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Assimilation of AIRS Data at NRL Assimilation of AIRS Data at NRL Benjamin Ruston, Clay Blankenship, William Campbell, Rolf Langland, and Nancy Baker Naval Research Laboratory, Monterey, CA, USA Data Use Data Use AIRS-324 channel subset,

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SLIDE 1

Benjamin Ruston, Clay Blankenship, William Campbell, Rolf Langland, and Nancy Baker Naval Research Laboratory, Monterey, CA, USA

Assimilation of AIRS Data at NRL Assimilation of AIRS Data at NRL

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SLIDE 2

Data Use Data Use

  • AIRS-324 channel subset, U1 and U2 alternate golfballs
  • Co-located with AMSU/A sensor, simultaneous assimilation
  • Thinned to approximately 300km resolution
  • Channels with sensitivity above model top (4mb) rejected
  • Ozone sensitive channels rejected
  • Near-infrared channel rejected in daytime
  • Approximately 4million observations per 6 hour watch before

thinning and quality control

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SLIDE 3

Channel Selection Channel Selection

NOGAPS model top 4hPa, and no prognostic O3

additional tools: ECMWF & MSC operational lists, adjoint senstivities

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SLIDE 4

Data Thinning Data Thinning thinned to ~300km resolution

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SLIDE 5

Quality Control Quality Control

  • Radiances modeled using JCSDA-CRTM
  • Histograms of observed minus simulated (ob-background)
  • Slope change in wavelength vs. (ob-background)*
  • Gross check on 2 window and 2 water vapor channels
  • Individual checks for each channel based on ob error

*A. McNally and P. W atts, 2 0 0 3 , A cloud detection alg orithm for hig h-spectral- resolution infrared sounders. Q. J. R. Meteorol. Soc., 2 0 9 , pp. 3 4 1 1 -3 4 2 3 .

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SLIDE 6

Forecast Impacts Forecast Impacts

Anomaly Correlations Anomaly Correlations

AQUA impacts AIRS+AMSU

SH better NH worse

AMSU only

SH slightly worse NH better

AQUA impacts (ocean only) (ocean only) AIRS+AMSU

SH better NH slightly worse

  • cean only improves

NH performance

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SLIDE 7

Forecast Impacts Forecast Impacts

Tropical Cyclone Tropical Cyclone

N O A A A M SU N O A A A M SU + A Q U A A M SU /A I RS = = = = A LL TRO PI CA L STO RM S FO R 2 0 0 6 0 7 2 6 0 0 TO 2 0 0 6 0 9 0 2 0 0 = = = = # storm s ddi s2 # storm s dsl p2 # strom s ddi s # storm s dsl p1 Tau 4 1 2 8 0 . 2 6 3 3

  • 0 .

5 6 4 1 2 6 3 . 8 5 3 3 0 . 2 7 1 2 0 4 9 2 3 7 . 9 7 4 0 0 . 0 1 4 9 2 2 4 . 0 6 4 0 0 . 6 9 1 0 8 5 7 1 9 7 . 4 8 4 8 0 . 5 6 5 7 1 9 1 . 7 8 4 8 1 . 1 8 9 6 6 5 1 6 7 . 8 7 5 6 1 . 0 9 6 6 1 6 9 . 4 6 5 7 1 . 5 4 8 4 7 6 1 4 7 . 7 3 6 7 1 . 4 3 7 7 1 5 0 . 8 0 6 8 1 . 8 4 7 2 8 9 1 2 8 . 0 8 7 9 1 . 6 6 9 0 1 3 6 . 4 3 8 0 1 . 9 7 6 0 1 0 3 1 0 5 . 2 9 9 5 1 . 8 0 1 0 5 1 1 6 . 9 5 9 7 2 . 2 7 4 8 1 1 9 8 4 . 7 2 1 1 3 1 . 9 3 1 2 2 9 3 . 5 9 1 1 6 2 . 2 1 3 6 1 3 7 6 4 . 1 2 1 3 2 1 . 6 2 1 3 8 7 0 . 9 4 1 3 4 1 . 8 8 2 4 1 5 6 4 8 . 0 9 1 5 3 1 . 2 3 1 5 4 5 1 . 3 2 1 5 1 1 . 3 4 1 2 1 7 2 2 8 . 9 5 1 7 2 0 . 0 0 1 7 2 3 2 . 5 8 1 7 2 0 . 0 0 better worse

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SLIDE 8

Adjoint Adjoint Sensitivities Sensitivities

  • Sensitivity to radiances assessed with adjoints of

NAVDAS & NOGAPS

  • Energy-weighted forecast error norm (moist TE-norm)

