High-resolution Precipitation Products Research Articles

Extreme precipitation detection ability of four high-resolution precipitation products dataset in Nepal

Given Nepal’s vulnerability to extreme precipitation (EP), it is imperative to conduct a comprehensive analysis to comprehend the historical trends of such events. However, acquiring precise precipitation data for EP remains challenging in mountainous countries like Nepal owing to the scarcity of densely gauged networks. This limitation impedes the dissemination of knowledge pertaining to EP variability events in Nepal. The current research on this topic is deficient for two main reasons: 1) there is a lack of studies leveraging recently released high-resolution precipitation products to identify their EP detection capabilities, which further hinders the usability of those products in data-scarce regions like Nepal, and 2) most studies have focused on the characterisation of EP events in Nepal rather than their spatial and temporal variability. To address these issues, this study evaluated the EP detection capabilities of four high-resolution precipitation product datasets (PPDs) across Nepal from 1985 to 2020. These datasets include the ERA5 Land reanalysis data, satellite-based precipitation data (PERSIANN_CCS_CDR and CHIRPS_V2.0) and a merged dataset (TPHiPr). We used various statistical and categorical indices to assess their ability to capture the spatial and temporal variability of EP events. The annual EP events were characterised by 11 indices divided into frequency and intensity categories. The TPHiPr merged dataset offered a robust depiction of monthly precipitation estimates, achieving the highest critical success index, accuracy, probability of detection and a low false alarm ratio for daily precipitation detection of 0.1 mm in Nepal. Conversely, the PERSIANN_CCS_CDR dataset exhibited poor performance. Most PPDs showed increasing trends in EP indices. However, the TPHiPr dataset showcased those trends with fewer errors and stronger correlations for many frequency (R10mm, R20mm and R25mm) and intensity (RX1day, RX5day, PRCPTOT and R99p) indices. The results indicate that TPHiPr outperformed other PPDs in accurately representing the spatial distribution of EP trends in Nepal from 1985 to 2020, particularly noting an exacerbation of EP events mostly in the eastern region of Nepal throughout the study period. While TPHiPr demonstrated superior performance in detecting various EP indices across Nepal, individual products like the ERA5 Land reanalysis dataset showed enhanced performance in the western region of Nepal. Conversely, PERSIANN_CCS_CDR and CHIRPS_V2.0 performed well in the eastern region compared to other PPDs.

Evaluation of high-resolution precipitation products (CMORPH-CRT, PERSIANN, and TRMM-3B42RT) and their performances as inputs to the hydrological model

Evaluation of high-resolution precipitation products (CMORPH-CRT, PERSIANN, and TRMM-3B42RT) and their performances as inputs to the hydrological model

Assessing drought impacts on groundwater and agriculture in Iran using high-resolution precipitation and evapotranspiration products

Accurate identification of drought events, supported by reliable and consistent spatiotemporal data, is essential for the development of efficient management and mitigation plans. The objectives of this study are twofold. First, we evaluate a wide range of global precipitation and potential evapotranspiration (PET) products versus in-situ observations for the period of 2001–2016 to identify the best-performing ones for drought assessment. Second, we assess the drought impact on groundwater storage and agricultural yield using Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI). The impact of drought on groundwater and agricultural yield was examined by investigating the association between SPI and SPEI with the Gravity Recovery and Climate Experiment (GRACE) satellite data, total crop production (CP), and total wheat production (WP). The results showed that the most severe and prolonged droughts occurred primarily in northwest highlands, northeastern regions, and southern regions, with SPI values indicating more intense drought events than SPEI values. While the SPEI index was found to be more suitable for assessing long-term drought severity, the SPI index was more sensitive to short-term precipitation deficits. Groundwater storage changes were found to be highly associated with SPEI/SPI at longer time scales. Furthermore, the results indicate that the strongest association between drought and yield loss occurs at the 9-month time scale. However, SPI exhibits a higher association with crop yield compared to SPEI. These findings can contribute to the development of drought management and response strategies.

