Cyclone Forecasts Research Articles

Performance of tropical cyclone forecasts in the western North Pacific in 2017

The forecasts of tropical cyclones (TC) in 2017 from five official guides, six global models, six regional models and six ensemble systems were assessed to study the current capability of track and intensity forecasts for the western North Pacific. The results show that the position errors for official agencies were under 100, 165, 265,335 and 425 km at the lead times of 24, 48, 72, 96 and 120 h, respectively. As the forecast lead times increased, the forecasted TCs propagated, on average, too slow for most official guides. It is encouraging to note that all the models had positive skill scores, there is an overall upward trend in the skill scores of the models during from 2010 to 2017. Furthermore, both global and regional models' intensity forecast skill was increasing year by year from 2010 to 2017. For the ensemble prediction systems (EPSs), ECMWF-EPS was the best forecast system for the lead time less than 72 h, beyond the 72 h, the best EPS belong to NCEP-GEFS.

Statistical Reconstruction of Seasonal Tropical Cyclone Variability in the North Atlantic Basin

AbstractSeasonal forecasting of tropical cyclones is a topic of considerable interest to the public, government and private sectors. To improve understanding of the dynamics controlling the predictability of tropical cyclone (TC) activity, and improve the accuracy of forecasts, multiple studies have related TC activity to empirically‐defined indices including the El Niño‐Southern Oscillation, the Atlantic Multidecadal Oscillation, and the North Atlantic Oscillation. These indices were not developed to forecast TC activity but rather summarize other aspects of atmosphere‐ocean variability. In this study we use a statistical approach, based on redundancy analysis, to define two indices related to overall activity and steering of TCs. We focus on North Atlantic TCs that reached tropical storm strength (≥34 kt) between August and October 1948–2016. TC occurrences are binned using an equal area grid that covers the North Atlantic. The redundancy indices are linear combinations of mean sea level pressure for the same season. Cross validation is used to guard against over fitting in the definition of the indices. This approach provides two physically interpretable redundancy indices related to North Atlantic TC activity. The leading redundancy index is used to successfully reconstruct the total number of TCs and the accumulated cyclone energy, over the extended period 1878–2014 using seasonal mean sea level pressures from the National Centers for Environmental Prediction 20th‐Century Reanalysis version 2c. Extensions of the approach for seasonal forecasting are discussed.

MTrack: Improved Center Fix of Tropical Cyclones from SMAP Wind Observations

AbstractMTrack is an automated algorithm that determines the center location (latitude and longitude) of a tropical cyclone from a scalar wind field derived from satellite observations. Accurate storm centers are useful for operational forecasting of tropical cyclones and for their reanalysis (e.g., research on storm surge modeling). Currently, storm center fixes have significantly larger errors for weak, disorganized storms. The MTrack algorithm presented here improves storm centers in some of those cases. It is also automated and, therefore, less subjective than manual fixes made by forecasters. The MTrack algorithm, which was originally designed to work with CYGNSS wind speed measurements, is applied to SMAP winds for the first time. The average difference between MTrack and Best Track storm center locations is 21, 36, and 46 km for major hurricanes, category 1–2 hurricanes, and tropical storms, respectively. MTrack is shown to operate successfully when a storm is only partially sampled by the observing satellite and when the eye of the storm is not resolved.

An Investigation of Large Cross-Track Errors in North Atlantic Tropical Cyclones in the GEFS and ECMWF Ensembles

