Typhoon Prediction Research Articles

Analysis of characteristics and evaluation of forecast accuracy for Super Typhoon Doksuri (2023)

Super Typhoon Doksuri is a significant meteorological challenge for China this year due to its strong intensity and wide influence range, as well as significant and prolonged hazards. In this work, we studied Doksuri's main characteristics and assessed its forecast accuracy meticulously based on official forecasts, global models and regional models with lead times varying from 1 to 5 days. The results indicate that Typhoon Doksuri underwent rapid intensification and made landfall at 09:55 BJT on July 28 with a powerful intensity of 50 m s−1 confirmed by the real-time operational warnings issued by China Meteorological Administration (CMA). The typhoon also caused significant wind and rainfall impacts, with precipitation at several stations reaching historical extremes, ranking eighth in terms of total rainfall impact during the event. The evaluation of forecast accuracy for Doksuri suggests that Shanghai Multi-model Ensemble Method (SSTC) and Fengwu Model are the most effective for short-term track forecasts. Meanwhile, the forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) and United Kingdom Meteorological Office (UKMO) are optimal for long-term predictions. It is worth noting that objective forecasts systematically underestimate the typhoon maximum intensity. The objective forecast is terribly poor when there is a sudden change in intensity. CMA-National Digital Forecast System (CMA-NDFS) provides a better reference value for typhoon accumulated rainfall forecasts, and regional models perform well in forecasting extreme rainfall. The analyses above assist forecasters in pinpointing challenges within typhoon predictions and gaining a comprehensive insight into the performance of each model. This improves the effective application of model products.

Effect of atmospheric response induced by preceding typhoon on movement of subsequent typhoon over Northwestern Pacific

This study investigates the atmospheric interactions between two closely located typhoons in 2019. Typhoons in the Western Pacific significantly impact Eastern and Southeastern Asian countries, leading to various damages. As global warming is expected to increase typhoon intensity, accurate track forecasting becomes crucial for coastal disaster management. Despite the existing knowledge about the influence of typhoon activities on the atmospheric background, limited research addresses the atmospheric response between two typhoons. The study focuses on the cases of LEKIMA and KROSA, occurring simultaneously in 2019, and utilizes the Weather Research and Forecasting (WRF) model for simulations. The experimental setup involves comparing two scenarios: one with both typhoons and one with LEKIMA removed. Results reveal LEKIMA-induced distinctive atmospheric responses, including the closure of the western North Pacific subtropical high (WNPSH) boundary and the formulation of a wave train, influencing KROSA’s stagnation. The absence of LEKIMA allows KROSA to move more freely along the steering flow. Furthermore, the study highlights the potential of atmospheric models for understanding typhoon effects at regional to mesoscale levels. A comprehensive analysis of similar cases could enhance typhoon predictions, contributing to better damage mitigation strategies.

Hierarchical Predictions of Fine-to-Coarse Time Span and Atmospheric Field Reconstruction for Typhoon Track Prediction

The prediction of typhoon tracks in the Northwest Pacific is key to reducing human casualties and property damage. Traditional numerical forecasting models often require substantial computational resources, are high-cost, and have significant limitations in prediction speed. This research is dedicated to using deep learning methods to address the shortcomings of traditional methods. Our method (AFR-SimVP) is based on a large-kernel convolutional spatio-temporal prediction network combined with multi-feature fusion for forecasting typhoon tracks in the Northwest Pacific. In order to more effectively suppress the effect of noise in the dataset to enhance the generalization ability of the model, we use a multi-branch structure, incorporate an atmospheric reconstruction subtask, and propose a second-order smoothing loss to further improve the prediction ability of the model. More importantly, we innovatively propose a multi-time-step typhoon prediction network (HTAFR-SimVP) that does not use the traditional recurrent neural network family of models at all. Instead, through fine-to-coarse hierarchical temporal feature extraction and dynamic self-distillation, multi-time-step prediction is achieved using only a single regression network. In addition, combined with atmospheric field reconstruction, the network achieves integrated prediction for multiple tasks, which greatly enhances the model’s range of applications. Experiments show that our proposed network achieves optimal performance in the 24 h typhoon track prediction task. Our regression network outperforms previous recurrent network-based typhoon prediction models in the multi-time-step prediction task and also performs well in multiple integration tasks.

