IMERG Products Research Articles

Urbanization-induced spatial and temporal patterns of local drought revealed by high-resolution fused remotely sensed datasets

Urbanization has exerted considerable impacts on urban water systems and ecological environments, yet its effects on local meteorological drought remain under-explored. The primary challenge to local-scale drought analysis is the scarcity of meteorological datasets with sufficient spatial and temporal resolution. To address the research gap, we initially proposed a two-step fusion framework, integrating both surface (i.e., gridded data)-surface and point (i.e., in-situ data)-surface fusion. The framework was applied to generate daily precipitation and average/maximum/minimum air temperature at a 1 km resolution through the integration of high-resolution remotely sensed datasets across the Yangtze River Basin (YRB), China. The final fused data demonstrated excellent performance, achieving a PCC (RMSE) of 0.806 (5.414 mm/day), 0.993 (1.138 °C), 0.987 (1.443 °C), and 0.988 (1.376 °C) for precipitation and average/maximum/minimum air temperature, respectively. A comparison of our fused data with CPC, ERA5-Land, CMFD, CHIRPS, IMERG, and TMPA products confirmed its capability in capturing local-scale meteorological dynamics by improving spatial resolution from 0.1°-0.25° to 1 km. Utilizing these high-resolution datasets, we quantified urbanization's impacts on local drought across 52 major cities in the YRB. We found that urbanization significantly magnified extreme Standardized Precipitation Evapotranspiration Index (SPEI) and drought severity in 69.2% and 61.5% of these cities, respectively. The effects of urbanization on extreme SPEI were amplified by the increase of urbanization rates, with a slope of −0.24 (p < 0.05). To further examine the spatial patterns of urbanization-induced local drought, we proposed a drought spatial field identification method, utilizing it in three representative urban regions: Chengdu, Wuhan, and the Yangtze River Delta. Our findings revealed that urbanization led to more intense peak drought intensity and average drought severity. In addition, urban drought fields showed lower effective radius, indicating more concentrated drought towards urban regions. While urbanization is projected to continue alongside rapid population growth in the future, the advanced application of remote sensing data and technology in this study not only improves our understanding of urban water resource challenges but also equips urban planners with the necessary data to develop effective drought mitigation strategies.

Precipitation variations associated with tropical cyclone intensity and intensity change in the Northwest Pacific using 21-Yr GPM IMERG product

ABSTRACT Precipitation variations associated with tropical cyclone (TC) intensity and intensity changes in the Northwest Pacific are examined using the Integrated Multi-satellite Retrievals for Global Precipitation Measurement product spanning from June 2000 to December 2020. The initial analysis focuses on the precipitation properties of TCs in a Neutral stage (TC-N). Results indicate a linear increase in both the precipitation area and the maximum rain rate with rising TC intensity, accompanied by a reduced radius of the maximum rain rate. Increased precipitation probability and intense rainfall occur in the down-shear quadrants relative to vertical wind shear. Compared to TC-N, TCs undergoing intensification display larger precipitation areas, a more symmetric spatial distribution, and an increased mean rain rate in the inner core area. The increase in precipitation area and the mean rain rate in the inner core area correlate with the intensification rate. TCs undergoing weakening exhibit a more asymmetric spatial distribution of precipitation. The increase in the asymmetry index is positively correlated with the weakening rate. Findings suggest that the precipitation area and the mean rain rate in the inner core area could serve as potential predictors for forecasting TC intensification rates. Additionally, an asymmetric spatial distribution of precipitation emerges as a preferred condition for predicting TC weakening.

