OPERATIONAL OCEAN FORECAST FOR THE NORTHERN IBERIA PENINSULA (OOF-NIP)

Transcription

1 OPERATIONAL OCEAN FORECAST FOR THE NORTHERN IBERIA PENINSULA (OOF-NIP) Investigación e Predición Numérica - MeteoGalicia - Consellería de Medio Ambiente e Ordenación do Territorio Xunta de Galicia Introduction The Regional Ocean Modelling System (ROMS) developed by Rutgers University has been used to set-up the Operational Ocean Forecast for the Northern Iberia Peninsula (OOF-NIP) which is being used as an additional operational ocean forecast system at MeteoGalicia. In ROMS model the primitive equations governing ocean dynamics and thermodynamics are discretized onto an Arakawa C-grid to obtain numerical solutions (Haidvogel et al., 2000; Shchepetkin and McWilliams, 2005). ROMS uses orthogonal curvilinear coordinates in the horizontal and stretched terrain following sigma coordinates in the vertical (Haidvogel et al., 2000; Song et al., 1994). For the purpose of computational efficiency the code utilises the natural time scale separation of barotropic and baroclinic processes by employing a mode-splitting algorithm which solves the vertically-integrated barotropic momentum equations using a much smaller time step (Mason et al., 2010). A specially designed fast-time-averaging procedure prevents aliasing of processes unresolved by the longer baroclinic time step and, at the same time, maintains all necessary conservation properties (Shchepetkin and McWilliams, 2005). Model setup The domain of the Northern Iberia ocean model based on ROMS extends from 14ºW to 4.5ºW in the west-east direction and from 38ºN to 46ºN in the south-north direction (Figure 1). The horizontal resolution of the Northern Iberia ocean model is 0.02 (approximately 2 km) and it has 41 sigma levels in the vertical. The vertical stretching parameters are chosen in such a way that the vertical resolution is highest in the upper part of the ocean. The lateral boundaries are treated as open where the tracer and momentum fields are relaxed to Mercator global ocean analysis and forecast system fields with radiation condition with nudging (Marchesiello et al., 2001). For the barotropic mode the normal velocity component uses a Flathertype condition (Flather 1976) based on radiation and the prescription of characteristic variables (Riemann invariants: Blayo and Debreu 2005). A Chapman boundary condition is applied to the free surface. In order to ensure a more realistic bathymetry near the coast the final topography (Figure 1) of the model was merged with the general bathymetric chart of the oceans (GEBCO) with 30 arcsecond resolution and cartography from two governmental institutions: Instituto Hidrográfico de la Marina, Cádiz (IHC) and from Instituto Hidrográfico Português (IH). Tides are included in the OOF-NIP with eleven tidal constituents (M2, S2, N2, K2, K1, O1, P1, Q1, mf, mm) from the OSU TOPEX/Poseidon Global Inverse Solution version 7.2. Atmospheric forcing Meteorological fields are extracted from the 12km horizontal resolution operational meteorological forecast system (covering the whole Iberian Peninsula) implemented in MeteoGalicia through the use of the bulk flux algorithm (Fairall et al. 2004). MeteoGalicia runs the WRF model twice a day (00 and 12 UTC) on three different resolution domains (36, 12 and 4km) using as a boundary condition the solution of the Global Forecast System (GFS) model.

2 Figure 1. Map of model domain used to set-up Northern Iberia ocean model ROMS. In the right panel the four sources of the ocean topography: GEBCO, Instituto Hidrográfico de la Marina, Cádiz (IHC) and Instituto Hidrográfico Português (IH) with 0.5 and 1 mile of resolution. River runoffs Flow measurements at the mouth of the rivers are very difficult to acquire so the alternative is to use a hydrological tool which will give flow forecasts for the coming days. The Soil water Assessment Tool (SWAT model) developed by Agricultural Research Service and Texas A&M University is the most commonly used. Along shoreline the daily average flow and temperature from the main rivers for the Cantabria, Galicia and Portuguese regions are introduced in the Northern Iberia Ocean model. From north to south: Sella, Nallon, Navia, Eo, Eume, Mandeo, Mero, Xallas, Tambre, Ulla, Umia, Lerez, Verdugo, Minho, Lima, Douro, Mondego and Tagus, (Figure 2). River Lima, Douro, Mondego, and Tagus daily flow measures are obtained from the Sistema Nacional de Informação de Recursos Hídricos (SNIRH - Portuguese Information System for Water Resources). Downscalling of Mercator global ocean analysis and forecast For acquiring satisfactory initial and boundaries conditions the interpolation process was divided into two stages: 1. For a given time a horizontal stage where the parent (Mercator global ocean analysis and forecast) variables 2D and 3D are interpolated to the horizontal coordinates (longitude, latitude) of the new child domain (Northern Iberia Ocean model with

