Aerosols - earthsystemcog.org

swedishstreakMécanique

22 févr. 2014 (il y a 3 années et 7 mois)

142 vue(s)

Aerosols

Sarah Lu

Sarah.Lu@noaa.gov

1

Outline


Introduction


NEMS GFS Aerosol Component (NGAC)


NGAC V1.0 dust forecasting


Future work

2

NEMS/GFS Modeling Summer School

3

1. Introduction

NEMS/GFS Modeling Summer School

EMC colleagues:

NEMS GFS team

AQ group


Project Collaborators

Arlindo da Silva, Mian Chin, and Peter Colarco (NASA GSFC)

Shobha Kondragunta and Xiaoyang Zhang (NOAA NESDIS)

Angela Benedetti, Jean Jacques Morcrette, Johannes Kaiser, Luke Jones (ECMWF)

Jeffrey Reid and Walter Sessions (NRL)

Development and operational implementation of the NEMS
-
GFS Aerosol Component
represents a successful three
-
year “research to operations” project sponsored by
NASA Applied Science Program, JCSDA (NESDIS) and NWS

Acknowledgements

4

NEMS/GFS Modeling Summer School

5



Radiation:
Aerosols affect radiation both directly (via scattering and
absorption) and indirectly (through cloud
-
radiation interaction)



Hurricane forecasts
: Dust
-
laden Saharan air layer reduces occurrence of
deep convection and suppresses tropical cyclone activities



Data Assimilation:
Aerosols are one of key sources of errors in SST
retrievals and an important component for accurate radiance data
assimilation



Regional air quality
: Aerosol (lateral an upper) boundary conditions are
needed for regional air quality predictions



Aviation and visibility
: Emissions from large wild fires and volcanic eruption
affect aviation route planning and visibility forecasts



Public Health:
Fine particulate matter (PM2.5) is the leading contributor to
premature deaths from poor air quality



Why Include Aerosols in the Predictive Systems?

NEMS/GFS Modeling Summer School


T126 L64 GFS/GSI
#

experiments for the 2006 summer period


PRC uses the OPAC climatology (as in the operational applications)


PRG uses the in
-
line GEOS4
-
GOCART
%

dataset (updated every 6 hr)

Aerosol
-
Radiation Feedback:

Impact of Aerosols on Weather Forecasts

Verification against analyses and observations indicates
a positive impact in temperature forecasts due to
realistic time
-
varying treatment of aerosols.

#: 2008 GFS package

%: In
-
line GEOS4
-
GOCART

6

NEMS/GFS Modeling Summer School

7

‘Dust
-
Free ’ vs. ‘Dusty’ Granule Retrievals

07/28/2011, 08/01/2011 IASI and
CrIMSS

AEROSE
-
2011
Matched IASI
(
RET
)
,
ECMWF and CrIMSS
(
RET
)
-

T
(
p
)
Dust
-
Free
/
Dusty
NOAA
-

AEROSE
-
2011
IASI
-
TRET vs
.
ECMWF
;
CrIMSS
-
TRET vs
.
ECMWF
07
/
28
/
2011
G
-

251
,
252
,
475
,
476
08
/
01
/
2011
G
-

448
,
449
·

From Eric Maddy’s findings and IASI Research Team at NOAA
·

IASI dust score is based on S
.
De
-
Souza Machado’s recipe of channel differences for AIRS
(
GSFC
,
JPL
,
UMBC
,
personal communication
)
for similar IASI channels
.
·

Score is calculated using IASI CCRs
(
operational version
+
new regressions
)
and can range
between
0
.
and
511
.
·

Warmer colors implies higher probability of contamination
·

Side note
:
AVHRR clear scenes can be dust contaminated
(
see black circles surrounding red dots
).
Murty

Divakarla

(NESDIS)

AEROSE
-
2011
Matched IASI
(
RET
)
,
CrIMSS
(
RET
)
-

T
(
p
)
Dusty
-

Improvements
NOAA
-

AEROSE
-
2011
IASI
-
TRET vs
.
ECMWF
;
CrIMSS
-
TRET vs
.
ECMWF
08
/
01
/
2011
G
-

448
,
449
Atmospheric Correction

NEMS/GFS Modeling Summer School

CMAQ

Baseline

CMAQ
Experimental

Whole domain

July 1


Aug 3

MB=
-
2.82

Y=1.627+0.583*X
R=0.42

MB=
-
0.88

Y=3.365+0.600*X
R=0.44

South of 38
°
N,

East of
-
105
°
W

July 1


Aug 3

MB=
-
4.54

Y=2.169+.442*X
R=0.37

MB=
-
1.76

Y=2.770+.617*X
R=0.41

Whole domain

July 18


July 30

MB=
-
2.79

Y=2.059+0.520*X
R=0.31

MB=
-
0.33

Y=2.584+0.795*X
R=0.37

South of 38
°
N,

East of
-
105
°
W

July 18


July 30

MB=
-
4.79

Y=2.804+.342*X
R=0.27

MB=
-
0.46

Y=
-
0.415+.980*X
R=0.41


Baseline CMAQ with static LBCs versus experimental
CMAQ with dynamic LBCs from NGAC, verified
against AIRNOW observations


