Subseasonal forecasting of extreme events is crucial for early warning systems.However,the forecast skills for extreme events are limited.Taking the extreme cold events in January 2018 as a specific example,and analyz...Subseasonal forecasting of extreme events is crucial for early warning systems.However,the forecast skills for extreme events are limited.Taking the extreme cold events in January 2018 as a specific example,and analyzing the 34 extreme cold events in East Asia from 1998 to 2020,the authors evaluated the forecast skills of the ECMWF model ensemble members on subseasonal time scales.The results show that while the ensemble mean has limited skills for forecasting extreme cold events at the 3-week lead time,some individual members demonstrate high forecast skills.For most extreme cold events,there are>10%of members among the total ensembles that can well predict the rapid temperature transitions at the 14-day lead time.This highlights the untapped potential of the ECMWF model to forecast extreme cold events on subseasonal time scales.High-skill ensemble members rely on accurate predictions of atmospheric circulation patterns(500-hPa geopotential height,mean sea level pressure)and key weather systems,including the Ural Blocking and Siberian High,that influence extreme cold events.展开更多
Taking short-duration heavy rainfall and convective wind gusts as examples, the present study examined the characteristics of radar reflectivity and several convective parameters. We analyzed nowcasting techniques by ...Taking short-duration heavy rainfall and convective wind gusts as examples, the present study examined the characteristics of radar reflectivity and several convective parameters. We analyzed nowcasting techniques by integrating a high-resolution numerical weather prediction model with these convective parameters. Based on the CMA-GD 1-km model and its assimilation system, we conducted repeated tests on radar reflectivity data assimilation and analyzed their impact on nowcasting accuracy. Based on these analyses, we proposed a method to improve model forecasts using the useful indicative information provided by high-frequency radar reflectivity data and convective parameters. The improved method was applied to the CMA-GD 1-km model for nowcasting tests. Evaluations from batch tests and case analysis show that the proposed method significantly reduced the model's false alarm rates and improved its nowcasting performance.展开更多
In the present study,multimodel ensemble forecast experiments of the global horizontal irradiance(GHI)were conducted using the dynamic variable weight technique.The study was based on the forecasts of four numerical m...In the present study,multimodel ensemble forecast experiments of the global horizontal irradiance(GHI)were conducted using the dynamic variable weight technique.The study was based on the forecasts of four numerical models,namely,the China Meteorological Administration Wind Energy and Solar Energy Prediction System,the Mesoscale Weather Numerical Prediction System of China Meteorological Administration,the China Meteorological Administration Regional Mesoscale Numerical Prediction System-Guangdong,and the Weather Research and Forecasting Model-Solar,and observational data from four photovoltaic(PV)power stations in Yangjiang City,Guangdong Province.The results show that compared with those of the monthly optimal numerical model forecasts,the dynamic variable weight-based ensemble forecasts exhibited 0.97%-15.96%smaller values of the mean absolute error and 3.31%-18.40%lower values of the root mean square error(RMSE).However,the increase in the correlation coefficient was not obvious.Specifically,the multimodel ensemble mainly improved the performance of GHI forecasts below 700 W m^(-2),particularly below 400 W m^(-2),with RMSE reductions as high as 7.56%-28.28%.In contrast,the RMSE increased at GHI levels above 700 W m^(-2).As for the key period of PV power station output(02:00-07:00),the accuracy of GHI forecasts could be improved by the multimodel ensemble:the multimodel ensemble could effectively decrease the daily maximum absolute error(AE max)of GHI forecasts.Moreover,with increasing forecasting difficulty under cloudy conditions,the multimodel ensemble,which yields data closer to the actual observations,could simulate GHI fluctuations more accurately.展开更多
In recent years, there has been increasing demand for high-resolution seasonal climate forecasts at sufficient lead times to allow response planning from users in agriculture, hydrology, disaster risk management, and ...In recent years, there has been increasing demand for high-resolution seasonal climate forecasts at sufficient lead times to allow response planning from users in agriculture, hydrology, disaster risk management, and health, among others. This paper examines the forecasting skill of the North American Multi-model Ensemble (NMME) over Ethiopia during the June to September (JJAS) season. The NMME, one of the multi-model seasonal forecasting systems, regularly generates monthly seasonal rainfall forecasts over the globe with 0.5 <span style="font-family:Verdana;">-</span><span style="font-family:;" "=""><span style="font-family:Verdana;"> 11.5 months lead time. The skill and predictability of