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# Evaluating synthetic aperture radar surface winds for convective squalls in the Gulf of Mexico

Tran Vu La, Christophe Messager

► To cite this version:

Tran Vu La, Christophe Messager. Evaluating synthetic aperture radar surface winds for convective squalls in the Gulf of Mexico. Quarterly Journal of the Royal Meteorological Society, 2024, 150 (764), pp.4178-4195. 10.1002/qj.4810. hal-04950249

HAL Id: hal-04950249

https://cnrs.hal.science/hal-04950249v1

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RESEARCH ARTICLE

# Evaluating synthetic aperture radar surface winds for convective squalls in the Gulf of Mexico

Tran Vu La<fnref n="1" /> | Christophe Messager<fnref n="2" />,<fnref n="3" />

**Correspondence**

Tran Vu La, Luxembourg Institute of
Science and Technology (LIST), 41 Rue du
Brill, 4422 Sanem, Luxembourg.

Email: tranvulaa@yahoo.com

**Funding information**

Fonds National de la Recherche
Luxembourg, Grant/Award Number:
CORE project C20/SR114703579

**Abstract**

The detection and quantification of strong sea surface winds, reaching up to 25 m·s⁻<fnref n="1" />, whether associated with deep convection aloft or not, have been extensively discussed in previous studies. This method involves the combined observation of the same event from both low-orbit altitude and geostationary (GEO) satellites. Strong surface winds observed by the Sentinel-1 C-band synthetic aperture radar (SAR) are robustly associated with deep convective clouds detected by GEO infrared sensors. The current paper aims to generalize the previous assessment of several convective wind events by collecting a larger dataset and comparing these data to in-situ wind observations. To achieve this, we evaluated wind speeds retrieved from Sentinel-1 SAR images against corresponding in-situ wind measurements from all active buoys/stations in the Gulf of Mexico. Significant agreement between satellite-based winds and in-situ data was achieved, particularly for wind speeds exceeding 3 m·s⁻<fnref n="1" />, with even better agreement for wind speeds over 10 m·s⁻<fnref n="1" />. From this dataset, three specific convective cases were extracted to illustrate various stages of convective squall events: before, during, and after the occurrence of a squall peak. In each case, comparison with in-situ measurements showed that SAR-estimated wind speeds closely matched observed speeds, including the peak convective winds, which were estimated at 18.90 m·s⁻<fnref n="1" /> and measured at 20.69 m·s⁻<fnref n="1" />. Furthermore, combining these findings with GOES-16 sequential images illustrates the temporal and spatial similarity between deep convection areas, estimated strong sea surface wind patterns, and measured wind speeds.

**KEYWORDS**

clouds, convection, geophysical sphere, mesoscale, observational data analysis, observations, ocean, physical phenomenon, remote sensing, scale, tools and methods, winds

# 1 | INTRODUCTION

Deep convection may produce mesoscale convective systems (CSs), supercell convective storms, thunderstorms and squall lines, all of which are frequently observed in the sub-tropical and tropical regions, for example, the Gulf of Guinea and Mexico, Lake Victoria, Southeast Asia,

India, etc. Severe CSs regularly produce hazards over the land and ocean with strong surface winds, heavy rainfall and intense lightning. Strong surface winds or wind gusts produced by deep convection can exceed 25 m·s⁻<fnref n="1" /> (Cotton et al., 2011; Van de Walle et al., 2021), and can significantly impact many onshore, offshore and coastal activities, including all power plants (onshore and offshore

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<footnote n="1">¹Luxembourg Institute of Science and Technology (LIST), Sanem, Luxembourg</footnote>

<footnote n="2">²Centre National de Recherches Météorologiques (CNRM), Université de Toulouse, Météo-France, CNRS, Toulouse, France</footnote>

<footnote n="3">³Centre Marine & Offshore, Brest, France</footnote>
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wind farms and oil/gas infrastructures), fishing, maritime transport and all ocean leisure activities. The observation and monitoring of deep CSs have significantly improved in recent years due to the geostationary (GEO) satellite network, including Meteosat, GOES, Himawari and Gaofen. For instance, GOES-16/17 satellites collect data over the western hemisphere (Americas and a part of the Pacific Ocean) every 5–15 min with a horizontal resolution of 0.5–2 km, depending on spectral bands. Meteosat-9/10 devices provide images of Europe and Africa every 15 min with a 3-km horizontal resolution for infrared (IR) channels. However, convective wind events at the land and sea surfaces cannot be estimated spatially and temporally from the GEO satellite data. Additionally, the limited resources of remote sensing and in-situ observation data, with high spatial and temporal resolutions, have created significant difficulties in observing and monitoring the time variability of convective wind events on a large scale. In particular, the lack of in-situ wind data is particularly pronounced over the sea. Compared to in-situ measurements over land, measuring wind fields over the sea surface with moored buoys is significantly more complex and costly due to environmental constraints during installation and maintenance. Consequently, most weather stations are strategically positioned along coastal regions rather than out at sea.

La et al. (2018, 2020) and La and Messager (2021a, 2021b, 2022) indicated that sea surface convective winds could be retrieved and estimated accurately (ranging from 3 to 25 m·s⁻¹) from Synthetic Aperture Radar (SAR) images (Sentinel-1 C-band SAR). These studies originated from the observation of the effect of deep convective cloud downdrafts on the sea surface, as inferred from sea surface roughness observations. As depicted in Figure 1 (La & Messager, 2021a) and discussed in previous studies (Cotton et al., 2011; Houze, 2004), warm air rises and cools as it ascends to higher altitudes. This cooled, denser air, now more humid, can induce downward-moving air parcels, known as downdrafts. Under favourable dynamic conditions at mid-levels, these downdrafts can intensify enough to descend all the way to the ocean surface. Upon reaching the sea surface, the downdrafts are deflected horizontally, potentially generating strong local surface winds within squall lines. The intensity of these surface winds correlates with the intensity of the downdrafts, as demonstrated by changes in sea surface roughness.

