Thesis (Ph. D.)--University of Nevada, Reno, 2006. / "December 2006." Includes bibliographical references. Online version available on the World Wide Web.

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Microphysical and dynamical interactions between aerosols and clouds are associated with some of the largest uncertainties in projections of future climate. Many possible aerosol effects on clouds have been suggested, but large uncertainties remain. In order to improve model projections of future climate, it is essential that we improve our quantitative understanding of anthropogenic aerosol effects. Several studies investigating interactions between satellite-observed aerosol and cloud properties have been published in recent years. However, the observed relationships are not necessarily due to aerosol effects on clouds. They may be due to cloud and precipitation effects on aerosol, meteorological covariation, observational data errors or methodological errors. An analysis of methodological errors arising through climatological spatial gradients is performed. For region sizes larger than 4°×4°, commonly used in the literature, spurious spatial variations in retrieved cloud and aerosol properties are found to introduce widespread significant errors to calculations of aerosol-cloud relationships. Small scale analysis prior to error-weighted aggregation to larger region sizes is recommended. Appropriate ways of quantifying relationships between aerosol optical depth (τ) and cloud properties are considered, and results are presented for three satellite datasets. There is much disagreement in observed relationships between τ and liquid cloud droplet number concentration and between τ and liquid cloud droplet effective radius, particularly over land. However, all three satellite datasets are in agreement about strong positive relationships between τ and cloud top height and between τ and cloud fraction (f_c). Using reanalysis τ data, which are less affected by retrieval artifacts, it is suggested that a large part of the observed f_c-τ signal may be due to cloud contamination of τ. General circulation model simulations further demonstrate that positive f_c-τ relationships may primarily arise due to covariation with relative humidity, and that negative f_c-τ relationships may arise due to scavenging of aerosol by precipitation. A new method of investigating the contribution of meteorological covariation to the observed relationships is introduced. Extratropical cyclone storm-centric composites of retrieved aerosol and cloud properties are investigated. A storm-centric description of the synoptics is found to be capable of explaining spurious f_c-τ relationships, although the spurious relationships explained are considerably smaller than observed relationships.

This thesis describes an investigation of, and improvements to, the microphysical modelling of aerosols in the Oxford-Rutherford Appleton Laboratory Aerosol and Clouds retrieval (ORAC), which is used to obtain aerosol properties from measurements by the Advanced Along Track Scanning Radiometer (AATSR). Modelling decisions determine the light scattering properties of the aerosol classes which in turn alter the retrieved aerosol properties: aerosol optical depth, and effective radius. The maritime, mineral dust, urban, and biomass burning aerosol classes were first investigated, and then improvements implemented. Major additions to the scheme include the ability to model non-spherical dust as spheroids, soot as fractal aggregates, and to coat spherical particles with an extra layer of differing refractive index (whose thickness can be modified by ambient relative humidity where necessary). Output from aerosol retrievals containing these new models is presented. Modelling of marine aerosol was found to be adequate, but an improvement in the relative humidity assumptions led to an average 5 % increase in aerosol optical depth (AOD). Modelling of mineral dust aerosols has been dramatically altered by the addition of non-spherical dust and hygroscopic particles, leading to increases in measured AOD of over 100 % during dust events, compared to the previous model. Measurement of biomass burning aerosol has been tested with an `ageing' aerosol scheme, leading to increases in over-land measured AOD of 0.14 (~50 % increase). With such significant changes in AOD, representation of aerosol light scattering properties is seen to be important factor in the accuracy of the ORAC scheme. Finally, a method of optimising the placement of detectors in an aerosol measurement device is presented.

When a tropical cyclone (TC) is within 360 nautical miles of Kadena AB, the Air Force's Typhoon Determination (TYDET) program is used to estimate TC-induced winds expected at the base. Best-track data and Joint Typhoon Warning Center (JTWC) forecasts are used to evaluate systematic errors in TYDET. The largest contributors to errors in TYDET are a systematic error by which wind speeds are too large and the lack of size and symmetry parameters. To examine these parameters, best-track and forecasts are used to classify TCs as small or large and symmetric or asymmetric. A linear regression technique is then used to adjust TYDET forecasts based on the best-track and forecast position, size, and symmetry categories. Using independent data, over 65 percent of the overall cross-wind forecasts were improved and more than 60 percent of the cross-wind forecasts were improved when verifying conditions noted a cross-wind of 20 knots or greater. The effectiveness of the corrections and implications for TYDET forecasts are examined in relation to errors in forecast data used to initialize TYDET. A similar approach as developed here for the TYDET model at Kadena AB is proposed for other bases within the Pacific theater.

