Exploiting Multisensor and Multiwavelength Precipitation Measurement Mission Synergy
A CREW proposal submitted to NASA-ROSES-2012 A20: PMM: Precipitation Measurement Missions Science Team (NNH12ZDA001N-PMM), , $488,000, 2 mo, 01/01/2013 – 12/31/2015.
I hypothesize that simultaneous multichannel retrievals of multiple water and energy variables in a vertical ocean/land-atmosphere column will (a) allow improved-accuracy retrievals that are not possible with isolated measurements, and (b) produce synergies that will substantially enhance understanding of the water and energy cycle as a system. The Precipitation Measurement Missions active and passive microwave instruments in combination with the occasionally aligned polar orbiting microwave instruments provide an ideal environment to test this hypothesis. Therefore, the project goal is to assemble and use this multisensory multifrequency active/passive microwave dataset in conjunction with complementary field verification data to demonstrate the potential of several different multivariate retrieval methods to examine the coupling within and among Earth’s terrestrial, aquatic, atmospheric, and cryospheric water and energy cycle.
The project objectives are as follows:
1. Identify and acquire the historical overlapping Precipitation Measurement Mission (PMM) multifrequency active/passive microwave observations – mostly TRMM (TMI and PR) and AQUA (AMSR and AMSU) for the DOE-ARM Southern Great Plains site.
2. Multi-Regression: Analyze the multi-sensor microwave dataset for useful relationships with water cycle variables using standard multi-regression and principal components analysis.
3. Neural Network: Develop, train and test a neural network framework to simulate complex relationships between the microwave observations and key water and energy cycle geophysical variables.
4. Create a simple model of vertically integrative water and energy cycle conservation equations.
5. Develop a coupled set of existing and proven forward radiative transfer models for the identified wavelengths, sensors, and desired geophysical variables.
6. Linked radiative transfer model retrieval: Perform the multivariable retrieval in a system of retrieval algorithms that is solved simultaneously for each parameter.
7. Statistical estimation: Starting with a reanalysis first guess for the water and energy conservation model, predict microwave response via the linked radiative transfer model, then statistically update the balance using difference in predicted versus observed microwave response.
8. Globally demonstrate the multi-variable energy and water cycle retrieval at times and locations when a reasonably complete suite of near-simultaneous satellite data is available to study spatial variability of the approach and resulting retrieved products.
9. Provide a quality and error assessment of the retrieved water and energy cycle observations using conservation constraints and multi-product error tracking.
10. Provide users access to the derived multi-sensor, multi-variable water cycle products.
This project is directly responsive to section 2.1, “algorithm/product validation and enhancement”, of the solicitation, as it will explore methodologies for the intercalibration of measurements from microwave sensors, combination of radar/radiometer algorithms, multi-satellite algorithm development, and development of innovative methods to improve the fusion of precipitation information from multi-instrument and multi-satellite platforms.