High-resolution Precipitation Datasets Research Articles

Intercomparison of reanalysis and satellite precipitation products in endorheic and exorheic basins on the Tibetan Plateau

Study Region: Endorheic and exorheic basins of the Tibetan Plateau (TP). Study Focus: Reanalysis and satellite precipitation products provide alternatives for regions of sparse ground precipitation observation, but pose a tough task to select a suitable one for the TP. This study conducts a multi-scale evaluation of six reanalysis and satellite precipitation products in endorheic and exorheic basins using water balance and extended triple collocation (ETC) methods. The reanalysis precipitation products include ECMWF Re-Analysis version 5 (ERA5-Land), China Meteorological Forcing Dataset (CMFD), Global Land Data Assimilation Systems (GLDAS), and High-resolution Precipitation dataset for the Third Pole region (TPHiPr). The satellite precipitation data include Global Precipitation Measurements (GPM) and Tropical Rainfall Measuring Mission (TRMM) products. New Hydrological Insights for the Region: The precipitation products vary in accuracy from basin to basin, with better performance in exorheic than endorheic basins. Reanalysis-based ERA5-land, TPHiPr, and CMFD perform well in most basins at annual scale, among which TPHiPr performs best at daily scale. At regional scale, GPM performs well in endorheic region, and ERA5-land in exorheic region. While all the products increase significantly in accuracy from basin to regional scale in endorheic region, ERA5-land shows best performance at annual and multi-year scales in the entire region. Our findings provide valuable supports for precipitation product selection in the Tibetan endorheic and exorheic basins.

PreciDBPN: A customized deep learning approach for hourly precipitation downscaling in eastern China

Long-term series of high-resolution gridded precipitation datasets are essential for hydrological and meteorological research. Producing high-resolution precipitation data from regional models demands substantial computational resources and labor. Global Reanalyses offer long-term coverage and effectively capture annual and seasonal precipitation patterns. However, they have inadequate resolution and frequently have difficulties depicting extreme conditions. This study proposes an efficient and accurate approach for generating long-term series of high spatial and temporal resolution precipitation. It is achieved by leveraging deep learning techniques to integrate the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) global climate reanalysis (ERA5, 0.25°, hourly) data with high-resolution precipitation fusion datasets. Considering the heavy-tailed distribution of precipitation, we developed the PreciDBPN model structure, which combines a classification network with a super-resolution network and incorporates physically relevant indices into the model's input. We trained and evaluated the PreciDBPN and baseline models in eastern China using the China Meteorological Administration Land Data Assimilation System (CLDAS) precipitation dataset (0.0625°, hourly, 2017–2022). When compared to baseline methods and ERA5, our model excels in multiple metrics and provides a more precise representation of relative rainfall frequency. Independent verification was performed using station observations during the period of 2010–2015 when CLDAS data were unavailable. During this verification, the PreciDBPN demonstrated exceptional performance and greater robustness compared to the baseline models. Because our method can efficiently downscale precipitation and bias-correct reanalysis data using minimal computational resources, it can be used to generate high-resolution precipitation datasets (0.0625°, hourly) from 1979 to 2022 while correcting for heavy precipitation underestimations in reanalysis data.

Uncertainty estimation of machine learning spatial precipitation predictions from satellite data