Assimilation of new rocket dropsonde data using WRFDA and its impact on numerical simulations of typhoon NORU

On September 26 at 2100 UTC and September 27 at 0900 and 2300 UTC, three rockets platform carrying dropsondes (TFTC-400) devices were launched off the east coast of Hainan Island to conduct a launch experiment aimed at detecting Typhoon NORU (2216). The experiment yielded valuable data that were subsequently analyzed to ascertain temperatures, wind speeds, and relative humidity in the atmosphere. Of the four experiments conducted, employing three distinct control variable configurations (CVs), we utilized the 3DVAR of WRF Data Assimilation (WRFDA) to assimilate rocket sounding data and the NCEP ADP Global Upper Air Observational Weather Data from the research data archive dataset that was jointly produced by the Center for Weather and Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR). In one experiment, no data assimilation was performed (CTL). These experiments were designed to assess the impact of these observational datasets on typhoon predictions of the Weather Research & Forecasting Model (WRF) numerical simulations. Utilizing the assimilated background field, a 24-hour forecast was conducted, and the assimilation simulation was analyzed with regard to typhoon path, intensity, precipitation, and improvements in the background field. The results reveal that, on average, the three-assimilation experiment led to a 30 % reduction in track error compared to the CTL. Additionally, the assimilation experiment for CV7 of control variable configurations brought the maximum wind speed closer to observed data than the CTL between 6 and 12 h. The TS (threat score) evaluation of simulated 24-hour precipitation in the model domain indicates that the three assimilation schemes exhibit a degree of improvement in the forecast scores for 24-hour cumulative typhoon precipitation. Nevertheless, the simulation results for minimum sea-level pressure are unsatisfactory. To establish statistical significance, additional cases within the relevant region are necessary for result validation.

A comprehensive review on the modeling of tropical cyclone boundary layer wind field

Tropical cyclone (TC) wind field models are becoming increasingly sophisticated and complex. This review systematically discusses a range of models capable of simulating TCs in terms of modifications or simplifications of the governing equation, the Navier–Stokes equations, as a starting point. The discussion focuses on linear models, which include slab models, height-resolving models, and numerical simulation methods, respectively. The linear model offers quick calculations and insights into physical mechanisms, while slab models have limitations in capturing important processes and site conditions. The height-resolving model is widely used for Monte Carlo simulations, providing realistic three-dimensional wind structures. Nonlinear simulations yield reliable results for typhoon trajectory prediction, although they require specific boundary and initial conditions. Integration of nonlinear simulation with artificial intelligence and machine learning shows promise for faster typhoon prediction. However, challenges remain in terms of data training for machine learning models. Future advancements in these areas have the potential to enhance hazard assessment and weather forecasting.

The WRF Simulation Influence of Assimilating GNSS Water Vapor and Parameterization Schemes on Typhoon Rumbia

The Weather Research and Forecasting (WRF) model was used to simulate Typhoon Rumbia in this paper. The sensitivity experiments were conducted with 16 different parameterization combination schemes, including four microphysics (WSM6, WSM5, Lin, and Thompson), two boundary layers (YSU and MYJ), and two cumulus convection (Kain–Fritsch and Grell–Freitas) schemes. The impacts of 16 parameterization combination schemes and the data assimilation (DA) of Global Navigation Satellite System (GNSS) water vapor were evaluated by the simulation accuracy of typhoon track and intensity. The results show that the typhoon track and intensity are significantly influenced by parameterization schemes of cumulus and boundary layers rather than microphysics. The averaged track error of Lin_KF_Y is 104.73 km in the entire 72-h simulation period. The track errors of all the other combination schemes are higher than Lin_KF_Y. During the entire 72-h, the averaged intensity error of Thompson_GF_M is 1.36 hPa. It is the lowest among all the combination schemes. As for data assimilation, the simulation accuracy of typhoon tracks can be significantly improved by adding the GNSS water vapor. Thompson_GF_M-DA combination scheme has the lowest average track error of 45.05 km in the initial 24 h. The Lin_KF_Y-DA combination scheme exhibits an average track error of 32.17 km on the second day, 28.03 km on the third day, and 35.33 km during 72-h. The study shows that the combination of parameterization schemes and the GNSS water vapor data assimilation significantly improve the initial conditions and the accuracy of typhoon predictions. The study results contribute to the selection of appropriate combinations of physical parameterization schemes for the WRF-ARW model in the mid-latitude region of the western Pacific coast.