seasonal rainfall are assessed using 28 years of hindcast data from the NMME models. The forecast skill is quantified using canonical correlation analysis (CCA) and root mean square error. The results show that the NMME models capture the JJAS seasonal rainfall over central, northern, and northeastern parts of Ethiopia while exhibiting weak or limited skill across western and southwestern Ethiopia. The performance of each model in predicting the JJAS seasonal rainfall is variable, showing greater skill in predicting dry conditions. Overall, the performance of the multi-model ensemble was not consistently better than any single ensemble member. The correlation of observed and predicted </span><span style="font-family:Verdana;">seasonal rainfall for the better performing models</span></span><span style="font-family:Verdana;">—GFDL-CM2p5-FLOR-A06,</span><span style="font-family:Verdana;"> CMC2-CanCM4, GFDL-CM2p5-FLOR-B01 and NASA-GMAO-062012</span><span style="font-family:Verdana;">—</span><span style="font-family:Verdana;">is 0.68, 0.58, 0.52, and 0.5, respectively. The COLA-RSMAS-CCSM4, CMC1-</span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">CanCM3 and NCEP-CFSv2 models exhibit less skill, with correlations less than 0.4. In general, the NMME offers promising skill to predict seasonal rainfall over Ethiopia during the June-September (JJAS) season, motivating further work to assess its performance at longer lead times.</span>展开更多
As a prominent mode of variability in the tropical stratosphere on the interannual timescale,the Quasi-Biennial Oscillation(QBO)can significantly influence global atmospheric circulation and weather patterns.This stud...As a prominent mode of variability in the tropical stratosphere on the interannual timescale,the Quasi-Biennial Oscillation(QBO)can significantly influence global atmospheric circulation and weather patterns.This study explores the dynamic processes of QBO disruptions using the integrated climate model of the China Meteorological Administration(CMA)by nudging the tropical zonal winds toward observations.A comparative analysis with ERA5 reanalysis data shows that the nudged runs accurately replicate the general characteristics of the QBO,including the alternating QBO wind regimes and QBO disruption events.The evolution of the QBO winds is diagnosed using empirical orthogonal function and root-mean-square difference analyses,and the rarity of the disruption events is confirmed in the CMA model.Different aspects of the QBO disruptions and the relevant dynamics are present in the model.Firstly,the momentum budget analysis highlights the crucial roles of extratropical Rossby waves and non-orographic gravity waves in the transition from westerly to easterly winds during a disruption.Secondly,Kelvin waves and non-orographic gravity waves explain much of the transition from easterly to westerly winds near 40 hPa.Thirdly,the positive tendency from enhanced vertical advection further accelerates westerly momentum development via secondary meridional circulation.These findings underscore the importance of nudging techniques in understanding QBO dynamics,which provides valuable insights for future climate model improvements toward better forecasting QBO-related climate variability.Notably,due to model limitations,no QBO disruptions were simulated in the free-run experiments.展开更多
As one of the participants in the Subseasonal to Seasonal(S2S)Prediction Project,the China Meteorological Administration(CMA)has adopted several model versions to participate in the S2S Project.This study evaluates th...As one of the participants in the Subseasonal to Seasonal(S2S)Prediction Project,the China Meteorological Administration(CMA)has adopted several model versions to participate in the S2S Project.This study evaluates the models’capability to simulate and predict the Madden-Julian Oscillation(MJO).Three versions of the Beijing Climate Center Climate System Model(BCC-CSM)are used to conduct historical simulations and re-forecast experiments(referred to as EXP1,EXP1-M,and EXP2,respectively).In simulating MJO characteristics,the newly-developed high-resolution BCC-CSM outperforms its predecessors.In terms of MJO prediction,the useful prediction skill of the MJO index is enhanced from 15 days in EXP1 to 22 days in EXP1-M,and further to 24 days in EXP2.Within the first forecast week,the better initial condition in EXP2 largely contributes to the enhancement of MJO prediction skill.However,during forecast weeks 2–3,EXP2 shows little advantage compared with EXP1-M because the increased skill at MJO initial phases 6–7 is largely offset by the degraded skill at MJO initial phases 2–3.Particularly at initial phases 2–3,EXP1-M skillfully captures the wind field and Kelvin-wave response to MJO convection,leading to the highest prediction skill of the MJO.Our results reveal that,during the participation of the CMA models in the S2S Project,both the improved model initialization and updated model physics played positive roles in improving MJO prediction.Future efforts should focus on improving the model physics to better simulate MJO convection over the Maritime Continent and further improve MJO prediction at long lead times.展开更多