The studies mentioned above utilized Sentinel-1 SAR images acquired in Interferometric Wide Swath (IW) mode, benefiting from their high spatial resolution (10-m pixel size) and wide swath (250 kilometres), enabling the observation of surface convective wind patterns at mesoscales and submesoscales. These wind patterns appeared as squall lines, bow echoes, nonlinear structures,

or convection cells, with wind intensities ranging from 10 to 25 m·s⁻¹, and peak speeds reaching 15–25 m·s⁻¹. The convective wind speeds estimated by Sentinel-1 closely matched those observed under deep convection by sensors installed on wind turbines (Imberger et al., 2021) and coastal radars (Waniha et al., 2019). Additionally, La and Messager (2021a, 2021b) demonstrated that the strong surface winds observed in Sentinel-1 images corresponded in both time and location to deep convective cold clouds (with brightness temperatures in the range of 200–220 K) observed by Meteosat GEO satellites. To further establish the relationship between severe surface winds and deep convection, La and Messager (2022) emphasized the collocated horizontal displacement between surface wind patterns observed through Sentinel-1 SAR, Windsat, and SMAP radiometers, as well as ASCAT-A/B/C, and deep convective clouds. Furthermore, La and Messager (2021a) provided a comprehensive view of a convective system and its vertical and horizontal dynamics, as illustrated in Figure 1, using collocated images acquired by Meteosat IR, Aeolus Lidar, and Sentinel-1 SAR sensors.

Most of the previous studies (Jang et al., 2019; Rana et al., 2019; Sergeev et al., 2023; Stoffelen et al., 2017; Zhou et al., 2022) dealt with a limited amount of in-situ data sources for limited locations. For instance, to verify C-band Geophysical Model Function (GMF) for wind speed retrieval in the open ocean and inland water conditions, Sergeev et al. (2023) collected in-situ wind measurements from five buoys installed in five different regions mostly in 2020. They compared 133 cases between the Sentinel-1-estimated wind speed and in-situ wind measurements. Rana et al. (2019) compared surface wind speed estimated from 19 Sentinel-1 images with in-situ measurements obtained from 13 stations installed in the two limited regions (Wadden Sea and Camargue). Zhou et al. (2022) utilized 4445 Sentinel-1 images to design and validate a new model of surface wind speed retrieval based on SAR image textures. However, they compared the estimated wind speed with the data obtained from numerical models. Only several cases of the measured wind speed under hurricanes were considered to compare with surface wind speed estimates.

To generalize the assessment between surface wind speeds estimated from Sentinel-1 images and in-situ measurements, regardless of the origin and scale of the events producing the winds, this study collected data from a large area in the Gulf of Mexico. Among the subtropical and tropical regions where severe convective squalls frequently occur, the Gulf of Mexico (Molina et al., 2016) is equipped with numerous moored buoys and weather stations installed by NOAA (National Oceanic and Atmospheric Administration) and their partners. Therefore, it is a relevant region for conducting a comprehensive and
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**FIGURE 1** Three-dimensional view of a convective system (CS) based on the collocation of low earth orbit (LEO) and geostationary (GEO) satellites: the coldest deep convection at the tropopause altitude is observed by Meteosat (GEO), intense downdrafts at the mid-levels (respective updrafts to balance the CS internal dynamics) are detected by the Aeolus LEO satellite and the associated high-resolution (HR) wind patterns at the sea surface are observed by the Sentinel-1 LEO satellite (La & Messenger, 2021a). [Colour figure can be viewed at wileyonlinelibrary.com]

systematic assessment of the estimated surface wind speeds from Sentinel-1 SAR data under deep convection, compared to the in-situ wind data provided by the moored buoys and weather stations still actively working at the time of data collection. For this end, we collected about 2900 Sentinel-1 IW images across the Gulf of Mexico, from 1 January 2018 to 30 September 2020.

The objective of this study is to evaluate surface convective winds estimated from SAR data across numerous cases and to validate the relationship between strong surface winds and deep convective clouds, as proposed in previous works (La et al., 2018, 2020 and La and Messenger, 2021a, 2021b, 2022), using an extensive in-situ sampling network. This research represents a significant step forward in enhancing our understanding of deep convection and its dynamics. Importantly, the results obtained are expected to enhance the prediction of convective winds.

In the paper, it was necessary to identify corresponding events in space and time observed by both satellite images and in-situ measurements from weather stations and buoys. The in-situ devices acquire data with a high time resolution (e.g., every second) for many hours, corresponding to the time before, during and after the occurrence of CSs at specific spatial locations. At the same time and across the same buoys/station measurement area, satellite data over the sea surface, corresponding to a significant part of the CS space extension and displacement, were collected.

Due to the short revisit time of Sentinel-1A/B over the Gulf of Mexico, including ascending and descending footprints, images covering nearly all the buoys/stations can be obtained almost every day. This advantage of Sentinel-1 significantly increases the number of corresponding cases between the Sentinel-1-estimated winds and in-situ measurements. For the studied period, from 1 January 2018

to 30 September 2020, out of 2900 Sentinel-1 IW images collected, we considered only the 447 images covering at least one buoy or weather station, before identifying those with strong surface winds associated with deep convection.

From our SAR wind database, we selected three distinct convective squall events to illustrate the three stages of a time series wind profile (Figure 2), as a convective squall line moves over a local station. In each event, the SAR-retrieved wind image corresponds to only a single part of the squall's lifetime, while the full lifetime of each event is observed solely through in-situ measurements. As shown in Figure 2, when a convective cell arrives over a station with background winds (stage 1), there is a sudden increase in wind magnitudes (stage 2), followed by a decrease in the winds (stage 3). However, the winds remain higher than the mean winds before the event, sometimes followed by several weaker wind peaks before a significant wind decrease close to the background winds. This latter stage occurs when the convective cell moves away or dissipates.

