Integrated Multi-satellitE Retrievals For GPM Research Articles

Implementation of a probability matching method in developing intensity–duration–frequency relationships for sub-daily durations using IMERG satellite-based data

ABSTRACT Reliable estimation of design rainfall in time and space can play a significant role in reducing flood-related losses. However, many parts of the world suffer from a lack of dense raingauge networks. Under this condition, the use of satellite-based data can be an alternative solution. In this research, using data from a dense ground-based raingauge network and Integrated Multi-satellitE Retrievals for GPM (IMERG) satellite-based precipitation product, the performance of a probability matching method for satellite-based extreme values has been investigated. The results indicate that the correction model, especially at shorter durations, is able to reduce the error of extreme values of raw satellite-based estimates, and its performance is not affected significantly by the density of the available raingauge network. In conclusion, implementation of the proposed method in poorly gauged regions can play an effective role in providing a reliable estimate of the design rainfall and eventually reducing the financial losses caused by floods.

Performance Assessment of Chirps, Persiann – CDR, IMERG, and TMPA Precipitation Products across Nepal

The observation of hydrological as well as meteorological data is very essential for any kind of hydraulic and hydrological study. In Nepal, due to the significant variation of topography and climatic characteristics as well it is necessary to establish the meteorological stations densely, here only 231 meteorological stations are available and are handled by government organizations Department of Hydrology and Meteorology (DHM). Even though, due to many limitations, the satellite data is very useful for the water resources study. There are so many satellite products available but the performance of these products varies from place to place. In this study, the performance of four satellite products i.e., Climate Hazards Group Infrared Precipitation with Station data (CHIRPS), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN-CDR), Integrated Multi-Satellite Retrievals for GPM (IMERG), and TRMM Multi-satellite Precipitation Analysis (TMPA) all over Nepal are evaluated with different elevation bands. The performance of each product is evaluated by Probability of Detection (POD), Critical Success Index (CSI), Frequency Bias Index (FBI), False Alarm Ratio (FAR), Root Mean Square Error (RMSE), and Percentage Bias (PBIAS). After analysis of each product, the PERSIANN-CDR data set gives a reasonable performance for all elevation bands after bias correction.

Statistical blending of global-gridded climatological products: an approach to inverse hydrological model

Abstract The growing use of global-scale environmental products in hydro-climatic modeling has increased the variety of their applications and the complications of their uncertainties and evaluations. Researchers have recently turned to statistical blending of these products to achieve optimal modeling. The proposed statistical blending in this study includes five large-scale and satellite precipitation (CHIRPS, ERA5-Land of ECMWF, GPM (IMERG), TRMM, and Terra) and evapotranspiration (GLEAM, SSEBop, MODIS, Terra, and ERA) products committed in three modeling scenarios. The blending procedures are organized using a conceptual water balance model to achieve the best precipitation and evapotranspiration results for the conceptual production of streamflow using hydrological inverse modeling. Based on the results, the proposed blending procedures of precipitation and evapotranspiration improved the performance of the model using different statistical metrics. In addition, the results show the conformity of the pattern and behavior of the blended precipitation calculated using the moving least square method in the study area. This happened by changing the estimation based on in situ values, particularly in cold months considering the orographic/snow effects. The combining method provides a good fusion procedure to improve the realistic estimation of precipitation and evapotranspiration in ungagged watersheds as well.

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.