Evaluation of Multi-Source Precipitation Products in the Hinterland of the Tibetan Plateau

High-resolution precipitation products have been crucial for hydrology, meteorology, and environmental ecosystems over the Tibetan Plateau (TP). However, these products are usually subject to systematic errors, which may vary with time and topographic conditions. The study evaluated the suitability of four satellite-derived products (GPM IMERG, GSMaP, CMORPH, and PERSIANN-CDR) and four fusion precipitation products (ERA5-Land, CHIRPS, CMFD, and TPHiPr) by comparing with 22 rain gauges at a daily scale from 1 January 2014 to 31 December 2018 over the hinterland of the TP. The main findings are as follows: (1) TPHiPr and CMFD are better than the satellite-derived products, while the performance of CHIRPS is worse; (2) among the satellite-derived products, the quality of GPM IMERG is the highest on different time scales, and PERSIANN-CDR is better in the months of June to October, while GSMaP and CMORPH have poor performance; (3) the eight precipitation products have weaker detection capability for heavy precipitation events, and the quality of each product decreases with the increase in the precipitation threshold, while the rate of descent of fusion precipitation products is slower than that of satellite-derived products. This study demonstrates the performance of eight precipitation products over the hinterland of the TP, which is expected to provide valuable information for hydrometeorology applications.

On the reliability of 12 high-resolution precipitation products for process-based hydrological modeling in China

Numerous satellite- and gauge-based precipitation products (SBPs/GBPs) have emerged in recent decades, providing unprecedented opportunities for process-based hydrological modeling. However, their reliability in high-resolution hydrological modeling at continental scales, especially for variables other than streamflow, remains to be investigated. Factors influencing their hydrological performance (e.g., catchment sizes, precipitation products’ spatial resolution and raw observation information) are also under debate. Here, we provide a comprehensive analysis of the hydrological reliability of 12 state-of-the-art precipitation datasets over China using a process-based land surface model at 0.125° resolution. Simulations driven by different precipitation products are evaluated across ∼ 200 basins with different catchment sizes. Results show that all simulations well reproduce the streamflow, soil moisture, evapotranspiration, and snow-depth at monthly timescale. None of the precipitation products provides best results for all variables, but the gauge-based and infrared-microwave-merged satellite products are superior to the infrared satellite products. The gauge-based regional precipitation product based on the 0.25° China precipitation dataset and the 6-km China Land Data Assimilation System (CN051_CLDAS) provides the best performance generally, followed by two global precipitation products from the Global Precipitation Climatology Centre (GPCC V2022) and the Global Precipitation Measurement Integrated Multi-satellite Retrievals (IMERGE-F V6). However, the advantages of CLDAS_CN051 and GPCC V2022 against satellite-based precipitation products are scale-dependent and tend to disappear over large-scale catchments (>106 km2). The spatial resolutions of precipitation products show no relationship with their hydrological performance, but the number of stations used to generate/bias-correct the precipitation is positively correlated with the performance of streamflow and soil moisture simulations. Differences among the climatology of terrestrial water-cycle components caused by using different precipitation products are notable and tend to increase over the dry and cold regions. Snow depth is the variable most sensitive to precipitation datasets, followed by runoff, ET, and soil moisture. This study suggests that using more station observations is more important than increasing spatial resolutions to enhance the applications of precipitation products in high-resolution hydrological modeling.

Evaluation of SPI and Rainfall Departure Based on Multi-Satellite Precipitation Products for Meteorological Drought Monitoring in Tamil Nadu

The prevalence of the frequent water stress conditions at present was found to be more frequent due to increased weather anomalies and climate change scenarios, among other reasons. Periodic drought assessment and subsequent management are essential in effectively utilizing and managing water resources. For effective drought monitoring/assessment, satellite-based precipitation products offer more reliable rainfall estimates with higher accuracy and spatial coverage than conventional rain gauge data. The present study on satellite-based drought monitoring and reliability evaluation was conducted using four high-resolution precipitation products, i.e., IMERGH, TRMM, CHIRPS, and PERSIANN, during the northeast monsoon season of 2015, 2016, and 2017 in the state of Tamil Nadu, India. These four precipitation products were evaluated for accuracy and confidence level by assessing the meteorological drought using standard precipitation index (SPI) and by comparing the results with automatic weather station (AWS) and rain gauge network data-derived SPI. Furthermore, considering the limited number of precipitation products available, the study also indirectly addressed the demanding need for high-resolution precipitation products with consistent temporal resolution. Among different products, IMERGH and TRMM rainfall estimates were found equipollent with the minimum range predictions, i.e., 149.8, 32.07, 80.05 mm and 144.31, 34.40, 75.01 mm, respectively, during NEM of 2015, 2016, and 2017. The rainfall data from CHIRPS were commensurable in the maximum range of 1564, 421, and 723 mm in these three consequent years (2015 to 2017) compared to AWS data. CHIRPS data recorded a higher per cent of agreement (>85%) compared to AWS data than other precipitation products in all the agro-climatic zones of Tamil Nadu. The SPI values were positive > 1.0 during 2015 and negative < −0.99 for 2016 and 2017, indicating normal/wet and dry conditions in the study area, respectively. This study highlighted discrepancies in the capability of the precipitation products IMERGH and TRMM estimates for low rainfall conditions and CHIRPS estimates in high rainfall regimes.

