11.6 Studying the Impact of Vertically Retrieved IR-Sensed Aerosol on Operational Data Assimilation Systems

Wednesday, 25 January 2017: 5:15 PM
Conference Center: Skagit 4 (Washington State Convention Center )
Mayra I. Oyola, ASEE/NRL Monterey, Monterey, CA; and J. W. Marquis, B. Ruston, J. R. Campbell, N. L. Baker, E. Hyer, J. Zhang, and D. Westphal

Brightness temperature (BT) retrieval techniques using passive infrared (IR) radiometric measurements are sensitive to absorbing and scattering constituents in the atmosphere. Dust aerosols absorb in the infrared, and are thus particularly significant due to their widespread extent over the globe and their high degree of spatial and temporal variability, including areas important for tropical cyclone development and other weather-related concerns. Previous studies have shown that dust aerosols exhibit the potential for inducing BT biases near 10K in some infrared channels assimilated to constrain atmospheric profiles. On the other hand, BT-derived parameters, such as sea surface temperatures (SSTs), are susceptible to negative biases of at least 1K or higher, which conflicts with the accuracy requirement for most research and operational applications (i.e., +/- 0.3 K). This problem is not limited to just satellite retrievals; it also impacts the incorporation of background fields from NWP analyses in data assimilation (DA) systems. The effect of aerosols on infrared fluxes at the ocean surface is a function of both aerosol loading and vertical profile. Therefore, a proper understanding and representation of the aerosol vertical distribution within NWP models is incumbent to ensure reconciliation of their impact on BT and DA processes. This understanding can be achieved by conducting modeling studies and by the exploitation of a robust observational dataset, such as satellite-based LIDAR profiling, which can be used to characterize aerosol type and distribution.

In this talk, we describe such an application using the Navy Aerosol Analysis Prediction System (NAAPS) and Naval Research Laboratory (NRL) Atmospheric Variational Data Assimilation System (NAVDAS).  We analyze the impact of aerosol-biased radiances on operational DA, and thus the quantitative impact that the presence of dust induces on model-innovated profiles of atmospheric parameters, using collocated hyperspectral Cross-track Infrared Sounder (CrIs) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) observations over the Tropical Atlantic.  We then describe how the NAVDAS responds when coupled with NAAPS alone, and thus how the model representation of dust compares with observation and what the residual error is due to use of the model alone.  The result is a conceptual description of how IR-absorbing dust impacts radiance DA for operational weather modeling, and a first-order description of how adept current aerosol transport models are for providing compulsory corrections.

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