This study processes and analyses hyperspectral measurements from the Atmospheric Infrared Sounder on 28 August 2006 covering day and night scenes of Hurricane Ioke with a temporal spacing of 12 hours. A semi-discrete multilevel cloud model is used to describe the perturbation of the outgoing thermal radiation caused by cloudiness in the field of view (FOV) of a satellite instrument. Cloud spectral effects in the model are represented by an effective cloud absorption vertical profile (CAVP). The CAVP estimate is considered an indicator of the presence of clouds at specific atmospheric layers. The CAVP estimates are compared with lidar measurements. Results indicate a realistic characterization of cloud top and cloud vertical scale. The spatial distribution of the CAVP estimates is used to describe the spatial structure of the hurricane and to monitor its changes over time. Comparison of daytime and night-time CAVP estimates shows that the effective hurricane radius is drastically reduced from about 578 km during the day to about 463 km at night at an atmospheric layer above 212 hPa and from about 604 km to about 499 km at atmospheric layer above 300 hPa. In addition, there is indication of a significant presence of ice crystals in the upper troposphere 10-14 km at night over the large areas adjacent to the hurricane. These crystal clouds have the potential to affect the hurricane energy budget by reducing the night-time cooling rate and trapping heat and moisture in the lower atmospheric layers.

Satellite high spectral resolution infrared measurements provide information for cloud vertical characterization when optically semi-transparent clouds at high altitude cover cloud layers at lower altitudes. It is an important issue because such atmospheric conditions are common and clouds are characterized by large-scale vertical development. An approximation radiative transfer model for a cloudy atmosphere is introduced. Cloud particle absorption of infrared (IR) radiation depends on the spectral frequency. So, the effective cloud parameters such as amount (absorption) and height, derived from IR spectral measurements, will be spectral functions as well. The degree of uncertainty in the determination of effective cloud parameters cannot be eliminated by increasing the number of spectral measurements. A cloud model should have extra degrees of freedom to address the spectral and spatial (vertical) variability of cloud absorption. A semi-discrete multilevel cloudmodel is used to describe the perturbation of the outgoing IR thermal radiation caused by cloudiness in the field of view of a satellite instrument. The model delineates cloudiness in a number of layers at fixed heights. Each layer (level) is characterized by the effective cloud absorption. An inverse problem of cloud absorption vertical profile (CAVP) estimation is described. The estimate of an effective cloud absorption profile is considered as predictor for identification of cloud presence at specific atmospheric layers. The problem is numerically examined for real satellite IR spectral measurements and solution estimates are compared with lidar measurements. Results show that the resulting estimate of CAVPs provides a realistic characterization of cloud top and cloud vertical scale.

Hurricane intensity forecast accuracy is extremely important in order to take necessary precautions for landfall events. Good intensity forecasts require a solid understanding of the underlying dynamics that cause a hurricane to strengthen or weaken. The causes of change in intensification of tropical storms and hurricanes have been widely studied, yet some aspects are still not well understood. Compared to the technical difficulty, cost, and danger associated with taking in situ measurements of these events, the use of satellite observation to study hurricanes presents a good way to attain timely data even in remote regions of the Earth. The EOS A-Train constellation of spacecraft provides unique insight into cloud geometric structure and atmospheric thermodynamic state from both active and passive sensors. The purpose of this study is to investigate the relationship between hurricane intensity and the temporal changes in cloud structure and water vapor distribution of the storm and its environment. The passive and active remote sensing instruments and their derived products will be used to examine the 3-D cloud structure, temperature profiles, and water vapor profiles of Hurricane/Super Typhoon Ioke at various points in its life cycle. The sensitivity of hyperspectral IR sounders is shown to provide unique insight into tropical cyclones.

The University of Wisconsin-Cloud Amount Vertical Profile (UW-CAVP) is a product currently being developed to provide a threedimensional view of cloud structure in the atmosphere obtained through passive remote sensing. It uses high spectral resolution infrared data from AIRS (Atmospheric Infrared Sounder) on the A-train satellite Aqua and a model temperature profile to create a cloud amount profile for 25 vertical layers from the surface to the tropopause.

An approach using spatial analysis of satellite IR spectral measurements for quality assessment is presented. The second spatial differential is used as a model of measurement noise for spatially smooth radiative fields. Spatial differentiation significantly magnifies the noise contribution and reduces the physical signal amplitude because of differences in spatial distributions of instrument noise and atmospheric thermal fields. The second spatial differential represents a convenient and effective tool for numerical analysis of satellite IR measurements. This paper demonstrates that statistics of the second spatial differential are informative predictors for data-quality characterization. Statistics of the second spatial differential are used for identifying anomalies in spectral channel data caused by detector noise, sensitivity loss to spatial shortwave thermal variations, and spatially (temporally) correlated noise.

