1. SMALL SCALE CASE STUDIES Aulikki Lehkonen Finnish Meteorological Institute Regional Services, Western Finland photos: Veera Lehkonen

2.  Why?  To whom?  How?  Benefits

3. Why?  To gather knowledge about weather situations, that are difficult to forecast, but occur frequently / are of special importance  To concentrate on local phenomena that can not be taken into account in big training courses  To pass on the “silent knowledge” to less experienced colleagues less experienced colleagues  To give information to researches, product developers and model experts product developers and model experts

4.  Colleagues  Product developers  Researchers  Modelling people To whom?

5. How?  Guidelines, presentation templates  Photos, newspaper clips  Soundings, surface charts, data from masts, ceilometers etc.  Satellite and radar images  Numerical fields  Analysis and conclusions

6. The FMI Case Study Archive  Ready case studies  Directions for making studies  A list of proposed subjects  A case archive, that can be searched by date, key word(s) or type of weather situation key word(s) or type of weather situation  Training courses

7. Some examples  Thick fog on the Gulf of Finland  Northeasterly flow and low clouds  A thunderstorm in a warm front  Freezing drizzle in Western Finland  Snow load caused collapse of roofs

8. An example of a report of a case: Situation type: Records broken, Front analysis Date and name:04-10-2002 - 05-10-2002 The first snowfall of the autumn in the western part of Finland Description: The first snowfall came 1,5 months earlier than usual. In Jokioinen the snow depth was 21 cm. Also the front analysis was difficult. Time: Evening and night Area: Western Finland Location: - Key words: Snowfall Reporter: Jenni Teittinen Further research: Soili Salmi Comment 1: I’ll research the case further, also considering damages caused by snow (power failures). Soili Salmi Comment 2:I was on duty that night and noticed that the HIRLAM-model differed remarkably from ECWMF and ICM-UKMO models. Vesa Nietosvaara

9.  Real situations, connected to the everyday work  The point of view is practical  Much visual material: images, charts etc.  A compact entity: can be studied in a relatively short time  Can be made by forecasters themselves Advantages over other training methods:

10. Benefits  New knowledge of local phenomena, that may lead to better forecasts that may lead to better forecasts  Experienced forecasters can share their “silent knowledge” with others their “silent knowledge” with others  The maker of the study gains knowledge  Forecasters are inspired in their work by a break from routines by a break from routines

11.  Raise of new questions and ideas among colleagues  Raise of discussion about working methods  Forecasting problems are presented to other groups