Hurricane Track Research Articles

Predictability of the Most Long‐Lived Tropical Cyclone Freddy (2023) During Its Westward Journey Through the Southern Tropical Indian Ocean

AbstractThis study aimed to explore the predictability of the most long‐lived tropical cyclone (TC) Freddy in 2023 while it traversed westward across the southern tropical Indian Ocean during the first 18 days of its existence. Global ensemble forecasts revealed southward track deflection and intensity underestimation of Freddy. We identified three key factors contributing to the limited predictability of Freddy, which are associated with the Mascarene High, Storm Dingani, and Freddy itself. The large track errors of Freddy can be attributed to the underestimated strength of the Mascarene High, the more northeastern position of Dingani, and the presence of excessively large or small sizes of Freddy. These findings were further validated through a high‐resolution regional model. Specifically, Freddy's track and intensity most closely matched the observations when these three factors were most closely represented. It underscores the pivotal role played by the interaction between TCs and multi‐scale systems in TC forecasts.

2022 real-time Hurricane forecasts from an experimental version of the Hurricane analysis and forecast system (HAFSV0.3S)

During the 2022 hurricane season, real-time forecasts were conducted using an experimental version of the Hurricane Analysis and Forecast System (HAFS). The version of HAFS detailed in this paper (HAFSV0.3S, hereafter HAFS-S) featured the moving nest recently developed at NOAA AOML, and also model physics upgrades: TC-specific modifications to the planetary boundary layer (PBL) scheme and introduction of the Thompson microphysics scheme. The real-time forecasts covered a large dataset of cases across the North Atlantic and eastern North Pacific 2022 hurricane seasons, providing an opportunity to evaluate this version of HAFS ahead of planned operational implementation of a similar version in 2023. The track forecast results show that HAFS-S outperformed the 2022 version of the operational HWRF model in the Atlantic, and was the best of several regional hurricane models in the eastern North Pacific for track. The intensity results were more mixed, with a dropoff in skill at Days 4–5 in the Atlantic but increased skill in the eastern North Pacific. HAFS-S also showed some larger errors than the long-time operational Hurricane Weather Research and Forecasting (HWRF) model in the radius of 34-knot wind, but other radii metrics are improved. Detailed analysis of Hurricane Ian in the Atlantic highlights both the strengths of HAFS and opportunities for further development and improvement.

Evaluation of Multiweek Tropical Cyclone Forecasts in the Philippines

Abstract In the pursuit of providing tropical cyclone (TC) forecasts beyond the conventional time scales covered by weather forecasting in the Philippines, this study has examined the multiweek (i.e., from week 1 to week 4) TC forecast skill in the country. TC forecasts derived from three ensemble models, namely, the NCEP Climate Forecast System version 2 (CFSv2), the European Centre for Medium-Range Weather Forecasts Ensemble Prediction System (ECMWF), and the NCEP Global Ensemble Forecast System version 12 (GEFSv12) from 6 October 2020 to 31 October 2021 were verified. Results revealed that the ECMWF model is consistently the most skillful in multiweek TC prediction over the domain bounded by 110°–155°E and 0°–27°N in the western North Pacific. The ECMWF obtained hit rates ranging from 0.25 to 0.31, low false alarm rates of 0–0.33, and the highest equitable threat scores among the models. In contrast to this, the GEFSv12 and CFSv2 models had varying skills, with the former performing better in the first two weeks and the latter in longer lead times. It is further revealed that the three models generally underestimate the observed number of storms, storm days, and accumulated cyclone energy. Moreover, the study shows that the forecast TC tracks have a significant (p < 0.05) positional bias toward the right of observed tracks beyond week 1, and that they tend to propagate slower than observations especially in week 1 and week 2. These findings contribute to better understanding the strengths and limitations of these ensemble models useful for eventual provision of multiweek TC forecasts in the Philippines.

