Modelo:

Actualização:

2 times per day, from 05:00 and 17:00 UTC

Greenwich Mean Time:

12:00 UTC = 12:00 WET

Resolution:

parâmetro:

Maximum wind velocity of convective wind gusts

Descrição:

The method of Ivens (1987) is used by the forecasters at KNMI to predict the maximum wind velocity associated with heavy showers or thunderstorms. The method of Ivens is based on two multiple regression equations that were derived using about 120 summertime cases (April to September) between 1980 and 1983. The upper-air data were derived from the soundings at De Bilt, and observations of thunder by synop stations were used as an indicator of the presence of convection. The regression equations for the maximum wind velocity (w_max ) in m/s according to Ivens (1987) are:

• if T_x - θ_w850 < 9°C

  w_max = 7.66 + 0.653⋅(θ_w850 - θ_w500 ) + 0.976⋅U₈₅₀
  

• if T_x - θ_w850 ≥ 9° C

w_max = 8.17 + 0.473⋅(θ_w850 - θ_w500 ) + (0.174⋅U₈₅₀ + 0.057⋅U₂₅₀)⋅√(T_x - θ_w850)

where

• T_x is the maximum day-time temperature at 2 m in K
• θ_wxxx the potential wet-bulb temperature at xxx hPa in K
• U_xxx the wind velocity at xxx hPa in m/s.

The amount of negative buoyancy, which is estimated in these equations by the difference of the potential wet-bulb temperature at 850 and at 500 hPa, and horizontal wind velocities at one or two fixed altitudes are used to estimate the maximum wind velocity. The effect of precipitation loading is not taken into account by the method of Ivens. (Source: KNMI)

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

NWP:

A previsão numérica do tempo usa o estado instantâneo da atmosfera como dados de entrada para modelos matemáticos da atmosfera, com vista à previsão do estado do tempo.
Apesar dos primeiros esforços para conseguir prever o tempo tivessem sido dados na década de 1920, foi apenas com o advento da era dos computadores que foi possível realizá-lo em tempo real. A manipulação de grandes conjuntos de dados e a realização de cálculos complexos para o conseguir com uma resolução suficientemente elevada para produzir resultados úteis requer o uso dos supercomputadores mais potentes do mundo. Um conjunto de modelos de previsão, quer à escala global quer à escala regional, são executados para criar previsões do tempo nacionais. O uso de previsões com modelos semelhantes ("model ensembles") ajuda a definir a incerteza da previsão e estender a previsão do tempo bastante mais no futuro, o que não seria possível conseguir de outro modo.

Contribuidores da Wikipédia, "Previsão numérica do tempo," Wikipédia, a enciclopédia livre, http://pt.wikipedia.org/w/index.php?title=Previs%C3%A3o_num%C3%A9rica_do_tempo&oldid=17351675 (accessed fevereiro 9, 2010).