GlobSnow Snow Extent and SWE datasets Kari Luojus, Jouni Pulliainen, Matias Takala, Juha Lemmetyinen Tuomo Smolander, Jaakko Ikonen, Juval Cohen (Finnish Meteorological Institute), Sari Metsämäki (Finnish Environment Institute), Chris Derksen (Environment Canada), T. Nagler (ENVEO) R. Solberg (NR), A. Wiesmann (GAMMA),S. Wunderle (Univ. Bern), F. Fontana (MeteoSwiss), E. Malnes (Norut), W. Schoener (ZAMG) + Bojan Bojkov (EUMETSAT) & Simon Pinnock (ESA) 13 June 2016 – Columbus, Ohio, USA WMO GCW “Snow Watch 2” workshop Finnish Meteorological Institute 6.5.2018

Outline ESA DUE GlobSnow-2 Snow Water Equivalent (SWE) dataset Snow Extent (SE) dataset Future evolution SWE & Snow Extent Finnish Meteorological Institute 6.5.2018

ESA DUE GlobSnow ESA DUE GlobSnow project: Production of novel hemispherical snow extent (SE) and snow water equivalent (SWE) climate data records. Generation of long time-series employing FMI supercomputing facilities at Helsinki (daily, weekly and monthly maps of SE and SWE for northern hemisphere) + NRT processing Consortium members: Finnish Meteorological Institute (FMI) with ENVEO IT GmbH (Austria), GAMMA Remote Sensing (Switzerland), Norwegian Computing Center, Finnish Environment Institute (SYKE), and Environment Canada (EC). + Univ. Bern, MeteoSwiss, ZAMG & Norut GlobSnow-1 (3.5 years): 11/2008 – 11/2011 (36 months) GlobSnow-2 (2 years): 05/2012 – 09/2014 (24 months) Details and products available at www.globsnow.info 6.5.2018

35 year-long CDR time-series on snow conditions of Northern Hemisphere First time reliable daily spatial information on SWE (snow cover): Snow Water Equivalent (SWE) Snow Extent and melt (+grain size) 25 km resolution (EASE-grid) Time-series for 1979-2015 Passive microwave radiometer data combined with ground-based synoptic snow observations Variational data-assimilation Available at open data archive (www.globsnow.info) Daily NRT production since 2010 => Methodology moved to EUMETSAT H-SAF Takala, M., Luojus, K., Pulliainen, J., Derksen, C., Lemmetyinen, J., Kärnä, J.-P, Koskinen, J., Bojkov, B., “Estimating northern hemisphere snow water equivalent for climate research through assimilation of spaceborne radiometer data and ground-based measurements”, Remote Sensing of Environment, Vol. 115, Issue 12, 15 December 2011, doi: 10.1016/j.rse.2011.08.014

GlobSnow SWE time series (FCDR) Northern Hemisphere 1979 to 1987 (SMMR) 1988 to 2013 (SSM/I, SSMIS) FPS v1.0 2003 to 2011 (AMSR-E) Daily, weekly, monthly products Includes error estimates (statistical std of the SWE estimate in mm) Data format HDF4 & NetCDF CF EASE-Grid projection (~25km resolution) snow grain data available as well Glaciers, mountains & Ice Sheets masked out Versions: 1.0; 1.3 and 2.0 (current)

Distributed validation data, e.g. Northern Eurasia & Canada Snow Survey data (from the former USSR and Russia) There are 517 snow path stations with data for (1979 – 2009) Manual ground-based measurements on snow depth/SWE 1 - 2km snow transects, measurements every 100m - 200m Land Cover Reference Dataset Year n Mean SWE (mm) Tundra Intensive Sites; SnowSTAR 2007 2006-2008 28 120 Northern Boreal EC Snow Surveys 2006-2007 105 135   SWE <150 mm 73 134 Southern Boreal 2005-2007 57 75 BERMS Towers 2005-2008 468 70 Prairie 41 44 Finnish Meteorological Institute

