Multi-satellite Precipitation Products Research Articles

Accuracy evaluation of multi-satellite precipitation products over Circum-Bohai-Sea Region.

Precipitation is a key variable of the global energy and water cycle. The accuracy of precipitation data is essential for monitoring and forecasting flood and drought, as well as water resources management. Based on daily precipitation data of 55 meteorological stations over Circum-Bohai-Sea region during 1998-2014, the accuracy was evaluated and compared among 3 satellite-based precipitation products, i.e., TRMM 3B42 V7, CMORPH, and PERSIANN from daily to monthly scale. Their errors were quantified for different seasons. Results showed that generally, TRMM 3B42 V7 had the best performance while PERSIANN had the largest error. However, CMORPH had the highest detection rate of rainfall events. The rainfall was slightly overestimated by 3B42 V7 but underestimated by CMORPH and PERSIANN. Seasonally, the skill score in summer was higher than that in other seasons, and the capturing capability was much lower in winter. Meanwhile, the correlation coefficient and root mean square error varied with seasons. The performance of 3 satellite-based precipitation products increased from the daily to monthly scale. Larger errors still existed in the daily rainfall estimates, and especially the rainstorm was seriously underestimated. This study provided a basis for the use of satellite precipitation products in precipitation estimation and its application in climate and hydrological simulation over Circum-Bohai-Sea region.

Hydrological Utility and Uncertainty of Multi-Satellite Precipitation Products in the Mountainous Region of South Korea

Satellite-derived precipitation can be a potential source of forcing data for assessing water availability and managing water supply in mountainous regions of East Asia. This study investigates the hydrological utility of satellite-derived precipitation and uncertainties attributed to error propagation of satellite products in hydrological modeling. To this end, four satellite precipitation products (tropical rainfall measuring mission (TRMM) multi-satellite precipitation analysis (TMPA) version 6 (TMPAv6) and version 7 (TMPAv7), the global satellite mapping of precipitation (GSMaP), and the climate prediction center (CPC) morphing technique (CMORPH)) were integrated into a physically-based hydrologic model for the mountainous region of South Korea. The satellite precipitation products displayed different levels of accuracy when compared to the intra- and inter-annual variations of ground-gauged precipitation. As compared to the GSMaP and CMORPH products, superior performances were seen when the TMPA products were used within streamflow simulations. Significant dry (negative) biases in the GSMaP and CMORPH products led to large underestimates of streamflow during wet-summer seasons. Although the TMPA products displayed a good level of performance for hydrologic modeling, there were some over/underestimates of precipitation by satellites during the winter season that were induced by snow accumulation and snowmelt processes. These differences resulted in streamflow simulation uncertainties during the winter and spring seasons. This study highlights the crucial need to understand hydrological uncertainties from satellite-derived precipitation for improved water resource management and planning in mountainous basins. Furthermore, it is suggested that a reliable snowfall detection algorithm is necessary for the new global precipitation measurement (GPM) mission.

Evaluation of latest TMPA and CMORPH precipitation products with independent rain gauge observation networks over high-latitude and low-latitude basins in China

The Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) and National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC) morphing technique (CMORPH) are two important multi-satellite precipitation products in TRMM-era and perform important functions in GPM-era. Both TMPA and CMORPH systems simultaneously upgraded their retrieval algorithms and released their latest version of precipitation data in 2013. In this study, the latest TMPA and CMORPH products (i.e., Version-7 real-time TMPA (T-rt) and gauge-adjusted TMPA (T-adj), and Version-1.0 real-time CMORPH (C-rt) and Version-1.0 gauge-adjusted CMORPH (C-adj)) are evaluated and intercompared by using independent rain gauge observations for a 12-year (2000–2011) period over two typical basins in China with different geographical and climate conditions. Results indicate that all TMPA and CMORPH products tend to overestimate precipitation for the high-latitude semiarid Laoha River Basin and underestimate it for the low-latitude humid Mishui Basin. Overall, the satellite precipitation products exhibit superior performance over Mishui Basin than that over Laoha River Basin. The C-adj presents the best performance over the high-latitude Laoha River Basin, whereas T-adj showed the best performance over the low-latitude Mishui Basin. The two gauge-adjusted products demonstrate potential in water resource management. However, the accuracy of two real-time satellite precipitation products demonstrates large variability in the two validation basins. The C-rt reaches a similar accuracy level with the gauge-adjusted satellite precipitation products in the high-latitude Laoha River Basin, and T-rt performs well in the low-latitude Mishui Basin. The study also reveals that all satellite precipitation products obviously overestimate light rain amounts and events over Laoha River Basin, whereas they underestimate the amount and events over Mishui Basin. The findings of the precision characteristics associated with the latest TMPA and CMORPH precipitation products at different basins will offer satellite precipitation users an enhanced understanding of the applicability of the latest TMPA and CMORPH for water resource management, hydrologic process simulation, and hydrometeorological disaster prediction in other similar regions in China. The findings will also be useful for IMERG algorithm development and update in GPM-era.