3 0.02 of resolution). 2. The vertical stage for each 3D variable a vertical interpolation transforms the data from the parent (Mercator global ocean analysis and forecast) z coordinates to the child (Northern Iberia Ocean model with 0.02 of resolution) sigma coordinates. Depth mismatches between child and parent grids may be significant in these regions and become a problem when they lie along open boundaries, because volume conservation is difficult to enforce (Mason et al., 2010). In order to prevent this problem a consistency check is made between the sea surface height and the ocean current velocities. Figure 2. Rivers implemented in Northern Iberia computational domain (North to South: Sella, Nallon, Navia, Eo, Eume, Mandeo, Mero, Xallas, Tambre, Ulla, Umia, Lerez, Verdugo, Minho, Lima, Douro, Mondego and Tagus). Data frame based on WGS84 datum. Operational implementation OOF-NIP is a high-resolution ocean forecasting system implemented over the Ocean Atlantic part of the Northern Iberia Peninsula it is run operationally on a daily basis producing 24 hour hind-cast and 96 hour forecasts providing a full 3D representation of the ocean (temperature, salinity, currents and sea surface height). This system is forced using WRF, SWAT and Mercator global ocean forecast fields.

4 Atmospheric forcing from WRF model - shortwave radiation revision In some particular cases we detected significant differences in the forecast of the Sea Surface Temperature (SST) forecast of the OOF-NIP when compared against in-situ and satellite observations. To improve the SST forecast we use observed satellite short-wave radiation form OSISAF on the hind-cast day of the simulation. Since the simulations are continued from the end of the hind-cast day the introduced BIAS it is much smaller and this has greatly helped to improve the SST prediction on the OOF-NIP model. Products publication OOF-NIP outputs (real-time and archived products) are freely available in a dedicated thredds server for research, educational and commercial use. Available data: 1. Model output interpolated to fixed depth layers. NetCDF files with ocean variables at fixed depths (1-hourly output). This variables are derived from the raw output from OOF-NIP model. 2. Model raw output. NetCDF files with original output (900-seconds window output) from OOF-NIP model. Figure 3. MeteoGalicia thredds server.

5 Results In this section we show the results from OOF-NIP model compared with four different sources of observations: data measured at moored stations belonging to the Puertos del Estado buoy network (Figure 4), ODYSSEA (ODYSSEA Sea Surface Temperature Analysis), Argo and Radar HF. Figura 4. Map with the locations of the ocean stations. Table 1. Statistical parameters BIAS, MAE and RMSE (time period between 7th of May 2014 to 25th of March 2015). OOF-NIP SILLEIRO OOF-NIP BARES OOF-NIP VILLANO TEMP SAL U VEL V VEL TEMP SAL TEMP SAL BIAS 0,70-0,01-0,01-0,01 0,54 0,05 0,66 0,04 MAE 0,73 0,09 0,17 0,18 0,66 0,08 0,78 0,06 RMSE 0,89 0,12 0,23 0,24 0,81 0,11 1,01 0,09

6 Ocean temperature and salinity at 3 meters. Sea surface height Figures 5 to 7 show the time series from the 7th of May 2014 to 25th of March 2015 of the OOF-NIP ocean temperature and salinity at 3 meters depth compared with Silleiro, Bares and Villano-Sisargas measured data. The OOF-NIP was quite succefull in capture the decrease-increase in temperature and salinity that occurred during this past 9 months. Although with some error during the rainy months the OOF-NIP was also able to reproduce the sudden salinity drops associated with river plumes. Evaluating the entire period, Table 1, the average magnitude of the difference between the forecast and observations, the mean absolute error and the root mean square error show that OOFNIP was able to produce lower scores when looking at salinity. There were also occasions when the OOF-NIP model show a tendency to overestimate the temperature at 3 meters depth. The bias and rmse observed in temperature were greather than 0.5 ºC but less than 1ºC. As can be seen in Figure 8 the sea surface height forecast obtained by the OOF-NIP model provides a good adjustment to the data observed by the tide gauge Vigo2 both in terms of phase and amplitude. Figure 5. Time series of the temperature and salinity at 3m depth time period between 7th of May 2014 to 25th of March 2015 for Silleiro Bouy (red line) and OOF-NIP model (green line)

