Evaluation of high-resolution satellite precipitation products with surface rain gauge observations from Laohahe Basin in northern China

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.

Runoff Simulation Using Radar and Rain Gauge Data

The conceptual rainfall-runoff model TOPMODEL is used to simulate runoffs of the Meishan and Nianyushan catchments during the summers of 1998 and 1999 in the GAME/HUBEX (GEWEX Asia Monsoon Experiment /HUAIHE River Basin Experiment) project. The rainfall distributions are estimated by weather radar and rain gauge networks according to different methods. Observed and simulated runoffs are compared and analyzed for both catchments. Results show that (1) the runoff of the catchment is best simulated by radar data combined with rain gauge network data from inside the catchment, and (2) the rainfall estimated by radar adjusted by a few rain gauges outside the catchment can be used to simulate runoff equally as well as using the dense rain gauge network alone.

Validation of TRMM and FEWS Satellite Rainfall Estimates with Rain Gauge Measurement over Ashanti Region, Ghana

Satellite rainfall estimates have predominantly been used for climate impact studies due to poor rain gauge network in sub-Saharan Africa. However, there are limited microscale studies within the sub-region that have assessed the performance of these satellite products, which is the focus of the present study. This paper therefore considers validation of Tropical Rainfall Measuring Mission (TRMM) and Famine Early Warning System (FEWS) satellite estimates with rain gauge measurements over Ashanti region of Ghana. First, a consistency assessment of the two gauge data products, the Automatic Rain Gauge (ARG) and Ghana Meteorological Agency (GMet) Standard Rain Gauge (SRG) measurements, was performed. This showed a very good agreement with correlation coefficient of 0.99. Secondly, satellite rainfall products from TRMM and FEWS were validated with the two gauge measurements. Validation results showed good agreement with correlation coefficients of 0.6 and 0.7 for TRMM and FEWS with SRG, and 0.87 and 0.86 for TRMM and FEWS with ARG respectively. Probability Of Detection (POD) and Volumetric Hit Index (VHI) were found to be greater than 0.9. Volumetric Critical Success Index (VCSI) was 0.9 and 0.8 for TRMM and FEWS respectively with low False Alarm Ratio (FAR) and insignificant Volumetric Miss Index (VMI). In general, relatively low biases and RMSE values were observed. The biases were less than 1.3 and 0.8 for TRMM and FEWS-RFE respectively. These indicate high rainfall detection capabilities of both satellite products. In addition, both TRMM and FEWS were able to capture the onset, peak and cessation of the rainy season, as well as the dry spells. Although TRMM and FEWS sometimes under/overestimated rainfall, they have the potential to be used for agricultural and other hydro-climatic impact studies over the region. The Dynamic-Aerosol-Cloud-Chemistry Interactions in West Africa (DACCIWA) project will provide an improved spatial gauge network database over the study area to enhance future validation and other climate impact studies.

Climatology Comparison Studies of Precipitations Between GPCP and Rain Gauges in China

The Global Precipitation Climatology Project （GPCP） monthly rainfall data and the rainfall records observed by 740 rain gauges in the mainland of China are used to analyze similarities and differences of the precipitation in China in the period from January 1980 to December 2000. Results expose significantly consistent rainfall distributions between the both data in multi-year mean, multi-year seasonal mean, and multi-year monthly mean. Departures of monthly rainfall for each dataset also show a high correlation with an over 0.8 correlation coefficient. Analysis indicates small differences of both datasets during autumn, winter, and spring, but relative large ones in summer. Generally, the GPCP has trend of overestimating the rainfall rate. Based on above good relationship of both datasets, the GPCP data, are used to represent distributions and variations of precipitation in the Tibetan Plateau and Northwest China. Results indicate positive departures of precipitation in summer in the west part of Tibetan Plateau in the 1980s and negative departures after the 1980s. For the west part of Northwest China, analysis illustrates precipitation decreases a little, but no clear variation tendency.

