Multi-satellite Precipitation Products Research Articles

An Innovative Correction–Fusion Approach for Multi-Satellite Precipitation Products Conditioned by Gauge Background Fields over the Lancang River Basin

Satellite precipitation products can help improve precipitation estimates where ground-based observations are lacking; however, their relative accuracy and applicability in data-scarce areas remain unclear. Here, we evaluated the accuracy of different satellite precipitation datasets for the Lancang River Basin, Western China, including the Tropical Rainfall Measuring Mission (TRMM) 3B42RT, the Global Precipitation Measurement Integrated Multi-satellitE Retrievals (GPM IMERG), and Fengyun 2G (FY-2G) datasets. The results showed that GPM IMERG and FY-2G are superior to TRMM 3B42RT for meeting local research needs. A subsequent bias correction on these two datasets significantly increased the correlation coefficient and probability of detection of the products and reduced error indices such as the root mean square error and mean absolute error. To further improve data quality, we proposed a novel correction–fusion method based on window sliding data correction and Bayesian data fusion. Specifically, the corrected FY-2G dataset was merged with GPM IMERG Early, Late, and Final Runs. The resulting FY-Early, FY-Late, and FY-Final fusion datasets showed high correlation coefficients, strong detection performances, and few observation errors, thereby effectively extending local precipitation data sources. The results of this study provide a scientific basis for the rational use of satellite precipitation products in data-scarce areas, as well as reliable data support for precipitation forecasting and water resource management in the Lancang River Basin.

Oceanic Validation of IMERG-GMI Version 6 Precipitation Using the GPM Validation Network

Abstract NASA’s multisatellite precipitation product from the Global Precipitation Measurement (GPM) mission, the Integrated Multi-satellitE Retrievals for GPM (IMERG) product, is validated over tropical and high-latitude oceans from June 2014 to August 2021. This oceanic study uses the GPM Validation Network’s island-based radars to assess IMERG when the GPM Core Observatory’s Microwave Imager (GMI) observes precipitation at these sites (i.e., IMERG-GMI). Error tracing from the Level 3 (gridded) IMERG V06B product back through to the input Level 2 (satellite footprint) Goddard Profiling Algorithm GMI V05 climate (GPROF-CLIM) product quantifies the errors separately associated with each step in the gridding and calibration of the estimates from GPROF-CLIM to IMERG-GMI. Mean relative bias results indicate that IMERG-GMI V06B overestimates Alaskan high-latitude oceanic precipitation by +147% and tropical oceanic precipitation by +12% with respect to surface radars. GPROF-CLIM V05 overestimates Alaskan oceanic precipitation by +15%, showing that the IMERG algorithm’s calibration adjustments to the input GPROF-CLIM precipitation estimates increase the mean relative bias in this region. In contrast, IMERG adjustments are minimal over tropical waters with GPROF-CLIM overestimating oceanic precipitation by +14%. This study discovered that the IMERG V06B gridding process incorrectly geolocated GPROF-CLIM V05 precipitation estimates by 0.1° eastward in the latitude band 75°N–75°S, which has been rectified in the IMERG V07 algorithm. Correcting for the geolocation error in IMERG-GMI V06B improved oceanic statistics, with improvements greater in tropical waters than Alaskan waters. This error tracing approach enables a high-precision diagnosis of how different IMERG algorithm steps contribute to and mitigate errors, demonstrating the importance of collaboration between evaluation studies and algorithm developers. Significance Statement Evaluation of IMERG’s oceanic performance is very limited to date. This study uses the GPM Validation Network to conduct the first extensive assessment of IMERG V06B at its native resolution over both high-latitude and tropical oceans, and traces errors in IMERG-GMI back through to the input GPROF-CLIM GMI product. IMERG-GMI overestimates tropical oceanic precipitation (+12%) and strongly overestimates Alaskan oceanic precipitation (+147%) with respect to the island-based radars studied. IMERG’s GMI estimates are assessed as these should be the optimal estimates within the multisatellite product due to the GMI’s status as calibrator of the GPM passive microwave constellation.

