critical issues concerning model applications in marine...
TRANSCRIPT
G. Kallos
UNIVERSITY OF ATHENSSCHOOL OF PHYSICS, DIVISION OF APPLIED PHYSICS
ATMOSPHERIC MODELING AND WEATHER FORECASTING GROUP
UNIVERSITY CAMPUS, Bldg PHYS-V, ATHENS-15784
http://forecast.uoa.gr
Critical Issues Concerning Model Applications in Marine Environment
RAMS and Other Models Applications
Topics to be discussed
Issues related to atmospheric modeling in marine environment
Mercury Model Development and Air Pollution Modeling
Wave Analysis and Prediction
Optimal Ship Routing and Ship Safety
Issues related to wind energy prediction
Marine Applications
• Operational Oceanography and sea-state forecasting are two subjects closely related to LAM modeling and operations
• At the atmosphere-ocean system the first is the fast moving system that defines to a certain degree exchange processes, sea status and ocean circulation at various scales
• The main development was performed on better coupling between the two systems
• Most of the work has been performed at the framework of the MFSPP, MFSTEP and ENVIWAVE projects funded by EU
• The evolution of these projects is the establishment of operation oceanographic predictions for the Mediterranean Sea and other places
Atmospheric model outputs for oceanographic applications
10 m wind components
2 and 10 m temperature
2 and 10 m moisture
Cloud deck information (cloud cover, cloud height, cloud fraction)
Accumulated precipitation (at short intervals)
Energy budget components (e.g. SW-in, SW-out, LW-in, LW-out,
sensible and latent heat flux) at 1 hr interval
Turbulence parameters like TKE and Kv
Viscous sub-layer parameters
Land-water mask,
Upper-air fields for any desired level
Desert dust deposition (in size bins)
What an atmospheric model needs from an ocean circulation or wave analysis model?
SST fields with equivalent spatiotemporal scales
Sea state conditions in order to redefine friction parameters
Sea salt in the atmosphere is a very active CCN
Fluxes of other species (e.g. Hg, DMS)
These parameters were considered as “a luxury” but as long as we move towards higher resolutions these parameters are considered as more and more necessary in order to describe intra-day features
Utilization of high-spatial resolution SSTs
High resolution SST product (1/16 x 1/16 degrees) for the
Mediterranean Region
SST differences (hires-coarse) on 3/11/04
What does it mean to atmospheric simulations?
T+12Case ofCase of
13/10/0413/10/04
Cyclone Cyclone
formation over formation over
Central Central
MediterraneanMediterranean
T+24
Front over the Front over the
Ionian SeaIonian Sea
CORFU
0
2
4
6
8
10
12
14
13 14 15 16
DATE (OCTOBER 2004)
10
m.
win
d s
pe
ed
(m
/s)
COARSE_SST
HIRES_SST
METAR
AKTIO
0
2
4
6
8
10
12
14
16
13 14 15 16
DATE (OCT. 2004)
10
m.
win
d s
pe
ed
(m
/s)
COARSE_SST
HIRES_SST
METAR
RED: 1/16 deg. SSTs
BLUE: 0.5 deg. SSTs
Viscous Sublayer and its Role on Fluxes
The viscous sublayer over the ocean is assumed to operate in 3 regimes:
smooth and transitional,
rough,
rough with spray,
depending on the Reynolds number which is a function of u*
Theoretical background
The viscous sub-layer is a layer next to the surface which is so thin that there is no room for turbulent eddies to develop.
Therefore, momentum, heat and moisture are transported through this layer by molecular diffusion.
Since the molecular diffusion is much weaker than the turbulent diffusion, the presence of the viscous sub-layer restricts the surface turbulent fluxes.
Differences in precipitation
(VISCOUSyes-VISCOUSno)
Higher precipitation amounts are predicted over the
Mediterranean without the use of the viscous sublayer.
This is in agreement with theory
Surface fluxes averagedover the sea-points and 2m Temperature
Mean Surface Latent Heat Fluxes (sea points)
0
50
100
150
200
250
300
1 6 11 16 21 26 31
DATE (JAN. 2003)
up
wa
rd L
HF
(W
/m2
)
Mean Surface Sensible Heat Fluxes (sea points)
0
10
20
30
40
50
60
70
80
90
1 6 11 16 21 26 31
DATE (JAN. 2003)
up
wa
rd S
HF
(W
/m2
)
Mean T2M (sea points)
10
11
12
13
14
15
16
1 6 11 16 21 26 31
DATE (JAN. 2003)
T2
M (
de
g.
C)
SST Perturbations in the Pacific Ocean and their impacts on regional weather in Europe and
Mediterranean
Is there any impact?
How they can be detected?
What are the characteristic spatiotemporal scales?
Katsafados et al. 2004, GRL
Main characteristics of the LAM experiments
ECMWF, 0.5°°°°x0.5º resolution, global coverage, 11 isobaric levelsInitial and Boundary conditions
August-September-October 1997Simulated period
32 unevenly spaced levelsVertical resolution
0.25ºx0.25º1.0ºx1.0ºHorizontal resolution
179.5°°°°W, 179.5°°°°E, 60.0°°°°S, 90.0°°°°NEdges of the domain
FineCoarse
30-day averaged differences (perturbed minus realistic) of precipitation rate (mm/day) valid for October 1997. Figure (a) refers to the LAM experiments with 1.00ºx1.00º
resolution (coarse) and (b) with 0.25ºx0.25º resolution (fine). The contours denote the difference of the precipitation rate with increment of 0.25 mm/day while the areas
exceeding the 95% confidence level are shaded. The red and blue contours correspond
to positive and negative differences respectively.
Low Resolution (a) High Resolution (b)
Temporal variation of the mean differences (perturbed minus realistic) for the MSLP (hPa) over 3 discrete locations during the first month of the simulation period. Figure
(a) refers to the LAM experiments with 1.00ºx1.00º resolution (coarse) and (b) with
0.25ºx0.25º resolution (fine).
