Global Precipitation Measurement Integrated Multi-satellitE Retrievals For GPM Research Articles

Assessing precipitation event characteristics throughout North Carolina derived from GPM IMERG data products

The Global Precipitation Measurement (GPM) mission provides near-real time precipitation estimates that can be used for monitoring water supply infrastructure. To better understand the potential use of GPM data products for use in relevant applications, this study examines the performance of the Integrated Multi-satellitE Retrievals for GPM (IMERG) precipitation products throughout North Carolina to capture rainfall events. Event characteristics derived from IMERG precipitation products: early (4 h latency), late (14 h latency), and final (3.5 month latency) are compared to characteristics derived from gauges (N = 282) for the period 2000–2021. Results show that the IMERG data products identify events reasonably well (mean event errors of 6.0, 6.8, and 1.7 mm for early, late, and final products; probability of detection: 0.83, 0.83, 0.86; false alarm ratio: 0.20, 0.18, 0.18, respectively). While the final product performs best, the early and late products perform similarly. While additional research is needed to better understand factors leading to under- and over-estimates of event magnitudes, the findings from this study support the potential use of early/late IMERG data products in water supply monitoring or flood warning systems, where rapid estimates of event precipitation characteristics are needed.

Assessment of Three GPM IMERG Products for GIS-Based Tropical Flood Hazard Mapping Using Analytical Hierarchy Process

The use of satellite precipitation products can overcome the limitations of rain gauges in flood hazard mapping for mitigation purposes. Hence, this study aims to evaluate the capabilities of three global precipitation measurement (GPM) integrated multisatellite retrievals for GPM (IMERG) products in tropical flood hazard mapping in the Kelantan River Basin (KRB), Malaysia, using the GIS-based analytic hierarchy process (AHP) method. In addition to the precipitation factor, another eleven factors that contribute to flooding in the KRB were included in the AHP method. The findings demonstrated that the spatial pattern and percentage area affected by floods simulated under the IMERG-Early (IMERG-E), IMERG-Late (IMERG-L), and IMERG-Final (IMERG-F) products did not differ significantly. The receiver operating characteristics curve analysis showed that all three IMERG products performed well in generating flood hazard maps, with area under the curve values greater than 0.8. Almost all the recorded historical floods were placed in the moderate-to-very-high flood hazard areas, with only 1–2% found in the low flood hazard areas. The middle and lower parts of the KRB were identified as regions of “very high” and “high” hazard levels that require particular attention from local stakeholders.

Assessment of GPM IMERG and GSMaP daily precipitation products and their utility in droughts and floods monitoring across Xijiang River Basin

The recent increasing concerns about floods and droughts have brought up a pressing need for high-precision and high-resolution precipitation records. As representative satellite precipitation products (SPPs) in Global Precipitation Measurement (GPM) era, the Integrated Multi-satellitE Retrievals for GPM (IMERG) product and the Global Satellite Mapping of Precipitation (GSMaP) are widely acknowledged as reliable alternative sources. A quantitative evaluation of these new products is of great importance before their extended applications. This study comprehensively assesses the statistical performance and the application potential of IMERG (IMERG-Early, IMERG-Late and IMERG-Final) and GSMaP (GSMaP-NRT, GSMaP-Gauge-NRT, GSMaP-Gauge) products over Xijiang River Basin during 2009–2018 at a daily timescale. Both IMERG and GSMaP products have their own advantages. The results indicate that (1) at the grid scale, the IMERG-Final exhibits the highest correlation with gauge-based precipitation data while GSMaP-Gauge yields the lowest relative mean error. IMERG-Early and IMERG-Late have superiorities in almost all evaluation metrics relative to GSMaP-NRT and GSMaP-Gauge-NRT. (2) The temporal performance in specific months visually demonstrates that GSMaP-Gauge has a lower precipitation detection capacity in wet seasons due to a higher FAR, in opposite to other SPPs. (3) The precipitation detection ability of SPPs varies largely with different precipitation intensities. GSMaP-Gauge can identify nearly 90% rainfall events at the lowest threshold of 1 mm/day, but it tends to overestimate the non- and slight precipitation events up to small or moderate classes and highly underestimate precipitation amount of strong events. At extreme precipitation intensity (≥50 mm/day), >50% of extreme precipitation events were missed by SPPs resulting in unsatisfactory PODs below 0.5. (4) For meteorological drought monitoring, IMERG-Final exhibits highest CCs in all inspected timescales, succeed by GSMaP-Gauge. IMERG-Early and IMERG-Late still perform better than near-real-time GSMaPs in drought research. (5) In the hydrological simulation with the Soil and Water Assessment Tool (SWAT), GSMaP-Gauge-simulation shows the best accuracy regardless of wet or dry seasons with highest NSEs of 0.64 and 0.73, respectively. All near-real-time SPPs present an unacceptable hydrological utility and an underestimation of streamflow in dry seasons. The systematic evaluation of extreme streamflow indices highlights that of the four timelier products, IMERG-Early is most reliable in capturing extreme streamflow. GSMaP-NRT tends to exaggerate the flood magnitude and could be used as reference in estimating severe and continuous floods.

