# A Convolutional Neural Network for Tropical Cyclone Wind Structure Identification in Kilometer-Scale Forecasts

Laure Raynaud, Ghislain Faure, M. Puig, C. Dauvilliers, J. -N. Trosino, Pierre Béjean

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To cite this version:

Laure Raynaud, Ghislain Faure, M. Puig, C. Dauvilliers, J. -N. Trosino, et al., A Convolutional Neural Network for Tropical Cyclone Wind Structure Identification in Kilometer-Scale Forecasts. Artificial Intelligence for the Earth Systems, 2024, 3, 10.1175/AIES-D-23-0059.1. insu-04752898

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# A Convolutional Neural Network for Tropical Cyclone Wind Structure Identification in Kilometer-Scale Forecasts

L. RAYNAUD,ª G. FAURE,ª M. PUIG,ᵇ C. DAUVILLIERS,ᶜ J.-N. TROSINO,ᵈ AND P. BÉJEANᵉ

(Manuscript received 17 July 2023, in final form 4 March 2024, accepted 25 March 2024)

**ABSTRACT:** Detection and tracking of tropical cyclones (TCs) in numerical weather prediction model outputs is essential for many applications, such as forecast guidance and real-time monitoring of events. While this task has been automated in the 1990s with heuristic models, relying on a set of empirical rules and thresholds, the recent success of machine learning methods to detect objects in images opens new perspectives. This paper introduces and evaluates the capacity of a convolutional neural network based on the U-Net architecture to detect the TC wind structure, including maximum wind speed area and hurricane-force wind speed area, in the outputs of the convective-scale AROME model. A dataset of 400 AROME forecasts over the West Indies domain has been entirely hand-labeled by experts, following a rigorous process to reduce heterogeneities. The U-Net performs well on a wide variety of TC intensities and shapes, with an average intersection-over-union metric of around 0.8. Its performances, however, strongly depend on the TC strength, and the detection of weak cyclones is more challenging since their structure is less well defined. The U-Net also significantly outperforms an operational heuristic detection model, with a significant gain for weak TCs, while running much faster. In the last part, the capacity of the U-Net to generalize on slightly different data is demonstrated in the context of a domain change and a resolution increase. In both cases, the pretrained U-Net achieves similar performances as the original dataset.

**KEYWORDS:** Atmosphere; Tropical cyclones; Neural networks; Forecasting; Nonhydrostatic models; Pattern recognition

## 1. Introduction

Detection of tropical storms and tropical cyclones (both abbreviated TC hereafter for clarity) in numerical weather prediction (NWP) outputs is essential to provide guidance products in real time. It is also useful for postevent activities such as verification and sensitivity studies. Such detection has been automated during the 1990s (Treut and Kalnay 1990; Heming 2017), when NWP models started to show some skills in TC prediction. These software packages, called TC trackers, identify TC at each lead time and associate them to provide a temporal track. There is now a large diversity of TC trackers available to the weather and climate community, which can be used for both global and regional models. For instance, TC trackers operationally used at ECMWF, NCEP, and Met Office have been, respectively, described in van der Grijn (2002), Marchok (2002, 2021), and Heming (2017). Other trackers are reviewed in Ullrich and Zarzycki (2017) and Bourdin et al. (2022).

The core step of TC identification is to find the center. For NWP TC trackers, this is achieved using one or several local extrema in fields, with a set of threshold-based rules (van der Grijn 2002; Heming 2017; Marchok 2002, 2021). In addition, the prior knowledge of an actual TC is generally used to restrict the search area. From this center, several characteristics of the TC intensity and structure can be computed. The most

common features are the TC maximum wind speed and the radius of maximum wind speed. The latter is measured radially in four geographic quadrants (in the northeast, southeast, southwest, and northwest directions) to have a raw depiction of the wind field structure. It is also standard to compute TC wind radii according to some reference wind speeds, including gale-force winds (34 kt; 1 kt ≈ 0.51 m s⁻¹), destructive winds (50 kt), and hurricane-force winds (64 kt). This information is even mandatory in advisories issued by operational centers. For an impact-based approach, the wind structure information is of primary interest, because standard characteristics of a TC, such as the location of the center and the maximum wind speed, are not sufficient to predict its consequences (Czajkowski and Done 2014; Powell and Reinhold 2007; Zhai and Jiang 2014). This is even more true for prediction over small islands, like the Lesser Antilles (Atlantic Ocean) or La Réunion (southwest Indian Ocean), where the margin between a hit and a miss is smaller than over the continent and depends heavily on both actual TC position and wind field structure. Current NWP mesoscale models simulate realistic structures of TC wind field (Tallapragada et al. 2014; Knaff and Sampson 2015; Davis 2018); hence, tracking the evolution of the TC wind structure during a forecast can provide useful guidance for forecasters.

