Typhoon Forecast Research Articles

Assessment of FY-3D SST Data on Typhoon In-Fa Simulation

Sea surface temperature (SST) plays an important role in the typhoon forecast. By comparison, satellite retrieval products can provide more accurate SST data than reanalysis data in typhoon simulations. However, the effect of SST data from Chinese Fengyun-3 (FY-3) satellites on typhoon simulation hasn’t been evaluated yet. In this paper, the impact of FY-3D SST retrieval data with ascending and descending orbits on the forecast of typhoon In-Fa, 2021, is investigated with the Weather Research Forecast (WRF) model. Compared to the control experiments with SST data from Global Data Assimilation System (GDAS) data, the replacement experiments with FY-3D SST data significantly improve the forecast of typhoon central sea level pressure, track, and precipitation. Especially, the SST from the descending orbit satellite data provides the optimal track and intensity forecast, which are verified against the best track data from the Japan Meteorological Agency.

Sensitivity of Typhoon Forecast to Prescribed Sea Surface Temperature Data

This study investigates the impact of the sea surface temperature (SST) on the forecast of two typhoons, which consecutively hit South Korea in 2020. SST data were obtained from the Daily Optimum Interpolation Sea Surface Temperature (OISST) version 2 and HYbrid Coordinate Ocean Model/Navy Coupled Ocean Data Assimilation (HYCOM/NCODA; GLBy0.08/expt_93.0). When verified using in situ observational data, the OISST data did not accurately estimate the changes in SST during each typhoon’s landfall period compared to the HYCOM data since it has a relatively low temporal resolution. To investigate the impact of these two SST data on typhoon forecasts, we conducted sensitivity experiments using the Weather Research and Forecasting (WRF) model. The results showed that simulated typhoon intensities were significantly improved in the simulations with HYCOM data (HY runs), while typhoon track forecast performances were similar in both runs. In addition, the forecast performances of the maximum wind speed at 10 m during the typhoon landfall period were improved in the HY runs. Therefore, this study showed that the overall typhoon intensity and forecast performances during the landfall period could be improved when the higher temporal-resolution SST data were prescribed in the model boundary conditions for a better representation of typhoon-induced SST changes.

Typhoon Forecasts with Dynamic Vortex Initialization Using an Unstructured Mesh Global Model

Abstract A dynamical vortex initialization (DVI) scheme is implemented on unstructured meshes for the global model MPAS for typhoon forecasts. The DVI extracts the departure vortex within a specified radius of the vortex center and implants this vortex at the observed vortex location in continuously cycled 1-h integrations of the model. The cycling integration is stopped when either the simulated central sea level pressure or maximum wind speed of the typhoon has reached the value in the best track data, denoted as P-match or V-match, respectively. The DVI may spin up the initial vortex with a more contracting eyewall, but still keeping the same size of the outer vortex. Forecasts for 16 typhoons over the western North Pacific in 2015–20 are investigated. Predictions from the experiments with the 60–15-km variable-resolution MPAS mesh show that both P-match and V-match significantly improve the track forecasts, where V-match mostly requires less cycle runs than P-match. Cycling results with P-match or V-match are also dependent on the choice of physics suites within MPAS. Positive impacts are larger for V-match than P-match using the mesoscale reference physics suite, with significantly improved track forecasts and earlier intensity forecasts. Intensity differences resulting from the DVI have gradually decreased with forecast time, which are closely correlated to the differences in the averaged tropospheric potential vorticity of the inner vortex. The DVI with the 60–15–3-km variable-resolution mesh also works well and improves intensity forecasts. The DVI can also help produce asymmetric structures and spin up inner vortex cores for typhoons near high topography, which leads to improved intensity forecasts.

