woensdag 29 oktober 2014

Seasonal forecasting

Numerical Models in Meteorology


Weather and climate are most often predicted using numerical models.
These models start from a snapshot of the current atmospheric conditions in the area of interest, from the surface to the upper atmosphere, at points on a three-dimensional grid. A set of atmospheric variables, such as the wind speed, temperature, pressure and humidity in each grid box are then stored and a set of equations are solved for each grid box to predict the values at that point a short time later. This process is repeated many times; each time the forecast stepping a few minutes further into the future to produce either a weather forecast of the next few days or a climate prediction of the coming 100 years.

The set of equations which are solved fall broadly into 2 categories: the dynamical core solves the equations of motion for a fluid, on a rotating sphere, to calculate the evolution of the atmospheric flow. Alongside the dynamical core, a large number of other Physical Processes  operate to warm/cool or moisten/dry the atmosphere, form clouds and precipitation and represent both the weather which we experience, and the effect of that weather on the evolution of the atmospheric flow.

 

Introduction to seasonal forecasting
(MetOffice)

 

What is a seasonal forecast?

Weather forecasts provide information about the weather expected over the next few days. While it is generally not possible to predict these day-to-day changes in detail beyond about a week ahead, it is possible to say something about likely conditions averaged over the next few months. Seasonal forecasts provide information about these long-term averages.

Bild

Why are seasonal forecasts possible?

Conditions at the Earth's surface, in particular slow fluctuations in the surface temperature of the global oceans, can influence patterns in the weather. These influences are not easily noticed in day-to-day weather events but become evident in long-term weather averages.
The slow fluctuations of sea-surface temperature (SST) can be predicted, to some extent, up to about 6 months ahead. The links between SST and weather can be represented in computer models of the atmosphere and ocean. Computer models developed at the Met Office, like those used in making both the familiar daily forecasts and for long-term climate change prediction, form the basis of our seasonal prediction systems.

The strongest links between SST patterns and seasonal weather trends are found in tropical regions, and it is here that seasonal forecasting is most successful. The best known links are those associated with the El Niño phenomenon, which is a warming of SST in the tropical Pacific that occurs on average every three or four years. El Niño can disrupt the normal pattern of weather around the globe, bringing for example large changes in seasonal rainfall that lead to droughts in some regions and floods in others.

Although the strongest links between SST and seasonal weather are found in the tropics, there is good evidence that similar, if weaker, links are present in other parts of the globe. The computer model forecasts can thus provide the best available guidance on likely seasonal conditions in many parts of the world, including Europe.

Because the link between weather and SST is best detected in long-term weather averages, and because the uncertainty in forecasts generally rises as the forecast range increases, seasonal forecasts look rather different in format compared to the familiar daily forecasts. The two key differences are:
  • forecasts are for conditions averaged over three-month periods.
  • forecasts are stated in terms of probability.
Our forecasts provide this information in the form of geographical maps of these probabilities for seasonally-averaged temperature and rainfall.

How are the forecasts produced?

The same computer models of the atmosphere that are used to make the familiar daily weather forecasts also lie at the heart of seasonal forecasts. Three additional features of the method are worth mentioning:
  • the models are run forward in time to a range of 6 months ahead, rather than just a few days.
  • the models have active oceanic as well as atmospheric components, to represent important ocean-atmosphere interactions.
  • the forecast models are run not once but many times, with slight variations to represent uncertainties in the forecast process.

ECMWF

Long range

Long range forecasts provide information about atmospheric and oceanic conditions averaged over the next few months. Despite the chaotic nature of the atmosphere, long term predictions rely on a number of components which themselves show variations on long time scales (seasons and years) and, to a certain extent, are predictable. The most important of these components is the ENSO (El Nino Southern Oscillation) cycle. Although ENSO is a coupled ocean-atmosphere phenomenon centered over the tropical Pacific the influence of its fluctuations extends around the world.
Similarly to the medium and extended ranges, the long range forecasts are produced by the IFS coupled ocean-atmosphere model. The model is run forward in time to a range of several months; this is repeated many times, with slight variations to represent uncertainties in
the forecast process, to produce the ensemble forecast.
See detailed documentation of the seasonal forecast.
Depending on who you are, you will be able to access different types of products. The linked charts are only displayed if you have correct access rights for that product. Find out about access to our forecasts.

Long range (seasonal) forecast

Forecasts are produced each month, giving an outlook to seven months ahead. Since November 2011 these have been produced using seasonal forecast System 4.
The history of the previous seasonal forecast (System 3) is accessible here.
An experimental annual range forecast is produced 4 times a year.

 

Spatial maps

Spatial maps of model probabilities stratified by terciles. Available parameters are: 2m Temperature, Mean sea level pressure, precipitation, Sea surface temperature, 850 hPa temperature and 500 hPa geopotential.
Charts for the tropical region are available to the public.

 

Tropical storms maps

Forecast of tropical storm frequency, mean location of tropical storm genesis, Hurricane/Typhoon frequency and Accumulated Cyclone Energy (ACE) valid for a six months period  for example: Accumulated Cyclone Energy - Long range forecast

 

Climagrams

Time-series of monthly mean anomalies. Available parameters are: area averages of sea-surface temperature, 2m temperatureprecipitation; teleconnection and monsoon indices.

 

Nino plumes

Forecast of Equatorial Pacific sea surface temperature anomalies averaged over NINO 3, NINO 3.4, NINO 4 areas.
Nino plumes (Public charts) - Long range forecast

EUROSIP Multi-model system

The EUROSIP multi-model seasonal forecasting system consists of a number of independent coupled seasonal forecasting systems integrated into a common framework. From September 2012, the systems include those from ECMWF, the Met Office, Météo-France and NCEP.
For more information see the documentation of the EUROSIP system.
EUROSIP multi-model forecast charts

 

Spatial maps

Spatial maps of model probabilities stratified by terciles. Available parameters are: 2m Temperature, Mean sea level pressure, precipitation, Sea surface temperature, 850 hPa temperature and 500 hPa geopotential. Forecast is made available on the 15th of each month.

 

Nino plumes

Forecast of Equatorial Pacific sea surface temperature anomalies averaged over NINO3, NINO3.4 and NINO4 areas from the European multi-model Seasonal to Inter-annual Prediction (EUROSIP) system. Forecast is made available on the 22nd of each month.

 

Tropical storm forecast

Forecasts of tropical storm frequency and mean location of tropical storm genesis valid for a six months period from the European multi-model Seasonal to Inter-annual Predictions (EUROSIP) system. Note that the tropical storm forecasts are created using data from ECMWF and Meteo France systems only.

Zie ook: http://old.ecmwf.int/products/forecasts/seasonal/documentation/system4/index.html

 

 

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