critical issues concerning model applications in marine...

$: Critical Issues Concerning Model Applications in Marine ...brams.cptec.inpe.br/~rbrams/RAMS_BRAMS_OLAM_6th... · Cloud deck information (cloud cover, cloud height, cloud fraction)$
G. Kallos

[email protected]

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 3day 3

00UTC00UTC



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 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 BiasBias


WH WH 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


advection and diffusion

Eulerian.

Horizontal

advection and

diffusion


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

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100

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Hg

P (

pg

/m3

)


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2 (

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/m3

)


0

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.Pau

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.Trie

ste

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.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

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0 5 10 15 20 25 30

Observed wind

Mo

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RAMS 12km - 1st model level 2nd model level 1:1

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12 km grid

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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

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30

246.0 246.5 247.0 247.5 248.0 248.5 249.0 249.5 250.0

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d s

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(m

/s)


2003/09/04 00UTC

0

5

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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)


2003/09/04 00UTC

Run 1: Initialisation

00UTC


00UTC + observations

assimilated


12UTC

0

5

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30

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d w

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0

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Observed wind

Mo

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d w

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0 5 10 15 20 25 30

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Mo

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d w

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246.0 246.5 247.0 247.5 248.0 248.5 249.0 249.5 250.0

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(m

/s)

Obs RAMS 6km - 1st level RAMS 2nd level

2003/09/04 00UTC

0

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/s)


2003/09/04 00UTC

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(m

/s)


2003/09/04 00UTC


00UTC



assimilated


12UTC

6 km grid

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RAMS 1.5km - 1st model level 2nd model level 1:1

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Obs RAMS 1.5 km - 1st level RAMS 2nd level

2003/09/04 00UTC

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(m

/s)

Obs RAMS 1.5km - 1st level RAMS 2nd level

2003/09/04 00UTC

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00UTC



assimilated


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Model direct output for RUN

1

Kalman filtered

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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


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


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

critical issues concerning model applications in marine...

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