These images combine data from three sources, precipitation data from NOGAPS, Satellite precipitation from the NRL-blended algorithm, and AOD from NAAPS. Each is a three color composite of model fields, and satellite derived rain rates resampled to model coordinates. The green graphic plane is assigned to the AOD, the red graphics plane is assigned to the NOGAPS model rain, and the blue graphics plane is assigned the satellite derived instantaneous rain rate.
The Navy Operational Global Atmospheric Prediction System (NOGAPS) is the Navy's global numerical weather prediction system, which includes components for data quality control, data assimilation, model initialization, and model forecasts. NOGAPS provides forcing fields for mesoscale weather prediction; tropical cyclone prediction; aerosol prediction; ocean, wave, and ice prediction; and aircraft and ship routing applications. NOGAPS also forms the backbone of the Navy's ensemble prediction system, which provides global forecasts out to 10 days.
The NRL-Blend precipitation dataset is based on a real time collection of time and space-matching pixels from all operational geostationary (GEO) visible/infrared (VIS/IR) imagers as well as passive microwave (PMW) imagers onboard low Earth orbiting (LEO) satellites. Essentially, these data are used to adjust intervening GEO data that arrive in between LEO satellite overpasses, unlike the motion-based techniques that use intervening GEO data to advect two-dimensional fields of precipitation in between LEO overpasses [Turk et. al, 2008]. The operation of the NRL-Blend is essentially described by three procedures as described in Turk and Miller . The 3, 6, 12 and 24-hour interval accumulations are updated at each 3-hourly synoptic time (00, 03, ... 21 UTC).
The Navy Aerosol Analysis and Prediction System (NAAPS), developed at the Naval Research Laboratory (NRL) in Monterey, CA, is a near-operational system for predicting the distribution of tropospheric aerosols. For a complete description see here.
Turk, F.J and S. Miller (2005), Toward improving estimates of remotely-sensed precipitation with MODIS/AMSR-E blended data techniques. IEEE Trans. Geosci. Rem. Sens., 43, 1059-1069.
Turk, F.J., P. Arkin, E. Ebert, and M. Sapiano (2008), Evaluating High Resolution Precipitation Products. Bull. Amer. Meteor. Soc., 89, 1911-1916.