The release of conditional symmetric instability (CSI; a type of moist symmetric instability), which yields slantwise convection, has been associated with mesoscale atmospheric phenomena such as frontal precipitation bands, cloud heads in rapidly developing extratropical cyclones and sting jets. A number of case studies of CSI have been previously published, but the climatological role of CSI release is not well understood. Slantwise convection is not parameterized in operational global-scale weather and climate models, but the justification for omitting this process has not been established. Towards answering these grand research questions, the aim of this thesis is to quantify the climatological role of CSI release, and link this to surface weather conditions. In Chapter 4, two contrasting extra-tropical cyclone case studies are examined to find the strengths and weaknesses of the following diagnostics for measuring CSI and its upright counterpart, conditional instability, and their release: convective available potential energy (CAPE), slantwise convective available potential energy (SCAPE), the vertical extent of upright instability (VRU) and the vertical extent of slantwise instability (VRS). An example of CSI release is studied. The climatologies of these diagnostics applied to the ERA-Interim reanalysis dataset are presented in Chapter 5. In Chapter 6, SCAPE and CAPE are correlated with observed and simulated precipitation at various spatial and temporal averaging. It is shown that CSI release is associated with extra-tropical cyclones, particularly enhancing precipitation. The different CSI diagnostics used in this study (VRS and SCAPE) depict the overall importance of CSI in a similar way, but differences in both case study and climatology should be noted.

Data assimilation (DA) methods combine a prior forecast (background state) with the latest observations of the atmosphere, to estimate the initial conditions for a weather forecast. Accurate and early forecasts of extreme weather events allow time for actions to be taken to protect populations from injury and death, and for the preservation of infrastructure as far as possible. Climate change is expected to increase the severity frequency of some extreme weather events over the coming Century. Advances in DA should improve the forecasts of these events.

No description available.

Long term weather forecasts are in great demand across many industries, such as the agricultural, tourism, and energy sectors. In this thesis a new long-term temperature forecasting benchmark is proposed. In particular, the Ensemble Random Analog Prediction (ERAP) dynamic resampler is developed. ERAP allows one to generate temperature scenarios over long time scales without making assumptions about the underlying model of temperature. ERAP works by identifying similar patterns in the historical data across multiple time scales. We also propose a new non-linear weather resembling test system - a weather-like process that mimics the real temperature with additional long term non-linear patterns. Finally we study the mixing of physical weather forecasts with the historical data. In particular, combination forecasts are developed that mix information from both physical forecasting models and historical data. The methodology is developed by exploring kernel dressing of forecast scenarios and ignorance skill-score based optimization of parameters. The new weather generator ERAP is then extensively tested in the perfect model scenario, by studying its performance in terms of the generated statistics, using both a noisy Lorenz system and the new weather like test process. ERAP is also tested on real weather data by assessing its performance on the Berlin daily maximum temperature in terms of the generated statistics. Finally ERAP is also used for pricing a weather derivative, and the prices compared to other existing techniques including pricing based on the historical statistics, Monte-Carlo using a fitted distribution, plus other statistical techniques from the weather derivative literature. For combination forecasts we study the sensitivity of parameters to ensemble size and the level of noise in the initial conditions, within the perfect model scenario. We show that ERAP performed well in the perfect model scenario, on the actual data and when used for pricing. The historical statistics were closely replicated, the statistics of a chosen verification set were also well replicated and in some cases, ERAP generated data provided a better match to the statistics of the verification set than the climatology (the statistics of the learning set itself). Some non-conventional statistics were better replicated using ERAP in both the perfect and imperfect model scenarios. Additionally, information is provided by ERAP on the uncertainty of the computed statistics. We also show that ERAP provides more reliable pricing, because it provides more reliable long-term simulations. The fake weather generator developed in this thesis has shown to provide a good test data set that is non-linear with patterns on multiple time scales and closely resembles the characteristics of real temperature time series. This process could be a viable alternative, if parameters are fully calibrated to the chosen weather data, to existing statistical temperature modeling approaches. This work could be further improved by the creating better parameter estimation techniques for ERAP. ERAP could also be extended to several dimensions allowing the generation of more enhanced synthetic weather data. For the purpose of pricing weather derivatives more work needs to be done to address the transformation of ERAP scenarios to probabilistic weather forecasts. Further work may also include studies of the performance of combined forecasts, which mix synthetic data and physical weather forecasts, in practice.