Global Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation Index

ABSTRACT This study presents a global, hourly surface soil moisture estimation procedure based on precipitation and temperature data. Information on soil composition further helps to define the local characteristics of soil moisture development. An advanced antecedent precipitation index (API) is utilized to generate a global soil moisture product of high temporal resolution with the Global Precipitation Measurement (GPM) Missions Integrated Multi-Satellite Retrievals for GPM (IMERG) as main driver. The resulting global GPM API data set is compared against in situ measurements from the International Soil Moisture Network (ISMN) and is also evaluated against the soil moisture data set from the European Space Agency’s Climate Change Initiative (ESA CCI SM). The study shows that with empirically derived dampening factors the GPM API achieves a mean ubRMSD across the utilized in situ stations in different climates and vegetation zones of 4.68 and a bias of 0.88 . The data set clearly represents the local soil moisture schemes with seasonal variations. When comparing with ESA CCI SM, the GPM API does perform better at the measurement sites concerning bias, correlation and error values. The data set is in most parts negatively biased compared to the ESA CCI SM, however better matches the mean soil moisture at ISMN stations. Overall, the GPM API delivers a very promising global, hourly surface soil moisture product at 0.1 0.1 spatial resolution.

A Spatial Downscaling Methodology for GRACE Total Water Storage Anomalies Using GPM IMERG Precipitation Estimates

A downscaling framework for coarse resolution Gravity Recovery and Climate Experiment (GRACE) Total Water Storage Anomaly (TWSA) data is described, exploiting the observations of precipitation from the Global Precipitation Measurement (GPM) mission, using the Integrated Multi-satellite Retrievals for GPM (IMERG). Considering that the major driving force for changes in TWS is precipitation, we tested our hypothesis that coarse resolution, i.e., 1°, GRACE TWSA can be effectively downscaled to 0.1° using GPM IMERG data. The algorithm for the downscaling process comprises the development of a regression equation at the coarse resolution between the GRACE and GPM IMERG data, which is then applied at the finer resolution with a subsequent residual correction procedure. An ensemble of GRACE data from three processing centers, i.e., GFZ, JPL and CSR, was used for the time period from June 2018 until March 2021. To verify our downscaling methodology, we applied it with GRACE data from 2005 to 2015, and we compared it against modeled TWSA from two independent datasets in the Thrace and Thessaly regions in Greece for the same period and found a high performance in all examined metrics. Our research indicates that the downscaled GRACE observations are comparable to the TWSA estimated with hydrological modeling, thus highlighting the potential of GRACE data to contribute to the improvement of hydrological model performance, especially in ungauged basins.