Abstract Merging satellite and gauge data with machine learning produces high-resolution precipitation datasets, but uncertainty estimates are often missing. We addressed the gap of how to optimally provide such estimates by benchmarking six algorithms, mostly novel even for the more general task of quantifying predictive uncertainty in spatial prediction settings. On 15 years of monthly data from over the contiguous United States (CONUS), we compared quantile regression (QR), quantile regression forests (QRF), generalized random forests (GRF), gradient boosting machines (GBM), light gradient boosting machines (LightGBM), and quantile regression neural networks (QRNN). Their ability to issue predictive precipitation quantiles at nine quantile levels (0.025, 0.050, 0.100, 0.250, 0.500, 0.750, 0.900, 0.950, 0.975), approximating the full probability distribution, was evaluated using quantile scoring functions and the quantile scoring rule. Predictors at a site were nearby values from two satellite precipitation retrievals, namely PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) and IMERG (Integrated Multi-satellitE Retrievals), and the site’s elevation. The dependent variable was the monthly mean gauge precipitation. With respect to QR, LightGBM showed improved performance in terms of the quantile scoring rule by 11.10%, also surpassing QRF (7.96%), GRF (7.44%), GBM (4.64%) and QRNN (1.73%). Notably, LightGBM outperformed all random forest variants, the current standard in spatial prediction with machine learning. To conclude, we propose a suite of machine learning algorithms for estimating uncertainty in spatial data prediction, supported with a formal evaluation framework based on scoring functions and scoring rules.

Global-scale evaluation of precipitation datasets for hydrological modelling

Abstract. Precipitation is the most important driver of the hydrological cycle, but it is challenging to estimate it over large scales from satellites and models. Here, we assessed the performance of six global and quasi-global high-resolution precipitation datasets (European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis version 5 (ERA5), Climate Hazards group Infrared Precipitation with Stations version 2.0 (CHIRPS), Multi-Source Weighted-Ensemble Precipitation version 2.80 (MSWEP), TerraClimate (TERRA), Climate Prediction Centre Unified version 1.0 (CPCU), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR, hereafter PERCCDR) for hydrological modelling globally and quasi-globally. We forced the WBMsed global hydrological model with the precipitation datasets to simulate river discharge from 1983 to 2019 and evaluated the predicted discharge against 1825 hydrological stations worldwide, using a range of statistical methods. The results show large differences in the accuracy of discharge predictions when using different precipitation input datasets. Based on evaluation at annual, monthly, and daily timescales, MSWEP followed by ERA5 demonstrated a higher correlation (CC) and Kling–Gupta efficiency (KGE) than other datasets for more than 50 % of the stations, whilst ERA5 was the second-highest-performing dataset, and it showed the highest error and bias for about 20 % of the stations. PERCCDR is the least-well-performing dataset, with a bias of up to 99 % and a normalised root mean square error of up to 247 %. PERCCDR only show a higher KGE and CC than the other products for less than 10 % of the stations. Even though MSWEP provided the highest performance overall, our analysis reveals high spatial variability, meaning that it is important to consider other datasets in areas where MSWEP showed a lower performance. The results of this study provide guidance on the selection of precipitation datasets for modelling river discharge for a basin, region, or climatic zone as there is no single best precipitation dataset globally. Finally, the large discrepancy in the performance of the datasets in different parts of the world highlights the need to improve global precipitation data products.

Spatio-Temporal Trends in Precipitation, Temperature, and Extremes: A Study of Malawi and Zambia (1981–2021)

Analyzing long-term climate changes is a prerequisite for identifying hotspot areas and developing site-specific adaptation measures. The current study focuses on assessing changes in precipitation, maximum and minimum temperatures, and potential evapotranspiration in Zambia and Malawi from 1981 to 2021. High-resolution precipitation and temperature datasets are used, namely, Climate Hazards Group InfraRed Precipitation with Station data (0.05°) and Multi-Source Weather (0.1°). The Mann–Kendall trend test and Sen’s Slope methods are employed to assess the changes. The trend analysis shows a non-significant increase in annual precipitation in many parts of Zambia and Central Malawi. In Zambia and Malawi, the average annual and seasonal maximum and minimum temperatures show a statistically significant increasing trend (up to 0.6 °C/decade). The change in precipitation during the major rainy seasons (December–April) shows a non-significant increasing trend (up to 3 mm/year) in a large part of Zambia and Central Malawi. However, Malawi and Northern Zambia show a non-significant decreasing trend (up to −5 mm/year). The change in December–April precipitation significantly correlates with El Niño–Southern Oscillation (Indian Ocean Dipole) in Southern (Northern) Zambia and Malawi. To minimize the impact of the observed changes, it is imperative to develop adaptation measures to foster sustainability in the region.