Improvement of Typhoon Intensity Forecasting by Using a Novel Spatio-Temporal Deep Learning Model

Typhoons can cause massive casualties and economic damage, and accurately predicting typhoon intensity has always been a hot topic both in theory and practice. In consideration with the spatial and temporal complexity of typhoons, machine learning methods have recently been applied in typhoon forecasting. In this paper, we attempt to improve typhoon intensity forecasting by treating it as a spatio-temporal problem in the deep learning field. In particular, we propose a novel typhoon intensity forecasting model named the Typhoon Intensity Spatio-temporal Prediction Network (TITP-Net). The proposed model takes multidimensional environmental variables and physical factors of typhoons into account and fully extracts the information from the datasets by capturing spatio-temporal dependencies with a spatial attention module, which includes two-dimensional and three-dimensional convolutional operations. A series of experiments with a comprehensive framework by using TITP-Net are conducted. The MAEs of the forecasts with 18, 24, 36 and 48 h lead time obtain a significant improvement by 7.02%, 6.53%, 6.25% and 5.37% compared with some existing deep learning models and dynamical models from official agencies.

Sensitivity analysis of the physics options in the Weather Research and Forecasting model for typhoon forecasting in Japan and its impacts on storm surge simulations

ABSTRACT Weather Research and Forecasting (WRF) model is useful for forecasting typhoons as an external force of storm surge forecasts. This study examines the variation in typhoon forecasts caused by different choices of arbitrary physics options in WRF and their influence on storm surge forecasts. Eight frequently used combinations of cloud microphysics and planetary boundary layers were extracted via a review of previous studies. Subsequently, sensitivity analyses of these physics options were performed, targeting nine typhoons that landed in Japan during 2015–2019. Additionally, we conducted case studies of storm surge ensemble forecasts in Tokyo Bay and Osaka Bay using WRF-simulated typhoons generated in the sensitivity analysis. As a result, the ensemble mean of the forecasts was comparable to the storm surge reanalysis simulation results obtained using an empirical typhoon model wherein the best track data is integrated to reproduce atmospheric fields. This may be attributed to the fact that the typhoon parameters (intensity, size, approaching angle, and velocity) obtained from the best track at landfall were generally within the range of the parameters that were simulated using WRF.

Impact-based forecasting for improving the capacity of typhoon-related disaster risk reduction in typhoon committee region

The two most common types of disasters caused by natural hazards in the Asia-Pacific region are floods and storms, many of them associated with typhoon (tropical cyclone) related impacts. To improve the capacity of typhoon-related disaster risk reduction so as to maximum reduce the losses of people’s life and properties, the decision makers and the public are imminently demanding the information of the targeted impact caused by typhoon. As the front line in hydro-meteorological disaster prevention and mitigation against the typhoon-related disasters, National Meteorological and Hydrological Services (NMHSs) in TC Members have recognized that forecasting impact became more important than forecasting pure causing-disaster elements. Impact-based forecasting signals an evolution from “what the weather will be” to “what the weather will do”. Many things change as impact based forecasts evolve from previous weather forecasts. To enhance impact-based typhoon forecasting, the Typhoon Committee added it into the new updated Strategic Plan 2022–2026. This paper briefed generally the concept of impact based forecasting, introduced the implementation and progresses on typhoon impact based forecasting in TC Members in recent years, and initially discussed the measures and direction for enhancement of impact-based typhoon forecasting and early warning services in future.

Impacts of Radio Occultation Data on Typhoon Forecasts as Explored by the Global MPAS-GSI System