Low Resolution (a) High Resolution (b)
T+0T+0 T+12T+12 T+36T+36 T+60T+60 T+84T+84 T+108T+108 T+120T+120
00UTC00UTC
day 1day 100UTC00UTC
day 2day 200UTC00UTC
day 3day 3
00UTC00UTC
day 4day 400UTC00UTC
day 5day 500UTC00UTC
SST SST
day 0day 0SST SST
day 1day 1SST SST
day 2day 2SST SST
day 3day 3SST SST
day 4day 4SST SST
day 5day 5
55--day SKIRON/day SKIRON/EtaEta model simulations with: model simulations with:
a)a) The OGCM SSTs of the initial time fixed during the runThe OGCM SSTs of the initial time fixed during the run
b)b) The OGCM SSTs of the initial time from T+0 to T+12 The OGCM SSTs of the initial time from T+0 to T+12
The predicted SST at the following times, updated during the runThe predicted SST at the following times, updated during the run
Nudging of Predicted SST in Atmospheric Model Predictions
SST difference day1SST difference day1--day0 day0
(day0 is the daily(day0 is the daily--average SST from 12UTC 30/11/2004 to 12UTC 01/12/2004)average SST from 12UTC 30/11/2004 to 12UTC 01/12/2004)
SUMMARY
• The short to medium range forecasts are sensitive, in local scales, to changes in the underlying SST field in the presence of strong synoptic or mesoscale flow.
• There are spatiotemporal differences in the distribution of precipitation from the use of fixed and updated OGCM SSTs. However, the precipitation amounts remain similar.
DATA ASSIMILATION IN WAVE MODELS
Assimilation Algorithms
Assimilation of altimeter data (RA2)
Assimilation of scatterometer data (ASAR)
Evaluation of the system in
– Mediterranean Sea (a wind sea dominated
area)
– Indian Ocean (swell dominated)
Assimilation of Significant Wave Height
• The analysis scheme is based on a modification of the traditional successive correction methods (Cressmann 1959)
• It is analogous to the statistical interpolation (Hollingsworth, 1987)
• The method is based on the following two iterative equations for Significant Wave Height (SWH):
Subscripts i, j refer to observation points, x to grid points, superscripts O, P, T and A to observed, first guess, true and analyzed value, N is the number of observations and k an iteration counter. mij and dij are model error and observation error covariances respectively. Mj is a function of mij and dijchosen so that the above equation converge.
1
1
( 1) ( ) ( ( )),
( 1) ( ) ( ( )), where
( ) / , /
=
=
+ = + −
+ = + −
= + =
∑
∑
NA A O A
i i ij j j
j
NA A O A
x x xj j j
j
ij ij ij j xj xj j
SWH k SWH k a SWH SWH k
SWH k SWH k a SWH SWH k
a m d M a m M
Assimilation of Wave Spectrum
• The code is based on an Optimal Interpolation Scheme
• The matrix equation used is :
where
R is the observation covariance matrix,
B the model covariance matrix,
H the observation operator,
y the observations,
xb model’s first guess and
xa the analyzed data
(J.E. Aarnes, 2003)
( ) , + = − = +T T
a b a b aR HBH w y Hx x x BH w
Experimental Regions – Real Time Operations
Set up WAM real time operations over 5 areas :
I. Mediterranean Sea with nested domains the Aegean Sea and the Saronic Gulf:
II. Global and Indian Ocean
6W–42E, 30N– 47N, Res: 0.1deg22E–29E, 34.5N – 41N, Res: 0.05deg
Res: 1.0 deg
40E – 100E, 0 – 30N, Res: 0.25deg
23E–25E, 37N – 38.5N, Res: 0.02deg
The results are available every day from http://forecast.uoa.gr
Satellite data density for operational use
ASARRA2
250-300 records/cycle
550-650 records/cycle 30 – 50 records/cycleIndian Ocean
almost no records
for operational use2
Mediterranean
Sea
Note 1: Time period 12UTC-end of the day for each operational cycle.
Note 2: The wave mode is not open in this area
Assimilation period: 12 hours
RA2 Data Assimilation : SWH time series for one assimilated
observation in Mediterranean Sea with moderate difference from
direct model output
0.5
0.55
0.6
0.65
0.7
0.75
0.8
0.85
0.9
0.95
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61
Integration Time (h)
Sig
n W
ave H
eig
ht
(m)
WH assim WH noassim
Initial conditions : WAM direct output = 0.69 m
Observation = 0.9 m
Assimilation time = T0 + 15 h
Energy spectrum distribution
(assimilation time)
WAM + AssimilationWAM
ASAR Data Assimilation: Location II: (Lat = -46.0, Lon = -161.0)
SWH and direction
WAM WAM + assim
2 May 2004, 12:00 UTC
No assimilated WAM vs WAM+RA2 assimilation scheme
Assimilation time +12h
Assimilation timeAssimilation time
WAM+RA2WAM
Bias RMSE
No assimilated WAM vs WAM+ASAR assimilation scheme
WAM WAM+ASAR
Assimilation timeAssimilation time
Assimilation time +12hAssimilation time +12h
RMSEBias
Assimilation time +12hAssimilation time +12h
Assimilation timeAssimilation timeBias RMSE
No assimilated WAM vs WAM+RA2 assimilation scheme
WAM WAM+RA2
Statistical results results for Aegean and Spanish buoys
WAM+Assim vs Buoys
Lesvos Buoy Horizontal axis : WAM + AssimVertical axis : Buoy
0.250.250.130.130.240.240.130.130.230.230.130.13GataGata
0.180.180.050.050.200.200.040.040.220.220.020.02AlicanteAlicante
0.300.300.270.270.270.270.250.250.260.260.230.23LesvosLesvos
0.420.420.270.270.430.430.190.190.470.470.130.13MykonosMykonos
0.160.16--0.110.110.160.16--0.060.060.180.18--0.070.07AvgoAvgo
0.310.310.200.200.250.250.180.180.240.240.170.17AthosAthos
WH WH Abs. Abs. BiasBias
WH WH BiasBias
WH WH Abs. Abs. BiasBias
WH WH BiasBias
WH WH Abs. Abs. BiasBias
WH WH BiasBiasBuoysBuoys
72 hour Forecast72 hour Forecast48 hour Forecast48 hour Forecast24 hour Forecast24 hour Forecast
0.250.250.130.130.240.240.130.130.230.230.130.13GataGata
0.180.180.050.050.200.200.040.040.220.220.020.02AlicanteAlicante
0.300.300.270.270.270.270.250.250.260.260.230.23LesvosLesvos
0.420.420.270.270.430.430.190.190.470.470.130.13MykonosMykonos
0.160.16--0.110.110.160.16--0.060.060.180.18--0.070.07AvgoAvgo
0.310.310.200.200.250.250.180.180.240.240.170.17AthosAthos
WH WH Abs. Abs. BiasBias
WH WH BiasBias
WH WH Abs. Abs. BiasBias
WH WH BiasBias
WH WH Abs. Abs. BiasBias
WH WH BiasBiasBuoysBuoys
72 hour Forecast72 hour Forecast48 hour Forecast48 hour Forecast24 hour Forecast24 hour Forecast
y = 1.1657x + 0.1734
R2 = 0.6502
0
0.5
1
1.5
2
2.5
0 0.5 1 1.5 2 2.5
Mediterranean Sea Model vs Buoy
0.8810.497-0.39RA2 vs
Model
(assim)
0.8860.49-0.37RA2 vs
Model
(noassim)
Cor. Coefficient
St.
