Evaluation of climate reanalysis and space-borne precipitation products over Bangladesh

ABSTRACT

This study aims to quantify the spatial distribution of errors in two climate reanalysis (ERA5 and CFSR) and two satellite (TMPA-RT and TMPA-V7) precipitation products over Bangladesh. The datasets are assessed against ground-based rain gauge observations to capture the extreme rainfall accumulations at daily temporal scale over a 5-year period (January 2010–December 2014). The bias ratio scores indicate that CFSR and TMPA-RT seriously overestimate the rainfall values over much of the study area. Whilst TMPA-V7 performs better than the other precipitation products, all datasets lose their detection skills substantially for higher quantile thresholds (i.e. above 50th and 75th percentiles). With respect to rainfall detection metrics – probability of detection (POD) and volumetric hit index (VHI) – both ERA5 and CFSR show superior performance (in the range 0.9–1.0 for all the analysis grid boxes). All rainfall datasets are equally good in terms of false alarm ratio (FAR) and volumetric FAR (VFAR), even though the lowest values are associated with ERA5 for higher quantiles. All products demonstrate a decrease in skill to capture the amount of rainfall but show satisfactory results to detect the rainfall events when using higher quantile thresholds (i.e. rainfall above the 50th and 75th percentiles) to sample the data before computing product skill.     

Comparing satellite derived precipitation datasets using the Hillslope River Routing (HRR) model in the Congo River Basin

In this paper, three satellite derived precipitation datasets (TRMM, CMORPH, PERSIANN) are used to drive the Hillslope River Routing (HRR) model in the Congo Basin. The precipitation data are compared spatially and temporally in two forms: (1) precipitation magnitudes, and (2) resulting streamflow and water storages. Simulated streamflow is assessed using historical monthly discharge data from in situ stream gauges and recent stage data based on water surface elevations derived from ENVISAT radar altimetry data. Simulated total water storage is assessed using monthly storage change values derived from GRACE data. The results show that the three precipitation datasets vary significantly in terms of magnitudes but generally produce a reasonable hydrograph throughout much of the basin, with the exception of the equatorial regions of the watershed. The satellite datasets provide unreasonably high values for specific periods (e.g. all three in Oct–Nov; only CMORPH and PERSIANN in Mar–Apr) in the equatorial regions. Overall, TRMM (3B42) provides the best spatial and temporal distributions and magnitudes or rainfall based on the assessment measures used here. Both CMORPH and PERSIANN tend to overestimate magnitudes, especially in the equatorial regions of the Basin. Copyright © 2011 John Wiley & Sons, Ltd.     

Evaluation of precipitation products over complex mountainous terrain: A water resources perspective

The availability of in situ measurements of precipitation in remote locations is limited. As a result, the use of satellite measurements of precipitation is attractive for water resources management. Combined precipitation products that rely partially or entirely on satellite measurements are becoming increasingly available. However, these products have several weaknesses, for example their failure to capture certain types of precipitation, limited accuracy and limited spatial and temporal resolution. This paper evaluates the usefulness of several commonly used precipitation products over data scarce, complex mountainous terrain from a water resources perspective. Spatially averaged precipitation time series were generated or obtained for 16 sub-basins of the Paute river basin in the Ecuadorian Andes and 13 sub-basins of the Baker river basin in Chilean Patagonia. Precipitation time series were generated using the European Centre for Medium Weather Range Forecasting (ECMWF) 40 year reanalysis (ERA-40) and the subsequent ERA-interim products, and the National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis dataset 1 (NCEP R1) hindcast products, as well as precipitation estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). The Tropical Rainfall Measurement Mission (TRMM) 3B42 is also used for the Ecuadorian Andes. These datasets were compared to both spatially averaged gauged precipitation and river discharge. In general, the time series of the remotely sensed and hindcast products show a low correlation with locally observed precipitation data. Large biases are also observed between the different products. Hydrological verification based on river flows reveals that water balance errors can be extremely high for all evaluated products, including interpolated local data, in basins smaller than 1000 km². The observations are consistent over the two study regions despite very different climatic settings and hydrological processes, which is encouraging for extrapolation to other mountainous regions.     

