2012 AGU Chapman Conference on Remote Sensing of the ...

support decision-making related to groundwatermanagement.Reichle, RolfAMSR-E Brightness Temperature Estimation overNorth America Using a Land Surface Model and anArtificial Neural NetworkForman, Barton 1, 2 ; Reichle, Rolf 1 ; Derksen, Chris 31. Global Modeling and Assimilation Office, NASA GSFC,Greenbelt, MD, USA2. NASA Postdoctoral Program, Oak Ridge AssociatedUniversities, Oak Ridge, TN, USA3. Climate Processes Branch, Environment Canada,Downsview, ON, CanadaAn artificial neural network (ANN) is presented for thepurpose of estimating passive microwave (PMW) emissionfrom snow covered land in North America. The NASACatchment Land Surface Model (Catchment) is used todefine snowpack properties; the Catchment-based ANN isthen trained with PMW measurements acquired by theAdvanced Microwave Scanning Radiometer (AMSR-E). Theintended use of the ANN is for eventual application as apredicted measurement operator in an ensemble-based dataassimilation (DA) framework to be presented in a follow-onstudy. The details shown here fulfill the necessaryrequirement of demonstrating the feasibility and efficacy ofthe ANN. A comparison of ANN output against AMSR-Emeasurements not used during training activities as well as acomparison against independent PMW measurementscollected during airborne surveys demonstrates thepredictive skill of the ANN. When averaged over the studydomain for the 9-year study period, computed statistics(relative to AMSR-E measurements not used during training)for multiple frequencies and polarizations yielded a nearzerobias, a root mean squared error less than 10K, and ananomaly correlation coefficient of approximately 0.7. TheANN demonstrates skill at reproducing brightnesstemperatures during the ablation phase when the snowpackis ripe and relatively wet. The ANN demonstrates evengreater skill during the accumulation phase when thesnowpack is relatively dry. Overall, the results suggest theANN should serve as an effective predicted measurementoperator that is computationally efficient at the continentalscale.Renzullo, Luigi J.An on-going intercomparison of near real-timeblended satellite-gauge precipitation estimates forAustraliaRaupach, Tim 1 ; Renzullo, Luigi J. 1 ; Chappell, Adrian 11. Commonwealth Scientific and Industrial ResearchOrganisation, Canberra, ACT, AustraliaSatellite-derived precipitation estimates have beenexamined as a useful auxiliary field to aid in theinterpolation of daily rain gauge observations and provideimproved estimates in the largely ungauged parts of123Australia. Blended satellite-gauge precipitation estimatesaim to produce a precipitation field that takes advantage ofboth the accuracy of gauge observations and the spatialcoverage of satellite estimates. We present results from thefirst two years of operation of a system that performs an ongoingintercomparison of near real-time blendedsatellite-gauge precipitation estimates for Australia. On acontinuing basis, we compare daily outputs of fifteenprecipitation products. Performance is measured using anovel technique in which outputs produced using near realtimegauge data are compared to an independent validationdataset of post real-time gauge observations. These post realtimeobservations are made on the day in question but theobservations only become available some days later. Oursystem automatically generates daily precipitation outputs,produces a range of performance statistics as post real-timeobservations become available, and publishes the results ona web portal. The results show extremely similarperformance between techniques, with the best techniquedepending on which specific statistic is examined. Thesystem is on-going and continues to amass a valuable archiveof performance statistics starting in mid-2009.Renzullo, Luigi J.Assimilating satellite-derived soil moisturealongside streamflow into the Australian waterresources assessment systemRenzullo, Luigi J. 1 ; Van Dijk, Albert I. 11. CSIRO, Canberra, ACT, AustraliaThe Australian water resources assessment (AWRA)system provides comprehensive water balance estimates thatunderpin the Australian Bureau of Meteorology’s nationalwater accounts and water resource assessments. The AWRAlandscape model component (AWRA-L) was developed toprovide daily estimates of water storages and fluxes at 0.05°resolution across the continent, constrained using a varietyof ground- and satellite-based observations and derivedproducts. Data used in model development and calibrationinclude: evaporative fluxes from tower measurements,streamflow and deep drainage observations, moderateresolution remotely-sensed vegetation properties (AVHRR,MODIS sensors), basin-scale terrestrial water storage(GRACE) and soil moisture estimates from passive (AMSR-E,TRMM) and active (ASAR GM) sensors. This paper describessome of the work towards an assimilation system for AWRA-L and examines the effect of assimilating streamflow andremotely-sensed soil moisture. The ensemble Kalman filter(EnKF) was applied in both lumped-catchment and gridbasedmodes. The EnKF lumped-catchment approachexamined the assimilation of AMSR-E soil moisture (SM)retrievals and/or streamflow observations for 719catchments across Australia. The EnKF grid-based approachexamined model estimates at 0.05° resolution across theMurrumbidgee catchment (New South Wales). In both cases1980–2005 was used as a spin-up period, and satellite SMwas linearly scaled using mean and variance of the modeltop-layer soil water storage for 2002–2005. The assimilation