Section 2 of the paper details the data selection and preparation process, which includes Sentinel-1 images covering the buoys/stations, in-situ measurements, and corresponding GOES GEO images. Section 3 discusses the methodology employed in this study, including the models used for sea surface wind speed retrieval from Sentinel-1 images, as well as the techniques used to process the raw in-situ wind measurements and identify deep convection events. In Section 4, we provide an overall comparison between Sentinel-1-estimated wind speeds and in-situ data from January 1, 2018, to September 30, 2020, covering various origins and scales of wind events. Section 5 presents three case studies of convective wind events. Finally, Section 6 comprises the discussion and conclusion of the study.
<figure><img src="image_6.png" /><figcaption>**FIGURE 2** Schematic typical wind magnitude time series of a squall line passing over a point location (a station or a buoy). Stage 1 is the presquall period; Stage 2 is the peak period, while Stage 3 is the postpeak period. [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

## 2 | DATA DESCRIPTION

The Gulf of Mexico is covered by a dense network of buoys and weather stations (Figure 3a). This network mainly comprises coastal buoys/stations with the most significant economic activities. Some buoys (red ones) are occasionally or definitively out of service, while the yellow ones routinely run to collect in-situ data. Several buoys sample 10-min data, while others offer 30-min samples. In order to generalize the comparisons between Sentinel-1-retrieved wind speeds and in-situ measurements, we used all active buoys and stations which offered data corresponding to Sentinel-1 images (in time and location). However, we only selected the buoys and stations offering 10-min data samples, and we assumed this selection because this sampling frequency limits the difference in observation time between satellite devices (Sentinel-1 and GOES) and in-situ buoys/stations, that is, not over 5 min. Moreover, this selection (short-term in-situ observation) is more appropriate to assess and correctly time-sample convective winds with its magnitude variability over few hours.

For illustration purposes, Figure 3b depicts a significant number of Sentinel-1A/B images (both ascending and descending directions) captured across the Gulf of Mexico within a span of 15 days. The dense coverage of Sentinel-1A/B images over a large number of buoys and stations enhances the potential for matching cases associated with deep convection between in-situ measurements and Sentinel-1 and GOES images. Figure 3c shows the coverage of GOES-16/17 over the Americas, including the Gulf of Mexico, with a short revisit time of every 5–10 min, depending on the channels. The collected GOES data were then resampled to the grid of Sentinel-1 images for location comparison with the Sentinel-1-estimated winds.

## 3 | METHODOLOGY

The methodology employed in this study is depicted in Figure 4 through a flowchart. Initially, Sentinel-1 images were gathered over the Gulf of Mexico. These data underwent preprocessing to derive the normalized radar cross section (NRCS), which is crucial for surface wind speed retrieval. The preprocessing steps are detailed in La et al., 2018, 2020 and La & Messager, 2021a, 2021b, 2022.

Subsequently, CMOD5.N was utilized to estimate sea surface wind speed from the extracted NRCS. Concurrently, in-situ measurements obtained from buoys and stations, corresponding to the time and space parameters of the Sentinel-1 images, were processed, considering they were collected at various heights.

The subsequent step involved comparing the wind speeds estimated by Sentinel-1 with the in-situ measured winds, corrected to the 10-m reference height. Additionally, strong surface winds associated with deep convection events were collected from GOES-16 GEO observations via the coldest clouds (200–220 K brightness temperature).

Finally, three cases corresponding to the three stages of squall events were selected: the early period (wind increase), wind peak, and late period (wind decrease).

### 3.1 | Sea surface wind speed estimation from SAR data

Sea surface wind speed at a height of 10 m can be indirectly imaged by the SAR sensors through the Bragg resonance mechanism (Valenzuela, 1978). This mechanism describes the relationship between radar wavelength and ocean surface waves at a small scale. The severe surface convective winds thus correspond to the high-intensity radar backscattering, or NRCS observed on the SAR images. Using the GMFs (La et al., 2017), one can describe the relationship between NRCS, radar parameters and surface wind vectors. Finally, surface wind speed can generally be retrieved from SAR data by inverting the GMFs.

La et al. (2018, 2020) and La and Messager (2021a, 2021b, 2022) demonstrated that the CMOD5.N GMF (Verlíek et al., 2010) could effectively estimate surface wind speeds associated with deep convection. The input data for CMOD5.N include the NRCS and incidence angle extracted from preprocessed Sentinel-1 images, along with wind direction obtained from Global Forecast System (GFS) weather analyses (NCEI, online access, 2021). It is important to note that CMOD5.N is utilized only for vertically polarized Sentinel-1 data. For surface wind speed retrieval from horizontally polarized Sentinel-1 data, a polarization ratio (La et al., 2017) is applied to transform CMOD5.N. As demonstrated in previous studies (La
<figure><img src="image_7.png" /><figcaption>FIGURE 3 (a) Gulf of Mexico with the buoys and weather stations installed by National Oceanic and Atmospheric Administration (NOAA) and others. The buoys in red are occasionally or definitively out of service, while the yellow buoys routinely run to collect in-situ data. (b) Gulf of Mexico imaged by Sentinel-1A/B from January 1 to 30, 2020 (Credit: EXWEXS). (c) Coverage of the GOES geostationary satellites, including the Gulf of Mexico, covered by GOES-16/17 (Courtesy: GOES-R.Gov). [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

et al., 2017; Sapp et al., 2016), CMOD5.N accurately estimates surface wind speed for wind speeds above 3 m·s⁻¹ and up to 25 m·s⁻¹, depending on the spatial resolution of wind maps. It is worth noting that the value of 25 m·s⁻¹ is close to the convective wind gust peaks reported in several studies (Chamberlain et al., 2014; Cotton et al., 2011; Imberger et al., 2021; Waniha et al., 2019).

Regarding the impact of precipitation on surface wind speed retrievals from SAR, as discussed in previous studies (La et al., 2018, 2020 and La and Messager, 2021a, 2021b, 2022), for C-band SAR, the contribution of rainfall to radar backscattering (or NRCS) observed on SAR images significantly depends on surface wind speed (Contreras & Plant, 2006; Xu et al., 2015). It is found that this contribution is minimal for winds above 7 m·s⁻¹. In other words, the high-intensity radar backscattering observed on C-band SAR images associated with deep convection is mainly induced by strong surface winds rather than precipitation. Similarly, in other studies (Lin et al., 2015;

Wang et al., 2021), it has been indicated that rainfall has little impact on surface wind speed estimation from C-band radar backscattering.