However, there are currently two main difficulties in providing an accurate TC tracking in real time. First, the accuracy of the depicted wind structure is very sensitive to the performance and tuning of the TC-tracker algorithms in determining the TC center. This detection can be particularly complex

<footnote>ª CNRM, University of Toulouse, Météo-France, CNRS, Toulouse, France</footnote>

<footnote>ᵇ University of Montpellier, Montpellier, France</footnote>

<footnote>ᶜ INRIA, Paris, France</footnote>

<footnote>ᵈ Ecole Polytechnique, Palaiseau, France</footnote>

<footnote>ᵉ Operations Department, Météo-France, Toulouse, France</footnote>

<footnote>Corresponding author: Laure Raynaud, laure.raynaud@meteo.fr</footnote>
in high-resolution fields (Ryglicki and Hart 2015). Several studies addressed this problem, for instance, using several predictor fields (Marchok 2021) or filtering some fine-scale features (Zhao et al. 2019). Second, the computational cost can limit the accuracy of the detected wind structure. The computation of wind radii is usually done in four quadrants, which is similar to the analysis TC forecasters make in real time, and this allows for a raw depiction of TC asymmetry. At the very small scales simulated by convection-permitting NWP models, it could be relevant to compute a more detailed view of TC wind structure, but the computational cost can increase dramatically. In addition, the first applications of kilometer-scale ensemble forecasts have shown great potential for TC prediction (Bousquet et al. 2020), and such systems may develop in operations in the very near future. A cost-efficient TC tracker to perform high-resolution ensemble predictions will then become crucial.

The rapid development of deep learning (DL) techniques in recent years has offered a new way to identify structures in images. In particular, convolutional neural networks (CNNs; LeCun et al. 1998) have become a major tool to perform image segmentation. They are based on the application of a series of convolution filters over the input fields, resulting in feature maps that summarize the structure of the inputs. For weather and climate applications, CNNs have been successfully used for the identification of several weather patterns in NWP model outputs and in observations. For instance, Lagerquist et al. (2019) use a CNN to identify weather fronts in the North American Regional Reanalysis, Mounier et al. (2021) explore the detection of bow echoes in convective-scale forecasts, and different cloud organizations are recognized in satellite images in Rasp et al. (2020).

Regarding TC prediction, state-of-the-art CNNs have been applied by several authors to weather and climate simulations (Liu et al. 2016; Mudigonda et al. 2017; Racah et al. 2017; Kurth et al. 2018; Kumler-Bonfanti et al. 2020; Prabhat et al. 2021; Galea et al. 2021; Gardoll and Boucher 2022) and satellite images (Nair et al. 2021; Pang et al. 2021), to perform classification (presence or absence of TC in a given image), detection (TC is roughly localized with a center or bounding box), or pixel-wise segmentation (TC is precisely located with a closed contour). The interested reader is referred to Table 1 of Gardoll and Boucher (2022) for an up-to-date comprehensive review of previous studies for the detection and segmentation of TCs in model and satellite data. Wang and Li (2023) also use CNNs to derive wind radii estimates from satellite images. The results are overall encouraging, showing that DL-based methods provide similar or even better detections and estimations than conventional algorithms, on top of running much faster.

Nonetheless, one of the main obstacles to a wider development of learning-based approaches for TC detection is the availability of high-quality labeled datasets. In the existing literature, the ground truth labels for TC center and spatial extent are most of the time given by outputs from a heuristic tracking model, such as the Toolkit for Extreme Climate Analysis (TECA; Prabhat et al. 2012), or from reference datasets such as the International Best Track Archive for Climate

Stewardship (IBTrACS; Knapp et al. 2010). Heuristics are useful for providing large datasets, but they have their own biases. On the other hand, human labeling is generally more accurate, but it is a time-consuming and subjective process. ClimateNet (Prabhat et al. 2021) is, to the authors' knowledge, the first initiative of expert-labeled open dataset (~500 carefully labeled images available) of weather events for DL applications, including TCs. The TC contours are drawn from the 25-km Community Atmosphere Model, version 5.1 (CAM5.1), outputs. Interestingly, Prabhat et al. (2021) noticed that the diversity of expert labels is smaller than the diversity arising from different heuristic models.

Following these recent successes of DL for TC detection, the goal of the present paper is to further explore the feasibility of such approaches for the real-time segmentation of TC surface wind structures in outputs of an operational kilometer-scale NWP model. This study presents the training of a state-of-the-art CNN on a specific dataset hand-labeled by the authors, and a thorough evaluation of the CNN performances, including a comparison with the detections of a heuristic model.

The paper is organized as follows. Section 2 introduces the data and the characteristics of the detection algorithms used. Subjective and objective evaluations of the CNN performances are detailed in Section 3. The generalization capacity of the trained CNN to other geographical domains and NWP model configurations is discussed in Section 4. Finally, conclusions and future work are provided in Section 5.

