Three high-resolution satellite precipitation products, the Tropical Rainfall Measuring Mission （TRMM） standard precipitation products 3B42V6 and 3B42RT and the Climate Precipitation Center＇s （CPC） morphing techn...Three high-resolution satellite precipitation products, the Tropical Rainfall Measuring Mission （TRMM） standard precipitation products 3B42V6 and 3B42RT and the Climate Precipitation Center＇s （CPC） morphing technique precipitation product （CMORPH）, were evaluated against surface rain gauge observations from the Laohahe Basin in northern China. Widely used statistical validation indices and categorical statistics were adopted. The evaluations were performed at multiple time scales, ranging from daily to yearly, for the years from 2003 to 2008. The results show that all three satellite precipitation products perform very well in detecting the occurrence of precipitation events, but there are some different biases in the amount of precipitation. 3B42V6, which has a bias of 21%, fits best with the surface rain gauge observations at both daily and monthly scales, while the biases of 3B42RT and CMORPH, with values of 81% and 67%, respectively, are much higher than a normal receivable threshold. The quality of the satellite precipitation products also shows monthly and yearly variation： 3B42RT has a large positive bias in the cold season from September to April, while CMORPH has a large positive bias in the warm season from May to August, and they all attained their best values in 2006 （with 10%, 50%, and -5% biases for 3B42V6, 3B42RT, and CMORPH, respectively）. Our evaluation shows that, for the Laohahe Basin, 3B42V6 has the best correspondence with the surface observations, and CMORPH performs much better than 3B42RT. The large errors of 3B42RT and CMORPH remind us of the need for new improvements to satellite precipitation retrieval algorithms or feasible bias adjusting methods.展开更多
Satellite-based Precipitation Estimates(SPEs)have gained importance due to enhanced spatial and temporal resolution,particularly in Indus basin,where raingauge network has fewer observation stations and drainage area ...Satellite-based Precipitation Estimates(SPEs)have gained importance due to enhanced spatial and temporal resolution,particularly in Indus basin,where raingauge network has fewer observation stations and drainage area is laying in many countries.Formulation of SPEs is based on indirect mechanism,therefore,assessment and correction of associated uncertainties is required.In the present study,disintegration of uncertainties associated with four prominent real time SPEs,IMERG,TMPA,CMORPH and PERSIANN has been conducted at grid level,regional scale,and summarized in terms of regions as well as whole study area basis.The bias has been disintegrated into hit,missed,false biases,and Root Mean Square Error(RMSE)into systematic and random errors.A comparison among gauge-and satellite-based precipitation estimates at annual scale,showed promising result,encouraging use of real time SPEs in the study area.On grid basis,at daily scale,from box plots,the median values of total bias(-0.5 to 0.5 mm)of the used SPEs were also encouraging although some under/over estimations were noted in terms of hit bias(-0.15 to 0.05 mm/day).Relatively higher values of missed(0.3 to 0.5 mm/day)and false(0.5 to 0.7 mm/day)biases were observed.The detected average daily RMSE,systematic errors,and random errors were also comparatively higher.Regional-scale spatial distribution of uncertainties revealed lower values of uncertainties in plain areas,depicting the better performance of satellite-based products in these areas.However,in areas of high altitude(>4000 m),due to complex topography and climatic conditions(orographic precipitation and glaciated peaks)higher values of biases and errors were observed.Topographic barriers and point scale gauge data could also be a cause of poor performance of SPEs in these areas,where precipitation is more on ridges and less in valleys where gauge stations are usually located.Precipitation system’s size and intensity can also be a reason of higher biases,because Microwave Imager underestimate precipitation in small systems(<200 km^(2))and overestimate in large systems(>2000 km^(2)).At present,use of bias correction techniques at daily time scale is compulsory to utilize real time SPEs in estimation of floods in the study area.Inter comparison of satellite products indicated that IMERG gave better results than the others with the lowest values of systematic errors,missed and false biases.展开更多
Satellite-and reanalysis-based precipitation products are important data source for precipitation, particularly in areas with a sparse gauge network. Here, five open-access precipitation products, including the newly ...Satellite-and reanalysis-based precipitation products are important data source for precipitation, particularly in areas with a sparse gauge network. Here, five open-access precipitation products, including the newly released China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool(SWAT) model(CMADS)reanalysis dataset and four widely used bias-adjusted satellite precipitation products [SPPs;i.e., Tropical Rainfall Measuring Mission(TRMM) Multisatellite Precipitation Analysis 3B42 Version 7(TMPA 3B42V7), Climate Prediction Center(CPC) morphing technique satellite–gauge blended product(CMORPH-BLD), Climate Hazards Group Infrared Precipitation with Station Data(CHIRPS), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record(PERSIANN-CDR)], were assessed. These products were first compared with the gauge observed data collected for the upper Huaihe River basin, and then were used as