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An Extended Triple Collocation for maximized Correlation (ETCC) method was proposed with a unique correlation function, the purpose of which is to maximize the correlation between the merged product and unknown truth. The method was tested over quasi‐global land by combining three independent precipitation products. The performance of the ETCC‐merged product was then evaluated against three reference data sets and compared with the existing Triple Collocation (TC) merging. The merged product was found to be generally superior to each contributor. Moreover, the ETCC method is better able to improve the correlation of merged product compared with the TC approach. Other improvements are also shown in the absolute difference of the ETCC‐merged product, such as regional validation for central North America and mainland China. These demonstrate the effectiveness of the ETCC method, and accordingly, it can provide a promising solution for maximizing the correlation of merged product without the truth. Plain Language Summary: Precipitation plays a significant role in global atmospheric and hydrological modeling systems. Owing to uncertainties in precipitation estimates from a single satellite or reanalysis product, it is necessary and challenging to blend multi‐source precipitation information to improve the quality of precipitation estimates. The present study proposed a new merging method, called Extended Triple Collocation for maximized Correlation (ETCC), which aims at maximizing the correlation of the merged product against unknown truth. The ETCC‐merged product combined three independent precipitation information derived from the satellite and reanalysis products, and then, was evaluated using three reference data sets. Meanwhile, we compared ETCC's performance with that of the original Triple Collocation (TC) merging. Results show that ETCC method not only performs better than each contributor, but also is more effective in improving precipitation accuracy relative to the TC approach especially in central North America and mainland China. Key Points: A new method is proposed for maximizing the correlation between the merged product and unknown truthThe merged product is more accurate than each contributorThe new method performs better than the original triple collocation [ABSTRACT FROM AUTHOR]

Precipitation is a major component of the water cycle. Accurate and reliable estimation of precipitation is essential for various applications. Generally, there are three main types of precipitation products: satellite based, reanalysis, and ground measurements from rain gauge stations. Each type has its advantages and disadvantages. Recent efforts have been made to develop various merging methods to improve precipitation estimates by combining multiple precipitation products. This study evaluated for the first time the performance of the random forest-based merging procedure (RF-MEP) method in enhancing the accuracy of daily precipitation estimates in Chongqing city, China with a complex terrain and sparse observational data. The RF-MEP method was used to merge three widely used gridded precipitation products (CHIRPS, ERA5-Land, and GPM IMERG) with ground measurements from a limited number of rain gauge stations to produce the merged precipitation dataset. Eight stations (approximately 70% of the available stations) were used to train the RF-MEP approach, while four stations (30%) were used for independent testing. Various statistical metrics were employed to assess the performance of the merged precipitation dataset and the three existing precipitation products against the ground measurements. Our results demonstrated that the RF-MEP approach significantly enhances the accuracy of daily precipitation estimates, surpassing the performance of the individual precipitation products and two other merging methods (the simple linear regression model and the simple averaging). Among the three existing products, ERA5-Land exhibited the best performance in capturing daily precipitation, followed by GPM IMERG, while CHIRPS performed the worst. Regarding precipitation intensity, all three existing products and the RF-MEP merged dataset performed well in capturing light precipitation events with an intensity of less than 1 mm/day, which accounts for the majority (more than 70%) of occurrences. However, all datasets showed rather poor capability in capturing precipitation events beyond 1 mm/day, with the worst performance observed for extreme heavy precipitation events exceeding 50 mm/day. The RF-MEP approach significantly improves the detection ability for all precipitation intensities, except for the most extreme intensity (>50 mm/day), where only marginal improvement is observed. Analysis of the spatial pattern of precipitation estimates and the temporal bias of daily precipitation estimates further confirms the superior performance of the RF-MEP merged precipitation dataset over the three existing products. [ABSTRACT FROM AUTHOR]