## 2. NWP data and TC detection methods

### a. The AROME-Overseas model

In this study, forecasts from the AROME-Overseas model are used (Faure et al. 2020). AROME-Overseas is a tropical version of the nonhydrostatic AROME model (Seité et al. 2011), which has been running operationally at Météo-France since 2016 over five tropical domains (Fig. 1). The vertical resolution is characterized by 90 vertical layers starting at 5 m, and a regular 2.5-km resolution is used on the horizontal, allowing explicit deep convection. Atmospheric initial and lateral boundary conditions are provided by the deterministic global model from the European Center for Medium-Range Weather Forecasts High Resolution (ECMWF-HRES). Forecasts at +48-h lead time are performed four times a day and are extended up to +78 h in case of tropical cyclone threat. AROME fields are provided on a regular latitude-longitude grid, at a resolution of 0.025°. To simulate air-sea interactions, AROME-Overseas is coupled to a 1D ocean model, initialized by the Mercator-Ocean global model (Lellouche et al. 2018). AROME significantly outperforms ECMWF-HRES for TC intensity (maximum wind) and structure (wind extensions, radius of maximum wind) forecasts at all lead times, while achieving similar performance for track forecasts (Bousquet et al. 2020). It is thus of particular interest to provide forecasters with guidance on TC structure from AROME-Overseas deterministic or ensemble forecasts.
<figure><img src="image_3.png" /><figcaption>FIG. 1. The five domains covered by the operational AROME–Overseas model, from left to right: French Polynesia, the West Indies, French Guiana, Mayotte, and La Réunion (southwest Indian Ocean), and New Caledonia.</figcaption></figure>

## b. The Cyclade TC tracker

To track TCs from various NWP models in real time, Météo-France runs its own software suite, called Cyclade. The TC identification performed by Cyclade follows the approach of standard heuristic detection and tracking models. The first step is to localize the center of a TC by finding extrema in two fields, 10-m wind speed and mean sea level pressure. The search area is limited either to the vicinity of an actual TC (for the initial lead time) or to the vicinity of a TC detected at the previous forecast lead time. From this center, characteristics of the 10-m wind field are computed along azimuth angles:

* Radius of the maximum wind (RMW): It is the distance from the center to the maximum value of wind speed along a given angle, with a 150-km upper bound; for mature cyclones, it represents the radius of the simulated eyewall.

* Radius of a specified wind speed threshold: It is the maximum distance from the center to the closest area above the specified wind speed threshold. In this study, we compute the 64-kt radius (R64), which represents the extension of the area where the wind speed is above the hurricane force (33 m s⁻¹).

RMW and R64 are both of great interest for forecasters to identify threatened areas.

In real time, these areas are described by Cyclade very roughly using only four values, one for each quadrant. Each quadrant is sampled using four different azimuth angles, which gives a total of 16 angles. The algorithm allows for finer sampling, but it is not done in operations due to its computational cost, which linearly increases according to the number of angles and to the number of members in the case of ensemble prediction. In this study, we use Cyclade as a baseline to compare the CNN results. A high-resolution setup of Cyclade is considered to compute TC characteristics every 5° (72 angles in total) in order to have a better depiction of the shape of the RMW and R64 structures. This also facilitates the comparison to the CNN detections, which provide continuous closed contours of wind extension areas. Nonetheless, there is no consistency check for two consecutive angles, and this can lead to spurious behavior for weak or asymmetric TC (Fig. 2a). Another caveat is that Cyclade produces only one value of R64 and thus cannot detect two structures of 64-kt wind along the same azimuth angle (Fig. 2b).

## c. The AROME–West Indies human-expert-labeled dataset

The outputs of Cyclade could be used to build the labeled datasets required to train and evaluate the CNN; however, as previously explained, Cyclade has some flaws that we want to alleviate with the DL approach. For this reason, a dataset of TC AROME forecasts has been designed and fully hand-labeled, following the procedure detailed in the next paragraphs.

To obtain a comprehensive dataset that is representative of TC simulations by AROME on a wide variety of situations, forecasts over the West Indies domain, which are issued four times a day up to 78-h lead time, have been examined during three Atlantic hurricane seasons (2016–18). Note that this dataset is rather small because the AROME–West Indies model has been running operationally only since 2016. Six different TC events are present in this domain during these seasons, and the corresponding forecasts have been sampled to finally obtain more than 400 cases, a case being an AROME forecast at a particular lead time. This dataset encompasses all maturity stages of TC, from tropical storms to very intense tropical cyclones. In addition, we paid particular attention to retaining a large diversity of scenarios, including both asymmetric and symmetric TCs, and different locations of the TCs, from open ocean to landfall, with several cases close to islands or close to the border of the domain. [Some examples are presented below in Fig. 5 (right column).]

The labeling process relies on two main fields, the 10-m wind speed and the absolute vorticity at 850 hPa. The latter helps primarily with weaker systems. Using wind speeds with a discrete color bar from 17 to 90 m s⁻¹, two parts of the TC are identified:

* The maximum velocity (Vmax) object, whose border encompasses the strongest winds within a 150-km radius from the center. For a mature TC, this feature aligns with the inner eyewall. For each TC, there is only one Vmax object. This object is comparable to the estimates provided by Cyclade through its values of RMW.

* The hurricane velocity (Vh) object, which is the area where the wind speed is above hurricane force. Depending on the TC case, there can be none, one, or several Vh objects. When there is just one object, it compares to the unique object given by Cyclade values of R64.

