Assessing the performance of satellite-based precipitation products and its dependence on topography over Poyang Lake basin

Transcription

1 Theor Appl Climatol (2014) 115: DOI /s x ORIGINAL PAPER Assessing the performance of satellite-based precipitation products and its dependence on topography over Poyang Lake basin Xianghu Li & Qi Zhang & Chong-Yu Xu Received: 3 September 2012 /Accepted: 22 April 2013 /Published online: 26 May 2013 # Springer-Verlag Wien 2013 Abstract Satellite-based precipitation products (SPPs) have greatly improved their applicability and are expected to offer an alternative to ground-based precipitation estimates in the present and the foreseeable future. There is a strong need for a quantitative evaluation of the usefulness and limitations of SPPs in operational meteorology and hydrology. This study compared two widely used high-resolution SPPs, the Tropical Rainfall Measuring Mission (TRMM) and Precipitation Estimation from Remote Sensing Information using Artificial Neural Network () in Poyang Lake basin which is located in the middle reach of the Yangtze River in China. The bias of rainfall amount and occurrence frequency under different rainfall intensities and the dependence of SPPs performance on elevation and slope were investigated using different statistical indices. The results revealed that (1) TRMM 3B42 usually underestimates the rainy days and overestimates the average rainfall as well as annual rainfall, while the data were markedly lower than rain gauge data; (2) the rainfall contribution rates were underestimated by TRMM 3B42 in the middle rainfall class but overestimated in the heavy rainfall class, X. Li : Q. Zhang (*) State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, 73 East Beijing Road, Nanjing , People s Republic of China qzhang@niglas.ac.cn X. Li xhli@niglas.ac.cn C.<Y. Xu Department of Hydrology and Water Resources, Wuhan University, Wuhan, China c.y.xu@geo.uio.no C.<Y. Xu Department of Geosciences, University of Oslo, Oslo, Norway while the opposite trend was observed for ; (3) although the temporal distribution characteristics of monthly rainfall were correctly described by both SPPs, tended to suffer a systematic underestimation of rainfall in every month; and (4) the performances of both SPPs had clear dependence on elevation and slope, and their relationships can be fitted using quadratic equations. 1 Introduction Precipitation is the key input for hydrological modeling and its temporal and spatial distribution has a significant impact on the land surface hydrological fluxes and states (Gottschalck et al. 2005; Tianetal.2007; Suet al. 2008). Therefore, accurate measurements of precipitation on fine spatial and temporal scales are very important for simulating land surface hydrologic processes, predicting drought and flood, and monitoring water resources (Sorooshian et al. 2005; Yong et al. 2010). However, in many populated regions of the world and especially in developing countries, groundbased measurement networks (either from rain gauge or weather radar) are either sparse in both time and space or nonexistent (Behrangi et al. 2011), and their limited sampling areas and problems inherent in point measurements represent a substantial difficulty when dealing with effective spatial coverage of rainfall over a large area (Pegram et al. 2004; Schulze 2006; Ghile et al. 2010). Although weather radar has enormous potential to offer rainfall estimates with high spatial resolution and temporal continuity (Sun et al. 2000; He et al. 2011), there is often a large space time variable bias (Smith et al. 2007; Krajewski and Smith 2002) andits accuracy is highly sensitive to atmospheric conditions, sampling height of the radar beam, beam blocking,