Cross-Validation Methods for Multisource Precipitation Datasets over the Sparse-Gauge Region: Applicability and Uncertainty

Abstract Evaluating the accuracy of various precipitation datasets over ungauged or even sparse-gauge areas is a challenging task. Cross-validation methods can evaluate three or more datasets based on the error independence from input data, without relying on ground observations. Here, the triple collocation (TC) method is employed to evaluate multisource precipitation datasets: China Gauge-based Daily Precipitation Analysis (CGDPA), model-based ERA5, and satellite-derived IMERG-Early, IMERG-Late, GSMaP in near–real time (GSMaP-NRT), and GSMaP moving vector with Kalman filter (GSMaP-MVK) over the Tibetan Plateau (TP). TC-based results show that ERA5 has better performances than satellite-only precipitation products over mountainous regions with complex terrains. For purely satellite-derived products, IMERG products outperform GSMaP products. Considering the potential existence of error dependencies among input datasets, caution should be exercised. Thus, it is necessary to introduce an alternative cross-validation method (generalized three-cornered hat) and explore the applicability of cross validation from the perspective of error independence. We found that cross-validation methods have high applicability in most TP regions with sparse-gauge density (accounting for about 80.1% of the total area). Additionally, we conducted simulation experiments to discuss the applicability and robustness of TC. The simulation results substantiated that cross validation can serve as a robust evaluation method over sparse-gauge regions. Although it is generally known that the cross-validation methods can be served in sparse-gauge regions, the application condition of different evaluation methods for precipitation products is identified quantitatively in TP now. Significance Statement Cross validation is a powerful assessment method for multisource precipitation datasets. This method is widely used by hydrologists in sparse-gauge or ungauged regions, like the African continent and the Tibetan Plateau (TP). However, as an indirect assessment method, the inherent uncertainty in cross validation warrants emphasis. Here, two cross-validation methods (the triple collocation method and the generalized three-cornered hat method) are employed to evaluate multisource precipitation datasets: China Gauge-based Daily Precipitation Analysis (CGDPA), model-based ERA5, and satellite-derived IMERG-Early, IMERG-Late, GSMaP in near–real time (GSMaP-NRT), and GSMaP moving vector with Kalman filter (GSMaP-MVK) over the TP. In this study, we not only assessed the current mainstream six precipitation datasets but also analyzed their uncertainties by combining these two cross-validation methods.

Identifying the spatiotemporal patterns of drought-flood alternation based on IMERG product in the humid subtropical Poyang Lake basin, China

Study RegionA humid subtropical basin, Poyang Lake Basin, China. Study focusDrought-flood alternation (DFA) is sub-seasonal precipitation anomaly, which has more serious impact on agricultural production, ecology and environment than a single drought or flood event. The study evaluated the suitability of high-resolution satellite-based precipitation product for identifying the DFA event and quantified the spatiotemporal patterns of the frequencies and intensities of DFA at various time scales. New hydrological insights for the regionThe Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) product could effectively identify the DFA events at 1-month scale, with high consistency with the results based on traditional rain-gauge data, however, its performance was not very satisfactory at 3-month and 6-month scales. Drought-to-Flood (DTF) events usually occurred from March to June, while Flood-to-Drought (FTD) events mainly occurred during from July to September. Frequencies and intensities of both DTF and FTD exhibited significant spatial heterogeneity. The lake area was mainly affected by DTF events, and the Ganjiang sub-catchment was mainly affected by FTD events. Especially, the southern part of the basin was a high-risk area affected by DFA, with higher frequencies of both severe DTF and FTD events. Furthermore, although the total area affected by DFA events showed a long-term decreasing trend from 2000 to 2019, the area affected by severe DTF and FTD events was gradually expanding in Poyang Lake basin.

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 &gt; 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.

IMERG BraMaL: An improved gridded monthly rainfall product for Brazil based on satellite‐based IMERG estimates and machine learning techniques

