Hit Bias Research Articles

Has IMERG_V07 Improved the Precision of Precipitation Retrieval in Mainland China Compared to IMERG_V06?

Integrated Multi-satellitE Retrievals for GPM (Global Precipitation Measurement) (IMERG) is the primary high spatiotemporal resolution precipitation product of the GPM era. To assess the applicability of the latest released IMERG_V07 in mainland China, this study systematically evaluates the error characteristics of IMERG_V07 from the perspective of different seasons, precipitation intensity, topography, and climate regions on an hourly scale. Ground-based meteorological observations are used as the reference, and the performance improvement of IMERG_V07 relative to IMERG_V06 is verified. Error evaluation is conducted in terms of precipitation amount and precipitation frequency, and an improved error component procedure is utilized to trace the error sources. The results indicate that IMERG_V07 exhibits a smaller RMSE in mainland China, especially with significant improvements in the southeastern region. IMERG_V07 shows better consistency with ground station data. IMERG_V07 shows an overall improvement of approximately 4% in capturing regional average precipitation events compared to IMERG_V06, with the northwest region showing particularly notable enhancement. The error components of IMERG_V06 and IMERG_V07 exhibit similar spatial distributions. IMERG_V07 outperforms V06 in terms of lower Missed bias but slightly underperforms in Hit bias and False bias compared to IMERG_V06. IMERG_V07 shows improved ability in capturing precipitation frequency for different intensities, but challenges remain in capturing heavy precipitation events, missing light precipitation, and winter precipitation events. Both IMERG_V06 and IMERG_V07 exhibit notable topography dependency in terms of Total bias and error components. False bias is the primary error source for both versions, except in winter, where high-altitude regions (DEM > 1200 m) primarily contribute to Missed bias. IMERG_V07 has enhanced the accuracy of precipitation retrieval in high-altitude areas, but there are still limitations in capturing precipitation events. Compared to IMERG_V06, IMERG_V07 demonstrates more concentrated error component values in the four climatic regions, with reduced data dispersion and significant improvement in Missed bias. The algorithm improvements in IMERG_V07 have the most significant impact in arid regions. False bias serves as the primary error source for both satellite-based precipitation estimations in the four climatic regions, with a secondary contribution from Hit bias. The evaluation results of this study offer scientific references for enhancing the algorithm of IMERG products and enhancing users’ understanding of error characteristics and sources in IMERG.

Rainfall estimation in the West African Sahel: comparison and cross-validation of top-down vs. bottom-up precipitation products in Burkina Faso

This study compares the performance of satellite precipitation products (SPPs) and soil moisture-based rainfall products (SM2RPPs) in capturing rainfall patterns in Burkina Faso, West Africa. The findings indicate that SPPs outperform SM2RPPs at daily to annual timescales. The decomposition of total bias reveals that hit bias are dominant, but are larger in magnitude in SM2RPPs. Systematic errors are the major component of hit biases, suggesting they could be further treated through bias-correction. Wavelet analysis indicates that SPPs and SM2RPPs well capture seasonal and annual rainfall variability. All products struggle to accurately represent the most extreme rainfall indices, with SPPs performing better than SM2RPPs. For meteorological droughts, RFE2 and CHIRPS show the strongest correlations with observed SPI values across 1- to 12-month timescales. Although SM2RPPs perform less than SPPs in Burkina Faso, they appear mature for hydrometeorological applications and could further benefit from bias-correction to attain similar or better performance than SPPs.

