A review on different methodologies employed in current SWE products from spaceborne passive microwave observations Nastaran Saberi, Richard Kelly Interdisciplinary Center on Climate Change (IC3) and Department of Geography and Environmental Management, University of Waterloo, ON, Canada

Outline Introduction –Snow physical properties retrieval using passive microwave observations Emission Modeling (HUT, MEMLS, DMRT) SWE Products & Validation Process Research questions and summary

Why measuring snowpack physical properties? How? Which properties?

Snow Properties Retrieval Using Passive Microwave RS Passive microwave observations Brightness temperature: T B –Appropriate frequency channels: 10 GHz-19GHz-37GHz X, Ku, Ka (IEEE) o Goal: Modeling microwave-medium interactions to retrieve snow physical properties AMSR2 instrument

Snow properties retrieval Empirical approaches SD & TB differences Emission modeling PhysicalDMRT-ML Semi- empirical MEMLSHUT (Matzler et al., 1982) (Wiesmann and Matzler, 1999) Snow Properties Retrieval Using Passive Microwave RS

Emission Modeling

HUT snowpack emission model Pulliainen et al. (1999) Semi-empirical model, adapted for remote sensing observations d 4

MEMLS Snowpack emission model A. Wiesmann and C. Matzler (1999) A semi-empirical six-flux radiative transfer model For a multi layered snowpack MEMLS inputs (for each layer of snowpack) o Grain size (correlation length) o LWC o Temperature o Depth o Density For substratum, reflectivity and temperature is needed

DMRT-ML Leung Tsang et al. (2000), Picard et al. (2012)  QCA-CP  DMRT (k s, k e )  DISORT (RT) o Mono disperse, stickiness o Poly disperse, Rayleigh DMRT-ML inputs (for each layer of snowpack) o Grain size (Optical) o Temperature o Depth o Density o Substratum model

Emission Models Summary Differences HUT, MEMLS, DMRT-ML: –Radiative transfer solution –Wave propagation –Substratum –Representation of a snow grain EmpiricalPhysical Complexity Sensitivity Analysis SimplicityCalibration Parsimonious modeling: physics-based | semi-empirical | empirical

Snow Depth & Snow Water Equivalent Products

Snow Depth and Snow Water Equivalent Products SWE estimation using in-situ data =>sparse observing networks Data assimilation Reanalysis snow cover using land surface or snow models Problem: dependency on precipitation data Satellite passive microwave derived SWE datasets Challenge: complex topography Data assimilation Reanalysis snow cover using land surface or snow models Problem: dependency on precipitation data Satellite passive microwave derived SWE datasets Challenge: complex topography

GlobSnow - SWE

AMSR2 Snow Depth and Snow Water Equivalent Product by R. Kelly V1 & V2 :Predicated on the AMSR-E algorithm (2003/4) V1: Regression based, came in response to deficiencies in static algorithm (Kelly,2009). V2: Physical modeling based (kelly, 2003) V2 Grain size & Density are dynamic Forest correction is model-based Atmospheric correction Lake ice addressed RFI determination (10 Ghz) AMSR2 - SWE V2 Snow Detection based on history of snow Snow Depth/SWE Retrieval using DMRT-ML

Methods that Use Machine Learning Techniques Neural network Emission model: HUT/MEMLS/DMRT Training Process TB SD/SWE Training Process TB SD/SWE Passive microwave observation (TB) Inversion by NN SD/SWE Inversion by NN SD/SWE Training dataset TB 19V TB 37V SWE/SD

MicroWave Radiation Imager (MWRI) on Feng-Yun 3 Grass LandBare Soil Farm Land Forest SD=fgrass×SDgrass+ fbarren×Sdbaren+fforest×Sdforest + ffarmland×Sdfarmland SDfarmland=  ×d18h36h+1.074×d89v89h;

Validation Process

Validation Process General overview of SWE dataset assessment by SnowPEx

Statistical Assessment Tools

Summary & Research Questions! Challenges in gathering In-situ data High spatial variability in snow physical properties and limitation in accessibility => Quantifying errors In electromagnetic modeling Adaptation of physical model to metamorphic processes and a layered snowpack structure, also adapting to spaceborne scale => Key emission controllers of seasonal snow evolution

Thank you Aknowledgements: Karem Chokmani