Performance Assessment of Global-EO-Based Precipitation Products against Gridded Rainfall from the Indian Meteorological Department

Monitoring water resources globally is crucial for forecasting future geo-hydro disasters across the Earth. In the present study, an attempt was made to assess the functional dimensionality of multi-satellite precipitation products, retrieved from CHIRPS, NASA POWER, ERA-5, and PERSIANN-CDR with respect to the gridded India Meteorological Department (IMD) precipitation dataset over a period of 30+ years (1990–2021) on monthly and yearly time scales at regional, sub regional, and pixel levels. The study findings showed that the performance of the PERSIANN-CDR dataset was significantly better in Central India, Northeast India, and Northwest India, whereas the NASA-POWER precipitation product performed better in Central India and South Peninsular of India. The other two precipitation products (CHIRPS and ERA-5) showed the intermediate performance over various sub regions of India. The CHIRPS and NASA POWER precipitation products underperformed from the mean value (3.05 mm/day) of the IMD gridded precipitation product, while the other two products ERA-5 and PERSIANN-CDR are over performed across all India. In addition, PERSIANN-CDR performed better in Central India, Northeast India, Northwest India, and the South Peninsula, when the yearly mean rainfall was between 0 and 7 mm/day, while ERA-5 performed better in Central India and the South Peninsula region for a yearly mean rainfall above 0–7 mm/day. Moreover, a peculiar observation was made from the investigation that the respective datasets were able to characterize the precipitation amount during the monsoon in Western Ghats. However, those products needed a regular calibration with the gauge-based datasets in order to improve the future applications and predictions of upcoming hydro-disasters for longer time periods with the very dense rain gauge data. The present study findings are expected to offer a valuable contribution toward assisting in the selection of an appropriate and significant datasets for various studies at regional and zonal scales.

Discussion of “Comparison of bias-corrected multisatellite precipitation products by deep learning framework”

• Comments about the application of satellite-derived evapotranspiration in the article by Shang et al. • Several key points/limitations of the article by Shang et al. were commented. • Some recommendations for overcoming the limitations were suggested.

Evaluation of SPI and Rainfall Departure Based on Multi-Satellite Precipitation Products for Meteorological Drought Monitoring in Tamil Nadu

The prevalence of the frequent water stress conditions at present was found to be more frequent due to increased weather anomalies and climate change scenarios, among other reasons. Periodic drought assessment and subsequent management are essential in effectively utilizing and managing water resources. For effective drought monitoring/assessment, satellite-based precipitation products offer more reliable rainfall estimates with higher accuracy and spatial coverage than conventional rain gauge data. The present study on satellite-based drought monitoring and reliability evaluation was conducted using four high-resolution precipitation products, i.e., IMERGH, TRMM, CHIRPS, and PERSIANN, during the northeast monsoon season of 2015, 2016, and 2017 in the state of Tamil Nadu, India. These four precipitation products were evaluated for accuracy and confidence level by assessing the meteorological drought using standard precipitation index (SPI) and by comparing the results with automatic weather station (AWS) and rain gauge network data-derived SPI. Furthermore, considering the limited number of precipitation products available, the study also indirectly addressed the demanding need for high-resolution precipitation products with consistent temporal resolution. Among different products, IMERGH and TRMM rainfall estimates were found equipollent with the minimum range predictions, i.e., 149.8, 32.07, 80.05 mm and 144.31, 34.40, 75.01 mm, respectively, during NEM of 2015, 2016, and 2017. The rainfall data from CHIRPS were commensurable in the maximum range of 1564, 421, and 723 mm in these three consequent years (2015 to 2017) compared to AWS data. CHIRPS data recorded a higher per cent of agreement (>85%) compared to AWS data than other precipitation products in all the agro-climatic zones of Tamil Nadu. The SPI values were positive > 1.0 during 2015 and negative < −0.99 for 2016 and 2017, indicating normal/wet and dry conditions in the study area, respectively. This study highlighted discrepancies in the capability of the precipitation products IMERGH and TRMM estimates for low rainfall conditions and CHIRPS estimates in high rainfall regimes.