DeviationBias
WAM+
Assim
Swh(m)
WAM
Swh(m)
*The impact of the assimilation of RA2 satellite data in Mediterranean Sea is limited mainly due to:
1. The lack of a sufficient number of data
2. The high correlation of available satellite records with model outputs
3. The local characteristics of Mediterranean basin
(Model) + (model+assim) vs RA2
RA2 swh (m)
RA2 swh (m)
General Remarks
The Statistical analysis performed showed a significant contribution of both assimilation methods to the improvement of the wave model forecasting skill spatially and temporarily
The assimilation effects are more significant in the vicinity of the available observations
The spectrum energy is translated to lower frequencies (swell)
However, most of the corrections are smoothed after a period of 12 hours
The different characteristics of the two domains used affect thequality as well as the amplitude of assimilation impact.
Simultaneous assimilation of wind and wav data is expected to give better results
Analysis of a ship accident near the Algerian coast
RAMS and SKIRON/Eta were used to analyze the weather conditions
during a ship accident that occurred close to the northern Algerian coast
at 07:15 on 31 January 2003.
The MFSTEP-SVP hindcasts were firstly used in order to analyze the
weather conditions during the event
Wind forecast
produced
during SVP
Ship accident
Ethernet
PC
PC
HELLENIC NATIONAL
METEOROLOGICAL
SERVICE
PC
Internet
Dial-Up
PSTN
NHREAS Server
THE NHREAS SYSTEM
SD
N et S er ver 5/ 100 L C
Ω
Hewlett-Packard
J-6000 (2cpu)
SD
N et Ser ve r 5 /10 0 LC
Ω
Hewlett-Packard
J-6000 (2cpu)
SD
N et S er ver 5/ 100 L C
Ω
Hewlett-Packard
J-6000 (2cpu)
Gbit Ethernet switch
Hewlett Packard C-3000
(control WS)
Modem
Router
SHIP
PC
GSM-900/1800
Modem
Inmarsat-B
Modem
Meteorological station
(GPS, compass wind-gauge,
thermometer barometer)
Dial-Up
Satellite dish
InMarsat
Satellite
Satellite dish
Central data collection
station
Telephone
ISDN
-network
OPTIMAL SHIP ROUTING
$0
$5.000
$10.000
$15.000
$20.000
$25.000
$30.000
$35.000
$40.000
$45.000
Runing Cost Bunker Cost Other Oper. Cost Revenue
Fact: Fuel is expensive on ship transportation especially today…….
ESTIMATED LOSS DUE TO WEATHER (ISEL 2002)
Accidents
related to bad
weather
37%
Other
63%
THE OPTIMAL ROUTING PROBLEM
Optimization of the multivariable cost function
dtuxyPossibilitCPFTCdtFOpricedtuxFOj
t g t
o
t
o
),(**),(0
∫ ∫ ∫++=
Where
FO = Fuel Oil Consumption
FOprice = Fuel Oil Price
TC = Market Time Charter Equivalent
CPF = Cost Penalty Factor for Specific Events
Possibility = Possibility for the event to happen
Weather and Sea Conditions Data Needed
Wind Waves (Significant wave height, direction, frequency)
Swell (Significant wave height, direction, frequency)
Wind Speed and Direction
Sea Currents (speed and direction)
5-12 days forecasts
Update every ΗU hours
Space Increment DS ml
Time Increment DT hours
SIMPLIFIED SOLUTION
Initial Conditions:
Allowed time
Topology
Efficiency of the engine
Ship traffic outside the borders
Vertical acceleration on the deck 0,15g
Side acceleration rms on the deck 0,12g
Slope rms 6 degrees (deviation from the vertical)
Extended Dynamic model Bellman Exploitation of all possible routes
Delays in order to avoid bad weather
Forecast for part of the route
Transatlantic Routes
Container Vessel
Hapag-Lloyd’s
HANNOVER EXRESS
VS= 23 Kn (Fn=0,225)
LOA =294 m
LPP =281,6 m
4839 TEU
67680 tn DWT
36510 KW
Calm Conditions
VMAX = 23 Kn
Distance = 3200 NM
VOPT = 17,60 Kn
TC = 20.000 $ / day
FO = 75 mt/day
FOprice = 200 $ / mt
SEAMIN = 139 hours
SEAMAX = 181 hours
Passage FO Costs in $
Min Time = 86.500
Optimum = 50.500
Time Savings
Hours = 42
$ = 35.000
Real Conditions
Passage FO Costs in $Passage FO Costs in $Passage FO Costs in $Passage FO Costs in $•Min Time = 114,000Min Time = 114,000Min Time = 114,000Min Time = 114,000•Optimum = 72,000Optimum = 72,000Optimum = 72,000Optimum = 72,000•Min Dist = 119,000Min Dist = 119,000Min Dist = 119,000Min Dist = 119,000
Time SavingsTime SavingsTime SavingsTime Savings•Hours = 38Hours = 38Hours = 38Hours = 38•$ = 31,000$ = 31,000$ = 31,000$ = 31,000
The The RAMSRAMS--HG HG ModelingModeling SystemSystem
Gas-phase and aqueous
chemistry.
Vertical advection and
diffusion
Eulerian. Horizontal
advection using a semi-
Lagrangian scheme.
Projected grid size 100km
x 100 km ; domain contains 63x47 grids; 6
vertical layers extending
up to 6 km in the vertical.
Point and area sources
AES/ENSR
EPRI
TEAM
Gas and aqueous phase
chemical reactions of Hg. Chemical
transformations of Hg
reactants. J-values
calculations using FAST-J (Wild et al., 2000)
Gas-phase and
aqueous chemistry Sorption of
aqueous Hg2
complexes.
Hg0 oxidation by O3
assumed balanced by reduction reactions;
HgCl2 scavenged using
Henry;s Law scavenging
coefficients; Hg (part.) scavenged by nucleation
Mercury Transformation
Options of the
atmospheric model
RAMS
Vertical advection
and diffusion
Vertical advection and
diffusion
Turbulent Diffusion
Eulerian. Horizontal
advection and diffusion
Eulerian.
Horizontal
advection and
diffusion
Eulerian. Horizontal
advection and diffusion.
