Precipitation Datasets Research Articles

Blue and green water re-distribution dependency on precipitation datasets for a tropical Indian River basin

Study regionThe Damodar River basin, India. Study focusWater resource assessment at the river basin scale is crucial for human well-being and ecosystem health, and it can be performed quantitatively through green water (GW) and blue water (BW) flow evaluation. However, the quantification of BW and GW through land surface models (LSMs) is significantly influenced by the accuracy of precipitation datasets. In this present study, the spatiotemporal variation of blue and green water resources under the influence of six secondary precipitation products (SPPs) [APHRODITE; IMDAA; WFDEI; PRINCETON; CHIRPS, PERSIANN-CDR] are investigated using JULES LSM, considering India Meteorological Department (IMD) precipitation product as an observed precipitation dataset. This will help in examining the dependencies of precipitation datasets on partitioning between BW and GW distribution in a tropical River basin. New hydrological insights for the study regionThe results suggest significant differences in BW and GW simulation with the use of six aforementioned SPPs. In comparison to the reference IMD-based JULES model simulation, the annual average BW and GW estimates varied significantly: from 9.05% to − 40% and 15.24% to − 11.79%, respectively due to changes in the SPPs. Furthermore, the higher correlation between BW and precipitation datasets (R2 = 0.84–0.96) suggested that BW is more sensitive to precipitation changes in comparison to GW. Overall, our study emphasizes the importance of carefully considering the specification of precipitation databases for estimating BW and GW in a tropical River basin.

An appraisal of the NEX-GDDP precipitation dataset across homogeneous precipitation sub-regions of Iran

An appraisal of the NEX-GDDP precipitation dataset across homogeneous precipitation sub-regions of Iran

Performance Analysis of Precipitation Datasets at Multiple Spatio-Temporal Scales over Dense Gauge Network in Mountainous Domain of Tajikistan, Central Asia

Cryospheric and ecological studies become very complicated due to the absence of observed data, particularly in the mountainous regions of Central Asia. Performance analysis of Satellite-Based Precipitation Datasets (SBPD) is very critical before their direct hydro-climatic applications. This study assessed the ground validation of four SBPDs (IMERG, TRMM, PERSIANN-CDR, and PERSIANN-CSS). From January 2000 to December 2013, all SBPD data were analyzed on daily, monthly, seasonal (winter, spring, summer, autumn), and annual scales at the entire spatial domain and point-to-pixel scale. The performance of SBPD was analyzed by using evaluation indices (root mean square error (RMSE), correlation coefficient (CC), bias, and relative bias (r-Bias)) along with categorical indices (false alarm ratio (FAR), probability of detection (POD), success ratio (SR), and critical success index (CSI). Results revealed that: (1) IMERG’s spatiotemporal tracking ability is better as compared to other datasets with appropriate ranges (CC > 0.8 and r-BIAS (±10)). The performance of all SBPDs is more capable on a monthly scale as compared to a daily scale. (2) In terms of POD, the IMERG outperformed all other SBPD on daily and seasonal scales. All SBPD showed underestimations in the summer season, and PERSIANN-CCS showed the most significant underestimation (−70). Moreover, the IMERG signposted the most satisfactory performance in all seasons. (3) All SBPD showed better performance in capturing the light precipitation events as indicated by the Probability Density Function (PDF%). Moreover, the performance of PERSIANN-CDR and TRMM is acceptable at low topography; the performance of PERSIANN-CCS is very poor in diverse topographical and climatic conditions over Tajikistan. Therefore, we advocate the use of daily, monthly, and seasonal estimations of IMERG precipitation product for hydro-climatic applications over the mountainous domain of Central Asia.

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.

Modelado hidrológico basado en el algoritmo KNN: una aplicación para el pronóstico de caudales diarios del río Ramis, Perú

The forecast of river stream flows is of significant importance for the development of early warning systems. Artificial intelligence algorithms have proven to be an effective tool in hydrological modeling data-driven, since they allow establishing relationships between input and output data of a watershed and thus make decisions data-driven. This article investigates the applicability of the k-nearest neighbor (KNN) algorithm for forecasting the mean daily flows of the Ramis river, at the Ramis hydrometric station. As input to the KNN machine learning algorithm, we used a data set of mean basin precipitation and mean daily flow from hydrometeorological stations with various lags. The performance of the KNN algorithm was quantitatively evaluated with hydrological ability metrics such as mean absolute percentage error (MAPE), anomaly correlation coefficient (ACC), Nash-Sutcliffe efficiency (NSE), Kling-Gupta efficiency (KGE') and the spectral angle (SA). The results for forecasting the flows of the Ramis river with the k-nearest neighbor machine learning algorithm reached high levels of reliability with flow lags of one and two days and precipitation with three days. The algorithm used is simple but robust to make short-term flow forecasts and can be integrated as an alternative to strengthen the daily hydrological forecast of the Ramis river.

