calibrating ensemble weather forecasts for warnings of ...i-react project aims to improve prediction...
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Calibrating ensemble weather forecasts for warnings of extreme weather events
K. Ylinen (1) and J. Kilpinen (1)(1) Finnish Meteorological Institute, Helsinki, Finland
ILMATIETEEN LAITOSMETEOROLOGISKA INSTITUTETFINNISH METEOROLOGICAL INSTITUTE
Contact: [email protected]
Finnish Meteorological Institute
P.O.Box 503, FI-00101 Helsinki, Finland
EMS Annual Meeting: European Conference for Applied Meteorology and Climatology 2017
4-8 September 2017 | Dublin, Ireland
A. INTRODUCTION B. GOALS
Finnish Meteorological Institute (FMI) is participating in
the EU-project I-REACT (www.i-react.eu) in which one
objective is to improve European level extreme weather
event detection. I-REACT aims to develop a European-
wide platform to integrate emergency management data
coming from multiple sources. The proposed system will
be targeted to public administration authorities, private
companies, as well as citizens in order to effectively
prevent and react against natural disasters.
E. CONCLUSIONS
FMI will provide the forecasted occurrence risk maps for
extreme weather events in terms of probabilities using
ECMWF (51 members, 16 km) and GLAMEPS (52 mbrs, 8 km)
ensemble models with lead times from a few hours to two
weeks for whole European region. Since ensemble forecasts
tend to be underdispersive and biased they are calibrated with
statistical methods. Calibrated ensemble forecasts will be used
also by other project partners who are providing wild fire and
heat wave hazard mapping services to the I-REACT system.
D. RESULTS
I-REACT project aims to improve prediction and management of natural disasters caused by extreme weather related events.
FMI develops and provides probabilistic weather forecasts of high-impact weather using ensemble models which are
calibrated to get more reliable forecasts.
Verification results (Fig 1 and 2) show that calibration methods used for gusts and temperature improve these ensemble
forecasts: especially spread correlates better with skill (RMSE), and also forecast skill is slightly improved after calibration.
Calibration methods
Temperature – normal ditribution
mean = a + b·MEAN + c·ELEV
stdev = exp (d + e·log(STD) + f·log(max(1,ELEV))
Wind speed and gust – box-cox t-distribution
mu (median) = a + b·MEAN + c·ELEV
sigma (variance) = exp (d + e·log(STD) + f·log(max(1,ELEV))
nu (skewness) = g + h·MEAN
tau (kurtosis) = exp(i)
Calibration coefficients
Estimated by training statistical
models using 30 most recent
point observations and
forecasts
For each lead time and for each
forecast cycle independently
Common for whole region
Updated once a week
Probabilistic
forecasts of extreme
weather events
Strong winds/gusts
Extreme high/low
temperatures
Heavy rainfall
Fig 1: Verification results for raw (blue) and calibrated (orange)
ECMWF-ENS maximum wind gust (3 hour) forecasts for lead
times from 3 to 144 hours. Verification period is June 2017
(00UTC analysis times), and verification includes all European
stations.
Fig 2: Verification results for raw (blue) and calibrated (orange)
ECMWF-ENS 2 meters temperature forecasts for lead times
from 3 to 360 hours. Verification period is June 2017 (00UTC
analysis times), and verification includes all European stations.
Fig 3: Probabilistic weather forecasts of extreme high
temperatures (left) and strong gusts (right). Forecasts are
made by using calibrated ECMWF-ENS model.
Fig 4: Fire Weather Index
(FWI) forecast which is
computed by Meteosim in
I-REACT project. FWI is
calculated using calibrated
ensemble weather
forecasts provided by FMI.
FMI
For a good ensemble system, ensemble spread and root-mean-square-error (RMSE) of ensemble mean should be positively
correlated on average.
Improving
Resilience to
Emergencies
through
Advanced
Cyber
Technologies
(MEAN~ensemble mean, ELEV~model elevation, STD~ensemble standard deviation, a-i~constants/coefficients)
C. METHODS
Fire Danger Classes FWI ranges
Very low < 5.2
Low 5.2 – 11.2
Moderate 11.2 – 21.3
High 21.3 – 38.0
Very high 38.0 – 50.0
Extreme >= 50.0
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 700256.