1. Validating NAVO’s Navy Coastal Ocean Model
LT Giancarlo Waghelstein
OC-3570
Summer 2009 Cruise

2. Validating NAVO’s Navy Coastal Ocean Model (NCOM)
Introduction
Data Collection
Results
Conclusion
Recommendations

3. All information taken from CNMOC INST 3140
Introduction
What is the NAVO G-Navy Ocean Coastal Model?
Physics based fluid dynamics ocean model that resolves mesoscale features
Temp, salinity, currents, elevation
Derives 3D sound speed for ASW, object drift track for MIW/environmental concerns
Uses statistics, satellite data, and physics to interpolate ocean structure between sparse insitu observations
Up to 42 vertical layers
1/8˚ horizontal resolution
Higher resolution in AOIs
FNMOC NOGAPS atmospheric forcing (winds, heat fields)
Mention oil spills
Classified resolution – if I told you I’d have to kill you
All information taken from CNMOC INST 3140

4. Data Collection
28 CTD casts along 2 tracks (SC & SNB runs) were used to compare salinity & temperature depth profiles from NCOM’s expected values (56 depth profiles for those keeping track)
CTD collection times taken throughout the course of the cruise had to be matched as closely as possible to spatially and temporally model runs (12 hr τ)
OPOC Results.xlsx
TIP: Learn to use for loops in Matlab. They are your friends. No seriously. Don’t look at me like I don’t know what I’m talking about. Just do it.

5. CTD Tracks

6. Results: Salinity (Whiskey Tango Foxtrot!)
Green – CTD Cast
Blue – Model Forecast
Station 5-8 show a significant under forecast of salinity temperatures. (Maybe use a correction in model output? Seems to be a fairly consistent under-forecast of expected salinity values)

7. Results: Salinity (Whiskey Tango Foxtrot!)
Green – CTD Cast
Blue – Model Forecast
Fairly consistent over all 4 stations so we don’t expect standard deviation between Observed-Model difference
Is it a function of space? Or a function of time? Need to find which model runs stations 5-8 came from

8. Results: Salinity Green – CTD Cast Blue – Model Forecast
Stations show a much smaller “over forecast” of expected salinity values

9. Results: Salinity Green – CTD Cast Blue – Model Forecast
Note the over forecast in the thermocline layer (0-75m)
Once again- this “better” data could be due to the model’s ability to forecast better in the relative op area or perhaps the model run at the given time of the observations was more accurate due to oparea mirroring BC better or insitu data become available in later runs to populate model background field.

10. Results: Temperature Red – CTD Cast Blue – Model Forecast
Looks like fairly good temperature predictions with these two examples. Barely any MLD? Summer time heating and light winds present very little chance for turbulence to increase MLD

11. Results: Temperature Red – CTD Cast Blue – Model Forecast
Still “looks” like the model might be better at forecasting T but at consider the x variation in the two profiles. Salinity changes between roughly PSU, while Temp variations range from 5 – 22 Deg C

12. Results: Temperature (Ruh Roh Rorge!)
Red – CTD Cast
Blue – Model Forecast
What happened? We see a large over forecast error in expected vs observed values. Fairly good at predicting depth of MLD throughout each scenario whether its 0-15m

13. Results: Temperature (Ruh Roh Rorge!)
Red – CTD Cast
Blue – Model Forecast
Over-forecast situation seems to permeate from surface to just below seasonal thermocline

14. Conclusions: Show Me the Money!
Entire Water Column
Thermocline
Taking the averaged difference between the 28 stations’ observed and expected values and plotted across depth shows where the model over the course of 3 days was most accurate.
Positive & large values in the shallower depths indicate the model under-forecasted (which we noted earlier). At deeper depths ( m) we see improvement in the forecast. Depths lower than 1000m can be thrown away due to much fewer collection points than >1000m

15. Conclusions Entire Water column thermocline
Large over-forecast in expected values throughout the thermocline leads to large negative values in first graph.
Look how close the model performed at deeper depths!

16. Conclusions Standard Deviation
Salinity
Temperature
No surprises here- standard deviations show that once again the model performed better at depths between m

17. Conclusions Spatial vs. Temporal
Better Model Salinity Results
Over-forecast TC
Better Model Temp Results
Model Under-forecasted Salinity
Model Over-forecast Temp

18. Theories
Model needs improvement in predicting seasonal thermocline temp
Currently too warm
Model needs improvement in predicting seasonal thermocline salinity
Currently not saline enough
Data
More obs
Better data in absence of obs
Use more accurate climo instead??
Better BC?
Bathy