Global Precipitation Measurement Products Research Articles

Evaluation of GPM IMERG satellite precipitation for rainfall-runoff modelling in Great Britain

Reliable hydrological simulations require accurate precipitation data. However, data uncertainties due to the indirect nature of satellite estimates can propagate through hydrologic models and lead to simulation errors. This study assesses the accuracy of Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG) products, comparing them directly with ground-based precipitation data and evaluating their performance in rainfall-runoff modelling across Great Britain. Three IMERG V-06 products (IMERG-Early, IMERG-Late, and IMERG-Final) are examined. Utilizing the Simple Water Balance Model (SWBM), the analysis covers 250 basins, revealing that the SWBM performs well in over 50% of the basins. Runoff estimations show that E-OBS ground-based data yield the highest Nash-Sutcliffe Efficiency (NSE) score (0.91), followed by IMERG-Final (0.85), IMERG-Late (0.82), and IMERG-Early (0.73). The findings underscore IMERG’s utility in hydrologic modelling for ungauged or poorly gauged basins.

Merged and Gridded GPM and Atmospheric River Data Product

AbstractThe Global Precipitation Measurement (GPM) Mission Core Observatory satellite launched in 2014 as a joint mission between National Aeronautics and Space Administration (NASA) and JAXA. Global Precipitation Measurement (GPM) has, since that time, provided continuous, valuable dual‐frequency radar and passive microwave radiometer observations. Here, we introduce a gridded data set of collocated GPM Core Observatory observational products merged with a reanalysis‐derived Atmospheric river (AR) data set in the North Atlantic and North Pacific sectors. The three data sets that are merged and gridded are: (a) the NASA Goddard Profiling (GPROF) precipitation product, which uses GPM passive microwave radiometer observations to derive surface precipitation rates, (b) a water vapor data product derived from the GPM Core Observatory radiometer, provided by Remote Sensing Systems (RSS), and (c) the Mattingly et al. (2018, https://doi.org/10.1029/2018jd028714) AR data set that is specifically tuned to the high‐latitude regions. This novel merged data set spans from May 2014 to December 2022 with plans to update annually through 2026 at minimum. This gridded product combines RSS passive water vapor and precipitation estimates with coincident AR detection. This data product benefits the scientific community by providing (a) user‐friendly gridded satellite data compared to standard satellite data sets, while maintaining high temporal resolution, and (b) coincident satellite observations to assess the link between ARs and precipitation.

Oceanic Validation of IMERG-GMI Version 6 Precipitation Using the GPM Validation Network

Abstract NASA’s multisatellite precipitation product from the Global Precipitation Measurement (GPM) mission, the Integrated Multi-satellitE Retrievals for GPM (IMERG) product, is validated over tropical and high-latitude oceans from June 2014 to August 2021. This oceanic study uses the GPM Validation Network’s island-based radars to assess IMERG when the GPM Core Observatory’s Microwave Imager (GMI) observes precipitation at these sites (i.e., IMERG-GMI). Error tracing from the Level 3 (gridded) IMERG V06B product back through to the input Level 2 (satellite footprint) Goddard Profiling Algorithm GMI V05 climate (GPROF-CLIM) product quantifies the errors separately associated with each step in the gridding and calibration of the estimates from GPROF-CLIM to IMERG-GMI. Mean relative bias results indicate that IMERG-GMI V06B overestimates Alaskan high-latitude oceanic precipitation by +147% and tropical oceanic precipitation by +12% with respect to surface radars. GPROF-CLIM V05 overestimates Alaskan oceanic precipitation by +15%, showing that the IMERG algorithm’s calibration adjustments to the input GPROF-CLIM precipitation estimates increase the mean relative bias in this region. In contrast, IMERG adjustments are minimal over tropical waters with GPROF-CLIM overestimating oceanic precipitation by +14%. This study discovered that the IMERG V06B gridding process incorrectly geolocated GPROF-CLIM V05 precipitation estimates by 0.1° eastward in the latitude band 75°N–75°S, which has been rectified in the IMERG V07 algorithm. Correcting for the geolocation error in IMERG-GMI V06B improved oceanic statistics, with improvements greater in tropical waters than Alaskan waters. This error tracing approach enables a high-precision diagnosis of how different IMERG algorithm steps contribute to and mitigate errors, demonstrating the importance of collaboration between evaluation studies and algorithm developers. Significance Statement Evaluation of IMERG’s oceanic performance is very limited to date. This study uses the GPM Validation Network to conduct the first extensive assessment of IMERG V06B at its native resolution over both high-latitude and tropical oceans, and traces errors in IMERG-GMI back through to the input GPROF-CLIM GMI product. IMERG-GMI overestimates tropical oceanic precipitation (+12%) and strongly overestimates Alaskan oceanic precipitation (+147%) with respect to the island-based radars studied. IMERG’s GMI estimates are assessed as these should be the optimal estimates within the multisatellite product due to the GMI’s status as calibrator of the GPM passive microwave constellation.