Operational weather prediction systems do not currently make full use of infra-red satellite observations that are affected by the presence of cloud. Observations that are affected by cloud are routinely discarded during pre-processing. This is because cloud causes large, unpredictable, and nonlinear changes in the observed radiances, and obscures the atmosphere underneath from view. This disrupts the finely-balanced calculations used to convert small changes in observed radiance into temperature and humidity profiles of the atmosphere. Areas that contain cloud are likely to be meteorologically interesting, so where information on the state of the atmosphere is most desired, it is also in shortest supply. This thesis explores the possibility of using the large changes over time of cloud-affected infra-red satellite observations to calculate the vertical component of wind. In order to explore the mathematical and practical issues of assimilating data from cloudy radiances, a study has been performed using an idealised single column atmospheric model developed for this purpose. The model simulates cloud development in an atmosphere with vertical motion and the effects on simulated infra-red satellite observations. An empirical method and a variational data assimilation system have been developed to process sequences of observations over a six hour time with the goal of calculating vertical velocity. These two methods combined allow vertical velocity to be determined with an RMS error of approximately 0.8 cm/s in 80% of cases. The system is capable of detecting the remaining cases where there is insufficient information in the observations to constrain vertical velocity. This result is the first step in the long term goal of using cloud-affected satellite imagery more effectively in operational weather prediction systems. The ability to use these observations in this way would improve the forecasting of severe weather events, helping to protect lives and property from loss or damage.

A suite of real-time statistical forecast models of the Madden-Julian Oscillation (MJO), a large scale, quasi-periodic phenomenon and the dominant mode of variability in the tropics, is presented along with a novel approach to performing real-time intraseasonal data filtering. The study introduces the new technique of empirical mode decomposition (EMD) in a meteorological-climate forecasting context. It identifies empirical adjustments that can be made to the basic EMD method to produce a band-pass filter that is highly efficient at extracting a broad-band signal such as the Madden-Julian oscillation (MJO) with minimal end effects, such that it is suitable for use in realtime. This process is used to efficiently extract the MJO signal from gridded time series of outgoing longwave radiation (OLR) data. A range of statistical models were then tested for their suitability in forecasting the MJO signal in the OLR data, as isolated by the EMD. These were from the general classes of vector autoregressive moving average (VARMA) and neural network models. The models were developed using 17 years of OLR data from 1980 to 1996. Forecasts (hindcasts) were then made on the remaining independent data from 1998 to 2004. These were made in real-time, as only data up to the date the forecast was made were used. A VARMA (5,1) model was selected and its parameters determined by a maximum likelihood method. The median skill of forecasts was accurate (defined as an anomaly correlation above 0.6) at lead times up to 25 days. Four neural network models were created with a range of degrees of freedom in their forecasts. .These had their parameters determined using back propagation methods in a supervised learning manner. The neural network comparable to the VARMA (5,1) model in terms of forecast resolution also had accurate median forecast skill at lead times of 25 days, while the remaining neural network models had accurate median forecast skill at lead times ranging from 15-25 days but with greater spatial resolution. The statistical MJO forecasts developed here perform significantly better than any current· dynamical models and are at least as skillful as other statistical MJO forecasts. Operational model forecasts are available at http://envam1.env.uea.ac.uk/mjo_forecast.html.

No description available.

