AIRS stratospheric temperature retrievals at full horizontal resolution Lars Hoffmann 1 and M. Joan Alexander 2 1 Forschungszentrum Jülich, ICG-1, Jülich, Germany 2 NWRA/CoRA, Boulder, CO AIRS Science Team Meeting, Washington, DC, October 2007
Outline Motivation 1 Why do we need temperature data at full horizontal resolution? Forward modelling for AIRS 2 Brief description of the JURASSIC forward model. Model optimization and validation. Stratospheric temperature retrievals 3 Brief description of the optimal estimation approach. Retrieval parameter studies and characteristics. First results and summary 4 Retrieved temperature data for selected AIRS granules.
Motivation AIRS radiance measurements provide information about stratospheric gravity waves on small horizontal scales... 10−SEP−2003, 04:26 UTC, near Antarctic peninsula −40° −45° −50° −55° −60° −65° 280° 285° 290° 295° 300° 305° 310° 315° 320° 325° −6 −4 −2 0 2 4 radiance pert. (667.7 cm −1 ) [mW/(m 2 sr cm −1 )] (Typical horizontal wavelength in this area: λ x ∼ 100 km)
Motivation Example of gravity waves produced by deep convection... 12−JAN−2003, 16:44 UTC, near Darwin, Australia −5° −10° −15° 450 km −20° 450 km −25° −30° 115° 120° 125° 130° 135° 140° 145° −0.010 −0.005 0.000 0.005 0.010 0.015 0.020 radiance pert. (2362 cm −1 ) [mW/(m 2 sr cm −1 )] ⇒ Loss of horizontal resolution in operational temperature retrieval (20 km → 60 km; cloud-clearing) is a drawback for gravity wave studies...
Forward Modelling for AIRS Juelich Rapid Spectral Simulation Code (JURASSIC) Fast radiative transfer model for the mid-infrared spectral region (4 . . . 15 micron, LTE, no scattering, no surface). Approximations for fast radiative transfer calculations: Band Transmittance Approximation Emissivity Growth Approximation Independent Gas Approximation Look-up tables for spectral mean emissivity Flexible handling of different types of observation geometry and atmospheric data: Interpolation of 1D, 2D or 3D atmospheric data (single profiles, satellite track, model output) Observer within or outside atmosphere Nadir, sub-limb, limb or zenith viewing
Forward Modelling for AIRS Modelling of instrument effects: Spectral filter functions (ILS, SRF,...) Vertical field of view (FOV) Offset and gain calibration Retrieval interface: Definition of state and measurement vector ( x , b , y ) Jacobians by numerical perturbation ( z , p , T , q i , k j , c 0 , c 1 ) Optimization studies and validation studies: Optimized ray-tracing step length Optimized emissivity look-up tables Comparisons against MIPAS RFM Comparisons against AIRS SARTA Documentation and download: https://jurassic.icg.kfa-juelich.de
Forward Modelling for AIRS Optimization of ray-tracing step size... model model error error 0.2% 0.5% 0.1 CPU-time [sec] 0.01 tropics mid latitudes polar summer polar winter 0.1 1 ray-tracing step length [km] ⇒ CPU-time for forward calculation is about 20 msec on a normal PC. Reduction by a factor 1000 compared to line-by-line reference calculations.
Forward Modelling for AIRS Comparison of JURASSIC and RFM... 15 micron temperature channels 0.0012 radiance difference (JURASSIC - RFM) AIRS noise 0.001 tropics mid latitudes 0.0008 polar summer [W/(m 2 sr cm -1 )] polar winter 0.0006 0.0004 0.0002 0 -0.0002 650 655 660 665 670 675 680 wavenumber [cm -1 ] ⇒ Reference model output is reproduced within AIRS noise. Results for 4 micron channels are similar.
Forward Modelling for AIRS Comparison of temperature kernel functions... 15 micron (667.0...670.1 cm -1 ) 4 micron (2360...2380 cm -1 ) JURASSIC JURASSIC 70 70 RFM RFM 60 60 SARTA SARTA 50 50 altitude [km] altitude [km] 40 40 30 30 20 20 10 10 0 0.02 0.04 0.06 0.08 0 0.0001 0.0002 0.0003 kernel function [mW/(m 2 sr cm -1 ) / K] kernel function [mW/(m 2 sr cm -1 ) / K] ⇒ Good agreement! 4 micron kernels are rather broad (due to broad SRFs), i. e. provide less information on vertical distribution, but help to reduce noise.