forcing data for streamflow simulation by the Xin’anjiang(XAJ) hydrological model under two scenarios with different calibration procedures. The performance of CMADS precipitation product for the Chinese mainland was also assessed. The results show that:(1) for the statistical assessment, CMADS and CMORPH-BLD perform the best, followed by TMPA 3B42V7, CHIRPS, and PERSIANN-CDR, among which the correlation coefficient(CC) and rootmean-square error(RMSE) values of CMADS are optimal, although it exhibits certain significant negative relative bias(BIAS;-22.72%);(2) CMORPH-BLD performs the best in capturing and detecting rainfall events, while CMADS tends to underestimate heavy and torrential precipitation;(3) for streamflow simulation, the performance of using CMADS as input is very good, with the highest Nash–Sutcliffe efficiency(NSE) values(0.85 and 0.75 for calibration period and validation period, respectively);and(4) CMADS exhibits high accuracy in eastern China while with significant negative BIAS, and the performance declines from southeast to northwest. The statistical and hydrological evaluations show that CMADS and CMORPH-BLD have high potential for observing precipitation. As high negative BIAS values showed up in CMADS evaluation, further study on the error sources from original data and calibration algorithms is necessary. This study can serve as a reference for selecting precipitation products in datascarce regions with similar climates and topography in the Global Precipitation Measurement(GPM) era.展开更多
Satellite-based precipitation observations with high spatiotemporal resolution are essential for studying rainfall-induced natural hazards,especially in alpine and canyon areas of the southeastern Tibetan Plateau,whic...Satellite-based precipitation observations with high spatiotemporal resolution are essential for studying rainfall-induced natural hazards,especially in alpine and canyon areas of the southeastern Tibetan Plateau,which are prone to such hazards yet sparsely gauged.Here,we evaluated precipitation estimated from the Chinese Fengyun-4A meteorological satellite(FY-4A AGRI)versus the Integrated Multi-satellitE Retrievals for GPM(IMERG),by using rain gauge data collected in the Parlung Zangbo Basin from May through September in both 2018 and 2019.Our results showed that(1)FY-4A AGRI generated smaller values of RMSE(root mean square error)on hourly to daily scales,and larger correlation coefficients(R-values)and smaller RMSE values for both moderate and heavy rain,indicating its greater accuracy at rainfall estimation,which is most likely due to the denser rain gauge network at a finer temporal scale used when calibrating FY-4A AGRI;(2)Both satellite products underestimated the volume of moderate and heavy rain,with the larger degree of underestimation by FY-4A AGRI,which could lower their performance in flood monitoring and forecasting;(3)Worse performance and greater inconsistency between the two products were observed in high-elevation areas,perhaps because of orographic cloud effects in these mountainous areas;and(4)Both products revealed that the Gangrigabu Range blocked incoming water vapor from the southwest monsoon,with a better representation of the spatial pattern and spatial variability produced by IMERG.To improve precipitation estimation,the effects of complex terrain should be explicitly incorporated into the retrieval algorithms,with more gauged observations in a denser network and at a finer temporal scale needed to robustly calibrate the satellite-based estimates.展开更多
The Tropical Rainfall Measuring Mission （TRMM） Multi-satellite Precipitation Analysis （TMPA） and National Oceanic and Atmospheric Administration （NOAA） Climate Prediction Center （CPC） morphing technique （CMO...The Tropical Rainfall Measuring Mission （TRMM） Multi-satellite Precipitation Analysis （TMPA） and National Oceanic and Atmospheric Administration （NOAA） Climate Prediction Center （CPC） morphing technique （CMORPH） are two important multi-satellite precipitation products in TRMM-era and perform important functions in GPM-era. Both TMPA and CMORPH systems simultaneously upgraded their retrieval algorithms and released their latest version of precipitation data in 2013. In this study, the latest TMPA and CMORPH products （i.e., Version-7 real-time TMPA （T-rt） and gauge-adjusted TMPA （T-adj）, and Version- 1.0 real-time CMORPH （C-rt） and Version-l.0 gauge-adjusted CMORPH （C-adj）） are evaluated and intercompared by using independent rain gauge observations for a 12-year （2000--2011） period over two typical basins in China with different geographical and climate conditions. Results indicate that all TMPA and CMORPH products tend to overestimate precipitation for the high-latitude semiarid Laoha River Basin and underestimate it for the low-latitude humid Mishui Basin. Overall, the satellite precipitation products exhibit superior performance over Mishui Basin than that over Laoha River Basin. The C-adj presents the best performance over the high-latitude Laoha River Basin, whereas T-adj showed the best performance over the low-latitude Mishui Basin. The two gauge-adjusted products demonstrate potential in water resource management. However, the accuracy of two real-time satellite precipitation products demonstrates large variability in the two validation basins. The C-rt reaches a similar accuracy level with the gauge-adjusted satellite precipitation products in the high-latitude Laoha River Basin, and T-rt performs well in the low-latitude Mishui Basin. The study also reveals that all satellite precipitation products obviously overestimate light rain amounts and events over Laoha River Basin, whereas they underestimate the amount and events over Mishui Basin. The findings of the precision characteristics associated with the latest TMPA and CMORPH precipitation products at different basins will offer satellite pre- cipitation users an enhanced understanding of the applicability of the latest TMPA and CMORPH for water resource management, hydrologic process simulation, and hydrometeorological disaster prediction in other similar regions in China. The findings will also be useful for IMERG algorithm development and update in GPM-era.展开更多