## 3.2 | In-situ-measured wind processing

The in-situ measurements are generally collected by the buoys and weather stations at different heights, as indicated by https://www.ndbc.noaa.gov/obs.shtml. To compare the in-situ wind measurements with the surface wind speed estimated from SAR satellite data, the measured data were corrected to the 10-m reference height. To extrapolate wind speed at low altitudes to the 10-m reference height, we utilized the wind profile power law (Hsu et al., 1994) or the equivalent neutral wind (Smith, 1988). As shown in La et al. (2017), the wind speeds obtained from the two methods were similar. Here, we selected the equivalent neutral wind method.
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<figure><img src="image_9.png" /><figcaption>FIGURE 4 Flowchart of the methodology used in this study. [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

## 3.3 | Collection of deep convection events for three squall stages

To identify the particular cases of strong surface winds associated with deep convection, we compared strong surface winds estimated from Sentinel-1 data with deep convective clouds detected by GOES at nearly the same time. We classify deep (cold) clouds as those with brightness temperatures ranging from 200 to 220 K, with hot spots (or the coldest patterns) reaching ~200 K.

For this study, convective events lasting between 3.5 and five hours were selected, consistent with previous research (La & Messager, 2022; Roca et al., 2017). This duration allows for the reasonable co-location of wind and deep cloud variabilities to be considered.

Table 1 presents the observation time of Sentinel-1, the buoys/stations (named 42012, SPGF1, and 41010), and GOES-16 for three cases. These cases are presented for comparison between the estimated and in-situ-observed surface convective winds. Cases #1, #2 and #3 correspond to the wind increase stage, the peak stage and the wind

decrease stage, respectively, of a time series of convective wind magnitude described in Figure 2.

The use of all the data available for these three sequences permitted us, in the next sections, to determine that each of the three cases corresponded to a particular stage of a squall event. As a result:

* Case #1 presents the early stage of a squall wind event measured by buoy 42012 offshore New Orleans (LA, USA).

* Case #2 corresponds to the peak of a squall wind event measured by the SPGF1 weather station offshore the Bahamas.

* Case #3 shows the late stage of a squall wind event measured by buoy 41010 off the east coast of Florida (USA).

## 4 | OVERALL ASSESSMENT OF SENTINEL-1-ESTIMATED WIND SPEED VERSUS IN-SITU WIND MEASUREMENTS

This section compares the Sentinel-1-estimated surface wind speeds and in-situ wind measurements with all available and relevant buoys/stations over the Gulf of Mexico from 1 January 2018 to 31 October 2020. We counted 447 matching cases between Sentinel-1 images and measured data for general meteorological situations.

As discussed in Section 3.2, the in-situ wind measurements used here for comparison should be considered as the estimates under the equivalent neutral wind approximation. Additionally, we used the mean wind speed collected by buoys and stations, averaged over a 10-min period for overall assessment. As indicated by NOAA (https://www.ndbc.noaa.gov/faq/wndav.shtml), the average wind speed is the simple scalar average of the wind speed observations.

To evaluate the effectiveness of CMOD5.N in estimating sea surface wind speed at a height of 10 m, a statistical assessment was conducted considering wind intensity across five speed ranges:

<table><caption>TABLE 1 Observation time of Sentinel-1, buoys/stations, referenced as 42012, SPGF1 and 41010, and GOES-16 for the three cases corresponding to the early, middle and late period of the peak of a convective wind gust.</caption><thead><tr><td>Case studies</td><td>Sentinel-1 sampling time (UTC)</td><td>Buoy (height, location, sampling time - UTC)</td><td>GOES-16 sampling time (UTC)</td></tr></thead><tbody><tr><td>Case #1: early stage of a squall wind event</td><td>30 August 2020, 114940–115008</td><td>42012 (3.8 m, offshore New Orleans, 1100–1600)</td><td>114616–144116</td></tr><tr><td>Case #2: peak stage of a squall wind event</td><td>12 July 2019, 110954–111113</td><td>SPGF1 (6.6 m, offshore the Bahamas, 0900–1300)</td><td>104132–120132</td></tr><tr><td>Case #3: late stage of a squall wind event</td><td>5 May 2019, 231209–231349</td><td>41010 (4.1 m, off the east coast of Florida, 2100–0030)</td><td>214125–233125</td></tr></tbody></table>
<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /><figcaption>FIGURE 5 Scatter plots of Sentinel-1-retrieved surface wind speed vs in-situ wind from the buoys/stations over the Gulf of Mexico, from 1 January 2018 to 31 October 2020, for (a) winds below 3 m·s⁻¹, (b) winds above 3 m·s⁻¹, (c) winds of 3–10 m·s⁻¹, (d) winds above 10 m·s⁻¹, and (e) all winds from 1 to 30 m·s⁻¹. (f) Taylor diagram for all the ranges represented by 1–5. Corr, correlation; RMSE, root-mean-squared error; Std, standard deviation. [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>
<figure><img src="image_12.png" /></figure>

* Low wind speed below 3 m·s⁻¹;

* Moderate wind 3–10 m·s⁻¹;

* High wind speed above 10 m·s⁻¹;

* Wind speeds strictly higher than 3 m·s⁻¹; and

* All winds ranging from 1 to 30 m·s⁻¹.

Cases where measured wind speeds exceeded 30 m·s⁻¹ were excluded from the analysis, as CMOD5.N-estimated wind speeds may not be accurate beyond this threshold.

For all wind magnitudes between 1 and 30 m·s⁻¹, the Sentinel-1-retrieved winds and the in-situ measurements present a correlation coefficient (R) of 0.88, a root-mean-squared error (RMSE) of 1.5 m·s⁻¹, and a bias of 0.17. These values are similar to those obtained in (Lu et al., 2018).

However, it should be noted that CMOD5.N underperforms for winds below 3 m·s⁻¹ with a low R of 0.3251, a higher bias of 1.2254 and a significant RMSE of 2.23 m·s⁻¹, as shown in (Verspek et al., 2010).