Paired Satellite and NWP Precipitation for Global Flood Forecasting

Abstract Precipitation data are known to be the key driver of hydrological simulations. Hence, reliable quantitative precipitation estimates and forecasts are vital for accurate hydrological forecasting. Satellite-based precipitation estimates from Integrated Multi-satellitE Retrievals for GPM Early Run (IMERG-E) and forecasted precipitation from NASA’s Goddard Earth Observing System Forward Processing (GEOS-FP) have shown values in global flood nowcasting and forecasting. However, few studies have comprehensively evaluated their hydrological performance let alone explored the potential value of combining them. Therefore, this study undertakes a quasi-global evaluation of their utility in real-time hydrological monitoring and 1–5-day forecasting with the Dominant River Tracing-Routing Integrated with Variable Infiltration Capacity (VIC) Environment (DRIVE) model. The gauge-corrected IMERG Final Run precipitation estimates and corresponding hydrological simulation are used as the references. Results showed that the hit bias is the dominant error source of IMERG-E, while the false precipitation is more noticeable in GEOS-FP. In terms of hydrological performance, the GEOS-FP-driven model (DRIVE-FP) performance is close to the IMERG-E-driven model (DRIVE-E) performance on day 1, indicating that GEOS-FP could nicely fill the gap of nowcasting caused by the IMERG-E time latency. For longer lead-time forecasts, the bias tends to diminish in most regions, likely because the under- or overestimation in IMERG-E is generally offset by the distinct types of misestimation in GEOS-FP. The skillful initial hydrological conditions present outperformed forecasts in most regions, except for tropical areas where the accuracy of GEOS-FP prevails. Overall, this study provides a valuable view of the combined use of IMERG-E and GEOS-FP precipitation in the context of hydrological nowcasts and forecasts.

Dependency of errors for four global reanalysis and satellite precipitation estimates on four crucial factors

Errors in the reanalysis and satellite precipitation products are closely related to four impacting factors, i.e., seasonality, topography, rainfall intensity, and climate type. Therefore, revealing the dependency of the errors of precipitation products on seasonality, topography, rainfall intensity, and climate type is important for data users and developers to improve the quality of precipitation products and enhance their applications in hydrometeorology. In this study, the links between errors and four impacting factors were used to investigate the dependency of errors in the ERA5 reanalysis and three satellite-only precipitation products (i.e., IMERG-Late, GSMaP-MVK, and PERSIANN-CCS) on the four impacting factors. Results from mainland China show that the errors of the four products depend on the four factors, whereas the dependency of different error indicators on the four factors differs significantly. In all seasons, ERA5 outperformed the three satellite-only precipitation products in most cases. In terms of topographic dependency, the probability of detection (POD) of ERA5 increases with topographic complexity but the opposite is true for the satellite-only precipitation products. Compared with purely infrared (IR) data, the fusion of passive microwave and IR data enhances the quality of precipitation estimates and reduces the topographical dependency of the errors. Regarding intensity-based performance, the performance of the four products in terms of the POD and normalized root-mean-squared error improves as the rain rate increases, whereas that of the three bias metrics (i.e., total bias, hit bias, and miss bias) deteriorates. In terms of the climatic dependency of the errors, the errors of the four products increase as the aridity increases in most cases. False bias contributes primarily to the total bias for the four products. More importantly, we found that the relationship between the false alarm ratio (FAR) and climate type cannot accurately characterize the dependency of false alarms based on climate type because of the interference of hit precipitation events. Based on this finding, the mean number of false alarms is recommended to replace the FAR to objectively investigate the false precipitation of various precipitation products.

Performance Evaluation of GPM IMERG Precipitation Products over the Tropical Oceans Using Buoys

Abstract The majority of global precipitation falls in tropical oceans. Nonetheless, due to the lack of in situ precipitation measurements, the number of studies over the tropical oceans remains limited. Similarly, the performance of IMERG products over the tropical oceans is unknown. In this context, this study quantitatively evaluates the 20 years (2001–20) of IMERG V06 Early, Late, and Final products against the in situ buoys’ estimates using the pixel–point approach at a daily scale across the tropical oceans. Results show that IMERG represents well the mean spatial pattern and spatial variation of precipitation, though significant differences exist in the magnitude of precipitation amount. Overall, IMERG notably overestimates precipitation across the tropical ocean, with maxima over the western Pacific and Indian Oceans, while it performs better over the eastern Pacific and Atlantic Oceans. Moreover, irrespective of the region, IMERG sufficiently detects precipitation events (i.e., >0.1 mm day−1) for high-precipitation regions, though it significantly overestimates the magnitude. Despite IMERG’s detection issues of precipitation events over the regions with lower precipitation, it is in good agreement with the buoys in total precipitation estimation. The positive hit bias and false alarm bias are the major contributions to the overall total positive bias. Furthermore, the detection capability of IMERG tends to decline with increasing precipitation rates. In terms of IMERG runs, the IMERG Final product performs slightly better than the Early and Late runs. More detailed studies over the tropical oceans are required to better characterize the biases and their sources.

