Thesis (M.A.)--University of Wisconsin--Madison, 1965. / eContent provider-neutral record in process. Description based on print version record. Bibliography: l. 26-28.

Thesis--Wisconsin. / Vita. Includes bibliographical references (leaves 218-220).

Thesis (M.S.)--Montana State University--Bozeman, 2008. / Typescript. Chairperson, Graduate Committee: Joseph A. Shaw. Includes bibliographical references (leaves 142-145).

Thesis (M.S.)--University of Wisconsin--Madison, 1965. / eContent provider-neutral record in process. Description based on print version record. Bibliography: l. 23.

Thesis (Ph.D.)--Rochester Institute of Technology, 2007. / Typescript. Includes bibliographical references (leaves 173-175).

Thesis (M.S.)--Montana State University--Bozeman, 2004. / Typescript. Chairperson, Graduate Committee: Joseph A. Shaw. Includes bibliographical references (leaves 91-94).

A 'model-to-radiance' comparison of simulated brightness temperatures and radiances from the Hadley Centre Global Environmental Model 2 (HadGEM2-A) with longwave measurements from the High Resolution Infrared Radiation Sounder/4 (HIRS/4) and the Infrared Atmospheric Sounding Interfermeter (IASI) onboard the MetOp-A satellite is presented for all-sky and clear-sky global means. The fast Radiative Transfer model for TOVS 10 (RTTOV-10) is applied to HadGEM2 output to simulate observational-equivalent data. The results are compared with corresponding broadband analyses. A method is developed to extend hyperspectral IASI radiances to cover the whole outgoing terrestrial spectrum, in order to identify any compensating biases, and explore wavebands in the unobserved Far Infrared (FIR) region. For the all-sky HIRS analysis, the model overestimates brightness temperatures in the atmospheric window region with the greatest biases over areas associated with deep convective cloud. In contrast to many global climate models, much smaller clear-sky biases are found indicating that model clouds are the dominating source of error. Simulated values in upper atmospheric CO2 channels approximate observations better as a result of compensating cold biases at the poles and warm biases at lower latitudes, due to a poor representation of the Brewer Dobson circulation in the 38 level 'low-top' configuration of the model. Simulated all and clear-sky outgoing longwave radiation evaluated against the Clouds and the Earth's Radiant Energy System (CERES) and HIRS OLR products reveal good agreement, in part due to cancellation of positive and negative biases. Through physical arguments relating to the spectral energy balance within a cloud, it is suggested that broadband agreement could be the result of a balance between positive window biases and unseen negative biases originating from the water vapour rotational band in the FIR (not sampled by HIRS). Simple sensitivity tests show that dramatically altering existing cloud properties has little effect on the prominent window biases, however raising clouds a maximum of 5 atmospheric levels minimises the error in cloud contaminated channels, due to the introduction of spatially compensating errors. Sensitivities to the way ice clouds are parameterised in RTTOV-10 display a range of up to 2.5 K in window channels but absolute biases still exceed 3 K for all choices. Because of the lack of satellite based FIR observations due to a technological gap in the spectral region, an algorithm is created to 'fill in' the available data. Correlations between selected IASI channels and simulated unobserved wavelengths in the far infrared are used to estimate radiances between 25.25 - 644.75 cm-1 at 0.5 cm-1 intervals. The same method is used in the 2760 - 3000 cm-1 region. The spectrum is validated by comparing the Integrated Nadir Longwave Radiance (INLR) product (spanning the whole 25.25 - 3000 cm-1 range) with the corresponding broadband measurements from the Clouds and the Earth's Radiant Energy System (CERES) instrument on the Terra and Aqua satellites at simultaneous nadir overpasses, revealing mean differences of 0.3 Wm-2sr-1 (0.5% relative difference) lower for IASI relative to CERES and significantly lower biases in nighttime only scenes. Averaged global data over a single month produces mean differences of about 1 Wm-2sr-1 in both the all and the clear-sky (1.2% relative difference). The new high resolution spectrum is presented for global mean clear and total skies where the far infrared is shown to contribute 44% and 47% to the total OLR respectively, which is consistent with previous estimates. In terms of spectral cloud radiative forcing, the FIR contributes 19% and in some subtropical instances appears to be negative, results that would go un-observed with a traditional broadband analysis. The equivalent complete IASI OLR model product is simulated from GCM data using RTTOV-10. The same process of applying predictors to the satellite measurements is applied to the model simulated radiances, with appropriate modifications, to produce a directly comparable model product. Annual mean all-sky radiances are still greatly overestimated at all wavenumbers with a total radiance bias of 4.52 Wm-2 across the whole range. Compensating negative biases outside of the HIRS coverage that were hypothesised are absent, with the far infrared contributing to the overall bias rather than cancelling it. Equivalent clear-sky biases are much lower overall at 0.39 Wm-2, in part due to spectral and spatial cancellation of errors. A flux-to-flux comparison is enabled by estimating the spatial distribution of anisotropic factors, using collated HIRS OLR fluxes and IASI OLR radiances, which yields global mean model fluxes in excess of 12 Wm-2 higher than observations in the all-sky. The difference between this and the fluxes calculated using the climate model's broadband radiation code (Edward-Slingo) are around 10 Wm-2 which is outside the range of uncertainty in the method used to estimate the flux. However, it is discussed that tuning of the climate model's broadband code to known flux values is a required practice to ensure global energy budgets balance but can produce inaccurate parameterised variables. An equivalent analysis adjusting the ice cloud parametrisation to reflect the radiances that have the biggest differences to the original configuration selected showed a bias reduction of 4.5 Wm-2, which is still not enough to completely explain its size, suggesting the existence of residual cloud problems. Finally, it is suggested that the way forward in separating and constraining cloud errors, in both radiative transfer codes, is a rigorous process of testing them with observation cloud properties and reanalysis data as inputs.