Note that, conversely to the usual heuristic algorithm used to compute RMW and R64, the human labeling does not need to find the TC center to draw the Vmax and Vh objects.
<figure><img src="image_4.png" /><figcaption>FIG. 2. Examples of Cyclade weaknesses and the improvements brought by human labeling for two cases. (a),(b) The Cyclade objects, with the values of RMW (R64) plotted in black (gray) from the center of the TC, on top of AROME 10-m wind speed field (m s⁻¹; shaded colors). For the case in (a) there is a sudden nonphysical increase in RMW in the southern part. For the case in (b) Cyclade is not able to determine the second R64 area in the south-eastern part, and the red area indicates values of wind speed above 33 m s⁻¹. (c),(d) The contours of the areas indicated by white lines corresponding to the maximum wind speed Vmax and to the hurricane-force wind Vh, obtained through human labeling. In both cases, the human labels appear more representative of AROME fields than Cyclade outputs [cf. (c) and (a); (d) and (b)].</figcaption></figure>

Figures 2 and 3 show the results of the labeling process for different cases. Figures 2c and 2d also show the improvements obtained with human labeling on two TCs compared with the outputs of Cyclade (Figs. 2a and 2b, respectively). The human-labeled objects have a more physical shape and remove the artifacts of Cyclade. Three experts participated in the labeling process, with supervision and correction by an independent expert to verify that the objective rules were applied.

The 400 hand-labeled samples of the West Indies database were split into 244 images for training and 156 for testing. Training and test cases correspond to independent TCs, and both include various cases of TCs sampled at different stages of their trajectory and life cycle. Training cases are sampled from TCs

[Irma, José, Maria (2017), Isaac and Kirk (2018)], and test cases correspond to TC [Matthew (2016)]. The limited size of the dataset constrained us to include only one TC in the test set. TC Matthew has been chosen because the AROME forecasts well capture the different stages of its development, from tropical storm when it enters the West Indies domain, to category 5 at the south of the domain and category 4 when heading to Haiti. A proper sampling of each stage thus allows for an evaluation of the detection methods in various situations.

### d. CNN architecture

The CNN used in this paper is inspired from the U-Net architecture (Ronneberger et al. 2015), originally developed for
<figure><img src="image_5.png" /><figcaption>FIG. 3. Examples of human-labeled Vmax and Vh objects from the West Indies database. The contour of the labeled objects is indicated by the white lines, and they are overlaid on the 10-m AROME wind speed field (shaded colors) used to draw these objects. (a) A case of intense TC with its Vmax object located on the eyewall and a Vh object that encompasses all the areas where the wind speed is above 33 m s⁻¹; (b) a case of tropical storm with only the Vmax object.</figcaption></figure>

the processing of 2D biomedical images. The U-Net is a state-of-the-art model for segmentation tasks that has been successfully applied for weather object identification by Mounier et al. (2021), Kumler-Bonfanti et al. (2020), among others.

The network (Fig. 4) consists of a contraction path (also called the encoder) and an expansion path (also called the decoder). The encoder part performs a downsampling of input images through repeated applications of convolution filters, each followed by a nonlinear activation function [the rectified linear unit (ReLU)] and a maximum pooling operation. The decoder part is essentially the reverse of the encoder part, performing an upsampling of the low-resolution feature maps learnt by the encoder. Skip connections between downsampling and upsampling layers are also included in order to provide the decoder with fine-grained details learnt

in the encoder part. The output of the U-Net is the probability, for each pixel, of belonging to the different categories considered.

### e. U-Net setup and training procedure

As shown in Fig. 4, the encoder is made of four convolution blocks with, respectively, 32, 64, 128, and 256 filters of size 3 × 3. Each block contains two convolutional layers followed by a maximum pooling layer. The network is trained for 200 epochs with a batch size of 32, using the Adam optimizer and a learning rate of 2 × 10⁻⁴. A dropout layer (value 0.3) is also added. The probabilities of three categories are issued by the U-Net, consistent with the human labeling: background (pixel is outside a TC), maximum wind area Vmax, and hurricane wind area Vh.

<figure><img src="image_6.png" /><figcaption>FIG. 4. The U-Net architecture used for TC wind structure segmentation. More details are given in section 2e. The inputs are the 10-m wind speed and 850-hPa vorticity AROME forecasts at a given time step.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>FIG. 5. Examples of TC hurricane and maximum wind areas detected in AROME-West Indies forecasts. (left) Segmentations from the U-Net, (middle) segmentations from the Cyclade model, and (right) expert-labeled segmentations (ground truth). The red and blue areas correspond to the maximum and hurricane wind objects, respectively. The 10-m wind speed forecast is overlaid (color bar; m s⁻¹), starting from 17 m s⁻¹ to filter tropical depressions. Hurricane winds correspond to 33 m s⁻¹. Various cases are illustrated, from top to bottom: a well-formed round-shaped TC, a more elongated TC, a very small TC, a TC near the domain border, a TC close to an island, and a tropical storm. The Jaccard indices between prediction and ground truth for maximum $J_{Vmax}$ and hurricane $J_{Vh}$ wind areas are indicated in the legend of each prediction panel. In each panel, a zoom on the TC allows for a detailed examination of the segmentations.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>FIG. 6. Jaccard indices computed over the West Indies test dataset for Cyclade (blue) and U-Net (red). (a) Total cyclone, (b) maximum wind area, and (c) hurricane wind area. The boxplots represent the distribution of the Jaccard indices over the samples of the test set, computed for each TC category (from 0 to 5, note that category 5 only contains one sample) and for the whole test set (on the right). Hurricane wind areas do not exist for category 0.</figcaption></figure>

Input data of the U-Net must be carefully chosen to provide the most suitable information for the segmentation task. For the detection of TC wind structures, two input variables are used: 10-m wind speed and vorticity at 850 hPa. Wind speed is necessary to identify the extension wind areas, while vorticity is mostly useful for locating the weaker TCs. This choice of variables is consistent with predictors used by conventional TC trackers. In addition, the feature importance results from Galea et al. (2021) show that 850-hPa vorticity is the most important variable for detecting the presence of TCs. A minimum-maximum normalization is applied to these input variables.