Seasonal snow cover in mountainous regions will affect local climate and hydrology. In this study, we assessed the role of altitude in determining the relative importance of temperature and precipitation in snow cover variability in the Central Tianshan Mountains. The results show that: (a) in the study area, temperature has a greater influence on snow cover than precipitation during most of the time period studied and in most altitudes. (b) In the high elevation area, there is a threshold altitude of 3,900 ± 400 m, below which temperature is negatively correlated whereas precipitation is positively correlated to snow cover, and above which the situation is the opposite. Besides, this threshold altitude decreases from snow accumulated period to snow stable period and then increases from snowmelt period to snow‐free period. (c) Below 2,000 m, there is another threshold altitude of 1,400 ± 100 m during the snow stable period, below (above) which precipitation (temperature) is the main driver of snow cover. [ABSTRACT FROM AUTHOR]

Diurnal rainfall variation plays an important role in the characterization of regional climate. Most existing research used predefined region boundaries to study the spatial differences of diurnal rainfall variation, which depends on experiential knowledge and is somewhat subjective. In this study, the k-means clustering algorithm was used to mine the spatial distribution of diurnal rainfall variation from gridded precipitation data over China. First, clustering was conducted according to the hourly rainfall frequency at each grid cell. A cluster number large enough to find the main types of diurnal rainfall variation was used. Then similar clusters were merged according to the peak time and amplitude of the diurnal rainfall variation. Each merged cluster corresponds to one type of diurnal variation, and the locations of grid cells in each merged cluster form the spatial distribution. Thus, the classification maps of diurnal rainfall variation can be obtained. From the spatial distribution maps, the conclusions of existing research (e.g., the distribution of well-known nocturnal rainfall in southwestern China, the prevailing afternoon rainfall regions) were confirmed. Some new findings were found, such as the spatial patterns of nocturnal rainfall regions along slopes of the macroterrain and the different types of diurnal rainfall variation in the North China Plain. The diurnal rainfall variation in some regions also shows a close relationship with land cover. The results of this study can provide valuable information for further mechanism studies, and the proposed approach can serve as a useful tool for studies on diurnal rainfall variation in other regions. [ABSTRACT FROM AUTHOR]

Abstract: Accurate precipitation data at high spatial and temporal resolution is deemed necessary for many hydrological and water management applications, and especially in data scarce river basins and regions where strong competition for water resources prevails. In this study we used a new downscaling–calibration procedure of the freely accessible Version 7 of TRMM (Tropical Rainfall Measuring Mission) 3B43 product, in conjunction with limited rain gauge data sets, to generate improved monthly pixel-based precipitation data at higher spatial resolution (1km). The spatial downscaling from 0.25° to 1km grids was achieved by using site-specific non-linear relationships between annual precipitation and annually averaged NDVI (Normalized Difference Vegetation Index). The calibration was based on Geograhical Difference Analysis (GDA) and Geographical Ratio Analysis (GRA). The new integrated procedure was tested for Lake Tana Basin (LTB) in Ethiopia with a humid climate (rainfall 1395 mm/yr during 1998–2004) and for the Caspian Sea Region (CSR) in Iran with a semi-arid climate (rainfall 442 mm/yr during 1999–2003). The best 1km annual precipitation data were achieved through downscaling followed by GDA calibration for most cases. The monthly fractions derived from the un-calibrated TRMM 3B43 product can be used to disaggregate 1km annual precipitation to 1km monthly precipitation. The disaggregated 1km monthly precipitation has not only significant improvement in the spatial resolution, but also good agreements with rain gauge data were achieved for both LTB (R2 =0.87, RMSE=56mm, MAE=32mm and Bias=0.01) and the CSR (R2 =0.79, RMSE=23mm, MAE=16mm and Bias=0.14). A similar calibration procedure using rain gauges at monthly time scale did not improve the level of performance. [Copyright &y& Elsevier]