Diverging Trends in Rain‐On‐Snow Over High Mountain Asia

AbstractRain‐on‐snow (ROS) over snow‐dominated regions such as High Mountain Asia (HMA) modulates snowmelt and runoff and is key contributor in influencing water availability and hazards (e.g., floods and landslides). We studied the trends in ROS in HMA over the past two decades from 2001 to 2018 using the land surface model Noah‐MP driven by an ensemble precipitation data set. Our results show that changes in precipitation phase and rainfall are altering ROS. Because of the strong physical heterogeneity and atmospheric dynamics of HMA, ROS characteristics and trends are region‐dependent and ROS occurs predominantly over the Indus, Ganges‐Brahmaputra, and northwestern basins. In the Indus, ROS representing ∼5% of the annual precipitation and ∼20% of the annual snowmelt, has an increasing trend. This is contrary to the Ganges‐Brahmaputra characterized by decreasing ROS trends, where it represents ∼11% of the annual precipitation and ∼60% of the annual snowmelt. In the northwestern basins, ROS has bidirectional trends due to elevation patterns and trends in rainfall, and it constitutes ∼5 to ∼10% of the annual precipitation. Increasing trends in ROS over Indus contribute to reducing the snowpack in late summer, with concerns of reduced water availability and increased groundwater exploitation. Similarly, because of its high amount and contribution to snowmelt, the decreasing ROS trends in the Ganges‐Brahmaputra will have consequences of decreased recharge from the headwaters and exacerbated use of groundwater unless increasing trends in rainfall compensate for the decreasing snowmelt. These results provide new insights on ROS‐driven changes in the hydrological cycle over HMA.

Performance Evaluation and Improvement of CMFD's Precipitation Products Over Shanghai City, China

AbstractPrecipitation products with high resolution for urban areas remain an important and challenging issue. The main purposes of this study are to evaluate the accuracy of the China Meteorological Forcing Data Set (CMFD) in the Shanghai megacity and to improve the accuracy of the CMFD precipitation. With the rain gauge observations for the 2013–2018 period, we evaluate the error and detection ability of CMFD at the sub‐daily, daily, and monthly scales, and examine the spatial variation of CMFD performance over the region. The Geographic Differential Analysis (GDA) method is used to calibrate the CMFD precipitation data set. The results show that the performance of CMFD precipitation varies from sub‐daily to monthly time scales with better accuracy at the monthly scale in the study region. The CMFD can capture the spatial variability over the region with less error in the north and outliers in the center. The GDA‐based calibration framework can significantly improve the capability of CMFD products, especially at the sub‐daily and daily scales. The calibrated CMFD can provide reliable precipitation estimates and capture more extreme precipitation events. The improved CMFD products can provide a key basis for applications related to hydrological and meteorological hazard monitoring and assessment in the Shanghai area. The overall framework is scalable and could be readily generalized to other regions, and the calibrated product can be used in the research of urban natural disaster risks and other studies.

Multiple Spatial and Temporal Scales Evaluation of Eight Satellite Precipitation Products in a Mountainous Catchment of South China

Satellite precipitation products (SPPs) have emerged as an important information source of precipitation with high spatio-temporal resolutions, with great potential to improve catchment water resource management and hydrologic modelling, especially in data-sparse regions. As an indirect precipitation measurement, satellite-derived precipitation accuracy is of major concern. There have been numerous evaluation/validation studies worldwide. However, a convincing systematic evaluation/validation of satellite precipitation remains unrealized. In particular, there are still only a limited number of hydrologic evaluations/validations with a long temporal period. Here we present a systematic evaluation of eight popular SPPs (CHIRPS, CMORPH, GPCP, GPM, GSMaP, MSWEP, PERSIANN, and SM2RAIN). The evaluation area used, using daily data from 2007 to 2020, is the Xiangjiang River basin, a mountainous catchment with a humid sub-tropical monsoon climate situated in south China. The evaluation was conducted at various spatial scales (both grid-gauge scale and watershed scale) and temporal scales (annual and seasonal scales). The evaluation paid particular attention to precipitation intensity and especially its impact on hydrologic modelling. In the evaluation of the results, the overall statistical metrics show that GSMaP and MSWEP rank as the two best-performing SPPs, with KGEGrid ≥ 0.48 and KGEWatershed ≥ 0.67, while CHIRPS and SM2RAIN were the two worst-performing SPPs with KGEGrid ≤ 0.25 and KGEWatershed ≤ 0.42. GSMaP gave the closest agreement with the observations. The GSMaP-driven model also was superior in depicting the rainfall-runoff relationship compared to the hydrologic models driven by other SPPs. This study further demonstrated that satellite remote sensing still has difficulty accurately estimating precipitation over a mountainous region. This study provides helpful information to optimize the generation of algorithms for satellite precipitation products, and valuable guidance for local communities to select suitable alternative precipitation datasets.