2 714 X. Li et al. variations in the reflectivity rainfall rate relationships, ground echoes, and distance from the radar (Deyzel et al. 2004; Pegram et al. 2004; Piccolo and Chirico 2005). This situation restricts these regions to manage water resources (Behrangi et al. 2011) and hampers the development and use of flood and drought warning models, extreme weather monitoring, and decisionmaking systems (AghaKouchak et al. 2011). Alternatively, satellite-based precipitation products (SPPs) are widely accepted as promising strategies to address the previously mentioned limitations (Ghile et al. 2010). Such data are especially valuable in developing countries or remote locations, where conventional rain gauge or weather radar data are sparse or of bad quality (Hughes 2006). Furthermore, the near real-time availability of the SPPs makes them suitable for modeling applications where water resources management is crucial and data gathering and quality assurance are cumbersome (Stisen and Sandholt 2010). Recent development in global and regional SPPs has greatly improved their applicability as input to large-scale distributed hydrological models (Stisen and Sandholt 2010;Li etal. 2012, 2013;Samaniego et al. 2012) and are expected to offer an alternative to groundbased rainfall estimates in the present and the foreseeable future (Sawunyama and Hughes 2008). This is mainly due to the increased temporal and spatial resolution of SPPs and also due to improved accuracy resulting from new methods to merge various data sources such as radar, microwave, and thermal infrared (TIR) remote sensing (Gottschalck et al. 2005; Tian and Peters-Lidard 2007; Stisen and Sandholt 2010). With suites of sensors flying on a variety of satellites over the last two decades, many satellite-based precipitation estimation algorithms have been developed (Behrangi et al. 2011) to combine measurements of different spaceborne sensors and gauge data allow the derivation of high-quality precipitation estimates. Since Huffman et al. (1995, 1997) created a scheme to combine satellite data of different sensors (microwave, infrared [IR], and longwave radiation) with gauge data and built the Global Precipitation Climatology Project (GPCP) combined precipitation dataset at a grid and monthly resolution, these algorithms have improved constantly by emerging further multisource products with higher resolutions (Scheel et al. 2011). Currently, several satellite-based gridded precipitation estimates are available for (at least) the lower latitudes and the tropics at high temporal (three hourly or shorter) and reasonably high spatial ( or finer) resolutions. Examples include the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) (Huffman et al. 2007), the Climate Prediction Center (CPC) morphing algorithm () (Joyce et al. 2004), the Precipitation Estimation from Remote Sensing Information using Artificial Neural Network () (Hsu et al. 1997; Sorooshian et al. 2000), the Global Satellite Mapping of Precipitation (Kubota et al. 2007; Aonashi et al. 2009; Ushio et al. 2009), the Naval Research Laboratory Global Blended- Statistical Precipitation Analysis (Turk et al. 2000), and so on. Although different in the precipitation estimation procedure, in all of the listed products, combined information from passive microwave (PMW) sensors in low earth orbiting satellites and IR radiometers in geostationary earth orbiting (GEO) satellites is used to improve the consistency, accuracy, coverage, and timeliness of high-resolution precipitation data (Kubota et al. 2009; Behrangietal.2011). However, satellite data also suffer from some inherent shortcomings and have biases and random errors that are caused by various factors like sampling frequency, nonuniform field of view of the sensors, and uncertainties in the rainfall retrieval algorithms (Nair et al. 2009). It is, therefore, essential to validate the satellite-derived products with conventional rain estimates to quantify the direct usability of these products (Nair et al. 2009; Li et al. 2012, 2013). Numerous researchers have examined the quality of satellite-derived precipitation datasets in various regions of the world. Table 1 summarizes recent studies on evaluations of three widely used high-resolution SPPs (TMPA,, and ) based on the work of Romilly and Gebremichael (2011). For instance, Dinku et al. (2010) evaluated two satellite rainfall estimation algorithms, TMPA and, over two locations (highlands of Ethiopia and Columbia) and found that total rainfall amount was overestimated by TMPA 3B42RT (13 %) and (11 %) in Ethiopia, while it was underestimated by TMPA 3B42RT (17 %), TMPA 3B42 (16 %), and (9 %) in Columbia. Stisen and Sandholt (2010) evaluatedfive satellite products, including TRMM 3B42,, and, in the Senegal River Basin using the MIKE SHE hydrological model and found that TRMM 3B42 performs better than the other satellite products. Yamamoto et al. (2011) compared several SPPs with rainfall data from the automated weather station in the Nepal Himalayas and found that showed large differences with the observed values in winter and had a tendency to overestimate precipitation in the pre-monsoon and postmonsoon seasons. Ward et al. (2011) believed that TRMM 3B42 and are unable to detect light rainfall amounts and underestimate rainfall in the dry season. Behrangi et al. (2011) found that TMPA 3B42RT,, and tend to overestimate intense precipitation during warm months. In addition, several studies have also validated the effects of topography on SPPs performance. For example, Bitew and Gebremichael (2010) found that the and -Cloud Classification System (CCS) underestimated 32 and 49 % of total rainfall, respectively, in a high-elevation region. Hong et al. (2007) evaluated the impact of topography on the performance of -CCS in western Mexico and found

3 Assessing the performance of satellite-based precipitation products 715 Table 1 Evaluations on high-resolution SPPs (supplement based on Romilly and Gebremichael 2011) Precipitation products Regions Main results References TMPA 3B42 Illinois River basin, USA TMPA 3B42RT,, and tend to overestimate intense Behrangi et al. TMPA 3B42RT TMPA 3B42 Nepal Himalayas precipitation during warm months; demonstrates higher skill to delineate precipitation area showed large differences in winter; overestimates rainfall (2011) Yamamoto et al. in the pre-monsoon and post-monsoon seasons; TMPA 3B42 and increase rainfall during the morning (2011) TRMM 3B42 Congo River basin TRMM 3B42 provides the best spatial and temporal distributions and magnitudes; and tend to overestimate magnitudes Beighley et al. (2011) TRMM Paute River, Ecuador and Both are unable to detect light rainfall amounts and underestimated in the Ward et al. (2011) 3B42 TMPA Baker basin, Patagonia Awash River basin, dry season; there is a systematic underestimation of rainfall occurrence by TRMM 3B42 3B42RT and show an increasing trend with elevation; Hirpa et al. (2010) 3B42RT Ethiopia considerably underestimates rainfall in high-elevation areas TRMM 3B42 Senegal River Basin, West African and have much larger biases than TRMM based on MIKE SHE hydrological model Stisen and Sandholt (2010) TMPA 3B42 Western Highlands, Occurrence of rain underestimated by all products; total amount underestimated by Dinku et al. TMPA 3B42RT Ethiopia TMPA 3B42 (14 %) and overestimated by TMPA 3B42RT (13 %) and (11 %) (2010) TMPA 3B42 Highlands, Columbia Occurrence of rain underestimated by all products; Total amount underestimated Dinku et al. TMPA 3B42RT by TMPA 3B42RT (17 %), TMPA 3B42 (16 %), and (9 %) (2010) TMPA Great Rift Valley, Ethiopia TMPA 3B42RT and show elevation-dependent trends, Hirpa et al. (2010) 3B42RT with underestimation at higher elevations; underestimates at higher elevations and not exhibit elevation-dependent trends TMPA 3B42 USA and the Pacific Ocean and overestimate rainfall as much as 125 % Sapiano and Berressa basin, Ethiopia in warm season over the USA; and TMPA 3B42 underestimate rainfall over the Pacific Ocean Both underestimate heavy rainfall by 50 %; total amount underestimated Arkin (2009) Bitew and - CCS by (32 %) and -CCS (49 %) Gebremichael (2010) TMPA 3B42 Mainland China Overestimate rainfall <1 mm/day; underestimate rainfall >1 mm/day Yu et al. (2009) TRMM 3B42 Sierra Madre Occidental, Mexico and overestimate the rainfall rate and frequency; TRMM 3B42 closely agrees with the rain gauge network Nesbitt et al. (2008) TRMM 3B42 Ethiopia and Zimbabwe All data detected the occurrence of rainfall well, the amount of rainfall was poorly estimated; the performance was better over Dinku et al. (2008) TRMM 3B42RT Zimbabwe (relatively flat area) as compared with Ethiopia (complex terrain)

