Systematic bias is a type of model error that can affect the accuracy of data assimilation and forecasting that must be addressed.An online bias correction scheme called the sequential bias correction scheme(SBCS),was...Systematic bias is a type of model error that can affect the accuracy of data assimilation and forecasting that must be addressed.An online bias correction scheme called the sequential bias correction scheme(SBCS),was developed using the6 h average bias to correct the systematic bias during model integration.The primary purpose of this study is to investigate the impact of the SBCS in the high-resolution China Meteorological Administration Meso-scale(CMA-MESO)numerical weather prediction(NWP)model to reduce the systematic bias and to improve the data assimilation and forecast results through this method.The SBCS is improved upon and applied to the CMA-MESO 3-km model in this study.Four-week sequential data assimilation and forecast experiments,driven by rapid update and cycling(RUC),were conducted for the period from 2–29 May 2022.In terms of the characteristics of systematic bias,both the background and analysis show diurnal bias,and these large biases are affected by complex underlying surfaces(e.g.,oceans,coasts,and mountains).After the application of the SBCS,the results of the data assimilation show that the SBCS can reduce the systematic bias of the background and yield a neutral to slightly positive result for the analysis fields.In addition,the SBCS can reduce forecast errors and improve forecast results,especially for surface variables.The above results indicate that this scheme has good prospects for high-resolution regional NWP models.展开更多
Unresolved small-scale orographic（SSO） drags are parameterized in a regional model based on the Global/Regional Assimilation and Prediction System for the Tropical Mesoscale Model（GRAPES TMM）. The SSO drags are re...Unresolved small-scale orographic（SSO） drags are parameterized in a regional model based on the Global/Regional Assimilation and Prediction System for the Tropical Mesoscale Model（GRAPES TMM）. The SSO drags are represented by adding a sink term in the momentum equations. The maximum height of the mountain within the grid box is adopted in the SSO parameterization（SSOP） scheme as compensation for the drag. The effects of the unresolved topography are parameterized as the feedbacks to the momentum tendencies on the first model level in planetary boundary layer（PBL）parameterization. The SSOP scheme has been implemented and coupled with the PBL parameterization scheme within the model physics package. A monthly simulation is designed to examine the performance of the SSOP scheme over the complex terrain areas located in the southwest of Guangdong. The verification results show that the surface wind speed bias has been much alleviated by adopting the SSOP scheme, in addition to reduction of the wind bias in the lower troposphere. The target verification over Xinyi shows that the simulations with the SSOP scheme provide improved wind estimation over the complex regions in the southwest of Guangdong.展开更多
Construction of high-order difference schemes based on Taylor series expansion has long been a hot topic in computational mathematics, while its application in comprehensive weather models is still very rare. Here, th...Construction of high-order difference schemes based on Taylor series expansion has long been a hot topic in computational mathematics, while its application in comprehensive weather models is still very rare. Here, the properties of high-order finite difference schemes are studied based on idealized numerical testing, for the purpose of their application in the Global/Regional Assimilation and Prediction System(GRAPES) model. It is found that the pros and cons due to grid staggering choices diminish with higher-order schemes based on linearized analysis of the one-dimensional gravity wave equation. The improvement of higher-order difference schemes is still obvious for the mesh with smooth varied grid distance. The results of discontinuous square wave testing also exhibits the superiority of high-order schemes. For a model grid with severe non-uniformity and non-orthogonality, the advantage of high-order difference schemes is inapparent, as shown by the results of two-dimensional idealized advection tests under a terrain-following coordinate. In addition, the increase in computational expense caused by high-order schemes can be avoided by the precondition technique used in the GRAPES model. In general, a high-order finite difference scheme is a preferable choice for the tropical regional GRAPES model with a quasi-uniform and quasi-orthogonal grid mesh.展开更多
Landfalling typhoons can cause disasters over large regions.The government and emergency responders need to take measures to mitigate disasters according to the forecast of landfall position,while slight timing error ...Landfalling typhoons can cause disasters over large regions.The government and emergency responders need to take measures to mitigate disasters according to the forecast of landfall position,while slight timing error can be ignored.The reliability of operational model forecasts of typhoon landfall position needs to be evaluated beforehand,according to the forecasts and observation of historical cases.In the evaluation of landfalling typhoon track,the traditional method based on point-to-point matching methods could be influenced by the predicted typhoon translation speed.Consequently,the traditional track evaluation method may result in a large track error even if the predicted landfall position is close to observation.The purpose of this paper is to address the above issue using a simple evaluation method of landfalling typhoon track forecast based on the time neighborhood approach.In this new method,the timing error was lessened to highlight the importance of the position error during the landfall of typhoon.The properties of the time neighborhood method are compared with the traditional method based on numerical forecast results of 12 landfalling typhoon cases.Results demonstrated that the new method is not sensitive to the sampling frequency,and that the difference between the time neighborhood and traditional method will be more obvious when the moving speed of typhoon is moderate(between 15−30 km h^(−1)).The time neighborhood concept can be easily extended to a broader context when one attempts to examine the position error more than the timing error.展开更多
