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This paper presents an in-depth investigation of the error properties of two high-resolution global-scale satellite rain retrievals verified against rainfall fields derived from a moderate-resolution rain-gauge network (25-30-km intergage distances) covering a region in the midwestern U.S. (Oklahoma Mesonet). Evaluated satellite retrievals include...

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... plot in Fig. 2 shows the number of data samples with values greater than a threshold rainfall value G th for the two seasons (cold and warm) and two years (2004 and 2006), while Fig. 3 shows the mean and standard deviation of the corresponding gauge data samples (Mesonet stations) in those periods. In general, the sample size gets smaller as the ...

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... As a solution for the abovementioned limitations and shortcomings identified in rain gauge data, multisatellite high-resolution precipitation products such as tropical rainfall measuring mission (TRMM) multisatellite precipitation analysis (TMPA), precipitation estimation from remotely sensed information using artificial neutral network (PERSIANN) system, multisatellite precipitation analysis, and multisatellite rainfall estimate with climate prediction center (CPM) and morphing technique (CMORPH) and weather radar observations are widely in use around the world [9][10][11]. ...
... However, these SbPPs are only used after a careful investigation in the desired study area. Since it was discovered that these SbPPs have certain uncertainties, such as accuracy that is affected by topographical features of the study area and precipitation mechanism due to seasonal and regional climate conditions, such accuracy evaluations of the products with respect to rain gauge data are done for each area of concern [7,9,10], which cannot be ignored if we plan to use them in hydrological applications [6]. A study done by et al., [13] in the Ganzi river basin of the Tibetan plateau to evaluate the impact of satellite data sets to be used in hydrological modeling for that area used CMORPH-CRT, PERSIANN-CDR, 3B42RT, and 3B42 satellite data sets against observed rainfall data using HEC-HMS model to find out that TRMM-3B42RT and CMORPH-CRT show good performance in the respective area and they also suggested that TRMM-3B42RT is a better choice overall for hydrological models in the Ganzi river basin of Tibetan plateau. ...
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... However, rainfall gauging station networks are often unevenly distributed sparsely across space, which imposes difficulties for properly capturing the spatial variability of precipitation systems [2]. In addition, precipitation samples obtained from ground-based gauges are frequently corrupted by long periods of missing data, which may hinder their use for continuous rainfall-runoff modeling and, accordingly, for the indirect estimation of streamflow-related variables [3]. ...
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... Since satellite QPEs contain data information from multiple sensors, such as the Microwave Imager (TMI) on TRMM, Special Sensor Microwave Imager (SSM/I) on Defense Meteorological Satellite Program (DMSP) satellites, and Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) on Aqua, they are subject to biases and uncertainties in estimating regional PR including PR extremes (Anagnostou et al., 2010;Liu and Zipser, 2015;Maggioni et al., 2014). A comprehensive assessment in the ability of QPEs to estimate the intensity, frequency and spatial distributions of PR extremes at different spatio-temporal scales is essential for the accurate application of QPEs in the monitoring and forecasting of extreme PR events (Tan et al., 2018;Trenberth et al., 2017). ...
Article
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... This difference in model performance between the two scenarios may be attributed to the difference in the value of satellite precipitation data with respect to the precipitation gauge data (Stisen and Sandholt, 2010). Past studies have shown that even rain gauge data are not free of error and contain uncertainties similar to SPP data (Ali et al., 2005;Anagnostou et al., 2010). Hence, such a difference may also be due to the poor quality, lack of spatial coverage and missing data, particularly for regions with sparse rain gauges. ...
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... Many different metrics to assess predictive skill can be defined, even when considering only two classes (Agha-Kouchak & Mehran, 2013;Wilks, 2006), however, the probabilities of a hit (known as probability of detection; POD) and of a false alarm (known as the false alarm ratio; FAR) are most commonly used in the literature (Anagnostou et al., 2010;Gourley et al., 2012;Hao et al., 2013). Here we used the critical success index (CSI), which combines the latter two metrics, as follows (Schaefer, 1990): ...
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... Besides, these stations are heterogeneously distributed around the earth (Kidd et al., 2016) and represent the rainfall in a limited area around each station (Kidd and Huffman, 2011). Therefore, rainfall monitoring through alternatives, including rainfall radars and satellite sensors, has been well-established (Anagnostou et al., 2010). ...
... However, large uncertainties might influence rain radars due to beam blockage and frozen hydrometeor (Villarini and Krajewski, 2010). In contrast, satellite rainfall products have been available since the 1970 s, and their accuracy and resolution (both temporal and spatial) have improved over time (Anagnostou et al., 2010;Zhang et al., 2020). Satellite products can measure various precipitation types such as snow, rainfall, and hail. ...
