IMERG Precipitation Products Research Articles

Precipitation Retrieval from FY-3G/MWRI-RM Based on SMOTE-LGBM

Using the FY-3G/MWRI-RM observations, this paper proposes a precipitation retrieval method that combines the Synthetic Minority Over-sampling Technique with Light Gradient Boosting Machine (SMOTE-LGBM) and analyzes the impact of MWRI-RM channel settings on precipitation retrieval. The SMOTE-LGBM-based model consists of two LGBM models for precipitation identification and estimation, respectively. The SMOTE method is used to address the imbalance between precipitation and non-precipitation samples. Using the Integrated Multi-Satellite Retrievals for the Global Precipitation Measurement (IMERG) product as a reference, we validate the retrieved precipitation by the SMOTE-LGBM-based model with an independent testing dataset. The critical success indexes are 0.483 and 0.526, and the Pearson correlation coefficients are 0.611 and 0.645 for the ocean and land regions, respectively. The spatial distributions of the retrieved and IMERG accumulated precipitation in the testing dataset are similar. In addition, we visualize and analyze the cases of Meiyu and two typhoons. The results indicate that the SMOTE-LGBM-based model effectively represents the spatial distribution characteristics of precipitation and achieves high agreement with IMERG precipitation products. Overall, the SMOTE-LGBM-based model successfully retrieves precipitation from MWRI-RM and provides accurate precipitation products for FY-3G/MWRI-RM for the first time.

Evaluation of Precipitation Frequency and Intensity as Estimated by the GPM IMERG Precipitation Product at Daily and Hourly Scales over the Tibetan Plateau

The IMERG V06 hourly rainfall product at daily and hourly scales was evaluated during the warm season (May to September) from 2015 to 2020 using 651 high-quality rain-gauge stations over the Tibetan Plateau (TP). Based on hourly observed rain-gauge precipitation, four categories were classified: light rainfall (0–12 mm·d−1), moderate rainfall (12–20.1 mm·d−1), torrential rainfall (20.1–32.2 mm·d−1), and extreme torrential rainfall (>32.2 mm·d−1). Precipitation frequency and intensity were calculated to further validate the accuracy and suitability of the IMERG estimated-precipitation product. At the daily scale, IMERG underestimated the number of days with less than moderate rainfall, but overestimated the frequency of torrential and extreme torrential rainfall. IMERG estimated the main characteristics of precipitation frequency at different daily precipitation amount levels better than the precipitation intensity, but its best estimate was at the moderate rainfall level, with the highest correlation coefficient (0.69) and the lowest root mean square error (0.17). At the hourly scale, IMERG underestimated the hourly precipitation amount mainly between the early morning and midday (the average deviation was 0.019 mm·h−1) and overestimated it between the afternoon and late night (the average deviation was 0.047 mm·h−1). IMERG overestimated precipitation frequency and underestimated precipitation intensity between the afternoon and the evening, which means that this analysis shows that IMERG estimated more precipitation hours than the observation and underestimated precipitation intensity. These results further our understanding of the suitability of the IMERG precipitation products over the TP and further improve the IMERG retrieval algorithm to better apply the corresponding precipitation product to light and extreme rainfall over regions with complicated topography.

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.

An Analysis for the Applicability of Global Precipitation Measurement Mission (GPM) IMERG Precipitation Data in Typhoons

This study selected examples of 17 typhoons that landed in Fujian after passing through Taiwan. The study evaluated the precipitation in different time scales and the spatial distribution of daily precipitation of varying magnitudes in the southeastern coastal area by comparing satellite precipitation estimation products with meteorological observation station data. The evaluation used a correlation coefficient, mean relative error, relative bias, and graded assessment indexes (probability of detection, false alarm rate, and critical success index). Correlation coefficient analysis revealed that maximum daily precipitation performed best, followed by process total precipitation. The relative bias indicates that the precipitation estimated by the satellite is lower than the rainfall recorded by the automatic weather station. Mean relative error analysis showed that hourly precipitation had the highest error, followed by maximum daily precipitation. The GPM IMERG precipitation products’ retrieval of daily precipitation of varying magnitudes was assessed using three indicators. The assessment revealed that the satellite had a low under-reporting rate for light rain events but a high under-reporting rate for torrential rain events, especially extremely heavy rainstorm events, in terms of probability of detection. For the false alarm rate, the satellite had a small probability of false predictions for light rain events, while extremely heavy rainstorm events had the highest probability. For the critical success index, the satellite’s estimation of light rain events was basically consistent with reality; however, its ability to estimate precipitations above rainstorm levels was low. The results of the spatial assessment of heavy precipitation show that the satellite’s ability to detect heavy precipitation’s structure, intensity, and location is fair and has some reference value, especially for regions where conventional information is scarce.

