# Fact Check: Analyzing Financial Events from Multilingual News Sources

Linyi Yang1 Tin Lok James Ng2 Barry Smyth1&Ruihai Dong1 1Insight Centre,
University College Dublin
2Trinity College Dublin {linyi.yang, barry.smyth, ruihai.dong}@insight-
centre.org<EMAIL_ADDRESS>
###### Abstract

The explosion in the sheer magnitude and complexity of financial news data in
recent years makes it increasingly challenging for investment analysts to
extract valuable insights and perform analysis. We propose FactCheck in
finance, a web-based news aggregator with deep learning models, to provide
analysts with a holistic view of important financial events from multilingual
news sources and extract events using an unsupervised clustering method. A web
interface is provided to examine the credibility of news articles using a
transformer-based fact checker. The performance of the fact checker is
evaluated using a dataset related to merger and acquisition (M&A) events and
is shown to outperform several strong baselines.

##  1 Introduction

A tremendous amount of financial news sources are available online nowadays.
The large weave of information generated by these news sources can be
overwhelming and confusing for financial investors, which makes the
discrimination of useful information out of tons of worthless or repetitive
data a challenging and tedious task. While an investor can rely on a
particular news source to learn about current financial events, the coverage
of the events will not be comprehensive and may be constrained to the
geographic location of the news source. On the other hand, the use of a
financial news aggregator can provide investors with a coverage of the same
financial events from multiple news sources.

Recently, the use of deep learning and natural language processing (NLP) in
the analysis of unstructured data has attracted much research interests in the
financial domain Ding et al. (2014); Yang et al. (2018, 2020b, 2020c).
Motivated by the impressive success in the applications of these methods, we
propose an event-based financial news aggregator along with a novel,
transformer-based architecture for assessing the validity of events. While
various general purpose and domain-specific news aggregators are available,
our objective is to develop a financial news aggregator which gathers relevant
news information from multiple sources in different languages. Unlike many
general purpose aggregatorsZhang et al. (2019), the news aggregator we
introduce identifies and constructs actual events that are being discussed in
the articles from various news sources, which aims to provide investors with a
holistic view of important events. Figure 1 describes an example of cross-
lingual linking of news articles related to accounting scandal of Luckin
Coffee which caused its share price to tumble as much as 83%.

The accuracy of each constructed event is assessed using a transformer-based
fact checker. Existing fact checking of news events in the financial domain is
typically performed by human
experts111http://www.factcheck.org/222http://www.snopes.com/, which is
constrained by the experience of the journalists and lacks scalability. The
transformer-based fact checker overcomes these shortcomings and achieved
reliable results, as demonstrated in our case study.

Figure 1: News articles related to the accounting scandal of Lucking Coffee in
both English and Chinese language.

##  2 Detailed Implementations

In this section, we describe our data first. Then, we introduce the process of
building our system based on AllenNLP333https://github.com/allenai/allennlp
with a micro web framework, Flask. Lastly, a novel transformer-based
prediction model will be discussed.

The architecture of our system is sketched in Figure 2. Our system mainly
contains two components: News Aggregator, and Fact Checker. In addition, we
have translated all news in bilingual of English and Simplified Chinese by a
online API-based translator444https://fanyi-api.baidu.com/. The primary
technical contributions include a event-based financial news aggregator and a
fact checker based on a novel, transformer-based architecture.

Figure 2: The overall architecture of our system.

###  2.1 Data

We collected news articles published between January 1st, 2020 and April 22nd,
2020 from Reuters, Fox and Sina. The resulting dataset contains a total of
183,991 news articles with 116,390 in English and 67,601 in Chinese.

###  2.2 News Aggregator

Our website provides both English and Chinese language versions. Non-English
news articles are first translated into English, and event extraction is
subsequently performed using an unsupervised news clustering model proposed by
Ribeiro et al. (2017). More specifically, hashtags, which correspond to the
events that are mentioned in the news article, are assigned to each article.
The result of each search query depends on both the hashtags and the content
of the news articles, and news articles mentioning the same events in
different languages will be jointly displayed (as shown in Figure 3).

###  2.3 Fact Checker

In order to assess the credibility of each news article, we adopt a
transformer-based fact checker. We leverage the adversarial training strategy,
namely, projected gradient descent (PGD) Madry et al. (2018) for training the
transformer. The adversarial training is implemented on WWM-BERT, whereby
perturbations are added to the input embedding to improve the robustness of
the model.

As a case study, we train a transformer-based classifier to predict the
outcome of an merger and acquisition (M&A) event Yang et al. (2020a) using
relevant news articles. A dataset which contains news articles related to
14,539 M&A deals between January 1st, 2007 and August 12th, 2019 were
collected from Zephyr. After preprocessing, the final dataset contains 4,098
instances which were further divided into a training, validation and test set.

Figure 3: The page of the news aggregator. Each column represents the news
from each source, while each row demonstrates the different reports of a given
event.

##  3 A Case Study on M&A Predictions

We evaluate the performance of the binary classification task described above.
The adversarial transformer is compared with several deep learning based
methods and an original transformer where the results are summarized in Table
1. The proposed model achieves significant improvement over strong baselines
in terms of MCC, accuracy and F1 scores. Additionally, the improvement over
the original transformer is statistically significant according to an
independent t-test (with a p-value of 0.014). The fine-tuned transformer model
can be leveraged to assess the credibility of news articles for a wide range
of financial events.

Evaluation | MCC | Acc | F1
---|---|---|---
Random Guess | 0.013 | 0.510 | 0.462
Baselines |  |  |
CNN-Text | 0.729 | 0.848 | 0.847
BiGRU | 0.734 | 0.836 | 0.849
HAN | 0.742 | 0.848 | 0.853
BERT-WWM | 0.751 | 0.874 | 0.879
Ours |  |  |
BERT-WWM+adv. | 0.788 | 0.894 | 0.894
Table 1: “MCC” is the score of Matthews Correlation Coefficient and “adv” is
short for the adversarial training by using Projected Gradient Descent (PGD).

##  4 Discussion

A website for aggregating financial news articles and assessing the
credibility of news articles is described in this paper. The developed
adversarial transformer based fact checker is shown to outperform a range of
strong baselines in terms of predicting the outcomes of M&A deals. The trained
transformer is readily applicable to predict the outcomes of various financial
events.

For the future work, the developed system can be expanded to include
additional news sources in multiple languages. Furthermore, the pre-trained
transformer can be transferred to a wide range of financial event prediction
problems.

## References

  * Ding et al. [2014] Xiao Ding, Yue Zhang, Ting Liu, and Junwen Duan.  Using structured events to predict stock price movement: An empirical investigation.  In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1415–1425, 2014.
  * Madry et al. [2018] Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu.  Towards deep learning models resistant to adversarial attacks.  In Proceedings of the International Conference on Learning Representations (ICLR), 2018.
  * Ribeiro et al. [2017] Swen Ribeiro, Olivier Ferret, and Xavier Tannier.  Unsupervised event clustering and aggregation from newswire and web articles.  In Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism, pages 62–67, 2017.
  * Yang et al. [2018] Linyi Yang, Zheng Zhang, Su Xiong, Lirui Wei, James Ng, Lina Xu, and Ruihai Dong.  Explainable text-driven neural network for stock prediction.  In 2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS), pages 441–445. IEEE, 2018.
  * Yang et al. [2020a] Linyi Yang, Eoin Kenny, Tin Lok James Ng, Yi Yang, Barry Smyth, and Ruihai Dong.  Generating plausible counterfactual explanations for deep transformers in financial text classification.  In Proceedings of the 28th International Conference on Computational Linguistics, pages 6150–6160, 2020.
  * Yang et al. [2020b] Linyi Yang, Jiazheng Li, Barry Smyth, and Ruihai Dong.  Maec: A multimodal aligned earnings conference call dataset for financial risk prediction.  In Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pages 3063–3070, 2020.
  * Yang et al. [2020c] Linyi Yang, Tin Lok James Ng, Barry Smyth, and Ruihai Dong.  Html: Hierarchical transformer-based multi-task learning for volatility prediction.  In Proceedings of The Web Conference 2020, WWW’20, page 441–451, 2020.
  * Zhang et al. [2019] Yifan Zhang, Giovanni Da San Martino, Alberto Barrón-Cedeno, Salvatore Romeo, Jisun An, Haewoon Kwak, Todor Staykovski, Israa Jaradat, Georgi Karadzhov, Ramy Baly, et al.  Tanbih: Get to know what you are reading.  EMNLP-IJCNLP 2019, pages 223–228, 2019.