Characteristics of convective precipitation over tropical Africa in storm‐resolving global simulations

AbstractWe analyse how the representation of deep convection affects the characteristics of convective precipitation over tropical Africa in global storm‐resolving simulations with the European Centre for Medium‐Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS). The simulations consist in 48 hr forecasts initiated daily at 0000 UTC for a 40‐day period in August and September 2016, at 9 and 4 km resolution. We find that results agree best with the satellite retrieval Global Precipitation Measurement (GPM) Integrated Multi‐satellitE Retrievals for GPM (IMERG) when a term for the integrated and scaled total advective moisture tendency is added to the convective instability closure. This allows for a better coupling of the convective parametrization to the mesoscale dynamics and improves precipitation intensity (increased), precipitating area fraction (decreased), the size of individual deep convective systems (decreased) and their propagation (westward), even though the duration of rain events remains underestimated. With explicit deep convection, the duration of rain events is more realistic, but precipitation intensity is significantly overestimated, and extreme rain events (>400 mmday) are three times more likely than in GPM IMERG, causing biases in the organization of deep convection. In particular, individual convective systems are too small, hindering the formation of mesoscale convective systems. These findings are also valid for two other regions: the Maritime Continent and the contiguous United States. Our results suggest that deep convection is not completely resolved at a resolution of 9 or even 4 km, and that some scale‐aware parametrization is still needed for global numerical weather prediction.

A Satellite View of an Intense Snowfall in Madrid (Spain): The Storm ‘Filomena’ in January 2021

Evaluating satellite ability in capturing sudden natural disasters such as heavy snowstorms is a topic of societal interest. This paper presents a rapid qualitative analysis of an intense snowfall in Madrid using data from the Global Precipitation Measurement (GPM) mission, specifically the GPM IMERG (Integrated Multi-satellitE Retrievals for GPM) Late Precipitation L3 Half Hourly 0.1° × 0.1° V06 estimates of precipitation (IMERG-Late), and Sentinel-2 imagery. The main research question addressed is the consistency of ground observations, model outputs and satellite data, a topic of major interest for an appropriate and timely societal response to severe weather episodes. Indeed, the choice of the ‘Late’ product over the IMERG ‘Final’ or other GPM datasets was motivated by the availability of data for near real-time response to the storm. Additionally, the 30-min temporal resolution of the product would in principle allow for a detailed analysis of the dynamic processes involved in the snowstorm. Using several complementary data sources, it is shown that optical remote sensing sensors (Sentinel) add value to existing ground data and that is invaluable for rapid response to severe meteorological events such as Filomena. Regarding the GPM precipitation radar, the sampling of the GPM-core satellite was insufficient to provide the IMERG algorithm with enough quality data to correctly represent the actual sequence of precipitation. Without corrections, the total precipitation differs from observations by a factor of two. The difficulties of retrieving precipitation with radiometers over snow-covered surfaces was a major factor for the mismatch. Thus, the calibrated precipitation product did not fully capture the historic storm, and neither did the IR-based element of the IMERG-Late product, which is a neural network merging of microwave and infrared data. It follows that increased temporal resolution of spaceborne microwave sensors and improved retrieval of precipitation from radiometers are critical in order to provide a complete account of these sorts of extreme, significant, short-duration cases. Otherwise, the high-quality, radar and radiometer data feeding the high temporal resolution algorithms simply slip through the grasp of the ascending and descending orbits, leaving little quality data to be interpolated into successive overpasses.