Input AROME images have a size of 945 × 529, each pixel being a 0.025° square. However, because of memory limitations, the U-Net is trained on smaller patches randomly extracted from the full images. 300 patches of size 96 × 96 are extracted in each image of the training dataset, leading to a total of 73200 patches used for the training. The size of the patches is chosen according to the typical size of a TC, and it should be large enough to extract information from the whole TC and its surroundings. Nonetheless, a majority of the selected patches are empty, meaning that no pixel is labeled as TC. This leads to a strong imbalance with only ~5% of patches labeled as “TC” (a patch is considered as TC if at least one pixel is labeled as maximum or hurricane-force wind speed). To reduce this imbalance, the proportion of TC patches is increased to 25% by removing some of the empty patches. In addition, the loss function used is the weighted categorical cross-entropy, which is a standard choice for handling imbalanced data (Nguyen and Thai 2023). The weights of the loss function have been manually tuned, with higher values given to the two TC classes (maximum and hurricane-force wind speed) compared with the background class.

When used for inference, the U-Net outputs probabilities for three classes at each pixel. The conversion from probabilities to a segmentation mask is performed by taking, for each pixel, the class index with the highest probability. In a post-processing step, a filtering of the U-Net outputs is applied to remove small-size maximum wind speed objects. These

spurious detections are rare and occasionally occur in cases of weak TCs, whose detection is more difficult and sometimes fragmented. Hence, a filtering of these objects has been implemented, based on thresholds regarding their size and maximum vorticity value.

### f. Evaluation of the detection accuracy

In addition to a subjective visual examination of TC detections, an objective evaluation is performed using a set of standard object-oriented metrics. The main metric used is the Jaccard index between prediction and ground truth, also called “intersection over union” (IoU). The Jaccard index is a widely used segmentation measure that accounts for true positives, false positives, and nondetections. It takes values between 0 (no overlap between prediction and ground truth) and 1 (perfect overlap). The Jaccard index has already been applied to TCs, for instance, in Kurth et al. (2018) and Prabhat et al. (2021). In addition, TC object characteristics such as size are considered to compare the accuracy of CNN and heuristic models to the ground truth. Each diagnostic is computed for maximum wind areas and hurricane wind areas, over all TCs in the test set and by filtering per TC category, according to the Saffir-Simpson scale.

## 3. Results

### a. Subjective evaluation

As a first step, we performed a visual examination of the 156 cases of the test set to roughly evaluate the U-Net capacity on a wide range of TCs. A correct detection is considered if the U-Net predicts an area of hurricane or maximum wind at the correct place, whatever the quality of the segmentation in terms of size and shape. This fast check revealed that there are no false alarms nor misses for both wind areas. Figure 5 presents the TC detections obtained for different cases using the U-Net approach and the heuristic Cyclade model. Human detections are also shown for reference. These examples show the ability of the U-Net to properly handle a large variety of TC stages, shapes, and geographical constraints (domain borders, landfall). There is also a relatively good agreement
<table><caption>TABLE 1. West Indies test dataset: number of TCs per category according to the Saffir–Simpson hurricane wind scale (category 0 being wind speed between 17 and 33 m s⁻¹).</caption><thead><tr><th>Category</th><th>0</th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th></tr></thead><tbody><tr><th>Number of TCs</th><td>36</td><td>18</td><td>21</td><td>54</td><td>26</td><td>1</td></tr></tbody></table>

between the U-Net and Cyclade on average, except for the weak cyclone (Figs. 5p–r). In that case, the Cyclade detection is very poor, while the U-Net prediction compares well with the expert labeling (similar behaviors are observed for other cases of weak cyclones, not shown). Jaccard indices are pretty good for all examples, with values around or larger than 0.8 on average for the U-Net and smaller values for Cyclade (around 0.6, except for the weak case).

### b. Intersection over union

Figure 6 presents the distributions of the Jaccard index for maximum wind, hurricane wind, and the whole TC object (corresponding to the union of maximum and hurricane wind areas). For each TC wind class, the Jaccard index is computed for the Cyclade and U-Net predictions over all cases of the test set and for each intensity category (category 0 for wind speed between 17 and 33 m s⁻¹ and from 1 to 5 according to the Saffir–Simpson scale). The number of TCs per category is given in Table 1.

