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Topic 2 – Numeric Weather Prediction
NWP modelling course in the frame of Serbia for Excell Summer School was held from
June 27th to July 1st 2016 in Novi Sad, Faculty of Agriculture.
Intention of NWP modelling course was to train students to install, run and present results
of Weather Research and Forecast numerical weather prediction model. We choose WRF
as a state-of-the-art atmospheric modelling system designed for both meteorological
research and numerical weather prediction. WRF has a large worldwide community of
registered users (over 30,000 in over 150 countries).
Some of the students didn't have any knowledge and experience in numerical modeling.
It was necessary to introduce the topics like software packages, libraries and Linux basics
that are needed for the installation, running and presentation of the model products.
Comprehensive step-by-step documentation with description of necessary libraries and
installation procedure was prepared in advance and distributed to the students. Students
had individual questions that we were solving during training course. They discussed
practical issues about how to run the model, where to download the data, how to read
and plot model products.
Lateral boundary conditions for actual dates where provided and students had an
opportunity to run the model and interpret their own forecast.
Final student's discussion and presentation convinced us that all of them finished
installation and running of the model and that they understood and learned NWP
fundamentals. After this summer school all students are capable to install and run WRF on
their own. This can be a starting point for their further research in providing
meteorological forecast for crop modelling purposes.
Parameterisation of processes in soil-vegetation-atmosphere transfer schemes: Water and energy balance
H2020-TWINN-2015
a Faculty of Agriculture, University of Novi Sad, Serbia
Branislava Lalić AgMnet INTERNATIONAL
SUMMER SCHOOL IN AGROMETEOROLOGY AND CROP MODELLING
27 June – 01 July 2016
Novi Sad, SERBIA
FOCUS LECTURES II Summer School, Novi Sad, June 2016
“The understanding of turbulent transfer within foliage canopies
provides the intellectual underpinning for the physical aspects of agricultural
meteorology ...”, Kaimal & Finnigan, 1994, Atmosperic Boudary Layer flows
Penman,H.L.andLong,I.F.(1960),Weatherinwheat:Anessayinmicro-meteorology.Q.J.R.Meteorol.Soc.,86:16–50.
PLANT:FROMOBJECTTOSUBJECTincropmodelsandSVATschemes
SVAT – Soil – Vegetation - Atmosphere Transfer Schemes
FOCUS LECTURES II Summer School, Novi Sad, June 2016
MeteorologistsneedinformaTonontheheatandmoistureinputtotheatmospherefromtheearth'ssurface(e.g.,soil,vegetaTon)andhowthesurfaceextractsmomentumandkineTcenergyoftheatmospherethrough"fricTon".
BiophysicistsandecologistsneedinformaTonaboutthetemperature,
humiditylevels,solarradiaTon,cloudcover,windspeed,precipitaTon,etc.to
determinehowplantsandplantcommuniTesrespondtoenvironmental
condiTons
FOCUS LECTURES II Summer School, Novi Sad, June 2016