Performance evaluation of IMERG precipitation products during typhoon Lekima (2019)

It is of great significance to evaluate satellite precipitation products performance for short-duration heavy precipitation in typhoon events. In this study, the performance of GPM (Global Precipitation Measurement) IMERG (Integrated Multi-satellitE Retrievals for GPM) with different run products (early run (IMERG-ER), late run (IMERG-LR), and final run (IMERG-FR)) were evaluated when typhoon Lekima landed in the eastern coastal area of China in 2019. Taking the rain gauge data as the reference, we carried out the study by comparing precipitation characteristics, verifying evaluation indices, and analyzing the heavy precipitation process. The differences between IMERG products in different stages and affected areas of the typhoon Lekima were compared. On the whole, the correlation coefficient (CC) of IMERG products was 0.51–0.56, and the IMERG-FR had the best probability of detection (POD, 0.88), followed by IMERG-LR (POD, 0.84) and IMERG-ER (POD, 0.81). With an increase in release latency, the precipitation retrieved by IMERG products increased and gradually approached the rain gauge observations. The evaluation indices result showed that IMERG products tend to underestimate precipitation and failed to show good performance for high-intensity precipitation. The error was positively related to the precipitation intensity. The performance of IMERG-LR was very close to that of IMERG-FR during the typhoon short-duration heavy precipitation, although IMERG-FR had higher latency and more processing.

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

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

Potential of GPM IMERG Precipitation Estimates to Monitor Natural Disaster Triggers in Urban Areas: The Case of Rio de Janeiro, Brazil

Extreme rainfall can be a catastrophic trigger for natural disaster events at urban scales. However, there remains large uncertainties as to how satellite precipitation can identify these triggers at a city scale. The objective of this study is to evaluate the potential of satellite-based rainfall estimates to monitor natural disaster triggers in urban areas. Rainfall estimates from the Global Precipitation Measurement (GPM) mission are evaluated over the city of Rio de Janeiro, Brazil, where urban floods and landslides occur periodically as a result of extreme rainfall events. Two rainfall products derived from the Integrated Multi-satellite Retrievals for GPM (IMERG), the IMERG Early and IMERG Final products, are integrated into the Noah Multi-Parameterization (Noah-MP) land surface model in order to simulate the spatial and temporal dynamics of two key hydrometeorological disaster triggers across the city over the wet seasons during 2001–2019. Here, total runoff (TR) and rootzone soil moisture (RZSM) are considered as flood and landslide triggers, respectively. Ground-based observations at 33 pluviometric stations are interpolated, and the resulting rainfall fields are used in an in-situ precipitation-based simulation, considered as the reference for evaluating the IMERG-driven simulations. The evaluation is performed during the wet seasons (November-April), when average rainfall over the city is 4.4 mm/day. Results show that IMERG products show low spatial variability at the city scale, generally overestimate rainfall rates by 12–35%, and impacts on TR and RZSM vary spatially mostly as a function of land cover and soil types. Results based on statistical and categorical metrics show that IMERG skill in detecting extreme events is moderate, with IMERG Final performing slightly better for most metrics. By analyzing two recent storms, we observe that IMERG detects mostly hourly extreme events, but underestimates rainfall rates, resulting in underestimated TR and RZSM. An evaluation of normalized time series using percentiles shows that both satellite products have significantly improved skill in detecting extreme events when compared to the evaluation using absolute values, indicating that IMERG precipitation could be potentially used as a predictor for natural disasters in urban areas.

Comparison of GPM IMERG and TRMM 3B43 Products over Cyprus

Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) high-resolution product and Tropical Rainfall Measuring Mission (TRMM) 3B43 product are validated against rain gauges over the island of Cyprus for the period from April 2014 to June 2018. The comparison performed is twofold: firstly, the Satellite Precipitation (SP) estimates are compared with the gauge stations’ records on a monthly basis and, secondly, on an annual basis. The validation is based on ground data from a dense and well-maintained network of rain gauges, available in high temporal (hourly) resolution. The results show high correlation coefficient values, on average reaching 0.92 and 0.91 for monthly 3B43 and IMERG estimates, respectively, although both IMERG and TRMM tend to underestimate precipitation (Bias values of −1.6 and −3.0, respectively), especially during the rainy season. On an annual basis, both SP estimates are underestimating precipitation, although IMERG estimates records (R = 0.82) are slightly closer to that of the corresponding gauge station records than those of 3B43 (R = 0.81). Finally, the influence of elevation of both SP estimates was considered by grouping rain gauge stations in three categories, with respect to their elevation. Results indicated that both SP estimates underestimate precipitation with increasing elevation and overestimate it at lower elevations.