Improving the Intensity Forecast of Tropical Cyclones in the Hurricane Analysis and Forecast System

Abstract A modification to the mixing length formulation in a planetary boundary layer (PBL) scheme is introduced to improve the intensity forecast of tropical cyclones (TCs) in a basin-scale Hurricane Analysis and Forecast System (HAFS) for the real-time experiment in 2021. The 2020 basin-scale HAFS with the physics suite of the NCEP operational Global Forecast System performs well in terms of the reduced root-mean-square (RMS) errors in track and intensity except for the mean intensity bias, compared with NCEP operational hurricane models. To address the large intensity bias issue, the vertical mixing length near the surface used in the PBL scheme is increased to follow the similarity theory, consistent with that used in the surface layer scheme. Test results show that the RMS error and bias in intensity are further reduced without the degradation of the track forecast. An idealized one-dimensional TC PBL model is used to understand the model response to the modification, indicating that the radial wind is strengthened to dynamically balance the enhanced downward momentum mixing. This is also exhibited in the case study of a three-dimensional HAFS simulation, with the improved vertical distribution of the simulated wind speed in the eyewall area. Given the improvement, the modification has been implemented in one of the configurations of the first version of the operational HAFS at NCEP. Finally, the adjustment of the parameterization of diffusion and mixing in TC simulations is discussed. Significance Statement A modification to the mixing length formulation in a PBL scheme is described, which improves the intensity forecast of tropical cyclones simulated in the Hurricane Analysis and Forecast System (HAFS). Retrospective tests indicate that the modification can reduce the root-mean-square error and bias of the simulated TC intensity by 5%–10% and 50%, respectively. This modification has been implemented in one of the operational configurations of HAFS, version 1, at NCEP, improving the hurricane model guidance.

Multiweek tropical cyclone prediction for the Southern Hemisphere in ACCESS‐S2: Maintaining operational skill and continuity of service

AbstractThe skill of subseasonal (multiweek) forecasts of tropical cyclone (TC) occurrence over the Southern Hemisphere is examined in the Australian Bureau of Meteorology's (BoM) multiweek to seasonal prediction system, ACCESS‐S2. Relative to its predecessor, ACCESS‐S1, ACCESS‐S2 shows improved biases in spatial TC frequency in the South Pacific and Southwest Indian Ocean. However, there is no improvement to the known negative bias in TC frequency off the coast of NW Australia. The ability of ACCESS‐S2 to provide probabilistic forecasts of TC occurrence for the Southern Hemisphere on multiweek timescales is examined using reliability measures and Brier skill scores. For the period November–February 1990–2012, both ACCESS‐S1 and ACCESS‐S2 show positive skill relative to climatology for calibrated forecasts out to week 5. However, the skill of ACCESS‐S2 is slightly reduced compared to ACCESS‐S1 at all lead times, which may be due to the fewer number of ensemble members available. For the full ACCESS‐S2 hindcast period, November–April 1981–2018, ACCESS‐S2 again shows positive skill of calibrated forecasts over climatology out to week 5. For weeks 1–2, skill is reduced compared to the shorter 1990–2012 period; whereas it is marginally improved for longer lead times (weeks 3–5). Use of lagged ensembles, an alternative linear regression calibration, as well as removing weaker model TCs were examined to potentially improve the skill of ACCESS‐S2 forecasts; however, none of these methods were able to significantly increase skill at all lead times. Continued use of the original calibration method is therefore recommended in order to retain skill and continuity of service of the BoM operational and public multiweek TC forecasts.

Impact of Assimilating Satellite and Glider Observations on Hurricane Isaias (2020) Forecast Using Marine JEDI

Abstract Realistic ocean initial conditions are essential for coupled hurricane forecasts. This study focuses on the impact of assimilating high-resolution ocean observations for initialization of the Modular Ocean Model (MOM6) in a coupled configuration with the Hurricane Analysis and Forecast System (HAFS). Based on the Joint Effort for Data Assimilation Integration (JEDI) framework, numerical experiments were performed for the Hurricane Isaias (2020) case, a category-1 hurricane, with use of underwater glider datasets and satellite observations. Assimilation of ocean glider data together with satellite observations provides opportunity to further advance understanding of ocean conditions and air–sea interactions in coupled model initialization and hurricane forecast systems. This comprehensive data assimilation approach has led to a more accurate prediction of the salinity-induced barrier layer thickness that suppresses vertical mixing and sea surface temperature cooling during the storm. Increased barrier layer thickness enhances ocean enthalpy flux into the lower atmosphere and potentially increases tropical cyclone intensity. Assimilation of satellite observations demonstrates improvement in Hurricane Isaias’s intensity forecast. Assimilating glider observations with broad spatial and temporal coverage along Isaias’s track in addition to satellite observations further increase Isaias’s intensity forecast. Overall, this case study demonstrates the importance of assimilating comprehensive marine observations to a more robust ocean and hurricane forecast under a unified JEDI–HAFS hurricane forecast system. Significance Statement This is the first comprehensive study of marine observations’ impact on hurricane forecast using marine JEDI. This study found that assimilating satellite observations increases upper-ocean stratification during the prestorm period of Isaias. Assimilating preprocessed observations from six gliders increases salinity-induced upper ocean barrier layer thickness, which reduces sea surface temperature cooling and increases enthalpy flux during the storm. This mechanism eventually enhances hurricane intensity forecast. Overall, this study demonstrates a positive impact of assimilating comprehensive marine observations to a successful ocean and hurricane forecast under a unified JEDI–HAFS hurricane forecast system.

