Third SRNWP Workshop on
Statistical Adaptation
12 June 2005, Vienna
(Austria)
Summaries of the Presentations
Session A: Ensemble Forecasts
J. Kilpinen, FMI
Calibration of EPS winds
Experiments with very recent data (October 2004  March 2005)
Correction of the 10m winds not only for the deterministic winds but also for the EPS winds.
Determination of probabilistic forecasts from deterministic forecasts by use of two methods:
 forecast error distribution
 neighbourhood method (the Suzanne Theis method).
No calibration of the EPS in the traditional way where a large sample of past forecasts and observations are
needed. Calibration is made by Kalman filtering. First, the ensemble mean is
Kalman filtered and after each member is Kalman filtered using the same
coefficients.
Kalman filtering is able to reduce
biases and produce better probability forecasts for most of the 6 stations
considered in terms of ROC curves, ROC areas and Brier Skill Scores.
A. Cofino
et al., University of Cantabria (Spain)
Analysing and downscaling ensemble forecasts with
topologypreserving clustering methods
The Institute for Artificial Intelligence and Meteorology of the University of Cantabria (Spain) has been with 3 highlevel presentations one of the major contributors of the Workshop.
A principal
component analysis of ECMWF grid point values of several parameters at several
levels over Iberia is realized in order to drop the number of predictors from
more than 6000 to about 600. Then a clustering based on these principal
components (PC) is performed. For each member of the cluster, a mean
precipitation amount (mm/24h) has been computed from the observations.
In operational
mode, there is a sharp drop in the quality of the precipitation forecasts after
day +3 due to the loss of skill of the ECMWF forecasts. To alleviate this
problem, the downscaling has been applied to the 51 members
of the ECMWF EPS. Each EPS member finds its place in one member of the cluster
in the PC space. Believing that the error is directly correlated with the
spread of the EPS members, the authors want to use the spread a measure of
predictability. In their case, the spread is immediately given by the
distribution of the 51 members over the cluster. To measure the dispersion over
the cluster, they use the EckertCattani "SelfOrganising Map"
algorithm and the "Generative Topographic Map"
concept which is a probabilistic reformulation of the SelfOrganizing Map
algorithm.
D. Cattani and T.
Comment, MeteoSwiss
Short presentation
about confidence index
How to inform the public about the uncertainty of a weather
forecast? Or: How much trust should the public give to a particular weather
forecast?
The solution of MeteoSwiss lies in the definition of a "confidence
index".
The confidence index (CI) is based on the dispersion of the ECMWF EPS members
in an adaptive table of 144 weather situations. The weather situations are
defined by their respective H500hPa and T850hPa patterns over Europe and Eastern Atlantic.
The confidence index is determined with respect to a climatological dispersion.
Is the EPS dispersion larger than the climatological one, the ECMWF
deterministic forecast is considered totally unreliable and receives the mark
zero. A confidence index of 10 corresponds to a certainty.
But since this CI is related to a large area of Europe, a low index may still
be related to the same kind of weather over the small Swiss area, or a high
index to different types of weather over Switzerland. In order to regionalize
the CI, we consider the dispersion of the EPS for 3 specific parameters
(temperature, cloudiness, precipitation), for 3 grid points over Switzerland.
Those dispersions are again compared to climatology. A new CI is then
calculated, as a weighted sum of the CI over Europe and the CI of each
parameter.
M. Vallée, Environment Canada, RPN, Dorval
(Canada)
Verification of weather elements forecast by
the Canadian EPS
It has become a nice tradition that scientists of
Environment Canada, more precisely from "Recherche en Prévision
Numérique" in Dorval, cross the pond to visit the different EUMETNET SRNWP
Workshops.
For the first time in the series of our Statistical
Adaptation Workshops was RPN represented. Marcel Vallée reviewed the past and
present EPS works of RPN and informed us about their future plans.
He also presented statistical scores for the
verification of the EPS forecasts which are not used in Europe (at least not by
the National Meteorological Services) as the "Probability Score", the
"Reduce Centered
Random Variable" and the "Continuous Ranked Probability Score".
