TRMM) Multisatellite Precipitation Analysis Research Articles

Hydrologic Evaluation of TRMM and GPM IMERG Satellite-Based Precipitation in a Humid Basin of China

Tropical Rainfall Measurement Mission (TRMM) is one of the most popular global high resolution satellite-based precipitation products with a goal of measuring precipitation over the oceans and tropics. However, in recent years, the TRMM mission has come to an end. Its successor, Global Precipitation Measurement (GPM) mission was launched to measure the earth’s precipitation structure, with an aim to improve upon the TRMM project. Both of the precipitation products have their own strengths and weaknesses in resolution, accuracy, and availability. The aim of this study is to evaluate the hydrologic utilization of the TRMM and GPM products in a humid basin of China. The main findings of this study can be summarized as follows: (1) 3B42V7 generally outperforms 3B42V6 in terms of hydrologic performance. Meanwhile, 3B42RTV7 significantly outperforms 3B42RTV6, and showed close performance with the bias-adjusted TRMM Multi-satellite Precipitation Analysis (TMPA) products. (2) The GPM showed better agreement with gauge observation than the TMPA products with lower RB and higher correlation coefficient (CC) values at different time scales. (3) The VIC hydrological model generally outperformed the XAJ hydrological model with lower RB, higher Nash–Sutcliffe Coefficient of Efficiency (NSCE) and CC values; though the 3B42RTV6 and 3B42RTV7 showed higher CC values in simulating the streamflow hydrograph by using the VIC and XAJ hydrological models. It can be found that the conceptual hydrological model was enough for the hydrologic evaluation of TRMM and GPM IMERG satellite-based precipitation in a humid basin of China. This study provides a reference for the comparison of multiple models on watershed scale.

Evaluation and Analysis of Grid Precipitation Fusion Products in Jinsha River Basin Based on China Meteorological Assimilation Datasets for the SWAT Model

Highly accurate and high-quality precipitation products that can act as substitutes for ground precipitation observations have important significance for research development in the meteorology and hydrology of river basins. In this paper, statistical analysis methods were employed to quantitatively assess the usage accuracy of three precipitation products, China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS), next-generation Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) and Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), for the Jinsha River Basin, a region characterized by a large spatial scale and complex terrain. The results of statistical analysis show that the three kinds of data have relatively high accuracy on the average grid scale and the correlation coefficients are all greater than 0.8 (CMADS:0.86, IMERG:0.88 and TMPA:0.81). The performance in the average grid scale is superior than that in grid scale. (CMADS: 0.86(basin), 0.6 (grid); IMERG:0.88 (basin),0.71(grid); TMPA:0.81(basin),0.42(grid)). According to the results of hydrological applicability analysis based on SWAT model, the three kinds of data fail to obtain higher accuracy on hydrological simulation. CMADS performs best (NSE:0.55), followed by TMPA (NSE:0.50) and IMERG (NSE:0.45) in the last. On the whole, the three types of satellite precipitation data have high accuracy on statistical analysis and average accuracy on hydrological simulation in the Jinsha River Basin, which have certain hydrological application potential.

Performance assessment of CHIRPS, MSWEP, SM2RAIN-CCI, and TMPA precipitation products across India

Accurate long-term estimates of precipitation at fine spatiotemporal resolution are vital for several applications ranging from hydrometeorology to climatology. The availability of a good network of rain gauges, and high precipitation variability associated with two annual monsoon systems and complex topography make India a suitable test-bed to assess the performance of any satellite-based precipitation product. This study assesses the performance of latest versions of four multi-satellite precipitation products: (i) Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), (ii) Multi-Source Weighted-Ensemble Precipitation (MSWEP), (iii) SM2RAIN-Climate Change Initiative (SM2RAIN-CCI), and (iv) TRMM Multisatellite Precipitation Analysis (TMPA) across India using gauge-based observations for the period of 1998–2015 at monthly scale. These four multi-satellite precipitation products are essentially based on different algorithms and input data sets. Among these multi-satellite precipitation products, SM2RAIN-CCI is the only product that does not use rain gauge observations for bias adjustment. Results indicate that CHIRPS and TMPA are comparable to gauge-based precipitation estimates at all-India and sub-regional scales followed by MSWEP estimates. However, SM2RAIN-CCI largely underestimates precipitation across the country as compared to gauge-based observations. The systematic error component in SM2RAIN-CCI dominates as compared to random error component, which suggests the need of a suitable bias correction to SM2RAIN-CCI before integrating it in any application. The overall results indicate that CHIRPS data set could be used for long-term precipitation analyses with rather higher confidence.

