# Evaluating Monthly Tropical Cyclone Forecasts Through the Lens of Track Clustering Over the Southwest Indian Ocean

Adrien Colomb, Hélène Veremes, François Bonnardot, Guillaume Jumaux, Sébastien Langlade, Sylvie Malardel

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Adrien Colomb, Hélène Veremes, François Bonnardot, Guillaume Jumaux, Sébastien Langlade, et al. Evaluating Monthly Tropical Cyclone Forecasts Through the Lens of Track Clustering Over the Southwest Indian Ocean. Meteorological Applications, 2025, 32 (6), pp.e70120. 10.1002/met.70120. hal-05474009

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RESEARCH ARTICLE

OPEN ACCESS

# Evaluating Monthly Tropical Cyclone Forecasts Through the Lens of Track Clustering Over the Southwest Indian Ocean

Adrien Colomb¹ ID | Hélène Veremes¹ ID | François Bonnardot² | Guillaume Jumaux² | Sébastien Langlade² | Sylvie Malardel³

<figure><img src="image_2.png" /></figure>

<figure><img src="image_3.png" /></figure>

¹Laboratoire de l'Atmosphère et des Cyclones, UMR8105, Météo-France, CNRS, Université de La Réunion, Saint-Denis La Réunion, France | ²Direction Interrégionale pour l'Océan Indien, Météo-France, Saint-Denis La Réunion, France | ³Centre National de Recherches Météorologiques, Université de Toulouse, Météo-France, CNRS, Toulouse, France

**Correspondence:** Adrien Colomb (adrien.colomb@meteo.fr)

**Received:** 15 May 2025 | **Revised:** 8 October 2025 | **Accepted:** 16 October 2025

**Funding:** This work was supported by the European Union and Région Réunion (ERDF, INTERREG V programs, PISSARO project).

**Keywords:** extended range weather forecast | model evaluation | monthly prediction | track classification | tropical cyclone | verification scores

## ABSTRACT

We apply a tropical cyclone (TC) track classification method to the subseasonal TC predictions issued by the European ensemble prediction model (EPS) on the Southwest Indian Ocean. This track typology is based on a subdivision of the basin into three areas. Each simulated storm track is assigned a three-digit code, where each digit represents the area of genesis, the westernmost position, and the easternmost position, respectively. To account for the intensity bias in the simulated TCs, we conduct sensitivity tests that result in lowering the tropical storm wind threshold to 29kt and filtering out systems with a lifetime maximum intensity lower than 34kt. The model skill is evaluated against the performance of a 1-month moving climatology, to validate its ability to capture intra-seasonal variations in TC activity and favoured track typology. Results show an overestimation of occurrence probabilities for all track types, with the central part of the basin and the Mozambique Channel being the regions most affected. These limitations confine the raw model's skill to the second week (W2, i.e., +7 to +14 days) of forecast only. However, when evaluating the model on its capacity to assign a track type to TC genesis known to be valid at the basin scale, the EPS exhibits a 20% performance gain for W2 and a 5% gain at W3 and W4, compared to the moving climatology. These results demonstrate that when TC forecasters consider an EPS genesis prediction to be reliable, they can leverage the corresponding track type predictions to characterise TC risk more precisely, even a month in advance. Furthermore, aggregating track types based on their likelihood of impacting inhabited areas within the basin further enhances predictive skill. An impact-based forecasting product is derived from this work and will be evaluated in operations by the TC forecasters in La Réunion.

## 1 | Introduction

Over the last decade, stakeholders and disaster-management authorities have been showing a growing interest in weather predictions extending beyond the typical 5-7-day lead times. Located in the blind spot between the fading influence of initial conditions and the timescale of climate drivers, monthly

forecasts are notoriously challenging for current numerical weather prediction (NWP) models (Vitart and Robertson 2018; Phakula et al. 2024). Although less precise than traditional weather forecasts, they still prove to be useful for decision makers in many situations owing to their longer notice period (White et al. 2017, 2022), that allows for the issuance of early warning messages. This anticipation of the meteorological risks,

<footnote>This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.</footnote>

<footnote>© 2025 The Author(s). Meteorological Applications published by John Wiley & Sons Ltd on behalf of Royal Meteorological Society.</footnote>
like heavy rainfall or tropical cyclones (TCs) impacts, better corresponds to the timeline needed to pre-position emergency supplies and prepare human resources deployment on the field (Vitart and Robertson 2018). In the case of TC forecasts, the cost of unnecessary anticipated measures triggered by false alarms is often outweighed by the severe impacts TCs have on human lives and economic activities. In this context, the subseasonal-to-seasonal (S2S) prediction project, conducted under the World Meteorological Organization (WMO) World Weather Research Programme (WWRP), gathered forecast data issued by 11 operational centers in the form of an open-access database. The project fostered an international effort in the evaluation and improvement of the skills of operational numerical weather prediction models from 7 to more than 30 days lead times (Vitart et al. 2017). Specifically, the predictability of TC activity at monthly lead times is clearly identified as one of the many lines of research explored by the S2S community (Camargo et al. 2019; Schreck III et al. 2023).

In the Southwest Indian Ocean (SWIO), the PISSARO project (Malardel et al., to be submitted) promoted the use of S2S forecast applied on TC and extreme rainfall predictions. More specifically, the project focused on assessing the performance of the monthly predictions available for the SWIO, as well as validating and refining tools and products for future operational use. TC forecasters from the La Réunion Regional Specialised Meteorological Center (RSMC) actively contributed to the initiative, sharing their expertise while familiarising themselves with subseasonal prediction concepts. Other risk management organizations, such as the local Red Cross Intervention Platform, also participated in PISSARO, providing insights into their needs and expected information. Stemming from this work, RSMC La Réunion now issues regular updates on the TC activity expected over the coming weeks on the SWIO. These bulletins establish a general scenario in terms of TC occurrence and preferential track shape (Météo-France 2024). They are mainly based on an expertise of the predictions of the Ensemble Prediction System (EPS), developed and operated by the European Centre for Medium-Range Weather Forecasts (ECMWF).

On the SWIO specifically, the skill of the EPS TC predictions has been evaluated by Emerton et al. (2024) up to day 7. Given their current performances, subseasonal predictions are not expected to have the same precision as those shorter-term forecasts. Classic metrics based on direct point-to-point comparison with the observations do not satisfyingly deal with the spatio-temporal uncertainty deemed reasonable at such lead times. In this context, a wide diversity of methods has been used for the evaluation of subseasonal TC predictions, as underlined by Camargo et al. (2019). In the previous S2S studies, metrics have been generally based around the probability of TC occurrence in predefined spatio-temporal ranges. The spatial resolution of these scores can vary, ranging from basin-wide assessments (Lee et al. 2018), based on coarser grids than the native resolutions of the forecasts (Yamaguchi et al. 2015), to large overlapping boxes (Vitart et al. 2010; Lee et al. 2020), with 7-day time windows being the most commonly used. Most of those studies identify the EPS as the most accurate model for TC prediction within the S2S panel. The model generally demonstrates skill in forecasting TC occurrence up to the second week of forecasts when compared to a climatology that varies by month, and up to

at least the third week when compared to a constant climatology in most ocean basins. Lee et al. (2020) further suggest that the main difficulty in predicting TC occurrence lies in forecasting genesis, with subsequent storm evolution appearing to be less challenging to predict.