Global Navigation Satellite System (GNSS) radio occultation (RO) provides plentiful sounding profiles over regions lacking conventional observations. The Gridpoint Statistical Interpolation (GSI) hybrid system for assimilating RO data is integrated in this study with the Model for Prediction Across Scales–Atmosphere (MPAS) to improve tropical cyclone forecasts. After the MPAS-GSI assimilation cycles, dynamical vortex initialization (DVI) that may effectively spin up the initial inner typhoon vortex through cycled model integration is implemented to improve the initial analysis fit to the best track position as well as maximum wind or pressure intensity for Typhoon Nepartak (2016) that moved northwestward toward southern Taiwan. During the cycling assimilation, assimilation with RO data improves the temperature and moisture analysis, and largely reduces the forecast errors compared to those without RO data assimilation. The two RO operators that assimilate local bending angle or refractivity produce similar analyses, but the temperature and moisture increments from bending angle assimilation are slightly larger than those from refractivity assimilation. The MPAS forecasts at 60-15 km resolution show that the typhoon track prediction is improved with RO data, especially using bending angle data. The reduction in track deviations is explained by the wavenumber-one potential vorticity budget for several forecasts associated with the track deflection near southern Taiwan. Assimilation of RO data has fewer impacts on the typhoon intensity forecast compared to the DVI that largely improves the initial and thus forecasted intensity of the typhoon but at the cost of a slightly degraded track. Use of the enhanced 3 km resolution in the typhoon path also further improved the forecasts with and without the DVI. The feasible performance of the MPAS-GSI system with the RO data impact is also illustrated for Typhoon Mitag (2019), that passed around northern Taiwan.

Offline Diagnostics of Skin Sea Surface Temperature from a Prognostic Scheme and Its Application in Typhoon Forecasting Using the CMA-TRAMS Model over South China

In the Tropical Regional Atmospherical Model System of South China of the China Meteorological Administration (CMA-TRAMS), the skin sea surface temperature (Ts) remains fixed during the forecast time. This limits the model’s performance in describing interactions between air and sea. The offline diagnostics and online analysis coupled with the CMA-TRAMS of Ts prognostic scheme were discussed. The results of the offline diagnostics showed that the profile shape parameter, ν, and initial temperature, Tb, were sensitive to the forecasted Ts. Based on our observations, when ν was set to 0.2 and Tb was the averaged Ts without obvious diurnal variation, the forecasted Ts was relatively reasonable. The forecasted Ts of CMA-TRAMS after coupling with the Ts scheme had diurnal variations during the overall forecast time, which was different from the fixed Ts from the uncoupled model. There existed a certain difference of forecasted Ts between uncoupled and coupled models in those days influenced by typhoons. The biases and Root Mean Square Errors (RMSEs) for the temperature and moisture in the lower layer and those for the wind speed in most layers were reduced and, therefore, the accuracy of environmental field forecasting was improved from the coupled model. The typhoon track errors after 36-h decreased due to the improvement of steering flow on the west side of subtropical high from the coupled model. However, the difference of typhoon intensity errors was insignificant, which might mean that the differences of forecasted Ts and heat flux between uncoupled and coupled model are small. The reasons for the small difference need to be further investigated.

Direct Assimilation of Radar Reflectivity Data Using Ensemble Kalman Filter Based on a Two-Moment Microphysics Scheme for the Analysis and Forecast of Typhoon Lekima (2019)

We investigate the impact of directly assimilating radar reflectivity data using an ensemble Kalman filter (EnKF) based on a double-moment (DM) microphysics parameterization (MP) scheme in GSI-EnKF data assimilation (DA) framework and WRF model for a landfall typhoon Lekima (2019). Observations from a single operational coastal Doppler are quality-controlled and assimilated. Compared with the baseline experiment initialized by GFS analysis, the reflectivity data assimilation experiment (Z-DA) resulted in an obvious improvement in both structural analysis and typhoon forecast skills in terms of intensity, precipitation, and track. Sensitivity experiments were conducted to evaluate the ability of EnKF to update certain state variables considering that the degree of freedom of analytical variables increased with a DM MP scheme. When either the total number concentration or other large-scale state variables that are not directly linked to reflectivity observations via the observation operator are not updated, the tendency of RMSIs and PS to be imbalanced is significantly increased during DA cycles compared to those of Z-DA with updating a full set of state variables, resulting in increased intensity and track forecast errors. These results indicate that the reliable ensemble covariance could handle the underconstraint issue associated with the DM scheme, and helps in obtaining more physically balanced analytical fields.