Evaluation of the climate prediction center (CPC) morphing technique (CMORPH) rainfall product on hourly time scales over the source of the Blue Nile River

Limited availability of surface‐based rainfall observations constrains the evaluation of satellite rainfall products over many regions. Observations are also often not available at time scales to allow evaluation of satellite products at their finest resolutions. In the present study, we utilized a 3‐month rainfall data set from an experimental network of eight automatic gauges in Gilgel Abbay watershed in Ethiopia to evaluate the 1‐hourly, 8 × 8‐km Climate Prediction Center morphing technique (CMORPH) rainfall product. The watershed is situated in the Lake Tana basin which is the source of the Blue Nile River. We applied a suite of statistical metrics that included mean difference, bias, standard deviation of differences and measures of association. Our results indicate that the accuracy of the CMORPH product shows a significant variation across the basin area. Its estimates are mostly within ±10 mm h^?1 of the gauge rainfall observations; however, the product does not satisfactorily capture the rainfall temporal variability and is poorly correlated (<0.27) to gauge observations. Its poor rain detection capability led to significant underestimation of the seasonal rainfall depth (total bias reaches up to ?52%) with large amounts of hit rain bias as well as missed rain and false rain biases. In the future refinement of CMORPH algorithm, more attention should be given to reducing missed rain bias over the mountains of Gilgel Abbay, whereas equal attention should be given to hit, missed rain and false rain biases over other parts of the watershed. Copyright © 2012 John Wiley & Sons, Ltd.     

Temporal analysis of Soil and Water Assessment Tool (SWAT) performance based on remotely sensed precipitation products

No study has systematically evaluated streamflow modelling between monthly and daily time scales. This study examines streamflow from seven watersheds across the USA where five different precipitation products were used as primary input into the Soil and Water Assessment Tool (SWAT) to generate simulated streamflow. Time scales examined include monthly, dekad (10 days), pentad (5 days), triad (3 days), and daily. The seven basins studied are the San Pedro (Arizona), Cimarron (north‐central Oklahoma), mid‐Nueces (south Texas), mid‐Rio Grande (south Texas and northern Mexico), Yocano (northern Mississippi), Alapaha (south Georgia), and mid‐St. Francis (eastern Arkansas). The precipitation products used to drive simulations include rain gauge, NWS Multisensor Precipitation Estimator, Tropical Rainfall Measurement Mission (TRMM), Multi‐Satellite Precipitation Analysis, TRMM 3B42‐V6, and Climate Prediction Center Morphing Method (CMORPH). Understanding how streamflow varies at sub‐monthly time scales is important because there are a host of hydrological applications such a flood forecast guidance and reservoir inflow forecasts that reside in a temporal domain between monthly and daily time scales. The major finding of this study is the quantification of a strong positive correlation between performance metrics and time step at which model performance deteriorates. Better performing simulations, with higher Nash–Sutcliffe values of 0.80 and above can support modeling at finer time scales to at least daily and perhaps beyond into the sub‐daily realm. These findings are significant in that they clearly document the ability of SWAT to support modeling at sub‐monthly time steps, which is beyond the capability for which SWAT was initially designed. Copyright © 2012 John Wiley & Sons, Ltd.     

A multi-sensor evaluation of precipitation uncertainty for landslide-triggering storm events

Extreme precipitation can have profound consequences for communities, resulting in natural hazards such as rainfall-triggered landslides that cause casualties and extensive property damage. A key challenge to understanding and predicting rainfall-triggered landslides comes from observational uncertainties in the depth and intensity of precipitation preceding the event. Practitioners and researchers must select from a wide range of precipitation products, often with little guidance. Here we evaluate the degree of precipitation uncertainty across multiple precipitation products for a large set of landslide-triggering storm events and investigate the impact of these uncertainties on predicted landslide probability using published intensity–duration thresholds. The average intensity, peak intensity, duration, and NOAA-Atlas return periods are compared ahead of 177 reported landslides across the continental United States and Canada. Precipitation data are taken from four products that cover disparate measurement methods: near real-time and post-processed satellite (IMERG), radar (MRMS), and gauge-based (NLDAS-2). Landslide-triggering precipitation was found to vary widely across precipitation products with the depth of individual storm events diverging by as much as 296 mm with an average range of 51 mm. Peak intensity measurements, which are typically influential in triggering landslides, were also highly variable with an average range of 7.8 mm/h and as much as 57 mm/h. The two products more reliant upon ground-based observations (MRMS and NLDAS-2) performed better at identifying landslides according to published intensity–duration storm thresholds, but all products exhibited hit ratios of greater than 0.56. A greater proportion of landslides were predicted when including only manually verified landslide locations. We recommend practitioners consider low-latency products like MRMS for investigating landslides, given their near-real time data availability and good performance in detecting landslides. Practitioners would be well-served considering more than one product as a way to confirm intense storm signals and minimize the influence of noise and false alarms.     