On the other hand, if we only consider the winds above 3 m·s⁻¹, the correlation increases to R = 0.8919 with a low bias of (−0.0326) while the RMSE remains at 1.3182 m·s⁻¹. Likewise, CMOD5.N performs well for estimating wind speed above 10 m·s⁻¹, with a correlation of 0.9468, a bias of −0.266 and an RMSE of 1.31 m·s⁻¹.

Figure 5 gives five scatter plots of SAR wind vs in-situ data for the five ranges of wind magnitude defined in Table 2. It also provides a summarized view of all the statistical results as a Taylor diagram.

For winds below 3 m·s⁻¹ (Figure 5a), a significant difference between the estimated and measured wind speed was observed. CMOD5.N overestimates low wind speed (with a bias of 1.2254), as mentioned in Verspek et al. (2010). However, CMOD5.N performs much better in estimating wind speed above 3 m·s⁻¹ (Figure 5b–d). In other words, CMOD5.N estimates moderate and high winds accurately and more generally for winds higher than 10 m·s⁻¹, as illustrated in the Taylor diagram (cf. Point 4 in Figure 5f).

Finally, as shown in Figure 5e, for winds higher than 3 m·s⁻¹ and even more so for winds higher than

10 m·s⁻¹, CMOD5.N delivers a relevant estimation of wind magnitudes. By extension, if we consider only the sea surface winds generated by deep convection, usually at a magnitude beyond 10 m·s⁻¹, one can conclude that CMOD5.N is relevant for estimating wind speeds in the range of 10–25 m·s⁻¹. Over 25 m·s⁻¹, as shown in Verspek et al. (2010), surface wind speed is underestimated by CMOD5.N.

Table 2 and Figure 5 indicate that CMOD5.N can be used for the estimation of wind speed from SAR satellite images for most wind ranges above 3 m·s⁻¹, whatever the type and scale of the wind events. These results strengthen and generalize the CMOD5.N-based methodology proposed in previous studies (La et al., 2018, 2020; La & Messager, 2021a, 2021b, 2022). Also, the method may be applied to estimate convective surface winds from SAR data, notably from Sentinel-1 images.

Although there is good agreement in the assessment between the estimated and measured wind speeds, uncertainties persist regarding the range of errors for both in-situ measurements and CMOD5.N computations. It would be valuable to provide information regarding the overlap of these two ranges of errors. However, while introducing and propagating error within the CMOD5.N model is manageable, collecting all the error sources associated with in-situ buoys/stations is a complex and challenging task that exceeds our current capabilities.

## 5 | COMPARISON OF THE ESTIMATED AND MEASURED CONVECTIVE WINDS FOR THE THREE CASES

Among the 447 cases processed in Section 3 without taking into account the meteorological situation, this section presents three cases that illustrate the surface wind variability associated with the occurrence of CSs, including three stages of squall events, that is, the increased wind stage, the peak stage, and the decreased wind stage. The observation is considered at a local point

<table><caption>TABLE 2 Comparisons between Sentinel-1-retrieved surface wind speed and in-situ wind measurements (from buoys and weather stations) over the Gulf of Mexico, from 1 January 2018 to 31 October 2020, for 447 cases, whatever the type and scale of the wind events.</caption><thead><tr><td>Range of wind speed</td><td>Data point</td><td>Bias</td><td>Correlation</td><td>Standard deviation (std)</td><td>Root-mean-squared error (RMSE)</td></tr></thead><tbody><tr><td>Wind &lt; 3 m·s<sup>-1</sup></td><td>91</td><td>1.2254</td><td>0.3251</td><td>1.8669 m·s<sup>-1</sup></td><td>2.2332 m·s<sup>-1</sup></td></tr><tr><td>Wind &gt; 3 m·s<sup>-1</sup></td><td>356</td><td>-0.0326</td><td>0.8919</td><td>1.3178 m·s<sup>-1</sup></td><td>1.3182 m·s<sup>-1</sup></td></tr><tr><td>3 &lt; Wind &lt; 10 m·s<sup>-1</sup></td><td>319</td><td>0</td><td>0.7241</td><td>1.3187 m·s<sup>-1</sup></td><td>1.3180 m·s<sup>-1</sup></td></tr><tr><td>Wind &gt; 10 m·s<sup>-1</sup></td><td>37</td><td>-0.266</td><td>0.9468</td><td>1.2902 m·s<sup>-1</sup></td><td>1.3170 m·s<sup>-1</sup></td></tr><tr><td>1 &lt; Wind &lt; 30 m·s<sup>-1</sup></td><td>431</td><td>0.1742</td><td>0.8826</td><td>1.4854 m·s<sup>-1</sup></td><td>1.4956 m·s<sup>-1</sup></td></tr></tbody></table>
<figure><img src="image_13.png" /></figure>

(a buoy or a station), thus following an Eulerian wind description.

## 5.1 | Case #1: Early period of a squall wind event

Figure 6a presents the estimated surface wind speed from a Sentinel-1 image from 30 August 2020, 114940–115008 UTC, as well as the position of buoy 42012. We note a mesoscale feature according to the wind pattern, extending zonally from 89° W to 87° W and meridionally from 29.5° N to 30.2° N, with a wind intensity of 10–25 m·s⁻¹. Some wind hot spots exceed 20–25 m·s⁻¹. Based on the assumed front this wind pattern moves northeastward (in accordance with the geostationary brightness temperature sequence illustration – Figure 7). Buoy 42012 was located

ahead of the wind front when the Sentinel-1 image was acquired.