Near-Real-Time Flood Forecasting Based on Satellite Precipitation Products

Floods, storms and hurricanes are devastating for human life and agricultural cropland. Near-real-time (NRT) discharge estimation is crucial to avoid the damages from flood disasters. The key input for the discharge estimation is precipitation. Directly using the ground stations to measure precipitation is not efficient, especially during a severe rainstorm, because precipitation varies even in the same region. This uncertainty might result in much less robust flood discharge estimation and forecasting models. The use of satellite precipitation products (SPPs) provides a larger area of coverage of rainstorms and a higher frequency of precipitation data compared to using the ground stations. In this paper, based on SPPs, a new NRT flood forecasting approach is proposed to reduce the time of the emergency response to flood disasters to minimize disaster damage. The proposed method allows us to forecast floods using a discharge hydrograph and to use the results to map flood extent by introducing SPPs into the rainfall–runoff model. In this study, we first evaluated the capacity of SPPs to estimate flood discharge and their accuracy in flood extent mapping. Two high temporal resolution SPPs were compared, integrated multi-satellite retrievals for global precipitation measurement (IMERG) and tropical rainfall measurement mission multi-satellite precipitation analysis (TMPA). The two products are evaluated over the Ottawa watershed in Canada during the period from 10 April 2017 to 10 May 2017. With TMPA, the results showed that the difference between the observed and modeled discharges was significant with a Nash–Sutcliffe efficiency (NSE) of −0.9241 and an adapted NSE (ANSE) of −1.0048 under high flow conditions. The TMPA-based model did not reproduce the shape of the observed hydrographs. However, with IMERG, the difference between the observed and modeled discharges was improved with an NSE equal to 0.80387 and an ANSE of 0.82874. Also, the IMERG-based model could reproduce the shape of the observed hydrographs, mainly under high flow conditions. Since IMERG products provide better accuracy, they were used for flood extent mapping in this study. Flood mapping results showed that the error was mostly within one pixel compared with the observed flood benchmark data of the Ottawa River acquired by RadarSat-2 during the flood event. The newly developed flood forecasting approach based on SPPs offers a solution for flood disaster management for poorly or totally ungauged watersheds regarding precipitation measurement. These findings could be referred to by others for NRT flood forecasting research and applications.

Applications of TRMM- and GPM-Era Multiple-Satellite Precipitation Products for Flood Simulations at Sub-Daily Scales in a Sparsely Gauged Watershed in Myanmar

Tropical Rainfall Measuring Mission (TRMM) and its successor, Global Precipitation Measurement (GPM), have provided hydrologists with important precipitation data sources for hydrological applications in sparsely gauged or ungauged basins. This study proposes a framework for statistical and hydrological assessment of the TRMM- and GPM-era satellite-based precipitation products (SPPs) in both near- and post-real-time versions at sub-daily temporal scales in a poorly gauged watershed in Myanmar. It evaluates six of the latest GPM-era SPPs: Integrated Multi-satellite Retrievals for GPM (IMERG) “Early”, “Late”, and “Final” run SPPs (IMERG-E, IMERG-L, and IMERG-F, respectively), and Global Satellite Mapping of Precipitation (GSMaP) near-real-time (GSMaP-NRT), standard version (GSMaP-MVK), and standard version with gauge-adjustment (GSMaP-GAUGE) SPPs, and two TRMM Multi-satellite Precipitation Analysis SPPs (3B42RT and 3B42V7). Statistical assessment at grid and basin scales shows that 3B42RT generally presents higher quality, followed by IMERG-F and 3B42V7. IMERG-E, IMERG-L, GSMaP-NRT, GSMaP-MVK, and GSMaP-GAUGE largely underestimate total precipitation, and the three GSMaP SPPs have the lowest accuracy. Given that 3B42RT demonstrates the best quality among the evaluated four near-real-time SPPs, 3B42RT obtains satisfactory hydrological performance in 3-hourly flood simulation, with a Nash–Sutcliffe model efficiency coefficient (NSE) of 0.868, and it is comparable with the rain-gauge-based precipitation data (NSE = 0.895). In terms of post-real-time SPPs, IMERG-F and 3B42V7 demonstrate acceptable hydrological utility, and IMERG-F (NSE = 0.840) slightly outperforms 3B42V7 (NSE = 0.828). This study found that IMERG-F demonstrates comparable or even slightly better accuracy in statistical and hydrological evaluations in comparison with its predecessor, 3B42V7, indicating that GPM-era IMERG-F is the reliable replacement for TRMM-era 3B42V7 in the study area. The GPM scientific community still needs to further refine precipitation retrieving algorithms and improve the accuracy of SPPs, particularly IMERG-E, IMERG-L, and GSMaP SPPs, because ungauged basins urgently require accurate and timely precipitation data for flood control and disaster mitigation.