AbstractThe largest medium-range (72–120 h) cross-track errors (CTE) of tropical cyclone (TC) forecasts from the Global Ensemble Forecast System (GEFS) over the northern Atlantic Ocean are examined for the 2008–16 seasons. The 38 unique forecasts within the upper quartile of most negative CTEs (i.e., left-of-track bias larger than 250 km by 72 h) do not have a clear common source of steering error, although 12 of the forecasts involve the underprediction of a weak upper-level trough to the west of the TC by 36 h. Meanwhile, at least 18 of the 36 most positive CTEs (right-of-track bias) are associated with TCs embedded in the southwest extent of a subtropical ridge, the strength of which is increasingly underpredicted during the first 24 h of the forecast. Excessive height falls north of the TC are driven by overpredicted divergence aloft, which corresponds to overpredicted TC outer-core convection. The convection is triggered by a 5%–20% overprediction of near-TC moisture and instability in the initial conditions. Weather Research and Forecasting (WRF) Model simulations are run at 36-, 12-, and 4-km grid spacing for select right-of-track cases, using the GEFS for initial and lateral boundary conditions. The 36-km WRF reproduces the same growth of errors as the GEFS because of, in part, sharing the same stability and moisture errors in the initial conditions. Changes in the convective parameterization affect how quickly these errors grow by affecting how much convection spins up. The addition of a 4-km nest with no convective parameterization causes the errors to grow ~20% faster, resulting in an even larger right-of-track error.

Applications of GNSS-RO to Numerical Weather Prediction and Tropical Cyclone Forecast

The global navigation satellite system (GNSS) radio occultation (RO) technique is an atmospheric sounding technique that originated in the 1990s. The data provided by this approach are playing a consistently significant role in atmospheric research and related applications. This paper mainly summarizes the applications of RO to numerical weather prediction (NWP) generally and specifically for tropical cyclone (TC) forecast and outlines the prospects of the RO technique. With advantages such as high precision and accuracy, high vertical resolution, full-time and all-weather, and global coverage, RO data have made a remarkable contribution to NWP and TC forecasts. While accounting for only 7% of the total observations in European Centre for Medium-Range Weather Forecasts’ (ECMWF’s) assimilation system, RO has the fourth-largest impact on NWP. The greater the amount of RO data, the better the forecast of NWP. In cases of TC forecasts, assimilating RO data from heights below 6 km and from the upper troposphere and lower stratosphere (UTLS) region contributes to the forecasting accuracy of the track and intensity of TCs in different stages. A statistical analysis showed that assimilating RO data can help restore the critical characteristics of TCs, such as the location and intensity of the eye, eyewall, and rain bands. Moreover, a non-local excess phase assimilation operator can be employed to optimize the assimilation results. With denser RO profiles expected in the future, the accuracy of TC forecast can be further improved. Finally, future trends in RO are discussed, including advanced features, such as polarimetric RO, and RO strategies to increase the number of soundings, such as the use of a cube satellite constellation.

On the seasonal and sub‐seasonal factors influencing East China tropical cyclone landfall

AbstractTo date it has proved difficult to make seasonal forecasts of tropical cyclones, particularly for landfall and in East China specifically. This study examines sources of predictability for the number of landfalling typhoons in East China on seasonal (June–October) and sub‐seasonal time scales. East China landfall count is shown to be independent of basin‐scale properties of TC tracks, such the genesis location, duration, basin track direction and length, and basin total count. Large‐scale environmental climate indices which are potential basin scale drivers are also shown to be largely uncorrelated with landfall prior to and throughout the season. The most important factor is the steering in the final stages to landfall. The seasonal landfall is strongly anti‐correlated with the more local zonal mid‐tropospheric wind field over the East China sea (r = −.61, p < .001). It is proposed that geopotential height anomalies over Korea/Japan cause anomalous easterly winds in the East China Sea and enhance landfall rates by steering typhoons onto the coast. Early, peak, and late sub‐seasonal landfall counts are shown to be independent of each other yet share this predictor. This local feature may be dynamically predictable allowing a potential hybrid dynamical‐statistical seasonal forecast of landfall.