The inclusion of LBCs from NGAC prediction is found
to improve PM forecasts (e.g., reduced mean biases,
improved correlations)

Youhua

Tang (NESDIS)

Long Range Dust Transport

8

NEMS/GFS Modeling Summer School

9

2. NEMS GFS Aerosol Component

NEMS/GFS Modeling Summer School

Developing an
Interactive Atmosphere
-
Aerosol Forecast System


In
-
line chemistry advantage


Consistency
: no spatial
-
temporal interpolation, same physics parameterization


Efficiency
: lower overall CPU costs and easier data management


Interaction
: Allows for aerosol
feedback to meteorology


NEMS GFS Aerosol Component


Model Configuration:


Forecast model: Global Forecast System (GFS) based on NOAA
Environmental Modeling System (NEMS),
NEMS
-
GFS


Aerosol model: NASA Goddard Chemistry Aerosol Radiation and Transport
Model,
GOCART


NEMS GFS and GOCART are
interactively

connected using
ESMF coupler
components


Despite the ESMF flavor in how GOCART is implemented, GOCART is
incorporated into NEMS GFS as
a column process




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NEMS/GFS Modeling Summer School

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NEMS/GFS Modeling Summer School


GOCART

Peter Colarco (GSFC)

Dynamics

Physics

Dyn
-
Phy

Coupler

GOCART

PHY2CHEM coupler component

transfers data
from
phys

export state to
chem

import state

Convert units (e.g.,
precip

rate, surface
roughness)

Calculations (e.g., soil wetness,
tropopause

pressure, relative humidity, air density,
geopotential

height)

Flip the vertical index for 3D fields from bottom
-
up to top
-
down


Phy
-
Chem

Coupler

Phy
-
Dyn

Coupler

Dynamics

Chem
-
Phy

Coupler

CHEM2PHY coupler component

transfers data
from
chem

export state to
phys

export state

Flip vertical index back to bottom
-
up

Update 2d aerosol diagnosis fields


GOCART gridded component
computes
source, sink, and transformation for aerosols

12

Primary Integration
Runstream

NEMS/GFS Modeling Summer School


NWP vs Chemistry Transport Model (CTM) modeling


Different focus for the same parameter


High wind speeds and heavy precipitation for NWP versus stagnant
conditions and low intensity rain for CTM


Different approaches are needed for emission estimates


Climate projection versus NRT forecasts


Are experiences in NWP applicable to chemistry modeling?


Multiple model ensemble


Verification and evaluation


The use of NWP model to transport chemical species


Need mass conserving, positive definite advection scheme


Requirements in operational environments


Code optimization


Concurrent code development


Near
-
real
-
time global emissions



Challenges for Incorporating
Aerosol Component into NEMS

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NEMS/GFS Modeling Summer School

T126

T382

High resolution run won’t help.

Gibbs phenomenon
in NGAC,

spurious oscillation in the vicinity of
sharp gradients

14

Gibbs phenomenon: Simulations of
Grimsvotn

ashes

NEMS/GFS Modeling Summer School

Dust Source Function

Function of surface topographic depression, surface wetness, and surface wind speed
(Ginoux et al. 2001)



S

: Source function

s
p
: fraction of clay and silt size

u
10
: wind speed at 10 m u
t
: threshold wind velocity





p

: particle diameter
ρ
p
,
ρ
a

: particle and air density

A : constant=6.5

w
t
: surface wetness









otherwise
u
u
u
u
u
s
S
Flux
Source
t
t
p
p
0
10
10
2
10


'
2
.
0
log
2
.
0
2
.
1
10











otherwise
w
if
w
g
A
u
t
t
p
a
a
p
t



Source function:
A static map
for probability of dust uplifting,
determined by the surface
bareness and topographical
depression features

15

NEMS/GFS Modeling Summer School

GOES
-
E and
GOES
-
W

METEOSAT

MTSAT

GBBEP
-
Geo

16


Hourly fire emissions for CO,
OC, BC, CO
2
, SO
2
, PM2.5


Limited coverage in high
latitudes and no coverage in
most regions across India and
parts of boreal
Asia


Globally, biomass burning is one of the primary sources of aerosols; burning varies
seasonally, geographically and is either natural (e.g., forest fires induced by lightning) or
human induced (e.g., agricultural burning for land clearing).