Validation of Integrated MultisatellitE Retrievals for GPM (IMERG) by using gauge-based analysis products of daily precipitation over East Asia

This paper documents the qualities of satellite-based daily precipitation products from Integrated MultisatellitE Retrievals for Global Precipitation Measurement (IMERG) over East Asia. Evaluations for a year from June 2014 to May 2015 are performed using gauge-based precipitation analysis from the Climate Prediction Center (CPC) and Global Precipitation Climatology Centre (GPCC) and other satellite-based high-resolution precipitation products, such as Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis (TMPA) and the Climate Prediction Center Morphing Method (CMORPH), are also compared. The results indicate that satellite products effectively capture seasonal variations in precipitation over East Asian land regions from spring to fall although overall underestimation with relatively low correlations is observed during winter. The verification of daily detection for various thresholds indicates that IMERG and TMPA products exhibit similarly higher correspondence to CPC while CMORPH exhibits persistent underestimation for all thresholds and especially in winter. The IMERG and TMPA products tend to underestimate with decreasing thresholds and overestimate with increasing thresholds against CPC, although this tendency is significantly reduced when validated with GPCC. Nevertheless, the overall performance of IMERG and TMPA are comparable and IMERG shows reliable performance in daily precipitation for all seasons, indicating less bias and higher skill scores against those of gauge-based precipitations. The assessment study suggests the validity of the IMERG product for daily precipitation over East Asia, and this exhibits the potential for use as reference precipitation data to validate numerical weather prediction models.

Evaluation of Three-Hourly TMPA Rainfall Products Using Telemetric Rain Gauge Observations at Lai Nullah Basin in Islamabad, Pakistan

Flash floods which occur due to heavy rainfall in hilly and semi-hilly areas may prove deleterious when they hit urban centers. The prediction of such localized and heterogeneous phenomena is a challenge due to a scarcity of in-situ rainfall. A possible solution is the utilization of satellite-based precipitation products. The current study evaluates the efficacy of Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) three-hourly products, i.e., 3B42 near-real-time (3B42RT) and 3B42 research version (3B42V7) at a sub-daily time scale. Various categorical indices have been used to assess the capability of products in the detection of rain/no-rain. Hourly rain rates are assessed by employing the most commonly used statistical measures, such as correlation coefficients (CC), mean bias error (MBE), mean absolute error (MAE), and root-mean-square error (RMSE). Further, a diurnal analysis is performed to authenticate TMPA’s performance in specific hours of the day. In general, the results show the good capability of both TMPA products in the detection of rain/no-rain events in all seasons except winter. Specifically, 3B42V7 performed better than 3B42RT. Moreover, both products detect a high number of rainy days falsely in light rain ranges. Regarding rainfall measurements, TMPA products exhibit an overall underestimation. Seasonally, 3B42V7 underestimates rainfall in monsoon and post-monsoon, and overestimates in winter and pre-monsoon. 3B42RT, on the other hand, underestimates rainfall in all seasons. A greater MBE and RMSE are found with both TMPA rain measurements in monsoon and post-monsoon seasons. Overall, a weak correlation and high MBE between the TMPA (3B42RT, 3B42V7) and reference gauge hourly rain rates are found at a three-hourly time scale (CC = 0.41, 0.38, MBE = −0.92, −0.70). The correlation is significant at decadal (CC = 0.79, 0.77) and monthly (CC = 0.91, 0,90) timescales. Furthermore, diurnal rainfall analysis indicates low credibility of 3B42RT to detect flash flooding. Within the parameters of this study, we conclude that the TMPA products are not the best choice at a three-hourly time scale in hilly/semi-hilly areas of Pakistan. However, both products can be used at daily, yet more reliably above daily, time scales, with 3B42V7 preferable due to its consistency.

