• the TRMM Multi-satellite Precipitation Analysis, which addresses inter-satellite calibration of precipitation estimates and monthly scale combination of satellite and gauge analyses;
• the CPC Morphing algorithm with Kalman Filtering, which provides quality-weighted time interpolation of precipitation patterns following storm motion; and
• the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks using a Cloud Classification System, which provides a neural-network-based scheme for generating microwave-calibrated precipitation estimates from geosynchronous infrared brightness temperatures, and filters out some non-raining cold clouds.
In this talk we will summarize the ingredients that go into IMERG, including the design requirements, plans for testing and starting to run the system, and important issues that drive the design and implementation. We will use early test results to illustrate the sequence of processing from input data to output fields in IMERG. In particular, we will address one of the key factors from the user's perspective, which is that the final output should contain ancillary information generated at intermediate processing steps that allows intelligent use of the combined precipitation estimates