Exploring the Benefits of the Satellite Hyperspectral Microwave Observations: Toward the New Generation of Microwave Sounders

The purpose of this project is to support the arguments for a Hyperspectral Microwave Sensor (HyMS) used in weather applications. The data used for this project is from a few different hurricane cases that come from the Global Forecasting System (GFS). Analysis for this project was done using simulated HyMS and ATMS data from the Community Radiative Transfer Model (CRTM). This model is useful for simulating satellite data since it has inputs for instrument noise amounts, the specified range of channels to use, etc. The ATMS sensor currently uses 22 channels spanning from 23 GHz to 183 GHz, and for the purposes of this project, the simulated HyMS data uses 1266 channels between 12 GHz and 220 GHz. The simulated HyMS and ATMS data are run through the Multi-Instrument Inversion and Data Assimilation Preprocessing System-Artificial Intelligence (MIIDAPS-AI) to acquire the Temperature, Water Vapor, and Cloud Water of each hurricane case. These final outputs were then compared to the GFS (true) output to see which sensor is more accurate.