A Methodological Framework to Retrospectively Obtain Downscaled Precipitation Estimates over the Tibetan Plateau

Long-term precipitation estimates with both finer spatial resolution and better quality are vital and highly needed in various related fields. Numerous downscaling algorithms have been investigated based on the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA), to obtain precipitation data with finer resolution (~1 km). However, this research was restricted by the time span of the TMPA dataset, as the starting time of TMPA was 1998. In this study, a new methodological framework incorporating wavelet coherence and Cubist was proposed to retrospectively obtain downscaled precipitation estimates (DS) over the Tibetan Plateau (TP), based on TMPA and ground observations, in 1990s. The correlations and similarities of precipitation patterns between the target years, from 1990 to 1999, and reference years, from 2000 to 2013, were firstly determined using wavelet coherence based on ground observations. Following this, the TMPA data in the reference years were regarded as the reference in the corresponding target years, which were adopted to be downscaled using Cubist models and land surface variables, to obtain the DS in the target years. We found that the DS showed continuous trends, which corresponded well with the ground observations. Additionally, the performances of the DS were better than those of the Climate Hazards group Infrared Precipitation with Stations (CHIRPS) data over the TP. Therefore, this methodological framework has great potential for obtaining precipitation estimates for the period of the 1990s for which TMPA data is inaccessible.

Statistical and Hydrological Evaluations of Multi-Satellite Precipitation Products over Fujiang River Basin in Humid Southeast China

The purpose of the paper is to evaluate the quality and hydrological utility of four popular satellite precipitation products, including the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) product (3B42V7), near real-time product (3B42RT), and the Climate Prediction Center (CPC) MORPHing technique (CMORPH) satellite–gauge merged product (CMORPH BLD) and bias-corrected product (CMORPH CRT) over Fujiang River basin, China. First, we provided a statistical assessment of the four precipitation products at multiple spatiotemporal scales. The results show that: (1) all the products except 3B42RT capture the spatial pattern of annual precipitation fairly well; (2) in general, CMORPH BLD benefits from the application of the probability density function-optimal interpolation (PDF-OI) gauge adjustment algorithm and performs best among all the products with Pearson correlation coefficients (CC) of 0.84 and 0.94, equitable threat score (ETS) of 0.56 and 0.63 in grid and basin scales, respectively, followed by 3B42V7 and CMORPH CRT; whereas 3B42RT performs worst across all the metrics; (3) according to the occurrence frequencies of rainfall, satellite estimates mainly fall into the bin of 0–1 mm/day and tend to underestimate light precipitation. In addition, the performance of all the products in warm season is much better than in cold season in both grid and basin scales. Subsequently, a physically based distributed model is established to further evaluate the hydrological utility of different precipitation products. The results reveal that: (1) the errors in precipitation products mainly propagate into hydrological simulations, resulting in the best hydrological performance in CMORPH BLD in both daily and monthly scales after recalibrating the model, while 3B42RT shows limited skills in reproducing the daily observed hydrograph; (2) after recalibrating the model with the respective satellite data, significant improvements are observed for all the products; (3) CMORPH BLD no longer shows its superiority during near-real-time monitoring of floods. There is still a great challenge for the application of current satellite-based estimates into local flood monitoring. This study could be used as guidance for choosing alternative satellite precipitation products for hydrological applications in a local community, particularly in basins in which rainfall gauges are scarce.