Pacific subsurface ocean temperature as a long-range predictor of South China tropical cyclone landfall

Seasonal forecasts of the tropical cyclones which frequently make landfall along the densely populated South China coast are highly desirable. Here, we analyse observations of landfalling tropical cyclones in South China and of subsurface ocean temperatures in the Pacific warm pool region, and identify the possibility of forecasts of South China tropical cyclone landfall a year ahead. Specifically, we define a subsurface temperature index, subNiño4, and build a predictive model based on subNiño4 anomalies with a robust double cross-validated forecast skill against climatology of 23%, similar in skill to existing forecasts issued much later in the spring. We suggest that subNiño4 ocean temperatures precede the surface El Niño/Southern Oscillation state by about 12 months, and that the zonal shifts in atmospheric heating then change mid-level winds to steer tropical cyclones towards landfall in South China. We note that regional subsurface ocean temperature anomalies may permit atmospheric predictions in other locations at a longer range than is currently thought possible.

A dipole of tropical cyclone outgoing long‐wave radiation

AbstractLarge‐scale (500 ≤ r ≤ 2,200 km) outgoing long‐wave radiation (OLR) and water vapour (WV) fields are investigated in satellite observations over the Pacific, linked to ERA‐5 reanalysis data and the ECMWF ensemble forecasts of tropical cyclones (TC) globally. A large‐scale OLR dipole pattern of low and high fluxes are found in both the observations and model. As expected, a low OLR region is positioned within the TC circulation, but there is also a robust high OLR region poleward and west of the TC centre. A dry “black hole” on WV grey‐scale imagery occupies the same region of high OLR. Relative to the central low OLR TC signal, the typical dipole magnitude, distance and orientation of the high OLR regions are 230 W·m−2, 1,150 km, and 145° anticlockwise from east. From the reanalysis we find that the interaction between the vortex and the environmental flows produces upper‐level convergence, low‐level divergence and subsidence throughout the troposphere in the region of high OLR. Analysis of the ECMWF model shows that the position of the high OLR region rotates anticlockwise about the TC centre as the TC moves from westward to eastward. Through use of a sub‐ensemble, we test if capturing the high OLR anomaly has a significant relationship with TC track. We apply a perfect model approach and find that sub‐ensembles that are composed of models whose large‐scale OLR fields closely match the target TC also have a better track. This improvement is mostly attributed to the high OLR component of the dipole.

Evaluating Benefits of Two-Way Ocean–Atmosphere Coupling for Global NWP Forecasts

AbstractWe evaluate the impact of adding two-way coupling between atmosphere and ocean to the Met Office deterministic global forecast model. As part of preoperational testing of this coupled NWP configuration we have three years of daily forecasts, run in parallel to the uncoupled operational forecasts. Skill in the middle and upper troposphere out to T + 168 h is generally increased compared to the uncoupled model. Improvements are strongest in the tropics and largely neutral in midlatitudes. We attribute the additional skill in the atmosphere to the ability of the coupled model to predict sea surface temperature (SST) variability in the (sub)tropics with greater skill than persisted SSTs as used in uncoupled forecasts. In the midlatitude, ocean skill for SST is currently marginally worse than persistence, possibly explaining why there is no additional skill for the atmosphere in midlatitudes. Sea ice is predicted more skillfully than persistence out to day 7 but the impact of this on skill in the atmosphere is difficult to verify. Two-way air–sea coupling benefits tropical cyclone forecasts by reducing median track and central pressure errors by around 5%, predominantly from T + 90 to T + 132 h. Benefits from coupling are largest for large cyclones, and for smaller storms coupling can be detrimental. In this study skill in forecasts of the Madden–Julian oscillation does not change with two-way air–sea coupling out to T + 168 h.