As described above, metrics used in the S2S literature are primarily based on the occurrence of TC points within large boxes, without explicitly considering the shape, continuity, or general bearing of the simulated TC tracks. In addition to predicting the presence of a TC within a given subregion, the ability to anticipate the general shape of a TC track can also provide valuable information. This is particularly relevant for countries with long and meridionally oriented coastlines, such as Madagascar or Mozambique, that are especially vulnerable to zonal-tracking TCs. Even when there is uncertainty in the precise storm location, forecasting that certain track types are more likely to occur in the coming weeks can offer meaningful guidance for preparedness and risk assessment, while potentially being less demanding to predict than precise subregional occurrence. This is why, in this study, we investigated the EPS subseasonal performance by considering TC track types, rather than focusing on individual TC points. To achieve this, we employ a TC track clustering method (Bonnardot et al. 2022) that has been used in seasonal predictions of TC activity over the SWIO since 2022 (Takaya et al. 2023). Bonnardot et al. (2022)'s work was directly inspired by the study of Ash and Matyas (2012). Other studies have used clustering tools on Best Track (BT) data like Camargo et al. (2007) on western North Pacific typhoons, which were later adapted to the southern hemisphere TCs by Ramsay et al. (2012). In both those reference studies, track clusters are defined using a regression mixture model based on the method of Gaffney (2004). The characteristics of each track type are then explored in terms of genesis location, intensity, lifetime, and landfall probability. Additionally, the intra-seasonal and inter-annual variability of different track types is investigated, with correlations to known climate drivers such as El Niño (ENSO) established. Ash and Matyas (2012) opted for a similar framework focusing on the SWIO but rather used a two-step hierarchical cluster analysis, first generating clusters based on genesis locations, then subdividing them into eastward versus westward moving TCs. Bonnardot et al. (2022) built on those results but streamlined the classification method for the context of operational seasonal TC predictions (see Section 2.1).

Our goal is to investigate the applicability of their track classification for the EPS monthly TC predictions to gain insight in the capacity of the EPS to capture intra-seasonal variations of the track typology on the SWIO, with the aim of ultimately leveraging the models skill to create a robust and meaningful forecasting product for RSMC forecasters. In the first section, we detail the classification method, review the key findings of Bonnardot et al. (2022), and present the model data. The second section outlines the necessary adaptations for applying the classification to simulated tracks and describes the metrics used to evaluate model performance. The results of this evaluation are presented and discussed in the subsequent section. In Section 4, we leverage these results to attempt a reliability calibration of EPS predictions, before evaluating the model through a more impact-based metric, obtained by aggregating track types based on their impact probability for each SWIO territory. Finally,
Section 2.3 presents the design of the forecasting product derived from this work.

## 2 | Data and Track Classification Method

### 2.1 | Classification Method

Bonnardot et al. (2022) track classification method is largely inspired by Ash and Matyas (2012) clusters. However, they aimed to rationalize the classification process, making it more directly interpretable and easier to use in the operational context, while retaining the same key information as the aforementioned reference studies. This includes the general shape of TC tracks at the basin scale, their genesis locations, and the inherent probability of impact on inhabited regions. The classification method is illustrated in Figure 1.

In practice, the SWIO basin is subdivided into three regions:

* Zone 1 is delineated by the 30° E and 50° E meridians. It contains the Mozambique Channel, the Comoros archipelago and the island of Madagascar.

* Zone 2 corresponds to the central part of the SWIO, between 50° E and 70° E. La Reunion, Mauritius and Rodrigues islands are located in this box.

* Zone 3 is the easternmost third of the basin, from 70° E to 90° E, with no inhabited islands.

Only the ‘active portion’ of the TC tracks (APT, where the sustained 10-min winds exceed 34kt) located on the SWIO basin is considered. This threshold corresponds to the operational tropical storm stage, as defined in World Meteorological Organization (2017). As in Camargo et al. (2007) study, TCs that did not exceed the tropical depression stage are discarded, to focus solely on systems that have completed their genesis process. Meanwhile, Ash and Matyas (2012) considered only mature TCs, corresponding to the tropical cyclone stage or higher in the SWIO classification. Additionally, all points of the track located outside of the three boxes, i.e., located southward of 25° S, over the African continent or eastward of 90° E, are not taken into account. Finally, each TC is tagged with a 3-digit code:

<figure><img src="image_4.png" /><figcaption>**FIGURE 1** | The classification method illustrated for two tropical cyclone examples. The colour along each track indicates whether the intensity of the system is above or below the 34-kt threshold. The abbreviations **Max Lon.** and **Min Lon.** refer to the box number where the maximum and minimum longitudes are reached, while the intensity exceeds this threshold. **Gen Lon.** denotes the genesis longitude, i.e., the box where the storm first exceeds the 34-kt threshold.</figcaption></figure>

* The first digit is set according to the number of the zone where the genesis is completed, i.e., the first point of the APT.

* The second digit corresponds to the zone number of the westernmost point of the APT.

* The third digit corresponds to the zone number of the easternmost point of the APT.

For example, a storm tracking exclusively over the Mozambique Channel is tagged with the code **111**. More generally, a same-digit class will be attributed to TCs with a generally meridian track, that never cross from one box to an other. On the other hand, parabolic tracks will be associated with the **323** or **212** classes. Note that codes like **123**, **233** or **322** are not associated with a track type, as the cyclogenesis longitude has to be located between the minimum and maximum longitudes.

### 2.2 | Track Typology Climatology

The Best Track (BT) records (Knapp et al. 2010b) serve as reference data for model validation. This data is produced by RSMC La Reunion TC meteorologists, after they have gathered all the observation data available on each mature system occurring over the basin (Knapp et al. 2010a). BT records omit the numerous tropical lows that do not reach the tropical depression stage, i.e., which intensity never exceeds 27 kt. Figure 2 displays the results of applying the Bonnardot et al. (2022) typology to BT records from 1986 to 2021. The central aspect of their work and main contribution compared to previous studies lies in a much more detailed exploration of the relationships between track types and their climatological probabilities of impact on the 6 inhabited territories most exposed to TCs in the SWIO (Figure 3). An impact is defined by the intersection of the TC track with the boundaries of a given region. These results are presented in Table 1.