7 Figure 6. Time series of the temperature and salinity at 3m depth time period between 7th of May 2014 to 25th of March 2015 for Estaca de Bares Bouy (red line) and OOF-NIP model (green line) Figure 7. Time series of the temperature and salinity at 3m depth time period between 7th of May 2014 to 25th of March 2015 for Villano-Sisargas Bouy (red line) and OOF-NIP model (green line)

8 Figure 8. Observed sea surface height in Vigo2 tide gauge (read line) and sea surface height forecast by OOF-NIP model (green line). Sea surface temperature Figures 9a and 9b shows the monthly average (3 month average) of the sea surface temperature forecast from the OOF-NIP model (above figure) compared with the analysed sea surface temperature from ODYSSEA (middle figure) and the estimated error standard deviation of analysed sea surface temperature (below figure). Figure 9a shows the monthly average from November 2014 to February 2015 and Figure 9b from June to September The most visible differences in the sea surface temperature occur along the north and south boundaries. The present results show slight tendency of OOF-NIP model to overestimate the sea surface temperature in the southern and underestimate over the northern probably influenced by the radiation lateral boundary condition with nudging. Despite not being able to state with total certainty because of the estimated error standard deviation of analysed sea surface temperature near the shore the OOF-NIP model was able to reasonable forecast the sea surface temperature. The spatial structures present in the satellite sea surface temperature observations are also present in OOF-NIP sea surface temperature forecast.

9 Figure 9a. Three month (November 2014 to February 2015) horizontal average comparison between the sea surface temperature forecast from the OOF-NIP model (above figure) and the analysed sea surface temperature from ODYSSEA (middle figure). In the below figure the estimated error standard deviation of analysed sea surface temperature.

10 Figure 9b.Three month (June to September 2015) horizontal average comparison between the sea surface temperature forecast from the OOF-NIP model (above figure) and the analysed sea surface temperature from ODYSSEA (middle figure). In the below figure the estimated error standard deviation of analysed sea surface temperature.

11 Argo temperature and salinity vertical profiles Argo profiles located within the model domain were downloaded on a daily basis from the IFREMER FTP site. On a daily basis the Argo temperature and salinity profiles were extracted and compared with model temperature and salinity profiles from the same location and closest timestamp. Float locations (Figures 10, 11) and profile plots (Figures 10a to 10d, 11a to 11d) as well as quantitative model skill metrics for each profile (bias, mae and RMSE) were calculated to evaluate the performance of the model. The temperature error increase with depth at all latitudes. Average errors below 1000 m are small less than 1.0ºC for the 40 S - 44 N region. The errors in salinity are smaller than 0.2. Figure 10. Float Argo Trajectory for platform (Float Cycles 102, 107, 122 and 122).

12 Figure 10a. Temperature and salinity profiles from ARGO at 42.11ºN and -9.89ºW compared to the OOF-NIP model on August 9, (Salinity is flagged as bad) Temperature profile: BIAS = -0.81; MAE = 0.92; RMSE = 1.22 Figure 10b. Temperature and salinity profiles from ARGO at 41.62ºN and ºW compared to the OOF-NIP model on September 28, (Salinity is flagged as bad) Temperature profile: BIAS = -0.85; MAE = 1.2; RMSE = 1.7

13 Figure 10c. Temperature and salinity profiles from ARGO at 41.11ºN and -9.60ºW compared to the OOF-NIP model on November 17, (Salinity is flagged as bad) Temperature profile: BIAS = -0.43; MAE = 0.89; RMSE = 1.16 Figure 10d. Temperature and salinity profiles from ARGO at 42.10ºN and ºW compared to the OOF-NIP model on February 25, (Salinity is flagged as bad) Temperature profile: BIAS = -0.90; MAE = 0.95; RMSE = 1.35