Evaluation on Radar Reflectivity-Rainfall Rate (Z-R) Relationships for Guyana

The constant development of science and technology in weather radar results in high-resolution spatial and temporal rainfall estimates and improved early warnings of meteorological phenomena such as flood [1]. Weather radars do not measure the rainfall amount directly, so a relationship between the reflectivity (Z) and rainfall rate (R), called the Z-R relationship (Z = aRb), where a and b are empirical constants, can be used to estimate the rainfall amount. In this research, mathematical techniques were used to find the best climatological Z-R relationships for the Low Coastal Plain of Guyana. The reflectivity data from the S-Band Doppler Weather Radar for February 17 and 21, 2011 and May 8, 2012 together with the daily rainfall depths at 29 rainfall stations located within a 150 km radius were investigated. A climatological Z-R relationship type Z = 200R1.6 (Marshall-Palmer) configured by default into the radar system was used to investigate the correlation between the radar reflectivity and the rainfall by gauges. The same data sets were used with two distinct experimental Z-R relationships, Z = 300R1.4 (WSR-88D Convective) and Z = 250R1.2 (Rosenfeld Tropical) to determine if any could be applicable for area of study. By comprehensive regression analysis, New Z-R and R-Z relationships for each of the three events aforementioned were developed. In addition, a combination of all the samples for all three events were used to produce another relationship called “All in One”. Statistical measures were then applied to detect BIAS and Error STD in order to produce more evidence-based results. It is proven that different Z-R relationships could be calibrated into the radar system to provide more accurate rainfall estimation.

Multi-Layer Forecast Project of Rain-Induced Debris Flow

Based on four kinds of methods—numerical weather prediction model, cloud image of stationary meteorological satellite, echo image of meteorological radar and telemetric rain gauge, multi space-time scale precipitation prediction products have been achieved, and multi-layer project of debris flow forecast is established with different space-time scale to get different forecast precision. The forecast system has the advantages in combination of regions and ravines, rational compounding of time and space scale. The project, which has debris flow forecast models of Sichuan province, Liangshan district and single ravine, can forecast debris flow in 3 layers and meets the demand of hazard mitigation in corresponding layer.

Evaluation of the integrated multi-satellite retrievals for global precipitation measurement over the Tibetan Plateau

The availability of high-resolution satellite precipitation measurement products provides an opportunity to monitor precipitation over large and complex terrain and thus accurately evaluate the climatic,hydrological and ecological conditions in those regions.The Global Precipitation Measurement(GPM)mission is an important new program designed for global satellite precipitation estimation,but little information has been reported on the applicability of the GPM’s products for the Tibetan Plateau(TP).The object of this study is to evaluate the accuracy of the Integrated Multi-Satellite Retrievals for GPM(IMERG)Final Run product under different terrain and climate conditions over the TP by using 78 ground gauges from April 2014 to December 2017.The results showed the following:(1)the 3-year average daily precipitation estimation in the IMERG agrees well with the rain gauge observations(R^2=0.58,P<0.01),and IMERG also has a considerable ability to detect precipitation,as indicated by a high probability of detection(78%-98%)and critical success index(65%-85%);(2)IMERG performed better at altitudes from 3000 m to 4000 m with a small relative bias(RB)of 6.4%.Precipitation change was not significantly affected by local relief;(3)the climate system of the TP was divided into four climate groups with a total of 12 climate types based on the K?ppen climate classification system,and IMERG performed well in all climate types with the exception of the arid-desert-cold climate(Bwk)type.Furthermore,although IMERG showed the potential to detect snowfall,it still exhibits deficiencies in identifying light and moderate snow.These results indicate that IMERG could provide more accurate precipitation data if its retrieval algorithm was improved for complex terrain and arid regions.

COMPARISON OF PERSIANN AND TMPA DAILY PRECIPITATION ESTIMATES OVER HUNAN PROVINCE OF CHINA

This study presented a detailed comparison of daily precipitation estimates from Precipitation Estimation from Remote Sensing Information using Artificial Neural Network(PERSIANN) and Tropical Rainfall Measuring Mission(TRMM) Multi-satellite Precipitation Analysis(TMPA) over Hunan province of China from 1998 to 2014. The ground gauge observations are taken as the reference. It is found that overall TMPA clearly outperforms PERSIANN, indicating by better statistical metrics(including correlation coefficient, root mean square error and relative bias). For the geospatial pattern, although both products are able to capture the major precipitation features(e.g., precipitation geospatial homogeneity) in Hunan, yet PERSIANN largely underestimates the precipitation intensity throughout all seasons. In contrast, there is no clear bias tendency from TMPA estimates. Precipitation intensity analysis showed that both the occurrence and amount histograms from TMPA are closer to the gauge observations from spring to autumn.However, in the winter season PERSIANN is closer to gauge observation, which is likely due to the ground contamination from the passive microwave sensors used by TMPA.