A preliminary assessment of GPM-based multi-satellite precipitation estimates over a monsoon dominated region

Summary Following the launch of the Global Precipitation Measurement (GPM) Core Observatory, two advanced high resolution multi-satellite precipitation products namely, Integrated Multi-satellitE Retrievals for GPM (IMERG) and Global Satellite Mapping of Precipitation (GSMaP) version 6 are released. A critical evaluation of these newly released precipitation data sets is very important for both the end users and data developers. This study provides a comprehensive assessment of IMERG research product and GSMaP estimates over India at a daily scale for the southwest monsoon season (June to September 2014). The GPM-based precipitation products are inter-compared with widely used TRMM Multi-satellite Precipitation Analysis (TMPA), and gauge-based observations over India. Results show that the IMERG estimates represent the mean monsoon rainfall and its variability more realistically than the gauge-adjusted TMPA and GSMaP data. However, GSMaP has relatively smaller root-mean-square error than IMERG and TMPA, especially over the low mean rainfall regimes and along the west coast of India. An entropy-based approach is employed to evaluate the distributions of the selected precipitation products. The results indicate that the distribution of precipitation in IMERG and GSMaP has been improved markedly, especially for low precipitation rates. IMERG shows a clear improvement in missed and false precipitation bias over India. However, all the three satellite-based rainfall estimates show exceptionally smaller correlation coefficient, larger RMSE, larger negative total bias and hit bias over the northeast India where precipitation is dominated by orographic effects. Similarly, the three satellite-based estimates show larger false precipitation over the southeast peninsular India which is a rain-shadow region. The categorical verification confirms that these satellite-based rainfall estimates have difficulties in detection of rain over the southeast peninsula and northeast India. These preliminary results need to be confirmed in other monsoon seasons in future studies when the fully GPM-based IMERG retrospectively processed data prior to 2014 are available.

Error characterization of TRMM Multisatellite Precipitation Analysis (TMPA-3B42) products over India for different seasons

Summary A comprehensive error characterization of satellite-based precipitation products is vital for advancement of precipitation algorithms, evaluation of numerical model outputs, and their integration in various hydro-meteorological applications. The Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) products are in high demand to users for numerous applications. The latest version 7 (V7) of TMPA products are assumed to be better than its previous versions due to substantial changes in input data sets and algorithm. The TMPA algorithm is also a benchmark algorithm for the Global Precipitation Measurement (GPM) mission multisatellite precipitation products. In this study, both near-real-time (TMPA-RT) and research-quality (TMPA-V7) TMPA data sets are extensively evaluated against gridded gauge-based data over India for pre-monsoon, monsoon, and post-monsoon seasons at daily scale for a 13-year (2001–2013) period. The systematic and random error components in TMPA data sets are decomposed for their wider usage in agricultural and hydrological applications. Although TMPA-V7 and RT data sets represent the mean seasonal rainfall characteristics reasonably well, both the satellite-based data sets show an overestimation of rainfall over most parts of the country except over the orographic regions. However, TMPA-V7 is better than TMPA-RT when compared with the reference observations. Both the TMPA products show larger systematic error over the mountainous regions of northeast India. TMPA-RT has larger random error than TMPA-V7 possibly due to difference in calibration methods for the development of these data sets. Both the satellite-based data sets show significant difference from the reference observations for light to moderate rainfall ranges especially in the monsoon season. Larger error associated with systematic component in both the data sets is found during the pre-monsoon season. Furthermore, bias and error in satellite-derived rainfall data show a considerable interannual variation. Overall results suggest that a suitable region and season dependent bias-correction is required in TMPA data sets before its integration in hydrological applications.

