Accurate weather forecasts are vital to air combat operations. Quantitative assessments of forecasts and their operational impacts are essential to improving weather support for war fighters. We adapted an existing U.S. Navy, web-based, near-real time system for collecting and analyzing data on the performance and operational impacts of military forecasts. We used the adapted system to collect and analyze data on Air Force Weather (AFW) forecasts, and the planning and execution of flying operations, at six Air Combat Command and Pacific Air Forces bases. We analyzed the data to develop quantitative metrics of forecast performance and operational impacts. Our results indicate that planning weather forecasts (PWFs) have a higher potential for making positive contributions to air operations than do mission execution forecasts (MEFs). This is notable because AFW units spend significantly less time developing PWFs than MEFs. Surface visibility, cloud ceilings, and cloud layers caused most negative mission impacts, indicating these phenomena should be a focus of future research and training. We found high levels of mission success even when forecasts were inaccurate, perhaps due to aircrew and mission flexibility. Our analyses revealed a need for improved education of flying units on the nature and availability of AFW products.

No description available.

No description available.

XTREND consists of the following methodical Parts. Time interval extraction (Part 1) to analyse different parts of a time series; extreme events detection (Part 2) with robust smoothing; magnitude classification (Part 3) by hand; occurrence rate estimation (Part 4) with kernel functions; bootstrap simulations (Part 5) to estimate confidence bands around the occurrence rate. You work interactively with XTREND (parameter adjustment, calculation, graphics) to acquire more intuition for your data. Although, using “normal” data sizes (less than, say, 1000) and modern machines, the computing time seems to be acceptable (less than a few minutes), parameter adjustment should be done carefully to avoid spurious results or, on the other hand, too long computing times. This Report helps you to achieve that. Although it explains the statistical concepts used, this is generally done with less detail, and you should consult the given references (which include some textbooks) for a deeper understanding.

The purpose of this research is to analyse pedestrian volumes on multi-use trails in Winnipeg, Canada. The research methodology consisted of collecting continuous automated pedestrian count volumes at seven locations on four multi-use trails in Winnipeg from January 1, 2014 to December 31, 2014. An average pedestrian volume was calculated for each count site over annual, seasonal, and monthly time periods. Pedestrian volumes were found to vary consistently by month of year and hour of day. Day-of-week patterns were not consistent in terms of pedestrian volume. There was a negative relationship between pedestrian volume and rainfall volume and duration, and average daily wind speed. There was a positive non-linear relationship between pedestrian volume and maximum daily temperature. While pedestrian volume correlates with weather factors, variability remains. This suggests that weather analysis may be useful as a complement, but not a replacement of traditional temporal analysis for estimation of pedestrian volumes. / May 2016

Our society needs to continuously perform transports of people and goods toensure that business is kept going. Every disturbance in the transportation ofpeople or goods affects the commerce and may result in economical losses forcompanies and society. Severe traffic accidents cause personal tragedies forpeople involved as well as huge costs for the society. Therefore the roadauthorities continuously try to improve the traffic safety. Traffic safety may beimproved by reduced speeds, crash safe cars, tires with better road grip andimproved road maintenance. The environmental effects from roadmaintenance when spreading de-icing chemicals need to be considered, i.e.how much chemicals should be used to maximize traffic safety and minimizethe environmental effects. Knowledge about the current and upcoming roadcondition can improve the road maintenance and hence improve traffic safety.This thesis deals with sensors and models that give information about the roadcondition.The performance and reliability of existing surface mounted sensors wereexamined by laboratory experiments. Further research involved field studies tocollect data used to develop surface status models based on road weather dataand camera images. Field studies have also been performed to find best usageof non intrusive IR technology.The research presented here showed that no single sensor give enoughinformation by itself to safely describe the road condition. However, the resultsindicated that among the traditional road surface mounted sensors only theactive freezing point sensor gave reliable freezing point results. Furtherresearch aimed to find a model that could classify the road condition indifferent road classes from existing road weather sensor data and road images.The result was a model that accurately could distinguish between the roadconditions dry, wet, snowy and icy. These road conditions are clearly dissimilarand are therefore used as the definition of the road classes used in this thesis.Finally, results from research regarding remote sensing IR technology showedthat it significantly improves knowledge of the road temperature and statuscompared to data from surface mounted sensors. / Vårt samhälle bygger på att det finns effektiva transporter av människor ochvaror för att säkerställa att samhällets funktioner fungerar och att företagenkan genomföra sina affärer. Störningar i transporterna av människor och varorpåverkar handeln och kan leda till ekonomiska förluster för både företag ochvårt samhälle. Allvarliga trafikolyckor orsakar personliga tragedier för deinblandade samt stora kostnader för samhället. Det är med denna bakgrundsom vägmyndigheterna kontinuerligt arbetar med att förbättratrafiksäkerheten. Trafiksäkerheten kan förbättras genom att minskahastigheterna, se till att bilarna blir krocksäkra, krav på däck med bättreväggrepp och ett bättre vägunderhåll. Miljöeffekterna från vinterväghållningdär avisningsmedel sprids på vägarna måste beaktas, d.v.s. hur mycketkemikalier bör användas för att maximera trafiksäkerheten och minimeramiljöpåverkan. Denna avhandling handlar om sensorer och modeller som gerinformation om väglaget. En kunskap om aktuellt och kommande väglag kanförbättra väghållningen och därmed öka trafiksäkerheten.I avhandlingen har prestanda och tillförlitlighet hos befintliga vägmonteradesensorer granskats i laboratorieexperiment. Data från fältstudier har använtsför att utveckla modeller som kan ge information om vägytans status baseratpå meteorologiska mätdata och kamerabilder. Det har också genomförtsfältstudier för att utforska den fördelaktigaste användningen av beröringsfriinfraröd sensorteknik.Den forskning som presenteras här visar att ingen enskild givare ger tillräckliginformation för att säkert beskriva väglaget. Från de traditionella ytmonteradesensorerna drogs slutsatsen att den aktiva fryspunktsgivaren gav de mesttillförlitliga fryspunktsresultaten. Det vidare arbetet handlade om att hitta enmodell som skulle kunna klassificera vägförhållanden i olika vägklassergenom att utnyttja information från befintliga sensorer och kamerabilder.Detta arbete resulterade i en modell som tillförlitligt kan särskilja väglagentorr, våt, snöig och isig. Dessa väglag är väsentligt olika och har därför valtssom väglagsklasser i denna avhandling. Under en säsong genomfördes ävenfältförsök med beröringsfri infraröd mätteknik där det visade sig att denberöringsfria teknologin förbättrar kunskapen om vägbanans temperatur och vägbanans status.