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.

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.

The evaluation and downscaling‐calibration of IMERG precipitation products at sub‐daily scales over a metropolitan region

AbstractThe Global Precipitation Measurement (GPM) Integrated Multi‐satellitE Retrievals for GPM products (i.e., IMERG) provide new‐generation satellite precipitation measurements. For urban contexts, however, the issues of its bias and insufficient resolutions still exist. This study aims to develop high‐precision and high‐resolution (e.g., 0.01°/1 h) data for a metropolitan region based on IMERG and gauge precipitations. The original IMERG product is evaluated using hourly in situ precipitations from 47 gauges. A spatial downscaling‐calibration (DC) technique is then developed to enhance the IMERG using the Normalized Difference Vegetation Index. The results show the limited capability of IMERG to capture sub‐daily precipitation and high‐intensity precipitation. The proposed DC method significantly improves IMERG performance, with correlation coefficient (CC) increasing from 0.07 to 0.75, and probability of detection improving from 0.34 to 0.90 at the hourly scale. In terms of spatial rainfall distribution, 86% of mean absolute error and 80% of RMSE are improved with CC increasing from 0.07 to 0.91 on average. Additionally, the calibrated downscaled product provides finer information in local areas, capturing three times more spatial variabilities of urban precipitation against the original IMERG input data. The results highlight the necessity of improving urban observations for flood risk management at fine spatiotemporal resolutions.

A multiple-step scheme for the improvement of satellite precipitation products over the Tibetan Plateau from multisource information

Precipitation data with high accuracy and spatial resolution characteristics play significant roles in the regional hydrological and eco-environmental system applications. Thus, satellite-based precipitation products (SPPs) with high spatial resolution and high accuracy must be developed. This study proposed a multiple-step scheme to improve the global precipitation measurement (GPM) at daily and monthly timescales over the Tibetan Plateau (TP) from 2014 to 2017. First, combined with the geographically weighted regression (GWR) method using geographic and topographic factors, the gamma-distribution mapping and local intensity scaling (GDM-LOCI) method is applied to effectively merge the observed data attributes and the spatial representation of SPPs at the daily scale by correcting precipitation volumes and frequencies. Second, the areal merged precipitation, normalized difference vegetation index (NDVI), and reanalyzed atmospheric data are used to improve the spatial resolution of monthly GPM with a random forest (RF) model that uses the 17 land cover types to establish the local downscaling model windows. The results show that daily merged precipitation can better reflect the spatial and temporal variability of precipitation than can satellite estimates, and the correlation coefficient (R), and critical success index (CSI) increased by 0.12 and 0.26, respectively. In the merged downscaling model, the merged precipitation factor can weaken the negative effect of the other auxiliary predictors due to its spatial autocorrelation in precipitation estimation. Most importantly, by using the land cover types to establish local model windows for the downscaling model, not only the spatial resolution of the GPM product is downscaled to 1 km, but also the spatial structure of the downscaled products is enhanced, with less deviation and a higher spatial correlation. The R, the root mean square error (RMSE) and the relative bias (BIAS) were 0.89, 50.19 mm and 0.57, respectively. This study presents a promising scheme for generating high-quality precipitation data for regional hydrometeorological research in data-scarce regions.

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.

