- Related Research Areas
- Water & Energy Cycles
Global measurements of soil moisture from the NASA SMAP mission will enable global climate and weather forecasting models to be evaluated and improved. Data from SMAP will give information on soil moisture at different scales, but research is needed to understand how to interpret and use these data effectively. A good physical understanding is required to optimise the retrieval system. The objectives of this proposal are to conduct trade-off studies to compensate for missing ancillary data, to identify and minimize the effects of heterogeneity, and to evaluate data latency limitations on weather forecasting. Studies on how to assimilate SMAP data into land surface models will also be carried out. Previous work conducted by this group has included the development of the Level 2 prototype processor for the ESA SMOS mission. We carried out a sensitivity analysis of the tau-omega model to quantify the soil moisture retrieval error from instrument noise and uncertainty in surface parameters. The effects of soil and vegetation heterogeneity were also investigated by generating synthetic scenes and retrieving soil moisture under the assumption of homogeneity. The tau-omega model was elaborated in order to mitigate the effects of heterogeneity in vegetation. Other relevant work has explored the development of techniques to estimate soil hydraulic properties from microwave emission dry-down curves, supported by an intensive field experiment to provide a suite of measurements suitable for L-band microwave model development and validation. As part of the SMAP team, we propose to carry out similar studies to the work that we have already done for SMOS in order to understand the parameter space. We will examine the sensitivity of the tau-omega model to uncertainties in the soil and vegetation parameters, and quantify soil moisture retrieval errors from heterogeneity in the soil and vegetation. Trade-off studies will then be carried out to investigate how frequently ancillary data are required. Missing data will be estimated from previous values and from nearest-neighbours to determine the most accurate method of accounting for missing data, and ancillary data latency requirements. Trade-off studies will also be carried out to inform the best use of the radar data with respect to its power requirements. The temporal distribution of radar measurements will be optimized for a dry-down curve, and the global requirements of the radar measurements will be assessed with global precipitation and flood modelling datasets. We will investigate whether techniques developed to assess soil and vegetation structure from LiDAR could also be applied to the radar measurements. Soil moisture data latency requirements will be identified for a 1-D SVAT, using observations taken from the Sonning Farm experiment and potentially other field experiments. We will liaise with the ECMWF and the UK Met Office to extend these results for GCMs. As part of the UK National Centre for Earth Observation (NCEO), we will establish methods to assimilate data from SMAP into regional and global models. This no-exchange-of-funds proposal provides significant relevant effort through the funding for the PI and two Co-Is agreed under the NCEO. As this research is needed for the development of the soil moisture product, which will be used to improve weather forecast and global models, this proposal addresses the following NASA Research Objectives: RO3A.2 Enable improved predictive capability for weather and extreme weather events, RO3A.4 Quantify the key reservoirs and fluxes in the global water cycle and improve models of water cycle change and fresh water availability, and RO3A.3 Quantify global land cover change and terrestrial and marine productivity, and improve carbon cycle and ecosystem models. This research will contribute to NASA Strategic Goal 3, and Subgoal 3A: Study Earth from space to advance scientific understanding and meet societal needs.
Project PI: Robert Gurney/University of Reading
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