IGU Regional Conference Moscow 2015 Implementation of SWAN model with COSMO-CLM and WRF-ARW wind forcing for the Barents Sea storm events (case study). Stanislav Myslenkov 1, Vladimir Platonov 2 and Pavel Toropov 2 Lomonosov Moscow State University, Faculty of Geography 1 department of oceanology; 2 department of meteorology and climatology.
OUTLINE Goals and objectives; Technique and data; Computational resources; Experiments description test (10/2000) and main (01/2010); Preliminary results of test study; Some results of the main study (COSMO-CLM/WRF + SWAN); Conclusion and future outlook.
GOALS AND OBJECTIVES Main goal is the implementation of ocean waves model SWAN with different high-resolution wind forcing from regional mesoscale atmospheric models for more detailed reproducing of waves characteristics during severe weather events. Corresponding objectives: - Use different reanalysis as driving conditions for atmospheric and wave models (ERA-Interim, NCEP-CFSR); - Use COSMO-CLM and WRF-ARW regional atmospheric models for producing high-resolution wind forcing; - Use SWAN model for extreme waves modeling; - The Barents sea and many periods with extreme events were chosen for these experiments. The main task was to develop a technology of detailed coupled modeling of the atmospheric circulation and sea waves.
TECHNIQUE Description of COSMO-CLM and WRF-ARW models COSMO-CLM is the climate version of mesoscale COSMO model, developed in DWD Most applicable resolutions: 0.44 0 ~ 48 km 0.15 0 ~ 16 km 0.22 0 ~ 24 km 0.0625 0 ~ 7 km 0.165 0 ~ 18.3 km 0.02 0 ~ 2.2 km 0.1 0 ~ 11 km 0.01 0 ~ 1.1 km 0.025 0 ~ 2.8 km WRF is well-known mesoscale model, developed by NCAR, NOAA/ESRL and NOAA/NCEP/EMC Detailed surface information (soil type, albedo, surface height, roughness length etc.) EXTernal PARarameters from CLM-Community web site Initial and boundary conditions from global driving models and reanalysis (ERA-Interim, NCEP- NCAR, NCEP-CFSR etc.) Nonhydrostatic regional climate model COSMO-CLM (version 5) Dynamical downscaling of initial fields taking into account surface characteristics
TECHNIQUE Wave Modeling tools SWAN (Simulating waves nearshore) is the most widely used computer model to calculate irregular waves in coastal environments, based on deep water wave conditions, wind, bottom topography, currents and tides (deep and shallow water). SWAN model is developed and distributed freely by Delft University of Technologies. In SWAN model could be realized many processes of waves evolution and propagation, dissipation, nonlinear waves interaction, etc. Main characteristics: unstructured computational mesh; wind forcing is combined from different sources; main output product significant wave heights. SMS (Surface Water Modeling System) is a comprehensive environment for one-, two, and three-dimensional hydrodynamic modeling. A pre- and post-processor for surface water modeling and design, SMS includes 2D finite element, 2D finite difference, 3D finite element modeling tools.
TECHNIQUE General scheme of experiments: Coupling between regional atmospheric models (COSMO-CLM and/or WRF-ARW) and SWAN model. Atmospheric component Driving data from global reanalysis (NCEP-CFSR, ERA-Interim etc.) Large domain of regional atmospheric model COSMO-CLM/WRF-ARW (~18 km res.) Small domain of regional atmospheric model (~2,8 km res.) Data used as driving and for validation: - Reanalysis data (ERA-Interim, NCEP-CFSR) - AVISO satellite altimetry data (http://www.aviso.altimetry.fr); - SOLAB satellite data sea ice, waves etc. (http://arctic.solab.rshu.ru); - meteorological stations archive (http://meteo.ru/data) wind forcing for SWAN model Next slide
TECHNIQUE Wind forcing from global reanalysis (NCEP-CFSR) for Northern Atlantic Previous slide General scheme of experiments: Coupling between regional atmospheric models (COSMO-CLM and/or WRF-ARW)and SWAN model. Ocean component Wind forcing from regional atmospheric model (large/small domain) for Barents Sea ~1 0 resolution ~15 km resolution and less Unstructured computational mesh for SWAN model, wind forcing is combined from different sources Detailed wave heights output, every 30 minutes
COMPUTATIONAL RESOURCES Supercomputer cluster Lomonosov in Moscow State University Mini cluster CRAY CX 48 core Server HP Proliant 80 core Server SuperMicro 64 core HDD raids 200 Tb Faculty of Geography