# Dry-to-Wet Soil Gradients Enhance Convection and Rainfall over Subtropical
South America

###### Abstract

Soil moisture-precipitation (SM-PPT) feedbacks at the mesoscale represent a
major challenge for numerical weather prediction, especially for subtropical
regions that exhibit large variability in surface SM. How does surface
heterogeneity, specifically mesoscale gradients in SM and land surface
temperature (LST), affect convective initiation (CI) over South America? Using
satellite data, we track nascent, daytime convective clouds and quantify the
underlying antecedent (morning) surface heterogeneity. We find that convection
initiates preferentially on the dry side of strong SM/LST boundaries with
spatial scales of tens of kilometers. The strongest alongwind gradients in LST
anomalies at 30 km length scale underlying the CI location occur during weak
background low-level wind ($<$2.5m/s), high convective available potential
energy ($>$1500J/kg) and low convective inhibition ($<$250J/kg) over sparse
vegetation. At 100 km scale, strong gradients occur at the CI location during
convectively unfavorable conditions and strong background flow. The location
of PPT is strongly sensitive to the strength of the background flow. The wind
profile during weak background flow inhibits propagation of convection away
from the dry regions leading to negative SM-PPT feedback whereas strong
background flow is related to longer lifecycle and rainfall hundreds of
kilometers away from the CI location. Thus, the sign of the SM-PPT feedback is
dependent on the background flow. This work presents the first observational
evidence that CI over subtropical South America is associated with dry soil
patches on the order of tens of kilometers. Convection-permitting numerical
weather prediction models need to be examined for accurately capturing the
effect of SM heterogeneity in initiating convection over such semi-arid
regions.

##  1 Introduction

The boundary layer evolution over a heterogeneously moist surface is different
from that over uniform terrain due to the spatial variability of surface
buoyancy flux and the resultant wind circulation (Pielke et al., 1991; Segal
and Arritt, 1992a; Taylor et al., 2007; Lee et al., 2019). This is of
particular importance for “transition zones” between dry and wet climatic
regimes where evaporation is limited by soil moisture (Koster, 2004), which
controls the partitioning of surface energy into heat and moisture fluxes.
Contingent on the background wind and the length scale of surface
heterogeneity, the induced circulation can increase the spatial variation in
boundary layer depth, mixed layer potential temperature and convective forcing
(Chen and Avissar, 1994; Cheng and Cotton, 2004), and provide vertical motion
triggers that can initiate deep convection (Taylor et al., 2011). The role of
soil moisture (SM) heterogeneity and mesoscale circulations in convective
initiation (CI) has not been studied over subtropical South America.

Numerical modeling studies using cloud-resolving models or large-eddy
simulations have shown that convergence and moist ascent is maximized over
strong dry-wet SM boundaries of an idealized heterogeneous surface and
convective triggering preferentially occurs near the downwind end of the drier
surface region (Segal and Arritt, 1992b; Chen and Avissar, 1994; Cheng and
Cotton, 2004; Adler et al., 2011; Garcia-Carreras et al., 2011; Kang and
Bryan, 2011; Rieck et al., 2014; Rochetin et al., 2017; Lee et al., 2019). The
strength of this induced flow is sensitive to the length scale of SM
heterogeneity, the magnitude of temperature gradient at the surface and the
strength of background wind. Using computationally-limited domain sizes
($<$200 km) and checkerboard-style representation of surface heterogeneity,
these studies have converged on a numerically-determined empirical limit of
3-5 m/s for the strongest background wind. This framework suffers major
drawbacks. The checkerboard representation of surface heterogeneity does not
provide a true picture of how this process occurs in nature. The simulated
maximum patch sizes of up to 40km is limited by domain size. We hypothesize
that the determined limit of background wind speed is a byproduct of the patch
size limitation. In the real world, SM heterogeneity can occur over a spectrum
of length scales from tens to hundreds of kilometers. The observed spectrum of
length scales of SM heterogeneity associated with CI has not been studied in
the context of mesoscale circulations.

Following CI over drier soils, the soil moisture-precipitation (SM-PPT)
feedback can vary in strength as well as sign due to the background conditions
and the length scale considered (Emori, 1998; Schär et al., 1999). For
example, Froidevaux et al. (2014) used an idealized cloud-resolving framework
to show positive feedback when convection initiates over dry soil patches but
the convective cells with longer lifetime propagate over wet soil patches,
strengthen and preferentially precipitate. Negative feedback occurs when a
weak wind profile cannot support the propagation of convective features from
dry to wet areas leading to more rainfall over the dry patch itself.
Understanding the role of SM anomalies in afternoon convection and accurately
quantifying convective scale SM-PPT feedbacks is critical for the prediction
of PPT from diurnal to seasonal time-scales as convective parameterizations
and spatial scale may confound both the feedback strength and the sign (Tuttle
and Salvucci, 2016). This scale-dependence of SM-PPT feedbacks has been
demonstrated in case of numerical model output but observational support is
lacking.

Recent work has used remotely-sensed observations to extract the “fingerprint”
of SM heterogeneity in CI occurrence and PPT accumulation. Variations in SM at
length scales of 10-40 km exert a strong control on storm initiation in the
Sahel (Taylor et al., 2011), Europe (Taylor, 2015) and the Tibetan plateau
(Barton et al., 2021). Taylor et al. (2011) showed that 37% of all analyzed
storm initiations over the Sahel region occurred over the steepest 25% of SM
gradients. Global analyses of mesoscale SM patterns antecedent to afternoon
PPT show a clear preference for rainfall over spatial minima in SM anomalies
(Taylor et al., 2012; Guillod et al., 2015). These studies provide strong
evidence for preferential daytime convection and rainfall over spatially drier
patches of soil of the order of tens of kilometers in semi-arid regions.

Daytime convection is the most important mechanism contributing to seasonal
rainfall accumulation over subtropical South America, specifically over the La
Plata river basin (LPB). Organized convective storms can contribute more than
70% of the rainfall in this region (Nesbitt et al., 2006; Rasmussen et al.,
2016). These storms typically originate in early afternoon hours that
coincides with the theoretical maximum probability of CI due to SM
heterogeneity-induced mesoscale circulations. Maximum PPT accumulation is
observed in late night and early morning hours when these storms progress
eastward and can last up to 18-24 hours after initiation (Rasmussen et al.,
2016). Past studies have highlighted the importance of SM in driving the
spatiotemporal variation of ET, air temperature and PPT (Baker et al., 2021)
over South America. Interactions between the land surface and the atmosphere
are important drivers of weather and climate over subtropical South America,
one of the “hotspots” of land-atmosphere coupling (Koster, 2004; Sörensson and
Menéndez, 2011; Ruscica et al., 2015). Typical coarse-grid analyses cannot
distinguish the effect of land-atmosphere interactions during the different
stages of convection (from initiation to dissipation) and yield an incomplete
picture of the role of surface feedbacks in regional PPT. The aforementioned
“fingerprint” technique applied to high-resolution observations over South
America can fill the identified gaps in convective-scale SM feedback studies.

The goals of this study are: a) quantify the relationship between daytime
convection and the antecedent, underlying, SM anomalies, b) formulate data-
driven hypothesis on how background geographical and atmospheric conditions
affect the SM-CI relationship, and c) analyze the controls on the strength and
sign of SM-PPT feedback for CI over drier soils. We use satellite observations
of infrared brightness temperature (Tb), surface SM, land surface temperature
(LST) and PPT to examine the influence of mesoscale SM variability on the
initiation of convection and subsequent rainfall during the warm season
(November-March; NDJFM) in South America.