Stratospheric Temperature Retrievals Optimal estimation approach: Find optimal estimate (i. e. MAP solution) of retrieval targets x for given measurements y by minimizing a cost function: J ( x ) = [ y − F ( x )] T S − 1 + ( x − x a ) T S − 1 ε [ y − F ( x )] a ( x − x a ) � �� � � �� � measurements – forward calculation atmospheric state – a priori x = atmospheric state y = radiance measurements = S ε measurement error covariance F ( x ) = simulated observations (forward model) x a = a priori state S a = a priori covariance
Stratospheric Temperature Retrievals Retrieval grid: 1D case: homogeneously stratified atmosphere Fixed altitudes: 3 km below 60 km, 5 km up to 90 km Retrieve only T, get p from hydrostatic equilibrium. Measurement error covariance: Consider only noise (uncorrelated). A priori data: Use AIRS operational retrieval results as a priori state (inter/extrapolate data gaps). Use a priori uncertainty of σ i = 20 K, correlations from first-order autoregressive model: S ij = σ i σ j exp ( − ∆ z / c z ) Correlation length c z is an important tuning parameter!
Stratospheric Temperature Retrievals Selection of AIRS channels for the retrieval... 4 micron temperature channels tropospheric fraction of kernel functions [%] 22.5 km 17.5 km 12.5 km 7.5 km 20 km 15 km 10 km 5 km 100 10 1 0.1 2300 2310 2320 2330 2340 2350 2360 2370 2380 2390 wavenumber [cm -1 ] ⇒ Exclude all channels where tropospheric fraction of kernel functions ( z trop = 17 . 5 km) exceeds 1% to minimize influence of clouds...
Stratospheric Temperature Retrievals Influence of a priori data... mid latitudes 80 dT = -20 K dT = -15 K 70 dT = -10 K dT = -5 K 60 dT = 0 K dT = 5 K altitude [km] 50 dT = 10 K dT = 15 K 40 dT = 20 K 30 20 10 180 200 220 240 260 280 300 temperature [K] ⇒ Varying the a priori profile by ± 20 K causes differences below ± 1 . 5 K in the retrieved profile at 20 . . . 55 km altitude.
Stratospheric Temperature Retrievals Retrieval error due to noise... 70 60 c z = 1 km c z = 2 km 50 altitude [km] c z = 5 km c z = 10 km 40 c z = 20 km c z = 50 km c z = 100 km 30 20 10 0 1 2 3 4 5 6 7 8 retrieval error (noise) [K] ⇒ We use an a priori vertical correlation length of 50 km to reduce the retrieval error due to noise: The resulting error is 1 . . . 2 K at 20 . . . 55 km.
Stratospheric Temperature Retrievals Vertical resolution... 70 60 c z = 1 km 50 c z = 2 km altitude [km] c z = 5 km c z = 10 km 40 c z = 20 km c z = 50 km c z = 100 km 30 20 10 4 6 8 10 12 14 16 18 20 vertical resolution [km] ⇒ For 50 km a priori vertical correlation length the vertical resolution is 7 . . . 11 km at 20 . . . 55 km.
Full Resolution Temperature Data – First results Gravity waves near Antarctic peninsula... full resolution retrieval operational retrieval −45° −45° −50° −50° −55° −55° −60° −60° 285° 290° 295° 300° 305° 310° 315° 320° 285° 290° 295° 300° 305° 310° 315° 320° 220 230 240 250 260 220 230 240 250 260 temperature (33 km) [K] temperature (33 km) [K] ⇒ Full resolution retrieval results resemble operational data, but gravity wave amplitudes are larger.
Full Resolution Temperature Data – First results Gravity waves produced by deep convection... full resolution retrieval operational retrieval −10° −10° −15° −15° −20° −20° −25° −25° 120° 125° 130° 135° 120° 125° 130° 135° 240 245 250 255 260 265 240 245 250 255 260 265 temperature (42 km) [K] temperature (42 km) [K] ⇒ Retrieval at full horizontal resolution reveals small-scale structures! Warm bias (about 3 . . . 5 K) in full resolution retrievals at the stratopause.
Summary We use the fast radiative transfer model JURASSIC to simulate AIRS measurements: The fast model helps to reduce CPU-time by a factor 1000. Reference calculations are reproduced within AIRS noise. We use the optimal estimation approach to retrieve temperature data for the stratosphere: Altitude Range: 20 . . . 55 km Vertical resolution: 7 . . . 11 km (about 6 dfs) A priori information: less than 5% Retrieval error (due to noise): 1 . . . 2 K First retrieval results for selected granules look promising: The full resolution data much better reveal the horizontal small-scale structures caused by gravity waves.
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