by Yeung Wai Lung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (leaves 120-122). / Text in English; abstracts in English and Chinese. / by Yeung Wai Lung. / Abstract --- p.i / Abstract (Chinese version) --- p.ii / Acknowledgements --- p.iii / Contents --- p.iv / List of figures --- p.vii / List of tables --- p.xi / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- The El Nino ´ؤ Southern Oscillation Phenomenon --- p.3 / Chapter 2.1 --- Introduction to El Nino - Southern Oscillation --- p.3 / Chapter 2.2 --- The mean conditions of the Tropical Ocean --- p.4 / Chapter 2.3 --- Life Cycle of ENSO --- p.8 / Chapter 2.4 --- Understanding ENSO --- p.12 / Chapter 3 --- ENSO prediction schemes --- p.18 / Chapter 3.1 --- Introduction --- p.18 / Chapter 3.2 --- Statistical prediction models --- p.19 / Chapter 3.2.1 --- Introduction --- p.19 / Chapter 3.2.2 --- Atmospheric models --- p.19 / Chapter 3.2.3 --- Oceanic models --- p.22 / Chapter 3.3 --- Coupled ocean-atmosphere models --- p.24 / Chapter 3.3.1 --- Introduction --- p.24 / Chapter 3.3.2 --- Intermediate coupled models --- p.25 / Chapter 3.3.3 --- Hybrid coupled models --- p.28 / Chapter 3.3.4 --- Coupled general circulation models --- p.30 / Chapter 3.4 --- Skill sensitivities --- p.36 / Chapter 3.4.1 --- Introduction --- p.36 / Chapter 3.4.2 --- Dependence of skill on season --- p.36 / Chapter 3.4.3 --- Dependence of skill on decade --- p.37 / Chapter 3.4.4 --- Dependence of skill on the Phase of the ENSO cycle --- p.38 / Chapter 4 --- The ENSO model --- p.40 / Chapter 4.1 --- Introduction --- p.40 / Chapter 4.2 --- Models formalism --- p.40 / Chapter 4.2.1 --- Oceanic model --- p.40 / Chapter 4.2.2 --- Atmospheric model --- p.43 / Chapter 4.2.3 --- Coupling of atmospheric and oceanic models --- p.44 / Chapter 4.3 --- Numerical implementation --- p.45 / Chapter 4.3.1 --- Oceanic model --- p.45 / Chapter 4.3.2 --- Atmospheric model --- p.49 / Chapter 4.3.3 --- Averaging and interpolation during coupling --- p.52 / Chapter 4.4 --- Standard response of the model --- p.54 / Chapter 4.5 --- Limitation of the model --- p.57 / Chapter 5 --- Behavior of the ENSO model --- p.59 / Chapter 5.1 --- Introduction --- p.59 / Chapter 5.2 --- Numerical results of the model --- p.60 / Chapter 5.3 --- Effects of the parameters --- p.62 / Chapter 5.3.1 --- Introduction --- p.62 / Chapter 5.3.2 --- Coupling constant parameters --- p.62 / Chapter 5.3.3 --- Rayleigh friction --- p.66 / Chapter 5.3.4 --- The oceanic Kelvin wave speed --- p.69 / Chapter 5.3.5 --- The ocean basin length --- p.69 / Chapter 5.3.6 --- Reflections at the western boundary --- p.74 / Chapter 5.4 --- Modification of the model background state --- p.76 / Chapter 5.4.1 --- Introduction --- p.76 / Chapter 5.4.2 --- The results of the modified model --- p.77 / Chapter 5.4.3 --- Sensitivity of the western boundary of the modified model --- p.81 / Chapter 5.4.4 --- Parameters sensitivities of the modified model --- p.83 / Chapter 5.5 --- Conclusion and discussion --- p.87 / Appendix A --- p.91 / Appendix B --- p.112 / Bibliography --- p.120

This thesis presents a methodological approach to developing the capability of the Infrared Atmospheric Sounding Interferometer (IASI) instrument to inform on the atmospheric concentrations of carbon monoxide (CO), focussing on three key studies: 1) an assessment of the radiometric accuracy of the instrument; 2) the development of the University of Leicester IASI Retrieval Scheme (ULIRS) to convert measured radiances into a CO product; and 3) an investigation into the reliability and possible use of the ULIRS product. An intercomparison between the radiances as measured by the IASI and Advanced Along Track Scanning Radiometer (AATSR) instruments is performed, and absolute differences at 11 µm of less than 0:1K are observed. Given the radiometric behaviour across the IASI instrument as a whole, it is also concluded that the IASI instrument is radiometrically accurate to < 0.3K in the 12 and 4.7 µm spectral regions. A retrieval scheme, the ULIRS, is developed with explicit digital elevation and emissivity information, and a correction for solar surface reflection with a high resolution solar spectrum. Typical random errors over the African region relating to the profiles are found to be ~10% at 5 and 12 km, and on the total columns to be ~12 %. The ULIRS dataset and the operational CO products from the Measurements Of Pollution In The Troposphere (MOPITT) are inter-compared. A methodology which uses the same a priori statistics, and which reduces the smoothing bias between the two sets of data shows that there is only a small bias between the ULIRS and MOPITT V4 products. A simplified top-down approach to estimating CO emissions from fires is also presented, highlighting the need for a better understanding of the correct detection of burnt area from space-based measurements.

