Aerosols

Aerosols

Sarah [email protected]

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Outline Introduction NEMS GFS Aerosol Component (NGAC) NGAC V1.0 dust forecasting Future work

2NEMS/GFS Modeling Summer School

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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


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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?


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


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‘Dust-Free ’ vs. ‘Dusty’ Granule Retrievals07/28/2011, 08/01/2011 IASI and CrIMSS

AEROSE-2011 Matched IASI(RET), ECMWF and CrIMSS (RET) - T(p) Dust-Free/Dusty

NO

AA- A

ERO

SE-2

011

IASI

-TRE

T vs

. ECM

WF;

CrIM

SS-T

RET

vs. E

CMW

F 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

NO

AA- A

ERO

SE-2

011

IASI

-TRE

T vs

. ECM

WF;

CrIM

SS-T

RET

vs. E

CMW

F

08/01/2011 G- 448, 449

Atmospheric Correction


CMAQ Baseline

CMAQ Experimental

Whole domainJuly 1 – Aug 3

MB= -2.82Y=1.627+0.583*X R=0.42

MB= -0.88Y=3.365+0.600*X R=0.44

South of 38°N, East of -105°WJuly 1 – Aug 3

MB= -4.54Y=2.169+.442*X R=0.37

MB= -1.76Y=2.770+.617*X R=0.41

Whole domainJuly 18– July 30

MB= -2.79Y=2.059+0.520*X R=0.31

MB= -0.33Y=2.584+0.795*X R=0.37

South of 38°N, East of -105°WJuly 18– July 30

MB= -4.79Y=2.804+.342*X R=0.27

MB= -0.46Y=-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


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2. NEMS GFS Aerosol Component


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



GOCART

Peter Colarco (GSFC)

Dynamics

Physics

Dyn-PhyCoupler

GOCART

PHY2CHEM coupler component transfers data from phys export state to chem import stateConvert 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-downPhy-Chem

Coupler

Phy-DynCoupler

Dynamics

Chem-PhyCoupler

CHEM2PHY coupler component transfers data from chem export state to phys export stateFlip vertical index back to bottom-upUpdate 2d aerosol diagnosis fields

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

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Primary Integration Runstream


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


T126

T382

High resolution run won’t help.

Gibbs phenomenon in NGAC, spurious oscillation in the vicinity of sharp gradients

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Gibbs phenomenon: Simulations of Grimsvotn ashes


Dust Source Function

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

S : Source function sp: fraction of clay and silt sizeu10: wind speed at 10 m ut: threshold wind velocity

p : particle diameter ρp, ρa : particle and air densityA : constant=6.5 wt: surface wetness

otherwise

uuuuusSFluxSource ttp

p 01010

210

'2.0log2.02.1 10

otherwise

wifwgAu ttpa

ap

t

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


GOES-E and GOES-W

METEOSAT MTSAT

GBBEP-Geo

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Hourly fire emissions for CO, OC, BC, CO2, SO2, 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) 16NEMS/GFS Modeling Summer School

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3. NGAC V1.0 dust forecasting


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)


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:


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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4. Future Work


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


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


Long Term Goal

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Thank You

Questions?


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Aerosols