This study evaluated Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) product i.e. TRMM 3B42 data, using data from 52 rain gauge stations around the Meichuan watershed, which is a representative watershed of Poyang Lake basin in China. Both the latest Version 7 (V7) and previous Version 6 (V6) of TRMM 3B42 data were compared and evaluated for a 9-year period covering 2001-2005 and 2007-2010. The evaluations were conducted at different spatial (grid and watershed) and temporal (daily, monthly and annual) scales. For evaluation at grid scale, the Thiessen polygon method was used to transform pointed-based rain gauge data to areal precipitation at the same grid scale (0.25°) as TRMM 3B42 data. The results showed that there was little difference in performances of V6 and V7 TRMM 3B42 products. Overall, both V6 and V7 products slightly overestimated precipitation with a bias of 0.04. At daily scale, both V6 and V7 data were considered to be unreliable with large relative RMSE (135%-199%) at the two spatial scales, and they were deficient in capturing large storms. These results suggest that local calibration with rain gauge data should be conducted before V6 and V7 TRMM 3B42 data are used at daily scale. At monthly and annual scales, V6 and V7 TRMM 3B42 data match the rain gauge data well (R2 = 0.91-0.99, relative RMSE = 4%-23%) at both grid and watershed scale and thus have good potential for hydrological applications. [ABSTRACT FROM AUTHOR]

The performance of the latest released Integrated Multi-satellitE Retrievals for GPM mission (IMERG) version 5 (IMERG v5) and the TRMM Multisatellite Precipitation Analysis 3B42 version 7 (3B42 v7) are evaluated and compared at multiple temporal scales over a semi-humid to humid climate transition area (Huaihe River basin) from 2015 to 2017. The impacts of rainfall rate, latitude and elevation on precipitation detection skills are also investigated. Results indicate that both satellite estimates showed a high Pearson correlation coefficient (r, above 0.89) with gauge observations, and an overestimation of precipitation at monthly and annual scales. Mean daily precipitation of IMERG v5 and 3B42 v7 display a consistent spatial pattern, and both characterize the observed precipitation distribution well, but 3B42 v7 tends to markedly overestimate precipitation over water bodies. Both satellite precipitation products overestimate rainfalls with intensity ranging from 0.5 to 25 mm/day, but tend to underestimate light (0–0.5 mm/day) and heavy (>25 mm/day) rainfalls, especially for torrential rains (above 100 mm/day). Regarding each gauge station, the IMERG v5 has larger mean r (0.36 for GPM, 0.33 for TRMM) and lower mean relative root mean square error (RRMSE, 1.73 for GPM, 1.88 for TRMM) than those of 3B42 v7. The higher probability of detection (POD), critical success index (CSI) and lower false alarm ratio (FAR) of IMERG v5 than those of 3B42 v7 at different rainfall rates indicates that IMERG v5 in general performs better in detecting the observed precipitations. This study provides a better understanding of the spatiotemporal distribution of accuracy of IMERG v5 and 3B42 v7 precipitation and the influencing factors, which is of great significance to hydrological applications. [ABSTRACT FROM AUTHOR]

• Four daily precipitation datasets were integrated based on the modifiable Bayesian model averaging (BMA) framework. • Seven blended precipitation products were generated by considering different inputs. • Some BMA products outperformed the representative merged product, MSWEP. • Impacts of the ensemble members on BMA predictions were quantitatively analyzed. 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. [ABSTRACT FROM AUTHOR]

