introduction to the development of a dynamic infrared land surface emissivity atlas...
TRANSCRIPT
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Rory Gray
Introduction to the Development of a Dynamic Infrared Land Surface Emissivity Atlas based on IASI Retrievals
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Contents
• Background and Motivation
• Atlas Construction
• Preliminary Runs
• Summary and Next Steps
• Extending Use of AMSU-A over Land
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NRT atlas- up-to-date information- short term variability
• assimilation surface sensitive IR channels over land for NWP (FG for 1dvar)
• apply to other IR instruments such as SEVIRI
• improve Tskin accuracy
Motivation
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e of sand (lab spectral analysis)
∂BT/ ∂esurf
surface emissivity sensitive IR wavelengths
Retrieved Emissivity at [lon,lat]=[-0.50,12.50] at 8.96mm 2013
month of year (2013)J F M A M J J A S O N D
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• CNES/EUMETSAT
• MetOp-A (2006), MetOp-B (2013)
• Hyperspectral IR Sounder (8461 channels)
• Spectral Range 3.62 – 15.5 mm
• Mid-morning orbit 09:30 (desc) / 21:30 (asc)
to sun
06:00
12:00
18:00
00:00O
09:30
21:30
orbital plane
IASI (Infrared Atmospheric Sounder Interferometer)
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1dvar Retrieval of Emissivity
• e(l) retrievals from estimation of PC coefficients in 1dvar
• high dimensional data set reconstructed from PC set of reduced dimensionality
• skin temperature, cloud top pressure and cloud fraction also retrieved
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1dvar Retrieved Emissivities, 2013010100
-Greenland [-45.0,75.0]
- Australia [130.0,-25.0]
- Sahara [-10.0,20.0]
Lab Spectral Emissivities (UCSB Emissivity Library)
- snow- pine leaf- soil- sand
8.96mm retrieved e
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Atlas Construction
• Gridded dataset
• emissivity spectral estimate for each gridbox
• NRT updates from 1dvar IASI emissivity retrievals -> initially mean for each gridbox
-> eventually data driven Kalman Filter
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Kalman Filter Implementation
• Initial e over each gridbox
• persistence model for each e(l) in each gridbox
• 1dvar retrievals as measurement updates
• measurement noise from 1dvar analysis covariance matrix
• update e for each relevant gridbox
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Atlas construction IASI chan.1884 (8.96mm)
Jan 2013
Emissivity
semissivity
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Jan 2013
Jul 2013
e variation over seasons, latitude variation of low e Sahara region
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Jan 2013 day-night emissivity differences 8.96mmno. pts per gridbox emissivity
eday - enight
day
night
day-night
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night
day-night
Apr 2013 day-night emissivity differences 8.96mm
day
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Summary and Next Steps
• KF implementation- R-noise from 1dvar analysis error cov matrix- Q-noise from UWIREMIS (or derived variation)- P cov estimate
• Snow consideratione(l) = (1-sfc)*eretrieved(l) + sfc*esnow(l)
• Diurnal Variation
• Scan angle dependence
• Test and Validation against other sources and instruments
• Application to other current and future IR instruments- SEVIRI, HIRS, MTG-IRS, IASI-NG
• Use in Met Office Data Assimilation system
• Available to all centres
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Extending our use of AMSU-A over land
(Stu Newman and Bill Bell)
Slides for Rory
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1D-Var retrieval of microwave emissivity and skin temperature
• Current operational usage at the Met Office: land surface emissivity fixed at 0.95 and lower-peaking channels AMSU 4 & 5 not assimilated over land• Aim: use microwave emissivity atlas (Fatima Karbou / Météo France) as a first guess in a 1D-Var retrieval • Atlas is tabulated for frequencies 23.8, 31.4, 50.3 & 89 GHz (AMSU channels 1, 2, 3 & 15) per calendar month• Use atlas uncertainty estimate to derive observation-dependent errors• 1D-Var retrieval of Tskin and microwave emissivity
AMSU ch 3 emissivity (March)
Atlas uncertainty estimate
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Improved modelling of surface sensitive channels
• Before 1D-Var the atlas provides a reasonable first guess
• Retrieved skin temperature and emissivity for AMSU-A channel 3 at 50.3 GHz is used as representative for channels 4 & 5 (52.8, 53.6 GHz)
• After 1D-Var retrieved skin temperature and emissivity are passed to 4D-Var where AMSU-A channels 4 and 5 are assimilated (as well as current channels 6-14)
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NWP global experiments
• Assimilation of AMSU-A channels 4 & 5 over land tested in global experiments for summer and winter seasons (on top of full observing system)
• Improvements seen in RMS forecast-analysis error for tropospheric temperature at short range in Southern Hemisphere; results mixed in Northern Hemisphere
• Overall verification considered positive, assimilation of AMSU-A 4&5 over land to be included in next set of planned operational changes mid-2016
RMS error reduction ~2% for SH lower atmospheric temperature vs analysis