This study focuses on an extreme rainfall event in East China during the mei-yu season,in which the capital city(Nanjing)of Jiangsu Province experienced a maximum 14-h rainfall accumulation of 209.6 mm and a peak hour...This study focuses on an extreme rainfall event in East China during the mei-yu season,in which the capital city(Nanjing)of Jiangsu Province experienced a maximum 14-h rainfall accumulation of 209.6 mm and a peak hourly rainfall of 118.8 mm.The performance of two sets of convection-permitting ensemble forecast systems(CEFSs),each with 30 members and a 3-km horizontal grid spacing,is evaluated.The CEFS_ICBCs,using multiple initial and boundary conditions(ICs and BCs),and the CEFS_ICBCs Phys,which incorporates both multi-physics schemes and ICs/BCs,are compared to the CMA-REPS(China Meteorological Administration-Regional Ensemble Prediction System)with a coarser 10-km grid spacing.The two CEFSs demonstrate more uniform rank histograms and lower Brier scores(with higher resolution),improving precipitation intensity predictions and providing more reliable probability forecasts,although they overestimate precipitation over Mt.Dabie.It is challenging for the CEFSs to capture the evolution of mesoscale rainstorms that are known to be related to the errors in predicting the southwesterly low-level winds.Sensitivity experiments reveal that the microphysics and radiation schemes introduce considerable uncertainty in predicting the intensity and location of heavy rainfall in and near Nanjing and Mt.Dabie.In particular,the Asymmetric Convection Model 2(ACM2)planetary boundary layer scheme combined with the Pleim-Xiu surface layer scheme tends to produce a biased northeastward extension of the boundary-layer jet,contributing to the northeastward bias of heavy precipitation around Nanjing in the CEFS_ICBCs.展开更多
Probabilistic forecasting is becoming increasingly important for a wide range of applications,especially for en-ergy systems such as forecasting wind power production.A need for proper evaluation of probabilistic fore...Probabilistic forecasting is becoming increasingly important for a wide range of applications,especially for en-ergy systems such as forecasting wind power production.A need for proper evaluation of probabilistic forecasts follows naturally with this,because evaluation is the key to improving the forecasts.Although plenty of excellent reviews and research papers on probabilistic forecast evaluation already exist,we find that there is a need for an introduction with some practical application.In particular,many forecast scenarios in energy systems are inher-ently multivariate,and while univariate evaluation methods are well understood and documented,only limited and scattered work has been done on their multivariate counterparts.This paper therefore contains a review of a selected set of probabilistic forecast evaluation methods,primarily scoring rules,as well as practical sections that explain how these methods can be calculated and estimated.In three case studies featuring simple autore-gressive models,stochastic differential equations and real wind power data,we implement,apply and discuss the logarithmic score,the continuous ranked probability score and the variogram score for forecasting problems of varying dimension.Finally,the advantages and disadvantages of the three scoring rules are highlighted,and this provides a significant step towards deciding on an evaluation method for a given multivariate forecast scenario including forecast scenarios relevant for energy systems.展开更多
Based on the reforecasts from ve models of the Subseasonal to Seasonal(S2S)Prediction project,the S2S prediction skill of surface soil moisture(SM)over East Asia during May September is evaluated against ERA-Interim.R...Based on the reforecasts from ve models of the Subseasonal to Seasonal(S2S)Prediction project,the S2S prediction skill of surface soil moisture(SM)over East Asia during May September is evaluated against ERA-Interim.Results show that good prediction skill of SM is generally 510 forecast days prior over southern and northeastern China in the majority of models.Over the Tibetan Plateau and northwestern China,only the ECMWF model has good prediction skill 20 days in advance.Generally,better prediction skill tends to appear over wet regions rather than dry regions.In terms of the seasonal variation of SM prediction skill,some diffierences are noticed among the models,but most of them show good prediction skill during September.Furthermore,the significant positive correlation between the prediction skill of SM and ENSO index indicates modulation by ENSO of the S2S prediction of SM.When there is an El Nino(a La Nina)event,the SM prediction skill over eastern China tends to be high(low).Through evaluation of the S2S prediction skill of SM in these models,it is found that the prediction skill of SM is lower than that of most atmospheric variables in S2S forecasts.Therefore,more attention needs to be given to the S2S forecasting of land processes.展开更多