The two most frequent classes are meridian tracks in the eastern and central regions, with their APTs strictly confined to box **3** or **2** respectively. Of those two most frequent types, only **222** systems pose a threat to inhabited islands. The parabolic tracks in the **323** class are the third most frequent and are associated with a low impact probability on the Mascarenes archipelago. In the West, the Mozambique Channel systems **111** only account for 14% of all SWIO systems but have a moderate probability of impacting either the western Madagascan coast or Mozambique. The last of the main track classes is the parabolic **212** type, which poses a great threat to the Eastern Madagascan coast. Other track types are much less frequent, with populations not exceeding 5% of all systems. However, rare and long-lived **313** systems (0.2 TCs per year on average and 115-kt average lifetime maximum intensity, LMI), such as the infamous TC Freddy (2023), are linked to the highest impact probability on the eastern coast of Madagascar.

Bonnardot et al. (2022) show a significant inter-annual modulation of their track typology, conditioned by the main climate drivers of TC activity identified over the SWIO. Like Ash and Matyas (2012), they considered the influence of both the ENSO and the Subtropical Indian Ocean Dipole (SIOD, Behera and
<figure><img src="image_5.png" /><figcaption>**FIGURE 2** | Tropical cyclone tracks of the five most frequent classes, when applying Bonnardot et al. (2022) classification on La Reunion RSMC Best Track data from 1986 to 2021. Above each map is indicated the class name, its frequency in the Best Track dataset, the corresponding average yearly occurrences and lifetime maximum intensity. The tracks are coloured according to the intensity of the system.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>**FIGURE 3** | Inhabited areas as defined in Bonnardot et al. (2022).</figcaption></figure>

Yamagata (2001), but also added the Indian Ocean Dipole (IOD, Saji et al. 1999) in their analysis. As climate drivers are usually well set at the beginning of each TC season, around mid-November, the observed seasonal context is used to infer the types of tracks that should be favoured during the upcoming months. For example, a combination of a negative ENSO (i.e., La Niña) and a positive SIOD event doubles the proportions of the meridian tracks on the Mozambique Channel and the Eastern region of the basin, while occurrences in the central area become

far less frequent than the climatology. This results in a lowered impact probability on the eastern Madagascan coastline, more exposed to zonal tracking TCs born in the central region of the SWIO. These considerations are used to construct the seasonal tropical cyclone activity forecast bulletin issued by the RMSC of La Réunion at the start of each season.

## 2.3 | Reforecast EPS Data

We used data made available on the S2S data portal for the ECMWF EPS predictions.¹ This study focuses on the 11-member reforecast ensemble, based on the 2022 operational version of the EPS (ECMWF 2021). The reforecast dataset guarantees the homogeneity of the model version over 20 years of data, from the 2002–2003 to the 2021–2022 active TC seasons on the SWIO. This allows the computation of meaningful performance metrics. However, it is worth noting that its lesser ensemble size compared to the 51 members of the real-time operational EPS is expected to negatively impact some statistical scores. A smaller ensemble possesses less sampling power of the probabilistic distribution of each meteorological situation, thus reducing its statistical acuity.
<table><caption>TABLE 1 | Impact probabilities for each inhabited area of the basin, associated with each TC track class, taken from Bonnardot et al. (2022).</caption><thead><tr><th></th><th>111</th><th>212</th><th>222</th><th>223</th><th>313</th><th>323</th><th>333</th></tr></thead><tbody><tr><td>(1) RODRIGUES</td><td>0.0</td><td>2.4</td><td>5.1</td><td>7.1</td><td>0.0</td><td>16.4</td><td>0.0</td></tr><tr><td>(2) REUNION-MAURITIUS</td><td>0.0</td><td>4.8</td><td>32.9</td><td>14.3</td><td>33.3</td><td>9.1</td><td>0.0</td></tr><tr><td>(3) MADA. EAST COAST</td><td>10.5</td><td>76.2</td><td>3.8</td><td>0.0</td><td>83.3</td><td>1.8</td><td>0.0</td></tr><tr><td>(4) MADA. WEST COAST</td><td>43.9</td><td>38.1</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr><tr><td>(5) MOZAMBIQUE COAST</td><td>24.6</td><td>14.3</td><td>0.0</td><td>0.0</td><td>33.3</td><td>0.0</td><td>0.0</td></tr><tr><td>(6) COMOROS-MAYOTTE</td><td>5.3</td><td>2.4</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr></tbody></table>

In the S2S data, TCs are tracked using the standard Vitart and Stockdale (2001) method. It defines a storm center at a local minimum of sea level pressure, while checking the presence of a mid-tropospheric warm core amongst other basic tropical characteristics. While the native resolution of the EPS forecasts is 0.25° for the first 10 days of forecast and 0.5° for the subsequent lead times, the tracking is operated on the 1.5°×1.5° S2S archive grid for all lead times, thereby smoothing out the resolution jump. As recommended by other S2S studies like (Lee et al. 2020; Camargo et al. 2019), the model evaluation is based exclusively on the active TC months of the SWIO basin, from November to April. As a way to remove noise introduced by the TC tracking method, only the simulated TCs with a minimum duration of 2 days have been included in the analysis, regardless of their intensity.

# 3 | Materials and Methods

## 3.1 | Classification Adjustments for the Model

Variations in TC intensity are driven by complex interactions between synoptic forcing and mesoscale convective phenomena occurring within the storm's inner core (Kaplan et al. 2010). With their coarser grid size than classic deterministic numerical models, S2S models do not have the means to accurately reproduce the intensity of mature TCs. In the case of EPS predictions, the reprojection on the S2S inter-comparison grid before the tracking also contributes to this underestimation (Lee et al. 2018). This results in an underestimation of tropical cyclone (TC) intensity. For tracks where the TC reaches at least 30 kt, the EPS produces an average LMI of 41 kt, compared to 73 kt in the Best Track (BT) records over the study period on the SWIO. Consequently, directly applying the method of Bonnardot et al. (2022) to the EPS data would be inappropriate, as the intensity threshold for defining the APT of simulated storms requires adjustment. If held at the 34-kt mark, the APT of the EPS tracks is expected to become too narrow. Consequently, the meridian classes become over-represented at the expense of zonal classes. Yamaguchi et al. (2015) already noted the sensitivity of basin-wide TC occurrence scores to the chosen wind threshold value.

To identify the optimal APT wind threshold, we ran sensitivity tests on both the occurrence and the frequency biases of each class. These metrics are defined as follows. For each class c,

$$OB_c = \frac{N_c^{\text{model}} - N_c^{\text{BT}}}{N_{\text{years}}} \qquad (1)$$

where $OB_c$ is the occurrence bias for class c, $N_{years}$ is the number of years in the dataset (here 20), $N_c^{\text{model}}$ is the number of storms predicted for class c by the model (all years combined), and $N_c^{\text{BT}}$ is the number of storms recorded in the best-track dataset for the same class c.

$$FB_c = \left( \frac{N_c^{\text{model}}}{N_{\text{total}}^{\text{model}}} - \frac{N_c^{\text{BT}}}{N_{\text{total}}^{\text{BT}}} \right) \times 100 \qquad (2)$$

where $FB_c$ is the frequency bias for class c, and $N_c^{\text{model}}$ and $N_c^{\text{BT}}$ are the total number of classified storms (all classes and all years combined) in the model and best-track datasets, respectively. A global root mean square error (RMSE) is also computed for both metrics by summing the squared biases across all classes. Together, these metrics provide complementary information: the occurrence bias reflects systematic over- or under-prediction of TC occurrence by the model, while the frequency bias evaluates the model's ability to reproduce the distribution of TC occurrences across all classes.