AbstractPrecipitation is one of the main components of the hydrological cycle and its precise quantification is fundamental to providing information for the understanding and prediction of physical processes. Precipitation observations based on ground‐based devices (manual and automatic rain gauges) are highly accurate but have limited spatial coverage. On the other hand, remote sensing products cover large areas but with lower accuracy. In this context, this study aims to provide a more accurate monthly precipitation estimating product, with lower latency than other products but without directly relying on field data. The methodology consists of applying a machine learning method (k‐nearest neighbours algorithm) to satellite‐based remote sensing data (IMERG Early Run product) and re‐analysis‐based (MERRA‐2) variables with a particular connection to precipitation. The method was applied over the Brazilian territory, which features a large range of precipitation regimes. This methodology resulted in the development of an adjusted IMERG product (IMERG BraMaL). Compared with the original IMERG products (Early Run and Final Run), IMERG BraMaL has improved the evaluated metrics between ground‐based and satellite data in almost all analyses. For instance, KGE (Kling‐Gupta efficiency) went from lower values (0.70 and 0.82 for Early and Late Run, respectively) to values above 0.86 in the IMERG BraMaL. The adjusted product also presented superior performance statistics compared with other global precipitation products (CHIRPS, PERSIANN‐CDR, and MSWEP). The main advantages of IMERG BraMaL compared with IMERG Final Run are (i) much faster availability to the end‐users; (ii) non‐dependency on any field data, allowing its application in areas where rain gauge data is unavailable or of low quality; (iii) the non‐relationship of errors to local features; and (iv) the much‐improved estimations in regions in Brazil where, historically, satellite‐based products usually underestimate the observed data.

A Machine Learning Method to Retrieve Global Rainfall and Snowfall Rates From the Passive Microwave Observations of FY‐3E

AbstractPassive microwave radiometers onboard satellites rely on the received upwelling radiation to retrieve precipitation, which is a mixed signal from the surface, atmosphere and precipitation hydrometeors. Liquid precipitation droplets increase the upwelling radiation from the surface at lower frequencies, while ice particles cause a decrease in upwelling radiation at higher frequencies. The task of the retrieval algorithm is to identify the precipitation phase and to isolate the signal of precipitation from that of the surface. This study develops a machine learning method to retrieve rainfall and snowfall rates based on observations from the Microwave Hydrometer Sounder and Microwave Temperature Sounder onboard FY‐3E. Self‐organized mapping (SOM) is selected to classify the precipitation and underlying surface types, and an artificial neural network (ANN) is subsequently used to relate the brightness temperature to the precipitation rate for the clusters derived from the SOM. The half‐hour product of the Integrated Multi‐Satellite Retrieval for Global Precipitation Measurement (IMERG) is used to train the ANN. To address the issue that number of heavy precipitation samples are not enough for training, the simulation of radiative transfer for TOVS is used as a supplement to heavy rain samples. The SOM‐ANN algorithm outperforms the IMERG and Goddard profiling algorithm (GPROF) retrieval products in both rainfall and snowfall retrieval. Compared with the hourly observations at ∼4,400 stations during a 2‐year period, the root mean square errors of SOM‐ANN proposed here are 1.06 and 0.34 mm/hr for the rainfall and snowfall rates, which are better than those of IMERG (1.23 and 0.42 mm/hr) and GPROF (1.22 and 0.44 mm/hr).

Spatial and conventional verifications of hurricanes Dorian and Fiona using the Canadian precipitation analysis & integrated multi-satellite retrievals for GPM products

Reanalysis and satellite-based rainfall packages are useful for monitoring hydroclimatic extremes. These advanced tools can be used as an early-warning system for decision-making during extreme events. Hurricane Dorian and Fiona impacted the North Atlantic from September 6–9, 2019 and September 22–25, 2022, respectively. This study evaluated the Canadian Precipitation Analysis (CaPA) in conjunction with integrated multi-satellite retrievals for the Global Precipitation Measurement Mission (GPM) in capturing these events at Early, Late and Final run stages (IMERG-E, IMERG-L & IMERG-F, respectively). Statistical verification was conducted through continuous, categorical and spatial methods to answer multiple research questions. The results show that CaPA outperformed all the IMERG products across various intensities throughout the duration of the extremes compared to station observations; therefore, CaPA can be a proxy for gauged observations. When CaPA was used as the reference for spatial verification, IMERG products correctly captured the spatial evolution of the hurricane from day to day, indicating they are reliable for hydroclimatic extreme applications. In addition, there are no striking statistical differences between the IMERG products despite the fact that IMERG-E and IMERG-L had no adjustment or gauge observation. In general, this study shows that IMERG-E has many potential applications such as CaPA assimilation, a hazard early warning system, and hurricane tracking and prediction. Further, including IMERG-E into CaPA will leverage its high resolution and latency. This study will benefit those involved in hydroclimatic studies and decision-making in the North Atlantic.