Session B: Dynamical Adaptation
T. Haiden, ZAMG
INCA  High resolution
downscaling of NWPmodel forecasts
Thomas presented the "Integrated Nowcasting through Comprehensive
Analysis" (INCA) system presently in development at the ZAMG.
Some
characteristics of this system:
 Effort
has been made to derive the best possible precipitation analyses by combination
of the radar and rain gauge information

Precipitations up to 6 hours are forecast by an extrapolation (of
the precipitation areas of the analyses) based on motion vectors determined by
precipitation pattern correlation.
Tests show that this
forecast method is better than the Aladin precipitations till +4 / +5 hours.
The 2D cloudiness
analyses are determined by using the cloud type analyses produced operationally
by EUMETSAT (from MSG) and the sunshine intensities measured by the Austrian
automatic observing network TAWES.
The 3D temperature
analyses are based on shortrange ALADIN forecasts which are modified with
respect to the 1km orography of the INCA grid. The INCA temperature forecasts
are clearly better than the Aladin ones till +7 / +8 hours and remain slightly
better till +12 hours.
The 1km winds are
determined in the following way: after a dynamical adaptation of the 9.6km
winds from the Aladin forecasts to a 2.3km mesh, the winds of the TAWES observing
stations are nudged simultaneously with a downscaling to the 1km INCA grid.
P.
Crochet, Icelandic Meteorological Office
Precipitation
mapping in Iceland using the linear theory model of orographic precipitation
A comparison of the yearly
accumulated precipitations between the observations and ERA40 shows large
difference in Iceland according to the type of stations. For station in open
terrain, ERA40 is very good, but it shows large precipitation overestimates
for station in rain shadow. For stations where precipitations are enhanced by
orography, this enhancement is too weak for the ERA40 precipitations.
It must not be
forgotten that a rain gauge network systematically underestimates precipitation
due to evaporation, plashing and aerodynamical effect, primarily for snow.
In order to improve
the ERA40 precipitations over Iceland and, consequently, the ECMWF
precipitations forecasts, the ERA40 precipitations have been corrected by the
use of a linear model for orographic precipitations [Smith and Barstad, 2004].
The first results look very promising.
F.
Wimmer and T. Haiden, ZAMG
Analysis
of residual errors in T2m nowcasting
For the station
Vienna, in February 2003, for the temperature, the persistence is better than
the Aladin DMO till +4 hours and the adapted climatology is better till +10
hours. This shows that there must be room for improvement by use of an adequate
method.
By introduction of
an advection algorithm, the number of better forecasts by use of this algorithm
largely exceeds the number of worse forecasts for the period Nov. 2004  March
2005.
Session
C: Operational Applications
S.
Roquelaure and T. Bergot, MétéoFrance
Predictability
of fog and low clouds at Paris CDG airport
Use of the 1D
version of the ISBA soil model and of the 1D COBEL model.
The initialization
of the low clouds and fog is made by explicit introduction into the analysis.
Several initial
conditions have been tested and the results of the integrations compared:
 with
initialization of low clouds and fog
 without
initialization of low clouds and fog
 with
initialization of low clouds and fog and cloudiness from the Aladin model
 with
initialization of low clouds and fog and persistence of the observed cloudiness
at initial time.
These tests showed a
large spread for the downward IR fluxes. The best setup has been the last one
(persistence of the observed cloudiness) and the worst the one without
initialization (its bias was 20 times larger).
It is interesting to
note that the weakest forecasts are for 3 UTC in the afternoon: highest false
alarm rate and lowest hit rate.
M.
Rohn et al., DWD
Objective
optimisation of local forecast guidances
The DWD presented a
very comprehensive review of adaptation and optimisation of its NWP forecasts
results.
From their global
model GME: 3500 point forecasts worldwide, each with 21 surface weather
elements, most of them MOS processed.
They also work on
objective optimisation and one of the methods used is to combine model results by
weighted averages. Example for 2 models (global and regional): the weights of
each model will vary during the integration as a function of the range.
They also intend to
use a Bayesian Model Average scheme in order to use the best model  not known
in advance  for any forecast range.
