# Deep Learning Techniques in Extreme Weather Events: A Review

Shikha Verma,
Kuldeep Srivastava,
Akhilesh Tiwari,
Shekhar Verma,

###### Abstract

Extreme weather events pose significant challenges, thereby demanding
techniques for accurate analysis and precise forecasting to mitigate its
impact. In recent years, deep learning techniques have emerged as a promising
approach for weather forecasting and understanding the dynamics of extreme
weather events. This review aims to provide a comprehensive overview of the
state-of-the-art deep learning in the field. We explore the utilization of
deep learning architectures, across various aspects of weather prediction such
as thunderstorm, lightning, precipitation, drought, heatwave, cold waves and
tropical cyclones. We highlight the potential of deep learning, such as its
ability to capture complex patterns and non-linear relationships.
Additionally, we discuss the limitations of current approaches and highlight
future directions for advancements in the field of meteorology. The insights
gained from this systematic review are crucial for the scientific community to
make informed decisions and mitigate the impacts of extreme weather events.

###### Index Terms:

Extreme Weather Events, Weather Prediction, Deep Learning

##  I Introduction

Weather refers to short-term natural events that occur in a certain location
and time which include characteristics such as temperature, pressure,
humidity, cloud cover, precipitation, wind speed and wind direction [1].
Extreme weather, on the other hand, refers to weather events that deviate
significantly from the expected conditions. Some instances of extreme weather
encompass include tropical cyclones, heatwaves, intense blizzards with heavy
snowfall, excessive rainfall leading to flooding, and droughts [2] [3] [4]
[5]. These occurrences pose serious challenges to society and the environment,
requiring careful planning and necessary measures to mitigate their
detrimental effects. Consequently, predicting weather holds significant
importance.

Weather prediction relies on a gathering information from weather stations,
satellites, radar systems, weather balloons, and buoys to access the current
atmospheric conditions [1] [6]. NWP utilizes mathematical models to simulate
the pattern of atmosphere which are based on initial conditions collected from
observational data [1] [6] [7]. Ensemble forecasting generates several
forecasts with slight modifications in initial variables and model parameters
to assess uncertainty and likelihood of possible outcomes. Climate models
utilize data assimilation, which integrates observational data with model
output, to generate long-term weather trends with increased forecast
precision.

Deep learning models are composed of multiple layers of interconnected
artificial neurons [8]. The distinguishing feature of deep learning models is
their ability to automatically learn and discover intricate patterns and
features directly from the data, without the need for explicit feature
engineering. This is achieved by passing the data through multiple layers of
interconnected neurons, where each layer learns to extract increasingly
abstract representations of the input data. The precision of weather forecasts
is heavily dependent on historic data. However, non-linear and complex nature
of weather phenomena poses inherent challenges to achieving absolute
precision. The traditional methods, including statistical, dynamical and
numerical models, have proven effective in forecasting weather events with
considerable lead time [9], they encounter limitations in capturing intricate
patterns and dynamics. As a result, achieving accurate predictions becomes
unattainable due to the intricate nature of weather systems. Continuous
advancements in research and the utilization of emerging technologies, such as
deep learning, offer promising avenues for further enhancing weather
prediction capabilities. This transformative approach enables highly accurate
weather predictions including severe weather events, empowering proactive
measures to mitigate their impacts effectively. Deep learning facilitates the
integration of diverse data sources, including satellites, radars, and weather
stations, to provide comprehensive and real-time meteorological insights for
improved public safety and resilience.

This review is organized as follows: section I introduces the paper by
providing an overview of the challenges, advancements, and applications of
weather forecasting using deep learning, as well as outlining the organization
of the paper. section II explores the realm of extreme weather events,
highlighting the need of accurate weather prediction. In section III, an
extensive literature review is presented, focusing on the utilization of deep
learning for extreme weather events. This section explores the existing
research, highlighting the different approaches, models, and findings in the
field. section IV focuses on the challenges encountered in the field of deep
learning for meteorology and weather forecasting. This section discusses the
limitations, data issues, interpretability concerns, and other obstacles faced
when applying deep learning techniques in this domain. section V highlights
how the integration of deep learning in extreme weather research advances
predictive capabilities and emphasizes the potential of hybrid models to
enhance forecast accuracy and proactive mitigation strategies. Additionally,
it outlines potential avenues for future research in weather forecasting using
deep learning, discussing promising areas of exploration, methodologies, and
potential advancements to enhance the accuracy and efficiency of deep learning
models for weather prediction. Finally, section VI offers a concise conclusion
that summarizes the key findings and contributions of the study.

##  II Extreme Weather Events

Exploring the fundamental elements that govern weather patterns and shape the
dynamics of atmosphere helps to obtain a broader perspective [1]. Temperature
plays a pivotal role in determining the thermal state of the atmosphere,
thereby exerting a substantial influence over the behaviors of gases, liquids,
and the overall human comfort. Air pressure, commonly referred to as
atmospheric pressure, significantly shapes weather patterns by creating low
and high-pressure systems. These systems contribute to a wide array of
meteorological conditions, including notable adverse conditions in case of
low-pressure systems. The pressure difference give rise to atmospheric winds
that traverse from low pressure area to high pressure area, enabling the
essential mechanism of air circulation. Humidity, the amount of water vapour
present in atmosphere, demonstrates a direct relationship with temperature;
higher temperatures allow the air to hold more water vapor, leading to the
formation of clouds. The extent of cloud cover plays a decisive role in
modulating solar radiation reaching the Earth’s surface, thereby affecting
temperature profile and atmospheric dynamics. Precipitation, which includes
rain, snow, and hail, occurs when moisture in the atmosphere condenses and is
then released from clouds. This process constitutes a primary mechanism
through which atmospheric water is returned to the Earth’s surface [10] When
these atmospheric elements surpass anticipated norms, they can trigger a
diverse range of extreme weather events that carry significant consequences.
These include heatwaves posing health risks and intensifying wildfires [11],
intense precipitation leading to flooding and landslides, warm ocean
conditions fueling cyclones with their devastating winds and storm surges,
severe thunderstorms generating tornadoes and hailstorms, prolonged droughts
affecting agriculture and water supply, snowstorms and blizzards disrupting
transportation and infrastructure, and freezing rain causing damaging ice
storms. Monitoring and understanding these factors are essential for early
detection, preparedness, and effective management to mitigate the potential
impacts of these diverse extreme weather phenomena [12]. To achieve this,
ensemble forecasting emerges as a powerful tool, generating several forecasts
with slight modifications in initial variables and model parameters to assess
uncertainty and the likelihood of possible outcomes. This strategic approach
is complemented by the utilization of climate models, which employ data
assimilation techniques. By integrating observational data with model outputs,
these models generate long-term weather trends with precise forecast. These
forecasts enable decision-makers and emergency response teams to better
understand the level of risk associated with different weather events and make
informed choices to protect communities and infrastructure.

##  III Deep Learning in Extreme Weather Events

Deep learning models are revolutionizing extreme weather prediction by
leveraging diverse data sources to accurately forecast events such as
cyclones, heatwaves, heavy rainfall, and severe storms. Their ability to
analyze complex patterns and relationships enables early warning systems and
proactive mitigation strategies, with the potential to minimize the impacts of
extreme weather on society and the environment.

A Deep Neural Network (DNN) is being developed for an early warning system to
predict extreme weather events such as floods, droughts, and heatwaves. The
DNN approach effectively downscales and bias corrects coarse resolution
seasonal forecast ensembles, generating realistic, high-resolution climate
information. The study demonstrates that the DNN model accurately predicts
extreme values while preserving the physical relationships and trends in the
variables [13]. Researchers aim to improve the forecast of SCW, including
thunderstorms, short-duration heavy rain, hail, and convective gusts by
employing deep-CNN algorithm to effectively extract the characteristics of SCW
and achieve better forecast performance as compared to traditional machine
learning algorithms [14]. Deep learning methods are being proposed for
detecting and forecasting anomalies in spatiotemporal data. The choice of
learning task depends on whether the anomalies are known or unknown. Anomalies
are often imbalanced and require specific data pre-processing. Leveraging
diverse data sources and modelling techniques, deep learning models exhibit
strong capabilities in accurately forecasting flood occurrence, severity, and
spatial distribution. These models play a crucial role in real-time
monitoring, enabling timely response and effective mitigation strategies.
Wasserstein Generative Adversarial Network (WGAN) is being utlized for
downscaling tropical cyclone rainfall to hazard-relevant spatial scales [15].
Additionally, a hybrid approach combining WGAN and Variational Autoencoder GAN
(VAEGAN) is being introduced to enhance the resolution of rainfall
measurements from 100 km to 10 km resolution, showing realistic power spectra
for various wave numbers [16]. A deep learning-based technique employing a
fully connected neural network is being proposed to accurately predict
rainfall-induced shallow landslides across Italy [17].

###  III-A Deep Learning in Thunderstorm and Lightning

Thunderstorms are complex atmospheric phenomena characterized by a combination
of thunder, lightning, heavy rainfall, and strong winds, with lightning
resulting from electrical discharges within clouds or between clouds and the
ground.

TABLE I: Deep Learning in Thunderstorm and Lightning Task | Approach | Ref
---|---|---
Thunderstorm Prediction | EEMD-ANN, EEMD-SVM, ARIMA | [18]
| LRCN-CNN, LSTM | [19]
Thunderstorm Severity Prediction | LSTM-FC, CNN-LSTM, ConvLSTM | [20]
Lightning Prediction | RNN | [21]
| ResNet | [22]
Lightning Identification | CNN | [23]

In a study two hybrid models, EEMD-ANN and EEMD-SVM, are being developed for
predicting thunderstorm frequency in Bangladesh. These models utilize ensemble
empirical mode decomposition (EEMD) to extract relevant features for accurate
prediction. The EEMD-ANN model consists of an input layer with 11 variables,
two hidden layers (4 and 2 neurons), sigmoid activation function, and a 0.1
learning rate. On the other hand, EEMD-SVM employs various kernel functions
for effective handling of non-stationary TSF data. The inpur variables include
CAPE, CPRCP, CRR, DP, KI, PRCP, RH, ST, TSD, TT, and WS50. EEMD, based on
Hilbert-Huang transform, mitigates challenges of EMD by introducing Gaussian
white noise. This enables precise decomposition of time series data into
intrinsic mode functions (IMFs), revealing underlying patterns. ARIMA models
effectively handle non-stationary time series data. The hybrid models, EEMD-
ANN and EEMD-SVM, capitalize on EEMD’s capabilities to handle non-stationary
data and capture nonlinear relationships. These models outperform like ANN,
SVM, and ARIMA in terms of prediction accuracy, with improvements ranging from
8.02% to 22.48% across TSF categories [18]. In another study, the aim is to
predict severe thunderstorm occurrences through an innovative approach using
lightning and radar video data in the Liguria region of Italy. The ensemble
technique outperforms traditional methods that optimized standard quality-
based scores. The architecture involves a LRCN, which combines a CNN for
extracting spatial features and an LSTM network for analyzing sequential
aspects. The training process spans 100 epochs, employing the Adam Optimizer
with a learning rate of 0.001 and a mini-batch size of 72. The training
process incorporates a class balanced cross-entropy loss function to fine-tune
the model’s performance. The model’s reliability is validated using a
historical radar video dataset comprising CAPPI images at 2 km, 3 km, and 5 km
above above sea level, demonstrating its effectiveness in probabilistic
forecasting of severe thunderstorms [19]. In a study focused at assessing the
accuracy of various LSTM neural network variants in predicting thunderstorm
severity through the utilization of remote sensing weather data, the primary
objective is to quantitatively forecast the intensity of thunderstorms by
analyzing the frequency of lightning flashes using deep learning models. The
study employs two main datasets: SALDN lightning detection network data and
SAWS weather station data. These datasets are used to train and evaluate
different LSTM neural network variants, including LSTM-FC, CNN-LSTM, and
ConvLSTM models. The LSTM-FC model consists of three LSTM layers and one dense
layer, with an optimizer based on the Adam algorithm. The activation function
used is the Leaky-Rectified Linear Unit with an alpha value of 0.15.
Similarly, the CNN-LSTM model comprises two Conv2D layers, one LSTM layer, and
one dense layer. This model also employs the Adam optimizer and utilizes the
Leaky-Rectified Linear Unit as the activation function, with an alpha value of
0.05. The ConvLSTM model is structured with two ConvLSTM2D layers and two
dense layers. The same Adam optimizer is employed, and the activation
functions are set to Leaky-Rectified Linear Unit (with an alpha value of 0.05)
and Rectified Linear Unit. The models are trained and evaluated using hourly
lightning flash data and weather variables based on the MAE and MSE. Among the
various LSTM model variants, the CNN-LSTM model outperforms the other models
with a MAE of 51 flashes per hour because of its ability to capture spatio-
temporal features, leading to more accurate predictions of thunderstorm
severity [20].

