Journal Club: 15-08-2017

Article Title:The importance of forecasting regional wind power ramping: A case study for the UK

This article studied a year of generation data from the Thames Estuary wind farm cluster for 2014, with particular interest given to a ramping event which occurred on the 3rd of November of that year.  This consisted of a ramp-up event of 67% Capacity Factor (CF) in 2h 45 min.  Followed closely by a 73% CF ramp-down event, which took 1h 45min.

Wind power generation data for 03/11/2014 for Thames Estuary (black) and GB-aggregated (blue)

High resolution forecast data, both deterministic and ensemble, from the UK Met Office was analysed on this day to assess the ability of these forecast systems to predict such extreme ramping events.

Deteministic (UKV) forecasts exhibited a consistent phase error of about 2 hours for all lead times, even for a forecast initialised 6 hours before the event. Choosing the fastest wind speed within 10km of each turbine produced a more accurate representation of the amplitude of the ramp compared to simply using the nearest model grid-point.

Ensemble (MOGREPS-UK) forecasts did exhibit more variability, though a phase error was again evident (a 24 member ensemble using the combination of 03Z and 09Z forecasts on 03/11/14 had 12.5% of its members producing a ramp of more than 40% CF within a 2 hour window of the observed ramp).

Thoughts for our research:

  • The use of fastest wind speed within an arbitrary spatial window, as opposed to the nearest model grid point, is worth keeping in mind for future forecast analysis.

Managing permissions on Sonic (chmod)

When creating a new directory on the ESIPP shared directory on Sonic, it is good practice to consider the permissions which are applied to that directory.

When a new directory has been created, by executing:

mkdir new_directory

A set of permissions are set up for this new directory for three categories:

  1. The user (u), i.e. the creator of the new directory.
  2. The group (g), e.g. our ESIPP metclim group.
  3. Others (o), i.e. other people who might be granted access to the folder.

The default on sonic is to provide “read, write and execute” permission to the user (u) and provide “read and execute” permission to both group members (g) and others (o).

To allow for group members to write to directories which have been created by another user, the permissions need to be altered using the “chmod” command.

There are two options which can be used to change permissions, one which uses letters and another which uses numbers.

Firstly using letters.  If you have created your new directory “new_directory”, you can change the permissions by entering the following command

chmod u=rwx,g=rwx,o=rx new_directory/

This will grant “read, write and execute” permission to both the user (u) and also the group members (g).  “Read and execute” permission is maintained for other users (o).

This can also be achieved used a numerical approach.  In this case:

  • 4 corresponds to “read”
  • 2 corresponds to “write”
  • 1 corresponds to “execute”
  • 0 corresponds to “no permission”

In this scenario you enter a sequence of 3 digits which correspond to the sum of the permissions for that category (ugo).  E.g. to replicate the chmod command shown earlier would correspond to the following:

chmod 775 new_directory/

The first digit represents the permission for the user as 7 = 4+2+1 (read + write + execute).  The same is the case for group members.  The 5 for other users is 4+1 (read+execute).

This should also be performed for any subsequent sub-directories within your new directory.

You can check the permissions for a given directory by entering “ls -lh” to display the contents of the directory in a human readable list. An example is shown below:

The letters which follow the letter d indicate the permissions, again for the 3 categories (ugo).  The “MERRA2” directory has permissions of “rwxrwxr-x” meaning that both the user and group members have “read, write and execute” permission, while other users just have “read and execute” permission.  On the other hand the “MetEireann” folder has permissions of “rwxr-xr-x”, meaning that Conor (as he is listed as the user who created the directory) is the only person who can write to this directory.

Journal Club: 18-07-2017

Article Title: Skilful seasonal predictions for the European energy industry

Published in the Environmental Research Letters journal, in 2017.

