The Day-1 algorithm for computing combined precipitation estimates as part of GPM, referred to as the Integrated Multi-satellitE Retrievals for GPM (IMERG), has the goal of providing long-term, fine-scale record of global precipitation from the diverse and evolving constellation of precipitation-relevant satellite sensors, with input from surface precipitation gauges. As such, the primary focus is obtaining the best fine-scale estimate, rather than the strict long-term homogeneity that characterizes Climate Data Records. IMERG is being developed as a unified U.S. algorithm that takes advantage of strengths in three groups:

• 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