Transport
Similar to the
atmospheric model RAMS
Grid size 36 km x
36 km. 21 vertical layers
Grid size 127 kmx127
km; domain contains 33x33 grids; 12 vertical
layers from 1 m to 10km.
Spatial
Resolution(Horizontal & Vertical)
Point and area sourcesPoint and area
sources
Point and area sourcesEmissions
(Sources)
UoA-IASAEPAAES/ENSR/
OMEE
Developer(s)
EUEPAAES/OMEE-Canada/
Germany
Sponsor(s)
RAMS-HgCMAQ-HgADOMModel
Models used for modelling atmospheric Hg cycle
Pai et al.,1997b
Simulated the transport
and fate of mercury
emissions in the
contiguous United
States; includes detailed model evaluation
Uncoupled
Wet deposition flux is calculated as the
product of cloud water
concentration of Hg
species and the precipitation amount.
Deposition velocities for
gases and particulates calculated for each grid
cell based on land use
and input meteorology.
TEAM RAMS-HgCMAQ-HgADOMModel (continued)
Voudouri and Kallos,
2005
Bullock and Brehme,
2002
Petersen et
al. 2001
Refs.
Mediterranean Sea
Region, Europe, North
Eastern United States
United States.Eastern
North
America,
Europe
Major Applications To Date
COUPLEDUncoupledUncoupledMeteorology Coupling
Deposition scheme
implemented on the
RAMS microphysical
scheme (Walko et al., 1995, Meyer et al.,
1997) . Calculation of
scavenging ratios for
Hg2 and HgΡ
Cloud-water concentration
of Hg0 , Hg2 and
Hg(part.). deposited to
the surface based on the simulated rate of
precipitation falling
from each clouded grid
volume.
(See Mercury
Transforma
tion above.)
Wet Deposition (Including Cloud & Precipitation)
Deposition velocities
using the resistancemodel for Hgp and
Hg2 for each grid cell
and timestep, based on
land use and onlineMeteorology
Deposition velocities for
Hg2 gas, HgPcalculated.
Deposition
velocities for Hg2 gas,
HgP
Dry Deposition
-Parameterization of air/soil, air/canopy fluxes (Capri and Lindberg 1998, Xu et al.
1999)
-Parameterization of air/water fluxes for u* ≤ 0.3 m s-1 (Mackay and Yeun, 1983)
-Parameterization of air/water fluxes for u* > 0.3 m s-1 and u10 ≤ 5 m s-1
-Parameterization of air/water fluxes for u10 > 5 m s-1 (Asher and Wanninkhof,
1998)
Hg air/surface exchange
- Hg Fluxes
-Calculation of scavenging ratios for Hg2 and HgΡ
-Deposition scheme implemented on the RAMS microphysical scheme (Walko et al.,
1995, Meyer et al., 1997) .
Wet Deposition
-Calculated based on Hg concentration and deposition velocity (Wesely and Hicks,
2000)
-Resistance model for Hg2 deposition velocity
-Deposition velocity according to the diameter of HgP particles (Pai et al., 1997)
Dry Deposition
-J-values calculations using FAST-J (Wild et al., 2000)
-Wet and aqueous phase chemical reactions of Hg
-Chemical transformations of Hg reactants
Chemical
Transformation
-As initial boundary conditionsHg boundary conditions
-1,6 to 0,02 ng/m3 for Hg0, 10 to 0,08 pg/m3 for Hg2 and HgP
-Tangent Hyperbolic profile (adjustable)
Hg Initial Conditions
-Options of the atmospheric model RAMSTurbulence
-Similar to the atmospheric model RAMSTransport
-Similar to the atmospheric model RAMSSpatial coordinates
-Point
-Area
Emissions
(Sources)
-Hg0, Hg2, HgPMercury species
Basic Features of RAMSBasic Features of RAMS--HgHg
AirAir--soil Fluxessoil Fluxes
•• HgHg00 fluxes from soil depend onfluxes from soil depend on
–– Soil temperatureSoil temperature
–– Solar radiation Solar radiation ((Capri and LindbergCapri and Lindberg 1998) 1998)
–– Soil moisture Soil moisture ((EPRIEPRI, 1998)., 1998).
•• Parameterization of Parameterization of HgHg00 fluxes from soil is based on the approach of fluxes from soil is based on the approach of Capri Capri and Lindbergand Lindberg (1998). (1998).
LogLog((FsFs) = ) = aTsaTs + + bb
wherewhere
Fs Fs ((ngng mm--2 2 hh--1) 1) fluxes from soilfluxes from soil
Ts Ts ((ooCC) ) soil temperaturesoil temperature
aa=0.057 =0.057 and and bb==--1.7 1.7 Capri and LindbergCapri and Lindberg 19981998
oror
aa=0.064 =0.064 andand bb==--2.032.03 XuXu et alet al. 1999 . 1999
AirAir--canopy fluxescanopy fluxes
•• HgHg00 fluxes from canopy fluxes from canopy FcFc, , ((ngng mm--22ss--11) depend on) depend on–– ΕΕcc evaportranspirationevaportranspiration rate (rate ( mm3 (Η2Ο) 3 (Η2Ο) mm--22ss--11) and ) and
–– CsCs HgHg00 concentrationconcentration ((ngng mm33))
•• FcFc = = ΕΕc Cs c Cs
•• EvaportranspirationEvaportranspiration isis–– calculated using the calculated using the PennmanPennman--MonteithMonteith equation modified with a soilequation modified with a soil--
waterwater--deficit factor deficit factor
–– minimal over wet canopy.minimal over wet canopy.
•• For offFor off--season crop fields, leaflessseason crop fields, leafless--season deciduous forests and season deciduous forests and grassgrass--land, ground is treated as bare soil.land, ground is treated as bare soil.