Extreme anomalies under El Niño and La Niña developing tendencies restrain subsequent severe typhoons in the western North Pacific

Forecasting typhoon features over a few upcoming months is necessary for planning disaster prevention and predicting the drought/flood situation. However, the current methods are more suitable for forecasting the interannual to interdecadal variation trends or demanding huge computing resources and are unsuitable for the operation of short-term climate forecasts. In order to devise other effective approaches, the correlation between the Oceanic Niño Index (ONI) in January and the frequency of severe typhoons (STYs) during 1959–2018 was analyzed for the two scenarios when the ONI trend during the previous year was higher and lower than zero. The reason for this classification is that the air‒sea conditions were different when the oceanic phenomena developed toward an El Niño or La Niña phase during the previous year, and the results of these trends affect the subsequent features of atmospheric dynamic factors favorable for TC development (relative vorticity and vertical velocity). The extreme anomalies under both the increasing and decreasing tendencies of ONI in January typically retrained the growth of STYs, as the subsequent decreasing ocean heat anomaly of the previous year was accompanied by a negative vorticity anomaly and downward vertical velocity anomaly in the lower atmosphere and vice versa. Results from the forecast test show that the accuracy of the new approach was acceptable and somewhat higher than that of the current methods. Thus, these results can assist in developing a practical approach for typhoon forecasts.

Data assimilation of adaptive observation and application for typhoon forecasts over the Western North Pacific

In this study, we added the post-processing module of the Ensemble Transform Sensitivity (ETS) to identify regions sensitive to adaptive observations of different variables at different levels for typhoon forecasts. We selected five cases between 2015 and 2020 over the Western North Pacific (WNP) using 50 ensemble forecasts from the European Center provided by the THORPEX Interactive Grand Global Ensemble (TIGGE) portal. Furthermore, intercomparison experiments were performed to explore the impact of adaptive observational data assimilation with conventional observations and Himawari-8 data over the sensitive (SEN), non-sensitive (NOSEN), and all studied regions (ALL) on typhoon forecasts using the Gridpoint Statistical Interpolation (GSI) assimilation system of the National Centers for Environmental Prediction (NCEP) of United States. The results show that the assimilation of less observational data in the SEN areas can achieve the forecast effect of assimilation in ALL regions, the intensity error can be reduced by approximately 15%, and the track error is reduced in some cases. Finally, detailed analysis of the circulation field and flux perspectives for Typhoon Lekima, which occurred in August 2019 over the WNP, confirmed our conclusion. At 1800 UTC on August 8, the data assimilation was performed over the SEN, NOSEN, and ALL areas. The initial circulation field, in which the vorticity increased in the southwest of the typhoon and decreased in the southeast, benefited the westward movement of the typhoon. The 18 h forecast indicated that the circulation in the SEN area assimilation experiment showed a negative vorticity increase in the southwest side of the typhoon and positive potential temperature as well as water vapor forecast differences on its north side, which contrasted with the results obtained in the NOSEN and ALL experiments. This provided favorable weather conditions for the typhoon to change direction and move northward. Targeted observation experiments investigating Typhoon Lekima indicated that assimilating observations of sensitive areas greatly improves typhoon forecasting and assimilation efficiency. However, the ALL experiment yielded the best forecast among the three cases.

Radiance-based assessment of bulk microphysics models with seven hydrometeor species in forecasting Super-typhoon Lekima (2019) near landfall