Comparisons of Spatially Downscaling TMPA and IMERG over the Tibetan Plateau

Accurate precipitation data is crucial in many applications such as hydrology, meteorology, and ecology. Compared with ground observations, satellite-based precipitation estimates can provide much more spatial information to characterize precipitation. In this study, the satellite-based precipitation products of Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) and Tropical Rainfall Measurement Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) were firstly evaluated over the Tibetan Plateau (TP) in 2015 against ground observations at both annual and monthly scales. Secondly, random forest algorithm was used to obtain the annual downscaled results (~1 km) based on IMERG and TMPA data and the downscaled results were examined against rain gauge data. Thirdly, a disaggregation algorithm was used to obtain the monthly downscaled results based on those at annual scale. The results indicated that (1) IMERG performed better than TMPA at both annual and monthly scales; (2) IMERG had few anomalies while TMPA displayed significant numbers of outliers in central and western parts of the TP; (3) random forest was a promising algorithm in acquiring high resolution precipitation data with improved accuracy; (4) the downscaled results based on IMERG had better performances than those based on TMPA.

Assessing the Use of Satellite-Based Estimates and High-Resolution Precipitation Datasets for the Study of Extreme Precipitation Events over the Iberian Peninsula

An assessment of daily accumulated precipitation during extreme precipitation events (EPEs) occurring over the period 2000–2008 in the Iberian Peninsula (IP) is presented. Different sources for precipitation data, namely ERA-Interim and ERA5 reanalysis by the European Centre for Medium-Range Weather Forecast (ECMWF) and Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), both in near-real-time and post-real-time releases, are compared with the best ground-based high-resolution (0.2° × 0.2°) gridded precipitation dataset available for the IP (IB02). In this study, accuracy metrics are analysed for different quartiles of daily precipitation amounts, and additional insights are provided for a subset of EPEs extracted from an objective ranking of extreme precipitation during the extended winter period (October to March) over the IP. Results show that both reanalysis and multi-satellite datasets overestimate (underestimate) daily precipitation sums for the least (most) extreme events over the IP. In addition, it is shown that the TRMM TMPA precipitation estimates from the near-real-time product may be considered for EPEs assessment over these latitudes. Finally, it is found that the new ERA5 reanalysis accounts for large improvements over ERA-Interim and it also outperforms the satellite-based datasets.

INTERCOMPARISON OF DIFFERENT RAINFALL PRODUCTS AND VALIDATION OF WRF MODELLED RAINFALL ESTIMATION IN N-W HIMALAYA DURING MONSOON PERIOD

Abstract. Extreme precipitation events are responsible for major floods in any part of the world. In recent years, simulations and projection of weather conditions to future, with Numerical Weather Prediction (NWP) models like Weather Research and Forecast (WRF), has become an imperative component of research in the field of atmospheric science and hydrology. The validation of modelled forecast is thus have become matter of paramount importance in case of forecasting. This study delivers an all-inclusive assessment of 5 high spatial resolution gridded precipitation products including satellite data products and also climate reanalysis product as compared to WRF precipitation product. The study was performed in river basins of North Western Himalaya (NWH) in India. Performance of WRF model is evaluated by comparing with observational gridded (0.25° × 0.25°) precipitation data from Indian Meteorological Department (IMD). Other products include TRMM Multi Satellite Precipitation Analysis (TMPA) 3B42-v7 product (0.25° × 0.25°) and Global Precipitation Measurement (GPM) product (0.1° × 0.1°). Moreover, climate reanalysis rainfall product from ERA Interim is also used. Bias, Mean Absolute Error, Root Mean Square Error, False Alarm Ratio (FAR), Probability of False Detection (POFD), and Probability of Detection (POD) were calculated with particular rainfall thresholds. TRMM and GPM products were found to be sufficiently close to the observations. All products showed better performance in the low altitude areas i.e. in planes of Upper Ganga and Yamuna basin and Indus basin, and increase in error as topographical variation increases. This study can be used for identifying suitability of WRF forecast data and assessing performance of other rainfall datasets as well.