•• HgHg fluxes from water surfaces depend on fluxes from water surfaces depend on
–– Atmospheric Hg concentrationAtmospheric Hg concentrationι ι
–– Existing Hg dissolved into waterExisting Hg dissolved into water
•• HgHg fluxes are calculated followingfluxes are calculated following ::
FwFw==KK ((CwCw –– CgCg//HH) )
•• Due to Due to HgHg0 0 low solubility,low solubility, FwFw is expressed through is expressed through
FwFw==FeFe –– FdFd = = KlCwKlCw –– KlCgKlCg//HH
•• HgHg fluxes from water surfaces are due to fluxes from water surfaces are due to
Temperature differencesTemperature differences Bubble mediated transferBubble mediated transfer
TurbulenceTurbulence
Breaking wavesBreaking waves
Other processesOther processes
AirAir--water fluxeswater fluxes
6 4 *2.2 0.51.0 10 144 10lK x x u Sc- - -= +
When When u* u* > > 0.3 m s0.3 m s--11 and and U U ≤ ≤ 55 m sm s--11
((Mackay and Mackay and YeunYeun, 1983) , 1983)
6 4 * 0.51.0 10 34.1 10lK x x u Sc- - -= +
where
u* (m s-1 ) friction velocitySc Schmidt number
when u* ≤ 0.3 m s-1
(Mackay and Yeun, 1983)
AirAir--Water FluxesWater Fluxes
wherewhere
•• UU ((msms--1) 1) wind speed atwind speed at 1010mm
•• α α Ostwald solubilityOstwald solubility
•• WcWc whitecap coveragewhitecap coverage
WcWc==cc1(1(UU--cc0)0)33 MonahanMonahan (1993) (1993)
whilewhile
•• cc1=2.561=2.56xx1010--6 και 6 και
•• cc0=1.770=1.77
•• When When U >U > 55 m sm s--11 ((Asher and Asher and WanninkhofWanninkhof, 1998 ), 1998 )
ChemistryChemistry
•• The modified chemistry module includes 107 reactions and deals wThe modified chemistry module includes 107 reactions and deals withith the gas and aqueous phase chemistry reactions of mercury speciesthe gas and aqueous phase chemistry reactions of mercury species with other with other
reactants reactants
Photochemical reactions of ozone (OPhotochemical reactions of ozone (O33) and hydrogen peroxide (H) and hydrogen peroxide (H22OO22) both in ) both in aqueous and gaseous phase aqueous and gaseous phase
bimolecular and bimolecular and termoleculartermolecular reactions that form these mercury reactants (e.g. reactions that form these mercury reactants (e.g. bimolecular reactions of bimolecular reactions of SOxSOx, CO and CO, CO and CO22 with Owith O22, H, H22O, OH and HO, OH and H22OO22). ).
•• The photochemical reactions of OThe photochemical reactions of O33 and Hand H22OO22 both in aqueous and gaseous both in aqueous and gaseous phase are treated within the chemistry module using the Fastphase are treated within the chemistry module using the Fast--J scheme J scheme proposed by Wild et al. (2000).proposed by Wild et al. (2000).
•• The gas and liquid phase reactions of mercury considered in the The gas and liquid phase reactions of mercury considered in the chemistry chemistry module are those with Omodule are those with O33, H, H22OO22, chlorines and sulphates (Munthe et al., , chlorines and sulphates (Munthe et al., 1991, Munthe 1992). 1991, Munthe 1992).
•• The Benefits of this chemistry module areThe Benefits of this chemistry module are flexibility flexibility
the ability to calculate on line the rate constants of the reactthe ability to calculate on line the rate constants of the reactions for various ions for various temperatures, pressures and water contenttemperatures, pressures and water content
the simplicity to add new reactions to the databasethe simplicity to add new reactions to the database
ChemistryChemistry
Gas phaseHg0 Hg2 Hgp
oxidation
Hg0
Hg2
Hgp
Hgp
oxidation
Aqueous Phase
adsorptionreduction
Dry DepositionDry Deposition
•• The dry deposition of both HgThe dry deposition of both Hg22 and and Hg(PHg(P) is calculated by using the ) is calculated by using the classical formulationclassical formulation
FF==--vvddCC
where the flux of a pollutant (F) to the surface is the product where the flux of a pollutant (F) to the surface is the product of a of a characteristic deposition characteristic deposition velocity(vvelocity(vdd) and its concentration (C) in the ) and its concentration (C) in the ““surface layersurface layer”” plant canopy and deposit on the ground surface.plant canopy and deposit on the ground surface.
•• The critical parameter here is the calculation of the depositionThe critical parameter here is the calculation of the depositionvelocity.velocity.
•• The deposition velocity is calculated according to the land use The deposition velocity is calculated according to the land use type type and the patches within the grid cell as they are defined in the and the patches within the grid cell as they are defined in the LEAFLEAF--2 sub2 sub--model. The deposition velocity is calculated separately for model. The deposition velocity is calculated separately for each vegetation category included on each grid cell (patching).each vegetation category included on each grid cell (patching).
Wet depositionWet deposition
•• The wet removal processes for Hg species have been developed by The wet removal processes for Hg species have been developed by following the following the facts and assumptions:facts and assumptions:
the soluble chemical species (Hgthe soluble chemical species (Hg22 and its compounds), and and its compounds), and
the particulate matter scavenged only from below the precipitatithe particulate matter scavenged only from below the precipitating clouds. ng clouds.
•• Wet scavenging of HgWet scavenging of Hg22 is assumed to occur in and below clouds. is assumed to occur in and below clouds.
•• HgHg22 is assumed to be an irreversibly soluble gas and its scavengingis assumed to be an irreversibly soluble gas and its scavenging coefficient is coefficient is calculated accordingly.calculated accordingly.
•• In cloud, HgIn cloud, Hg22 can be removed by interstitial cloud air by dissolution into clcan be removed by interstitial cloud air by dissolution into cloud oud drops. drops.
•• The calculated local rate of removal of the irreversibly solublThe calculated local rate of removal of the irreversibly soluble gas with a e gas with a concentration depends on the concentration depends on the scavenging coefficient of the gas in the cloud and scavenging coefficient of the gas in the cloud and on the concentration of Hg.on the concentration of Hg.
•• Scavenging coefficients in and below the clouds are different anScavenging coefficients in and below the clouds are different and in general are d in general are calculated according to Seinfeld and calculated according to Seinfeld and PandisPandis (1998) and Pielke (2002). (1998) and Pielke (2002).
•• The mass of each Hg specie is following the water category transThe mass of each Hg specie is following the water category transformation formation processes (e.g. nucleation, collision, breakup, shedding and melprocesses (e.g. nucleation, collision, breakup, shedding and melting) as well as ting) as well as droplet evaporationdroplet evaporation
2Λ( ) ( ) ( , )
4p p t p p p D
πd D U D E D d N=
*
1 / 2 1 / 3 1 / 2 1 / 2 1 1 / 2 3 / 2
*
4[1 0.4 Re 0.16 Re ] 4 [ (1 2 Re ) ] ( )
2Re
3
St SE Sc Sc φ ω φ
ScSt S
- -= + + + + + +
- +
Wet DepositionWet DepositionScavenging CoefficientScavenging Coefficient
Scavenging coefficient for Hg2
Scavenging coefficient for HgP
where E is the collision coefficient and Ut droplet settling velocity
where
Wbc Hg rate transferred to the droplet
Cg Hg gas phase concentration
Dp the droplet diameter
Kc mass transfer coefficient of the gas in cm s-1
Model validation Model validation –– comparison with CMAQcomparison with CMAQ--HgHg
•• RAMSRAMS--Hg has been validated through model Hg has been validated through model intercomparisonintercomparison with the with the CMAQCMAQ--Hg used on previous work of Bullock and Hg used on previous work of Bullock and BrehmeBrehme (2002).(2002).
