Night-time Microphysics RGB Imagery Assists with Aviation Forecasting

WFO Raleigh, NC (RAH) has been collaborating with the NASA/MSFC’s Short-term Prediction Research and Transition (SPoRT) team and evaluating the Night-time Microphysics RGB product from VIIRS and MODIS for use with observing fog and low clouds. The Night-time Microphysics RGB imagery uses the common IR spectral difference for fog/low cloud detection as well as inputs related to optical thickness and the cloud’s thermal property in order to help differentiate low clouds and near-surface fog.

The image below is the Night-Time Microphysics RGB satellite product from 0741 UTC 24 August 2013 along with the 0800 UTC surface ceiling and visibility plot across the mid-Atlantic and Southeast as displayed in AWIPS D2D. This image shows complex cloud conditions with regions of low stratus and higher based stratus clouds across central NC.

blog.20130824.rgb.a1If you examine the RGB closely, you will notice a difference in the shading with the low stratus across the western and northwestern area of the cloud shield shaded in aqua with ceilings of 900-2,500 feet (high-end IFR or MVFR) near Greensboro while further east the clouds are more tan colored and associated with ceilings in the 4,000-5,000 feet range (VFR) near Roanoke Rapids.

The example below shows the 11-3.9 micron product from 0745 UTC on the left and the Night-Time Microphysics RGB satellite product from 0741 UTC on the right. While the 11-3.9 micron product highlights the location of the stratus, the Night-Time Microphysics RGB satellite product provides the user with much more detail including information on the cloud bases and the associated aviation flight category. The increased resolution in the Night-Time Microphysics RGB product also allows the user to much more easily identify details in the clouds field and attribute more confidence to the surface observations. This information was used by the forecaster to correctly update the Terminal Aviation Forecast and produce a better quality forecast product.


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