This article, produced by the UK’s Met Office Hadley Centre, is a follow up on previous recent research conducted by scientists from the same centre. That previous research has been able to reproduce the North Atlantic Oscillation (NAO) teleconnection signal months ahead of Winter using their seasonal forecast system, the Global Seasonal forecast System 5 (GloSea5) – this system incorporates and represents elements and subsystems of the climate system (ocean, stratosphere, sea ice) which have been shown to mediate predictable teleconnections such as the NAO.

With this forecast system shown to be useful in predicting the NAO months ahead; and with previous research pieces showing significant and relevant impacts of the NAO on components of climate over Britain, such as wind speed and temperature (which can be linked to electricity production and demand variability); this article’s objective was to assess how the GloSea5 forecast system can be useful to predict electricity demand over Britain.

So, after validating GloSea5’s forecast hindcasts’ accuracy against “observed” (ERA-interim) conditions in the UK (average), for Sea Level Pressure (SLP), wind speed and air temperature; the authors check the reliability of the GloSea5 wind and temperature hindcasts for most of Europe (Maps of reliability), where they confirm that indeed Britain (and Ireland; and some parts of Western Europe) and surrounding areas fall on the category of reliable or underconfident seasonal forecasts (regarding the usage of the GloSea5 system).

Therefore, the article proceeds to its centre objective, which is testing the forecast system, through it’s NAO predicted index, against electricity demand using real data from Britain. This NAO-electricity demand relationship is first expressed using the real-observed NAO index. We can see both in the figure bellow.

Figure 1: Time series (left) and scatter plot (right) of the standardised observed winter mean NAO index and Britain’s electricity demand. Both use the same real electricity demand data. The top row plots use the real-observed NAO index. The bottom plots use the forecasted NAO index, from the GloSea5 hindcast. Note: low-frequency variability of electricity demand was removed from data, as it is associated with socio-economical change.

As we can see in the Figure, there is a strong negative relationship between Britain’s Winter (DJF) electricity demand and the NAO, expressed using both the real-observed NAO index (top plots) and using the 1 month-ahead predicted NAO index (bottom plots). This is the first time that seasonal forecasts of the weather dependent component of Britain’s Winter electricity demand have been demonstrated, offering multiple potential benefits to the energy industry.

 

Relevancy for our research:

  • Having this electricity demand-Winter NAO relationship demonstrated, one can expect to have similar skill for similar applications in other Northern-Western European countries, specialy Ireland (due to its proximity to the UK), which is of our interest. This relationship has multiple benefits, specialy for the energy industry, and can be very useful if the NAO-associated Winter average conditions (wind, temperature) or the NAO index itself are sucessfully forecasted months ahead, and the relevant information integrated in the energy grid management.

 

  • Should we apply the same approach using all-Ireland aggregated electricity demand? Maybe with more spatially resolved demand and production data, as NAO signal may vary significantly accross Ireland.

 

  • We need to engage with ESIPP researchers working with demand data in Ireland (Dr. Lucy Cradden) to assess how viable it is to perform this analysis for Ireland.

 

 

 

Journal Club: 01-08-2017

Article Title: Using ERA-Interim Reanalysis for creating datasets of energy-relevant climate variables.

This article was published from the construction of a bias-adjusted dataset of climate variables at the near surface using ERA-Interim reanalysis which is presented in this interactive website. Variables include; wind speed at 10m, temperature and solar radiation. Users in the energy sector are much more interested in the extremes of the distribution so this article adjusts the whole ERA-Interim distribution on the daily and sub-daily timescales, using a different statistical distribution for each variable.

Wind speed uses a 2-parameter Weibull distribution. Surface air temperature and dewpoint temperature use a normal distribution. Precipitation uses a gamma distribution. Three methods are described for solar radiation; ratio, affine and quantile mapping. Quantile mapping consists of adjusting the cumulative distribution function of ERA-Interim to the observations.

Comparison of statistical distributions of wind speed at 10m, for observations (black), ERA-Interim (orange) and bias-adjusted ERA-Interim (green).

Thoughts for our research:

  • Input and output data is available online.
  • These bias-adjustment methods can be replicated.