A New Method for Hour-by-Hour Bias Adjustment of Satellite Precipitation Estimates over Mainland China

Highly accurate near-real-time satellite precipitation estimates (SPEs) are important for hydrological forecasting and disaster warning. The near-real quantitative precipitation estimates (REGC) of the recently developed Chinese geostationary meteorological satellite Fengyun 4A (FY4A) have the advantage of high spatial and temporal resolution, but there are errors and uncertainties to some extent. In this paper, a self-adaptive ill-posed least squares scheme based on sequential processing (SISP) is proposed and practiced in mainland China to correct the real-time biases of REGC hour by hour. Specifically, the scheme adaptively acquires sample data by setting temporal and spatial windows and constructs an error-correction model based on the ill-posed least squares method from the perspectives of climate regions, topography, and rainfall intensity. The model adopts the sequential idea to update satellite precipitation data within time windows on an hour-by-hour basis and can correct the biases of real-time satellite precipitation data using dynamically changing parameters, fully taking into account the influence of precipitation spatial and temporal variability. Only short-term historical data are needed to accurately rate the parameters. The results show that the SISP algorithm can significantly reduce the biases of the original REGC, in which the values of relative bias (RB) in mainland China are reduced from 11.2% to 3.3%, and the values of root mean square error (RMSE) are also reduced by about 17%. The SISP algorithm has a better correction in humid and semi-humid regions than in arid and semi-arid regions and is effective in reducing the negative biases of precipitation in each climate region. In terms of rain intensity, the SISP algorithm can improve the overestimation of satellite precipitation estimates for low rain intensity (0.2–1 mm/h), but the correction for high rain intensity (>1 mm/h) needs further improvement. The error component analysis shows that the SISP algorithm can effectively correct the hit bias. This study serves as a valuable reference for real-time bias correction using short-term accumulated precipitation data.

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.

Errors of five satellite precipitation products for different rainfall intensities

Revealing the errors of the satellite precipitation products (SPPs) for various rainfall intensity groups is crucial for their applications in the hydro-meteorological field. However, the spatial maps of errors of SPPs for different rainfall intensity groups and the impact of elevation on them are still unclear. In this study, the spatial maps of errors of the five satellite precipitation products (i.e., IMERG-Late, IMERG-Final, GSMaP-MVK, GSMaP-Gauge, and PERSIANN-CCS) for the four rainfall intensity groups (i.e., light precipitation, moderate precipitation, heavy precipitation, and storm) are depicted. A novel score, namely the relative rainfall occurrence bias (ROB), is proposed as a means to evaluate the reproducibility of the spatial patterns of SPPs for different surface rainfall intensity occurrences. All five SPPs cannot reproduce the spatial patterns of the four surface rainfall intensity occurrences. Despite that, all SPPs except for PERSIANN-CCS can capture almost all rainfall intensity groups (except light precipitation) because their probability of detection (POD) values exceeds 0.8. Moreover, we found that the detection capability of SPPs for light precipitation decreases with the increasing standard deviation of elevation (SDE) but is the opposite for bias, while the impacts of elevation on the errors of SPPs for other rainfall intensity groups are unapparent. The overestimations of the number of surface heavy precipitation and storm occurrences for IMERG-Late and GSMaP-MVK are mainly due to overestimating the rainfall amount of the lower rain-rate surface rainfall events, while their total biases for heavy precipitation and storms originate from hit bias. The potential limitations of the bias correction algorithms used in the IMERG and GSMaP retrieval systems in correcting different rainfall intensity groups are identified by comparing the performance between gauge-adjusted products and satellite-only products. Finally, the optimal SPP precipitation product for each rainfall intensity group is discussed. The findings reported in this study can provide valuable information for data users to select a product that best meets the requirements of the applications.

