Presented in this study is a wide-ranging investigation into the use of properties of balance in an operational numerical weather prediction context. Initially, a joint numerical and observational study is undertaken. We used the Unified Model (UM), the suite of atmospheric and oceanic prediction software used at the UK Met Office (UKMO), to locate symmetric instabilities (SIs), an indicator of imbalanced motion. These are areas of negative Ertel potential vorticity (in the Northern hemisphere) calculated on surfaces of constant potential temperature. Once located, the SIs were compared with satellite and aircraft observational data. As a full three-dimensional calculation of Ertel PV proved outwith the scope of this study we calculated the two-dimensional, vertical component of the absolute vorticity, to assess the inertial stability criterion. We found that at the synoptic scale in the atmosphere, if there existed a symmetric instability, it was dominated by an inertial instability. With the appropriate observational data, evidence of inertial instability from the vertical component of the absolute vorticity, predicted by the UM was found at 12km horizontal grid resolution. Varying the horizontal grid resolution allowed the estimation of a grid length scale, above which, the inertial instability was not captured by the observational data, of approximately 20km. Independently, aircraft data was used to estimate that horizontal grid resolutions above 20-25km should not model any features of imbalance providing a real world estimate of the lower bound of the grid resolution that should be employed by a balanced atmospheric prediction model. A further investigation of the UM concluded that the data assimilation scheme and time of initialisation had no effect on the generation of SIs. An investigation was then made into the robustness of balanced models in the shallow water context, employing the contour-advective semi-Lagrangian (CASL) algorithm, Dritschel & Ambaum (1997), a novel numerical algorithm that exploits the underlying balance observed within a geophysical flow at leading order. Initially two algorithms were considered, which differed by the prognostic variables employed. Each algorithm had their three-time-level semi-implicit time integration scheme de-centred to mirror the time integration scheme of the UM. We found that the version with potential vorticity (PV), divergence and acceleration divergence, CA[subscript(δ,γ)], as prognostic variables preserved the Bolin-Charney balance to a much greater degree than the model with PV, divergence and depth anomaly CA[subscript(tilde{h},δ)], as prognostic variables. This demonstrated that CA[subscript(δ,γ)] was better equipped to benefit from de-centring, an essential property of any operational numerical weather prediction (NWP) model. We then investigate the robustness of CA[subscript(δ,γ)] by simulating flows with Rossby and Froude number O(1), to find the operational limits of the algorithm. We also investigated increasing the efficiency of CA[subscript(δ,γ)] by increasing the time-step Δt employed while decreasing specific convergence criteria of the algorithm while preserving accuracy. We find that significant efficiency gains are possible for predominantly mid-latitude flows, a necessary step for the use of CA[subscript(δ,γ)] in an operational NWP context. The study is concluded by employing CASL in the non-hydrostatic context under the Boussinesq approximation, which allows weak stratification to be considered, a step closer to physical reality than the shallow water case. CASL is compared to the primitive equation pseudospectral (PEPS) and vorticity-based pseudospectral (VPS) algorithms, both as the names suggest, spectral-based algorithms, which again differ by the prognostic variables employed. This comparison is drawn to highlight the computational advantages that CASL has over common numerical methods used in many operational forecast centres. We find that CASL requires significantly less artificial numerical diffusion than its pseudospectral counterparts in simulations of Rossby number ~O(1). Consequently, CASL obtains a much less diffuse, more accurate solution, at a lower resolution and therefore lower computational cost. At low Rossby number, where the flow is strongly influence by the Earth's rotation, it is found that CASL is the most cost-effective method. In addition, CASL also preserves a much greater proportion of balance, diagnosed with nonlinear quasigeostrophic balance (NQG), another significant advantage over its pseudospectral counterparts.

Thesis (Ph. D.)--University of Hawaii at Manoa, 2005. / Includes bibliographical references (leaves 181-193).

No description available.

Thesis (M.Sc.(Geography, Geoinformatics & Meteorology))--University Pretoria, 2008. / Summary in English. Includes bibliographical references (leaves 94-98).

Thesis (M.Sc.) - University of Queensland, 2004. / Includes bibliographical references.

Thesis (M.S.)--Florida State University, 2006. / Advisor: Xiaolei Zou, Florida State University, College of Arts and Sciences, Dept. of Meteorology. Title and description from dissertation home page (viewed June 13, 2006). Document formatted into pages; containsxi, 65 pages. Includes bibliographical references.

Reliability and accuracy of the forcing data plays a vital role in the Hydrological Streamflow Prediction. Reliability of the forcing data leads to accurate predictions and ultimately reduction of uncertainty. Currently, Numerical Weather Prediction (NWP) models are developing ensemble forecasts for various temporal and spatial scales. However, it is proven that the raw products of the NWP models may be biased at the basin scale; unlike model grid scale, depending on the size of the catchment. Due to the large space-time variability of precipitation, bias-correcting the ensemble forecasts has proven to be a challenging task. In recent years, Ensemble Pre-Processing (EPP), a statistical approach, has proven to be helpful in reduction of bias and generation of reliable forecast. The procedure is based on the bivariate probability distribution between observation and single-value precipitation forecasts. In the current work, we have applied and evaluated a Bayesian approach, based on the Copula density functions, to develop an ensemble precipitation forecasts from the conditional distribution of the single-value precipitation. Copula functions are the multivariate joint distribution of univariate marginal distributions and are capable of modeling the joint distribution of two variables with any level of correlation and dependency. The advantage of using Copulas, amongst others, includes its capability of modeling the joint distribution independent of the type of marginal distribution. In the present study, we have evaluated the capability of copula-based functions in EPP and comparison is made against an existing and commonly used procedure for same i.e. meta-Gaussian distribution. Monthly precipitation forecast from Climate Forecast System (CFS) and gridded observation from Parameter-elevation Relationships on Independent Slopes Model (PRISM) have been utilized to create ensemble pre-processed precipitation over three sub-basins in the western USA at 0.5-degree spatial resolution. The comparison has been made using both deterministic and probabilistic frameworks of evaluation. Across all the sub-basins and evaluation techniques, copula-based technique shows more reliability and robustness as compared to the meta-Gaussian approach.

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Meteorology, 1980. / Microfiche copy available in Archives and Science. / Bibliography: leaves 147-151. / by Ross N. Hoffman. / Ph.D.

Thesis. 1978. M.S.--Massachusetts Institute of Technology. Dept. of Meteorology. / Microfiche copy available in Archives and Science. / Bibliography: leaves 177-178. / by John P. Sheldon. / M.S.

Thesis. 1975. Ph.D.--Massachusetts Institute of Technology. Dept. of Meteorology. / Vita. / Bibliography: leaves 202-206. / Ph.D.