Sensor performance plays a central role in the quality of weather data products produced by weather satellite observing systems. Next generation sensors harness commercial technologies and small satellite architectures that may include fewer spectral bands. The Aerospace Corporation is developing an end-to-end modeling and simulation testbed for performance analysis using CLAVR-x, a cloud detection algorithm made available by the Cooperative Institute of Meteorological Satellite Studies (CIMSS) at the University of Wisconsin. The current version of the testbed is being developed specifically for the U.S. Space Force’s EO/IR Weather System (EWS) mission to fill two top priority observational needs: cloud characterization and theater weather imagery. We present simulations of sensor performance based on MODIS data, characterizing the sensitivity of cloud products to sensor errors.
Traditional cloud detection algorithms for weather monitoring require radiometrically calibrated multispectral visible and infrared (IR) sensors such as those on dedicated weather satellites. In contrast, deep learning methods facilitate the use of proliferated earth observing constellations with uncalibrated sensors and fewer spectral bands for weather applications. This capability detects clouds and classifies types by recognizing spatial and spectral features using a deep neural network. In this study, we leverage existing U-Net architectures and train on several different satellite datasets. Following model development, we compare several ways to segment clouds in remote sensing images, including the number of spectral bands and separation of thin and thick clouds. The separation of thin and thick clouds is a first step in segmenting clouds by type. The capability developed in this work will facilitate the exploitation of rapidly growing data sources from the expanding market of proliferated commercial remote sensing systems.
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