Gennady Uymin

Infrared Radiance Modeling by Optimal Spectral Sampling

This paper describes a rapid and accurate technique for the numerical modeling of band transmittances and radiances in media with nonhomogeneous thermodynamic properties (i.e., temperature and pressure), containing a mixture of absorbing gases with variable concentrations. The optimal spectral sampling (OSS) method has been designed specifically for the modeling of radiances measured by sounding radiometers in the infrared and has been extended to the microwave; it is applicable also through the visible and ultraviolet spectrum. OSS is particularly well suited for remote sensing applications and for the assimilation of satellite observations in numerical weather prediction models. The novel OSS approach is an extension of the exponential sum fitting of transmittances technique in that channel-average radiative transfer is obtained from a weighted sum of monochromatic calculations. The fact that OSS is fundamentally a monochromatic method provides the ability to accurately treat surface reflectance and spectral variations of the Planck function and surface emissivity within the channel passband, given that the proper training is applied. In addition, the method is readily coupled to multiple scattering calculations, an important factor for treating cloudy radiances. The OSS method is directly applicable to nonpositive instrument line shapes such as unapodized or weakly apodized interferometric measurements. Among the advantages of the OSS method is that its numerical accuracy, with respect to a reference line-by-line model, is selectable, allowing the model to provide whatever balance of accuracy and computational speed is optimal for a particular application. Generally only a few monochromatic points are required to model channel radiances with a brightness temperature accuracy of 0.05 K, and computation of Jacobians in a monochromatic radiative transfer scheme is straightforward. These efficiencies yield execution speeds that compare favorably to those achieved with other existing, less accurate parameterizations.

Fast and Accurate Radiative Transfer in the Microwave With Optimum Spectral Sampling

Optimal spectral sampling (OSS) is a computationally fast and accurate method for modeling sensor-band transmittances and radiances. The spectral response of a sensor channel is approximated by an optimally weighted average of monochromatic radiative transfer calculations at optimally selected points. The absorption coefficients for the selected points are obtained from prestored lookup tables. Analytical Jacobians are produced in conjunction with the radiances with very little added computational burden. A microwave version of the OSS training algorithm and forward model has been developed in parallel with the infrared version. The microwave model treats O2 and N2 as fixed gases and H2O and O3 as variable gases. Several reference line-by-line (LBL) models are available for training. The method of tabulating and interpolating absorption coefficients has been optimized for execution speed. Results are shown for OSS application to several current and future microwave sounders. With a selected requirement of 0.05-K rms error (with respect to the reference LBL model), the number of monochromatic points required varies from one, for most window channels, to about four for channels embedded in the 60-GHz O2 line complex and along the 183-GHz H2O line. Even in the cases where a single point is adequate, the optimal point does not necessarily coincide with the center frequency of the channel.

Atmospheric and surface retrievals in the Mars polar regions from the Thermal Emission Spectrometer measurements

Received 26 February 2008; revised 12 June 2008; accepted 31 July 2008; published 31 October 2008. [1] Retrievals of atmospheric temperatures, surface emissivities, and dust opacities in the Mars polar regions from the Thermal Emission Spectrometer (TES) spectra are presented. The retrievals correspond to two types of spectra, characterized by small and large band depths BD25 in the 25-mm band of solid CO2. These two types of spectra have previously been identified with locations covered by slab ice and fluffy CO2 frost, respectively. Above the first atmospheric scale height, there is a correlation between the degree of saturation in the retrieved atmospheric temperatures and the two types of surface, with the high BD25 spectra (‘‘cold spots’’) showing larger supersaturations around 1 mbar. This supports the hypothesis that coldspotscorrespondtolocationswithpotentialor actual atmospheric precipitation. Furthermore, the retrieved temperature profiles exhibit a warming above 1 mbar (15 km), which appears real even when the limited number of independent pieces of information from the measurement (� 3) and coarse vertical resolution of the TESinstrumentabove 15 kmare considered.The spectralshape ofthe retrievedsurface emissivities in the cold spot locations is consistent with modeling results attributing high BD25 to porosity. For the low BD25 spectra, the retrieved emissivities are spectrally flat but significantly less than unity (0.8–0.9). The cause of these spectrally uniform deviations from blackbody behavior (which are not supported by modeling) remains to be investigated, with a noticeable reduction in the deviation from the blackbody behavior achieved through a zero-radiance-level correction to the TES spectra available from the Planetary Data System.

