Surface soil moisture has great impact on both meso-and microscale atmospheric processes,especially on severe local convection processes and on the dynamics of short-lived torrential rains.To promote the performance o...Surface soil moisture has great impact on both meso-and microscale atmospheric processes,especially on severe local convection processes and on the dynamics of short-lived torrential rains.To promote the performance of the land surface model (LSM) in surface soil moisture simulations,a hybrid hydrologic runoff parameterization scheme based upon the essential modeling theories of the Xin'anjiang model and Topography based hydrological Model (TOPMODEL) was developed in preference to the simple water balance model (SWB) in the Noah LSM.Using a strategy for coupling and integrating this modified Noah LSM to the Global/Regional Assimilation and Prediction System (GRAPES) analogous to that used with the standard Noah LSM,a simulation of atmosphere-land surface interactions for a torrential event during 2007 in Shandong was attempted.The results suggested that the surface,10-cm depth soil moisture simulated by GRAPES using the modified hydrologic approach agrees well with the observations.Improvements from the simulated results were found,especially over eastern Shandong.The simulated results,compared with the products of the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) soil moisture datasets,indicated a consistent spatial pattern over all of China.The temporal variation of surface soil moisture was validated with the data at an observation station,also demonstrated that GRAPES with modified Noah LSM exhibits a more reasonable response to precipitation events,even though biases and systematic trends may still exist.展开更多
Air pollution is an issue of great concern in any urban region due to its serious health implications.The capital of India,New Delhi continues to be in the list of most polluted cities since 2014.The air quality of an...Air pollution is an issue of great concern in any urban region due to its serious health implications.The capital of India,New Delhi continues to be in the list of most polluted cities since 2014.The air quality of any region depends on the ability of dispersion of air pollutants.The height or depth of the atmospheric boundary layer(ABL)is one measure of dispersion of air pollutants.Ventilation coefficient is another crucial parameter in determining the air quality of any region.Both of these parameters are obtained over Delhi from the operational global numerical weather prediction(NWP)model of National Centre for Medium Range Weather forecasting(NCMRWF)known as NCMRWF Unified Model(NCUM).The height of ABL over Delhi,is also obtained from radiosonde observations using the parcel method.A good agreement is found between the observed and predicted values of ABL height.The maximum height of ABL is obtained during summer season and minimum is obtained in winter season.High values of air pollutants are found when the values of ABL height and ventilation coefficient are low.展开更多
Snow depth and sea ice thickness were observed applying an ice mass balance buoy(IMB)in the drifting ice station Tara during the International Polar Year in 2007.Detailed in situ observations on meteorological variabl...Snow depth and sea ice thickness were observed applying an ice mass balance buoy(IMB)in the drifting ice station Tara during the International Polar Year in 2007.Detailed in situ observations on meteorological variables and surface fluxes were taken during May to August.For this study,the operational analyses and short-term forecasts from two numerical weather prediction(NWP)models(ECMWF and HIRLAM)were extracted for the Tara drift trajectory.We compared the IMB,meteorological and surface flux observations against the NWP products,also applying a one-dimensional thermodynamic sea ice model(HIGHTSI)to calculate the snow and ice mass balance and its sensitivity to atmospheric forcing.The modelled snow depth time series,controlled by NWP-based precipitation,was in line with the observed one.HIGHTSI reproduced well the snowmelt onset,the progress of the melt,and the first date of snow-free conditions.HIGHTSI performed well also in the late August freezing season.Challenges remain to model the“false bottom”observed during the melting season.The evolution of the vertical temperature profiles in snow and ice was better simulated when the model was forced by in situ observations instead of NWP results.During the melting period,the nonlinear ice temperature profile was successfully modelled with both forcing options.During spring and the melting season,total sea ice mass balance was most sensitive to uncertainties in NWP results for the downward longwave radiation,followed by the downward shortwave radiation,air temperature,and wind speed.展开更多
Accurate predictions of wind power generation several months in advance are crucial for the effective operation and maintenance of wind farms and for facilitating efficient power purchase planning.This study evaluates...Accurate predictions of wind power generation several months in advance are crucial for the effective operation and maintenance of wind farms and for facilitating efficient power purchase planning.This study evaluates the performance of the seasonal prediction system of the National Centre for Medium-Range Weather Forecasting in forecasting near-surface winds.An analysis of 23 years of hindcast data,from 1993 to 2015,indicates that the seasonal prediction system effectively captures the inter-annual variability of near-surface winds.Specifically,predictions initialized in May demonstrate notable accuracy,with a skill score of 0.78 in predicting the sign of wind speed anomalies aggregated across various wind farms during the high wind season(June to August).Additionally,we critically examine the peculiarity of a case study from 2020,when the Indian wind industry experienced low power generation.To enhance forecasting accuracy,we employ statistical techniques to produce bias-corrected forecasts on a seasonal scale.This approach improves the accuracy of wind speed predictions at turbine hub height.Our assessment,based on root mean square error,reveals that bias-corrected wind speed forecasts show a significant improvement,ranging from 54%to 93%.展开更多
