A WATER CYCLE PREDICTION SYSTEM
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1 A WATER CYCLE PREDICTION SYSTEM FOR THE GREAT LAKES AND ST. LAWRENCE RIVER V. Fortin 1, D. Durnford 2, G. Smith 1, P. Matte 1, M. Mackay 1, N. Bernier 1... and many others 1 Meteorological Research Division, ECCC 2 Meteorological Service of Canada, ECCC Coastal Hydrology and Surface Processes linked to Air/Sea Modeling: 1st community of users workshop Funchal, Madeira, Sept
2 Authors work for units of the Meteorological Research Division (MRD) or the Meteorological Service of Canada (MSC) located at the Canadian Meteorological Centre (CMC) of Environment and Climate Change Canada (ECCC) Dorval, Québec, close to Montreal's main airport CMC National Weather Forecasting Global Ocean Forecasting Hydrological Forecasting (G.L.+St.L. for now) National Support for Environmental Emergency Response (e.g. oil spills)
3 Water Cycle Prediction Forecasting the processes that impact the continental water cycle at scales of hours to days the atmosphere, and in particular precipitation the land surface (vegetated land, wetlands, glaciers, urban areas), and in particular, soil moisture, snowpack, evapotranspiration and runoff the continental water surface (lakes, rivers, ocean), and in particular water levels, water velocity, water temperature and ice cover Page 3 27 septembre 2017
4 U.A. EnVAR GEM GDPS 25 km / 10 days Surface OI GEM
5 U.A. 4DVAR GEM GDPS 25 km / 10 days Surface OI GEM RDPS 10 km / 84 h GEM
6 GEM GDPS 25 km / 10 days GEM RDPS 10 km / 84 h PR OI GEM HRDPS 2.5 km / 48 h Surface EnKF GEM
7
8 H2D2 GEM OASIS/ Gossip coupler CICE WATROUTE
9 GEM OASIS/ Gossip coupler CICE
10 Surface pressure and water surface temperature in 10-day uncoupled and coupled global weather forecasts issued UTC (Hurricanes Irma and José) Uncoupled (GEM only) Coupled (GEM+NEMO+CICE) Accepted for operational implementation on September 19, 2017 see Smith et al. (2015) QJRMS and Smith et al. (2017) MWR (submitted)
11 Impact of GEM+NEMO+CICE coupling Tropics, Geopotential Height, August August 2017 Std. dev. of error at 120 h Std. dev. of error at 850 hpa Error reduction at 850 hpa
12 Applications and domains Global 1 resolution Seasonal ensemble forecast Global 1/4 resolution 10 day forecast Gulf of St. Lawrence: 5 km 2 day forecast Great Lakes : 2 km 3.5 day forecast 1 Global Bathymetry GEM Resolution 2-km Great Lakes CICE 1/4 Global Surface currents 5-km Gulf of St. Lawrence Surface temperature
13 Needs for water cycle prediction systems at ECCC To help manage transboundary watersheds (including locks, dams, and hydropower facilities shared with the United States) To improve weather forecasting, incl. marine forecasts for large lakes and rivers To help respond to environmental emergencies (e.g. oil spills) Page septembre 2017
14 The Great Lakes and St. Lawrence River Watershed: ~1.5M km² at Quebec City, average discharge of ~ cms Ontario, Canada Quebec, Canada Lake Superior Lake Huron Montreal, QC pop: 4M Lake Michigan Toronto, ON pop: 6M Lake Ontario Chicago, IL pop: 10M Detroit, MI pop: 4M Lake Erie United States of America Page 14 September-27-17
15 Kaniatarowanenneh: the Great Waterway Duluth, MN Quebec City, QC Sault Ste-Marie, ON Chicago, IL Page 15 September-27-17
16 Kaniatarowanenneh: the Great Waterway Nantes, FR Budapest, HU Geneva, CH Barcelona, ES Page 16 September-27-17
17 The Great Lakes and St. Lawrence River Watershed: Control structures and power plants Lake Superior Sault Ste-Marie: Outlet of Lake Superior Lake Michigan Lake Huron Lake Ontario Cornwall: Outlet of Lake Ontario Lake St. Clair Lake Erie Niagara Falls: Outlet of Lake Erie Page 17 September-27-17
18 Long-term fluctuations in water levels Page septembre 2017
19 Page septembre 2017
20 Net basin supplies: component vs residual method Residual Component
21 Water Cycle Prediction System for the Great Lakes: the starting point GEM RDPS (15 km) atmospheric model ISBA land-surface model WATROUTE routing model (1km) Open ocean bulk formula for evaporation Page septembre 2017
22 Water Cycle Prediction System for the Great Lakes: the starting point GEM RDPS (15 km) atmospheric model ISBA land-surface model WATROUTE routing model (1km) P Lake R Basin NBS = P Lake - E Lake + R Basin E Lake Open ocean bulk formula for evaporation Page septembre 2017
23 Monthly NBS, June May 2009 Deacu et al. (2012), Journal of Hydrometeorology Residual NBS (observed) Component NBS (modelled)