How accurate are infrared-only and rain gauge-adjusted multi-satellite precipitation products in the southwest monsoon precipitation estimation across India?

A dense network of rain gauges and considerably large variability of the southwest monsoon precipitation across the country make India a suitable test-bed to evaluate any satellite-based precipitation product. In this paper, three real-time infrared-only precipitation products derived from the INSAT-3D satellite namely, INSAT Multispectral Rainfall (IMR), Corrected IMR (IMC) and Hydro-Estimator (HEM) and three rain gauge-adjusted Global Precipitation Measurement (GPM)-based multi-satellite precipitation products namely, Integrated Multi-satellitE Retrievals for GPM (IMERG), Global Satellite Mapping of Precipitation (GSMaP) and an Indian merged satellite-gauge product (INMSG) have been evaluated over India at a daily timescale for the southwest monsoon seasons of 2020 and 2021. An evaluation against rain gauge-based gridded reference dataset shows noticeable reduction of bias in IMC product over IMR, primarily over the orographic regions. However, INSAT-3D infrared-only precipitation retrieval algorithms have limitations in shallow and convective precipitation estimation. Among rain gauge-adjusted multi-satellite products, INMSG is shown to be the best product in the monsoon precipitation estimation over India due to use of rather larger number of rain gauges than IMERG and GSMaP products. All satellite-derived precipitation products, i.e. infrared-only and gauge-adjusted multi-satellite products underestimate heavy monsoon precipitation by 50-70%. The bias decomposition analysis indicates that a simple statistical bias correction would considerably improve the performance of the INSAT-3D precipitation products over the central India, but the same might not work over the west coast due to rather larger contributions of both positive and negative hit bias components. Although rain gauge-adjusted multi-satellite precipitation products show very small or negligible total biases in the monsoon precipitation estimation, positive and negative hit bias components are considerable over the west coast and central India. Furthermore, rain gauge-adjusted multi-satellite precipitation products underestimate very heavy to extremely heavy precipitation with larger magnitudes than the INSAT-3D derived precipitation products over the central India. Among the rain gauge-adjusted multi-satellite precipitation products, INMSG has smaller bias and error than IMERG and GSMaP products for very heavy to extremely heavy monsoon precipitation over the west coast and central India. Preliminary results of this study would be useful for end users in choosing a better precipitation product for real-time and research applications as well as for algorithm developers in further improving these products.

Merging multisatellite precipitation products using stacking method and the censored-shifted gamma ensemble model output statistics in china's Beimiaoji basin

High-quality precipitation data are crucial for hydrological analyses, water resource management, and drought monitoring. Merging precipitation products from different sensors and algorithms provides more reliable spatial information and can obtain high-quality precipitation data. To obtain high-spatiotemporal-resolution precipitation, in this study, we proposed a method to merge multisource precipitation products by downscaling coarse resolution products(geographically weighted regression (GWR) algorithm) and then eliminating biases in individual datasets(stacking algorithm), and finally merging the bias-adjusted precipitation by weighted average(the ensemble model output statistics-censored, shifted gamma (EMOS-CSG) algorithm). The multisource precipitation products included the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation 3B42 in Real-Time (TMPA-3B42RT), Climate Precipitation Center Morphing Technique (CMORPH), Global Satellite Mapping of Precipitation Near-Real-Time (GSMaP_NRT), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) products). The merging method was applied to the Beimiaoji basin from April to October in the 2016–2019 period. The precipitation observations at 38 gauges were spatially and randomly divided into two parts, 28 gauges were used for the training, while the other for the performance evaluations. The results showed that (1) the daily merged precipitation product had a better performance than the original satellite products in terms of the six considered statistical indexes, with the lowest root mean square error (RMSE) at 4.33 mm and the highest correlation coefficient (CC) at 0.64; (2) the utilized merging method not only increased the spatial resolution to 1 km but also captured more detailed precipitation distribution information; and (3) considering the influence of the gauge density, the performance of the merged product was still improving after the number of gauge stations exceeds 16, but the improvement was not significant.