Introducing large‐scale analysis constraints in regional hybrid EnVar data assimilation for the prediction of triple typhoons

AbstractLarge‐scale environment fields play an important role in the accurate prediction of typhoons. However, regional predictions for typhoons often suffer from inadequate representation of large‐scale flow pattern such as those from global models due to limited domain size and observations employed in regional models, especially when multiple typhoons that interact concurrently occur in a regional domain. This study merges the large‐scale information from global model forecasts with the mesoscale information from regional model forecasts in the hybrid ensemble‐variational (EnVar) data assimilation by adding an analysis constraint in the EnVar cost function, which is defined by the departure of the regional model EnVar analysis from the global model fields and takes advantage of flow‐dependent ensemble background error covariance for the introduction of large scales using data assimilation. The EnVar assimilation impacts of the large‐scale fields on predictions of triple typhoons are assessed by conducting cycling assimilation and forecast experiments for a 13‐day‐long period in July 2015 when three typhoons concurrently occurred. Results show that the large‐scale constraint for EnVar can clearly improve the triple‐typhoons' track and intensity forecasts of the regional model. The large‐scale information introduced by the proposed method is also shown to reduce forecast errors of wind, temperature and humidity, respectively. Predictions of the rainfall caused by typhoons are also ameliorated. Besides, the analysis‐constrained regional predictions provide better model dynamic fields in terms of sea surface pressure, geopotential height, and water vapor transport, as well as developed typhoon structures. In addition, the adaptive bias correction for radiance assimilation presents a stable performance under the influence of introducing extra background large‐scale fields. The results indicate that the large‐scale analysis constraint introduced in the hybrid EnVar takes advantages of the multiscale information from the global model and the regional model respectively, thus improving the final results of the predictions of multiple typhoons.

Assimilation of Additional Radiosonde Observation Helps Improve the Prediction of Typhoon-Related Rainfall in the Pearl River Delta

Abstract Typhoons frequently hit the Pearl River Delta (PRD), threatening the region’s dense population and assets. Typhoon precipitation forecasting in this region is challenging, in part because of the complex hydrometeorological effects over the coast and the scarcity of upstream marine meteorological observations. Typhoon Mun was formed in the South China Sea on 2 July 2019, and it brought heavy rainfall to the PRD when its center moved to the Beibu Gulf. During Typhoon Mun, an additional sounding was conducted offshore in the PRD every 12 h to assess the incremental impact on the skill of precipitation forecasting. A precipitation prediction based on the Weather Research and Forecasting (WRF) Model underestimated the 12-h accumulated precipitation over the PRD by 87%, with the Final Analysis (FNL) data from the National Centers for Environmental Prediction in the United States as initial fields. To address this issue, we implemented a solution by reconstructing the initial field through the assimilation of the additional radiosonde observations using the WRF three-dimensional variational (3D-Var) method. The prediction with the new initial fields reduced the rainfall underestimation by 24%. A difference analysis indicates that the planetary boundary layer scheme used in FNL underestimates the low-level temperature and humidity, especially after the rainfall peak. In contrast, assimilation gives a more realistic lower-tropospheric structure, significantly enhancing the moisture flux convergence around 925 hPa and divergence around 700 hPa around the PRD. Sensitivity experiments show that assimilating atmospheric thermal (i.e., temperature and humidity) profiles is more helpful than dynamic (wind) profiles in improving the rainfall prediction of the typhoon. Significance Statement The impact of typhoon-related precipitation in the Pearl River Delta (PRD) is significant. Improving the numerical forecast precision in this region poses challenges, partly due to the influences of land–sea thermal and topographic factors near the boundary layer, as well as the scarcity of upstream observational data. This study proposes a practical method to improve typhoon-related precipitation prediction from a case study of Typhoon Mun. By assimilating additional sounding observations, we obtain a more realistic structure of the lower atmosphere, better spatial patterns of water vapor fluxes, and, ultimately, better precipitation forecasts. The results of our study suggest that a more advanced observing system of vertical atmospheric structure, especially the thermal one, over the South China Sea is important for improving typhoon predictions.