Evaluating the Performance of Merged Multi-Satellite Precipitation Products Over a Complex Terrain

The aim of this paper is to investigate the effectiveness of merging satellite precipitation products on rainfall estimates over different terrains in Iran. Four widely used satellite rainfall estimates (SREs) namely PERSIANN, TMPA-3B42, TMPA-3B42RT, and CMORPH were evaluated and blended in order to generate new combinations in which we expect to estimate rainfall more accurately than with using each individual product. For this purpose, first, the daily bias of each satellite algorithm was calculated at each pixel by comparing with gauge observed data for the time period of 2003–2008. Then, for each pixel, a linear combination of SREs was selected which resulted in the least bias for that pixel. This combination named as merged satellite rainfall estimate (MSRE). In order to test the proposed method, several statistical indices including bias, root mean square error (RMSE), and correlation coefficient (CC) were employed. The results indicate that the merged product used in this study can improve the accuracy of rainfall estimates compared with each individual satellite estimate over all the study regions for different seasons. However, the efficiency of improvement depends highly on the number of rain gauges distributed over the study area.

Impact of Missing Passive Microwave Sensors on Multi-Satellite Precipitation Retrieval Algorithm

The impact of one or two missing passive microwave (PMW) input sensors on the end product of multi-satellite precipitation products is an interesting but obscure issue for both algorithm developers and data users. On 28 January 2013, the Version-7 TRMM Multi-satellite Precipitation Analysis (TMPA) products were reproduced and re-released by National Aeronautics and Space Administration (NASA) Goddard Space Flight Center because the Advanced Microwave Sounding Unit-B (AMSU-B) and the Special Sensor Microwave Imager-Sounder-F16 (SSMIS-F16) input data were unintentionally disregarded in the prior retrieval. Thus, this study investigates the sensitivity of TMPA algorithm results to missing PMW sensors by intercomparing the “early” and “late” Version-7 TMPA real-time (TMPA-RT) precipitation estimates (i.e., without and with AMSU-B, SSMIS-F16 sensors) with an independent high-density gauge network of 200 tipping-bucket rain gauges over the Chinese Jinghe river basin (45,421 km2). The retrieval counts and retrieval frequency of various PMW and Infrared (IR) sensors incorporated into the TMPA system were also analyzed to identify and diagnose the impacts of sensor availability on the TMPA-RT retrieval accuracy. Results show that the incorporation of AMSU-B and SSMIS-F16 has substantially reduced systematic errors. The improvement exhibits rather strong seasonal and topographic dependencies. Our analyses suggest that one or two single PMW sensors might play a key role in affecting the end product of current combined microwave-infrared precipitation estimates. This finding supports algorithm developers’ current endeavor in spatiotemporally incorporating as many PMW sensors as possible in the multi-satellite precipitation retrieval system called Integrated Multi-satellitE Retrievals for Global Precipitation Measurement mission (IMERG). This study also recommends users of satellite precipitation products to switch to the newest Version-7 TMPA datasets and the forthcoming IMERG products whenever they become available.

Satellite-driven downscaling of global reanalysis precipitation products for hydrological applications