Diarrhea is the second leading cause of death in children under-5; it kills more children than HIV/AIDS, measles, and malaria combined. Despite this significant health burden, our ability to anticipate and prepare for diarrhea outbreaks remains limited. Precipitation and temperature variability have been shown to affect diarrhea dynamics and therefore contribute to outbreak predictions, but the observed environment-diarrhea relationships are complex and context-specific, depending on local pathogen distribution, host population behavior, and physical environments. To date, studies in sub-Saharan Africa, where the burden of under-5 diarrhea is particularly high, are limited due to sparse diarrheal disease surveillance data. In this dissertation, we leverage unique under-5 diarrhea incidence data to explore the effects of meteorological variability on childhood diarrhea incidence and develop a real-time forecasting system for diarrheal disease in Botswana, where diarrhea remains an important cause of childhood morbidity and mortality. The study focuses in Chobe District, which has an annual dry (April – September) and wet (October – March) season, during which the Chobe River, the primary source of drinking water in the region, floods. Weekly cases of under-5 diarrhea in Chobe District exhibit strong seasonal dynamics with biannual outbreaks occurring during the wet and the dry season. In Chapter 1, we show that wet season diarrhea incidence is strongly associated with increased rainfall and Escherichia coli concentrations in the Chobe River, while dry season incidence is associated with declines in Chobe River flood height and increased total suspended solids in the river. In Chapter 2, we confirm the existence of an El Niño-Southern Oscillation teleconnection with southern Africa by demonstrating that La Niña conditions are associated with cooler temperatures, increased rainfall, and higher flooding in Chobe District during the wet season. In turn, we show that La Niña conditions lagged 0-5 months are associated with higher than average incidence of under-5 diarrhea in the early wet season (December – February). In Chapter 4, we develop and test an epidemiological forecast model for childhood diarrheal disease in Chobe District. The prediction system uses a compartmental susceptible-infected-recovered-susceptible (SIRS) model coupled with Bayesian data assimilation to infer relevant epidemiological parameter values and generate retrospective forecasts. The model system accurately forecasts diarrhea outbreaks up to six weeks before the predicted peak of the outbreak, and prediction accuracy increases over the progression of the outbreak. Many forecasts generated by the model system are more accurate than predictions made using only historical data trends. This dissertation work is an important step forward in our understanding of the links between proximal and distal climatic variability and childhood diarrhea in arid regions of sub-Saharan Africa. Furthermore, it advances methods for generating accurate long-term and short-term forecasts of under-5 diarrhea. We demonstrates the potential use of ENSO data, which are publicly available, to prepare for and mitigate diarrheal disease outbreaks in a low-resource setting up to 5 months in advance, and develop a model-inference system that can generate accurate predictions during an outbreak. Deaths caused by diarrhea are preventable using low-cost treatments. Hence, accurate predictions of diarrhea outbreak magnitudes could help healthcare providers and public health officials prepare for and mitigate the significant morbidity and mortality resulting from diarrhea outbreaks.

