Spaghetti plots:
are a method of viewing data from an ensemble forecast.
A meteorological variable e.g. pressure, temperature is drawn on a chart for a number of slightly different model runs from an ensemble. The model can then be stepped forward in time and the results compared and be used to gauge the amount of uncertainty in the forecast.
If there is good agreement and the contours follow a recognisable pattern through the sequence then the confidence in the forecast can be high, conversely if the pattern is chaotic i.e resembling a plate of spaghetti then confidence will be low. Ensemble members will generally diverge over time and spaghetti plots are quick way to see when this happens.
Spaghetti plot. (2009, July 7). In Wikipedia, The Free Encyclopedia. Retrieved 20:22, February 9, 2010, from
http://en.wikipedia.org/w/index.php?title=Spaghetti_plot&oldid=300824682
SST:
A daily, high-resolution, real-time, global, sea surface temperature (RTG_SST) analysis has been developed at the National Centers for Environmental Prediction/Marine Modeling and Analysis Branch (NCEP / MMAB). The analysis was implemented in the NCEP parallel production suite 16 August 2005. It became fully operational on September 27, 2005.
The daily sea surface temperature product is produced on a twelfth-degree (latitude, longitude) grid, with a two-dimensional variational interpolation analysis of the most recent 24-hours buoy and ship data, satellite-retrieved SST data, and SST's derived from satellite-observed sea-ice coverage. The algorithm employs the following data-handling and analysis techniques:
Satellite retrieved SST values are averaged within 1/12 o grid boxes with day and night 'superobs' created separately for each satellite;
Bias calculation and removal, for satellite retrieved SST, is the technique employed in the 7-day Reynolds-Smith climatological analysis;
Currently, the satellite SST retrievals are generated by a physically-based algorithm from the Joint Center for Satellite Data Assimilation. Retrievals are from NOAA-17 and NOAA-18 AVHRR data;
SST reports from individual ships and buoys are separately averaged within grid boxes;
The first-guess is the prior (un-smoothed) analysis with one-day's climate adjustment added;
Late-arriving data which did not make it into the previous SST analysis are accepted if they are less than 36 hours old;
Surface temperature is calculated for water where the ice cover exceeds 50%, using salinity climatology in Millero's formula for the freezing point of salt water:
t(S) = -0.0575 S + 0.0017 S3/2 - 0.0002 S2,
with S in psu.
An inhomogeneous correlation-scale-parameter l, for the correlation function: exp(-d2/l2) , is calculated from a climatological temperature gradient, as
l = min ( 450 , max( 2.25 / |grad T| , 100 )),
with d and l in kilometers. "grad T" is in oC / km
Evaluations of the analysis products have shown it to produce realistically tight gradients in the Gulf Stream regions of the Atlantic and the Kuroshio region of the Pacific, and to be in close agreement with SST reports from moored buoys in both oceans. Also, it has been shown to properly depict the wintertime colder shelf water -- a feature critical in getting an accurate model prediction for coastal winter storms.
NWP:
Numerical weather prediction uses current weather conditions as input into mathematical models of the atmosphere to predict the weather. Although the first efforts to accomplish this were done in the 1920s, it wasn't until the advent of the computer and computer simulation that it was feasible to do in real-time. Manipulating the huge datasets and performing the complex calculations necessary to do this on a resolution fine enough to make the results useful requires the use of some of the most powerful supercomputers in the world. A number of forecast models, both global and regional in scale, are run to help create forecasts for nations worldwide. Use of model ensemble forecasts helps to define the forecast uncertainty and extend weather forecasting farther into the future than would otherwise be possible.
Wikipedia, Numerical weather prediction,
http://en.wikipedia.org/wiki/Numerical_weather_prediction(as of Feb. 9, 2010, 20:50 UTC).