Meteorological disasters usually exert huge impacts on the development of both human society and the economy. According to statistics from the United Nations International Strategy for Disaster Reduction, the annual m...Meteorological disasters usually exert huge impacts on the development of both human society and the economy. According to statistics from the United Nations International Strategy for Disaster Reduction, the annual mean economic loss caused by meteorological disasters accounts for 3%-6% of the total amount of global GDP. China is a country that has been one of the most severely influenced by natural disasters.展开更多
The principle of middle and long-term earthquake forecast model of spatial and temporal synthesized probability gain and the evaluation of forecast efficiency (R-values) of various forecast methods are introduced in t...The principle of middle and long-term earthquake forecast model of spatial and temporal synthesized probability gain and the evaluation of forecast efficiency (R-values) of various forecast methods are introduced in this paper. The R-value method, developed by Xu (1989), is further developed here, and can be applied to more complicated cases. Probability gains in spatial and/or temporal domains and the R-values for different forecast methods are estimated in North China. The synthesized probability gain is then estimated as an example.展开更多
The Yunnan–Guizhou quasi-stationary front(YGQSF)is a critical weather system in Southwest China during the cold season(November to April),frequently introducing low-temperature disasters.Accurate forecasting of YGQSF...The Yunnan–Guizhou quasi-stationary front(YGQSF)is a critical weather system in Southwest China during the cold season(November to April),frequently introducing low-temperature disasters.Accurate forecasting of YGQSF poses significant challenges for operational models.This study evaluates the performance of ECMWF Integrated Forecasting System(IFS)in forecasting YGQSFs by utilizing hourly observational data and a linear fitting method for frontal line identification.From 2020 to 2023,a total of 392 and 261 YGQSFs were identified in observational data and the IFS forecast,respectively.From east to west,the frontal lines gradually change from northwest–southeast-oriented to quasi-north-south-oriented.The northern segments of the frontal lines are concentrated east of the Hengduan Mountains,while the southern segments are dispersed across four high-occurrence regions on the Yungui Plateau.These high-occurrence regions are located on local highlands or their eastern slopes,highlighting the influence of topographic blocking effects.These spatial characteristics of the YGQSFs are well captured by the IFS,especially for the high-occurrence region between 103.5°E and 105°E,which is related to the pronounced spatial temperature gradients enhanced by the large elevation difference.Further analysis focuses on the impact of spatial temperature gradients on the YGQSF forecasting,particularly examining Hits,Misses,and False alarms.The model demonstrates superior performance in forecasting YGQSFs characterized by large temperature gradients and substantial cold air accumulation.Conversely,reduced temperature gradients,stemming from weaker cold air to the east or weaker warm air to the west,increase the risk of Misses.Temporally,the missed YGQSFs are uniformly distributed,while spatially they are scattered.False alarms,however,peak between February and March and are concentrated between 103.5°E and 107.25°E.Overall,forecasting YGQSFs with weak intensity,short frontal lines,or meridional orientation remains particularly challenging.The biases revealed in this study provide valuable insights for enhancing operational forecasting accuracy.展开更多
In view of the poor precision of the theoretical model of labor demand estimation,it is difficult to estimate and predict the actual production problems accurately.Based on the actual production conditions and the rel...In view of the poor precision of the theoretical model of labor demand estimation,it is difficult to estimate and predict the actual production problems accurately.Based on the actual production conditions and the relationship between the degree of mechanization of planting and the demand of labor force,this study established an estimation model for the labor demand of planting industry considering the factors of planting structure and mechanization degree.In order to ensure high reliability of data,the method of checking out abnormal data was adopted to obtain the cultivated land area index when the mechanization degree is from 0 to 100%.Taking Suihua region(Heilongjiang Province,China)as an example,the theory of the research was analyzed and applied.This study accessed to the data of cultivated land area per labor can afford when the mechanization level in Suihua area were 0 and 100%respectively through the investigation,and the average cultivated land area data of each labor force in two cases were sorted out and the abnormal data were eliminated at the same time.Finally,using the derived model,the data obtained and the mechanization level and cultivated land area of Suihua in the future,the labor demand amount in Suihua area from 2015 to 2019 were predicted.The model established in this study can be used to calculate the quantity of both current labor demand in planting industry and the labor demand in the various moments in the future through forecasting the future mechanization level and cultivated area which are the two main factors influencing the quantity of labor demand in planting structure.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.42030610 and 42205006)the Startup Foundation for Introducing Talent of NUIST(2023r121)。