Fast and Accurate Radiative Transfer in the Thermal Regime by Simultaneous Optimal Spectral Sampling over All Channels

AbstractThe optimal spectral sampling (OSS) method provides a fast and accurate way to model radiometric observations and their Jacobians (required for inversion problems) as a linear combination of monochromatic quantities. The method is flexible and versatile with respect to the treatment of variable constituents, and the method’s fidelity to reference line-by-line (LBL) calculations is tunable. The focus of this paper is on the modeling of radiances from hyperspectral infrared sounders in both clear and cloudy (scattering) atmospheres for application to retrieval and data assimilation. In earlier articles, the authors presented an approach that performed spectral sampling for each channel sequentially. This approach is particularly robust in terms of preserving fidelity to LBL models and yields ratios of monochromatic calculations per channel of approximately 1:1 for such hyperspectral sensors as the Infrared Atmospheric Sounding Interferometer (IASI) or the Atmospheric Infrared Sounder (AIRS) (when tu...

Sources of discrepancies between satellite‐derived and land surface model estimates of latent heat fluxes

Monthly-average estimates of latent heat flux have been derived from a combination of satellite-derived microwave emissivities, day-night differences in land surface temperature (from microwave AMSR-E), downward solar and infrared fluxes from ISCCP cloud analysis, and MODIS visible and near-infrared surface reflectances. The estimates, produced with a neural network, were compared with data from the Noah land surface model, as produced for GLDAS-2, and with two alternative estimates derived from different datasets and methods. Areas with extensive, persistent, substantial discrepancies between the satellite and land surface model fluxes have been analyzed with the aid of data from flux towers. The sources of discrepancies were found to include problems with the model surface roughness length and turbulent exchange coefficients for midlatitude cropland areas in summer, inaccuracies in the precipitation data that were used as forcing for the land surface model, and model underestimation of transpiration in some forests during dry periods. At the tower sites analyzed, agreement with tower data was generally closer for our satellite-derived fluxes than for the land surface model fluxes, in terms of monthly averages.

OSS Radiative Transfer Method Performance in Real Time Atmosphere Characterization from Satellite Sounding and Imaging Data

The optimal spectral sampling (OSS) method is a rapid and accurate technique for numerical modeling of narrow-band transmittances in media with non-homogeneous thermodynamic properties containing a mixture of absorbing gases with variable concentrations. The method was initially designed for the modeling of radiances measured by satellite or aircraft-borne sensors in the infrared and microwave and is particularly well suited for remote sensing applications and for the assimilation of satellite observations in numerical weather prediction (NWP) models. In this paper we provide a brief description of the method and discuss its accuracy and computational performance in the context of retrieval of atmospheric parameters from sounding and imaging sensor (e.g. AIRS and MODIS) observations. Both clear and cloudy conditions are addressed.

Approximations of the Planck Function for Models and Measurements Into the Submillimeter Range

A brightness temperature is defined as a linear function of the Planck radiance, with the linear coefficients optimized to minimize the difference between the brightness temperature and the physical temperatures of atmospheric and terrestrial emitters. Radiative transfer (RT) calculations can be accelerated by formulating the integration in terms of this brightness temperature while producing output in terms of radiance or brightness temperature. Approximation errors are < 0.012 K for RT model applications up to 400 GHz, for any upward, downward, or limb-view geometry, which is about an order of magnitude smaller than for the common brightness temperature derived from a second-order expansion of the Planck function. When products of an RT model that uses this optimized Planck approximation are compared with measurements and the measured radiance is high (equivalent brightness temperature is >170 K), it can be advantageous to apply a complementary approximation to the measurements to benefit from error compensation between the model and the measurements. Alternatively, error compensation can be obtained if the calibration and RT equations use consistent brightness temperature approximations.