Validation of GPM IMERG V05 and V06 Precipitation Products over Iran

2020 ◽  
Vol 21 (5) ◽  
pp. 1011-1037 ◽  
Author(s):  
Seyed-Mohammad Hosseini-Moghari ◽  
Qiuhong Tang
Keyword(s):  
Probability Of Detection ◽  
Validation Metrics ◽  
Precipitation Measurement ◽  
Retrieval Algorithms ◽  
Satellite Sensors ◽  
Daily Scale ◽  
Global Precipitation Measurement ◽  
Global Precipitation ◽  
Early Late ◽  
Better Than

AbstractThis study attempts to assess the validity of the Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) products across Iran. Six IMERG precipitation products (IPPs) including early, late, and final runs for versions 05 and 06 were compared with precipitation data from 76 synoptic stations on a daily scale for the period from June 2014 to June 2018. According to the results, V05 performed better than V06, particularly in early and late runs. The IPPs overestimate precipitation ranging from 5% to 32%; however, IPPs tended to underestimate (overestimate) the amount of precipitation for wet (dry) areas and precipitation classes higher than 5 mm day−1 (less than 5 mm day−1). The probability of detection (POD) in IPPs was almost similar (with a median equal to 0.60), whereas other categorical validation metrics like false alarm ratio (FAR) improved in the final run. Our assessments revealed that the dependency of IPPs to the elevation was low, while the error characteristics of IPPs were strongly dependent on the climate and precipitation intensity. For instance, the systematic error varied between less than 12% in dry regions to more than 60% in wet regions. Also, according to modified Kling–Gupta efficiency (KGE) and relative bias (RBias), the performance of IPPs in winter with the highest KGE (ranging from 0.47 to 0.63) and lowest RBias (ranging from 0% to 16%) was better than other seasons. Further improvement is recommended in the satellite sensors and the precipitation retrieval algorithms to achieve a reliable precipitation source.

scholarly journals Validation of GPM Precipitation Products by Comparison with Ground-Based Parsivel Disdrometers over Jianghuai Region

Water ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 1260 ◽  
Author(s):  
Zuhang Wu ◽  
Yun Zhang ◽  
Lifeng Zhang ◽  
Xiaolong Hao ◽  
Hengchi Lei ◽  
...  
Keyword(s):  
Shape Parameter ◽  
Rain Rate ◽  
Dual Frequency ◽  
Raindrop Size Distribution ◽  
Precipitation Measurement ◽  
Retrieval Algorithms ◽  
Raindrop Size ◽  
Global Precipitation Measurement ◽  
Global Precipitation ◽  
Rain Retrieval

In this study, we evaluated the performance of rain-retrieval algorithms for the Version 6 Global Precipitation Measurement Dual-frequency Precipitation Radar (GPM DPR) products, against disdrometer observations and improved their retrieval algorithms by using a revised shape parameter µ derived from long-term Particle Size Velocity (Parsivel) disdrometer observations in Jianghuai region from 2014 to 2018. To obtain the optimized shape parameter, raindrop size distribution (DSD) characteristics of summer and winter seasons over Jianghuai region are analyzed, in terms of six rain rate classes and two rain categories (convective and stratiform). The results suggest that the GPM DPR may have better performance for winter rain than summer rain over Jianghuai region with biases of 40% (80%) in winter (summer). The retrieval errors of rain category-based µ (3–5%) were proved to be the smallest in comparison with rain rate-based µ (11–13%) or a constant µ (20–22%) in rain-retrieval algorithms, with a possible application to rainfall estimations over Jianghuai region. Empirical Dm–Ze and Nw–Dm relationships were also derived preliminarily to improve the GPM rainfall estimates over Jianghuai region.

scholarly journals Performance Evaluation of Version 5 (V05) of Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) over the Tianshan Mountains of China

