second galion workshop, wmo, geneva 20-23 september 2010 aerosol lidar observations: a missing...

Second GALION workshop, WMO, Geneva 20-23 September 2010

Aerosol Lidar Observations: A Missing Component Of Near-Real-Time Data Assimilation

Requirements for Weather and Air Quality Models

S. Nickovic and L. BarrieWMO Research Department, Geneva

G. Pejanovic, A. Vukovic, M. Vujadinovic, M. Dacic SEEVCCC, Met Service, Serbia

L. Mona and G. Pappalardo CNR-IMAA, Potenza

F. RussoISAC, Bologna

Current Pre-operational Assimilation SystemsUS Navy (NAAPS aerosol model)

- operational- satellite (MODIS, etc)- global

GEMS/ECMWF aerosol model - operational- MODIS- global

Met Service (Serbia) DREAM model - operational- ECMWF objective analysis of dust (MODIS)- regional

CMA (CUACE-Dust aerosol model) - operational- visibility data, satellite FY-2C AOD

Meteorological Research Institute (Japan) aerosol model - research- CALIPSO- global


• None of the pre-operational systems use vertical profile observations

• Only the Japanese experimental system uses CALIPSO for reconstructing the vertical structure;

• The frequency of observation by CALIPSO is insufficient for routine assimilation


NAAPS AOD (no assimilation)

NAAPS AOD(w/ assimilation)

1) Convert NAAPS mass concentration to aerosol optical depth

2) Two-D variational assimilation of the optical depth field (MODIS etc)

3) Convert optical depth to NAAPS three-D mass concentration

NAAPS Data Assimilation Methodology

AERONET versus NAAPS

(January –May 2006)

Zhang, J., J. S. Reid, D. L. Westphal, N. L. Baker, and E. J. Hyer, 2008, A system for operational aerosol optical depth data assimilation over global oceans, J. Geophys. Res., 113, doi:10.1029/2007JD009065.

without AOD assimilation

with AOD assimilation


GEMS/ECMWF 4-D Variation Assimilation System

• Prognostic variables (mass concentrations):– 3 bins for dust– 3 bins for sea salt– Organic matter– Black carbon– Sulphate

• Assimilated observations: MODIS AOD• Validation data: AERONET, AEROCE• Validated variables: AOD, Angstrom exponent

• Regional dust model (DREAM)• Blended DREAM 24-h forecast and

ECMWF 3D MODIS-based objective dust analysis

• Assimilation based on Newtonian relaxation

First operational dust assimilation at regional scale;

http://www.seevccc.rs/

Assimilation System: South East Europe Regional Climate Centre (Serbian Met Service)

C n+ 1=Cn+KΔtC ECMWF

1+KΔt

Pejanovic et al., 2010, Assimilation of satellite information on mineral dust using dynamic relaxation approach. AGU 2010 General Assembly.


WET DEPOSITIONNO ASSIMILATION

WET DEPOSITION ASSIMILATION

04 March 2010Case of yellow snow observed in the Kopaonik sky resort (location marked with )


Assimilation System of Meteorological Research Institute,

National Institute for Environmental Studies, and Japan Meteorological Agency

• Global aerosol model

• Assimilation based on 4-D Kalman filter

• First use of CALIPSO data for assimilation


Without assimilation With assimilation

no observed dust

observed dust

Importance of observing vertical aerosol profiles: Early ideas (1996)

• Suggested analogy between TEMP meteorology reports and aerosol vertical profile observations

• Recognizing that satellite column variables (e.g. AOD) are not sufficient for model assimilation

• Proposed blending of the vertical profiling data (not available at that time) and model forecasts

Nickovic, S., 1996: Modelling of dust processes for the Saharan and Mediterranean area

First Step

For a selected dust intrusion into Europe assemble EARLINET lidar profiles from Munich, Aberystwyth, Barcelona, Leipzig, Neuchatel

Second Step

• Specify lidar observations at a selected model level using objective analyses based on successive correction method.