Validation of Daily Precipitation from Two High-Resolution Satellite Precipitation Datasets over the Tibetan Plateau and the Regions to Its East

Daily precipitation amounts and frequencies from the CMORPH （Climate Prediction Center Morphing Technique） and TRMM （Tropical Rainfall Measuring Mission） 3B42 precipitation products are validated against warm season in-situ precipitation observations from 2003 to 2008 over the Tibetan Plateau and the regions to its east. The results indicate that these two satellite datasets can better detect daily precipitation frequency than daily precipitation amount. The ability of CMORPH and TRMM 3B42 to accurately detect daily precipitation amount is dependent on the underlying terrain. Both datasets are more reliable over the relatively flat terrain of the northeastern Tibetan Plateau, the Sichuan basin, and the mid-lower reaches of the Yangtze River than over the complex terrain of the Tibetan Plateau. Both satellite products are able to detect the occurrence of daily rainfall events; however, their performance is worse in regions of complex topography, such as the Tibetan Plateau. Regional distributions of precipitation amount by precipitation intensity based on TRMM 3B42 are close to those based on rain gauge data. By contrast, similar distributions based on CMORPH differ substantially. CMORPH overestimates the amount of rain associated with the most intense precipitation events over the mid-lower reaches of the Yangtze River while underestimating the amount of rain associated with lighter precipitation events. CMORPH underestimates the amount of intense precipitation and overestimates the amount of lighter precipitation over the other analyzed regions. TRMM 3B42 underestimates the frequency of light precipitation over the Sichuan basin and the mid-lower reaches of the Yangtze River. CMORPH overestimates the frequencies of weak and intense precipitation over the mid-lower reaches of the Yangtze River, and underestimates the frequencies of moderate and heavy precipitation. CMORPH also overestimates the frequency of light precipitation and underestimates the frequency of intense precipitation over the other three regions. The TRMM 3B42 product provides better characterizations of the regional gamma distributions of daily precipitation amount than the CMORPH product, for which the cumulative distribution functions are biased toward lighter precipitation events.

Quantitative evaluation of NEXRAD data and its application to the distributed hydrologic model BPCC

The next-generation weather radar(NEXRAD) can generally capture the spatial variability of rainfall fields,but fails to provide accurate depth measurements.A systematic strategy to evaluate the accuracy of radar data in depth measurement and its performance in hydrologic model is outlined.Statistical evaluation coefficients are calculated by comparing NEXRAD data with individual raingauges as well as subbasin-averaged interpolations,and point-and surface-average factors are introduced to revise radar data successively.Hydrologic simulations are then performed with a distributed hydrologic model,called basin pollution calculation center(BPCC) with both raingauge observations and revised NEXRAD estimates inputs.The BPCC model is applied to Clear Creek Watershed,IA,USA,on an hourly scale,and the calibration and validation parameters are semi-automatically optimized to improve manual calibration shortcomings.Results show that hydrographs generated from both gauge and NEXRAD are in good agreement with observed flow hydrographs.Coefficient statistics reveal that NEXRAD contributes to model performance,indicating that NEXRAD data has the potential to be used as an alternative source of precipitation data and improve the accuracy of hydrologic simulations.

EXPERIMENT OF QUANTITATIVE MEASUREMENTS OF PRECIPITATION BY USE OF WEATHER RADAR IN THE HUAIHE RIVER BASIN

With the scheme of the variation analysis and Kalman filter,the radar data were adjusted by the real-time rain gauge data.The accuracy of areal rainfall calculation was improved and the results can be basically used for flood forecasting.It is concluded that the scheme is suitable in the upper and middle reaches of the Huaihe River.