Multi-year evaluation of CloudSat, ERA5, and IMERG global snowfall products

Snowfall products derived from satellite-based radar observations, reanalysis products and phase-classified precipitation products are the three primary sources of information on surface snowfall. This study compares snowfall rate products from CloudSat, ERA5 and IMERG using the Triple Collocation (TC) method on a global scale covering almost the entire period of CloudSat cloud-profiling radar (CPR) 2C products from 2006 to 2019, especially considering the precipitation phase classification field of IMERG (probability of Liquid Precipitation, pLP). The main conclusions include but are not limited to the following: (1) The pLP threshold significantly impacts the ability of IMERG to identify snowfall from precipitation, with an optimal pLP threshold of 25 percent determined by matching IMERG with CloudSat and ERA5 snowfall events globally. (2) CloudSat presents the highest performance and stability on a global scale, with average TC-estimated CC and RMSE of 0.472 and 1.465, respectively. (3) The TC-estimated CC of IMERG exhibit significant spatiotemporal heterogeneities, but IMERG product with the optimal pLP threshold show comparable performance with CloudSat, with an average TC-estimated RMSE of ∼ 1.533. (4) ERA5 performed worse than the other two products, except in some high-latitude and high-altitude areas. This study provides novel insights into the performances of snowfall rate products from different sources at the global scales and provides a foundation for improving satellite- and model-based snowfall rate retrievals.

Evaluation of IMERG Data over Open Ocean Using Observations of Tropical Cyclones

The IMERG data product is an optimal combination of precipitation estimates from the Global Precipitation Mission (GPM), making use of a variety of data types, primarily data from various spaceborne passive instruments. Previous versions of the IMERG product have been extensively validated by comparisons with gauge data and ground-based radars over land. However, IMERG rain rates, especially sub-daily, over open ocean are less validated due to the scarcity of comparison data, particularly with the relatively new Version 07. To address this issue, we consider IMERG V07 30-min data acquired in tropical cyclones over open ocean. We perform two tasks. The first is a straightforward comparison between IMERG precipitation rates and those retrieved from the GPM Dual-frequency Precipitation Radar (DPR). From this, we find that IMERG and DPR are close at low rain rates, while, at high rain rates, IMERG tends to be lower than DPR. The second task is the assessment of IMERG’s ability to represent or detect structures commonly seen in tropical cyclones, including the annular structure and concentric eyewalls. For this, we operate on IMERG data with many machine learning algorithms and are able to achieve a 96% classification accuracy, indicating that IMERG does indeed contain TC structural information.

Characterizing the performances of different observational precipitation products and their uncertainties over Africa

Validation of observed gridded precipitation datasets sourced from satellites or reanalysis over Africa remains a challenge due to the dearth of in-situ products that can act as a true estimate. To address this gap, this study compares the performance of different precipitation products (gauge, reanalysis, and satellite-based) sourced from the Frequent Rainfall Observations on GridS (FROGS) database over Africa. Satellite products are classified as corrected (incorporating gauge observations into their algorithms) or uncorrected, which implies that temporal variations depend entirely on the satellite. The main aim is to identify regions where precipitation products depict minimal uncertainties, supporting the use of the datasets in understanding precipitation variability in the specific regions. This is achieved by applying the triple collocation approach, which takes advantage of three collocated datasets of the same variable to derive the mean square error without requiring knowledge of the true value. The results show that light precipitation (1–5 mm d−1) was prevalent in most regions of Africa during the study duration (2001–2016). Estimating the spatial distribution of daily precipitation greater than the 90th percentiles suggests that extreme precipitation is mainly detected over the Central Africa region and coastal regions of West Africa, where the majority of uncorrected satellite products show consistent performance. The satellite product CMORPH_V1_RAW shows higher estimates of 90th percentile precipitation among the uncorrected satellite products. The ability of precipitation products to detect rainy or non-rainy days shows that corrected satellite products depict notable agreement for probability of detection and false alarm ratio over most regions of Africa. Overall, better performance is demonstrated by the IMERG products, ARCv2, CHIRPSv2 and PERSIANN_CDRv1r1 (corrected), and GPCC, CPC_v1.0 and REGEN_ALL (gauge) during the study period. Among the reanalysis products, ERA5 datasets shows good performance in estimating daily precipitation over Africa. The optimal maps that show the classification of products in regions where they depict reliable performance can be recommended for various usage by different stakeholders.