Assessment of IMERG v06 Satellite Precipitation Products in the Canadian Great Lakes Region

Abstract The Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM; IMERG) is a high-resolution gridded precipitation dataset widely used around the world. This study assessed the performance of the half-hourly IMERG v06 Early and Final Runs over a 5-yr period versus 19 high-quality surface stations in the Great Lakes region of North America. This assessment not only looked at precipitation occurrence and amount, but also studied the IMERG Quality Index (QI) and errors related to passive microwave (PMW) sources. Analysis of bias in accumulated precipitation amount and precipitation occurrence statistics suggests that IMERG presents various uncertainties with respect to time scale, meteorological season, PMW source, QI, and land surface type. Results indicate that 1) the cold season’s (November–April) larger relative bias can be mitigated via backward morphing; 2) IMERG 6-h precipitation amount scored best in the warmest season (JJA) with a consistent overestimation of the frequency bias index − 1 (FBI-1); 3) the performance of five PMW sources is affected by the season to different degrees; 4) in terms of some metrics, skills do not always enhance with increasing QI; 5) local lake effects lead to higher correlation and equitable threat score (ETS) for the stations closest to the lakes. Results of this study will be beneficial to both developers and users of IMERG precipitation products. Significance Statement The purpose of the study was to assess the performance of the gridded precipitation product from the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) version 6 over the Great Lakes region of North America. The assessment performs a statistical comparison of precipitation amounts from IMERG versus surface stations as a function of time scale, season, precipitation event threshold, and input source among satellites. Interpretation of the results identifies shortcomings in the IMERG algorithms, particularly in extreme precipitation events and over ice-covered surfaces. The results also describe spatial variability in the IMERG data quality due to the complex geography of the study area and offer a clear threshold in the Quality Index (QI) flag for optimal application of the precipitation products.

Statistical comparison and hydrological utility evaluation of ERA5-Land and IMERG precipitation products on the Tibetan Plateau

In the context of global warming, precipitation information over the Tibetan Plateau (TP), known as the water tower of Asia, is crucial for both atmospheric and hydrological cycle research. As important alternatives of gauge observations, the satellite-based precipitation estimations and atmospheric reanalysis precipitation prodcuts have undergone rapid development over the past few decades. However, their performance and usabilities are not effectively investigated over the TP, particularly for the latestly released products. Hence, this study presents a comprehensive evaluation of a state-of-the-art reanalysis precipitation (ERA5-Land) and compares it with two IMERG products (i.e., IMERG-E and IMERG-L) from 2001 to 2018, with a special focus on their hydrological utility. Results indicate that ERA5-Land presents a severe overestimation of precipitation amount and precipitation frequency but well reflects their spatial–temporal patterns over the entire TP. The IMERG products, particularly for IMERG-F, outperform ERA5-Land in estimating precipitation amount. In terms of hydrological utility in the upper Yellow River basin, both ERA5-Land and IMERG-E show poor simulation performance using gauge-based model parameters, which is closely related to their overestimation/underestimation of precipitation amount. In contrast, IMERG-F forced simulation performs much better due to its low systematic error. When the hydrological model is recalibrated based on individual precipitation input, the simulation performance of ERA5-Land is comparable to IMERG-F, slightly inferior to gauge observation. Therefore, it is recommended that the selection of appropriate precipitation products need to accord their own research purposes. IMERG-F provides a more accurate estimation of precipitation amount, while, employing input-specific calibrated model parameters, ERA5-Land can provide long term hydrological simulations.

