High-resolution real-time satellite-based precipitation estimation datasets can play a more essential role in flood forecasting and risk analysis of infrastructures. This is particularly true for extended deserts or mountainous areas with sparse rain gauges like Iran. However, there are discrepancies between these satellite-based estimations and ground measurements, and it is necessary to apply adjustment methods to reduce systematic bias in these products. In this study, we apply a quantile mapping method with gauge information to reduce the systematic error of the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS). Due to the availability and quality of the ground-based measurements, we divide Iran into seven climate regions to increase the sample size for generating cumulative probability distributions within each region. The cumulative distribution functions (CDFs) are then employed with a quantile mapping 0.6° × 0.6° filter to adjust the values of PERSIANN-CCS. We use eight years (2009–2016) of historical data to calibrate our method, generating nonparametric cumulative distribution functions of ground-based measurements and satellite estimations for each climate region, as well as two years (2017–2018) of additional data to validate our approach. The results show that the bias correction approach improves PERSIANN-CCS data at aggregated to monthly, seasonal and annual scales for both the calibration and validation periods. The areal average of the annual bias and annual root mean square errors are reduced by 98% and 56% during the calibration and validation periods, respectively. Furthermore, the averages of the bias and root mean square error of the monthly time series decrease by 96% and 26% during the calibration and validation periods, respectively. There are some limitations in bias correction in the Southern region of the Caspian Sea because of shortcomings of the satellite-based products in recognizing orographic clouds.

中文翻译：

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基于分位数映射方法的伊朗不同气候区基于卫星的降水估计偏差校正

基于高分辨率实时卫星的降水估计数据集在基础设施的洪水预报和风险分析中可以发挥更重要的作用。对于大片沙漠或雨量稀疏的山区，例如伊朗，尤其如此。但是，这些基于卫星的估计与地面测量之间存在差异，因此有必要应用调整方法来减少这些产品中的系统偏差。在这项研究中，我们应用带有量规信息的分位数映射方法，以减少使用人工神经网络-云分类系统（PERSIANN-CCS）从遥感信息中进行降水估算的系统误差。由于地面测量的可用性和质量，我们将伊朗划分为七个气候区域，以增加样本数量，以在每个区域内生成累积概率分布。然后，将累积分布函数（CDF）与分位数映射0.6°×0.6°滤波器一起使用以调整PERSIANN-CCS的值。我们使用八年（2009–2016）的历史数据来校准我们的方法，生成每个气候区域的地面测量和卫星估算的非参数累积分布函数，以及两年（2017–2018）的其他数据进行验证我们的方法。结果表明，在校正和验证期间，偏差校正方法可将PERSIANN-CCS数据汇总到月度，季节和年度尺度，从而得到改善。在校准和验证期间，年度偏差的面积平均值和年度均方根误差分别减少了98％和56％。此外，在校准和验证期间，每月时间序列的偏差和均方根误差的平均值分别降低了96％和26％。里海南部地区的偏差校正存在某些局限性，因为基于卫星的产品在识别地形云方面存在缺陷。