Can IMERG QPE product capture the heavy rain on urban flood scale?

Urban areas are increasingly vulnerable to sudden flooding disasters caused by intense rainfall and high imperviousness degree, resulting in great economic losses and human casualties. Interactions between rainfall data and urban catchment characteristics highlight the urgent need of accurate and effective precipitation data to apply in reliable hydrological simulations. However, it remains a challenge to obtain accurate rainfall datasets on such small scales in urban areas. As satellite remote sensing is the only method that can achieve global observation, it is important to evaluate satellite precipitation products in their ability to accurately capture intense precipitation on urban flood scales. This study evaluates the performance of the latest version 06B (V06B) Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) in North China Plain, with using the Radar-Gauge merged precipitation estimates as reference data. First, it could be concluded that IMERG fails to accurately estimate precipitation in the whole study area, having the problem of overestimating light precipitation and underestimating heavy precipitation. Second, results show that IMERG has poor ability to capture heavy precipitation on small scales, with the percentage of Hit nearly 0 and the percentage of Miss higher than 40 % for all the precipitation cases. Third, with the expansion of heavy precipitation centers' coverage, the problem of IMERG not to detect heavy precipitation gets mitigated, with the percentage of Miss decreasing by 14 % (19 %). However, the ability to capture both spatial location and precipitation intensity is still not good, the percentage of Hit ranging from 0.05 % to 7 %, without obvious improvement. When IMERG is able to capture the center of strong precipitation, it also tends to overestimate the weak precipitation around the center of strong precipitation. Results of this study provide an improved understanding of how well the V06B IMERG products capture the heavy precipitation center at small scales in urban areas, which will be useful for both developers and users of IMERG.

Precipitation Estimation Using FY-4B/AGRI Satellite Data Based on Random Forest

Precipitation is the basic component of the Earth’s water cycle. Obtaining high-resolution and high-precision precipitation data is of great significance. This paper establishes a precipitation retrieval model based on a random forest classification and regression model during the day and at night with FY-4B/AGRI Level1 data on China from July to August 2022. To evaluate the retrieval effect of the model, the GPM IMERG product is used as a reference, and the retrieval results are compared against those of the FY-4B/AGRI operational precipitation product. In addition, the retrieval results are analyzed according to different underlying surfaces. The results showed that compared with the FY-4B/AGRI operational precipitation product, the retrieval model can better identify precipitation and capture precipitation areas of light rain, moderate rain, heavy rain and torrential rain. Among them, the probability of detection (POD) of the day model increased from 0.328 to 0.680, and the equitable threat score (ETS) increased from 0.252 to 0.432. The POD of the night model increased from 0.337 to 0.639, and the ETS score increased from 0.239 to 0.369. Meanwhile, the precipitation estimation accuracy of the day model increased by 38.98% and that of the night model increased by 40.85%. Our results also showed that due to the surface uniformity of the ocean, the model can identify precipitation better on the ocean than on the land. Our findings also indicated that for the different underlying surfaces of the land, there is no significant difference in each evaluation index of the model. This is a strong argument for the universal applicability of the model. Notably, the results showed that, especially for more vegetated areas and areas covered by water, the model is capable of estimating precipitation. In conclusion, the precipitation retrieval model that is proposed herein can better determine precipitation regions and estimate precipitation intensities compared with the FY-4B/AGRI operational precipitation product. It can provide some reference value for future precipitation retrieval research on FY-4B/AGRI.