FORECAST VERIFICATION OF AN ENSEMBLE TROPICAL CYCLONE STORM SURGE SYSTEM

Tropical Cyclone (TC) induced storm surges can be especially damaging and require emergency preparations. A unique challenge of providing TC storm surge advice to emergency services relates to the sensitivity of storm surge forecasts to uncertainty in the TC forecast. Probabilistic forecast products are a good fit for such advice, as long as forecast skill characteristics are known and understood. The Australian Bureau of Meteorology operates a dynamical ensemble prediction system to provide forecasts of storm surge driven by TCs. This is a step change from the legacy parametric and/or scenario-based techniques previously used at the Bureau. There are a number of benefits to be gained from dynamic storm surge ensemble forecasts over more traditional systems. Most importantly, the surge forecast is very sensitive to errors in the TC location, velocity, intensity, and size. Ensemble storm surge forecasting is required in order to take account of this uncertainty in the TC forecast.

Recent advances in seasonal and multi-annual tropical cyclone forecasting

Seasonal tropical cyclone (TC) forecasting has evolved substantially since its commencement in the early 1980s. However, present operational seasonal TC forecasting services still do not meet the requirements of society and stakeholders: current operational products are mainly basin-scale information, while more detailed sub-basin scale information such as potential risks of TC landfall is anticipated for decision making. To fill this gap and make the TC science and services move forward, this paper reviews recent research and development in seasonal tropical cyclone (TC) forecasting. In particular, this paper features new research topics on seasonal TC predictability in neutral conditions of El Niño–Southern Oscillation (ENSO), emerging forecasting techniques of seasonal TC activity including Machine Learning/Artificial Intelligence, and multi-annual TC predictions. We also review the skill of forecast systems at predicting landfalling statistics for certain regions of the North Atlantic, Western North Pacific and South Indian oceans and discuss the gap that remains between current products and potential user's expectations. New knowledge and advanced forecasting techniques are expected to further enhance the capability of seasonal TC forecasting and lead to more actionable and fit-for-purpose products.

SHYTCWAVES: A STOP-MOTION HYBRID TC-INDUCED WAVES METAMODEL

Waves produced by tropical cyclones (TCs) can be estimated using numerical models and non-stationary simulations forced with vortex-type time-varying wind fields. However, dynamical simulations are time and computationally demanding at regional-scale domains since high temporal resolutions are required to correctly simulate wave propagation. Early warning systems, as well as risk induced assessments, would benefit from fast and accurate estimates of the wave climate induced by close-to-real tracks geometry. For this reason, the objective of the proposed methodology is to obtain a tool capable of estimating the spatial and temporal variability of directional wave spectra produced by TCs at regional scale in deep waters using a hybrid approach, and statistical techniques, to reduce CPU time effort. SHyTCWaves is a metamodel that provides estimates of the directional wave spectra produced by TCs, which can be used as boundary conditions for local-scale models at nearshore coastal areas, in addition to providing faster than dynamically simulated TC forecast predictions.