Figure 1: a) Elevation (m; blue contours) and standard deviation of elevation
(m; orange shading) over every 40km x 40km area (i.e. topographic complexity).
Green dashed box shows the domain for the analysis. b) Eco-regions within the
domain denoted by AL (Amazonian Lowlands), CA (Central Andes), EH (Eastern
Highlands), GC (Gran Chaco) and PP (Pampas Pantanal) and location of lake
bodies greater than 10 km2 in area (shading). c) Frequency of detected CI
events resampled to a 0.25∘ x 0.25∘ grid. Red delineated boundary shows the La
Plata basin. c) Diurnal probability distribution of tracked CI events; the red
dashed lines from left to right show the start time of CI events considered
for MODIS and SMAP analysis, local noon, the start time of CI events
considered from AMSR2 analysis and the end of daytime respectively.

##  2 Data and Methods

###  2.1 Study region

The La Plata basin (LPB) in South America covers 3.17 million km2
hydrographical area between 14∘S-38∘S latitudes (red delineated boundary in
Fig. 1c). The land surface over LPB is dominantly covered by savannas,
grasslands and croplands along with deciduous broadleaf forests in
southeastern Bolivia and southeastern Brazil. Both PPT and vegetation density
generally decrease from north to south and from east to west within the basin
with a maximum located in central-east LPB at the climatological location of
the convergence of the northerly moisture flux transported by the South
American low-level jet (see Fig. 1 of Chug and Dominguez, 2019). The domain
for this analysis broadly encompasses LPB (green dashed box in Fig. 1a). The
elevation within the domain ranges up to 3200 $m$ and the topographic
complexity (TC), defined as the local standard deviation of elevation in every
40 km-by-40 km area, ranges from 2 $m$ to 1074 $m$ (orange shading in Fig.
1a).

###  2.2 Tracking nascent-stage convective features

To understand SM feedbacks to CI, we need to detect the locations and timings
of the first initiation of deep convective clouds. Convective clouds can be
detected from infrared satellite images as local minima of brightness
temperature which are strongly correlated with deep convection. We use the
half-hourly infrared brightness temperature data (Tb; equivalent blackbody
temperature) at 4 km horizontal resolution globally-merged from multiple
geostationary satellites Janowiak et al. (2017). This dataset has been pre-
corrected for zenith angle dependence to reduce the cold bias at locations far
from satellite nadir.

Following Barton et al. (2021), we define a procedure to detect deep
convective clouds in early stages of growth such that the horizontal area of
the detected clouds is always smaller than or equal to a maximum size
threshold, chosen to be 20 km-by-20 km in this study. This limits the cloud
window to a 5 x 5 grid in the satellite Tb field at 4 km resolution. The
procedure tracks the location of such cloud windows in the Tb field that have
a cold core, i.e., all 5 x 5 grids where the coldest temperature is found at
the central pixel. This coldest temperature must be less than a threshold core
temperature for deep convective clouds (235 K in this study). All such
detected windows represent potential convective clouds in early stages of
growth.

To reduce errors due to pre-existing and moving clouds, the detected clouds
need to be newly emergent and rapidly cooling. Hence, we apply the following
additional criteria:

  1. 1.

To ensure a newly emergent cloud, the periphery of the 5 x 5 feature in the
time step of detection should be free of cloud-related pixels. Each pixel
within 12 km of the cloud window should be warmer than a maximum brightness
temperature found in a cloud layer. This highest cloud layer Tb associated
with deep convection is set to 253K (Mapes and Houze, 1993).

  2. 2.

To ensure that this feature is rapidly cooling and not associated with a
moving cloud, we check the progression of minimum Tb within the cloud window
and its periphery. We find the linear regression slope (K hr-1) of the minimum
Tb over the last 1 hour using 2 earlier time steps. This slope must be less
(more negative) than a threshold slope that represents rapid cooling (-10K
hr-1 in this study).

If all criteria are satisfied, the center of the detected cloud feature is
recorded as the location of CI along with the respective timing of detection.
This algorithm is applied to all 5 x 5 windows in the domain during the local
daytime hours (13 UTC-23 UTC or 10 am-8pm local time) for the wet season over
subtropical South America (November through March; NDJFM) over the period
2001-2020. Parallax correction is applied to reduce the error in sensing the
ground location of tracked cloud features. We estimate the approximate cloud-
top heights using monthly mean temperature profile and geopotential heights
from ERA5 reanalysis (Hersbach et al., 2020). To minimize the impact of
convection over or related to water bodies, all CI events within 10 $km$ of a
water body greater than 10 $km^{2}$ in area (Fig. 1b) are removed. This leads
to a 10% reduction in detected convective events within the domain.

###  2.3 Composite patterns of antecedent surface wetness

After detecting daytime CI events over the domain, the next step is to
quantify the antecedent surface anomalies underlying each event. There is no
single measurement system that can accurately map SM and the associated
surface flux variability at a resolution appropriate to study convective
feedbacks. Hence, we employ three different satellite datasets.

####  2.3.1 Surface soil moisture from microwave radiometer

Daily morning surface SM (units: cm3cm-3) estimates at approximately 6 am
local time from the Soil Moisture Active Passive (SMAP) enhanced level-3
radiometer dataset (O’Neill et al., 2021) at 9 km horizontal resolution are
employed to extract SM anomaly (SMA) patterns underlying the detected CI
events. This dataset is available from 2015 onward. The SMAP SM estimates are
derived from the natural thermal emission of the Earth’s surface at low
microwave frequency ($\sim$1 GHz), providing the following advantages: a) This
frequency lies within the spectral window where the atmosphere is nearly
transparent to outgoing energy, b) measurements are independent of solar
illumination, and c) thermal emission from the soil underlying vegetation can
be retrieved for sparse to moderate foliage, up to atleast 5 kg m-2 vegetation
water content (O’Neill et al., 2021).

A 31-day running mean climatology over all years (2015-2020) centered at each
calendar day is subtracted from the respective day to calculate SMA. We adopt
a similar methodology to Taylor et al. (2011) and Barton et al. (2021). SMA
over a surface transect of 250 km-by-250 km centered at the closest SMAP grid
point directly underlying the CI location was extracted for each CI event. The
analysis is limited to cases with the highest quality data at the CI location.

####  2.3.2 Land surface temperature from thermal infrared radiometer

Daily morning observations of LST (units: K) at approximately 10:30 am local
time from level-3 MODIS/Terra dataset version 6.1 at 0.05∘ ($\sim$5600 m at
equator) horizontal resolution (Wan et al., 2021) are used for the entire
duration of the study (2001-2020). MODIS LST is available for longer duration
at a higher spatial resolution compared to SMAP SM, provides more detailed
retrieval quality description and is directly linked to the surface energy
budget. Infrared LSTA is employed as a proxy for spatial variability in SM
because moister soils are associated with a cooler temperature due to higher
specific heat capacity and exhibit higher thermal emissivity due to enhanced
absorption and emission in the thermal infrared range. LST is determined from
the thermal infrared signature received by the MODIS sensors in the spectral
bands between 3.66-12.27 $\mu$m range after accounting for atmospheric
emission, absorption and scattering of thermal radiation from the surface and
from the sun. Since clouds are not transparent in this wavelength range, MODIS
LST data is produced only for clear sky conditions. We follow a similar
procedure as with the SMAP dataset but with key differences. We subtracted a
15-day running mean climatology (instead of 31-day) while calculating the
temporal LST anomalies (LSTAs) to account for the larger sample size of the
data. Due to lack of ground validation studies of MODIS LST data over
subtropical South America and the errors related to the cloud removal
algorithm. i.e., frequent cold bias due to cloud shadow effects, we limit this
analysis to pixels with the best quality data - a) cloud-free conditions, b)
average LST error $<$ 1 K, and c) average emissivity error $<$ 0.01. To
alleviate the subsequent sampling issues due to the filtering, we only include
cases when - a) there is a valid retrieval underlying the initiation point,
and b) the surface domain contains the best quality retrievals for more than
50% pixels.

