Simulation of micrometeorological fields during a frost event in the Po Plane
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mulation of micrometeorological fields during a frost event mulation of micrometeorological fields during a frost event the Po Plane the Po Plane Nardino, G. Antolini, F. Rossi, T. Georgiadis, G. Leoncini, R. Piel CONSIGLIO NAZIONALE DELLE RICERCHE ISTITUTO DI BIOMETEOROLOGIA COST ACTION OF THE EUROPEAN SCIENCE FOUNDATION WORLD METEOROLOGICAL ORGANIZATION WORKSHOP ON CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE (14-17 June 2005, Bologna, Italy) FOOD AND AGRICULTURE ORGANIZATION
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WORLD METEOROLOGICAL ORGANIZATION. FOOD AND AGRICULTURE ORGANIZATION. COST ACTION OF THE EUROPEAN SCIENCE FOUNDATION. WORKSHOP ON CLIMATIC ANALYSIS AND MAPPING FOR AGRICULTURE (14-17 June 2005, Bologna, Italy). Simulation of micrometeorological fields during a frost event in - PowerPoint PPT Presentation
Transcript of Simulation of micrometeorological fields during a frost event in the Po Plane
Simulation of micrometeorological fields during a frost event inSimulation of micrometeorological fields during a frost event inthe Po Planethe Po Plane
M. Nardino, G. Antolini, F. Rossi, T. Georgiadis, G. Leoncini, R. Pielke
CONSIGLIO NAZIONALE DELLE RICERCHE ISTITUTO DI BIOMETEOROLOGIA
COST ACTION OF THE EUROPEANSCIENCE FOUNDATION
WORLD METEOROLOGICAL ORGANIZATION
WORKSHOP ON CLIMATIC ANALYSIS AND MAPPING
FOR AGRICULTURE (14-17 June 2005, Bologna, Italy)
FOOD AND AGRICULTUREORGANIZATION
A RADIATIVE FROSTA 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.
THE ATMOSPHERIC THE ATMOSPHERIC CONDITIONSCONDITIONS
INVERSION LAYER
CLEAR SKY
GOALSGOALS
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 input for the frost risk mapping of the Emilia Romagna region.
To have the local micrometeorology starting 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 model for other agrometeorological applications (i.e. fire risk index, ecophysiology modeling, crop production,….).
MODAMBO_2DMODAMBO_2D
THE MODEL
INPUT_1: geometrical characteristics of the domain.1) topography map;2) land use map;
Surface surface roughnessalbedo 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).
MODAMBO_2DMODAMBO_2DTHE 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.
MODAMBO_2DMODAMBO_2DTHE MODEL
THEORY
2D terrain following model
For each grid cell the slope and the azimuth is computed :
Cell (i,j)
N3 (i+1,j+1)
N2 (i+1,j)N1 (i,j)
SLOPE
AZIMUTH
ghz
yY
xX
MODAMBO_2DMODAMBO_2DTHE 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.
222 )()( stationstation YyyXxxD
Nstations
kk
Nstations
kk
D
kValDjiVal
1
2
1
2
/1
)( /1),(
MODAMBO_2DMODAMBO_2DTHE MODEL
WIND INTERPOLATIONThe model takes into account the effects of:
1) Surface roughness
MODAMBO_2DMODAMBO_2DTHE MODEL
WIND INTERPOLATIONThe model takes into account the effects of:
2) Topography:
MODAMBO_2DMODAMBO_2DTHE 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.