文摘This study focuses on an extreme rainfall event in East China during the mei-yu season,in which the capital city(Nanjing)of Jiangsu Province experienced a maximum 14-h rainfall accumulation of 209.6 mm and a peak hourly rainfall of 118.8 mm.The performance of two sets of convection-permitting ensemble forecast systems(CEFSs),each with 30 members and a 3-km horizontal grid spacing,is evaluated.The CEFS_ICBCs,using multiple initial and boundary conditions(ICs and BCs),and the CEFS_ICBCs Phys,which incorporates both multi-physics schemes and ICs/BCs,are compared to the CMA-REPS(China Meteorological Administration-Regional Ensemble Prediction System)with a coarser 10-km grid spacing.The two CEFSs demonstrate more uniform rank histograms and lower Brier scores(with higher resolution),improving precipitation intensity predictions and providing more reliable probability forecasts,although they overestimate precipitation over Mt.Dabie.It is challenging for the CEFSs to capture the evolution of mesoscale rainstorms that are known to be related to the errors in predicting the southwesterly low-level winds.Sensitivity experiments reveal that the microphysics and radiation schemes introduce considerable uncertainty in predicting the intensity and location of heavy rainfall in and near Nanjing and Mt.Dabie.In particular,the Asymmetric Convection Model 2(ACM2)planetary boundary layer scheme combined with the Pleim-Xiu surface layer scheme tends to produce a biased northeastward extension of the boundary-layer jet,contributing to the northeastward bias of heavy precipitation around Nanjing in the CEFS_ICBCs.
文摘Probabilistic forecasting is becoming increasingly important for a wide range of applications,especially for en-ergy systems such as forecasting wind power production.A need for proper evaluation of probabilistic forecasts follows naturally with this,because evaluation is the key to improving the forecasts.Although plenty of excellent reviews and research papers on probabilistic forecast evaluation already exist,we find that there is a need for an introduction with some practical application.In particular,many forecast scenarios in energy systems are inher-ently multivariate,and while univariate evaluation methods are well understood and documented,only limited and scattered work has been done on their multivariate counterparts.This paper therefore contains a review of a selected set of probabilistic forecast evaluation methods,primarily scoring rules,as well as practical sections that explain how these methods can be calculated and estimated.In three case studies featuring simple autore-gressive models,stochastic differential equations and real wind power data,we implement,apply and discuss the logarithmic score,the continuous ranked probability score and the variogram score for forecasting problems of varying dimension.Finally,the advantages and disadvantages of the three scoring rules are highlighted,and this provides a significant step towards deciding on an evaluation method for a given multivariate forecast scenario including forecast scenarios relevant for energy systems.
基金supported by the National Key R&D Program of China [grant number 2016YFA0602100]
文摘Based on the reforecasts from ve models of the Subseasonal to Seasonal(S2S)Prediction project,the S2S prediction skill of surface soil moisture(SM)over East Asia during May September is evaluated against ERA-Interim.Results show that good prediction skill of SM is generally 510 forecast days prior over southern and northeastern China in the majority of models.Over the Tibetan Plateau and northwestern China,only the ECMWF model has good prediction skill 20 days in advance.Generally,better prediction skill tends to appear over wet regions rather than dry regions.In terms of the seasonal variation of SM prediction skill,some diffierences are noticed among the models,but most of them show good prediction skill during September.Furthermore,the significant positive correlation between the prediction skill of SM and ENSO index indicates modulation by ENSO of the S2S prediction of SM.When there is an El Nino(a La Nina)event,the SM prediction skill over eastern China tends to be high(low).Through evaluation of the S2S prediction skill of SM in these models,it is found that the prediction skill of SM is lower than that of most atmospheric variables in S2S forecasts.Therefore,more attention needs to be given to the S2S forecasting of land processes.