Consistency of precipitation products over the Arabian Peninsula and interactions with soil moisture and water storage

The regional-scale consistency between four precipitation products from the GPCC, TRMM, WM, and CMORPH datasets over the Arabian Peninsula was assessed. Their macroscale relationships were inter-compared with soil moisture and total water storage (TWS) estimates from AMSR-E and GRACE. The consistency analysis was studied with multivariate statistical hypothesis testing and Pearson correlation metrics for the period from January 2000 to December 2010. The products and GRACE estimates were assessed over a representative sub-domain (United Arab Emirates) with available in situ well observations. Next, geographically temporally weighted regression (GTWR) was employed to examine the interdependencies among the peninsula’s hydrological components. The results showed GPCC-TRMM recording the highest correlation (0.85) with insignificant mean differences over more than 90% of the peninsula. The highest GTWR predictive performance of TWS (R² = 0.84) was achieved with TRMM forcing, which indicates its potential to monitor changes in TWS over the arid peninsular region.     

Hydrological appraisal of rainfall estimates from radar,satellite, raingauge and satellite–gauge combination on the Qinhuai River Basin,China

ABSTRACT

Multisource rainfall products can be used to overcome the absence of gauged precipitation data for hydrological applications. This study aims to evaluate rainfall estimates from the Chinese S-band weather radar (CINRAD-SA), operational raingauges, multiple satellites (CMORPH, ERA-Interim, GPM, TRMM-3B42RT) and the merged satellite–gauge rainfall products, CMORPH-GC, as inputs to a calibrated probability distribution model (PDM) on the Qinhuai River Basin in Nanjing, China. The Qinhuai is a middle-sized catchment with an area of 799 km². All sources used in this study are capable of recording rainfall at high spatial and temporal resolution (3 h). The discrepancies between satellite and radar data are analysed by statistical comparison with raingauge data. The streamflow simulation results from three flood events suggest that rainfall estimates using CMORPH-GC, TRMM-3B42RT and S-band radar are more accurate than those using the other rainfall sources. These findings indicate the potential to use satellite and radar data as alternatives to raingauge data in hydrological applications for ungauged or poorly gauged basins.     

Daytime precipitation identification scheme based on multiple cloud parameters retrieved from visible and infrared measurements

Visible and infrared (VIR) measurements and the retrieved cloud parameters are commonly used in precipitation identification algorithms, since the VIR observations from satellites, especially geostationary satellites, have high spatial and temporal resolutions. Combined measurements from visible/infrared scanner (VIRS) and precipitation radar (PR) aboard the Tropical Rainfall Measuring Mission (TRMM) satellite are analyzed, and three cloud parameters, i.e., cloud optical thickness (COT), effective radius (Re), and brightness temperature of VIRS channel 4 (BT4), are particularly considered to characterize the cloud status. By associating the information from VIRS-derived cloud parameters with those from precipitation detected by PR, we propose a new method for discriminating precipitation in daytime called Precipitation Identification Scheme from Cloud Parameters information (PISCP). It is essentially a lookup table (LUT) approach that is deduced from the optimal equitable threat score (ETS) statistics within 3-dimensional space of the chosen cloud parameters. South and East China is selected as a typical area representing land surface, and the East China Sea and Yellow Sea is selected as typical oceanic area to assess the performance of the new scheme. It is proved that PISCP performs well in discriminating precipitation over both land and oceanic areas. Especially, over ocean, precipitating clouds (PCs) and non-precipitating clouds (N-PCs) are well distinguished by PISCP, with the probability of detection (POD) near 0.80, the probability of false detection (POFD) about 0.07, and the ETS higher than 0.43. The overall spatial distribution of PCs fraction estimated by PISCP is consistent with that by PR, implying that the precipitation data produced by PISCP have great potentials in relevant applications where radar data are unavailable.     