Comparison of bias-corrected multisatellite precipitation products by deep learning framework

Comparison of bias-corrected multisatellite precipitation products by deep learning framework

Where Can IMERG Provide a Better Precipitation Estimate than Interpolated Gauge Data?

Although rain gauges provide valuable point-based precipitation observations, gauge data is globally sparse, necessitating interpolation between often-distant measurement locations. Interpolated gauge data is subject to uncertainty just as other precipitation data sources. Previous studies have focused either on the effect of decreasing gauge density on interpolated gauge estimate performance or on the ability of gauge data to accurately assess satellite multi-sensor precipitation data as a function of gauge density. No previous work has directly compared the performance of interpolated gauge estimates and satellite precipitation data as a function of gauge density to identify the gauge density at which satellite precipitation data and interpolated estimates have similar accuracy. This study seeks to provide insight into interpolated gauge product accuracy at low gage densities using a Monte Carlo interpolation scheme at locations across the continental U.S. and Brazil. We hypothesize that the error in interpolated precipitation estimates increases drastically at low rain gauge densities and at high distances to the nearest gauge. Results show that the multisatellite precipitation product, IMERG, has comparable performance in precipitation detection to interpolated gauge data at very low gauge densities (i.e., less than 2 gauges/10,000 km2) and that IMERG often outperforms interpolated data when the distance to the nearest gauge used during interpolation is greater than 80–100 km. However, there does not appear to be a consistent relationship between this performance ‘break point’ and the geographical variables of elevation, distance to coast, and annual precipitation.

How Well Do Multisatellite Products Capture the Space–Time Dynamics of Precipitation? Part II: Building an Error Model through Spectral System Identification

Abstract Satellite precipitation products, as all quantitative estimates, come with some inherent degree of uncertainty. To associate a quantitative value of the uncertainty to each individual estimate, error modeling is necessary. Most of the error models proposed so far compute the uncertainty as a function of precipitation intensity only, and only at one specific spatiotemporal scale. We propose a spectral error model that accounts for the neighboring space–time dynamics of precipitation into the uncertainty quantification. Systematic distortions of the precipitation signal and random errors are characterized distinctively in every frequency–wavenumber band in the Fourier domain, to accurately characterize error across scales. The systematic distortions are represented as a deterministic space–time linear filtering term. The random errors are represented as a nonstationary additive noise. The spectral error model is applied to the IMERG multisatellite precipitation product, and its parameters are estimated empirically through a system identification approach using the GV-MRMS gauge–radar measurements as reference (“truth”) over the eastern United States. The filtering term is found to be essentially low-pass (attenuating the fine-scale variability). While traditional error models attribute most of the error variance to random errors, it is found here that the systematic filtering term explains 48% of the error variance at the native resolution of IMERG. This fact confirms that, at high resolution, filtering effects in satellite precipitation products cannot be ignored, and that the error cannot be represented as a purely random additive or multiplicative term. An important consequence is that precipitation estimates derived from different sources shall not be expected to automatically have statistically independent errors. Significance Statement Satellite precipitation products are nowadays widely used for climate and environmental research, water management, risk analysis, and decision support at the local, regional, and global scales. For all these applications, knowledge about the accuracy of the products is critical for their usability. However, products are not systematically provided with a quantitative measure of the uncertainty associated with each individual estimate. Various parametric error models have been proposed for uncertainty quantification, mostly assuming that the uncertainty is only a function of the precipitation intensity at the pixel and time of interest. By projecting satellite precipitation fields and their retrieval errors into the Fourier frequency–wavenumber domain, we show that we can explicitly take into account the neighboring space–time multiscale dynamics of precipitation and compute a scale-dependent uncertainty.

