6.2A Data Fusion of Environmental Data Based on Converging Remote Sensing and Data Assimilation Techniques

Tuesday, 24 January 2017: 2:00 PM
620 (Washington State Convention Center )
Kevin Garrett, Riverside Technology, Inc. and NOAA/NESDIS/STAR, College Park, MD; and E. Maddy, S. A. Boukabara, E. Jones, B. Johnson, L. Liu, and K. Kumar

A new data fusion approach which leverages current National Oceanic and Atmospheric Administration (NOAA) data assimilation systems as well as established remote sensing inversion algorithms has been under development at NOAA’s Center for Satellite Applications and Research in the context of a pilot project. The concept uses data assimilation as a data fusion tool and integrates remote sensing algorithms in order to increase the diversity of the parameters being generated. It is a single tool that combines actual algorithms to produce geophysically consistent parameters, in contrast to conventional data fusion applications which attempt to blend or fuse selected product types from multiple sources. Additionally, the data fusion concept presented in this study includes both preprocessing and post processing of satellite observations to fully integrate and synergize remote sensing and data assimilation applications. The data fusion analysis is capable of providing the full complement of traditional remote sensing products at hourly or sub-hourly intervals (e.g. soundings, trace gases, precipitable water, cloud, rainfall rate, cryospheric parameters), as well as added-value parameters such as geopotential height, potential vorticity, helicity, Convective Available Potential Energy (CAPE), and Convective Inhibition (CIN). The availability of a consolidated and consistent source of information for multiple products from multiple satellites and conventional data sources, at high resolution, should provide superior benefit to the NOAA user community with emphasis on forecasters providing situational awareness and short-term forecast guidance. It presents an elegant way to merge all sources of observation data including spaceborne and conventional. Here we present an overview of the data fusion processing system components: the preprocessor which combines multiple satellite retrieval algorithms (called MIIDAPS) to perform an adjustment to the data assimilation background field as well as provide quality control and boundary conditions to assimilated satellite data; the 3DVAR data assimilation leveraging the NOAA Gridpoint Statistical Interpolation (GSI); and the post processing which includes derived products based on the analysis, and visualization and validation of the data fusion outputs. The data fusion system can provide global, hourly analyses at 30 km spatial resolution with the prospect of increasing to 15 km. Product performances are detailed in comparison to in-situ and Numerical Weather Prediction (NWP) analyses, along with case studies to illustrate the added value for operational forecasting needs.
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