The overarching Earth science mission objective of GPM is to develop a scientific understanding of the earth system and its response to natural and human-induced changes. This will enable improved prediction of climate, weather, and natural hazards for present and future generations. The specific scientific objectives of the GPM Mission are summarized below.

1. Precipitation Measurement Capability.
GPM will advance precipitation measurement capability from space through combined use of active and passive remote-sensing techniques. These measurements will be used to calibrate dedicated and operational passive microwave sensors with the goal of achieving global sampling.

• Retrieve precipitation from radiometric observables (including reference radar-radiometer core algorithm/radar simulator studies, parametric radiometer constellation algorithm/radiometer simulator studies, cross-satellite calibration transfer and bias removal)
• Calibrate and validate of satellite precipitation measurements
• Measure snow and lighter rain rates through the use of high-frequency passive microwave radiometry
• Improve passive microwave retrieval (PWR) algorithms over land
• Improve precipitation measurements in mid- and high-latitudes during cold seasons
• Test and evaluate the constellation passive microwave algorithms using the DPR and high frequency channels

2. Water/Energy Cycle Variability.
GPM will advance knowledge about the global water/energy cycle and fresh water availability. Improved measurements of the space-time variability of global precipitation will close the water/energy budget and elucidate the interactions between precipitation and other climate parameters.

• Quantify variability of regional and global water cycle processes directly or closely related to precipitation, on diurnal through decadal time scales, and based on past, present, and future global precipitation datasets
• Diagnose the rate of water transfer within Earth's atmosphere and surface while reconciling individual terms in water budget equations, for detection of accelerations (decelerations) in global water cycle and how such accelerations respond to and influence the global climate system

3. Climate Prediction.
GPM can improve climate prediction through better understanding of surface water fluxes, soil moisture storage, cloud/precipitation microphysics and latent heat release in the EarthÕs atmosphere.

• Understand the role of cloud-scale precipitation processes in climate sensitivity and feedback
• Study climate system variability and climate diagnostics
• Perform climate model simulations and reanalysis
• Elucidate the distribution and variance of latent heat release and bulk precipitation microphysics
• Better understand the 4-D precipitation distribution and variability from diurnal to inter-annual time scales
• Improve datasets for climate analysis through assimilation of satellite-based precipitation information into global data assimilation systems

4. Weather Prediction.
GPM will advance numerical weather prediction (NWP) skills through more accurate and frequent measurements of instantaneous rain rates with better error characterizations and improved assimilation methods.

• Improve NWP skills through assimilation of satellite-based precipitation information into numerical weather prediction systems
• Estimate and correct systematic errors in moist processes parameterizations contained in numerical prediction models through effective use of satellite precipitation observations
• Employ advanced assimilation methods to extract maximum information from precipitation data in the presence of forecast model errors
• Improve the understanding and modeling of precipitation error properties
• Monitor extreme precipitation events and freshwater availability
• Improve position fixes for typhoon/hurricane predictions

5. Hydrometeorological Prediction.
GPM will improve flood-hazard and fresh-water-resource prediction capabilities through better temporal sampling and spatial coverage of high-resolution precipitation measurements and innovative designs in hydro-meteorological modeling.

• Develop innovative hydrological modeling and prediction systems including downscaling of precipitation observations
• Develop hydrometeorological testbeds for evaluating the quality of satellite products in hydrological applications
• Improve techniques for global precipitation products based on combined satellite and ground measurements
• Quantify anticipated improvements in surface runoff, infiltration, storage, surface moisture flux transfer, and other hydrological processes commonly represented in hydrometeorological models
• Understand the quantitative nature and physical causes of differences in surface fluxes simulated by hydrometeorological models operating in different modes
• Improve land surface data assimilation schemes for use in high resolution, nonhydrostatic, regional models, suited to availability of 3-hourly, high spatial resolution, globally available precipitation products