Figure 6b illustrates the 10-min average wind speed and wind gust defined by NOAA as the maximum 5-s peak gust (NDBC, Online Access, 2021) measured at a 10-m height by buoy 42012 between 1100 and 1600 UTC, with 10-min sampling intervals. The measured wind is nearly stable from 1100 to 1220 UTC (about 6–7 m·s⁻¹ for synoptic background winds) but suddenly increases from 6 m·s⁻¹ to 15 m·s⁻¹ for 20 min (1220–1240 UTC) and then drops quickly from 15 to 9 m·s⁻¹ also for 20 min (1240–1300 UTC). The rapid increase and decrease in wind speed measurements are also observed from 1350 to 1430 UTC (increasing from 7.5 to 12.5 m·s⁻¹) and from 1430 to 1450 UTC (dropping from 12.5 to 7 m·s⁻¹). For five hours of observation (1100–1600 UTC), we notice two peaks of

<figure><img src="image_14.png" /></figure>

FIGURE 6 (a) Surface wind speed estimated from Sentinel-1 image, 30 August 2020, 114940–115008 UTC. Buoy 42012, offshore New Orleans (LA, USA), is located ahead of the wind front at the Sentinel-1 observation time. (b) In-situ 10-m height wind speed measured by buoy 42012, from 1100 to 1600 UTC, every 10 minutes. The wind peaks are observed at 1230–1240 and 1420–1430 UTC, respectively. Buoy 42012 is located ahead of the wind front illustrated on the Sentinel-1 image. [Colour figure can be viewed at wileyonlinelibrary.com]
<figure><img src="image_15.png" /><figcaption>FIGURE 7 Deep convective cloud (220–230 K brightness temperature) observed by GOES-16 (channel 14), 30 August 2020, 114616–145116 UTC. The GOES-16 image, 114616 UTC, corresponds to the Sentinel-1 observation time, 114940–115008 UTC. The images for 123116 and 124116 UTC, match the observation time of the first convective wind peak (1230–1240 UTC), while the second peak (1420–1430 UTC) corresponds to the images for 142116 and 143116 UTC. [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

the in-situ wind measurements at 1240 UTC (15 m·s⁻¹) and 1430 UTC (12.5 m·s⁻¹), as well as the rapid increase and drop in wind intensity. The time series wind at the point location is typical of a convective squall line event already described by Kuchera and Parker (2006): the wind increases quickly in the initial period at the squall front, then reaches a magnitude peak, and finally quickly drops down in the second period at the rear of the wind front.

The Sentinel-1-estimated wind speed at the buoy location (6.45 m·s⁻¹) is similar to that observed by the buoy

(6.41 m·s⁻¹) at 1150 UTC (Figure 6b), corresponding to the early period of the squall line since the buoy is located ahead of the wind front. This wind front is expected to move towards the buoy while it induces two wind peaks measured at 1240 and 1430 UTC.

This displacement and convective reinforcement is clearly illustrated by the deep convective clouds detected by GOES-16 (Figure 7) on 30 August 2020, from 114616 to 145116 UTC, collocated with the Sentinel-1 image (Figure 6a). The GOES observation duration
<figure><img src="image_16.png" /></figure>

(114616–145116 UTC) thus covers the Sentinel-1 acquisition time (114940–115008 UTC), as well as the occurrence of the first convective wind peak, that is, 1220–1300 UTC, followed by the second peak at 1350–1450 UTC.

The cold cloud part (220–230 K brightness temperature) observed by GOES-16 at 114616 UTC (Figure 7) matches, in location (for the section of 29.5–30.2° N and 87–89° W) and shape, the Sentinel-1 wind pattern (Figure 6a). This collocation, as already mentioned in La et al., 2018, 2020 and La & Messager, 2021a, 2021b, 2022, illustrates the link between the Sentinel-1-observed strong surface winds and deep convective cells.

The collocation of the GOES-16 images at 114616, 121116, and 122116 UTC (Figure 7) shows the eastward and northeastward deep convection movement with a spread of the areas experiencing convection deepening (the coldest parts 200–210 K). This northeastward deep convection spreading corresponds to the wind front observed on the Sentinel-1 image (Figure 6a). The deep convective cloud arrives over buoy 42012 at 121116–122116 UTC, corresponding to the early period of the first convective wind peak from 1220 to 1240 UTC (Figure 6b).

Between 122116 and 124116 UTC, when the coldest/deepest cloud part (200–210 K) covers the buoy, a rapid increase of the wind intensity is observed (from 6 to 15 m·s⁻¹), with a peak of 15 m·s⁻¹ at 1240 UTC. This observation illustrates that the Sentinel-1 wind pattern (Figure 6a), 114940–115008 UTC, associated with the cold cloud part (Figure 7), actually moves eastward and northeastward, before reaching the buoy at 1220–1240 UTC. It significantly increases local winds with the peak at 1240 UTC, as shown in Figure 6b. From 124116 to 130116 UTC, the cold cloud part, as well as the wind pattern, continued moving in the same direction (away from the buoy). Thus, a significant decrease in the observed wind speed (from 15 to 9 m·s⁻¹) occurred. Indeed, it returns to its background magnitude observed before the event (about 7.5–9 m·s⁻¹).

From 134116 to 145116 UTC, as the convective cloud feature still moves between eastward and northeastward, the second wind peak is measured by the buoy at 1430 UTC, following a corresponding wind increase. This observation corresponds to the results shown in La and Messager (2022) and Roca et al. (2017) that convective wind patterns may exist for many hours with several changes in wind intensity.

## 5.2 | Case #2: Wind peak of a squall wind event

The second case illustrates the estimated surface wind speed from Sentinel-1 images on 12 July 2019, 110954–111113 UTC, according to the peak observed by

the SPGF1 coastal station. Figure 8a shows a mesoscale surface wind feature as a squall line spreading meridionally from 24° N to 28° N with a wind magnitude of 10 to 25 m·s⁻¹. This high-magnitude wind pattern moves eastward (in accordance with the geostationary brightness temperature sequence illustration – Figure 9) to reach Grand Bahama Island, where the SPGF1 coastal weather station is located, with severe winds (20–25 m·s⁻¹).

Figure 8b presents the 10-min in-situ wind speed collected by the SPGF1 station from 0900 to 1300 UTC. For this case, the one-hour wind gusts are also available from 0900 to 1020 UTC. The observed mean wind remains at around 2.5 m·s⁻¹ up to 3.5 m·s⁻¹. At 1020 UTC, it suddenly increases to reach 17.5 m·s⁻¹ at 1110 UTC. A corresponding gust increase occurs from 3.12 m·s⁻¹ (1000 UTC) to 20.69 m·s⁻¹ (1100 UTC). From 1100 to 1200 UTC, a rapid decrease of mean winds and gusts is observed to reach nearly the magnitude observed before the event.