SMPD: a soil moisture-based precipitation downscaling method for high-resolution daily satellite precipitation estimation

Abstract. As a key component in the water and energy cycle, estimates of precipitation with high resolution and accuracy is of great significance for hydrological, meteorological, and ecological studies. However, current satellite-based precipitation products have a coarse spatial resolution (from 10 to 50 km) not meeting the needs of several applications (e.g., flash floods and landslides). The implementation of spatial downscaling methods can be a suitable approach to overcome this shortcoming. In this study, we developed a soil moisture-based precipitation downscaling (SMPD) method for spatially downscaling the integrated multisatellite retrievals for global precipitation measurement (IMERG) V06B daily precipitation product over a complex topographic and climatic area in southwestern Europe (Iberian Peninsula) in the period 2016–2018. By exploiting the soil-water balance equation, high-resolution surface soil moisture (SSM) and normalized difference vegetation index (NDVI) products were used as auxiliary variables. The spatial resolution of the IMERG daily precipitation product was downscaled from 10 to 1 km. An evaluation using 1027 rain gauge stations highlighted the good performance of the downscaled 1 km IMERG product compared to the original 10 km product, with a correlation coefficient of 0.61, root mean square error (RMSE) of 4.83 mm and a relative bias of 5 %. Meanwhile, the 1 km downscaled results can also capture the typical temporal and spatial variation behaviors of precipitation in the study area during dry and wet seasons. Overall, the SMPD method greatly improves the spatial details of the original 10 km IMERG product also with a slight enhancement of accuracy. It shows good potential to be applied for the development of high-quality and high-resolution precipitation products in any region of interest.

Evaluation of ERA5 and ERA5-Land reanalysis precipitation datasets over Spain (1951–2020)

Reanalysis precipitation estimates are widely used in the fields of meteorology and hydrology because they can provide physical, spatial, and temporal coherent long time series at a global scale. Nevertheless, as a pre-requisite for many applications their performance needs to be assessed. The objective of this study was to evaluate the European Centre for Medium-Range Weather Forecasts (ECMWF) latest fifth-generation reanalysis precipitation products, i.e., ERA5 and ERA5-Land, at country scale in Spain. For doing so, we compared it against a high-resolution precipitation product of the Spanish Meteorological Agency which spans approximately 70 years (1951–2020). A comprehensive assessment (continuous, categorical, probability distribution function (pdf), spatial pattern, and temporal trend) was performed in order to ascertain the quality of the reanalysis products. Results of the analysis revealed a general agreement between observations and ERA5-Land/ERA5 estimates: spearman correlation values between 0.5 and 0.9, Root Mean Square Error (RMSE) mostly between 2 and 8 mm/d and Kling Gupta Efficiency (KGE) values >0.4. Categorical assessment additionally indicated a good performance (Heiken Skill score (HSS) score, also known as kappa, between 0.4 and 0.8). Nevertheless, performance was found to be dependent on the climatic region, precipitation intensity and orography. Correlation revealed a north-west (higher values) south-east (lower values) spatial gradient while relative bias (RBIAS) and RMSE spatial patterns were positively correlated with slope (ρ = 0.41/0.35, 0.69/0.70, respectively). In addition, as indicated by the categorical analysis, along the Mediterranean coast a wet bias (i.e., overestimation of days with precipitation) was found. Reanalysis detection capacity (kappa) shown a negative correlation with the slope (ρ = −0.29/−0.34). Worst model performance is obtained during summer months, with a generalized overestimation. The pdf assessment revealed that the ERA5-Land/ERA5 tended to overestimate light (≥1 and < 5 mm/day), and moderate (≥5 and < 20 mm/day) precipitation categories while underestimating the heavy (≥20 and < 40 mm/day) and violent (≥40 mm/day) categories. Moderate precipitation provided the best detection capacity, as indicated by the precipitation-intensity analysis. ERA5-Land/ERA5 showed a good capacity to reproduce the spatial patterns and temporal trends of the observations. ERA5-Land and ERA5, with a different spatial resolution, performed very similar in all the analysis considered. Mediterranean and northern coast were highlighted as the most critical for reanalysis modelling purposes because of its performance.