Advanced Tropical Cyclone Prediction Using the Experimental Global ECMWF and Operational Regional COAMPS-TC Systems

Abstract Structure and intensity forecasts of 19 tropical cyclones (TCs) during the 2020 Atlantic hurricane season are investigated using two NWP systems. An experimental 4-km global ECMWF model (EC4) with upgraded moist physics is compared with a 9-km version (EC9) to evaluate the influence of resolution. EC4 is then benchmarked against the 4-km regional COAMPS–Tropical Cyclones (COAMPS-TC) system (CO4) to compare systems with similar resolutions. EC4 produced stronger TCs than EC9, with a >30% reduction of the maximum wind speed bias in EC4, resulting in lower forecast errors. However, both ECMWF predictions struggled to intensify initially weak TCs, and the radius of maximum winds (RMW) was often too large. In contrast, CO4 had lower biases in central pressure, maximum wind speed, and RMW. Regardless, minimal statistical differences between CO4 and EC4 intensity errors were found for ≥36-h forecasts. Rapid intensification cases yielded especially large intensity errors. CO4 produced superior forecasts of RMW, together with an excellent pressure–wind relationship. Differences in the results are due to contrasting physics and initialization schemes. ECMWF uses global data assimilation with no special treatment of TCs, whereas COAMPS-TC constructs a vortex for TCs with initial intensity ≥55 kt (∼28 m s−1) based on data provided by forecasters. Two additional ECMWF experiments were conducted. The first yielded improvements when the drag coefficient was reduced at high wind speeds, thereby weakening the coupling between the low-level winds and the surface. The second produced overly intense TCs when explicit deep convection was used, due to unrealistic mid–upper-tropospheric heating. Significance Statement Improved forecasts of tropical storms and hurricanes depend on advances in computer weather models. We tested an experimental high-resolution (4 km) version of the global ECMWF model against its 9-km counterpart to evaluate the influence of resolution on storm position and intensity. We also compared this with the 4-km U.S. Navy model, which is designed for tropical storms and hurricanes. Over a 3-month period during the active 2020 Atlantic hurricane season, we found that increasing the horizontal resolution improved intensity forecasts. The Navy model forecasts were superior for the radius of maximum winds and had lower intensity biases. Two additional experiments with the ECMWF model revealed the importance of simulating air–sea interaction in high winds and current challenges with explicitly simulating deep thunderstorm clouds in their system.

The Development and Evaluation of a Tropical Cyclone Probabilistic Landfall Forecast Product

Abstract Improving estimates of tropical cyclone forecast uncertainty remains an important goal of the Hurricane Forecast Improvement Project (HFIP). Intensity forecast uncertainty near landfall is especially complicated because intensity forecasts depend on track forecasts. Ensembles can be difficult to interpret near land due to differences in both spatial and temporal resolution and differences in landfall timing (if at all) and location. The Monte Carlo Wind Speed Probability (WSP) model is a statistical ensemble based on the error characteristics of forecasts by the National Hurricane Center (NHC) and the spread of several track forecast models. The landfall distribution product (LDP) introduced in this paper was developed to use the statistical ensemble of forecasts from the WSP model to estimate both the track and intensity forecast uncertainty associated with potential landfalls. The LDP includes probabilistic intensity estimates as well as estimates of the most likely and reasonable strongest intensity at landfall. These products could communicate concise intensity uncertainty information to users at risk for tropical cyclone impacts. Demonstration on a retrospective dataset from 2010 to 2018 and evaluation of the LDP on the 2020–21 Atlantic hurricane seasons shows that the probability of landfall and the landfall intensity probabilities generated by the WSP model are reliable and potentially useful for preparedness decision-making. A case study of Hurricane Ida (2021) highlights how the LDP can be implemented to communicate landfall uncertainty to a broad range of users. Significance Statement With the new landfall distribution product (LDP), the National Hurricane Center can provide both track and intensity forecast uncertainty surrounding the landfall of hurricanes. The issuance of a reasonable worst case scenario for the strongest winds that could impact a region could amplify messaging to encourage people to take appropriate action prior to a landfall.

Improvements of Lagrangian Data Assimilation Tested in the Gulf of Mexico

Abstract This study extends initial work by Sun and Penny and Sun et al. to explore the inclusion of path information from surface drifters using an augmented-state Lagrangian data assimilation based on the local ensemble transform Kalman filter (LETKF-LaDA) with vertical localization to improve analysis of the ocean. The region of interest is the Gulf of Mexico during the passage of Hurricane Isaac in the summer of 2012. Results from experiments with a regional ocean model at eddy-permitting and eddy-resolving model resolutions are used to quantify improvements to the analysis of sea surface velocity, sea surface temperature, and sea surface height in a data assimilation system. The data assimilation system assimilates surface drifter positions, as well as vertical profiles of temperature and salinity. Data were used from drifters deployed as a part of the Grand Lagrangian Deployment beginning 20 July 2012. Comparison of experiment results shows that at both eddy-permitting and eddy-resolving horizontal resolutions Lagrangian assimilation of drifter positions significantly improves analysis of the ocean state responding to hurricane conditions. These results, which should be applicable to other tropical oceans such as the Bay of Bengal, open new avenues for estimating ocean initial conditions to improve tropical cyclone forecasting.