Evaluation of remote‐sensing‐based rainfall products through predictive capability in hydrological runoff modelling

The emergence of regional and global satellite‐based rainfall products with high spatial and temporal resolution has opened up new large‐scale hydrological applications in data‐sparse or ungauged catchments. Particularly, distributed hydrological models can benefit from the good spatial coverage and distributed nature of satellite‐based rainfall estimates (SRFE). In this study, five SRFEs with temporal resolution of 24 h and spatial resolution between 8 and 27 km have been evaluated through their predictive capability in a distributed hydrological model of the Senegal River basin in West Africa. The main advantage of this evaluation methodology is the integration of the rainfall model input in time and space when evaluated at the sub‐catchment scale. An initial data analysis revealed significant biases in the SRFE products and large variations in rainfall amounts between SRFEs, although the spatial patterns were similar. The results showed that the Climate Prediction Center/Famine Early Warning System (CPC‐FEWS) and cold cloud duration (CCD) products, which are partly based on rain gauge data and produced specifically for the African continent, performed better in the modelling context than the global SRFEs, Climate Prediction Center MORPHing technique (CMORPH), Tropical Rainfall Measuring Mission (TRMM) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). The best performing SRFE, CPC‐FEWS, produced good results with values of R²_NS between 0·84 and 0·87 after bias correction and model recalibration. This was comparable to model simulations based on traditional rain gauge data. The study highlights the need for input specific calibration of hydrological models, since major differences were observed in model performances even when all SRFEs were scaled to the same mean rainfall amounts. This is mainly attributed to differences in temporal dynamics between products. Copyright © 2009 John Wiley & Sons, Ltd.     

Validation of a new meteorological forcing data in analysis of spatial and temporal variability of precipitation in India

During the past two decades, numerous datasets have been developed for global/regional hydrological assessment and modeling, but these datasets often show differences in their spatial and temporal distributions of precipitation, which is one of the most critical input variables in global/regional hydrological modeling. This paper is aimed to explore the precipitation characteristics of the Water and Global Change (WATCH) forcing data (WFD) and compare these with the corresponding characteristics derived from satellite-gauge data (TRMM 3B42 and GPCP 1DD) and rain gauge data. It compared the consistency and difference between the WFD and satellite-gauge data in India and examined whether the pattern of seasonal (winter, pre-monsoon, monsoon and post-monsoon) precipitation over six regions [e.g. North Mountainous India (NMI), Northwest India (NWI), North Central India (NCI), West Peninsular India (WPI), East Peninsular India (EPI) and South Peninsular India (SPI)] of India agrees well for the gridded data to be useful in precipitation variability analyses. The multi-time scale of precipitation in India was analysed by wavelet transformation method using gauged and WFD precipitation data. In general, precipitation from WFD is larger than that from satellite-gauge data in NMI and Western Ghats region whereas it is smaller in the dry region of NWI. Both WFD and satellite-gauge datasets underestimate precipitation compared to the measured data but the precipitation from WFD is better estimated than that from satellite-gauge data. It was found that the wavelet power spectrum of precipitation based on WFD is reasonably close to that of measured precipitation in NWI and NCI, while slightly different in NMI. It is felt that the WFD data can be used as a potential dataset for hydrological study in India.     

Spatial downscaling of TRMM precipitation data based on the orographical effect and meteorological conditions in a mountainous area

The lack of high resolution precipitation data has posed great challenges to the study and management of extreme rainfall events. Satellite-based rainfall products with large areal coverage provide a potential alternative source of data where in situ measurements are not available. However, the mismatch in scale between these products and model requirements has limited their application and demonstrates that satellite data must be downscaled before being used. This study developed a statistical spatial downscaling scheme based on the relationships between precipitation and related environmental factors such as local topography and pre-storm meteorological conditions. The method was applied to disaggregate the Tropical Rainfall Measuring Mission (TRMM) 3B42 products, which have a resolution of 0.25° × 0.25°, to 1 × 1 km gridded rainfall fields. The TRMM datasets in accord with six rainstorm events in the Xiao River basin were used to validate the effectiveness of this approach. The downscaled precipitation data were compared with ground observations and exhibited good agreement with r² values ranging from 0.612 to 0.838. In addition, the proposed approach provided better results than the conventional spline and kriging interpolation methods, indicating its promise in the management of extreme rainfall events. The uncertainties in the final results and the implications for further study were discussed, and the needs for additional rigorous investigations of the rainfall physical process prior to institutionalizing the use of satellite data were highlighted.     