Numerical simulation of upper ocean responses to Typhoon Maria (2018) in the Northwest Pacific: Behavior of sub-mesoscale processes

A deeper understanding of the upper ocean response to typhoons is beneficial to the further improvement of typhoon forecasting. However, because of model horizontal resolution limitations, we still do not understand how sub-mesoscale processes in the ocean may modulate the response of the upper ocean to typhoons. In this study, we set up one-way online nested parent (mesoscale resolving: Exp_9 km) and child (sub-mesoscale permitting: Exp_3 km) models to investigate how the 15–45-km sub-mesoscale processes in the ocean affected the thermal and dynamical response processes of the upper ocean to Super Typhoon Maria (2018). It was found that the sub-mesoscale processes and their induced vertical heat transport (VHT) were mainly concentrated at the eddy, and most of the sub-mesoscale processes and their accompanying VHT disappeared after the typhoon, indicating that typhoons may be destroyers of sub-mesoscale processes. The differences in the sea temperature response between Exp_3 km and Exp_9 km can reached 1.5 °C in the eddy-rich sea surface region and 3 °C at the thermocline. This thermal response difference could be attributed to the difference in vertical diffusion and the difference in total advection processes simulated by the two models. Furthermore, the sub-mesoscale VHT played an important role in causing smaller sea surface cooling (SSC) and larger subsurface warming to the right side of the typhoon, and considering more sub-mesoscale processes improved the simulation accuracy of the SSC on the right side of the typhoon. The difference in near-inertial kinetic energy (NIKE) and the difference in fD1 (a peak frequency approximately equal to the sum of f and D1) kinetic energy between the two experiments were mainly found in the eddy-rich region to the right side of the typhoon, accounting for, at most, 15% of Exp_3 km's NIKE and 50% of Exp_3 km's fD1 kinetic energy, respectively, and resolving more sub-mesoscale processes could limit the lateral propagation of NIKE, especially in warm eddy. The kinetic energy and vertical shear of the near-inertial currents (NICs) and fD1 currents of Exp_3 km were larger than those of Exp_9 km, suggesting that sub-mesoscale processes may enhance ocean vertical mixing. Moreover, sub-mesoscale processes promoted the downward propagation of NIKE, which may be of great significance for promoting NICs-induced ocean mixing into the deep ocean and accelerating the decay of NICs. This work is of great importance for improving the understanding of typhoon-induced thermal and dynamical responses, and thus for improving the typhoon prediction.

USFP: An unbalanced severe typhoon formation prediction framework based on transfer learning

IntroductionSevere typhoons, as extreme weather events, can cause a large number of casualties and property damage in coastal areas. There are mainly three kinds of methods for the prediction of severe typhoon formation, which are the numerical-based methods, the statistical-based methods, and the machine learning-based methods. However, existing methods do not consider the unbalance between the number of ordinary typhoon samples and severe typhoon samples, which makes the accuracies of existing methods in the prediction of severe typhoons much lower than that of ordinary typhoons.MethodsIn this paper, we propose an unbalanced severe typhoon formation prediction (USFP) framework based on transfer learning. We first propose a severe typhoon pre-learning model which is used to learn prior knowledge from a constructed balanced dataset. Then, we propose an unbalanced severe typhoon re-learning model which utilizes the prior knowledge learning from the pre-learning model. Our USFP framework fuses three different variables, which are atmospheric variables, sea surface variables, and ocean hydrographic variables.ResultsExtensive experiments based on datasets of three different regions show that our USFP framework outperforms the numerical model IFS of ECMWF and existing machine learning methods.