A systematic approach to quantifying uncertainty in climate projections is through the application of observational constraints to an ensemble of climate model simulations. In this thesis we investigate how large perturbed physics ensembles of atmosphere-ocean general circulation model (AOGCM) simulations can be used to represent modelling uncertainty in climate projections. We start by considering the challenges involved in ensemble design owing to the high dimensional parameter space in AOGCMs, introducing the technique of emulation that can be used to efficiently target regions of parameter space having properties of interest for a particular research question. We then present results from the climateprediction.net BBC climate change experiment (BBC CCE), the first multi-thousand member coupled AOGCM ensemble exploring un- certainties in the transient response. We find a "likely" range (66% confidence interval) of 1.4-3K for global mean warming by 2050 relative to 1961-1990 under a mid-range emissions scenario. The range is larger than observed in multi-model ensembles of opportunity, especially at the upper end and is more consistent with the subjective estimate given in the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4). The results provide the first direct AOGCM evidence of high response worlds that are consistent with the recent observed warming, providing an additional set of physically coherent input scenarios that can support climate impact assessments. Application of observational constraints in calculating model error requires a met- ric to weight individual components, often through an inverse covariance matrix. We demonstrate how the sample covariance matrix is a poor estimator in many situations faced in climate research and follow up previous work introducing regularized covariance estimation, which is applied to the analysis of the BBC CCE. This stabilises uncertainty estimates removing the need for empirical choices of the truncation in the model error calculation. Finally we introduce objective Bayesian statistics as a methodology to address some of the difficulties faced when specifying prior distributions for AOGCM parameters. Results from applying Jeffreys' prior to a simplified energy balance model of the climate system suggest the approach can be applied to complex AOGCMs and therefore provides a complementary alternative to more traditional subjective Bayesian methods. However, we conclude that the probabilistic framework adopted in quantifying uncertainty should be partially motivated by the forecast of interest and strength of relevant observational constraints in the climate system.

The mid-winter polar stratosphere has great potential as a source of additional predictability for medium and extended-range weather forecasts. Several studies have demonstrated that during particularly dynamically active periods in the stratosphere known as stratospheric sudden warmings (SSW), tropospheric forecasts can be sensitive to the stratospheric evolution on timescales of 10 days. A key limitation of these studies has been their idealised nature. Similarly, there has been little investigation of how and why changes to the stratosphere influence tropospheric behaviour and what this means for the design of future numerical weather prediction systems. This thesis addresses the above issues by performing a series of ensemble experiments using the Met Office Unified Model with a range of different vertical resolutions. These include a "low top" model with an upper boundary in the mid-stratosphere and two "high top" models with a similar upper boundary in the mesosphere, but with differing stratospheric vertical resolutions. The forecasts were run at these resolutions around the SSW of February 2010. The focus is on short to medium range timescales and so the forecasts are only out to 30 days. Statistically significant differences in surface fields, with mean differences in surface pressure of up to 3hPa are found between "high top" and "low top" simulations as soon as 5 days into the forecast. These tropospheric differences resemble a negative North Atlantic Oscillation pattern, and are likely related to the inability of the "low top" model to effectively capture the SSW. No statistically significant differences are detected in surface fields between the two "high top" models, suggesting that the extra vertical resolution does not influence the surface forecast at this timescale. The second part of this thesis investigates whether the dynamical interaction between the stratosphere and the troposphere is mediated by changes to the development of baroclinic eddies. Two innovative wave breaking detection methods are developed to assess Rossby wave breaking in the troposphere.

The dynamics of the atmosphere span a tremendous range of spatial and temporal scales which presents a great challenge to those who seek to forecast the weather. To aid understanding of and facilitate research into such complex physical systems, `idealised' models can be developed that embody essential characteristics of these systems. This thesis concerns the development of an idealised fluid model of convective-scale Numerical Weather Prediction (NWP) and its use in inexpensive data assimilation (DA) experiments. The model modifies the rotating shallow water equations to include some simplified dynamics of cumulus convection and associated precipitation, extending the model of Wuersch and Craig (2014). Despite the non-trivial modifications to the parent equations, it is shown that the model remains hyperbolic in character and can be integrated accordingly using a discontinuous Galerkin finite element method for nonconservative hyperbolic systems of partial differential equations. Combined with methods to ensure well-balancedness and non-negativity, the resulting numerical solver is novel, efficient, and robust. Classical numerical experiments in shallow water theory, based on the Rossby geostrophic adjustment problem and non-rotating flow over topography, elucidate the model's distinctive dynamics, including the disruption of large-scale balanced flows and other features of convecting and precipitating weather systems. When using such intermediate-complexity models for DA research, it is important to justify their relevance in the context of NWP. A well-tuned observing system and filter configuration is achieved using the ensemble Kalman filter that adequately estimates the forecast error and has an average observational influence similar to NWP. Furthermore, the resulting error-doubling time statistics reflect those of convection-permitting models in a cycled forecast-assimilation system, further demonstrating the model's suitability for conducting DA experiments in the presence of convection and precipitation. In particular, the numerical solver arising from this research provides a useful tool to the community and facilitates other studies in the field of convective-scale DA research.