4 716 X. Li et al. Table 1 (continued) Precipitation products Regions Main results References TMPA 3B42 Continental USA TMPA Continental USA 3B42RT TMPA 3B42 has near zero biases for both summer and winter months; overestimates rainfall over central; underestimates over the northeast during the summer overestimate total rainfall over central and western during spring and summer, underestimate during fall and winter; TMPA 3B42RT overestimate during spring and summer, overestimate during fall and winter Tian et al. (2007) Gottschalck et al. (2005) that light precipitation events were underestimated in the high-elevation regions and precipitation events in the lowerelevation regions were overestimated. Hirpa et al. (2010) also found elevation-dependent trends of performance in the TMPA 3B42RT and products. Many researchers have testified that the accuracy of SPPs is influenced by location, season, rain type (i.e., convective, stratiform), topography, climatological factors, and so on (Artan et al. 2007; Dinku et al. 2008; Jiang et al. 2008; Han et al. 2011). However, very few of previous studies have so far fully and comprehensively analyzed these aspects. Their performances under different rainfall intensities and in different seasons are still unclear. Moreover, although several recent studies (i.e., Dinku et al. 2008; Ward et al. 2011; Romilly and Gebremichael 2011) have taken into account the effects of elevation, they have only compared the performances of SPPs in high-elevation and lowelevation regions and the quantitative relationships between accuracy and elevation are not mentioned. On the other hand, slope as another important topographic factor is not considered yet in these corresponding studies; the relationship between SPPs performance and slope is still unclear. Poyang Lake, located in the middle reach of the Yangtze River (Fig. 1), is the largest freshwater lake in China and plays a crucial role in flood protection for the lower reaches of the Yangtze River. It has recently been shown that the frequency and severity of the floods have increased since 1990 (Guo et al. 2008) and the surface runoffs from the five subbasins have been the primary source of the major floods in the Poyang Lake basin (Hu et al. 2007). To implement flood protection and regulation and ensure water safety in areas around the lake, it is necessary to understand the flood development and the rainfall runoff processes in the catchment. However, the applications of satellite-based precipitation, as complementary rainfall data, are seldom in Poyang Lake basin. The scarcity on the accuracy evaluation of SPPs in this basin has hampered their extensive application and development of flood warning models to a certain extent. Therefore, the objectives of the study are designed to evaluate and compare two high-resolution SPPs (TRMM 3B42 and ) with rain gauge data and investigate their spatial and temporal characteristics in the Poyang Lake basin. Also, the bias of rainfall amount and occurrence frequency under different rainfall intensities in each month and the quantitative relationships of accuracy with elevation and slope are investigated. By doing so, different statistical measures and methods are calculated and used in the study. The study is expected to serve as useful reference and valuable information for future study and application of satellite rainfall data in the Poyang Lake basin as well as in other regions. The rest of this paper is organized as follows. In the next section, details of the study area and climate, along with a brief discussion on the rain gauge and SPPs, are presented. In Section 3, the indexes and methods used in the study are briefly described with the help of cited references. Major results of this study are presented in Section 4. Section 5 mainly discusses the possible sources of errors of SPPs from various aspects and the further challenges that we face in using SPPs for hydrological studies, and Section 6 summarizes the conclusions. 2 Study area and data 2.1 Study area Poyang Lake basin is located in the middle and lower reaches of the Yangtze River, China and the lake receives water flows mainly from the five rivers: Xiushui River, Ganjiang River, Fuhe River, Xinjiang River, and Raohe River and discharges into the Yangtze River through a channel in its northern part (Fig. 1). The total drainage area of the water systems is km 2, accounting for 9 % of the drainage area of the Yangtze River basin. The topography in the basin varies from highly mountainous and hilly areas (with the maximum elevation of 2,200 m above mean sea level) to alluvial plains in the lower reaches of the primary watercourses. Poyang Lake basin has a subtropical wet climate characterized with a mean annual precipitation of 1,680 mm for the period of and annual mean temperature of 17.5 C. Annual precipitation shows a wet season and a dry season and a short transition period in between. Precipitation increases quickly from January to

5 Assessing the performance of satellite-based precipitation products 717 June and decreases sharply in July, and after September, the dry season sets in and lasts through December. In response to the annual cycle of precipitation, the Poyang Lake can expand to a large water surface of 3,800 km 2 and volume of m 3 in the wet season, but shrinks to little more than a river during the dry season (Xu et al. 2001) and exposes extensive floodplains and wetland areas. 2.2 Data Ground data Daily precipitation data, during the period for 34 stations in the Poyang Lake basin, are obtained from National Meteorological Information Center of China, which are used to compare and evaluate the accuracy of satellite-based rainfall data in the study. The distribution of rain gauges is shown in Fig. 1. These data have been widely used for different studies previously and the qualities have been approved to be reliable (Hu et al. 2007; Guo et al. 2008; Li et al. 2012). Daily precipitation data from all the stations are averaged to obtain the areal daily precipitation for the Poyang Lake basin, and the spatial distribution of annual rainfall is interpolated by the inverse distance weighted (IDW) technique with a power of 2. In addition, the digital elevation model data are derived from the National Aeronautics and Space Administration (NASA) Shuttle Radar Topographic Mission at a spatial resolution of 90 m ( which are used to obtain the altitude of each rain gauge and the average slope in pixel size of Satellite data The high-resolution SPPs investigated in this study are TRMM 3B42 and. TRMM was launched in November 1997 as a joint effort by NASA and the Japan Aerospace Exploratory Agency with the specific objectives of studying and monitoring the tropical rainfall (Kummerow et al. 1998). The TRMM includes a number of precipitation-related instruments, such as a precipitation radar, a visible and IR sensor, and a special sensor microwave imager (SSM/I) like the TRMM Fig. 1 Location of Poyang Lake basin and the distribution of rain gauges (black squares represent the six selected grids for statistical comparison)

