A downscaled ensemble prediction system for offshore weather

A downscaled ensemble prediction system
for offshore weather forecasts
PO.003
Torge Lorenz
Uni Research
Abstract
Results from the ensemble forecast
Sea-surface temperature perturbations
A short-term weather forecast for the North Sea is
being developed to reduce costs and improve safety of
marine operations during installation and maintenance
of offshore wind turbines.
We apply the mesoscale Weather Research and
Forecasting (WRF) Model to downscale the global
ensemble prediction system from the European Centre
for Medium-Range Weather Forecasts (ECMWF) to a
horizontal grid spacing of 3 km in the North Sea.
Near-surface wind speed is one of the key
variables in determining safety of marine operations. To
increase the ensemble spread and improve the
probabilistic forecast of near-surface wind speed, we
introduce perturbations of the sea-surface temperature
(SST) into our downscaled ensemble.
The SST perturbations are designed to represent smallscale SST features which are not resolved by the largescale ECMWF model but should be present on the
smaller scale of our WRF ensemble. Their amplitude is
based on statistical analysis of the Multi-scale Ultrahigh Resolution (MUR) Sea Surface Temperature
Analysis on different spatial scales.
Both the dynamical downscaling and the additional
small-scale SST perturbations are shown to increase
the ensemble spread in near-surface winds.
The large-scale ensemble forecast at the ECMWF is
initialized twice every day, at 00 and 12 UTC, with 50+1
ensemble members. For a test period of 5 continuous
weeks in Autumn 2014, we dynamically downscale
each of these global ensemble forecasts to a 3-km grid
in the North Sea for a short-term forecast of 27 hours.
The sensitivity of the ensemble spread and the
ensemble mean to the downscaling and introduction of
additional small-scale perturbations of sea-surface
temperature will be investigated.
The figures below show an example of the actual smallscale SST features which are missing when the WRF
ensemble is initialized with the large-scale ECMWF
ensemble data (Figure 4) and the small-scale SST
perturbations which we introduce to represent these
missing features (Figure 5).
The aim of the perturbations is not to exactly recreate
the missing features, but to create SST features with
the same properties. These perturbations can then be
used to create different initial states of SST, which on
average all have the same spatial variability.
Sea-surface temperature analysis
When the regional WRF ensemble is initialized with
large-scale SST data from the ECMWF ensemble, the
SST in the WRF model will be too smooth and lacking
genuine features on the 3-km scale of the WRF model.
To quantify the loss in spatial variability in the SST, we
compute the spatial variance between high-resolution
MUR SST data (0.011 grid spacing) smoothed to 3km grid spacing and to ECMWF resolution.
From Figure 1 below we can see that most of the
additional variability in SST on the 3-km scale of the
WRF model can be accounted for by a proportional
relationship of 1.8 times the spatial variability present in
the large-scale SST data inherited from the ECMWF
ensemble.
Our small-scale SST perturbations are thus designed to
increase, on average, the spatial variability in SST by
80%.
Figure 3. Original ECMWF EPS forecast for 10-m wind speed
at the location of research platform FINO3. Shown are only the
21 ensemble members which are downscaled in the regional
WRF ensemble.
The example above illustrates how the spread in the
original ECMWF ensemble forecast of 10-m wind
speed (Figure 3) is increased by the dynamical
downscaling
with
additional
small-scale
SST
perturbations (Figure 4).
An increase in ensemble spread is visible already in the
first hours of forecast time. This indicates that the
downscaling with additional SST perturbations may
improve the common issue of under-dispersive shortterm ensemble forecasts.
The mean value (blue line), here shown for the
medians of the first 98 percentiles of the large-scale
SST data set, is followed well by the according leastsquares linear fit (red line).
The proportionality factor from the least-squares linear
fit is 1.84 ( 0.01), with a near-zero constant of 2*10-4
K2.
Figure 5 (below). Example of small-scale SST perturbations
applied to the regional WRF ensemble forecast.
Summary and Conclusions
Figure 4. Forecast of 10-m wind speed from the regional WRF
ensemble forecast with additional small-scale SST
perturbations.
Figure 1. Spatial variance of SST on the 3-km scale of the
WRF model as a function of spatial variance of SST on the
larger scale of the ECMWF ensemble forecasts.
Figure 4 (above). Example of SST features missing when
WRF is initialized with large-scale ECMWF EPS data.
The improvements in terms of accuracy (RMSE)
and other common ensemble verification metrics, such
as continuous rank probability score, reliability and rank
histograms, will be quantified using the in-house
verification software from the Norwegian Meteorologisk
Institutt, HARP (Hirlam-Aladin R Package).
With HARP, the ensemble forecasts can be compared
to a vast array of meteorological observations, both
before and after downscaling and with and without
additional SST perturbations.
Most of the SST variance on smaller spatial scales can
be accounted for by proportionality with SST variance
on larger spatial scales (Figure 1).
SST perturbations on the spatial scale of the
downscaled ensemble forecast are developed
exploiting this proportional relationship. These
perturbations qualitatively represent the actual missing
features on that scale well (Figures 4 and 5).
The dynamical downscaling, together with the
additional small-scale SST perturbations, improves the
ensemble spread already in the first hours of the
forecast (Figures 2 and 3).
Both the dynamical downscaling and the SST
perturbations help to accommodate additional
uncertainty in the short-term weather forecast and add
value to the risk assessment for marine operations
related to offshore wind energy.
Acknowledgments
The MUR SST analyses are freely available at the Physical
Oceanography Distributed Active Archive Center.
The ECMWF EPS data were provided via an exclusive
operational data stream from the European Centre for MediumRange Weather Forecasts.