The mean Jaccard index over the whole dataset for maximum and hurricane wind areas is around 0.8 for the U-Net and 0.6 for Cyclade. Another advantage for the U-Net is its greater performance consistency, as indicated by the smaller size of the boxplots compared with Cyclade. It is also interesting to notice that the detection accuracy of the Cyclade model is significantly improved when considering the whole TC object, with an average Jaccard close to the U-Net, but its performance is more variable. This may suggest some compensating biases in the estimation of maximum and hurricane wind areas, while the whole structure is better captured. It is finally worth pointing out that our U-Net has better performances than values found in previous studies. For instance, Prabhat

et al. (2021) obtained a Jaccard index of 0.44 when the DL model is trained on heuristics and 0.24 when the model is trained on the hand-labeled ClimateNet dataset. This difference is likely to be related to the absence of clear definition of TC boundaries in ClimateNet, while our definitions of maximum and hurricane wind areas are precise and thus less prone to expert subjectivity.

Looking at the performances per TC category shows that the U-Net clearly outperforms Cyclade for all categories and reveals a clear improvement as TC intensity increases, with higher Jaccard indices and reduced variability. This behavior is observed for both models, especially for the prediction of hurricane wind and total TC areas. There are at least two reasons that can explain this improvement. As the TC intensity increases, the stronger vorticity gradients allow for an easier and more accurate detection of TC location and wind extension areas. Second, the size of hurricane wind areas significantly increases with TC intensity (from 200 pixels for category 1 to more than 1000 pixels for category 4 on average), which contributes to reducing the impact of small differences between prediction and ground truth in the Jaccard index. This result is also consistent with the works of Galea et al. (2021) and Pang et al. (2021), which indicate a higher quality of detection as TCs strengthen.

### c. TC sizes

To further examine the characteristics of the U-Net and Cyclade segmentations, Fig. 7 presents the distributions of TC sizes for total, maximum, and hurricane wind areas. The U-Net predictions are on average in good agreement with the ground truth, with only a marginal underestimation of maximum wind areas and an overestimation of hurricane wind areas. On the other hand, the sizes of Cyclade detections show larger deviations from the ground truth, with a clear overestimation of maximum wind areas and underestimation of hurricane wind areas. A visual examination of test cases indicates that this is linked to maximum wind areas encroaching on hurricane wind areas. Figure 8 details the size distributions per TC category. For brevity, results are only shown for the total TC area. Conclusions drawn from the average distributions remain mostly true when looking separately at

<figure><img src="image_9.png" /><figcaption>FIG. 7. Distributions of TC sizes over the West Indies test dataset for Cyclade (purple), U-Net (pink), and ground truth (black). (left) Total cyclade area, (middle) maximum wind area, and (right) hurricane wind area. The distributions are computed over the 156 cases of the test dataset.</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>FIG. 8. Distributions of total TC sizes over the West Indies test dataset for Cyclade (purple), U-Net (pink), and ground truth (black). Distributions are computed for each TC category, from 0 to 4. Category 5 is not shown because it contains only one sample.</figcaption></figure>

each category. One can also notice an improved consistency between Cyclade and ground truth for TC categories 3 and 4, which are mainly explained by compensations between underestimation of hurricane wind areas and overestimation of maximum wind areas.

### d. Numerical efficiency

Finally, another important comparison point of the two detection models is their computational cost. A U-Net prediction for one image of size 945 × 529 is done in 0.05 s [which is consistent with the inference time indicated in Kumler-Bonfanti et al. (2020) of 0.03 s for a 720 × 361 image], while it takes about 20 s for Cyclade with the high-resolution sampling used in this study. This substantial improvement with the U-Net approach is particularly interesting for operational forecasting and postprocessing of ensemble forecasts.

## 4. Generalization to other AROME configurations

As the development of specific training datasets for each NWP model configuration is time-consuming when it requires hand labeling, the generalization properties of the U-Net trained on the West Indies dataset are examined in this section. For that purpose, one considers both “domain generalization,” using outputs of the AROME model run on a different geographical domain, and “resolution generalization,” using outputs from a higher-resolution AROME-West Indies model.

These two case studies are important in practice since the AROME-Overseas model runs operationally on five domains (Fig. 1), and resolution upgrades are common during a model life to improve forecast performances. In particular, a resolution increase in AROME-Overseas model from 2.5 to 1.3 km has been done in June 2022. For both cases, test datasets have been hand-labeled following the rules described in section 2 in order to perform objective evaluations. The Cyclade heuristic model has not been run for these generalization studies since the primary goal is to examine the U-Net transfer capability.

### a. Domain generalization

For the domain generalization, we consider outputs from the AROME model run over the southwest Indian Ocean Basin (called the AROME-Indian Ocean). The dynamics and physics configuration of the AROME-Indian Ocean model is the same as the AROME-West Indies model, and only the geographical domain changes. The Indian Ocean dataset