The Role of Water Vapor Observations in Satellite Rainfall Detection Highlighted by a Deep Learning Approach

West African food systems and rural socio-economics are based on rainfed agriculture, which makes society highly vulnerable to rainfall uncertainty and frequent floods and droughts. Reliable rainfall information is currently missing. There is a sparse and uneven rain gauge distribution and, despite continuous efforts, rainfall satellite products continue to show weak correlations with ground measurements. This paper aims to investigate whether water vapor (WV) observations together with temporal information can complement thermal infrared (TIR) data for satellite rainfall retrieval in a Deep Learning (DL) framework. This is motivated by the fact that water vapor plays a key role in the highly seasonal West African rainfall dynamics. We present a DL model for satellite rainfall detection based on WV and TIR channels of Meteosat Second Generation and temporal information. Results show that the WV inhibition of low-level features enables the depiction of strong convective motions usually related to heavy rainfall. This is especially relevant in areas where convective rainfall is dominant, such as the tropics. Additionally, WV data allow us to detect dry air masses over our study area, that are advected from the Sahara Desert and create discontinuities in precipitation events. The developed DL model shows strong performance in rainfall binary classification, with less false alarms and lower rainfall overdetection (FBias <2.0) than the state-of-the-art Integrated MultisatellitE Retrievals for GPM (IMERG) Final Run.

Insights on Satellite-Based IMERG Precipitation Estimates at Multiple Space and Time Scales for a Developing Urban Region in India

Abstract Satellite-based rainfall estimates are a great resource for data-scarce regions, including urban regions, because of its finer resolution. Integrated Multi-satellitE Retrievals for GPM (IMERG) is a widely used product and is evaluated at a city scale for the Hyderabad region using two different ground truths, i.e., India Meteorological Department (IMD) gridded rainfall and Telangana State Development Planning Society (TSDPS) automatic weather station (AWS) measured rainfall. The IMERG rainfall estimates are evaluated on multiple spatial and temporal scales as well as on a rainfall event scale. Both continuous and categorical verification metrics suggest good performance of IMERG on the daily scale; however, relatively decreased performance was observed on the hourly scale. Underestimated and overestimated IMERG estimates with respect to IMD gridded rainfall and AWS measured rainfall, respectively, suggest the performance depends on type of ground truth. Unlike categorical metrics, RMSE and PBIAS have a pattern implying a systematic error with respect to rainfall amount. Further, sample size, diurnal variations, and season are found to have a role in IMERG estimates’ performance. Temporal aggregation of hourly to daily time scales showed the improved IMERG performance; however, no spatial-scale dependence was observed among zonewise and Hyderabad region–wise rainfall estimates. Comparison of raw and bias-corrected IMERG rainfall-based intensity–duration–frequency (IDF) curves with corresponding hourly rain gauge IDF curves showcases the value addition via simple bias correction techniques. Overall, the study suggests the IMERG estimates can be used as an alternative data source, and it can be further improved by modifying the retrieval algorithm. Significance Statement Many urban regions are typically data sparse, which limits scientific understanding and reliable engineering designs of various urban hydrometeorology-relevant tasks, including climatological and extreme rainfall characterization, flood hazard assessment, and stormwater management systems. Satellite rainfall estimates come as a great resource and Integrated Multi-satellitE Retrievals for GPM (IMERG) acts as a best alternative. The Hyderabad region, the sixth-largest metropolitan area in India, is selected to analyze the widely used satellite estimates, i.e., retrievals for GPM. The study observed inaccuracies in the IMERG estimates that varied with rainfall magnitudes and space and time scales; nonetheless, the estimates can be used as an alternative data source for decision-making such as whether rain exceeds a certain threshold or not.

Evaluation of IMERG for GPM satellite-based precipitation products for extreme precipitation indices over Turkiye