Given the interpretability,accuracy,and stability of numerical weather prediction(NWP)models,current operational weather forecasting relies heavily on the NWP approach[1].In the past two years,the rapid development of...Given the interpretability,accuracy,and stability of numerical weather prediction(NWP)models,current operational weather forecasting relies heavily on the NWP approach[1].In the past two years,the rapid development of Artificial Intelligence(AI)has provided an alternative solution for medium-range(1-10 d)weather forecasting.展开更多
Although business groups are prevalent globally,limited attention is paid to how financial pressure transmission between parent and subsidiary firms elicits tax avoidance.We explore the impact of parent firms’dividen...Although business groups are prevalent globally,limited attention is paid to how financial pressure transmission between parent and subsidiary firms elicits tax avoidance.We explore the impact of parent firms’dividend policy on their subsidiaries’tax strategy using China’s mandatory dividend policy as a quasi-natural experiment.We find that parent firm dividends elevate tax avoidance among their subsidiaries.Mechanism tests show that parent firms transmit the pressure of paying dividends to their subsidiaries,compelling them to adopt tax avoidance strategies to alleviate the pressure.The effect is more pronounced among subsidiaries facing greater dividend pressure and external financing constraints and operating in weaker corporate governance environments.Finally,subsidiaries engaging in greater tax avoidance subsequently pay higher dividends.Our findings highlight how intra-group financial dynamics influence tax avoidance among subsidiaries and the significance of financial pressure transmission from parent firms to subsidiaries.展开更多
The operational Tropical Regional Atmospheric Model System(TRAMS)often underestimates initial typhoon intensity when using the global analysis field as the initial condition.The TRAMS tropical cyclone(TC)initializatio...The operational Tropical Regional Atmospheric Model System(TRAMS)often underestimates initial typhoon intensity when using the global analysis field as the initial condition.The TRAMS tropical cyclone(TC)initialization scheme,developed based on the incremental analysis updates(IAU)technique,effectively reduces initial bias.However,the original IAU-based TC initialization scheme only adjusts the wind field at the analysis moment,with other variables adjusted implicitly under the model's constraints according to a gradually inserted wind increment(named“univariate adjustment scheme”hereafter).The univariate adjustment scheme requires approximately 3 h to reach a dynamic equilibrium state,which constrains the assimilation of hourly TC observations and causes excessive dissipation of meaningful short-wave information in adjustment increments.To address this limitation,this study develops a multivariate adjustment IAU-based TC initialization scheme that incorporates gradient wind balance and hydrostatic balance as its largescale constraints.Numerical experiments with TC Hato(2017)demonstrate that the multivariate adjustment scheme reduces the IAU relaxation time to 1 h while marginally improving forecast skill.These findings are consistently replicated across 12 additional TC cases.The development of the IAU-based multivariate adjustment initialization scheme establishes a foundation for 4-D initialization using hourly TC observations.展开更多
In traditional simulations of heavy rainfall events, the regional model is often initialized by using a global reanalysis dataset and a cold start method. An alternative to using global analysis data is to gradually i...In traditional simulations of heavy rainfall events, the regional model is often initialized by using a global reanalysis dataset and a cold start method. An alternative to using global analysis data is to gradually introduce the analysis field via an incremental analysis update(IAU) method under the replay configuration. We found substantial differences in the forecast of a heavy rainfall event in southern China between a precipitation forecast using the traditional method and a forecast using the IAU method in the Tropical Regional Atmospheric Modeling System(TRAMS),based on the ECMWF global analysis. The IAU method is efficient in removing spurious high-frequency gravity wave noise, especially when the relaxation time is more than 90 min. The regional model needs to be pre-integrated for about 12 h to warm up the convective system in the background field. The improvement by the IAU method is supported by verification of simulations over 1 month(1–30 April 2019). In general, the IAU technique improves the initialization and spin-up process in the simulation of the heavy rainfall event.展开更多
Variations in the initial structure of tropical cyclones(TCs) inevitably affect prediction results;however, the bogus model cannot accurately describe the structure of a weak tropical cyclone with increased initial fi...Variations in the initial structure of tropical cyclones(TCs) inevitably affect prediction results;however, the bogus model cannot accurately describe the structure of a weak tropical cyclone with increased initial field resolution. This study aims to construct a model to improve the prediction of weak TC in southern China. Based on the ECMWF 0.1° analysis data, several vortices were filtered out from tropical depressions and tropical storms in 2018 and 2019 to represent a weak TC reservoir in the South China Sea. For different simulation objects, filtered vortices were combined with the TC environmental field to form ensemble members. The observed TC information was assimilated for simulating TCs Bebinca, Mun, and Ewiniar to verify the feasibility of the proposed model, based on the Global/Regional Assimilation and Prediction Enhanced System(GRAPES) 9-km model developed by the Guangzhou Institute of Tropical and Marine Meteorology. The results show that the initialization scheme of the weak tropical cyclone model improved the intensity prediction of the TC by 26.81%(Bebinca), 18.65%(Mun), and 47.00%(Ewiniar), compared with the control experiment. Because typhoon intensity forecasting has not notably improved for many years, this scheme has certain scientific and operational significance.展开更多