PRSOT: Precipitation Retrieval from Satellite Observations Based on Transformer

Precipitation with high spatial and temporal resolution can improve the defense capability of meteorological disasters and provide indispensable instruction and early warning for social public services, such as agriculture, forestry, and transportation. Therefore, a deep learning-based algorithm entitled precipitation retrieval from satellite observations based on Transformer (PRSOT) is proposed to fill the observation gap of ground rain gauges and weather radars in deserts, oceans, and other regions. In this algorithm, the multispectral infrared brightness temperatures from Himawari-8, the new-generation geostationary satellite, have been used as predictor variables and the Global Precipitation Measurement (GPM) precipitation product has been employed to train the retrieval model. We utilized two data normalization schemes, area-based and pixel-based normalization, and conducted comparative experiments. Comparing the estimated results with the GPM product on the test set, PRSOT_Pixel_based model achieved a Probability Of Detection (POD) of 0.74, a False Alarm Ratio (FAR) of 0.44 and a Critical Success Index (CSI) of 0.47 for two-class metrics, and an Accuracy (ACC) of 0.75 for multi-class metrics. Pixel-based normalization is more suitable for meteorological data, highlighting the precipitation characteristics and obtaining better comprehensive retrieval performance in visualization and evaluation metrics. In conclusion, the proposed PRSOT model has made a remarkable and essential contribution to precipitation retrieval and outperforms the benchmark machine learning model Random Forests.

Exploring the potential of satellite precipitation after bias correction in streamflow simulation in a semi-arid watershed in northeastern China

Study RegionThe Upper Taoer River Watershed (UTRW), Northeastern China Study FocusThis study presents a comprehensive hydrometeorological evaluation of six satellite precipitation products (SPPs) over a sparsely gauged semi-arid watershed, including the adjusted and unadjusted versions of Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA), Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM products (IMERG), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERISIANN) series. The effectiveness of monthly bias correction in improving SPPs estimation accuracy and in enhancing their feasibility in hydrological applications was also analyzed. New hydrological insights for the regionThe SPPs, especially bias-corrected SPPs, adequately provide complementary precipitation information for UTRW where ground measurement is insufficient. The adjusted SPPs after bias correction obtained a higher Nash-Sutcliffe Efficiency (NSE) than gauge data when driving streamflow simulation. The unadjusted SPPs hydrological applicability was significantly enhanced after bias correction, with NSE< 0 (except for IMERG product) and NSE> 0.34 before and after correction, respectively. Performance of the TMPA series improved the most through bias correction, making it the preferred choice for daily and monthly simulations. Additionally, SPPs were more applicable in wet and normal years and require improvement in dry years. This study provides valuable references for identifying better alternative precipitation sources for local water resource management and exploring an effective SPP utilization approach in practical applications.

Event-Based Bias Correction of the GPM IMERG V06 Product by Random Forest Method over Mainland China

The Global Precipitation Measurement (GPM) IMERG V06 product showed excellent performance in detecting precipitation, but still have room to improve. This study proposed an event-based bias correction strategy through random forest (RF) method to improve accuracy of the GPM IMERG V06 product over mainland China. Results showed that, over mainland China, biases caused by ‘hits’ events are most responsible for the errors of the GPM product, and ‘falseAlarms’ events took the least responsibility for that because of the small GPM values for ‘falseAlarms’ events. Compared with the raw GPM product, the bias-corrected GPM product showed better performance in both fitting observed precipitation values and in detecting precipitation events, thus the event-based bias-strategy in this study is credible. After bias correction, the ability of the bias-corrected GPM product was significantly improved for ‘hits’ events, but showed slight deterioration in RMSE and MAE and significant improvements in FAR and CSI for ‘falseAlarms’ events. This is because the established RF classification model tends to learn characteristics of the event with larger proportion, and then performed better in correctly identifying the event with larger proportion in the subregion.

Cloud water path, precipitation amount, and precipitation efficiency derived from multiple datasets on the Qilian Mountains, Northeastern Tibetan Plateau

Orographic clouds and precipitation are critical to the water cycle and ecology of arid and semiarid mountainous regions. This study intended to investigate the spatial and seasonal distribution of the cloud water path (CWP), precipitation amount, and precipitation efficiency (PE) on the Qilian Mountains (QM) of the northeastern Tibetan Plateau, as well as the related mechanisms linked with topographic distribution and water vapor transport. To solve the limitations of rain gauges and radar observations at high elevations of the QM, we used the latest multiple datasets, including the Global Precipitation Measurement (GPM), Advanced Very High Resolution Radiometer, and ECMWF Reanalysis v5 data from 2016 to 2019. The amount of precipitation retrieved from the two GPM products was evaluated using rain gauge data. The results show that the GPM data generally overestimate the precipitation amount on the QM and have a high correlation with rain gauge data in warm seasons and a low correlation in winter seasons, primarily due to the significant influences of snow and ice coverage. The terrain height and water vapor supply below 600 hPa are two critical factors that determine the spatial and seasonal distributions of clouds and precipitation in the QM. The precipitation generally increased with an increase in terrain elevation below 3000 m and decreased above 3000 m on the QM. CWP, precipitation amount, and PE in the mountainous areas are much higher than those in the surroundings and decrease from the southeast to the northwest because of the decrease in low-level water vapor supply. CWP, precipitation amount, and PE were the highest in summer because of the strong moisture transport from the southeast and the lowest in winter because of the weakest water vapor transport. Water vapor fluxes from the eastern, southern, and northern boundaries contributed to the wetness of the eastern and northeastern slopes of the QM. The lack of low-level water vapor in the westerly winds was the main reason for drought in the western QM.