C = matrix of energy-weighting coefficients f = NOGAPS forecast t = verifying NAVDAS / NOGAPS analysis

x = NOGAPS state vector (u, v, θ, q, pt)

ef has units of J kg-1

〈 , 〉 = scalar inner product

Langland and Baker (Tellus, 2004), slide courtesy of Rolf Langland

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SLIDE 9

NAVDAS adjoint Observation Impact

Innovations assimilated for Xa Sensitivity gradient in

  • bservation space

(J kg-1)

0.5 deg, current to ops version of NAVDAS

Ob Impact Calculation Ob Impact Calculation

Langland and Baker (Tellus, 2004), slide courtesy of Rolf Langland

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SLIDE 10

30 24 30 24 30 24 30 24

  • f

ion approximat an is BENEFICIAL

  • NON

is n

  • bservatio

the 0.0 BENEFICIAL is n

  • bservatio

the 0.0 e e e e e

n

  • >

<

  • Observation Impact

Observation Impact

Ob sensitivity summary: Ob sensitivity summary: Aug 17-31, 2006 Aug 17-31, 2006

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SLIDE 11

Observation Impact Observation Impact

Ob sensitivity summary: Ob sensitivity summary: Aug 15-26, 2006 Aug 15-26, 2006

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SLIDE 12

Ob sensitivity summary: Ob sensitivity summary: Aug 15-26, 2006 Aug 15-26, 2006 spatial distribution spatial distribution shows strong impacts shows strong impacts are generally outliers are generally outliers beneficial channels have beneficial channels have slightly positively slightly positively skewed distributions skewed distributions

Observation Impact Observation Impact

good bad good good

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SLIDE 13

1DVAR preprocessor 1DVAR preprocessor

GOAL: improve atmospheric profiling over land improve the Land Surface Temperature (LST) reduce rejects over desert & elevated terrain ISSUES:

  • true background land emission (validation)
  • uncertainty in land surface temperature analysis
  • behavior of surface emission characteristics (scales)

How do the emission characteristics vary ?

  • canopy properties
  • soil properties
  • surface roughness effects
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SLIDE 14

1DVAR preprocessor 1DVAR preprocessor

  • Combined microwave and infrared
  • AMSU/A, AMSU/B, and HIRS/3
  • AMSU, AIRS under development

– Retrieve

  • profiles of T,q
  • Land Surface temperature (LST) – single value
  • Surface emissivity (spectrally)

– a priori

  • Atmospheric profiles

– NOGAPS 3,6,9 hr forecast interpolated to observation – error covariance global

  • Infrared Emissivity

– Indexed surface type to spectral library – error covariance from retrieval statistics, NO channel correlation

  • Microwave Emissivity

– JCSDA MEM – error covariance from retrieval statistics

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Ancillary Data Ancillary Data

  • Vegetation Data (1km – global)

– static (based largely on AVHRR 1992) – needed for indexing and input to JCSDA MEM

  • Soil Data (1/12th degree – global)

– static, modeled in many regions – needed for indexing and input to JCSDA MEM

  • Snow cover

– Air Force product

  • Sea Ice

– NRL in-house analysis

  • ASTER spectral library

– Infrared spectra of soils and vegetation

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Initial Initial emissivity emissivity estimate estimate Infrared: Land Databases indexed to spectral library Microwave: JCSDA MEM

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SLIDE 17

1DVAR retrieval 1DVAR retrieval

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1DVAR retrieval 1DVAR retrieval

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Scan Dependence Scan Dependence

investigation indicates weak dependence empirical correction possible

mean from retrieval Δε (mean – LUT*)

*LUT: look-up-table, emissivities from ASTER spectral library indexed to soil and vegetation databases

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SLIDE 20

Summary Summary

  • AIRS assimilation cycling in NRL NAVDAS/NOGAPS system
  • forecast impacts mixed, continuing to broaden study, examine quality control, bias

correction

  • observation sensitivities helping to guide channel selection assess sensor performance

Future Work Future Work

  • AIRS experience to help guide transition to and use of IASI
  • 1dvar preprocessor used to estimate effective land surface emissivity and assimilate

sounding channels over land

  • NAVDAS operational with COAMPS, test regional assimilation
  • Cloudy radiance assimilation with COAMPS (5km inner nest)
  • NAVDAS-AR (4DVAR) better scalability with increase spectral spation observation density
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SLIDE 21

Scan dependence Scan dependence greatest at lower freq dampens with increasing freq & vegetation

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Scan dependence Scan dependence MEM generally good, slight

  • vercorrection

at edge of scan IR small dependence