EXPERIMENTS TEST STUDY (10/2000) COSMO-CLM Forcing Resolution 18 km nesting COSMO-CLM Forcing Resolution 2.8 km 26. October 2. November 2000 Driving reanalysis data - ERA-Interim, ~0.75 0 res. SWAN unstructured computational grid and western boundary conditions for Atlantic waves
PRELIMINARY RESULTS TEST STUDY (10/2000) COSMO-CLM output: wind fields and synoptic situation 18 km - large domain 2,8 km small domain
PRELIMINARY RESULTS TEST STUDY (10/2000) SWAN significant waves height output results Comparison with NOAA WaveWatch3. NOAA WaveWatch3 (1.25 0 * 1 0 ) and SWAN (~15 km and less) models WaveWatch3 waves modeling results SWAN waves modeling results NOAA WAVEWATCH III CFSRR Reanalysis Hindcasts wind forcing from NCEP- CFSR reanalysis, http://polar.ncep.noaa.gov/waves/index2.shtm - description; Data source: http://apdrc.soest.hawaii.edu/data/data.php; Output: 1.25 0 * 1 0, every 3 hours
EXPERIMENTS MAIN STUDY (10/2010) COSMO-CLM and WRF-ARW wind forcing over large domain; SWAN unstructured grid and different wind forcing sources Driving reanalysis data NCEP-CFSR, ~0.3 0 res. Barentsburg Teriberka Medvezhiy island Malye Karmakuly Kolguev Severnyi Amderma January 2010 Long-term run. Reason about 3 storms over Barents Sea during this period. COSMO-CLM and WRF-ARW large model domain (18 km) and meteorological stations for validation Output frequency for SWAN was 1 hour SWAN unstructured computational grid and different wind forcing zones
10 m wind velocity, m/s 10 m wind vellocity, m/s PRELIMINARY RESULTS MAIN STUDY (01/2010) 30,0 25,0 20,0 15,0 10,0 5,0 0,0 30,0 25,0 20,0 15,0 10,0 5,0 0,0 COSMO-CLM and WRF-ARW wind characteristics: Time plots and mean wind roses for 2 stations st. Malye Karmakuly 1 1 2 3 4 5 6 7 8 8 9 10111213141515161718192021222223242526272829293031 Days of January 2010 Observations WRF COSMO-CLM st. Amderma 1 1 2 3 4 5 6 7 8 8 9 10111213141515161718192021222223242526272829293031 Days of January 2010 Mean error for 6 stations ~ +/- 1.5 m/s RMS for 6 stations ~3.5 4 m/s WNW W WSW st. Malye Karmakuly NW SW WRF NNW SSW observations WNW W WSW NW SW WRF 60 N NNE 50 NE 40 30 20 ENE 10 0 ENE S SSE st. Amderma NNW SSW observations SE ESE COSMO-CLM 60 N NNE 50 NE 40 30 20 ENE 10 0 ENE S SSE SE ESE COSMO-CLM
PRELIMINARY RESULTS MAIN STUDY (01/2010) SWAN significant waves height output results for 74N, 34E point Results for different wind forcing (NCEP-CFSR, COSMO-CLM, WRF-ARW) and comparison with WaveWatch3 model and AVISO satellite data 11 10 swan_from_cfsr wavewatch3 aviso swan_from_wrf swan_from_cosmo Significant waves height, m 9 8 7 6 5 4 3 2 1 0 01.01.10 06.01.10 11.01.10 16.01.10 21.01.10 26.01.10 31.01.10
SUMMARY AND CONCLUSION The technique of coupling between regional atmospheric and ocean waves models was successfully tested and implemented using COSMO-CLM and WRF-ARW models and SWAN model; Synoptic characteristics were reproduced well, leading to minimal mean errors. However, local wind properties were reproduced significantly worse; Experiments have shown that many extreme waves characteristics were reproduced better using COSMO-CLM forcing, than WRF-ARW. OUTLOOK OF FUTURE WORK - Fine-tuning and adaptation of the models configuration to the Arctic basin; - Using the polar version of WRF-ARW model; - Using more detailed nesting and lower model resolutions; - Testing this technique for many other extreme cases, such as polar lows etc. -
Barents sea big Area big Problem
Example of western boundary condition problem
SOLab Arctic Portal http://arctic.solab.rshu.ru 1 Jan 2010 11 Jan 2010
SOLab Arctic Portal http://arctic.solab.rshu.ru
STATISTICS mean error max error min error rms correlation coefficient COSMO COSMO COSMO COSMO COSMO Station WRF -CLM WRF -CLM WRF -CLM WRF -CLM WRF -CLM Amderma -1,9-1,1 4,4 4,9-13,9-9,9 2,9 2,6 0,78 0,82 Barentsburg 1,4 0,9 14,0 12,1-7,7-8,9 3,6 3,4 0,51 0,57 Medvezhiy island Malye Karmakuly Kolguev Severnyi 1,2 1,6 13,7 12,5-10,7-16,5 3,5 3,7 0,65 0,60-1,0-2 12,8 7,6-20,3-19,8 4,6 4,2 0,50 0,62 1,7 0,8 15,1 7,0-8,9-6,7 4,9 2,3 0,79 0,86 Teriberka -1,3-2,1 6,5 7,5-11,5-13,4 3,4 3,5 0,75 0,72 All stations average 0,0-0,3 11,1 8,6-12,2-12,5 3,8 3,3 0,70 0,70
Models Characteristics
Models Characteristics