Performance of New Near-Real-Time PERSIANN Product (PDIR-Now) for Atmospheric River Events over the Russian River Basin, California

Abstract Most heavy precipitation events and extreme flooding over the U.S. Pacific coast can be linked to prevalent atmospheric river (AR) conditions. Thus, reliable quantitative precipitation estimation with a rich spatiotemporal resolution is vital for water management and early warning systems of flooding and landslides over these regions. At the same time, high-quality near-real-time measurements of AR precipitation remain challenging due to the complex topographic features of land surface and meteorological conditions of the region: specifically, orographic features occlude radar measurements while infrared-based algorithms face challenges, differentiating between both cold brightband (BB) precipitation and the warmer nonbrightband (NBB) precipitation. It should be noted that the latter precipitation is characterized by greater orographic enhancement. In this study, we evaluate the performance of a recently developed near-real-time satellite precipitation algorithm: Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) Dynamic Infrared–Rain Rate-Now (PDIR-Now). This model is primarily dependent on infrared information from geostationary satellites as input; consequently, PDIR-Now has the advantage of short data latency, 15–60-min delay between observation to precipitation product delivery. The performance of PDIR-Now is analyzed with a focus on AR-related events for cases dominated by NBB and BB precipitation over the Russian River basin. In our investigations, we utilize S-band (3-GHz) precipitation profilers with Joss/Parsivel disdrometer measurements at the Middletown and Santa Rosa stations to classify BB and NBB precipitation events. In general, our analysis shows that PDIR-Now is more skillful in retrieving precipitation rates over both BB and NBB events across the topologically complex study area as compared to PERSIANN-Cloud Classification System (CCS). Also, we discuss the performance of well-known operational near-real-time precipitation products from 2017 to 2019. Conventional categorical and volumetric categorical indices, as well as continuous statistical metrics, are used to show the differences between various high-resolution precipitation products such as Multi-Radar Multi-Sensor (MRMS).

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

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

Comparing and contrasting the performance of high-resolution precipitation products via error decomposition and triple collocation: An application to different climate classes of the central Iran

Precipitation is a vital pillar in the most of hydro-climatological studies. To measure its stochastic behavior, recent technological advancements have provided new sources of High-Resolution Precipitation Products (HRPPs), which could be utilized to overcome limitations of the ground measurements. However, accuracy of HRPPs is not the same in different regions and climates and therefore, should be assessed prior to any practical application. In this study, monthly datasets of ten HRPPs, known as CHIRPS, CMORPH, ERA5-Land, GPM_3IMERGM, MSWEP V2, PERSIANN, PERSIANN-CCS, PERSIANN-CDR, TerraClimate, and TRMM_3B43, are assessed over Central Plateau watershed located in central Iran during 2005 to 2015. Lack of previous studies as well as remarkable variations in altitude and climate of this watershed, make it a suitable region for studying spatiotemporal pattern of precipitation and evaluating HRPPs. For this purpose, two approaches are implemented; comparing the products with ground measurements and together using Triple Collocation (TC). Through the first approach, an error decomposition scheme is utilized besides the other statistical metrics to further investigate total and seasonal accuracy of the HRPPs; Köppen-Geiger climate classification indicators are used to assess climate-based spatial performance of the HRPPs. According to the results, most of the HRPPs underestimate and overestimate precipitation values in wetter and drier climates, respectively. Additionally, winter contributes more than any other season to the biases of the products. While GPM_3IMERGM is the most accurate HRPP in the region with NRMSE, NSE, and KGE of 0.95, 0.62, and 0.73, respectively, PERSIANN-CCS results in the lowest accuracy with NRMSE, NSE, and KGE of 2.09, −0.82, and − 0.02, respectively.

