“CMORPH” (CPC MORPHed precipitation) is global precipitation analysis technique described by Joyce et al (2004) and developed by NOAA's Climate Prediction Center (CPC) for the real-time monitoring of global precipitation. CMORPH provides precipitation estimates on an 8 kilometer latitude/longitude grid (at the equator) from 60oN-60oS with a temporal resolution of 30 minute, and daily precipitation products are produced by using the CPC MORPHing technique on a global basis and accumulating the 30-minute segments into a 24-hour time period.

The satellite data inputs from CMORPH include half-hourly geostationary satellite infrared (IR) temperature fields and polar orbiting passive microwave (PMW) brightness temperature retrievals with irregular-intervals. The morphing process involves using the relatively poor temporal resolution of passive microwave (PMW) precipitation estimate data and interpolating its movement between retrieval periods. The motivation for developing such precipitation products stem from the fact that passive microwave observations yield more direct information about precipitation than is available from infrared data, but PMW-derived precipitation estimates have poor spatial and temporal sampling characteristics due to their polar orbits. Conversely, while the IR data provide relatively poor estimates of precipitation, they provide extremely good spatial and temporal sampling. Therefore, CPC combines or morphs the data from these two disparate sensors to take advantage of the strengths that each has to offer (Joyce et al 2004).

Correspondingly, FAS/OGA aggregates daily CMORPH data into 10-day (or dekadal) periods for agriculture monitoring and the 10-day CMORPH data in Crop Explorer is not displayed in the original 8-km spatial resolution. In addition, it should be noted that the CMOPRH product does not blend rainfall station gauge (SG) data into its estimates although studies have shown that algorithms which combine both satellite and ground rainfall SG data tend to provide more accurate precipitation estimates than those precipitation products that rely only on satellite sensors without SG data. The lack of SG data in the CMORPH product is a weakness and CMOPRH comparisons with the other rainfall products tend to show positive biases when compared to daily SG data. In addition, for crop monitoring during the growing season of four months or more, these daily positive bias errors accumulate and make the CMOPRH product not very useful for seasonal crop monitoring purposes.