•• In RAMSIn RAMS--Hg wet deposition mechanisms used to describe the removal of Hg wet deposition mechanisms used to describe the removal of Hg2 and Hg2 and HgPHgP are merged with the detailed cloud microphysical scheme in are merged with the detailed cloud microphysical scheme in order to provide better representation of the wet deposition proorder to provide better representation of the wet deposition processes. cesses.
•• Simulated Hg wet deposition has been also compared with weekly Simulated Hg wet deposition has been also compared with weekly observations. observations.
•• Horizontal Horizontal modelingmodeling domain covers the central and eastern United States domain covers the central and eastern United States and adjacent southern Canada . and adjacent southern Canada .
•• Simulations performed for two evaluation periods: 4 AprilSimulations performed for two evaluation periods: 4 April––2 May 1995, and 2 May 1995, and 20 June20 June––18 July 1995. 18 July 1995.
•• Results indicate that the RAMSResults indicate that the RAMS--Hg simulates reasonably well the specific Hg Hg simulates reasonably well the specific Hg wet deposition measurements made by the Hg deposition network (Mwet deposition measurements made by the Hg deposition network (MDN). DN).
RAMSRAMS--Hg vs. CMAQHg vs. CMAQ--HgHg
Summary statistics for wet deposited Hg in Summary statistics for wet deposited Hg in ng/mng/m22
539,51539,51258,52258,52141,64141,6488,9688,9600132,37132,37179,179,33RAMSRAMS--HgHg
2598,502598,50576,30576,30247,10247,1082,5082,5000531,4531,4430,5430,5CMAQCMAQ--HgHg6363SummerSummer
1293129340440418218265,5965,5900307,6307,6283,0283,0MDNMDNSpring &Spring &
1143,91143,9583,5583,5388,2388,2192,2192,200318,9318,9409,6409,6Lin & Tao Lin & Tao
503,96503,96270,96270,96184,1184,199100,9100,96,856,85124,9124,9187,187,66RAMSRAMS--HgHg
2598,52598,5759,7759,7482,5482,5202,1202,100621,1621,1623,7623,7CMAQCMAQ--HgHg3535SummerSummer
12931293561561347347171,5171,500327,3327,3389,3389,3MDNMDN
539,51539,51199,87199,87139,82139,8273,773,700145,0145,0168,92168,92RAMSRAMS--HgHg
843,5843,5268,6268,6103,1103,121,621,600232,0232,0189,1189,1CMAQCMAQ--HgHg2828SpringSpring
9059051571577070313100222,3222,3150,17150,17MDNMDN
75th75th50th50th2525thth
MaxMax
(ng/m2)(ng/m2)
Percentiles (ng/m2)Percentiles (ng/m2)MinMin
(ng/m2)(ng/m2)
σσ
(ng/m2)(ng/m2)
AverageAverage
(ng/m2)(ng/m2)
SourceSourceΝΝPeriodPeriod
Ν : sample number, MDN: Mercury Deposition Network observations, CMAQ –Hg, RAMS- Hg modelled data
ΒΙΑΒΙΑSS ((inin ng/m2) ng/m2) and Pearson correlation coefficient and Pearson correlation coefficient
for wet deposition of for wet deposition of Hg Hg
•• Model Model ΒΙΑΒΙΑS S with MDN observations with MDN observations waswas improved using improved using RAMSRAMS--HgHg versus CMAQversus CMAQ--Hg Hg by:by:
–– 29,6% 29,6% for both periodsfor both periods
–– 13.9% 13.9% for summerfor summer
–– 51.8% 51.8% for springfor spring
•• Other statistical measures between RAMSOther statistical measures between RAMS--Hg and MDN observations.Hg and MDN observations.
–– ABSBIASABSBIAS == 197,2 197,2 ngng//mm22
–– RMSE RMSE = 286,9 = 286,9 ngng//mm22
0,4740,4740,4840,4840,3290,3290,3960,3960,6570,6570,7010,701PearsonPearson
147,5147,5--103,7103,7234,4234,4--201,7201,738,9338,9318,7518,75BIASBIAS
(ng/m2)(ng/m2)
CMAQCMAQ--HgHgRAMSRAMS--HgHgCMAQCMAQ--HgHgRAMSRAMS--HgHgCMAQCMAQ--HgHgRAMSRAMS--HgHg
SpringSpring & & SummerSummerSummerSummerSpringSpringMeasureMeasure
Model Model -- measurement measurement intercomparisonintercomparison for four for four
experimental campaigns for Europeexperimental campaigns for Europe
Simulation Periods and measurement sites
18 July – 3 August 2004Summer
25 April – 11 May 2004Spring
19 January – 3 February 2004Winter
20 October - 4 November 2003Fall
SIMULATION PERIODSEASON
Model-Measurement intercomparison for the Winter Experimental
period
19 January – 2 February 2004
0
20
40
60
80
100
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.Pau
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Hg
P (
pg
/m3
)
19 January – 2 February 2004
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Obs
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Hg
2 (
pg
/m3
)
19 January – 2 February 2004
0
0,5
1
1,5
2
2,5
3
Cap
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ste
Mod
.Trie
ste
Obs
.
Hg
0 (
ng
/m3
)
Red squares stand for the average value.
Bars indicate the width between minimum and maximum value
Annual (wet and dry) Hg Annual (wet and dry) Hg
depositiondeposition
for for 20042004 (ng/m(ng/m22))
Annual Hg Budget in the Mediterranean RegionAnnual Hg Budget in the Mediterranean Region
Annual emittedAnnual emitted HgHg forfor 20042004
((µµg/mg/m22))
20042004 Hg budget (depositedHg budget (deposited--emitted) (emitted) (µµg/mg/m22))
Are water surfaces sources of Hg ?
Are soil surfaces sinks of Hg ?
Concluding Remarks Concerning RAMSConcluding Remarks Concerning RAMS--Hg Hg
ModelingModeling EffortEffort
•• Model validation indicated that the comprehensive model simulateModel validation indicated that the comprehensive model simulated d reasonably well the wet deposition measurements of Hg at the MDNreasonably well the wet deposition measurements of Hg at the MDN sites. sites.