Simultaneous Observations of Atmosphere and Ocean Directly under Typhoons Using Autonomous Surface Vehicles

This paper presents experimental observations to improve typhoon prediction accuracy and to understand interactions between atmosphere and ocean directly under typhoons. Two unmanned surface vehicles (Wave Gliders (WGs)) equipped with interchangeable sensors were sailed toward the path of an approaching Category 5 typhoon (Hinnamnor), which began on 29 August 2022 and subsided on 6 September, reaching a minimum pressure of 920 hPa and a maximum wind speed of 55 m/s (105 knots). Sensors on WGs measured atmospheric pressure, wind speed, atmospheric and seawater temperature, wave height, currents, salinity, and chlorophyll-a concentrations in different parts of the typhoon. These observations made it possible to clarify changes in various phenomena as the typhoon approached and to compare differences in storm characteristics measured by the two WGs. Sea surface pressure in the core of a typhoon is useful as an initial predictor of its intensity. Data assimilation into numerical models and other observations are expected to improve prediction accuracy of typhoon phenomena. Furthermore, simultaneous observations of atmosphere and ocean will also be useful for modeling interactions.

Strategy for the Prediction of Typhoon Wind and Storm Surge Height Using the Parametric Typhoon Model: Case Study for Hinnamnor in 2022

The parametric typhoon model is a powerful typhoon prediction and reproduction tool with advantages in accuracy, and computational speed. To simulate typhoons’ horizontal features, the longitude and latitude of the typhoon center, central pressure, radius of maximum wind speed (Rmax), and background states (such as surface air pressure and wind speed) are required. When a typhoon approaches or is predicted to affect Korea, the Korea Meteorological Agency (KMA) notifies the above-mentioned parameters, except for the Rmax and background state. The contribution of background wind and pressure is not very significant; however, Rmax is essential for calculating typhoon winds. Therefore, the optimized Rmax for the typhoons over the past five years was estimated at each time step compared with the in situ wind observation record. After that, a fifth-order polynomial fitting was performed between the estimated Rmax and the radius of strong wind (RSW; >15 m/s) provided by the KMA. Finally, the Rmax was calculated from the RSW via the empirical equation, and the horizontal fields of typhoon Hinnamnor (2211) were reproduced using a parametric model. Furthermore, the ocean storm surge height was adequately simulated in the surge model.

SASTA-Net: self-attention spatiotemporal adversarial network for typhoon prediction

To solve the problems of poor authenticity and lack of clarity for short-time typhoon prediction, we propose a self-attentional spatiotemporal adversarial network (SASTA-Net). First, we introduce a multispatiotemporal feature fusion method to fully extract and fuse the multichannel spatiotemporal feature information to effectively enhance feature expression. Second, we propose an SATA-LSTM prediction model that incorporates spatial memory cell and attention mechanisms in order to capture spatial features and important details in sequences. Finally, a spatiotemporal 3D discriminator is designed to correctly distinguish the generated predicted cloud image from the real cloud image and generate a more accurate and real typhoon cloud image by adversarial training. The evaluation results on the typhoon cloud image data set show that the proposed SASTA-Net achieves 67.3, 0.878, 31.27, and 56.48 in mean square error, structural similarity, peak signal to noise ratio, and sharpness, respectively, which is superior to the most advanced prediction algorithm.

Impacts of FY-4A AGRI Radiance Data Assimilation on the Forecast of the Super Typhoon “In-Fa” (2021)

This study assessed the impact of assimilating the Fengyun-4A (FY-4A) Advanced Geosynchronous Radiation Imager (AGRI) observations on the Super Typhoon “In-Fa” event based on the Weather Research and Forecasting Data Assimilation (WRFDA) system of the three-dimensional variational data assimilation (3DVAR) method. It was found that the two water vapor channels 9–10 from the full-disk AGRI datasets yield relatively stable results in terms of the track forecast of In-Fa. A new cloud-detection method using a Particle Filter (PF) was firstly employed to remove the cloud-affected observations by identifying the channel’s weighting function. Compared to the other cloud-detection schemes based on the AGRI “Cloud_Binary_Mask” (CLM) products, the PF method is conducive to reducing the track error of typhoon prediction after improving the utilization of observations under clear-sky conditions. Furthermore, the proposed cycling assimilation scheme has a potential positive effect on the intensity forecast of In-Fa. It seems that assimilating the FY-4A AGRI radiance data improves the predictability of Typhoon In-Fa by adjusting the atmospheric environment.

Evaluation of a Regional Ensemble Data Assimilation System for Typhoon Prediction

An ensemble Kalman filter (EnKF) combined with the Advanced Research Weather Research and Forecasting model (WRF) is cycled and evaluated for western North Pacific (WNP) typhoons of year 2016. Conventional in situ data, radiance observations, and tropical cyclone (TC) minimum sea level pressure (SLP) are assimilated every 6 h using an 80-member ensemble. For all TC categories, the 6-h ensemble priors from the WRF/EnKF system have an appropriate amount of variance for TC tracks but have insufficient variance for TC intensity. The 6-h ensemble priors from the WRF/EnKF system tend to overestimate the intensity for weak storms but underestimate the intensity for strong storms. The 5-d deterministic forecasts launched from the ensemble mean analyses of WRF/EnKF are compared to the NCEP and ECMWF operational control forecasts. Results show that the WRF/EnKF forecasts generally have larger track errors than the NCEP and ECMWF forecasts for all TC categories because the regional simulation cannot represent the large-scale environment better than the global simulation. The WRF/EnKF forecasts produce smaller intensity errors and biases than the NCEP and ECMWF forecasts for typhoons, but the opposite is true for tropical storms and severe tropical storms. The 5-d ensemble forecasts from the WRF/EnKF system for seven typhoon cases show appropriate variance for TC track and intensity with short forecast lead times but have insufficient spread with long forecast lead times. The WRF/EnKF system provides better ensemble forecasts and higher predictability for TC intensity than the NCEP and ECMWF ensemble forecasts.

Response of Temperature to Successive Typhoons in the South China Sea

Typhoons are serious natural disasters in coastal areas. During the summer of 2011, successive typhoons Nesat and Nalgae appeared in the South China Sea, providing a unique opportunity for us to study the response of the upper ocean to successive typhoons. We comprehensively use satellite data and COAWST model data to explore the effects of successive typhoons on the temperature structure of the South China Sea. Nesat caused the sea surface temperature to decrease by up to 4.4 °C on the right side of the typhoon path, and the ensuing Nalgae caused the temperature to decrease by up to 2.2 °C. Because Nesat had already cooled the ocean, the response to Nalgae was more to the left of the track than one would normally expect. The upwelling dominates the change in subsurface temperature. Based on the increase caused by Nesat, the isotherm was further raised by Nalgae. The isotherm rising amplitude is larger in the upper and deeper layer and is smaller in the middle layer in the depth range of 0–200 m. Heat budget analysis indicates that in the area close to the typhoon path, vertical diffusion is the main reason for the decrease in ocean surface temperature, while total advection suppresses the decrease in temperature. In the area with a larger distance from the typhoon path, vertical diffusion and total advection lead to the decrease in ocean surface temperature, and total advection will gradually contribute more to temperature change and become the dominant factor. On the right side of the typhoon track, the reduction of the contribution rate of vertical diffusion with distance from typhoon track is slower than that on the left side of the typhoon track. Whether Nesat or Nalgae, the intensity and depth of effects of vertical diffusion on the right side of typhoon path are greater than those on the left side of typhoon path, and the near-inertial periodic oscillation of local temperature change rate is more obvious. When the vertical diffusion is weak, the influence of vertical advection and horizontal advection is deeper. Moreover, the near-inertial periodic oscillation of the local temperature change occurs in lower depth after Nalgae passed through than that after Nesat. The typhoon intensity of the two typhoons shows the opposite change: the first typhoon increases, and the second typhoon weakens. Therefore, the special case of successive typhoons should be fully considered in typhoon prediction to improve accuracy.

Improving typhoon predictions by assimilating the retrieval of atmospheric temperature profiles from the FengYun-4A's Geostationary Interferometric Infrared Sounder (GIIRS)

The Geostationary Interferometric Infrared Sounder (GIIRS) onboard China's FengYun-4A geostationary satellite provides an unprecedented opportunity to observe the three-dimensional thermodynamic structure of typhoons in the western North Pacific Ocean with high spatiotemporal resolutions. Field campaigns of targeting observation based on the conditional nonlinear optimal perturbation (CNOP) sensitivity were carried out utilizing the GIIRS on FY-4A for five typhoons: Chan-hom, Maysak, and Higos in 2020 and typhoons Chanthu and Conson in 2021 and collected temporally continuous observations for the thermodynamic structure of typhoons and their environments.This study investigated the impact of atmospheric temperature profile retrieved from the collected GIIRS radiance on typhoon analysis and prediction in a regional Hurricane WRF (HWRF) model using the ensemble-variational data assimilation scheme. Despite the case dependence, the assimilation of the additional satellite retrieval generally improves the typhoon track forecasts relative to those with simply the operational observations assimilated. The improvement mainly occurs beyond two days and the average reduction of track errors reaches up to 100 km at about day 3. Diagnostics show that the improvement of initial temperature condition influences the geopotential height and wind fields roughly through the hydrostatic relationship. The assimilation of temperature retrieval also shows some potential in improving wind forecasts in the near-coast areas at upper and lower levels and the precipitation forecasts on land.

Seasonal prediction of typhoons approaching the Korean Peninsula using several statistical methods

Seasonal prediction of typhoons approaching the Korean Peninsula using several statistical methods

An improved typhoon simulation method based on Latin hypercube sampling method

In order to further improve the prediction accuracy of typhoon simulation method for extreme wind speed in typhoon prone areas, an improved typhoon simulation method is proposed by introducing the Latin hypercube sampling method into the traditional typhoon simulation method. In this paper, the improved typhoon simulation method is first given a detailed introduction. Then, this method is applied to the prediction of extreme wind speeds under various return periods in Hong Kong. To validate this method, two aspects of analysis is carried out, including correlation analysis among typhoon key parameters and prediction of extreme wind speeds under various return periods. The results show that the correlation coefficients among typhoon key parameters can be maintained satisfactorily with this improved typhoon simulation method. The results show that the improved typhoon simulation method can generate the correlations among all typhoon key parameters satisfactorily. Compared with the traditional typhoon simulation method, the improved typhoon simulation method has higher accuracy in predicting the typhoon extreme wind speed in Hong Kong, increasing by about 8% and 11% respectively at 200 m height and gradient height.

Comparison of the Potential Impact to the Prediction of Typhoons of Various Microwave Sounders Onboard a Geostationary Satellite

A microwave radiometer onboard a geostationary satellite can provide for the continuous atmospheric sounding of rapidly evolving convective events even in the presence of clouds, which has aroused great research interest in recent decades. To approach the problem of high-spatial resolution and large-size antennas, three promising geostationary microwave (GEO-MW) solutions—geostationary microwave radiometer (GMR) with a 5 m real aperture antenna, geostationary synthetic thinned aperture radiometer (GeoSTAR) with a Y-shaped synthetic aperture array, and geostationary interferometric microwave sounder (GIMS) with a rotating circular synthetic aperture array—have been proposed. To compare the potential impact of assimilating the three GEO-MW sounders to typhoon prediction, observing system simulation experiments (OSSEs) with the simulated 50–60 GHz observing brightness temperature data were conducted using the mesoscale numerical model Weather Research and Forecasting (WRF) and WRF Date Assimilation-Four dimensional variational (WRFDA-4Dvar) assimilation system for Typhoons Hagibis and Bualoi which occurred in 2019. The results show that the assimilation of the three GEO-MW instruments with 4 channels of data at 50–60 GHz could lead to general positive impacts in this study. Compared with the control experiment, for the two cases of Bualoi and Hagibis, GMR improves the average 72 h typhoon track forecast accuracy by 24% and 43%, GeoSTAR by 33% and 50%, and GIMS by 10% and 29%, respectively. Overall, the three GEO-MW instruments show considerable promise in atmospheric sounding and data assimilation. The difference among these positive impacts seems to depend on the observation error of the three potential instruments. GeoSTAR is slightly better than the other two GEO-MW sounders, which may be because it has the smallest observation error of the 4 assimilation channels. Generally, this study illustrates that the performance of these three GEO-MW sounders is potentially adequate to support assimilation into numerical weather prediction models for typhoon prediction.