SWE retrieval (data assimilation vs. channel diff.) Density scatterplot (assimilated vs. satellite only SWE) Russian INTAS SCCONE SWE transect data as reference

SWE product intercomparison (ESA SnowPEx): GlobSnow-2, NASA Standard, NASA Prototype SWE

GlobSnow-2 SWE vs. RIHMI WDC (2002-2011) 38197 Coinciding samples of GlobSnow, NASA Standard and NASA prototype SWE Evaluations for the samples available in all 3 products! ”Blended product” = combines satellite and ground-based WS-data

NASA Standard SWE vs. RIHMI WDC (2002-2011) 38197 Coinciding samples of GlobSnow, NASA Standard and NASA prototype SWE Evaluations for the samples available in all 3 products! ”Pure satellite product” = utilizes only satellite data for retrieval

NASA Prototype SWE vs. RIHMI WDC (2002-2011) 38197 Coinciding samples of GlobSnow, NASA Standard and NASA prototype SWE Evaluations for the samples available in all 3 products! ”Pure satellite product” = utilizes only satellite data for retrieval

Density scatterplots SWE vs. ref Density scatterplots SWE vs. ref. data (ESA SnowPEx): GlobSnow-2, NASA Standard, NASA Prototype SWE

SWE analysis on a monthly basis, RMSE Differences increase towards the end of the snow accumulation season

Conclusions (satellite-based SWE) NASA Pro GlobSnow NASA Std Observed under-estimation of NASA SWE products due to high negative bias with deep snow. Total snow estimates of GlobSnow for NH are more accurate.

Summary – All SWE products Assessed for an uniform time period, ranked by retrieval performance (RMSE) Time period 2002-2010, Russian snow course data as reference Datasets Dataset availability Retrieval performance (RMSE) 2002-2010 Bias 2002-2010 Retrieval performance (RMSE) 1981-2010 Bias 1981-2010 GlobSnow v2.0 1979-2015 42.6 mm -3.8 mm 44.9 mm -4.3 mm CROCUS-Era-Interim 1981-2010 45.8 mm +1.1 mm 48.0 mm +4.7 mm GLDAS2.0-Noah -8.4 mm 49.5 mm -10.8 mm MERRA (Standard) 54.9 mm +12.9 mm 57.9 mm +15.2 mm ERA-Interim (ERA-Land) 67.3 mm +35.4 mm 74.7 mm +42.4 mm NASA Standard 2002-2011 67.4 mm -24.3 mm - NASA Prototype 72.4 mm -19.9 mm

Long term consistency of GlobSnow SWE FPS RMS error and retrieval bias calculated independently for each year 1980-2011 Reference data: snow transects from Russia (INTAS-SCCONE) SWE<150 mm

GlobSnow Snow Extent (SE) dataset 17 years SE data record has been produced using optical imagery from ESA ATSR-2 (1995-) and AATSR (2002-) on a hemispherical scale. NPP VIIRS from 2012- SYKE’s SCAmod method for fractional snow cover mapping implemented for Northern hemisphere Cloud detection algorithm developed by SYKE (+ contributed by ENVEO, FMI & NR) Methodology developed especially for forested regions – basically a tough challenge for optical SE retrieval Uncertainty estimate provided for each grid cell, data available as NetCDF CF Operational data production at the Finnish Meteorological Institute (FMI) Metsämäki, S., Mattila, O.-P., Pulliainen, J., Niemi, K., Luojus, K., Böttcher, K. "An optical reflectance model-based method for fractional snow cover mapping applicable to continental scale", Remote Sensing of Environment, Vol. 123, August 2012, pp. 508-521, doi: 10.1016/j.rse.2012.04.010.