Assimilation of GPM Rain Rate Products With GSI Data Assimilation System for Heavy and Light Precipitation Events

AbstractThe National Aeronautics and Space Administration‐Japan Aerospace Exploration Agency Global Precipitation Measurement (GPM) mission consists of a multisatellite constellation that provides real‐time or near‐real‐time global observations of rain and snow. In this study, GPM Level 3 Integrated Multi‐satellitE Retrievals for GPM (IMERG) and Level 2 GPM Microwave Imager Goddard Profiling rainfall products have been assimilated into the Weather Research and Forecasting model using the community Gridpoint Statistical Interpolation (GSI) data assimilation system. Experiments have been conducted and compared to demonstrate the impact of rain rate data assimilation on forecasts of heavy rainfall related to Hurricane Harvey (2017) and moderate to light rainfall observed during the GPM Integrated Precipitation and Hydrology Experiment field campaign. The results indicate that both GPM Microwave Imager Goddard Profiling and IMERG data could generate apparent increments in moisture, temperature, wind, and pressure fields for Hurricane Harvey, which led to significant improvement in the precipitation forecast. Frequent (every 3 hr) assimilation of IMERG data also positively impacted the short‐term precipitation forecast skill for the Integrated Precipitation and Hydrology Experiment moderate to light rain events. However, results also indicate that the impact of rain data assimilation was limited for a system that had a small horizontal dimension with low rain rates and within a relatively stable synoptic environment.

Adequacy of Satellite-derived Precipitation Estimate for Hydrological Modeling in Vietnam Basins

This study evaluates eight Satellite-derived Precipitation Estimate (SPE) datasets, which include uncorrected SPE and gauge-corrected SPE products from Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis (TMPA), Global Precipitation Measurement (GPM), Climate Hazards group Infrared Precipitation (CHIRP), and Precipitation Estimation form Remotely Sensed Information using Artificial Neural Networks (PERSIANN). These datasets are utilized with six representative river basins, corresponding to six sub-climate zones in Vietnam, during the period 2002–2017. The evaluations were carried out in two parts: 1) inter-comparison of the SPE products with rain gauges, for the six basins; 2) comparison of streamflow simulations, using the Soil and Water Assessment Tool (SWAT) forced by precipitation from rain gauge and SPE products. The results indicated that the gauge-corrected SPE datasets exhibited slightly better over the uncorrected datasets in comparison with rain gauges, but showed much higher performances as inputs in hydrological simulations. The GPM Integrated Multi-satellitE Retrievals for GPM (IMERG) Final run version 06B (GPM IMERGF-V6) exhibited the best overall performances among SPE products, in comparison with the rain gauges for the simulation of streamflow. This study is the first of its kind to validate GPM IMERG products in Vietnam, indicating the strong capability of the new IMERG retrieval algorithms. The CHIRP with stations (CHIRPS) dataset demonstrates a relatively low bias, could benefit long-term water resources planning for droughts. In monthly streamflow simulations, the SPE-driven simulations outperformed rain gauge-driven simulations in a larger basin (North West Region), which has low rain-gauge density. The results of this study could be a guide to determine the suitability of different SPE products for hydrological simulations.