14 Figure 11. Float Argo Trajectory for platform (Float Cycles 10, 14, 22 and 26). Figure 11a. Temperature and salinity profiles from ARGO at 40.89ºN and -9.76ºW compared to the OOF-NIP model on January 25, Temperature profile: BIAS = 0.08; MAE = 0.62; RMSE = 0.87 Salinity profile: BIAS = 0.06; MAE = 0.14; RMSE = 0.17

15 Figure 11b. Temperature and salinity profiles from ARGO at 41.19ºN and ºW compared to the OOF-NIP model on December 16, Temperature profile: BIAS = -0.43; MAE = 0.68; RMSE = 1.01 Salinity profile: BIAS = -0.11; MAE = 0.13; RMSE = 0.18 Figure 11c. Temperature and salinity profiles from ARGO at 41.58ºN and ºW compared to the OOF-NIP model on September 27, Temperature profile: BIAS = -0.74; MAE = 0.78; RMSE = 1.03 Salinity profile: BIAS = -0.09; MAE = 0.14; RMSE = 0.18

16 Figure 11d. Temperature and salinity profiles from ARGO at 41.35ºN and ºW compared to the OOF-NIP model on August 18, Temperature profile: BIAS = -0.57; MAE = 0.77; RMSE = 1.19 Salinity profile: BIAS = -0.05; MAE = 0.13; RMSE = 0.17 Ocean currents at 3 meters Figures 12 to 14 show the time series from 2th of July 2014 to 3th September 2014 and Figures 15 to 16 show the time series from 7th of January 2015 to 18th March 2015 of the ocean current at 3 meters depth compared with Silleiro, Bares and Villano-Sisargas measured data. The OOF-NIP in the first period from 2th of July 2014 to 3th September 2014 show a tendency to overestimate in Silleiro, Bares and Villano-Sisargas the intensity of the ocean current at 3 meters depth. On days 11 and 27 of August 2014 the OOF-NIP was almost able to reproduce the observed increase in the ocean current at 3 meters in Villano-Sisargas buoy. In the second period from 7th of January 2015 to 18th March 2015 the OOF-NIP underestimate the intensity of the ocean current at 3 meters depth in Villano-Sisargas particularly on the last two months. Figure 17 show the time series from the 7th of May 2014 to 25th of March 2015 of the OOF-NIP model ocean current North/South component (v) and East/West component (u) at 3 meters depth compared with Silleiro measured data. The OOF-NIP was successful in capture the decrease-increase that occurred during this past 9 months. When evaluating the entire period, Table 1, the average magnitude of the difference between the forecast and observations, the mean absolute error and the root mean square error show that OOF-NIP was able to produce lower scores. From the 7th of May 2014 to 25th of March 2015 the current rose at 3 meters depth (Figure 18) show that the frequency of the ocean currents observed in Silleiro buoy is between 180º and 240º. The OOF-NIP model show good agreement since the frequency of the ocean currents are between 200º and 270º.

17 Silleiro Buoy Puertos del Estado July 2, 2014 to September 3, 2014 Figure 12a. Time series of the ocean current at 3m depth (velocity sticks, speed and east [blue line] north [green line] velocity) time period between 2th of July to 3th of September 2014 for Silleiro Bouy. Figure 12b. Time series of the ocean current at 3m depth (velocity sticks, speed and east [blue line] north [green line] velocity) time period between 2th of July to 3th of September 2014 for OOF-NIP model.

18 Estaca de Bares Buoy Puertos del Estado July 2, 2014 to September 3, 2014 Figure 13a. Time series of the ocean current at 3m depth (velocity sticks, speed and east [blue line] north [green line] velocity) time period between 2th of July to 3th of September 2014 for Estaca de Bares Bouy. Figure 13b. Time series of the ocean current at 3m depth (velocity sticks, speed and east [blue line] north [green line] velocity) time period between 2th of July to 3th of September 2014 for OOF-NIP model.

19 Villano-Sisargas Buoy Puertos del Estado July 2, 2014 to September 3, 2014 Figure 14a. Time series of the ocean current at 3m depth (velocity sticks, speed and east [blue line] north [green line] velocity) time period between 2th of July to 3th of September 2014 for Villano-Sisargas Bouy. Figure 14b. Time series of the ocean current at 3m depth (velocity sticks, speed and east [blue line] north [green line] velocity) time period between 2th of July to 3th of September 2014 for OOF-NIP model.

