GOES-R Advanced Baseline Imager (ABI) instrument launch, and ported for use with MODIS.
The Aerosol Visibility software provides information about potential obstructions to surface visibility due solely to atmospheric aerosols for a given 10 km region, based upon multivariate regressions against Automated Surface Observing System (ASOS) measurements. The algorithm uses the MODIS Collect 6 aerosol product (MOD04 - part of the IMAPP MODIS level 2 package) as the key input. It is produced only during the daytime. The technique was developed at the University of Wisconsin, and published in 2016: Brunner, J., R. B. Pierce, and A. Lenzen, “Development and validation of satellite-based estimates of surface visibility”, Atmos. Meas. Tech., 9, 409–422, 2016.
The second algorithm is the MODIS Fog/Low Stratus (FLS) software that provides an estimate of the probability of Instrument Flight Rules (MVFR, IFR and LIFR) for a given MODIS 1km Field-of-View (FOV). The product is created both day and night, and uses the MODIS Collect 6 Cloud Top Properties product (MOD06 - part of the IMAPP MODIS level 2 package) as input. This algorithm was developed at the University of Wisconsin by Mike Pavolonis (NOAA/STAR) and Corey Calvert (CIMSS/SSEC). The Algorithm Theoretical Basis Document (ATBD) is included as part of the release. It is important to note that the GOES-R algorithm was developed and tested using high spatial and temporal resolution RAPid Refresh (RAP) Numerical Weather Prediction (NWP) files as input. Using this higher resolution model improves the skill of the FLS product. For IMAPP global implementation, the .5 degree Global Forecast System (GFS) model fields (issued 4 times per day) are used, which means that the results may not meet the quality standards set for GOES-R implementation. However, our validation efforts have shown that the products are in reasonably good agreement with the current GOES product equivalents.
The output consists of one HDF4 data file, as well as output product imagery showing regions of reduced visibility due to aerosols, and probabilities of Instrument Flight Rules (MVFR,IFR,LIFR) on a map.
This is a binary only release, and is supported on 64 bit Intel Linux CentOS6 machines, with 2 GB of RAM and 33 GB of disk space required.
The package includes the GEOstationary Cloud Algorithm Test-bed (GEOCAT) software that was developed by Michael Pavolonis of NOAA/STAR, and serves as a framework for running the algorithms.
Complete installation and run instructions are included with the release.