Comparisons of Gauge, TMPA and IMERG Products for Monsoon and Tropical Cyclone Precipitation in Southern China

The Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) and Integrated Multi-satellite Retrievals for Global Precipitation Measurement (GPM) Mission (IMERG) products are compared against 447 quality-controlled rain gauges in southern China (SC) during summer (May to August) for the period 2014–2016. The differences of TMPA and IMERG in measuring the monsoon and tropical cyclone (TC) precipitation, considering the mean spatial patterns, and rainfall intensities are evaluated quantitatively. Statistical analysis shows that IMERG has much higher correlations of precipitation frequency (PF) with gauge observations for monsoon and TC precipitation. However, both satellite-based products show much poorer capability in estimating conditional rainfall and PF for TC precipitation, especially for estimating large conditional rainfall in TC systems. The capability of capturing the precipitation events at different rainfall intensities decreases with the increase of rainfall threshold for both monsoon and TC precipitation. Generally, IMERG exhibits a superior ability to detect precipitation at different rainfall intensities than TMPA. In depicting the diurnal cycles, IMERG shows a much better performance in estimating rainfall and PF for monsoon precipitation than TMPA by revealing the comparable diurnal amplitudes to the rain gauges, whereas TMPA fails to accurately estimate the early morning rainfall and PF. As a successor to TMPA, IMERG is a reliable source of precipitation estimates for future studies on precipitation meteorology.

Drought monitoring utility of satellite-based precipitation products across mainland China

This study mainly evaluated and compared satellite-based quantitative precipitation estimate products (QPEs) for the drought monitoring of mainland China. Two long-term (more than 30 a) satellite-based QPEs, i.e. the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) and the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS), and a short-term (18a) QPE, i.e. the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) 3B42V7 are considered. Two widely used drought indices, the Standardized Precipitation Index (SPI) and the Palmer Drought Severity Index (PDSI), are chosen to evaluate the drought monitoring utility. The 3B42V7 was only evaluated with PDSI due to the short data records. The results show that all the three QPEs perform satisfactorily in the eastern part of China when using both SPI and PDSI. However, their performances for west China could not be clearly determined due to the sparse gauge networks. 3B42V7 features best performance among the three QPEs in the evaluation using PDSI. To further spatiotemporally evaluate the drought utility of the QPEs, four typical drought-affected regions, i.e. northeast China (NEC), Huang-Huai-Hai plain (3HP), southwest China (SWC), and Loess plateau (LP) were extracted from mainland China for specific case studies. Temporally, all three QPEs are able to detect the typical drought of the four regions with both SPI and PDSI, and 3B42V7 presents the least deviation in PDSI estimate. Spatially, both CHIRPS and 3B42V7 accurately catch the spatial centers and extent of the typical drought events, while PERSIANN-CDR could not match the spatial patterns of drought events well. Generally, the long-term PERSIANN-CDR and CHIRPS perform satisfactorily in drought detection and are suitable for drought utility; however, caution should be applied when studying the spatial variation of drought using PERSIANN-CDR. CHIRPS could also be suitable for near-real-time drought monitoring for its shorter time latency of data release. The short-term 3B42V7 also performs well in many cases, and has thus considerable potential for drought monitoring.

Precipitation Retrieval Using 118.75-GHz and 183.31-GHz Channels From MWHTS on FY-3C Satellite

In this paper, the focus is on precipitation rate retrieval using 118.75 and 183.31-GHz channels from the microwave humidity and temperature sounder (MWHTS) onboard the FY-3C satellite. These channels provide not only atmospheric humidity profile measurements but they also provide temperature profile measurements. These measurements play important roles detecting and retrieving precipitation rates. Data from the MWHTS and the Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis (TMPA) 3B42 are matched in time and space. Matched datasets are used to verify precipitation detection algorithms and precipitation rate retrieval algorithms. To retrieve the precipitation rate, the multiple linear regression algorithm and back propagation (BP) neutral network algorithm are applied in the ten models proposed in this paper. After detailed retrieval analysis between these two algorithms, advanced algorithms are presented this paper to improve the accuracy of the precipitation rate retrieval. In our algorithms, the correlation coefficient of the precipitation rate over the ocean can reach 0.82, the bias is approximately 0.07 mm/hr, and the root mean square error (RMSe) is less than 0.4 mm/hr. Over land, the correlation coefficient is approximately 0.73, the bias is 0.1 mm/hr, and the RMSe is less than 0.5 mm/hr. These results show that our retrieval algorithms improve the retrieval accuracy. Moreover, we study a number of typhoons and find that the FY-3C MWHTS works well to retrieve typhoon precipitation rates. The potential sensitivities of the temperature and humidity channels are analyzed, which can improve the retrieval accuracy and lay the foundation for further research.

