Published datasets in MDPI remote sensing entitled "Improving the regional precipitation simulation corrected by satellite observation using quantile mapping"

<b>Abstract</b>: <br>This study investigates how to use the gridded satellite datasets of observational precipitation to improve the performance of the climatological simulation by using the method of non-parametric quantile mapping (QM). The precipitation in Southeast Asia is simulated in 2001–2005 using the climate model of Weather Research and Forecasting (WRF). Two satellite datasets of observational precipitation, GSMaP and CHIRPS, are used for model training, simulation evaluation, and cross-validation. The evaluations of simulation and bias correction suggest that QM is able to perfectly correct the overall quantile distributions of the simulated precipitation, which has overestimation at most quantiles, especially for light and extreme precipitation. After the QM correction based on GSMaP (CHIRPS), the relative bias of the monthly average for all months is reduced from 39.3% to 4.1% (from 57.2% to 4.2%). The biases of spatial patterns are largely narrowed from 43.5% (59.4%) to 4.0% (2.5%) for annual-mean precipitation and from 43.5% (59.4%) to 4.0% (2.5%) for extreme precipitation. The results indicate that the QM correction based on the gridded satellite datasets outperforms the raw model output and greatly improves the estimates of the simulated precipitation.<br><b>Dataset description:</b>Name regulation: XXX05: 5km; XXX10: 10km; 01-12: 12 months; OBS: observation; WRF: WRF simulation; QMC: quantile mapping correction.<br>Folder "grid_data_OBS": daily grid data of observation;<br>Folder "grid_data_WRF": daily grid data of WRF simulation;<br>Folder "grid_data_QMC": daily grid data of QM correction;Folder "Figure 2 - cdf_OBS_WRF_QMC": data for results in Figure 2;<br>Folder "Figure 3 - statistics_mon_QMC": data for results in Figure 3;Folder "Figure 4 - annual precipitation": data for results in Figure 4;<br>Folder "Figure 5 - extreme precipitation": data for results in Figure 5.<b>摘要</b>：本研究探讨如何利用观测降水的网格化卫星数据集，结合非参数分位数映射（non-parametric quantile mapping, QM）方法，提升气候模拟的性能。本研究采用天气研究与预报模式（Weather Research and Forecasting, WRF），对2001-2005年东南亚地区的降水进行模拟。研究选用两款观测降水卫星数据集GSMaP与CHIRPS，用于模型训练、模拟评估与交叉验证。模拟与偏差校正评估结果表明，QM可有效校正模拟降水的整体分位数分布：原始模拟在多数分位数上存在高估现象，尤其在弱降水与极端降水场景下更为显著。基于GSMaP的QM校正后，所有月份的月平均相对偏差从39.3%降至4.1%；基于CHIRPS的QM校正则使该偏差从57.2%降至4.2%。年平均降水与极端降水的空间分布偏差均从43.5%（59.4%）大幅收窄至4.0%（2.5%）。研究结果表明，基于网格化卫星数据集的QM校正效果优于原始模式输出，可显著提升模拟降水的估算精度。<br><b>数据集说明</b>：命名规则：XXX05代表5km分辨率；XXX10代表10km分辨率；01-12代表12个月份；OBS为观测数据；WRF为WRF模拟结果；QMC为分位数映射校正（quantile mapping correction, QMC）。<br>文件夹"grid_data_OBS"：观测逐日网格化数据；<br>文件夹"grid_data_WRF"：WRF模拟逐日网格化数据；<br>文件夹"grid_data_QMC"：QM校正逐日网格化数据；<br>文件夹"Figure 2 - cdf_OBS_WRF_QMC"：图2相关结果数据；<br>文件夹"Figure 3 - statistics_mon_QMC"：图3相关结果数据；<br>文件夹"Figure 4 - annual precipitation"：图4相关结果数据；<br>文件夹"Figure 5 - extreme precipitation"：图5相关结果数据。