A new high-resolution global precipitation analysis technique dubbed “CMORPH” (CPC MORPHed precipitation) has been developed at NOAA's Climate Prediction Center (CPC) for the real-time monitoring of global precipitation. CMORPH provides precipitation estimates on an 8 km lat/lon grid (at the equator) from 60^oN-60^oS with a temporal resolution of 30 minutes. Analyses are available back to December 1, 2002 and are updated routinely. Plans have been made to extend these analyses back to early 1998. The spatial and temporal scales of CMORPH make it potentially useful for input into hydrologic models.

The motivation for developing such an analysis system stems from the well-known fact that that passive microwave (MW) observations yield more direct information about precipitation than is available from IR data, but because platforms that house these instruments are relegated to polar orbits, the MW-derived estimates have poor spatial and temporal sampling characteristics. Conversely, while the IR data provide relatively poor estimates of precipitation, they provide extremely good spatial and temporal sampling. Given these facts, the natural course of action is to attempt to combine the data from these disparate sensors to take advantage of the strengths that each has to offer. To this end, a number of techniques have been developed in which the IR data are manipulated in a statistical fashion to mimic the behavior of microwave-derived precipitation estimates in which IR data are used to estimate rainfall in locations and instances where microwave data are not available.

CMORPH is a considerably different method that uses precipitation estimates that have been derived from low orbiter satellite microwave observations exclusively, and whose features are transported via spatial advection information that is obtained entirely from geostationary satellite IR data. The method is extremely flexible, permitting precipitation estimates from any satellite source to be incorporated. Advection vector matrices are produced by computing spatial lag correlations on successive images of geostationary satellite IR and are then used to propagate the microwave-derived precipitation estimates. This process governs only the movement of the precipitation features. At a given location, the shape and intensity of the precipitation features in the intervening ½ hour periods between microwave scans are determined from a time-weighting interpolation between microwave-derived features that have been propagated forward in time from the previous microwave observation and those that have been propagated backward in time from the following microwave scan. We refer to this latter step as “morphing” of the features.