New Normal in ITCZ and Its Role in Altering Agroclimatic Suitability for Rice Production

Intertropical Convergence Zone (ITCZ) primarily governs the convective rainfall potential of the summer monsoon in Asia. In the present study, non-parametric trend test with outgoing longwave radiation (OLR) for the summer monsoon period for the last 42 years (1980–2021) have been analyzed for ITCZ zone, representative zones of Hadley circulation and Walker circulation for exploring trend of the deep convection activity. Besides, various climatic variables like temperature (maximum, minimum, mean), precipitation, and cloud cover dataset are used for exploring trend in major rice growing regions of the world. The results indicate that there is a significantly decreasing trend of OLR in ITCZ zone during summer monsoon season. Contrarily, major rice growing regions of the world have witnessed a significantly increasing trend for the temperature parameter among all the zones. Rainfall and cloud cover have shown a typical trend i.e., increasing rainfall but decreasing cloud cover in the Southeast Asian and Maritime Continent rice growing regions. In rice suitable climate assessment, it has been found that the Maritime Continent rice growing region, the Indo-Gangetic Plain and the Southeast Asian rice growing regions have witnessed better rice suitable climates than other rice growing regions during the last 42 years (1980–2021).

Changes in extreme precipitation over Mpologoma catchment in Uganda, East Africa

Changes of five Extreme Precipitation Indices (EPIs) over Mpologoma catchment were examined. The EPIs were obtained from the long-term (1948–2016) gridded (0.25° × 0.25°) Princeton Global Forcing (PGF) daily precipitation dataset. The number of days with precipitation intensity greater than 5 mm/day (NWD5) and 10 mm/day (NWD10) were part of the five EPIs. Trend and variability analyses were by the nonparametric technique based on the cumulative sum of the difference between exceedance and non-exceedance counts of data. The simple multiplicative bias correction method was employed to bias correct the PGF dataset using observed rainfall from nine weather stations. The number of wet days were decreasing although insignificant (p > 0.05). On the other hand, the annual total precipitation considering days with rainfall intensities greater than 5 mm/day (SPre5) exhibited a significant (p < 0.05) decrease. However, annual maxima rainfall (ANMS) increased even though insignificant. This indicates that less frequent rains are received over the study area but some events having high intensity. The insights from the findings of this study constitute a good baseline for predictive planning of water resources management applications across Mpologoma catchment.

Copula-Based Assessment and Regionalization of Drought Risk in China.

Droughts are widespread in China and have brought considerable losses to the economy and society. Droughts are intricate, stochastic processes with multi-attributes (e.g., duration, severity, intensity, and return period). However, most drought assessments tend to focus on univariate drought characteristics, which are inadequate to describe the intrinsic characteristics of droughts due to the existence of correlations between drought attributes. In this study, we employed the standardized precipitation index to identify drought events using China's monthly gridded precipitation dataset from 1961 to 2020. Univariate and copula-based bivariate methods were then used to examine drought duration and severity on 3-, 6-, and 12-month time scales. Finally, we used the hierarchical cluster method to identify drought-prone regions in mainland China at various return periods. Results revealed that time scale played an essential role in the spatial heterogeneity of drought behaviors, such as average characteristics, joint probability, and risk regionalization. The main findings were as follows: (1) 3- and 6-month time scales yielded comparable regional drought features, but not 12-month time scales; (2) higher drought severity was associated with longer drought duration; (3) drought risk was higher in the northern Xinjiang, western Qinghai, southern Tibet, southwest China, and the middle and lower reaches of the Yangtze River, and lower in the southeastern coastal areas of China, the Changbai Mountains, and the Greater Khingan Mountains; (4) mainland China was divided into six subregions according to joint probabilities of drought duration and severity. Our study is expected to contribute to better drought risk assessment in mainland China.

