Unmitigated tropospheric delay is one of the major error sources in precise point positioning(PPP).Precise Slant Tropospheric Delay(STD)estimation could help to provide cleaner observables for PPP,and improve its conv...Unmitigated tropospheric delay is one of the major error sources in precise point positioning(PPP).Precise Slant Tropospheric Delay(STD)estimation could help to provide cleaner observables for PPP,and improve its convergence,accuracy,and stability.STD is difficult to model accurately due to the rapid spatial and temporal variation of the water vapor in the troposphere.In the traditional approach,the STD is mapped from the zenith direction,which assumes a spherically symmetric local tropospheric profile and has limitations.In this paper,a new approach of directly estimating the STD from high resolution numerical weather modeling(NWM)products is introduced.This approach benefits from the best available meteorological information to improve real time STD estimation,with the RMS residual lower than 3.5 cm above 15°elevation,and 2 cm above 30°.Therefore,the new method can provide sufficient accuracy to improve PPP convergence time.To improve the performance of the new method in highly variable tropospheric conditions,a correction scheme is proposed which combines NWM information with multi-GNSS observations from a network of local continuously operating reference stations.It is demonstrated through a case study that this correction scheme is quite effective in reducing the STD estimation residuals and PPP convergence time.展开更多
Numerical weather simulation data usually comprises various meteorological variables, such as precipitation, temperature and pressure. In practical applications, data generated with several different numerical simulat...Numerical weather simulation data usually comprises various meteorological variables, such as precipitation, temperature and pressure. In practical applications, data generated with several different numerical simulation models are usually used together by forecasters to generate the final forecast. However, it is difficult for forecasters to obtain a clear view of all the data due to its complexity. This has been a great limitation for domain experts to take advantage of all the data in their routine work. In order to help explore the multi-variate and multi-model data, we propose a stamp based exploration framework to assist domain experts in analyzing the data. The framework is used to assist domain experts in detecting the bias patterns between numerical simulation data and observation data. The exploration pipeline originates from a single meteorological variable and extends to multiple variables under the guidance of a designed stamp board. Regional data patterns can be detected by analyzing distinctive stamps on the board or generating extending stamps using the Boolean set operations. Experiment results show that some meteorological phenomena and regional data patterns can be easily detected through the exploration. These can help domain experts conduct the data analysis efficiently and further guide forecasters in producing reliable weather forecast.展开更多
The data of several rainfall products, including those estimated from satellite measurements and those forecasted via numerical weather modeling, for a severe debris-flow event in Zhouqu, Northwest China, are compared...The data of several rainfall products, including those estimated from satellite measurements and those forecasted via numerical weather modeling, for a severe debris-flow event in Zhouqu, Northwest China, are compared and analyzed in this paper. The satellite products, including CPC MORPHing technique（CMORPH）, TMPA-RT, and PERSIANN are all near-real-time retrieved with high temporal and spatial resolutions. The numerical weather model used in this paper for precipitation forecasting is WRF. The results show that all three satellite products can basically reproduce the rainfall pattern, distribution, timing, scale, and extreme values of the event, compared with gauge data. Their temporal and spatial correlation coefficients with gauge data are as high as about 0.6, which is statistically significant at 0.01 level. The performance of the forecasted results modeled with different spatial resolutions are not as good as the satellite-estimated results, although their correlation coefficients are still statistically significant at 0.05 level. From the total rainfall and extreme value time series for the domain, it is clear that, from the grid-to-grid perspective, the passive microwave-based CMORPH and TRMM products are more accurate than the infrared-based PERSIANN, while PERSIANN performs very well from the general point of view, especially when considering the whole domain or the whole convective precipitation system. The forecasted data — especially the highest resolution model domain data — are able to represent the total or mean precipitation very well in the research domain, while for extreme values the errors are large. This study suggests that satellite-retrieved and model-forecasted rainfall data are a useful complement to gauge data, especially for areas without gauge stations and areas not covered by weather radars.展开更多
This paper introduces several alternative statistical approaches to modeling and prediction of electric energy generated by photovoltaic farms. The statistical models use outputs of a numerical weather prediction mode...This paper introduces several alternative statistical approaches to modeling and prediction of electric energy generated by photovoltaic farms. The statistical models use outputs of a numerical weather prediction model as their inputs. Presented statistical models allow for easy-to-compute predictions, both in temporal sense and for out-of-sample individual farms. Model performance is illustrated on a sample of real photovoltaic farms located in the Czech Republic.展开更多
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