SVAT – Soil – Vegetation - Atmosphere Transfer Schemes Usedtomoreaccuratelydescribehowsoil,vegetaTon,andwatersurfacesexchangefluxeswiththeatmosphere MeteorologistsandclimatologistsneedinformaTonontheheatandmoistureinputtotheatmospherefromtheearth'ssurface(e.g.,soil,vegetaTon,waterbodies)andhowthesurfaceextractsmomentumandkineTcenergyoftheatmospherethrough"fricTon".BiophysicistsandecologistsneedinformaTonaboutthetemperature,humiditylevels,solarradiaTon,cloudcover,windspeed,precipitaTon,etc.todeterminehowplantsandplantcommuniTesrespondtoenvironmentalcondiTons
q Soil–threelayermodel
SVAT:SoilandVegeta9onModel
FOCUS LECTURES II Summer School, Novi Sad, June 2016
q VegetaTon-singlelayermodelGoverningequaTons
LAPS:Radia9onModule
tTCHER c
cccnc ∂∂
++λ=
ThenetradiaTonabsorbedbythe
canopy
soil
parTTonedintosensibleheat,latentheat,andstorageterms
tT
CHER ggggng ∂
∂++λ=
( ) )365C/(EHR2tT
gggngd πλ−−=
∂∂
Deepsoiltemperature
Mihailovicetal.(1992,1993,1995),J.App.Meteor
SensibleandlatentheatfluxparTToning
LAPS:Radia9onModule
( )b
acpc r
TT2cH −ρ= ( ) ⎟⎟
⎠
⎞⎜⎜⎝
⎛
+
−+
γ
ρ−=λ
cb
c
b
cpac*c rr
W1rWc
e)T(eE
( )d
agpg r
TT2cH
−ρ=
( )γ
ρ
+
−α=λ p
db
ag*sg
crre)T(e
E
( )a
rapgc r
TTcHHH −ρ=+= ( )
a
rapgc r
eecEEE −
γ
ρ=λ+λ=λ
q Sensible(Hc)andlatent(λEc)heatfluxfromvegetaTontocanopyairspace
,
q Sensible(Hg)andlatent(λEg)heatfluxfromgroundtocanopyairspace
q Sensible(H)andlatent(λE)heatfluxfromcanopyairspacetoreferencelevel
,
Governing equations
LAPS:HydrologicalModule
Waterstoredonthecanopy
Thevolumetricsoilwatercontent
ρ−=∂∂
wwfff /EPt
w
⎭⎬⎫
⎩⎨⎧
−−ρ
+−−=
∂∂
RREE
FPD1
tw
10w
l,tfg2,1l
l
1
{ }R/EFFD1
tw
2w2,tf3,22,12
2 −ρ−−=∂∂
{ }RFFD1
tw
333,23
3 −−=∂∂
“The Hitchhiker's Guide to the Galaxy” LAI ⇄ ALBEDO
Moore,K.E.,D.R.Fitzjarrald,R.K.Sakai,M.L.Goulden,J.W.Munger,S.C.Wofsy,1996:SeasonalVariaToninRadiaTveandTurbulentExchangeataDeciduousForestinCentralMassachusees,JournalofAppliedMeteorology,35(1),122-134.
FOCUS LECTURES II Summer School, Novi Sad, June 2016
Lalic, B., Fitzjarrald, D.R., Firanj Sremac, A., 2016: “Climatology” of Radiative and Turbulent Exchange at a Deciduous Forest in Central Massachusetts (manuscript in preparation)
FOCUS LECTURES II Summer School, Novi Sad, June 2016
PAR albedo 1991-2009 Solar albedo 1991-2009
Energy balance – IMPACT OF PLANTS ON ENERGY BALANCE
yxy
FOCUS LECTURES II Summer School, Novi Sad, June 2016
Energy balance – IMPACT OF PLANTS ON ENERGY BALANCE
FOCUS LECTURES II Summer School, Novi Sad, June 2016
Energy balance – IMPACT OF “ENERGY BALANCE” ON PLANTS
PredicTonsofcanopy-levelCO2assimilaTonrate(Ac)asafuncTonofLAI(panelA)andGPPasafuncTonofLAD(panelB).AcwaspredictedusingthebiochemicalmodelsofFarquharandvonCaemmerer(1982)coupledtothreedifferenttypesofcanopyfluxmodels;abig-leafmodel,amulTplelayermodel,andasun/shadebig-leafmodel.
“The Hitchhiker's Guide to the Galaxy” LAI ⇄ PHOTOSYNTHESYS CALCULATION
Monson,R.,Baldocchi,D.,2014:TerrestrialBiosphere-AtmosphereFluxes.CambridgeUniversityPress,pp.518.Firanj,A.,Lalic,B.,Podrascanin,Z.,(2014)TheImpactofForestArchitectureParameterizaTononGPPsimulaTons.Theore@calandAppliedClimatology,121,3,529-544
B
FOCUS LECTURES II Summer School, Novi Sad, June 2016
Water balance – IMPACT OF PLANTS ON WATER BALANCE
Vegetation can intercept up to 50% of the rain that falls on its leaves. The leaves of deciduous trees commonly intercept anywhere from 20 to 30% of the falling rain.