•• RAMSRAMS--Hg can accurately calculate wet deposited Hg when regional scaleHg can accurately calculate wet deposited Hg when regional scalemeteorological systems prevail. meteorological systems prevail.
•• The proposed approach seems to derogate limitations or uncertainThe proposed approach seems to derogate limitations or uncertainties ties derived from meteorology prediction and reflected mainly on wet derived from meteorology prediction and reflected mainly on wet and dry and dry deposition treatment.deposition treatment.
•• Considering the rather small sample size, model results are encoConsidering the rather small sample size, model results are encouraging. uraging.
•• Further development and model validation is planned based on theFurther development and model validation is planned based on the results results of this study. of this study.
Wind Energy ApplicationsWind Energy Applications
Forecasting wind energy production is a major issue with Forecasting wind energy production is a major issue with considerable economical and environmental implicationsconsiderable economical and environmental implications
EU regulations require from the member states to cover EU regulations require from the member states to cover 20% of the energy production from wind and wave energy 20% of the energy production from wind and wave energy until 2012until 2012
Meteorological Meteorological modelingmodeling related to this subject:related to this subject: Wind park Wind park sitingsiting
Wind forecasting for short and long periodsWind forecasting for short and long periods
Project ANEMOS: Project ANEMOS: Medium and High resolution forecasting for 36 and 120 hoursMedium and High resolution forecasting for 36 and 120 hours
KalmanKalman filtering to remove systematic errors from the predictionsfiltering to remove systematic errors from the predictions
Very high resolution simulations: Is it worth doing it operationally?
RAMS
domains
0
5
10
15
20
25
30
0 5 10 15 20 25 30
Observed wind
Mo
de
lle
d w
ind
RAMS 12km - 1st model level 2nd model level 1:1
0
5
10
15
20
25
30
0 5 10 15 20 25 30
Observed wind
Mo
de
lle
d w
ind
RAMS 12km - 1st model level 2nd model level 1:1
0
5
10
15
20
25
30
0 5 10 15 20 25 30
Observed wind
Mo
de
lle
d w
ind
RAMS 12km - 1st model level 2nd model level 1:1
12 km grid
0
5
10
15
20
25
30
246.0 246.5 247.0 247.5 248.0 248.5 249.0 249.5 250.0
Time UTC
Win
d s
peed
(m
/s)
Obs RAMS 12 km - 1st level RAMS 2nd level
2003/09/04 00UTC
0
5
10
15
20
25
30
246.0 246.5 247.0 247.5 248.0 248.5 249.0 249.5 250.0
Time UTC
Win
d s
peed
(m
/s)
Obs RAMS 12 km - 1st level RAMS 2nd level
2003/09/04 00UTC
0
5
10
15
20
25
30
246.0 246.5 247.0 247.5 248.0 248.5 249.0 249.5 250.0
Time UTC
Win
d s
peed
(m
/s)
Obs RAMS 12 km - 1st level RAMS 2nd level
2003/09/04 00UTC
Run 1: Initialisation
00UTC
Run 2: Initialisation
00UTC + observations
assimilated
Run 3: Initialisation
12UTC
0
5
10
15
20
25
30
0 5 10 15 20 25 30
Observed wind
Mo
de
lle
d w
ind
RAMS 6km - 1st model level 2nd model level 1:1
0
5
10
15
20
25
30
0 5 10 15 20 25 30
Observed wind
Mo
de
lle
d w
ind
RAMS 6km - 1st model level 2nd model level 1:1
0
5
10
15
20
25
30
0 5 10 15 20 25 30
Observed wind
Mo
de
lle
d w
ind
RAMS 6km - 1st model level 2nd model level 1:1
0
5
10
15
20
25
30
246.0 246.5 247.0 247.5 248.0 248.5 249.0 249.5 250.0
Time UTC
Win
d s
peed
(m
/s)
Obs RAMS 6km - 1st level RAMS 2nd level
2003/09/04 00UTC
0
5
10
15
20
25
30
246.0 246.5 247.0 247.5 248.0 248.5 249.0 249.5 250.0
Time UTC
Win
d s
peed
(m
/s)
Obs RAMS 6km - 1st level RAMS 2nd level
2003/09/04 00UTC
0
5
10
15
20
25
30
246.0 246.5 247.0 247.5 248.0 248.5 249.0 249.5 250.0
Time UTC
Win
d s
peed
(m
/s)
Obs RAMS 6km - 1st level RAMS 2nd level
2003/09/04 00UTC
Run 1: Initialisation
00UTC
Run 2: Initialisation
00UTC + observations
assimilated
Run 3: Initialisation
12UTC
6 km grid
0
5
10
15
20
25
30
0 5 10 15 20 25 30
Observed wind
Mo
de
lle
d w
ind
RAMS 1.5km - 1st model level 2nd model level 1:1
0
5
10
15
20
25
30
0 5 10 15 20 25 30
Observed wind
Mo
de
lle
d w
ind
RAMS 1.5km - 1st model level 2nd model level 1:1
0
5
10
15
20
25
30
0 5 10 15 20 25 30
Observed wind
Mo
de
lle
d w
ind
RAMS 1.5km - 1st model level 2nd model level 1:1
1.5 km grid
0
5
10
15
20
25
30
246.0 246.5 247.0 247.5 248.0 248.5 249.0 249.5 250.0
Time UTC
Win
d s
peed
(m
/s)
Obs RAMS 1.5 km - 1st level RAMS 2nd level
2003/09/04 00UTC
0
5
10
15
20
25
30
246.0 246.5 247.0 247.5 248.0 248.5 249.0 249.5 250.0
Time UTC
Win
d s
peed
(m
/s)
Obs RAMS 1.5km - 1st level RAMS 2nd level
2003/09/04 00UTC
0
5
10
15
20
25
30
246.0 246.5 247.0 247.5 248.0 248.5 249.0 249.5 250.0
Time UTC
Win
d s
peed
(m
/s)
Obs RAMS 1.5km - 1st level RAMS 2nd level
2003/09/04 00UTC
Run 1: Initialisation
00UTC
Run 2: Initialisation
00UTC + observations
assimilated
Run 3: Initialisation
12UTC
0
5
10
15
20
25
30
0 5 10 15 20 25 30
Observed wind
Mo
de
lle
d w
ind
RAMS 0.5km - 1st model level 2nd model level 1:1
0
5
10
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30
0 5 10 15 20 25 30
Observed wind
Mo
de
lle
d w
ind
RAMS 0.5km - 1st model level 2nd model level 1:1