####  2.3.3 Land surface temperature from microwave radiometer

We employed afternoon LST observations at approximately 1:30 pm local time
from the 10 km AMSR2 downscaled LPRM level-2 product (Owe et al., 2008) as a
third dataset to validate the surface anomaly patterns. Similar to SMAP, AMSR2
provides all-sky observations. However, its high microwave frequency (36.5
GHz) is sensitive to hydrometeors (Gao et al., 2008; Holmes et al., 2009).
Hence, we filter the dataset using GPM IMERG Level 3 High Quality
precipitation based on microwave-only data (Huffman et al., 2019). Following
Barton et al. (2021), all AMSR2 pixels with rainfall rates greater than 1 mm
hr-1 between 1–3 pm local time are excluded from the analysis. AMSR2 LSTAs are
calculated after removing a 31-day running mean climatology for 2013-2021 at
each grid point. Since the study is focused on the effect of pre-storm surface
heterogeneity, we sample AMSR2 LSTAs only for CI cases that initiate after 2
pm local time.

####  2.3.4 Alongwind rotation and composite analysis

Before compositing the surface anomaly transects for the respective surface
datasets, each transect was rotated in the direction of the background wind.
The background wind was calculated as the 10-m wind from ERA5 dataset
spatially averaged over the 1∘x1∘ area centered at the CI location and
temporally averaged over 2 hours preceding the corresponding initiation time.
A composite of mean surface conditions was generated as the average of rotated
surface transects for all cases for a respective dataset. Since the CI events
occur over a range of latitudes, topography, daytime hours, etc., we
subtracted the spatial mean SMA (or LSTA) from each transect before
calculating the composite pattern.

###  2.4 Rainfall patterns after convective onset

Half-hourly high quality microwave-only rainfall rate estimates from the IMERG
final run dataset (Huffman et al., 2019) were used for analyzing the 24-hour
rainfall following the CI events. This product provides PPT estimates that are
intercalibrated and merged from various passive microwave sensors, and then
corrected using monthly gauge-based PPT to produce the final IMERG output of
rain rate at a $\sim$10 km grid. The 24-hour PPT composites corresponding to
each CI event are constructed from 250 km-by-250 km transects of PPT rate
centered at the location of each CI event. The alongwind composites of PPT
anomaly are derived similarly as the surface anomaly composites.

We also used this dataset to ensures that our signal is not contaminated by
the effect of early morning PPT on SM conditions. All CI cases with morning
(7-10 am local time) PPT within the domain exceeding 1 mm hr-1 were removed
before the composite analysis.

###  2.5 Other datasets used in the study

  * •

GMTED2010: Global Multi-resolution Terrain Elevation Data at 30 arc-second
resolution was used for inferring elevation over the study domain and
calculating TC, the local standard deviation of elevation over a 40 km-by-40
km region.

  * •

ERA5: Wind vectors at 10 m above ground and multiple pressure levels in the
atmosphere, convective available potential energy (CAPE) and convective
inhibition (CIN) from the ERA5 dataset (Hersbach et al., 2020).

  * •

Other MODIS datasets: The Terra MODIS land water mask (MOD44W) version 6
dataset (Carroll et al., 2017) for the most recent year (2015) was used to
filter out all CI events related to water bodies. The global map of surface
water at 0.25 km spatial resolution was conservatively regridded to 4 km to
preserve the integral of water body area within each pixel.

Monthly-mean Enhanced Vegetation Index (EVI) at 0.05∘ resolution (Didan, 2015)
was used to estimate background vegetation density for each CI event. EVI is
derived from atmospherically-corrected differential reflectance in the red,
near-infrared, and blue wavelength bands. The usage of the blue band minimizes
canopy-soil variations and improves saturation issues over dense vegetation
conditions that can be expected in the domain.

###  2.6 Significance testing

We sampled initiation gradients as the slope of the linear regression of
surface anomalies with distance, between the CI location and a point L km
downwind, where L represents the length scales of surface heterogeneity
considered in this study. To compare the initiation gradients with surface
conditions not linked to CI, we sampled up to eight non-initiation gradients
close to the edges of the rotated surface transects extracted for each CI
case. The statistical significance of the mean initiation gradient was
assessed through bootstrapping (refer to Wilks, 2019) against a “superset” of
all initiation and non-initiation gradients. To assess the statistical
significance of the mean initiation gradient for a set of N initiation
gradients, a distribution of the mean values of randomly sampled N gradients
was generated from the “superset” by repeating the sampling 1000 times with
replacement. The mean value of the initiation set was deemed significant at a
particular confidence level if it lay in the respective tails of the
distribution of randomly sampled gradients.

##  3 Results

###  3.1 Tracked convective initiation events

The CI tracking criteria applied to the domain during NDJFM months of
2001-2020 resulted in the identification of 77,745 CI events. Figure 1c shows
a map of CI frequency resampled to a 0.25∘ x 0.25∘ grid over the domain.
Maximum CI frequency is located over the lower latitudes in northern LPB. The
pattern shows a southward bulge likely associated with the northerly flow that
transports moisture and momentum from lower toward higher latitudes over South
America. Figure 1d shows a diurnal peak in CI frequency in early afternoon
hours between 15 UTC ($\sim$local noon) and 19 UTC ($\sim$4 pm). The
normalized diurnal probability of CI occurrence in the domain reduces to less
than 0.05 after 23 UTC ($\sim$8 pm local time). We consider daytime CI events
as those detected between 13 UTC and 23 UTC.

###  3.2 Mean surface anomalies across all satellite datasets

Figure 2: a) Composite mean SMAP soil moisture anomaly (SMA; cm3 cm-3)
underlying CI cases, where the SMA transects for each case were first aligned
in the direction of the low-level (10 m) average wind in the two hours before
the detected CI time. The center (0,0) denotes the initiation location for
each case. Black rectangle (10km x 30km) at the center used to calculate the
downwind SMA gradient at the initiation point. Grey rectangles were similarly
used to calculate the downwind gradients over non-initiation locations for
significance testing. b) Downwind cross-section of mean SMA for all CI cases
(thick red curve and left ordinate) along with its standard deviation across
all CI cases in the downwind direction (thin red curve and right ordinate).
Dashed lines denote the extent of the box used to calculate the initiation
gradient. c) Same as a but for MODIS land surface temperature anomaly (LSTA;
K). d) Same as b but for MODIS LSTA. e) Same as a but for LSTA from AMSR2
dataset. f) Same as b but for AMSR2 LSTA.

Figure 2 presents the composite patterns of alongwind surface conditions
before and directly underlying daytime CI over subtropical South America from
independent sensors aboard the three different low earth orbit satellites used
in our analysis. Table 1 shows the variables used along with their sample
size, the mean initiation gradient between the CI location and 30 km downwind,
and the associated p-values.

Volumetric SM anomalies from the SMAP level-3 dataset show the mean location
of 4,467 CI events at the downwind end of a spatially drier surface patch
(Fig. 2a). The dry patch is located close to relatively moister regions
contributing to enhanced heterogeneity at the surface and strong dry-wet soil
boundaries at the location of CI. The one-dimensional downwind cross-section
through the CI location (Fig. 2b) shows that CI preferentially occurs downwind
of the driest SM anomaly. A rapid increase in SM occurs on the downwind side
of CI. The strength of the mesoscale gradient is quantified as the regression
slope of the SMA anomalies between the ground location of CI and those
approximately 30 km downwind (black rectangle in Fig. 2a), similar to (Barton
et al., 2021). The mean downwind gradient value of +0.002 cm3 cm-3 (100 km)-1
is statistically significant (p-value = 0.064) at the 90% confidence level.
The magnitude of the mean gradient is similar to the one reported by Taylor et
al. (2011) over the Sahel region. This pattern suggests preferential
convective initiation over dry-wet SM boundaries, at the downwind edge of the
drier patch, in agreement with the hypothesis of this study.

The analysis of MODIS LST dataset yields 4,173 CI events with valid, high
quality retrievals. The mean LSTA pattern (Fig. 2c) shows a spatially warmer
surface anomaly upwind of the initiation location with cooler conditions
downwind. Convection preferentially initiates over the downwind edge of the
warm patch. The one-dimensional downwind cross-section through the CI location
shows that the strongest LSTA gradient is located at and downwind of the CI
location (Fig. 2d). The mean gradient value of -0.909 K (100 km)-1 over all
4,173 cases is statistically significant at the 99% level and is stronger than
similar metrics reported over the Sahel and the Tibetan Plateau.

