8.08 REAL-TIME VERIFICATION OF MESOSCALE MODEL PRODUCTS OVER COMPLEX TERRAIN

Manfred Dorninger*, R. Steinacker, T. Gorgas, B. Chimani, S. Tschannett, B. Bica

University of Vienna

Vienna, Austria

1. INTRODUCTION

Verification of NWP model products is done

primarily through comparison of model analysed

fields or through a comparison of selected surface

stations with nearby grid points. The new generation

of operational mesoscale forecast models with a

horizontal resolution of around 4km does not

contain its own data assimilation system in general

and consequently does not produce an analysis

field. Further, the horizontal resolution is still too

rough to compare instantaneous values of surface

stations especially over complex terrain with model

grid points with success.

The analysis tool VERA (Vienna Enhanced

Resolution Analysis) fits this gap. It produces

surface fields of potential temperature, equivalent

potential temperature, mean sea level pressure and

10m wind on a regular basis in real time without

using a first guess field. It is therefore best suited to

serve as an independent reference for model

evaluation. Downscaling of the fields in data sparse

areas is performed by using the fingerprint

technique, which introduces an a priori physical

knowledge of the field distribution over complex

terrain. For a detailed discussion on VERA including

the treating of the data quality problem see e.g.

Steinacker et al. (2000), Häberli et al. (2004), Bica

et al. (2005) , Steinacker et al. (2005) and

Tschannett et al. (2005).

* Corresponding author address:

Manfred Dorninger, University of Vienna,

Dept. of Meteorology and Geophysics , UZA II,

Althanstraße 14, 1090 Vienna, Austria;

e-mail: Manfred.Dorninger@univie.ac.at

2. NWP-MODELS

Currently products of three operational NWP-

models are compared with the VERA fields. These

are the ALADIN, the LM and the IFS-ECMWF

model. The evaluation is performed over the larger

Alpine area. ALADIN and LM are limited area

models with a horizontal resolution of 9 km and 7

km, respectively. Both, the surface fields of the 00

UTC-run as well as of the 12 UTC run are available

and evaluated on an hourly interval. The IFS model

of the ECMWF is a spectral (T511) and global

model. From this model the surface fields of the 12

UTC run on a three hourly interval enter the

evaluation procedure

3. COMPARISON METHOD

Since the VERA parameters like potential

temperature and equivalent potential temperature

are not standard model output parameters, they are

calculated from the appropriate model parameters

of the model surface. In a second step the model

data are interpolated to the VERA grid by using a

simple inverse distance weighting scheme

(Cressman scheme). In a final step the differences

between the VERA topography and the model

topography are taken into account by using the

values of the standard atmosphere for the vertical

interpolation.

3. RESULTS

3.1 Case study: Cold front with squall line

A squall line forms in front of a cold front over

southern Germany on June 23rd, 2002. The cold air

outflow stemming from this squall line rushes down

Figure 1. VERA analysis of potential temperature for June 24th, 12 UTC, 2002 (colour shading, oC, spacing 1 oC, ranging from

17 oC to 37 oC). Diamonds indicate stations used for the analysis. Dashed-dot lines represents state borders, blue lines show

main rivers and coastlines. Cold front impinges the Alps from the NW.

Danube valley and spreads over the western

Hungarian planes on June 24th, 12 UTC as can be

seen in Fig. 1. The cold front itself reaches at that

time the Viennese region and the main Alpine chain.

In the following some results of the comparison with

the ALADIN model are presented. Fig. 2 shows the

48 h forecast. The model could not describe the

cold air out flow properly. We find large differences

over the western Hungarian planes. The model

predicts too warm in the southern part and too cold

in the northern part. In general the whole position

and strength of the cold front over the Alpine barrier

was to some extent (3-4 oC) off compared to the

VERA analysis . These findings are supported by the

wind analysis. Fig. 3 shows the 2D surface wind

vectors as predicted by ALADIN and as analysed by

VERA. The VERA analysis shows a northwesterly

flow over the whole area of western Hungary, the

model instead shows southwesterly flow in the

southern parts indicating inflow of warm air

The example highlights the usefulness of such a

real-time evaluation for detecting deficits in the

current model run at a very early stage. The

differences are monitored and give the forecaster

valuable indications on which forecast he can rely

on. Currently, this method is applied at the Austrian

aviation weather service with success.