INPUT MAPSINPUT MAPS
Topography Resolution: 900 m
INPUT MAPSINPUT MAPS
Land use
550000 600000 650000 700000 750000 800000
850000
900000
950000
0
0.05
0.1
0.15
0.2
0.25
550000 600000 650000 700000 750000 800000
850000
900000
950000
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1.1
1.2
-10
0
25
50
100
200
300
400
700
900
120 0
200 0
55 0000 600 000 650 000 7000 00 7 5000 0 80 0000
850 000
900 000
950 000
INPUT DATAINPUT DATA
00:00 GMT
16 meteo stations
04:00 GMT
23 meteo stations
-10
0
25
50
100
200
300
400
700
900
120 0
200 0
55 0000 600 000 650 000 7000 00 7 5000 0 80 0000
850 000
900 000
950 000
GOODNESS of GOODNESS of INTERPOLATIONINTERPOLATION
16 meteorological stations
149 meteorological stations
- 1 0
- 8
- 6
- 5
- 4
- 3
- 2
- 1
0
2
4
6
8
2 0
5 5 0 0 0 0 6 0 0 0 0 0 6 5 0 0 0 0 7 0 0 0 0 0 7 5 0 0 0 0 8 0 0 0 0 0
8 5 0 0 0 0
9 0 0 0 0 0
9 5 0 0 0 0
- 1 0
- 8
- 6
- 5
- 4
- 3
- 2
- 1
0
2
4
6
8
2 0
5 5 0 0 0 0 6 0 0 0 0 0 6 5 0 0 0 0 7 0 0 0 0 0 7 5 0 0 0 0 8 0 0 0 0 0
8 5 0 0 0 0
9 0 0 0 0 0
9 5 0 0 0 0
No data
No data
00:00 (GMT)
04:00 (GMT)
0 2 0
550000 6000 00 650000 70000 0 750000 800000
850000
900000
950000
0
1 00
2 00
3 00
4 00
5 00
6 00
7 00
8 00
9 00
1 000
1 100
1 200
1 300
1 400
1 500
1 600
1 700
1 800
550000 6000 00 650000 70000 0 750000 800000
850000
900000
950000
0
1 00
2 00
3 00
4 00
5 00
6 00
7 00
8 00
9 00
1 000
1 100
1 200
1 300
1 400
1 500
1 600
1 700
1 800
00:00 (GMT)
04:00 (GMT)
- 1 0
- 8
- 6
- 5
- 4
- 3
- 2
- 1
0
2
4
6
8
2 0
5 5 0 0 0 0 6 0 0 0 0 0 6 5 0 0 0 0 7 0 0 0 0 0 7 5 0 0 0 0 8 0 0 0 0 0
8 5 0 0 0 0
9 0 0 0 0 0
9 5 0 0 0 0
- 1 4
- 1 2
- 1 0
- 8
- 6
- 5
- 4
- 3
- 2
- 1
0
2
4
6
8
2 0
5 5 0 0 0 0 6 0 0 0 0 0 6 5 0 0 0 0 7 0 0 0 0 0 7 5 0 0 0 0 8 0 0 0 0 0
8 5 0 0 0 0
9 0 0 0 0 0
9 5 0 0 0 0
No data
Air Temperature (°C)
No data
00:00 (GMT)
04:00 (GMT)
Relative Humidity (%)
55 0000 6 00000 650000 700000 750000 800000
85000 0
90000 0
95000 0
0
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
1 00
55 0000 6 00000 650000 700000 750000 800000
85000 0
90000 0
95000 0
0
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
1 00
00:00 (GMT)
04:00 (GMT)
550000 600000 650000 700000 750000 800000
850000
900000
950000
-200-190-180-170-160-150-140-130-120-110-100-90-80-70-60-50-40-30-20-100
Sensible Heat Flux (W m-2)
No data
No data
55 0000 60 0000 6 50000 7 00000 750000 800000
85000 0
90000 0
95000 0
-200-190
-180
-170-160
-150-140
-130-120
-110-100
-90-80
-70-60
-50-40
-30
-20-10
0
00:00 (GMT)
04:00 (GMT)
550000 600000 650000 700000 750000 800000
850000
900000
950000
-60
-50
-40
-30
-20
-10
0
10
20
30
40
50
60
550000 600000 650000 700000 750000 800000
850000
900000
950000
-60
-50
-40
-30
-20
-10
0
10
20
30
40
50
60
Latent Heat Flux (W m-2)
No data
No data
Air Temperature (°C) 04:00
RAMS simulation:
Resolution: 2.5 km
550000 600000 650000 700000 750000 800000
850000
900000
950000
-20
-18
-15
-12
-9
-6
-3
0
3
6
9
-20
-18
-15
-12
-9
-6
-3
0
3
6
9
Sensible Heat Flux (W m-2) 04:00
550000 600000 650000 700000 750000 800000
850000
900000
950000
-120
-110
-100
-90
-80
-70
-60
-50
-40
-30
-20
-10
0
-120
-110
-100
-90
-80
-70
-60
-50
-40
-30
-20
-10
0
RAMS simulation:
Resolution: 2.5 km
REMARKSREMARKS 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.
REMARKSREMARKS
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.