Comparison and Bias Correction of TMPA Precipitation Products over the Lower Part of Red–Thai Binh River Basin of Vietnam

As the limitation of rainfall collection by ground measurement has been widely recognized, satellite-based rainfall estimate is a promising high-resolution alternative in both time and space. This study is aimed at exploring the capacity of the satellite-based rainfall product Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), including 3B42V7 research data and its real-time 3B42RT data, by comparing them against data from 29 ground observation stations over the lower part of the Red–Thai Binh River Basin from March 2000 to December 2016. Various statistical metrics were applied to evaluate the TMPA products. The results showed that both 3B42V7 and 3B42RT had weak relationships with daily observations, but 3B42V7 data had strong agreement on the monthly scale compared to 3B42RT. Seasonal analysis showed that 3B42V7 and 3B42RT underestimated rainfall during the dry season and overestimated rainfall during the wet season, with high bias observed for 3B42RT. In addition, detection metrics demonstrated that TMPA products could detect rainfall events in the wet season much better than in the dry season. When rainfall intensity was analyzed, both 3B42V7 and 3B42RT overestimated the no rainfall event during the dry season but underestimated these events during the wet season. Finally, based on the moderate correlation between climatology–topography characteristics and correction factors of linear-scaling (LS) approach, a set of multiple linear models was developed to reduce the error between TMPA products and the observations. The results showed that climatology–topography-based linear-scaling approach (CTLS) significantly reduced the percentage bias (PBIAS) score and moderately improved the Nash–Sutcliffe efficiency (NSE) score. The finding of this paper gives an overview of the capacity of TMPA products in the lower part of the Red–Thai Binh River Basin regarding water resource applications and provides a simple bias correction that can be used to improve the correctness of TMPA products.

Using multiple satellite-gauge merged precipitation products ensemble for hydrologic uncertainty analysis over the Huaihe River basin

Global satellite–gauge merged precipitation (SGMP) products combine the advantages of satellite precipitation estimates with rain gauge data, providing great potential to hydrological applications. However, the inaccuracies of the precipitation products together with hydrologic model limitations, could cause great uncertainty in streamflow predictions. Therefore, this study investigates the hydrological value of three mainstream global SGMP products, including the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42V7 product, the Climate Prediction Center (CPC) MORPHing technique (CMORPH) satellite–gauge merged product (CMORPH BLD), the Global Satellite Mapping of Precipitation (GSMaP) Gauge-calibrated product (GSMaP Gauge). They are used as the precipitation input of the Variable Infiltration Capacity (VIC) hydrologic model over the Huaihe River basin in China. To better quantify their effects on parameter calibration and streamflow predictions, a newly developed residual error model accompanied with the Bayesian uncertainty analysis are performed. CMORPH satellite-gauge merged precipitation product, recently developed by the China Meteorological Administration (CMA) (CMORPH CMA), is a high-quality regional precipitation product. Thus, this study applies the CMORPH CMA within the same framework to provide a benchmark. The results show that the parameter uncertainty are influenced significantly by the input of various precipitation products. There is a tradeoff between the deterministic streamflow performance and the probabilistic predictive performance for selecting the best input among the three global precipitation products. The streamflow uncertainty intervals of the three global precipitation products are then merged using the Bayesian Model Averaging (BMA) method. The BMA results show satisfying hydrological performance in terms of deterministic streamflow predictions, with the largest Nash-Sutcliffe coefficient of Efficiency (NSCE) values of 0.86 and 0.64, and the smallest absolute relative error (RE) values of 0% and 10.2% in the calibration and validation periods, respectively. In addition, the BMA results also produce much more reliable probabilistic predictions, which even outperform the outcomes of the high-quality CMORPH CMA. Our study demonstrates the potential uncertainty of various SGMP products for model calibration and streamflow predictions. The hydrologic ensemble using multiple global SGMP products provides a promising and advantageous approach to support water management and decision making, especially in ungauged basins.