Intercomparison of multiple high-resolution precipitation products over China: Climatology and extremes

An accurate and high spatiotemporal resolution precipitation observation dataset is crucial to the studies of climate change and related impact assessments. Great efforts have been made to evaluate the performance of a variety of precipitation products. However, most studies mainly focused on the assessment of precipitation estimation or precipitation occurrence detection, and seldom evaluated the capabilities of monitoring extreme precipitation of the utilized datasets. We conduct a study to quantitatively evaluate eight high-resolution precipitation products, including APHRODITE, CHIRPS, CMFD, CMORPH, IMERG, MSWEP, PERSIANN, and TMPA, against dense rain gauges in terms of precipitation climatology and extremes over China from 2001 to 2015. Results indicate that CMFD is superior to the rest products in terms of estimating precipitation amounts and monitoring precipitation extremes in China. APHRODITE performs best among all products in detecting precipitation occurrences. Precipitation products also exhibit different levels of performance in four subregions with distinct regional features. For both precipitation climatology and extremes, CMFD has the best applicability in the humid region and the Tibetan Plateau, while APHRODITE and MSWEP outperform other products in the wet-to-arid transition and arid regions, respectively. This work could provide a valuable basis for bias correction and downscaling of global climate model outputs. It also has implications for the selection of precipitation products for local studies and the future development of precipitation products in China.

Evaluation of High-Resolution Precipitation Products over the Rwenzori Mountains (Uganda)

Abstract The Rwenzori Mountains, in southwest Uganda, are prone to precipitation-related hazards such as flash floods and landslides. These natural hazards highly impact the lives and livelihoods of the people living in the region. However, our understanding of the precipitation patterns and their impact on related hazardous events and/or agricultural productivity is hampered by a dearth of in situ precipitation observations. Here, we propose an evaluation of gridded precipitation products as potential candidates filling this hiatus. We evaluate three state-of-the-art gridded products, the ERA5 reanalysis, IMERG satellite observations, and a simulation from the convection-permitting climate model (CPM), COSMO-CLM, for their ability to represent precipitation totals, timing, and precipitation probability density function. The evaluation is performed against observations from 11 gauge stations that provide at least 2.5 years of hourly and half-hourly data, recorded between 2011 and 2016. Results indicate a poor performance of ERA5 with a persistent wet bias, mostly for stations in the rain shadow of the mountains. IMERG gives the best representation of the precipitation totals as indicated by bias score comparisons. The CPM outperforms both ERA5 and IMERG in representing the probability density function, while both IMERG and the CPM have a good skill in capturing precipitation seasonal and diurnal cycles. The better performance of CPM is attributable to its higher resolution. This study highlights the potential of using IMERG and CPM precipitation estimates for hydrological and impact modeling over the Rwenzori Mountains, preferring IMERG for precipitation totals and CPM for precipitation extremes.

A New Downscaling-Calibration Procedure for TRMM Precipitation Data Over Yangtze River Economic Belt Region Based on a Multivariate Adaptive Regression Spline Model

High-resolution precipitation products are essential for accurate hydrological and meteorological applications. To improve the spatial resolution and accuracy of monthly satellite precipitation products, we developed a new downscaling-calibration framework with three key steps: 1) coarse-resolution satellite precipitation data are downscaled to 1-km resolution precipitation data with multivariate adaptive regression spline (MARS) model; 2) residual correction is applied to bridge the difference between the satellite precipitation data and downscaled precipitation data; and 3) the geographical differential analysis (GDA) calibration method is implemented to improve accuracy by merging the residual-corrected data with rain gauge data. In this study, geolocation variables (longitude and latitude), a digital elevation model (DEM) data, daytime and nighttime land surface temperatures, and four remote sensing indices were used to downscale monthly Tropical Rainfall Measuring Mission (TRMM) 3B43 precipitation datasets over the Yangtze River Economic Belt. The downscaled results showed that the MARS model can avoid “boxy artifact” and pixel-level anomalies, which are often found in geographically weighted regression (GWR) and random forest (RF) results. According to the validation, the step of residual correction is not necessary. With GDA calibration, the MARS-based estimated results were more accurate than the results of the other methods (i.e., GWR and RF) and original TRMM products. Therefore, the developed MARS-based downscaling-calibration procedure can improve not only the spatial resolution but also the quality of the TRMM 3B43 products.