Evaluation of GPM-IMERG Precipitation Product at Multiple Spatial and Sub-Daily Temporal Scales over Mainland China

The Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) provides new-generation satellite precipitation datasets with high spatio-temporal resolution and accuracy, which is widely applied in hydrology and meteorology. However, most examinations of the IMERG were conducted on daily, monthly, and annual scales, and inadequate research focused on the sub-daily scale. Thus, this study set up four sub-daily scales (1 h, 3 h, 12 h, and 24 h at 0.1° spatial resolution) and four spatial scales (0.1°, 0.25°, 0.5°, and 1° at 1 h temporal resolution) to finely evaluate the performance of IMERG products in the summer seasons from 2014 to 2019 over mainland China. The precipitation amount (PA), frequency (PF), and intensity (PI) were adopted to assess the performance of the IMERG referenced by the ground-based precipitation product of the China Meteorological Administration (CMA). The results show that the IMERG can capture the spatial patterns of precipitation characters over mainland China, but the PA and PI are overestimated and the PF is underestimated, and the evaluation results are highly sensitive to the different temporal and spatial resolutions. Compared with fine spatio-temporal scales, the performance of the IMERG is significantly improved when scaled up to coarser scales. Moreover, the IMERG shows a better performance of PA and PI in larger regions and during longer periods. This study provided a reference for the application of IMERG products in different spatial and temporal scales.

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

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

Utilizing Satellite Data to Establish Rainfall Intensity-Duration-Frequency Curves for Major Cities in Iraq

This study generates intensity-duration-frequency curves for three important cities in Iraq using Global Precipitation Measurement Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG), Global Satellite Mapping of Precipitation near real-time (GSMaP NRT), and gauge corrected (GSMaP GC) satellite precipitation datasets. Many probability distribution functions were used to fit the maximum yearly rainfall data. The Sherman equation was used to create intensity-duration-frequency (IDF) curves for rainfall intensities with 2-, 5-, 10-, 25-, 50-, and 100-year return periods, with the estimated coefficients of the best-fit distribution serving as the fitting parameters. The discrepancy between the IDF curves produced from the satellites and the observed data was used to bias correct the satellite IDF curves. The Generalized Extreme Value Distribution model best describes the hourly rainfall distribution of satellite data. GSMaP GC was the best option for creating IDF curves with higher correlations with observed data at Baghdad, Basra, and Mosul. The study indicates the necessity of gauge correction of satellite rainfall data to reduce under- and over-estimating observed rainfall. GSMaP GC can reasonably estimate rainfall in a predominantly arid climate region like Iraq. The generated IDF curves may be an important step toward achieving sustainable urban stormwater management in the country.

A New Agricultural Drought Disaster Risk Assessment Framework: Coupled a Copula Function to Select Return Periods and the Jensen Model to Calculate Yield Loss

China is one of the regions with the most frequent drought disasters and serious social and economic losses. Agricultural drought is the most serious natural disaster. Due to climate change, the regional agricultural drought risk assessment has always been the focus of the academic circle. This study takes Zunyi City as an example, which is the most typical city of karst landform development. The monthly precipitation data set of ground meteorological observation stations in Zunyi City from 1956 to 2020 was selected, and the drought characteristic variables were extracted by the coupled use of the precipitation anomaly percentage (Pa) index and the theory of runs. A copula function was applied to establish the joint distribution model of characteristic variables, obtaining the drought frequency and drought return periods. Combined with the Jensen model, the agricultural drought loss rate under different drought return periods in the target year (2020) was calculated and evaluated. The results showed that the Gumbel-Hougaard copula function was suitable for the joint distribution of drought joint variables in Zunyi City. From 1956 to 2020, fewer droughts occurred in Zhengan and Wuchuan, and the most droughts took place in Fenggang, Meitan, and Yuqing. The average drought duration in each county was about 1.5 months, and the average drought severity was about 0.35 in spatial distribution. Crop loss rate caused by drought increased and the affected area expanded with the increase of drought return periods (5, 10, 20, 50, and 100 years) in temporal distribution. Meanwhile, the drought disaster was most drastic in the eastern region, followed by the south, north, west, and central area. The results were highly consistent with the historical drought in Zunyi City, which verified the validity of the model. This study could provide scientific knowledge for drought resistance and reasonable mitigation programing for the security of the regional agricultural production and the sustainability of social and economic development.

Diverging identifications of extreme precipitation events from satellite observations and reanalysis products: A global perspective based on an object-tracking method

Extreme precipitation can trigger various natural hazards, causing catastrophic losses worldwide. As global warming accelerates, it is widely acknowledged that extreme precipitation will become more frequent and intense, calling for more accurate historical and projected precipitation estimation. Identifying extreme precipitation events is understudied particularly on the global scale, given that most studies only focus on pixel-by-pixel characteristics while ignoring the space-time continuity and evolution of extreme events. This study utilizes an object-based tracking method to track the precipitation system objects in time, extracting spatiotemporal attributes of precipitation events and investigates variable definitions of extremes for a better understanding of the performance of existing precipitation datasets. Five satellite and two reanalysis precipitation products (IMERG, GSMaP, PERSIANN-CCS, TRMM 3B42, CMORPH, ERA5, and ERA-Interim) are involved to investigate (1) the difference and similarity of those datasets in depicting the global pattern of extreme precipitation, (2) the impact of various definitions of extreme events, and (3) the trend of global extreme precipitation events. Results show that the object-based method can capture the motion of precipitation events. Different extreme definitions have a large impact on the distribution, trend, and features of extreme precipitation. The inter-comparison of extreme precipitation events from different products shows low correlation, indicating that accurately capturing the evolution of extreme events is still challenging. Reanalysis product-based analysis shows variable trends (magnitudes and signs) based on different attributes of precipitation objects and notable scale effect. Satellite precipitation products exhibit distinct inter-annual variability which could affect their hydrometeorological applications. In summary, this study leverages the object-based tracking method for comprehensive extreme precipitation analysis from the multi-dataset, multi-scale, and multi-definition perspectives, which can advance the understanding of the status and trend of global extreme precipitation.

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

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

Variation Characteristics of Pavement Temperature in Winter and Its Nowcasting for Xianyang Airport Expressway, China

Based on the pavement temperature observation data of the transportation meteorological stations along the Xianyang Airport Expressway, China, as well as the datasets of precipitation and sunshine hours obtained from the nearby weather stations, the variation characteristics of local pavement temperatures are investigated for winter in this study. Results indicate that during the daytime, the pavement temperatures are always higher on sunny and cloudy days than those on rainy and snowy days, while during the nighttime, the temperatures on sunny and cloudy days are higher than those on the days with freezing rain and snow, and with the temperatures on rainy and snowy days without icing being further higher. In general, the pavement temperatures in winter features significant periodic oscillations with cycles of roughly 24 h, 12 h, 8 h, 6 h, 5 h and 4 h, which differ slightly at different times for different stations. Moreover, the nowcasting experiments on the local pavement temperatures are also carried out using a regression model via extracting the corresponding periodic features. It shows the mean absolute errors of about 0.6 °C, 1.2 °C, and 1.5 °C for lead times of 1 h, 2 h, and 3 h, respectively. The nowcasting skills are higher on rainy and snowy days, while are inferior on sunny days. For nowcasting cases initialized at nighttime (daytime), the mean absolute errors are 0.4 °C (0.7 °C) and 0.9 °C (1.4 °C) for lead times of 1 h and 2 h. Examinations suggest that the nowcasting system could be well utilized in plain areas of China, whereas it shows relatively larger biases in plateau areas with complex terrain.

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).

Fusion of gauge-based, reanalysis, and satellite precipitation products using Bayesian model averaging approach: Determination of the influence of different input sources

Selection of the number and which of multisource precipitation datasets is crucially important for precipitation fusion. Considering the effects of different inputs, this study proposes a new framework based on the Bayesian model averaging (BMA) algorithm to integrate precipitation information from gauge-based analysis CPC, reanalysis-derived dataset ERA5, and satellite-retrieval products IMERG-E and GSMaP-RT. The BMA weights were optimized for the period 2001–2010 using daily measurements and then applied to the period 2011–2015 for model validation. Seven BMA-merged precipitation products (i.e., MCE, MCI, MCG, MCEI, MCEG, MCIG, and MCEIG) were thoroughly evaluated across mainland China and then compared against the state-of-the-art ensemble-based product, MSWEP. The results indicate that the BMA predictions performed substantially better than the reanalysis and satellite precipitation datasets in both daily statistics and seasonal analyses. MCE, MCI, and MCEG demonstrated better performances relative to CPC in terms of individual metrics. Moreover, MCI, MCG, and MCEI generally outperformed MSWEP over the entire study area, particularly in local regions, such as southwestern China and the eastern Tibetan Plateau. During Typhoon Rammasun in 2014, MCG and MCEG provided greater detail for heavy rainfall events than the four ensemble members and the MSWEP product. Thus, the performance of the BMA predictions exhibited evident differences because of various input sources. CPC was the major internal influencing factor with the highest weight score. Meanwhile, the increased-input CPC dataset into the BMA-based schemes exerted a significant influence on the precipitation estimates, which markedly facilitated the performance improvement of the fusion model, and its improved degree (greater than 14 %) was obtained using a ‘changed-initial’ comparison method. Our results demonstrate that the developed modifiable BMA framework is useful for analyzing the impacts of ensemble members on BMA predictions and suggests that it is considerate in the use of different input sources for generating ensemble-based precipitation products.