Satellites can provide smoke emissions information on a real time basis.


A joint NASA/GMAO
-
NESDIS/STAR
-
NWS/NCEP project to develop near real time
biomass burning emissions product covering the whole globe from polar and
geostationary satellites (Shobha Kondragunta and Xiaoyang Zhang, STAR; Arlindo da
Silva, GMAO; Sarah Lu, NCEP)

Near
-
Real
-
Time Smoke Emissions

Shobha Kondragunta (STAR)

16

NEMS/GFS Modeling Summer School

17

3. NGAC V1.0 dust forecasting

NEMS/GFS Modeling Summer School

Model Configuration:


Forecast model:
NEMS GFS


Aerosol model:
GOCART

Phased Implementation:


Dust
-
only guidance
is established in
Q4FY12


Full
-
package aerosol forecast after real
-
time global smoke emissions are
developed and implemented

Near
-
Real
-
Time Dust Forecasts


5
-
day dust forecast
once per day (at 00Z),
output every 3 hour, at T126 L64
resolution


ICs: Aerosols from previous day forecast
and meteorology from operational GDAS

Overview of NOAA GFS Aerosol Component (NGAC)

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NEMS/GFS Modeling Summer School



ngac.t00z.aod_$CH, CH=340nm, 440nm, 550nm, 660nm, 860nm, 1p63um, 11p1um


AOD at specified wavelength from 0 to 120 hour



ngac.t00z.a2df$FH, FH=00, 03, 06, ….120


AOD at 0.55 micron


Dust emission, sedimentation, dry deposition, and wet deposition fluxes


Dust fine mode and coarse mode surface mass concentration


Dust fine mode and coarse mode column mass density



ngac.t00z.a3df$FH, FH=00, 03, 06, ….120


Pressure, temperature, relative humidity at model levels


Mixing ratios for 5 dust bins (0.1
-
1, 1
-
1.8, 1.8
-
3, 3
-
6, 6
-
10 micron) at model levels


NGAC Product Suite and Applications

UV index forecasts

DA and ensemble

AVHRR SST

AIRS retrievals

Budget, ocean productivity

Air quality

Budget

Atmospheric correction



NGAC provides 1x1 degree output in GRIB2 format once per day.

Output files and their contents include:

19

NEMS/GFS Modeling Summer School

WMO Sand and Dust Storm Warning Advisory and Assessment
System (SDS
-
WAS): Model Inter comparison

BSC
-
DREAM8b


UKMO

MACC
-
ECMWF

Median

NMMB/BSC
-
Dust

NCEP NGAC

DREAM
-
NMME
-
MACC



SDS
-
WAS Regional Centre for Northern Africa,
Middle East, and Europe, hosted by Spain,
conducts daily dust AOD inter comparison

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NEMS/GFS Modeling Summer School

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NEMS/GFS Modeling Summer School

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4. Future Work

NEMS/GFS Modeling Summer School


Enables future operational global short
-
range (e.g., 5
-
day)
aerosol
prediction


Provides a first step toward an operational
aerosol data assimilation
capability at NOAA


Allows
aerosol impacts
on medium range weather forecasts to be
considered


Allows NOAA to exploe
aerosol
-
chemistry
-
climate interaction
in the Climate
Forecast System (CFS)


Provides global aerosol information required for various applications (e.g.,
satellite radiance data assimilation,
satellite retrievals, SST analysis, UV
-
index forecasts, solar electricity production)


Provides
lateral aerosol boundary conditions
for regional aerosol forecast
system




23

NEMS/GFS Modeling Summer School

Future Operational Benefits Associated with

NEMS GFS Aerosol Component


With further development and resources, the NEMS GFS can be used for
modeling and assimilation of reactive gases (including ozone) and aerosols
(including volcanic ashes) on a global
-
scale



Enable
global atmospheric constituents forecasting capability
to provide
low
-
resolution aerosols forecasts routinely as well as high
-
resolution air
quality predictions and volcanic ash forecasts on
-
demand.



Provide quality atmospheric constituents forecast products to serve a wide
-
range stakeholders, such as health professionals, aviation authorities, policy
makers, climate scientists and solar energy plant managers





24

NEMS/GFS Modeling Summer School


Long Term Goal

25

Thank You


Questions?

NEMS/GFS Modeling Summer School