Adaptation Analysis in IMERG Precipitation Estimation for the Dongting Lake Basin, China

Precipitation data from ground-based observatories in the Dongting Lake basin are often missing, resulting in large errors in surface precipitation data obtained by interpolation, which affects the accuracy of hydro-meteorological studies. Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) is the main high-resolution precipitation product, which is available to supplement measured missing data. To evaluate the applicability of this product in the Dongting Lake basin at multiple spatial and temporal scales, this paper analyzes daily, monthly, seasonal, annual, and extreme precipitation events of the three latest IMERG precipitation products (IPPs) (IMERG-F, IMERG-E, and IMERG-L) using eight statistical evaluation metrics. We find that the spatial and temporal performance of IMERG precipitation products varies over different time scales and topographic conditions. However, all three metrics (CC, RMSE, and RB) of the IMERG-F precipitation products outperform the IMERG-E and IMERG-L precipitation products for the same period. In the comparison of IMERG and TRMM (Tropical Rainfall Measuring Mission) precipitation products on monthly and seasonal scales, IMERG-F performed the best. IPPs can capture precipitation more accurately on seasonal scales and perform better in winter, indicating good detection of trace precipitation. Both high and low altitudes are not favorable for the satellite detection of extreme precipitation in both general and extreme precipitation events. Overall, the accuracy of IMERG-F with correction delay is slightly better than that of IMERG-E and IMERG-L without correction under near-real-time conditions, which is applicable in the Dongting Lake basin. However, the correction process also exacerbates overestimation of the precipitation extent.

A comprehensive validation for GPM IMERG precipitation products to detect extremes and drought over mainland China

This study provides a comprehensive validation of Integrate Multi-SatellitE Retrievals of (IMERG) Global Precipitation Measurement (GPM) products in detecting extremes and drought over mainland China. The estimated values of extreme precipitation and drought are provided by three runs (early, late and final) of the latest IMERG products (V06) and 696 in-situ gauges over mainland China during 2008–2017. The results demonstrate that the three runs of IMERG V06 exhibit a relatively good performance in detecting the spatial patterns of extreme precipitation volume. The early and late runs present limited capability to capture extreme precipitation events, while the final run performs slightly better. Based on the extreme value theory, the three runs show better performances in estimating extreme precipitation within short return period (10-yr) compared to 50-yr and 100-yr return periods. All runs consistently underestimate extreme precipitation in all investigated return periods over eastern China. The standardized precipitation index (SPI) is used as a drought monitoring tool. The SPI of three runs of IMERG is well aligned with the in-situ data in southern and eastern China at both time and space scales, which demonstrates that the great potential of IMERG V06 in monitoring drought in these regions. However, the three runs of IMERG V06 products have inferior performances in monitoring drought over western China at four investigated timescales (1-, 3-, 6- and 12-month). This study highlights discrepancies in the capability of the IMERG products to estimate major extreme climatic variables and suggests potential avenues of research to improve the algorithm associated with the products. Meanwhile, the results of this study can benefit both developers and users of the new generation GPM IMERG products.

A Global Evaluation of IMERG Precipitation Occurrence Using SMAP Detected Soil Moisture Change

Abstract A globally consistent ground validation method for remotely sensed precipitation products is crucial for building confidence in these products. This study develops a new methodology to validate the IMERG precipitation products through the use of SMAP soil moisture changes as a proxy for precipitation occurrence. Using a standard 2 × 2 contingency table method, preliminary results provide confidence in SMAP’s ability to be utilized as a validation tool for IMERG as results are comparable to previous validation studies. However, the method allows for an overestimate of false alarm frequency due to light precipitation events that can evaporate before the subsequent SMAP overpass and changes in overpass-to-overpass SMAP soil moisture that are within the range of SMAP uncertainty. To counter these issues, a 3 × 3 contingency table is used to reduce noise and extract more signal from the detection method. Through the use of this novel approach, the validation method produces a global mean POD of 0.64 and global mean FAR of 0.40, the first global-scale ground validation skill scores for the IMERG products. Advancing the method to validate precipitation quantity and the development of a real-time validation for the IMERG Early product are the crucial next developments. Significance Statement We wanted to see if there was a method in which remotely sensed precipitation observations could be validated at a near-global scale for land areas. Scientific literature is filled with studies that validate various precipitation datasets over local-to-regional scales, with very few extending beyond that domain. This study provides a robust first attempt at validating a global precipitation product at a global scale using changes in remotely sensed soil moisture as an independent proxy for precipitation presence/absence. While the method demonstrates that there is skill in using soil moisture as a tool to validate precipitation at the global scale, we find that there are still instances of a systemic bias for arid climate regimes. This method lays the groundwork for future studies to provide a comprehensive global validation in a globally consistent manner.