Analysis of the AMSR2 LST dataset serves as another validation of the surface
temperature pattern. It yields valid surface transects for 7,755 daytime CI
events that initiate after the GCOM-W1 overpass time, i.e. CI events between
17 UTC and 23 UTC. The general characteristics of this pattern (Fig. 2e,f)
match that of the MODIS LSTA analysis, although poorly resolved by comparison.
The differences between the two LSTA patterns are likely due to the spatial
resolution as the AMSR2 dataset is interpolated to the $\sim$10 km resolution
from its native resolution of $\sim$25 km using a forward radiative transfer
model. The all-weather retrieval of LST from the microwave radiometer can
likely dilute the warming signal and the gradient strength due to a cool bias
whenever a shallow cloud is present at the overpass time. This can be expected
at the detected initiation location given that, by definition, deep convection
develops at that location within minutes to a few hours.

In summary, the three satellite datasets agree on preferential CI at the
downwind end of a spatially drier patch of the order of tens of kilometers,
close to strong dry-wet soil boundaries. The signal from the LSTA datasets is
consistent (anti-correlated) with that from the SM dataset.

Table 1: Downwind initiation gradients Dataset | Variable | Initiation Gradient | P-value | Number of Cases
---|---|---|---|---
SMAP | SM | +0.002 (100 km)-1 | 0.064 | 4467
MODIS | LST | -0.909 K (100 km)-1 | 0.001 | 4173
AMSR2 | LST | -0.105 K (100 km)-1 | 0.124 | 7755

###  3.3 Sensitivity to environmental conditions

Figure 3: Wavelet transform computed from the composite alongwind land surface
temperature anomaly pattern on the central transect passing through the CI
location in the alongwind direction, for all valid CI cases from the MODIS
dataset analysis.

Convection due to mesoscale circulations induced by spatially drier soils can
be modulated by environmental conditions that can be static (e.g., underlying
topography) or dynamic (e.g., background wind speed) in nature. We analyze the
mean spatial patterns of surface anomalies after reclassifying the CI events
based on background conditions to gain insights on such interactions. Since
the prestorm LSTA signal from MODIS emerges clearly above the noise and shows
high statistical significance (p-value = 0.001), we employ this dataset in the
subsequent analysis. The discrete spatial scales associated with the strongest
surface heterogeneity were empirically determined using a wavelet analysis on
the alongwind LSTA transects underlying CI. The mean wavelet transform (Fig.
3) captures the strongest variability between spatial scales of 30 km and 100
km, centered close to the initiation point. The weighted amplitude of the
decomposed signal is remarkably similar to the weighted wavelet transform from
alongwind LSTA anomalies underlying CI over the Sahel region (Fig. 2b of
Taylor et al., 2011). Here, we present the downwind initiation gradients for
the lower (30 km) and upper (100 km) ends of the dominant wavelength spectrum.

Table 2 shows the mean initiation gradients from the MODIS dataset at 30 km
and 100 km downwind of the initiation point respectively, after resampling the
CI events based on various thresholds of a) seasonality, represented through
bimonthly periods, b) topographic complexity, i.e. the local standard
deviation of elevation in the 40 km-by-40 km region centered at the initiation
point, c) background low-level (10 m) wind, d) background CAPE and e)
background CIN in the two hours before the detected initiation time averaged
over the 1∘-by-1∘ box centered at the initiation location; and f) background
vegetation, represented through monthly EVI at the CI location. The
statistical significance of the mean initiation gradient at a particular
length scale for each of the sub-classes was assessed against the “superset”
of initiation and non-initiation gradients as done for Figure 2c but for the
respective length scale.

Table 2: Downwind initiation gradients from MODIS LSTA dataset at 30 km and
100 km length scales along with the p-values from permutation tests shown in
parenthesis. The criteria or thresholds for the different sub-classes are
shown in italics. Bold quantities denote statistical significance at the 95%
confidence level.

Parameter |  | 30 km gradient
---
[K (100 km)-1]
| 100 km gradient
---
[K (100 km)-1]
Months | ND | DJ | JF | FM | ND | DJ | JF | FM
| -0.934
---
(0.007)
| -0.756
---
(0.015)
| -0.892
---
(0.003)
| -0.995
---
(0.002)
| -0.543
---
(0.001)
| -0.479
---
(0.001)
| -0.451
---
(0.001)
| -0.485
---
(0.001)
TC [m] | 2-36 | 36-89 | 89-1074 | 2-36 | 36-89 | 89-1074
| -0.867
---
(0.008)
| -0.985
---
(0.001)
| -0.867
---
(0.01)
| -0.471
---
(0.001)
| -0.337
---
(0.025)
| -0.602
---
(0.001)
EVI [ratio] | 0.1-0.4 | 0.4-0.5 | 0.5-0.9 | 0.1-0.4 | 0.4-0.5 | 0.5-0.9
| -1.367
---
(0.001)
| -0.622
---
(0.069)
| -0.676
---
(0.055)
| -0.766
---
(0.001)
| -0.248
---
(0.168)
| -0.365
---
(0.029)
CAPE [J/kg] | 0-600 | 600-1500 | 1500-4000 | 0-600 | 600-1500 | 1500-4000
| -0.548
---
(0.147)
| -1.014
---
(0.001)
| -1.316
---
(0.001)
| -0.324
---
(0.027)
| -0.578
---
(0.001)
| -0.566
---
(0.001)
CIN [J/kg] | 0-100 | 100-250 | 250-1000 | 0-100 | 100-250 | 250-1000
| -1.052
---
(0.001)
| -0.934
---
(0.001)
| -0.885
---
(0.016)
| -0.313
---
(0.096)
| -0.51
---
(0.001)
| -0.644
---
(0.001)
Wind speed [m/s] | 0-1.5 | 1.5-2.5 | 2.5-10.0 | 0-1.5 | 1.5-2.5 | 2.5-10.0
| -1.032
---
(0.001)
| -1.012
---
(0.001)
| -0.613
---
(0.104)
| -0.371
---
(0.012)
| -0.669
---
(0.001)
| -0.347
---
(0.043)

####  3.3.1 Seasonality

Figure 4: Location of detected CI events (first column), composite mean MODIS
LSTA (K; shading; middle column) and downwind cross-section of mean LSTA
passing through the CI location (last column) for CI cases classified based on
the bimonthly period in which they occur. a-c) November-December, d-f)
December-January, g-i) January-February, and j-l) February-March. The LSTA
data for each case is first aligned in the direction of the low-level (10 m)
average wind in the two hours before the detected initiation time. For the
middle column, the center (0,0) denotes the initiation location for each case.
For the last column, dashed lines denote the extent of the boxes used to
calculate the initiation gradients at the 30 km and the 100 km length scales.

The location of CI events is similarly distributed over the domain during all
bimonthly periods (left column of Fig. 4). These results show a strong
negative LSTA gradient underlying CI events throughout the season over both
length scales (Fig. 4 and Table 2). The spatial LSTA pattern shows slightly
stronger negative anomalies over a larger region earlier in the season
(November through January) compared to later in the season (January through
March) when the negative anomalies are confined to a smaller patch. The
initiation gradient at 100 km length scale is also strongest earlier in the
season (Nov-Dec = -0.543 K/100 km) and weakens as the season progresses (Table
2). Contrary to expectations, we do not find strong evidence for intra-
seasonal variability of the hypothesized negative feedback.

####  3.3.2 Topographic complexity

Figure 5:  Same as Fig. 4 but for CI cases classified based on terciles of
topographic complexity over which they occur. a-c) 2 to 36 m , d-f) 36 to 89
m, and g-i) greater than 89 m.

Topography can induce upslope circulations due to efficient heating on
elevated surfaces as well as through mechanical lifting of unstable near-
surface air, leading to convective onset. We explore the differences in the
downwind initiation gradients for CI cases re-classified into terciles of
topographic complexity, i.e. the local standard deviation of elevation in a 40
km-by-40 km region centered at the ground location of CI (Fig. 5). The size of
the region to define TC reflects a $\beta$-mesoscale region associated with
convective onset where the mechanical and buoyancy effects of topography in
forcing upslope flow are effective.