To predict the occurrence of lightning, an innovative data-driven neural
network model called Attention-Based Dual-Source Spatiotemporal Neural Network
(ADSNet) is being introduced. ADSNet is designed for accurate hourly lightning
forecasting and utilizes both numerical simulations and historical lightning
observations, resulting in a comprehensive and effective approach. A diverse
dataset, combining WRF simulation data with Cloud-to-Ground Lightning Location
System (CGLLS) observations from North China, is being employed. The model
consists of dual RNN encoder-decoder units, several CNN modules, DCNN modules,
and attention mechanisms. ConvLSTM is chosen for its adeptness in capturing
intricate spatiotemporal dependencies. This intricate framework is tailored
for conducting 12-hour lightning forecasts in the North China region. The
model adopts the Adam optimizer with an initial learning rate of 0.0001 and
Weighted Binary Cross-Entropy as a loss function. Experimental results are
validating the superiority of ADSNet over baseline methods in terms of
lightning forecast accuracy [21]. In another study, an innovative approach
known as Lightning Monitoring Residual Network (LM-ResNet) is being
introduced, leveraging deep learning for effective lightning location
monitoring in Ningbo, China. By transforming the task into binary
classification, radar data (PPI, CR, ET, V) and essential land attributes
(DEM, aspect, slope, land use, NDVI) are being harnessed to create a
comprehensive lightning feature dataset. LM-ResNet employs Rectified Linear
Unit (ReLU) activation for effective learning and addresses data imbalances
through Focal Loss, a specialized cross-entropy-based loss function. The
model’s training configuration includes an initial learning rate of 0.1 and
utilizes the SGD optimizer with a batch size of 64, incorporating momentum of
0.9 and weight attenuation of 0.0004 to enhance learning while mitigating
overfitting. The study is demonstrating LM-ResNet’s superiority over competing
architectures like GoogLeNet and DenseNet, highlighting its potential for
accurate and reliable lightning incident tracking. [22].

An approach called Lightning-SN is introduced, designed for precise cloud-to-
ground (CG) lightning identification using deep learning techniques. This
model effectively utilizes S-band Doppler radar data and CG lightning records
of the Ningbo area in Zhejiang Province, China, collected from August 2009 to
December 2021 via the ADTD lightning positioning system. Lightning-SN
leverages an encoder-decoder structure with 25 convolutional layers, five
pooling layers, five upsampling layers, and a sigmoid activation function
layer. The architecture capitalizes on symmetry, boundary preservation
techniques, and a 1x1 convolution kernel in the final layer. The model’s
optimization is driven by the Adam optimizer and guided by the GHM loss
function. Training involves the BP algorithm, employing iterative refinement
and validation testing. Additionally, the study includes a comprehensive
comparative analysis with other semantic segmentation algorithms—FCNN,
DeepLab-V3, and BiSeNet—evaluated under identical conditions. Lightning-SN
demonstrates substantial performance improvements over traditional threshold-
based methods, particularly in scenarios involving high-resolution radar data.
[23].

###  III-B Deep Learning in Precipitation

Precipitation is the process by which water, in either liquid or solid form,
falls from the atmosphere to the Earth’s surface. Hail, snow, and rainfall are
the three most common types of precipitation. The most frequent type of
precipitation is rain, which occurs when water droplets congregate and become
heavy enough to fall to the ground. When raindrops are pushed higher into the
freezing parts of the sky during violent thunderstorms, they freeze and pile
in layers, resulting in hailstones of varied sizes that can cause property and
crop damage. Snow is formed when water vapour condenses straight into ice
crystals in cold atmospheric conditions. Accurate forecasting and
understanding of precipitation patterns are critical for many industries,
including agriculture, water resource management, and transportation.

TABLE II: Deep Learning in Precipitation Task | Approach | Ref
---|---|---
Precipitation Forecast | RNN | [24]
| CNN | [25]
Precipitation Data Merging | CNN, ANN | [26]
Quantitative Precipitation Estimation | CNN-based | [27]
TPW and CAPE Estimation | MLP | [28]
Hailstorm Detection | CNN, DNN | [29]
Hailstorm Forecast | Atuencoder, CNN | [30]
| CNN | [31]
Hail Size Estimation | PCA, BPNN | [32]
Cloud or Snow Identification | DeepLab-CRF | [33]
| CNN | [34]
| U-Net | [35]
| U-Net | [36]
| U-Net | [37]
| CNN | [38]
Snow Depth Estimation | BPNN | [39]
| CNN, ResNet | [40]
| deep CNN | [41]
Snow Water Equivalent Estimation | ANN, ANFIS | [42]
| MNLR, NNGA | [43]

####  III-B1 Rainfall

Deep learning uses meteorological data such as historical rainfall records,
satellite images, and atmospheric conditions. Rainfall forecasts give
essential information for disaster preparedness, agricultural planning, water
resource management, and climate modelling.

A nowcasting model is designed to address extreme weather phenomena
encompassing both precipitation and landfalling hurricanes. The research
employs a comprehensive dataset spanning five years (2015–2020) of radar
observations over South Texas, including 22 hurricane events that occurred in
the United States. The architectural design of the model comprises four core
components: RNN, up-sample, down-sample, and convolution. The architecture is
built upon a three-layer encoder-decoder structure, incorporating distinct
filter arrangements for the RNN, while seamlessly integrating convolution and
deconvolution operations. GRU is selected as the foundational RNN unit,
organised in multiple layers to effectively capture intricate spatiotemporal
patterns. The model effectively predicted future radar reflectivity echo maps
based on five preceding observations, enabling forecasts for up to a 3-hour
lead time. Model parameter optimization is achieved using the Adam optimizer,
fine-tuned with a learning rate of 104 and a momentum of 0.5. To further
enhance predictive performance, the research incorporates Balanced Mean
Squared Error (B-MSE) and Balanced Mean Absolute Error (B-MAE) as loss
functions. The model’s forecasting capabilities are evaluated using
established metrics—HSS, CSI, POD, and FAR—all of which collectively highlight
its proficiency in precipitation nowcasting. [24]. MetNet-2, a deep neural
network-based weather model, outperforms existing physics-based models in
predicting high-resolution precipitation up to 12 hours ahead. The study
utilizes data sources such as MRMS, GOES-2, and HRRR datasets. Input
observations from various sources, including radar, satellite, and
assimilation features, are processed through a CNN to capture temporal
dynamics. Efficient computation is achieved through model parallelism across
16 interconnected TPU cores, allowing accurate forecasts over a 512 km x 512
km target patch. The model’s architecture consists of three stacks of 8
residual blocks with exponentially increasing dilation factors. Operating
within the Continental United States, MetNet-2 generates forecasts at a
2-minute frequency with a spatial resolution of 1 km. It operates within a
probabilistic framework, producing categorical predictions across 512
precipitation levels for each target position. The model’s performance exceeds
that of the High-Resolution Ensemble Forecast (HREF) when assessed using the
Cumulative Ranked Probability Score (CRPS). [25].

The utilization of deep learning techniques to merge precipitation data from
diverse sources across the Tibetan Plateau, with the aim of enhancing data
precision. The study explores three methodologies: ANN, CNN and a statistical
Extended Triple Collocation (ETC) method. The neural network architecture
employed consists of an ANN with four fully connected layers and a CNN
enhanced by two additional convolutional layers to capture spatial features.
To mitigate overfitting, dropout layers with a 0.1 dropout rate followed each
fully connected or convolutional layer. The optimization employs the Adam
algorithm with a learning rate of 0.0001, the RMSE serves as the loss
function, and the ReLU function acts as the activation function. The
hyperparameters consist of 500 epochs and a batch size of 2500 for effective
training. Meteorological and hydrological evaluations reveal that the CNN
approach consistently demonstrates superior performance compared to the
others, showcasing enhanced spatial distribution and heightened accuracy. The
meteorological evaluation employs eight metrics: CC, BIAS, STDRATIO, MAE,
RMSE, POD, FAR, and CSI. The hydrological assessment utilizes NSE and PBIAS
for model parameter validation, with KGE employed to counter NSE’s flow peak
bias and emphasize runoff variability [26].

Using the U.S. Weather Surveillance Radar-1988 Doppler (WSR-88D) observations
dataset, researchers developed four DL models for QPE (Quantitative
Precipitation Estimation) with a CNN-VGG architecture. These models, named
RQPENetD1, RQPENetD2, RQPENetV, and RQPENetR, incorporate dense blocks, RepVGG
blocks, and residual blocks. The architecture of RQPENetD1 features an initial
convolution layer, four dense blocks with varying bottleneck layers, and
transition layers for spatial reduction. It processes 3-D radar data from two
elevation angles to estimate rainfall rate using a fully connected layer with
adaptive average pooling and utilizes MSE as the loss function. RQPENetD2
shares a similar structure with dense blocks featuring (24, 16) and (36, 24)
bottleneck layers, along with transition layers involving 1x1 Convolution and
average pooling. RQPENetV incorporates RepVGG blocks in a multi-branch
structure across five stages, while RQPENetR utilizes residual modules in four
sequential blocks with varying bottleneck layers for feature enhancement from
3-D radar data. The evaluation of RQPENet’s radar precipitation estimation
includes metrics such as RMSE, MAE, CC, and NSE, along with additional
atmospheric science metrics: POD, FAR, CSI, HSS, and GSS. The findings
indicate the superior performance of dense blocks-based models, particularly
RQPENet D1 and RQPENet D2, compared to residual blocks and RepVGG blocks-based
models, as well as five traditional Z-R relations [27].