Abstract. Deriving flood hazard maps for ungauged basins typically requires simulating a long record of annual maximum discharges. To improve this approach, precipitation from global reanalysis systems must be downscaled to a spatial and temporal resolution applicable for flood modeling. This study evaluates such downscaling and error correction approaches for improving hydrologic applications using a combination of NASA's Global Land Data Assimilation System (GLDAS) precipitation data set and a higher resolution multi-satellite precipitation product (TRMM). The study focuses on 437 flood-inducing storm events that occurred over a period of ten years (2002–2011) in the Susquehanna River basin located in the northeastern United States. A validation strategy was devised for assessing error metrics in rainfall and simulated runoff as function of basin area, storm severity, and season. The WSR-88D gauge-adjusted radar-rainfall (stage IV) product was used as the reference rainfall data set, while runoff simulations forced with the stage IV precipitation data set were considered as the runoff reference. Results show that the generated rainfall ensembles from the downscaled reanalysis product encapsulate the reference rainfall. The statistical analysis consists of frequency and quantile plots plus mean relative error and root-mean-square error statistics. The results demonstrated improvements in the precipitation and runoff simulation error statistics of the satellite-driven downscaled reanalysis data set compared to the original reanalysis precipitation product. Results vary by season and less by basin scale. In the fall season specifically, the downscaled product has 3 times lower mean relative error than the original product; this ratio increases to 4 times for the simulated runoff values. The proposed downscaling scheme is modular in design and can be applied on any gridded satellite and reanalysis data set.

An Error Model for Uncertainty Quantification in High-Time-Resolution Precipitation Products

Abstract This study proposes a new framework, Precipitation Uncertainties for Satellite Hydrology (PUSH), to provide time-varying, global estimates of errors for high-time-resolution, multisatellite precipitation products using a technique calibrated with high-quality validation data. Errors are estimated for the widely used Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42 product at daily/0.25° resolution, using the NOAA Climate Prediction Center (CPC) Unified gauge dataset as the benchmark. PUSH estimates the probability distribution of reference precipitation given the satellite observation, from which the error can be computed as the difference (or ratio) between the satellite product and the estimated reference. The framework proposes different modeling approaches for each combination of rain and no-rain cases: correct no-precipitation detection (both satellite and gauges measure no precipitation), missed precipitation (satellite records a zero, but the gauges detect precipitation), false alarm (satellite detects precipitation, but the reference is zero), and hit (both satellite and gauges detect precipitation). Each case is explored and explicitly modeled to create a unified approach that combines all four scenarios. Results show that the estimated probability distributions are able to reproduce the probability density functions of the benchmark precipitation, in terms of both expected values and quantiles of the distribution. The spatial pattern of the error is also adequately reproduced by PUSH, and good agreement between observed and estimated errors is observed. The model is also able to capture missed precipitation and false detection uncertainties, whose contribution to the total error can be significant. The resulting error estimates could be attached to the corresponding high-resolution satellite precipitation products.

국내 수문학적 활용성 평가를 위한 다중위성 강수자료의 검증

다중위성 기반의 강수자료는 국내에서 다양한 수문학적 응용분야에 활용될 수 있다. 본 연구에서는 위성강수의 수문학적 활용성을 평가하기 위하여 TRMM 3B42v6, CMORPH, GSMaP-MVK 등 3가지 다중위성 강수자료를 이용하여 4개강 권역에 대해 2002년부터 2009년까지의 지상관측강수자료와 비교하였다. 분석결과, TRMM 3B42v6은 강수의 발생 빈도와 정량적 크기에서 관측치에 근접한 결과를 보여주었다. CMORPH와 GSMaP-MVK의 경우 TRMM 3B42v6에 비해 상대적으로 관측치에 대한 일치성이 낮고, 과소 추정하는 경향을 보였다. 이러한 다중위성 강수자료와 관측강수량 비교는 4대강 권역에서 모두 일관된 결과를 보여주었다. Multi-satellite precipitation products can provide possible opportunity for diverse hydrological applications in Korea. This study investigated the potential utility of three multi-satellite precipitation estimates, including TRMM(Tropical Rainfall Measuring Mission) 3B42v6, CMORPH(Climate Prediction Center Morphing), and GSMaP(Global Satellite Mapping of Precipitation)-MVK(Moving Vector with Kalman filter), from 2002 to 2009 by comparing with ground-gauged precipitation data over four major river basins of South Korea. The results showed that the frequency and amount of TRMM 3B42v6 estimates agreed well with observations at daily scale. However, CMORPH and GSMaP-MVK exhibited relatively poor performance than TRMM 3B42v6 with significantly underestimated precipitation. Overall, our results were consistent across four major river basins.