Evaluation of Gridded Multi-Satellite Precipitation Estimation (TRMM-3B42-V7) Performance in the Upper Indus Basin (UIB)

The present study aims to evaluate the capability of the Tropical Rainfall Measurement Mission (TRMM), Multi-satellite Precipitation Analysis (TMPA), version 7 (TRMM-3B42-V7) precipitation product to estimate appropriate precipitation rates in the Upper Indus Basin (UIB) by analyzing the dependency of the estimates’ accuracies on the time scale. To that avail, various statistical analyses and comparison of Multisatellite Precipitation Analysis (TMPA) products with gauge measurements in the UIB are carried out. The dependency of the TMPA estimates’ quality on the aggregation time scale is analyzed by comparisons of daily, monthly, seasonal and annual sums for the UIB. The results show considerable biases in the TMPA Tropical Rainfall Measurement Mission (TRMM) precipitation estimates for the UIB, as well as high numbers of false alarms and miss ratios. The correlation of the TMPA estimates with ground-based gauge data increases considerably and almost in a linear fashion with increasing temporal aggregation, i.e., time scale. There is a predominant trend of underestimation of the TRMM product across the UIB at most of the gauge stations, i.e., TRMM-estimated rainfall is generally lower than the gauge-measured rainfall. For the seasonal aggregates, the bias is mostly positive for the summer but predominantly negative for the winter season, thereby showing a slight overestimation of the precipitation in summer and underestimation in winter. The results of the study suggest that, in spite of these discrepancies between TMPA estimates and gauge data, the use of the former in hydrological watershed modeling undertaken by the authors may be a valuable alternative in data-scarce regions like the UIB, but still must be taken with a grain of salt.

Evaluating TMPA Rainfall over the Sparsely Gauged East African Rift

Abstract Accurate precipitation data are fundamental for understanding and mitigating the disastrous effects of many natural hazards in mountainous areas. Floods and landslides, in particular, are potentially deadly events that can be mitigated with advanced warning, but accurate forecasts require timely estimation of precipitation, which is problematic in regions such as tropical Africa with limited gauge measurements. Satellite rainfall estimates (SREs) are of great value in such areas, but rigorous validation is required to identify the uncertainties linked to SREs for hazard applications. This paper presents results of an unprecedented record of gauge data in the western branch of the East African Rift, with temporal resolutions ranging from 30 min to 24 h and records from 1998 to 2018. These data were used to validate the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) research version and near-real-time products for 3-hourly, daily, and monthly rainfall accumulations, over multiple spatial scales. Results indicate that there are at least two factors that led to the underestimation of TMPA at the regional level: complex topography and high rainfall intensities. The TMPA near-real-time product shows overall stronger rainfall underestimations but lower absolute errors and a better performance at higher rainfall intensities compared to the research version. We found area-averaged TMPA rainfall estimates relatively more suitable in order to move toward regional hazard assessment, compared to data from scarcely distributed gauges with limited representativeness in the context of high rainfall variability.

Intercomparison of trend analysis of Multisatellite Monthly Precipitation Products and Gauge Measurements for River Basins of India

This study compares the precipitation trend from the gridded rain gauge data collected by India Meteorological Department (IMD) and Multisatellite High Resolution Precipitation Products (HRPPs) including Tropical Rainfall Measurement Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) version 7, Climate Prediction Center Morphing (CMORPH) version 1.0, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and Multi-Source Weighted-Ensemble Precipitation (MSWEP) version 1.1 for the river basins of India. The IMD and HRPPs are of the same spatial resolution (0.25° × 0.25°) and extend from 1998 to 2015. The gridded precipitation datasets are compared for 25 river basins of India. TRMM, CMORPH, PERSIANN, and MSWEP datasets accuracy for the river basins are assessed by comparison with IMD using root mean square error (RMSE) and correlation coefficient (CC) methods. The Mann–Kendall (MK) and modified Mann–Kendall (MMK) tests are applied for analyzing the data trend, and the change is detected by Sen's Slope using all datasets for annual and seasonal time periods. Variation in precipitation is high (>20%) in the northern part of India in all datasets. All these basins located at the elevations above 2000 m. The annual and monsoon trend pattern for TMPA, CMORPH, PERSIANN, and MSWEP matched with IMD data in the north, northwest, and central part in 1–2, 22–25, and parts of 3, 12, and 21 river basins (1998–2015). The calculated results implied that the TMPA precipitation product (in terms of accuracy) and PERSIANN (in terms of annual and monsoon trend) show a better agreement with IMD and they can be used in climate studies and hydrological simulation in locations/river basins where the number of rain gauges is not adequate to quantify the spatial variability of precipitation.