Simulation of micrometeorological fields during a frost event in the Po Plane

1. WORLD METEOROLOGICAL ORGANIZATION FOOD AND AGRICULTUREORGANIZATION COST ACTION OF THE EUROPEANSCIENCE FOUNDATION WORKSHOP ON CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE (14-17 June 2005, Bologna, Italy) Simulation of micrometeorological fields during a frost event in the Po Plane M. Nardino, G. Antolini, F. Rossi, T. Georgiadis, G. Leoncini, R. Pielke CONSIGLIO NAZIONALE DELLE RICERCHEISTITUTO DI BIOMETEOROLOGIA

2. A RADIATIVE FROST • THE PROBLEMA strong spring frost episode was recorded in the Emilia Romagna region during the 17 March 2003 night. The event was a typical radiative late frost frequent in this region. WHAT is a RADIATIVE FROST? • Clear sky nights; • heat cumulated during the day is rapidly transferred to the atmosphere causing a strong decrease of the surface temperature leading to an inversion layer; • the air temperature increases with the height; • the inversion layer height depends on the local atmospheric conditions.

3. INVERSION LAYER CLEAR SKY THE ATMOSPHERIC CONDITIONS

4. GOALS • To simulate this frost event with an atmospheric diagnostic model (MODAMBO_2D) to obtain a regional map of the principal micrometeorological fields. • To give an inputfor the frost risk mapping of the Emilia Romagna region. • To have the local micrometeorologystarting from the results of a fluido_dynamic model (RAMS- Regional Atmospheric Model System). • To forecast the frost events(RAMS+MODAMBO) in order to give a early warning to farmers. • To use the diagnostic modelfor other agrometeorological applications (i.e. fire risk index, ecophysiology modeling, crop production,….).

5. MODAMBO_2D THE MODEL INPUT_1: geometrical characteristics of the domain.1) topography map;2) land use map; Surface surface roughness albedo length INPUT_2: meteorological conditions.the model needs:1) air temperature 2) relative humidity3) wind speed4) wind direction obtained from the meteo stations of the regional hydrometeorological service (ARPA-SIM).

6. MODAMBO_2D THE MODEL OUTPUT_1For each grid point:1) air temperature (°C)2) relative humidity (%)3) cloud fraction (tenths)4) Global Radiation (W m-2)5) Net Radiation (W m-2)6) Soil Heat Flux (W m-2)7) Sensible heat flux (W m-2)8) Latent heat flux (W m-2)9) friction velocity (m/s)10) U wind speed component (m/s)11) V wind speed component (m/s)12) mixing height (m) THE MODEL OUTPUT_2Some files that can be utilized by MODAMBO_3D, able to compute the vertical profiles of the principal micrometeorological fields.

7. SLOPE AZIMUTH MODAMBO_2D THE MODEL THEORY 2D terrain following model  For each grid cell the slope and the azimuth is computed : N3 (i+1,j+1) Cell (i,j) N1 (i,j) N2 (i+1,j)

8. MODAMBO_2D THE MODEL GEOMETRIC INTERPOLATION For each meteorological station and for each grid cell we compute: The geometric interpolation is utilized to calculate the values for each grid point of air temperature, relative humidity and cloud fraction.

9. MODAMBO_2D • THE MODEL • WIND INTERPOLATIONThe model takes into account the effects of: • Surface roughness

10. MODAMBO_2D THE MODEL WIND INTERPOLATIONThe model takes into account the effects of: 2) Topography:

11. MODAMBO_2D • THE MODEL • MICROMETEOROLOGY PARAMETERIZATIONSThrough the measurements of air temperature, wind speed and relative humidity for each grid cell are computed: • global radiation; • cloud fraction; • net radiation; • soil heat flux; • friction velocity; • Monin-Obukhov length; • sensible heat flux; • latent heat flux; • mixing height; • …. By using parameterizations verified through micrometeorological experimental campaigns.

12. INPUT MAPS Topography Resolution: 900 m

13. INPUT MAPS Land use

14. INPUT DATA 00:00 GMT 16 meteo stations 04:00 GMT 23 meteo stations

15. GOODNESS of INTERPOLATION No data 16 meteorological stations No data 149 meteorological stations

16. 00:00 (GMT) 04:00 (GMT)

17. No data Air Temperature (°C) 00:00 (GMT) No data 04:00 (GMT)

18. Relative Humidity (%) 00:00 (GMT) 04:00 (GMT)

19. No data Sensible Heat Flux (W m-2) 00:00 (GMT) No data 04:00 (GMT)

20. No data Latent Heat Flux (W m-2) 00:00 (GMT) No data 04:00 (GMT)

21. RAMS simulation: Resolution: 2.5 km Air Temperature (°C) 04:00

22. Sensible Heat Flux (W m-2) 04:00 RAMS simulation: Resolution: 2.5 km

23. REMARKS • MODAMBO (Environmental Diagnostic Model) is a mass consistent model developed at IBIMET Bologna Institute; • RAMS (Regional Atmospheric Modeling System) is a fluido-dynamic prognostic model. RAMS, as used in its standard mode (land use and soil characteristics data downloaded from USGS site) was not able to simulate the frost event as well as MODAMBO model, that has been developed ad hoc for this kind of applications. MODAMBO proved to be able to offer good simulation of frost events, but it obviously does not take into account the meteorological conditions (synoptic, but also mesoscale) out of its domain.

24. REMARKS Moreover, RAMS is not a so “easy and portable instrument” while MODAMBO can be installed in a simple PC and can run on real time with standard meteorological stations data. It can be hence a very useful instrument for the regional agrometeorological services. The next step is to feed RAMS with the Emilia Romagna land use and soil characteristics for forecast purposes and then feed MODAMBO with the output of RAMS to obtain a more realizable local characterization of micrometeorological features of extreme events.