Spatiotemporal evaluation of five satellite-based precipitation products under the arid environment of Saudi Arabia

In arid regions like Saudi Arabia, accurate precipitation data are crucial for water resource management and climate studies. However, satellite-based precipitation products (SPPs) can contain uncertainties, impacting their reliability. This study evaluated the accuracy of five high-resolution SPPs [IMERG-V06 variants, Tropical Rainfall Measuring Mission (TRMM)-3B42V7, and Soil Moisture to Rain (SM2RAIN)-Advanced SCATterometer (ASCAT)] over Saudi Arabia. We compared daily, monthly, and yearly precipitation estimates from SPPs with in situ rain gauge data (2010–2022) using both continuous and categorical metrics. The evaluation encompassed point-to-pixel comparisons, regional analysis, and national assessments. All SPPs effectively captured the spatiotemporal patterns of precipitation across the country. Notably, monthly estimates showed stronger agreement with rain gauge data than daily estimates, as indicated by higher correlation coefficients. IMERG products generally outperformed SM2RAIN-ASCAT and TRMM, with IMERG-LR exhibiting superior performance in estimating monthly precipitation. However, underestimation of light precipitation events (&amp;lt;2 mm/day) was observed across all SPPs. In addition, their ability to detect moderate and heavy precipitation events remained uncertain, requiring further investigation. While IMERG-FR showed reduced bias and root mean square error compared to IMERG-ER and IMERG-LR, its capability for precipitation event detection did not exhibit significant improvement. This study highlights the need for bias correction of IMERG-LR and IMERG-FR monthly estimates for improved application in hydrometeorological studies in Saudi Arabia. Our findings contribute valuable insights for both data users and SPP algorithm developers, aiming to enhance the accuracy and reliability of satellite-derived precipitation data in arid environments.

The performance of IMERG near-real-time estimations during the record-breaking Meiyu season in 2020

The extreme Meiyu season, characterized by continuous heavy precipitation, is often leads to numerous flood-related disasters. Accurate and timely monitoring of extreme Meiyu precipitation is crucial for the effective prevention and mitigation of these disasters. The near-real-time (NRT) products from Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), specifically IMERG-E and IMERG-L products, have high potential utility to acquire timely precipitation data. Therefore, it is important to quantify their errors and uncertainties during the extreme Meiyu season. In this study, we assess and compare the performance of IMERG NRT products with Global Satellite Mapping of Precipitation Motion Vector Kalman product (GSMaP-MVK) using hourly gauge observations during the recor-breaking Meiyu event in 2020. The main conclusions are as follows: all three satellite products generally capture the spatial–temporal evolution of Meiyu precipitation, with IMERG-L showing the closest agreement with gauge observations, particularly for the precipitation center. In terms of the statistical and volumetric categorical metrics, IMERG-L performs best among the three products, while IMERG-E does not exhibit superior performance compared to GSMaP-MVK. Regarding the diurnal cycle, IMERG-L generally reproduces the diurnal cycle of observed precipitation amount with a peak time occurring two hours earlier. In contrast, both IMERG-E and GSMaP-MVK exhibit notably poorer performance. When it comes to the diurnal cycle of precipitation frequency and intensity, both IMERG products display bad performance in reproducing their diurnal variations. Furthermore, all three satellite products show a wet biases for light precipitation and a dry biases for heavy precipitation, which is consistent with previous evaluation studies. However, a novel phenomenon is noticed that theses biases tend to worsen significantly in the afternoon compared to the morning. Although this study is limited to a case study, the findings can serve as a valuable reference for the NRT applications of IMERG products during the extreme Meiyu seasons and provide insights for the improvement of IMERG algorithm.