In a recent study, researchers propose an ANN model with incremental learning
to derive total precipitable water (TPW) and convective available potential
energy (CAPE) from GEO-KOMPSAT-2A satellite imagery over Northeast Asia. The
study utilizes AMI satellite imagery, ERA5 data, and radiosonde observations
for training and evaluation. An MLP feedforward backpropagation ANN model is
employed for the retrieval algorithm. The model architecture includes an input
layer with 20 neurons, a hidden layer with 40 neurons using a hyperbolic
tangent activation function, and an output layer with a linear activation
function. The optimization process utilizes the Adam optimizer with a mean
squared error loss function. The accuracy assessment involves the utilization
of statistical metrics, including correlation coefficient, bias, and RMSE. The
incremental ANN model demonstrates improved accuracy and stability compared to
static learning methods, indicating its potential to accurately estimate TPW
and CAPE [28].

####  III-B2 Hail

Hail prediction helps to improve our understanding and readiness for this
dangerous weather occurrence. We can increase hail forecasting accuracy,
giving advanced warnings to prevent possible infrastructure, agriculture, and
community damage. Because hail storms may have significant socioeconomic
consequences, applying deep learning techniques into hail prediction models is
critical for timely and effective risk management and disaster response
measures.

In a test case study, researchers applied deep learning networks for hailstorm
detection using CNN and DNN architectures. The approach involves training
these networks on GOES satellite imagery and MERRA-2 atmospheric parameters to
identify hail storms. Different architectures are utilized, including a CNN
for processing satellite imagery and a DNN for atmospheric parameters, aimed
at capturing pertinent features. The CNN architecture for satellite imagery
uses four convolutional layers with ReLU activation functions, combined with
max-pooling layers for downsizing and a batch normalization layer for
streamlined training. Fully connected layers are also integrated into the
architecture to enable classification. Concurrently, the DNN architecture for
atmospheric parameters features four fully connected layers with ReLU
activation, utilizing a Softmax function for classification. Both
architectures converge within a merged network, amalgamating outputs from the
CNN and DNN via concatenation and incorporating additional fully connected
layers for the final classification. This approach harnesses the capabilities
of deep learning to enhance hail detection by merging multi-source data and
recognizing spatial patterns. The CNN model achieves heightened precision by
accurately identifying decreased infrared brightness temperatures linked to
hail storms. [29]. In an effort to forecast hailstorms, researchers introduced
an architecture that comprises three distinct models: an Autoencoder (AE) with
encoder and decoder layers, each containing 32 neurons; a CNN constructed with
CNN layers featuring 64 and 32 filters; and an RF model characterized by an
ensemble of decision trees and decision tree aggregation through majority
voting. Both AE and CNN are optimized using the Adam optimizer and MSE as the
loss function. The dataset utilized in the study consists of observations from
the TRMM and reanalysis data from the ECMWF spanning one year. The selected
attributes for training the models include convective potential energy,
convective inhibition, wind shear within the 1–3 km range, and warm cloud
depth. The study aims to predict global hailstorms using these models and to
compare their performance in terms of accuracy, precision, and error rates.
Surprisingly, contrary to expectations, RF outperforms the deep learning
methods in terms of hailstorm prediction performance [30]. In a study focusing
on severe hail prediction, researchers employed CNN to encode spatial weather
data and compared its performance with traditional statistical approaches like
Logistic Mean and Logistic PCA. The dataset utilized in this study includes
geopotential height, temperature, dewpoint, zonal wind, and meridional wind
variables from the NCAR ensemble model output. These variables are collected
at different pressure levels: 500 hPa, 700 hPa, and 800 hPa. The study uses
upper-air dynamic and thermodynamic fields from an NCAR NWP model. The CNN
architecture used in this study comprises three strided convolutional layers
with 5x5 gridcell filters. A range of hyperparameters is tested, including the
initial number of filters, dropout rates, activation functions (ReLU and Leaky
ReLU), L2 norm regularization coefficients, and optimizers (Stochastic
Gradient Descent and Adam) with different learning rates. The model’s
evaluation is conducted using the Brier Score as the prediction error
function, and standard probabilistic verification metrics are employed to
assess the quality of probabilistic forecasts. The results demonstrate a
significant enhancement in various measures of prediction skill achieved by
the CNN architecture, leading to improved probabilistic predictions when
compared to the logistic PCA approach [31].

Accurately estimating hail size remains crucial for evaluating potential
damage caused by hailstorms. In order to address this challenge, a model
consisting of two main components has been proposed. The PCA-based technique
selects 18 features that strongly correlate with hail sizes, while the BPNN
regression model with a two-layer architecture and 35 hidden layer neurons is
employed to estimate the size of hailstones from satellite images. Using a MSE
loss function, the BPNN regression model achieves an R-squared value of 0.52
through linear fitting when assessing the correspondence between predicted and
observed Maximum Hail Diameters on the test set [32].

####  III-B3 Snow

Understanding snowfall patterns is critical for many industries, including
transportation, agriculture, and disaster planning, resulting to more
effective resource management and risk mitigation techniques. However, the
intricate task of accurately differentiating between snow and clouds arises
from their comparable white appearance in satellite imagery, highlighting the
utmost importance of precise discrimination. Therefore, in an attempt to
accurately identify cloud and snow in high-resolution remote sensing images, a
study introduced the DeepLab v3+ neural network with a CRF model. The research
utilized data from China Gaofen-1 (GF-1) satellite’s Wide Field View (WFV)
sensor, comprising four bands and a spatial resolution of 16 m, encompassing a
total of ten images spanning three years. The DeepLab v3+ adopts an encode-
decoder architecture and employs the Adam optimizer with a learning rate of
0.001, a batch size of 5, and 200 epochs. The study analyzes accuracy
variations resulting from distinct loss functions, including the Cross Entropy
(CE) loss function, Dice loss function, and Focal loss function. Evaluation
metrics encompass Mean Intersection over Union (MIoU) and Mean Pixel Accuracy
(MPA) to assess model performance. This methodology effectively mitigates
misclassification issues, enhancing cloud and snow identification precision
through refined boundary delineation and reduced isolated patches [33]. An
end-to-end fully-CNN with a multiscale prediction approach is proposed to
differentiate cloud and snow using a dataset of 50 high-resolution Gaofen
satellite images (13400×12000 pixels each), meticulously labeled for cloud and
snow regions. The network adopts the VGG network architecture with stride
reduction and atrous convolution techniques. Due to the frequent co-occurrence
of snow and cloud in images, a pixel-level approach is employed, involving the
replacement of the last two fully connected layers in the VGG model with two
convolutional layers. The final layer employs a three-class softmax loss for
classifying snow, cloud, and other land types, using batch normalization and
rectified linear units. The Multiscale Prediction Module merges feature maps
from diverse intermediate layers, functioning as an ensemble learning
approach. This approach allows for the simultaneous utilization of low-level
spatial information and high-level semantic information, enabling accurate
differentiation between cloud and snow [34]. A deep learning-based method is
developed by utilizing the Unet3+ network with Resnet50 and the Convolutional
Block Attention Module (CBAM) to accurately detect cloud and snow in remote
sensing images. This approach effectively eliminates interference information.
The feature extraction process of UNet3+ includes five encoders with effective
convolutional and pooling layers. In an enhanced version, multiple
convolutional layers, regularization, ReLU activation, and residual modules
are added to each of the five encoders, resulting in feature graphs. The
decoders consist of convolution and activation layers. To address bias, a
weighted cross-entropy loss is employed, emphasizing cloud and snow regions.
For enhanced focus and deeper feature extraction, the Convolutional Block
Attention Module (CBAM) is incorporated into ResNet50. CBAM harnesses channel
attention through global pooling, multi-layer perceptron processing, and
sigmoid activation to generate attention feature maps. The model’s performance
is assessed using various metrics, including Mean Intersection over Union
(mIoU), Mean Pixel Accuracy (mPA), Mean Precision (mPrecision), and Estimated
Total Size. These evaluations successfully mitigate interference data,
resulting in accurate cloud and snow extraction from diverse landforms within
remote sensing images.[35]. The effectiveness of U-Net based deep learning
models in delineating glacier boundaries and identifying snow/ice is
demonstrated in a study that developed ENVINet5 and ENVI Net-Multi deep
learning classifiers to analyze Landsat-8 satellite data over the Bara Shigri
glacier region in Himachal Pradesh, India. The ENVINet5 architecture, based on
a mask-based encoder-decoder U-Net model, is employed for single-class
categorization, while ENVI Net-Multi is used for multi-class classification of
features like snow, ice, and barren areas. The ENVINet5 architecture comprises
five levels with twenty-three convolutional layers, incorporating input
patches, feature maps, various convolutions, feature fusion, max-pooling, co-
convolution, and 1x1 convolutions. For ENVINet-Multi, training parameters
include 25 epochs, a patch sampling rate of 16, class weight of 2.5, loss
weight of 0.5, 200 patches per epoch, 464x464 pixel patch size, and 2 patches
per batch [36].

The performance of U-Net, RF, and Sen2Cor models for snow coverage mapping is
compared in a study using Sentinel-2 satellite multispectral images across 40
diverse sites spanning all continents except Antarctica. A Random Forest model
is built using Bayesian Hyperparameter Optimization for improved performance.
The Sentinel-2 Level-2A product incorporates cloud and snow confidence masks
derived from Sen2Cor, which employs threshold tests on spectral bands, ratios,
and indices like NDVI and NDSI. A U-Net network architecture is employed,
featuring an encoding path with repeated 3x3 convolutions, batch
normalization, and ReLU activation, followed by 2x2 max pooling for
downsampling. The decoding path utilizes transpose convolutions for
upsampling, concatenating with corresponding encoding path features, and
applying 3x3 convolutions with BN and ReLU. The final layer comprises a 1x1
convolution for class prediction. Training involves weighted cross-entropy
loss, stochastic gradient descent with a learning rate of 0.01 and momentum of
0.9, resulting in effective semantic segmentation. The model’s performance is
assessed using precision, recall, F1 score, Intersection Over Union (IoU), and
accuracy metrics. The results demonstrate that U-Net models exhibit superior
performance compared to RF and Sen2Cor in accurately mapping snow coverage
[37]. An open-source machine learning-based system for snow mapping,
AutoSMILE, was developed to automate the process using image processing,
machine learning, deep learning, and visual inspection. It was applied in a
mountainous area in the northern Tibetan Plateau using RF and CNN algorithms,
achieving accurate snow cover mapping. The CNN architecture comprises four
layers: convolutional layers for feature extraction, activation layers like
ReLU to expedite training, pooling layers for non-linear downsampling, and
fundamental components like fully connected and flatten layers. For model
evaluation, key metrics include producer’s accuracy (PA), user’s accuracy
(UA), intersection over union (IoU), and overall accuracy (OA) [38].

A deep learning approach is introduced to downscale snow depth retrieval
across an alpine region by integrating satellite remote-sensing data with
diverse spatial scales and characteristics. The study focuses on collaborative
snow parameter retrieval in Northern Xinjiang, China, utilizing MODIS and MWRI
data. A three-hidden-layer neural network is designed with 20, 20, and 10
neurons in each layer. The network processes resampled BTD, topographic, and
meteorological data at a 500m resolution, utilizing a sigmoid function for
capturing nonlinear patterns. Backpropagation, guided by MSE and SGD,
optimizes the network to enhance the accuracy of snow depth observations. The
optimization process entails weight and bias adjustments, informed by a
learning rate of 0.001. This approach aims to boost the precision of snow
depth observations through deep neural network downscaling. Reference data
from ground station snow depth measurements are employed to evaluate the
downscaling model’s performance and retrieval accuracy, employing assessment
metrics encompassing R2, RMSE, PME, NME, MAE, and BIAS [39].