Modification of falling precipitation by vegetation The relative quantity of precipitation entering the soil is indicated in dark brown
FOCUS LECTURES II Summer School, Novi Sad, June 2016
FOCUS LECTURES II Summer School, Novi Sad, June 2016
Water balance – IMPACT OF “WATER BALANCE” ON PLANTS
EvapotranspiraToncoefficient
FOCUS LECTURES II Summer School, Novi Sad, June 2016
Water balance – IMPACT OF “WATER BALANCE” ON PLANTS
FOCUS LECTURES II Summer School, Novi Sad, June 2016
Air flow - IMPACT OF AIR FLOWS ON PLANTS
TURBULENT DIFUSION ⇄ Venturia inaequalis spatial distribution
Summer school lessons about
building SVAT models
H2020-TWINN-2015
Faculty of Agriculture, University of Novi Sad, Serbia
Dragutin T. Mihailović
AgMnet INTERNATIONAL SUMMER SCHOOL
IN AGROMETEOROLOGY AND CROP MODELLING
27 June – 01 July 2016
Novi Sad, SERBIA
Summer school lessons about building
SVAT models Dragutin T. Mihailović
Lesson 1 Epistemological features
CROP MODELLING Summer School, Novi Sad, June 2016
CROP MODELLING Summer School, Novi Sad, June 2016
Problems in building SVAT models a) How we see the object of interest? b) How to make proper approximations? c) Mathematical problems d) Scalling problems
CROP MODELLING Summer School, Novi Sad, June 2016
Problems in building SVAT models a) How we see the object of interest? b) ...................................................... c) ....................................................... d) .......................................................
CROP MODELLING Summer School, Novi Sad, June 2016
People “naively” say: Model is the only way to approach to reality! NO! Formalization of theory cannot be older than the theory itself!!
Model is the only way to simulate a reality! Icarus
CROP MODELLING Summer School, Novi Sad, June 2016
Mihailović, D.T., I. Balaž and D. Kapor, 2016: Time and methods in modelling environmental interfaces: personal insights. Elsevier, Amsterdam (in publishig dept.)
Mihailović et al., 2006, J. App. Meteorol.
CROP MODELLING Summer School, Novi Sad, June 2016
What we see as a reality?
Is it a: 1) duck 2) rabbit
or 3) both
CROP MODELLING Summer School, Novi Sad, June 2016
MinimumofprocessesEvapotranspira2onPrecipita2onCanopyintercep2onThroughflowStemflowIntercep2on&inflitra2on
Our reality
CROP MODELLING Summer School, Novi Sad, June 2016
Problems in building SVAT models a) .......................................................... b) How to make proper approximations? c) .......................................................... d) ..........................................................
CROP MODELLING Summer School, Novi Sad, June 2016
The most famous approximation – material point
CROP MODELLING Summer School, Novi Sad, June 2016
bird's-eye view seen by bugs CROP MODELLING
Summer School, Novi Sad, June 2016
HowPicasso“saw”awomen1901-1968?
Howwecan“see”atreeinSVATmodels?
CROP MODELLING Summer School, Novi Sad, June 2016
Leaf
Steam
Root
Portential / resistance Evapotranspiration =
Analogy to electornic circuit
CROP MODELLING Summer School, Novi Sad, June 2016
Canopy
Air
Air
H
h
CROP MODELLING Summer School, Novi Sad, June 2016
Why does a train clatter when it’s
moving along the tracks?
CROP MODELLING Summer School, Novi Sad, June 2016
CROP MODELLING Summer School, Novi Sad, June 2016
CROP MODELLING Summer School, Novi Sad, June 2016
Problems in building SVAT models a) ........................................................... b) ........................................................... c) Mathematical problems d) ...........................................................
CROP MODELLING Summer School, Novi Sad, June 2016
Mihailović and Eitzinger, 2007, Ecol. Modelling
CROP MODELLING Summer School, Novi Sad, June 2016
CROP MODELLING Summer School, Novi Sad, June 2016
Chaotic behaviour of model surface variables
Mimić and Mihailović, 2013, Modern Phys. Lett.
CROP MODELLING Summer School, Novi Sad, June 2016
Problems in building SVAT models a) .......................................................... b) .......................................................... c) .......................................................... d) Scaling problems
CROP MODELLING Summer School, Novi Sad, June 2016
Scalling problem is one of the major concern in building SVAT models
ItisthelackofinformaBononhowonecanaggregateand/ordisaggregatevarioussurfacefluxes(heat,momentumandwatervapor)fromonescaletoanother.
CROP MODELLING Summer School, Novi Sad, June 2016
GCMmodelresolu2on
Topography
Vegeta2on
Soil
Aggrega2
on
Disaggrega2o
n
RgCMmodels
OceanLand
GCMisrunonthecoarsegridwhileLSSonthefinersubgridinputparameterstoLSSneedstobedisaggregated
CROP MODELLING Summer School, Novi Sad, June 2016
Disggregation of fluxes
Mihailović et al., 2016, Science Total Enviro.
CROP MODELLING Summer School, Novi Sad, June 2016
Aggregation of fluxes (Schmidt’s paradox)
The grid-box over the Prospect park NY, USA illustrating the heterogeneous grid-box used in environmental model simulations.
Mihailović et al., 2015, App. Math. Comp.
Schematic diagram illustrating how the sub-grid scale surface patch-use classes are treated within the different parameterization scheme
CROP MODELLING Summer School, Novi Sad, June 2016
Schmidt’s paradoxLowestmodellevel
Averaged sensible heat flux
Single sensible heat fluxes
Countergradient
Summer school lessons about
building SVAT models
H2020-TWINN-2015
Faculty of Agriculture, University of Novi Sad, Serbia
Dragutin T. Mihailović
AgMnet INTERNATIONAL SUMMER SCHOOL
IN AGROMETEOROLOGY AND CROP MODELLING
27 June – 01 July 2016
Novi Sad, SERBIA
Summer school lessons about building
SVAT models Dragutin T. Mihailović
Lesson 2
An example with the LAPS scheme
CROP MODELLING Summer School, Novi Sad, June 2016
CROP MODELLING Summer School, Novi Sad, June 2016
Content a) Introduction b) The main features of the LAPS scheme c) Comment of outputs d) Uncertainities in modelling procedure
CROP MODELLING Summer School, Novi Sad, June 2016
Content a) Introduction b) ............................................................ c) ............................................................ d) ............................................................
CROP MODELLING Summer School, Novi Sad, June 2016
How LSS works?
Soil
Reference level
CROP MODELLING Summer School, Novi Sad, June 2016
Content a) ............................................................... b) The main features of the LAPS scheme c) ............................................................... d) ...............................................................
L A P S
LAPS: Soil-Vegetation-Atmosphere-Transfer model Forcing data q Air temperature
q Short wave radiation
q Long wave radiation
q Humidity
q Wind speed
q Precipitation
q Initial conditions
q Aerodynamic ch.
q Morphological ch.
Prognostic variables Temperatures:
leaf, ground and deep soil temperature
q Soil moisture content at three soil layers
q Leaf wetness Main outputs Sensible and latent heat fluxes vegetation - canopy air space
ground - canopy air space canopy air space - reference level
q Momentum flux q Wind within the canopy
Serbian-French Project Meeting, 23 June 2008, Avignon (France)
LAPS: resistance representation
q Mihailovic et al. (1992), J. App. Meteor. q Mihailovic et al. (1993), J. App. Meteor. q Mihailovic (1994), Workshop, McQuary Univ., Sydney q Mihailovic et al. (1995), J. App. Meteor. q Mihailovic (1996), Global and Planetary Chang. q Mihailovic and Kallos (1997), Bound. Layer Met. q Mihailovic et al. (1999), Bound. Layer Met. q Mihailovic et al., (2006), J. App. Meteor.
Serbian-French Project Meeting, 23 June 2008, Avignon (France)
q Soil – three layer model
LAPS: submodels used
q Vegetation-single layer model
Serbian-French Project Meeting, 23 June 2008, Avignon (France)
Governing equations in the LAPSThe net radiation absorbed by the canopy and soilis assumed to be partitioned into sensible heat,latent heat, and storage terms, as
tT
CHER ffffnf ∂
∂++λ= (1) Prognostic
tT
CHER ggggng ∂
∂++λ= (2) Prognostic
Deep soil temperature
( ) )365/(2 πλ−−=∂∂
CEHRtT
gggnetg
d
(3) Prognostic
Serbian-French Project Meeting, 23 June 2008, Avignon (France)
AGRIDEMA, Vienna, 21 November – 2 December 2005 Page 1, DTM
Governing equations in the LAPS
Water stored on the canopy
ρ−=∂
∂wwff
fEPt
w/ Prognostic
The volumetric soil water content
,11,2 0 1
1
1 g tf ll
w
E Ew P F R Rt D⎧ ⎫+∂
= − − − −⎨ ⎬∂ ρ⎩ ⎭
{ }REFFDt
wwtf 22,3,22,1
2
2 /1−ρ−−=
∂
∂
{ }RFFDt
w333,2
3
3 1−−=
∂
∂ Prognostic
Serbian-French Project Meeting, 23 June 2008, Avignon (France)
CROP MODELLING Summer School, Novi Sad, June 2016
Content a) ............................................................... b) ............................................................... c) Comment of outputs d) ...............................................................
Outputs
Ground surface temperature Tg Leaf temperature Tl Water intercepted by leafes wl Soil moisture content in three layers wl, w2 and w3
15
20
25
30
35
Folia
ge te
mpe
ratu
re, T
f (o C
)
Sunflower (NS-Velja)Rimski Sancevi (Serbia)
Simulated (solid line)Observed (square)
(b)
0000 0400 0800 1200 1600 2000 2400 Hours (LST)
Daily variations (19 July 1998) of the sunflower foliage temperature: (1) simulated by the LAPS model and (2) measured (Rimski Sancevi, SERBIA) (Mihailovic and Eitzinger, Ecol. Modell., 2007)
NCEP LAPS
Serbian-French Project Meeting, 23 June 2008, Avignon (France)
150 151 152 153 154 155Day of Year
-100
0
100
200
300
400
500
Late
nt h
eat f
lux
(Wm
-2)
OLDNEW
Observed
(a)
Temporal variation of latent heat flux andobtained by the LAPS scheme comparedwith the observations over a soybean field inCaumont site (France) during its growingseason in 1986(Mihailovic et al., 2006, J. App. Meteor.)
0 2 4 6 8 10 12 14 16 18 20 22 24Hours (UTC)
-100
-50
0
50
100
150
200
250
300
Surf
ace
fluxe
s (W
m-2
)
Apple orchard16 May 2002Chlewiska (Poland)
SIMULATED
Sensible
OBSERVED
(a)
Sensible
Latent Latent
Diurnal variations of latent andsensible heat fluxes over(a) apple orchard, (b) rapeseed in Chlewiska (Poland) for 16 May 2002(Mihailovic et al.,Ther. App. Climat.– in revision)Measured values provided by thePoznan Agric. University Group
0 2 4 6 8 10 12 14 16 18 20 22 24Hours (UTC)
-100
-50
0
50
100
150
200
250
300
Surf
ace
fluxe
s (W
m-2
)
Rapeseed16 May 2002Chlewiska (Poland)
SIMULATED OBSERVED
Sensible
Latent
(b)
Sensible
Latent
Serbian-French Project Meeting, 23 June 2008, Avignon (France)
188 190 192 194 196 198 200Julian day
0
10
20
30
40
Air t
empe
ratu
re in
can
opy,
Ta
(o C)
Simulated (thin)Observed (thick)
Ten-day variation (8-17 July 2002) of within-canopy air temperature simulated by the LAPS and observed inside a sunflower canopy at the Rimski Sancevi (SERBIA) (Mihailovic and Eitzinger, Ecol. Modell., 2007)
NCEP LAPS
Serbian-French Project Meeting, 23 June 2008, Avignon (France)
Water transfer modelling: Soil
Daily averages of total soil water content (mm) over a depth of 1.6 m simulated by the LAPS and observed beneath a soybean canopy in HAPEX experiment at the Caumont (France) (Mihailovic et al., 1998, J. Hydrol.)
0 100 200 300Julian day
200
400
600
Observed (squares) Calculated (solid line)
θSo
il m
oist
ure,
t(m
m)
Field capacity (480 mm)
(WRF Modelling System - NMM) + LAPS (forcing data)
Serbian-French Project Meeting, 23 June 2008, Avignon (France)
CROP MODELLING Summer School, Novi Sad, June 2016
Content a) ............................................................... b) ............................................................... c) ............................................................... d) Uncertainities in modelling procedure
The energy balance equation is valid on LOCAL and GLOBAL scale (Rosenberg, 1983) In order to calculate surface temperature Ts we can use one of the following methods 1) solving this transcendental equation for Ts 2) solving this equation in F.D. form
Rn (Ts) + S(Ts) + LE (Ts) + PS (Ts) + M (Ts) = 0
Cg ∂Ts/ ∂t = Rn (Ts) - S(Ts) - LE (Ts) - PS (Ts) - M (Ts)
( ) ( ) ( )rgDrgHrgLrggg TTCTTCeTECTTatTC −−−−−−−=∂∂ ])([/
Net radiation
Latent heat Sensible heat
Soil heat
Transfer coefficient
radiation wat. vap. press. heat in
atmosp. soil heat
ground surface TEMPERATURE at reference level
surface capacity
))/(//(1 ng
ng
nng
ng TFCtFTT δδ+Δ+=+
in this form n time level Δt time step solution
backward scheme in time
5 10 15 20 25 30Local time (h)
15
20
25
30
Soil
surfa
ce te
mpe
ratu
re, T
g (C
)
6 July 1982NS-10
Modeled (solid)Observed (square)
(Mihailovic and Lalic, 1995)
0 20 40 60 80 100 120 140 160 180 200Time lag
0.0
0.2
0.4
0.6
0.8
1.0
X
Solution of energy balance equation plotted as the renormalised “effective”surface temperature X in terms of time.
