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.

Approved for public release, distribution is unlimited / Performance of high resolution mesoscale models has been in a continuous state of refinement since their inception. Mesoscale models have become quite skillful in forecasting synoptic scale events such as mid-latitude cyclones. However, atmospheric forcing becomes a much more complicated process when faced with the challenge of forecasting near topography along the coastline. Phenomena such as gap flows, blocked flow winds and low level stratification become important to predictability at these scales. The problem is further complicated by the dynamics of a frontal passage event. The skill of mesoscale models in predicting these winds is not as well developed. This study examines several forecasts by the Coupled Ocean Atmospheric Mesoscale Prediction System (COAMPS) during frontal passage events for the Winter of 2003-2004. An attempt is made to characterize the predictability of the wind speed and direction both before and after frontal passage along the California coast. Synoptic forcing during this time is strong due to the effects of the mid-latitude cyclones propagate across the Pacific. The study's results indicate that the wind field predictability is subject to several consistent errors associated with the passage of fronts over topography. These errors arise due to difficulty in the model capturing weak thermal advection events and topographic wind funneling. The deficiencies in model representation of topography contributes to these errors. / Lieutenant, United States Navy

Approved for public release, distribution is unlimited / Vertically propagating waves (VPWs) generated by prominent mountain ridges are a severe hazard to military aircraft operations. Properly forecasting the initiation and duration of such a phenomenon is critical, yet quite often missed by turbulence forecasters. A primary reason for poor forecast skill is vague VPW forecasting guidelines at the Air Force operational centers, focusing a majority of attention on the less severe, more common trapped lee wave response. The United States Air Forces in Europe Operational Weather Squadron (USAFE OWS) has requested a tool to aid in improving forecast ability of VPW events. Satellite analysis from October 2003 through March 2004 indicated an occurrence of six major VPW events to the lee of the Alps. Actual verification of turbulence in each VPW was unavailable due to the minimal pilot report (PIREP) database kept for military flights over Europe, therefore, a subjective assessment of turbulent conditions was determined depending on the resulting cloud signature. Using NCEP GFS model analysis and upstream upper air soundings during these events, an average synoptic condition and critical weather parameters were created. These developed tools were then tested from October 2004 through March 2005 to prove their reliability. In a limited data set these tools identified all VPW events, with only a 25% false alarm rate. This is compared to a 6% forecast ability with 0% false alarm rate determined during the 2003-2004 winter season by USAFE OWS forecasters. These new rules should be valuable in that they will provide a much needed capability for synoptic scale turbulence forecasters to better determine hazardous aviation conditions associated with VPWs. / Captain, United States Air Force

The purpose of this thesis is to investigate the value-added of the Navy's nowcast/forecast and GFO satellite to the naval antisubmarine warfare (ASW) and anti-surface warfare. For the former, the nowcast/forecast versus observational fields were used by the WAPP to determine the suggested presets for MK 48 variant torpedo. The metric used to compare the two sets of outputs is the relative difference in acoustic coverage area generated by WAPP. Output presets are created for five different scenarios, two anti-surface warfare scenarios and three ASW scenarios, in each of two regions: the East China Sea and South China Sea. Analysis of the output reveals that POM outperforms MODAS in all tactic scenarios. For the latter, the MODAS (T, S) profiles were used by the WAPP to determine suggested presets for Mk 48 variant torpedo. The only difference in the MODAS fields was the altimeter used to initialize the respective MODAS fields. The same metrics used in the nowcast/forecast case were used to generate and compare the acoustic coverages. Analysis of the output reveals that, in most situations, WAPP output is not very sensitive to the difference in altimeter orbit.

Submitted to the Information Technology (IT) Department in conformity with the requirements for the degree of Doctor of Philosophy in Information Technology, Durban University of Technology. Durban, South Africa, 2016. / This doctoral research introduces an integration of satellite systems and new stratospheric platforms for weather observation, imaging and transfer of meteorological data to the ground infrastructures. Terrestrial configuration and satellite communication subsystems represent well-established technologies that have been involved in global satellite sensing and weather observation area for years. However, in recent times, a new alternative has emerged based on quasi-stationary aerial platforms located in the Stratosphere called High Altitude Platform (HAP) or Stratospheric Communication Platforms (SCP). The SCP systems seem to represent a dream come true for communication engineers since they preserve most of the advantages of both terrestrial and satellite communication systems. Today, SCP systems are able to help, in a more cost effective way, developments of space Earth sensing and weather observation and weather sensing and observation. This new system can provide a number of forms ranging from a low altitude tethered balloon to a high altitude (18 – 25 km) fuel-powered piloted aircraft, solar-powered unmanned airplanes and solar-powered airship.