The first iteration of our tests indicates that a 29-kt threshold minimises the class frequency RMSE. This value optimizes for the repartition of simulated tracks between each type. On the other hand, the test focused on the occurrence bias yields a much higher 34-kt threshold. We address this significant gap between the two values by converting the 34-kt APT threshold found to optimise the occurrences bias in each track type into a filter to be added to the data, that discards all systems with an LMI lower than 34kt. A similar LMI threshold of the IFS TC tracks has already been used in other studies over the southern hemisphere (Gregory et al. 2020). In the second iteration of our tests, we applied the LMI filter before rerunning the sensitivity tests, whose results are presented in Figure 4. This method results in closer optimal thresholds of 29 kt and 27 kt for minimising the RMSE of class frequency and class occurrence, respectively. We faced a choice between optimizing the wind threshold to better match the observed distribution of the track typologies or reducing class occurrence biases. We ultimately selected the 29-kt threshold, prioritising the minimization of frequency biases, with overall occurrence bias already addressed via the LMI threshold.

The main contributors to the global RMSE appear to be the **323** and **222** classes, in both graphs. The total RMSE is minimised when the individual biases of these two classes intersect, indicating that delineating the APT of the systems tracking across the 70° E meridian is the main difficulty for adapting
the classification to the simulated storms. To investigate this
spatial bias, we computed the weekly number of APT TC
points within simulated storms for the fourth week of forecast,
using a 4° resolution grid and the APT definition adaptations
described earlier. These values were compared to the weekly
frequencies derived from BT records to calculate anomalies

(Figure 5). Similar spatial patterns are observed for the sec-
ond and third week of forecasts, although less pronounced (not
shown).

The EPS model demonstrates a general overestimation of
TC points occurrences across most areas in the SWIO, with

<figure><img src="image_7.png" /><figcaption>**FIGURE 4** | Mean prediction bias of frequency (top) and occurrence (bottom) for each class, shown as a function of the intensity threshold used for the TC classification. The global RMSE for each threshold is also computed and shown as a black dotted line. Best Track data is used as reference. Only tracks with at least one point within the 8–28 days lead times window are taken into account. Optimal value based on the RSME minimum is highlighted by the arrow.</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>**FIGURE 5** | Anomaly of weekly APT tropical cyclone (TC) points in EPS predictions (wind threshold: 29 kt) over the fourth week of forecast com-
pared to Best Track data (wind threshold: 34 kt), spanning 2002–2022 and limited to the active TC season (November and April).</figcaption></figure>
the central basin and the Mozambique Channel (i.e., Zones 1 and 2) showing particularly high positive anomalies. Conversely, the southern part of Region 3 exhibits a negative anomaly. This dipole pattern, straddling the 70°E boundary, highlights the EPS model's challenges in delivering consistent TC predictions over the eastern half of the basin. Interestingly, Gregory et al. (2020) also identified this region as particularly problematic for the EPS TC predictions, albeit in their analysis of the operational 51-member version of the model.

## 3.2 | Time Window

Bulletins of monthly forecasts are usually issued following a week-by-week format, so we chose to compute scores presented on weekly time steps. For example, scores for the second week of forecast (noted W2) are computed for simulated systems with at least part of their APT intersecting with the lead time window between the eighth and the fourteenth day of the forecast. Naturally, the classes are determined considering the complete APT of a given system, whether or not it fits entirely in the lead time window. This process is illustrated in the schematic representation shown in Figure 6.

However, when active systems are already present at the time of model analysis, part of the observed track may lie 'in the past' relative to the forecast start date. To ensure consistency and a fair evaluation of the model, the observed track types are defined relative to each analysis date, by considering only the portion of the BT that occurs after the forecast start. Earlier best-track positions are ignored. This adjustment is particularly relevant for W2 forecasts and for zonal tracks, which typically have longer lifespans. For example, a 313 system in the full BT records can become a 212 system when evaluating an EPS run that starts when the TC is already located in zone 2.

Finally, note that if a single ensemble member predicts more than one system of the same class within a given lead time week (i.e., a double occurrence), it still contributes only 1/11 to the total occurrence probability forecast for that class.

<figure><img src="image_9.png" /><figcaption>FIGURE 6 | Schematic drawing of the filtering method used to separate each week of the forecast. Systems with an APT intersecting $W_n$ are taken into account to compute the scores, points where the TC intensity is below the APT threshold are not taken into account. In this case, for $W_n$ scores, system A counts while system B does not because its APT does not intersect with $W_n$. Note that long-lived systems may contribute to multiple weeks. For instance, system A contributes to both the $W_{n-1}$ and $W_n$ scores, whereas system B contributes only to the $W_{n+1}$ score.</figcaption></figure>

## 3.3 | Reliability and Precision-Recall Diagrams

For a given TC track type, the probability of occurrence estimated by the EPS reforecast is obtained by counting the proportion of the members predicting an occurrence relative to the ensemble size. With its 11 members, the reforecast is able to output 12 probability values, from 0 to 1 with each member contributing for 1/11 of the total predicted probability. The accuracy of the predicted occurrence probabilities provided by the model is assessed within each class through reliability diagrams. The reliability plots are generated by comparing the observed occurrence frequency in the 12 subsamples relative to the predicted probability values. Reliability diagrams also allow us to evaluate the model's statistical sharpness (European Centre for Medium-Range Weather Forecasts (ECMWF) 2024). Sharpness measures the forecast's utility, with the capacity to issue probabilities distinct from climatology being crucial for delivering valuable information to end users. The occurrence of a TC within a specific class during a single week is a relatively rare event, with the maximum climatological rate found in the moving climatology standing at 10.7%. In those conditions, the more the model predicts probabilities different from 10%, the better its sharpness.

Precision-Recall (PR) diagrams are employed to assess the model's performance in distinguishing true positives from false alarms. PR diagrams emphasise the prediction of event occurrences and do not reward the correct prediction of non-occurrences. While identifying quiet periods of TC activity at the basin scale can be valuable for stakeholders, this study focuses on the ability of the EPS to predict the occurrence of TCs within a given class. Because such events are relatively rare, the model should not be overly rewarded for systematically predicting non-occurrence. For this reason, the PR metric is well aligned with the objectives of this study. Precision is defined as the ratio of correct occurrence forecasts to the total number of occurrence forecasts (correct and incorrect), while recall is calculated as the ratio of correct occurrence forecasts to the total number of observed occurrences (true positives and false negatives). These graphs evaluate the model's effectiveness by considering each probability step as a threshold for issuing a binary prediction of TC occurrence in each class. The area under the PR curve is called the Average Precision (AP) score. It measures the performance of the model to predict occurrences of the different track types, rather than their absence.