Water ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 1139 ◽  
Author(s):  
Min Yang ◽  
Zhongqin Li ◽  
Muhammad Naveed Anjum ◽  
Yayu Gao
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Heavy Precipitation ◽  
Tianshan Mountains ◽  
Spatiotemporal Variability ◽  
Precipitation Measurement ◽  
Satellite Retrievals ◽  
Precipitation Estimates ◽  
Global Precipitation Measurement ◽  
Global Precipitation ◽  
Better Than

This study evaluated the performance of the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) version 5 (V05) Early-run and Final-run (IMERG-E and IMERG-F, respectively) products over the Tianshan Mountains. For comparison, the accuracies of two Tropical Rainfall Measuring Mission (TRMM) products (3B42RT and 3B42V7) were also analyzed. Performance of the satellite-based precipitation products (SPPs) was analyzed at daily to annual scales from April 2014 to October 2017. Results showed that: (1) IMERG-F and 3B42V7 performed better than IMERG-E and 3B42RT in the characterization of spatiotemporal variability of precipitation; (2) Precipitation estimates from IMERG-F were in the best overall agreement with the gauge-based data, followed by IMERG-E and 3B42V7 on all temporal scales; (3) IMERG-E and 3B42RT products were failed to provide accurate precipitation amounts, whereas IMERG-F and 3B42V7 were able to provide accurate precipitation estimates with the lowest relative biases (4.98% and −1.71%, respectively) and RMSE (0.58 mm/day and 0.76 mm/day, respectively); (4) The enhancement from the IMERG Early-run to the Final-run to capture the moderate to heavy precipitation events was not evident; (5) On seasonal scale, IMEGR-F performed better than all other SPPs, particularly during the spring season with negligible bias (0.28%). It was deduced that IMERG-F was capable of replacing TRMM products.

scholarly journals Performance of the IMERG Precipitation Products over High-latitudes Region of Finland

2021 ◽  
Vol 13 (11) ◽  
pp. 2073
Author(s):  
Mohammed T. Mahmoud ◽  
Safa A. Mohammed ◽  
Mohamed A. Hamouda ◽  
Miikka Dal Maso ◽  
Mohamed M. Mohamed
Keyword(s):  
Spatial Scales ◽  
Time Estimation ◽  
Observation Data ◽  
High Latitudes ◽  
Precipitation Measurement ◽  
Spatial Coverage ◽  
Satellite Sensors ◽  
Temporal And Spatial ◽  
Global Precipitation Measurement ◽  
Global Precipitation

Highly accurate and real-time estimation of precipitation over large areas remains a fundamental challenge for the hydrological and meteorological community. This is primarily attributed to the high heterogeneity of precipitation across temporal and spatial scales. Rapid developments in remote sensing technologies have made the quantitative measurement of precipitation by satellite sensors a significant data source. The Global Precipitation Measurement (GPM) mission makes precipitation data with high temporal and spatial resolutions available to different users. The objective of this study is to evaluate the accuracy of Integrated Multi-satellite Retrievals for GPM (IMERG) V06 (Early, Late, and Final) satellite precipitation products (SPPs) at high latitudes. Ground-based observation data across Finland were used as a reference and compared with IMERG data from 2014 to 2019. Three aspects were evaluated: the spatial coverage of the satellite estimates over Finland; the accuracy of the satellite estimates at various temporal scales (half-hourly, daily, and monthly); and the variation in the performance of SPPs over different spatial regions. The results showed that IMERG SPPs can be used with high confidence over Southern, Eastern, and Western Finland. These SPPs can be used with caution over the region of the historical province of Oulu but are not recommended for higher latitudes over Lapland. In general, the IMERG-Final SPP performed the best, and it is recommended for use because of its low number of errors and high correlation with ground observation. Furthermore, this SPP can be used to complement or substitute ground precipitation measurements in ungauged and poorly gauged regions in Southern Finland.

scholarly journals Assessment on IMERG V06 Precipitation Products Using Rain Gauge Data in Jinan City, Shandong Province, China