20 Silleiro Buoy Puertos del Estado January 7, 2015 to March 18, 2015 Figure 15a. Time series of the ocean current at 3m depth (velocity sticks, speed and east [blue line] north [green line] velocity) time period between 7th of January to 18th of March 2015 for Silleiro Bouy. Figure 15b. Time series of the ocean current at 3m depth (velocity sticks, speed and east [blue line] north [green line] velocity) time period between 7th of January to 18th of March 2015 for OOF-NIP model.

21 Villano-Sisargas Buoy Puertos del Estado January 7, 2015 to March 18, 2015 Figure 16a. Time series of the ocean current at 3m depth (velocity sticks, speed and east [blue line] north [green line] velocity) time period between 7th of January to 18th of March 2015 for Villano-Sisargas Bouy. Figure 16b. Time series of the ocean current at 3m depth (velocity sticks, speed and east [blue line] north [green line] velocity) time period between 7th of January to 18th of March 2015 for OOF-NIP model.

22 Figure 17. Time series of the ocean current North/South component (v) and East/West component (u) at 3m depth time period between 7th of May 2014 to 25th of March 2015 for Silleiro Bouy (red line) and OOF-NIP model (green line). Figure 18. Silleiro buoy and OOF-NIP model current rose at 3 meters depth (time period from 7th of May 2014 to 25th of March 2015).

23 Sea surface currents. HF radar Real-time monitoring systems such as HF radar were used for validation of the real-time prediction system. Daily averaged OFF-NIP model current fields for two typical observed patterns East to West above Figure 17 and North to South below Figure 17 were compared with HF radar currents. The comparison show good qualitative agreement to HF radar current near the coast, however offshore the OFF-NIP model exhibit smaller ocean current magnitude than the radar HF. Figure 17. Radar HF (left figure) and OOP-NIP model (right figure) surface ocean current direction averaged for 14th December 2014 (above figure) and 21th January 2015 (below image).

24 Conclusions An ocean modelling system was developed for the Northern Iberia Peninsula based on the ROMS model. Forced with meteorological, hydrological and tidal data. A statistical study was performed for 10 months (7th of May 2014 to 25th of March 2015) comparing OOF-NIP model against moored stations belonging to the Puertos del Estado buoy network, ODYSSEA sea surface temperature analysis, Argo buoys and radar HF. The OOF-NIP model have considerable good skill at reproducing the temperature and salinity near the coast. The forecast of the sea surface temperature is coherent although exhibiting a tendency to overestimate in the southern boundary. The vertical structure of ocean temperature and salinity of the OOF-NIP model is in agreement with the observations. For example the errors in salinity are smaller than 0.2 psu and in temperature is always less than 1ºC. In general the OOF-NIP model slight overestimate the ocean currents at 3 meters depth. Finally, it is important to mention that the aim of this report is to give an overview of the chosen operational setup and present some results. References Blayo E., Debreu L Revisiting open boundary conditions from the point of view of characteristic variables. Ocean Model. 9: Flather, R.A A tidal model of the north-west European continental shelf. Mem. Soc. R. Sci. Liège 6: Fairall C.W., Bradley E.F, Rogers D.P., Edson J.B., Young G.S Bulk parameterization of air sea fluxes for tropical ocean global atmosphere Coupled Ocean Atmosphere Response Experiment. J. Geophys. Res. 101: Haidvogel, D.B., Beckman A Numerical Ocean Circulation Modeling. Imperial College Press, 318 pp. Marchesiello P., McWilliams J., Shchepetkin A Open boundary conditions for long-term integration of regional oceanic models. Ocean Modelling, 3, Mason E., Molemaker J., Shchepetkin A., Colas F., McWilliams J., Sangrà P Procedures for offline grid nesting in regional ocean models. Ocean Modelling. Elsevier. 35:1-15 Shchepetkin A.F., McWilliams J.C The Regional Ocean Modeling System: A split-explicit, free-surface, topography following coordinates ocean model. Ocean Model. 9: Song Y., Haidvogel D.B A semi-implicit ocean circulation model using a generalized topography-following coordinate system. J. Comp. Phys. 115: