Retrieving boundary-layer thermodynamic profiles and cloud properties from infrared spectra: An update on AERIoe operational processing

Poster PDF

Description

ARM has greatly expanded the Southern Great Plains (SGP) site by establishing four “profiling facilities” (PFs) approximately 40 km away from the Central Facility (CF). One of the main objectives of these PFs is to provide profiles of temperature, humidity, and wind that could be used to improve model forcing data sets that are used to drive models and evaluate model output, quantify the advection of moisture and temperature across the SGP site, and investigate land-atmosphere interactions. Each PF includes an Atmospheric Emitted Radiance Interferometer (AERI), which measures downwelling infrared (3-19 µm) radiance at high spectral resolution every 20-30 s. These observations contain information on the temperature and humidity profile above the instrument, and the AERIoe algorithm was developed to retrieve these profiles from the radiance observations. The AERIoe algorithm uses an optimal estimation approach to solve this ill-posed problem. A historical radiosonde data set provides the level-to-level covariance matrix used to constrain the retrieval to physically realistic solutions. In the last year, additional data sets have been included into AERIoe to further constrain the algorithm and hence improve the solution. This includes profiles of temperature and humidity in the free troposphere from NOAA’s operational Rapid-Refresh model RAP, surface meteorology and microwave brightness temperature observations, and (partial) profiles of temperature and/or humidity from lidar. The inclusion of these new data sets has greatly improved the consistency and accuracy of the retrieved profiles. Furthermore, the addition of the MWR brightness temperatures allows liquid water path (LWP) to be retrieved over its entire dynamic range, whereas retrievals that only used AERI observations resulted in biased LWP retrievals if the LWP was larger than ~50 g/m2. The AERIoe retrievals agree very well with collocated radiosonde observations at both the SGP and NSA/OLI sites. We will show examples of the variability of temperature and moisture across the SGP site using retrievals from the CF and PFs. We are in the process of making the AERIoe algorithm an operational Value-Added Product. This requires development to handle the computational cost of running a complex radiative transfer model and real-time monitoring of input data quality. We will provide an update on the plans for real-time and historical processing of the AERI data from the different ARM facilities.