Coupling of satellite-derived precipitation products with Bartlett-Lewis model to estimate intensity-frequency-duration curves for remote areas

Intensity-frequency-duration (IFD) curves (also known as intensity-duration-frequency (IDF) curves in some regions) are widely used to take into consideration the probability of occurrence of extreme precipitation for the design of water related infrastructure. Derivation of IFD curves is based on statistical analysis of observed extreme precipitation events and thus the reliability of the curves mostly depends on the record length of observations. At sub-hourly timescales however, ground-based precipitation observations are often absent or very limited in terms of spatial coverage and record length, particularly in remote areas around the world. Given the availability of satellite-derived precipitation products (SPPs) with finer spatiotemporal resolution and near-global coverage, there is great potential to use these products in the planning and design of infrastructure. This paper proposes a novel framework, SIFD (Satellite-derived IFD curves) to combine long-term daily gauge data and sub-hourly SPP to estimate IFD curves for time intervals from 0.5 to 24 h. A recent variant of the Random Parameter Bartlett-Lewis Rectangular Pulse model (RPBLRPX) was used to disaggregate daily gauge data to derive IFD curves. In the absence of sub-daily rain gauge data, the statistics required for the RPBLRPX model were obtained using the Integrated Multi-satellitE Retrievals for GPM (IMERG; 30-min, 0.1° resolution) product derived from the Global Precipitation Measurement (GPM) mission. Performance of the SIFD framework was tested against gauge data from seven Australian sites across a wide range of climatologies. The disaggregation scheme coupled with the RPBLRPX model was capable of reproducing the wet-period mean intensity, wet-period standard deviation, and wet-period fraction with reasonable accuracy across the sub-daily timescales. The coefficient of determination (r2) values were 0.39 (mean intensity), 0.38 (standard deviation), and 0.87 (wet-period fraction) at 0.5 h timescale. Furthermore, despite some differences in methodology and record length, the IFD estimates computed using the SIFD framework were fairly close to those of the Bureau of Meteorology (BoM). The skill metrics for Sydney were the best with a bias ratio (BR) of 1.01, root mean square error (RMSE) of 13% of the mean of the BoM estimates, and a Nash-Sutcliffe coefficient of efficiency (NSE) of 0.97. The worst case was found at Hobart (BR = 1.43, RMSE = 66%, and NSE = 0.26). Overall, the results indicate that the proposed SIFD framework could provide a viable alternative to estimate unbiased IFD curves at the sub-hourly timescale in the absence of adequate sub-hourly ground-based observations.

Spatial Downscaling Model Combined with the Geographically Weighted Regression and Multifractal Models for Monthly GPM/IMERG Precipitation in Hubei Province, China

High spatial resolution (1 km or finer) precipitation data fields are crucial for understanding the Earth’s water and energy cycles at the regional scale for applications. The spatial resolution of the Global Precipitation Measurement (GPM) mission (IMERG) satellite precipitation products is 0.1° (latitude) × 0.1° (longitude), which is too coarse for regional-scale analysis. This study combined the Geographically Weighted Regression (GWR) and the Multifractal Random Cascade (MFRC) model to downscale monthly GPM/IMERG precipitation products from 0.1° × 0.1° (approximately 11 km × 11 km) to 1 km in Hubei Province, China. This work’s results indicate the following: (1) The original GPM product can accurately express the precipitation in the study area, which highly correlates with the site data from 2015 to 2017 (R2 = 0.79) and overall presents the phenomenon of overestimation. (2) The GWR model maintains the precipitation field’s overall accuracy and smoothness, with even improvements in accuracy for specific months. In contrast, the MFRC model causes a slight decrease in the overall accuracy of the precipitation field but performs better in reducing the bias. (3) The GWR-MF combined with the GWR and MFRC model improves the observation accuracy of the downscaling results and reduces the bias value by introducing the MFRC to correct the deviation of GWR. The conclusion and analysis of this paper can provide a meaningful experience for 1 km high-resolution data to support related applications.