Error Decomposition of CRA40-Land and ERA5-Land Reanalysis Precipitation Products over the Yongding River Basin in North China

Long-term and high-resolution reanalysis precipitation datasets provide important support for research on climate change, hydrological forecasting, etc. The comprehensive evaluation of the error performances of the newly released ERA5-Land and CRA40-Land reanalysis precipitation datasets over the Yongding River Basin in North China was based on the two error decomposition schemes, namely, decomposition of the total mean square error into systematic and random errors and decomposition of the total precipitation bias into hit bias, missed precipitation, and false precipitation. Then, the error features of the two datasets and precipitation intensity and terrain effects against error features were analyzed in this study. The results indicated the following: (1) Based on the decomposition approach of systematic and random errors, the total error of ERA5-Land is generally greater than that of CRA40-Land. Additionally, the proportion of random errors was higher in summer and over mountainous areas, specifically, the ERA5-Land accounts for more than 75%, while the other was less than 70%; (2) Considering the decomposition method of hit, missed, and false bias, the total precipitation bias of ERA5-Land and CRA40-Land was consistent with the hit bias. The magnitude of missed precipitation and false precipitation was less than the hit bias. (3) When the precipitation intensity is less than 38 mm/d, the random errors of ERA5-Land and CRA40-Land are larger than the systematic error. The relationship between precipitation intensity and hit, missed, and false precipitation is complicated, for the hit bias of ERA5-L is always smaller than that of CRA40-L, and the missed precipitation and false precipitation are larger than those ofCRA40-L when the precipitation is small. The error of ERA5-Land and CRA40-Land was significantly correlated with elevation. A comprehensive understanding of the error features of the two reanalysis precipitation datasets is valuable for error correction and the construction of a multi-source fusion model with gauge-based and satellite-based precipitation datasets.

Systematic assessment of GPM IMERG V06 precipitation products with dense rain gauge observations over Zhejiang Province, China

AbstractThis study systematically assessed the performance of the Integrated Multi‐satellitE Retrievals (IMERG) for Global Precipitation Measurement V06, including the near‐real‐time “Late Run” (IMERG‐L) and the post‐real‐time “Final Run” (IMERG‐F), over Zhejiang Province (ZJP), China. The evaluation was conducted at daily and hourly timescales for a full year and for each season, based on dense rain gauge observations and continuous and categorical validation statistics. For the full year and for each season, IMERG‐F outperformed IMERG‐L in representing the spatial pattern of multiyear mean precipitation. For regional mean of ZJP, IMERG‐F and IMERG‐L overestimated the daily/hourly precipitation for the full year by 6.51 and 4.98%, respectively. Among seasons, the regional mean relative biases for IMERG‐F were between 5.65 and 8.63%; however, for IMERG‐L, they exhibited notable variations with a maximum of 11.09% in fall and a minimum of 0 in spring. Bias composition suggested that the regional mean overestimations were largely due to false bias for the full year and for each season, except in winter, wherein it was due to hit bias. Spatially, the biases for the full year and for each season commonly arose from false and hit biases at daily timescale, and from false and miss biases at hourly timescale. Based on the remaining continuous metrics (i.e., root‐mean‐square‐error [RMSE], correlation coefficient [CC], and Kling–Gupta Efficiency [KGE]) and all categorical metrics, the IMERG daily/hourly performance was acceptable on regional and grid scales throughout the year and in all seasons. From a region‐average perspective, IMERG‐F outperformed IMERG‐L according to CC, RMSE, and KGE, but both products showed the same performance overall based on all categorical metrics; most grids also share these characteristics. This study provides a valuable reference for IMERG developers to improve product accuracy from the perspective of the final postprocessing step and for potential users in ZJP.

From TRMM to GPM, how do improvements of post/near-real-time satellite precipitation estimates manifest?