## 3.4 | Brier Skill Score

The skill of the model to predict TC occurrences in each class is evaluated through the Brier skill score (BSS). We use the classic formulation for the application of the Brier Score (BS) to ensemble forecast, as:

$$BS = \frac{1}{N} \sum_{n=1}^{N} \sum_{c=1}^{C} (p_{n,c} - o_{n,c})^2 \quad (3)$$

where N is the total number of forecasts, C is the number of classes, $o_{n,c}$ is the nth observation for class c and $p_{n,c}$ is the
predicted probability of TC occurrence in class c for the nth forecast, defined as

$$p_{n,c} = \frac{1}{M} \sum_{m=1}^{M} P_{n,c,m} \quad (4)$$

where M is the number of ensemble members, $P_{n,c,m}$ is the TC occurrence prediction in class c from the mth ensemble member for the nth forecast. $P_{n,c,m}$ and $o_{n,c}$ are 0 if there is no storm and 1 if there is a storm. BS is defined as the sum over all available classes of the mean squared probability forecast error in each class.

The skill of the model is evaluated against the performance of a varying climatology forecast. This climatology of TC occurrence is computed for each class over the BT data, and then averaged on a moving 1-month window centred around each date of the November-April period of study. It provides a dynamic and contextually relevant benchmark that accounts for intraseasonal variations in TC occurrence and track typology. The comparison between the model and the climatology scores defines the Brier skill score (BSS) as follows:

$$BSS = 1 - \frac{BS}{BS_{\text{clim}}} \quad (5)$$

A positive BSS indicates better performance of the model compared to the climatology used as a reference.

Additionally, we adopt a more impact-based approach than simply considering track types. We effectively aggregate classes, but rather than grouping them by shape similarity, we use their associated climatological impact probabilities. In practice, we compute a predicted and observed probability of impact on each of the 6 territories from all the predicted and observed track type occurrence over each forecast week. Formally, for the nth forecast on the tth territory, on each weekly lead time, we get:

$$p_{\text{impact}(n,t)} = \sum_{c=1}^{C} p_{n,c} * P_{\text{impact}(c,t)}^{\text{clim}} \quad \text{and} \quad o_{\text{impact}(n,t)} = \sum_{c=1}^{C} o_{n,c} * P_{\text{impact}(c,t)}^{\text{clim}} \quad (6)$$

where $P_{\text{impact}(c,t)}^{\text{clim}}$ is the climatological probability of impact for class c on the tth territory, as presented in Table 1. This gives:

$$BS_{\text{impact}} = \frac{1}{N} \sum_{n=1}^{N} \sum_{t=1}^{T} (p_{\text{impact}(n,t)} - o_{\text{impact}(n,t)})^2 \quad (7)$$

where T is the total number of territories considered. The derived BSS$_{\text{impact}}$ based on impact probabilities is then defined in the same way as the score based on track types, presented in Equation (5).

# 4 | Model Evaluation

## 4.1 | Reliability and Precision Assessment

Figure 7 presents a global reliability diagram when considering TC occurrences in all classes (a) and the individual diagrams for the five most common classes (b–f), for W2 to W4.

Focusing on the global diagram first (Figure 7a), the predicted occurrence probabilities are systematically higher than those observed in the associated samples, for all predicted probability values. For TC occurrence predicted with elevated probability, i.e., with a predicted probability over 50% compared to the maximum value of 10% observed in the climatology, the gap between the forecast and the observation tends to be larger. As an illustration, when the model predicts a 63% chance of occurrence, it is only observed 30% of the time. This translates to a tendency towards overconfidence, more pronounced for high predicted probabilities. This behaviour is observed for all three considered lead time weeks. Caution is warranted when interpreting probabilities exceeding 40% for **W3** and **W4** due to limited sample size (less than 100 cases), as they do not ensure an accurate evaluation of observed frequency in those instances.

When investigating the reliability for the individual track types, most classes demonstrate behaviour consistent with the global diagram. Notably, predicted probabilities at **W2** for classes **333** and **222** (Figure 7b,c respectively) remain close to the observed occurrence rate for all probabilities below 50%, with prediction errors contained under 10%. This result translates quite well to **W3** and **W4**. For higher predicted probabilities, the gap between the forecast and the observed frequency exceeds 30%. This is especially true for the **333** class, as displayed in Figure 7b. Track type **111** tends towards overestimation at lower predicted probabilities (Figure 7e), particularly for those exceeding 30% and from **W3** lead times. However, the major reliability flaw is displayed for the parabolic **323** systems (Figure 7d). When considering predicted probabilities under 70%, the observed frequency never exceeds 15%.

The model exhibits a consistent pattern of overconfidence for predicting the occurrence of all classes across all lead times. As seen on the overall diagram, the predicted probabilities remain quite reliable under the 50% threshold, but tend to diverge and become noisy for higher values and deeper lead times. In particular, the **323** predictions show a pronounced overconfidence at all lead times. This result aligns with the sensitivity tests laid out in Section 2.1, and confirms that these tracks remain very challenging to identify for the EPS predictions within our classification framework.

The PR diagram in Figure 8 evaluates forecasts for occurrences across all classes. At the **W2** lead time, the model achieves higher precision rates than the moving climatology across all recall levels. This is reflected in the average precision (AP) of the **W2** curve, almost 1.5 times higher than that of the reference. As seen on the reliability diagram, a notable change in the slope of the PR curve occurs at the 50% probability level. Predicted probabilities above this threshold have good precision, exceeding 50%, but at the expense of low recall values, remaining below 20%. At longer lead times, the PR curves closely align with the climatology's performance for probability thresholds between 0% and 50%. This underscores the model's difficulty in effectively distinguishing false alarms from true positives at these extended lead times. For higher predicted probabilities at **W3** and **W4**, recall is very low, less than 10%, meaning that those values are rarely predicted. This
<figure><img src="image_10.png" /></figure>

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**FIGURE 7** | Reliability diagrams for the three considered lead times: W2 (dark orange), W3 (light orange) and W4 (yellow), as detailed in bottom right corner of subfigure a. The reliability is computed over (a) all classes, and the most common classes: (b) 333, (c) 222, (d) 323, (e) 111 and (f) 212. The size of the points denote the number of cases attributed to each subsample; points corresponding to fewer than five cases are not displayed. Grey lines represent 10% probability intervals parallel to the reference diagonal in dashed black, indicating a perfect reliability.

is expected for those deeper lead times. However, **W3** predictions manage to gain some precision there while the precision at **W4** decreases, indicating a further decline in model performance.

Overall, the capacity of the model to distinguish occurrences at **W2** is much higher than that of the climatology, illustrating the utility of the forecast at this lead time. Predictions with elevated probabilities (exceeding 50%) are relatively rare at all lead times, but when they occur at **W2**, they correspond to a class occurrence more than half the time. Results are less encouraging for forecasts at longer ranges. Despite being rare, high predicted probabilities at **W3** are indicative of an improved precision, between 30% and 50%. However, high probabilities at **W4** prove to be quite deceptive, with no improvement in precision compared to lower predicted probabilities.

