Retrieval of microwave effective grain size for seasonally and spatially varying snow cover Juha Lemmetyinen 1), Chris Derksen 2), Peter Toose 2), Martin Proksch 3), Jouni Pulliainen 1), Anna Kontu 1), Kimmo Rautiainen 1), Jaakko Seppänen 4), and Martti Hallikainen 4) 1) Arctic Research, Finnish Meteorological Institute, Finland 2) Environment Canada, Climate Research Division, Canada. 3) WSL Institute for Snow and Avalanche Research SLF, Switzerland 4) Aalto University, Department of Radio Science and Engineering, Finland Microsnow2014

Overview Aim: Data: Results: Study performance of HUT snow emission model over a whole winter season & over varying landscapes Variability of effective grain size Data: Multi-scale radiometer observations from same test site (tower; mobile ground-based; airborne; satellite) in situ information on snow structure (snow pits) and distribution (SWE/depth transects) Results: Analysis of forward model simulations at multiple scales, using in situ snow information for input Retrieval of effective grain size at varying scales; seasonal changes and land cover effects.

ESA GlobSnow SWE Based on variational data assimilation ‘Calibration’ of forward model by calculation of effective grain size deff,r compensation for incomplete input information (snow stratigraphy, land cover effects) mitigation of forward model errors & deficiencies Satellite scale retrieval: is there any physical connection left between deff,r and actual snow cover characteristics? 18.9.2018

Grain size metrics Metrics of snow grain size used in this study: ’classical’ Grain size (Dmax) Maximum extent of a snow grain, averaged for a layer Determined visually from snow pits Retrieved effective rain size (deff,r) Precise retrieved grain size matching emission model output to brightness temperature observations. All retrievals performed for 19-37 GHz V-pol Empirical effective grain size (Dmax,eff) Empirical modification to Dmax matching HUT model to observations. Ideally, Dmax,eff = deff,r

Radiometry – multi-scale sensors Fixed ground-based (tower) Mobile ground based (sled) Airborne TB temporal evolution through whole season Spatial variability of TB under forest canopy Spatial variability of TB including forest canopy 18.9.2018

Tower based observations Sodankylä Site of Nordic Snow Radar Experiment (NoSREx) 2009-2013 Focus on winter 2010-2011

Test site Dominant land cover in test area: Forest 70.9 % Bog 23.3 % Lakes & rivers 3.7 % Open (”barren”) 1.2 % Other 0.8 % 18.9.2018

Airborne TB at 18.7 GHz V-pol Anna Kontu IGARSS 2014, Quebec, Canada

Microwave radiometer system L, X-, Ku-, Ka- and W- band dual pol radiometers (Elbara-II & RPG-8CH-DP ‘SodRad’) 30 min measurement every 4 h Elevations 30 – 70 , azimuth scanning possible (except L-band) Sky tip calibration every 12 h Sky zenith used to verify stability between snow observations 18.9.2018

Mobile radiometer observations Environment Canada Radiometers Frequency (GHz) 19.0 37.0 Bandwidth [MHz] 1000 2000 Sensitivity [K] 0.04 0.03 Accuracy [K] <2 <1 3dB [°] 6 i [°] 53 Spatial Footprint (m) 0.6 x 0.6 Radiometer calibration uncertainty Frequency 19V 19H 37V 37H RMSE 1.3 1.2 1.1 18.9.2018

Airborne observations March 17th 2011 HUTRAD multi-frequency radiometer system 6.8 GHz, 10.65 GHz, 18.7 GHz, 36.5 GHz (dual pol) 15 x 7 km transects over study area Two-point antenna calibration before and after flight operations SC-7 Skyvan research aircraft

Snow cover observations Weekly manual snow pit measurements Snow stratigraphy Density profile (snow fork and snow scale) Grain size profile Temperature profile Moisture profile (snow fork) Bulk values for SD, SWE, density Additional snowpits and sampling during mobile radiometer and flight campaigns Automated observations AWS Several snow depth sensors Snow and soil temperature profile Soil moisture profile SWE from gamma-ray extinction