The estimated wind speed from the Sentinel-1 image (110954–111113 UTC) at the station location is close to the measured wind peak at 1100 UTC (18.90 m·s⁻¹ vs 20.69 m·s⁻¹). This result is in accordance with the observation shown on the wind magnitude map (Figure 8a), where the SPGF1 station is located in the strong wind areas.

In the meantime, Figure 9 illustrates the deep convection (200–220 K brightness temperature) observed by GOES-16, 12 July 2019, 100132–120132 UTC, collocated with the Sentinel-1 image (Figure 8a). The GOES-16 images, 110132–111132 UTC, correspond to the Sentinel-1 observation time, 110954–111113 UTC, as well as the observed wind peak (at nearly 1100 UTC). The time-sequence images in Figure 9 illustrate the eastward displacement of the deep convective cloud, while the displacement direction agrees with that assumed for the surface wind pattern. From 100132 to 102132 UTC (early period of the event), as well as for Sentinel-1 surface wind pattern, one can notice that the deep cloud reaches Grand Bahama Island. However, the coldest brightness temperature pattern (nearly 200 K) is not located at the SPGF1 coastal station. It then moves eastward and reaches the station from 103132 UTC, at the beginning of the significant in-situ wind increase. The peak of the measured wind speed (mean value of 17.5 m·s⁻¹ and 20.69 m·s⁻¹ for gust) is observed at 1100–1110 UTC when the SPGF1 station is reached by the coldest part of the convective cloud, as shown on the GOES-16 image at 111132 UTC. Likewise, the coldest cloud observed at that time matches the hot spots (20–25 m·s⁻¹) of the Sentinel-1 wind pattern (Figure 8a). After 1110 UTC, according to the cold/deep cloud, the wind front moves eastward, away from the station, and then the measured wind intensity significantly drops to the values of synoptic background wind magnitude, at about 3 m·s⁻¹.
<figure><img src="image_17.png" /><figcaption>FIGURE 8 (a) Surface wind speed estimated from Sentinel-1 images, 12 July 2019, 110954–111113 UTC, showing a mesoscale wind pattern (10–25 m·s⁻¹). The coastal SPGF1 station (Bahamas) experiences the maximum of the wind front observed by Sentinel-1. (b) 10-m height in-situ 10-min mean wind magnitude measured by the SPGF1 station, from 0900 to 1300 UTC, as well as gust values over 1 h. The magnitude peak of gust is observed at 1100 UTC. [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

**5.3 | Case #3: Late period of a squall wind event**

The third case illustrates the final stage of a squall line, that is, the wind magnitude decreases after the peak stage (Figure 2). This case is based on the estimated surface wind speed from Sentinel-1 images, 5 May 2019, 231209–231349 UTC, and that measured by buoy 41010.

Figure 10a shows a surface wind pattern in the shape of a mesoscale squall line spreading meridionally from 28° N to 32° N, with a wind magnitude varying from 10 to 25 m·s⁻¹. This wind front moves eastward in accordance with the geostationary brightness temperature sequence illustration (Figure 11). Buoy 41010 is located on the west of the wind pattern (where the wind is moderate). Thus, we can assume that the squall line had already overtaken
<figure><img src="image_18.png" /><figcaption>**FIGURE 9** Deep convective cloud with low brightness temperature from 200 to 220 K, observed by GOES-16 (channel 14), 12 July 2019, 100132–120132 UTC. The GOES-16 images, 110132–111132 UTC, correspond to the Sentinel-1 observation time, 110954–111113 UTC, as well as to the peak of the in-situ winds at 1100 UTC for wind gust and 1110 UTC for mean wind. [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

buoy 41010 a while earlier than the Sentinel-1 observation time.

Figure 11 presents a deep convective cloud (200–220 K brightness temperature) observed by GOES-16, 5 May 2019, 214125–233125 UTC, collocated with the Sentinel-1 image (Figure 10a). The GOES-16 time-sequence images illustrate an eastward displacement, assumed to be the one for the Sentinel-1 wind pattern. From 214125–221125

UTC, at the early stage of the measured wind gust, we notice that buoy 41010 is under the deep convection area but not covered by the coldest part (about 200 K). From 221125 to 225125 UTC, as the coldest/deepest pattern is moving over buoy 41010, a significant increase in wind intensity is observed as a peak at 2250 UTC. Then, the wind magnitude significantly decreases to the values of the background wind.
<figure><img src="image_19.png" /><figcaption>**FIGURE 10** Surface wind speed estimated from Sentinel-1 images, 5 May 2019, 231209–231349 UTC, illustrating a mesoscale wind front (10–25 m·s⁻¹). Buoy 41010, offshore the Florida east coast (USA), is located at the edge of the wind pattern observed by Sentinel-1. (b) 10-m height in-situ wind speed measured by buoy 41010, from 2100 to 0030 UTC, at 10 min sampling. The peak of the wind gust is observed at 2250 UTC. [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

Figure 10b presents the in-situ wind measurements by buoy 41010 every 10 minutes from 2100 to 0030 UTC. From 2100 to 2210 UTC, the mean wind intensity remains in

the range of 6–8 m·s⁻¹ and 7–10 m·s⁻¹ for the gust. After 2210 UTC, a significant increase occurs with a gust peak of 18 m·s⁻¹ (mean value of 13.5 m·s⁻¹). After 2250 UTC,
<figure><img src="image_20.png" /><figcaption>**FIGURE 11** Deep convective cloud (220–230 K brightness temperature) observed by GOES-16 (channel 14), 5 May 2019, 214125–233125 UTC. The GOES-16 image, 231125 UTC, corresponds to the Sentinel-1 observation time, 231209–231349 UTC. The image, 225125 UTC, matches the observation time of the peak of the wind gust at 2250 UTC. [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

the wind drops rapidly from 13.5 m·s⁻¹ to 5 m·s⁻¹ at 2330 UTC. Like the two previous cases, this observed time series wind corresponds to the time occurrence feature of a convective squall line in a short time (about 40 min).

