Use of sea level observations in DMIs storm surge model Jacob L. Høyer, Weiwei Fu, Kristine S. Madsen & Lars Jonasson Center for Ocean and Ice, Danish Meteorological Institute

Outline DMI and Storm surge modelling Reanalysis experiments Blended satellite and tide gauge observations Data assimilation experiments Conclusions

Operational storm surge modelling and warning DMI created after storm surge in 1872 DMI holds the national responsibility to forecast and issue warnings of storm surges. 18/24 hours warning Hourly updates Yearly user meetings The service is based upon: Tide gauge observations Models

Tide gauge stations Dense network of stations DMI Operate 15 tide gauge stations NRT storm surge modelling and warnings Long term sea level changes (10 with > 100 years record) Extensive collaboration within BOOS and NOOS

Model Information Two-way nested, free surface, hydrostatic three-dimensional (3D) circulation model HIROBM-BOOS (HBM). A staggered Arakawa C grid is applied on a horizontally spherical coordinate with a resolution of 6 nm. There are 50 vertical levels in z-coordinate. In the Danish Water (boxed), the horizontal resolution is increased to 1 nm. A detailed description of the model can be found in Berg and Poulsen (2011). The meteorological forcing is the High Resolution Limited Area Model (HIRLAM) - a numerical short-range weather forecasting system developed by the international HIRLAM Programme ( 3D hydrodynamic model HBM Spatial resolution: 6/1 nm 4 forecasts a day, 5 days ahead Hourly 3-D fields, 10 minutes values extracted for 133 stations.

Motivation: Compare wind forcing from HIRLAM (NWP) and HIRHAM (reanalysis) and examine if the assimilated winds in HIRHAM improve the storm surge forecasts. Method: Five events are compared between 2002 and o Same surges on the outer boundary o Same tides o Compare the modeled sea level to tide gauge at 8 locations in the Norths Sea and the Baltic Sea Reanalysis simulations Case 1: Case 2: – Case 3: Case 4: Case 5: –

Minor differences in modelled sea level Modelled sea level has too large standard deviation when forced with HIRHAM (probably due to the stronger winds). Reason for the very similar results: Tides and surges are same in both simulation which probably is the reason for the similar results Observations are located close to the coast where the model coast line has a large impact Reanalysis results Example: Sea level at HvideSandeKyst (Danish East coast, Jan 2005)

Statistical blending Method Multivariate regression where data from 17 tide gauge stations are regressed onto the satellite altimetry observations (Høyer et al., 2002, Madsen & Høyer, 2007) Tide gauges selected based on the correlation with satellite observations Allows real-time sea level estimation in points where satellite data are available Sea level estimate independent upon ocean and atm. Model performance Assumes stationarity New version for eSurge uses coastal altimetry observations

Coastal Altimetry observations Jason-2: Pistach 20 Hz (2008-present) Envisat 18 Hz observations from ALES (see poster for validation ) Spike removal and along track smoothing Data used within 3 km “Flat” “Steep”

Model verification Initial regression results from Jason-2 data Surge part only Tide gauge weights determined for every 20 Hz time series. Fitting errorHindcast skill

Output Hourly gridded fields Surge + tidal components 1 km spatial resolution 5 test cases of historical events NRT fields operational Netcdf files Available to users ~1st Feb, 2014

Data assimilation experiments Assimilation experiments are carried out for the storm surge event in January Old version of blended tide gauge and satellite data is tested. Results are compared with independent tide gauge data with-held from the assimilation experiment.

Tide Gauge Station in the North and Baltic Sea. Independent tide gauges are marked with red dots. Hiromb-BOOS model HIRLAM NWP forcing Assimilation method: Ensemble OI 5 days event Data assimilation test case

Example of the blended water level product on Jan 8, Only the surge part is shown above. Example, blended sea level product, Jan 2005

Case 1: Jan 5-11, 2005

Reanalysis: Not much impact using different wind forcings Assimilation: Test case show improvements in the data assimilation test cases New blended product ready, based upon coastal altimetry high resolution products Blending method could be applied to other semi-enclosed seas like Adriatic Conclusions