Diapositiva 1
Download
Report
Transcript Diapositiva 1
COST ACTION OF THE EUROPEAN
SCIENCE FOUNDATION
FOOD AND AGRICULTURE
ORGANIZATION
WORLD METEOROLOGICAL
ORGANIZATION
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 RICERCHE
ISTITUTO DI BIOMETEOROLOGIA
A RADIATIVE FROST
THE PROBLEM
A 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 CONDITIONS
INVERSION
LAYER
CLEAR SKY
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 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_2D
THE MODEL
INPUT_1: geometrical characteristics of the domain.
1) topography map;
2) land use map;
albedo
Surface
length
surface roughness
INPUT_2: meteorological conditions.
the model needs:
1) air temperature
2) relative humidity
3) wind speed
4) wind direction
obtained
from
the
meteo
stations
hydrometeorological service (ARPA-SIM).
of
the
regional
MODAMBO_2D
THE MODEL
THE MODEL
OUTPUT_1
For 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)
OUTPUT_2
Some files that can be utilized by
MODAMBO_3D, able to compute
the vertical profiles of the principal
micrometeorological fields.
MODAMBO_2D
THE MODEL
THEORY
X x
2D terrain following model
Yy
z hg
For each grid cell the slope and the
azimuth is computed :
SLOPE
N3 (i+1,j+1)
Cell (i,j)
AZIMUTH
N1 (i,j)
N2 (i+1,j)
MODAMBO_2D
THE MODEL
GEOMETRIC INTERPOLATION
For each meteorological station and for each grid cell we compute:
D2 ( xx X station )2 ( yy Ystation )2
Nstations
Val (i, j )
2
1
/
D
k Val(k )
k 1
Nstations
2
1
/
D
k
k 1
The geometric interpolation is utilized to calculate the values for each
grid point of air temperature, relative humidity and cloud fraction.
MODAMBO_2D
THE MODEL
WIND INTERPOLATION
The model takes into account the effects of:
1) Surface roughness
MODAMBO_2D
THE MODEL
WIND INTERPOLATION
The model takes into account the effects of:
2) Topography:
MODAMBO_2D
THE MODEL
MICROMETEOROLOGY PARAMETERIZATIONS
Through 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 MAPS
Topography
Resolution: 900 m
INPUT MAPS
0.25
Land use
0.2
950000
0.15
900000
0.1
0.05
850000
550000
600000
650000
700000
750000
800000
0
1.2
1.1
1
0.9
950000
0.8
0.7
0.6
0.5
900000
0.4
0.3
0.2
850000
0.1
550000
600000
650000
700000
750000
800000
0
INPUT DATA
2000
1200
900
950000
700
400
300
00:00 GMT
16 meteo
stations
200
100
900000
50
25
0
-10
850000
550000
600000
650000
700000
750000
800000
2000
1200
900
950000
700
400
04:00 GMT
23 meteo
stations
300
200
100
900000
50
25
0
-10
850000
550000
600000
650000
700000
750000
800000
GOODNESS of INTERPOLATION
No data
20
8
6
4
950000
2
0
16
meteorological
stations
-1
-2
-3
900000
-4
-5
-6
-8
-10
850000
550000
600000
650000
700000
750000
20No
800000
8
6
4
950000
2
0
149
meteorological
900000
stations
-1
-2
-3
-4
-5
-6
-8
-10
850000
550000
600000
650000
700000
750000
800000
data
1800
0 20
1700
1600
1500
1400
950000
1300
1200
1100
1000
900
800
700
600
900000
500
400
300
200
00:00 (GMT)
100
0
850000
550000
600000
650000
700000
750000
800000
1800
1700
1600
1500
1400
950000
1300
1200
1100
1000
900
800
700
600
900000
500
400
300
200
100
850000
0
04:00 (GMT)
550000
600000
650000
700000
750000
800000
20
No data
8
Air Temperature
(°C)
Climatological Minimum Temperature during
frost
events 1987-2003
6
4
950000
2
0
-1
-2
-3
900000
-4
-5
-6
-8
850000
-10
00:00 (GMT)
550000
600000
650000
700000
750000
800000
No da
20
8
6
4
950000
2
0
-1
-2
-3
-4
900000
-5
-6
-8
-10
-12
850000
-14
04:00 (GMT)
550000
600000
650000
700000
750000
800000
Relative Humidity (%)
100
90
950000
80
70
60
50
40
900000
30
20
10
00:00 (GMT)
0
850000
550000
600000
650000
700000
750000
800000
100
90
950000
80
70
60
50
40
900000
30
20
10
04:00 (GMT)
0
850000
550000
600000
650000
700000
750000
800000
No data
0
-10
-20
-30
-40
-50
-60
-70
-80
-90
-100
-110
-120
-130
-140
-150
-160
-170
-180
-190
-200
950000
900000
00:00 (GMT)
850000
550000
600000
650000
700000
750000
Sensible Heat Flux
(W m-2)
800000
0No
-10
-20
-30
-40
-50
-60
-70
-80
-90
-100
-110
-120
-130
-140
-150
-160
-170
-180
-190
-200
950000
900000
04:00 (GMT)
850000
550000
600000
650000
700000
750000
800000
data
No data
60
50
40
950000
30
20
10
0
Latent Heat Flux
(W m-2)
-10
900000
-20
-30
-40
-50
00:00 (GMT)
-60
850000
550000
600000
650000
700000
750000
800000
60
No data
50
40
950000
30
20
10
0
-10
900000
-20
-30
-40
-50
04:00 (GMT)
-60
850000
550000
600000
650000
700000
750000
800000
RAMS simulation:
Resolution: 2.5 km
9
950000
6
3
0
Air Temperature (°C)
04:00
-3
-6
-9
-12
900000
-15
-18
-20
Sensible Heat Flux
(W m-2) 04:00
950000
RAMS simulation:
Resolution: 2.5 km
0
-10
-20
-30
-40
-50
-60
-70
-80
900000
-90
-100
-110
-120
REMARKS
MODAMBO (Environmental Diagnostic Model) is a mass
consistent model developed at IBIMET Bologna Institute;
RAMS (Regional Atmospheric Modeling System) is a fluidodynamic 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.
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.