Reliability of reanalysis and remotely sensed precipitation products for hydrological simulation over the Sefidrood River Basin,Iran

ABSTRACT

Hydrological models require different inputs for the simulation of processes, among which precipitation is essential. For hydrological simulation, four different precipitation products – Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE); European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-Interim); Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) real time (RT); and Precipitation Estimation from Remotely Sensed Information using Arti?cial Neural Networks (PERSIANN) – are compared against ground-based datasets. The variable infiltration capacity (VIC) model was calibrated for the Sefidrood River Basin (SRB), Iran. APHRODITE and ERA-Interim gave better rainfall estimates at daily time scale than other products, with Nash-Sutcliffe efficiency (NSE) values of 0.79 and 0.63, and correlation coefficient (CC) of 0.91 and 0.82, respectively. At the monthly time scale, the CC between all rainfall datasets and ground observations is greater than 0.9, except for TMPA-RT. Hydrological assessment indicates that PERSIANN is the best rainfall dataset for capturing the streamflow and peak flows for the studied area (CC: 0.91, NSE: 0.80).     

Correction of TRMM 3B43 monthly precipitation data over the mountainous areas of Peru during the period 1998–2007

In an attempt to estimate the spatial and temporal behaviour of rainfall over the mountainous areas of the Peruvian Andes, a new in situ monthly rainfall dataset has been collected (1998–2007) and compared with Tropical Rainfall Measuring Mission (TRMM) 3B43 monthly precipitation data for regions located above 3000 m. The reliability of the TRMM 3B43 data varies depending on the root mean squared error ratio (%RMSE) and correlation coefficient. Because of the discrepancy between the two datasets, the use of additive and multiplicative correction models is proposed for the TRMM 3B43 data. In the Peruvian mountain ranges, these correction models better approximate TRMM rainfall monthly values, as already verified for annual values. Copyright © 2011 John Wiley & Sons, Ltd.     

Effects of temporal scales and space mismatches on the TRMM 3B42 v7 precipitation product in a remote mountainous area

The accurate measurement of precipitation is essential to understanding regional hydrological processes and hydrological cycling. Quantification of precipitation over remote regions such as the Tibetan Plateau is highly unreliable because of the scarcity of rain gauges. The objective of this study is to evaluate the performance of the satellite precipitation product of tropical rainfall measuring mission (TRMM) 3B42 v7 at daily, weekly, monthly, and seasonal scales. Comparison between TRMM grid precipitation and point‐based rain gauge precipitation was conducted using nearest neighbour and bilinear weighted interpolation methods. The results showed that the TRMM product could not capture daily precipitation well due to some rainfall events being missed at short time scales but provided reasonably good precipitation data at weekly, monthly, and seasonal scales. TRMM tended to underestimate the precipitation of small rainfall events (less than 1 mm/day), while it overestimated the precipitation of large rainfall events (greater than 20 mm/day). Consequently, TRMM showed better performance in the summer monsoon season than in the winter season. Through comparison, it was also found that the bilinear weighted interpolation method performs better than the nearest neighbour method in TRMM precipitation extraction.     