A deep learning model is presented for ’area-to-point’ snow depth estimation,
which integrates AMSR2 TB, MODIS, and NDSI data, achieving high accuracy with
a spatial resolution of 0.005°. The model utilizes CNN and residual blocks to
capture spatial heterogeneity and leverage high-resolution snow information
from MODIS. The CNN comprises convolutional and ReLU activation layers,
pooling for downsampling, measures to prevent overfitting, and concludes with
a fully connected layer for output. The proposed deep residual network
contains a 35-layer input patch, applies convolutions with batch normalization
and max pooling, followed by 4 residual blocks for feature extraction. After
adaptive average pooling and fully connected layers, it predicts snow depth at
the patch center. It has 9 convolutions and 4 fully connected layers, using
ReLU activation except for the linear output layer. The model is trained for
50 epochs, using a learning rate of 0.0001, with exponential decay of 0.5
every 20 epochs, and a batch size of 32, while employing stochastic gradient
descent (SGD) as the optimizer. The evaluation metrics in this study include
RMSE, MAE, MBE, and R2. The results demonstrate that by incorporating spatial
heterogeneity and leveraging high-resolution MODIS snow cover data, the
proposed model achieves promising accuracy in snow depth estimation, with
potential applicability to other regions [40].

A novel inverse method is presented for extracting snow layer thickness and
temperature from passive microwave remote sensing data. Utilizing
convolutional, pooling, and fully-connected layers, the study employs a
ConvNet to inversely estimate the thickness and temperature of a snowpack from
its corresponding vertical polarization brightness temperature and horizontal
polarization brightness temperature. The model chooses the Adam optimizer with
a learning rate of 0.01 to optimize the half mean squared error loss function,
and L2 regularization is employed to enhance prediction accuracy by mitigating
over-fitting. Furthermore, a comparative analysis is conducted between the
ConvNet outcomes and those of conventional ANN and SVM. The model assessment
is carried out using RMSE and R2 metrics, underscoring the effectiveness of
the advanced ConvNet approach. The utilized ANN architecture comprises three
layers - input, hidden, and output - with 20 hidden-layer units utilizing
hyperbolic tangent basis functions [41].

A study on predicting snow water equivalent (SWE) in a semi-arid region of
Iran was conducted using regression, ANN, and adaptive neuro-fuzzy inference
system (ANFIS) models. The study proposes a three-layer ANN alongside ANFIS,
which integrates ANN with fuzzy logic, utilizing a five-layer structure based
on the Sugeno model featuring two fuzzy if-then rules. In the ANFIS
architecture, a hyperbolic tangent activation function is utilized in the
hidden layer, and optimal neuron counts are ascertained for both hidden and
input layers through iterative refinement. The handling of numerous
independent input variables in the ANFIS approach is accomplished using
backpropagation training and a sub-clustering method. The assessment of ANN,
ANFIS, and regression models involves statistical metrics such as MBE, MAE,
RMSE, correlation coefficient, relative error percentage, and Nash-Sutcliffe
coefficient efficiency. The results demonstrate the superior performance of
both ANN and ANFIS models compared to the regression method, with ANN and
ANFIS exhibiting similar prediction accuracy for SWE [42]. In a study,
researchers focused on estimating SWE in the Samsami basin of Iran using MNLR,
NNGA, and ANN architectures. The study aims to estimate snow water equivalent,
a critical component of water resources in mountainous areas, based on
climatic and topographic parameters such as elevation, slope, aspect,
longitude, and latitude. The MNLR architecture employed in the study aims to
model the complex non-linear relationship between SWE and a set of independent
parameters. Moreover, four different ANN architectures are investigated: MLP
for supervised prediction with input, hidden, and output layers; GFF for
efficient problem-solving through multi-layer connections; RBF for rapid
learning via self-organizing hidden layers; and MNN, a specialized MLP with
parallel sub-modules for specialized function and faster training. The study
evaluates these architectures to enhance the prediction of SWE values. The
NNGA model utilizes genetic algorithms to optimize neural network parameters,
enhancing accuracy by iteratively refining weights through selection,
crossover, and mutation. Additionally, six diverse learning algorithms are
examined for training neural network components in the NNGA model. These
algorithms include Levenberg-Marquardt for adaptive MSE minimization, Delta-
Bar-Delta for efficient step size adaptation, Step for gradient descent with
step size adjustment, Momentum for inertia-infused gradient descent, Conjugate
Gradient for second-order optimization, and Quickprop for error surface
curvature-based weight adjustments. The study evaluates three activation
functions: Sigmoid, Tanh, and Linear, and assesses all models using standard
statistical criteria, including correlation coefficient, RSME, ratio of
average estimated to observed values, and MAE. The NNGA model, specifically
the NNGA5 variant, proves to be the most effective approach, offering valuable
insights for water resource management in mountainous regions [43].

###  III-C Deep Learning in Drought

Drought refers to an extended period of unusually low precipitation within the
natural climate cycle caused by deficiency in rainfall. It can have far-
reaching impacts, including water shortages, crop failure, livestock losses,
increased wildfire risk, and ecosystem degradation. Drought can be categorized
into four types: meteorological, hydrological, agricultural, and socio-
economic [44] [45], with each type influenced by critical climatic factors
such as increased evaporation, transpiration, and insufficient precipitation.
In this review, we will consider only meteorological drought.

Meteorological Drought forecasting is a complex process that involves
analyzing various climatic and environmental factors to anticipate future
drought conditions. Statistical models and drought indices, such as the
Standardized Precipitation Index (SPI) or Palmer Drought Severity Index (PDSI)
[46], are utilized to quantify drought severity and monitor changes over time.
These methodologies contribute to a comprehensive understanding of drought
dynamics and aid in the formulation of effective drought management and
adaptation measures. However, they often face challenges in capturing the
complexities of drought dynamics.

TABLE III: Deep Learning in Drought Task | Approach | Ref
---|---|---
SPI Forecast | ANFIS, FFNN, MLR | [47]
| ARIMA, ANN, SVR, WA-ANN, WA-SVR | [48]
| ANN, SVR, WANN | [49]
| ANN, WANN, ARIMA, SARIMA | [50]
| ARIMA, ANN, WANN | [51]
| EMD-DBN | [52]
| WP-ANN, WP-SVR | [53]
SWSI and SIAP Forecast | ANN | [54]
SPEI Forecast | ANFIS, hybrid WT-ARIMA-ANN | [55]

The study utilized ANFIS to forecast SPI-based drought indices using rainfall
data from 10 stations in Central Anatolia, Turkey. The ANFIS architecture
consists of five layers: input, rule, average, consequent, and output nodes.
These models, named SPI-1, SPI-3, SPI-6, SPI-9, and SPI-12 for different time
scales, are designed to capture diverse drought patterns by integrating SPI
and precipitation data, addressing short-term, seasonal, and long-term
variations. For each phase, a total of 20 distinct models with varied input
combinations are developed. The evaluation includes performance metrics such
as Root Mean Square Error (RMSE), Efficiency (E), and Correlation (CORR), and
comparisons are made against Feed Forward Neural Networks (FFNN) and Multiple
Linear Regression (MLR) models. Significantly, the ANFIS models exhibit
exceptional performance, showcasing a notable ability to accurately identify
dry and wet periods, particularly across extended time scales [47]. In a
different study, the focus is on long-term Standard Precipitation Index (SPI)
drought forecasting (6 and 12 months lead time) in the Awash River Basin,
Ethiopia. The study compares the efficacy of five data-driven models: ARIMA,
ANN, SVR, WA-ANN, and WA-SVR. The ARIMA model involves three essential steps:
model identification, parameterization, and validation. The significant lags
are determined using ACF and PACF, guiding the selection of accurate and
precise parameters. The ANN model employs a MLP structure with input, hidden,
and output layers, trained using the Levenberg-Marquardt (LM) backpropagation
algorithm. Lagged SPI values are utilized as input, with optimal input layer
neurons determined via trial and error, while hidden layer neurons are
selected using empirical methods. The SVR model employs a non-linear RBF
kernel. The Wavelet Decomposition process encompasses CWT for time-frequency
representation and DWT for efficient computation. The transformed time series
serves as input for both ANN and SVR models. The performance of models is
evaluated using RMSE, MAE, R2, and persistence. It is found that the WA-ANN
model outperforms other models for forecasting SPI values over lead times of 6
and 12 months[48]. In another study, the WA-ANN models outperform alternative
approaches, providing the most accurate forecasts for SPI 3 and SPI 6 values
over lead times of 1 and 3 months. This underscores the effectiveness of the
WA-ANN architecture, characterized by 3 to 5 neurons in the input layer, 4 to
7 neurons in the hidden layer, and a single neuron in the output layer,
contributing to improved short-term drought forecasting [49]. A comparative
approach is employed to evaluate the performance of various forecasting models
for drought using SPI as the indicator. Three primary models are investigated:
ANN, WANN, and traditional stochastic models, namely ARIMA and Seasonal ARIMA
(SARIMA). The focus is on SPI-3, SPI-6, and SPI-12 time scales, and the impact
of wavelet preprocessing on model accuracy is explored for the Algerois Basin
in North Algeria. ARIMA and Seasonal ARIMA models offer an empirical framework
for modeling and predicting complex hydrologic systems, with nonseasonal ARIMA
addressing stationary data and seasonal ARIMA handling nonstationarity through
AR, MA operators, and differentiation parameters. An implementation of ANN-MLP
involves a network structure comprising interlinked input, hidden, and output
layers. The WA-ANN model utilizes Discrete Wavelet (DW) inputs derived from
original SPI time series and corresponding un-decomposed SPI outputs, with a
focus on assessing the impact of various mother wavelets to enhance model
efficiency. The model performance is assessed using the Nash-Sutcliffe model
efficiency coefficient (NSE), RSME, and MAE as evaluation metrics. The results
demonstrate that the WANN model outperforms the ANN model for SPI-3 forecasts
over up to six months while the SARIMA model shows satisfactory results for
SPI-12 forecasts with a one-month lead time. However, all models experience
reduced accuracy as lead times increase [50]. In another study, three data-
driven models, namely ARIMA, ANN, and WANN, are employed for drought
forecasting based on the SPI at two time scales (SPI-6 and SPI-12) in the
north of the Haihe River Basin. The effectiveness of the models is assessed
using statistical tests like the Kolmogorov-Smirnov (K-S) test, Kendall rank
correlation, and correlation coefficients (R2). ARIMA models with varying
parameter combinations are employed, selecting the model that minimizes K–S
distance and maximizes Kendall rank correlation. The chosen model’s efficacy
is validated through ACF and PACF plots. The ANN model underestimates certain
instances of extreme drought or extreme precipitation, where the observed SPI
values correspond to such extreme conditions. The WANN model outperforms other
models, exhibiting higher correlation, lower K–S distance, and enhanced
Kendall rank correlation. The comparison results show that the WANN model is
the most suitable and effective for forecasting SPI-6 and SPI-12 values in the
study area [51]. Furthermore, a hybrid predictive model is presented,
combining EMD with a DBN for drought forecasting using the SSI across the
Colorado River basin. DBN is constructed by stacking multiple RBM on top of
each other, and the RBMs are trained using the contrastive divergence
algorithm. EMD is utilized to decompose the data into IMF with varying
frequencies. Some IMFs are found to contain noise or irrelevant information. A
denoising technique is proposed, involving the use of Detrended Fluctuation
Analysis (DFA) scaling exponents. A threshold (Hurst exponent 0.5) is applied
to identify noisy IMFs, which are subsequently eliminated, and the relevant
IMFs are aggregated for reconstruction. The DBN model, along with other models
(MLP, SVR, EMD-MLP, EMD-SVR), is used to predict SSI-12 with lead times of one
and two months. The evaluation metrics include RMSE, MAE, and NSE, and the
EMD-DBN model outperforms all other models in the two-step ahead prediction
[52].