New insights into the 2020 Sardoba dam failure in Uzbekistan from Earth observation

On 1 May 2020, the Sardoba Reservoir in Uzbekistan breached its western wall, and the uncontrolled release of water caused casualties, environmental damages and economic losses. We investigate the dam failure based on three sets of Earth observation data, including: (i) satellite altimetry products, i.e., ICESat-2 data, with the aim of understanding the topographic features in the study area; (ii) multi-geometry Sentinel-1 SAR data to retrieve the pre-failure deformation along the vertical and horizontaleast-west directions between 2017 and 2020; (iii) optical images from Sentinel-2 satellites and Global Precipitation Measurement (GPM) products, which are involved in exploring the environmental status before the failure. We analyse the possible causes of the collapse in terms of both physical and human factors. The differential settlement of ∼60 mm revealed by InSAR at the failure section is a sign of internal erosion through the embankment, which is the physical factor contributing to the failure. The opportunity to prevent the collapse were missed due to the human factor of ignorance brought by limitations of the conventional monitoring methods. Neither ground observations nor satellite-based GPM products show extreme precipitation in the region, ruling out the likelihood of rainfall-induced overtopping. The settlement rate of the embankment shows no decaying trend, indicating that the dam is undergoing the primary consolidation phase of total settlement. Maximum settlement of ∼270 mm (∼0.8% of the dam height) has happened on the north bank since the reservoir impoundment, which should raise concern in future monitoring and surveillance. The results reveal that InSAR can discern the failure precursor by detecting surface motion, and that the deformation signals can help to warn of risks and avoid dam damage. We recommend InSAR deformation monitoring be included in future safety programs, providing detailed deformation and resisting risks of ignorance.

Estimating Rainfall with Multi-Resource Data over East Asia Based on Machine Learning

The lack of accurate estimation of intense precipitation is a universal limitation in precipitation retrieval. Therefore, a new rainfall retrieval technique based on the Random Forest (RF) algorithm is presented using the Advanced Himawari Imager-8 (Himawari-8/AHI) infrared spectrum data and the NCEP operational Global Forecast System (GFS) forecast information. And the gauge-calibrated rainfall estimates from the Global Precipitation Measurement (GPM) product served as the ground truth to train the model. The two-step RF classification model was established for (1) rain area delineation and (2) precipitation grades’ estimation to improve the accuracy of moderate rain and heavy rain. In view of the imbalance categories’ distribution in the datasets, the resampling technique including the Random Under-sampling algorithm and Synthetic Minority Over-sampling Technique (SMOTE) was implemented throughout the whole training process to fully learn the characteristics among the samples. Among the features used, the contributions of meteorological variables to the trained models were generally greater than those of infrared information; in particular, the contribution of precipitable water was the largest, indicating the sufficient necessity of water vapor conditions in rainfall forecasting. The simulation results by the RF model were compared with the GPM product pixel-by-pixel. To prove the universality of the model, we used independent validation sets which are not used for training and two independent testing sets with different periods from the training set. In addition, the algorithm was validated against independent rain gauge data and compared with GFS model rainfall. Consequently, the RF model identified rainfall areas with a Probability Of Detection (POD) of around 0.77 and a False-Alarm Ratio (FAR) of around 0.23 for validation, as well as a POD of 0.60–0.70 and a FAR of around 0.30 for testing. To estimate precipitation grades, the value of classification was 0.70 in validation and in testing the accuracy was 0.60 despite a certain overestimation. In summary, the performance on the validation and test data indicated the great adaptability and superiority of the RF algorithm in rainfall retrieval in East Asia. To a certain extent, our study provides a meaningful range division and powerful guidance for quantitative precipitation estimation.