With the advent of the phased array radar(PAR)technology,it is possible to capture the development and evolution of convective systems in a much shorter time interval and with higher spatial resolution than via tradit...With the advent of the phased array radar(PAR)technology,it is possible to capture the development and evolution of convective systems in a much shorter time interval and with higher spatial resolution than via traditional Doppler radar.Research on the assimilation of PAR observations in numerical weather prediction models is still in its infancy in China.In this paper,the impact of assimilating PAR data on model forecasts was investigated by a case study of a local heavy rainfall event that occurred over Foshan city of Guangdong Province on 26 August 2020,via a series of sensitivity experiments.Both the retrieved three-dimensional wind and hydrometeor fields were assimilated through the nudging method with the Tropical Regional Assimilation Model for South China Sea_Rapid Update Cycle_1km(TRAMS_RUC_1km).The temperature and moisture fields were also adjusted accordingly.The results show that significant improvements are made in the experiments with latent heat nudging and adjustment of the water vapor field,which implies the importance of thermodynamic balance in the initialization of the convective system and highlights the need to assimilate PAR radar observations in a continuous manner to maximize the impact of the data.Sensitivity tests also indicate that the relaxation time should be less than 5 min.In general,for this case,the assimilation of PAR data can significantly improve the nowcasting skill of the regional heavy precipitation.This study is the first step towards operational PAR data assimilation in numerical weather prediction in southern China.展开更多
Each physical process in a numerical weather prediction(NWP)system may have many different parameterization schemes.Early studies have shown that the performance of different physical parameterization schemes varies w...Each physical process in a numerical weather prediction(NWP)system may have many different parameterization schemes.Early studies have shown that the performance of different physical parameterization schemes varies with the weather situation to be simulated.Thus,it is necessary to select a suitable combination of physical parameterization schemes according to the variation of weather systems.However,it is rather difficult to identify an optimal combination among millions of possible parameterization scheme combinations.This study applied a simple genetic algorithm(SGA)to optimizing the combination of parameterization schemes in NWP models for typhoon forecasting.The feasibility of SGA was verified with the simulation of Typhoon Mujigae(2015)by using the Weather Research and Forecasting(WRF)model and Typhoon Higos(2020)by using the Coupled Ocean–Atmosphere–Wave–Sediment Transport(COAWST)modeling system.The results show that SGA can efficiently obtain the optimal combination of schemes.For Typhoon Mujigae(2015),the optimal combination can be found from the 1,304,576 possible combinations by running only 488 trials.Similar results can be obtained for Typhoon Higos(2020).Compared to the default combination proposed by the COAWST model system,the optimal combination scheme significantly improves the simulation of typhoon track and intensity.This study provides a feasible way to search for the optimal combinations of physical parameterization schemes in WRF and COAWST for more accurate typhoon simulation.This can help provide references for future development of NWP models,and for analyzing the coordination and adaptability of different physical process parameterization schemes under specific weather backgrounds.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.U2242213,U2142213,42305167,42175105)。
文摘Systematic bias is a type of model error that can affect the accuracy of data assimilation and forecasting that must be addressed.An online bias correction scheme called the sequential bias correction scheme(SBCS),was developed using the6 h average bias to correct the systematic bias during model integration.The primary purpose of this study is to investigate the impact of the SBCS in the high-resolution China Meteorological Administration Meso-scale(CMA-MESO)numerical weather prediction(NWP)model to reduce the systematic bias and to improve the data assimilation and forecast results through this method.The SBCS is improved upon and applied to the CMA-MESO 3-km model in this study.Four-week sequential data assimilation and forecast experiments,driven by rapid update and cycling(RUC),were conducted for the period from 2–29 May 2022.In terms of the characteristics of systematic bias,both the background and analysis show diurnal bias,and these large biases are affected by complex underlying surfaces(e.g.,oceans,coasts,and mountains).After the application of the SBCS,the results of the data assimilation show that the SBCS can reduce the systematic bias of the background and yield a neutral to slightly positive result for the analysis fields.In addition,the SBCS can reduce forecast errors and improve forecast results,especially for surface variables.The above results indicate that this scheme has good prospects for high-resolution regional NWP models.