The estimated wind speed from Sentinel-1 images (231209–231349 UTC) over the buoy is very close to the measured mean intensity at 2310 UTC (8.89 vs 9.1 m). The Sentinel-1 observation time corresponds to the period of the significant drop in the measured winds (2250–2330 UTC). This result strengthens the assumption that the Sentinel-1 wind pattern moved through buoy 41010's location earlier when it displaced eastward, as well as that it produces the observed peak at 2250 UTC.

## 6 | DISCUSSION AND CONCLUSION

Previous papers (La et al., 2018, 2020; La & Messager, 2021a, 2021b, 2022) have already detailed the ability of the SAR sensors to deliver, after the relevant processing, HR (infra-kilometric) wind fields at the ocean surface. These wind fields were then correlated to the meteorological activity above the sea surface. In particular, it has been illustrated that deep convection and its particular and powerful dynamics may be exhibited through the SAR-derived wind field (La et al., 2020; La & Messager, 2021a, 2021b, 2022). Moreover, the combination of vertical and
horizontal atmospheric fluxes at the sea surface, from which the ocean surface wind gust appears, may be observed by SAR devices. The correlation with deep convection activity was illustrated by the utilization of the infrared channel of the sensors installed onboard geostationary satellites, but with a kilometric resolution. The connection with the vertical atmospheric movements was also performed by a low-altitude satellite (e.g., Aeolus) to deliver the vertical wind components (updraft and downdraft) – even if the vertical resolution was poor (La & Messager, 2021a, 2021b).

All these papers (La et al., 2018, 2020 and La & Messager, 2021a, 2021b, 2022) were performed using particular case studies. It was indeed necessary to dissect the ability of the SAR images to deliver the wind information at a high resolution representative of the convective downdrafts. As a result, it has been shown that the winds from the convective clouds may be calculated from the SAR images over the sea like all other winds, whatever the wind sources.

In order to add a time view to the convective observation by SAR images, it was necessary to move towards an Eulerian view of a CS and consider the wind time variability at a location.

The current paper targeted the generalization and assessment of the SAR image ability to deliver ocean wind fields at a high resolution (e.g., Sentinel-1 at 1-km wind grid). For the current work, all the available in-situ surface wind observations were collected and compared to all the SAR-derived winds within the area and the period selected. This comparison was performed over a large sea area covered by the largest available amount of observation stations and buoys. For this goal, the Gulf of Mexico should be considered as an appropriate region.

The wind magnitude deduced from SAR images in this assessment conforms well with the in-situ observations. The higher the wind magnitude, the more the proposed methodology (for winds under 20 m·s⁻¹). Thus, the study illustrates definitively the relevance of the proposed method to give snapshot wind views over the ocean surface at a high resolution by using SAR data.

The use of SAR images must be considered as a serious and relevant methodology to observe sea surface winds in general and convective-originated winds in particular. Additionally, the different stages of convective events may be observed on SAR images. This result is significant for forecasters that follow the CSs in near real-time or real-time in order to characterize an event (if they have access to SAR data) and deliver a short- or medium-term alert/warning related to the squall lines.

The comparison results presented in Section 4 indicate that strong surface winds (above 10 m·s⁻¹) estimated from Sentinel-1 images closely match in-situ

measurements (cf. Figures 5d, 4f, Table 2). These severe winds encompass both convective and non-convective events. To identify convective winds, we collocated the detected wind patterns from Sentinel-1 images with deep convective clouds observed by GEO sensors (Meteosat, GOES, and Himawari) taken nearly at the same time. This approach can be generalized for all assessment cases to evaluate the accuracy of surface wind speed retrieval under deep convection. If necessary, adjustments or modifications to the GMF can be proposed to improve surface wind speed estimation from SAR images.

Additionally, the discrimination of convective and non-convective situations that produce weak, moderate, or strong winds is crucial for: (i) developing the quantitative and qualitative climatology of convective events (vs synoptic events) across various regions, and (ii) weather monitoring and forecasting, enabling the qualification of specific situations and their subsequent tracking.

The benefit of such sea wind fields in the assimilation part of numerical weather models for forecast could also be investigated. On the other hand, abundant ocean surface data are available and could be used to develop and improve some deep-learning (DL) models, for example, a Generative Adversarial Network and Recurrent Neural Networks, in order to detect or forecast convective events. To this end, SAR-estimated convective winds may be used for training and validating DL models, while the synoptic winds derived from numerical models would be a part of the input.

## ACKNOWLEDGEMENTS

This work is supported by the Luxembourg National Research Fund (FNR) in the framework of the CORE project C20/SR114703579, as well as Météo-France, its research laboratory CNRM (Centre National de Recherche Météorologiques) and the Centre Marine et Offshore (CMO) – Météo-France. We would like to express our sincere gratitude to the three reviewers, Dr Jennie Bukowski (Colorado State University) and the two anonymous reviewers, for their many helpful and constructive comments, suggestions, and recommendations that have significantly improved this paper. We also extend our thanks to Dr Andrew Ross, Chief Editor, and Dr Simon Peatman, Associate Editor, for their valuable suggestions and fair decisions regarding the publication of this paper.

## DATA AVAILABILITY STATEMENT

Sentinel-1A/B images were downloaded via Copernicus Open Access Hub (https://scihub.copernicus.eu/). Sentinel-1A/B images were provided by the European Copernicus Programme. The in-situ wind data were obtained from the NOAA buoys and weather stations, via https://www.ndbc.noaa.gov/. The GOES-16
data were provided by NOAA, via the Amazon cloud (https://noaa-goes17.s3.amazonaws.com/index.html).
The GFS weather analysis data were downloaded via https://www.ncei.noaa.gov/products/weather-climate-models/global-forecast.

ORCID

Tran Vu La https://orcid.org/0000-0003-2160-6004

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**How to cite this article:** La, T.V. & Messager, C. (2024) Evaluating synthetic aperture radar surface winds for convective squalls in the Gulf of Mexico. *Quarterly Journal of the Royal Meteorological Society*, 150(764), 4178–4195. Available from: https://doi.org/10.1002/qj.4810