Article
Sparse distribution of rain gauge networks challenges the estimation of rainfall variability over space and time. The SM2RAIN algorithm was developed to estimate rainfall from the knowledge of soil moisture (SM) by inverting the soil‐water balance equation. The algorithm was simplified by neglecting the contribution of evapotranspiration and surface runoff rate during the rainfall event. A recent study developed an analytical model to estimate the net water flux (NWF) from SM data via inversion of analytical Warrick’s equation. In this study, the SM2RAIN-NWF algorithm was developed by integrating the SM2RAIN algorithm and the NWF model to improve the accuracy of rainfall estimation. The applicability of the SM2RAIN-NWF algorithm was evaluated based on observed rainfall data in the Lake Urmia basin, Iran. Satellite SM data was obtained from the Advanced Microwave Scanning Radiometer 2 (AMSR2). The algorithm calibrated based on the data from July 3, 2012, to December 31, 2017, was then used to estimate rainfall for two years extending from January 2018 to December 2019. Estimated rainfall through SM2RAIN-NWF algorithm improved compared to SM2RAIN by 14% and 37.4% increase in the average values of correlation coefficient (R) and Nash–Sutcliffe (NS), and 11.5% decrease in the Percentage Root Mean Square Error (PRMSE) over the calibration period. Validating the estimated rainfall showed a considerable improvement in the performance of the SM2RAIN-NWF algorithm compared to the SM2RAIN algorithm by 8.6% and 30.4% increase in the average values of R and NS, and 13.4% decrease in the PRMSE. It was also found that the SM2RAIN-NWF algorithm contributes to the improvement of error indices in rainfall estimation and simulates the rainfall variation trend in a better fashion than the SM2RAIN algorithm.
... Compared with rain gauge and radar observations, SPPs can provide a larger view of spatial precipitation distribution. However, it is well known that the precipitation estimates made by SPPs for various regions have different deviations (O et al., 2017;Tan and Duan, 2017;Ma et al., 2019;Nashwan et al., 2019;Liu et al., 2020), and the performance of SPPs is both regionally (Dinku et al., 2010;Derin et al., 2016;Maggioni et al., 2016;Derin et al., 2019) and seasonally dependent (Anagnostou et al., 2010;Stampoulis and Anagnostou, 2012;Tan et al., 2015;Ning et al., 2016). Therefore, before applying a SPP, research must be conducted to verify its performance in different regions and seasons. ...
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This study evaluates the performance of five satellite precipitation products (SPPs) from the Precipitation Remotely Sensed Information using Artificial Neural Networks (PERSIANN) family for depicting precipitation changes in Taiwan over multiple timescales. Rain gauge data provided by the Central Weather Bureau (CWB) of Taiwan were used as a reference for evaluation, which focused on the wet seasons (May to October) during the period 2003–2019. All SPPs were found to have good ability in expressing the temporal phase changes over most of Taiwan on all the timescales examined, with significant temporal correlation coefficients (TCC) observed between the SPPs and the CWB data. We further evaluated the performance of the SPPs in making quantitative precipitation estimates based on the root mean square error (RMSE). For all examined timescales, the comparison between the best and worst performance shows greater normalized differences in quantitative estimates (i.e., RMSE) than in temporal phase depiction (i.e., TCC). In general, all SPPs tend to underestimate precipitation over most of Taiwan; however, two relatively new products (PDIR-Now and PERSIANN-CCS-CDR) have better RMSE performance than other SPPs on different timescales. PDIR-Now is the best product for quantitatively estimating precipitation on interannual, annual, and seasonal timescales, while PERSIANN-CCS-CDR is superior for daily and diurnal timescales. The findings also highlight that the performance of the PERSIANN-family in quantitatively estimating Taiwan precipitation does not depend primarily on the spatiotemporal resolution of SPPs, but may be related to the use of the cloud patch approach and the inclusion of weather station information in producing PDIR-Now and PERSIANN-CCS-CDR.
... High-resolution rainfall data have an extensive application in the fields of agriculture, forestry, transportation, and marine monitoring [6][7][8][9]. There are obvious weaknesses in traditional ground station measurement, due to the limited coverage of rain gauges, which is confined by the complex terrain and coastline, and the uncertainty of accuracy in the weather radar detection [10]. Contrastingly, satellites can make comprehensive observations and provide intuitive remote sensing image information; therefore, satellite rainfall products are superior and have good prospects to improve the ability of monitoring grid rainfall [11]. ...