This study examines the track and intensity forecasts of two typical Bay of Bengal tropical cyclones(TC)ASANI and MOCHA.The analysis of various Numerical Weather Prediction(NWP)model forecasts[ECMWF(European Centre fo...This study examines the track and intensity forecasts of two typical Bay of Bengal tropical cyclones(TC)ASANI and MOCHA.The analysis of various Numerical Weather Prediction(NWP)model forecasts[ECMWF(European Centre for Medium range Weather Forecast),NCEP(National Centers for Environmental Prediction),NCUM(National Centre for Medium Range Weather Forecast-Unified Model),IMD(India Meteorological Department),HWRF(Hurricane Weather Research and Forecasting)],MME(Multi-model Ensemble),SCIP(Statistical Cyclone Intensity Prediction)model,and OFCL(Official)forecasts shows that intensity forecasts of ASANI and track forecasts of MOCHA were reasonably good,but there were large errors and wide variation in track forecasts of ASANI and in intensity forecasts of MOCHA.Among all model forecasts,the track forecast errors of IMD model and MME were least in general for ASANI and MOCHA respectively.Also,the landfall point forecast errors of IMD were least for ASANI,and the MME and OFCL forecast errors were least for MOCHA.No model is found to be consistently better for landfall time forecast for ASANI,and the errors of ECMWF,IMD and HWRF were least and of same order for MOCHA.The intensity forecast errors of OFCL and SCIP were least for ASANI,and the forecast errors of HWRF,IMD,NCEP,SCIP and OFCL were comparable and least for MOCHA up to 48 h forecast and HWRF errors were least thereafter in general.The ECMWF model forecast errors for intensity were found to be highest for both the TCs.The results also show that although there is significant improvement of track forecasts and limited or no improvement of intensity forecast in previous decades but challenges still persists in real time forecasting of both track and intensity due to wide variation and inconsistency of model forecasts for different TC cases.展开更多
基金funded by the National BasicResearch Program of China (Grant No. 2010CB951404)the National Natural Science Foundation of China (Grant No. 40971024)CMA Special Meteorology Project (Grant No.GYHY200706001)
文摘Surface soil moisture has great impact on both meso-and microscale atmospheric processes,especially on severe local convection processes and on the dynamics of short-lived torrential rains.To promote the performance of the land surface model (LSM) in surface soil moisture simulations,a hybrid hydrologic runoff parameterization scheme based upon the essential modeling theories of the Xin'anjiang model and Topography based hydrological Model (TOPMODEL) was developed in preference to the simple water balance model (SWB) in the Noah LSM.Using a strategy for coupling and integrating this modified Noah LSM to the Global/Regional Assimilation and Prediction System (GRAPES) analogous to that used with the standard Noah LSM,a simulation of atmosphere-land surface interactions for a torrential event during 2007 in Shandong was attempted.The results suggested that the surface,10-cm depth soil moisture simulated by GRAPES using the modified hydrologic approach agrees well with the observations.Improvements from the simulated results were found,especially over eastern Shandong.The simulated results,compared with the products of the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) soil moisture datasets,indicated a consistent spatial pattern over all of China.The temporal variation of surface soil moisture was validated with the data at an observation station,also demonstrated that GRAPES with modified Noah LSM exhibits a more reasonable response to precipitation events,even though biases and systematic trends may still exist.
文摘Air pollution is an issue of great concern in any urban region due to its serious health implications.The capital of India,New Delhi continues to be in the list of most polluted cities since 2014.The air quality of any region depends on the ability of dispersion of air pollutants.The height or depth of the atmospheric boundary layer(ABL)is one measure of dispersion of air pollutants.Ventilation coefficient is another crucial parameter in determining the air quality of any region.Both of these parameters are obtained over Delhi from the operational global numerical weather prediction(NWP)model of National Centre for Medium Range Weather forecasting(NCMRWF)known as NCMRWF Unified Model(NCUM).The height of ABL over Delhi,is also obtained from radiosonde observations using the parcel method.A good agreement is found between the observed and predicted values of ABL height.The maximum height of ABL is obtained during summer season and minimum is obtained in winter season.High values of air pollutants are found when the values of ABL height and ventilation coefficient are low.
