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首页> 外文期刊>Journal of hydrometeorology >LMODEL: A Satellite Precipitation Methodology Using Cloud Development Modeling. Part I: Algorithm Construction and Calibration
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LMODEL: A Satellite Precipitation Methodology Using Cloud Development Modeling. Part I: Algorithm Construction and Calibration

机译:模型:使用云开发建模的卫星降水方法。第一部分:算法构建和校准

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摘要

The Lagrangian Model (LMODEL) is a new multisensor satellite rainfall monitoring methodology based on the use of a conceptual cloud-development model that is driven by geostationary satellite imagery and is locally updated using microwave-based rainfall measurements from low earth-orbiting platforms. This paper describes the cloud development model and updating procedures; the companion paper presents model validation results. The model uses single-band thermal infrared geostationary satellite imagery to characterize cloud motion, growth, and dispersal at high spatial resolution (similar to 4 km). These inputs drive a simple, linear, semi-Lagrangian, conceptual cloud mass balance model, incorporating separate representations of convective and stratiform processes. The model is locally updated against microwave satellite data using a two-stage process that scales precipitable water fluxes into the model and then updates model states using a Kalman filter. Model calibration and updating employ an empirical rainfall collocation methodology designed to compensate for the effects of measurement time difference, geolocation error, cloud parallax, and rainfall shear.
机译:拉格朗日模型(LMODEL)是一种新的多传感器卫星降雨监测方法,其基于概念性云发展模型的使用,该模型由对地静止卫星图像驱动,并使用来自低地球轨道平台的基于微波的降雨测量值进行了本地更新。本文介绍了云开发模型和更新过程;随附的论文介绍了模型验证结果。该模型使用单波段热红外对地静止卫星图像来表征高空间分辨率(约4 km)下的云运动,生长和扩散。这些输入驱动一个简单的线性半拉格朗日概念性云量平衡模型,其中包含对流过程和层状过程的独立表示。使用两阶段过程针对微波卫星数据在本地更新模型,该过程将可沉淀的水通量缩放到模型中,然后使用卡尔曼滤波器更新模型状态。模型校准和更新采用经验性降雨搭配方法,旨在补偿测量时间差,地理位置误差,云视差和降雨剪切的影响。

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