基金supported by the National Natural Science Foundation of China(Grant Nos.41505084,41275053and 41461164006)the China Meteorological Administration Special Public Welfare Research Fund(Grant Nos.GYHY201406003 and GYHY201406009)+1 种基金the Guangdong Meteorological Service Project(Grant No.2015B01)the Guangdong Province Public Welfare Research and Capacity Construction Project(Grant No.2017B020218003)
文摘Unresolved small-scale orographic（SSO） drags are parameterized in a regional model based on the Global/Regional Assimilation and Prediction System for the Tropical Mesoscale Model（GRAPES TMM）. The SSO drags are represented by adding a sink term in the momentum equations. The maximum height of the mountain within the grid box is adopted in the SSO parameterization（SSOP） scheme as compensation for the drag. The effects of the unresolved topography are parameterized as the feedbacks to the momentum tendencies on the first model level in planetary boundary layer（PBL）parameterization. The SSOP scheme has been implemented and coupled with the PBL parameterization scheme within the model physics package. A monthly simulation is designed to examine the performance of the SSOP scheme over the complex terrain areas located in the southwest of Guangdong. The verification results show that the surface wind speed bias has been much alleviated by adopting the SSOP scheme, in addition to reduction of the wind bias in the lower troposphere. The target verification over Xinyi shows that the simulations with the SSOP scheme provide improved wind estimation over the complex regions in the southwest of Guangdong.
基金supported by the National Natural Science Foundation of China (Grant No. U1811464)。
文摘Construction of high-order difference schemes based on Taylor series expansion has long been a hot topic in computational mathematics, while its application in comprehensive weather models is still very rare. Here, the properties of high-order finite difference schemes are studied based on idealized numerical testing, for the purpose of their application in the Global/Regional Assimilation and Prediction System(GRAPES) model. It is found that the pros and cons due to grid staggering choices diminish with higher-order schemes based on linearized analysis of the one-dimensional gravity wave equation. The improvement of higher-order difference schemes is still obvious for the mesh with smooth varied grid distance. The results of discontinuous square wave testing also exhibits the superiority of high-order schemes. For a model grid with severe non-uniformity and non-orthogonality, the advantage of high-order difference schemes is inapparent, as shown by the results of two-dimensional idealized advection tests under a terrain-following coordinate. In addition, the increase in computational expense caused by high-order schemes can be avoided by the precondition technique used in the GRAPES model. In general, a high-order finite difference scheme is a preferable choice for the tropical regional GRAPES model with a quasi-uniform and quasi-orthogonal grid mesh.
基金supported by the National Natural Science Foundation of China(Grant Nos.U1811464,U2142213)Guangdong Basic and Applied Basic Research Foundation(Grants Nos.2020A1515110275,2020A1515110040,2022A1515011870)the Special program for innovation and development of China Meteorological Administration(CXFZ2021Z006,CXFZ2022P026).
文摘Landfalling typhoons can cause disasters over large regions.The government and emergency responders need to take measures to mitigate disasters according to the forecast of landfall position,while slight timing error can be ignored.The reliability of operational model forecasts of typhoon landfall position needs to be evaluated beforehand,according to the forecasts and observation of historical cases.In the evaluation of landfalling typhoon track,the traditional method based on point-to-point matching methods could be influenced by the predicted typhoon translation speed.Consequently,the traditional track evaluation method may result in a large track error even if the predicted landfall position is close to observation.The purpose of this paper is to address the above issue using a simple evaluation method of landfalling typhoon track forecast based on the time neighborhood approach.In this new method,the timing error was lessened to highlight the importance of the position error during the landfall of typhoon.The properties of the time neighborhood method are compared with the traditional method based on numerical forecast results of 12 landfalling typhoon cases.Results demonstrated that the new method is not sensitive to the sampling frequency,and that the difference between the time neighborhood and traditional method will be more obvious when the moving speed of typhoon is moderate(between 15−30 km h^(−1)).The time neighborhood concept can be easily extended to a broader context when one attempts to examine the position error more than the timing error.