Impact of Cloud Microphysics Schemes on Tropical Cyclone Forecast Over the Western North Pacific

AbstractIn high‐resolution numerical modeling, cloud microphysics schemes can affect the forecasting of tropical cyclones (TCs) by controlling the phase changes of water. The simulated TC characteristics such as motion, intensity, and structure can change depending on the number of hydrometeors used in these schemes. In this study, we investigate the sensitivity of real‐time track and intensity forecasts of TCs to cloud microphysics schemes using the Weather Research and Forecasting (WRF) model. For the sensitivity test, we selected WRF‐single‐moment‐microphysics Class 3 (WSM3) and Class 6 (WSM6) schemes as simple and sophisticated schemes, respectively. A total of 20 forecasts for 10 TCs were conducted. For TCs moving westward in the subtropics, track forecasts were similar in the different sensitivity tests, although the WSM6 scheme considerably reduced the TC intensity errors. However, for TCs moving to the midlatitudes, the WSM6 scheme improved both track and intensity prediction compared to the WSM3 scheme. Particularly, track errors were prominently reduced by the WSM6 scheme, which realistically captured westward shifted track during the rapid intensification process. This can be attributed to the improved simulations of TC intensity, size, and associated β effect by WSM6 scheme. In contrast, the WSM3 scheme underestimated the above characteristics due to low latent heat release compared to the WSM6 scheme. Consequently, TC track moving northwestward was unreasonably shifted eastward. This indicates that a sophisticated cloud microphysics scheme is necessary to improve the track and intensity forecasts for TCs moving to the midlatitudes.

On the Prospects for Improved Tropical Cyclone Track Forecasts

AbstractThe success story of numerical weather prediction is often illustrated with the dramatic decrease of errors in tropical cyclone track forecasts over the past decades. In a recent essay, Landsea and Cangialosi, however, note a diminishing trend in the reduction of perceived positional error (PPE; difference between forecast and observed positions) in National Hurricane Center tropical cyclone (TC) forecasts as they contemplate whether “the approaching limit of predictability for tropical cyclone track prediction is near or has already been reached.” In this study we consider a different interpretation of the PPE data. First, we note that PPE is different from true positional error (TPE; difference between forecast and true positions) as it is influenced by the error in the observed position of TCs. PPE is still customarily used as a proxy for TPE since the latter is not directly measurable. As an alternative, TPE is estimated here with an inverse method, using PPE measurements and a theoretically based assumption about the exponential growth of TPE as a function of lead time. Eighty-nine percent variance in the behavior of 36–120-h lead-time 2001–17 seasonally averaged PPE measurements is explained with an error model using just four parameters. Assuming that the level of investments, and the pace of improvements to the observing, modeling, and data assimilation systems continue unabated, the four-parameter error model indicates that the time limit of predictability at the 181 nautical mile error level (n mi; 1 n mi = 1.85 km), reached at day 5 in 2017, may be extended beyond 6 and 8 days in 10 and 30 years’ time, respectively.

Subseasonal Forecasts of Tropical Cyclones in the Southern Hemisphere Using a Dynamical Multimodel Ensemble

AbstractSubseasonal tropical cyclone forecasts from two operational forecast models are verified for the 2017/18 and 2018/19 Southern Hemisphere cyclone seasons. The forecasts are generated using the ECMWF’s Medium- and Extended-Range Ensemble Integrated Forecasting System (IFS), and the Bureau of Meteorology’s seasonal forecasting system ACCESS-S1. Results show the IFS is more skillful than ACCESS-S1, which is attributed to the IFS’s greater ensemble size, increased spatial resolution, and data assimilation schemes. Applying a lagged ensemble with ACCESS-S1 increases forecast reliability, with the optimum number of lagged members being dependent on forecast lead time. To investigate the impacts of atmospheric assimilation at shorter lead times, comparisons were made between the Bureau of Meteorology’s ACCESS-S1 and ACCESS-GE2 systems, the latter a global Numerical Weather Prediction system running with the same resolution and model physics as ACCESS-S1 but using an ensemble Kalman filter for data assimilation. This comparison showed the data assimilation scheme used in the GE2 system gave improvements in forecast skill for days 8–10, despite the smaller ensemble size used in GE2 (24 members per forecast compared to 33). Finally, a multimodel ensemble was created by combining forecasts from both the IFS and ACCESS-S1. Using the multimodel ensemble gave improvements in forecast skill and reliability. This improvement is attributed to complementary spatial errors in both systems occurring across much of the Southern Hemisphere as well as an increase in the ensemble size.