The high resolution satellite precipitation products bear great potential for large-scale drought monitoring, especially for those regions with sparsely or even without gauge coverage. This study focuses on utilizing ...The high resolution satellite precipitation products bear great potential for large-scale drought monitoring, especially for those regions with sparsely or even without gauge coverage. This study focuses on utilizing the latest Version-7 TRMM Multi-satellite Precipitation Analysis （TMPA 3B42V7） data for drought condition monitoring in the Weihe River Basin （0.135×10^6 km2）. The accuracy of the monthly TMPA 3B42V7 satellite precipitation data was firstly evaluated against the ground rain gauge observations. The statistical characteristics between a short period data series （1998-2013） and a long period data series （1961-2013） were then compared. The TMPA 3B42V7-based SPI （Standardized Precipitation Index） sequences were finally validated and analyzed at various temporal scales for assessing the drought conditions. The results indicate that the monthly TMPA 3B42V7 precipitation is in a high agreement with the rain gauge observations and can accurately capture the temporal and spatial characteristics of rainfall within the Weihe River Basin. The short period data can present the characteristics of long period record, and it is thus acceptable to use the short period data series to estimate the cumulative probability function in the SPI calculation. The TMPA 3B42V7-based SPI matches well with that based on the rain gauge observations at multiple time scales （i.e., 1-, 3-, 6-, 9-, and 12-month） and can give an acceptable temporal distribution of drought conditions. It suggests that the TMPA 3B42V7 precipitation data can be used for monitoring the occurrence of drought in the Weihe River Basin.展开更多
This paper compares monthly and seasonal rain rates derived from the Version 5 （V5） and Version 6 （V6） TRMM Precipitation Radar （TPR, TSDIS reference 2A25）, TRMM Microwave Imager （TMI, 2A12）, TRMM Combined Ins...This paper compares monthly and seasonal rain rates derived from the Version 5 （V5） and Version 6 （V6） TRMM Precipitation Radar （TPR, TSDIS reference 2A25）, TRMM Microwave Imager （TMI, 2A12）, TRMM Combined Instrument （TCI, 2B31）, TRMM calibrated IR rain estimates （3B42） and TRMM merged gauge and satellite analysis （3B43） algorithms over New Mexico （NM） with rain gauge analyses provided by the New Mexico water districts （WD）. The average rain rates over the NM region for 1998- 2002 are 0.91 mm d^-1 for WD and 0.75, 1.38, 1.49, 1.27, and 1.07 mm d^-1 for V5 3B43, 3B42, TMI, PR and TCA; and 0.74, 1.38, 0.87 and 0.97 mm d^-1 for V6 3B43, TMI, TPR and TCA, respectively. Comparison of V5 3B43 with WD rain rates and the daily TRMM mission index （TPR and TMI） suggests that the low bias of V5 3B43 for the wet months （summer to early fall） may be due to the non-inclusion of some rain events in the operational gauge analyses that. are used in the production of V5 3B43. Correlation analyses show that the WD rain rates vary in phase, with higher correlation between neighboring WDs. High temporal correlations （〉0.8） exist between WD and the combined algorithms （3B42, 3B43 and TCA for both V5 and V6） while satellite instrument algorithms （PR, TMI and TCI） are correlated best among themselves at the monthly scale. Paired t-tests of the monthly time series show that V5 3B42 and TMI are statistically different from the WD rain rates while no significant difference exists between WD and the other products. The agreements between the TRMM satellite and WD gauge estimates are best for the spring and fall and worst for winter and summer. The reduction in V6 TMI （-7.4%） and TPR （-31%） rain rates （compared to V5） results in better agreement between WD estimates and TMI in winter and TPR during summer.展开更多
This study employs a newly defined regional-rainfall-event （RRE） concept to compare the hourly charac- teristics of warm-season （May September） rainfall among rain gauge observations, China merged hourly pre- cipi...This study employs a newly defined regional-rainfall-event （RRE） concept to compare the hourly charac- teristics of warm-season （May September） rainfall among rain gauge observations, China merged hourly pre- cipitation analysis （CMPA-Hourly）, and two commonly used satellite products （TRMM 3B42 and CMORPH） By considering the rainfall characteristics in a given limited area rather than a single point or grid, this method largely eliminates the differences in rainfall characteristics among different observations or measure- ments over central-eastern China. The results show that the spatial distribution and diurnal variation of RRE frequency and intensity are quite consistent among different datasets, and the performance of CMPA- Hourly is better than the satellite products when compared with station observations. A regional rainfall coefficient （RRC）, which can be used to classify local rain and regional rain, is employed to represent the spatial spread of rainfall in the limited region defining the RRE. It is found that rainfall spread in the selected grid