Precipitation Retrieval over the Tibetan Plateau from the Geostationary Orbit - Part 1: Precipitation Area Delineation with Elektro-L2 and Insat-3D
Precipitation Retrieval over the Tibetan Plateau from the Geostationary Orbit - Part 1: Precipitation Area Delineation with Elektro-L2 and Insat-3D
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DOI：
10.3390/rs11192302
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发表时间：
2019-10
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通讯作者：
Christine Kolbe;B. Thies;Sebastian Egli;L. Lehnert;H. Schulz;J. Bendix
中科院分区：
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作者：
Christine Kolbe;B. Thies;Sebastian Egli;L. Lehnert;H. Schulz;J. Bendix
由于青藏高原地形复杂，缺乏长期的、分布均匀的降水观测资料，这就需要其他来源的降水资料。基于卫星的降水反演可以填补这些数据空白。在从卫星图像中检索降水率之前，需要对降水区域进行适当的分类。在这里，我们提出了一个可行性研究的降水区划定计划的基础上，从地球静止轨道（GEO，Insat-3D和Elektro-L2）和机器学习方法（随机森林，RF）的数据融合的多光谱数据的TiP。GEO数据被用作RF模型的预测因子，并由独立的GPM（全球降水测量使命）IMERG（GPM综合多卫星检索）仪表校准微波（MW）最佳质量降水估计进行了广泛验证。为了提高RF模型的性能，我们测试了不同的优化方案。在这里，我们发现（1）在训练过程中使用更多的沉淀像素并减少非沉淀像素的数量大大改善了分类结果。降水区划分的准确性也受益于（2）将时间分辨率改为更小的段。我们特别将我们的结果与来自GPM IMERG的仅红外（IR）降水产品进行了比较，发现新的多光谱产品的性能明显改善（Heidke技能评分（HSS）为0.19（仅IR），而0.57（新的多光谱产品））。其他研究与降水区划定得到的检测概率（POD）为0.61，而我们的POD是可比的，平均为0.56。新的多光谱产品表现最好（差）的降水率高于第90百分位数（低于第10百分位数）。我们的研究结果指出了一个明确的战略，以提高IMERG产品在MW辐射的情况下。
The lack of long term and well distributed precipitation observations on the Tibetan Plateau (TiP) with its complex terrain raises the need for other sources of precipitation data for this area. Satellite-based precipitation retrievals can fill those data gaps. Before precipitation rates can be retrieved from satellite imagery, the precipitating area needs to be classified properly. Here, we present a feasibility study of a precipitation area delineation scheme for the TiP based on multispectral data with data fusion from the geostationary orbit (GEO, Insat-3D and Elektro-L2) and a machine learning approach (Random Forest, RF). The GEO data are used as predictors for the RF model, extensively validated by independent GPM (Global Precipitation Measurement Mission) IMERG (Integrated Multi-satellitE Retrievals for GPM) gauge calibrated microwave (MW) best-quality precipitation estimates. To improve the RF model performance, we tested different optimization schemes. Here, we find that (1) using more precipitating pixels and reducing the amount of non-precipitating pixels during training greatly improved the classification results. The accuracy of the precipitation area delineation also benefits from (2) changing the temporal resolution into smaller segments. We particularly compared our results to the Infrared (IR) only precipitation product from GPM IMERG and found a markedly improved performance of the new multispectral product (Heidke Skill Score (HSS) of 0.19 (IR only) compared to 0.57 (new multispectral product)). Other studies with a precipitation area delineation obtained a probability of detection (POD) of 0.61, whereas our POD is comparable, with 0.56 on average. The new multispectral product performs best (worse) for precipitation rates above the 90th percentile (below the 10th percentile). Our results point to a clear strategy to improve the IMERG product in the absence of MW radiances.