Bias-corrected high-resolution precipitation datasets assessment over a tropical mountainous region in Colombia: A case of study in Upper Cauca River Basin

Surface gauge measurements have been commonly employed to analyze the precipitation's high spatial and temporal variability. However, incomplete area coverage and deficiencies over most tropical and complex topography mean significant limitations of this measurement type. Satellite-derived datasets, combined with the integration of in-situ observations with satellite data, are an alternative to address these limitations by offering a more spatially homogeneous and temporally comprehensive coverage for scarce data areas of the globe. Nevertheless, applying a bias correction technique on the precipitation datasets is still necessary before they are used for research due to their considerable bias. Here, we analyze the performance of CHIRPS, WorldClim, and TerraClimate datasets compared to data from 30 rain gauge stations over the South-West of Colombia, specifically in the Upper Cauca River Basin-UCRB between 1981 and 2018. Additionally, we applied the Quantile Mapping correction to all gridded precipitation products, and subsequently, the corrected rainfall is compared to the observed data on the monthly, seasonal, and annual scale. Our results show that the CHIRPS dataset better captures the seasonal and monthly variability. CHIRPS presents the best performance during less rainy seasons and at low elevation zones (900–2000 m above sea level-m.a.s.l.), followed by TerraClimate. Utilizing the bias correction methodology, we generated a new, corrected, and more reliable monthly precipitation time series for each location from all gridded precipitation products. Additionally, we found that the correction of the CHIRPS dataset presented the best performance across all spatiotemporal scales in the UCRB. Therefore, this study provides an accurate precipitation database for a complex topographic tropical region with limited data availability.

Rainfall Erosivity Mapping for Tibetan Plateau Using High-Resolution Temporal and Spatial Precipitation Datasets for the Third Pole

Low-density weather station and high topographic variance limited rainfall erosivity (RE) calculation for Tibetan Plateau (TP). The accuracy of RE prediction from three data sources (a High-resolution Precipitation dataset for the Third Pole (TPHiPr), IMERG Final Run (IMERG-F) and weather station daily precipitation data) were evaluated for the TP, and the variations were analyzed from 2001 to 2020. The results showed that TPHiPr can more accurately characterize spatial and temporal variations of the RE on the TP. TPHiPr can better represent the impact of topography on precipitation, effectively compensating the deficiencies in precipitation data from low-density stations. The R2 and NSE between the mean annual/monthly RE of TPHiPr and the station data were around 0.9. TPHiPr effectively revealed rain shadow areas on the northern slopes of the Himalayas and calculated RE more accurately in the broad-leaved evergreen forest zone on the southern flank of the Himalayas and the arid regions to the northwest. RE from 2001 to 2020 showed an overall increasing trend. However, TPHiPr produced underestimates in the southern valleys and the eastern Hengduan Mountains, while overestimates in the southeastern area at lower elevations. This research provided a new and more accurate RE data for the TP.