Evaluation and hydrologic validation of TMPA satellite precipitation product downstream of the Pearl River Basin,China

With high spatio‐temporal resolution and wide coverage, satellite‐based precipitation products can potentially fill the deficiencies of traditional in situ gauge precipitation observations and provide an alternative data source for ungauged areas. However, due to the relatively poor accuracy and high uncertainty of satellite‐based precipitation products, it remains necessary to assess the quality and applicability of the products for each investigated area. This study evaluated the accuracy and error of the latest Tropical Rainfall Measuring Mission Multi‐satellites Precipitation Analysis 3B42‐V7 satellite‐based precipitation product and validated the applicability of the product for the Beijiang and Dongjiang River Basins, downstream of the Pearl River Basin in China. The study first evaluated the accuracy, error, and bias of the 3B42‐V7 product during 1998–2006 at daily and monthly scale via comparison with in situ observations. The study further validated the applicability of the product via hydrologic simulation using the variable infiltration capacity hydrological model for three hydrological stations in the Beijiang River Basin, considering two scenarios: a streamflow simulation with gauge‐calibrated parameters (Scenario I) and a simulation after recalibration with the 3B42‐V7 product (Scenario II). The results revealed that (a) the 3B42‐V7 product produced acceptable accuracy both at the daily scale and high accuracy at the monthly scale while generally tending to overestimate precipitation; (b) the product clearly overestimated the frequency of no rainfall events at the grid cell scale and light rainfall (<1 mm/day) events at the region scale and also overestimated the amount of heavy rain (25–50 mm/day) and hard rain (≥50 mm/day) events; (c) under Scenario I, the 3B42‐V7 product performed poorly at three stations with gauge‐calibrated parameters; under Scenario II, the recalibrated model provided significantly improved performance of streamflow simulation with the 3B42‐V7 product; (d) the variable infiltration capacity model has the ability to reveal the hydrological characteristics of the karst landform in the Beijiang Basin when using the 3B42‐V7 product.     

Geospatial blending to improve spatial mapping of precipitation with high spatial resolution by merging satellite‐based and ground‐based data

Estimating accurate spatial distribution of precipitation is important for understanding the hydrologic cycle and various hydro‐environmental applications. Satellite‐based precipitation data have been widely used to measure the spatial distribution of precipitation over large extents, but an improvement in accuracy is still needed. In this study, three different merging techniques (Conditional Merging, Geographical Differential Analysis and Geographical Ratio Analysis) were used to merge precipitation estimations from Communication, Ocean and Meteorological Satellite (COMS) Rainfall Intensity data and ground‐based measurements. Merged products were evaluated with varying rain‐gauge network densities and accumulation times. The results confirmed that accuracy of detecting quantitative rainfall was improved as the accumulation time and network density increased. Also, the impact of spatial heterogeneity of precipitation on the merged estimates was investigated. Our merging techniques reproduced accurate spatial distribution of rainfall by adopting the advantages of both gauge and COMS estimates. The efficacy of the merging techniques was particularly pronounced when the spatial heterogeneity of hourly rainfall, quantified by variance of rainfall, was greater than 10 mm²/accumulation time². Among the techniques analysed, Conditional Merging performed the best, especially when the gauge density was low. This study demonstrates the utility of the COMS Rainfall Intensity product, which has a shorter latency time (1 h) and higher spatio‐temporal resolution (hourly, 4 km by 4 km) than other widely used satellite precipitation products in estimating precipitation using merging techniques with ground‐based point measurements. The outcome has important implications for various hydrologic modelling approaches, especially for producing near real‐time products. Copyright © 2016 John Wiley & Sons, Ltd.     

Evaluation of dynamically downscaled reanalysis precipitation data for hydrological application

Skilful and reliable precipitation data are essential for seasonal hydrologic forecasting and generation of hydrological data. Although output from dynamic downscaling methods is used for hydrological application, the existence of systematic errors in dynamically downscaled data adversely affects the skill of hydrologic forecasting. This study evaluates the precipitation data derived by dynamically downscaling the global atmospheric reanalysis data by propagating them through three hydrological models. Hydrological models are calibrated for 28 watersheds located across the southeastern United States that is minimally affected by human intervention. Calibrated hydrological models are forced with five different types of datasets: global atmospheric reanalysis (National Centers for Environmental Prediction/Department of Energy Global Reanalysis and European Centre for Medium‐Range Weather Forecasts 40‐year Reanalysis) at their native resolution; dynamically downscaled global atmospheric reanalysis at 10‐km grid resolution; stochastically generated data from weather generator; bias‐corrected dynamically downscaled; and bias‐corrected global reanalysis. The reanalysis products are considered as surrogates for large‐scale observations. Our study indicates that over the 28 watersheds in the southeastern United States, the simulated hydrological response to the bias‐corrected dynamically downscaled data is superior to the other four meteorological datasets. In comparison with synthetically generated meteorological forcing (from weather generator), the dynamically downscaled data from global atmospheric reanalysis result in more realistic hydrological simulations. Therefore, we conclude that dynamical downscaling of global reanalysis, which offers data for sufficient number of years (in this case 22 years), although resource intensive, is relatively more useful than other sources of meteorological data with comparable period in simulating realistic hydrological response at watershed scales. Copyright © 2013 John Wiley & Sons, Ltd.     