Merging Multisatellite and Gauge Precipitation Based on Geographically Weighted Regression and Long Short-Term Memory Network

To generate high-quality spatial precipitation estimates, merging rain gauges with a single-satellite precipitation product (SPP) is a common approach. However, a single SPP cannot capture the spatial pattern of precipitation well, and its resolution is also too low. This study proposed an integrated framework for merging multisatellite and gauge precipitation. The framework integrates the geographically weighted regression (GWR) for improving the spatial resolution of precipitation estimations and the long short-term memory (LSTM) network for improving the precipitation estimation accuracy by exploiting the spatiotemporal correlation pattern between multisatellite precipitation products and rain gauges. Specifically, the integrated framework was applied to the Han River Basin of China for generating daily precipitation estimates from the data of both rain gauges and four SPPs (TRMM_3B42, CMORPH, PERSIANN-CDR, and GPM-IMAGE) during the period of 2007–2018. The results show that the GWR-LSTM framework significantly improves the spatial resolution and accuracy of precipitation estimates (resolution of 0.05°, correlation coefficient of 0.86, and Kling–Gupta efficiency of 0.6) over original SPPs (resolution of 0.25° or 0.1°, correlation coefficient of 0.36–0.54, Kling–Gupta efficiency of 0.30–0.52). Compared with other methods, the correlation coefficient for the whole basin is improved by approximately 4%. Especially in the lower reaches of the Han River, the correlation coefficient is improved by 15%. In addition, this study demonstrates that merging multiple-satellite and gauge precipitation is much better than merging partial products of multiple satellite with gauge observations.

Evaluation of multi-satellite precipitation products in estimating precipitation extremes over mainland China at annual, seasonal and monthly scales

The wide and consistent global coverage of satellite-based quantitative precipitation estimates (QPEs) has shown great potential for monitoring precipitation (PR) at large spatial scales. Evaluation of QPEs in estimating PR extremes is vital to forecasting hydrologic extremes. Here, we present a systematic evaluation of four commonly used QPEs: (1) the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS), (2) Tropical Rainfall Measuring Mission 3B42 Version 7 (TRMM 3B42 V7), (3) Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and (4) Multi-Source Weighted-Ensemble Precipitation (MSWEP), in their abilities to detect PR extremes in mainland China at annual-seasonal-monthly scales. Four Standard Extreme Precipitation Indices (SEPIs) are chosen as the assessment metrics, including the maximum 1-day PR (Rx1day), the simple PR intensity index (SDII), the count of days with PR ≥ 20 mm (R20mm), and the consecutive dry days (CDD). Results indicate that PERSIANN-CDR performs best for all SEPIs, followed by CHIRPS and TRMM 3B42 V7. All QPEs (except MSWEP) perform better (worse) in capturing CDD (SDII) than other SEPIs at all three timescales. However, large differences among the performance of QPEs in estimated seasonal SEPIs are found - CHIRPS (PERSIANN-CDR) outperforms the other QPEs in spring (summer and autumn), while PERSIANN-CDR and CHIRPS outperform the other QPEs in winter. All QPEs perform better in detecting extreme PR occurrence in summer than in other seasons, and spatially most of them perform better in humid southeastern China than in arid northwestern China. CHIRPS and TRMM 3B42 V7 overestimate Rx1day, R20mm and SDII in most of China, while MSWEP notably underestimates CDD in most of China and overestimates Rx1day, R20mm and SDII in western China.

Assessment of Multi-Satellite Precipitation Products over the Himalayan Mountains of Pakistan, South Asia

Performance assessment of satellite-based precipitation products (SPPs) is critical for their application and development. This study assessed the accuracies of four satellite-based precipitation products (PERSIANN-CDR, PERSIANN-CCS, PERSIANN-DIR, and PERSIANN) using data of in situ weather stations installed over the Himalayan Mountains of Pakistan. All SPPs were evaluated on annual, seasonal, monthly, and daily bases from 2010 to 2017, over the whole spatial domain and at point-to-pixel scale. The assessment was conducted using widely used evaluation indices (root mean square error (RMSE), correlation coefficient (CC), bias, and relative bias (rBias)) along with categorical indices (false alarm ratio (FAR), probability of detection (POD), success ratio (SR), and critical success index (CSI)). Results showed: (1) PERSIANN and PERSIANN-DIR products efficiently traced the spatio-temporal distribution of precipitation over the Himalayan Mountains. (2) On monthly scale, the estimates of all SPPs were more consistent with the reference data than on the daily scale. (3) On seasonal scale, PERSIANN and PERSIANN-DIR showed better performances than the PERSIANN-CDR and PERSIANN-CCS products. (4) All SPPs were less accurate in sensing daily light to medium intensity precipitation events. Subsequently, for future hydro-meteorological investigations in the Himalayan range, we advocate the use of monthly PERSIANN and PERSIANN-DIR products.

