Development of Improved Forward Models for Retrievals of Snow Properties

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

2. 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. SWE from Remote Sensing • Potential exits for improved retrieval of SWE at large-scales from space-borne microwave radiometry. Challenging because microwave Tb 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 Tb • Direct assimilation of Tb is a challenging problem - requires comprehensive land surface microwave emission model (LSMEM).

4. 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 Tb sufficiently accurate and useful for assimilation into operational NWP?

5. 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. Observed emission = surface (snow/soil) emission + reflection + vegetation emission + attenuation +atmospheric emission + attenuation + water/ice emission + reflection Task 1: All-Season LSMEM development Radiometer • Based on NCEP community emission model, Princeton’s warm-season LSMEM (see Gao et al., 2003), and related microwave emission models • Tb = F(Atmos, Tveg, Tsnow, Tsoil, snow, soil, scattering albedo, optical depth) • Processes needed for cold seasons: • frozen ground • snow covered surface • tall vegetation (snow or no snow). AtmosphericEmission Vegetation Emission Soil Emission Surface Reflection Water Emission

7. Observed emission = surface (snow/soil) emission + reflection + vegetation emission + attenuation +atmospheric emission + attenuation + water/ice emission + reflection Task 1: All-Season LSMEM development Radiometer • Based on NCEP community emission model, Princeton’s warm-season LSMEM (see Gao et al., 2003), and related microwave emission models • Tb = F(Atmos, Tveg, Tsnow, Tsoil, snow, soil, scattering albedo, optical depth) • Processes needed for cold seasons: • frozen ground • snow covered surface • tall vegetation (snow or no snow). AtmosphericEmission 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. Vegetation Emission Soil Emission Surface Reflection Water Emission

8. 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. 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 Brightness Temperature 1.2mm 19H 19V 36H 36V 89H 89V Frequency (GHz) Validation of TB at constant incidence angle 0.4mm Brightness Temperature 1.3mm 19H 19V 36H 36V 89H 89V Frequency (GHz)

10. 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 0.4mm 0.4mm Brightness Temperature Brightness Temperature Angle=65º Angle=30º 1.1mm 1.1mm 19H 19V 36H 36V 89H 89V Frequency (GHz) 19H 19V 36H 36V 89H 89V Frequency (GHz) Validation of TB at varying incidence angle

11. 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) 0.6mm 0.6mm Brightness Temperature Brightness Temperature Full Coverage Partial Coverage 1.0mm 1.0mm 19H 19V 36H 36V 89H 89V Frequency (GHz) 19H 19V 36H 36V 89H 89V Frequency (GHz) Validation of TB at full/partial snow coverage

12. Sensitivity at low frequencies is small Much higher at higher frequencies Linear dependence (similar for other measurement dates) 18.7 GHz -.1 0 .1 .2 .3 .4 .5 TB, obs – T B, LSMEM (K) 36.5 GHz 0 .1 .2 .3 .4 .5 .6 89.0 GHz -.1 0 .1 .2 .3 .4 .5 .6 Grain size difference from optical size (mm) Sensitivity of Results to Grain Size • Difference between model TB and GBMR observation, with difference from optical grain size

13. 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 Tb. • Sensitivity and Parameter Estimation: continue sensitivity studies of Tb to snow and surface parameters (based on CLPX field data), start to develop strategies for estimating parameters at larger scales (NWP operational data).

14. Title:Development of Improved Forward Models for Retrievals of Snow Properties PIs: E. Wood (Princeton U) and D. Lettenmaier (U. Washington) • 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: • Further model development to try and incorporate the model into the NOAA Community Radiative Transfer Model system; • Continue model testing using additional NASA’s CLPX ground data, airborne PSR data and comparison to AMSR-E; • Continue sensitivity studies of Tb to snow and surface parameters