고해상도 다중위성 강수자료와 분포형 수문모형의 유출모의 적용

본 연구에서는 다중위성 강수자료의 수문학적 적용성을 평가하기 위하여 Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), Global Satellite Mapping of Precipitation (GSMaP), Climate Prediction Center (CPC) Morphing technique(CMORPH) 등 전 지구 규모의 고해상도 다중위성 강수자료와 분포형 수문모형을 이용하여 유출모의를 수행하였다. 충주댐 유역에 대하여 2002년 1월 1일부터 2009년 12월 31일까지의 기간에 대하여 Coupled Routing and Excess Storage (CREST) 모형을 적용하였다. 분석기간은 준비기간(2002-2003년, 2006-2007년), 보정기간(2004-2005년), 그리고 검증기간(2008-2009년)으로 구분하여 모의를 수행하였다. 각 다중위성 강수자료를 지상관측자료와 비교결과, 강수의 계절적 변동특성은 잘 반영하고 있으나 연강수량합계 및 월평균강수량에서 TMPA는 과대추정을, GSMaP과 CMORPH는 과소추정하는 경향을 보여주었다. 또한 유출분석결과, TMPA를 제외한 GSMaP과 CMORPH의 충주댐 유역에 대한 수문학적 적용성이 매우 낮은 것을 알 수 있었으며, 향후 다중위성 강수자료의 활용에 앞서 통계적 보정이나 강수알고리즘에 대한 개선이 필요한 것으로 판단된다. In this study we evaluated the hydrological applicability of multi-satellite precipitation estimates. Three high-resolution global multi-satellite precipitation products, the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), the Global Satellite Mapping of Precipitation (GSMaP), and the Climate Precipitation Center (CPC) Morphing technique (CMORPH), were applied to the Coupled Routing and Excess Storage (CREST) model for the evaluation of their hydrological utility. The CREST model was calibrated from 2002 to 2005 and validated from 2006 to 2009 in the Chungju Dam watershed, including two years of warm-up periods (2002-2003 and 2006-2007). Areal-averaged precipitation time series of the multi-satellite data were compared with those of the ground records. The results indicate that the multi-satellite precipitation can reflect the seasonal variation of precipitation in the Chungju Dam watershed. However, TMPA overestimates the amount of annual and monthly precipitation while GSMaP and CMORPH underestimate the precipitation during the period from 2002 to 2009. These biases of multi-satellite precipitation products induce poor performances in hydrological simulation, although TMPA is better than both of GSMaP and CMORPH. Our results indicate that advanced rainfall algorithms may be required to improve its hydrological applicability in South Korea.

Precipitation from Space: Advancing Earth System Science

Of the three primary sources of spatially contiguous precipitation observations (surface networks, ground-based radar, and satellite-based radar/radiometers), only the last is a viable source over ocean and much of the Earth's land. As recently as 15 years ago, users needing quantitative detail of precipitation on anything under a monthly time scale relied upon products derived from geostationary satellite thermal infrared (IR) indices. The Special Sensor Microwave Imager (SSMI) passive microwave (PMW) imagers originated in 1987 and continue today with the SSMI sounder (SSMIS) sensor. The fortunate longevity of the joint National Aeronautics and Space Administration (NASA) and Japan Aerospace Exploration Agency (JAXA) Tropical Rainfall Measuring Mission (TRMM) is providing the environmental science community a nearly unbroken data record (as of April 2012, over 14 years) of tropical and sub-tropical precipitation processes. TRMM was originally conceived in the mid-1980s as a climate mission with relatively modest goals, including monthly averaged precipitation. TRMM data were quickly exploited for model data assimilation and, beginning in 1999 with the availability of near real time data, for tropical cyclone warnings. To overcome the intermittently spaced revisit from these and other low Earth-orbiting satellites, many methods to merge PMW-based precipitation data and geostationary satellite observations have been developed, such as the TRMM Multisatellite Precipitation Product and the Climate Prediction Center (CPC) morphing method (CMORPH. The purpose of this article is not to provide a survey or assessment of these and other satellite-based precipitation datasets, which are well summarized in several recent articles. Rather, the intent is to demonstrate how the availability and continuity of satellite-based precipitation data records is transforming the ways that scientific and societal issues related to precipitation are addressed, in ways that would not be otherwise possible. These developments have taken place in parallel with the growth of an increasingly interconnected scientific environment. Scientists from different disciplines can easily interact with each other via information and materials they encounter online, and collaborate remotely without ever meeting each other in person. Likewise, these precipitation datasets are quickly and easily available via various data portals and are widely used. Within the framework of the NASA/JAXA Global Precipitation Measurement (GPM mission, these applications will become increasingly interconnected. We emphasize that precipitation observations by themselves provide an incomplete picture of the state of the atmosphere. For example, it is unlikely that a richer understanding of the global water cycle will be possible by standalone missions and algorithms, but must also involve some component of data, where model analyses of the physical state are constrained alongside multiple observations (e.g., precipitation, evaporation, radiation). The next section provides examples extracted from the many applications that use various high-resolution precipitation products. The final section summarizes the future system for global precipitation processing.