Daily, weekly and monthly products Optical data ~ 1km spatial resolution 10 April 2003 3 March 2010 1 March 2010 19 April 2003 April 2003 March 2010 Finnish Meteorological Institute 6.5.2018

SCAmod retrieval algorithm Based on reflectance model where forest canopy effect into the observed reflectance is compensated through pre-determined canopy transmissivity Designed to provide FSC in forested areas, and overall well applicable globally over any terrain A single band approach - Applicable to optical wavelengths and to a variety of sensors Surface scattering Volume scattering r λ,obs(FSC) observed reflectance from unit area r λ, snow wet snow reflectance r λ ,ground snow-free ground reflectance r λ ,forest forest canopy reflectance forest canopy transmissivity for unit area FSC fraction of snow covered area

Mean Absolute FSC Difference MOD10 versus GlobSnow - 1.3.-31.5.2010 (S|FSCMOD10 – FSCGlobSnow|)/N forest unforested number of pixels (N) Thomas Nagler / ENVEO

GlobSnow-2, Suomi NPP VIIRS SE product ~1km spatial resolution, daily hemispherical coverage 10 April 2003 3 March 2010 1 March 2010 19 April 2003 April 2003 March 2010 Finnish Meteorological Institute 6.5.2018

Higher resolution retrieval Future evolution SWE Higher resolution retrieval Augmenting SWE retrieval using SE information Snow Extent Sentinel-3 OLCI & SLSTR Extending time series back from 1995 (1980?) Finnish Meteorological Institute 6.5.2018

Super resolution (5km) Pan-European SWE product Super resolution 5km SWE SWE retrieval based on enhanced GlobSnow approach Fractional snow cover information used in SWE retrieval for: improved snow detection during winter snow accumulation period Improved melt detection during spring Utilization of optical (VIIRS-based) FSC-data in combination with NOAA IMS (4km) product Processing for Pan-European domain in NRT since 2015 Example of super resolution SWE product

Super resolution (5km) Pan-European SWE product 5km resolution with a new processing approach. New Aux data, including an improved forest stem volume map Improved handling of snow grain size and variance in emission model, artefacts filtered from proc. chain Masking of snow extent area using IMS/VIIRS data RMSE similar to GS-2, but output shows added spatial information with the high resolution SWE retrieval Applying the 5km SWE data for hydrologial modelling, the model output is improved, compared using GS-2 data Even as radiometer resolution is ~25km, we are able to achieve sub-pixel resolution on the level of 5km without losing SWE retrieval sensitivity Shows improved results when SWE data applied for hydrological modeling

Super resolution (5km) Pan-European SWE product Future Evolution Potential Long term ECV with 5km resolution?? (ESA Snow CCI)? Utilization of NSIDC super-resolution SSMIS Tb data for 5km retrieval?

Combination of SE & SWE products for the generation of concise snow cover information NRT north-hemisphere daily snow monitoring product combining GlobSnow SWE and SE products (based on SSMI-S and VIIRS+Sentinel-3) SE information from VIIRS & NOAA IMS applied for (NRT) SWE production Finnish Meteorological Institute 6.5.2018

Conclusions ESA GlobSnow produced NH snow extent (SE) and snow water equivalent (SWE) climate data records (17 and 35 years of snow cover information) The NRT processing system still online! info & data available at www.globsnow.info SWE NRT production moved under EUMETSAT HSAF initiative! Future evolution: Combination of SE and SWE information (for improved SWE time series) Moving to 5km resolution with SWE Future plans (ESA Snow CCI+ ? or a similar initiative) Improved snow emission model for (SWE) Improved handling of forests and lake rich regions (SWE) Dynamic snow grain size (SWE) Sentinel-3 (SE) Improved cloud masking & FSC retrieval (SE) Extended time series (from 1980s to present day?)

Impact of Radiometer Derived Information Difference between final assimilated SWE and background SWE from interpolated synoptic weather station data.

SWE Retrieval ‘Saturation’ (PMW signal) “what if we would not apply the synop data?” Chang et al. EC GlobSnow