<table><caption>TABLE 2. Indian Ocean test dataset: number of TCs per category according to the Saffir-Simpson hurricane wind scale (category 0 being wind speed between 17 and 33 m s⁻¹).</caption><thead><tr><th>Category</th><th>0</th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th></tr></thead><tbody><tr><th>Number of TCs</th><td>53</td><td>14</td><td>12</td><td>46</td><td>29</td><td>2</td></tr></tbody></table>
<figure><img src="image_11.png" /><figcaption>FIG. 9. Examples of TC hurricane and maximum wind areas over the Indian Ocean domain. (left) Segmentation from the U-Net and (right) expert-labeled segmentations (ground truth). The red and blue areas correspond to the maximum and hurricane wind objects, respectively. The 10-m wind speed forecast is overlaid (color bar; m s⁻¹), starting from 17 m s⁻¹ to filter tropical depressions. Various cases are illustrated, from top to bottom: a TC near the domain border, a weak TC, a case with two TCs, a TC close to the African coast, and a TC over small islands. The Jaccard indices between prediction and ground truth for maximum $J_{Vmax}$ and hurricane $J_{Vh}$ wind areas are indicated in the legend of each prediction panel. In each panel, a zoom on the TC allows for a detailed examination of the segmentations.</figcaption></figure>
<figure><img src="image_12.png" /><figcaption>FIG. 10. Jaccard indices computed over the Indian Ocean dataset for the U-Net predictions. (a) Total cyclone, (b) maximum wind area, and (c) hurricane wind area. The boxplots represent the distribution of the Jaccard indices over the samples of the dataset, computed for each category (from 0 to 5, note that category 5 only contains two samples) and for the whole dataset (on the right). Hurricane wind areas do not exist for category 0.</figcaption></figure>

comprises 156 cases that occurred during the 2018/19 season. The labeled systems are Cilida, Funani, Gelena, Idai, and Joaquinha. The number of TCs per category is given in Table 2, and the distribution of cases is quite similar to the West Indies dataset. One may mention that the expert who labeled this dataset is different from those who labeled the West Indies dataset; hence, even if the labeling rules are clear, one cannot exclude some differences or biases between these two hand-labeled datasets.

A visual inspection of the detections over the 156 cases of the Indian Ocean dataset showed that no false alarms nor misses are observed for both hurricane and maximum wind areas. Figure 9 presents various cases of TCs detected in this domain. One can see that the quality of the detections seems as good as on the West Indies data for different situations, with the Jaccard index of around 0.8 on average. In addition, the U-Net is able to properly handle cases with multiple TCs.

Jaccard indices computed over the whole dataset for total, maximum, and hurricane wind areas are presented in Fig. 10. Similarly to the West Indies domain, a performance improvement as the TC severity increases is observed, especially for hurricane wind and total TC objects. Jaccard values are also

in good agreement with those obtained on the West Indies. For hurricane wind area, the average Jaccard value increases from 0.6 for category 0 to around 0.8/0.9 for categories 3 and above, while for maximum wind area values, it is around 0.8 whatever the category. There is, however, one prediction with a small Jaccard index of maximum wind (~0.2) for a category 1 TC, which corresponds to a difficult case of landfall on the Madagascar Coast (not shown). For the whole TC object, Jaccard indices are even better than on the West Indies domain, with average values equal to or larger than 0.9 for categories 1 and above.

Finally, the distributions of predicted object sizes (Fig. 11) are consistent with those of hand-labeled objects for total TC. A small overestimation of hurricane wind areas and underestimation of maximum wind areas are observed, as was the case on the West Indies domain.

b. Generalization to higher resolution

To examine the generalization capabilities of the U-Net on input data provided at different resolutions, we consider outputs from the AROME-West Indies model run at a higher resolution than the AROME model used for training the U-Net. In this part, the AROME model used is the operational

<figure><img src="image_13.png" /><figcaption>FIG. 11. Distribution of TC sizes over the Indian Ocean dataset for the U-Net predictions (pink) and hand labels (black). (left) Total cyclone area, (middle) maximum wind area, and (right) hurricane wind area. The distributions are computed over the 156 cases of the test dataset.</figcaption></figure>
<table><caption>TABLE 3. High-resolution West Indies test dataset: number of TCs per category according to the Saffir–Simpson hurricane wind scale (category 0 being wind speed between 17 and 33 m s⁻¹).</caption><tr><td>Category</td><td>0</td><td>1</td><td>2</td><td>3</td><td>4</td><td>5</td></tr><tr><td>Number of TCs</td><td>25</td><td>21</td><td>18</td><td>6</td><td>7</td><td>7</td></tr></table>

version released in June 2022, running at a 1.3-km horizontal resolution (instead of 2.5 km) and including some updates in the physics, the most important one for TC simulation being an enhancement of the scheme for turbulent air-sea fluxes. The dataset encompasses 84 different cases, taken from TC Matthew (2016) and TC Fiona (2022), at various stages and different locations like for the previous datasets. This 1.3-km dataset is thus fully independent from the training data and different from the test dataset used at 2.5 km to some extent. As in operations, the output files from the AROME model are available on a 0.025° grid for both horizontal resolutions (1.3 and 2.5 km), which reduces the differences between the two configurations and facilitates the use of a common U-Net. As before, this dataset has been labeled by another expert compared with the 2.5-km dataset, which can impact the scores. The number of TCs per intensity category is given in Table 3.