DWD works also
intensively in the nowcasting range. Already developed is the
"SatelliteWeather": analysis with METEOSAT and SYNOP; forecast by
extrapolation.
Planned is BlitzMOS
whose predictand will be the probability of lightning.
H. Hoffmann and V. Renner, DWD
Interpretation
of the new DWD high resolution LMK
Present configuration of the LMK: 2.8 km grid resolution, 50 levels, forecast lead time: 18 hours.
Highresolution numerical weather forecasts include noticeable stochastic elements already in the short range. Therefore direct model output for deterministic forecasts should be transformed in order to suppress essentially unpredictable small scale structures.
Probability information for the exceedance of given thresholds should be derived by statistical means.
The following methods are planned to reach these aims:
 Derivation of statistical (probabilistic) forecasts from a single (deterministic) model integration by means of the neighbourhood method (NM)
 As there will be 8 LMK integrations per day (every 3 hours), use of timelagged ensembles.
First results based on two short periods of time:
= Deterministic precipitation forecasts:
Neighbourhood averaging (space and time) not better than simple spatial averaging over 5x5 or 15x15 grid points. Averaging over 5x5 points better than over 15x15.
For some of
the elements (e.g. precipitation, gusts) a recalibration of the distributions
of the smoothed field towards the distribution of the original field will be
done.
= Probabilistic precipitation forecasts:
Quality improves by increasing the size of the spatiotemporal neighbourhood. The optimal neighbourhood has not yet been determined.
Increasing the temporal neighbourhood size leads to better results than increasing the spatial neighbourhood (with the same number of points in the spatiotemporal neighbourhood).
H. Petithomme, MétéoFrance
New operational methods
and forecast parameters at MétéoFrance
The author reviewed the operational production at MétéoFrance.
Postprocessing is made for 2500 sites in France and up to 6000 worldwide. The method used depends on the parameter: linear regression + filtering for T2m and U; linear discrimination for dd/ff, vis and N.
Wind gusts:
Computed for 374 sites over France. 2 thresholds: 28 kt and 43 kt.
Several methods tested: regression, linear discriminant analysis, pseudo PPM, direct wind gust computation. In the predictor selection procedure, the surface wind stress came first, which gives to the turbulence produced by wind shear the main cause for wind gusts.
We can say that
 higher threshold (43 kt) much more difficult to predict
 regression gives poor results
 high HR can be achieved (for example with linear discriminant analysis) but they are always linked with high FAR.
Low visibilities:
Five different classes are forecasted with linear discriminant analysis at 16 locations over France.
F. Schubiger, MeteoSwiss
Shortreport of some operational
adaptations at MeteoSwiss with aLMo
MeteoSwiss presented an overview of its presently operational statistical and dynamical adaptations applied to its highresolution model aLMo.
Statistical adaptations:
 Kalman filtering: T2m, Td2m
 Verification of the precipitations by averaging over 5 and 13 grid points around observation point
 Verification of the cloudiness by averaging over all grid points up to 30 km of observation point
Dynamical adaptation:
 Wind gust computation with the Brasseur method
Other products:
For airplane icing warnings: production of
 liquid water content charts at 700 hPa
 temperature charts at 700 hPa.
Supercooled cloud droplets play a very important role in aviation safety as they are the cause of airplane icing.
Forecast maps showing the amount of cloud liquid water can be of great importance for the aviation. It is by temperatures of 2/3 degrees that icing is the most dangerous, when glazed, transparent ice forms. At 6/7 degrees, rimed, opaque ice forms, which is less dangerous as it remains on the edges of the wings and does not spread on the wings as the glazed ice.
Session D: Statistical Adaptation /
Neural Networks
A. Manzato
Short term rainfall
forecasts from soundingderived indices using neural networks
In the plain of FriuliVeneziaGiulia in Northern Italy, there is a radiosounding (RS) station (UdineCampoformido) and 15 automatic observing stations.