Development and Evaluation of Multi-week Tropical Cyclone Strike Probability Forecasts in the Philippines

Tropical cyclone (TC) forecast, provided a few weeks in advance, can be beneficial for the preparation and mitigation of disaster risks in TC-vulnerable countries such as the Philippines. In this study, TC strike probability forecasts with a lead time of up to 4 wk were derived by combining the TC tracks from each ensemble member of the three models – namely, NCEP Coupled Forecast System version 2 (CFSv2), European Centre for Medium-Range Weather Forecasts Ensemble Prediction System (ECMWF), and NCEP Global Ensemble Forecast System version 12 (GEFSv12) – to show how likely it is for a TC to form or strike an area within the 300-km radius of the TC center. To assess its performance, real-time-derived TC strike probability forecasts over the Tropical Cyclone Information Domain (TCID) of the Philippines (bounded by 0–27 °N and 110–155 °E) covering the period from 06 Oct 2020–31 Oct 2022 were evaluated. Verification metrics revealed that the skill and reliability of the TC forecasts vary with lead time and the TC being forecasted. Week 1 forecasts are reliable and can be helpful for decision-making, whereas Week 2 forecasts are considered most reliable only up to the 51–60% probability interval. On the other hand, forecasts with 3–4-wk lead times are reliable for probabilities less than 20%. The case study performed using five TCs with different intensity classifications has shown that generally, forecasts for TCs with stronger intensity have higher skill than forecasts for relatively weaker ones. It was also observed that the magnitude of the probability values varies with the intensity changes within the validity period. These findings suggest that multi-model ensemble forecasts can be utilized for the improvement and eventual operationalization of multi-week TC strike probability forecasts over the Philippines.

The influence of large-scale climate modes on tropical cyclone tracks in the southwest Pacific

Tropical cyclones (TCs) impact the economy, properties, lives and infrastructure of island nations and territories of the southwest Pacific (SWP), accounting for three in four regional disasters each year. To increase the resilience of the SWP to the destructive impacts of TCs, improved TC track forecasts are needed since a high degree of uncertainty exists around the likely path a TC will take in this region post-formation. This requires better comprehension of the factors contributing to TC track variability occurring at different timescales. Therefore, we examine the modulating impact of key Indo-Pacific climate drivers: the El Niño-Southern Oscillation (ENSO), Interdecadal Pacific Oscillation (IPO), Southern Annular Mode (SAM) and the Indian Ocean Dipole (IOD), on SWP TC track variability. We present new insights into the spatial (i.e. prevailing trajectories) and temporal (i.e. track length, average speed and duration) components of TC tracks, being modulated by both individual and combined climate modes. Overall, TC tracks tend to shift northeast during El Niño, IPO positive, IOD east positive and/or SAM negative phases (with a southwest shift observed during the opposite climate phases). Further, we show that when two of these climate modes are in their positive phase (e.g. El Niño with the positive phases of IPO or eastern pole of IOD and SAM), TC track length and average speed are enhanced. However, for cases where either one (e.g. El Niño/negative phase of IPO and IOD east) or two (La Niña/negative phase of IPO, IOD east and SAM) climate modes were in the negative phase, an increase in TC track duration was observed. The findings of this study may be used to improve TC forecasting and better quantify TC-related risks.