With the end of the Tropical Rainfall Measuring Mission (TRMM) era, its successor of Global Precipitation Measurement (GPM) aims to provide a new generation of satellite precipitation estimates (SPE) with higher spatiotemporal resolution and wider spatial coverage. As the representative precipitation retrieval algorithms for GPM and TRMM eras, it is necessary to compare the Integrated Multi-satellite Retrievals for GPM (IMERG) with the TRMM Multi-satellite Precipitation Analysis (TMPA) to investigate the improvements of SPEs from TRMM to GPM. This study analyzes the post/near-real-time SPEs from TMPA and IMERG over Mainland China from April 2014 to March 2018 at daily scale, using precipitation observations from 510 meteorological stations as reference. A very comprehensive evaluation system, which contains four indicators of continuous metrics, categorical metrics, error components, and systematic and random errors is adopted in this study. Besides, four patterns with spatial distribution, temporal pattern, precipitation intensity distribution, and overall metrics are applied to illustrate each indicator's performance. The results show that IMERG products have lower RB and RMSE and higher CC than TMPA and greatly reduce the three error components (hit bias, missed precipitation, and false precipitation). Furthermore, IMERG products present a higher capability to capture the precipitation events correctly and generally have lower systematic and random errors than TMPA products. However, there are still rooms for IMERG products to improve, such as higher false alarm ratio (FAR) and more false precipitation of IMERG products at relatively low rain rates, the 25% higher systematic error of IMERG-E (IMERG “Early Run” near-real-time SPE) compared to TMPA-RT (TMPA near-real-time SPE). In addition, we find the gauge adjustment from GPCC is not a very effective way to improve the detecting capability and reduce the systematic errors for near-real-time SPEs over Mainland China. This study provides some valuable information on the transition from TMPA to IMERG, which may advance the algorithm's development and the hydrometeorological applications.

Accuracy assessment and error cause analysis of GPM (V06) in Xiangjiang river catchment

Abstract Application potential and development prospect of satellite precipitation products such as Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Mission (GPM) have promising implications. This study discusses causes of spatiotemporal differences on GPM data through the following steps: Initially, calculate bias between satellite-based data and rain gauge data of Xiangjiang river catchment to assess the accuracy of GPM (06E, 06 L, and 06F) products. Second, total errors of satellite precipitation data are divided into hit bias (HBIAS: precipitation detected by both GPM and rain gauge station), missed precipitation (MBIAS: precipitation detected only by rain gauge station), and false precipitation (FBIAS: precipitation detected only by GPM). Third, evaluate the impact of precipitation intensity and total precipitation on accuracy of GPM data and their influence on three error components. Several conclusions are drawn from the results above. (1) Satellite-based precipitation measurements perform better on a larger temporal-spatial scale. (2) The accuracy of TRMM and GPM data displays significant variances on space and time. Season, precipitation intensity, and total precipitation are main factors influencing the accuracy of TRMM and GPM data. (3) The detection capability of satellite products change with seasonal variation and different precipitation intensity level.

Investigating the potential of a global precipitation forecast to inform landslide prediction

Extreme rainfall events within landslide-prone areas can be catastrophic, resulting in loss of property, infrastructure, and life. A global Landslide Hazard Assessment for Situational Awareness (LHASA) model provides routine near-real time estimates of landslide hazard using Integrated Multi-Satellite Precipitation Retrievals for the Global Precipitation Mission (IMERG). However, it does not provide information on potential landslide hazard in the future. Forecasting potential landslide events at a global scale presents an area of open research. This study compares a global precipitation forecast provided by NASA's Goddard Earth Observing System (GEOS) with near-real time satellite precipitation estimates. The Multi-Radar Multi-Sensor gauge corrected (MRMS-GC) reference is used to assess the performance of both satellite and model-based precipitation products over the contiguous United States (CONUS). The forecast lead time of 24hrs is considered, with a focus on extreme precipitation events. The performance of IMERG and GEOS-Forecast products is assessed in terms of the probability of detection, success ratio, critical success index and hit bias as well as continuous statistics. The results show that seasonality influences the performance of both satellite and model-based precipitation products. Comparison of IMERG and GEOS-Forecast globally as well as in several event case studies (Colombia, southeast Asia, and Tajikistan) reveals that GEOS-Forecast detects extreme rainfall more frequently relative to IMERG for these specific analyses. For recent landslide points across the globe, the 24hr accumulated precipitation forecast >100 mm corresponds well with near-real time daily accumulated IMERG precipitation estimates. GEOS-Forecast and IMERG precipitation match more closely for tropical cyclones than for other types of storms. The main intention of this study is to assess the viability of using a global forecast for landslide predictions and understand the extent of the variability between these products to inform where we would expect the landslide modeling results to most prominently diverge. Results of this study will be used to inform how forecasted precipitation estimates can be incorporated into the LHASA model to provide the first global predictive view of landslide hazards.

