Actual

Actual

© 2019 Lake Street Consulting Ltd.Whilst the contents have been composed with reasonable care by Lake Street Consulting Ltd, data used in the construction of this document are subject to uncertainty and contingencies

outside our control. We do not make any representation or warranty, express or implied, as to the accuracy or completeness of the information contained here.

Strength of ensembles in applications: from hourly to sub-seasonalIsla Finney1, Bethany Jackson1 and Josh Dorrington2

1 Lake Street Consulting; 2on a NERC internship from AOPP, Oxford to Lake Street

Hourly wind generation forecast for Germany• Below the plot of forecasts initialised at 00z on April 25th 2019• Ensemble members give some measure of uncertainty in extent or timing of

waning and building winds.• A poster at the April ECMWF workshop discussed the additional information provided

by ensemble members (Stanger and Finney, 2019).

ReferencesHintze and Nelson, 1998, Violin plots: A box plot-density trace synergism , The American Statistician; May 1998; 52, 2

Monhart et al, 2017, Skill of Subseasonal Forecasts in Europe: Effect of Bias Correction and Downscaling Using Surface Observations, Journal of Geophysical Research: Atmospheres, 123, 7999–8016

Stanger and Finney, 2019, Optimising the use of ensemble information in forecasts of wind power generation, Poster at

Workshop on Predictability, dynamics and applications research using the TIGGE and S2S ensembles, ECMWF, April 2019

Quantile bias correctionAnalysis: ERA5, 1° grid, data at 12z, from 1999-2019For each forecast system, for each grid point, for each forecast verification time, for each quantile a bias is calculated - by lead time, - by representative time period (month or 15 day period) from all years except the forecast year.

This ‘drop one out’ methodology is an evolution from Monhart, 2017 Fig 1 from Monhart, 2017

Reliability of EC45 day forecasts for weekly mean 2m temperature in France

Data so far is for Mondays only, 2000-2018, for the 10 hindcast ensemble members

DJF

MAM

JJA

SON

Actual temperature

Coming soon (MARS allowing...) the same for EC45 day twice weekly with 11 ensemble members. And then a comparison with ECMWF system 5 and SUBX.

Fore

cast

tem

per

atu

re

Sub seasonal: a work in progress• At the User Voice corner at UEF 2018, we commented on the apparent ‘jumpiness’ in the ECMWF seasonal

forecast for European T2m. Forecasts for the same verification time ‘jumped’ as the initialisation time varied.• Whilst some understood what was meant, others did not.• Laura Ferranti’s talk at UEF 2018 had a plot by Linus Magnusson where

all distributions in one plot are for the same verification time.So we put our results into this format…. Here is the Feb 2018 plot

• Analysis is ERA5 • Forecast ECMWF system 5 seasonal, 50 ensemble member forecast• Boxes are 25-50% and 50-75%; whiskers the ensemble max and min• The point was made, but the sample size was small (6 months)• So here are some preliminary results from a larger dataset (20 years)…

‘Violin’ plots for ECMWF system 5 seasonal, SUBX, and ECMWF 45 day forecasts

• An alternative plot design from the 90s:see Hintze and Nelson 1998

• As in Magnusson’s plots, all for the sameverification time

• But here we have the pdfs plotted side by side• With horizontal lines representing

the ensemble members

• Analysis: ERA5, 1° grid, data at 12z, from 1999-2019• 2m temperature for France at 12z• Data frequency is as available,

except EC45 where Thursdays yet to be added.

Next seasonal steps• Complete the datasets• Investigate the ebb and flow of skill

S2S forecasts and their specs

Hourly : availability of hourly data enables improved renewable power forecasts• Hourly data from the ECMWF IFS became available (to all) from October 2018• For renewable power management, which is weather variable, hourly (and ideally 15’) forecasts are key• Below we compare the EC00z runs with the Euro4 LAM

for German wind generation forecasts in the short term (0-48 hours).• Given the ECMWF cycle change planned for mid-June, the wind power curve

(see right) for the EC models is built from historic ERA5 data. • Once sufficient runs are available from the new cycle, this can be improved upon.• Error calculations below are for mid-December 2018 through mid-May 2019:

the bias of the EC00 runs is 65-75% that of the Euro4, the RMSE 85-90%.

Name Centre (source) Forecast Duration Initialisation frequency Resolution Time span Ensemble size

EC45 ECMWF (MARS) 46 days 2 x week 0.5° / 6h May 1999 - 11 (hind), 51 (fore)

SEA5 EMCWF (Copernicus CDS) 7 months 1 x month 1.0° / 12h Jan 1992 - 25 (hind), 51 (fore)

GEFS EMC (SUBX project) 35 days 1 x week 1.0° / 24h Jan 1999 - Dec 2016; July 2017 11 (hind), 21 (fore)

CNRM MeteoFrance (S2S database) 32 days 2 x month (hind), 1 x week (fore) 1.5° / 24h Jan 1993 - Jan 2014; May 2015 - 15 (hind), 51 (fore)

HMCR HMCR (S2S database) 61 days 1 x week 1.5° / 24h Jan 1985 - Jan 2010; Jan 2015 - 10 (hind), 20 (fore)

BoM BoM (S2S database) 62 days 6 x month 1.5° / 24h Jan 1981- Jan 2013; Jan 2015 - 33

Utilised S2S forecasts

Selected unutilised S2S forecasts