6 718 X. Li et al. microwave imager (TMI) (Kummerow et al. 2001), and detailed information is shown in Table 2. Several algorithms have been developed to make use of data from the TRMM mission, and the purpose of the 3B42 class of algorithm is to produce TRMM-adjusted merged IR precipitation and root mean square precipitation error estimates. The TRMM 3B42 precipitation product was produced using the following four steps (Vila et al. 2009). The first stage of the algorithm consists of the calibration and combination of microwave precipitation estimates. Passive microwave observations from the TMI, Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), and SSM/I are converted to precipitation estimates at the TRMM Science Data and Information System with sensor-specific versions of the Goddard profiling algorithm (Kummerow et al. 2001). In the second step, the IR precipitation estimates are created using the calibrated microwave precipitation. Histograms of time space matched combined microwave (high-quality precipitation rates) and IR brightness temperatures (TBs), each represented on the same three hourly grid, are accumulated for 1 month into histograms on a 1 1 grid and aggregated to overlapping 3 3 windows, which are then used to create spatially varying calibration coefficients that convert IR TBs to precipitation rates (Huffman et al. 2007; Vila et al. 2009). In the third stage, the microwave and IR estimates are combined. The physically based combined microwave estimates are taken as is where available, and the remaining grid boxes are filled with microwave-calibrated IR estimates. And the final step is the indirect use of rain gauge data. The GPCP monthly rain gauge analysis data developed by the Global Precipitation Climatology Center and the Climate Assessment and Monitoring System monthly rain gauge analysis data developed by the CPC are integrated using a histogram-matching technique (Huffman et al. 2007). A detailed description of this algorithm can be found in Huffman et al. (2007) and Dinku and Anagnostou (2006). The dataset, from University of California, Irvine, uses an adaptive neural network function classification/approximation procedure to estimate rainfall rates at each pixel of the IR TB image provided by high-frequency (48 readings a day) geostationary satellites (Geostationary Operational Environmental Satellites (GOES)-8, GOES-9 and GOES-10; Geostationary Meteorological Satellite-5, Meteorological Satellite (MetSat)-6 and MetSat-7) (Hsu et al. 1997; Sorooshian et al. 2000; Asadullah et al. 2008). Model parameters are regularly updated using rainfall estimates from low-orbit satellites, including the TRMM, the National Oceanic and Atmospheric Administration (NOAA)-15, NOAA-16, and NOAA-17 satellites, and the Defense Meteorological Satellite Program (DMSP) F-13, DMSP F-14, and DMSP F-15 satellites (Ferraro and Marks 1995; Kummerow et al. 1998; Hsu and Sorooshian 2008). An adaptive training feature facilitates updating of the network parameters whenever independent estimates of rainfall are available. Initially, the neural network was trained using radar data and the input was limited to TIR data and later extended to include the use of both daytime visible imagery (Hsu et al. 1999)andtheTMI rainfall estimates (Sorooshian et al. 2000). In the operation of, two algorithms are running in parallel (Hsu and Sorooshian 2008): one is run in the simulation mode and the other in the update mode. The simulation mode generates the surface rain rate at the resolution at every 30 min from the GEO satellites IR images, while the update mode continuously adjusts the mapping function parameters of based on the fitting error of any pixel for which a PMW instantaneous rainfall estimate is available. The simulation mode generates the regular rainfall rate output, and the update mode improves the quality of the product. A full description of the algorithm was given by Sorooshian et al. (2000) and Hsu et al. (1997). TRMM 3B42 and precipitation estimates are available at the grid, three hourly and six hourly resolution, respectively, with global coverage between 50 N and 50 S, and the data used in the study cover the period from January and March 2000, respectively, to December Table 2 Summary of the high-resolution SPPs Datasets Spatial coverage Temporal coverage Spatial resolution Temporal resolution Main product data sources References TRMM 3B42 V6 50 S 50 N globally 50 S 50 N globally January 1998 present March 2000 present hourly Geostationary IR, TRMM TMI, SSM/I, AMSU, AMSR-E, and rain gauge data hourly Neural network using geostationary IR, TRMM TMI, SSM/I, and AMSU Huffman et al. (2007) Hsu et al. (1997, 1999); Sorooshian et al. (2000) IR infrared, SSM/I special sensor microwave/imager, AMSU advanced microwave sounding unit, AMSR-E Advanced Microwave Sounding Radiometer for the Earth Observing System