Quantifying the snowfall variations in the Third Pole region from 1980 to 2020

Snowfall is critical in the mountainous cryosphere cycle and is vulnerable to climate change. Due to data-scarcity and strong surface heterogeneity, its information is not sufficiently revealed across the Third Pole (TP) under warming. Accordingly, this study investigated the spatiotemporal variability of snowfall and its entropy information across the TP from 1980 to 2020 based on a new high-accuracy snowfall dataset (TPHiS), which was estimated from a long-term high-resolution precipitation dataset for the TP (TPHiPr) and High Asia Refined analysis version 2 reanalysis data (HARv2) using a threshold temperature method. The results indicated that the correlation coefficient, mean bias, and root-mean-square error of the daily TPHiS were 0.77, 0.03 mm/daily, and 0.18 mm/daily, respectively, compared against in-situ observation, illustrating that this dataset performs better than other snowfall datasets. The annual mean snowfall measured 259.38 mm across the TP and experienced a slight negative trend (−0.77 mm/decade, p > 0.05). A significantly positive trend in annual snowfall was observed in the intersection of the West Kunlun, Karakoram, and Western Inner Tibet, with elevations over 5500 m. In contrast, a significantly negative trend prevailed in the Pamir, Hengduan Shan, and West and Central Himalaya intersections, especially at elevations varied from 3000 to 4500 m. Compared with other seasons, a significantly negative trend in snowfall was observed in summer (−1.94 mm/decade, p < 0.05). Change in snowfall frequency entropy was more significant than that in snowfall amount entropy. A significantly positive trend in light snowfall frequency dominated the augmentation in snowfall frequency, causing a negative trend in snowfall frequency entropy, while a substantial reduction in snowfall frequency associated with the decline in light snowfall frequency induced a positive trend in snowfall frequency entropy. The outcomes provide a finer snowfall dataset for regional cryospheric research and might inform sustainable water resource management in alpines.

Construction of high-resolution precipitation dataset and its implication to drought over the Tianshan Mountains, China

Understanding the drought characteristics of mountainous areas in northwest China with sparse rainfall stations requires high precision, as well as high-resolution precipitation data. Considering the spatial relationship of precipitation and environmental factors, this study downscales Global Precipitation Measurement (GPM) and Multi-Source Weighted-Ensemble Precipitation (MSWEP) based on the geographically weighted regression (GWR) and multi-scale geographically weighted regression (MGWR) models integrated with interpolation. A high-resolution (1 km×1 km) precipitation dataset during 1979–2020 is reconstructed in the Tianshan Mountains, and the drought characteristics are analyzed by using the optimal dataset. The results show that: 1) Compared with GWR, MGWR model has higher downscaling accuracy; 2) The optimal MSWEP downscaling dataset (CC = 0.93, |BIAS| = 0.48%) compared to GPM (CC = 0.81, |BIAS| = 1.87%) is closer to the observed precipitation; 3) In the past 40 years, 71% and 9% of the Tianshan Mountains show significant wetting and drying trends respectively, and 16 drought events are identified. 4) The West subregion of the Tianshan Mountains is characterized by low frequency, long duration and high severity of drought events. The characteristics of the East are opposite to those of the West. Occasional extreme drought events occur in the North and South. This paper provides data support and method reference for the study of water-vapor balance and regional ecohydrological process in the arid area of Northwest China.

TPHiPr: a long-term (1979–2020) high-accuracy precipitation dataset (1∕30°, daily) for the Third Pole region based on high-resolution atmospheric modeling and dense observations

Abstract. Reliable precipitation data are highly necessary for geoscience research in the Third Pole (TP) region but still lacking, due to the complex terrain and high spatial variability of precipitation here. Accordingly, this study produces a long-term (1979–2020) high-resolution (1/30∘, daily) precipitation dataset (TPHiPr) for the TP by merging the atmospheric simulation-based ERA5_CNN with gauge observations from more than 9000 rain gauges, using the climatologically aided interpolation and random forest methods. Validation shows that TPHiPr is generally unbiased and has a root mean square error of 5.0 mm d−1, a correlation of 0.76 and a critical success index of 0.61 with respect to 197 independent rain gauges in the TP, demonstrating that this dataset is remarkably better than the widely used datasets, including the latest generation of reanalysis (ERA5-Land), the state-of-the-art satellite-based dataset (IMERG) and the multi-source merging datasets (MSWEP v2 and AERA5-Asia). Moreover, TPHiPr can better detect precipitation extremes compared with these widely used datasets. Overall, this study provides a new precipitation dataset with high accuracy for the TP, which may have broad applications in meteorological, hydrological and ecological studies. The produced dataset can be accessed via https://doi.org/10.11888/Atmos.tpdc.272763 (Yang and Jiang, 2022).