Comprehensive Evaluation of Global Precipitation Measurement Mission (GPM) IMERG Precipitation Products over Mainland China

Due to the difficulty involved in obtaining and processing a large amount of data, the spatial distribution of the quality and error structure of satellite precipitation products and the climatic dependence of the error sources have not been studied sufficiently. Eight statistical and detection indicators were used to compare and evaluate the accuracy of the Integrated Multi-Satellite Retrievals for Global Precipitation Measurement Mission (GPM IMERG) precipitation products in China, including IMERG Early, Late, and Final Run. (1) Based on the correlation coefficient between GPM IMERG precipitation products and measured precipitation, the precipitation detection ability is good in eastern China, whereas the root-mean-square error increases from northwest to southeast. (2) Compared with the Early and Late Run, the accuracy of the detection of a light rain of the IMERG Final Run is higher, but the precipitation is overestimated. With the increase in the precipitation intensity, the detection ability weakens, and the precipitation is underestimated. (3) The Final Run has a higher estimation accuracy regarding light rain in western high-altitude areas, whereas the accuracy of the detection of moderate rain, heavy rain, and rainstorms is higher in eastern coastal low-altitude areas. This phenomenon is related to the performance and detection principles of satellites. The altitude and magnitude of the precipitation affect the detection accuracy of the satellite. This study provides guidance for the application of GPM IMERG precipitation products in hydrological research and water resource management in China.

Statistical Comparison of IMERG Precipitation Products with Optical Rain Gauge Observations over Kototabang, Indonesia

Satellite-based precipitation estimates play a crucial role in many hydrological and numerical weather models, especially to overcome the scarcity of rain gauge data. Globally gridded rainfall product from Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (GPM) (IMERG) has been used in a wide range of hydrological applications. However, the IMERG is inherently prone to errors and biases. This study evaluated the performance of the IMERG-Final run (IMERG-F) product to estimate rainfall in a mountainous area of Sumatra. Validation was carried out using optical rain gauge (ORG) data for 15 years (2002-2016), at Kototabang, West Sumatra, Indonesia. In general, IMERG-F overestimated rainfall in all time scales. The longer the time scale was, the better the performance of IMERG-F we obtained. This feature was indicated by all quantities of continuous and categorical statistical matrices used. The performance of IMERG-F was lower than in other areas of the Maritime Continent, except for the probability of detection (POD) value. IMERG-F could detect rain very well, including for daily and hourly data, but the false alarm rate (FAR) was also relatively high. Such high FAR value may indicate a significant small-scale spatial rainfall variability in mountainous area of Sumatra.

A New HASM-Based Downscaling Method for High-Resolution Precipitation Estimates

Obtaining high-quality precipitation datasets with a fine spatial resolution is of great importance for a variety of hydrological, meteorological and environmental applications. Satellite-based remote sensing can measure precipitation in large areas but suffers from inherent bias and relatively coarse resolutions. Based on the high accuracy surface modeling method (HASM), this study proposed a new downscaling method, the high accuracy surface modeling-based downscaling method (HASMD), to derive high-quality monthly precipitation estimates at a spatial resolution of 0.01° by downscaling the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) precipitation estimates in China. A scale transformation equation was introduced in HASMD, and the initial value was set by including the explanatory variables related to precipitation. The performance of HASMD was evaluated by comparing the results yielded by HASM and the combined method of HASM, Kriging, IDW and the geographical weighted regression (GWR) method (GWR-HASM, GWR-Kriging, GWR-IDW). Analysis results indicated that HASMD performed better than the other four methods. High agreement was achieved for HASMD, with bias values ranging from 0.07 to 0.29, root mean square error (RMSE) values ranging from 9.53 mm to 47.03 mm, and R2 values ranging from 0.75 to 0.96. Compared with the original IMERG precipitation products, the downscaling accuracy with HASMD improved up to 47%, 47%, and 14% according to bias, RMSE and R2, respectively. HASMD was able to capture the spatial variation in monthly precipitation in a vast region, and it might be potentially applicable for enhancing the spatial resolution and accuracy of remotely sensed precipitation data and facilitating their application at large scales.