基金supported by the National University of Defense Technology(NUDT)Research Initiation Funding for High-Level Scientific and Technological Innovative Talents(202402-YJRC-LJ-001)the Independent Innovation Science Fund of National University of Defense Technology(22-ZZCX-081)the Guangdong Province Introduction of Innovative R&D Team Project China(2019ZT08G669)。
文摘Given the interpretability,accuracy,and stability of numerical weather prediction(NWP)models,current operational weather forecasting relies heavily on the NWP approach[1].In the past two years,the rapid development of Artificial Intelligence(AI)has provided an alternative solution for medium-range(1-10 d)weather forecasting.
文摘Although business groups are prevalent globally,limited attention is paid to how financial pressure transmission between parent and subsidiary firms elicits tax avoidance.We explore the impact of parent firms’dividend policy on their subsidiaries’tax strategy using China’s mandatory dividend policy as a quasi-natural experiment.We find that parent firm dividends elevate tax avoidance among their subsidiaries.Mechanism tests show that parent firms transmit the pressure of paying dividends to their subsidiaries,compelling them to adopt tax avoidance strategies to alleviate the pressure.The effect is more pronounced among subsidiaries facing greater dividend pressure and external financing constraints and operating in weaker corporate governance environments.Finally,subsidiaries engaging in greater tax avoidance subsequently pay higher dividends.Our findings highlight how intra-group financial dynamics influence tax avoidance among subsidiaries and the significance of financial pressure transmission from parent firms to subsidiaries.
基金supported by the National University of Defense Technology(NUDT)Research Initiation Funding for High-Level Scientific and Technological Innovative Talents(202402-YJRC-LJ-001)the National Natural Science Foundation of China(Grant No.U2142213)+1 种基金the Basic and Applied Basic Research Foundation of Guangdong Province(Grants 2025A1515011835,2022A1515011870)the National Natural Science Foundation of China(Grant No.42305167)。
文摘The operational Tropical Regional Atmospheric Model System(TRAMS)often underestimates initial typhoon intensity when using the global analysis field as the initial condition.The TRAMS tropical cyclone(TC)initialization scheme,developed based on the incremental analysis updates(IAU)technique,effectively reduces initial bias.However,the original IAU-based TC initialization scheme only adjusts the wind field at the analysis moment,with other variables adjusted implicitly under the model's constraints according to a gradually inserted wind increment(named“univariate adjustment scheme”hereafter).The univariate adjustment scheme requires approximately 3 h to reach a dynamic equilibrium state,which constrains the assimilation of hourly TC observations and causes excessive dissipation of meaningful short-wave information in adjustment increments.To address this limitation,this study develops a multivariate adjustment IAU-based TC initialization scheme that incorporates gradient wind balance and hydrostatic balance as its largescale constraints.Numerical experiments with TC Hato(2017)demonstrate that the multivariate adjustment scheme reduces the IAU relaxation time to 1 h while marginally improving forecast skill.These findings are consistently replicated across 12 additional TC cases.The development of the IAU-based multivariate adjustment initialization scheme establishes a foundation for 4-D initialization using hourly TC observations.
基金Supported by the National Natural Science Foundation of China (U1811464)Science and Technology Planning Project of Guangdong Province,China (2018B020208004)。
文摘In traditional simulations of heavy rainfall events, the regional model is often initialized by using a global reanalysis dataset and a cold start method. An alternative to using global analysis data is to gradually introduce the analysis field via an incremental analysis update(IAU) method under the replay configuration. We found substantial differences in the forecast of a heavy rainfall event in southern China between a precipitation forecast using the traditional method and a forecast using the IAU method in the Tropical Regional Atmospheric Modeling System(TRAMS),based on the ECMWF global analysis. The IAU method is efficient in removing spurious high-frequency gravity wave noise, especially when the relaxation time is more than 90 min. The regional model needs to be pre-integrated for about 12 h to warm up the convective system in the background field. The improvement by the IAU method is supported by verification of simulations over 1 month(1–30 April 2019). In general, the IAU technique improves the initialization and spin-up process in the simulation of the heavy rainfall event.
