Abstract

1. Information Content for Standard Retrievals Cross-Validation With Data from AVE-Houston During the Aura Validation Experiment 11, extensive multi-platform measurements were conducted with the Aqua and Aura platforms and heavily-instrumented aircraft underflights. In situ data from various instruments aboard the WB-57 was used here. Results Using TES Extended Staring Mode References: • Mlawer, E. J., Taubman, S. J. , et al. (1997). Journal of Geophysical Research-Atmospheres 102(D14): 16663-16682. • McClatchey, R., et al. (1971). Optical Properties of the Atmosphere ARCRL-71-0279. Air Force Geophysical Lab, Bedford, MA. • Clough, S.A., Shepard, M.W., et al. (2005). Journal of Quantitative Spectroscopy and Radiative Transfer 91:233-244. • Hanel, R. A., Schlachman, B. et al. (1971). Applied Optics 10(6): 1376-1382. • Aumann, H. H., Chahine, M. T. et al. (2003). IEEE Transactions on Geoscience and Remote Sensing 41(2): 253-264. • Beer, R., Glavich, T., et al. (2001). Applied Optics 40(15): 2356-2367. • Mlynczak, M.G., Johnson, D.G. et al. (2006). Geophysical Research Letters 33(L07704). • Liou, K. N., Xue, Y. K. (1988). Meteorology and Atmospheric Physics 38(3): 131-139. • Feldman, D. R., Liou, K.N., Yung, Y.L., et al. (2006). Geophysical Research Letters 33(L11803). • Rodgers, C. D. (2000). Inverse Methods for Atmospheric Sounding: Theory and Practice. London, World Scientific. • AVE (2006). AURA Validation Data Center ( http://avdc.gsfc.nasa.gov ). Greenbelt, MD, NASA. • Li, J. (2000). Journal of the Atmospheric Sciences. 57(5): 753-765. A13B-0891 1. Dept. of Environmental Science and Engineering, Caltech 2. Dept. of Atmospheric and Oceanic Sciences, UCLA 3. Division of Geological and Planetary Sciences, Caltech †Corresponding Author Contact Information: feldman@caltech.edu1200 E. California Blvd. MC 150-21; Pasadena, CA 91125 USA; 626-395-6447 http://www.gps.caltech.edu/~drf/misc/agu2006/ Abstract Eq. (4) An accurate and computationally-efficient means for deriving heating and cooling rate profiles at small horizontal resolution in the Tropical Tropopause Layer (TTL) is necessary for the understanding of stratospheric water vapor transport. We first present a formal error budget analysis for clear-sky infrared radiative cooling rates in that region with respect to uncertainties in atmospheric temperature, water vapor, and other pertinent profiles. Subsequently, we explore the extent to which current hyperspectral thermal infrared sounders such as AIRS and TES describe the clear-sky infrared cooling rate profiles within a retrieval-based optimal estimation framework. Next, we detail formal and ad hoc methods for treating the absence of spectral coverage for wavelengths longer than 15.4 μm. Our approach to directly infer cooling rate profile from space is cross-validated using in situ water vapor, temperature, and ozone profile data from the Fall 2004 Aura Validation Experiment. Finally, we show that data from the advanced, though under-utilized, extended staring capability of the TES instrument is well-suited for application to the understanding of cooling rate profiles. With a formal calculation of the error covariance according to Eq. (4), the cooling rate profile information content can be calculated for various mid-IR instruments. Validation of a Direct Cooling Rate Retrieval Method Using AIRS and TES DataD.R. Feldman 1,†, K.N. Liou 2, Y.L. Yung 3Research supported by the NASA Earth Systems Science Fellowship, grant number NNG05GP90H, the AIRS and TES Projects at JPL, and the RT web codes from AER, Inc. Figure 5:Comparison between conventionally-derived cooling rates from satellite platforms (blue and black), directly-retrieved cooling rates from satellite radiances (magenta and green), and cooling rates derived from in situ T, H2O, O3measurements (red). Data were collocated with a WB-57 spiral descent at (27 ° N, 89 ° W). Eq. (3) Introduction Retrieval Setup The infrared cooling rate profile is a quantity of interest as an input into circulation models. The accuracy of cooling rate calculations has been shown to impact directly the performance of these models1. The cooling rate is a function of spectral interval and altitude though circulation models are only concerned with total IR cooling in each layer. A