Evaluation and Hydrological Utility of the GPM IMERG Precipitation Products over the Xinfengjiang River Reservoir Basin, China

As a supplement to gauge observation data, many satellite observations have been used for hydrology and water resource research. This study aims to analyze the quality of the Integrated Multisatellite Retrieval for Global Precipitation Measurement (GPM IMERG) products and their hydrological utility in the Xinfengjiang River reservoir basin (XRRB), a mountainous region in southern China. The grid-based soil and water assessment tool (SWAT) model was used to construct a hydrological model of the XRRB based on two scenarios. The results showed that on a daily scale, the IMERG final run (FR) product was more accurate than the others, with Pearson’s correlation coefficients (CORR) of 0.61 and 0.71 on the grid accumulation scale and the average scale, respectively, and a relative bias (BIAS) of 0.01. In Scenario I (the SWAT model calibrated by rain gauge data), the IMERG-based simulation showed acceptable hydrologic prediction ability on the daily scale and satisfactory hydrological performance on the monthly scale. In Scenario II (the SWAT model calibrated by the FR), the hydrological performances of the FR on the daily and monthly scales were slightly better than those in Scenario I (the CORR was 0.64 and 0.85, the BIAS was 0.01 and −0.02, and the NSE was 0.43 and 0.84). These results showed the potential of the FR for hydrological modeling in tropical mountain watersheds in areas where information is scarce. This study is useful for hydrological, meteorological, and disaster studies in developing countries or remote areas with sparse or low-quality networks of ground-based observation stations.

Capability of IMERG V6 Early, Late, and Final Precipitation Products for Monitoring Extreme Precipitation Events

The monitoring of extreme precipitation events is an important task in environmental research, but the ability of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) precipitation products to monitor extreme precipitation events remains poorly understood. In this study, three precipitation products for IMERG version 6, early-, late-, and final-run products (IMERG-E, IMERG-L, and IMERG-F, respectively), were used to capture extreme precipitation, and their applicability to monitor extreme precipitation events over Hubei province in China was evaluated. We found that the accuracy of the three IMERG precipitation products is inconsistent in areas of complex and less complex topography. Compared with gauge-based precipitation data, the results reveal the following: (1) All products can accurately capture the spatiotemporal variation patterns in precipitation during extreme precipitation events. (2) The ability of IMERG-F was good in areas of complex topography, followed by IMERG-E and IMERG-L. In areas of less complex topography, IMERG-E and IMERG-L produced outcomes that were consistent with those of IMERG-F. (3) The three IMERG precipitation products can capture the actual hourly precipitation tendencies of extreme precipitation events. (4) In areas of complex topography, the rainfall intensity estimation ability of IMERG-F is better than those of IMERG-E and IMERG-L.

Performance evaluation and correction of precipitation data using the 20-year IMERG and TMPA precipitation products in diverse subregions of China

The latest Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) dataset version 6, which is the successor of the multi-satellite 3B42 dataset from the Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA), is now recommended for many purposes. It is unclear how the quality of the latest IMERG data products compares to that of the delisted TMPA series data sets. This study evaluated the accuracy of TRMM 3B42 version 7 and IMERG final version 6 products at 677 stations in the 7 subregions of China over 2000–2019 at the daily, monthly and annual timescales based on gauge-observed precipitation data. The performance of TMPA and IMERG precipitation products was assessed by six statistical indices. The product with the best performance was corrected based on the linear relationship of monthly precipitation between remote sensing products and ground-observed precipitation. The correction was then used for the predicting and mapping of precipitation in July 2019. The results showed that (1) IMERG outperformed TMPA at all three timescales, and the accuracy increased with the expanding timescale. (2) IMERG exhibited better performance in all subregions than TMPA and performed best in the mid-temperate semihumid zone and worst in the mid-temperature arid zone. (3) IMERG showed higher accuracy than TMPA in all months for most subregions and mainland China but performed worse in dry (December, January and February) and wet (July, August and September) months. (4) The linear relationship between remote sensing products and ground-observed precipitation could be used to correct and predict satellite precipitation data without up-to-date gauge observations, the predicted map indicated the good spatial variation in precipitation and better accuracy than the original IMERG data. In conclusion, the IMERG dataset was more accurate and could be used for precipitation investigations at large scales in China.