## 4.2 | Brier Scores Analysis

Figure 9 illustrates the model's performance evaluated through the *BSS*, as detailed in Section 3.4. The model performs better than the moving climatology in **W2** only, while **W3** and **W4** exhibit similarly unskillful scores (approximately 10% lower *BSS* compared to climatology). When considering individual classes, **333** and **222** track types appear to enhance the overall performance of the predictions for **W2**, whereas accurate prediction in the less frequent **323** class proves challenging again, in agreement with the corresponding reliability results (cf Figure 7). Model performance on this class does not exhibit a trend with lead times akin to other classes, and consistently falls below the -34% skill threshold. For the two other most frequent classes, **111** and **212**, the predictions also appear to lack skill, but to a lesser extent, with a loss of less than 5%.
<figure><img src="image_16.png" /><figcaption>**FIGURE 8** | Precision-Recall curves for lead times W2 (red), W3 (orange), and W4 (yellow), based on occurrence predictions across all classes. The grey shaded area represents the performance of the moving climatology, i.e., its average precision (AP). In the top-right corner, the AP value for each lead time is shown, along with its relative gain or loss compared to the climatology AP used as reference in parentheses. The numbers within each point correspond to the probability threshold used to calculate the recall and precision for that point.</figcaption></figure>

<figure><img src="image_17.png" /><figcaption>**FIGURE 9** | Brier skill scores depending on the lead time week, evaluated on classes prediction computed on all weeks in the dataset (solid) and only weeks with at least one system observed and one system predicted by at least one member on the basin (dotted line). The grey area represents forecasts with a lower performance than the 1-month moving climatology.</figcaption></figure>

To evaluate the model's ability to classify a TC occurrence signal into a specific track type, given that the occurrence is already correctly identified at the basin scale, we computed a new *BSS* score, noted *BSS*<sub>Class</sub>. This score is defined in the same way as the *BSS*, but only considers cases where at least one member of the EPS predicted an occurrence while a system was observed over the basin. As outlined in the introduction, previous studies in the S2S literature have demonstrated the difficulty of predicting TC genesis at subseasonal lead times. With *BSS*<sub>Class</sub>, our objective is to investigate error sources by separating the challenge of track-type classification from that of basinwide TC genesis prediction. Under these restricted conditions, the model demonstrates great skill at **W2**, with a 20% performance gain over the reference forecast. The performance gain is still there for **W3** and **W4**, although more marginal with 6% and 4% *BSS*<sub>Class</sub> values

respectively. This overall skill gain translates to all classes but the **323** systems. Despite remaining unskillful, the predictions for the **323** systems see a +25% improvement compared to the general case.

This leads to the conclusion that when TC genesis is well predicted on the basin, the subsequent track information provided by the EPS proves to be more reliable than a 1-month moving climatology. TC motion is heavily influenced by the surrounding synoptic circulation, which is governed by synoptic-scale weather systems like troughs embedded in the midlatitude westernlies or the subtropical ridge (Galarneau and Davis 2013). These results suggest that the EPS has significant skill in forecasting the position and intensity of these large-scale action centers, which in turn dictate the speed and direction of TC tracks.

All leadtimes considered, cases where none of the EPS members predict a basin-wide TC occurrence while one is observed constitute only 0.1% of the total observed occurrence weeks, and all of these cases occur at **W4**. In contrast, cases where at least one EPS member predicts a TC occurrence while none is observed over the basin, all leadtimes considered, make up 53% of all predicted TC occurrence cases. Consequently, the performance improvement seen between the *BSS* and *BSS*<sub>Class</sub> metrics primarily stems from reducing false alarms regarding TC genesis at the basin scale, despite the imposition of a rather strong constraint on LMI during pre-processing (see Section 2.1).

## 5 | Translating Forecast Performance Into Operational Tools

To translate these results into the operational context, we first aim to optimise the performance of the EPS predictions using a reliability-based calibration method. Next, we convert track type predictions into impact probabilities, drawing on Bonnardot et al. (2022) results, and evaluate this approach using the *BSS*<sub>impact</sub> metric. Finally, we propose a prototype tool for cyclone forecasters, designed to leverage the specifics of our approach in the context of monthly TC forecasts.

### 5.1 | Reliability-Based Calibration

The results presented in Section 3.1 suggest that a reliability-based calibration could potentially enhance the performance of EPS predictions. In this section, we calibrate the raw occurrence probabilities predicted by the EPS for each class. To achieve this, we split the dataset, using 70% to construct reliability diagrams and the remaining 30% to assess the calibration's performance. This split ensures the evaluation is conducted on unknown data. Specifically, we use data from 13 of the 19 active TC seasons to compute reliability diagrams for each track type and evaluate calibration on the remaining 6 seasons. The calibration involves replacing the raw TC occurrence probabilities for a given track type with the corresponding observed frequencies derived from the reliability diagram of this particular track type (hereafter referred to as the direct method). Since the reliability diagrams produced for each class seem affected by sampling variability for probabilities exceeding 40% (see Figure 7), we also implemented a fitted reliability calibration following the method described by
Atger (2004). In this approach, a bi-normal model is fitted to the receiver operating characteristic (ROC) curve, and the projected hit rates and false alarm rates are then used to reconstruct the expected reliability at each probability level. To further assess the reliability of both approaches, we conduct 200 random data split iterations as a simple sensitivity test on train years selection. The effectiveness and variability of the calibration processes are then evaluated through the mean and standard deviation BSS values obtained over all 200 splits.

The results are presented in Figure 10 for both train and test datasets (left and right graphs, respectively). The uncalibrated predictions (blue lines) exhibit very comparable mean BSS values for both the training and test samples across all lead times. This consistency indicates that the mean BSS is largely unaffected by the specific data split used, confirming the adequacy of the chosen number of random splits for robust evaluation. On the train dataset, both calibrations lead to a substantial improvement in the model predictions, with a 10% average performance gain consistent across all lead times. The variability of this result caused by train years selection is limited, as shown by the standard deviation interval. When evaluated on unseen data, both calibration methods still show systematic improvements in mean BSS, with the fitted method achieving up to a 6% gain and the direct method 5%. The fitted method consistently outperforms the direct method by 1%–2%. The standard deviation intervals for both calibrated predictions are considerably larger for the test sample than for the calibration sample. Interestingly, the fitted method yields slightly lower standard deviations than the direct method, corresponding to a 1% reduction at W2. Smoothing the reliability curves probably mitigates overfitting of low-sampled probability levels in the training samples. This is particularly evident at **W2**, where the fitted method shows slightly lower skill than the direct calibration on the calibration sample but achieves a 2.5% gain in BSS on the test sample. However, even for the fitted method the standard deviation interval between calibrated and uncalibrated forecasts overlaps at all lead times, especially at W2.