The focus of the study is on drought forecasting with lead times of 1 month
and 6 months for the Gulbarga district in Karnataka, India, using the SPI as
the drought quantifying parameter. WPT is employed to preprocess SPI time
series, generating decomposed coefficients used as inputs for ANN and SVR
models. The SPI time series forecasting utilizes a BPNN with a 3-4-1
architecture. The network’s weights and biases are determined using the
gradient descent optimization algorithm, incorporating an adaptive learning
rate of 0.45, momentum rate of 0.15, and 5000 learning cycles. The SVR
utilizes a loss function based on Vapnik’s $\epsilon$-insensitive approach and
incorporates the Gaussian radial basis function (RBF) kernel. This approach
creates hybrid WP-ANN and WP-SVR models for drought forecasting, with
Daubechies 4 (db4) wavelet chosen as the mother wavelet. It is observed that
the hybrid WP-ANN model performs better than standalone approaches, with the
forecast accuracy decreasing as the lead time increases [53].

In another study, an ANN model is used to forecast drought indices, including
the Standardised Water Storage Index (SWSI) and the Standard Index of Annual
Precipitation (SIAP). The dataset encompasses rainfall and water level
information originating from the Langat River Basin in Malaysia, covering a
time span of three decades (1986-2016). A feed-forward multilayer perceptron
(MLP) structure is employed, comprising input, hidden, and output layers. This
architecture is trained using the Levenberg–Marquardt (LM) back-propagation
algorithm for both traditional artificial neural network (ANN) models and the
wavelet-based artificial neural network (W-ANN) models. In the W-ANN approach,
discrete wavelet transform (DWT) is applied to the input data, yielding
subseries components. Subsequently, pertinent components are chosen from these
subseries and integrated into the MLP to enhance the accuracy of forecasting.
The outcomes demonstrate that the W-ANN model showcases improved performance,
achieving heightened correlation coefficients [54].

Another study employs two hybrid models, namely Wavelet-ARIMA-ANN (WAANN) and
Wavelet-Adaptive Neuro-Fuzzy Inference System (WANFIS), to predict the
Standardized Precipitation Evapotranspiration Index (SPEI) at the Langat River
Basin for different time scales (1-month, 3-months, and 6-months). The input
data are subjected to wavelet decomposition at a level of three, and the
resulting components are employed as inputs for both ANN and ANFIS models. ANN
models are constructed using Bayesian regularization backpropagation with a
total of 1000 training epochs, and the optimal number of hidden neurons is
identified through trial and error. The WANFIS involves normalizing decomposed
historical SPEI series as input for Sugeno-type Fuzzy Inference System (FIS),
chosen for its computational efficiency and compatibility with optimization
techniques. This is followed by applying the ANFIS algorithm with determined
training parameters to enhance model performance. It is found that the hybrid
WT-ARIMA-ANN technique outperforms other models, providing better forecasts
for both short-term and mid-term drought indices (SPEI 1, SPEI 3, and SPEI
6).[55].

###  III-D Deep Learning in Heatwave and Cold waves

Heatwaves are extreme weather event characterized by prolonged periods of
excessively hot weather [56], often accompanied by high humidity. During a
heatwave, temperatures rise significantly above the average for a particular
location and persist for an extended period, typically several days.
Conversely, a coldwave is a meteorological phenomenon marked by a sudden and
significant decrease in air temperature at the Earth’s surface. This results
in extremely low temperatures that can give rise to hazardous weather
conditions, including frost formation and the formation of ice. Both events
can have profound impacts on human health [12], [57] particularly in
vulnerable populations such as the elderly, children, and individuals with
pre-existing health conditions, infrastructure, agriculture, ecosystems, and
can even result in mortality for human beings and livestocks. Additionally,
heatwaves strain power grids due to increased air conditioning use, resulting
in power outages, and can cause crop failures, wildfires and damage to
infrastructure like roads, bridges, railways, and airports [11]. The World
Meteorological Organization’s (WMO) annual report for 2023 highlighted the
unprecedented heatwaves experienced in Europe during the summer, exacerbated
by abnormally dry conditions. Tragically, these extreme heat events resulted
in over 15,000 excess deaths across several countries, including Spain,
Germany, the United Kingdom, France, and Portugal. These alarming findings
underscore the pressing need for urgent and effective heatwave mitigation
strategies and adaptive measures to safeguard vulnerable populations in the
face of escalating climate challenges [58].

Monitoring and predicting heatwaves and cold waves are crucial for
preparedness [59] and mitigating potential risks, such as implementing
appropriate measures to protect vulnerable populations and ensuring the
efficient functioning of critical systems during extreme cold episodes. Both
the events can be predicted using a range of approaches, including statistical
models [60] and dynamic models such as GMC [61] [62] and RCM [63] [64] [65]
and machine learning techniques that analyze extensive datasets to identify
patterns associated with heatwave occurrences. Deep learning can be utilized
to extract features from various meteorological variables, such as
temperature, humidity, wind patterns, and atmospheric pressure, to forecast
the likelihood, frequency, duration and intensity of both heatwaves and
coldwaves.

TABLE IV: Deep Learning in Heatwaves and Cold waves Task | Approach | Ref
---|---|---
Heatwave Forecast | LSTM | [66]
| GNN | [67]
Heatwave and Cold wave Forecast | ConvNet, CapsNets | [68]
SAT Forecast | CNN, CNN-RP, CNN-RP-BIN | [69]
SAT and LST Forecast | LSTM | [70]

The focus of the study is on heatwave monitoring and prediction, employing
index-based monitoring, and LSTM-based prediction model in northern India up
to 5-6 days ahead. The study employs IMD daily mean gridded surface
temperature data (1951–2020) and NCMRWF-IMDAA reanalysis dataset for humidity
and wind data (1979–2020). The objective of this study is to develop an
operational framework that can monitor, track, and predict heatwaves in real-
time over the Indian region, utilizing a combination of temperature indices,
synoptic information, and an LSTM-based prediction model. The model’s
performance is evaluated using a correlation coefficient and root mean square
error (RMSE). The results demonstrate that the proposed approach offers a
promising approach to enhance heatwave preparedness and response strategies
[66]. In another study, a GNN model is developed to predict regional summer
heatwaves in North America. By utilizing daily weather data of 91 stations
across CONUS and analyzing key meteorological variables, the model reduces
computational burdens for immediate heatwave warnings and facilitates fast
decision-making. The model utilizes an encoder-processor-decoder architecture
for binary classification of heatwave events. Each node within the graph
corresponds to a weather station, while the model employs a GAL – a form of
nonlinear graph convolution. This GAL dynamically adjusts the adjacency matrix
based on node features via attention mechanisms, thereby enhancing its
expressiveness. The GAL computes specialized attention weights (AW) to capture
interactions between nodes, encompassing influences from neighbors, to
neighbors, and historical data. Moreover, the model’s training incorporates a
soft F1-score metric, effectively combining recall and precision to mitigate
bias and maximize the F1-score. As a result, the GNN model achieves an
impressive 90% accuracy in predicting regional heatwave occurrences [67].
Another study employs ConvNet and CapsNet to predict heatwaves and cold waves,
predicting the occurrence and region of extreme weather patterns in North
America. The study employs daily data from the Large-Ensemble Community
Project (LENS) for surface air temperature (T2m) and geopotential height at
500 mb (Z500) during boreal summer and winter months from 1920 to 2005. The
ConvNet architecture comprises 4 convolutional layers with ReLU activation,
where the last two layers are followed by max-pooling (2x2, stride 1). The
output feeds into a fully connected neural network with 200 neurons, featuring
dropout regularization and L2 regularization to prevent overfitting. An
adaptive learning rate is implemented through the ADAM optimizer, while a
softmax layer assigns patterns to cluster indices based on the highest
probability. On the other hand, CapsNet includes two convolutional layers with
ReLU activation, followed by a primary capsule layer (eight capsules with
eight convolution layers), utilizing the routing-by-agreement algorithm to
convey information to a secondary capsule layer for cluster probability
prediction. The squash function introduces nonlinearity, and a decoding layer
with three fully connected layers aids pattern reconstruction. The framework’s
performance is evaluated using accuracy and recall metrics and compared
against CNN and logistic regression. The CapsNet-based framework achieves
notable accuracy and recall in predicting extreme temperature events based on
Z500 patterns [68].

The challenge of long-term air temperature prediction in summer using AI
techniques is addressed in the study. ECMWF’s ERA5 reanalysis data spanning
1950 to 2021 for Paris (France) and Córdoba (Spain) is employed, incorporating
nine vital meteorological variables, including 2m air temperature, sea surface
temperature, wind components (10m and 100m), mean sea level pressure, soil
water layer, and geopotential pressure. For each region, two experiments are
carried out: one for shorter-term prediction and another for prolonged
prediction time-horizons, therefore possibly indicating a heatwave or a
coldwave occurrence. The research explores a diverse array of nine modeling
approaches, encompassing Linear Regression (LR), Lasso Regression (Lasso),
Polynomial Regression (Poly), AdaBoost, Decision Trees (DT), Random Forest
(RF), Convolutional Neural Network (CNN), CNN with Recurrence Plots (RP+CNN),
and RP+CNN with binary fusion (RP+CNN+BIN). Each method is assessed using MSE,
MAE, Pearson and Spearman rank correlation coefficients, and optimal predictor
variable subsets from exhaustive search. The CNN combined with RP approaches
accurately detects maximum temperatures, indicating heatwaves, outperforming
classical CNN and other machine learning techniques. [69]. In another study,
the investigation of the association between surface air temperature (SAT) and
land surface temperature (LST) considering land use during heat and cold wave
events is undertaken. The author employs LSTM with a memory block containing
forget, input, and output gates. These gates utilize sigmoid layers and
pointwise multiplication to govern the flow of data across the cell and neural
networks, effectively managing data dynamics. The study uses Terra and Aqua
MODIS daytime and nighttime LST data, along with observed air temperature data
obtained from 79 weather stations under the Korea Meteorological
Administration spanning the years 2008 to 2018. The performance of the model
is conducted using metrics such as R-squared, Root Mean Square Error (RMSE),
and Index of Agreement (IoA) [70].

###  III-E Deep Learning in Tropical Cyclone

Tropical cyclones are low-pressure weather systems that form over warm
tropical oceans between latitudes 23.5 degrees North and South, except in the
South-Atlantic Ocean region [71].