layer’s cooling rate is a function of the absorption of the upwelling flux from the surface and two competing layer-interaction terms as given in Eq. (1). The importance of these latter terms depends largely on the rate at which the flux transmittance is changing in the vicinity of the layer in question. The rotational bands of H2O are responsible for most mid-tropospheric cooling while the O3ν3and CO2ν2 bands effect stratospheric cooling. In the TTL, H2O, CO2, and O3 cooling are all significant. A direct cooling rate retrieval requires a formula that describes the relationship between the spectral cooling rate profile and a remote sensing measurement 8,9. As given in the formulation in Eq. (7), the weighting function calculation is trivial. The computational burden of the retrieval shifts from calculating accurate weighting functions to estimating the a priori covariance and the errors in the net surface flux and the conversion from spectral radiance to spectral flux. Measurement Eq. (5) The TES instrument supports a configurable, extended staring capability which allows it to point at one ground footprint for along-track zenith angles ranging from ±45°. Several test cases utilizing these spectra over Lake Tahoe (39 ° N, 120 ° W) have been explored below in a cross-validation framework. TOA spectral flux is estimated from 3-point, moment-power 1 quadrature 12. Retrieval Quantity Eq. (6) Tropical Atmosphere Band and Total IR Cooling Rate Profiles Linear Bayesian Update 10 (a) (b) Eq. (7) A priori and A posteriori Error Comparisons a b Pressure (mbar) Eq. (1) (b) (a) Figure 1:Spectral broadband clear-sky cooling rate profiles for the McClatchey Tropical Atmosphere 2 calculated using the AER suite of radiative transfer models 3. Values less than 0 imply IR heating. Figure 6:(a) Estimated spectral flux from TES (black), Noise-effective Flux (green), estimated quadrature error (blue), and spectral flux derived from collocated RAOB (red). (b) Total IR cooling rate profiles derived from remote sensing and in situ measurements. Cooling Rate (K/day) Conclusions Cooling Rate Error Budget Calculation • To understand how remote measurements impact knowledge of the cooling rate profile, it is first necessary to characterize the error budget formally for which the knowledge of the covariance of standard atmospheric state vector quantities is imperative. • An information content comparison of several IR sounders reveals that the TES instrument is expected to perform best for cooling rate retrievals. • The information content of remote measurements w.r.t. the cooling rate profile can be better incorporated through a direct retrieval, though treatment of the out-of-band cooling is non-trivial. • Cross-validation results from the AVE instrument show direct retrieval robustness as compared to in situ measurements, though results are biased by a priori. • The unique staring capability of the TES instrument may be ideally suited towards cooling rate retrievals. Increases in T, H2O, and O3 at a given layer increase the layer’s cooling but decrease the cooling in adjacent layers. Consequently, the error budget must include, and is inflated by cross-layer sensitivity as seen in Eq. (2). Figure 3:Comparison of 1 cm-1-wide spectral cooling rate profile information incorporated from a standard atmospheric state retrieval and a direct cooling rate retrieval with AIRS for a Tropical model atmosphere at (a) 650 cm-1 and (b) 800 cm-1. Eq. (2) Out-of-Band Cooling Rates (a) Most (though not all) past, present, and future infrared spectrometers under development for space-borne remote sensing will not measure spectra beyond 15.4 μm. Significant troposphere cooling takes place from bands in the far infrared. Assuming that the HITRAN spectroscopic parameters are accurate, the retrieval must be designed to minimize the error budget contribution from the far infrared bands. (b) b a X Figure 4:Percent contribution to total TTL cooling rate uncertainty at 94 mbar from the far infrared (λ>15.4 μm) for varying levels of T and H2O profile uncertainty. AIRS Specs Figure 2:(a) Band cooling rate response to a decreasing the temperature of the layer from 94 to 111 mbar (b) Band cooling rate uncertainty for T, H2O and O3 covariance matrices derived from Eq. (3).