Use of Multiensemble Track Clustering to Inform Medium-Range Tropical Cyclone Forecasts

AbstractTropical cyclone ensemble track forecasts from 153 initialization times during 2017–18 are clustered using regression mixture models. Clustering is performed on a four-ensemble dataset [ECMWF + GEFS + UKMET + CMC (EGUC)], and a three-ensemble dataset that excludes the CMC (EGU). For both datasets, five-cluster partitions are selected to analyze, and the relationship between cluster properties (size, ensemble composition) and 96–144-h cluster-mean error is evaluated. For both datasets, small clusters produce very large errors, with the least populous cluster producing the largest error in more than 50% of forecasts. The mean of the most populous EGUC cluster outperforms the most accurate (EGU) ensemble mean in only 43% of forecasts; however, when the most populous EGUC cluster from each forecast contains ≥30% of the ensemble population, its average cluster-mean error is significantly reduced compared to when the most populous cluster is smaller. Forecasts with a highly populous EGUC cluster also appear to have smaller EGUC-, EGU-, and ECMWF-mean errors. Cluster-mean errors also vary substantially by the ensembles composing the cluster. The most accurate clusters are EGUC clusters that contain threshold memberships of ECMWF, GEFS, and UKMET, but not CMC. The elevated accuracy of EGUC CMC-excluding clusters indicates the potential utility of including the CMC in clustering, despite its large ensemble-mean errors. Pruning ensembles by removing members that belong to small clusters reduces 96–144-h forecast errors for both EGUC and EGU clustering. For five-cluster partitions, a pruning threshold of 10% affects 49% and 35% of EGUC and EGU ensembles, respectively, improving 69%–74% of the forecasts affected by pruning.

Impact of Refractivity Profiles from a Proposed GNSS-RO Constellation on Tropical Cyclone Forecasts in a Global Modeling System

Abstract A global observing system simulation experiment (OSSE) was used to assess the potential impact of a proposed Global Navigation Satellite System (GNSS) radio occultation (RO) constellation on tropical cyclone (TC) track, maximum 10-m wind speed (Vmax), and integrated kinetic energy (IKE) forecasts. The OSSE system was based on the 7-km NASA nature run and simulated RO refractivity determined by the spatial distribution of observations from the original planned (i.e., including both equatorial and polar orbits) Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2). Data were assimilated using the NOAA operational weather analysis and forecasting system. Three experiments generated global TC track, Vmax, and IKE forecasts over 6 weeks of the North Atlantic hurricane season in the North Atlantic, east Pacific, and west Pacific basins. Confidence in our results was bolstered because track forecast errors were similar to those of official National Hurricane Center forecasts, and Vmax errors and IKE errors showed similar results. GNSS-RO assimilation did not significantly impact global track forecasts, but did slightly degrade Vmax and IKE forecasts in the first 30–60 h of lead time. Global forecast error statistics show adding or excluding explicit random errors to RO profiles made little difference to forecasts. There was large forecast-to-forecast variability in RO impact. For two cases studied in depth, track and Vmax improvements and degradations were traced backward through the previous 24 h of assimilation cycles. The largest Vmax degradation was traced to particularly good control analyses rather than poor analyses caused by GNSS-RO.