Accurate models of aerosol transformation including partitioning between the gas- and particle-phase are needed for estimating their effects on climate and air quality. In this thesis, the process of partitioning between phases and its determining factors are introduced and investigated. Three studies assess: the accuracy of estimation methods for aerosol component volatility and its effect on particulate concentration and composition; the consistency of different solutions to models of particle-phase diffusion; and, a method to analytically solve particle-phase diffusion. These studies contribute to the ongoing effort of improving aerosol models, such that their wide-ranging effects can be accurately estimated.

Representing model uncertainty in atmospheric simulators is essential for the production of reliable probabilistic forecasts, and stochastic parametrisation schemes have been proposed for this purpose. Such schemes have been shown to improve the skill of ensemble forecasts, resulting in a growing use of stochastic parametrisation schemes in numerical weather prediction. However, little research has explicitly tested the ability of stochastic parametrisations to represent model uncertainty, since the presence of other sources of forecast uncertainty has complicated the results. This study seeks to provide firm foundations for the use of stochastic parametrisation schemes as a representation of model uncertainty in numerical weather prediction models. Idealised experiments are carried out in the Lorenz `96 (L96) simplified model of the atmosphere, in which all sources of uncertainty apart from model uncertainty can be removed. Stochastic parametrisations are found to be a skilful way of representing model uncertainty in weather forecasts in this system. Stochastic schemes which have a realistic representation of model error produce reliable forecasts, improving on the deterministic and the more "traditional" perturbed parameter schemes tested. The potential of using stochastic parametrisations for simulating the climate is considered, an area in which there has been little research. A significant improvement is observed when stochastic parametrisation schemes are used to represent model uncertainty in climate simulations in the L96 system. This improvement is particularly pronounced when considering the regime behaviour of the L96 system - the stochastic forecast models are significantly more skilful than using a deterministic perturbed parameter ensemble to represent model uncertainty. The reliability of a model at forecasting the weather is found to be linked to that model's ability to simulate the climate, providing some support for the seamless prediction paradigm. The lessons learned in the L96 system are then used to test and develop stochastic and perturbed parameter representations of model uncertainty for use in an operational numerical weather prediction model, the Integrated Forecasting System (IFS). A particular focus is on improving the representation of model uncertainty in the convection parametrisation scheme. Perturbed parameter schemes are tested, which improve on the operational stochastic scheme in some regards, but are not as skilful as a new generalised version of the stochastic scheme. The proposed stochastic scheme has a potentially more realistic representation of model error than the operational scheme, and improves the reliability of the forecasts. While studying the L96 system, it was found that there is a need for a proper score which is particularly sensitive to forecast reliability. A suitable score is proposed and tested, before being used for verification of the forecasts made in the IFS. This study demonstrates the power of using stochastic over perturbed parameter representations of model uncertainty in weather and climate simulations. It is hoped that these results motivate further research into physically-based stochastic parametrisation schemes, as well as triggering the development of stochastic Earth-system models for probabilistic climate prediction.

The Infrared Atmospheric Sounding Interferometer (IASI) provides atmospheric observations with high spectral resolution and its data have been shown to have a significant positive impact on global Numerical Weather Prediction (NWP) and trace gas retrievals. A fundamental component of the retrieval of atmospheric composition is the radiative transfer model used to simulate the observations. An accurate representation of the expected emission spectrum measured by the satellite is essential given that differences in the reproduced atmospheric spectra propagate through a retrieval procedure and produce an altered estimate of the atmospheric state. The importance of the assumptions within the forward model are discussed and it is established that in the simulation of spectra from satellite-borne instruments the choice of the model parameters can have a large impact upon the resulting output. These assumptions are explored in the context of the Reference Forward Model (RFM), which is further configured to optimise its output for simulating the IASI spectrum in the troposphere. In order to ascertain the consistency of different radiative transfer models, comparisons are carried out between the RFM and the Radiative Transfer model for TOVS (RTTOV) in order to quantify any discrepancies in the reproduction of IASI measurements. Good agreement is shown across the majority of the spectrum, with exceptions caused by CO₂ line mixing effects and the H₂O continuum. Alongside model comparisons, the RFM is validated against real IASI measurements. Being a Fourier Transform Spectrometer, there are a large number of channels available from the IASI instrument, which leads to a very large quantity of data. However, this can lead to problems within retrievals and data assimilation. Choosing an optimal subset of the channels is an established method to reduce the amount of data; maintaining the information contained within it whilst eliminating spectral regions with large uncertainties. The method currently used at the UK Met Office to select their spectral channels is re-assessed and a modified method is presented that improves upon the modelling of spectrally correlated errors.