基金the National Key Research and Development Program of China (2018YFC1507602)Science and Technology Planning Project of Guangdong Province (2017B020244002 and 2018B020208004)+1 种基金Natural Science Foundation of Guangdong Province(2019A1515011118)National Natural Science Foundation of China (41705089)。
文摘Variations in the initial structure of tropical cyclones(TCs) inevitably affect prediction results;however, the bogus model cannot accurately describe the structure of a weak tropical cyclone with increased initial field resolution. This study aims to construct a model to improve the prediction of weak TC in southern China. Based on the ECMWF 0.1° analysis data, several vortices were filtered out from tropical depressions and tropical storms in 2018 and 2019 to represent a weak TC reservoir in the South China Sea. For different simulation objects, filtered vortices were combined with the TC environmental field to form ensemble members. The observed TC information was assimilated for simulating TCs Bebinca, Mun, and Ewiniar to verify the feasibility of the proposed model, based on the Global/Regional Assimilation and Prediction Enhanced System(GRAPES) 9-km model developed by the Guangzhou Institute of Tropical and Marine Meteorology. The results show that the initialization scheme of the weak tropical cyclone model improved the intensity prediction of the TC by 26.81%(Bebinca), 18.65%(Mun), and 47.00%(Ewiniar), compared with the control experiment. Because typhoon intensity forecasting has not notably improved for many years, this scheme has certain scientific and operational significance.
基金Supported by the National Natural Science Foundation of China(U1811464 and 40675099)National Key Research and Development Program of China(2018YFC1506900)。
文摘With the advent of the phased array radar(PAR)technology,it is possible to capture the development and evolution of convective systems in a much shorter time interval and with higher spatial resolution than via traditional Doppler radar.Research on the assimilation of PAR observations in numerical weather prediction models is still in its infancy in China.In this paper,the impact of assimilating PAR data on model forecasts was investigated by a case study of a local heavy rainfall event that occurred over Foshan city of Guangdong Province on 26 August 2020,via a series of sensitivity experiments.Both the retrieved three-dimensional wind and hydrometeor fields were assimilated through the nudging method with the Tropical Regional Assimilation Model for South China Sea_Rapid Update Cycle_1km(TRAMS_RUC_1km).The temperature and moisture fields were also adjusted accordingly.The results show that significant improvements are made in the experiments with latent heat nudging and adjustment of the water vapor field,which implies the importance of thermodynamic balance in the initialization of the convective system and highlights the need to assimilate PAR radar observations in a continuous manner to maximize the impact of the data.Sensitivity tests also indicate that the relaxation time should be less than 5 min.In general,for this case,the assimilation of PAR data can significantly improve the nowcasting skill of the regional heavy precipitation.This study is the first step towards operational PAR data assimilation in numerical weather prediction in southern China.
基金Supported by the National Natural Science Foundation of China(42130605)Shenzhen Science and Technology Program(JCYJ20210324131810029)Guangdong Province Introduction of Innovative Research and Development Team Project China(2019ZT08G669)。
文摘Each physical process in a numerical weather prediction(NWP)system may have many different parameterization schemes.Early studies have shown that the performance of different physical parameterization schemes varies with the weather situation to be simulated.Thus,it is necessary to select a suitable combination of physical parameterization schemes according to the variation of weather systems.However,it is rather difficult to identify an optimal combination among millions of possible parameterization scheme combinations.This study applied a simple genetic algorithm(SGA)to optimizing the combination of parameterization schemes in NWP models for typhoon forecasting.The feasibility of SGA was verified with the simulation of Typhoon Mujigae(2015)by using the Weather Research and Forecasting(WRF)model and Typhoon Higos(2020)by using the Coupled Ocean–Atmosphere–Wave–Sediment Transport(COAWST)modeling system.The results show that SGA can efficiently obtain the optimal combination of schemes.For Typhoon Mujigae(2015),the optimal combination can be found from the 1,304,576 possible combinations by running only 488 trials.Similar results can be obtained for Typhoon Higos(2020).Compared to the default combination proposed by the COAWST model system,the optimal combination scheme significantly improves the simulation of typhoon track and intensity.This study provides a feasible way to search for the optimal combinations of physical parameterization schemes in WRF and COAWST for more accurate typhoon simulation.This can help provide references for future development of NWP models,and for analyzing the coordination and adaptability of different physical process parameterization schemes under specific weather backgrounds.
