A new prototype AMSR-E SWE operational algorithm M. Tedesco The City College of New York, CUNY, NYC With contributions from : Chris Derksen, Jouni Pulliainen, James Foster, Richard Kelly Acknowledgment: NASA Terrestrial Hydrology and Energy and Water Cycle Programs

✤ Current status of the AMSR-E SWE operational algorithm ✤ A new operational algorithm for SWE ✤ Validation stage ✤ Refinement and future directions for the product OUTLINE

Current status No major issues to report The code from SIPS is up and running at CCNY and will be modified once the new ATBD (see next) is going to be approved Testing of the new code at CCNY and then migrating to UAH for production undergoing

Current algorithm Ingestion of Tbs and check on precipitation, wet snow and shallow snow

Identifying major issues: Current AMSR-E SWE vs. CMC CMC CMC – AMSR-E AMSR-E [cm]

January Surface Temperature from AMSR-E [C] CMC – AMSR-E Surface temperature from AMSR-E [cm]

MW sensitivity to snow

K ext ≈ D 2 * f 2.8

The current algorithm has been attempting to account for the evolution of grain size to consider this aspect in the retrieval scheme However, spatio-temporal evolution of grain size is difficult to model without ancillary data (given the sensitivity of PMW data to this parameter) This is a large source of error

Proposed changes Use of neural networks and electromagnetic model to derive quantities related used in retrieval coefficients (e.g., effective grain size) Formula for retrieval coefficients (e.g., those relating the snow depth to Tbs) Density used to convert snow depth to SWE New formula for surface temperature estimate

What is available now that was not in the past, when the algorithm was originally developed ? Electromagnetic theory advances Computational power Long time series of PMW data Other snow depth products (from ground obs. and models) Numerical techniques

Snow depth monthly climatology Ground obs. Kriging (or similar) Kriging (or similar) Other avail. prod Module 1.1 Density model Density model Snow depth Day of the year Module 1.2 Emissivity approach AMSR-E 10 GHz AMSR-E 10 GHz Monthly Climatology Snow density Maps Set of Snow parameters Set of Snow parameters Simulated Tbs Electromagnetic model Electromagnetic model Module 2.1 Simulated TBs Grain size ANN Training ANN Training Module 2.2 Ts, Tg, et al. ANN AMSR-E All channels AMSR-E All channels Ground and surface temperatur e

Example: map of effective grain size January 2006

Current dynamic coefficient map at 37 GHz Current vs. new coefficients [cm/K][cm/K] New dynamic coefficient map at 37 GHz [cm/K]

Density Current algorithmNew algorithm g/cm 3 Using Sturm et al., 2011

OLD AMSR-E [cm] Old vs. new algorithm e.g., January 2004

Assessment and validation: Comparison with CMC product

Assessment: NEW vs. OLD algorithm CMC data set 2002 – 2010 Data (CMC data set is used as ‘truth’)

2002 – 2010 Data (CMC data set is used as ‘truth’) Assessment: NEW vs. OLD algorithm CMC data set (cont’d)

Assessment: NEW vs. OLD algorithm WMO data set As in the case of CMC, the new algorithm provides better results than the original one for all months

Assessment: NEW vs. OLD algorithm GlobSNOW (SWE) As in the case of CMC and WMO data, the new algorithm provides better results than the original one for all months

MULTIPLE “LAYERS” WITHIN THE SAME PRODUCT Second layer = migrating operational algorithm Third layer = future operational algorithm -Effective grain size -Snow depth -Surface temperature -Snow bulk density

Steps for operational implementation ATBD readySubmitted to NASA next w.e.Evaluation and review ATBD and software to UAH Implementation and testing at UAH NSIDC distribution request RESEARCH product distribution Parallel distribution Of current and new products Migration to new product

Summary -The new proposed AMSR-E SWE algorithm makes use of ANN, electromagnetic models to compute grain size and use this information in the new retrieval scheme -Density is computed as a function of depth, day of the year and snow class in a dynamic fashion -New approach proposed for surface temperature -The new approach provides better results than the current algorithm when considering three different and independent validation data sets -The comparison of the two products allows also to move the AMSR-E SWE product to validated stage 2

Future plans -Introduce atmospheric correction (e.g., using other AMSR products) -Lake fraction (e.g., using a different ‘tuned’ algorithm for lake-rich areas) -Introduction of uncertainty maps -Extension to AMSR2 -Modifying the first part of the algorithm for flags and wet snow, precipitation events detection

The future generation of SWE global products Multivariate outputs (snow density, depth, effective grain size) Uncertainty will be included based upon quality flags depending on factors such as forest cover, lakes, atmosphere, etc. The proposed approach is applicable ‘as is’ to other platforms (AMSR2, SSM/I) The modules can be replaced -e.g., Snow depth climatology with ground obs.  assimilation -EM models can be replaced -Density model