The weather derivative concept was created in the United States of America as a result of the deregulation of its energy industry. When other countries learnt of this concept they decided to enter the market as well. Thus a body called the Weather Risk Management Association was established. This body’s main function is to collate information pertaining to weather risk and to help the process of advancement and growth within the market. The weather risk market has grown tremendously and various participants across the world are using weather derivative products to protect the revenue of their respective companies against adverse weather condition. South Africa entered the weather risk market and it’s contracted its first weather contract in February 2000. The objective of this study is to evaluate the South African financial market perceptions on weather derivatives and to establish the feasibility of use. The study also places emphasis on the importance of evaluating the South African economic conditions in order to achieve the goal of the study. Hence the study evaluates the different aspects in terms of the legal, accounting, taxation, weather data, and structuring and pricing implications of a weather derivative transaction. Thus a survey was designed, forwarded, and received back from professionals in the legal, accounting, taxation, weather data, and structuring and pricing fields. This analysis was conducted to evaluate the South African financial market’s perceptions on weather derivative applications. / Prof. C.H. van Schalkwyk

Traditional methods such as distance weighing, correlation and data driven methods have been used in the estimation of missing precipitation data. Also common is the use of radar (NEXRAD) data to provide better spatial distribution of precipitation as well as infilling missing rain gage data. Conventional regression models are often used to capture highly variant nonlinear spatial and temporal relationships between NEXRAD and rain gage data. This study aims to understand and model the relationships between radar (NEXRAD) estimated rainfall data and the data measured by conventional rain gages. The study is also an investigation into the use of emerging computational data modeling (inductive) techniques and mathematical programming formulations to develop new optimal functional approximations. Radar based rainfall data and rain gage data are analyzed to understand the spatio-temporal associations, as well as the effect of changes in the length or availability of data on the models. The upper and lower Kissimmee basins of south Florida form the test-bed to evaluate the proposed and developed approaches and also to check the validity and operational applicability of these functional relationships among NEXRAD and rain gage data for infilling of missing data. / by Delroy Peters. / Thesis (M.S.)--Florida Atlantic University, 2008. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2008. Mode of access: World Wide Web.

Radar rainfall estimates have become a decision making tool for scientists, engineers and water managers in their tasks for developing hydrologic models, water supply planning, restoration of ecosystems, and flood control. In the present study, the utility of a power function for linking the rain gauge and radar estimates has been assessed. Mean daily rainfall data from 163 rain gauges installed within the South Florida Water Management District network have been used and their records from January 1st, 2002 to October 31st, 2007 analyzed. Results indicate that the power function coefficients and exponents obtained by using a non-linear optimization formulation, show spatial variability mostly affected by type of rainfall events occurring in the dry or wet seasons, and that the linear distance from the radar location to the rain gauge has a significant effect on the computed values of the coefficients and exponents. / by Mario Mayes-Fernandez. / Thesis (M.S.C.S.)--Florida Atlantic University, 2009. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2009. Mode of access: World Wide Web.

The physical mechanisms and predictability associated with extreme daily rainfall in South East South America (SESA) are investigated for the December-February season. Through a k-mean analysis, a robust set of daily circulation regimes is identified and then it is used to link the frequency of rainfall extreme events with large-scale potential predictors at subseasonal-to-seasonal scales. This basic set of daily circulation regimes is related to the continental and oceanic phases of the South Atlantic Convergence Zone (SACZ) and wave train patterns superimposed on the Southern Hemisphere Polar Jet. Some of these recurrent synoptic circulation types are conducive to extreme rainfall events in the region through synoptic control of different meso-scale physical features and, at the same time, are influenced by climate phenomena that could be used as sources of potential predictability. Extremely high rainfall (as measured by the 95th- and 99th-percentiles) is preferentially associated with two of these weather types, which are characterized by moisture advection intrusions from lower latitudes and the Pacific; another three weather types, characterized by above-normal moisture advection toward lower latitudes or the Andes, are preferentially associated with dry days (days with no rain). The analysis permits the identification of several subseasonal-to-seasonal scale potential predictors that modulate the occurrence of circulation regimes conducive to extreme rainfall events in SESA. It is conjectured that a cross-timescale interference between the different climate drivers improves the predictive skill of extreme precipitation in the region. The potential and real predictive skill of the frequency of extreme rainfall is then evaluated, finding evidence indicating that mechanisms of climate variability at one timescale contribute to the predictability at another scale, i.e., taking into account the interference of different potential sources of predictability at different timescales increases the predictive skill. This fact is in agreement with the Cross-timescale Interference Conjecture proposed in the first part of the thesis. At seasonal scale, a combination of those weather types tends to outperform all the other potential predictors explored, i.e., sea surface temperature patterns, phases of the Madden-Julian Oscillation, and combinations of both. Spatially averaged Kendall’s τ improvements of 43% for the potential predictability and 23% for realtime predictions are attained with respect to standard models considering sea-surface temperature fields alone. A new subseasonal-to-seasonal predictive methodology for extreme rainfall events is proposed, based on probability forecasts of seasonal sequences of these weather types. The cross-validated realtime skill of the new probabilistic approach, as measured by the Hit Score and the Heidke Skill Score, is on the order of twice that associated with climatological values. The approach is designed to offer useful subseasonal-to-seasonal climate information to decision-makers interested not only in how many extreme events will happen in the season, but also in how, when and where those events will probably occur. In order to gain further understanding about how the cross-timescale interference occurs, an externally-forced Lorenz model is used to explore the impact of different kind of forcings, at inter-annual and decadal scales, in the establishment of constructive interactions associated with the simulated “extreme events”. Using a wavelet analysis, it is shown that this simple model is capable of reproducing the same kind of cross-timescale structures observed in the wavelet power spectrum of the Niño3.4 index only when it is externally forced by both inter-annual and decadal signals: the annual cycle and a decadal forcing associated with the natural solar variability. The nature of this interaction is non-linear, and it impacts both mean and extreme values in the time series. No predictive power was found when using metrics like standard deviation and auto-correlation. Nonetheless, it was proposed that an early warning signal for occurrence of extreme rainfall in SESA may be possible via a continuous monitoring of relative phases between the cross-timescale leading components.

Abstract not available