Precipitable Water （PW） derived from Global Positioning System （GPS） measurements and numerical weather prediction （NWP） model analysis data were compared to further evaluate the effcacy of applying GPS-derived ...Precipitable Water （PW） derived from Global Positioning System （GPS） measurements and numerical weather prediction （NWP） model analysis data were compared to further evaluate the effcacy of applying GPS-derived PW to the NWP model. The spatial and temporal variations of GPS-derived PW during a rainfall event were also examined. GPS-derived PW measurements show good agreement with the behavior of water vapor at a high spatial resolution during the analysis period. Temporal anomalies of GPS-derived PW moving along with the front are successfully detected by the GPS array. Large positive anomalies of GPS-derived PW are indicated immediately before a rainfall event, and the intensity of these positive anomalies do not seem to decrease significantly as the precipitation system passes. These results indicate that the Korean GPS network may have great potential as a PW sensor over the Korean Peninsula. In contrast with GPS-derived PW, NWP-derived PW shows negative biases. These biases appear to stem mainly from the differences between modeled and actual GPS site elevations, as GPS sites were generally located at elevations lower than those employed by the NWP model. However, there still exists a discernable dry bias after a PW correction is applied to NWP-derived PW. GPS-derived PW better reflects the spatial and temporal moisture variations of precipitation systems, as compared to NWP-derived PW. These results provide entirely new information for improving the regional NWP system, since GPS-derived PW produced with data from the Korean GPS network may be incorporated into the NWP model to improve rainfall forecasts.展开更多
Forecasting wind structure of tropical cyclone(TC)is vital in assessment of impact due to high winds using Numerical Weather Prediction(NWP)model.The usual verification technique on TC wind structure forecasts are bas...Forecasting wind structure of tropical cyclone(TC)is vital in assessment of impact due to high winds using Numerical Weather Prediction(NWP)model.The usual verification technique on TC wind structure forecasts are based on grid-to-grid comparisons between forecast field and the actual field.However,precision of traditional verification measures is easily affected by small scale errors and thus cannot well discriminate the accuracy or effectiveness of NWP model forecast.In this study,the Method for Object-Based Diagnostic Evaluation(MODE),which has been widely adopted in verifying precipitation fields,is utilized in TC’s wind field verification for the first time.The TC wind field forecast of deterministic NWP model and Ensemble Prediction System(EPS)of the European Centre for Medium-Range Weather Forecasts(ECMWF)over the western North Pacific and the South China Sea in 2020 were evaluated.A MODE score of 0.5 is used as a threshold value to represent a skillful(or good)forecast.It is found that the R34(radius of 34 knots)wind field structure forecasts within 72 h are good regardless of DET or EPS.The performance of R50 and R64 is slightly worse but the R50 forecasts within 48 h remain good,with MODE exceeded 0.5.The R64forecast within 48 h are worth for reference as well with MODE of around 0.5.This study states that the TC wind field structure forecast by ECMWF is skillful for TCs over the western North Pacific and the South China Sea.展开更多
基金This study is carried out as part of the project Innovative Navigation using new GNSS Signals with Hybridized Technologies(iNsight),which is funded by the UK Engineering and Physical Sciences Research Council(EPSRC).
文摘Unmitigated tropospheric delay is one of the major error sources in precise point positioning(PPP).Precise Slant Tropospheric Delay(STD)estimation could help to provide cleaner observables for PPP,and improve its convergence,accuracy,and stability.STD is difficult to model accurately due to the rapid spatial and temporal variation of the water vapor in the troposphere.In the traditional approach,the STD is mapped from the zenith direction,which assumes a spherically symmetric local tropospheric profile and has limitations.In this paper,a new approach of directly estimating the STD from high resolution numerical weather modeling(NWM)products is introduced.This approach benefits from the best available meteorological information to improve real time STD estimation,with the RMS residual lower than 3.5 cm above 15°elevation,and 2 cm above 30°.Therefore,the new method can provide sufficient accuracy to improve PPP convergence time.To improve the performance of the new method in highly variable tropospheric conditions,a correction scheme is proposed which combines NWM information with multi-GNSS observations from a network of local continuously operating reference stations.It is demonstrated through a case study that this correction scheme is quite effective in reducing the STD estimation residuals and PPP convergence time.
基金Supported by National Natural Science Foundation of China(61572274,61672307,61272225,51261120376)the National Key Technologies R&D Program of China(2015BAF23B03)
文摘Numerical weather simulation data usually comprises various meteorological variables, such as precipitation, temperature and pressure. In practical applications, data generated with several different numerical simulation models are usually used together by forecasters to generate the final forecast. However, it is difficult for forecasters to obtain a clear view of all the data due to its complexity. This has been a great limitation for domain experts to take advantage of all the data in their routine work. In order to help explore the multi-variate and multi-model data, we propose a stamp based exploration framework to assist domain experts in analyzing the data. The framework is used to assist domain experts in detecting the bias patterns between numerical simulation data and observation data. The exploration pipeline originates from a single meteorological variable and extends to multiple variables under the guidance of a designed stamp board. Regional data patterns can be detected by analyzing distinctive stamps on the board or generating extending stamps using the Boolean set operations. Experiment results show that some meteorological phenomena and regional data patterns can be easily detected through the exploration. These can help domain experts conduct the data analysis efficiently and further guide forecasters in producing reliable weather forecast.