The RS station makes 4 soundings a day. Each sounding is associated with the maximum of the 15 6hourly accumulated precipitations measured by the automatic stations (the 6hourly periods start at sounding launch times). Only precipitation events with at least 5mm in 6h at at least one of the 15 stations are considered.
For each RS of UdineCampoformido, a very large number of indices (as CAPE and K index) and indications (as height of the tropopause and lifting condensation level) are recorded.
These indices will form the input of a neural network whose output will be the maximum of precipitation to be expected for the next 6 hours at at least one of the 15 observing stations.
The setup and the computation of the neural network are explained with great details in the presentations.
An interesting point of the work is to look at the ranking of the indices chosen as input by the forward selection algorithm for all the precipitation cases (>=5mm/6h).
1. mean relative humidity in the lower 500hPa
2. maximum buoyancy
3. midlevel density weighted wind (vcomponent)
4. mean water vapour flux (vcomponent) at the previous sounding (6 hour before).
The same work has been done for the forecast of two precipitation classes:
one class is >20 mm / 6h at at least one station; the other class is >40mm / 6h at at least one station.
For the 20 mm class as well as for the 40 mm class, the forward selection algorithm picked up in both cases the same indices as inputs and in the same (decreasing) order:
1. mean water vapour flux (vcomponent)
2. Kindex
3. standard deviation of the radiosonde vertical velocity.
But the forecasts given by the neural network for this last class are very poor. They are better for the 20mm class and, according to the author, good for RR> 5mm/6h.
C. Sordo and J. Gutiérrez, University of Cantabria (Spain)
A
comparison of PCA and CCA predictors for wind speed downscaling using logistic
regression and neural networks
PCA = Principal
Components
CCA = Canonical
Correlation Analysis
The authors have
started their presentation by showing for a simple example (temperature in
Santander to be deduced from the temperature of the ERA nearest grid point)
that a linear relationship (regression) is totally inappropriate. Thus
nonlinear relationship between predictands and predictors must be used.
The problem is the
determination of the probabilities of wind speeds > 50 kt at 11 stations in
the North of Spain using as input T, Z, U, V and H at 27 grid points of ERA40 for the period January 1977  August
2002.
The first step is to
reduce the amount of data of the predictors.
Two methods are
used:
 Principal
Component Analysis (PCA). With only 10% of the PCs, they can reconstruct fields
presenting a RMSE of 2 % only.
 Canonical
Correlation Analysis (CCA): an output data vector on a lower dimensional space
which should have the maximum correlation with the equally reduced input data
vector must be defined.
The PCA and the CCA
data are used as input for two models:
 Logistic
Regression
 Neural Network.
The best results
measured by the Brier Skill Score are given by the Neural Network using as
input 10 Principal Components. The use of a larger number of PCs has not
improved the results significantly.
J.
Vehovar, Environment Agency of the Republic of Slovenia
Temperature
forecasting in terms of quantiles
Instead of having as
predictand for a given range and location only the value of the parameter we
are interested in (for example temperature), the method will yield a
distribution of its possible values in terms of quantiles. For temperature, the
regressions to be performed will give the temperature of the different
quantiles, for example 5%, 25% 50%, 75% and 95%, and the median. As predictors,
DMO (Aladin/SI) and observations have been used.
J.
Bremnes, met.no
Quantile
forecasting in practice using local quantile regression
The aim is to
forecast quantiles. For example: for a given probability of rain (say: 40%),
which will be to forecast the probabilies of precipitation more than a given
amount. The method defines a weight for each data point; the most similar data
to historical ones should get most weight.
In the training
phase, evaluate the quantile function at predictor values by looking at the
corresponding observed precipitations.
H.
Seidl, ZAMG
Experiences with PPMmethods applied to forecast
local and areal amounts of precipitation in Austria
PPM = Perfect Prognosis Method
Firstly the observed precipitations (6hourly and 12hourly
accumulated) are interpolated onto a regular grid with height correction (the
increase of precipitation with height is deduced from the seasonal climatology
of Austria).
Method for the Areal PPM:
1. Determination of a multiple regression equation for each area.
Predictors values are taken from ECMWF archived analyses for the grid
point most representative of the area considered. Corresponding areal
precipitations are taken from the observation data base. Then the regression
coefficients are computed. This work must be done once for each area.