• It mixes lidar profiles and 24-h predicted concentration;

• Convert Bscat coefficients mass concentration

used Ansmann et al. (2003)

Dust Concentration (gm-3) = Backscatter Coefficient *60/0.7

Early Attempts (2002) at Assimilation of EARLINET Lidar Data in DREAM (I)


∂C∂ t+K t,z C−C =0

Third Step

Assimilate lidar profiles by applying Newtonian relaxation (nudging) method :

C – concentration

C* - target concentration

K – nudging coefficient; increases with relaxing time; has max at 3.5 km

Early Attempts (2002) at Assimilation of EARLINET Lidar Data in DREAM (II)

Concentration (g/m^3) at 2 km Height

13 October 2002 at 0000 UTC

NO ASSIMILATION ASSIMILATION


DIFFERENCE: (ASSIM-NOASSIM)

2 km concentration (g/m^3)


Model validation against lidar observations

A systematic comparison between DREAM model and lidar observations is currently in progress

Among all EARLINET stations, the Potenza station was selected as the one with the largest database of Saharan dust observations to develop a methodology for the comparison.

Comparison for May 2000 – April 2005 period between lidar observations and DREAM forecasts over Potenza.

Comparison procedure taking into account different temporal and vertical resolution has been developed.


Comparisons in terms of:

Geometrical properties base, top and center of mass of layers identified layers above the PBL

Extensive properties mean backscatter and extinction for lidar profiles

mean concentration for DREAM profiles

Integrated backscatter and optical depth for lidar profiles aerosol load for DREAM profiles

2 3 4 5 6 7 8 90

10

20

30

40

50

60

Co

un

ts

Layer Center of Mass a.s.l. [km]

Lidar Dream

COMLidar = (4.5 ± 1.2) kmCOMDream = (4.4 ± 1.1) km

Profiles mean (and variability) of profiles of extinction and backscatter for Lidar

mean concentration (and its variability) profile for DREAM

correlation coefficient for each identified case between extinction (or backscatter) and concentration in the identified layer

2

4

6

8

10

0.0 5.0x10-5 1.0x10-4 1.5x10-4

0 20 40 60 80

Aerosol Concentration [ g m-3]

Aerosol Extinction Coefficient @ 355 nm

[m-1]A

ltit

ud

e a.

s.l.

[km

]


Proposed conversions: lidar parameters to model concentrations

The first step for lidar data assimilation is the conversion of concentration into lidar measured optical quantities (like aerosol extinction). The BOLCHEM group at ISAC/CNR in Bologna started to work on these conversions.

BOLCHEM is a coupled meteorology-chemistry model.

• Meteorology is based on BOLAM (Bologna Limited Area Model) developed at ISAC-CNR by the Dynamic Meteorology group.

• Modelling of aerosols is in a test phase and is supported by the atmospheric chemistry group.

BOLCHEM interest in aerosol is due to:

• Implementation of the aerosol feedback on radiation.

• Aerosol optical depth validation by comparison with MODIS.


AOD computation from BOLCHEMIndividual aerosol species:

• SO4, organic, black carbon, sea salt, NH4, NO3.

Example of monthly averaged total aerosol optical depthin the Po Valley

Sulfate component

Sea salt component


Possible Way Forward For Near-Real-Time Data Delivery And Data Assimilation Of GALION

Observations • WMO frameworks in support of such approach:

– WIS– GAW supported integrated global aerosol observing and analysis

system (GALION, AOD consortium, surface in situ consortium, IAGOS partners, satellite partners) www.wmo.int/gaw

– Sand and Dust Storm Warning and Advisory System(SDS-WAS) www.wmo.int/sdswas

• Proposed GALION data exchange concept– Time frequency: 3 hours (alternative, 6 hours)– Vertical resolution: 100m– Delivery triggering: as indicated by aerosol models (e.g. EARLINET

stations may operate according to routine dust forecasts) – Format: WMO/WIS BUFER or CIREX– Data quality level: not controlled– Extend GALION network form a core of sophisticated research

based stations to include stations operating with relative cheap Lidar equipment (so that met services, airports and/or environment agencies could perform observation operations)

http://www.wmo.int/gaw

http://www.wmo.int/sdswas

second galion workshop, wmo, geneva 20-23 september 2010 aerosol lidar observations: a missing...

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