####  III-E1 Frequency and Identification

A study focuses on predicting TC frequency during the post-monsoon season
based on large-scale climate variables such as geopotential height, relative
humidity, sea-level pressure, and zonal wind. Three types of artificial neural
networks, namely MLP, RBF, and GRNN, are employed to develop prediction
models. The research methodology involves selecting significant predictors
using correlation analysis and utilizing historical TC frequency data from
1971 to 2013. The models are trained with data from 1971 to 2002 and evaluated
with independent data from 2003 to 2013. The MLP architecture consists of two
hidden layers with five nodes in the first layer, three nodes in the second
layer, and an output layer. The RBF network employs radial basis functions
with optimized spread parameters of 0.6, while the GRNN employs a parallel
structure with spread factors of 0.2. Results demonstrate that the MLP model
outperforms RBF and GRNN models across various evaluation metrics, showing
lower RMSE, higher correlation, and better agreement between predicted and
observed TC counts [72]. Furthermore, a multistaged deep learning framework
proposes incorporating a Mask R-CNN detector, a wind speed filter, and a
classifier based on CNN to detect TCs. The Mask R-CNN detector with the R50
FPN model predicts TC locations, trained on RGB satellite images, and
generates predictions with class labels, scores, segmentation masks, and
bounding box coordinates. A Wind Speed Filter is applied to reduce false
positives using a threshold of 34KT or higher. Cropped images based on
bounding box coordinates from the detector are fed to the DenseNet169 CNN
classifier to differentiate between true TCs and non-TCs. This methodology is
optimized using Bayesian optimization techniques. The study uses Meteosat
Visible Infra-Red Imager (MVIRI) IR satellite images from Meteosat 5 and
Meteosat 7 in the Indian Ocean Data Coverage (IODC) region. The model is
tested on a dataset of 171 images, including 88 TCs, indicating promising
performance [73].

####  III-E2 Genesis Forecast

Traditionally, meteorologists relies on various physical models and
statistical techniques to predict tropical cyclone (TC) genesis. However,
these physical models often have limitations and simplifications, which can
affect their accuracy in capturing the complex interactions and dynamics
involved in TC genesis. On the other hand, statistical models have been used
to analyze historical data and identify patterns and relationships between
different meteorological variables and TC genesis. While statistical models
can provide valuable insights and correlations, they may struggle to capture
nonlinear and complex relationships present in the data.

Short-term tropical cyclogenesis forecasting plays a critical role in
predicting the formation and development of TCs within a relatively short time
frame. A study investigated the detectability of TCs and their precursors
using a CNN model across different basins, seasons, and lead times. The CNN
architecture consists of four convolutional layers, three pooling layers, and
three fully connected layers, and finally an output layer with two units for
binary classification. The Adam optimizer is applied to the CNN to update the
network parameters to minimize the loss function called binary cross-entropy.
In the western North Pacific, the CNN successfully detects TCs and their
precursors during the period of July to November, achieving high POD ranging
from 79.0% to 89.1%, along with relatively low FAR ranging from 32.8% to
53.4%. Notably, the CNN exhibits impressive performance in detecting
precursors, with detection results of 91.2%, 77.8%, and 74.8% for precursors
occurring 2, 5, and 7 days before their formation, respectively. This method
displays promise for studying tropical cyclogenesis and exhibits robust
performance even in regions with limited training data and short TC lifetimes.
However, the detection of TCs and their precursors is found to be limited in
cases where cloud cover is extremely small (¡ 30%) or extremely large (¿ 95%).
Considering developing TCs and precursors as one category potentially affects
the ability to detect pre-TCs. Additionally, model-specific biases are
identified due to the CNN being trained solely on Nonhydrostatic ICosahedral
Atmospheric Model (NICAM) dataset. Notably, the detection performance in the
North Atlantic is relatively lower, which could be attributed to the scarcity
of training data and shorter lifetimes of TCs in that particular region [74].

In the realm of long-term cyclogenesis forecasting, various approaches have
been employed to improve the accuracy of predictions and provide insights into
the behavior of TCs over an extended period. Taking a distinctive route, a
study combines SOM and FFNN to investigate changes in TCs’ GPI and its
contributing factors for a global climate model. This study introduces a
comprehensive methodology employing two types of artificial neural networks to
project changes in North Atlantic tropical cyclone genesis potential under the
warming climate of the twenty-first century. Through SOMs, archetypal patterns
of GPI-related environmental variables are captured, arranging them on a two-
dimensional grid to retain data topology. Concurrently, FBNNs identify the
relative importance of these variables in driving projected GP changes. SOMs’
training ensures the preservation of data relationships, while FBNNs calculate
variable relevance for GP outcomes. The FBNNs’ training involves conveying
input signals to hidden-layer nodes, generating output via sigmoid functions.
The neural network framework NEVPROP4 is employed for FBNN implementation.
This dual-network approach yields significant insights into the intricate
trends of TC genesis potential as they respond to evolving environmental
conditions [75].

TABLE V: Deep Learning in Tropical Cyclone Task | Approach | Ref
---|---|---
Frequency | MLP, RBF, GRNN | [72]
Identification | CNN, Mask R-CNN | [73]
Cyclogenesis Forecast | CNN | [74]
| SOM-FNN | [75]
Track Forecast | HRBF | [76]
| ANN | [77]
| GAN | [78]
| LSTM | [79]
| CNN | [80]
Intensity Forecast | NN | [81]
| MLP | [82]
| CNN | [83]
| RNN | [84]
| CNN | [85]
| CNN | [86]
| double cascade CNN | [87]
| CNN, VGG-19 | [88]
| CNN | [89]
| CNN | [90]
| CNN, VGG | [91]
RI Prediction | CNN | [92]
| RNN, LSTM | [93]

####  III-E3 Track Forecast

The accurate prediction of TC tracks is crucial for effective disaster
preparedness and response. In recent years, deep learning techniques have
emerged as promising tools for improving TC track forecasting. In a study,
researchers aimed to utilize the neural oscillatory elastic graph matching
(NOEGM) technique for tropical cyclone (TC) pattern identification, and a
hybrid radial basis function (HRBF) network integrated with time difference
and structural learning (TDSL) algorithm for TC track prediction. The HRBF
network employs three layers, with past network outputs introduced through
time-delay units and influenced by a decay factor. The evaluation encompassed
120 TC cases spanning from 1985 to 1998. The NOEGM model achieved noteworthy
results, with 98% accurate segmentation and 97% correct classification rates
for TC pattern recognition. The HRBF model showcased an accuracy of over 86%
in TC track and intensity mining. In comparison with prevailing TC prediction
models, the proposed approach demonstrated substantial enhancements, reducing
forecast errors by more than 30% and achieving a remarkable 14% enhancement in
48-hour track forecast accuracy. [76]. Utilizing an extensive dataset spanning
32 years of cyclone best track analysis, researchers constructed an ANN model
to forecast TC positions 24 hours in advance. Notably, this model incorporates
inputs from the two most recent 6-hourly positions, along with the present
latitude and longitude, while predicting positions for a 24-hour lead time.
Through a systematic exploration, a range of both linear and nonlinear
transfer functions, such as Radial Basis Function and linear least squares
optimization, are evaluated. Furthermore, different configurations of hidden
layers and neurons are experimented with to optimize performance. The chosen
linear neural network architecture, driven by a pseudo invert learning
algorithm, yields remarkable results, achieving MAE as low as 0.75 degrees for
latitude and 0.87 degrees for longitude. The model’s effectiveness is
reinforced by a comparison of average errors: the Limited Area Model (LAM),
the National Centre for Environmental Prediction based Quasi Lagrangian Model
(QLM), and ANN models exhibit errors of 132.6 km, 142.0 km, and 127.5 km,
respectively. These findings underscore the potential accuracy of the ANN-
based approach in cyclone tracking, particularly within a 24-hour prediction
window [77]. Rüttgers et al. leveraged a Generative Adversarial Network (GAN)
to anticipate the paths of typhoons. This was accomplished by merging
satellite images from the KMA and reanalysis data from the ECMWF dataset,
covering the time span from 1993 to 2017, with a focus on typhoons that could
impact the Korean Peninsula. Training data comprised cropped segments of
historical typhoon images, while full-scale images were employed for testing
purposes. The GAN framework consisted of a generator, which utilized multi-
scale capabilities to generate diverse images, and a discriminator to
differentiate between authentic and generated images. Inputs encompassed
meteorological variables such as Sea Surface Temperature (SST), Sea Pressure,
Relative Humidity (RH), Surface velocity field (zonal and meridional
components), Velocity field at 950 mb pressure level, and Vertical wind shear
(at 850 mb and 200 mb pressure levels). The training process involved
iteratively optimizing both networks using distinct loss functions: L2 loss
for quantifying image disparities, gradient difference loss to amplify image
clarity, and adversarial loss to challenge the discriminator’s ability to
distinguish real from generated images. The findings underscored the GAN’s
efficacy in predicting typhoon trajectories and cloud formations. Accuracy in
predicting typhoon center positions was assessed, revealing that a majority
fell within 80 km (65.5%), a notable portion within 80-120 km (31.5%), and a
smaller fraction exceeded 120 km (3.0%). The overall prediction error was
significantly reduced to 67.2 km, compared to 95.6 km when relying solely on
observational data. The GAN’s ability to anticipate cloud movement patterns
underscored its potential in capturing dynamic phenomena [78]. An algorithm
based on LSTM is employed for desirable 6-24 hour nowcasting of typhoon tracks
on historical typhoon data from 1949 to 2011 in China’s Mainland. The model’s
architecture encompasses three layers: input, hidden, and output, featuring 20
LSTM cells in the hidden layer and 2 neurons in the output layer. Through
backpropagation utilizing the BPTT algorithm, errors are minimized by
comparing predictions with actual observed tracks, thus offering a substantial
advancement in typhoon track prediction [79]. An innovative approach is
introduced to predict tropical cyclone movement over a 24-hour timeframe by
combining historical trajectory data and reanalysis atmospheric images,
particularly wind and pressure fields. The technique involves adopting a
dynamic frame of reference that follows the storm center, thus enhancing the
precision of forecasts. The model’s versatility is demonstrated by its
capability to rapidly provide forecasts for newly emerging storms, a crucial
asset for real-time predictions. Leveraging an extensive database spanning
more than three decades and over 3,000 storms, sampled at six-hour intervals,
the approach integrates past displacement data, metadata, wind fields, and
geopotential height fields to capture diverse information. The methodology
involves separate training of the Wind CNN, Pressure CNN, and Past Tracks +
Meta NN, followed by integration into a fused network. Training incorporates
root mean square error (RMSE) as the loss function, with regularization to
prevent overfitting. This fusion network not only enhances prediction accuracy
but also significantly reduces testing time, making it a promising advancement
for real-time forecasting in the realm of tropical cyclones [80].