Comparative Evaluation of the GPM IMERG Early, Late, and Final Hourly Precipitation Products Using the CMPA Data over Sichuan Basin of China

The Global Precipitation Measurement (GPM) mission has generated global precipitation products of improved accuracy and coverage that are promising for advanced hydrological and meteorological studies. This study evaluates three Integrated Multi-satellitE Retrievals for GPM (IMERG) Hourly products, including the Early-, Late-, and Final-run products (IMERG-HE, IMERG-HL, and IMERG-HF, respectively), over Sichuan Basin of China. This highly complex terrain of the steep mountainous region offers further scrutiny on the quality and applicability of the data. The China Meteorological Precipitation Analysis (CMPA) data from January 2016 to December 2018 are used as the reference for the evaluation. Results show that: (1) At grid scale, IMERG-HL and IMERG-HF outperform IMERG-HE in terms of correlation coefficient (CC) and root-mean-square error (RMSE), but IMERG-HL has smaller relative bias (RB) than that of the IMERG-HF (by 21.16%). IMERG-HF presents the highest probability of detection (POD = 0.52) and critical success index (CSI = 0.32), except for high false alarm ratio (FAR) for light precipitation. (2) At regional scale, IMERG-HF outperforms IMERG-HE and IMERG-HL in annual evaluation in all the metrics except for the serious overestimation as shown in RB (20.18%, 3.84%, and 4.97%, respectively). Its accumulative precipitation deviation mainly comes from moderate precipitation events (1–10 mm/h), while better detection capability is seen in light precipitation (<1 mm/h). Seasonally, IMERG-HF performs the best in winter, while IMERG-HL performs the best in the other seasons. (3) IMERG-HF captures the peak precipitation more accurately in all seasons. In reproducing the diurnal cycle, IMERG-HF performs better in winter, while IMERG-HL performs better in summer and autumn, and IMERG-HE in spring. However, all three products overestimate the early morning precipitation (01:00–08:00 local standard time) of the diurnal cycle in spring, summer, and autumn.

Correcting GPM IMERG precipitation data over the Tianshan Mountains in China

Point-scale gauge observations have inherent limitations, making it difficult to study the spatial and temporal distributions of precipitation in alpine regions due to gauge undercatch and complex terrains. The Global Precipitation Measurement (GPM) mission provides new-generation satellite precipitation products that are promising alternative data sources in mountainous areas. However, quality evaluations and bias corrections should be conducted prior to the application of satellite data. In this study, an unprecedentedly dense ground station network composed of more than 1000 automatic weather stations (AWSs) over the Tianshan Mountains in China are used for bias correction of the Integrated Multisatellite Retrievals for GPM (IMERG) product. First, universal kriging interpolation is used to downscale IMERG from 0.1° to 500 m to ensure a fair comparison with the gauge observations. Then, the downscaled IMERG precipitation data over this region are corrected by two methods, i.e., stepwise regression (STEP) and geographically weighted regression (GWR). Both methods are established on various terrain factors and vegetation indexes that have strong relations with precipitation. The results show that (1) GWR outperform the conventional STEP method as well as the original IMERG; (2) the original IMERG performs best over the plain region (less than 1000 m), while the best correction effect was found in middle and low-elevation region (1000–1500 m); and (3) the performance of the GWR model is largely dependent on the number of available training stations in mountainous areas. Overall, the methods and results presented in this study provide insight into the correction of satellite precipitation data in mountainous areas with scarce ground observations.

Assessment of an Extreme Rainfall Detection System for Flood Prediction over Queensland (Australia)

