Case 2Case 2

15 March 200315 March 2003

Case 3Case 3

2 – 3 April 20032 – 3 April 2003

FORECAST WEATHER

LAMI (01-04-03, 00 UTC run)

step +36h

LIGHTNINGS

02-04-03, 12.30 UTC

METAR - 02 april 2003, 15.00 UTC

LIVP 021455Z 01012KT 0000 - SN FZFG VV000 M04/M04 Q0996 RMK MON INVIS VAL INVIS

LIVE 021455Z 34012KT 2000 BR SCT008 BKN020 00/M04 Q0999 RESN RMK OVC

LIVO 021455Z 13001KT 1200 SN BR OVC005 02/02 Q1001 RMK OVC

LIVD 021455Z 00000KT 1200 -SN OVC005 M00/M00 Q1000 RMK OVC

Very good agreement with the snow-falls recorded in the meteo observations

LIGHTNINGS – 02/04/2003, 23.30 UTC

FORECAST WEATHER

LAMI (02-04-03, 00 UTC run)

step +24h

LIGHTNINGS – 03/04/2003, 06.00 UTC

FORECAST WEATHER

LAMI (02-04-03, 00 UTC run)

step +30h

Planned Planned DevelopmentsDevelopments

Developments in progressA verification procedure has been implemented, but at the moment we cannot show any representative data because of our poor statistics(about only 50 days of forecast LAMI data)

The observation forming the control dataset were collected on 6-hourly basis from the synoptic network managed by UGM

Weather phenomena are being verified for about one hundred synop lowland stations satisfying the COSMO WG5 specification ( , where is the station height and the height of the neighbour land grid points)

mHHmH nss 100,700 sHnH

A synchronus and co-location comparison between observation and forecast has appeared as a too strong condition and a not very reasonable criterion for a fair objective verification, because we have to take into account the origin and the nature of our errors

Verification procedure

The post-processing output, actually, may not match well with near-surface point observations due to several reasons:

• intrinsic wrong behaviour of the model (systematic error, model error)

• inconsistency in the surface representation of the model

(as far as the terrain elevation and the percentage of the surface covered by water)

• inconsistency between the model definition of the precipitation (a value representing an integral over the grid cell) and the meaning of the observed precipitation (a local measure)

• many parameters under control are not explicit variables of the model but are produced through some internal post-processing which can be an extra source of error

• mismatchting between the spatial representativity of the observations and the horizontal model grid size (representativity mismatch)

• assumption of wrong or unsuitable thresholds or procedures in the post-processing routines

To cope with these problems we tought to realize our comparison observation-forecast by means of an upscaling procedure.

For each observation-point we are considering all grid points with spatial distance from the former less or equal to 3 (grid spacing, about 7 km for LAMI ), so that we probabilisticly perform our comparison in such a procedure that, at the same time, gives a more adequate weight to the shape of the considered weather system in spite of little phase errors of the model.

Δs

Future developments- To improve the current AWI version by tuning

thresholds thanks to the objective verification results and the forecasters‘ feedback

- To apply AWI post-processing to LM runs driven by IFS model, i.e. with ECMWF boundary condition (performed by Lucio Torrisi) in order to evaluate the importance of analysis and BC quality to a successful weather phenomena detection

- To make further refinements to the postprocessing method integrating the current suite by means of new and parameter-oriented approaches (MOS, neural networks)