Figure 5 shows the CI locations, spatial patterns of surface anomalies and the
associated central transects for three non-overlapping TC classes. The
thresholds for the TC bins are determined to approximately divide the CI
events into terciles. These results show preferential CI over strong negative
LSTA gradients across all terciles of TC at the CI location (Table 2).

Figure 6: Downwind LSTA gradients at the initiation point from the MODIS
dataset that occur over a minimum topographic complexity indicated by the
x-axis labels. Filled circles denote statistically significant gradient at the
99% confidence level. Black crosses indicate the ratio of total CI events
associated with each sub-class.

The spatial pattern for the highest TC tercile shows negative LSTAs over a
larger region. At the 100 km scale, the gradient strength for the highest TC
tercile (-0.602 K/100 km) is approximately twice that of the lower tercile
(-0.337 K/100 km). This suggests that larger scale LSTA gradients could be
important for convection over higher terrain. We further investigate the
highest TC tercile (TC $>$ 89 m) by incrementally increasing the minimum TC
value, e.g. TC $>$100 m, 150 m, and so on. Figure 6 shows the downwind
initiation gradients, their statistical significance at the 99% level and the
ratio of total CI cases (out of 4173) associated with these new sub-classes
within the highest tercile. The 30 km gradient is significant for TC sub-
classes up to 250 m (similar to Barton et al., 2021). We find increasing
strength of the gradient with increasing TC.

Figure 7 shows the composite pattern of the alongwind elevation transects
centered at the CI locations. While the pattern for the lowest TC tercile
shows a relatively flat region, the pattern for the highest tercile shows
steep alongwind decline in elevation. The CI cases in the highest tercile
occur when the induced upslope circulation due to elevated warming over a
spatially drier patch opposes the downslope background flow. These observed
patterns are consistent with the modeling study of Imamovic et al. (2017) who
found that the superposition of mountain-valley circulation and the SM
heterogeneity-induced flow due to an idealized small mountain can lead to
increased rainfall accumulation because mountains that are drier than the
nearby low-lying regions can induce stronger circulations.

Figure 7:  Composite mean elevation (m; shading) underlying CI cases, where
the elevation data for each case is first aligned in the direction of the low-
level (10 m) in a similar way as Fig. 5, for CI cases classified based on
terciles of topographic complexity over which they occur. a) 2 to 36 m , b) 36
to 89 m, and c) greater than 89 m. Note the alongwind decline in elevation for
the highest tercile.

####  3.3.3 Background vegetation index

Figure 8: Same as Fig. 4 but for CI cases classified based on terciles of
background EVI. a-c) Less than 0.4, d-f) 0.4 to 0.5, and g-i) greater than
0.5.

The idealized mechanism for CI over spatially drier bare-soils can be
modulated by the intermediary role of vegetation that can affect the exchange
of moisture, energy and momentum with the above-canopy boundary layer.
Subtropical South America exhibits a variety of land cover types - evergreen
and deciduous broadleaf forests, savannas and woody savannas, grasslands and
croplands. To test the sensitivity of convective onset to vegetation density,
we employ the MODIS EVI to re-classify the CI cases approximately into
terciles of low ($<$0.4), moderate (0.4-0.5) and high EVI ($>$0.5). Figure 8
shows a clear signal of statistically significant (99% level), negative LSTA
gradient underlying CI cases in the lowest EVI tercile (EVI $<$ 0.4). These
regions correspond to savanna, grassland and cropland land cover classes
mainly over northern Argentina, Paraguay and southern Brazil. This sensitivity
is clear and consistent across both length scales (Table 2). Results for
higher EVI classes show negative LSTA gradients that are relatively weaker
compared to sparse vegetation cases.

####  3.3.4 Convective potential and inhibition

Figure 9: Same as Fig. 4 but for CI cases classified based on terciles of
background CAPE in the two hours before initiation. a-c) Less than 600 J/kg ,
d-f) 600 to 1500 J/kg, and g-i) greater than 1500 J/kg.

CAPE provides a useful measure of the maximum possible intensity of surface-
based convection. At convective-scales, the structure and length scale of the
surface heterogeneity can strongly influence the ability of mesoscale flows to
concentrate CAPE within regions where the likelihood of stronger thunderstorm
is locally maximized (Avissar and Liu, 1996; Pielke Sr., 2001). Notably, the
estimates of CAPE from ERA5 dataset at 0.25∘ resolution used in this study are
- a) strongly influenced by model physics rather than observed profiles, and
b) likely represent the domain mean conditions, i.e. they do not adequately
capture the effects of surface heterogeneity at convective scales.

While CAPE provides a good measure of the potential for strong convection,
breaking the energy barrier below the level of free convection is critical to
initiate convection. CIN is a strong determinant of the location of CI when
expansive regions may have similarly positive CAPE. Unlike CAPE, changes in
surface temperature have a greater effect on the CIN than corresponding
changes in low-level humidity that lead to the same change in moist static
energy within a well-mixed PBL (Trier, 2003). Spatially drier/warmer soils are
ideally situated to help reduce the CIN locally before the development of
convection.

Figure 9 shows that the highest CAPE cases are limited to the low-lying
regions of southern Amazon and the Gran Chaco in northern LPB. For both length
scales, the strength of the negative downwind gradient increases with
increasing CAPE (Table 2). The strongest 30 km gradients are observed for
cases with high background CAPE ($>$ 1500 J/kg). This suggests that convective
forcing from small scale gradients is effective when the lower troposphere
outside the boundary layer is more conducive to convection.

Figure 10: Same as Fig. 4 but for CI cases classified based on terciles of
background CIN in the two hours before initiation. a-c) Less than 100 J/kg ,
d-f) 100 to 250 J/kg, and g-i) greater than 250 J/kg.

The statistically significant gradient at the 100 km scale in the lowest CAPE
tercile suggests that the surface heterogeneity of a larger length scale can
play an important role in initiating convection during less favorable synoptic
conditions. This is evident in the contrasting relationship of the downwind
initiation gradient with increasing CIN at the CI location (Table 2). While
strong LSTA gradients at the 30 km length scale are effective in initiating
convection in convectively favorable environments up to moderate CIN
conditions ($<$ 250 J/kg), the strength and significance of the 100 km
gradients consistently increases with increasing CIN. Figure 10 shows a
stronger and more expansive pattern of warmer anomalies with increasing
thresholds of background CIN. Convection in high CIN environments is
insensitive to smaller scale SMA/LSTA gradients. However, when present over a
larger scale, these gradients can generate persistent mesoscale circulations
that a) increase the CAPE locally through convergence, and b) reduce the CIN
locally through surface warming to overcome the larger inhibition.

####  3.3.5 Low-level wind

Figure 11: Same as Fig. 4 but for CI cases classified based on terciles of
background 10 m wind speed in the two hours before initiation. a-c) Less than
1.5 m/s , d-f) 1.5 to 2.5 m/s, and g-i) greater than 2.5 m/s.

Low-level wind speed (10 m) is used as a proxy for background flow that can
lead to enhanced horizontal mixing and affect the persistence of surface-
induced variability propagating into the daytime boundary layer. The
distribution of meridional 10 m wind speeds before the detected CI events at
the initiation location (Supplementary Fig. S1) shows higher probability for
convective onset during northerly flow.

We find significantly stronger negative LSTA gradients at 30 km scale
underlying CI for background wind speeds less than 2.5 m/s (Table 2). This is
in agreement with results from large-eddy simulations that show a suppression
of the effect of surface heterogeneity on CI when wind speeds are greater than
2.5 m/s on length scales up to 40 km (Lee et al., 2019, and reference
therein). Similar sensitivity to background wind speed was found in Barton et
al. (2021) over the Tibetan Plateau for CI cases with underlying TC less 300
m.

The location of initiations (left column of Fig. 11) shows a southwestward
migration of CI locations with increasing background wind speeds. Stronger
low-level flow in this region is associated with the southward penetration of
the SALLJ.

