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.[1] An extensive set of airborne and satellite observations of volcanic ash from the Eyjafjallajökull Icelandic eruption are analyzed for a case study on 17 May 2010. Data collected from particle scattering probes and backscatter lidar on the Facility for Airborne Atmospheric Measurements (FAAM) BAe 146 aircraft allow estimates of ash concentration to be derived. Using radiative transfer simulations we show that airborne and satellite infrared radiances can be accurately modeled based on the in situ measured size distribution and a mineral dust refractive index. Furthermore, airborne irradiance measurements in the 0.3-1.7 mm range are well modeled with these properties. Retrievals of ash mass column loading using Infrared Atmospheric Sounding Interferometer (IASI) observations are shown to be in accord with lidar-derived mass estimates, giving for the first time an independent verification of a hyperspectral ash variational retrieval method. The agreement of the observed and modeled solar and terrestrial irradiances suggests a reasonable degree of radiative closure implying that the physical and optical properties of volcanic ash can be relatively well constrained using data from state-of-the-science airborne platforms such as the FAAM BAe 146 aircraft. Comparisons with IASI measurements during recent Grímsvötn and Puyehue volcanic eruptions demonstrate the importance of accurately specifying the refractive index when modeling the observed spectra.

This article is published with the permission of the Controller of HMSO and the Queen's Printer for Scotland.The predictive quality of an ensemble model of cirrus ice crystals to model passive and active measurements of ice cloud, from the ultraviolet (UV) to the microwave, is tested. The ensemble model predicts m ∝ D 2 , where D is the maximum dimension of the ice crystal, and m is its mass. This predicted m-D relationship is applied to a moment estimation parametrization of the particle size distribution (PSD), to estimate the PSD shape, given ice water content (IWC) and in-cloud temperature. The same microphysics is applied across the electromagnetic spectrum to model UV, infrared, microwave and radar observations. The short-wave measurements consist of airborne UV backscatter lidar (light detection and ranging) estimates of the volume extinction coefficient, total solar optical depth, and spacebased multi-directional spherical albedo retrievals, at 0.865 µm, between the scattering angles 85 • and 125 • . The airborne long-wave measurements consist of high-resolution interferometer upwelling brightness temperatures, obtained between the wavelengths of about 3.45 µm and 4.1 µm, and 8.0 µm to 12.0 µm. The low-frequency measurements consist of ground-based Chilbolton 35 GHz radar reflectivity measurements and spacebased upwelling 190 GHz brightness temperature measurements. The predictive quality of the ensemble model is demonstrated to be generally within the experimental uncertainty of the lidar backscatter estimates of the volume extinction coefficient and total solar optical depth. The ensemble model prediction of the high-resolution brightness temperature measurements is generally within ±2 K and ±1 K at solar and infrared wavelengths, respectively. The 35 GHz radar reflectivity and 190 GHz brightness temperatures are generally simulated to within ±2 dBZ e , and ±2 K, respectively. The directional spherical albedo observations suggest that the scattering phase function of the most randomized ensemble model gives the best fit to the measurements (generally within ±3%). This article demonstrates that the ensemble model, assuming the same microphysics, is physically consistent across the electromagnetic spectrum.

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Data from hyperspectral infrared sounders are routinely ingested worldwide by the National Weather Centers. The cloud‐free fraction of this data is used for initializing forecasts which include temperature, water vapor, water cloud, and ice cloud profiles on a global grid. Although the data from these sounders are sensitive to the vertical distribution of ice and liquid water in clouds, this information is not fully utilized. In the future, this information could be used for validating clouds in National Weather Center models and for initializing forecasts. We evaluate how well the calculated radiances from hyperspectral Radiative Transfer Models (RTMs) compare to cloudy radiances observed by AIRS and to one another. Vertical profiles of the clouds, temperature, and water vapor from the European Center for Medium‐Range Weather Forecasting were used as input for the RTMs. For nonfrozen ocean day and night data, the histograms derived from the calculations by several RTMs at 900 cm−1 have a better than 0.95 correlation with the histogram derived from the AIRS observations, with a bias relative to AIRS of typically less than 2 K. Differences in the cloud physics and cloud overlap assumptions result in little bias between the RTMs, but the standard deviation of the differences ranges from 6 to 12 K. Results at 2,616 cm−1 at night are reasonably consistent with results at 900 cm−1. Except for RTMs which use full scattering calculations, the bias and histogram correlations at 2,616 cm−1 are inferior to those at 900 cm−1 for daytime calculations.