Sigma coordinates.

Sigma coordinates are widely used in oceanography due to the differences in the elevation of the seabed.  The elevation can vary from several meters deep near the coast and several kilo-meters in the deep basins. The regular z coordinates are mostly compatible only with uniform basins.

z is the vertical length from water surface to any point. H is the depth of the total water column. η is the sea surface height that is affected by the tidal force. Sigma varies from 0 to -1 where -1 is at the  bottom earth surface where z=-H and 0 is at the top of water surface z=η.

From Wikipedia

 In sigma coordinates system the number of vertical levels in the water column is the same everywhere in the domain though the depth of water column is different from place to place. The Navier Stokes equation will then be represented in sigma coordinates.

Zonal and meridional

They are used to describe the directions on earth

Zonal refers to the direction from east to west

Meridional refers to the directions from North to South of earth

Zonal temperature or meridional wind flows are some commonly used terms

Reference:

http://en.wikipedia.org/wiki/Zonal_and_meridional

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Arctic dipole anomaly

There is a pressure difference between the two regions of the Arctic. High pressure on the North American region and low pressure on the Eurasian region give rice to this Arctic dipole. Arctic dipole leads to drive more Southern winds in to the arctic ocean which affects the melting of sea ice. This said to be the reason behind the recorded sea ice minima during the past few years.

Reference
http://en.wikipedia.org/wiki/Arctic_dipole_anomaly

Ice albedo feedback

You can read about Ice albedo at my previous post. In simple terms ice albedo refers to how much light is being reflected by the ice. Ice albedo feedback however refers to a positive feedback climate process.

A positive feedback climate process is a  process in which the outcome is being amplified by a feedback. In this scenario when ice albedo decreases ice absorb large amount of radiation and it starts to melt. when it melts ice albedo is further reduced and it starts a vicious cycle that increase the melting of sea ice

Reference:
http://en.wikipedia.org/wiki/Ice-albedo_feedback

Ice Albedo

The first new word I found is Ice albedo. This comes from the two words Ice and albedo.

Albedo is the reflectivity. In simple terms it is the measure of how white something is. It is also a measure of how much light does something absorbs. The albedo scale ranges from 0 to 1, where 0 being perfect absorption (pitch black) and 1 being perfect reflection (As white as white can be).

Talking about ice albedo it ranges from 0.5 to 0.7 which is quite high reflectivity. On the other hand ocean albedo is around 0.06 in other words ocean absorbs most of the visible light. Snow has the albedo of 0.9 therefore it acts as a protecting layer over the sea ice.

When snow melts it creates melting ponds that has the albedo of 0.2 to 0.4 in that case albedo drops to 0.75 when the melting pond deepens the albedo can drop to 0.15

You can do further reading at
http://nsidc.org/cryosphere/seaice/processes/albedo.html

Where to start?

As I've mentioned before I've got a title. Yey!!

"Incorporating data assimilation in numerically predicting sea ice to assist navigation in the Arctic Ocean"

I also need to take classes as well. This semester I'm just taking three classes but until add drop period I'll be taking nearly 5 classes. I hope I'll be able to figure-out the classes I'm taking soon enough so that I can focus on my real research.

Right now I'm taking Marine Environment modeling class, also I take propulsion engines, Coastal environment studies, Urban engineering. That's a nice mix. The first one was suppose to be in English but the lecturer decided to do it in Japanese, Therefore I might want to drop the class later. My favorite out of all the classes is propulsion engines. It's about space propulsion engines. It's damn interesting.

 Getting back to my title, the key words are data assimilation, numerically predicting sea ice, Arctic ocean. My knowledge about them is really low. But one time I read in an article, that getting a PhD and being an expert in an area are two things. Therefore I'll not make the same mistake as  in Msc by going in to the details of my minor. I'd rather try to find answers to the following questions.

1. What is data assimilation?
2. What data assimilation methods are most suitable?
3. Which parameters should be used in data assimilation?
4.Where in Arctic should I focus first?
5. What is POM?
6. What is ICEPOM?
7. How are they compiled?
8. How does one create a mesh, assign initial conditions and boundary conditons and run a simulation in the cluster using ICE-POM?

These are to be answered during the first few months. First of all I'm gonna read the manual of ICE-POM to get started.