0
5
10
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25
30
0 5 10 15 20 25 30
Observed wind
Mo
de
lle
d w
ind
RAMS 0.5km - 1st model level 2nd model level 1:1
0.5 km grid
0
5
10
15
20
25
30
246.0 246.5 247.0 247.5 248.0 248.5 249.0 249.5 250.0
Time UTC
Win
d s
peed
(m
/s)
Obs RAMS 0.5km - 1st level RAMS 2nd level
2003/09/04 00UTC
0
5
10
15
20
25
30
246.0 246.5 247.0 247.5 248.0 248.5 249.0 249.5 250.0
Time UTC
Win
d s
peed
(m
/s)
Obs RAMS 0.5km - 1st level RAMS 2nd level
2003/09/04 00UTC
0
5
10
15
20
25
30
246.0 246.5 247.0 247.5 248.0 248.5 249.0 249.5 250.0
Time UTC
Win
d s
peed
(m
/s)
Obs RAMS 0.5km - 1st level RAMS 2nd level
2003/09/04 00UTC
Run 1: Initialisation
00UTC
Run 2: Initialisation
00UTC + observations
assimilated
Run 3: Initialisation
12UTC
Model direct output for RUN
1
Kalman filtered
0
5
10
15
20
25
30
35
0 5 10 15 20 25 30 35
Observed wind
Mo
de
lle
d w
ind
RAMS 12km 1:1
0
5
10
15
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35
0 5 10 15 20 25 30 35
Observed wind
Mo
de
lle
d w
ind
Kalman 1:1
0
5
10
15
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30
35
0 5 10 15 20 25 30 35
Observed wind
Mo
de
lle
d w
ind
RAMS 6km 1:1
0
5
10
15
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25
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35
0 5 10 15 20 25 30 35
Observed wind
Mo
dell
ed
win
d
Kalman 1:1
Kalman filtered
0
5
10
15
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35
0 5 10 15 20 25 30 35
Observed wind
Mo
dell
ed
win
d
RAMS 1.5km 1:1
0
5
10
15
20
25
30
35
0 5 10 15 20 25 30 35
Observed wind
Mo
dell
ed
win
d
Kalman 1:1
0
5
10
15
20
25
30
35
0 5 10 15 20 25 30 35
Observed wind
Mo
dell
ed
win
d
RAMS 0.5km 1:1
0
5
10
15
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35
0 5 10 15 20 25 30 35
Observed wind
Mo
dell
ed
win
d
Kalman 1:1
Model direct output for RUN
1
0.010.01
-- 0.790.79
0.5k0.5k
mm
0.100.10
-- 1.061.06
1.5k1.5k
mm
0.070.07
--
0.620.62
6km6km
0.210.21
--
2.532.53
12k12k
mm
Run 1: Run 1: InitialisationInitialisation 00UTC00UTC
-- 0.040.04
-- 1.361.36
0.5k0.5k
mm
-- 0.130.13
-- 1.651.65
1.5k1.5k
mm
0.530.53
--
1.221.22
6km6km
0.410.41
--
2.972.97
12k12k
mm
Run 2 : Run 2 : InitialisationInitialisation 00UTC + 00UTC +
observations assimilatedobservations assimilated Run 3 : Run 3 : InitialisationInitialisation 12UTC12UTC
BiasBias
-- 0.020.02
-- 2.672.67
12k12k
mm
0.400.40
--
0.650.65
6km6km
0.350.35
-- 1.891.89
1.5k1.5k
mm 0.5km0.5km
0.03 0.03 m/sm/sKalman:Kalman:
-- 1.67 1.67
m/sm/sModel:Model:
Summary of statistics for the period 4Summary of statistics for the period 4--7/9/20037/9/2003
2.912.91
3.583.58
0.5k0.5k
mm
3.343.34
3.983.98
1.5k1.5k
mm
3.173.17
4.034.03
6km6km
2.822.82
4.334.33
12k12k
mm
Run 1Run 1
2.662.66
4.174.17
0.5k0.5k
mm
2.992.99
4.384.38
1.5k1.5k
mm
3.323.32
4.194.19
6km6km
3.013.01
4.764.76
12k12k
mm
Run 2Run 2 Run 3Run 3
RMSERMSE
2.372.37
4.074.07
12k12k
mm
2.652.65
3.543.54
6km6km
2.222.22
3.503.50
1.5k1.5k
mm 0.5km0.5km
2.25 2.25 m/sm/sKalman:Kalman:
3.36 3.36 m/sm/sModel:Model:
19.2119.21
18.4118.41
0.5km0.5km
19.3019.30
18.1418.14
1.5km1.5km
19.219.2
77
18.518.5
88
6km6km
19.419.4
11
16.616.6
77
12k12k
mm
Run 1Run 1
19.1619.16
17.8417.84
0.5km0.5km
19.0719.07
17.5517.55
1.5km1.5km
19.719.7
33
17.917.9
88
6km6km
19.619.6
11
16.216.2
33
12k12k
mm
Run 2Run 2 Run 3Run 3
MeanMean
20.0820.08
17.4317.43
12km12km
20.520.5
00
19.419.4
55
6km6km
20.4520.45
18.2118.21
1.5km1.5km 0.5km0.5km
20.13 20.13 m/sm/sKalman:Kalman:
18.43 18.43 m/sm/sModel:Model:
Concluding RemarksConcluding Remarks
•• Wind Power Energy production needs good quality weather forecastWind Power Energy production needs good quality weather forecastss
•• Model resolution does not necessarily solves all the problems reModel resolution does not necessarily solves all the problems related to lated to accurate wind predictionsaccurate wind predictions
•• Certain difficulties arise from the fact that in most of the casCertain difficulties arise from the fact that in most of the cases the wind es the wind generators are at the mountain (or hill crests)generators are at the mountain (or hill crests)
•• The short term wind (and therefore energy) prediction depends onThe short term wind (and therefore energy) prediction depends on the the resolution and the existence of a dense meteorological network aresolution and the existence of a dense meteorological network around the round the park(spark(s) )
•• According to our experimental simulations the wind field improveAccording to our experimental simulations the wind field improvement ment beyond the 6 km horizontal grid is not considered as worth the beyond the 6 km horizontal grid is not considered as worth the computational costs, at least with the present status of the comcomputational costs, at least with the present status of the computer power puter power to cost ratioto cost ratio
•• Since this is not feasible other techniques like the Since this is not feasible other techniques like the KalmanKalman filtering can filtering can provide acceptable accuracy with less moneyprovide acceptable accuracy with less money