模式:

Times Series from the ECMWF

更新:
Update monthly
格林尼治平时:
12:00 UTC = 20:00 北京时间
Resolution:
1.0° x 1.0°
参量:
降水:
东亚降水(毫米或升/平方米)
描述:
降水图 - 每6小时更新一次 - 显示东亚地区模式计算的降水分布情况。 降水区用等雨量线标出。 然而,目前模式算出的降水还不是很可靠。如果您比较一下模式结果和降水实测值,您会 发现模式结果只能算得上降水的一级近似值。不过,这幅图对于专业气象预报员却是个重 参考。

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
Introduction to seasonal forecasting:
The production of seasonal forecasts, also known as seasonal climate forecasts, has undergone a huge transformation in the last few decades: from a purely academic and research exercise in the early '90s to the current situation where several meteorological forecast services, throughout the world, conduct routine operational seasonal forecasting activities. Such activities are devoted to providing estimates of statistics of weather on monthly and seasonal time scales, which places them somewhere between conventional weather forecasts and climate predictions.
 
In that sense, even though seasonal forecasts share some methods and tools with weather forecasting, they are part of a different paradigm which requires treating them in a different way. Instead of trying to answer to the question "how is the weather going to look like on a particular location in an specific day?", seasonal forecasts will tell us how likely it is that the coming season will be wetter, drier, warmer or colder than 'usual' for that time of year. This kind of long term predictions are feasible due to the behaviour of some of the Earth system components which evolve more slowly than the atmosphere (e.g. the ocean, the cryosphere) and in a predictable fashion, so their influence on the atmosphere can add a noticeable signal.
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