ABSTRACT

A new satellite-based algorithm for rainfall retrieval in high spatio-temporal resolution for Ecuador is presented. The algorithm relies on the precipitation information from the Integrated Multi-SatEllite Retrieval for the Global Precipitation Measurement (GPM) (IMERG) and infrared (IR) data from the Geostationary Operational Environmental Satellite-16 (GOES-16). It was developed to (i) classify the rainfall area (ii) assign the rainfall rate. In each step, we selected the most important predictors and hyperparameter tuning parameters monthly. Between 19 April 2017 and 30 November 2017, brightness temperature derived from the GOES-16 IR channels and ancillary geo-information were trained with microwave-only IMERG-V06 using random forest (RF). Validation was done against independent microwave-only IMERG-V06 information not used for training. The validation results showed the new rainfall retrieval technique (multispectral) outperforms the IR-only IMERG rainfall product. This offers using the multispectral IR data can improve the retrieval performance compared to single-spectrum IR approaches. The standard verification scored a median Heidke skill score of ~0.6 for the rain area delineation and R between ~0.5 and ~0.62 for the rainfall rate assignment, indicating uncertainties for Andes’s high elevation. Comparison of RF rainfall rates in 2 km² resolution with daily rain gauge measurements reveals the correlation of R = ~0.33.

摘要

提出了一种新的基于卫星的厄瓜多尔高时空分辨率降雨恢复算法。该算法依赖于来自用于全球降水量测量（GPM）（IMERG）的综合多卫星观测的降水信息和来自对地静止作战环境卫星16（GOES-16）的红外（IR）数据。它被开发为（i）对降雨区域进行分类（ii）分配降雨率。在每个步骤中，我们每月选择最重要的预测变量和超参数调整参数。在2017年4月19日至2017年11月30日之间，仅使用微波的IMERG-V06使用随机森林（RF）训练了来自GOES-16红外通道和辅助地理信息的亮度温度。针对未用于培训的独立的仅限微波的IMERG-V06信息进行了验证。验证结果表明，新的降雨检索技术（多光谱）优于仅使用IR的IMERG降雨产品。与单光谱IR方法相比，使用多光谱IR数据可以提高检索性能。对于雨区轮廓，标准验证的Heidke技能得分中位数约为0.6，而对于降雨率分配，R的中位数R值介于0.5至0.62之间，这表明安第斯山脉的高海拔存在不确定性。2 km的RF降雨率比较 降雨区域轮廓为6，降雨率分配的R在〜0.5和〜0.62之间，这表明安第斯山脉的高海拔地区存在不确定性。2 km的RF降雨率比较 降雨区域轮廓为6，降雨率分配的R在〜0.5和〜0.62之间，这表明安第斯山脉的高海拔地区存在不确定性。2 km的RF降雨率比较 每日雨量计测量的²分辨率揭示了R =〜0.33的相关性。