Radiative transfer simulations and remote sensing studies fundamentally require accurate and efficient computation of the optical properties of non-spherical particles.This paper proposes a deep learning(DL)scheme in ...Radiative transfer simulations and remote sensing studies fundamentally require accurate and efficient computation of the optical properties of non-spherical particles.This paper proposes a deep learning(DL)scheme in conjunction with an optical property database to achieve this goal.Deep neural network(DNN)architectures were obtained from a dataset of the optical properties of super-spheroids with extensive shape parameters,size parameters,and refractive indices.The dataset was computed through the invariant imbedding T-matrix method.Four separate DNN architectures were created to compute the extinction efficiency factor,single-scattering albedo,asymmetry factor,and phase matrix.The criterion for designing these neural networks was the achievement of the highest prediction accuracy with minimal DNN parameters.The numerical results demonstrate that the determination coefficients are greater than 0.999 between the prediction values from the neural networks and the truth values from the database,which indicates that the DNN can reproduce the optical properties in the dataset with high accuracy.In addition,the DNN model can robustly predict the optical properties of particles with high accuracy for shape parameters or refractive indices that are unavailable in the database.Importantly,the ratio of the database size(~127 GB)to that of the DNN parameters(~20 MB)is approximately 6810,implying that the DNN model can be treated as a highly compressed database that can be used as an alternative to the original database for real-time computing of the optical properties of non-spherical particles in radiative transfer and atmospheric models.展开更多
An obvious trend shift in the annual mean and winter mixed layer depth(MLD)in the Antarctic Circumpolar Current(ACC)region was detected during the 1960–2021 period.Shallowing trends stopped in mid-1980s,followed by a...An obvious trend shift in the annual mean and winter mixed layer depth(MLD)in the Antarctic Circumpolar Current(ACC)region was detected during the 1960–2021 period.Shallowing trends stopped in mid-1980s,followed by a period of weak trends.The MLD deepening trend difference between the two periods were mainly distributed in the western areas in the Drake Passage,the areas north to Victoria Land and Wilkes Land,and the central parts of the South Indian sector.The newly formed ocean current shear due to the meridional shift of the ACC flow axis between the two periods is the dominant driver for the MLD trends shift distributed in the western areas in the Drake Passage and the central parts of the South Indian sector.The saltier trends in the regions north to Victoria Land and Wilkes Land could be responsible for the strengthening mixing processes in this region.展开更多
基金supported by the National Key Research and Development Program[grant number 2022YFC3004203]the S&T Development Fund of CAMS(Chinese Academy of Meteorological Sciences)[grant numbers 2023KJ040 and 2024KJ013].
文摘Subseasonal forecasting of extreme events is crucial for early warning systems.However,the forecast skills for extreme events are limited.Taking the extreme cold events in January 2018 as a specific example,and analyzing the 34 extreme cold events in East Asia from 1998 to 2020,the authors evaluated the forecast skills of the ECMWF model ensemble members on subseasonal time scales.The results show that while the ensemble mean has limited skills for forecasting extreme cold events at the 3-week lead time,some individual members demonstrate high forecast skills.For most extreme cold events,there are>10%of members among the total ensembles that can well predict the rapid temperature transitions at the 14-day lead time.This highlights the untapped potential of the ECMWF model to forecast extreme cold events on subseasonal time scales.High-skill ensemble members rely on accurate predictions of atmospheric circulation patterns(500-hPa geopotential height,mean sea level pressure)and key weather systems,including the Ural Blocking and Siberian High,that influence extreme cold events.
基金Key-Area Research and Development Program of Guangdong (2020B1111200001)National Natural Science Foundation of China (42230105, U2142213, 42175167)。
文摘Taking short-duration heavy rainfall and convective wind gusts as examples, the present study examined the characteristics of radar reflectivity and several convective parameters. We analyzed nowcasting techniques by integrating a high-resolution numerical weather prediction model with these convective parameters. Based on the CMA-GD 1-km model and its assimilation system, we conducted repeated tests on radar reflectivity data assimilation and analyzed their impact on nowcasting accuracy. Based on these analyses, we proposed a method to improve model forecasts using the useful indicative information provided by high-frequency radar reflectivity data and convective parameters. The improved method was applied to the CMA-GD 1-km model for nowcasting tests. Evaluations from batch tests and case analysis show that the proposed method significantly reduced the model's false alarm rates and improved its nowcasting performance.