00 /)( gnggn TTTX −= , where 0
gT is the initial“effective“ surface temperature.
300 350 400 450 500 550DOY (27 October 2005 - 26 June 2006)
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Frac
tion
al c
over
of
vege
tation
Reference
Error 2%
Error 5%
Error 10%
0.0 1.0 2.0 3.0 4.0 5.0 6.0Leaf area index (LAI)
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Frac
tion
al c
over
of
vege
tation
Reference Error 2%
Error 5%
Error 10%
Error in estimation of vegetation fractional cover
Avignon (France) Wheat (2005/2006)
Δσ =(1-exp(-k*LAI*r))/exp(k*LAI) K=0.34 r = ΔLAI/LAI
Error in estimation of vegetation fractional cover
300 350 400 450 500 550DOY (27 October 2005 - 26 June 2006)
0.00
0.10
0.20
0.30
0.40
0.50H
eigh
ts (m
)
Canopy height (M)
Displacement height (C)
Roughnes length (C)
Canopy source height (C)
Avignon (France) Wheat (2005/2006)
Error in estimation of vegetation fractional cover
300 350 400 450 500 550DOY (27 October 2005 - 26 June 2006)
0.0
1.0
2.0
3.0
4.0
5.0
6.0
Evap
orat
ion
(mm
)
LAIError in estimation of VEG_COVBLACK referenceRED -2%BLUE -5%
Avignon (France) Wheat (2005/2006)
Error in estimation of vegetation fractional cover
300 350 400 450 500 550DOY (27 October 2005 - 26 June 2006)
-5
0
5
10
15
20
25
30
Leaf
tem
pera
ture
(deg
C)
LAI
300 350 400 450 500 550DOY (27 October 2005 - 26 June 2006)
-5
0
5
10
15
20
25
30
Insi
de c
anop
y te
mpe
ratu
re (d
eg C
)
LAI
Avignon (France) Wheat (2005/2006)
( Theor.App. Clim., Mihailovic et al., 2008)
Calculation performed according to LAPS scheme
Summer school lessons about
building SVAT models
H2020-TWINN-2015
Faculty of Agriculture, University of Novi Sad, Serbia
Dragutin T. Mihailović
AgMnet INTERNATIONAL SUMMER SCHOOL
IN AGROMETEOROLOGY AND CROP MODELLING
27 June – 01 July 2016
Novi Sad, SERBIA
Summer school lessons about building
SVAT models Dragutin T. Mihailović
Lesson 3
Links with other models
CROP MODELLING Summer School, Novi Sad, June 2016
Regional climate 3- D simulations
Temporal ). 651 grid points
Mihailovic et al., 2004, JAM
Surface temperature
).
0 6 12 18 24Hours (GMT)
0
5
10
15
20
25
Surf
ace
tem
pera
ture
(de
g C)
(blue ) Tall grass (Tmin=12.1, Tmax=18.0)(pink ) Crop (Tmin=11.5, Tmax=21.0)(orange) Mixed woodland (Tmin=12.0, Tmax=17.0)(black ) Control (Tmin=11.8, Tmax=19.6)
Sensitivity of mean surface temperature to cover type. The hourly means were made using all grid points in the domain of interest.
Mihailovic, D. T., 2003: Implementation of Land-Air Parameterization Scheme (LAPS) in a limited area model. Final Report, The New York State Energy Conservation and Development Authority, Albany, NY, 110 pp.
Air temperature at 2m
).
0 6 12 18 24Hours (GMT)
0
5
10
15
20
25
Air
tem
pera
ture
at
2m (
deg
C)
(blue ) Tall grass (Tmin=12.3, Tmax=17.6)(pink ) Crop (Tmin=11.7, Tmax=19.8)(orange) Mixed woodland (Tmin=12.1, Tmax=16.9)(black ) Control (Tmin=11.9, Tmax=18.7)
The hourly means were made using all grid points in the domain of interest.