基金This study was initialized during DAMOCLES project(Grant no.18509)which was funded by the 6th Framework Programme of the European Commission+2 种基金The initial data analysis was funded by the Research Council of Norway’s AMORA project(Grant no.#193592)The modelling work has been supported by the Academy of Finland(Contract 317999)The finalization of this work was supported by the European Union’s Horizon 2020 research and innovation programme(Grant no.727890–INTAROS).
文摘Snow depth and sea ice thickness were observed applying an ice mass balance buoy(IMB)in the drifting ice station Tara during the International Polar Year in 2007.Detailed in situ observations on meteorological variables and surface fluxes were taken during May to August.For this study,the operational analyses and short-term forecasts from two numerical weather prediction(NWP)models(ECMWF and HIRLAM)were extracted for the Tara drift trajectory.We compared the IMB,meteorological and surface flux observations against the NWP products,also applying a one-dimensional thermodynamic sea ice model(HIGHTSI)to calculate the snow and ice mass balance and its sensitivity to atmospheric forcing.The modelled snow depth time series,controlled by NWP-based precipitation,was in line with the observed one.HIGHTSI reproduced well the snowmelt onset,the progress of the melt,and the first date of snow-free conditions.HIGHTSI performed well also in the late August freezing season.Challenges remain to model the“false bottom”observed during the melting season.The evolution of the vertical temperature profiles in snow and ice was better simulated when the model was forced by in situ observations instead of NWP results.During the melting period,the nonlinear ice temperature profile was successfully modelled with both forcing options.During spring and the melting season,total sea ice mass balance was most sensitive to uncertainties in NWP results for the downward longwave radiation,followed by the downward shortwave radiation,air temperature,and wind speed.
文摘Accurate predictions of wind power generation several months in advance are crucial for the effective operation and maintenance of wind farms and for facilitating efficient power purchase planning.This study evaluates the performance of the seasonal prediction system of the National Centre for Medium-Range Weather Forecasting in forecasting near-surface winds.An analysis of 23 years of hindcast data,from 1993 to 2015,indicates that the seasonal prediction system effectively captures the inter-annual variability of near-surface winds.Specifically,predictions initialized in May demonstrate notable accuracy,with a skill score of 0.78 in predicting the sign of wind speed anomalies aggregated across various wind farms during the high wind season(June to August).Additionally,we critically examine the peculiarity of a case study from 2020,when the Indian wind industry experienced low power generation.To enhance forecasting accuracy,we employ statistical techniques to produce bias-corrected forecasts on a seasonal scale.This approach improves the accuracy of wind speed predictions at turbine hub height.Our assessment,based on root mean square error,reveals that bias-corrected wind speed forecasts show a significant improvement,ranging from 54%to 93%.
文摘This study examines the track and intensity forecasts of two typical Bay of Bengal tropical cyclones(TC)ASANI and MOCHA.The analysis of various Numerical Weather Prediction(NWP)model forecasts[ECMWF(European Centre for Medium range Weather Forecast),NCEP(National Centers for Environmental Prediction),NCUM(National Centre for Medium Range Weather Forecast-Unified Model),IMD(India Meteorological Department),HWRF(Hurricane Weather Research and Forecasting)],MME(Multi-model Ensemble),SCIP(Statistical Cyclone Intensity Prediction)model,and OFCL(Official)forecasts shows that intensity forecasts of ASANI and track forecasts of MOCHA were reasonably good,but there were large errors and wide variation in track forecasts of ASANI and in intensity forecasts of MOCHA.Among all model forecasts,the track forecast errors of IMD model and MME were least in general for ASANI and MOCHA respectively.Also,the landfall point forecast errors of IMD were least for ASANI,and the MME and OFCL forecast errors were least for MOCHA.No model is found to be consistently better for landfall time forecast for ASANI,and the errors of ECMWF,IMD and HWRF were least and of same order for MOCHA.The intensity forecast errors of OFCL and SCIP were least for ASANI,and the forecast errors of HWRF,IMD,NCEP,SCIP and OFCL were comparable and least for MOCHA up to 48 h forecast and HWRF errors were least thereafter in general.The ECMWF model forecast errors for intensity were found to be highest for both the TCs.The results also show that although there is significant improvement of track forecasts and limited or no improvement of intensity forecast in previous decades but challenges still persists in real time forecasting of both track and intensity due to wide variation and inconsistency of model forecasts for different TC cases.