box is more uniform during the nocturnal to morning hours over central-eastern China. The RRC tends to reach its diurnal maximum several hours after the RRE intensity peaks, implying an in- termediate transition stage from convective to stratiform rainfall. In the afternoon, the RRC reaches its minimum, implying the dominance of local convections on small spatial scale in those hours, which could cause large differences in rain gauge and satellite observations. Since the RRE method reflects the overall features of rainfall in a limited region rather than at a fixed point or in a single grid, the widely recognized overestimation of afternoon rainfall in satellite products is more reliable in representing sub-daily variation of rainfall a reasonable method to compare satellite products with which also has great potential to be used in evaluating the numerical models. not obvious, and thus the satellite estimates are from the RRE perspective. This study proposes rain gauge observations on the sub-daily scale, spatiotemporal variation of cloud and rainfall in展开更多
Seasonal variations in weather have significant impacts on crop yields.The accuracy of weather data is an important consideration for crop yield models.This study uses an independent in-situ weather station network to...Seasonal variations in weather have significant impacts on crop yields.The accuracy of weather data is an important consideration for crop yield models.This study uses an independent in-situ weather station network to validate the accuracy of monthly temperature and precipitation data from the in-situ weather station network operated by the Bureau of Meteorology(BOM),interpolated gridded data from this network,and satellite weather data for the South West Agricultural Region of Australia.This region covers five classes of the K?ppen-Geiger climate classification system and is responsible for 10 billion AUD of agricultural produce annually.A strong bias was found for the maximum temperatures in the Copernicus LST(land surface temperature)satellite product.This bias was linearly correlated with the in-situ temperature and exceeded 20℃in warmer months.Due to the bias’s linear nature,a linear correction was able to reduce the root-mean-square error(RMSE)of the Copernicus LST product by 82%.This process was tested for other regions of Australia and,despite some regional differences,a linear correction consistently reduced RMSE by 80%.The validation process demonstrated that the dataset with reliably the lowest RMSE is the gridded weather data calculated from BOM’s in-situ weather stations.Nearest neighbor in-situ weather stations generally had the next lowest RMSE,followed by weather-station corrected satellite products and lastly the non-weather station corrected satellite products.While the in-situ gridded product generally had the lowest RMSE,there were spatial and seasonal variations.Monthly maximum temperatures were more accurately measured by the bias-corrected Copernicus LST product in the northern and eastern extents(where there is a lower density of BOM in-situ stations).Monthly minimum temperatures from the Copernicus LST product had similar to slightly better RMSE than the Australian Water Availability Project(AWAP)product for the southern half of the study area and the rain-gauge corrected GSMaP(Global Satellite Mapping of Precipitation)product performed similarly to AWAP in the drier months(November-April).展开更多
Heavy precipitation induced by typhoons is the main driver of catastrophic flooding,and studying precipitation patterns is important for flood forecasting and early warning.Studying the space-time characteristics of h...Heavy precipitation induced by typhoons is the main driver of catastrophic flooding,and studying precipitation patterns is important for flood forecasting and early warning.Studying the space-time characteristics of heavy precipitation induced by typhoons requires a large range of observation data that cannot be obtained by ground-based rain gauge networks.Satellite-based estimation provides large domains of precipitation with high space-time resolution,facilitating the analysis of heavy precipitation patterns induced by typhoons.In this study,Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks(PERSIANN)satellite data were used to study the temporal and spatial features of precipitation induced by Typhoon Hato,which was the strongest typhoon of 2017 to make landfall in China.The results show that rainfall on the land lasted for six days from the typhoon making landfall to disappearing,reaching the maximum when the typhoon made landfall.Hato produced extremely high accumulated rainfall in South China,almost 300 mm in Guangdong Province and Guangxi Zhuang Autonomous Region and 260 mm in Hainan Province.The rainfall process was separated into three stages and rainfall was the focus in the second stage(5 h before making landfall to 35 h after making landfall).展开更多