Figure 12 presents two examples of TC detected in the high-resolution AROME model outputs, which are in very good agreement with the hand labeling. Figure 13 presents the Jaccard indices computed for each wind object over the whole dataset and per TC category. The performances are very similar to those obtained on the lower-resolution dataset, with a noticeable improvement of hurricane and total wind area accuracy as TC strength increases, with TCs of categories 4 and 5 reaching Jaccard values of 0.9. The identification of maximum wind areas is more stable across categories, with an average Jaccard index of around 0.8, which is consistent with what has been observed in previous datasets. Looking at the area distributions (Fig. 14), there is again a very good consistency between the U-Net predictions and the ground truth.

## 5. Conclusions and future work

A convolutional neural network, inspired from the U-Net architecture, has been trained to perform the detection of tropical cyclone wind structures in the outputs of the convective-scale AROME model. For that purpose, a specific dataset of 400 AROME forecasts over the West Indies domain has been entirely hand-labeled by experts, following a rigorous

<figure><img src="image_14.png" /><figcaption>FIG. 12. Examples of TC hurricane and maximum wind areas detected in the outputs of the high-resolution AROME-West Indies model. (left) Segmentations from the U-Net and (right) expert-labeled segmentations (ground truth). The red and blue areas correspond to the maximum and hurricane wind objects, respectively. The 10-m wind speed forecast is overlaid (color bar; m s⁻¹), starting from 17 m s⁻¹ to filter tropical depressions. The Jaccard indices between prediction and ground truth for maximum $J_{Vmax}$ and hurricane $J_{Vh}$ wind areas are indicated in the legend of each prediction panel. In each panel, a zoom on the TC allows for a detailed examination of the segmentations.</figcaption></figure>
<figure><img src="image_15.png" /><figcaption>FIG. 13. Jaccard indices computed over the high-resolution West Indies dataset for the U-Net predictions. (a) Total cyclone, (b) maximum wind area, and (c) hurricane wind area. The boxplots represent the distribution of the Jaccard indices over the samples of the dataset, computed for each intensity category (from 0 to 5) and for the whole dataset (on the right). Hurricane wind areas do not exist for category 0, for which wind speed is between 17 and 33 m s⁻¹.</figcaption></figure>

procedure to define maximum and hurricane force wind speed areas. The dataset includes six TCs sampled at different stages of their life cycle to have a reasonable variety in TC intensity and shape. The CNN has been trained on ~60% of the dataset, and the evaluation has been performed on the remaining 40%.

The performance of the U-Net for the detection of both maximum and hurricane wind areas is overall very promising. Even trained on a relatively small sample of TC cases, Jaccard index values are around 0.8 on average, which is far better than results from previous studies. This increased performance is likely to be due to an accurate definition of TC, which limits heterogeneities in the labels. It has also been shown that the performance noticeably increases with TC intensity, when the TC location and wind areas are better structured and easier to find. A comparison with the heuristic TC-tracker Cyclade, operationally used at Météo-France, indicates that the U-Net performs better in most cases, particularly for weak cyclones. In addition to enhanced skill, the U-Net has the advantage of being more computationally efficient than Cyclade. This computational efficiency then offers interesting perspectives for real-time processing of high-resolution forecasts, especially large ensemble forecasts.

As NWP is constantly evolving and hand-labeled training datasets are time-consuming to develop, evaluating the transferability of the trained U-Net to slightly different data is a key aspect. We looked at two types of transfer: first, using similar NWP data on another domain in the opposite hemisphere, and second, using data from a higher-resolution NWP. These two configurations are of particular interest for operational applications. In both cases, the quality of the U-Net detections is very similar to that obtained on the original dataset. While this generalization capacity is promising to avoid retraining CNNs each time NWP models are upgraded, it requires further investigation to fully understand the conditions for successful transfer. We notably recommend exploring additional configurations, using, for instance, larger-resolution differences or different NWP models between train and test. Generalization to observations, such as synthetic aperture radar (SAR) wind speed data, should also be considered.

While the CNN performances are already very satisfying, several directions could be investigated to further enhance their capability. Since weak cyclones are harder to properly identify, it may be worth increasing the dataset with more tropical storms and category 1 TCs and adding predictor variables. Other loss functions could also be tested to address the

<figure><img src="image_16.png" /><figcaption>FIG. 14. Distribution of TC sizes over the high-resolution West Indies dataset for the U-Net predictions (pink) and hand labels (black). (left) Total cyclone area, (middle) maximum wind area, and (right) hurricane wind area. The distributions are computed over the 84 cases of the test dataset.</figcaption></figure>
data imbalance. The temporal stability of TC detections has not been thoroughly evaluated in this study; however, it is an important aspect since the next step would be to use this tool for TC tracking. A rapid visual analysis of some cases did not reveal instability issues, but this should be further explored. Using a short sequence of forecast fields as input to the U-Net, rather than a single forecast, may also be an option to ensure consistency in the detections at successive time steps.

Representing TCs as continuous objects also offers new perspectives for refined forecast guidance products, which may allow for a more accurate prediction of the area under threat than with the standard wind radii. Another straightforward application is forecast evaluation. Using object-based scores can certainly provide new and valuable insight into forecast verification and process studies (Zick et al. 2022).

*Acknowledgments.* We thank the LACy/Cyclones team in La Réunion Island for providing the Cyclade outputs and colleagues for their valuable feedback on this manuscript.

*Data availability statement.* The hand-labeled datasets will be made available after paper acceptance.

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