1. Princeton University Development of Improved Forward Models for Retrievals of Snow Properties Eric. F. Wood, Princeton University Dennis. P. Lettenmaier, University of Washington

2. Princeton University Motivation for the Research Snow cover extent (SCE) and water equivalent (SWE) are key factors in land-atmosphere feedbacks Operational large-scale SCE and SWE would likely enhance the accuracy of NWP Improved NWP forecasts would also benefit flood forecasting and drought monitoring. Current observations of snow: Standard in-situ observations (e.g. SNOTEL sites) may not be representative due to their limited deployment and non- representativeness (e.g land surface heterogeneity). Satellite coverage of snow cover hampered by clouds and SWE algorithms (from passive radiometers) not well developed.

3. Princeton University SWE from Remote Sensing Potential exits for improved retrieval of SWE at large-scales from space- borne microwave radiometry. Challenging because microwave T b derives from surface, snow pack, vegetation, and atmosphere SWE retrieval theory: dielectric constant of frozen water differs from liquid form snow crystals are effective scatterers of microwave radiation (snow density, grain size, stratigraphic structure and liquid water) deeper snow packs --> more snow crystals --> lower T b Direct assimilation of T b is a challenging problem - requires comprehensive land surface microwave emission model (LSMEM).

4. Princeton University Project Research Questions Can a forward model of surface microwave emission be developed that is capable of providing realistic brightness temperatures for snow covered areas, and can the inputs for the forward model be provided by operational observations and NWP model output? Is the modeled/predicted T b sufficiently accurate and useful for assimilation into operational NWP?

5. Princeton University Project Task Activities (04-05) Task 1: Algorithm development (Princeton). Develop and test an all-season microwave emission model, designed for use in updating NWP snow and frozen ground with satellite data. Task 2: Algorithm testing and validation (UW). Use field data from the NASA CLPX experiment to test the emission model, and determine the model sensitivity to snow pack parameters.

6. Princeton University Task 1: All-Season LSMEM development Based on NCEP community emission model, Princeton’s warm-season LSMEM (see Gao et al., 2003), and related microwave emission models T b = F(Atmos , T veg, T snow, T soil,  snow,  soil, scattering albedo, optical depth) Processes needed for cold seasons: frozen ground snow covered surface tall vegetation (snow or no snow). Observed emission = surface (snow/soil) emission + reflection + vegetation emission + attenuation + atmospheric emission + attenuation + water/ice emission + reflection Vegetation Emission Surface Reflection Atmospheric Emission Soil Emission Radiometer Water Emission

7. Princeton University Task 1: All-Season LSMEM development Based on NCEP community emission model, Princeton’s warm-season LSMEM (see Gao et al., 2003), and related microwave emission models T b = F(Atmos , T veg, T snow, T soil,  snow,  soil, scattering albedo, optical depth) Processes needed for cold seasons: frozen ground snow covered surface tall vegetation (snow or no snow). Observed emission = surface (snow/soil) emission + reflection + vegetation emission + attenuation + atmospheric emission + attenuation + water/ice emission + reflection Vegetation Emission Surface Reflection Atmospheric Emission Soil Emission Radiometer Water Emission Initial version developed and is being tested. The NOAA Community Radiative Transfer Model code is being studied to determine how to couple in the AS-LSMEM.

8. Princeton University Task 2: Algorithm testing and validation Validation with CLPX Observations Ground-Based Microwave Radiometer Dense Snow Pit measurements 12-13 Dec 2002 & 19-24 Mar 2003 Snow on bare ground (no vegetation) Assume snow measurements representative of entire LSOS (100 x 100 m) http://nsidc.org/data/docs/daac/nsidc0165_clpx_gbmr/index.html

9. Princeton University Validation of T B at constant incidence angle Microwave emission from full snow coverage at 55 ° AS-LSMEM was run for different grain sizes to capture observed stratigraphy Strong dependence of results with assumed grain size 0.7mm 1.2mm Brightness Temperature 19H 19V 36H 36V 89H 89V Frequency (GHz) Brightness Temperature 19H 19V 36H 36V 89H 89V Frequency (GHz) 0.4mm 1.3mm

10. Princeton University Validation of T B at varying incidence angle Coincident measurements at incidence angles from 30º to 70º Dependence of optical grain size on incidence angle is small Similar results for other dates and incidence angles Angle=30º 0.4mm 1.1mm Brightness Temperature 19H 19V 36H 36V 89H 89V Frequency (GHz) Angle=65º Brightness Temperature 19H 19V 36H 36V 89H 89V Frequency (GHz) 0.4mm 1.1mm

11. Princeton University Validation of T B at full/partial snow coverage Full coverage observations at 51 cm snow depth Subsequent removal of about 20 cm of snow (partial coverage) Optical grain size increases (deeper layers solely affecting microwave emission) Full Coverage 0.6mm 1.0mm Brightness Temperature 19H 19V 36H 36V 89H 89V Frequency (GHz) Partial Coverage Brightness Temperature 19H 19V 36H 36V 89H 89V Frequency (GHz) 0.6mm 1.0mm

12. Princeton University Sensitivity of Results to Grain Size Sensitivity at low frequencies is small Much higher at higher frequencies Linear dependence (similar for other measurement dates) ● Difference between model TB and GBMR observation, with difference from optical grain size 18.7 GHz 36.5 GHz 89.0 GHz -.1 0.1.2.3.4.5.6 Grain size difference from optical size (mm) 0.1.2.3.4.5.6 -.1 0.1.2.3.4.5 T B, obs – T B, LSMEM (K)

13. Princeton University 05-06 Work Plan and Project Tasks Algorithm development: Develop hooks to add the AS-LSMEM into the NOAA Community Radiative Transfer Model Testing and Validation: Continue model testing using additional NASA’s Cold Land Process eXperiment ( CLPX) ground data, airborne PSR data and comparison to AMSR-E T b. Sensitivity and Parameter Estimation: continue sensitivity studies of T b to snow and surface parameters (based on CLPX field data), start to develop strategies for estimating parameters at larger scales (NWP operational data).

14. Princeton University Purpose: Develop a forward model of surface microwave emission for snow covered areas, and test its usefulness for assimilating operational Tb into NCEP/NWP models for improved snow estimation. Progress so far: An initial version of the model has been developed and is undergoing testing using field data from the NASA Cold Land Process eXperiment ( CLPX). Sensitivity studies have started to evaluate the effect of grain size, partial coverage and incidence angle on the modeled brightness temperatures. Future plans 05-06: 1.Further model development to try and incorporate the model into the NOAA Community Radiative Transfer Model system; 2.Continue model testing using additional NASA’s CLPX ground data, airborne PSR data and comparison to AMSR-E; 3.Continue sensitivity studies of T b to snow and surface parameters Title: Development of Improved Forward Models for Retrievals of Snow Properties PIs: E. Wood (Princeton U) and D. Lettenmaier (U. Washington)