As hydrological disasters become more common, the need for monitoring extreme precipitation events is growing globally. Remote sensing technologies developed in recent years help to meet this demand with satellite-based precipitation products, particularly for ungauged basins. However, due to limitations and uncertainties, these products must be validated before being used in hydrometeorological/climatological applications. Validation studies also help improve retrieval algorithms by identifying errors and biases in the data that can be corrected or adjusted. One of NASA's Global Precipitation Measurement products, called Integrated Multi-satellitE Retrievals for GPM (IMERG), has been extensively validated for its ability to detect extreme precipitation events in drastically different climate regions across the world. However, such studies remain limited for Turkiye. In this study, we examined the ability of IMERG Early (E), Late (L), and Final (F) products to detect extreme precipitation in Turkiye using rain gauge data over an eleven-year period (2010−2020). We used extreme precipitation indices recommended by the Expert Team on Climate Change Detection Indices (ETCCDI) to evaluate the ability of IMERG products to detect extreme precipitation. For the stations selected across Turkiye, the Pearson correlation coefficients (R) between daily ground-based precipitation measurements and the three IMERG precipitation products (IMERG-E, IMERG-L, and IMERG-F) were found to be 0.54, 0.55, and 0.64, respectively. IMERG products performed better in detecting the precipitation extreme based on the 85% percentile compared to the 95% and 99% percentiles. IMERG underestimates consecutive dry days by about 25% while overestimating consecutive wet days at the same rate. The performance of IMERG Early, Late, and Final products is inversely proportional to the daily precipitation totals. The annual intensity of precipitation outperformed in the Final run compared to other products across the country. Light and moderate rainfall was detected well, while the detection capability of IMERG decreased for heavy and heavy torrential precipitation. Geographically, the Aegean, Mediterranean, and Southeastern Anatolia regions generally performed better, while Eastern Anatolia and interior parts of the Black Sea region exhibited poorer performance. The low performance in Eastern Anatolia may be attributed to the region's mountainous terrain, while the low performance in the Black Sea region may be due to orographic forcing and land-sea interaction. Overall, the findings of this study showed that the detection capability of IMERG products performed better with increasing latency.

Machine Learning-Based Bias Correction of Precipitation Measurements at High Altitude

Accurate precipitation measurements are essential for understanding hydrological processes in high-altitude regions. Conventional gauge measurements often yield large underestimations of actual precipitation, prompting the development of statistical methods to correct the measurement bias. However, the complex conditions at high altitudes pose additional challenges to the statistical methods. To improve the correction of precipitation measurements in high-altitude areas, we selected the Yakou station, situated at an altitude of 4147 m on the Tibetan plateau, as the study site. In this study, we employed the machine learning method XGBoost regression to correct precipitation measurements using meteorological variables and remote sensing data, including Global Satellite Mapping of Precipitation (GSMaP), Integrated Multi-satellitE Retrievals for GPM (IMERG) and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). Additionally, we examined the transferability of this method between different stations in our study site, Norway, and the United States. Our results show that the Yakou station experiences a large underestimation of precipitation, with a magnitude of 51.4%. This is significantly higher than similar measurements taken in the Arctic or lower altitudes. Furthermore, the remote sensing precipitation datasets underestimated precipitation when compared to the Double Fence Intercomparison Reference (DFIR) precipitation observation. Our findings suggest that the machine learning method outperformed the traditional statistical method in accuracy metrics and frequency distribution. Introducing remote sensing data, especially the GSMaP precipitation, could potentially replace the role of in situ wind speed in precipitation correction, highlighting the potential of remote sensing data for correcting precipitation rather than in situ meteorological observation. Moreover, our results indicate that the machine learning method with remote sensing data demonstrated better transferability than the traditional statistical method when we cross-validated the method with sites located in different countries. This study offers a promising strategy for obtaining more accurate precipitation measurements in high-altitude regions.

Spatial Variation in the Synoptic Structure of Convective Systems over the Great Plains

