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Journal Club: 20-11-2017

Article Title: North Atlantic Oscillation amplifies orographic precipitation and river flow in upland Britain

Published in the Water Resources Research journal in June 2013. Authors: T. P. Burt and N. J. K. Howden.

This article assessed the relationship between both precipitation and river flow and the North Atlantic Oscillation (NAO) in Britain. For this, the authors got station data from all over the UK mainland and computed the seasonal totals of precipitation and river flow. Afterwards they correlated every season’s total of those variables with the NAO index. Although it was previously known that the NAO has considerable effect on European precipitation, the authors detailed this knowledge for Britain, finding that NAO variations cause large differences in seasonal precipitation and river flow totals. Additionally, because the stations were relatively spread out through the region – including low land and upland areas – the authors found that the influence of the NAO on precipitation and river flow is higher on upland areas, an effect they named “double orographic enhancement”, and which has relevant implication to water management in the region (both for flood and drought situations).

Projections of precipitation(a)/flow change (b) [%] per NAO deviation from the neutral conditions. The x axis contains the winter precipitation/flow total, which is correlated with altitude (proxy for altitude, as demonstrated previously in the paper). The y axis contains the percentage of enhancement. The (c) and (d) plots were estimated for the highest (blue) and lowest (red) NAO index on record, based on the regression coefficients found previously in the paper for the rate of change of precipitation/flow per unit change of the NAO index, for every precipitation/flow total.  

 The authors also performed similar preliminary analysis on other areas of the world both influenced by large scale patterns and with orographic arrangements (Pacific North West of the USA and Sri Lanka, both influenced by the El Nino Southern Oscillation). This was done to confirm that the “double orographic enhancement” effect was also present in other locations facing a similar set of conditions as Britain (high interannual variability of precipitation due to large scale patterns and high spatial variability of precipitation due to wind direction, orography and rain-shadow effects).

 

Conclusions:

The North Atlantic Oscillation has considerable influence on interannual variability of precipitation in Britain, an effect which is amplified with altitude.

Relevancy for our research:

To some extent, understanding precipitation variability is understanding solar radiation variability (as both depend highly on cloudiness), which is one of the focus of our group. This work, although not directly related to our research, has given insights on how the impact of a large scale variability pattern such as the North Atlantic Oscillation on some meteorological variables can vary drastically over small regions, as a result of interaction between atmosphere and topography.

 

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Probabilistic Skill Scores

Assessments of the skill of probabilistic forecast models utilize various skil scores. Those range from being scalar scores, to visual graphs that express scores according to different forecasting conditions (e.g. extreme events’ probability of occurrence).

Attached is a summary document that briefly describes, explains and showcases some of the most common probabilistic skill scores used in climate, meteorology and weather-dependent renewable energy forecasting. This document includes:

  • Introduction to Skill Scores
  • The Generic Form of a Skill Score
  • Reliability Diagram
  • Sharpness Diagram
  • ROC Diagram (also called ROC curve)

Probabilistic Skill Scores

Hope you enjoy!

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Journal Club: 29-08-2017

Article Title: The relationship between wind power, electricity demand and winter weather patterns in Great Britain

Published in the Environmental Research Letters journal, in 2017. Main author: Hazel E. Thornton, from UK’s Met Office.

This article assessed the relationship between electricity demand and the availability of wind power, relating both of these to weather patterns affecting the UK region.

Like the previous article by some of the same authors, also covered in the our journal club (https://metclim.ucd.ie/2017/08/journal-club-18-07-2017/), the current article focused on the winter season, although for different reasons: the winter season encompasses both the highest electricity demand (therefore ED) and the highest variability in wind capacity factor (WP), when comparing with the rest of the year (see Figure 1 of the article).

Due to its intermittent nature, highest on winter, wind power has been questioned as a safe provider of electricity during high and peak electricity demand periods. Understanding what atmospheric patterns are associated with both winter wind power production and electricity demand will offer security to the energy grids by way of securing reserve power production – this can be achieved through forecasting of the atmospheric conditions associated with different WP and ED.