7 Assessing the performance of satellite-based precipitation products Methods To quantitatively compare SPPs with rain gauge observations, several widely used validation statistical indices are selected in the study. The correlation coefficient (R) is used to reflect the degree of linear correlation between satellite-based precipitation and gauge observations, the mean error (ME) simply scales the average difference between the satellite-based estimates and observations, the root mean square error (RMSE) measures the average error magnitude but gives greater weight to the larger errors, and the relative bias (BIAS) is used to assess the systematic bias of satellite precipitation. The values of R, ME, RMSE, and BIAS are calculated, respectively, as Eqs. 1, 2, 3 and 4: P n G i G Si S i¼1 R ¼ sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P n 2 P G i G n 2 S i S ME ¼ 1 n i¼1 RMSE ¼ BIAS ¼ X n i¼1 i¼1 ð1þ ðs i G i Þ ð2þ sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 X n ðs i G i Þ 2 n P n i¼1 P n i¼1 ðs i G i Þ i¼1 ð3þ G i 100 % ð4þ where n is number of samples, G i are gauge observations, S i are satellite-based precipitation, and G and S are mean gauge and satellite-based precipitation, respectively. In addition, evaluation and comparison are carried out by detecting rain events at different precipitation thresholds over the Poyang Lake basin at a daily time step. It is performed by computing the frequency bias index (FBI), probability of detection (POD), false alarm ratio (FAR), and equitable threat score (ETS) (Wilks 1995, 2006) based on a 2 2 contingency table, as shown in Table 3. The FBI indicates whether the dataset tends to underestimate (FBI<1) or overestimate (FBI>1) rain events, and it ranges from 0 to infinity, with a perfect score of 1. The POD gives the fraction of rain occurrences that was correctly detected. It ranges from 0 to 1, with a perfect score of 1. Table 3 Contingency table for comparing SPPs with rain gauge data Satellite-based rainfall data The FAR measures the fraction of rain detections that was actually false alarms. It ranges from 0 to 1, with a perfect score of 0. The ETS provides the fraction of rain events (observed and/or detected) which was correctly detected, and the perfect score is 1 (Su et al. 2008; Koo et al. 2009). The ETS is commonly used as an overall skill measure by the numerical weather prediction community, whereas the FBI, POD, and FAR provide complementary information about bias, misses, and false alarms (Koo et al. 2009). Those indices have been successfully applied in many studies (Layberry et al. 2006; Ebert et al. 2007; Suetal. 2008; Kubotaetal. 2009; Yong et al. 2010; Shrestha et al. 2011) and are believed to be robust and provide a sound basis for the assessment of the rainfall detection capabilities of the satellite products. For a more detailed explanation of FBI, POD, FAR, and ETS, please refer to Wilks (2006) and Ebert et al. (2007) and their values are calculated, respectively, using Eqs. 5, 6, 7, 8, and9: FBI ¼ a þ b ð5þ a þ c POD ¼ FAR ¼ ETS ¼ a a þ c b a þ b a He a þ b þ c He Rain gauges data Greater than or equal to the threshold Greater than or equal to a b the threshold Less than the threshold c d Less than the threshold a the number of observed rain events correctly detected, b the number of false alarms (rainfall events detected but not observed), c the number of observed rain events not detected, d the sum of cases when neither observed nor detected rain events occurred ð6þ ð7þ ð8þ He ¼ ða þ bþða þ cþ ð9þ N where N is the total number of estimates, a is the number of observed rain events correctly detected, b is the number of false alarms (rainfall events detected but not observed), c stands for the number of observed rain events not detected, and d is the sum of cases when neither observed nor detected rain events occurred.

8 720 X. Li et al. All of these indicators are calculated based on the domainaveraged precipitation amount over Poyang Lake basin. Moreover, to quantify the ability of each dataset in predicting light and heavy rainfall events, the FBI, POD, FAR, and ETS are calculated for precipitation thresholds of 1, 2, 5, 10, 25, and 50 mm/day, respectively. 4 Results 4.1 Grid-based statistical comparison For the comparison between satellite-based rainfall and rain gauge data at grid scale, several statistical indices such as average rainy day in a year, average rainfall in rainy days, maximal daily rainfall, maximal 5-day rainfall, and average annual rainfall were firstly analyzed. Considering the relative location of rain gauges in the grid (central is best), representative of Poyang Lake basin s topography and the spatial distribution in the catchment, the six grids were selected for the comparison between satellite pixel ( grid) and the gauging stations inside the grids (namely, Gaoan, Jinggang, Ganzhou, Nancheng, Yiyang, and Duchang) (see Fig. 1) and the results were shown in Table 4. It is seen that the average rainy days (rainfall 1 mm/day) were days/year for different rain gauges, but and days/year for TRMM 3B42 and, respectively. This indicated that average rainy days were underestimated by both SPPs. The average rainfall (in rainy days) is another important and useful index to reflect the precision of rainfall amount. The average rainfalls estimated from rain gauge data ranged between 13.4 and 14.7 mm/day, with an average of 14.1 mm/day. However, the average rainfalls from TRMM 3B42 data were larger than those from rain gauge data in every grid, and the opposite was true for. As for the maximal daily rainfall, the TRMM 3B42 data were smaller than rain gauge data, except in Nancheng and Duchang grid, while data were acutely smaller than rain gauge data in every grid. The similar situations were observed further in the comparison of maximal 5-day rainfall. It is shown that the maximal 5-day rainfall from data were lower markedly than that from rain gauge data, but the performance of TRMM 3B42 was acceptable. Lastly, the average annual total rainfall estimated from rain gauge, TRMM 3B42, and data were 1,619, 1,657, and 839 mm, respectively. TRMM 3B42 overestimated the annual rainfall slightly, but underestimated it greatly. 4.2 Evaluation of the rainfall data under different rainfall intensity Figure 2 shows the intensity distributions of daily rainfall in different classes and their contributions to the total rainfall Table 4 Comparison of statistical indexes between satellite-based and rain gauge rainfall Grids Rainy day a (day/year) Average rainfall b (mm/day) Max. daily rainfall (mm/day) Max. 5-day rainfall (mm/5day) Annual rainfall (mm/year) Gauge TRMM Gauge TRMM Gauge TRMM Gauge TRMM Gauge TRMM Gaoan ,585 1, Jinggang ,990 1, Ganzhou ,430 1, Nancheng ,640 1, Yiyang ,718 1, Duchang ,349 1, Average ,619 1, a Days with rainfall 1 mm b Average rainfall in rainy day