Performance of high-resolution precipitation datasets CHIRPS and TerraClimate in a Colombian high Andean Basin

High-resolution reanalysis products could improve the representativeness of rainfall on high Andean basins, but their performance must be locally validated. We addressed the performance, accuracy, and ability of TerraClimate and CHIRPSv.2 to represent 36 years of rainfall (1985–2020) from 23 stations in the Upper Chicamocha River, a Colombian basin of complex terrain and tropical hydrometeorology. Using several statistical metrics at monthly, seasonal and annual scales, we found how both datasets overestimate rainfall as a function of elevation, with better performance and accuracy from CHIRPS (r ∼0.76, R2 ∼0.58, NSE ∼0.56, and low RMSE ∼33.7 mm/month, MAE ∼25.2 mm/month, ME ∼6.4 mm/month, and PBIAS ∼9.3), while TerraClimate overestimates inter-annual variability, especially between June and August. Seasonally, the datasets exhibit different spatial patterns and magnitudes, even after bias correction. The findings highlight the potential use and challenges of high-resolution datasets in basins with similar topography and hydrometeorology in the Andean region.

Atmospheric River activities and their effects on precipitation over South Korea

Atmospheric rivers (ARs) have been recognized as the main mechanism of water supply to many coastal regions across the globe, and large fractions of extreme precipitation events have been attributed to them in coastal landforms. Though more than 70% boundary of South Korea is coastal, the research literature on ARs and their impacts on precipitation is scarce, if not absent. In this study, we aim to explore the AR activity over South Korea and estimate their seasonal and spatial impact on the precipitation and its extremes over the region. To this end, we use gridded reanalysis data for identifying ARs and a recently developed high-resolution precipitation dataset for South Korea. As results, we find that ARs are an important source of precipitation over South Korea by occurring in all seasons, though their frequency varies significantly across the seasons. Further, we conclude that more than half of precipitation extremes may be attributed to ARs, and most of the top-ten extreme precipitation events witnessed by South Korea happen concurrently with ARs, especially in winter and spring seasons. Lastly, our analysis indicates that recent the changes in inter-annual variations of winter precipitation may be attributed to the strengthening of AR contributions, implying that more research on ARs and their impacts in a warmer world is critical for the region. Overall, our findings demonstrate that ARs are one of the main mechanisms of oceanic moisture inflow into South Korea, which will likely have important implications to water management in the country, through targeted forecasting of ARs and by utilizing the information in climate mechanism-based water planning and management.

Spatiotemporal estimation of 6-hour high-resolution precipitation across China based on Himawari-8 using a stacking ensemble machine learning model

Precipitation plays a significant role in the hydrological cycle and atmospheric circulation. However, estimating precipitation is a challenging task due to the high spatiotemporal variability of precipitation. The development of machine learning has marked a new approach to precipitation estimation. In this study, a stacking ensemble machine learning model was developed to estimate 6-hour precipitation at a high spatial resolution of 5 km based on Himawari-8 and ground stations data over China. Metrological and topographic factors are considered ancillary data. The stacking model consists of two levels. Three sub-models, namely, extremely randomized trees, extreme gradient boosting, and deep neural networks, were trained respectively in level-1 of the stacking model. Level-2 linearly combines sub-models to achieve the final estimation. Evaluation results over China indicate that the stacking model outperforms the individual model, with a correlation coefficient of 0.630, a mean absolute error of 1.431 mm/6hr, and a root mean square error of 4.248 mm/6hr. The performance of the proposed model is better than several widely used precipitation products (IMERG, GSMaP, and ERA5). In particular, the detection rate of heavy precipitation (≥16.9 mm/6hr) in our model, with a probability of detection of 0.97, is distinctly superior to that of the other products. The precipitation estimations are accumulated and plotted at different temporal scales, contrasting with these products. The spatial patterns appear to be relatively coincident at annual and daily scales. More importantly, the spatial patterns of our model are more reliable than those of the products due to its smaller mean bias. The study provided a novel alternative for producing improved high-resolution precipitation datasets with high accuracy.