Evaluation of the IMERG Product in the MATOPIBA Region, Brazil

Evaluation of the IMERG Product in the MATOPIBA Region, Brazil

Comparative analysis of bias correction methods for IMERG V06 precipitation products: case study in Guangxi, China

ABSTRACT This study investigates the effectiveness of three bias correction methods: linear scaling (LS), local intensity scaling (LOCI), and quantile mapping (QM) in correcting the bias of Integrated Multi-SatellitE Retrieval for Global Precipitation Measurement (IMERG) V06 products (IMERG-Early run (IMERG-E), IMERG-Late run (IMERG-L) and IMERG-Final run (IMERG-F)) at different spatial and temporal scales in Guangxi, China. The results reveal that the LS and LOCI are effective in reducing the relative bias (RB) and root mean square error (RMSE) of IMERG products at the regional and monthly scales, while the QM tends to increase the overestimation or underestimation of precipitation. The QM outperforms the other two methods in improving the correlation coefficient (CC) of IMERG products at the monthly and daily scales, especially for stations with low correlation before correction. None of the methods can effectively correct the extreme values of IMERG products at a daily scale, and the correction efficacy of the three methods is limited for IMERG-F, which has a high initial accuracy. The QM has an advantage in enhancing the correlation between IMERG and gauge data. LS and LOCI have great potential in correcting the bias of IMERG near-real-time products.

Evaluation of GPM IMERG Product Over the Yellow River Basin Using an Improved Error-Component Procedure

Evaluation of GPM IMERG Product Over the Yellow River Basin Using an Improved Error-Component Procedure

Evaluation of IMERG precipitation product over various temporal scales in a semi-arid region of southern Iran

In the recent decades, poor spatial coverage of rain gauges and weather radars, mostly in less developed countries, has caused satellite-based precipitation products to gain more popularity. However, they are considered less precise compared to traditional rain gauges and weather radars. Therefore, their thorough evaluations are essential before implementing these products in practice. In this study, IMERG satellite-based precipitation product was evaluated in the Fars province, Iran, using daily rain gauges as reference data to find the relationships between the uncertainties in the product and various factors such as elevation, temperature, and rainfall intensity by employing continuous and categorical statistical metrics. The findings revealed that: a) The IMERG product overestimates light rainfall and underestimates heavy rainfall, while showing the best performance in the range 40–80 mm/day. b) The accuracy of the product tends to vary in different months of the year and it is less biased in ones with milder temperatures. c) Higher correlation in mid-elevated areas (up to 0.96), positive bias in low-elevated areas (up to 2.5) and negative bias in high-elevated areas (down to −0.4) were observed. d) A considerable improvement in the IMERG estimates was observed for longer time scales for almost all validation indices. Overall, IMERG products show potential for hydrological and atmospheric decision-making, but further improvements in retrieval algorithms and developing local correction models, considering the trends found in this study, are needed for increased reliability.

A Comprehensive Assessment of Multiple High-Resolution Precipitation Grid Products for Monitoring Heavy Rainfall during the “7.20” Extreme Rainstorm Event in China

Precipitation products play an important role in monitoring rainstorm processes. This study takes a rare historical event of extreme, heavy precipitation that occurred in Henan Province, China, in July 2021 as a research case. By analyzing the distribution of the spatial and temporal characteristics of precipitation errors, using a probability density function of the occurrence of precipitation and the daily variation pattern, we assess the capability of a radar precipitation estimation product (RADAR), satellite precipitation products (IMERG and GSMAP), a reanalysis product (ERA5) and a precipitation fusion product (the CMPAS) to monitor an extreme rainstorm in the Henan region. The CMPAS has the best fit with the gauge observations in terms of the precipitation area, precipitation maximum and the evolution of the whole process, with a low spatial variability of errors. However, the CMPAS slightly underestimated the precipitation extremum at the peak moment (06:00–08:00). The RADAR product was prone to a spurious overestimation of the originally small rainfall, especially during peak precipitation times, with deviations concentrated in the core precipitation area. The IMERG, GSMAP and ERA5 products have similar performances, all of which failed to effectively capture heavy precipitation in excess of 60 mm/h, with negative deviations in precipitation at mountainfront locations west of northern Henan Province. There is still a need for terrain-specific error revisions for areas with large topographic relief. By merging and processing precipitation data from multiple sources, the accuracy of the CMPAS is better than any single-source precipitation product. The CMPAS has the characteristic advantage of high spatial and temporal resolutions (0.01° × 0.01°/1 h), which play a positive role in precipitation dynamic monitoring, providing early warnings of heavy rainfall processes and hydrological application research.