Assimilation of GlobSnow Data in HIRLAM. Suleiman Mostamandy Kalle Eerola Laura Rontu Katya Kourzeneva

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1 Assimilation of GlobSnow Data in HIRLAM Suleiman Mostamandy Kalle Eerola Laura Rontu Katya Kourzeneva 10/03/2011

2 Contents Introduction Snow from satellites Globsnow Other satellites The current study Experiment design Preliminary results Concluding remarks and plans SNAPS

3 Why snow Time-series of snow depth from RCR Helsinki-Vantaa(left) Utti (right) Finnish Meteorological Institute 10/03/2011 3

4 Snow analysis is by no means a trivial task! Snow edge is difficult to analyze

5 Snow analysis in Hirlam at the moment Only conventional SYNOP snow depth observations are used We know the problems Distribution of SYNOP observations 00 cm of snow problem, analysis of snow edge Snow depth SWE (snow water equivalent) Snow density needed

6 Snow from satellites Globsnow Other satellites Finnish Meteorological Institute 10/03/2011 6

7 ESA GlobSnow ( ) Production of new global snow extent (SE) and snow water equivalent (SWE) climate data records, with a demonstration of a near-real-time processing capability. Consortium led by the Finnish Meteorological Institute (FMI) with collaborators: ENVEO IT (Austria), GAMMA Remote Sensing (Switzerland), Norwegian Computing Center (NR), Finnish Environment Institute (SYKE), Environment Canada and Norut (Norway). Project details including technical reports and newsletters available at globsnow.fmi.fi. or Finnish Meteorological Institute 10/03/2011 7

8 30 year ECV/FCDR time-series on snow conditions of Northern Hemisphere (ESA-GlobSnow led by FMI) Spaceborne passive microwave radiometer data combined with ground-based synop observations 25km spatial resolution Uncertainty for each grid cell Temporal coverage Demonstration of NRT (near-realtime) service on-going Reference: Pulliainen J Mapping of snow water equivalent and snow depth in boreal and subarctic zones by assimilating space-borne microwave radiometer data and ground-based observations. Remote Sensing of Environment, Vol. 101, pp ) Finnish Meteorological Institute 10/03/2011 8

9 Main features for SWE: The snow water equivalent product will be based on the combination of satellite-based microwave radiometer and ground-based weather station data (Synop-observations). The long term SWE data set will span the years The snow cover maps are produced as daily, weekly and monthly composites Nimbus-7 SMMR, DMSP SSM/I and Aqua AMSR-E are the main data sources, SSM/I for , AMSR-E for Non-mountainous areas of Northern Hemisphere are covered The SWE maps will be generated using in a 25km spatial resolution Demonstration of an operational near-real time SWE mapping service was initiated on October 2010 Finnish Meteorological Institute 10/03/2011 9

10 Main features for SE: The long term SE data set will be generated for the years The snow cover maps are produced as daily, weekly and monthly composites ESA ERS-2 ATSR-2 and Envisat AATSR data are the main employed data sources Areas with seasonal snow cover on both Northern and Southern Hemispheres are covered The SE maps will be generate in a 0.01 degree (approx. 1km) spatial resolution Demonstration of an operational near-real time SE mapping service will be conducted out at a later stage of the project Finnish Meteorological Institute 10/03/

11 More details important for us Normally the data of previous day is ready around midnight If conflict with satellite and Synop information, more weight to Synop In autumn and early winter the quality is worse (snow appearing and disappearing) SE and SWE are based on different instrument, clouds problem to SE In the future SE and SWE are combined Development this year and operational already perhaps next year Globsnow Newsletter published 3-4 times a year SWE on mountains not very good ( mountains based on slopes, plateau is not mountain ) In longer perspective FMI GlobSnow (non-mountainous) and Turkish data (over mountains) will be combined in HydroSAF project, but it is possible to use only the no-mountains data (mask) The file with coordinates will be created as an ascii file for easier use Finnish Meteorological Institute 10/03/

12 Most recent Globsnow map ( ) Finnish Meteorological Institute 10/03/

13 GlobSnow SE - Northern Hemisphere (monthly) January 2003 February 2003 March 2003 Finnish Meteorological Institute 10/03/

14 Optical SE Iceland (AATSR monthly -> MODIS?) January 2003 February 2003 March 2003 Finnish Meteorological Institute 10/03/

15 Other satellite data LandSaf file for the previous day are available about 3 am via Eumetcast, one product per day Comparison of IMN and LandSAF: an accepted paper by Niilo Siljamo and Otto Hyvärinen. Kalle has a copy of it (pdf) In IMN the priority in manual checking is in North America sometimes European side is not checked LandSAF is more fresh data, INM can contain older information LandSAF has only binary data (snow no snow unclassified) while GlobSnow has the fractional snow cover (percentage), to produce them GlobCover physiography database is used LansSAF data is better in Central Europe (angle of the geostationary satellite), but similar product from polar orbiting satellite is coming (next year?) Resolution in Central Europe ~ 5 km SYKE (Finnish Environmental Administration) data only available on the Baltic Sea area and only melting period Same algorithm as in HydroSAF data, MODIS data not very useful for us Finnish Meteorological Institute 10/03/