An improved error decomposition scheme for satellite-based precipitation products

The conventional error decomposition schemes of satellite-based precipitation products (SPPs) suffer from significant drawbacks such as misrepresentation of hit bias (H) component and magnification of random error, which limits their accuracy in deducing reliable conclusions. We propose a novel error decomposition technique by disintegrating the total bias (TB) in SPPs into four different independent components - over-hit (OH), under-hit (–UH), missed (–M) and false (F) precipitation. Further, the systematic and random parts are identified by adopting a multiplicative error model. The efficacy of the proposed scheme is demonstrated by comparing six different SPPs over India with a high-resolution gridded observed reference rainfall dataset by the India Meteorological Department, at daily-time scale for 16 years (2001–2016). The results of the study highlight that the traditional hit component is mostly underestimated due to the cancelling of opposite natured OH and –UH bias components and hence is unreliable. Often, the magnitudes of OH and –UH are found to be higher than TB and H. The proposed OH and –UH bias accurately estimates the “intensity” error in satellite precipitation retrieval process and can be used by both the data producers and users for deriving reliable conclusions. The findings of the study will aid in a more reliable and accurate characterization of error in SPPs, which is a fundamental step in the development of uncertainty modelling and bias-reduction algorithms.

Validation of Satellite-Based Precipitation Products from TRMM to GPM

The global precipitation measurement mission (GPM) has been in operation for seven years and continues to provide a vast quantity of global precipitation data at finer temporospatial resolutions with improved accuracy and coverage. GPM’s signature algorithm, the integrated multisatellite retrievals for GPM (IMERG) is a next-generation of precipitation product expected for wide variety of research and operational applications. This study evaluates the latest version (V06B) of IMERG and its predecessor, the tropical rainfall measuring mission (TRMM) multisatellite precipitation (TMPA) 3B42 (V7) using ground-based and gauge-corrected multiradar multisensor system (MRMS) precipitation products over the conterminous United States (CONUS). The spatial distributions of all products are analyzed. The error characteristics are further examined for 3B42 and IMERG in winter and summer by an error decomposition approach, which partitions total bias into hit bias, biases due to missed precipitation and false precipitation. The volumetric and categorical statistical metrics are used to quantitatively evaluate the performance of the two satellite-based products. All products show a similar precipitation climatology with some regional differences. The two satellite-based products perform better in the eastern CONUS than in the mountainous Western CONUS. The evaluation demonstrates the clear improvement in IMERG precipitation product in comparison with its predecessor 3B42, especially in reducing missed precipitation in winter and summer, and hit bias in winter, resulting in better performance in capturing lighter and heavier precipitation.

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.

Evaluation of the GPM IMERG V06 products for light rain over Mainland China

Monitoring the spatiotemporal characteristics of light rain events is vital to water resources assessment and environmental protection. The Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG) products provide new-generation satellite precipitation measurements with improved light rain estimates. However, the spatial distribution of the quality and error structure of IMERG light precipitation detections and the climatic dependence of the error sources have not been studied sufficiently. In this study, we evaluated the GPM IMERG daily light precipitation estimates' performance from 2015 to 2018 over mainland China. We found that: (1) IMERG captured the spatial patterns of light precipitation over mainland China but has limited ability to detect light rainfall events below 5 mm/d; (2) the hit bias is the dominant error of IMERG under 5 mm/d; (3) IMERG exhibits a worse performance and has relatively higher miss bias for light rainfall in areas with a complex winter precipitation phase; (4) the evaluation of IMERG for light rain is affected by the mismatch between gauge data and IMERG data caused by air pollution. These findings can improve the IMERG algorithms for light rainfall and provide a reference for applications based on these datasets.

Seasonal Error Component Analysis of the GPM IMERG Version 05 Precipitation Estimations Over Sichuan Basin of China