Mobile radiometer observations Basic snow pit information from all sites Density, temperature profiles Snow grain size/type profile Snow characterization from selected sites SSA from NIR photography SSA & correlation length from CT analysis of snow samples Snow pits made directly in instrument footprints 18.9.2018

Mobile radiometer observations Basic sampling of snow depth and density SWE every 500 meters, SD every 100 meters Provides reference for SWE spatial variability and connection to vegetation/land cover 18.9.2018

Forward modeling experiments 18.9.2018

Winter time series 1-layer Forward modeling of winter time series Snowpits, made outside of instrument footprints, were aggregated to 1- and 2-layer representative pits (depth weight averaging for grain size) 3rd order fit applied to grain size data Reduces random error 18.9.2018

Winter time series 2-layer Forward modeling of winter time series Snowpits, made outside of instrument footprints, were aggregated to 1- and 2-layer representative pits (depth weight averaging for grain size) 3rd order fit applied to grain size data Reduces random error Soil parameters optimized using 10.65 GHz (model by Wegmüller & Mätzler) 18.9.2018

Winter time series 1-layer simulations: underestimation for both 19 and 37 GHz Use of empirical Dmax,eff formulation improves results 18.9.2018

Winter time series 1-layer simulations: underestimation for both 19 and 37 GHz Use of empirical Dmax,eff formulation improves results 18.9.2018

Winter time series 2-layer simulations: Underestimation improved (still apparent especially for 19 GHz) Improvement largely due to better representativity of grain size value 18.9.2018

Winter time series 2-layer simulations: Underestimation improved (still apparent especially for 19 GHz) Improvement largely due to better representativity of grain size value 18.9.2018

Sled based 1- & n-layer simulations: In situ measurements made directly in instrument footprints -> n-layer simulations applied Vegetation canopy effects compensated for forested sites (downwelling Tb reflected from snow) Results again improved using multi-layer simulation Use of empirical Dmax,eff required to obtain reasonable results Sled based Model bias using in situ data 18.9.2018

Airborne Airborne simulations: Snow pit data used to assign typical characteristics for snow for varying land cover (1-layer sim) Vegetation canopy effects compensated based on forest biomass in instrument FOV Results improved using multi-layer simulation Use of empirical Dmax,eff required to obtain reasonable results 18.9.2018

Results: tower based Tb vs. SWE 18.9.2018

Results: mobile radiometers vs. SWE 18.9.2018

Results: airborne radiometers vs. SWE 18.9.2018

Results: airborne radiometers vs. SWE 18.9.2018

Retrieval of effective grain size 18.9.2018

Effective grain size Retrieval from AMSR-E over whole season: Deff,r retrieved for satellite scene over Sodankylä test site Forward model observation 18.9.2018

Effective grain size Trend & overall level corresponds well to tower based retrieval – forward model is able to compensate for land cover effects 18.9.2018

Effective grain size Trend & overall level corresponds well to tower based retrieval – forward model is able to compensate for land cover effects Reasonable match even with ’classical’ Dmax, averaged over whole snowpack (RMSE < 0.3 mm) 18.9.2018

Effective grain size airborne & sled 18.9.2018

Applicability of single deff for varying land cover Effective grain diameter retrieved from average of 19-37 GHz V-pol for whole measured scene Forward simulation of individual footprints using mean snow depth (+/- std) and common deff,r 18.9.2018

Summary For a well-controlled test setup (no vegetation, land cover effects) the retrieved deff,r for the HUT snow emission model can bear a relation to the measured Dmax HUT model was empirically formulated using Dmax to calculate extinction coefficient For varying land cover, deff,r reacts to Lack of input information Model deficiencies Actual differences in snow structure For deff,r to be applicable over versatile land cover, 1.,2. should be minimized Error in retrieved SWE (GlobSnow algorithm) as function of error in effective grain size deff,r underestimated deff,r overestimated 18.9.2018