Ground-clutter is a significant cause of missed-detection and underestimation of precipitation in complex terrain from space-based radars such as the Global Precipitation Measurement Mission (GPM) Dual-frequency Precipitation Radar (DPR). This research proposes an Artificial Intelligence (AI) framework consisting of a precipitation detection model (PDM) and a precipitation regime classification model (PCM) to improve orographic precipitation retrievals from GPM-DPR using machine learning.

The PDM is a Random Forest Classifier using GPM Microwave Imager (GMI) calibrated brightness temperatures (Tbs) and low-level precipitation mixing ratios from the High-Resolution Rapid Refresh (HRRR) analysis as inputs. The PCM is a Convolutional Neural Network that predicts the precipitation regime class, defined independently based on quantitative features of ground-based radar reflectivity profiles, using GPM DPR Ku-band (Ku-PR) reflectivity profiles and GMI Tbs. The AI framework is demonstrated for warm-season precipitation in the Southern Appalachian Mountains over three years (2016–2019), achieving large reductions in false alarms (77%) and missed detections (82%) relative to GPM Ku-PR precipitation products. The spatial distribution of predicted precipitation classes within the GPM overpass reflects the complex interactions between storms and topography that determine orographic precipitation regimes. For each GPM pixel, the local precipitation class informs on the vertical structure of rainfall microphysics aiming to capture low-level processes missed in GPM DPR reflectivity profiles contaminated by ground-clutter (i.e., the radar blind-zone).

地面杂波是诸如全球降水量测量任务（GPM）双频降水雷达（DPR）等天基雷达在复杂地形中漏检和低估降水的重要原因。这项研究提出了一个由降水检测模型（PDM）和降水状态分类模型（PCM）组成的人工智能（AI）框架，以改善使用机器学习从GPM-DPR进行地形降水的检索。

PDM是一种随机森林分类器，它使用GPM微波成像仪（GMI）校准的亮度温度（Tbs）和高分辨率快速刷新（HRRR）分析中的低水平降水混合比作为输入。PCM是一个卷积神经网络，它使用GPM DPR Ku波段（Ku-PR）反射率剖面图和GMI Tbs来预测降水状况类别，该预测机制是基于地面雷达反射率剖面图的定量特征独立定义的。在三年（2016-2019年）内，已证明了AI框架可用于南部阿巴拉契亚山脉的暖季降水，相对于GPM Ku-PR降水产品，虚假警报（77％）和漏检（82％）的大幅度减少。GPM立交桥内的预测降水类别的空间分布反映了风暴和地形之间复杂的相互作用，这些相互作用决定了地形上的降水方式。对于每个GPM像素，本地降水类别会告知降雨微观物理学的垂直结构，旨在捕获地面杂波（即雷达盲区）污染的GPM DPR反射率剖面中遗漏的低水平过程。