Regulating Fine‐Scale Resolved Convection in High‐Resolution Models for Better Hurricane Track Prediction

AbstractHigh‐resolution atmospheric models are powerful tools for hurricane track and intensity predictions. Although using high resolution contributes to better representation of hurricane structure and intensity, its value in the prediction of steering flow and storm tracks is uncertain. Here we present experiments suggesting that biases in the predicted North Atlantic hurricane tracks in a high‐resolution (approximately 3 km grid‐spacing) model originates from the model's explicit simulation of deep convection. Differing behavior of explicit convection leads to changes in the synoptic‐scale pattern and thereby to the steering flow. Our results suggest that optimizing small‐scale convection activity, for example, through the model's horizontal advection scheme, can lead to significantly improved hurricane track prediction (∼10% reduction of mean track error) at lead times beyond 72 hr. This work calls attention to the behavior of explicit convection in high‐resolution models, and its often overlooked role in affecting larger‐scale circulations and hurricane track prediction.

Tropical Cyclone Forecasts in the DIMOSIC Project—Medium‐Range Forecast Models With Common Initial Conditions

AbstractThe tropical cyclone (TC) forecast skill of the eight global medium‐range forecast models which are participating in the DIfferent Models, Same Initial Conditions project is investigated in this study. Each model was used to generate 10‐day forecasts from the same initial conditions provided by the European Centre for Medium‐Range Weather Forecasts. There are a total of 123 initial dates spanning in one year from June 2018 to June 2019 at 3‐day intervals. The TC track and intensity forecasts are evaluated against the best track data set. TC‐related precipitation and tropical cyclogenesis forecasts are also compared to explore the differences and similarities of TC forecasts across the models. This comparison of TC forecasts allows model developers in different centers to benchmark their model against other models, with the impact of the initial condition quality removed. The verifications reveal that most models show slow‐moving and right‐of‐track biases in their TC track forecasts. Also, a common dry bias in TC‐related precipitation indicates a general deficiency in TC intensity and convection in the models which should be related to insufficient model resolution. These findings provide important references for future model developments.

Evaluating the Impacts of COSMIC-2 GNSS RO Bending Angle Assimilation on Atlantic Hurricane Forecasts Using the HWRF Model

Abstract This study evaluates the impact of assimilating Global Navigation Satellite System (GNSS) radio occultation (RO) bending angles from Formosa Satellite Mission-7/Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2) receiver satellites on Hurricane Weather Research and Forecasting (HWRF) Model tropical cyclone (TC) forecasts. Launched in June 2019, the COSMIC-2 mission provides significantly higher tropics data coverage compared to its predecessor COSMIC constellation. GNSS RO measurements yield information about atmospheric pressure, temperature, and water vapor profiles. HWRF is cycled with and without COSMIC-2 bending angle data assimilation for six 2020 Atlantic hurricane cases. COSMIC-2 assimilation has little impact on HWRF track forecasts, consistent with HWRF’s design limiting cycled data assimilation impacts on surrounding large-scale flows; however, COSMIC-2 assimilation results in a statistically significant ∼8%–12% mean absolute forecast error reduction in minimum central sea level pressure for t = 36-, 54-, 60-, and 108–120-h lead times. Forecasts initialized from analyses assimilating COSMIC-2 observations also have a 1%–4% smaller 600–700-hPa specific humidity (SPFH) root-mean-squared deviation compared to radiosondes and dropwindsondes for most lead times. While not all HWRF intensity forecasts benefit from COSMIC-2 assimilation, a few show notable improvement. For example, assimilating two COSMIC-2 profiles within the inner core of developing Hurricane Hanna (2020) increases 800-hPa SPFH by up to 1 g kg−1 locally, helping to correct a dry bias. The forecast initialized from this analysis better captures Hanna’s observed intensification rate, likely because its moister inner core facilitates development of persistent deep convection near the TC center, where diabatic heating is more efficiently converted to cyclonic wind kinetic energy. Significance Statement Tropical cyclone (TC) intensification can be strongly sensitive to the lower-to-midtropospheric water vapor distribution near the storm. The COSMIC-2 GNSS radio occultation (RO) receiver satellite mission provides denser spatial coverage of atmospheric water vapor and temperature profiles over the tropics compared to other GNSS RO observation platforms. Herein, using six 2020 Atlantic TC cases, we evaluate the impacts of assimilating COSMIC-2 RO bending angles into a regional forecast model that already assimilates clear-sky satellite radiances. It is shown that COSMIC-2 assimilation yields a modest ∼10% intensity forecast skill improvement for several lead times, although more substantial intensity forecast improvement is found for a few forecasts where the COSMIC-2 observation assimilation helps correct a lower-to-midtropospheric water vapor bias.