Rainstorms are one of the extreme rainfall events that cause serious disasters,such as urban flooding and mountain torrents.Traditional studies have used rain gauge observations to analyze rainstorm events,but relevan...Rainstorms are one of the extreme rainfall events that cause serious disasters,such as urban flooding and mountain torrents.Traditional studies have used rain gauge observations to analyze rainstorm events,but relevant information is usually missing in gauge-sparse areas.Satellite-derived precipitation datasets serve as excellent supplements or substitutes for the gauge observations.By developing a grid-based rainstorm-identification tool,we used the Tropical Rainfall Measurement Mission(TRMM)Multi-satellite Precipitation Analysis(TMPA)time series product to reveal the spatial and temporal variabilities of rainstorms over China during 1998–2017.Significant patterns of both increasing and decreasing rainstorm occurrences were detected,with no spatially uniform trend being observed across the whole country.There was an increase in the area being affected by rainstorms during the 20-year period,with rainstorm centers shifting along the southwest–northeast direction.Rainstorm occurrence was found to be correlated with local total precipitation.By comparing rainstorm occurrence with climate variables such as the El Ni?o-Southern Oscillation and Pacific Decadal Oscillation,we also found that climate change was likely to be the primary reason for rainstorm occurrence in China.This study complements previous studies that used gauge observations by providing a better understanding of the spatiotemporal dynamics of China’s rainstorms.展开更多
基金supported by the National Key Basic Research Program of China (the 973 Program,Grant No.2006CB400502)the Innovative Research Team Project of the State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering (Grant No. 2009585412)+3 种基金the Special Basic Research Fund by the Ministry of Science and Technology,China (Grant No. 2009IM020104)the Programme of Introducing Talents of Discipline to Universities by the Ministry of Educationthe State Administration of Foreign Experts Affairs,China (the 111 Project,Grant No. B08048)the Fundamental Research Funds for the Central Universities (Grants No. 2010B13614 and 2009B11614)
文摘Three high-resolution satellite precipitation products, the Tropical Rainfall Measuring Mission （TRMM） standard precipitation products 3B42V6 and 3B42RT and the Climate Precipitation Center＇s （CPC） morphing technique precipitation product （CMORPH）, were evaluated against surface rain gauge observations from the Laohahe Basin in northern China. Widely used statistical validation indices and categorical statistics were adopted. The evaluations were performed at multiple time scales, ranging from daily to yearly, for the years from 2003 to 2008. The results show that all three satellite precipitation products perform very well in detecting the occurrence of precipitation events, but there are some different biases in the amount of precipitation. 3B42V6, which has a bias of 21%, fits best with the surface rain gauge observations at both daily and monthly scales, while the biases of 3B42RT and CMORPH, with values of 81% and 67%, respectively, are much higher than a normal receivable threshold. The quality of the satellite precipitation products also shows monthly and yearly variation： 3B42RT has a large positive bias in the cold season from September to April, while CMORPH has a large positive bias in the warm season from May to August, and they all attained their best values in 2006 （with 10%, 50%, and -5% biases for 3B42V6, 3B42RT, and CMORPH, respectively）. Our evaluation shows that, for the Laohahe Basin, 3B42V6 has the best correspondence with the surface observations, and CMORPH performs much better than 3B42RT. The large errors of 3B42RT and CMORPH remind us of the need for new improvements to satellite precipitation retrieval algorithms or feasible bias adjusting methods.
文摘Satellite-based Precipitation Estimates(SPEs)have gained importance due to enhanced spatial and temporal resolution,particularly in Indus basin,where raingauge network has fewer observation stations and drainage area is laying in many countries.Formulation of SPEs is based on indirect mechanism,therefore,assessment and correction of associated uncertainties is required.In the present study,disintegration of uncertainties associated with four prominent real time SPEs,IMERG,TMPA,CMORPH and PERSIANN has been conducted at grid level,regional scale,and summarized in terms of regions as well as whole study area basis.The bias has been disintegrated into hit,missed,false biases,and Root Mean Square Error(RMSE)into systematic and random errors.A comparison among gauge-and satellite-based precipitation estimates at annual scale,showed promising result,encouraging use of real time SPEs in the study area.On grid basis,at daily scale,from box plots,the median values of total bias(-0.5 to 0.5 mm)of the used SPEs were also encouraging although some under/over estimations were noted in terms of hit bias(-0.15 to 0.05 mm/day).Relatively higher values of missed(0.3 to 0.5 mm/day)and false(0.5 to 0.7 mm/day)biases were observed.The detected average daily RMSE,systematic errors,and random errors were also comparatively higher.Regional-scale spatial distribution of uncertainties revealed lower values of uncertainties in plain areas,depicting the better performance of satellite-based products in these areas.However,in areas of high altitude(>4000 m),due to complex topography and climatic conditions(orographic precipitation and glaciated peaks)higher values of biases and errors were observed.Topographic barriers and point scale gauge data could also be a cause of poor performance of SPEs in these areas,where precipitation is more on ridges and less in valleys where gauge stations are usually located.Precipitation system’s size and intensity can also be a reason of higher biases,because Microwave Imager underestimate precipitation in small systems(<200 km^(2))and overestimate in large systems(>2000 km^(2)).At present,use of bias correction techniques at daily time scale is compulsory to utilize real time SPEs in estimation of floods in the study area.Inter comparison of satellite products indicated that IMERG gave better results than the others with the lowest values of systematic errors,missed and false biases.