2021 ◽  
Vol 13 (7) ◽  
pp. 1241
Author(s):  
Peng Li ◽  
Zongxue Xu ◽  
Chenlei Ye ◽  
Meifang Ren ◽  
Hao Chen ◽  
...  
Keyword(s):  
Extreme Precipitation ◽  
Rain Gauge ◽  
Precipitation Measurement ◽  
Precipitation Estimation ◽  
Daily Scale ◽  
Extreme Precipitation Indices ◽  
Precipitation Indices ◽  
Jinan City ◽  
Global Precipitation Measurement ◽  
Global Precipitation

In this study, a comprehensive assessment on precipitation estimation from the latest Version 06 release of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) algorithm is conducted by using 24 rain gauge observations at daily scale from 2001 to 2016. The IMERG V06 dataset fuses Tropical Rainfall Measuring Mission (TRMM) satellite data (2000–2015) and Global Precipitation Measurement (GPM) satellite data (2014–present), enabling the use of IMERG data for long-term study. Correlation coefficient (CC), root mean square error (RMSE), relative bias (RB), probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) were used to assess the accuracy of satellite-derived precipitation estimation and measure the correspondence between satellite-derived and observed occurrence of precipitation events. The probability density distributions of precipitation intensity and influence of elevation on precipitation estimation were also examined. Results showed that, with high CC and low RMSE and RB, the IMERG Final Run product (IMERG-F) performs better than two other IMERG products at daily, monthly, and yearly scales. At daily scale, the ability of satellite products to detect general precipitation is clearly superior to the ability to detect heavy and extreme precipitation. In addition, CC and RMSE of IMERG products are high in Southeastern Jinan City, while RMSE is relatively low in Southwestern Jinan City. Considering the fact that the IMERG estimation of extreme precipitation indices showed an acceptable level of accuracy, IMERG products can be used to derive extreme precipitation indices in areas without gauged data. At all elevations, IMERG-F exhibits a better performance than the other two IMERG products. However, POD and FAR decrease and CSI increase with the increase of elevation, indicating the need for improvement. This study will provide valuable information for the application of IMERG products at the scale of a large city.

scholarly journals Evaluation and Correction of GPM IMERG Precipitation Products over the Capital Circle in Northeast China at Multiple Spatiotemporal Scales

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Wei Sun ◽  
Yonghua Sun ◽  
Xiaojuan Li ◽  
Tao Wang ◽  
Yanbing Wang ◽  
...  
Keyword(s):  
Cumulative Distribution ◽  
Precipitation Data ◽  
Evaluation Indexes ◽  
Ground Station ◽  
Critical Success ◽  
Precipitation Measurement ◽  
Long Time ◽  
Global Precipitation Measurement ◽  
Global Precipitation ◽  
Better Than

Accurate remote-sensed precipitation data are crucial to the effective monitoring and analysis of floods and climate change. The Global Precipitation Measurement (GPM) satellite product offers new options for the global study of precipitation. This paper evaluates the applicability of GPM IMERG products at different time resolutions in comparison to ground-measured data. Based on precipitation data from 107 meteorological stations in the Beijing-Tianjin-Hebei region, GPM products were analysed at three timescales: half-hourly (GPM-HH), daily (GPM-D), and monthly (GPM-M). We use a cumulative distribution function (CDF) model to correct GPM-D and GPM-M products to analyse temporal and spatial distributions of precipitation. We came to the following conclusions: (1) The GPM-M product is strongly correlated with ground station data. Based on five evaluation indexes, NRMSE (Normalized Root Mean Square Error), NSE (Nash-Sutcliffe), FAR (False Alarm Ratio), UR (Underreporting Rate), and CSI (Critical Success Index), the monthly GPM products showed the best performance, better than GPM-HH products and GPM-D products. (2) The performance of GPM products in summer and autumn was better than in winter and spring. However, the GPM satellite’s precision in undulating terrain was poor, which could easily lead to serious errors. (3) CDF models were successfully used to modify GPM-D and GPM-M products and improve their accuracy. (4) The range of 0–100 mm precipitation could be corrected best, but the GPM-M products were underestimated. Corrected GPM-M data in the range >100 mm were overestimated. According to this analysis, the GPM IMERG Final Run products at daily and monthly timescales have good detection ability and can provide data support for long-time series analyses in the Beijing-Tianjin-Hebei region.