Response of Precipitation in Tianshan to Global Climate Change Based on the Berkeley Earth and ERA5 Reanalysis Products

Global climate change has readjusted a global-scale precipitation distribution in magnitude and timing. In mountainous areas, meteorological stations and observation data are very limited, making it difficult to accurately understand the response of precipitation to global climate change. Based on ECMWF Reanalysis v5 precipitation products, Berkeley Earth global temperature, and typical atmospheric circulation indexes, we integrated a gradient descent-nonlinear regression downscaling model, cross wavelet transform, and wavelet correlation method to analyze the precipitation response in Tianshan to global climate change. This study provides a high-resolution (90 m × 90 m) precipitation dataset in Tianshan and confirms that global warming, the North Pacific Pattern (NP), the Western Hemisphere Warm Pool (WHWP), and the Atlantic Multidecadal Oscillation (AMO) are related to the humidification of Tianshan over the past 40 years. The precipitation in Tianshan and global temperature have a resonance period of 8–15 months, and the correlation coefficient is above 0.9. In Tianshan, spring precipitation is determined mainly by AMO, North Tropical Atlantic Sea Level Temperature, Pacific Interdecadal Oscillation (PDO), Tropical North Atlantic Index, WHWP, NP, summer by NP, North Atlantic Oscillation, and PDO, autumn by AMO, and winter by Arctic Oscillation. This research can serve the precipitation forecast of Tianshan and help in the understanding of the regional response to global climate change.

Characterizing extreme values of precipitation at very high resolution: An experiment over twenty European cities

The paper presents a new hourly high-resolution (i.e., at 0.02°, ≃2.2 km) precipitation dataset, labelled as ERA5@2km, obtained by dynamically downscaling ERA5 reanalysis at convection permitting scale (CPS) over 20 European cities for the recent past thirty years (1989–2018). The downscaling activity is performed within the framework of the Contract implemented by Fondazione CMCC to support Sectoral Information System about “Disaster Risk Reduction” (see https://climate.copernicus.eu/pluvial-flood-risk-assessment-urban-areas) of Copernicus Climate Change Service (C3S). Specifically, such an additional precipitation dataset is developed to provide precipitation data for estimating expected precipitations at fixed return periods used as input for pluvial flooding risk analysis where hazard (inundated areas, water levels) and risk (estimated damages) are assessed. The ambition is to support the Disaster Risk Reduction (DRR) community involved in pluvial flood risk assessment by providing a basis for impact analysis at city scale, in terms of extreme hourly precipitation, that matches with the expected spatial and temporal requirements. In this work, ERA5@2km precipitation dataset is introduced for the first time and its reliability and coherence are evaluated as for spatial patterns and trends as for extreme values against a set of available high-resolution observational datasets (comparable in terms of spatial and temporal resolution). Such an evaluation provides a clearer understanding about the added value of very high-resolution (VHR) dynamical downscaling reanalysis in terms of localization and magnitude of precipitation events at urban scale, confirming a general and relevant added value of this new configuration for the assessment of extreme atmospheric events (such as heavy precipitations).