The spatial patterns of LSTA anomalies underlying convection show a distinct,
spatial gradient on the order of tens of kilometers for wind speeds less than
2.5 m/s, however, the length scale of the upwind warmer area seems to increase
with increasing wind speed (middle column in Fig. 11). This is reflected in
the statistically significant downwind gradients at 100 km scale for all three
terciles of wind speed (Table 2). Under conditions of strong background wind,
the atmosphere is insensitive to spatial variability of smaller length scale.
However, convection is still favored over larger scale negative LSTA
gradients, consistent with Barton et al. (2021).

###  3.4 What determines the sign of SM-PPT feedbacks?

Figure 12: a) Composite mean precipitation (PPT) rate and b) PPT anomaly
averaged over 24 hours following the initiation of convection for all valid
cases in the MODIS analysis set (N = 4173). The PPT and PPT anomaly data for
each case is first aligned in the direction of the low-level (10 m) average
wind. The white cross at (0,0) denotes the initiation location for each case.

We analyzed the composite PPT and PPT anomaly patterns over 24 hours following
the initiation time of each CI event using the IMERG half-hourly high quality
microwave-only dataset (Fig. 12). We rotated each PPT transect using the same
low level wind direction used in the respective MODIS transects in Fig. 2c.
Figure 12 shows that the rainfall-producing storms preferentially translate
leftward of the downwind direction in the rotated composite pattern. Since the
majority of storms initiate under the climatological northerly low-level flow,
the composite pattern implies eastward propagation of rain-producing storms in
a geographic coordinate system (Supplementary Fig. S2). Similar eastward
progression of extreme rain features in subtropical South America has
previously been shown by Janowiak et al. (2005) and Rasmussen et al. (2016)
who analyzed mesoscale convective systems over their lifecycle of several
hours. The composite pattern of PPT and PPT anomaly in Fig. 12 along with Fig.
2 show that - a) afternoon storm initiation is favored through the convergence
produced by the induced mesoscale circulation against the background flow over
strong dry-wet SM boundaries, and b) the location of maximum PPT can vary
between the initiation point and hundreds of kilometers eastward of the CI
location. The translation of maximum PPT confounds the sign of the convective-
scale SM-PPT feedback if collocated values of SM and PPT are considered. This
demonstrates a fundamental issue in defining convective scale SM-PPT feedbacks
similar to “local” SM-PPT feedbacks that have previously been derived from
coarse-scale climate model outputs.

Figure 13: Composite alongwind PPT anomaly (mm/d) averaged over 24 hours
following the initiation of convection for all valid cases from the MODIS
dataset classified approximately based on terciles of background wind (similar
to Fig. 11) shown as a) Less than 1.5 m/s , b) 1.5 to 2.5 m/s, and c) greater
than 2.5 m/s. Grey box indicates the area used for computing spatial
correlation between LSTA and PPT anomalies. d) Mean PPT anomaly (thick curves)
and standard deviation (thin curves) for each tercile of CI events after
averaging in the alongwind direction over the domain shown in (a-c). Dashed
vertical lines show the peak alongwind-averaged PPT in the crosswind direction
for each tercile.

We postulate that the sign of the SM-PPT feedback can be modulated by the wind
profiles that steer the convective structures and affect the organization and
lifetime of PPT while its strength is likely controlled by the convective
instability that leads to the generation of PPT. We tested the sensitivity of
the PPT anomaly patterns to the strength of the low-level (10 m) wind when CI
occurred. The low-level wind was chosen because it is related to convection
over subtropical South America (Salio et al., 2007), advecting heat and
moisture from the Amazon and the LPB southward, and is strongly correlated
with vertical wind shear in the lower troposphere. This classification is
based on the same thresholds of background wind as in Section 3.3.5.

The spatial location of the largest PPT anomaly shows a strong sensitivity to
the background wind speed (Fig. 13). The lowest tercile is associated with
enhanced rainfall close to the initiation point as the lack of background wind
weakens the propagation of convective cells and lack of shear reduces their
lifetime (Rotunno et al., 1988). Similar to the idealized modeling study of
Froidevaux et al. (2014), the convective cycle during weak wind speed is
anchored to the CI location likely leading to negative SM-PPT feedback. The
statistically significant and positive spatial correlation (+0.59) between
composite LSTA (antecedent to CI) and PPT anomalies (in the 24 hours following
CI) within a 0.5∘ x 0.5∘ box centered at the CI location (Table 3) confirms
this negative feedback. In contrast to Froidevaux et al. (2014) who showed
clear positive feedback during stronger wind speeds, the translation of PPT
occurs perpendicular to the direction of the low-level wind (as opposed to
parallel). The highest tercile shows the largest PPT anomalies between 150-250
kilometers away from the CI location in the negative crosswind direction (Fig.
13c) as stronger low-level wind enhances the propagation of convective
features and stronger vertical shear in the environment leads to enhanced
mesoscale convective organization and increased spatiotemporal extent of
rainfall. The lagged spatial correlation between composite LSTA and PPT
anomalies is statistically significant and weakly negative (-0.20) which
implies a weak positive SM-PPT feedback near the CI location.

Table 3: Spatial correlation between composite LSTA and PPT anomalies in 0.5∘ x 0.5∘ box centered at the CI location Sub-class | Thresholds | Pearson-r | P-value | Number of Cases
---|---|---|---|---
Low wind | 0-1.5 m/s | +0.59 | 9.45e-43 | 1824
Moderate wind | 1.5-2.5 m/s | -0.05 | 0.24 | 1675
High wind | 2.5-10 m/s | -0.20 | 3.56e-05 | 1246
All events |  | +0.23 | 5.03e-07 | 4745

In summary, we show distinct peaks in PPT anomalies 150 km away from the CI
location for the highest wind tercile and closer to the initiation location
for the lower terciles (Fig. 13d). Our results show strong sensitivity of the
peak PPT location and the sign of the SM-PPT feedback to the strength of the
low-level wind.

##  4 Discussion

There is a large pool of numerical simulation studies showing heterogeneous
SM/LSTA conditions triggering convection over spatially drier/warmer soils due
to induced mesoscale circulations (Cheng and Cotton, 2004; Adler et al., 2011;
Garcia-Carreras et al., 2011; Kang and Bryan, 2011; Rieck et al., 2014;
Rochetin et al., 2017; Lee et al., 2019). Previous observational evidence has
found that SM heterogeneity of the order of 30-50 km exerts a strong influence
on CI (Taylor et al., 2011; Taylor, 2015; Barton et al., 2021). Our results
agree with past studies and show that the length scale of the surface
heterogeneity linked to CI over subtropical South America can vary between
30-100 km based on the background static and dynamic conditions of topography,
wind, CAPE and CIN. This study is the first assessment of preferential
convection over drier soils near strong dry-wet SM boundaries for the South
American continent.

This observational evidence of enhanced CI over drier/warmer soils is
supported by three different satellite datasets of land surface
characteristics and advocates for a realistic representation of mesoscale SM
boundaries for an adequate prediction of afternoon convection. In forecasting
mode, near realtime information of SM/LST conditions can potentially enhance
skill in predicting the spatial location and timing of convective storms as
demonstrated by Taylor et al. (2022). Simulating the lifecycle of convective
storms without adequately capturing the role of SM heterogeneity will result
in biases in weather prediction and climate simulations.

The observed role of spatially drier soils over complex topography in
initiating convection through induced upslope flow against the downslope
background flow is a novel finding of this study. Our results in Figures 5, 6
and 7 suggest that larger scale surface heterogeneity can induce upslope flow
and CI over complex terrain. In past modeling studies (e.g., Imamovic et al.,
2017), the spatial scale of the induced mesoscale circulation is constrained
by the relatively small domain size. The importance of the larger scale SM
gradients warrants closer examination.