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The lack of accurate estimation of intense precipitation is a universal limitation in precipitation retrieval. Therefore, a new rainfall retrieval technique based on the Random Forest (RF) algorithm is presented using the Advanced Himawari Imager-8 (Himawari-8/AHI) infrared spectrum data and the NCEP operational Global Forecast System (GFS) forecast information. And the gauge-calibrated rainfall estimates from the Global Precipitation Measurement (GPM) product served as the ground truth to train the model. The two-step RF classification model was established for (1) rain area delineation and (2) precipitation grades’ estimation to improve the accuracy of moderate rain and heavy rain. In view of the imbalance categories’ distribution in the datasets, the resampling technique including the Random Under-sampling algorithm and Synthetic Minority Over-sampling Technique (SMOTE) was implemented throughout the whole training process to fully learn the characteristics among the samples. Among the features used, the contributions of meteorological variables to the trained models were generally greater than those of infrared information; in particular, the contribution of precipitable water was the largest, indicating the sufficient necessity of water vapor conditions in rainfall forecasting. The simulation results by the RF model were compared with the GPM product pixel-by-pixel. To prove the universality of the model, we used independent validation sets which are not used for training and two independent testing sets with different periods from the training set. In addition, the algorithm was validated against independent rain gauge data and compared with GFS model rainfall. Consequently, the RF model identified rainfall areas with a Probability Of Detection (POD) of around 0.77 and a False-Alarm Ratio (FAR) of around 0.23 for validation, as well as a POD of 0.60–0.70 and a FAR of around 0.30 for testing. To estimate precipitation grades, the value of classification was 0.70 in validation and in testing the accuracy was 0.60 despite a certain overestimation. In summary, the performance on the validation and test data indicated the great adaptability and superiority of the RF algorithm in rainfall retrieval in East Asia. To a certain extent, our study provides a meaningful range division and powerful guidance for quantitative precipitation estimation.
... Nowadays, rain gauges are the most traditional and direct way to obtain reliable rainfall data at a fine temporal resolution. Traditional conceptual rainfall-runoff models commonly use catchment-average rainfall as inputs [16]; however, rainfall is usually biased due to the spatial variability and low density of unevenly distributed rain gauging stations, especially in developing areas [15,[17][18][19][20][21]. Therefore, finding an appropriate way to describe rainfall variability for traditional rain gauging stations in conceptual rainfall-runoff models remains an unresolved issue. ...
Article
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Rainfall is an important input to conceptual hydrological models, and its accuracy would have a considerable effect on that of the model simulations. However, traditional conceptual rainfall-runoff models commonly use catchment-average rainfall as inputs without recognizing its spatial variability. To solve this, a seamless integration framework that couples rainfall spatial variability with a conceptual rainfall-runoff model, named the statistical rainfall-runoff (SRR) model, is built in this study. In the SRR model, the exponential difference distribution (EDD) is proposed to describe the spatial variability of rainfall for traditional rain gauging stations. The EDD is then incorporated into the vertically mixed runoff (VMR) model to estimate the statistical runoff component. Then, the stochastic differential equation is adopted to deal with the flow routing under stochastic inflow. To test the performance, the SRR model is then calibrated and validated in a Chinese catchment. The results indicate that the EDD performs well in describing rainfall spatial variability, and that the SRR model is superior to the Xinanjiang model because it provides more accurate mean simulations. The seamless integration framework considering rainfall spatial variability can help build a more reasonable statistical rainfall-runoff model.
... For instance, Tian et al. (2007) showed that the bias in the North-Western US changes from positive to negative if either the gauge only or the radar + gauge dataset is considered. Anagnostou et al. (2010) demonstrated the need to benchmark reference data sources prior to their quantitative use in validating remote sensing retrievals. Ali et al. (2005) used different gauge networks for performing an empirical evaluation of (1) the error of the reference network (often used in validation of global products as the ground truth) and (2) the covariance between the errors in the product and the errors in the reference. ...
Chapter
Quantifying errors and uncertainties associated with satellite precipitation products (SPPs) is fundamental to guarantee their correct use in several applications, including hydrological predictions, climate studies, and water resource management. Numerous factors affect the accuracy and precision of these products, including the sensor frequencies and channels, the type of precipitation, the heterogeneity of precipitation within the sensor footprint, as well as the choice of algorithm that transfers the sensor retrieval information to a precipitation rate. This chapter analyses these sources and summarizes the most common methods to estimate, quantify, and model errors and uncertainties associated with SPPs.