3.2 Statistical comparison

A systematic investigation of the differences may

give some hints about general model problems,

e.g., speed of frontal movement along the northern

Alpine barrier, pressure gradients across the Alps

during Foehn situations or systematic differences of

the wind direction between model and analysis.

Figure 2. ALADIN-VERA model comparison of potential temperature for June 24th, 12 UTC, 2002. Difference field (colour

shading, oC, spacing 2 oC, ranging from -9 oC to 7 oC), red colours: predicted values higher than analysed; blue colours:

predicted values lower than analysed. Dashed-dot lines represents state borders, blue lines show main rivers and coastlines.

Figure 3. ALADIN-VERA model comparison of surface winds for June 24th, 12 UTC, 2002. Red arrows: predicted surface

winds, black arrows: analysed winds. Dashed-dot lines represents state borders, blue lines show main rivers and coastlines

Figure 4. rms of difference field of mean sea level pressure for January 1st to January 16th, 2004. Dots represent all 13h to 24h

forecasts. Orange: IFS-ECMWF (overall rms=1.33 hPa), greenish: LM (overall rms=1.80 hPa), blue: ALADIN (overall rms=1.76

hPa).

As an example of a statistical evaluation Fig. 4

presents the pressure differences for the first half of

Jan 2004. It seems that we have to allow for a

certain limit of 1 hPa in the rms which can be

interpreted as noise in the difference fields . At the

beginning around January 2nd all models are very

close in their forecasts. Later in the month the IFS-

ECMWF model seems to have the better scores in

the rms. However, one has to interpret this with

care, since high resolution models predicting small

scale features on the wrong place have a higher

rms error than lower resolution models which do not

predict the small scale features anyway. Further, at

certain dates a single model has higher rms values

than the others (e.g., LM model at January 15th ).

This helps to identify model problems for specific

weather situations and can give the modellers some

hints about model deficiences .

A further example of a statistical evaluation is

shown if Fig. 5. We find for all models a positive

bias of the wind direction from 8o to 20o. Positive

values mean that the model wind has a too strong

geostrophic component. At this stage we can only

hypothesize for the reason. It might happen in the

post processing step when the 2D wind field is

interpolated from the lowes t model level to the

surface or it indicates a general problem in the

parameterization of the boundary layer. This has to

be investigated in more detail.

4. SUMMARY AND CONCLUSION

The NWP-model independent analysis tool VERA

has been introduced as an approach for real-time

model evaluation. The monitoring of differences

between model and analysis acts as an early

recognition system to identify model forecast

problems. The computationally inexpensive analysis

tool makes the difference fields already 20 minutes

after observation time available. This makes it to a

valuable tool for the nowcasting business. Statistical

evaluations over a longer time period can give some

hints about general model deficits. These all helps

to gain a better understanding of model results.

Figure 5. Differences of the wind direction (model-analysis) for January, 2004 for all 13h to 24h forecasts in

percent. Orange: IFS-ECMWF (BIAS=8o), greenish: LM (BIAS=20o), blue: ALADIN (BIAS=11o).

4. REFERENCES

Bica, B., S. Schneider, S. Tschannett, M. Dorninger,

M. Ratheiser, and R. Steinacker, 2005: High

Resolution Analysis and Nowcasting over Complex

Terrain by using Physical a priori Knowledge.

WWRP Internat. Symp. on nowcasting and very

short range Forecasting. 5-9. Sep. 2005, Toulouse,

France. Extended abstract 3.04.

Häberli, C., I. Groehn, R. Steinacker, W.

Pöttschacher, and M. Dorninger, 2004: Performance

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