16 The current study Use of satellite data in HIRLAM data assimilation Globsnow (to improve data coverage) Downloading GlobSnow SWE data from FTP/HTTP server ( Converting data from HDF to ASCII (specob format) Converting SWE to snow depth using constant snow density, as done while producing Globsnow data Using the HIRLAM 7.3 with new surface scheme More sensitive to snow analysis than the old scheme Three experiments were run: 1. Reference: only synop 2. The Globsnow and Synop observations are included 3. Only Globsnow without Synop observation No independent snow observations to verify against Differences in the analysis Changes in the forecast (t2m etc.) Verify the forecasts

17 Design of the experiments Hirlam version Time Latest 7.3 version New surface scheme ( newsnow ) Resolution 0.15 deg., 60 levels 3DVAR 15.Oct 24.Dec Mar 15. Apr (30 Mar) 2010 Globsnow observations Use them when available, some days with missing Globsnow file If exist, use the same file for every cycle of the day for winter they are available near real time

18 Different data scenarios Reference: only SYNOP observations Globsnow data in addition to the reference Globsnow data without SYNOP data Globsnow data using HIRLAM orography Original resolution of the Globsnow data: ~25km In the current study thinning of data to 75 km done (technical aspect)

19 Preliminary results The analysis of the results has just started only first impressions and findings can be shown Results only for the autumn/early winter 2009 (more problematic season) Finnish Meteorological Institute 10/03/

20 Finnish Meteorological Institute 10/03/

21 Blue: Light blue: 1 3 Green: Orange: Red: > 10 Finnish Meteorological Institute 10/03/

22 Compare of Synop and Globsnow data Finnish Meteorological Institute 10/03/

23 Reference Globsnow + synop Finnish Meteorological Institute 10/03/

24 Finnish Meteorological Institute 10/03/

25 Preliminary conclusions and plans Technically the system is working: data from Globsnow to Span The autumn experiment has been preliminary examined The snow extent is reasonable in Globsnow Underestimating the snow depth in some areas Conversion from SWE to depth? Problems in Globsnow data in autumn? Peaks in analysis when Globsnow data missing analyse directly SWE, convert Synop to SWE Problem in data quality check close to eastern boundary So thinning of data to 25 km should be tried More careful analysis of autumn period and analysis of spring period Can other data (IMN) be used for verification? Use of same Globsnow data in all daily cycles: should it be used only once? Finnish Meteorological Institute 10/03/

26 FMI expertise in snow mapping - contribution to SNAPS Kari Luojus, Jouni Pulliainen, Petteri Ahonen, Riika Autio 26

27 Snow mapping (FMI) Snow cover maps in SNAPS Snow extent Optical (AATSR/MODIS) SAR work by Norut Snow status (wet snow / dry snow) Passive microwave SWE (Passive microwave) Investigated for SNAPS Issue with glaciers & mountains Finnish Meteorological Institute 10/03/

28 FMI objectives in SNAPS Snow cover maps Near-real time snow cover maps updated shortly after satellite data has been received Snowdrift forecasts Pilot system based on visibility sensors compared to radar data Special case road snowdrift, observed from pilot road weather station History of snow cover FMI data archives exploited, special case Sodankylä dense data Avalanche forecasts for specific areas. Supporting role, as avalanches not occurring in Finland Finnish Meteorological Institute 10/03/

29 HIRLAM and HARMONIE - possible FMI contribution to SNAPS snow mapping Laura Rontu, Kalle Eerola, Suleiman Mostamandy, Ekaterina Kurzeneva Richard Essery (University of Edinburgh) 29

30 Present operational 30

31 HIRLAM = High Resolution Limited Area Model - Operational NWP in 9 European countries - Used for research over any domain over the globe - Contains atmospheric and surface data assimilation and weather forecast model, including snow assimilation and parametrizations - Maintained and developed by the international HIRLAM programme - Close cooperation with the international ALADIN programme > HARMONIE NWP framework

32 We suggest for SNAPS To provide SNAPS with kilometer-scale gridded snow depth/water equivalent maps based on HIRLAM/HARMONIE data assimilation To provide dedicated stand-alone snow data-assimilation system (by the Edinburgh university) with atmospheric forcing data including temperature, humidity, wind, snowfall, downwelling radiation fluxes To obtain this, we suggest to

33 To set up two NWP experiment domains with a 2.5 km horizontal resolution: Iceland and North of Scandinavia To perform data-assimilation + forecast experiments over these domains during agreed test periods with assimilation of - conventional SYNOP observations on snow depth, - satellite snow water equivalent from Globsnow - available local snow depth/density measurements To work in cooperation with the partners in Icelandic Meteorological Office and European Space Agency projects Globsnow, CoSDAS and North Hydrology 33

34 Measured and Modelled Snow Profiles Sodankylä, 23 March /03/2011

35 Measured and Modelled Snow Water Equivalent Hirlam analyses CoSDAS model CoSDAS model + assimilation Finnish Meteorological Institute 10/03/