People who live in rural areas in low-income countries not only live in poverty, they also have to deal with an extremely variable income. In the absence of a well-functioning credit market, these fluctuations can be costly for households. This study aims to provide knowledge to the cost of these fluctuations, more precisely the study aims to answer how transitory weather shocks in Kenya affect children´s cognitive ability. Where weather shocks are assumed to be aggregated shocks that temporarily change the productivity in districts. The result from the study shows that drought, which can be seen as a negative shock, decrease the cognitive ability of children 11 to 16. The effect is marginally significant at the 10 percent level. No significant effect on cognitive ability is found for children aged 6 to 10. The result for older children is robust to alternative specifications. The study does not show any conclusive evidence on different effects on how boys and girls are affected by droughts.

This thesis considers one-dimensional moving mesh (MM) methods coupled with semi-Lagrangian (SL) discretisations of partial differential equations (PDEs) for meteorological applications. We analyse a semi-Lagrangian numerical solution to the viscous Burgers’ equation when using linear interpolation. This gives expressions for the phase and shape errors of travelling wave solutions which decay slowly with increasing spatial and temporal resolution. These results are verified numerically and demonstrate qualitative agreement for high order interpolants. The semi-Lagrangian discretisation is coupled with a 1D moving mesh, resulting in a moving mesh semi-Lagrangian (MMSL) method. This is compared against two moving mesh Eulerian methods, a two-step remeshing approach, solved with the theta-method, and a coupled moving mesh PDE approach, which is solved using the MATLAB solver ODE45. At each time step of the SL method, the mesh is updated using a curvature based monitor function in order to reduce the interpolation error, and hence numerical viscosity. This MMSL method exhibits good stability properties, and captures the shape and speed of the travelling wave well. A meteorologically based 1D vertical column model is described with its SL solution procedure. Some potential benefits of adaptivity are demonstrated, with static meshes adapted to initial conditions. A moisture species is introduced into the model, although the effects are limited.

La connaissance du flux UltraViolet (UV) solaire et de sa variabilité dans le temps est un problème clé aussi bien dans le domaine de l’aéronomie qu’en physique solaire. Alors que l’extrême UV, entre 10 et 121 nm, est important pour la caractérisation de l’ionosphère, l’UV entre 121 et 300 nm l’est tout autant pour les modélisations climatiques. La mesure continue de l’irradiance dans l’UV est cependant une tâche ardue. En effet, les instruments spatiaux étant dans un environnement hostile se dégradent rapidement. De nombreux modèles basés sur des indices solaires sont alors utilisées lorsque peu de données sont disponibles. Pourtant, l’utilisation de ces indices ne permet pas d’atteindre aujourd’hui une précision suffisante pour les différentes applications en météorologie de l’espace. Comme alternative, ce travail de thèse met en avant l’utilisation de bandes passantes pour reconstruire l’irradiance solaire dans l’UV. En utilisant des méthodes d’analyse statistique multivariée, ce travail met tout d’abord en évidence la forte cohérence de la variabilité spectrale de l’irradiance dans l’UV, ainsi que ses principales caractéristiques. Une première étape consiste à utiliser des bandes passantes existantes afin de tester la faisabilité de notre approche : le flux UV peut ainsi être reconstruit avec une erreur relative d’environ 20%, une bien meilleure performance qu’avec l’utilisation d’indices solaires. Afin de limiter les problèmes de dégradation liés à l’utilisation des filtres, nous proposons un instrument d’un genre nouveau basé uniquement sur des détecteurs à larges bande interdite permettant de sélectionner une bande spectrale (notamment pour l’UV à partir de 120 nm). Un tel radiomètre permettrait de reconstruire les raies spectrales importantes pour la spécification de la thermosphère terrestre avec une bonne précision. Enfin, une modélisation de l’impact du flux UV solaire sur l’atmosphère de Ganymède est exposée. Les émissions atmosphériques pour quelques espèces sont alors calculées, afin de proposer quelques recommandations pour les futures missions pour Jupiter. / The knowledge of the solar spectral irradiance in the UV and its variation in time is a key problem in aeronomy but also in climatology and in solar physics. While the Extreme UV (10-121 nm) range is important for thermosphere/ionosphere specification, the Far UV and Middle UV ranges are essential for climate modelling. However, the continuous monitoring of the UV irradiance is a difficult task. Space instruments are indeed suffering from ageing but also signal contamination of many kinds. Because of the lack of long-term measurements of the whole UV range, most thermosphere/ionosphere and climate models rely today on proxies for the solar irradiance, which may however not reflect very well the variability. As an alternative, we proposed in this work to use a few radiometers with properly chosen passbands in order to reconstruct the solar UV irradiance. Using a multivariate statistical approach, we first characterize the high redundancy as well as the different features of the solar UV irradiance. With four passbands from already existing instrument, we test our concept : the solar UV flux is reconstructed with a relative error of about 20%. This work proposes then to define a new kind of instrument, which may use wide bandgap materials as detectors selecting moreover the spectral range without using filters. Filters are indeed very sensitive to the degradation. This new instrument could reconstruct very well some spectral lines important to the Earth thermosphere specification. This thesis finally proposes to model the impact of the solar UV flux on the atmosphere of Ganymede. We predict some atmospheric emissions in the framework of future space mission to Jupiter.