Mihailovic, D. T., 2003: Implementation of Land-Air Parameterization Scheme (LAPS) in a limited area model. Final Report, The New York State Energy Conservation and Development Authority, Albany, NY, 110 pp.
3) Chemical Transport Models
5th International Summer School “Renewable Energy & Energy Efficiency in South East Europe”
10 – 21 August 2009, Fojnica, Bosnia and Herzegovina
Chemical transport models While related general circulation models (GCMs) focus on simulating overall atmospheric dynamics (e.g. fluid and heat flows), a CTM instead focuses on the stocks and flows of one or more chemical species
iEMSs 2006 July 9-13, 2006 The Wyndham Hotel, Burlington, Vermont, USA Page 10
Domain of integration
5th International Summer School “Renewable Energy & Energy Efficiency in South East Europe”
10 – 21 August 2009, Fojnica, Bosnia and Herzegovina
5th International Summer School “Renewable Energy & Energy Efficiency in South East Europe”
10 – 21 August 2009, Fojnica, Bosnia and Herzegovina
Mihailović et al., Environ. Sci. Poll. Res. Int. (2008, 2009) To activate EMEP with new meteorology (advection, surface processes and boundary layer)
5th International Summer School “Renewable Energy & Energy Efficiency in South East Europe”
10 – 21 August 2009, Fojnica, Bosnia and Herzegovina
5th International Summer School “Renewable Energy & Energy Efficiency in South East Europe”
10 – 21 August 2009, Fojnica, Bosnia and Herzegovina
6) Hydrological Models
5th International Summer School “Renewable Energy & Energy Efficiency in South East Europe”
10 – 21 August 2009, Fojnica, Bosnia and Herzegovina
Water transfer modelling: Soil
Daily averages of total soil water content (mm) over a depth of 1.6 m simulated by the LAPS and observed beneath a soybean canopy in HAPEX experiment at the Caumont (France) (Mihailovic et al., 1998, J. Hydrol.)
0 100 200 300Julian day
200
400
600
Observed (squares) Calculated (solid line)
θSo
il m
oist
ure,
t(m
m)
Field capacity (480 mm)
(WRF Modelling System - NMM) + LAPS (forcing data)
Serbian-French Project Meeting, 23 June 2008, Avignon (France)
7) Forecasting the Occurrence of Plant Diseases
5th International Summer School “Renewable Energy & Energy Efficiency in South East Europe”
10 – 21 August 2009, Fojnica, Bosnia and Herzegovina
BAHUSbiometeorological system for predicting the occurrence of diseases inplant production (Center for Meteorology and Envoronmental Predictions)
1) Clamatological data 2) Automatic weather station 3) NCEP outputs (mesoscale) 4) LAPS surface scheme
(Mihailovic et al., Envir. Modell. Soft., 2001)
Venturia inequalis Ervinia amylovora Plasmopra vitiocola
FP6 project ADAGIO
Serbian-French Project Meeting, 23 June 2008, Avignon (France)
8) Modelling of the UV radiation
5th International Summer School “Renewable Energy & Energy Efficiency in South East Europe”
10 – 21 August 2009, Fojnica, Bosnia and Herzegovina
NEOPLANTA computes the: 1) solar direct and diffuse UV irradiances under cloud free conditions for the wavelength range 280–400 nm and UV index 2) effects of O3, SO2, NO2, aerosols, and nine different ground surface types on UV radiation are included. 3) instantaneous spectral irradiance for a given solar zenith angle 4) UV index for the whole day at half-hour intervals from sunrise to sunset Modelling details: 5) atmosphere is divided into several parallel layers (maximum 40) in the model 6) it is assumed that the layers are homogeneous with constant values of meteorological parameters 7) vertical resolution of the model is 1 km for altitudes below 25 km and 5 km above this height. 8) upper boundary of the highest layer in the model is 100 km 9) model uses standard atmosphere meteorological profiles 10) there is also an option of including the real-time meteorological data profiles from the high-level resolution mesoscale
Serbian-French Project Meeting, 23 June 2008, Avignon (France)
NEOPLANTA: A Short Description of the First Serbian UV Index Model Authors: Malinovic, S., Mihailovic, D.T., Kapor, D., Mijatovic, Z., Arsenic, I.D., Publication: Journal of Applied Meteorology and Climatology, vol. 45, Issue 8, p.1171-1177
(WRF Modelling System - NMM) + LAPS (forcing data)
Serbian-French Project Meeting, 23 June 2008, Avignon (France)
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