non-linear colormaps in matplotlib

Here is a way to do non-linear colormaps in matplotlib. You can select a base colormap, and a series of levels, and this class will remap the (linear) colormap such that your new selected levels define the linear positions along the map. This can be used for plotting bathymetry, specifically, where you may want to have more contours in the shallows than deep. Grab the code here: nlcmap.py.

Map Projections Poster

Although 'poster' seems a bit of an exageration, the Map Projections Poster is the best source I have found so far for describing the different mapping projections.

Merrimack river discharge

Here is a python script (merrimack_discharge.py) that downloads river discharge based on the USGS station code. The class provides time-dependent dischrge data and a few simple methods for deriving anual statistics. Running the script creates a plot of the historical (for statistics) and recent disharge of the Merrimack river in New England. This was developed for my near-field plume study with Dan Macdonald.

UPDATED: script now includes real-time data from the USGS, and the figure has been updated to look at the recent flood as compared to historical floods.

Real-time stream flow data in Google Earth.

The USGS has released two kml files for Google Earth that show real time river guage data relative to climatologies. You can click on the colored points and get information about the station. A sample screen grab from New England today shows the rain New England has been getting recently.

Python as a platform for ROMS

It appears that python is gaining a lot of momentum in scientific computing. There are a few packages in particular that seem interesting to me right now:
SciPy Scientific Tools for Python - SciPy - Scientific tools for Python
Matplotlib / pylab - matlab style python plotting (plots, graphs, charts)

nanohub offers some tips on using pyton for scientific computing (in particular, using scipy, since they are the primary developers):
NANOHUB.ORG - Scientific Computing with Python

Of particular interest to people who use the right kind of computers:
SciPy Scientific Tools for Python - OSX Build instructions

Underneath both of these programs is a library that allows pyton to deal with arrays and other numerical issues: Numerical Python.

Perhaps it would be good to model any system built around ROMS on the Earth System Modeling Framework, or even better, steel the best parts of their code.

Shelf break front simulation

Here is a sample cross-section from an idealized shelf break front simulation. The contours are salinity, the colorfill velocity.



Click the image for an animated gif movie.

Skill paper.

I just submitted this paper to Ocean Modelling. The paper discusses estimating model skill when the primary feature is a descrete event.

Hurricane Ivan surface currents

Ivan ran through the Gulf, and stirred up some strong currents. Click below to see an animation


MOVIE - whole Gulf [31 MB]
MOVIE - Texas/Louisiana shelf [3 MB]

Summertime TABS model skill assessment

In summertime, the TGLO model does a much poorer job at predicting coastal sea-level. We attribute this primarily to fresh water run off from the Mississippi and other local rivers. Note how the time series diverges in late May, with the measured values higher than the simulated values. Also the simulated SSH seems to 'ring' too much, indicating that the drag in the model is too low, so that the coatally trapped waves are not damped out quickly enough. This seems to be a problem only for very strong storms (compare this figure to the late 2001 figure -- the variations here are much larger due to the magnitude of the blue northers that pass by).


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Comparison of ETA winds and met buoy winds

Les Bender did a very careful study of wind observations as compared to the ETA wind forcasts. The bottom line is that correlation coefficients are between 0.8 and 0.9, meaning that we will not be able to improve this much. This is good news, however, since the correlation is better than we were thinking it might be. It seems that the modeled winds are indeed capturing most of the sea breeze signal.


Les writes:

Here are the preliminary results of analyzing the ETA-12 model winds and
observations taken from 5 TABS buoys, 6 NDBC buoys, and 3 CMAN stations. I
used the ETA-12 hindcast winds, provided by Matt, for the period from
15-Feb-2003 to 31-Oct-2003.

Bottom line: There is room for improvement, though it appears to me that the
NDBC and CMAN winds are already being assimilated.

The Kundu vector correlations and angle of rotation between model and
observations are as follows:

Buoy Correlation Angle, deg Comments
---- ----------- ---------- --------
TABS B 0.858 +1.73
TABS J 0.686 -34.57 The TABS wind sensor is highly suspect during this
period.
TABS K 0.816 +6.06
TABS N 0.832 +10.89
TABS V 0.763 -18.76
NDBC 42001 0.810 -2.91
NDBC 42002 0.800 -15.79
NDBC 42019 0.889 -6.24
NDBC 42020 0.900 -5.20
NDBC 42035 0.893 -5.59
NDBC 42041 0.851 -6.30
CMAN BURL1 0.858 +1.59
CMAN PTAT2 0.885 -17.81
CMAN SRST2 0.848 -4.27

The difference between N and V is striking. It could indicate that one or
both of the wind sensors was faulty.

I have attached a map showing the LATEX region, the model grid points, and
the observation sites. Each of the other figures are, I hope, self
explanatory.

Images are available for comparisons between the winds at these station positions.

NDBC buoy 42001
NDBC buoy 42002
NDBC buoy 42019
NDBC buoy 42020
NDBC buoy 42035
NDBC buoy 42041
NDBC buoy BURL1
NDBC buoy PTAT2
NDBC buoy SRST2

TABS buoy B
TABS buoy J
TABS buoy K
TABS buoy N
TABS buoy V

Subtidal sea level data skill

A quick look at a hindcast shows pretty good skill when predicting coastal sea level (skill = 0.61). This is surprising, since there seem to be essentially no loop current effects. The errors that are there are most likely due to buoyancy forcing. Take a look (grey line is unfiltered sea level measurements, black is lowpassed (33 hr) measurements, and red is lowpassed model sea level. All time series are demeaned over the time interval shown, but not detrended):


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