Precipitation estimations based on remote sensing compared with data from weather stations over agricultural region of Argentina pampas

Global patterns of precipitation have changed due to the increase in temperature as a result of climate change. Measuring the amount of precipitation at a given location using surface instruments is relatively simple. However, the great spatial and temporal variability of the intensity, type and occurrence of this phenomenon, makes direct and uniformly calibrated measurements difficult in large regions. Satellite information is an important alternative to describe precipitation events; the Global Precipitation Measurement (GPM) mission estimates precipitation, considering different time periods, with three products Integrated Multi-Satellite Retrievals for GPM (IMERG), in near real time. This study evaluates and quantifies, temporal and spatially, the monthly precipitation estimated by Early (IMERG-E), Late (IMERG-L) and Final (IMERG-F) products compared with data from weather stations located in agricultural areas of the Pampas region in Argentina. Data of precipitation belonging to meteorological stations located at four provinces: Buenos Aires, Córdoba, La Pampa and Santa Fe, for 2014–2018 periods, were considered. The spatial performance of IMERG was evaluated using statistical coefficients and Taylor diagrams, considering at region, province and stations level. The adjustment of the products increased from IMERG-E to IMERG–F, obtaining R2 values between 0.86 and 0.95 and RMSE from 14.2 to 29.3 mm, the best results corresponding to Córdoba and the worst to La Pampa. The performance of GPM products varies temporally; IMERG-F presented a higher correlation coefficient and a lower percent root mean square error in warm than in cold seasons. The results indicate that GPM can effectively capture the amount and patterns of monthly precipitation over the Pampas region of Argentina, which is important for its application to agricultural production and disaster prevention.

Satellite rainfall products outperform ground observations for landslide prediction in India

Abstract. Landslides are among the most dangerous natural hazards, particularly in developing countries, where ground observations for operative early warning systems are lacking. In these areas, remote sensing can represent an important detection and monitoring process to predict landslide occurrence in space and time, particularly satellite rainfall products that have improved in terms of accuracy and resolution in recent times. Surprisingly, only a few studies have investigated the capability and effectiveness of these products in landslide prediction in reducing the impact of this hazard on the population. We have performed a comparative study of ground- and satellite-based rainfall products for landslide prediction in India by using empirical rainfall thresholds derived from the analysis of historical landslide events. Specifically, we have tested Global Precipitation Measurement (GPM) and SM2RAIN-ASCAT satellite rainfall products, and their merging, at daily and hourly temporal resolution, and Indian Meteorological Department (IMD) daily rain gauge observations. A catalogue of 197 rainfall-induced landslides that occurred throughout India in the 13-year period between April 2007 and October 2019 has been used. Results indicate that satellite rainfall products outperform ground observations thanks to their better spatial (0.1∘ vs. 0.25∘) and temporal (hourly vs. daily) resolutions. The better performance is obtained through the merged GPM and SM2RAIN-ASCAT products, even though improvements in reproducing the daily rainfall (e.g. overestimation of the number of rainy days) are likely needed. These findings open a new avenue for using such satellite products in landslide early warning systems, particularly in poorly gauged areas.

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.

Heavy Winter Precipitation Events with Extratropical Cyclone Diagnosed by GPM Products and Trajectory Analysis

Heavy precipitation events were identified during the cold seasons of 2014–2019 using two-day accumulated precipitation data at 137 stations of the Japan Meteorological Agency. The mechanisms for producing heavy precipitation regarding the structure of an occluding extratropical cyclone were analyzed using the products of the Dual-frequency Precipitation Radar (DPR) onboard the Global Precipitation Measurement (GPM) core satellite and trajectory analysis on the European Centre for Medium-range Weather Forecasts atmospheric reanalysis data. Upper-ranked events with heavy precipitation were predominantly caused by extratropical cyclones and were in mature stages. In the top 50 ranked events, three south-coast cyclones were nominated, and the relationships between developing the mesoscale precipitation system and airstreams were intensively diagnosed. Hourly precipitation changes at stations that recorded heavy precipitation were primarily affected by a combination of the warm conveyor belt (WCB), cold conveyor belt (CCB), and dry intrusion (DI). Wide-ranging stratiform precipitation east of the cyclone center was composed of low-level WCB over the CCB and upper WCB, and convective clouds around the cyclone center were associated with the upper DI over the WCB that provided an extreme precipitation rate at the surface, including the formation of a band-shaped precipitation system. Convective cloud activities also contributed to moist air advection over the stationary stratiform precipitation areas recognized as the upper WCB. DPR products also identified deep stratiform precipitation in the cloud-head area behind the cyclone center with mid-level (near-surface) latent heat release (absorption) with increased potential vorticity along the CCB, making feedback intensification of the cyclone possible.