These results demonstrate that, despite an improvement in terms of average performance, the improvements observed for calibration years do not consistently generalise to unseen seasons. This inconsistency may stem from the strong inter-annual variability in TC track type occurrences (Bonnardot et al. 2022) and the model's potential limitations in fully capturing year-to-year variations in TC genesis, especially around the 70° E region. These mixed results are consistent with previous calibration efforts (Lee et al. 2020; Camargo et al. 2019; Camp et al. 2018), despite different methodologies. Given those results, calibration was not retained for the subsequent stages of the study and the transition to operational use. It should also be noted that the EPS reforecast is conducted with only 11 members, compared to 101 members in the current operational configuration. As a result, a reliability-based calibration constructed from the reforecast configuration may not be directly applicable to operational outputs, even though the fitted reliability can provide estimates for all probability levels, including those finer than those produced by the reforecast. More generally, the calibration should, in principle, be recomputed and its contribution reassessed for each new version of the ensemble.

## 5.2 | Impact-Based Evaluation

Figure 11 is similar to Figure 9, but using the BSS<sub>impact</sub> score as defined in Section 3.4. Considering impact predictions rather than track types, Figure 11 still shows a clear skill loss between **W2** and **W3**, that become less significant between **W3** and **W4**. The model is still more skillful when considering solely active weeks rather than all weeks.

The impact-based approach basically translates the challenges and qualities of the class predictions to the new framework. It should be noted that the challenging 70°E area is not as important in this framework, as **333** systems are totally discarded. However, **323** tracks are still taken into account, as they pose a regular threat to Rodrigues island. This small island is positioned

<figure><img src="image_18.png" /><figcaption>**FIGURE 10** | Brier Skill Scores of the track typology forecasts, comparing uncalibrated probabilities (blue), calibration using raw reliability correspondence (green), and calibration using fitted reliability curves (red), for each lead time week. The calibration is made on 200 subsamples of 6 randomly picked seasons out of the 19 seasons available. The left graph presents the results on the subsample used for calibration and the right column presents the performance on the unknown years (i.e., the test subsample). Solid lines delineate the mean scores, while the dash-dotted lines indicate the ±1σ interval. The grey area denotes negative BSS values, showing unskillful forecasts compared to the performance of a 1-month sliding climatology.</figcaption></figure>
as the easternmost inhabited region and emerges as the area with the least skillful predictions, probably because of the poor performances of the model for the **323** systems. Conversely, the central Mascarenes islands stand out as the archipelago where the model performs the best. Here, the predictions do not depend on the challenging **323** and **111** systems, while well-predicted **222** systems dominate the predicted impact probabilities. When focusing solely on well-predicted active weeks within the basin, the EPS demonstrates consistent skill across all lead times, with *BSS<sub>impact</sub>* values reaching 28% at **W2** and still remaining over 14% for **W3** and **W4** lead times. These results explicitly suggest that genesis prediction aside, the EPS model is able to provide valuable insight on the impact risk up to 4 weeks in advance compared to a varying climatology.

<figure><img src="image_19.png" /><figcaption>**FIGURE 11** | Brier Skill Scores depending on the lead time week, evaluated on the prediction of impact probabilities on each inhabited area of the basin, computed on all weeks in the dataset (solid) and only weeks with at least one system observed and one system predicted by at least one member on the basin (dotted line). The reference is the performance of a 1-month moving climatology.</figcaption></figure>

## 5.3 | Operational Product

Building on the evaluation presented above, a forecast product has been developed in the form of a bar chart. Each bar represents predictions for a specific weekly lead time, from **W2** to **W4**. The bars display stacked occurrence probabilities predicted by the EPS for each track type, with the total occurrence probability for the entire basin shown at the top of each bar. Track types originating from similar genesis areas (i.e., sharing the same first digit) are grouped within the bar and differentiated by colour: green for systems from the Mozambique Channel, yellow for those from the central region, and blue for the eastern region. The overall shape of the track is indicated by hatching: vertical lines correspond to meridional tracks, horizontal lines to zonal tracks, and the absence of hatching denotes parabolic tracks. Additionally, impact probabilities on inhabited regions are computed as described in the previous section for each lead time but are displayed only if they exceed those of the moving climatology. Due to the mixed results of the reliability-based calibration, it has not been implemented in the operational product.

Figure 12 is an example of this product, for the 12-01-21 EPS run. This EPS run correctly indicates a heightened TC activity over the SWIO for the following month. For **W2** and **W3** lead times, the preferred scenario is a genesis in the central part of the basin, with equal probabilities for the occurrence of either a parabolic (212) or meridian (222) track type. An alternative or co-occurring scenario is suggested by the presence of a signal in the **333** class. The product furthermore indicates a low probability of impact on the eastern Madagascan coastline. Comparing these predictions with the observations, we see that the **212** storm **CHALANE** actually formed in the central zone during **W2**, then made landfall on Madagascar on the 26th of December and intensified again over the Mozambique

<figure><img src="image_20.png" /><figcaption>**FIGURE 12** | Bar chart of the weekly occurrence probabilities for each track type predicted by the 12/01/21 Reforecast EPS run. For each forecast week, the sum of the predicted probabilities is indicated on top of each bar. For each inhabited region, the associated impact probability is only displayed if it exceeds that of the climatology, to alert on an anomalously high value. Observed track types from the Best Track dataset, along with their associated system names, are displayed above the graph for reference. Active portions of the tracks are shown in full colour, while inactive portions are depicted in faded tones.</figcaption></figure>
Channel during **W3**. The predictions were quite relevant in this case, hinting towards a central genesis but not predicting the exact nature of the awaited track type. As a consequence, the impact information given by the product for Madagascar was relevant but the uncertainty on the track type did not allow for a higher probability that would have given a clearer signal.

The **111** occurrence signal may correspond to simulated tracks associated with **CHALANE**, regenerating over the Mozambique Channel after a track discontinuity occurring when the storm crosses the Madagascan landmass. This highlights the limitations associated with the tracking method. For **W4**, the summed occurrence probability across classes suggests the presence of two systems in the basin simultaneously. The second system is expected to form preferentially over the eastern or central areas, with a meridian track type (either a **222** or **333** system). However, in the BT records, this signal corresponds to the formation of the **323** storm **DANILO**, which developed during *W3* and *W4*. This in turn showcases the challenge of characterising the TCs that track close to the 70° E meridian, and can either stay in their genesis box or cross into the central area. This uncertainty resulted in the display of impact probabilities for Madagascar and the central Mascarenes at **W4**. Although these probabilities remained low, they would nevertheless have drawn the forecasters' attention, requiring monitoring of the signal in subsequent model runs.

The primary advantage of this bar chart over traditional products, which display all TC tracks predicted by all ensemble members on a single map, lies in its clarity and conciseness. Additionally, the inclusion of highlighted impact probabilities draws the forecaster's attention to key risk areas, and enables a quick assessment of the relevance of this information. This streamlined format is particularly valuable in today's context, where TC forecasters face an ever-growing volume of data to analyze within the tight constraints of fixed operational schedules. The classification method proposed by Bonnardot et al. (2022) uses fixed criteria for assigning classes to TC tracks. This consistency ensures that each model run is directly comparable, enabling forecasters to track changes in the predicted probabilities for each track type across successive runs. As a result, forecasters can effectively assess the consistency of predicted track type occurrences and impact probabilities over time.

