4A.1 Using Radio Occultation Data for Atmospheric Numerical Weather Prediction, Climate Sciences, and Atmospheric Studies: Recent Results from COSMIC-2, Commercial RO Data, and Recent RO Missions at NESDIS/STAR

Monday, 29 January 2024: 4:30 PM
320 (The Baltimore Convention Center)
Shu-Peng Ho, NOAA, College Park, MD; and X. zhou, X. Shao, Y. Chen, and W. Miller

The Global Navigation Satellite System (GNSS) Radio Occultation (RO) is an active remote sensing technique for probing the atmosphere. The measurements from the RO limb-sounding technique provide and high vertical resolution ranging from ~60 m near the surface to ~1.5 km at 40 km altitude, which is very suitable for studying atmospheric phenomena. Because RO measurements are unaffected by clouds and precipitation, RO measurements complement the satellite microwave and infrared data and provide all-weather measurements globally. NOAA has included GNSS RO data as one of the crucial long-term observables for weather and climate applications. Over four important RO satellite missions have been launched in the past five years. New missions included the Taiwan–U.S. FORMOSAT-7/ COSMIC-2 and the ESA–EUMETSAT–U.S. Sentinel-6. The commercial vendors, GeoOptics, Inc., and Spire Global, Inc., expanded their RO satellite constellation capability to provide more occultation observations. Each of these missions has different spatial and temporal coverage and tracking systems. This study aims to summarize the recent NWP applications, climate research, and atmospheric applications using COSMIC-2, commercial RO data, and current RO missions at NOAA Center for Satellite Applications and Research (STAR). NOAA STAR has developed capabilities of processing measurements of COSMIC-2, Spire, and many RO missions and become a GNSS RO data and science (DSC). STAR RO DSC aims to develop enterprise RO processing algorithms for all RO missions. In this study, I will first highlight the current development of the STAR RO inversion package and STAR COSMIC-2 and Spire data products processed by using the STAR inversion package. Then I will present our analysis to answer the following questions: i) Does lower Signal-Noise-Ratio (SNR) commercial CubeSats RO data lead to lower precision and more significant observation errors? ii) Does lower SNR Spire RO data lead to less accurate retrieval results? iii) Does Spire and COSMIC-2 data have impacts on tropical cyclone forecasts? and iv) Can we determine whether the RO data improve water vapor forecasts, especially in the lower troposphere? I will then highlight the current studies conducted at STAR using GNSS RO measurements for atmospheric applications and climate diagnostics and monitoring. I will also highlight the procedures to quantify the error covariance matrix for multiple RO missions in the NWP system.

Disclaimer: The scientific results, conclusions, and views or opinions expressed herein are those of the author(s) and do not necessarily reflect those of NOAA or the Department of Commerce.

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