Multi-source merging is an established tool for improving large-scale precipitation estimates. Existing merging frameworks typically use gauge-based precipitation error statistics and neglect the inter-dependence of various precipitation products. However, gauge-observation uncertainties at daily and sub-daily time scales can bias merging weights and yield sub-optimal precipitation estimates, particularly over data-sparse regions. Likewise, frameworks ignoring inter-product error cross-correlation will overfit precipitation observation noise. Here, a Statistical Uncertainty analysis-based Precipitation mERging framework (SUPER) is proposed for addressing these challenges. Specifically, a quadruple collocation analysis is employed to estimate precipitation error variances and covariances for commonly used precipitation products. These error estimates are subsequently used for merging all products via a least-squares minimization approach. In addition, false-alarm precipitation events are removed via a reference rain/no-rain time series estimated by a newly developed categorical variable merging method. As such, SUPER does not require any rain gauge observations to reduce daily random and rain/no-rain classification errors. Additionally, by considering precipitation product inter-dependency, SUPER avoids overfitting measurement noise present in multi-source precipitation products. Results show that the overall RMSE of SUPER-based precipitation is 3.35 mm/day and the daily correlation with gauge observations is 0.71 [−] – metrics that are generally superior to recent precipitation reanalyses and remote sensing products. In this way, we seek to propose a new framework for robustly generating global precipitation datasets that can improve land surface and hydrological modeling skill in data-sparse regions. • We develop a new framework for global precipitation merging. • This framework leverages inter-product error dependency to avoid overfitting. • This framework minimizes the use of gauge-based precipitation observations. • This framework is theoretically more robust in data-sparse regions. [ABSTRACT FROM AUTHOR]

Timely and accurate detection of drought events using reliable data with continuous spatial coverage is of great importance to adapt efficient mitigation strategies. Despite several studies that have been carried out to validate satellite precipitation products in Iran, the performance of satellite precipitation products in precipitation amount estimation and drought events detection has not been assessed yet simultaneously. It is unclear if good estimates of mean precipitation values by satellites can also provide good performance in detecting drought events. Here, we used a comprehensive framework to evaluate the accuracy of the satellite-based precipitation and their performance in detecting drought events against ground-based data. We evaluated three satellite precipitation products, namely, TRMM TMPA 3B43 v7, CHIRPS, and PERSIANN-CDR using multiple evaluation metrics, and three different drought indices (Standardized Precipitation Index-SPI, Standardized Precipitation Evapotranspiration Index-SPEI, and Multivariate Standardized Precipitation Index-MSPI) over different climate zones of Iran during 1983–2017. Our results indicated that TRMM and PERSIANN-CDR had mainly overestimation while CHIRPS had mostly underestimation in precipitation over the different climates of Iran. The precipitation overestimation (underestimation) in TRMM, PERSIANN-CDR, and CHIRPS were observed in 80.0% (20.0%), 71.4% (28.6%), and 45.7% (54.3%) of stations, respectively. The most overestimation in satellite products was found to be in the arid-related climate regions, while the most underestimation was detected in humid-related climate regions. The average correlation coefficient values of PERSIANN-CDR (0.6) and TRMM (0.6) products against ground observations were higher than CHIRPS (0.4) dataset. Further, we found a lower correlation coefficient (0.3) between monthly satellite-based and ground-based precipitation in arid-related regions (with Root Mean Square Error, RMSE, ~ 10 mm per month) of the country but a high correlation coefficient (0.6) in humid-related climates (with RMSE ~100 mm per month). The results of the drought assessment indicated that the most severe meteorological drought events identified by SPI, SPEI, and MSPI were in 1998–2001, 2008–2009, 2010–2012, and 2016–2017 in all climate zones. The most intense drought events were in arid and semi-arid climate zones. Overall, in terms of the statistical metrics at multiple timescales, TRMM and PERSIANN-CDR outperformed CHIRPS, when compared against ground-based observations, for drought events detection. Also, TRMM and PERSIANN-CDR with higher scores for Probability of Detection (POD) and Critical Success Index (CSI) and lower scores for False Alarm Ratio (FAR) showed a good ability to detect drought events in arid and semi-arid climates compared to other climate zones. Although the CHIRPS estimated precipitation with acceptable accuracy, it did not detect drought events as the TRMM and PERSIANN-CDR did. [Display omitted] • Developing a comprehensive framework to detect accurate precipitation and drought events. • CHIRPS did not detect drought events as good as the TRMM and PERSIANN-CDR. • Satellite products detected more accurate drought events in arid-related climates. [ABSTRACT FROM AUTHOR]