Abstract During the summer, rainfall over the Rocky Mountains peaks in the afternoon. The diurnal time of maximum rain becomes progressively delayed in going eastward from the Rocky Mountains, such that over much of the Great Plains, maximum rain occurs near 0300 local time. We use Rapid Refresh (RAP) reanalysis data from July and August of 2009–19 to show that there is continuous spatial variation in the mean structure of the high-rain events that occur along this anomalous diurnally propagating rainfall feature. High-rain events that occur farther from the Rockies are more likely to be associated with a larger warm anomaly to the south of the rain event center, a smaller cold anomaly, a larger negative surface pressure anomaly, and increased low-level southerly meridional wind. We also use the Integrated Multi-Satellite Retrievals for GPM (IMERG) rainfall dataset to show that, on hourly time scales, afternoon rainfall that occurs over the Rocky Mountains propagates eastward onto the Great Plains but is rapidly attenuated and becomes negligible east of 100°W. This eastward rainfall propagation appears to be mediated in part by the formation of a low-level cold anomaly and increased surface pressure over the Rocky Mountains, a reversal of the upslope wind, and increased low-level zonal mass convergence over the adjacent near plains. Significance Statement Models have difficulty in simulating the diurnal time of nocturnal rainfall over the Great Plains. Deep convection in this region is often associated with severe weather. Rainfall variability over the Great Plains has a significant impact on soil moisture and agricultural productivity. This paper explores how the structure of deep convective rain events over the Great Plains varies with longitude. The results improve our understanding of deep convection development in the Great Plains and have the potential to contribute to improved forecasts of convective rainfall.

Higher Frozen Soil Permeability Represented in a Hydrological Model Improves Spring Streamflow Prediction From River Basin to Continental Scales

AbstractDespite plentiful evidence of frozen ground effects on snowmelt infiltration from lab experiments at pedon scales, streamflow observations show a weaker or no effect in terms of timing and magnitude at larger scales. We aim to understand this conflicting phenomenon through modeling using the Noah land surface model with multi‐physics (MP; Noah‐MP) options and the Routing Application for Parallel computatIon of Discharge (RAPID) over the Mississippi River Basin. We conduct 16 experiments with combinations of two supercooled liquid water (SLW) parameterization schemes and four soil hydraulic property schemes in Noah‐MP driven by two gridded precipitation products of the North American Land Data Assimilation System (NLDAS) and the Integrated Multi‐satellitE Retrievals for GPM (IMERG) Final. We then use RAPID to route Noah‐MP modeled surface runoff and groundwater discharge to predict daily streamflow at 52 United States Geological Survey gauges from 2015 to 2019. A model with the highest permeability performs better than other schemes on daily streamflow predictions by 20%–57% throughout a water year and 29%–113% for the spring as measured by the mean Kling‐Gupta Efficiency of the 52 gauges. Different SLW schemes demonstrate negligible effects on streamflow predictions. Models forced by IMERG show a better prediction skill compared with those forced by NLDAS at most of the gauges. Both precipitation products confirm that a scheme of higher permeability yields more accurate streamflow predictions over frozen ground. Future models should represent preferential flows through macropore networks to improve the understanding of frozen soil effects on infiltration and discharge across scales.

Evaluation and Error Decomposition of IMERG Product Based on Multiple Satellite Sensors

The Integrated Multisatellite Retrievals for GPM (IMERG) is designed to derive precipitation by merging data from all the passive microwave (PMW) and infrared (IR) sensors. While the input source errors originating from the PMW and IR sensors are important, their structure, characteristics, and algorithm improvement remain unclear. Our study utilized a four-component error decomposition (4CED) method and a systematic and random error decomposition method to evaluate the detectability of IMERG dataset and identify the precipitation errors based on the multi-sensors. The 30 min data from 30 precipitation stations in the Tunxi Watershed were used to evaluate the IMERG data from 2018 to 2020. The input source includes five types of PMW sensors and IR instruments. The results show that the sample ratio for IR (Morph, IR + Morph, and IR only) is much higher than that for PMW (AMSR2, SSMIS, GMI, MHS, and ATMS), with a ratio of 72.8% for IR sources and a ratio of 27.2% for PMW sources. The high false ratio of the IR sensor leads to poor detectability performance of the false alarm ratio (FAR, 0.5854), critical success index (CSI, 0.3014), and Brier score (BS, 0.1126). As for the 4CED, Morph and Morph + IR have a large magnitude of high total bias (TB), hit overestimate bias (HOB), hit underestimate bias (HUB), false bias (FB), and miss bias (MB), which is related to the prediction ability and sample size. In addition, systematic error is the prominent component for AMSR2, SSMIS, GMI, and Morph + IR, indicating some inherent error (retrieval algorithm) that needs to be removed. These findings can support improving the retrieval algorithm and reducing errors in the IMERG dataset.