Evaluation of GPM IMERG precipitation products with the point rain gauge records over Sichuan, China

The objective of this study is to comprehensively evaluate the Integrated Multi-satellite Retrievals for Global Precipitation Measurement Mission (IMERG), especially on heavy and extreme rainfall events, by using 144 rain gauge observations at daily/hourly scale from April 1, 2014 to December 31, 2017 over the Sichuan Province, China. Terrain in the study area is very complex which is composed of mountains, hills, plain basins and plateaus. The evaluation was conducted based on general assessments, graphical comparisons, precipitation detection capability, probability density function, extreme indicators and one specific extreme precipitation event.The results show that: For the general analysis, IMERG_F tends to overestimate rainfall events especially light rainfall events, which is contrary to IMERG_E and IMERG_L. The precipitation detection ability of IMERG is influenced by the rainfall type and topography. For the extreme indicator analysis, IMERG has a better estimation of the rainfall intensity than the precipitation detection ability. For one specific extreme rainfall event, all the three IMERG products could capture the rainfall center and basic spatial changes. But the amount of rainfall has a bigger uncertainty especially at the beginning and the end of a rainfall. The Final Run product has a better performance, but the difference is insignificant. In addition, when the interval time of two heavy rainfall events is small, IMERG may regard them as one event. These findings provide valuable feedbacks to satellite heavy rainfall studies in complex terrain region regarding the development of the IMERG algorithm and data use.

Validation of GPM IMERG V05 and V06 Precipitation Products over Iran

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.

An applicability evaluation of version 05 IMERG precipitation products over a coastal basin located in the tropics with hilly and karst combined Landform, China

ABSTRACTVersion 05 of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) includes IMERG-E, IMERG-L, and IMERG-F with spatial resolution of 0.1° × 0.1°. Evaluation over a hilly and Karst landform combined basin in the tropics is necessary to be done to verify the acceptability of the use of IMERG V05. In this study, IMERG Data (ID) are compared with Ground Precipitation Data (GPD) from 15 stations over the Nanliujiang River Basin in China. The comparison is quantified using some statistical indices to analysis detection capability, correlation, and error evaluation. The results show that IMERG-F performs better than IMERG-E and IMERG-L. All three products can detect precipitation under different rainfall intensity effectively. A positive relationship between Pearson’s correlation coefficient (r) and temporal scales has been found within both single station and whole basin scales. At a single station scale, r of 14 stations are greater than 0.6 (daily scale) and 0.8 (monthly scale), Relative Bias (RB) ranges from 0.458 to 0.013, and the value of Root-Mean Square Error (RMSE) on daily scale and monthly scale is ranging from 7.38 to 12.51 mm and from 40.17 to 69.01 mm, respectively. At the whole basin scale, products got r values in daily, monthly, and seasonal scale within the range of 0.698 to 0.741, 0.867 to 0.918 and 0.940 to 0.950, respectively. RB in summer is smaller than 0.35, and a more underestimation of precipitation occurs during the winter season of the Northern Hemisphere. The values of monthly RMSE are distributed from 2.841 to 18.949 mm, while the seasonal RMSE has a higher value in summer. Conclusively, IMERG V05 can be considered as an auxiliary tool for precipitation observation in the regions with similar characteristics.

Ground Validation of GPM IMERG Precipitation Products over Iran

Accurate estimation of precipitation is crucial for fundamental input to various hydrometeorological applications. Ground-based precipitation data suffer limitations associated with spatial resolution and coverage; hence, satellite precipitation products can be used to complement traditional rain gauge systems. However, the satellite precipitation data need to be validated before extensive use in the applications. Hence, we conducted a thorough validation of the Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals (IMERG) product for all of Iran. The study focused on investigating the performance of daily and monthly GPM IMERG (early, late, final, and monthly) products by comparing them with ground-based precipitation data at synoptic stations throughout the country (2014–2017). The spatial and temporal performance of the GPM IMERG was evaluated using eight statistical criteria considering the rainfall index at the country level. The rainfall detection ability index (POD) showed that the best IMERG product’s performance is for the spring season while the false alarm ratio (FAR) index indicated the inferior performance of the IMERG products for the summer season. The performance of the products generally increased from IMERG-Early to –Final according to the relative bias (rBIAS) results while, based on the quantile-quantile (Q-Q) plots, the IMERG-Final could not be suggested for the applications relying on extreme rainfall estimates compared to IMERG-Early and -Late. The results in this paper improve the understanding of IMERG product’s performance and open a door to future studies regarding hydrometeorological applications of these products in Iran.

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

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.