基金Innovation and Development Project of China Meteorological Administration(CXFZ2023J044)Innovation Foundation of CMA Public Meteorological Service Center(K2023002)+1 种基金“Tianchi Talents”Introduction Plan(2023)Key Innovation Team for Energy and Meteorology of China Meteorological Administration。
文摘In the present study,multimodel ensemble forecast experiments of the global horizontal irradiance(GHI)were conducted using the dynamic variable weight technique.The study was based on the forecasts of four numerical models,namely,the China Meteorological Administration Wind Energy and Solar Energy Prediction System,the Mesoscale Weather Numerical Prediction System of China Meteorological Administration,the China Meteorological Administration Regional Mesoscale Numerical Prediction System-Guangdong,and the Weather Research and Forecasting Model-Solar,and observational data from four photovoltaic(PV)power stations in Yangjiang City,Guangdong Province.The results show that compared with those of the monthly optimal numerical model forecasts,the dynamic variable weight-based ensemble forecasts exhibited 0.97%-15.96%smaller values of the mean absolute error and 3.31%-18.40%lower values of the root mean square error(RMSE).However,the increase in the correlation coefficient was not obvious.Specifically,the multimodel ensemble mainly improved the performance of GHI forecasts below 700 W m^(-2),particularly below 400 W m^(-2),with RMSE reductions as high as 7.56%-28.28%.In contrast,the RMSE increased at GHI levels above 700 W m^(-2).As for the key period of PV power station output(02:00-07:00),the accuracy of GHI forecasts could be improved by the multimodel ensemble:the multimodel ensemble could effectively decrease the daily maximum absolute error(AE max)of GHI forecasts.Moreover,with increasing forecasting difficulty under cloudy conditions,the multimodel ensemble,which yields data closer to the actual observations,could simulate GHI fluctuations more accurately.
文摘In recent years, there has been increasing demand for high-resolution seasonal climate forecasts at sufficient lead times to allow response planning from users in agriculture, hydrology, disaster risk management, and health, among others. This paper examines the forecasting skill of the North American Multi-model Ensemble (NMME) over Ethiopia during the June to September (JJAS) season. The NMME, one of the multi-model seasonal forecasting systems, regularly generates monthly seasonal rainfall forecasts over the globe with 0.5 <span style="font-family:Verdana;">-</span><span style="font-family:;" "=""><span style="font-family:Verdana;"> 11.5 months lead time. The skill and predictability of seasonal rainfall are assessed using 28 years of hindcast data from the NMME models. The forecast skill is quantified using canonical correlation analysis (CCA) and root mean square error. The results show that the NMME models capture the JJAS seasonal rainfall over central, northern, and northeastern parts of Ethiopia while exhibiting weak or limited skill across western and southwestern Ethiopia. The performance of each model in predicting the JJAS seasonal rainfall is variable, showing greater skill in predicting dry conditions. Overall, the performance of the multi-model ensemble was not consistently better than any single ensemble member. The correlation of observed and predicted </span><span style="font-family:Verdana;">seasonal rainfall for the better performing models</span></span><span style="font-family:Verdana;">—GFDL-CM2p5-FLOR-A06,</span><span style="font-family:Verdana;"> CMC2-CanCM4, GFDL-CM2p5-FLOR-B01 and NASA-GMAO-062012</span><span style="font-family:Verdana;">—</span><span style="font-family:Verdana;">is 0.68, 0.58, 0.52, and 0.5, respectively. The COLA-RSMAS-CCSM4, CMC1-</span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">CanCM3 and NCEP-CFSv2 models exhibit less skill, with correlations less than 0.4. In general, the NMME offers promising skill to predict seasonal rainfall over Ethiopia during the June-September (JJAS) season, motivating further work to assess its performance at longer lead times.</span>
基金funded by the National Natural Science Foundation of China(Grant No.42275056).
文摘As a prominent mode of variability in the tropical stratosphere on the interannual timescale,the Quasi-Biennial Oscillation(QBO)can significantly influence global atmospheric circulation and weather patterns.This study explores the dynamic processes of QBO disruptions using the integrated climate model of the China Meteorological Administration(CMA)by nudging the tropical zonal winds toward observations.A comparative analysis with ERA5 reanalysis data shows that the nudged runs accurately replicate the general characteristics of the QBO,including the alternating QBO wind regimes and QBO disruption events.The evolution of the QBO winds is diagnosed using empirical orthogonal function and root-mean-square difference analyses,and the rarity of the disruption events is confirmed in the CMA model.Different aspects of the QBO disruptions and the relevant dynamics are present in the model.Firstly,the momentum budget analysis highlights the crucial roles of extratropical Rossby waves and non-orographic gravity waves in the transition from westerly to easterly winds during a disruption.Secondly,Kelvin waves and non-orographic gravity waves explain much of the transition from easterly to westerly winds near 40 hPa.Thirdly,the positive tendency from enhanced vertical advection further accelerates westerly momentum development via secondary meridional circulation.These findings underscore the importance of nudging techniques in understanding QBO dynamics,which provides valuable insights for future climate model improvements toward better forecasting QBO-related climate variability.Notably,due to model limitations,no QBO disruptions were simulated in the free-run experiments.