Evaluation of multi-satellite precipitation products for soil loss and sediment export modeling over eastern regions of the Koshi River Basin, Nepal

Evaluation of multi-satellite precipitation products for soil loss and sediment export modeling over eastern regions of the Koshi River Basin, Nepal

Assessment of SM2RAIN derived and IMERG based precipitation products for hydrological simulation

Land surface processes significantly influence weather thereby affecting terrestrial water balance. Even though land surface models (LSMs) offer a complementary means of examining fluxes, their simulations are often subjected to uncertainties from model parameters, model structure and forcing data. Of these, precipitation forcing uncertainty is known to significantly contribute to LSM output. This study examines the impact of different satellite-based precipitation products on LSM simulated soil moisture over India based on a data period of 8 years (2008 to 2015). The precipitation products used are namely, the Global Precipitation Measurement mission (GPM) integrated Multi-satellite Retrievals (IMERG) Late run, SM2RAIN-Climate Change Initiative (SM2RAIN-CCI) and SM2RAIN-Advanced SCATerometer (SM2RAIN-ASCAT). The first product is a multi-satellite precipitation product (top-down approach) with the remaining two products derived from surface satellite soil moisture (bottom up approach). The uncertainty in these three precipitation products are evaluated against simulations resulting from India Meteorological Department (IMD) precipitation dataset. The IMERG precipitation indicates a bias (mm) of 1.3014 and RMSE (mm) of 1.8024 during monsoon. Whereas for SM2RAIN-ASCAT it shows a bias of 0.0206 and RMSE of 0.9368 during monsoon and SM2RAIN-CCI which showed a bias of −0.3711 and RMSE of 0.9345 during monsoon. VIC simulated soil moisture using IMERG indicate a bias (m3/m3) of 0.0356 and RMSE (m3/m3) of 0.0402 during monsoon. This is in comparison to SM2RAIN-ASCAT which showed a bias of 0.0211 and RMSE of 0.0286 during monsoon and SM2RAIN-CCI which showed a bias of −0.0178 and RMSE of 0.0327 during monsoon. Our study has evaluated precipitation products derived from two different algorithms i.e top down and bottom up approach, it will provide insights on which algorithm is performing better in a country like India on different topographic condition.

A Comprehensive Evaluation of Near-Real-Time and Research Products of IMERG Precipitation over India for the Southwest Monsoon Period

Precipitation is one of the integral components of the global hydrological cycle. Accurate estimation of precipitation is vital for numerous applications ranging from hydrology to climatology. Following the launch of the Global Precipitation Measurement (GPM) Core Observatory, the Integrated Multi-satellite Retrievals for GPM (IMERG) precipitation product was released. The IMERG provides global precipitation estimates at finer spatiotemporal resolution (e.g., 0.1°/half-hourly) and has shown to be better than other contemporary multi-satellite precipitation products over most parts of the globe. In this study, near-real-time and research products of IMERG have been extensively evaluated against a daily rain-gauge-based precipitation dataset over India for the southwest monsoon period. In addition, the current version 6 of the IMERG research product or Final Run (IMERG-F V6) has been compared with its predecessor, version 5, and error characteristics of IMERG-F V6 for pre-GPM and GPM periods have been assessed. The spatial distributions of different error metrics over the country show that both near-real-time IMERG products (e.g., Early and Late Runs) have similar error characteristics in precipitation estimation. However, near-real-time products have larger errors than IMERG-F V6, as expected. Bias in all-India daily mean rainfall in the near-real-time IMERG products is about 3–4 times larger than research product. Both V5 and V6 IMERG-F estimates show similar error characteristics in daily precipitation estimation over the country. Similarly, both near-real-time and research products show similar characteristics in the detection of rainy days. However, IMERG-F V6 exhibits better performance in precipitation estimation and detection of rainy days during the GPM period (2014–2017) than the pre-GPM period (2010–2013). The contribution of different rainfall intensity intervals to total monsoon rainfall is captured well by the IMERG estimates. Furthermore, results reveal that IMERG estimates under-detect and overestimate light rainfall intensity of 2.5–7.5 mm day−1, which needs to be improved in the next release. The results of this study would be beneficial for end-users to integrate this multi-satellite product in any specific application.