9 Assessing the performance of satellite-based precipitation products 721 in different grids. It is seen that nonrainy and puny rain (<1 mm/day) had the largest occurrence, occurring about 70 % of the total days, in all datasets. Difference between satellite-based data and rain gauge data was also quite small, except in Jinggang grid that was located in a mountainous area which may bring uncertainties to the observation. The occurrence of the small rainfall class ranges (1 mm/day< rainfall 3 mm/day) from TRMM 3B42 was lower than those from rain gauge data, but it was larger for. It can also be seen that, although the occurrences of the first two classes, i.e., nonrainy/puny rain and small rain classes, accounted for as high as % of the total days, their contributions to the total rainfall amount were very small. The occurrence of the middle rainfall class ranges (3 mm/day<rainfall 25 mm/day) estimated by TRMM 3B42 and were generally equivalent (accounting for about 17 % on average) to that of rain gauge data, but with different contribution rates to the total rainfall. For the TRMM 3B42, rainfall contribution rates were mildly lower than that of rain gauge rainfall in classes of 3 10 and mm/day. But for the, the contribution rates were overestimated in both classes. It is important to note that the high rainfall ranges (>25 mm/day) play a significant role in contributing to the total rain amount. This kind of information is essential because thunder showers cause the geographical slides and flash floods and hence threaten the economy and human life. Although the two high rainfall classes (25 50 and >50 mm/day) Fig. 2 Distribution of daily rainfall in different rainfall classes and their relative contributions to the total rainfall in different grids

10 722 X. Li et al. occurred only about 5 % of the total days together, their contributions to the total rainfall were as high as 30 and 22 %, respectively, for rain gauge data. TRMM 3B42 performed perfectly for both occurrence and contribution rates in the rainfall class of mm/day and the statistics matched well with their counterparts in every grid. However, in the rainstorm class (>50 mm/day), rainfall contribution rates were larger than that of observation data. obviously underestimated both occurrence and contribution rates for the high rainfall ranges, especially for the class of >50 mm/day. So, the SPPs had difficulties in accurately estimating the rainstorm in Poyang Lake basin, TRMM 3B42 inclined to overestimate the occurrence and contribution rates, whereas usually underestimated them. In order to further elucidate the differences between the two datasets, a rain event detection analysis over the Poyang Lake basin had also been performed. Figure 3 shows the verification results of FBI, POD, FAR, and ETS scores for domainaveraged daily precipitation at different rainfall thresholds. It is found that the TRMM 3B42 tended to overestimate the frequency of intense rain events slightly, but there was a systematic underestimation of precipitation occurrence by, as shown in Fig. 3a. The FBI values of the latter decreased from 0.7 to 0.1 as the precipitation threshold increases, indicating that the products were less skillful to correctly capture the magnitude of intense rain events. Figure 3b shows that the POD of both SPPs had a consistent trend and decreased as the precipitation threshold increases, and the POD values of TRMM 3B42 tended to be higher than those of the products. The TRMM 3B42 produced a fine result, with POD values being larger than 0.70 for thresholds 1 5 mm/day, while these values decreased rapidly (POD<0.5) for thresholds of 25 and 50 mm/day. Oppositely, the FAR results (Fig. 3c) show an increasing trend as the precipitation threshold increases. TRMM 3B42 and had an equivalent performance with the small FAR scores up to the threshold of 10 mm/day, but the FAR of the latter increased rapidly (>0.7) for precipitation threshold >25 mm/day. The ability to detect rain events was also evaluated in terms of the ETS. Both SPPs showed increasing ETS scores for the precipitation thresholds up to 2 mm/day; then, the ETS scores started dropping for the higher thresholds (Fig. 3d). On the other hand, the ETS scores of TRMM 3B42 were higher than in all precipitation thresholds. Figure 3d, together with Fig. 3b, c, demonstrated that SPPs (TRMM 3B42 and ) can identify the small rain events but failed to capture the intense rain events, especially for precipitation products. 4.3 Temporal characteristics of SPPs performance The distribution of monthly rainfall for various precipitation estimates was summarized using box plot for the mean, upper and lower quartiles, and max and min of rainfall as shown in Fig. 4. From the rain gauge data, we can see that the rainfall of Poyang Lake basin increased very fast from January and reached its peak from April to June, then the rainfall decreased sharply from July to September and the dry season set in and lasted through December. Both TRMM 3B42 and data described this distribution characteristic correctly, but the latter tended to suffer from a systematic underestimation of monthly rainfall, regardless of maximum, minimum, or mean. In Fig. 5 several statistical indices, such as RMSE, ME, and BIAS, of monthly averaged daily rainfall between satellitebased and rain gauge data are shown. It is noticeable from Fig. 3 Precipitation detection of daily average TRMM 3B42 and data versus rain gauges at different rainfall thresholds (a FBI, b POD, c FAR, d ETS)