Intercomparison of Multiple High-Resolution Precipitation Products Over China: Climatology and Extremes

Intercomparison of Multiple High-Resolution Precipitation Products Over China: Climatology and Extremes

Testing the accuracy of high-resolution satellite-based and numerical model output precipitation products over Ethiopia

The indirect rainfall estimates by satellites and numerical models are the alternative options for the regions lacking enough and accurate ground observations. However, these indirect estimates often lack homogeneity and need to be evaluated before application. This study used gauge observations to test the accuracy of recently produced high-resolution satellite-based and numerical model output rainfall products over Ethiopia. Tropical Applications of Meteorology Using Satellite data and Ground-Based Observations (TAMSAT v3.1), Climate Hazard group Infrared Precipitation with Stations (CHIRPS v2.0), and the ERA5 reanalysis products were evaluated at monthly, seasonal, and annual temporal scales for the years 1992–2009. The satellite products showed nearly similar characteristics with much better accuracy than the model reanalysis output, which underestimated the rainfall amounts. Both satellite and reanalysis products captured the shapes of the rainfall at a monthly scale but less accurately at a seasonal scale. In general, the satellite-based products outperformed the reanalysis data set with a high correlation coefficient and index of agreement values, as well as low Root Mean Square Error and BIAS values. On the other hand, the reanalysis (ERA5) product showed a considerable underestimation in all sites. Therefore, satellite-based products are more reliable for researches in the region. However, the algorithms in both satellites need further calibration for a better estimation of seasonal rainfall amounts.

Evaluation of the CMORPH high-resolution precipitation product for hydrological applications over South Korea

The use of a high-resolution satellite-based precipitation product on a global-scale is attractive to hydrological applications. However, it should be systematically evaluated from various perspectives as the quality property is dependent on the geographical and topographical features of a region. This study aims to comprehensively assess the Climate Prediction Center morphing technique (CMORPH) precipitation product over South Korea. Two evaluation approaches, the general evaluation using statistical metrics and the detection evaluation (to measure skill in detecting precipitation and non-precipitation) using categorical metrics, are employed based on an 18-year long-term period of record. As a result, the CMORPH product tended to underestimate precipitation over South Korea, and the level of the underestimation varied with the seasons and regions. The overall quality was adequate for hydrological applications that require precipitation data at the annual-to-daily resolution but not at hourly resolution. Skill in detecting hourly precipitation in a storm event was 60% of the rain-gauge data, and the accuracies of total volume and peak value were 45% and 40%, respectively. The quality in the coastal regions and islands was not as good as in inland areas at low altitudes. The accuracy in a wet season was better than that in a dry/winter season. Notably, the CMORPH precipitation product was not suitable for snowfall data. Ultimately, the CMORPH product at hourly resolution needs a correction process using the local measurement systems for enhancing the quality property over South Korea.

Exploring the effect of urbanization on hourly extreme rainfall over Yangtze River Delta of China

Short-term extreme rainfall events (EREs) often produce urban flooding and result in the loss of life and property. In this study, three high spatial-temporal resolution precipitation products were used to analyze hourly EREs during May-October from 2001 to 2018 over the Yangtze River Delta (YRD). To quantify the possible link between EREs and environmental factors (such as urbanization, climate change, and orography), the Hovmoller diagrams and Generalized additive model for location, scale, and shape (GAMLSS) nonstationary model were used. The results showed that the eastward propagating precipitation systems created a heavy rainfall center in the southwest mountainous of YRD due to orographic lifting, and enhanced in the plains over urbanized areas, especially in the vicinity of Nanjing, Changzhou, and Nantong metropolitan regions. The variability of hourly rainfall extreme in the urbanized regions is high correlated to the multivariate ENSO index (MEI), and shows a greater magnitude in the urban than rural regions. This phenomenon was intensified by large-scale circulation patterns due to strong-ENSO over the Nanjing-Changzhou-Nantong (NCN) urban regions, but weakened over the Shanghai-Hangzhou-Ningbo (SHN) urban regions. An analysis of nonstationary GAMLSS also suggests the large-scale circulation presents a greater impact on the hourly extreme rainfall than the urbanization, while the hourly rainfall extremes in the urban areas are still higher than that in rural regions under the nonstationary scenario. Those results suggest intense rainfall occurred more frequently in the urban region under observed climate change and urbanization in YRD.