Machine Learning in Tropical Cyclone Forecast Modeling: A Review

Tropical cyclones have always been a concern of meteorologists, and there are many studies regarding the axisymmetric structures, dynamic mechanisms, and forecasting techniques from the past 100 years. This research demonstrates the ongoing progress as well as the many remaining problems. Machine learning, as a means of artificial intelligence, has been certified by many researchers as being able to provide a new way to solve the bottlenecks of tropical cyclone forecasts, whether using a pure data-driven model or improving numerical models by incorporating machine learning. Through summarizing and analyzing the challenges of tropical cyclone forecasts in recent years and successful cases of machine learning methods in these aspects, this review introduces progress based on machine learning in genesis forecasts, track forecasts, intensity forecasts, extreme weather forecasts associated with tropical cyclones (such as strong winds and rainstorms, and their disastrous impacts), and storm surge forecasts, as well as in improving numerical forecast models. All of these can be regarded as both an opportunity and a challenge. The opportunity is that at present, the potential of machine learning has not been completely exploited, and a large amount of multi-source data have also not been fully utilized to improve the accuracy of tropical cyclone forecasting. The challenge is that the predictable period and stability of tropical cyclone prediction can be difficult to guarantee, because tropical cyclones are different from normal weather phenomena and oceanographic processes and they have complex dynamic mechanisms and are easily influenced by many factors.

Strategies for Assimilating High-Density Atmospheric Motion Vectors into a Regional Tropical Cyclone Forecast Model (HWRF)

In recent years, atmospheric numerical modeling frameworks and satellite observing systems have both undergone significant advances. While these developments offer considerable potential for improving forecasts of high-impact weather events such as tropical cyclones (TC), much work remains to be done regarding the targeted processing and optimal use of observations now becoming available with high spatiotemporal resolution. Using the 2019 version of NCEP’s HWRF model, we explore several different strategies for the assimilation of TC-scale, high-density atmospheric motion vectors (AMVs) derived from the new-generation GOES-R series of geostationary satellites. Using 2017’s Atlantic Hurricane Irma as a case study, we examine the HWRF forecast impacts of observation pre-processing, including thinning and adjustments to observation errors. It is demonstrated that enhanced vortex-scale GOES-16 AMVs contribute to notable improvements in HWRF track forecast error compared to a baseline control experiment that does not incorporate the high-density AMVs. Impacts on TC intensity and structure (i.e., wind radii) forecast errors are less robust, but results from the optimization experiments suggest that further work (both with regard to data assimilation strategies and advancements in the methods themselves) should lead to improvements in these forecast variables as well.

The Evaluation of Real-Time Hurricane Analysis and Forecast System (HAFS) Stand-Alone Regional (SAR) Model Performance for the 2019 Atlantic Hurricane Season

The next generation Hurricane Analysis and Forecast System (HAFS) has been developed recently in the National Oceanic and Atmospheric Administration (NOAA) to accelerate the improvement of tropical cyclone (TC) forecasts within the Unified Forecast System (UFS) framework. The finite-volume cubed sphere (FV3) based convection-allowing HAFS Stand-Alone Regional model (HAFS-SAR) was successfully implemented during Hurricane Forecast Improvement Project (HFIP) real-time experiments for the 2019 Atlantic TC season. HAFS-SAR has a single large 3-km horizontal resolution regional domain covering the North Atlantic basin. A total of 273 cases during the 2019 TC season are systematically evaluated against the best track and compared with three operational forecasting systems: Global Forecast System (GFS), Hurricane Weather Research and Forecasting model (HWRF), and Hurricanes in a Multi-scale Ocean-coupled Non-hydrostatic model (HMON). HAFS-SAR has the best performance in track forecasts among the models presented in this study. The intensity forecasts are improved over GFS, but show less skill compared to HWRF and HMON. The radius of gale force wind is over-predicted in HAFS-SAR, while the hurricane force wind radius has lower error than other models.