Global Assessment of the Capability of Satellite Precipitation Products to Retrieve Landslide-Triggering Extreme Rainfall Events

Abstract Rainfall-induced landsliding is a global and systemic hazard that is likely to increase with the projections of increased frequency of extreme precipitation with current climate change. However, our ability to understand and mitigate landslide risk is strongly limited by the availability of relevant rainfall measurements in many landslide prone areas. In the last decade, global satellite multisensor precipitation products (SMPP) have been proposed as a solution, but very few studies have assessed their ability to adequately characterize rainfall events triggering landsliding. Here, we address this issue by testing the rainfall pattern retrieved by two SMPPs (IMERG and GSMaP) and one hybrid product [Multi-Source Weighted-Ensemble Precipitation (MSWEP)] against a large, global database of 20 comprehensive landslide inventories associated with well-identified storm events. We found that, after converting total rainfall amounts to an anomaly relative to the 10-yr return rainfall R*, the three products do retrieve the largest anomaly (of the last 20 years) during the major landslide event for many cases. However, the degree of spatial collocation of R* and landsliding varies from case to case and across products, and we often retrieved R* &gt; 1 in years without reported landsliding. In addition, the few (four) landslide events caused by short and localized storms are most often undetected. We also show that, in at least five cases, the SMPP’s spatial pattern of rainfall anomaly matches landsliding less well than does ground-based radar rainfall pattern or lightning maps, underlining the limited accuracy of the SMPPs. We conclude on some potential avenues to improve SMPPs’ retrieval and their relation to landsliding. Significance Statement Rainfall-induced landsliding is a global hazard that is expected to increase as a result of anthropogenic climate change. Our ability to understand and mitigate this hazard is strongly limited by the lack of rainfall measurements in mountainous areas. Here, we perform the first global assessment of the potential of three high-resolution precipitation datasets, derived from satellite observations, to capture the rainfall characteristics of 20 storms that led to widespread landsliding. We find that, accounting for past extreme rainfall statistics (i.e., the rainfall returning every 10 years), most storms causing landslides are retrieved by the datasets. However, the shortest storms (i.e., ∼3 h) are often undetected, and the detailed spatial pattern of extreme rainfall often appears to be distorted. Our work opens new ways to study global landslide hazard but also warns against overinterpreting rainfall derived from satellites.

A GWR downscaling method to reconstruct high-resolution precipitation dataset based on GSMaP-Gauge data: A case study in the Qilian Mountains, Northwest China

Accurate precipitation data are crucial for hydrological, meteorological, and ecological research. However, it is difficult to obtain high-precision and high-resolution spatiotemporal distributions of precipitation in remote mountain regions with complex topography and sparse rain gauges. In addition, the spatial resolutions of existing satellite precipitation products are too coarse to apply them in the mountain regions with great spatiotemporal heterogeneity. To overcome the disadvantage, downscaling satellite precipitation products has been an effectively method to develop high-resolution precipitation data. In this study, a geographically weighted regression (GWR) model, coupling with topographical and water vapor source variables filtered by stepwise regression analysis (SRA), is applied to downscale the GSMaP-Gauge precipitation products (0.1° × 0.1°) to obtain high-resolution (1 km × 1 km) precipitation from 2000 to 2020 at annual, seasonal, and monthly scales over the Qilian Mountains. Besides, the accuracy of the downscaled precipitation based on all meteorological stations and the stations at high altitude (i.e., over 3000 m) are validated. Furtherly, the spatiotemporal variations of precipitation are analyzed. The results show that: (1) the accuracy after downscaling has been improved comparing with that of the original data. The accuracy of precipitation simulated at high-altitude stations is lower than that at all stations; (2) The trend of precipitation before and after downscaling is consistent in space. The spatial distributions of precipitation at annual, spring, summer, autumn, and months from March to November are decreased from the southeast to the northwest; (3) The spatial variations of precipitation show an increasing trend in most areas (>50%) at different time scales, except for March and September. Along with the time, the annual precipitation shows an increasing trend with a slope of 3.83 over the last 20 years. These findings suggest that the GWR method can be applied effectively to downscale annual, seasonal, and monthly precipitation of GSMaP-Gauge products in the Qilian Mountains.