This is the motivation and rationale of the article.

As we can see in the figure bellow (left plot), for most of the ED percentiles, there is linear inverted relationship between WP and ED; for the peak ED though, there is an upturn, meaning that the highest percentiles of ED occur when WP starts to increase again. This same plot also shows us that the higher the meridional pressure difference (two boxes, one north of the UK, another south), the higher the WP. This pressure difference can be used to identify the general direction of the winds, and therefore the general temperature of the airmasses – e.g. winter air masses coming from the East are generally colder than airmasses coming from the West. This pressure difference can also be interpreted as a regional proxy of the NAO index.

If we look at the right plot of the same figure, we can see that the colder the air temperature, the higher the ED, which can probably be explained by the higher levels of heating – therefore associating temperature (therefore T) with ED, for the UK region.

Figure 1: Variation in UK’s average WP and meridional pressure difference between two regions-boxes north and south of the UK (left plot). Variation in ED and UK’s average temperature (right) 

The authors then got the Sea Level Pressure (MSLP), T and WP fields (all from ERA reanalysis) associated with high ED (~30 years of real UK data) and applied a clustering technique (k-means) to see the pressure, temperature and WP patterns associated with those ED conditions (the last two fields, T and WP, are presented as anomalies and not absolute values). As one can see in the Figure 2, bellow, the authors identified 4 clusters with frequencies between ~20% and ~30% of occurrence each. All of the identified clusters demonstrate high pressure systems, although in different regions of Europe. The location of the pressure centers will condition the degree and extent of temperature anomalies in the UK and neighbours. Regarding WP, the picture is not that “consistent”, as expected from the previous plot – two of the clusters are associated with lower than normal WP, one associated with average WP, and one associated with above average WP (Cluster 1). Note: In this work, the authors assumed a uniform and geographical balanced distribution of wind farms, although they also performed other types of sensitivity analysis (using OBS of WP instead of reanalysis; averaging WP over different regions of the UK, both onshore and offsore) that guaranteed that the results are consistent, namely the ED-WP relationship.

One also has to notice that 3 of clusters/weather types show that in those weather conditions not only the UK but also its neighbours suffer from T and WP anomalies, which might make inter-national electricity balancing harder and/or more expensive, specially if those conditions are not predicted sufficiently ahead of time.

On the other hand, 3 of the clusters (not all the same as the previous) show some advantages on continental scale balancing of power, as southern Europe faces opposite anomalies to northern Europe, at least regarding WP.

Figure 2: Clusters of MSLP (left column) and T (middle) and WP (right) anomalies, obtained by K-means technique on those variables, when the ED is high.

 

Conclusions:

  • WP and T anomalous conditions are associated with different weather types with similar frequency of ocurrence in the winter;
  • In general, weather patterns that bring low T and high ED to the UK also cause anomalously low temperatures across many parts of Europe. Consequently, regional balancing of power may not be enough to satisfy ED, as these weather patterns affect all of a region and its neighbours. On the other hand, continental balancing may offer a safety net for these extreme conditions – this is an argument for climate variability informed interconnections, as the identification of these patterns and weather types may provide information important for security of electricity supply (e.g. predictability/forecasting; interconnection capacity and location; spatial range of anomalies).

Relevancy for our research:

  • How is Ireland’s ED related to these weather patterns? Can Ireland be a good neighbour of the UK/France for extreme WP and ED conditions? As in, are extreme conditions happening in in both countries at the same time?
  • Is Ireland’s ED dependent on T as much as the UK’s ED is?
  • What is the relationship between ED and WP, for Ireland?
  • Answering these questions may facilitate future decisions on the production, transmission and consuming of electricity.

 

Eadaoin, João and Seánie had the chance to see Hazel Thornton presenting this work at the European Meteorological Society Conference in Dublin, September 2017. She also presented a follow up work of this article, which is about predicting some of these weather dependent variables using Met Office’s forecast system.

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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.