Feasibility of Downscaling Satellite-Based Precipitation Estimates Using Soil Moisture Derived from Land Surface Temperature

For many areas, satellite-based precipitation products or reanalysis model data represent the only available precipitation information. Unfortunately, the resolution of these datasets is generally too coarse for many applications. A very promising downscaling approach is to use soil moisture due to its clear physical connection to precipitation. We investigate the feasibility of using soil moisture derived from land surface temperature in this context. These data are more widely available in the required resolution compared to other soil moisture data. Rain gauge-adjusted radar data from Namoi serves as a spatial reference dataset for two objectives: to identify the most suitable globally available precipitation dataset and to explore the precipitation information contained in the soil moisture data. The results show that these soil moisture data cannot be used to downscale satellite-based precipitation data to a high resolution because of cloud cover interference. Therefore, the Integrated Multi-satellitE Retrievals for GPM (IMERG) late data represents the best precipitation dataset for many areas in Australia that require timely precipitation information, according to this study.

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.

Quantification of Gridded Precipitation Products for the Streamflow Simulation on the Mekong River Basin Using Rainfall Assessment Framework: A Case Study for the Srepok River Subbasin, Central Highland Vietnam

Many fields have identified an increasing need to use global satellite precipitation products for hydrological applications, especially in ungauged basins. In this study, we conduct a comprehensive evaluation of three Satellite-based Precipitation Products (SPPs): Integrated Multi–satellitE Retrievals for GPM (IMERG) Final run V6, Soil Moisture to Rain (SM2RAIN)-Advanced SCATterometer (ASCAT) V1.5, and Multi-Source Weighted-Ensemble Precipitation (MSWEP) V2.2 for a subbasin of the Mekong River Basin (MRB). The study area of the Srepok River basin (SRB) represents the Central Highland sub-climatic zone in Vietnam under the impacts of newly built reservoirs during 2001–2018. In this study, our evaluation was performed using the Rainfall Assessment Framework (RAF) with two separated parts: (1) an intercomparison of rainfall characteristics between rain gauges and SPPs; and (2) a hydrological comparison of simulated streamflow driven by SPPs and rain gauges. Several key findings are: (1) IMERGF-V6 shows the highest performance compared to other SPP products, followed by SM2RAIN-ASCAT V1.5 and MSWEP V2.2 over assessments in the RAF framework; (2) MSWEP V2.2 shows discrepancies during the dry and wet seasons, exhibiting very low correlation compared to rain gauges when the precipitation intensity is greater than 15 mm/day; (3) SM2RAIN–ASCAT V1.5 is ranked as the second best SPP, after IMERGF-V6, and shows good streamflow simulation, but overestimates the wet seasonal rainfall and underestimates the dry seasonal rainfall, especially when the precipitation intensity is greater than 20 mm/day, suggesting the need for a recalibration and validation of its algorithm; (4) SM2RAIN-ASCAT had the lowest bias score during the dry season, indicating the product’s usefulness for trend analysis and drought detection; and (5) RAF shows good performance to evaluate the performance of SPPs under the impacts of reservoirs, indicating a good framework for use in other similar studies. The results of this study are the first to reveal the performance of MSWEP V2.2 and SM2RAIN-ASCAT V1.5. Additionally, this study proposes a new rainfall assessment framework for a Vietnam basin which could support future studies when selecting suitable products for input into hydrological model simulations in similar regions.