Explaining recent trends in extreme precipitation in the Southwestern Alps by changes in atmospheric influences

This article analyzes recent trends in extreme precipitation in the Southwestern Alps and compares these trends to changes in the occurrence of the atmospheric influences generating extremes. We consider a high-resolution precipitation dataset of 1 × 1 km2 for the period 1958–2017. A robust method of trend estimation in extreme precipitation is considered, based on nonstationary extreme value distribution and a homogeneous neighborhood approach. The results show contrasting trends in extreme precipitation depending on the season. In autumn, most of Southern France shows significant increasing trends, with increase in the 20-year return level between 1958 and 2017 as large as its average value over the period, while the Northern French Alps and the Swiss Valais show decreasing extremes. In winter, significant increasing extremes are found in the valleys and medium mountain areas surrounding the Northern French Alps, while the inner French Alps, the Swiss Valais and the Aosta Valley show significant decreasing trends. In the other seasons, the significant trends are mostly negative in the Mediterranean area. Comparing these trends to changes in the occurrence of the atmospheric influences generating extremes shows that part of the significant changes in extremes can be explained by changes in the dominant influences, in particular in the Mediterranean influenced region that shows the most organized trends. In particular, the strong positive trends in extreme precipitation in autumn in Southern France are concomitant with an increase in Mediterranean influence generating extremes.

A PCA-based clustering algorithm for the identification of stratiform and convective precipitation at the event scale: an application to the sub-hourly precipitation of Sicily, Italy

Understanding the structure of precipitation and its separation into stratiform and convective components is still today one of the important and interesting challenges for the scientific community. Despite this interest and the advances made in this field, the classification of rainfall into convective and stratiform components is still today not trivial. This study applies a novel criterion based on a clustering approach to analyze a high temporal resolution precipitation dataset collected for the period 2002–2018 over the Sicily (Italy). Starting from the rainfall events obtained from this dataset, the developed methodology makes it possible to classify the rainfall events into four different classes, which can be related to the convective and stratiform components of the events on the basis of their hyetograph shapes and average intensities. The results show that the occurrence of stratiform events is always much higher than the convective ones, especially in the winter and spring seasons, while from the summer to the mid-autumn the rainfall depth due to convective events results to be higher than that due to the stratiform events. Moreover, the comparison with a more widely accepted separation methodology demonstrates the physical consistency of the proposed methodology.

A downscaling approach for constructing high-resolution precipitation dataset over the Tibetan Plateau from ERA5 reanalysis

Current gridded precipitation datasets are hard to meet the requirements of hydrological and meteorological applications in complex-terrain areas due to their coarse spatial resolution and large uncertainties. High-resolution atmospheric simulations are capable of describing the influence of topography on precipitation but are difficult to be used to obtain long-term precipitation datasets because they are computationally expensive, while reanalysis data has a long-term coverage and can provide reasonable large-scale spatial and temporal variability of precipitation. This study presents an approach to obtain long-term high-resolution precipitation datasets over complex-terrain areas by combining the ERA5 reanalysis with short-term high-resolution atmospheric simulation. The approach consists of two main steps: first, the ERA5 precipitation is corrected by the high-resolution simulation at the coarse spatial resolution; second, the corrected data is downscaled using a convolution neural network (CNN) based model at daily scale. The proposed approach is applied to the Tibetan Plateau (TP). The downscaled results from ERA5 have a finer spatial structure than ERA5 and can reproduce the spatial patterns of precipitation revealed by the high-resolution simulation. An evaluation based on rain gauge data shows that the downscaled ERA5 has remarkably lower biases than the original ERA5 which overestimates precipitation a lot, and even higher accuracy than the high-resolution simulation data over the TP. The downscaled ERA5 preserves the temporal characteristics of ERA5 which are more consistent with the rain gauge data than that of high-resolution simulation. Since this approach is much less computing resources consuming than the high-resolution simulation, it is an effective method to obtain long-term high-resolution precipitation datasets in complex-terrain areas and is expected to have extensive applications.