基金Supported by the National Key Research and Development Program of China(2016YFA0601504)National Natural Science Foundation of China(51979069)+1 种基金Fundamental Research Funds for the Central Universities(B200204029)Program of Introducing Talents of Discipline to Universities by the Ministry of Education and State Administration of Foreign Experts Affairs,China(B08048)。
文摘Satellite-and reanalysis-based precipitation products are important data source for precipitation, particularly in areas with a sparse gauge network. Here, five open-access precipitation products, including the newly released China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool(SWAT) model(CMADS)reanalysis dataset and four widely used bias-adjusted satellite precipitation products [SPPs;i.e., Tropical Rainfall Measuring Mission(TRMM) Multisatellite Precipitation Analysis 3B42 Version 7(TMPA 3B42V7), Climate Prediction Center(CPC) morphing technique satellite–gauge blended product(CMORPH-BLD), Climate Hazards Group Infrared Precipitation with Station Data(CHIRPS), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record(PERSIANN-CDR)], were assessed. These products were first compared with the gauge observed data collected for the upper Huaihe River basin, and then were used as forcing data for streamflow simulation by the Xin’anjiang(XAJ) hydrological model under two scenarios with different calibration procedures. The performance of CMADS precipitation product for the Chinese mainland was also assessed. The results show that:(1) for the statistical assessment, CMADS and CMORPH-BLD perform the best, followed by TMPA 3B42V7, CHIRPS, and PERSIANN-CDR, among which the correlation coefficient(CC) and rootmean-square error(RMSE) values of CMADS are optimal, although it exhibits certain significant negative relative bias(BIAS;-22.72%);(2) CMORPH-BLD performs the best in capturing and detecting rainfall events, while CMADS tends to underestimate heavy and torrential precipitation;(3) for streamflow simulation, the performance of using CMADS as input is very good, with the highest Nash–Sutcliffe efficiency(NSE) values(0.85 and 0.75 for calibration period and validation period, respectively);and(4) CMADS exhibits high accuracy in eastern China while with significant negative BIAS, and the performance declines from southeast to northwest. The statistical and hydrological evaluations show that CMADS and CMORPH-BLD have high potential for observing precipitation. As high negative BIAS values showed up in CMADS evaluation, further study on the error sources from original data and calibration algorithms is necessary. This study can serve as a reference for selecting precipitation products in datascarce regions with similar climates and topography in the Global Precipitation Measurement(GPM) era.
基金funded by the Science&Technology Department of Sichuan Province,China(Grant No.2020YFS0356)the Natural Science Foundation of China(Grants No.42201520)the National Cryosphere Desert Data Center(Grants No.E01Z790201)。
文摘Satellite-based precipitation observations with high spatiotemporal resolution are essential for studying rainfall-induced natural hazards,especially in alpine and canyon areas of the southeastern Tibetan Plateau,which are prone to such hazards yet sparsely gauged.Here,we evaluated precipitation estimated from the Chinese Fengyun-4A meteorological satellite(FY-4A AGRI)versus the Integrated Multi-satellitE Retrievals for GPM(IMERG),by using rain gauge data collected in the Parlung Zangbo Basin from May through September in both 2018 and 2019.Our results showed that(1)FY-4A AGRI generated smaller values of RMSE(root mean square error)on hourly to daily scales,and larger correlation coefficients(R-values)and smaller RMSE values for both moderate and heavy rain,indicating its greater accuracy at rainfall estimation,which is most likely due to the denser rain gauge network at a finer temporal scale used when calibrating FY-4A AGRI;(2)Both satellite products underestimated the volume of moderate and heavy rain,with the larger degree of underestimation by FY-4A AGRI,which could lower their performance in flood monitoring and forecasting;(3)Worse performance and greater inconsistency between the two products were observed in high-elevation areas,perhaps because of orographic cloud effects in these mountainous areas;and(4)Both products revealed that the Gangrigabu Range blocked incoming water vapor from the southwest monsoon,with a better representation of the spatial pattern and spatial variability produced by IMERG.To improve precipitation estimation,the effects of complex terrain should be explicitly incorporated into the retrieval algorithms,with more gauged observations in a denser network and at a finer temporal scale needed to robustly calibrate the satellite-based estimates.
基金Under the auspices of Programme of Introducing Talents of Discipline to Universities by Ministry of Education and the State Administration of Foreign Experts Affairs, China (the 111 Project, No. B08048)National Natural Science Foundation of China (No. 41501017)Natural Science Foundation of Jiangsu Province (No. BK20150815)
文摘The Tropical Rainfall Measuring Mission （TRMM） Multi-satellite Precipitation Analysis （TMPA） and National Oceanic and Atmospheric Administration （NOAA） Climate Prediction Center （CPC） morphing technique （CMORPH） are two important multi-satellite precipitation products in TRMM-era and perform important functions in GPM-era. Both TMPA and CMORPH systems simultaneously upgraded their retrieval algorithms and released their latest version of precipitation data in 2013. In this study, the latest TMPA and CMORPH products （i.e., Version-7 real-time TMPA （T-rt） and gauge-adjusted TMPA （T-adj）, and Version- 1.0 real-time CMORPH （C-rt） and Version-l.0 gauge-adjusted CMORPH （C-adj）） are evaluated and intercompared by using independent rain gauge observations for a 12-year （2000--2011） period over two typical basins in China with different geographical and climate conditions. Results indicate that all TMPA and CMORPH products tend to overestimate precipitation for the high-latitude semiarid Laoha River Basin and underestimate it for the low-latitude humid Mishui Basin. Overall, the satellite precipitation products exhibit superior performance over Mishui Basin than that over Laoha River Basin. The C-adj presents the best performance over the high-latitude Laoha River Basin, whereas T-adj showed the best performance over the low-latitude Mishui Basin. The two gauge-adjusted products demonstrate potential in water resource management. However, the accuracy of two real-time satellite precipitation products demonstrates large variability in the two validation basins. The C-rt reaches a similar accuracy level with the gauge-adjusted satellite precipitation products in the high-latitude Laoha River Basin, and T-rt performs well in the low-latitude Mishui Basin. The study also reveals that all satellite precipitation products obviously overestimate light rain amounts and events over Laoha River Basin, whereas they underestimate the amount and events over Mishui Basin. The findings of the precision characteristics associated with the latest TMPA and CMORPH precipitation products at different basins will offer satellite pre- cipitation users an enhanced understanding of the applicability of the latest TMPA and CMORPH for water resource management, hydrologic process simulation, and hydrometeorological disaster prediction in other similar regions in China. The findings will also be useful for IMERG algorithm development and update in GPM-era.