11 Assessing the performance of satellite-based precipitation products 723 Fig. 4 Box plot of monthly rainfall for different precipitation products Fig. 5a that RMSEs ranged from 4 to 10 mm and showed similar temporal pattern for both SPPs. Greater RMSEs were mainly observed in the wet season (April to June), but the lower values were mainly observed in the dry season (October to December). However, the MEs of two SPPs presented the opposite structure (as Fig. 5b). TRMM 3B42 showed positive errors in general and ME values were <1 mm, while the negative errors were mainly found in with large ME values even more than 3 mm.likeme,biasofboth SPPs also showed a similar temporal pattern. had a better performance in the summer season with the smaller BIAS (almost 0 in July), but it performed much worse in the winter season with the BIAS value as much as 80 % (Fig. 5b). 4.4 Spatial characteristics of SPPs performance A spatial performance analysis was adopted to examine and compare the spatial variability of SPPs. Figure 6a c shows the spatial distribution of averaged annual rainfall for the period derived from rain gauges, TRMM 3B42, and, respectively. The spatial distribution of rain gauge data was directly interpolated by the IDW technique with a power of 2. The rainfall characteristics varied strongly in different areas (Fig. 6a). The largest annual rainfall occurred in the eastern part of Poyang Lake basin (with annual rainfall as high as 2,000 mm), while the lowest one was observed in the southern and northern parts (about 1,400 mm). The median rainfall (about 1,600 1,800 mm) was observed in other areas, i.e., the central parts of the basin. Overall, good agreement existed between the SPPs and rain gauge estimates in terms of relative values within the basin. Both TRMM 3B42 and showed higher rainfall rates in the eastern side of the basin than at the central and western sides, although the high rainfall rates covered a larger area than that of the rain gauge estimates (Fig. 6b, c). However, absolute values varied considerably from one dataset to another. For instance, the northeast northwest rainfall gradient observed in rain gauge estimates, as shown in Fig. 6a, was not reproduced in satellite-based estimates, and local median rainfall in the central parts of the basin was weakened in TRMM 3B42 and. Visual inspections of the results also revealed that the lowest rainfall amount (about 1,050 mm for TRMM 3B42 and 770 mm for ) and their distribution regions differ from that of rain gauge estimates, especially for. These biases maybe caused by two aspects: on one hand, there was a weakness for both SPPs to accurately reflect the spatial distribution of precipitation in some regions; on the other hand, a great deal of uncertainties was also exhibited in the spatial distribution of rain gauge data due to the sparsity and bad quality of rain gauges in mountain area as well as the weakness of the interpolation technique. Subsequently, the spatial distribution of R, ME, RMSE, and BIAS between satellite-based and rain gauge data was also examined and compared, which was calculated for every rain gauge and their nearest satellite pixel ( grid) during the period of at the daily scale. The results are shown in Fig. 7. The TRMM 3B42 correlated best with the rain gauge observations, with most R values >0.45 (even >0.55). While for, R values mainly ranged between 0.35 and The ME values varied considerably in the two SPPs. TRMM 3B42 showed smaller MEs in the central parts of the basin, with the ME falling into the 0.5 to 0.5 mm class; only several Fig. 5 Annual distribution of a RMSE and b ME and BIAS between satellite-based and rain gauge data

12 724 X. Li et al. Fig. 6 Spatial distribution of average annual rainfall for the period derived from a rain gauges, b TRMM 3B42, and c pixels in the peripheral area produced small positive errors ( mm). However, the products presented large negative errors in all calculated pixels. Evaluated from the index of the RMSE, performed better than TRMM 3B42. The RMSE values of the former (about 11 mm) were lower than the latter (about mm). As for BIAS, its spatial distribution was similar to ME, TRMM 3B42 performed better with smaller relative bias than in general. 4.5 Dependence of SPPs performance on elevation and slope This study also investigated the dependence of SPPs performance on elevation and slope. In order to do so, statistical Fig. 7 Spatial distribution of R, ME, RMSE, and BIAS between satellite-based and rain gauge data

13 Assessing the performance of satellite-based precipitation products 725 Fig. 8 Scatter plots of the correlation coefficient and RMSE versus elevation over the Poyang Lake basin indices such as the correlation coefficient and RMSE between satellite-based and rain gauge data were used, and for elevation, a logarithm transformation (ln(elevation), simply denoted by ln(e)) was made to obtain a better fit at the lowest values. Figure 8 shows the scatter plots of the correlation coefficient and RMSE versus ln(e) over the Poyang Lake basin. It is seen that the correlation coefficient as well as RMSE has a clear dependence on elevation in both SPPs, and their relationship can be fitted using quadratic equations. The correlation coefficient, either from TRMM 3B42 or, reached maximum at approximately ln(e)= 4.5, and then started dropping for higher ln(e). The RSME values for TRMM 3B42 decreased with increasing ln(e) at lower elevation (ln(e) <4.5) and increased after that. The RMSE from had a feeble dependence on ln(e) at lower elevation and increased clearly at higher elevation. The scatter plots of the correlation coefficient and RMSE versus slope are shown in Fig. 9. It is found that the correlation coefficient and RMSE varied with the slope in both SPPs, and their relationships can also be fitted using quadratic equations. Similar with Fig. 8, the correlation coefficient reached a maximum at slope of about 0.2 and then dropped when the slope became steeper for both SPPs. The RMSE of both SPPs presented slight decreasing trends at gently sloping area but became larger at steeper area. In general, TRMM 3B42 performed best within the ln(e) range of and slope range of , but for, the dependence on elevation and slope were trivial at lower elevation and gently sloping area. At higher elevation (ln(e) >5.0) and steeper area (slope>0.3), the validity of both SPPs decreased with increasing elevation and slope. Similar conclusions were also indicated by Barros et al. (2006), which found that the TRMM s precipitation radar has difficulties in detecting precipitation at high elevations. 5 Discussions The previous section presented the error statistics, which showed that the different satellite rainfall products have very different strengths and weaknesses under different rainfall intensities and in different seasons. Possible sources of errors may be associated with the effects of different sensors, topographies, and retrieval algorithms used in the rainfall estimates (Beighley et al. 2011). Poyang Lake basin consists of mountainous and hilly areas, where the complex topography could cause strong scattering signals in the microwave region, especially at cold land surfaces and ice-covered or snow-covered areas (Huffman et al. 2007; Scheel et al. 2011) and also a strong influence on TB and its polarization property with varying snow cover conditions, depending on exposure and the altitude in mountainous terrain (Amlien 2008; Scheel et al. 2011). Mountainous regions have relatively warm clouds, and the satellite sensors may not detect the rainfall from the warm clouds as the cloud tops would be too warm for IR thresholds to discriminate between raining and nonraining clouds Fig. 9 Scatter plots of the correlation coefficient and RMSE versus slope over the Poyang Lake basin