scholarly journals Assessment of GPM-Era Satellite Products’ (IMERG and GSMaP) Ability to Detect Precipitation Extremes over Mountainous Country Nepal

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 254
Author(s):  
Bikash Nepal ◽  
Dibas Shrestha ◽  
Shankar Sharma ◽  
Mandira Singh Shrestha ◽  
Deepak Aryal ◽  
...  
Keyword(s):  
Extreme Precipitation ◽  
Daily Precipitation ◽  
Precipitation Amount ◽  
Probability Of Detection ◽  
Precipitation Extremes ◽  
Extreme Precipitation Events ◽  
Precipitation Measurement ◽  
Global Precipitation Measurement ◽  
Global Precipitation ◽  
Precipitation Events

The reliability of satellite precipitation products is important in climatic and hydro-meteorological studies, which is especially true in mountainous regions because of the lack of observations in these areas. Two recent satellite rainfall estimates (SREs) from Global Precipitation Measurement (GPM)-era—Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG-V06) and gauge calibrated Global Satellite Mapping of Precipitation (GSMaP-V07) are evaluated for their spatiotemporal accuracy and ability to capture extreme precipitation events using 279 gauge stations from southern slope of central Himalaya, Nepal, between 2014 and 2019. The overall result suggests that both SREs can capture the spatiotemporal precipitation variability, although they both underestimated the observed precipitation amount. Between the two, the IMERG product shows a more consistent performance with a higher correlation coefficient (0.52) and smaller bias (−2.49 mm/day) than the GSMaP product. It is worth mentioning that the monthly gauge-calibrated IMERG product yields better detection capability (higher probability of detection (POD) values) of daily precipitation events than the daily gauge calibrated GSMaP product; however, they both show similar performance in terms of false alarm ratio (FAR) and critical success index (CSI). Assessment based on extreme precipitation indices revealed that the IMERG product outperforms GSMaP in capturing daily precipitation extremes (RX1Day and RX5Day). In contrast, the GSMaP product tends to be more consistent in capturing the duration and threshold-based precipitation extremes (consecutive dry days (CDD), consecutive wet days (CWD), number of heavy precipitation days (R10mm), and number of extreme precipitation days (R25mm)). Therefore, it is suggested that the IMERG product can be a good alternative for monitoring daily extremes; meanwhile, GSMaP could be a better option for duration-based extremes in the mountainous region.

Preliminary Assessment of GPM Satellite Rainfall over Myanmar

2018 ◽  
Vol 13 (1) ◽  
pp. 22-30 ◽  
Author(s):  
Muhammad Mohsan ◽  
Ralph Allen Acierto ◽  
Akiyuki Kawasaki ◽  
Win Win Zin ◽  
◽  
...  
Keyword(s):  
Daily Rainfall ◽  
Heavy Rain ◽  
Probability Of Detection ◽  
Light Rain ◽  
Precipitation Measurement ◽  
Observation Network ◽  
Satellite Rainfall ◽  
Global Precipitation Measurement ◽  
Global Precipitation ◽  
Statistical Metrics