Improving Cross-track Scanning Radiometers’ Precipitation Retrieval over Ocean by Morphing

AbstractPrevious studies showed that conical scanning radiometers greatly outperform cross-track scanning radiometers for precipitation retrieval over ocean. This study demonstrates a novel approach to improve precipitation rates at the cross-track scanning radiometers’ observation time by propagating the conical scanning radiometers’ retrievals to the cross-track scanning radiometers’ observation time. The improved precipitation rate is a weighted average of original cross-track radiometers’ retrievals and retrievals propagated from a conical scanning radiometer. The cross-track scanning radiometers include the Advanced Technology Microwave Sounder (ATMS) onboard the NPP satellite and four Microwave Humidity Sounders (MHSs). The conical scanning radiometers include the Advanced Microwave Scanning Radiometer 2 (AMSR2) and three Special Sensor Microwave Imager/Sounders (SSMISs), while the precipitation retrievals from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) are taken as the reference. Results show that the morphed precipitation rates agree much better with the reference. The degree of improvement depends on several factors, including the propagated precipitation source, the time interval between the cross-track scanning radiometer and the conical scanning radiometer, the precipitation type (convective vs. stratiform), the precipitation events’ size, and the geolocation. The study has potential to greatly improve high-impact weather systems monitoring (e.g., hurricanes) and multi-satellite precipitation products. It may also enhance the usefulness of future satellite missions with cross-track scanning radiometers onboard.

Disintegration of uncertainties associated with real-time multi-satellite precipitation products in diverse topographic and climatic area in Pakistan

Satellite-based Precipitation Estimates (SPEs) have gained importance due to enhanced spatial and temporal resolution, particularly in Indus basin, where raingauge network has fewer observation stations and drainage area is laying in many countries. Formulation of SPEs is based on indirect mechanism, therefore, assessment and correction of associated uncertainties is required. In the present study, disintegration of uncertainties associated with four prominent real time SPEs, IMERG, TMPA, CMORPH and PERSIANN has been conducted at grid level, regional scale, and summarized in terms of regions as well as whole study area basis. The bias has been disintegrated into hit, missed, false biases, and Root Mean Square Error (RMSE) into systematic and random errors. A comparison among gauge- and satellite-based precipitation estimates at annual scale, showed promising result, encouraging use of real time SPEs in the study area. On grid basis, at daily scale, from box plots, the median values of total bias (−0.5 to 0.5 mm) of the used SPEs were also encouraging although some under/over estimations were noted in terms of hit bias (−0.15 to 0.05 mm/day). Relatively higher values of missed (0.3 to 0.5 mm/day) and false (0.5 to 0.7 mm/day) biases were observed. The detected average daily RMSE, systematic errors, and random errors were also comparatively higher. Regional-scale spatial distribution of uncertainties revealed lower values of uncertainties in plain areas, depicting the better performance of satellite-based products in these areas. However, in areas of high altitude (>4000 m), due to complex topography and climatic conditions (orographic precipitation and glaciated peaks) higher values of biases and errors were observed. Topographic barriers and point scale gauge data could also be a cause of poor performance of SPEs in these areas, where precipitation is more on ridges and less in valleys where gauge stations are usually located. Precipitation system’s size and intensity can also be a reason of higher biases, because Microwave Imager underestimate precipitation in small systems ( 2000 km2). At present, use of bias correction techniques at daily time scale is compulsory to utilize real time SPEs in estimation of floods in the study area. Inter comparison of satellite products indicated that IMERG gave better results than the others with the lowest values of systematic errors, missed and false biases.