基金supported by the National Natural Science Foundation of China[grant numbers 41421004 and 41210007]the International Innovation Team project of the Chinese Academy of Sciences entitled ‘High Resolution Numerical Simulation of Regional Environment’
文摘The data of several rainfall products, including those estimated from satellite measurements and those forecasted via numerical weather modeling, for a severe debris-flow event in Zhouqu, Northwest China, are compared and analyzed in this paper. The satellite products, including CPC MORPHing technique（CMORPH）, TMPA-RT, and PERSIANN are all near-real-time retrieved with high temporal and spatial resolutions. The numerical weather model used in this paper for precipitation forecasting is WRF. The results show that all three satellite products can basically reproduce the rainfall pattern, distribution, timing, scale, and extreme values of the event, compared with gauge data. Their temporal and spatial correlation coefficients with gauge data are as high as about 0.6, which is statistically significant at 0.01 level. The performance of the forecasted results modeled with different spatial resolutions are not as good as the satellite-estimated results, although their correlation coefficients are still statistically significant at 0.05 level. From the total rainfall and extreme value time series for the domain, it is clear that, from the grid-to-grid perspective, the passive microwave-based CMORPH and TRMM products are more accurate than the infrared-based PERSIANN, while PERSIANN performs very well from the general point of view, especially when considering the whole domain or the whole convective precipitation system. The forecasted data — especially the highest resolution model domain data — are able to represent the total or mean precipitation very well in the research domain, while for extreme values the errors are large. This study suggests that satellite-retrieved and model-forecasted rainfall data are a useful complement to gauge data, especially for areas without gauge stations and areas not covered by weather radars.
文摘This paper introduces several alternative statistical approaches to modeling and prediction of electric energy generated by photovoltaic farms. The statistical models use outputs of a numerical weather prediction model as their inputs. Presented statistical models allow for easy-to-compute predictions, both in temporal sense and for out-of-sample individual farms. Model performance is illustrated on a sample of real photovoltaic farms located in the Czech Republic.
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.
基金funded by the Korea Meteorological Administration Research and Development Program under Grant GATER 2006-2201 supported by the Brain Korea 21 Project
文摘Precipitable Water （PW） derived from Global Positioning System （GPS） measurements and numerical weather prediction （NWP） model analysis data were compared to further evaluate the effcacy of applying GPS-derived PW to the NWP model. The spatial and temporal variations of GPS-derived PW during a rainfall event were also examined. GPS-derived PW measurements show good agreement with the behavior of water vapor at a high spatial resolution during the analysis period. Temporal anomalies of GPS-derived PW moving along with the front are successfully detected by the GPS array. Large positive anomalies of GPS-derived PW are indicated immediately before a rainfall event, and the intensity of these positive anomalies do not seem to decrease significantly as the precipitation system passes. These results indicate that the Korean GPS network may have great potential as a PW sensor over the Korean Peninsula. In contrast with GPS-derived PW, NWP-derived PW shows negative biases. These biases appear to stem mainly from the differences between modeled and actual GPS site elevations, as GPS sites were generally located at elevations lower than those employed by the NWP model. However, there still exists a discernable dry bias after a PW correction is applied to NWP-derived PW. GPS-derived PW better reflects the spatial and temporal moisture variations of precipitation systems, as compared to NWP-derived PW. These results provide entirely new information for improving the regional NWP system, since GPS-derived PW produced with data from the Korean GPS network may be incorporated into the NWP model to improve rainfall forecasts.
基金supported by the ESCAP/WMO Typhoon Committee Research Fellowship Scheme 2020 hosted by the Hong Kong Observatorythe Shanghai Natural Science Foundation(21ZR1477300)+2 种基金FengYun Application Pioneering Project(FY-APP-2021.0106)WMO Typhoon Landfall Forecast Demonstration Project(TLFDP)the Typhoon Scientific and Technological Innovation Group of Shanghai Meteorological Service。
文摘Forecasting wind structure of tropical cyclone(TC)is vital in assessment of impact due to high winds using Numerical Weather Prediction(NWP)model.The usual verification technique on TC wind structure forecasts are based on grid-to-grid comparisons between forecast field and the actual field.However,precision of traditional verification measures is easily affected by small scale errors and thus cannot well discriminate the accuracy or effectiveness of NWP model forecast.In this study,the Method for Object-Based Diagnostic Evaluation(MODE),which has been widely adopted in verifying precipitation fields,is utilized in TC’s wind field verification for the first time.The TC wind field forecast of deterministic NWP model and Ensemble Prediction System(EPS)of the European Centre for Medium-Range Weather Forecasts(ECMWF)over the western North Pacific and the South China Sea in 2020 were evaluated.A MODE score of 0.5 is used as a threshold value to represent a skillful(or good)forecast.It is found that the R34(radius of 34 knots)wind field structure forecasts within 72 h are good regardless of DET or EPS.The performance of R50 and R64 is slightly worse but the R50 forecasts within 48 h remain good,with MODE exceeded 0.5.The R64forecast within 48 h are worth for reference as well with MODE of around 0.5.This study states that the TC wind field structure forecast by ECMWF is skillful for TCs over the western North Pacific and the South China Sea.