AbstractAn error decomposition method is used to analyze and track seasonal error sources of the Integrated Multi‐satellitE Retrievals for the Global Precipitation Measurement (IMERG) Version 05 daily precipitation estimations, including Early‐, Late‐, and Final‐run products (IMERG‐DE, IMERG‐DL, and IMERG‐DF, respectively) over the Sichuan Basin of China from January 2016 to December 2018. The method decomposes the total errors into hit bias, missed precipitation, and false precipitation, which can be attributed to the satellite retrieval processes. Results show that in spring, all the estimations had serious overestimation in the northwest of the basin. The major error source is the hit bias of the intensity over 25 mm/day. In summer, the three IMERG estimations overestimated the precipitation in almost the entire Sichuan Basin, especially in the south and southwest. The principal determinants of the error sources are missed and false light precipitation (<25 mm/day), while hit bias and false precipitation are the leading error sources for the moderate precipitation (25–54 mm/day). In autumn, the IMERG‐DF's overestimation mainly comes from hit and false moderate precipitation (15–64 mm/day), while the underestimation of the IMERG‐DE and DL is mainly determined by missed and false precipitation of the precipitation less than 15 mm/day. In winter, the main contributors to the underestimation of the IMERG‐DE and DL estimations are missed and false lighter precipitation (<8 mm/day). However, the total errors of the IMERG‐DF are resulting from the three components canceling one another. Additionally, missed precipitation often takes place in the autumn and winter and false precipitation in summer and winter. The IMERG‐DL shows better error control of positive hit bias and false precipitation than do the other estimations in summer.

Comparison of TRMM Microwave Imager Rainfall Datasets from NASA and JAXA

AbstractThis study compares three TMI rainfall datasets generated by two versions of NASA’s Goddard Profiling algorithm (GPROF2010 and GPROF2017) and JAXA’s Global Satellite Mapping of Precipitation algorithm (GSMaP) over land, coast, and ocean. We use TRMM precipitation radar observations as the reference, and also include CloudSat cloud profiling radar (CPR) observations as the reference over ocean. First, the dynamic thresholds for rainfall detection used by GSMaP and GPROF2017 have better detection capability, indicating by larger Heidke skill score (HSS) values, compared with GPROF2010 over both land and coast. Over ocean, all three datasets have very similar HSS regardless of including CPR observations. Next, intensity analysis shows that no single dataset performs the best according to all three statistical metrics (correlation, root-mean-square error, and relative bias), except that GSMaP performs the best for stratiform precipitation over coast, and GPROF2017 performs the best for convective precipitation over ocean, based on all three metrics. Finally, an error decomposition analysis shows that the total error and its three components have very different characteristics over several regions among these three datasets. For example, the positive total error in GPROF2010 and GSMaP is primarily caused by the positive hit bias over central Africa, while the false bias in GPROF2017 is largely responsible for this positive total error. For future algorithm development, results from this study imply that a convective–stratiform separation technique may be necessary to reduce the large underestimation for convective rain intensity.

Comparison analysis of six purely satellite-derived global precipitation estimates

We executed a comprehensive evaluation and intercomparison between six purely satellite-derived precipitation estimates (i.e., IMERG-Late, IMERG-Early, GSMaP-NRT, GSMaP-MVK, TMPA-RT and PERSIANN-CCS) at global and regional scales for the period from February 2017 to January 2019. The results show that IMERG-Late exhibits the best performance among six evaluated products, while the worst performance was found in GSMaP-NRT and GSMaP-MVK. The root mean squared error (RMSE) has a power function to the logarithm of precipitation intensity in all six satellite products. On the basis of our findings, the RMSE of all products in rainfall events with intensity exceeding 32 mm/day (or 8 mm/h) accounts for beyond 30% of the corresponding precipitation intensity, which might result in a significant impact on the detectability and forecast of flash floods simulated by satellite precipitation. Additionally, both IMERG and GSMaP overestimate the proportions of light rainfall occurrences, and also display relatively larger errors in light precipitation (0.2–0.4 mm/h or 1–2 mm/day) with the RMSE values exceeding 0.5 mm (or 2 mm) at hourly (or daily) time scale. As for the error analysis, we decomposed the total bias of each product into hits, misses and false biases at hourly and 0.1° resolution over mainland China except for TMPA-RT. We found that the false bias is the dominated error sources for these five products in cold season over semi-humid areas despite that the hit bias accounts for a non-negligible proportion for GSMaP suite. The missed precipitation is the dominated error sources of PERSIANN-CCS both in two seasons over most of humid regions, and meanwhile is one of major error sources for other four products. We expect that the findings of this study not only provide some valuable feedbacks for algorithm developers to improve the GPM-based satellite precipitation retrievals, but also provide some guidance for data users across the world.