Comprehensive evaluation of multi-satellite precipitation products with a dense rain gauge network and optimally merging their simulated hydrological flows using the Bayesian model averaging method

Summary This study first focuses on comprehensive evaluating three widely used satellite precipitation products (TMPA 3B42V6, TMPA 3B42RT, and CMORPH) with a dense rain gauge network in the Mishui basin (9972 km 2 ) in South China and then optimally merge their simulated hydrologic flows with the semi-distributed Xinanjiang model using the Bayesian model averaging method. The initial satellite precipitation data comparisons show that the reanalyzed 3B42V6, with a bias of −4.54%, matched best with the rain gauge observations, while the two near real-time satellite datasets (3B42RT and CMORPH) largely underestimated precipitation by 42.72% and 40.81% respectively. With the model parameters first benchmarked by the rain gauge data, the behavior of the streamflow simulation from the 3B42V6 was also the most optimal amongst the three products, while the two near real-time satellite datasets produced deteriorated biases and Nash–Sutcliffe coefficients (NSCEs). Still, when the model parameters were recalibrated by each individual satellite data, the performance of the streamflow simulations from the two near real-time satellite products were significantly improved, thus demonstrating the need for specific calibrations of the hydrological models for the near real-time satellite inputs. Moreover, when optimally merged with respect to the streamflows forced by the two near real-time satellite precipitation products and all the three satellite precipitation products using the Bayesian model averaging method, the resulted streamflow series further improved and became more robust. In summary, the three current state-of-the-art satellite precipitation products have demonstrated potential in hydrological research and applications. The benchmarking, recalibration, and optimal merging schemes for streamflow simulation at a basin scale described in the present work will hopefully be a reference for future utilizations of satellite precipitation products in global and regional hydrological applications.

Comparison of Satellite Precipitation Products with Rain Gauge Data for the Khumb Region, Nepal Himalayas

Rain gauge data for the period of 1998–2004 from the Syangboche Automated Weather Station (S-AWS) site in the Nepal Himalayas were compared with multi-satellite precipitation products for the period of 2003–2005, employing several retrieval algorithms: the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), the Climate Prediction Center (CPC) morphing algorithm (CMORPH), the Precipitation Estimation from Remote Sensing Information using an artificial neural network (PERSIANN), and the Global Satellite Mapping of Precipitation (GSMaP). All the products, except GSMaP, showed an increase in precipitation during the summer monsoon, in agreement with S-AWS. However, PERSIANN showed large differences with the observed values in winter and CMORPH had a tendency to overestimate precipitation in the pre- and post-monsoon seasons relative to S-AWS/TMPA. Summer monsoon precipitation showed an increase in the evening and mid-night in all products except GSMaP, but the local-time peak in PERSIANN lagged compared to that of S-AWS by several hours, and the peak in other products was ahead of that in S-AWS by several hours. All products except for PERSIANN showed an increase in precipitation during the morning. The differences among the products may reflect microwave signatures from convection, the sun-synchronous satellite orbit, and infrared-merging processes. A verification of rain detection by GSMaP revealed good scores over global land areas except for the Tibetan Plateau (including the present study area) due to insufficient resolution for rain/no-rain classification. The peak local-time distribution of precipitation showed a relationship with the topography in the order of precipitation radar (strongest relationship), microwave radiometer, and infrared products.