Comparison of Tree-Based Ensemble Algorithms for Merging Satellite and Earth-Observed Precipitation Data at the Daily Time Scale

Merging satellite products and ground-based measurements is often required for obtaining precipitation datasets that simultaneously cover large regions with high density and are more accurate than pure satellite precipitation products. Machine and statistical learning regression algorithms are regularly utilized in this endeavor. At the same time, tree-based ensemble algorithms are adopted in various fields for solving regression problems with high accuracy and low computational costs. Still, information on which tree-based ensemble algorithm to select for correcting satellite precipitation products for the contiguous United States (US) at the daily time scale is missing from the literature. In this study, we worked towards filling this methodological gap by conducting an extensive comparison between three algorithms of the category of interest, specifically between random forests, gradient boosting machines (gbm) and extreme gradient boosting (XGBoost). We used daily data from the PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) and the IMERG (Integrated Multi-satellitE Retrievals for GPM) gridded datasets. We also used earth-observed precipitation data from the Global Historical Climatology Network daily (GHCNd) database. The experiments referred to the entire contiguous US and additionally included the application of the linear regression algorithm for benchmarking purposes. The results suggest that XGBoost is the best-performing tree-based ensemble algorithm among those compared. Indeed, the mean relative improvements that it provided with respect to linear regression (for the case that the latter algorithm was run with the same predictors as XGBoost) are equal to 52.66%, 56.26% and 64.55% (for three different predictor sets), while the respective values are 37.57%, 53.99% and 54.39% for random forests, and 34.72%, 47.99% and 62.61% for gbm. Lastly, the results suggest that IMERG is more useful than PERSIANN in the context investigated.

Performance evaluation of IMERG products based on the extremely heavy rainstorm event (2021) once in a thousand years in Henan, China

Henan, a province of central China, experienced rainfall at the level of rare extremely heavy rainstorms once in a millennium during the period from July 17th to July 23rd, 2021. Based on ground-based rainfall data from 116 gauges, the performance of Global Rainfall Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG) products (IMERG-Early run (IMERG-E), IMERG-Late run (IMERG-L), and IMERG-Final run (IMERG-F)) were evaluated from the seven-day rainfall change process, the hourly rainfall change process on July 20th and the six traditional indexes. Results indicate that the overall performance of IMERG-F is the best, relative bias (RB) is −0.03 and correlation coefficient (CC) is 0.51. IMERG-L (RB, −0.09; CC, 0.49) is very close to IMERG-F More outstandingly, in the three days of 19th, 20th and 21st, RB of IMERG-L per day is −0.01, 0.02, and − 0.13, respectively. IMERG-L shows a greater ability to capture extreme short-duration heavy rainfall in the peak period compared to the other two products. Since weak rainfall overestimation coefficient (WROC) is >0.5, it can be concluded that RB of excessively overestimated gauges are >100% mainly determined by the overestimation during weak rainfall; Because of overestimation coefficient (OC) and weak rainfall coefficient (WRC), there is an obvious error trend that gauges with weak rainfall (≤ 1 mm/h) are easily overestimated and gauges with non-weak rainfall (> 1 mm/h) are easily underestimated; IMERG-F has higher entire accuracy and reduces the error trend because of the large number of PMW estimates.

Assessment of the IMERG Early-Run Precipitation Estimates over South American Country of Chile