The strength and significance of the negative LSTA gradients in this study are
strongly sensitive to the background vegetation density (Fig. 8). Dense
vegetation can a) homogenize the spatial variability in surface sensible heat
fluxes by accessing deeper soil moisture (Ivanov et al., 2010), and b) enhance
spatial gradients in heat fluxes through the effect of roughness length on
boundary layer winds. While the observed negative LSTA gradient is
statistically significant at the 95% level for all terciles of EVI, its
strength reduces to less than half for EVI greater than 0.4. This suggests -
a) the important role of roughness effects due to spatial heterogeneity in
vegetation density in providing convective forcing, and b) a limitation of
this current framework in quantifying the surface heterogeneity-related
convective forcing in the presence of dense vegetation. While some past work
has linked local circulations developing over anthropogenic land surface
heterogeneities to the observations of cumulus development over the U.S
Central Plains (Weaver and Avissar, 2001) and the Amazonia (Baidya Roy and
Avissar, 2002), further investigation of the observed role of vegetation
heterogeneity in initiating convection is needed to improve understanding.

The prevalence of negative SM-PPT feedback has been observed by quantifying
afternoon rainfall over spatially drier soils in multiple regions around the
world (Taylor et al., 2012; Guillod et al., 2015; Welty et al., 2020). When
dry SM anomalies lead to enhanced PPT, the feedback is classified as negative.
This is contrary to the positive SM-PPT feedback shown by a plethora of
observational (Findell et al., 2011; Koster et al., 2003) and global climate
model studies with parametrized convection (Guo et al., 2006; Taylor et al.,
2012). Our results show observational evidence that while convection can
preferentially initiate over drier soils, the location of peak PPT can vary
based on the background low-level wind. The description of SM-PPT feedbacks in
a manner that captures the complete duration of convection from initiation to
dissipation is lacking and needs to be addressed in future studies.

The simple algorithm to detect CI in our study is likely missing the
convective hotspot in the Sierras de Cordoba mountains located in northern
Argentina. This region is known for the most intense convective storms on
Earth (Zipser et al., 2006; Nesbitt et al., 2021). The fixed thresholds of
cloud size, cloud core temperature and conservative filtering of cases with
any cloud pixel in the vicinity fail to detect CI that occurs under conditions
of strong wind shear and/or a pre-existing layer of cirrus clouds. Similar
issues in designing a simple CI detection algorithm for satellite imagery have
been discussed by Cancelada et al. (2020); Barton et al. (2021); Lee et al.
(2021). Future work can benefit from an adaptive algorithm that can capture
the regional and latitudinal dependence of CI signature in satellite imagery.

##  5 Conclusion

Figure 14: a) Idealized SM heterogeneity-induced mesoscale transport (dashed
arrows) under light synoptic wind (thick arrow) lead to ascent and convective
initiation over the downwind edge of the dry patch, as hypothesized by Taylor
et al. (2011). b) Under strong background wind conditions, surface
heterogeneity of a larger length scale ($\sim$100 km) can induce mesoscale
flows strong enough to overcome the mixing effect of the stronger wind and
lead to convection. c) Dry-wet soil patches located on elevated terrain
directly heated by solar insolation can induce upslope flow to increase the
convective forcing. d) Surface-induced storms that initiate under weak
background flow result in peak rainfall accumulation over dry soils (negative
SM-PPT feedback). e) Strong background wind leads to stronger propagation and
longer lifecycle of convection resulting in PPT accumulation upto hundreds of
kilometers east of the initiation location in subtropical South America.

The goal of this study is to observe and understand the role of surface
heterogeneity at the mesoscale in initiating daytime convection under a range
of environmental conditions and the consequent SM-PPT feedback during the warm
season over subtropical South America. Major findings from this analysis are
as follows:

  1. 1.

Convection over subtropical South America preferentially initiates on the dry
side of strong dry-wet SM boundaries that correspond to positive alongwind SMA
gradients and negative alongwind LSTA gradients on the order of tens of
kilometers (Fig. 14a).

  2. 2.

The strength of the induced circulation is sensitive to the length scale of
heterogeneity and the background wind. Convection is preferred over surface
gradients of 30 km length scale during calm conditions with low-level wind
speeds less than 2.5 ms-1. Surface heterogeneity of a larger length scale (100
km) can lead to secondary circulations able to overcome the mixing effect of
stronger background wind ($>$ 2.5 ms-1) to initiate convection (Fig. 14b).

  3. 3.

In the presence of complex topography, SM patchiness of a larger length scale
(100 km) can induce upslope flow (Fig. 14c) that opposes the background
downslope winds and leads to enhanced convection and rainfall over the eastern
slope of the Andes High Plateau and the Brazilian Highlands.

  4. 4.

The convective forcing due to surface heterogeneity is strongly sensitive to
the presence of dense vegetation. While SM patchiness provides a strong and
clear signal of convective forcing over sparse vegetation, roughness effects
are likely crucial in the presence of dense vegetation.

  5. 5.

Under convectively unfavorable conditions, strong gradients over a larger
length scale can - a) induce mesoscale flows that concentrate CAPE within
local regions and increase the likelihood of deep convection, and b) locally
reduce CIN through enhanced surface heating.

The analysis of PPT anomalies over 24 hours following the tracked CI events
shows:

  1. 1.

During weak background wind, the convective lifecycle is anchored to the
initiation location and PPT occurs over the drier patch leading to negative
SM-PPT feedback (Fig. 14d).

  2. 2.

Stronger background wind and shear support the propagation of convective
structures hundreds of kilometers away from the initiation location, thus,
confounding the sign of the SM-PPT feedback at convective scales (Fig. 14e).

Our results show that mesoscale circulations induced by SM heterogeneity of
the order of tens of kilometers represent an important forcing for convection
over subtropical South America. The evaluation of SM-PPT feedbacks at
convective scale for CI over drier soils is confounded due to the sensitivity
of convective propagation to the background wind. This represents a major
challenge as parametrized convection in climate models does not account for
these subgrid circulations making it difficult to reproduce the observed
diurnal cycle of precipitation. A better understanding of SM-PPT feedbacks is
crucial for land management, adaptations to future climate change and our
ability to forecast weather and make climate projections. Convection-
permitting climate simulations that can explicitly capture the effect of
surface heterogeneity in boundary layer transport and moist convection are
necessary for accurately simulating the terrestrial water and energy cycles.

###### Acknowledgements.

DC was funded by the Future Investigators in NASA Earth and Space Science and
Technology (FINESST) Award 80NSSC19K1352. FD was supported by the National
Science Foundation (NSF) Award 1852709. CMT and CK acknowledge funding from
the UK Natural Environment Research Council (NERC) under the LMCS project
(NE/W001888/1). SWN acknowledges funding by NASA Precipitation Measurement
Missions Grant 80NSSC19K0713. The authors declare no conflict of interest. All
datasets used in this study are publicly available. The information provided
in Table 4 can help readers locate and download the data used in this
analysis:

Table 4: Additional information about the datasets used in this study.

Variable | Dataset name | Shortname | Download Source
---|---|---|---
Infrared Tb | NCEP/CPC L3 Merged IR V1 | GPM_MERGIR | https://disc.gsfc.nasa.gov/datasets/GPM_MERGIR_1/summary
Microwave SM | SMAP Enhanced L3 Radiometer EASE-Grid SM V3 | SPL3SMP_E | https://nsidc.org/data/spl3smp_e/versions/5
Infrared LST | MODIS/Terra LST L3 V061 | MOD11C1 | https://lpdaac.usgs.gov/products/mod11c1v061/
Microwave LST | AMSR2/GCOM-W1 downscaled LPRM L2B V001 | LPRM_AMSR2_DS_SOILM2 | https://disc.gsfc.nasa.gov/datasets/LPRM_AMSR2_DS_SOILM2_001/summary
Wind, CAPE, CIN | ERA5 hourly data on single levels | ERA5 | https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=form
Precipitation | GPM IMERG Final Precipitation L3 V06 | GPM_3IMERGHH | https://disc.gsfc.nasa.gov/datasets/GPM_3IMERGHH_06/summary
Elevation | Global Multi-resolution Terrain Elevation Data 2010 | GMTED2010 | https://www.usgs.gov/coastal-changes-and-impacts/gmted2010
Lake mask | Terra/MODIS land water mask V6 | MOD44W | https://lpdaac.usgs.gov/products/mod44wv006/
EVI | MODIS/Terra Vegetation Indices | MOD13C2 | https://lpdaac.usgs.gov/products/mod13c2v006/

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