####  III-E4 Intensity Prediction

Tropical cyclone intensity prediction is a critical aspect of forecasting, and
various approaches have been explored in recent research. In a study, an
advanced neural network model is employed to predict tropical cyclone
intensity changes in the western North Pacific, incorporating climatology,
persistence, and synoptic factors. The neural network architecture consists of
three layers: an input layer with 11 units representing climatology,
persistence, and synoptic predictors; a hidden layer with 11 units capturing
complex relationships; and an output layer predicting intensity changes. The
models analyzed include a multiple linear regression model with climatology
and persistence predictors (R-CP), a neural network model with the same
predictors (N-CP), a multiple linear regression model with climatology,
persistence, and synoptic predictors (R-CPS), and a neural network model
incorporating all predictors (N-CPS). The performance of these models is
assessed through average intensity prediction errors across different
prediction intervals. The N-CPS model demonstrates superior performance in
predicting tropical cyclone intensity changes, especially over shorter time
intervals, while the N-CP model shows slight superiority over the R-CPS model
[81]. In a study, the use of MLP models capable of forecasting intensity
changes at 3-hour intervals beyond 72 hours in the North Indian Ocean,
specifically in the Bay of Bengal and Arabian Sea, is explored. The
architecture of the MLP model incorporates central pressure (CP), maximum
sustained wind speed (MSWS), pressure drop (PD), total ozone column (TOC), and
sea surface temperature (SST) as inputs for predicting cyclone intensity. The
model’s effectiveness is assessed using metrics like RSME and MAE, revealing
the MLP’s superior performance compared to other models like RBFN, MLR, and
OLR for forecasting cyclone intensity. The models’ individual performances are
evaluated for various cyclones, accounting for varying sea surface
temperatures over the Arabian Sea and Bay of Bengal [82]. In a study, a CNN
model is used to estimate the intensity of tropical cyclones in the Atlantic
and Pacific regions. The proposed model uses a comprehensive dataset
comprising two distinct components: a collection of 48,828 infrared (IR)
hurricane images sourced from the Marine Meteorology Division of the U.S.
Naval Research Laboratory, and HURDAT2 data to label these images. The model’s
architecture integrates convolutional layers with varying filter sizes and
strides, followed by strategic max-pooling for down-sampling. Complemented by
local response normalization and fully connected layers with ReLU activation,
the model incorporates regularization techniques like dropout to prevent
overfitting. Weight updates are fine-tuned through SGD optimization with
momentum, and a specialized Softmax loss layer facilitates accurate multi-
class classification. By autonomously extracting pivotal features from TC
images, this methodology achieves better accuracy and reduced RSME, indicating
a significant advancement in tropical cyclone intensity estimation [83].
Additionally, a study investigates the application of RNN for forecasting TC
intensity by leveraging historical observation data in the Western North
Pacific since 1949. The RNN architecture captures intricate relationships
among sequential elements—longitude, latitude, and intensity—across input,
hidden, and output layers. Integrating a backpropagation through time
optimization algorithm, the model refines weights and biases. Employing a
cross-entropy loss function, it gauges disparities between predicted and
actual TC intensity, with the hidden layer employing the tanh activation
function. Notably, the model excels, achieving a compelling 5.1 m/s error in
24-hour forecasts, outperforming select dynamical models and closely
approximating subjective predictions.[84]. A dual-branch CNN model is proposed
for estimating tropical cyclone (TC) intensity in the Northwest Pacific. The
model exhibits strong performance for tropical storm and super typhoon
categories but demonstrates reduced accuracy for moderate intensity and the
weakest tropical depression category. The architecture of the TCIENet model
comprises two parallel CNN branches designed for processing infrared and water
vapor images. Each branch includes essential modules for feature extraction,
water vapor attention, and intensity regression, with the overall goal of
capturing the intricate relationship between image patterns and TC intensity.
The training is facilitated by the Adam optimizer, utilizing techniques such
as Softmax operation, dropout regularization, and L1 and L2 loss functions to
enhance its predictive capability. The research also delves into the impact of
diverse image sizes and model components on intensity estimation accuracy,
leveraging metrics like RMSE, MAE, bias, and absolute error to evaluate the
model’s effectiveness [85]. Tian et al. presented a novel CNN-based hybrid
model designed to accurately estimate tropical cyclone intensity by harnessing
46,919 infrared images sourced from the Pacific Northwest and Atlantic Ocean.
This architecture incorporates a classification model, fine-grained regression
models, and a Back-propagation neural network. The classification model
effectively categorizes TC samples into distinct intensity levels, thereby
guiding the selection of appropriate regression models. The model’s
optimization is carried out using the Adam optimization algorithm, while a
cross-validation loss function is employed for both classification and
regression tasks. Notably, the model achieves exceptional accuracy and
remarkably low RSME, outperforming the existing methodologies [86]. The
TCICENet model offers a novel approach to accurately classifying and
estimating tropical cyclone intensity using infrared satellite images from the
northwest Pacific Ocean basin. This model adopts a cascading deep-CNN
architecture consisting of two essential components: TC intensity
classification (TCIC) and TC intensity estimation (TCIE). The TCIC module
employs convolutional layers to categorize TC intensity into three specific
classes, while the TCIE module, inspired by a modified AlexNet structure,
predicts intensity values across different TC intensity categories. Notably,
the TCIC module employs a cross-entropy loss with L2 regularization, and the
TCIE module employs a SmoothL1 loss function for precise intensity estimation.
The model’s effectiveness is validated using a dataset encompassing 1001 TCs
from 1981 to 2019, partitioned into distinct sets for training, validation,
and testing. Evaluation based on intensity estimation metrics reveals
impressive performance, achieving an overall root mean square error of 8.60 kt
and a mean absolute error of 6.67 kt in comparison to best track data. [87].
In a separate study, a CNN model is utilized to predict the intensity levels
of hurricanes using IR satellite imagery data from HURSAT and wind speed data
from the HURDAT2 of the Greater Houston region. The architecture involves
sequential layers: input, convolution, pooling, and fully connected, guided by
ReLU activations, MSE loss, and RmsProp/Adam optimizers. This facilitates
accurate hurricane intensity estimation, pattern recognition for storm
categorization by severity, achieving lower RMSE (7.6 knots) and MSE (6.68
knots) through batch normalization and dropout layers. Additionally, a VGG19
model is employed to evaluate the extent of damage and automate annotation of
satellite imagery data. The VGG 19 model undergoes fine-tuning for hurricane
damage prediction and classification of severe weather events. The
optimization process is guided by the Adam Optimizer, utilizing MSE as the
foundational loss function. The models are subjected to rigorous evaluation,
encompassing a diverse set of metrics including RMSE, MAE, MSE, and Relative
RSME. Notably, the model demonstrates remarkable performance, achieving a 98%
accuracy in predicting hurricane damage and a 97% accuracy in classifying
severe weather events [88]. Furthermore, a model called DeepTCNet is proposed
specifically designed for TC intensity and size estimation in the North
Atlantic by using IBTrACS and the Hurricane Satellite dataset. The study
harnesses CNN as the core architecture within DeepTCNet to estimate TC
intensity and wind radii from IR imagery. Extensive experimentation
establishes VGGNet with 13 layers and compact (3 × 3) convolutional filters as
the optimal configuration, forming the foundational structure for DeepTCNet.
The evaluation presents MAE for TC intensity estimation (measured in knots) on
the test dataset across various depths and kernel sizes in VGGNet’s initial
convolutional layer. Leveraging the Adam optimization with default parameters,
learning occurs through the adoption of MAE as the loss function. This
holistic approach exemplifies the seamless fusion of physics-augmented deep
learning, culminating in enhanced TC analysis and prediction capabilities
[89]. The study focuses on estimating TC intensity using a CNN model.
Satellite IR imagery and Best Track data are employed to analyze 97 TC cases
over the Northwest Pacific Ocean from 2015 to 2018. The CNN architecture
encompasses an input layer, four convolutional layers, four pooling layers,
two fully connected layers, and an output layer, resulting in the derivation
of eight intensity values. Notably, the multicategory CNN achieves an accuracy
of 84.8% for TC intensity estimation, which further improves to 88.9% through
conversion to a binary classification task. [90]. Another study proposes a CNN
model for estimating TC intensity using Himawari-8 satellite cloud products,
including cloud optical thickness (CLOT), cloud top temperature (CLTT), cloud
top height (CLTH), cloud effective radius (CLER), and cloud type (CLTY). The
model’s architecture is based on the VGG framework, enhanced with attention
mechanisms and residual learning to improve precision while reducing parameter
count. The CNN comprises four convolutional blocks with progressively larger
filter sizes, integrating residual learning at different levels and a
Convolutional Block Attention Module (CBAM) after a maximum pooling layer.
Batch normalization and dropout layers are employed to counter overfitting.
The model is optimized using the Adam optimizer and MAE loss function. It
undergoes training and tuning through six-fold cross-validation and is
evaluated on independent test data, utilizing real-time typhoon track
information from the western North Pacific basin alongside Himawari-8 cloud
products [91]. These studies contribute valuable insights into improving
tropical cyclone intensity forecasts through the utilization of advanced
neural network models.

In addition to general tropical cyclone intensity forecasting, a particularly
challenging aspect is predicting Rapid Intensification (RI), where a tropical
cyclone undergoes a sudden and significant strengthening over a short
timeframe. RI is a critical phenomenon due to its potential to escalate a
relatively mild storm into a highly destructive force, posing severe threats
to coastal communities and infrastructure.

To address the complexities of RI prediction, a CNN model called TCNET was
developed to enhance the prediction of RI in tropical cyclones by extracting
features from large-scale environmental conditions. The study used ECMWF ERA-
Interim reanalysis data and the SHIPS database. TCNET’s architecture consists
of data filters, a customized sampler (GMM-SMOTE), an XGBoost classifier, and
hyperparameter tuning. This model’s performance outperforms COR-SHIPS and LLE-
SHIPS in RI prediction, yielding superior results in terms of kappa, PSS, POD,
and FAR metrics. Moreover, TCNET identifies previously unexplored variables,
such as ozone mass mixing ratio, that influence RI. The training of TCNET
involves backpropagation, utilizing mean square error as the loss function and
Adam optimizer for weight updates of the filters [92]. In another study, deep
learning models including RNN and LSTM were explored for predicting tropical
cyclone intensity and rapid intensification (RI). The proposed approach
involved convolutional layers for autonomous feature extraction from satellite
images, an RNN block with ConvLSTM cells for feature evolution, and a final
output regressor composed of convolutional and dense layers to forecast
tropical cyclone intensity (Vmax) at +24 hours. Additionally, the study
introduced a deep learning ensemble strategy involving 20 models with diverse
designs, effectively improving TC intensity and RI prediction by incorporating
both conventional and satellite-derived features. This ensemble method offered
intensity distributions for deterministic predictions, RI likelihood
estimation, and prediction uncertainty assessment, yielding improved RI
detection probabilities and reduced false-alarm rates compared to operational
forecasts for western Pacific TCs [93].

##  IV Challenges

The effective utilization of DL models in weather forecasting is accompanied
by several challenges that require careful consideration. In this section, we
explore several key challenges associated with the application of deep
learning in the field of weather forecasting.

####  IV-1 Data Availability

Data availability is crucial for advancing the capabilities of deep learning
models in meteorological applications. Limited access to historical records,
real-time observations, and specialized data sources can hamper model
development and evaluation [94]. Addressing data availability challenges
requires establishing robust data-sharing frameworks, promoting data
collaboration between meteorological organizations, and exploring innovative
approaches to gather and enhance meteorological data.

####  IV-2 Data Quality

Ensuring data quality poses a significant challenge for deep learning models
in meteorology. Weather data, obtained from various sources like weather
stations, satellites, and radars, may have limitations in terms of spatial
coverage, temporal resolution, and accuracy. Missing or inaccurate
observations can introduce biases and errors. For example, inadequate
temperature measurements in remote regions due to limited weather station
distribution can lead to incomplete climate models, potentially affecting the
accuracy of long-term weather predcitions.