Flood events represent some of the most catastrophic natural disasters, especially in localities where appropriate measurement instruments and early warning systems are not available. Remotely sensed data can often help to obtain near real-time rainfall information with a global spatial coverage without the limitations that characterize other instruments. In order to achieve this goal, a freely accessible Extreme Rainfall Detection System (ERDS—erds.ithacaweb.org) was developed and implemented by ITHACA with the aim of monitoring and forecasting exceptional rainfall events and providing information in an understandable way for researchers as well as non-specialized users. The near real-time rainfall monitoring is performed by taking advantage of NASA GPM (Global Precipitation Measurement) IMERG (Integrated Multi-satellite Retrievals for GPM) half-hourly data (one of the most advanced rainfall measurements provided by satellite). This study aims to evaluate ERDS performance in the detection of the extreme rainfall that led to a massive flood event in Queensland (Australia) between January and February 2019. Due to the impressive amount of rainfall that affected the area, Flinders River (one of the longest Australian rivers) overflowed, expanding to a width of tens of kilometers. Several cities were also partially affected and Copernicus Emergency Management Service was activated with the aim of providing an assessment of the impact of the event. In this research, ERDS outputs were validated using both in situ and open source remotely sensed data. Specifically, taking advantage of both NASA MODIS (Moderate-resolution Imaging Spectroradiometer) and Copernicus Sentinel datasets, it was possible to gain a clear look at the full extent of the flood event. GPM data proved to be a reliable source of rainfall information for the evaluation of areas affected by heavy rainfall. By merging these data, it was possible to recreate the dynamics of the event.

Systematical Evaluation of GPM IMERG and TRMM 3B42V7 Precipitation Products in the Huang-Huai-Hai Plain, China

Accurate estimation of high-resolution satellite precipitation products like Global Precipitation Measurement (GPM) and Tropical Rainfall Measuring Mission (TRMM) is critical for hydrological and meteorological research, providing a benchmark for the continued development and future improvement of these products. This study aims to comprehensively evaluate the Integrated Multi-Satellite Retrievals for GPM (IMERG) and TRMM 3B42V7 products at multiple temporal scales from 1 January 2015 to 31 December 2017 over the Huang-Huai-Hai Plain in China, using daily precipitation data from 59 meteorological stations. Three commonly used statistical metrics (CC, RB, and RMSE) are adopted to quantitatively verify the accuracy of two satellite precipitation products. The assessment also takes into account the precipitation detection capability (POD, FAR, CSI, and ACC) and frequency of different precipitation intensities. The results show that the IMERG and 3B42V7 present strong correlation with meteorological stations observations at annual and monthly scales (CC > 0.90), whereas moderate at the daily scale (CC = 0.76 and 0.69 for IMERG and 3B42V7, respectively). The spatial variability of the annual and seasonal precipitation is well captured by these two satellite products. And spatial patterns of precipitation gradually decrease from south to north over the Huang-Huai-Hai Plain. Both IMERG and 3B42V7 products overestimate precipitation compared with the station observations, of which 3B42V7 has a lower degree of overestimation. Relative to the IMERG, annual precipitation estimates from 3B42V7 show lower RMSE (118.96 mm and 142.67 mm, respectively), but opposite at the daily, monthly, and seasonal scales. IMERG has a better precipitation detection capability than 3B42V7 (POD = 0.83 and 0.67, respectively), especially when detecting trace and solid precipitation. The two precipitation products tend to overestimate moderate (2–10 mm/d) and heavy (10–50 mm/d) precipitation events, but underestimate violent (>50 mm/d) precipitation events. The IMERG is not found capable to detecting precipitation events of different frequencies more precisely. In general, the accuracy of IMERG is better than 3B42V7 product in the Huang-Huai-Hai Plain. The IMERG satellite precipitation product with higher temporal and spatial resolutions can be regarded a reliable data sources in studying hydrological and climatic research.

Similarities and improvements of GPM IMERG upon TRMM 3B42 precipitation product under complex topographic and climatic conditions over Hexi region, Northeastern Tibetan Plateau