Downscaling Satellite and Reanalysis Precipitation Products Using Attention-Based Deep Convolutional Neural Nets

High-quality and high-resolution precipitation products are critically important to many hydrological applications. Advances in satellite remote sensing instruments and data retrieval algorithms continue to improve the quality of the operational precipitation products. However, most satellite products existing today are still too coarse to be ingested for local water management and planning purposes. Recent advances in deep learning algorithms enable the fusion of multi-source, high-dimensional data for statistical learning. In this study, we investigated the efficacy of an attention-based, deep convolutional neural network (AU-Net) for learning spatial and temporal mappings from coarse-resolution to fine-resolution precipitation products. The skills of AU-Net models, developed using combinations of static and dynamic predictors, were evaluated over a 3 × 3° study area in Central Texas, U.S., a region known for its complex precipitation patterns and low predictability. Three coarse-resolution satellite/reanalysis precipitation products, ERA5-Land (0.1°), TRMM (0.25°), and IMERG (0.1°), are used as part of the inputs, while the predictand is the 1-km PRISM data. Auxiliary predictors include elevation, vegetation index, and air temperature. The study period includes 18 years of data (2001–2018) at the monthly scale for training, validation, and testing. Results show that the trained AU-Net models achieve different degrees of success in downscaling the baseline coarse-resolution products, depending on the total precipitation, the accuracy of large-scale patterns captured by the baseline products, and the amount of information transferable from predictors. Higher precipitation rate tends to affect AU-Net model performance negatively. Use of the attention mechanism in the AU-Net models allows for infilling of multiscale features and generation of sharper images. Correction using gauge data, if there is any, can further improve the results significantly.

Spatial and Temporal Evaluation of the Latest High-Resolution Precipitation Products over the Upper Blue Nile River Basin, Ethiopia

Quality and representative precipitation data play an essential role in hydro-meteorological analyses. However, the required reliability and coverage is often unavailable from conventional gauge observations. As a result, globally available precipitation datasets are being used as an alternative or supplementary to gauge observations. In this study, the accuracy of three recently released, high-resolution precipitation datasets with a spatial resolution of 0.1° and a daily temporal resolution is evaluated over the Upper Blue Nile River Basin (UBNRB) for the period of 2007 to 2016. The datasets are Integrated Multi-satellitE Retrievals for GPM version 6 (IMERG6), Multi-Source Weighted-Ensemble Precipitation version 2.2 (MSWEP2.2) and soil moisture to rain using Advanced SCATterometer version 1.1 (SM2RAIN-ASCAT1.1). The comparison was made between rain gauge observations and two other high-resolution precipitation datasets named Enhancing National Climate Services (ENACTS) and Climate Hazards Group Infrared Precipitation with Stations version 2 (CHIRPS2). The modified Kling-Gupta efficiency (KGE’) and four categorical indices named probability of detection (POD), false alarm ratio (FAR), critical success index (CSI), and frequency bias (fBIAS) was used to measure the skills of each dataset. Results revealed that, except SM2RAIN-ASCAT1.1, all other datasets show a better ability on a monthly time scale for areas with an elevation below 1500 m above sea level (m.a.s.l). The overall performance was better in the wetter months of March to August than the drier months of September to February. Besides, all products including SM2RAIN-ASCAT1.1 could detect no rain events (rain &lt; 1 mm) correctly, but their skill deteriorates on identifying higher intensity events. By comparison, ENACTS (calibrated with most quality gauges of Ethiopia) and CHIRPS2 exhibited the best performance due to their high-resolution nature and inclusion of physiographic information in their data generation procedures. IMERG6 and MSWEP2.2 showed the next best performance according to both the continuous and categorical indices used. SM2RAIN-ASCAT1.1 demonstrates the least skill everywhere due to problems that could be associated with misinterpretations of soil moisture signals by the SM2RAIN algorithm. Considering the scarcity of gauged datasets over UBNRB, IMERG6 and MSWEP2.2 could be regarded as valuable datasets for hydro-climatic analysis, mainly where gauge density is low. SM2RAIN-ASCAT1.1, on the other hand, needs significant bias correction to treat its apparent wet biases before any application.