# 6 | Conclusion and Future Work

The classification method of Bonnardot et al. (2022) is adapted to account for TC intensity biases in the EPS forecasts. The evaluation of the predictions yields promising results in **W2**, but a significant decline in skill is observed at longer lead times. Part of this decline is linked to the overestimation of TC genesis, an obstacle in monthly TC forecasting already described in previous studies by Lee et al. (2018, 2020) and Vitart and Robertson (2018). Additionally, certain types of tracks in our classification framework, such as parabolic **323** systems in the central area and **111** storms in the Mozambique Channel, pose greater difficulties in prediction than the others. Although none of the predictions for individual classes

have more skill than the climatology beyond **W2** (Figure 9), these track types systematically drag the global performance down at all lead times. Forecasting the evolution of TCs in the Mozambique Channel remains particularly challenging, even at shorter lead times. This difficulty stems from frequent and complex interactions between TCs and dry land air masses due to proximity to the coastlines, combined with a climatologically high oceanic heat content that facilitates rapid intensity changes. For the **323** systems, the difficulties shown by the model are likely driven by a spatial bias in TC occurrence in the central SWIO region (Figure 5), but also by limitations in the classification design. Specifically, the 70° E boundary is less physically meaningful than the 50° E limit, which aligns with the physical barrier of Madagascar's eastern coastline. TCs following meridional tracks near 70° E or reaching the genesis intensity threshold near this boundary introduce ambiguity between the **323**, **222**, and **333** track types, leading to misclassification and reduced forecast accuracy.

To address these challenges, we attempted to implement two reliability-based calibration methods. However, the reliability corrections derived from the training seasons did not systematically lead to significant improvements when applied to the remaining, unseen seasons. This limitation may stem from the EPS's difficulty in fully capturing the mechanisms driving the inter-annual variability of each track type frequencies. As a result, model tendencies learned from certain years do not generalise well to others, thus reducing the effectiveness of the calibration. To enable calibration of rare events with significant inter-annual variability, like TCs, a 20-year reforecast period may be somewhat short. Similarly, as computational power increases, it could be valuable to extend the reforecast ensemble size to improve confidence in probabilistic metrics.

Excluding cases of incorrect genesis predictions at the basin scale significantly improves the track type predictions provided by the EPS. Under these conditions, the EPS outperforms the reference moving climatology in skill, maintaining this advantage up to the fourth week of the forecast. Our results highlight the EPS's ability to skillfully predict the general shape of TC tracks, once the challenge of genesis prediction is removed. In operations, however, it is not possible to determine a priori which weeks will feature a reliable basin-wide genesis signal. This is where the work conducted during the PISSARO campaign shows its relevance. During PISSARO, TC forecasters assessed week-by-week genesis risk on a monthly basis, yielding promising results. They learned to use products based on frequency-wavenumber filtering methods (Wheeler and Kiladis 1999; Schreck III et al. 2012) available on the MISVA platform² (Peyrillé 2023), which allowed them to assess and compare the individual contributions of intra-seasonal variability modes to a given TC genesis signal. These large-scale intra-seasonal influences include the MJO (Wheeler and Hendon 2004; Camargo et al. 2009) and other convectively coupled equatorial waves such as equatorial atmospheric Kelvin waves (Schreck 2015, 2016). Finally, they evaluate the reliability of a simulated TC genesis signal by considering the combination of these influencing factors, which are more predictable than TC genesis itself and therefore enhance confidence in the signal. This method of expertise builds on an adaptation of the 'window of opportunity' concept initially developed by Mariotti et al. (2020).
Our findings suggest that when forecasters identify a credible signal for TC activity in a given week, the EPS is able to provide skillful indications on track types, even at extended lead times. Furthermore, by grouping track types based on their impact probabilities for SWIO territories, we observed increased predictive skill compared to predictions for individual track types alone. These results showcase the potential of EPS forecasts to aid in the elaboration of effective early warning messages for crisis management authorities in the SWIO region, even up to a month in advance. To capitalise on this potential, we developed a forecasting tool that provides an intuitive visualisation of EPS predictions within our framework, presented as a bar chart. This chart enables forecasters to examine predicted probabilities for each track type, assess the resulting impact probabilities on SWIO territories, and monitor run-to-run variability in these signals.

Moving forward, our future efforts should focus on evaluating the EPS extended predictions in the new configuration that was deployed in 2024. In particular, it should be interesting to investigate the effects of the improved statistical acuity afforded by a doubled ensemble size (101 members). In this new model version, the extended-range EPS is now completely separated from the medium-range full-resolution ensemble. This means that the grid used for the first 2 weeks of forecasts is effectively coarser than in previous ensemble versions, where the extended-range forecast was launched the last day of the medium-range forecast at a lower resolution, introducing a 'resolution jump' from W2 to W3. We should therefore verify whether this change in resolution negatively impacts the forecast skill in our framework. Additionally, there's a pressing need to improve the representation of TC intensity and genesis processes within the models operating at the subseasonal lead times to further refine forecasting capabilities. Finally, a more in-depth analysis of the regional skill differences highlighted by our evaluation framework would be of great interest. Potential links with biases in the large-scale environment predicted by the EPS and other S2S models could be further explored. It would also be valuable to determine whether these errors stem from model deficiencies or from intrinsically challenging subseasonal processes in this region, such as the influence of the MJO or the triggering of SIOD events.

## Author Contributions

**Adrien Colomb:** investigation, conceptualization, writing – original draft, methodology, validation, visualization, software, data curation. **Hélène Veremes:** conceptualization, writing – review and editing, supervision. **François Bonnardot:** writing – review and editing. **Guillaume Jumaux:** writing – review and editing. **Sébastien Langlade:** writing – review and editing. **Sylvie Malardel:** writing – review and editing, methodology.

## Acknowledgments

We would like to thank Simon Charpigny for performing the preliminary study that demonstrated the potential of the proposed approach and helped build confidence in the chosen research direction. The authors thank the reviewers for their careful reading and constructive comments, which helped improve the manuscript. This work is based on S2S data. S2S is a joint initiative of the World Weather Research Programme (WWRP) and the World Climate Research Programme

(WCRP). The original S2S database is hosted at ECMWF as an extension of the TIGGE database. During the preparation of this work, the authors used chatGPT in order to improve readability and language. After using this tool, the authors reviewed and edited the content as needed and took full responsibility for the content of the publication.

## Conflicts of Interest

The authors declare no conflicts of interest.

## Data Availability Statement

The data that support the findings of this study are openly available on the S2S project FTP server: s2sidx:s2sidx@aux.ecmwf.int.

## Endnotes

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<footnote n="1">¹Accessible via FTP on s2sidx:s2sidx@aux.ecmwf.int.</footnote>

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