全球降水计划多卫星降水联合反演IMERG卫星降水产品在中国大陆地区的多尺度精度评估

以中国气象局逐小时地面降水数据集为参考基准,采用8种统计评价指标综合评估对比了美国NASA研发的全球降水计划（GPM）多卫星降水联合反演IMERG（Integrated Multi-satellitE Retrievals for GPM）卫星降水产品的三个不同版本的Final数据,分析了三套卫星降水在中国大陆地区多时空尺度下的反演精度,探讨了IMERG最新版本V5数据的改进情况及反演中仍然存在的问题.结果表明：IMERG数据能够准确地捕捉到中国大陆地区的降水区域特征,但是在中国西北部地面站点稀疏地区误差较大,精度较低,难以精确估测该地区的实际降水值.最新版本V5数据精度整体上优于先前的V3和V4数据,V5与地面观测数据的相关系数为0.75,均方根误差为7.03 mm/d,较V3、V4有明显提高,改善了V3、V4在中国西北部出现的降水低估问题;但是V5在冬季表现较差且没有解决前期版本存在的高估问题,整体上相对实际降水仍处于高估状态;同时V5在对高雨强事件的捕捉监测能力方面还存在一定的不足,因此建议在强降雨事件监测中需谨慎使用卫星降水IMERG数据集.目前V5系统中的校正算法还存在部分缺陷：为消除全球降水系统性低估问题,目前的校正算法整体性抬升了卫星降水值,从而导致卫星降水反演在中国地区高雨强事件下出现高误报以及高估问题,进而影响到IMERG数据回推以及后续再生数据的精度.     

Evaluation and hydrological application of precipitation estimates derived from PERSIANN‐CDR,TRMM 3B42V7, and NCEP‐CFSR over humid regions in China

Satellite‐based and reanalysis quantitative precipitation estimates are attractive for hydrologic prediction or forecasting and reliable water resources management, especially for ungauged regions. This study evaluates three widely used global high‐resolution precipitation products [Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks‐Climate Data Record (PERSIANN‐CDR), Tropical Rainfall Measuring Mission 3B42 Version 7 (TRMM 3B42V7), and National Centers for Environment Prediction‐Climate Forecast System Reanalysis (NCEP‐CFSR)] against gauge observations with seven statistical indices over two humid regions in China. Furthermore, the study investigates whether the three precipitation products can be reliably utilized as inputs in Soil and Water Assessment Tool, a semi‐distributed hydrological model, to simulate streamflows. Results show that the precipitation estimates derived from TRMM 3B42V7 outperform the other two products with the smallest errors and bias, and highest correlation at monthly scale, which is followed by PERSIANN‐CDR and NCEP‐CFSR in this rank. However, the superiority of TRMM 3B42V7 in errors, bias, and correlations is not warranted at daily scale. PERSIANN‐CDR and 3B42V7 present encouraging potential for streamflow prediction at daily and monthly scale respectively over the two humid regions, whilst the performance of NCEP‐CFSR for hydrological applications varies from basin to basin. Simulations forced with 3B42V7 are the best among the three precipitation products in capturing daily measured streamflows, whilst PERSIANN‐CDR‐driven simulations underestimate high streamflows and high streamflow simulations driven by NCEP‐CFSR mostly are overestimated significantly. In terms of extreme events analysis, PERSIANN‐CDR often underestimates the extreme precipitation, so do extreme streamflow simulations forced with it. NCEP‐CFSR performs just the reverse, compared with PERSIANN‐CDR. The performance pattern of TRMM 3B42V7 on extremes is not certain, with coexisting underestimation and overestimation. Copyright © 2016 John Wiley & Sons, Ltd.