PDF(Pubmed)
Abstract：
In Indonesia, the monitoring of rainfall requires an estimation system with a high resolution and wide spatial coverage because of the complexities of the rainfall patterns. This study built a rainfall estimation model for Indonesia through the integration of data from various instruments, namely, rain gauges, weather radars, and weather satellites. An ensemble learning technique, specifically, extreme gradient boosting (XGBoost), was applied to overcome the sparse data due to the limited number of rain gauge points, limited weather radar coverage, and imbalanced rain data. The model includes bias correction of the satellite data to increase the estimation accuracy. In addition, the data from several weather radars installed in Indonesia were also combined. This research handled rainfall estimates in various rain patterns in Indonesia, such as seasonal, equatorial, and local patterns, with a high temporal resolution, close to real time. The validation was carried out at six points, namely, Bandar Lampung, Banjarmasin, Pontianak, Deli Serdang, Gorontalo, and Biak. The research results show good estimation accuracy, with respective values of 0.89, 0.91, 0.89, 0.9, 0.92, and 0.9, and root mean square error (RMSE) values of 2.75 mm/h, 2.57 mm/h, 3.08 mm/h, 2.64 mm/h, 1.85 mm/h, and 2.48 mm/h. Our research highlights the potential of this model to accurately capture diverse rainfall patterns in Indonesia at high spatial and temporal scales.
摘要：
在印度尼西亚,由于降雨模式的复杂性，对降雨的监测需要具有高分辨率和广泛空间覆盖范围的估算系统。本研究通过整合各种仪器的数据，建立了印度尼西亚的降雨量估算模型，即,雨量计,天气雷达,和气象卫星。一种合奏学习技术，具体来说,极端梯度提升(XGBoost)，用于克服由于雨量计点数量有限而导致的稀疏数据，天气雷达覆盖范围有限，和不平衡的降雨数据。该模型包括卫星数据的偏差校正以提高估计精度。此外,来自印度尼西亚安装的几个气象雷达的数据也被合并在一起。这项研究处理了印度尼西亚各种降雨模式下的降雨量估计，比如季节性的,赤道,和当地的模式，具有很高的时间分辨率，接近实时。验证在六个点进行，即,BandarLampung,Banjarmasin,庞蒂亚克,DeliSerdang,Gorontalo,Biak研究结果表明，估计精度较好，分别为0.89、0.91、0.89、0.9、0.92和0.9，均方根误差（RMSE）值为2.75mm/h，2.57mm/h,3.08mm/h,2.64mm/h,1.85mm/h,和2.48毫米/小时。我们的研究强调了该模型在高时空尺度上准确捕获印度尼西亚各种降雨模式的潜力。