14 726 X. Li et al. (Hong et al. 2007; Dinku et al. 2008; Bitew and Gebremichael 2010). Moreover, clouds over mountainous area could produce heavy rainfall without much ice aloft in PMW algorithms (Dinku et al. 2010). However, the sensors could accurately detect rainfall from the deep convection, as Fig. 5b shows a better performance in the summer season with the smaller BIAS (almost 0inJuly)fordata.Zhouetal.(2008) also gained similar conclusions that rainfall is more convective with higher rainfall intensities during the warm season and could be accurately estimated in satellite precipitation products. But, on the other hand, the heavy rainfall may cause signal attenuation which is significant and most frequently encountered (Villarini and Krajewski 2010). This is a possible explanation for the bad performances under higher rainfall intensity for both SPPs (Fig. 3). Additionally, although the topography obviously influences the accuracy of satellite products, the retrieval algorithm may significantly dominate the contributions of satellite error sources for high-resolution estimates (Yan and Gebremichael 2009; AghaKouchak et al. 2009). The current global algorithms estimate precipitation indirectly from the TB at the cloud top (Levizzani and Amorati 2002) and do not consider the altitude of the object and the sub-cloud evaporation (Scheel et al. 2011; Dinku et al. 2011), which significantly affect the retrieval accuracy of precipitation (Petty 2001). At the same time, further challenges arise from the processing scheme for microwave and IR data (Scheel et al. 2011). The definition of the underlying surface should satisfy the interpretation of the measured microwave signal and globally applied algorithms need to cope with highly heterogeneous terrain with varying TBs (Scheel et al. 2011). Furthermore, it is indispensable to calibrate the retrieval algorithms using locally available rain gauge observations, which is not just for the selection of appropriate temperature thresholds but also involves determining the other relevant calibration parameters (Dinku et al. 2008, 2011). Local calibration could be one of the most potent approaches to alleviate the satellite precipitation errors. Certainly, the current research is only limited to evaluate and compare the SPPs at the daily scale. The sub-daily (or three hourly) precipitation data are not involved due to the temporal scale limitation of rain gauge data, although they aremorecriticaltodrivethefloodwarningmodelsand decision-making systems in Poyang Lake basin. Moreover, the effects of topography are more complex, as it includes other factors than just elevation and slope, i.e., the orientation of the slope with respect to wind direction at a given time, and geographical location of the slopes (Dinku et al. 2008). The current research is limited to describing the roles that elevation and slope might have played in the performance of satellite rainfall products; however, the physical mechanisms are not addressed adequately here. So, extensive efforts on the evaluation of satellite-based products and thorough understanding of the errors in satellite rainfall need to continue in Poyang Lake basin as well as in other regions. 6 Conclusions This paper evaluated and compared two widely used highresolution satellite-based precipitation data (TRMM 3B42 V6 and ) with rain gauge data in Poyang Lake basin and investigated their spatial and temporal characteristics, including their relationship with evaluation and slope. It is concluded that: & & & & & TRMM 3B42 and were better suited to determining rain occurrence frequency than to determining the rainfall amount. In the study region, the former slightly underestimated the rainfall amount contribution rates in middle rainfall class ranges (3 mm/day<rainfall 25 mm/day) but overestimated it in the heavy rainfall class (> 50 mm/day), and the opposite trend was observed for. The temporal distribution characteristics of monthly rainfall were correctly described by both SPPs, and greater RMSEs were mainly observed between April and June. performed much worse in the winter season with the BIAS value being as much as 80 %, while TRMM 3B42 gained better accuracy in the winter season than in the summer season. TRMM 3B42 had better performance in estimating the frequency and locality of precipitation occurrence and had potential for useful application in regions where rain gauge observations were sparse or of bad quality. Shortcomings of TRMM 3B42 are as follows: it usually underestimates the rainy days and overestimates the average rainfall and intense rain events, which may reduce the accuracy of land surface hydrological processes simulation or flood forecasting. tended to suffer from a large systematic underestimation of rainfall, and during high precipitation events, the occurrence frequency and rainfall amount were underestimated greatly, which clearly revealed that IR-based rainfall algorithms had major limitations in reproducing rainfall fields in the Poyang Lake basin. The performances of both SPPs had clear dependence on elevation and slope and their relationships can be fitted using quadratic equations. TRMM 3B42 and performed best at a gently rolling landscape and the accuracy would decrease at higher elevation or steeper area.