2. Operational procedure: From the ECMWF forecast fields, the same predictors are extracted and inserted into the multiple regression equation.
Next to this PPM for area precipitation forecasts, ZAMG has also in a similar way developed a PPM for forecasting local amounts of precipitation, but the method has a tendency to underestimate severe precipitations. This has been alleviated with the "MAXMIN method" which, according to the values of the parameters (predictors), will compute the precipitations either in a maximum mode or in a minimum mode. This method deteriorates the classical scores as MAE or RMSE, but the HR for strong precipitations is increased, concomitantly with an acceptable increase of the FAR.
G. Csima, HMS
Using multiple linear
regression for postprocessing model
output data
At the HMS,
forecasts of the 00 and 12 UTC of the ECMWF and ALADIN models are 3hourly
processed between +12 / +60 for ECMWF and +3 / +48 for ALADIN.
The
postprocessing is done by multiple linear regression at every SYNOP station.
There is a different set of regression equations for each month.
The predictors
are: MSLP, T2m, RH2m, U10m, V10m, N. At 925, 850,700 and 500 hPa: Z, T,
U, V, RH. Together 26 predictors.
The selection of the predictors for the linear regression equations has been done by forward selection. The quality of the procedure has been assessed by the ANOVA method (ANalysis Of VAriance).
In simple linear regression mode for the T2m, it is with 1314 predictors that the reduction of the variance of the errors  the difference between the computed temperatures and their corresponding observations  is maximum. By adding supplementary predictors, the reduction of variance decreases.
For the full test period (Jan, Feb, Mar 2005) for the T2m as predictand, the most significant model corrections took place in mountainous and hilly terrain.
P. Crochet, Icelandic Meteorological Office
Prediction of T2m and 10m
windspeed in Iceland using a Kalman filter
A precise mathematical description of the Kalman filter technique has been presented by the author. The author presented results obtained with quantification of the noise statistics (in many NWS, Kalman filter is used with an observation noise sets equal to zero).
A. Persson, SMHI
From 2D to 3D Kalman
filtering of NWP output
The author
claims that Kalman filtering of a twodimensional expression (2DKF) not only
corrects the bias but also the variance of the forecasts.
This can be
understood if we consider the correction C = A + Bx where x is the forecast and
A (the "bias") and B (the "slope" and therefore the
variance) which are recursively estimated by the Kalman filter. The improvement
in variance meant that an underforecasting of extreme events such as
temperature < 20° was almost avoided.
This has
been very well shown by the HIRLAM 2m temperature and 10m wind speed
corrections for different observing stations in Sweden. The author has also
compared the application of a 2D versus a 3DKF to
the temperatures of a very cold observing station. Account was now taken also
of the forecast with respect to the last available observation. This type of
3DKF decreased the RMSE, but had  compared to the 2DKF  a dampening
influence with fewer forecasts with temperature < 20°.
If the
anomalies are used instead of the full values, a large change in B will not
necessarily imply large changes in A as well and the Kalman filter will achieve
a quicker adaptation.
J.M. Gutierrez et al., University
of Cantabria (Spain)
Multisite
Spatial Analog Downscaling Methods with Bayesian Networks
The main point of this presentation was to show the use of the Bayesian Networks (BN) as a nonlinear statistical downscaling method linking the ERA40 grid point values over Iberia with the local observations.
BM are very popular in several fields (as biology and medicine), but are just starting to be used in meteorology.
BN have the advantage to be global in the sense that only joint probabilities encompassing all the variables (largescale and smallscale) can be computed. The graphs defining the dependence structures do not separate for the downscaling process the large scale (ERA40 gridpoint values) from the local observations, as other downscaling methods  for example the multiple regression  do it.
The authors have not restricted themselves to the case of singly connected networks over Spain (ie. networks with only one path between any two nodes). This limitation would have rendered the search of a solution for the joint probabilities much easier.
This presentation can be considered as a breakthrough in the field of the meteorological statistical downscaling.
The end