Accurate rainfall measurement is a challenge, especially in regions with diverse climates and complex topography. Thus, knowledge of precipitation patterns requires observational networks with a very high spatial and temporal resolution, which is very difficult to construct in remote areas with complex geological features such as desert areas and mountains, particularly in countries with high topographical variability such as Chile. This study evaluated the performance of the near-real-time Integrated Multi-satellite Retrievals for GPM (IMERG) Early product throughout Chile, a country located in South America between 16°S–66°S latitude. The accuracy of the IMERG Early was assessed at different special and temporal scales from 2015 to 2020. Relative Bias (PBIAS), Mean Absolute Error (MAE), and Root-Mean-Squared Error (RMSE) were used to quantify the errors in the satellite estimates, while the Probability of Detection (POD), False Alarm Ratio (FAR), and Critical Success Index (CSI) were used to evaluate product detection accuracy. In addition, the consistency between the satellite estimates and the ground observations was assessed using the Correlation Coefficient (CC). The spatial results show that the IMERG Early had the best performance over the central zone, while the best temporal performance was detected for the yearly precipitation dataset. In addition, as latitude increases, so do errors. Also, the satellite product tends to slightly overestimate the precipitation throughout the country. The results of this study could contribute towards the improvement of the IMERG algorithms and open research opportunities in areas with high latitudes, such as Chile.

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.

Evaluation of Satellite-Based Precipitation Products over Complex Topography in Mountainous Southwestern China

Satellite-based precipitation products (SBPPs) are essential for rainfall quantification in areas where ground-based observation is scarce. However, the accuracy of SBPPs is greatly influenced by complex topography. This study evaluates the performance of Integrated Multi-satellite Retrievals for GPM (IMERG) and Global Satellite Mapping of Precipitation (GSMaP) in characterizing rainfall in a mountainous catchment of southwestern China, with an emphasis on the effect of three topographic variables (elevation, slope, aspect). The SBPPs are evaluated by comparing rain gauge observations at eight ground stations from May to October in 2014–2018. Results show that IMERG and GSMaP have good rainfall detection capability for the entire region, with POD = 0.75 and 0.93, respectively. In addition, IMERG overestimates rainfall (BIAS = −48.8%), while GSMaP is consistent with gauge rainfall (BIAS = −0.4%). Comprehensive analysis shows that IMERG and GSMaP are more impacted by elevation, and then slope, whereas aspect has little impact. The independent evaluations suggest that variability of elevation and slope negatively correlate with the accuracy of SBPPs. The accuracy of GSMaP presents weaker dependence on topography than that of IMERG in the study area. Our findings demonstrate the applicability of IMERG and GSMaP in mountainous catchments of Southwest China. We confirm that complex topography impacts the performance of SBPPs, especially for complex topography in mountainous areas. It is suggested that taking topographical factors into account is needed for hydrometeorological applications such as flood forecasting, and SBPP evaluations and retrieval technology require further improvement in the future for better applications.

Effects of Land Cover Change and Deforestation on Rainfall and Surface Temperature in New Capital City of Indonesia

Land cover change and deforestation have a significant impact on climate change. This study investigates the effect of land cover change and deforestation on surface temperatures and extreme rainfall in the New Capital City of Indonesia (IKN), particularly in Samboja and Sepaku Subdistricts, East Kalimantan Province. Land cover change and deforestation were analyzed from Landsat 5 TM, 7 ETM+, and 8 OLI satellite data during 2001-2020. Land cover is divided into four classes: built-up land, water bodies, vegetation, and agricultural area. Rainfall data were obtained from the Integrated Multi-satellite Retrievals for GPM (IMERG) version 6 satellite, and extreme temperatures were taken from fifth-generation ECMWF reanalysis (ERA5) data. A significant decrease in vegetated land area is observed every year, followed by an increase in residential land and buildings (built-up area) and agricultural land. The highest rate of increase was observed in the area of ​​agricultural land. Such change is correlated with an increase in surface temperature in the IKN. The strongest correlation is shown by increased built-up area and agricultural land. The relationship between rainfall and land cover is weak, and the body of water shows a relatively strong relationship. The extreme number of very wet days (R95p), ​​consecutive dry days (CDD), and max 1-day precipitation (RX1day) rain index showed a decreasing trend during 2001-2020. However, the consecutive wet days (CWDs) index showed an increase. This needs to be a concern because consecutive precipitation extremes may cause more catastrophability than occasional extreme events.