EWEA Wind Power Forecasting 2015 – Leuven – 1-2 October 2015

1. Scaife, A. A., et al. (2014), Skillful long-range prediction of European

and North American winters, Geophys. Res. Lett., 41,2514–2519, doi:

10.1002/2014GL059637.

2. MacLachlan, C., et al. (2015), Global Seasonal forecast system version

5 (GloSea5): a high-resolution seasonal forecast system., Q.J.R.

Meteorol. Soc., 141: 1072–1084. doi: 10.1002/qj.2396

1) To communicate recent advances in forecasting

ahead of a European winter season.

2) To understand decisions that can be made by the

energy industry ahead of the winter season.

3) To find out the extent to which seasonal forecasts

could improve decision making.

In 2014 the Met Office revealed remarkable accuracy

for seasonal predictions of European winter climate.

Probabilistic forecasts of wind, temperature and

storminess were now possible ahead of the winter

season[1,2], bringing with them potential for efficiency

and cost savings for the energy industry. However there

was a significant mismatch between the exciting

research result, and the adoption by end-users. This

poster describes a user-engagement activity and

prototype winter 2014/15 forecast service undertaken

to understand this mismatch.

Please ask me to explain what the prototypes looked

like, and how they were used.

Based on our understanding of user needs, prototype forecasts were produced for

“Supply and demand forecasters” and “Meteorologists”, and supplied on a weekly basis.

Some of these forecasts, and comments from users can be found below:

Regional wind forecasts:

“What does this ‘% change’ mean for my site?” Wind farm operator

Regional maps:

“We used these maps to summarise the

forecast to traders”

Meteorologist for traders

NAO plume: “We need to know how the forecast

changes from week to week”

Energy Trading,

“I want to see how this index

compares with the observations

that I’m familiar with.

Utility,

“This gives me a clear steer for

the season ahead.”

Meteorologist for traders

Sudden Stratospheric Warming index “The sudden stratospheric

warming index gave us the best guidance. We had confidence the winter was unlikely to

be very cold.” Meteorologist for traders

European winter conditions can be predicted better than

ever before ahead of the season.

Winter forecasts could provide useful guidance to some

stakeholders in the energy industry:

Resilience planners need bespoke guidance to make

resourcing decisions.

Supply and demand forecasters can make business

decisions based on forecasts of average winter

temperature and wind speed.

Traders “would not be without” a forecast from such a

skillful system, and can make use of highly technical

information.

All need the forecast in a context they are familiar with.User engagement campaign: A user engagement activity was

conducted to find out to what extent the exciting scientific result could

improve decision making for the energy industry.

Abstract

Methods

Background: Winter predictability

Objectives of project

Conclusions

References

Winter forecasting for wind energy:

A journey from scientific breakthrough to useful applicationEmily Wallace

Met Office

PO.001

Introduce the science

Understand user needs

Produce prototype draft

Provide prototypes regularly

Feedback

Is this useful?

Select the users: Wind farm operators, utilities, energy traders and

distribution network operators were selected as prototype testers. All users

were affected by winter conditions, and in particular by the strength of wind

over the winter.

“I hadn’t realised the significance of this result” UK

utility

Figure 1: In the positive phase of the NAO northern Europe will tend to be mild and

wet with stronger winds, and southern Europe cool and dry with lighter winds (left).

In the negative phase of the NAO this pattern reverses (right).

Ensemble

MeanObservations

Ensemble

member

What controls winter climate? The most important factor influencing

European winters is the North Atlantic Oscillation (NAO). The NAO is often defined as the

difference in surface pressure between the Azores and Iceland. When this difference is

larger than normal it is said that the NAO is in a positive phase, and when the difference

is smaller the NAO is said to be in a negative phase (Figure 1).

How predictable is winter? Targeted improvements made to the Met

Office seasonal forecast system have resulted in remarkable predictability of the winter

NAO (Figure 2). It is now possible to predict average winter wind

speed/temperature/storminess over a region. This result was not generally well known by

industry.

Figure 2: A 20 year set of retrospective forecasts showed a correlation coefficient

of 0.6 between predicted and observed winter NAO from November.

Understanding user need

NA

O

How useful are these forecasts?

To find out to what extent seasonal predictability was useful we asked:

“How does winter weather affect your business?”

“How do you currently prepare for winter?”

“What would you do if you knew that a winter like 2009/10 was more likely? Or 2013/14?”

2009/10: UK covered in snow 2013/14: Run-off from flooding visible in UK waters

What do stakeholders want?

Three stakeholder groups emerged.

1) Resilience planners: Interpreted output required (out of scope)

Owners of infrastructure such as wind farms wanted to know about the expected

frequency of extreme winds and flooding. They needed interpreted output indicating,for

example, the number of floods or faults. Requirements differed significantly between

different organizations. It was decided that this group was out of scope for the project.

2) Supply and demand forecasters: Bespoke data feeds and

summaries required

Wind farm operators, utility companies and commodity traders all need to estimate supply

and demand of energy as far ahead as possible. Temperature, wind and radiation are the

most important variables. Some in this group already used bespoke decision making tools

based on climatological variability – a suitable application of seasonal forecasts, but out

of scope. We tested whether they could also make decisions more subjective output.

3) Meteorologists: Summary information and technical output

required

Several commodity traders, utility companies and large renewable farm operators employ

highly expert meteorologists to provide summaries of the expected upcoming conditions.

This group was particularly excited by the new forecast capability, and was experienced

in making decisions using long-range forecasts. We provided both summary information

and technical output to understand how these were used.

Example displays

Temperature anomaly (ensemble mean)