Abstract. Latent heating (LH) is an important quantity in both weather forecasting and climate analysis, being the essential factor driving convective systems. Yet, inferring LH rates from our current observing systems is challenging at best. For climate studies, LH has been retrieved from the Precipitation Radar (PR) on the Tropical Rainfall Measuring Mission (TRMM) using model simulations in the look-up table (LUT) that relates instantaneous radar profiles to corresponding heating profiles. These radars, first on TRMM and then Global Precipitation Measurement (GPM), provide a continuous record of LH. However, with observations approximately 3 days apart, its temporal resolution is too coarse to be used to initiate convection in forecast models. In operational forecast models such as High-Resolution Rapid Refresh (HRRR), convection is initiated from LH derived from ground based radar. Despite the high spatial and temporal resolution of ground-based radars, one disadvantage of using it is that its data are only available over well observed land areas. This study suggests a method to derive LH from the Geostationary Operational-Environmental Satellite-16 (GOES-16) in near-real time. Even though the visible and infrared channels on the Advanced Baseline Imager (ABI) provide mostly cloud top information, rapid changes in cloud top visible and infrared properties, when coupled to a LUT similar to those used by the TRMM and GPM radars, can equally be used to derive LH profiles for convective regions using model simulations coupled to a convective classification scheme and channel 14 (11.2 μm) brightness temperature. Convective regions detected by GOES-16 are assigned LH from the LUT, and they are compared with LH from NEXRAD and one of Dual-frequency Precipitation Radar (DPR) products, Goddard Convective-Stratiform Heating (CSH). LH obtained from GOES-16 show similar magnitude with NEXRAD and CSH, and vertical distribution of LH is also very similar with CSH. Overall, GOES LH appear to have the ability to mimic LH from radars, although the area identified as convective is roughly 25 % smaller than the current HRRR model, while the heating is correspondingly higher.

中文翻译：

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GOES-16 的潜热分布及其与 NEXRAD 和 GPM 的加热比较

摘要。潜热 (LH) 是天气预报和气候分析中的重要量，是驱动对流系统的基本因素。然而，从我们当前的观测系统推断 LH 率充其量是具有挑战性的。对于气候研究，已使用查找表 (LUT) 中的模型模拟从热带降雨测量任务 (TRMM) 的降水雷达 (PR) 中检索到 LH，该表将瞬时雷达剖面与相应的加热剖面联系起来。这些雷达，首先是 TRMM，然后是全球降水测量 (GPM)，提供 LH 的连续记录。然而，由于观测间隔大约为 3 天，其时间分辨率太粗糙，无法用于在预测模型中启动对流。在高分辨率快速刷新 (HRRR) 等运营预测模型中，对流由来自地基雷达的 LH 发起。尽管地基雷达具有很高的空间和时间分辨率，但使用它的一个缺点是其数据只能在经过良好观察的陆地区域内可用。这项研究提出了一种近乎实时地从地球同步运行环境卫星 16 (GOES-16) 中推导出 LH 的方法。尽管高级基线成像仪 (ABI) 上的可见光和红外通道主要提供云顶信息，但当耦合到类似于 TRMM 和 GPM 雷达使用的 LUT 时，云顶可见光和红外特性的快速变化同样可以用于使用耦合到对流分类方案和通道 14 (11.2 μm) 亮度温度的模型模拟得出对流区域的 LH 剖面。GOES-16 检测到的对流区域被指定为来自 LUT 的 LH，并将它们与来自 NEXRAD 的 LH 和双频降水雷达 (DPR) 产品之一戈达德对流层状加热 (CSH) 进行比较。从 GOES-16 获得的 LH 显示出与 NEXRAD 和 CSH 相似的震级，LH 的垂直分布也与 CSH 非常相似。总体而言，GOES LH 似乎有能力从雷达模拟 LH，尽管确定为对流的区域比当前的 HRRR 模型小约 25%，而热量相应地更高。