基金jointly supported by the National Key Research and Development Program approved by Ministry of Science and Technology,China(2016YFA0601504)the Program of Introducing Talents of Discipline to Universities by the Ministry of Education and the State Administration of Foreign Experts Affairs,China(B08048)+1 种基金the National Natural Science Foundation of China(41501017,51579066)the Natural Science Foundation of Jiangsu Province(BK20150815)
文摘The high resolution satellite precipitation products bear great potential for large-scale drought monitoring, especially for those regions with sparsely or even without gauge coverage. This study focuses on utilizing the latest Version-7 TRMM Multi-satellite Precipitation Analysis （TMPA 3B42V7） data for drought condition monitoring in the Weihe River Basin （0.135×10^6 km2）. The accuracy of the monthly TMPA 3B42V7 satellite precipitation data was firstly evaluated against the ground rain gauge observations. The statistical characteristics between a short period data series （1998-2013） and a long period data series （1961-2013） were then compared. The TMPA 3B42V7-based SPI （Standardized Precipitation Index） sequences were finally validated and analyzed at various temporal scales for assessing the drought conditions. The results indicate that the monthly TMPA 3B42V7 precipitation is in a high agreement with the rain gauge observations and can accurately capture the temporal and spatial characteristics of rainfall within the Weihe River Basin. The short period data can present the characteristics of long period record, and it is thus acceptable to use the short period data series to estimate the cumulative probability function in the SPI calculation. The TMPA 3B42V7-based SPI matches well with that based on the rain gauge observations at multiple time scales （i.e., 1-, 3-, 6-, 9-, and 12-month） and can give an acceptable temporal distribution of drought conditions. It suggests that the TMPA 3B42V7 precipitation data can be used for monitoring the occurrence of drought in the Weihe River Basin.
文摘This paper compares monthly and seasonal rain rates derived from the Version 5 （V5） and Version 6 （V6） TRMM Precipitation Radar （TPR, TSDIS reference 2A25）, TRMM Microwave Imager （TMI, 2A12）, TRMM Combined Instrument （TCI, 2B31）, TRMM calibrated IR rain estimates （3B42） and TRMM merged gauge and satellite analysis （3B43） algorithms over New Mexico （NM） with rain gauge analyses provided by the New Mexico water districts （WD）. The average rain rates over the NM region for 1998- 2002 are 0.91 mm d^-1 for WD and 0.75, 1.38, 1.49, 1.27, and 1.07 mm d^-1 for V5 3B43, 3B42, TMI, PR and TCA; and 0.74, 1.38, 0.87 and 0.97 mm d^-1 for V6 3B43, TMI, TPR and TCA, respectively. Comparison of V5 3B43 with WD rain rates and the daily TRMM mission index （TPR and TMI） suggests that the low bias of V5 3B43 for the wet months （summer to early fall） may be due to the non-inclusion of some rain events in the operational gauge analyses that. are used in the production of V5 3B43. Correlation analyses show that the WD rain rates vary in phase, with higher correlation between neighboring WDs. High temporal correlations （〉0.8） exist between WD and the combined algorithms （3B42, 3B43 and TCA for both V5 and V6） while satellite instrument algorithms （PR, TMI and TCI） are correlated best among themselves at the monthly scale. Paired t-tests of the monthly time series show that V5 3B42 and TMI are statistically different from the WD rain rates while no significant difference exists between WD and the other products. The agreements between the TRMM satellite and WD gauge estimates are best for the spring and fall and worst for winter and summer. The reduction in V6 TMI （-7.4%） and TPR （-31%） rain rates （compared to V5） results in better agreement between WD estimates and TMI in winter and TPR during summer.