基金supported by the National Natural Science Foundation of China(Grant No.42075161).
文摘As one of the participants in the Subseasonal to Seasonal(S2S)Prediction Project,the China Meteorological Administration(CMA)has adopted several model versions to participate in the S2S Project.This study evaluates the models’capability to simulate and predict the Madden-Julian Oscillation(MJO).Three versions of the Beijing Climate Center Climate System Model(BCC-CSM)are used to conduct historical simulations and re-forecast experiments(referred to as EXP1,EXP1-M,and EXP2,respectively).In simulating MJO characteristics,the newly-developed high-resolution BCC-CSM outperforms its predecessors.In terms of MJO prediction,the useful prediction skill of the MJO index is enhanced from 15 days in EXP1 to 22 days in EXP1-M,and further to 24 days in EXP2.Within the first forecast week,the better initial condition in EXP2 largely contributes to the enhancement of MJO prediction skill.However,during forecast weeks 2–3,EXP2 shows little advantage compared with EXP1-M because the increased skill at MJO initial phases 6–7 is largely offset by the degraded skill at MJO initial phases 2–3.Particularly at initial phases 2–3,EXP1-M skillfully captures the wind field and Kelvin-wave response to MJO convection,leading to the highest prediction skill of the MJO.Our results reveal that,during the participation of the CMA models in the S2S Project,both the improved model initialization and updated model physics played positive roles in improving MJO prediction.Future efforts should focus on improving the model physics to better simulate MJO convection over the Maritime Continent and further improve MJO prediction at long lead times.
基金supported by the NSFC Major Project (Grant Nos. 42090030, and 42090032)the National Natural Science Foundation of China (Grant Nos. 42022038, and 42075155)the National Key Research and Development Program (2019YFC1510400)
文摘Radiative transfer simulations and remote sensing studies fundamentally require accurate and efficient computation of the optical properties of non-spherical particles.This paper proposes a deep learning(DL)scheme in conjunction with an optical property database to achieve this goal.Deep neural network(DNN)architectures were obtained from a dataset of the optical properties of super-spheroids with extensive shape parameters,size parameters,and refractive indices.The dataset was computed through the invariant imbedding T-matrix method.Four separate DNN architectures were created to compute the extinction efficiency factor,single-scattering albedo,asymmetry factor,and phase matrix.The criterion for designing these neural networks was the achievement of the highest prediction accuracy with minimal DNN parameters.The numerical results demonstrate that the determination coefficients are greater than 0.999 between the prediction values from the neural networks and the truth values from the database,which indicates that the DNN can reproduce the optical properties in the dataset with high accuracy.In addition,the DNN model can robustly predict the optical properties of particles with high accuracy for shape parameters or refractive indices that are unavailable in the database.Importantly,the ratio of the database size(~127 GB)to that of the DNN parameters(~20 MB)is approximately 6810,implying that the DNN model can be treated as a highly compressed database that can be used as an alternative to the original database for real-time computing of the optical properties of non-spherical particles in radiative transfer and atmospheric models.
基金This study was supported by the Hunan Provincial Natural Science Foundation of China[grant number 2021JC0009]the Natural Science Foundation of China[grant number U2142212]the National Key R&D Program of China[grant number 2022YFC3004200].
基金The National Natural Science Foundation of China under contract No.41605052。
文摘An obvious trend shift in the annual mean and winter mixed layer depth(MLD)in the Antarctic Circumpolar Current(ACC)region was detected during the 1960–2021 period.Shallowing trends stopped in mid-1980s,followed by a period of weak trends.The MLD deepening trend difference between the two periods were mainly distributed in the western areas in the Drake Passage,the areas north to Victoria Land and Wilkes Land,and the central parts of the South Indian sector.The newly formed ocean current shear due to the meridional shift of the ACC flow axis between the two periods is the dominant driver for the MLD trends shift distributed in the western areas in the Drake Passage and the central parts of the South Indian sector.The saltier trends in the regions north to Victoria Land and Wilkes Land could be responsible for the strengthening mixing processes in this region.