24 Canadian Precipitation Analysis: real-time QPE for North America Optimal interpolation technique used to merge gauges and radar data with a background provided by the GEM model Fully automated quality control 6-h and 24-h accumulations 2.5 km and 10 km horizontal resolutions now available Early (T+1h) and late (T+7h) analyses Archive going back to 2002 also available online Page 30 September Mahfouf et al. (2007) A.-O. Lespinas et al. (2015) J. Hydromet. Fortin et al. (2015) J. Hydrology
25 Monthly NBS, June May 2009 Deacu et al. (2012), Journal of Hydrometeorology Residual NBS Component NBS GEM precip CaPA precip
26 Page 32 September-27-17
27 Monitoring of evaporation on the Great Lakes Network started in 2008 Page 33 September-27-17
28 Deacu et al. (2012), Journal of Hydrometeorology Improving the turbulent flux parameterization over water Eddy-covariance data used to improve evaporation prediction ORIG: Parameterization used by Reg. GEM 15 EXP1: PR T changed from 0.85 to 1 EXP2: z 0H z 0M Page 34 September-27-17
29 Monthly NBS, June May 2009 Deacu et al. (2012), Journal of Hydrometeorology Residual NBS Component NBS GEM precip CaPA precip Improved FPW
30 Water Cycle Prediction System for the Great Lakes: the starting point GEM RDPS (15 km) atmospheric model GEM LAM (15 km) atmospheric model (ISBA land-surface scheme) WATROUTE routing model (1km) Improved flux parameterization over water Page septembre 2017
31 Impact of coupling(using improved flux parameterization) on precipitation forecasts (sum of 31 daily forecasts, Dec ember 2009) milimiters
32 Monthly NBS, June May 2009 Component NBS Residual NBS GEM precip CaPA precip Improved FPW Coupled system Deacu et al. (2012), Journal of Hydrometeorology
33 Operational implementation of improved flux param. in Fall 2012 (old) (new) Page 39 September-27-17
34 Short-term fluctuations in water levels Storm surge 12 h Meters above mean water level 0.5 Page septembre
35 Water Cycle Prediction System for the Great Lakes GEM RDPS (10 km) atmospheric model GEM LAM (10 km) atmospheric model (ISBA land-surface scheme) WATROUTE routing model (1km) In production since June 2016: 2 runs per day 3.5 day forecasts Durnford et al. (2017) submitted to BAMS NEMO+CICE (2 km) ocean-ice model over the Great Lakes Page septembre 2017
36 Halloween storm, 2014: NEMO forecast +80 cm +60 cm +40 cm Forecast issued on Oct 30 th, 2014 for Chicago +20 cm Obs. Model 0h 24h 48h
37 NEMO ice and surface current forecast Z Z Page septembre 2017
38 Impact of coupling: Winter 2014 temperature forecasts Uncoupled forecast Coupled forecast Bias improved by 1C at night, 0.5C during the day Standard deviation improved by 0.1 C Page 47 September-27-17
39 Streamflow analysis cycle Z Z Page septembre 2017
40 Streamflow analysis cycle Z Z Page septembre 2017
41 Black: observed inflows at most downstream Canadian gauge Red: simulated flow at the same gauges
42 Water Cycle Prediction System for the Great Lakes and St. Lawrence GEM RDPS (10 km) atmospheric model GEM LAM (10 km) atmospheric model (ISBA land-surface scheme) WATROUTE routing model (1km) NEMO+CICE (2 km) ocean-ice model over the Great Lakes Page septembre 2017 H2D2 finite element hydrodynamic model from Montreal to the Gulf of St. Lawrence
43 Lake St. Pierre water extent and velocity, May April
44 Surface currents near Quebec City Page 53 September-27-17
45 Advantages of using coupled environmental models Accuracy gains: Capturing interactions between the atmosphere and the lakes, in particular in the presence of upwelling events and ice cover (Durnford et al., BAMS, 2017, submitted) Tuning of the NWP model based on water supply and over-lake evaporation observations (Deacu et al., J. Hydrometeor., 2012) Consistency gains: Evaporation and evapotranspiration are the same in the atmosphere, hydrology and lake models Efficiency gains: Latency: environmental predictions are available at the same time as the weather forecast Robustness: 24/7 support at almost no additional cost Maintenance: a single land-surface model to maintain Page 58 September-27-17
46 Next steps Deterministic system increase resolution of atmospheric model component from 10-km to 2.5-km increase resolution of lake model to hundreds of meters (TBD) upgrade land-surface model (SVS) and land-data assimilation system (CaLDAS) improved data assimilation of streamflow observations and lake levels (method TBD) Ensemble prediction system increase lead time from 3.5 days to 30 days! Develop a similar system for the Arctic basin Page septembre 2017
47 Hudson Bay watershed 1/12 N. Atlantic and Arctic
48 Total Hudson Bay Inflow Changes in average salinity (PSU) 2010
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