Surface fluxes added to TGLO/TABS model

We will use monthly averaged, global surface flux data to improve the mixed layer calculations in the TGLO/TABS model. Hopefully, this will improve estimates of mixed layer depth, and estimates of inertial oscillation phase with respect to diurnal wind forcing. Data are available here.

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

Running ROMS on clusters - speed comparison

From Rich Signell's Blog, a comparison of clustered computers. Our cluster comes out pretty well.

ROMS "small" benchmark


Here's what I've got so far for the ROMS "BENCHMARK1" test:
(ROMS/TOMS 2.1 - Benchmark Test, Idealized Southern Ocean
Resolution, Grid 01: 0512x0064x030):

8 cpu, 8 node, Scott Doney's 2.8 GHz Xeon cluster (MPI/MPICH, Linux,Myrinet) 2.37 minutes
8 cpu, 8 node, Rob Hetland's 2.8 GHz P4 cluster (MPI/LAM, Linux,Gigabit) 3.95 minutes
8 cpu, 4 node, Scott Doney's 2.8 GHz Xeon cluster (MPI/MPICH, Linux,Myrinet) 4.38 minutes

4 cpu, 4 node, Rob Hetland's 2.8 GHz P4 cluster (MPI/LAM, Linux,Gigabit) 7.43 minutes
4 cpu, 1 node, USGS Alpha ES40 (MPI/HP, Tru64) 9.83 minutes
4 cpu, 1 node, USGS Alpha ES40 (OpenMP, Tru64) 9.53 minutes
4 cpu, 1 node, ERDC Alpha SC40 (MPI/HP, Tru64) 9.83 minutes

2 cpu, 1 node, Sandro Carniel's Dual 1.3 GHz Itanium II (OpenMP, Linux) 10.82 minutes
2 cpu, 2 node, Rob Hetland's 2.8 GHz P4 cluster (MPI/LAM, Linux,Gigabit) 12.53 minutes
2 cpu, 1 node, John Warner's Dual 3.0 GHz Xeon (OpenMP, Cygwin) 16.56 minutes
2 cpu, 1 node, USGS Alpha ES40: (OpenMP, Tru64) 17.68 minutes

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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