Assimilation of Himawari-8 Rapid-Scan Atmospheric Motion Vectors on Tropical Cyclone in HWRF System

This study investigates the assimilation impact of rapid-scan (RS) atmospheric motion vectors (AMVs) derived from the geostationary satellite Himawari-8 on tropical cyclone (TC) forecasts. Forecast experiments for three TCs in 2016 in the western North Pacific basin are performed using the National Centers for Environmental Prediction (NCEP) operational Hurricane Weather Research and Forecasting Model (HWRF). An ensemble-variational hybrid data assimilation system is used as an initialization. The results show that the assimilation of RS-AMVs can improve the track forecast skill, while the weak bias or slow intensification bias increases at the shorter forecast lead time. A vortex initialization in HWRF has a substantial impact on TC structure, but it has neutral impacts on the track and intensity forecasts. A thinning of AMVs mitigates the weak bias caused by RS-AMV assimilation, resulting in the reduction of intensity error. However, it degrades the track forecast skill for a longer lead time. A decomposition of the TC steering flows demonstrated that the change in TC-induced flow was a primary factor for reducing the track forecast error, and the change in environmental flow has less impact on the track forecast. The investigation of the structural change from the assimilation of RS-AMV revealed that the following two factors are likely related to the intensity forecast degradation: (1) an increase of inertial stability outside the radius of maximum wind (RMW), which weakens the boundary layer inflow; and (2) a drying around and outside the RMW.

Improvements in tropical cyclone forecasting through ensemble prediction system at NCMRWF in India

This paper deals with the comparison of cyclone forecasts from the two versions of the operational global ensemble prediction system (EPS) at the National Centre for Medium Range Weather Forecasting (NEPS). The previous version had a horizontal resolution of 33 km with 44 ensemble members (NEPS) whereas the updated version of this EPS has a resolution of 12 km with 11 members (NEPS-UP). The ensemble mean forecasts from both the models are compared using the direct position (DPE), along (ATE) and cross track (CTE) errors. For the verification of strike probability, Brier Score (BS), Brier Skill Score (BSS), Reliability Diagram, Relative Operating Characteristic (ROC) Curve and Root Mean Square Error (RMSE) in mean Vs Spread in members are used. For verification of intensity, RMSE in maximum wind speed from the ensemble mean forecasts are compared.Comparison of ensemble mean tracks from both models showed lower errors in NEPS-UP for all forecast lead times. The decrease in the DPE, ATE and CTE in NEPS-UP was around 38%, 48% and 15% respectively. NEPS-UP showed lower BS and higher BSS values indicating a better match between observed frequencies and forecast probabilities as well as higher prediction skills. The reliability diagram showed higher accuracy for NEPS-UP as compared to NEPS. The ROC curves showed that for forecasts with higher probabilities the hit rate was high in NEPS-UP. There was a greater consensus between the RMSE and Spread for NEPS-UP at all lead times. It was also seen that the RMSE in mean showed a 41% decrease from NEPS to NEPS-UP. On comparing maximum wind, it was found that for all lead times the RMSE in maximum wind speed for NEPS-UP was lower than NEPS.

An evaluation of tropical cyclone forecast in the Southwest Indian Ocean basin with AROME‐Indian Ocean convection‐permitting numerical weather predicting system

AbstractIn order to contribute to ongoing efforts on tropical cyclone (TC) forecasting, a new, convection‐permitting, limited‐area coupled model called AROME‐Indian Ocean (AROME‐IO) was deployed in the Southwest Indian Ocean basin (SWIO) in April 2016. The skill of this numerical weather predicting system for TC prediction is evaluated against its coupling model (European Center for Medium Range Weather Forecasting‐Integrated Forecasting System [ECMWF‐IFS]) using 120‐hr reforecasts of 11 major storms that developed in this area over TC seasons 2017–2018 and 2018–2019. Results show that AROME‐IO generally provides significantly better performance than IFS for intensity (maximum wind) and structure (wind extensions, radius of maximum wind) forecasts at all lead times, with similar performance in terms of trajectories. The performance of a prototype, 12‐member ensemble prediction system (EPS), of AROME‐IO is also evaluated on the case of TC Fakir (April 2018), a storm characterized by an extremely low predictability in global deterministic and ensemble models. AROME‐IO EPS is shown to significantly improve the predictability of the system with two scenarios being produced: a most probable one (~66%), which follows the prediction of AROME‐IO, and a second one (~33%) that closely matches reality.