Slide 1
Hello everybody. My name is Maria Aristizabal and the title of my talk is “Validation of the HWRF-POM and HWRF-HYCOM Hurricane Forecasting Systems during Hurricane Dorian using Glider Observations”. This work was done is collaboration with Travis Miles and Scott Glenn from Rutgers University, and Hyun-Sook Kim and Avichal Mehra from National Center from Environmental Prediction.

Side 2
During the 2019 hurricane season, Hurricane Dorian made landfall in the Bahamas when it underwent rapid intensification and transitioned from a category 4 to a category 5 hurricane, becoming the strongest hurricane in record to make landfall in the Bahamas.

Slide 3:
As we all know, Hurricane Dorian caused a devastation in human lives and property of historic proportions. This kind of very destructive storms will happen again in the future  and this is why is so important to have an accurate hurricane forecast. 

Slide 4:
During Dorian, none of the hurricane forecasting models captured the intensity evolution five days prior to Dorian reaching its maximum strength. In the last three decades there has been significant progress reducing the error in the storm track in the operational models, however the error reduction in the intensity has been marginal. It has been demonstrated that the sea surface thermal structure plays a very important role in the intensification of storms and that an accurate representation of the upper ocean processes that affect air-sea heat fluxes in ocean-atmosphere coupled models is necessary for an accurate intensity forecast. 

Slide 5:
So our main objective in this work was evaluate different upper ocean metrics: temperature and salinity in the mixed layer, ocean heat content and average temperature in the top 100 meters, that are relevant to air-sea heat fluxes, and therefore to hurricane intensity, in three different hurricane forecasting models,  using temperature and salinity data from a fleet of autonomous underwater gliders, dedicated to hurricane research and operations.

Slides 6:
During the 2019 hurricane season, a fleet of underwater gliders was deployed in the Caribbean Sea, Gulf of Mexico, the South and Middle Atlantic Bight as part of the NOAA hurricane underwater glider project. During the passage of Hurricane Dorian through the Caribbean, there were six underwater gliders deployed in the region north and south of Puerto Rico, shown here in the map to the right. The glider data is publicly available through the IOOS glider data assembly center (DAC) (https://data.ioos.us/gliders/erddap)

Slide 7:
The three hurricane models that we evaluated are: 
- HWRF2019 coupled to the Princeton Ocean Model (POM). This was one of the NOAA hurricane forecasting systems during 2019. The Initial conditions come from climatology and a feature-based model 
- The second model was HWRF2020 also coupled to POM
- and the third model is HWRF2020 coupled to HYCOM 
- In addition, we contrasted all our findings with the results from the Global Ocean Forecasting System GOFS 3.1. This is the global navy operational ocean model and implements a 3D-Var data assimilation method. It is publicly available in the hycom thredds server

Slide 8:
We used three different surface ocean metrics to evaluate the models. 
The first metric is the temperature in the surface mixed layer. The surface mixed layer is the portion of the water column that presents very small changes in vertical stratification. We used two different criteria to define it: the temperature criteria, in which the temperature differences between different depths and 10 m is less than to 0.2 degrees, and the density criteria, where the density differences are less than to 0.125 kg/m^3

Slide 9:
Our second metric is the ocean heat content. OHC is defined as the depth integrated heat content of the water column between the depth of the 26 degrees isotherm and the surface. The OHC is a metric that has been shown to be correlated with the intensification of storms in the open ocean

Slide 10:
Our last metric is the T100 that is the depth average temperature in the top 100 meters. This metric roughly estimates the resulting SST after the passage of a hurricane due to vertical mixing processes in the ocean interior

Slide 11:
One of the tools that we used to visualize a model’s performance is the normalized Taylor diagram. A Taylor diagram is polar plot in which the radial axis is the normalized standard deviation and the angle is the correlation between the model and the observations. The closer the model estimate in this diagram is to the observations, located on the 1 on the standard deviation axis, the better the model skill 

Slide 12:
Here we see comparisons of temperature and salinity between the glider that was closer to Dorian’s track and the different models.
The top two panel shows the temperature and salinity from the observations and the rest of the panels show the corresponding glider transect from the different ocean models. We see that overall the temperate and salinity agree well with the glider observations. We also see that in the observations and the models, the mixed layer depth based on the temp criteria, shown in red, is deeper than the mixed layer depth based on the density criteria. The reason for this discrepancy is the presence of the so called barrier layer, a relatively fresh surface layer that characterized the Caribbean Sea and is produced by the spreading of the Amazon and Orinoco River Plumes. As a consequence, salinity controls stratification at the surface in the Caribbean Sea. So for this analysis, we define the mixed layer depth based on the density criteria

Slide 13:
Despite the general agreement in temperature and salinity, there are important differences in the surface metrics between the observations and the models.
To quantify the models skill, we obtained the normalized Taylor diagram for the different surface metrics that were introduced, plus the temperature and salinity over the full water column, for all the different models. Remember that in the diagram, the 1 on the normalized standard deviation represents all the available observations. The closer the points are to the observations, the better the model’s skill. There is a lot of information in the diagram, so to help us digest this information, I first grouped all the skills by metric so every oval is showing a different metric. We see that all the models have a very god skill in the temperature (orange oval) over the full water column. The salinity over the full water column (shown as the dark green oval)  also has a good skill. However, the model’s skill considerably deteriorates for the upper ocean metrics that are relevant for the air-sea heat fluxes such temp ML (in purple), OHC (in red) and the depth average temp in the top 100 meters (in blue)

Slide 14:
When we take exactly the same Taylor diagram, but now we group the different skills by models, we see that HWRF2020-HYCOM is the hurricane forecasting model with the best skill for the surface ocean metrics and its skill is close to the skill of data asimilitave GOFS 3.1. But the other two hurricane models coupled to POM have a poor skill for these metrics

Slide 15:
The time series of the mixed layer temperature (the top panel in the figure) shows that all the models tend to be colder than the observations (in blue), in particular, HWRF2109-POM initialized from climatology, is significantly	colder. This result if confirmed by the mean bias for the mixed layer temperature between all the observation and the models.   

Slide 16:
There is also a similar patter for the OHC (second panel in the figure).  All the models have a deficit in the OHC, with HWRF2019-POM initialized with climatology with a 22% deficit while GOFS 3.1 has only a deficit 0f 3.6%

Slide 17:
Conclusions

Slide 18:
Future work 

Thanks for listening and I am happy to take any questions