Extreme precipitation concerning typhoons brings great losses to coastal cities every year. How to accurately describe the natural cloud and precipitation processes in model remains a major challenge in typhoon forecasts. Observations found rain and hail can both exist under severe typhoon convections. But current operational forecasting models fail to take hail into consideration in super-typhoon prediction. This study assesses the performances of five typical bulk microphysical schemes (BMSs) of the Weather Research and Forecasting (WRF) model in the forecast of Super-typhoon Lekima (2019) near landfall. Unlike other microphysical schemes, all the selected BMSs in this study incorporate seven hydrometeor species including hail, which can represent the hydrometeor distribution within natural super-storm more realistically as supported by field observational facts. Through statistical-physical evaluation processes and using radar-satellite joint observations, the goal is to identify the best scheme to accurately forecast Super-typhoon Lekima and to find the future avenue to possible microphysical scheme improvement. It is found that the WRF double-moment 7-class (WDM7) scheme exhibits the smallest root mean square error (RMSE) of approximately 1.5 for both composite reflectivity and brightness temperature signatures, and predicts the closest macro-physical hydrometeors structure, amplitude and location to observations. Moreover, the azimuthal mean brightness temperature distributions suggest large forecasting errors over typhoon inner rainband, with WDM7 scheme performing best. The vertical reflectivity profiles further suggest that WDM7 scheme could well characterize the updraft, water content, and cold-cloud processes, but needs further improvement in the representation of warm-cloud processes. The performances of BMSs are found to be closely related to the moments used to describe the evolution of the drop size distribution. Among all the schemes, the double-moment schemes generally perform better in simulating warm-cloud processes, while the single-moment schemes show better performance in simulating cold-cloud processes. As a partially double-moment and partially single-moment scheme, WDM7 scheme exhibits unique advantages over the others. The optimum choice of moments is an important step toward the future improvement of BMS.

Assimilation of FY-3D MWTS-II Radiance with 3D Precipitation Detection and the Impacts on Typhoon Forecasts

Assimilation of FY-3D MWTS-II Radiance with 3D Precipitation Detection and the Impacts on Typhoon Forecasts

Discriminating Technique of Typhoon Rapid Intensification Trend Based on Artificial Intelligence

The trend detection of the sudden change of typhoon intensity has always been a difficult issue in typhoon forecast. Artificial intelligence (AI) can implicitly extract the complex features in the images through learning a large number of samples, and it has been widely applied in the meteorological field nowadays. In this study, based on the deep residual network (ResNet) model and the long short-term memory (LSTM) model, an automatic and objective method of identifying the trend of typhoon rapid intensification (RI) is presented through marking and learning the key information on the satellite images of the typhoons on the Northwest Pacific and South China Sea from 2005 to 2018. This method introduces the typhoon lifecycle indication and can effectively forecast and identify the trend of typhoon RI. By applying the detecting method in analyzing the operational typhoon satellite cloud images in 2019, we find that the method can well capture the sudden change tendency of typhoon intensity, and the threat score of independent sample estimation in 2019 reached 0.24. In addition, four typhoon cases with RI processes from 2019 to 2021 are tested, and the results show that the AI-based identification method of typhoon RI is superior to the traditional subjective intensity prediction method, and it has important application values.

Role of Salinity-Induced Barrier Layer in Air-Sea Interaction During the Intensification of a Typhoon

A pronounced increase in the intensification of Typhoon Bavi in 2020 was detected when the typhoon passed over the Changjiang plume in the northern East China Sea. Using a coupled atmosphere-ocean modeling system, this study investigates the role of the plume-induced barrier layer (BL) in the air–sea interaction during the intensification of a typhoon. Simple comparative experiments with and without the river plume revealed a strong relationship between BL formation and typhoon intensification as a result of the significant surface freshening discharged from the Changjiang River. The plume-induced BL maintained a warm sea surface before the typhoon approached, thereby influencing the energy transfer at the air–sea interface. The enthalpy and moisture reaching the atmosphere were increased by approximately 20%, leading to the intensification of Typhoon Bavi and providing further support for the results observed in the best-track record. The model comparison also indicates that the salinity-induced BL led to the reduction of the typhoon-induced SST cooling by restricting the vertical diffusion between the surface and the thermocline, and consequently contributed to maintaining the typhoon intensity. This study suggests that the effect of river-induced surface freshening in a coupled atmosphere-ocean model may help in improving typhoon forecasts and may aid in mitigating against the destructive power of typhoons in the future.

A time series image prediction method combining a CNN and LSTM and its application in typhoon track prediction.

Typhoon forecasting has always been a vital function of the meteorological department. Accurate typhoon forecasts can provide a priori information for the relevant meteorological departments and help make more scientific decisions to reduce the losses caused by typhoons. However, current mainstream typhoon forecast methods are very challenging and expensive due to the complexity of typhoon motion and the scarcity of ocean observation stations. In this paper, we propose a typhoon track prediction model, DeepTyphoon, which integrates convolutional neural networks and long short-term memory (LSTM). To establish the relationship between the satellite image and the typhoon center, we mark the typhoon center on the satellite image. Then, we use hybrid dilated convolution to extract the cloud features of the typhoon from satellite images and use LSTM to predict these features. Finally, we detect the location of the typhoon according to the predictive markers in the output image. Experiments are conducted using 13, 400 satellite images of time series of the Northwest Pacific from 1980 to 2020 and 8420 satellite images of time series of the Southwest Pacific released by the Japan Meteorological Agency. From the experimentation, the mean average error of the 6-hour typhoon prediction result is 64.17 km, which shows that the DeepTyphoon prediction model significantly outperforms existing deep learning approaches. It achieves successful typhoon track prediction based on satellite images.

Prediction of Typhoon Track and Intensity Using a Generative Adversarial Network With Observational and Meteorological Data

To save lives and reduce damage from the destructive impacts of a typhoon, an accurate and fast forecast method is highly demanded. Particularly, predictions for short lead times, known as nowcasting, rely on fast forecasts allowing immediate emergency plannings in the affected areas. In this paper, we propose a generative adversarial network that operates on a single graphics processing unit, to predict both the track and intensity of typhoons for short lead times within fractions of a second. To investigate the effects of meteorological variables on typhoon forecasts, we conducted a parameter study for 6-h track predictions. The results of the study indicate that learning velocity, temperature, pressure, and humidity along with satellite images have positive effects on prediction accuracy. To address the limited access to observational data and facilitate predictions for 12-h intervals, we replaced satellite images with reanalysis data of the total cloud cover and vorticity fields. This replacement led to an increase in data from 76 to 757 typhoons, and it reduced the error of the 6-h track forecasts by 23.5%. The best combination of the parameter study yields track predictions in intervals of 6 and 12 h with the corresponding averaged absolute errors of 44.5 and 68.7 km. Typhoon intensities are predicted by extracting information from generated velocity fields with averaged hit rates of 87.3% and 83.2% for 6- and 12-h interval forecasts, respectively. For typhoons after 1994, tracks and intensities for 12-h intervals are compared to forecasts from the Joint Typhoon Warning Center and Regional Specialized Meteorological Center Tokyo.

Updated Scheme for Data Assimilation of Adaptive Observation And Application for Typhoon Forecasts Over the Western North Pacific

Updated Scheme for Data Assimilation of Adaptive Observation And Application for Typhoon Forecasts Over the Western North Pacific

Media History of Typhoon Forecast: Experiences of Watching Typhoons on Television

Media History of Typhoon Forecast: Experiences of Watching Typhoons on Television

Impact of High Temporal Resolution FY‐4A Geostationary Interferometric Infrared Sounder (GIIRS) Radiance Measurements on Typhoon Forecasts: Maria (2018) Case With GRAPES Global 4D‐Var Assimilation System

AbstractTargeted observations for Typhoon Maria (2018) using the Geostationary Interferometric Infrared Sounder (GIIRS) at a temporal resolution of 15 min provide unprecedented information for impact studies of high‐temporal‐resolution from geostationary hyperspectral IR sounders. This study investigates the impacts of different temporal resolutions for GIIRS assimilation on Maria analyses and forecasts. Results reveal that assimilating higher resolution data captures more detailed temporal and spatial structures and stronger warm anomalies. Additionally, the track forecast for Maria from assimilating higher temporal resolution GIIRS radiances is better than those from assimilating radiances with lower temporal resolution, and both are better than the control experiment. The accuracy of the different temporal resolutions GIIRS experiments (from 3 h to 15 min) is improved (from 18% to 43%), and the intensity forecast errors are also reduced (from 12% to 18%). Meanwhile, high‐temporal GIIRS assimilation also improves the coastal precipitation forecasts during typhoon landfall.