####  IV-3 Model Architecture

DL models often consist of complex architectures with numerous layers and
parameters, posing challenges in their design and optimization for weather
forecasting. Determining the optimal network architecture, selecting
appropriate activation functions, and managing computational resources are
critical task in developing efficient DL models for meteorological
applications [8].

####  IV-4 Hybrid Approach

Combining DL techniques with traditional physical models can leverage the
strengths of both approaches, leading to more accurate and reliable
predictions. DL models excel at learning complex patterns and capturing
nonlinear relationships in large datasets [94], while traditional physical
models provide valuable insights into the underlying physical processes. For
instance, coupling a deep learning algorithm with a NWP model can allow the DL
component to capture intricate spatial patterns in satellite imagery, while
the physical model contributes its understanding of atmospheric physics. This
collaborative approach offers the potential for more precise predictions of
complex meteorological events. However, integrating DL models with existing
frameworks triggers challenges such as resource requirement, data quality and
interpretability.

####  IV-5 Data Heterogeneity

Data heterogeneity refers to the diversity of data sources, formats, and
features, which can complicate the integration and analysis of different data
types. For instance, in the development of a deep learning model for weather
prediction, information must be accumulated from various sources such as
satellites, radar systems, automatic weather stations, numerical models and
manual observations. However, each of these sources employs its own distinct
method of storing data. Satellites often employ formats like TIFF or GeoTIFF,
while radar data may utilize formats such as HDF5 or NetCDF, and other sources
could have unique formats. To ensure the seamless operation of a weather
prediction model, these different types of data must be harmonized, enabling
the model to effectively learn from the combined data and resulting in
accurate and reliable weather forecasts.

####  IV-6 Model Explainability

Model explainibility refers to the how a model process and transform input
into corresponding output, making the process transparent and easy to
comprehend [95]. For instance, if the model predicts upcoming rain,
meteorologists need to comprehend the specific meteorological variables and
potential biases influencing its predictions. This understanding becomes
crucial as the model transitions to real-world application, allowing its
developers to provide insights into its functioning.

##  V Discussion and Future Directions

The integration of deep learning methods into the study of extreme weather
events brings about a significant transformation, expanding our understanding
and predictive abilities. These advanced models not only excel in deciphering
intricate spatial relationships but also stand out in unraveling the complex
timing patterns inherent in meteorological phenomena. This advancement holds
the potential to greatly improve weather forecasting accuracy across a wide
range of events, from thunderstorms and lightning occurrences to the tracking
of tropical cyclones.

At the heart of this innovation lies the natural capacity of deep learning
models to identify and replicate non-linear relationships within the complex
fabric of atmospheric data. By analyzing extensive sets of information, these
models unearth hidden patterns and interactions that conventional methods
struggle to capture. However, as we move forward with these promising
advancements, it becomes crucial to address certain key challenges that must
be overcome to fully realize the potential of deep learning in meteorology.
One such challenge revolves around the continuous need for high-quality and
comprehensive data. The effectiveness of deep learning models relies on their
exposure to a diverse array of carefully curated data points. This emphasizes
the importance of creating robust data pipelines and well-organized datasets.
Furthermore, the inherently complex nature of deep learning architectures
presents a dilemma regarding their interpretability. Ensuring that the
decisions made by these intricate models can be comprehended and validated by
experts in meteorology remains an ongoing endeavor.

The future of weather prediction calls for the exploration of ensemble
techniques that combine the strengths of various models to produce more
comprehensive and accurate forecasts. This pursuit involves developing
innovative approaches that seamlessly integrate deep learning models with
traditional numerical weather prediction methods, drawing on the well-
established physical understanding of atmospheric processes. The convergence
of deep learning capabilities with specialized insights in meteorology emerges
as a fertile area for further exploration. Hybrid models that blend empirical
meteorological knowledge with the computational power of deep learning offer a
promising path to enhancing forecast accuracy and reinforcing our ability to
handle the multifaceted impacts of extreme weather events.

In the broader context of this review, a clear message underscores the
persistent drive for progress, necessitating ongoing collaboration and
interdisciplinary synergy. By harnessing the capabilities of deep learning and
pushing the boundaries of meteorological understanding, we are positioned to
empower decision-makers and stakeholders with invaluable tools to proactively
mitigate the far-reaching consequences of the ever-evolving realm of extreme
weather events.

In weather forecasting, there exist several research gaps that need to be
addressed to enhance the capabilities and effectiveness of these models.
Firstly, in drought prediction, current studies lack long-term forecasting
capabilities and are limited in spatial resolution. Improving these aspects is
crucial to provide accurate and detailed information on drought conditions,
enabling proactive mitigation measures. In the case of tropical cyclones,
there is a notable absence of studies focused on pattern identification.
Efforts should be directed towards reducing the cone of uncertainty by
improving track accuracy, size estimation, and spatial distribution of
cyclones. More research is needed in predicting RI and associated weather
phenomena such as storm surge, floods, and quantitative precipitation
forecasts. The lack of practical success stories in this area underscores the
need for further investigation and advancements. In heatwave prediction, there
is a paucity of research focused on the frequency and duration of heatwaves.
The prediction of severe thunderstorms poses its own set of challenges. To
improve forecasts in this domain, exploring other ensemble techniques,
incorporating feature selection methods, and leveraging dynamic graph modeling
approaches can be beneficial. Integrating data from multiple NWP models, along
with high-resolution NWP models, holds promise for enhancing thunderstorm
forecasts. The intensity, frequency, and location prediction of lightning
strikes require further attention. Monitoring and predicting lightning
strikes, especially in discreetly distributed scenarios, remain complex tasks
that require advanced techniques and data integration. Radar and satellite
data play a crucial role in weather forecasting. However, challenges persist
in utilizing radar data to make predictions without clear indications of
initial convections. Exploiting the early-stage signals of convections using
radar and satellite data can aid in improving forecast accuracy, particularly
in mitigating false alarms. Cloud-related weather forecasting also faces
challenges, including high computation time and resource requirements.
Inefficient observations due to rain, strong winds, foggy conditions, and
sunsets further hinder the efficiency of cloud-related forecasting methods.
Addressing these challenges is essential to unlock the full potential of deep
learning in cloud prediction. Further research is required to enhance hail
detection, size estimation, forecasting, and damage assessment methods using
deep learning techniques, despite recent advancements in hail-related studies.
Improving models, such as NWP, remains a substantial challenge.

##  VI Conclusion

This review highlighted the significant advancements and promising potential
of deep learning techniques in the field of meteorology, specifically in
extreme weather events. Deep learning models, such as CNN and RNN,
demonstrated their effectiveness in various applications, including cyclone
prediction, severe rainfall and hail prediction, cloud and snow detection,
rainfall-induced flood, landslide forecasting, and more. The utilization of
deep learning algorithms allowed researchers to extract intricate patterns and
features from complex meteorological datasets, leading to improved accuracy
and performance in weather prediction and analysis. These models showed
remarkable skill in capturing spatial and temporal dependencies in weather
data, enabling more accurate predictions of extreme events and enhancing our
understanding of their underlying dynamics. Furthermore, deep learning methods
offered advantages over traditional statistical approaches by automatically
learning representations and hierarchies of features, eliminating the need for
manual feature engineering. This allowed for more efficient and effective
analysis of large-scale meteorological datasets, facilitating the development
of advanced forecasting models and decision-support systems. Deep learning
models also provided an alternative approach by directly learning the
relationships between input observations and output variables from data,
circumventing the computational bottlenecks and time lags associated with
physics-based models. However, further advancements were needed to enhance the
performance and efficiency of these models in weather forecasting
applications. Closing these research gaps and advancing the field of deep
learning in weather forecasting would contribute to more accurate, reliable,
and timely predictions, ultimately benefiting various sectors and society as a
whole.

## Acknowledgment

The author would like to express heartfelt gratitude to the India
Meteorological Development (IMD) and the Indian Institute of Information
Technology, Allahabad (IIIT Allahabad) for their invaluable support and
contributions to this journal. Their guidance, resources, and assistance have
been instrumental in the successful completion of this research.

## Conflicts of Interest

The authors declare no conflict of interest.

## Abbreviations

The following abbreviations are used in this manuscript:

ANFIS | Adaptive Neuro-Fuzzy Inference System
---|---
ANN | Artificial Neural Networks
ARIMA | Autoregressive Integrated Moving Average
BPNN | Back Propagation Neural Network
CAPE | Convective Available Potential Energy
CapsNets | Capsule Neural Networks
CBAM | Convolutional Block Attention Module
CFS | Climate Forecast System
CG | Cloud-To-Ground
CNN | Convolutional Neural Networks
Conv2d | 2D Convolution Layer
CRF | Conditional Random Fields
CRPS | Cumulative Ranked Probability Score
DBN | Deep Belief Network
DL | Deep Learning
DLNN | Deep Learning Neural Network
DNN | Deep Neural Network
EEMD | Ensemble Empirical Mode Decomposition
EMD | Empirical Mode Decomposition
ENSO | El Niño-Southern Oscillation
FAR | False Alarm Rate
FFNN | Feed Forward Neural Networks
GAN | Generative Adversarial Network
GNN | Graph Neural Network
GNSS | Global Navigation Satellite System
GPI | Genesis Potential Index
GRNN | Generalized Regression Neural Network
GRU | Gated Recurrent Unit
HR | Heavy Rain
HREF | High-Resolution Ensemble Forecast
IMD | India Meteorological Department
IMF | Intrinsic Mode Function
IR | Infrared
JTWC | Joint Typhoon Warning Center
LM-ResNet | Lightning Monitoring Residual Network
LRCN | Long-Term Recurrent Convolutional Network
LST | Land Surface Temperature
LSTM | Long Short-Term Memory
MAE | Mean Absolute Error
ML | Machine Learning
MLP | Multi-Layer Perceptrons
MLR | Multiple Linear Regression
MNLR | Multivariate Non-Linear Regression
NARX | Nonlinear Autoregressive Exogenous
NCEP | National Centers For Environmental Prediction
NIO | North Indian Ocean
NNGA | Neural Network-Genetic Algorithm
NWP | Numerical Weather Prediction
PCA | Principal Component Analysis
PDSI | Palmer Drought Severity Index
POD | Probability Of Detection
POSH | Probability Of Severe Hail
QPE | Quantitative Precipitation Estimation
RBF | Radial Basis Function
RBM | Restricted Boltzmann Machines
RF | Random Forest
RI | Rapid Intensification
---|---
RMSE | Root Mean Square Error
RNN | Recurrent Neural Networks
RP | Recurrence Plots
SAT | Surface Air Temperature
SCW | Severe Convective Weather
SGD | Stochastic Gradient Descent
SIAP | Standard Index Of Annual Precipitation
SOM | Self-Organizing Maps
SPEI | Standardized Precipitation Evapotranspiration Index
SPI | Standardized Precipitation Index
SSI | Standardized Streamflow Index
SVM | Support Vector Machine
SWE | Snow Water Equivalent
SWG | Stochastic Weather Generator
SWSI | Standardised Water Storage Index
TC | Tropical Cyclone
TPW | Total Precipitable Water
VAEGAN | Variational Autoencoder Gan
VGG | Visual Geometry Group
WANN | Wavelet-Artificial Neural Network
WGAN | Wasserstein Generative Adversarial Network
WMO | World Meteorological Organization’S
WPT | Wavelet Packet Transform

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