The potential and uncertainty of triple collocation in assessing satellite precipitation products in Central Asia

Although satellite-based precipitation estimation has extensive application potential, validation of its reliability is challenging for areas lacking ground-based data which is particularly true for many arid and semiarid regions. The triple collocation (TC) method can be used to evaluate three independent inputs with unknown true values, and thus provides an appealing alternative for assessment of satellite precipitation products in lack of observation regions. This study is the first to utilize TC to comprehensively assess the uncertainties of various satellite precipitation products in Central Asia (CA) with a distinctive continental arid and semi-arid climate. TC requires the errors of inputs to be independent with each other, while many multi-satellite precipitation products use overlapped data sources. To address this problem, this study uses a soil moisture-based product (SM2RAIN) and a reanalysis model-based product (ERA5) as two inputs of a triplet with the last input coming from each one of the six satellite precipitation products (3B42, CHIRPS, CMORPH, GSMaP, IMERG, PERSIANN). Six independent triplets are obtained in this way. The temporal/spatial resolution is daily/0.1° and the period is 2007–2019. The results show the overall performance of GSMaP is best among eight gridded precipitation products over CA, followed by IMERG, CMORPH and PERSIANN. All precipitation products show degraded performance with increasing altitude. Moreover, the accuracy estimates are subjected to uncertainties caused by the TC method and data inputs. Overall, the study concludes the TC method can provide a new perspective for the assessment of precipitation products over data-absent arid and semiarid regions, while careful check and explanation of evaluation results are always necessary to defend the rationality of TC in specific cases.

Evaluation of TMPA Satellite Precipitation in Driving VIC Hydrological Model over the Upper Yangtze River Basin

Although the Tropical Rainfall Measurement Mission (TRMM) has come to an end, the evaluation of TRMM satellite precipitation is still of great significance for the improvement of the Global Precipitation Measurement (GPM). In this paper, the hydrological utility of TRMM Multi-satellite Precipitation Analysis (TMPA) 3B42 RTV7/V7 precipitation products was evaluated using the variable infiltration capacity (VIC) hydrological model in the upper Yangtze River basin. The main results show that (1) TMPA 3B42V7 had a reliable performance in precipitation estimation compared with the gauged precipitation on both spatial and temporal scales over the upper Yangtze River basin. Although TMPA 3B42V7 slightly underestimated precipitation, TMPA 3B42RTV7 significantly overestimated precipitation at daily and monthly time scales; (2) the simulated runoff by the VIC hydrological model showed a high correlation with the gauged runoff and lower bias at daily and monthly time scales. The Nash–Sutcliffe coefficient of efficiency (NSCE) value was as high as 0.85, the relative bias (RB) was −6.36% and the correlation coefficient (CC) was 0.93 at the daily scale; (3) the accuracy of the 3B42RTV7-driven runoff simulation had been greatly improved by using the hydrological calibration parameters obtained from 3B42RTV7 compared with that of gauged precipitation. A lower RB (14.38% vs. 66.58%) and a higher CC (0.87 vs. 0.85) and NSCE (0.71 vs. −0.92) can be found at daily time scales when we use satellite data instead of gauged precipitation data to calibrate the VIC model. However, the performance of the 3B42V7-driven runoff simulation did not improve in the same operation accordingly. The cause might be that the 3B42V7 satellite products have been adjusted by gauged precipitation. This study suggests that it might be better to calibrate the parameters using satellite data in hydrological simulations, especially for unadjusted satellite data. This study is not only helpful for understanding the assessment of multi-satellite precipitation products in large-scale and complex areas in the upper reaches of the Yangtze River, but also can provide a reference for the hydrological utility of the satellite precipitation products in other river basins of the world.