Climatology of Tropical Cyclone Rainfall Magnitude at Different Landfalling Stages: An Emphasis on After-Landfall Rain

Abstract Estimating the magnitude of tropical cyclone (TC) rainfall at different landfalling stages is an important aspect of the TC forecast that directly affects the level of response from emergency managers. In this study, a climatology of the TC rainfall magnitude as a function of the location of the TC centers within distance intervals from the coast and the percentage of the raining area over the land is presented on a global scale. A total of 1834 TCs in the period from 2000 until 2019 are analyzed using satellite information to characterize the precipitation magnitude, volumetric rain, rainfall area, and axial-symmetric properties within the proposed landfalling categories, with an emphasis on the postlandfall stages. We found that TCs experience rainfall maxima in regions adjacent to the coast when more than 50% of their rainfall area is over the water. TC rainfall is also analyzed over the entire TC extent and the portion over land. When the total extent is considered, rainfall intensity, volumetric rain, and rainfall area increase with wind speed intensity. However, once it is quantified over the land only, we found that rainfall intensity exhibits a nearly perfect inversely proportional relation with the increase in TC rainfall area. In addition, when a TC with life maximum intensity of a major hurricane makes landfall as a tropical depression or tropical storm, it usually produces the largest spatial extent and the highest volumetric rain. Significant Statement This study aims to describe the cycle of tropical cyclone (TC) precipitation magnitude through a new approach that defines the landfall categories as a function of the percentage of the TC precipitating area over the land and ocean, along with the location of the TC centers within distance intervals from the coast. Our central hypothesis is that TC rainfall should exhibit distinct features in the long-term satellite time series for each of the proposed stages. We particularly focused on the overland events due to their effects on human activities, finding that the TCs that at some point of their life cycle reached major hurricane strength and made landfall as a tropical storm or tropical depression produced the highest volumetric rain over the land surface. This research also presents key observational evidence of the relationship between the rain rate, raining area, and volumetric rain for landfalling TCs.

MGTCF: Multi-Generator Tropical Cyclone Forecasting with Heterogeneous Meteorological Data

Accurate forecasting of tropical cyclone (TC) plays a critical role in the prevention and defense of TC disasters. We must explore a more accurate method for TC prediction. Deep learning methods are increasingly being implemented to make TC prediction more accurate. However, most existing methods lack a generic framework for adapting heterogeneous meteorological data and do not focus on the importance of the environment. Therefore, we propose a Multi-Generator Tropical Cyclone Forecasting model (MGTCF), a generic, extensible, multi-modal TC prediction model with the key modules of Generator Chooser Network (GC-Net) and Environment Net (Env-Net). The proposed method can utilize heterogeneous meteorologic data efficiently and mine environmental factors. In addition, the Multi-generator with Generator Chooser Net is proposed to tackle the drawbacks of single-generator TC prediction methods: the prediction of undesired out-of-distribution samples and the problems stemming from insufficient learning ability. To prove the effectiveness of MGTCF, we conduct extensive experiments on the China Meteorological Administration Tropical Cyclone Best Track Dataset. MGTCF obtains better performance compared with other deep learning methods and outperforms the official prediction method of the China Central Meteorological Observatory in most indexes.

A new approach to represent model uncertainty in the forecasting of tropical cyclones: The orthogonal nonlinear forcing singular vectors

AbstractTropical cyclone (TC) track forecasting has been considerably improved in recent decades, while TC intensity forecasting remain challenging. In this study, orthogonal nonlinear forcing singular vectors (O‐NFSVs) for emulating the impact of model uncertainties are used to conduct TC ensemble forecasting experiments with the Weather Research and Forecasting (WRF) model, with a focus on improving TC intensity forecasting skill. The O‐NFSVs approach is compared with the traditional stochastic kinetic‐energy backscatter (SKEB) and stochastically perturbed parametrization tendency (SPPT) schemes. The results demonstrate that the O‐NFSVs ensembles generally provide a better representation of the model uncertainties affecting TC intensification, with much better deterministic and probabilistic skills. These results also extend to the ability to forecast TC track, although the perturbations have not been optimized for that specific purpose. The O‐NFSVs are therefore appropriate perturbation structures for describing the uncertainties of the TC intensity and track forecasting and are also favourable for recognizing the rapid intensification process.