文摘Meteorological disasters usually exert huge impacts on the development of both human society and the economy. According to statistics from the United Nations International Strategy for Disaster Reduction, the annual mean economic loss caused by meteorological disasters accounts for 3%-6% of the total amount of global GDP. China is a country that has been one of the most severely influenced by natural disasters.
文摘The principle of middle and long-term earthquake forecast model of spatial and temporal synthesized probability gain and the evaluation of forecast efficiency (R-values) of various forecast methods are introduced in this paper. The R-value method, developed by Xu (1989), is further developed here, and can be applied to more complicated cases. Probability gains in spatial and/or temporal domains and the R-values for different forecast methods are estimated in North China. The synthesized probability gain is then estimated as an example.
基金Supported by the National Natural Science Foundation of China(42225505 and U2142204)S&T Development Fund of Chinese Academy of Meteorological Sciences(2023KJ033 and 2022KJ007)+1 种基金Jiangsu Collaborative Innovation Center for Climate ChangeKey Research and Development Program of Yunnan Province(202403AC100040)。
文摘The Yunnan–Guizhou quasi-stationary front(YGQSF)is a critical weather system in Southwest China during the cold season(November to April),frequently introducing low-temperature disasters.Accurate forecasting of YGQSF poses significant challenges for operational models.This study evaluates the performance of ECMWF Integrated Forecasting System(IFS)in forecasting YGQSFs by utilizing hourly observational data and a linear fitting method for frontal line identification.From 2020 to 2023,a total of 392 and 261 YGQSFs were identified in observational data and the IFS forecast,respectively.From east to west,the frontal lines gradually change from northwest–southeast-oriented to quasi-north-south-oriented.The northern segments of the frontal lines are concentrated east of the Hengduan Mountains,while the southern segments are dispersed across four high-occurrence regions on the Yungui Plateau.These high-occurrence regions are located on local highlands or their eastern slopes,highlighting the influence of topographic blocking effects.These spatial characteristics of the YGQSFs are well captured by the IFS,especially for the high-occurrence region between 103.5°E and 105°E,which is related to the pronounced spatial temperature gradients enhanced by the large elevation difference.Further analysis focuses on the impact of spatial temperature gradients on the YGQSF forecasting,particularly examining Hits,Misses,and False alarms.The model demonstrates superior performance in forecasting YGQSFs characterized by large temperature gradients and substantial cold air accumulation.Conversely,reduced temperature gradients,stemming from weaker cold air to the east or weaker warm air to the west,increase the risk of Misses.Temporally,the missed YGQSFs are uniformly distributed,while spatially they are scattered.False alarms,however,peak between February and March and are concentrated between 103.5°E and 107.25°E.Overall,forecasting YGQSFs with weak intensity,short frontal lines,or meridional orientation remains particularly challenging.The biases revealed in this study provide valuable insights for enhancing operational forecasting accuracy.
基金This work was supported by National Social Science Foundation of China(13BJY098)Social Science Foundation of Heilongjiang Province(16JYB06).
文摘In view of the poor precision of the theoretical model of labor demand estimation,it is difficult to estimate and predict the actual production problems accurately.Based on the actual production conditions and the relationship between the degree of mechanization of planting and the demand of labor force,this study established an estimation model for the labor demand of planting industry considering the factors of planting structure and mechanization degree.In order to ensure high reliability of data,the method of checking out abnormal data was adopted to obtain the cultivated land area index when the mechanization degree is from 0 to 100%.Taking Suihua region(Heilongjiang Province,China)as an example,the theory of the research was analyzed and applied.This study accessed to the data of cultivated land area per labor can afford when the mechanization level in Suihua area were 0 and 100%respectively through the investigation,and the average cultivated land area data of each labor force in two cases were sorted out and the abnormal data were eliminated at the same time.Finally,using the derived model,the data obtained and the mechanization level and cultivated land area of Suihua in the future,the labor demand amount in Suihua area from 2015 to 2019 were predicted.The model established in this study can be used to calculate the quantity of both current labor demand in planting industry and the labor demand in the various moments in the future through forecasting the future mechanization level and cultivated area which are the two main factors influencing the quantity of labor demand in planting structure.