Building upon the success of TRMM launched in 1997, NASA and JAXA successfully deployed the Global Precipitation Measurement (GPM) mission on February 28, 2014. GPM started to release the new-generation global precipitation products Integrated Multi-satellite Retrievals for GPM (IMERG) since April 2014. Critical data validation over diverse topographic and climatic regions could effectively help users and algorithm developers maximize the accuracy, strengths and weaknesses of the new product. To this end, this study comprehensively evaluated and compared this newly released precipitation product (IMERG V05B) and its predecessor TRMM 3B42V7 based upon the ground-based observations under complex topographic and climatic conditions over the Hexi Region in the northwest arid region of China. The evaluation was conducted at daily, monthly, seasonal and annual scales from April 2014 to September 2017. Results indicated that: (1) compared to ground-based observations, both IMERG and 3B42V7 showed good performance with slightly overestimation (with RB values of 4.58% and 2.27%, respectively) at basin scale. While at the point-to-pixel scale, both products showed overestimation in low-elevation regions and underestimation in high-altitude mountainous areas; (2) on multiple temporal and spatial scales, IMERG correlated better with ground-based observations than 3B42V7, whereas its bias were larger than 3B42V7; (3) both IMERG and 3B42V7 showed better performance in warm seasons (summer and autumn) than in cold seasons (spring and winter); (4) Compared to 3B42V7, IMERG represented both larger hit rate (POD) and false alarm ratio (FAR), but similar correct detection rate (CSI); (5) IMERG showed poorer detection capabilities in light precipitation events (0–2 mm/day), while represented a certain advantage in detecting moderate (5–10 mm/day) and heavy (>25 mm/day) rain events. Most of precipitation estimates over Hexi region are unreliable on daily and monthly time scales. Results suggest that IMERG do not show significant improvement compared to its predecessor 3B42V7, and performed even worse in estimating precipitation amounts. Further data bias-correction is needed before IMERG can be used for climatic and hydrologic research in the Hexi region.

Evaluation and Hydrological Utility of the Latest GPM IMERG V5 and GSMaP V7 Precipitation Products over the Tibetan Plateau

Satellite precipitation products provide alternative precipitation data in mountain areas. This study aimed to assess the performance of the latest Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG) version 5 (IMERG V5) and Global Satellite Mapping of Precipitation version 7 (GSMaP V7) products and their hydrological utilities over the Tibetan Plateau (TP). Here, two IMERG Final Run products (uncalibrated IMERG (IMERG-UC) and gauge-calibrated IMERG (IMEEG-C)) and two GSMaP products (GSMaP Moving Vector with Kalman Filter (GSMaP-MVK) and gauge-adjusted GSMaP (GSMaP-Gauge)) were evaluated from April 2014 to March 2017. Results show that all four satellite precipitation products could generally capture the spatial patterns of precipitation over the TP. The two gauge-adjusted products were more consistent with the ground measurements than the satellite-only products in terms of statistical assessment. For hydrological simulation, IMERG-UC and GSMaP-MVK showed unsatisfactory performance for hydrological utility, while GSMaP-Gauge demonstrated comparable performance with gauge reference data, suggesting that GSMaP-Gauge can be selected for hydrological application in the TP. Our study also indicates that accurately measuring light rainfall and winter snow is still a challenging task for the current satellite precipitation retrievals.

STUDY OF GPM-IMERG RAINFALL DATA PRODUCT FOR GANGOTRI GLACIER

Abstract. This study presents a comparison of new generation weather observatory satellites Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG) rainfall products with field data collected for Gangotri glacier in India. The meteorological analysis of rainfall estimates has been performed on GPM IMERG Final, Late and Early precipitation products available at daily scale with a spatial resolution of 0.1° × 0.1° for melting season from May to September for the year 2014 and 2015 respectively. The comparison of satellite products with field data was done using correlation coefficient and standard anomaly. The Late run curve showed a high degree of similarity with final run curve while early run showed variation from them. The satellite meteorological data correctly identified non-rainy days with an average of ∼86.7%, ∼67.5% and ∼95% for pre-monsoon, monsoon and post-monsoon season respectively. The rmse for final run data product for 2014 and 2015 are 4.5, 1.23, 1.55, 1.24, 0.8 and 1.14, 7.1, 1.82, 1.15, 1.52 from May to September respectively. Overall, it has been observed that for medium to heavy rainfall final run estimates are close to field data and for light to medium rainfall late run estimates are close. Similar results have been obtained from both datasets for non-rainy days in the study area.