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Kari Luojus ( Finnish Meteorological Institute) J. Pulliainen, M. Takala, J. Lemmetyinen (FMI)

ESA DUE GLOBSNOW Investigating Hemispherical Trends in Snow Accumulation Using GlobSnow Snow Water Equivalent Data. Kari Luojus ( Finnish Meteorological Institute) J. Pulliainen, M. Takala, J. Lemmetyinen (FMI) C. Derksen, L. Wang (EC) S. Metsämäki (SYKE), B. Bojkov (ESA)

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Kari Luojus ( Finnish Meteorological Institute) J. Pulliainen, M. Takala, J. Lemmetyinen (FMI)

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  1. ESA DUE GLOBSNOWInvestigating Hemispherical Trends in Snow Accumulation Using GlobSnow Snow Water Equivalent Data Kari Luojus (Finnish Meteorological Institute) J. Pulliainen, M. Takala, J. Lemmetyinen (FMI) C. Derksen, L. Wang (EC) S. Metsämäki (SYKE), B. Bojkov (ESA) GlobSnow project consortium: FMI, NR, ENVEO, GAMMA, SYKE, EC, Norut

  2. ESA GlobSnow ESA-funded GlobSnow project: Production of hemispherical scale snow extent (SE) and snow water equivalent (SWE) climate data records; along with a demonstration of a near real time processing and data dissemination chain for the products. Consortium; Finnish Meteorological Institute (FMI) with ENVEO IT GmbH (Austria), GAMMA Remote Sensing (Switzerland), Norwegian Computing Center, Finnish Environment Institute (SYKE), and Environment Canada (EC). Products and project details: www.globsnow.info Near-real-time snow map production on-going since: 1 October 2010 Finnish Meteorological Institute

  3. GlobSnow: Long term snow products • SWE (30 years) and SE product (15 years) + NRT demonstration • Primary products: • Daily SWE / Daily fractional SE • Secondary products: • Weekly SWE / SE (mean and maximum) • Sliding average: data from current day + previous 6 days • 1 file for every day • Monthly SWE / SE(mean and maximum) • 12 files per year: January, February, March… • Spatial coverage: Northern Hemisphere (both SE & SWE) Finnish Meteorological Institute

  4. GlobSnow – SWE ECV • A new State-of-the-Art Snow Water Equivalent (SWE) climate data record • Work initiated at HUT/LST during 1990’s by Profs Pulliainen and Hallikainen • SWE Methodology (main body of work) published 2006 in RSE by Pulliainen • Past 5 years the work has been carried at FMI Arctic research centre • Shifted in high gear for ESA DUE GlobSnow (T.O. ESA/ESRIN: B.Bojkov) • -> Past 3 years of work by an experienced team (key people originally HUT/LST + team at Environment Canada) have resulted in a new SWE climate record (daily, 1979 – 2010, hemispherical SWE record) • Validation, spanning NH, indicates a new level of accuracy for SWE retrieval • Production of 30+ years SWE data on a (FMI Cray 5 XTm) super computer • Results presented in earlier conferences & a journal article in press (and a project document library for GlobSnow users) Finnish Meteorological Institute

  5. SWE product (development by FMI & EC) • SWE climate record for 1979 - 2010(+ NRT demonstration) • Spaceborne microwave radiometer data • AMSR-E data (2003 – present) • SSM/I data (1987 – 2002) • SMMR data (1979 – 1987) + Global weather station data from WMO stations • Produced in EASE-grid projection ~ 25km resolution • Product includes error estimates for each SWE value Finnish Meteorological Institute

  6. Overview for the SWE product Daily maps of hemispherical snow cover: SWE for the permanent seasonal snow area Total snow area (Snow Extent) Regions with high topograpical variability are masked out Alpine regions Glaciers Greenland NRT production of SWE demonstrated in 2010-2011 NRT to be transferred to EUMETSAT H-SAF late 2011 Data format HDF4 & NetCDF CF Data access through FTP & WWW Finnish Meteorological Institute

  7. Daily SWE product: 15 February 2008Northern Hemisphere Finnish Meteorological Institute

  8. SWE algorithm selection & retrieval validation Daily SWE estimates acquired with 5 algorithms evaluated for Eurasia (1994-1997) • FMI Assimilation algorithm (Pulliainen 2006) • EC SWE suite (Goodison, Walker, Goita, Derksen et al. 1993-2009) • Chang et al. 1987 (original channel difference algorithm) • SPD-algorithm (Asbacher 1989) • Armstrong and Brodzik 2001-algorithm (Improved channel difference) SWE estimates computed between 09/1994 and 12/1997 (SSM/I data) • Chang, SPD and Armstrong –algorithms were evaluated for both asc & desc nodes; FMI uses desc node data complemented with asc node data • Evaluations for North America and Finland were carried out in addition to the analyses for Eurasia Finnish Meteorological Institute