Intensive and long-term rainfall in Myanmar causes floods and landslides that affect thousands of people every year. However, the rainfall observation network is still limited in number and extent, so satellite rainfall products have been shown to supplement observations over the ungauged areas. One example is the estimates from Global Precipitation Measurement (GPM) called Integrated Multi-satellite Retrievals for GPM (IMERG), which has high spatial (0.1 × 0.1 degree) and temporal (30 min) resolution. This has potential to be used for modeling streamflow, early warnings, and forecasting systems. This study investigates the utility of these GPM satellite estimates for representing the daily rainfall for 25 rain gauges over Myanmar. Statistical metrics were used to understand the characteristic performance of the GPM satellite estimates. Daily rainfall estimates from GPM show a range of 29.3% to 81.1% probability of detection (POD). The satellite estimates show a capability of detecting no-rain days between 61.4 and 93.5%. For different rainfall intensities, the satellite estimates have a 12.9 to 39.1% POD for light rain (1–10 mm/day), 11.1 to 49% POD for moderate rain (10–50 mm/day), a maximum of 36% for heavy rain (50–150 mm/day), and a maximum of 12.5% for extreme rain (=150 mm/day). However, the correlation coefficient (CC) only ranges from 0.064 to 0.581, which is considered low, and is not uniform for all the stations. The highest CC scores and POD scores tend to be located in the northern part and deltaic region extending to the southern coasts in Myanmar, indicating a dependency of the statistical metrics on rainfall magnitude. The high POD scores indicate the utility of the estimates without correction for early warning purposes, but the estimates have low reliability for rainfall intensity. The satellite estimates can be used for forecasting and modeling purposes in the region, but the estimates require bias-correction before application.

scholarly journals Comprehensive Comparisons of State-of-the-Art Gridded Precipitation Estimates for Hydrological Applications over Southern China

2020 ◽  
Vol 12 (23) ◽  
pp. 3997
Author(s):  
Zhen Gao ◽  
Bensheng Huang ◽  
Ziqiang Ma ◽  
Xiaohong Chen ◽  
Jing Qiu ◽  
...  
Keyword(s):  
Southern China ◽  
Temporal Distribution ◽  
Rain Gauge ◽  
Vital Role ◽  
Probability Of Detection ◽  
Precipitation Estimates ◽  
Accumulated Rainfall ◽  
Daily Scale ◽  
Global Precipitation ◽  
Better Than

Satellite-based precipitation estimates with high quality and spatial-temporal resolutions play a vital role in forcing global or regional meteorological, hydrological, and agricultural models, which are especially useful over large poorly gauged regions. In this study, we apply various statistical indicators to comprehensively analyze the quality and compare the performance of five newly released satellite and reanalysis precipitation products against China Merged Precipitation Analysis (CMPA) rain gauge data, respectively, with 0.1° × 0.1° spatial resolution and two temporal scales (daily and hourly) over southern China from June to August in 2019. These include Precipitation Estimates from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS), European Center for Medium-Range Weather Forecasts Reanalysis v5 (ERA5-Land), Fengyun-4 (FY-4A), Global Satellite Mapping of Precipitation (GSMaP), and Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG). Results indicate that: (1) all five products overestimate the accumulated rainfall in the summer, with FY-4A being the most severe; additionally, FY-4A cannot capture the spatial and temporal distribution characteristics of precipitation over southern China. (2) IMERG and GSMaP perform better than the other three datasets at both daily and hourly scales; IMERG correlates slightly better than GSMaP against CMPA data, while it performs worse than GSMaP in terms of probability of detection (POD). (3) ERA5-Land performs better than PERSIANN-CCS and FY-4A at daily scale but shows the worst correlation coefficient (CC), false alarm ratio (FAR), and equitable threat score (ETS) of all precipitation products at hourly scale. (4) The rankings of overall performance on precipitation estimations for this region are IMERG, GSMaP, ERA5-Land, PERSIANN-CCS, and FY-4A at daily scale; and IMERG, GSMaP, PERSIANN-CCS, FY-4A, and ERA5-Land at hourly scale. These findings will provide valuable feedback for improving the current satellite-based precipitation retrieval algorithms and also provide preliminary references for flood forecasting and natural disaster early warning.

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