基金Supported by the Outstanding Tutors for Doctoral Dissertations of S&T Project in Beijing(20138005801)National Natural Science Foundation of China(41375004)Basic Scientific Research and Operation Foundation of the Chinese Academy of Meteorological Sciences(2014R013)
文摘This study employs a newly defined regional-rainfall-event （RRE） concept to compare the hourly charac- teristics of warm-season （May September） rainfall among rain gauge observations, China merged hourly pre- cipitation analysis （CMPA-Hourly）, and two commonly used satellite products （TRMM 3B42 and CMORPH） By considering the rainfall characteristics in a given limited area rather than a single point or grid, this method largely eliminates the differences in rainfall characteristics among different observations or measure- ments over central-eastern China. The results show that the spatial distribution and diurnal variation of RRE frequency and intensity are quite consistent among different datasets, and the performance of CMPA- Hourly is better than the satellite products when compared with station observations. A regional rainfall coefficient （RRC）, which can be used to classify local rain and regional rain, is employed to represent the spatial spread of rainfall in the limited region defining the RRE. It is found that rainfall spread in the selected grid box is more uniform during the nocturnal to morning hours over central-eastern China. The RRC tends to reach its diurnal maximum several hours after the RRE intensity peaks, implying an in- termediate transition stage from convective to stratiform rainfall. In the afternoon, the RRC reaches its minimum, implying the dominance of local convections on small spatial scale in those hours, which could cause large differences in rain gauge and satellite observations. Since the RRE method reflects the overall features of rainfall in a limited region rather than at a fixed point or in a single grid, the widely recognized overestimation of afternoon rainfall in satellite products is more reliable in representing sub-daily variation of rainfall a reasonable method to compare satellite products with which also has great potential to be used in evaluating the numerical models. not obvious, and thus the satellite estimates are from the RRE perspective. This study proposes rain gauge observations on the sub-daily scale, spatiotemporal variation of cloud and rainfall in
文摘Seasonal variations in weather have significant impacts on crop yields.The accuracy of weather data is an important consideration for crop yield models.This study uses an independent in-situ weather station network to validate the accuracy of monthly temperature and precipitation data from the in-situ weather station network operated by the Bureau of Meteorology(BOM),interpolated gridded data from this network,and satellite weather data for the South West Agricultural Region of Australia.This region covers five classes of the K?ppen-Geiger climate classification system and is responsible for 10 billion AUD of agricultural produce annually.A strong bias was found for the maximum temperatures in the Copernicus LST(land surface temperature)satellite product.This bias was linearly correlated with the in-situ temperature and exceeded 20℃in warmer months.Due to the bias’s linear nature,a linear correction was able to reduce the root-mean-square error(RMSE)of the Copernicus LST product by 82%.This process was tested for other regions of Australia and,despite some regional differences,a linear correction consistently reduced RMSE by 80%.The validation process demonstrated that the dataset with reliably the lowest RMSE is the gridded weather data calculated from BOM’s in-situ weather stations.Nearest neighbor in-situ weather stations generally had the next lowest RMSE,followed by weather-station corrected satellite products and lastly the non-weather station corrected satellite products.While the in-situ gridded product generally had the lowest RMSE,there were spatial and seasonal variations.Monthly maximum temperatures were more accurately measured by the bias-corrected Copernicus LST product in the northern and eastern extents(where there is a lower density of BOM in-situ stations).Monthly minimum temperatures from the Copernicus LST product had similar to slightly better RMSE than the Australian Water Availability Project(AWAP)product for the southern half of the study area and the rain-gauge corrected GSMaP(Global Satellite Mapping of Precipitation)product performed similarly to AWAP in the drier months(November-April).
基金supported by the National Key Research and Development Program of China,China(funding no.2017YFC1502702)
文摘Heavy precipitation induced by typhoons is the main driver of catastrophic flooding,and studying precipitation patterns is important for flood forecasting and early warning.Studying the space-time characteristics of heavy precipitation induced by typhoons requires a large range of observation data that cannot be obtained by ground-based rain gauge networks.Satellite-based estimation provides large domains of precipitation with high space-time resolution,facilitating the analysis of heavy precipitation patterns induced by typhoons.In this study,Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks(PERSIANN)satellite data were used to study the temporal and spatial features of precipitation induced by Typhoon Hato,which was the strongest typhoon of 2017 to make landfall in China.The results show that rainfall on the land lasted for six days from the typhoon making landfall to disappearing,reaching the maximum when the typhoon made landfall.Hato produced extremely high accumulated rainfall in South China,almost 300 mm in Guangdong Province and Guangxi Zhuang Autonomous Region and 260 mm in Hainan Province.The rainfall process was separated into three stages and rainfall was the focus in the second stage(5 h before making landfall to 35 h after making landfall).
基金National Key Research and Development Program of China,No.2017YFC1502501,No.2017YFC0404302National Natural Science Foundation of China,No.41501460。
文摘Rainstorms are one of the extreme rainfall events that cause serious disasters,such as urban flooding and mountain torrents.Traditional studies have used rain gauge observations to analyze rainstorm events,but relevant information is usually missing in gauge-sparse areas.Satellite-derived precipitation datasets serve as excellent supplements or substitutes for the gauge observations.By developing a grid-based rainstorm-identification tool,we used the Tropical Rainfall Measurement Mission(TRMM)Multi-satellite Precipitation Analysis(TMPA)time series product to reveal the spatial and temporal variabilities of rainstorms over China during 1998–2017.Significant patterns of both increasing and decreasing rainstorm occurrences were detected,with no spatially uniform trend being observed across the whole country.There was an increase in the area being affected by rainstorms during the 20-year period,with rainstorm centers shifting along the southwest–northeast direction.Rainstorm occurrence was found to be correlated with local total precipitation.By comparing rainstorm occurrence with climate variables such as the El Ni?o-Southern Oscillation and Pacific Decadal Oscillation,we also found that climate change was likely to be the primary reason for rainstorm occurrence in China.This study complements previous studies that used gauge observations by providing a better understanding of the spatiotemporal dynamics of China’s rainstorms.