  9. Validation data – INTAS SSCONE data INTAS SSCONE data (from the former USSR and Russia) • There are 1294 snow path stations with data from the USSR • Manual ground-based measurements on snow depth • Used as the validation data for the GlobSnow evaluations • There were 450 path stations with data for 1994-1997 Finnish Meteorological Institute

  10. Overall performance for SWE algorithmsEurasia 09/1994 – 12/1997Evaluation with 450 INTAS snow courses Finnish Meteorological Institute

  11. F 8 SSM/I F 8 SSM/I F 8 SSM/I F 8 SSM/I F 11 SSM/I F 11 SSM/I F 11 SSM/I F 11 SSM/I F 13 SSM/I F 13 SSM/I F 13 SSM/I F 13 SSM/I SMMR SMMR SMMR SMMR AMSR-E AMSR-E AMSR-E AMSR-E Consistency of GlobSnow SWE retrieval 1980 - 2009 • RMS error and retrieval bias calculated independently for each year • Consistent inter-annual characteristics & minimal difference between sensors SWE<150 mm All SWE Finnish Meteorological Institute

  12. AMSR-E vs. SSM/I evaluations (2003 - 2010) GlobSnow SWE PMW sensor changes from SSM/I to AMSR-E at the beginning of 2003 FPS v1.0 (current sensor set) SMMR 1979 – 1987 SSM/I 1987 – 2002 AMSR-E 2003 – 2010 Possible alternative sensor set (e.g. NSIDC sea ice) SMMR 1979 – 1987 SSM/I 1987 - 2010 Comparisons between for SSM/I and AMSR-E derived SWE datasets (2003-2010) conducted Finnish Meteorological Institute

  13. Change of bias: DMSP-F13 results in higher snow mass (SWE) than AMSR-E, but DMSP-F17 gives a lower snow mass after summer 2009 AMSR-E vs. SSM/I evaluations (2003 - 2010) 2003 – 2008 DMSP-F13 2009-2010 DMSP-F17 Finnish Meteorological Institute

  14. AMSR-E vs. SSM/I trend (1980 - 2010) Finnish Meteorological Institute

  15. AMSR-E vs. SSM/I trend (1980 - 2010) AMSR-E, -9.6% trend for 30 years SSMI, -8,2% trend for 30 years Finnish Meteorological Institute

  16. Summary on SWE product • Current long term datasets on Global scale: Monthly from 1978, daily from 2002 GlobSnow: Daily 30+ years ( … from launch of SMMR 1978) • Thematic accuracy • Current alternative algorithms • Global scale 40mm – 200mm • Regional scale 20mm – 50mm (methods typically regionally adjusted) • GlobSnow algorithm (Global scale): RMSE of 43.2 mm for Eurasia (diagnostic dataset; > 26 000 samples) RMSE of 33.5 mm for Eurasia (for SWE < 150mm; >23 000 samples) Error estimates (error bars) • Current methods do not provide information on estimation error GlobSnow SWE algorithm: Error estimates for each SWE estimate Finnish Meteorological Institute

  17. GlobSnow Snow Extent Dataset • SE retrieval using ERS-2 ATSR-2 and Envisat AATSR data • - Finnish Environment Institute’s SCAmod algorithm. • - Norwegian Linear Reflectance’ (NLR) fractional snow cover (FSC) algorithm; • - 15 years SE data record has been produced using optical imagery from ATSR-2 (1995-) and AATSR (2002-) on a hemispherical scale. yellow – clouds green – bare ground white – snow cover Finnish Meteorological Institute Finnish Meteorological Institute 1.1.2020 17

  18. Daily, weekly and monthly products Optical data ~ 1km spatial resolution 10 April 2003 19 April 2003 April 2003 Finnish Meteorological Institute

  19. Examples of monthly products, 2003 April 2003 May 2003 June 2003 Finnish Meteorological Institute

  20. GlobSnow - Summary • 30+ year daily Snow Water Equivalent climate record (25 km resolution) • 30+ year daily Snow Extent (PMW) climate record (25 km resolution) • 15+ year daily Snow Extent (optical) climate record (1km resolution) • Version 1.0 of low resolution SWE/SE (30 years) and high resolution SE (15 years) are available now • The extensive validation effort indicates anew level of accuracy for hemispherical scale SWE retrieval for the 30+ year evaluation • Long term trends indicate a clear decline in hemispherical snow mass • The near-real-time GlobSnow processing system is on-going an operational demonstration phase (ESA GlobSnow -> Eumetsat H-SAF) • Products (freely available) and additional details:www.globsnow.info Finnish Meteorological Institute Finnish Meteorological Institute 1.1.2020 20

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