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Journal Club: 05-09-2018

Article: The link between eddy-driven jet variability and weather regimes in the North Atlantic-European sector (Madonna et al, 2017)

This study aims to reconcile the 2 perspectives on wintertime atmospheric variability in the North Atlantic – European sector: 

  1. The zonal-mean framework comprising of 3 preferred locations of the eddy-driven jet.
  2. The weather regime framework comprising of four classical North Atlantic- European regimes.

Questions?

  • Are there common jet structures that do not fit within the three preferred jet locations?
  • Can we reconcile the three preferred jet locations with the four classical weather regimes?

Eddy momentum flux convergence from transient eddies creates a deep barotropic eddy-driven jet in mid latitudes. This is not always clearly separated from the subtropical jet. The leading pattern of variability in eddy driven jet is a latitudinal shifting (30-40% monthly variance) related to phase of the NAO. We would expect jet and weather regimes to be associated but there is a mismatch. Also, flow regimes tend to have characteristic Rossby Wave breaking patterns – pushing the jet polewards/equatorwards. 

The literature on the eddy driven jet stream has identified three preferred jet configurations using a zonally averaged jet latitudinal index method (Woolings et al, 2010).

However, this paper applies a clustering analysis to 10 day low pass filtered low level zonal wind to identify 3, 4 and 5 jet configurations.

 

The 4 cluster analysis identifies a new jet configuration (Mixed) along with the previous identified ones (Southern, Central and Northern). A persistence analysis shows that this new configuration is not merely a transition pattern but a jet regime in its own right.

Comparing new representation of jet regimes to the classical weather regimes, there is a clear match. Most notably the Scandinavian Blocking weather regime matches the new Mixed jet configuration very well.

This paper has identified a new jet configuration (mixed) and reconciled eddy driven jet variability with the four classical weather regimes.

This study uses methods such as filtering and clustering that we may apply to our own research in the future. It ties in well with the work on jet stream interactions and Rossby waves. It would be good to find links between the upper level steering flow and the eddy driven jet stream. We would like to find out if a similar analysis has been carried out of the upper level jet stream and would it yield similar results.

 

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Radiation: Clear Sky and Top of Atmosphere

A question arose in regard to the meaning of certain short wave radiation variables, in particular when they are described at the top of the atmosphere (TOA) or at the surface in clear sky conditions.

A good document describing the TOA and clear sky conditions in the ECMWF model can be found here.  The TOA in this model, corresponds to the 0 hPa pressure level. While NASA say that TOA can be described arbitrarily as 400,000 ft (about 120 km) above the surface of the Earth, or in meteorological terms as the location where pressure is 0.1 mb.

Clear sky is defined in the ECMWF model as as a hypothetical atmosphere where no clouds are present, however the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol exist. So the short wave radiation at the Earth’s surface in clear sky conditions is that which would be present if all clouds were removed. In clear sky conditions, when the solar radiation passes through the Earth’s atmosphere, it is reduced only due to scattering and absorption. The radiation that is not scattered or absorbed will reach the Earth’s surface as a direct (beam) radiation, while the scatter radiation that reaches the ground is called diffuse radiation. The scattering and absorption of the solar radiation can be attributed to ozone, water vapor, aerosol and dry-air particles in the atmosphere.

Figure from description of the ECMWF model  below showing the short and long wave radiation entering the top of the atmosphere (TOA) and being scattered and absorbed before reaching the Earth’s surface.

 

Summer daily average accumulated short wave radiation at the TOA and at the Earth’s surface in clear sky conditions are shown below. Data from MERRA2.

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Journal Club: 04-04-2018

Article Title: Using weather ensemble prediction in electricity forecasting, Taylor and Buizza (2013).

The authors compare different methods of including weather into an electricity demand forecasting model. They compare the traditional method (inputting the mean of an ensemble of weather forecasts), to that of applying each weather forecast from the ensemble to the demand model, and then taking the mean of the scenarios. They show that the second method (x) performs better than the traditional method (o) for lead times of 1-10days (see Figure 3 below).

The difference in accuracy of the 2 methods increases with lead time. It is concluded that the reason for the improved performance is due to the fact that the demand is a non-linear function of weather. Further, they show that excluding weather variables from the electricity demand forecast model reduces the accuracy substantially.

The demand model is a regression model based on the UK National Grid. Weather related components depend on effective temperature, cooling power of the wind and effective illumination (replaced by cloud cover here). The model is weighted to reflect different population densities across 5 English cities.

The authors also present different methods of estimating the demand forecast error and the demand prediction intervals. From this they conclude that including weather ensemble information can improve these estimates compared to traditional volatility forecasting methods.

Applications to our research:

  • Implement this demand model for Ireland.
  • Use this model to identify atmospheric spatial patterns associated with certain demand profiles.
  • Apply Shortwave Radiation data to model instead of cloud cover.
  • Potentially use the model to link demand profiles to climate patterns (eg. NAO).
  • First check if there are improved versions of this model.