Surface emissivity (SE) variations cause measurable changes in infrared radiances. To improve the accuracy of vertical temperature-moisture profiles retrieved from AIRS sounder infrared measurements, the surface emissivity must be accounted for in the solution of the inverse problem. The accuracy of atmospheric parameters retrieved depends on the measurement accuracy and accurate definition of measurement model. The associated inverse problem based upon the numerical solution of the radiative transfer equation (RTE) is ill posed. Disregarding the spectral-spatial variations of SE in the RTE magnifies the errors. Different types of surface cover, with different surface optical properties and extremely high spatial and temporal variations, restrict the use of a priori estimates of SE. The direct evaluation of SE is an effective alternative. The RTE solution includes the surface emissivity, the surface temperature, and the temperature-moisture profile. The RTE equation is solved using the method of least squares in coordinate descent based on the Gauss-Newton numerical schema. Results of SE estimation are demonstrated. The SE estimates over land show significant spectral-spatial variability. Accounting for the emissivity positively affects the atmospheric temperature-moisture profile estimates. Introduction To estimate the atmospheric temperature-humidity vertical distributions from the multi- spectral infrared measurements requires a numerical solution of the radiative transfer equation (RTE). Accounting for emissions from both the earth surface and the atmosphere is critical since even small SE variations cause measurable changes in the infrared radiances. The spectral-spatial variations of SE in RTE, if ignored, can drastically reduce the accuracy of the solution. An algorithm is described in Plokhenko and Menzel (2000 and 2003). Results of data analysis and experimental processing of AIRS nighttime spectral measurements from the granule 016 of September 6, 2002 are presented. Data analysis and physical interpretation The Atmospheric Infrared Sounder (AIRS) spectral channels along with their intended purposes are given in Aumann and Miller 1995. The measurements demonstrate the following properties: (a) surface reflection is substantially larger in shortwave (SW) bands than in longwave (LW) bands, (b) radiation absorption by cloud ice particles is substantially larger in LW than in SW bands, and (c) SW bands are less affected by variations of atmospheric moisture than LW bands. In processing the AIRS measurements, the solution of the inverse problem includes the surface emissivity, the surface temperature, and the vertical temperature-humidity profile. Only spectral measurements at cloud free pixels are processed. The RTE for the cloud free atmosphere is: (, ) 1

Infrared radiative transfer solutions for surface emissivities using GOES Sounder measurements show temporal consistency and significant spatial variability over non-homogeneous land scenes; associated lower tropospheriuc profiles agree well with radiosonde observations.

The spatial and temporal continuity of the infrared measurements from the Geostationary Operational Environmental Satellite (GOES)-8 sounder data are investigated, and an experimental processing approach is presented. Spatial filtering and cloud detection are performed in a joint algorithm: the preparation of the data for sounding analysis starts with spatial smoothing, followed by cloud detection, followed by averaging the clear-sky (cloud free) subsamples. Analysis of the sounder images reveals the presence of coherent noise on large spatial scales in some of the spectral bands. Analysis of a temporal sequence of spatially smoothed sounder images reveals regions of unphysical hourly change likely induced by instrument noise. A nonlinear temporal-spatial filtering algorithm is presented and tested that improves the noise filtering for the sounder spectral measurements and the thermodynamical spatial and temporal consistency of the sounding retrievals in the troposphere.

To retrieve vertical profiles of temperature and moisture from infrared spectral measurements, surface emissivity must be accounted for in the physical solution of the inverse problem. A radiative model that includes the emission and reflection on the lower atmospheric boundary is introduced. An algorithm is developed for the solution of the vertical temperature-humidity profile and the estimation of an effective surface emissivity and temperature within the sounding area. Results using spectral measurements from the Geostationary Operational Environmental Satellite (GOES)-8 sounder are presented. It is found that accounting for the surface emissivity in the solution of the inverse problem has a positive impact on the meteorological profiles.

Surface emissivity (SE) must be accounted for in the retrieval of atmospheric profiles from multi-spectral infrared measurements. A model accounting for SE and an algorithm of solution have been developed and tested. The solution includes SE, the surface temperature, and the temperature-moisture profile. The temporal and spatial consistency of the results over land is discussed.

To improve the accuracy of vertical profiles of temperature and moisture retrieved from infrared spectral measurements, the surface emissivity must be accounted for in the solution of the inverse problem (based upon the radiative transfer equation). An algorithm that considers emission and reflection from the lower atmospheric boundary is applied to spectral measurements from an airborne radiometer over summer and winter land surfaces. The surface emissivity and temperature are estimated directly from the infrared multispectral radiances. Vertical temperature-humidity profiles generated with and without surface emissivity consideration are compared; accounting for the surface emissivity in the solution of the inverse problem substantially and positively changes the meteorological profiles.

To improve the accuracy of vertical temperature-moisture profiles retrieved from GOES sounder IR measurements, the surface emissivity (SE) must be accounted for in the solution of the inverse problem. A model accounting for SE and an algorithm of solution are presented. The solution includes SE, the surface temperature, and the temperaturemoisture profile. Results over land are discussed. Accounting for SE positively effects the solution.

The accuracy of temperature and moisture vertical profiles retrieved from infrared spectral measurements is dependent on accurate definition of all contributions from the observed "surface-atmosphere'' system to the outgoing radiances. The associated inverse problem is ill posed. Instrument noise is a major contributor to errors in modeling spectral measurements. This paper considers an approach for noise reduction in the Geostationary Operational Environmental Satellite (GOES) spectral channels using spatial averaging that is based upon spectral characteristics of the measurements, spatial properties of atmospheric fields of temperature and moisture, and properties of the inverse problem. Spatial averaging over different fields of regard is studied for the GOES-8 sounder spectral bands. Results of the statistical analysis are presented.

To improve the accuracy of vertical profiles of temperature and moisture retrieved from infrared spectral measurements, the surface emissivity must be accounted for in the solution of the inverse problem (based upon the radiative transfer equation). A model that accounts for the emission and reflection on the lower atmospheric boundary and an algorithm of solution are presented. Results using spectral measurements from an airborne radiometer over land surfaces are discussed. The solution of the inverse problem includes the surface emissivity, the surface temperature, and the vertical temperature-humidity profile. It is shown that accounting for the surface emissivity in the solution of the inverse problem substantially and positively changes the meteorological profiles.