Assimilation of OMI Data Into NCEP’s GFS

Assimilation of OMI Data Into NCEP’s GFS

Craig Long,

S. Zhou, T. Beck, A.J. MillerNOAA/NWS/NCEP/Climate Prediction Center

L.Flynn

NOAA/NESDIS/STAR

Outline

Background Improvements due to OMI coverage OMI Issues Comparisons between SSI and GSI How OMI data is assimilated

Thinning possibilities

Summary What’s Next

Aspects of Ozone in NWP

Three aspects of dealing with ozone in NWP Assimilation of ozone observations

• Horizontal and vertical

• Agreement between multiple sources

Transport of ozone once in the model• Brewer Dobson Circulation

Ozone Chemistry• Homogeneous: Production and Loss

– f: Latitude, Pressure, Season

• Heterogeneous: 'Ozone Hole' type depletion

– Need additional observations

Background

Currently NCEP GFS assimilates SBUV/2 total and profile ozone measurements from both NOAA-16 and 17.

SBUV/2 provides about 90 nadir observations per orbit. Replacement instrument is the OMPS (Ozone Mapping

and Profiler Suite) Combination of scanning mapper and limb profile On NPP and NPOESS Will provide higher vertical and horizontal resolution

Current additional sources of ozone data available: Aura: OMI*, HIRDLS*, MLS, TES *NRT MetOp: GOME2*

Background cont.

Why is ozone assimilated? LW and SW radiation schemes need realistic ozone. Used to extract correct temperature component from the ozone

sensitive HIRS channels. Biggest impacts in terms of temperature and dynamics and

should occur in the UT/LS.• Won't improve short term skill (days 1-3)

• But should improve days >3

Ozone forecasts used in UV Index forecasts. Used for boundary conditions in Air Quality forecasts.

OMI

GFS

OMI Comparison with GFS using SBUV/2

OMI shows finer structure than the GFS, e.g., the relatively high ozone off the East coast is captured by OMI but

missed by GFS.

November 11, 2005

SBUV/2 only Adding OMI TOMS obs.

Adding OMI makes 5 day total ozone forecast agree more with NASA/TOMS

November 12, 2005


November 13, 2005


November 14, 2005


November 15, 2005


end

OMI Issues• Conflicts with SBUV/2 at high SZA

Also SBUV/2 is V6 product Is V8 much different? Where? When?

• Noise in some channels affects TO3 at high SZA Cloud climatology may degrade quality of TO3

• Comparisons with DOAS products DOAS has striping But, better estimate of cloud top heights

• High density of data 840 points per single SBUV/2 ob Needs thinning

• Comparisons with surface obs

N16 SBUV/2 & OMTO3 Nadir Total Ozone - 20051023

140160180200

220240260280300320340360

380400420440

-90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80 90

Tot

al O

zon

e (D

U)

N16 OMTO3

Comparison between OMTO3 (NASA/TOMS) and OMDOAO3 (KNMI/DOAS)

OMTO3 vs OMDOAO3 Zonal Mean Total Ozone

Mean may average out to near zero, but variability is quite high!

Striping in DOAS Total ozone makes it unusable

TOMS and SBUV/2 V8 Clim Cloud Tops Results in Total Ozone being too High

DOAS Cloud Top PressuresOMTO3 Cloud Top Pressures

OMTO3 Cloud Top Pressure Climatology Issue

0 10000 1000500 500

OMTO3 using cloud own climatology OMTO3 using DOAS cloud top heights

If DOAS Cloud Top Pressures are used,OMTO3 Total Ozone usually is lower

208 258 208 258

OMI N-17 SBUV/2 N-16 SBUV/2

(12Z)

GSI vs SSI

SBUV/2 only (N16,N17)

OMI only

SBUV/2 andOMI

GSI SSI

Total Ozone increment (DU)

SBUV/2 only

OMI only

SBUV/2 and OMI

GSI – SSI differences

Data Thinning• There are many ways to thin massive amounts of sat. obs.

• Experimentation is only way to determine best density: Sometimes “less is more”

• OMI vs SBUV # of obs 60 OMI obs/scan x 14 scans/SBUV retrieval

Or 840 points per SBUV retrieval

~76,000 points per orbit

• Need to restrict OMI to quality data points

• Thin by selection Fewer points in flat regions - more points in dynamic regions

Background errors may be adjusted to be more sensitive in dynamic regions

• Thin by averaging Uniform coverage

Average out noisy data

Flat region Dynamic regions

Dynamic ozoneregionsFlat region

Total Ozone Variability within Scan - 20051023

0

10

20

30

40

50

60

70

80

-90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80 90

Var

iab

ilit

y w

ith

in S

can

(D

U)

Orb1 Orb2 Orb3 Orb4 Orb5 Orb6 Orb7 Orb8 Orb9 Orb10 Orb11 Orb12 Orb13 Orb14

Dynamic ozoneregions

Flat region

Data thinning method tested

• Method: averaging data in 1o x 1o model grid box.

• Selection: when there are overlapped data from multiple orbits within a 1o x 1o box, select data only from one major orbit.

• Reduction: total number of data is reduced to ~ 6%.

OMI N-17 SBUV/2 N-16 SBUV/2

(12Z)

1o (lat) x 1o (lon) thinning

12Z

From ~ 76,000 obs per orbit to ~ 4000

1o (lat) x 2o (lon) thinning

12Z

From ~ 76,000 obs per orbit to ~ 2000

DU

Ozone difference of thinning and non-thinning (GSI)SBUV/2 and OMI

DU

GSI and SSI TOZ difference (1o x 1o thinning)

GSI and SSI TOZ difference (1o x 2o thinning)

DU

Summary

OMI adds additional information in horizontal

OMI data have issues to be rectified Are ways to improve it!

GSI assimilation of OMI data not significantly different from SSI

What’s Next

• Move to Aqua computer when available.

• Continue experimenting with thinning options.– Quality assessment of data

• Assess impacts in forecast mode.– Determine resolution dependence

– Impacts to temperatures and dynamics

– Strive for improvement in multi-day forecasts.

• Begin looking at OMI profile products– Profile total ozone may be better than ‘best’ ozone

– Additional profiles

• Use March 2006 as test month

• Compare profiles with ozonesonde and Lidar data.

• HIRDLS data

fini

MLS

OMI

TESHIRDLS

EOS AURA was launched in July 2004, which has 4 ozone measuring instruments.

Aura instruments

• OMI (ozone Monitoring Instrument)– total ozone and ozone profile, high horizontal resolution

• HIRDLS (High Resolution Dynamics Limb Sounder)

– ozone profile, high vertical resolution (1.25 km, 10-80 km)

• MLS (Microwave Limb Sounder)– ozone profile (3 km, 8-50 km)

• TES (Tropospheric Emission Spectrometer)

– tropospheric ozone (0-34 km)

Documents

Assimilation of OMI Data Into NCEP’s GFS