Kernel-based Mapping of Meteorological Fields in Complex Orography

Due to the advances in sensor networks and remote sensing technologies, the acquisition and storage rates of meteorological and climatological data increases every day and ask for novel and efficient processing algorithms. A fundamental problem of data analysis and modeling is the spatial prediction of meteorological variables in complex orography, which serves among others to extended climatological analyses, for the assimilation of data into numerical weather prediction models, for preparing inputs to hydrological models and for real time monitoring and short-term forecasting of weather.In this thesis, a new framework for spatial estimation is proposed by taking advantage of a class of algorithms emerging from the statistical learning theory. Nonparametric kernel-based methods for nonlinear data classification, regression and target detection, known as support vector machines (SVM), are adapted for mapping of meteorological variables in complex orography.With the advent of high resolution digital elevation models, the field of spatial prediction met new horizons. In fact, by exploiting image processing tools along with physical heuristics, an incredible number of terrain features which account for the topographic conditions at multiple spatial scales can be extracted. Such features are highly relevant for the mapping of meteorological variables because they control a considerable part of the spatial variability of meteorological fields in the complex Alpine orography. For instance, patterns of orographic rainfall, wind speed and cold air pools are known to be correlated with particular terrain forms, e.g. convex/concave surfaces and upwind sides of mountain slopes.Kernel-based methods are employed to learn the nonlinear statistical dependence which links the multidimensional space of geographical and topographic explanatory variables to the variable of interest, that is the wind speed as measured at the weather stations or the occurrence of orographic rainfall patterns as extracted from sequences of radar images. Compared to low dimensional models integrating only the geographical coordinates, the proposed framework opens a way to regionalize meteorological variables which are multidimensional in nature and rarely show spatial auto-correlation in the original space making the use of classical geostatistics tangled.The challenges which are explored during the thesis are manifolds. First, the complexity of models is optimized to impose appropriate smoothness properties and reduce the impact of noisy measurements. Secondly, a multiple kernel extension of SVM is considered to select the multiscale features which explain most of the spatial variability of wind speed. Then, SVM target detection methods are implemented to describe the orographic conditions which cause persistent and stationary rainfall patterns. Finally, the optimal splitting of the data is studied to estimate realistic performances and confidence intervals characterizing the uncertainty of predictions.The resulting maps of average wind speeds find applications within renewable resources assessment and opens a route to decrease the temporal scale of analysis to meet hydrological requirements. Furthermore, the maps depicting the susceptibility to orographic rainfall enhancement can be used to improve current radar-based quantitative precipitation estimation and forecasting systems and to generate stochastic ensembles of precipitation fields conditioned upon the orography.

Intra-individual variation in body temperature and pectoral muscle size in nestling Alpine swifts Apus melba in response to an episode of inclement weather

The Alpine swift (Apus melba) forages on insects caught exclusively on the wing, implying that dependent nestlings face acute food shortage in periods of cold and rainy weather. Therefore, there should be strong selection on nestling swifts to evolve physiological strategies to cope with periods of undernutrition. We have investigated intra-individual changes in nestling pectoral muscle and body temperature in response to a 1-week period of inclement weather. The pectoral muscle is the largest reserves of proteins, and nestlings have to devote a large amount of energy in the maintenance of body temperature. The results show that nestling pectoral muscle size and body temperature were significantly reduced during the episode of inclement weather. Assuming that these physiological changes are adaptive, our study suggests that nestling swifts spare energy by a pronounced reduction (up to 18 degrees C) in body temperature and use proteins from the pectoral muscle as a source of extra energy to survive prolonged periods of fasting.

Demographic effects of extreme winter weather in the barn owl.

Extreme weather events can lead to immediate catastrophic mortality. Due to their rare occurrence, however, the long-term impacts of such events for ecological processes are unclear. We examined the effect of extreme winters on barn owl (Tyto alba) survival and reproduction in Switzerland over a 68-year period (approximately 20 generations). This long-term data set allowed us to compare events that occurred only once in several decades to more frequent events. Winter harshness explained 17 and 49% of the variance in juvenile and adult survival, respectively, and the two harshest winters were associated with major population crashes caused by simultaneous low juvenile and adult survival. These two winters increased the correlation between juvenile and adult survival from 0.63 to 0.69. Overall, survival decreased non-linearly with increasing winter harshness in adults, and linearly in juveniles. In contrast, brood size was not related to the harshness of the preceding winter. Our results thus reveal complex interactions between climate and demography. The relationship between weather and survival observed during regular years is likely to underestimate the importance of climate variation for population dynamics.

Sudden weather deterioration but not brood size affects baseline corticosterone levels in nestling Alpine swifts.

While evidence is accumulating that stress-induced glucocorticoid responses help organisms to quickly adjust their physiology and behaviour to life-threatening environmental perturbations, the function and the ecological factors inducing variation in baseline glucocorticoid levels remain poorly understood. In this study we investigated the effects of brood size by experimentally manipulating the number of nestlings per brood and the effect of weather condition on baseline corticosterone levels of nestling Alpine swifts (Apus melba). We also examined the potential negative consequences of an elevation of baseline corticosterone on nestling immunity by correlating corticosterone levels with ectoparasite intensity and the antibody production towards a vaccine. Although nestlings reared in enlarged broods were in poorer condition than nestlings reared in reduced broods, they showed similar baseline corticosterone levels. In contrast, nestling baseline corticosterone levels were higher immediately after cold and rainy episodes with strong winds. Neither nestling infestation rate by ectoparastic flies nor nestling antibody production against a vaccine was correlated with baseline corticosterone levels. Thus, our results suggest that altricial Alpine swift nestlings can quickly modulate baseline corticosterone levels in response to unpredictable variations in meteorological perturbation but not to brood size which may be associated with the degree of sibling competition. Apparently, short-term elevations of baseline corticosterone have no negative effects on nestling immunocompetence.

Influence of weather, rank, and home advantage on football outcomes in the gulf region.

PURPOSE: The objective of this study was to investigate the effects of weather, rank, and home advantage on international football match results and scores in the Gulf Cooperation Council (GCC) region. METHODS: Football matches (n = 2008) in six GCC countries were analyzed. To determine the weather influence on the likelihood of favorable outcome and goal difference, generalized linear model with a logit link function and multiple regression analysis were performed. RESULTS: In the GCC region, home teams tend to have greater likelihood of a favorable outcome (P < 0.001) and higher goal difference (P < 0.001). Temperature difference was identified as a significant explanatory variable when used independently (P < 0.001) or after adjustment for home advantage and team ranking (P < 0.001). The likelihood of favorable outcome for GCC teams increases by 3% for every 1-unit increase in temperature difference. After inclusion of interaction with opposition, this advantage remains significant only when playing against non-GCC opponents. While home advantage increased the odds of favorable outcome (P < 0.001) and goal difference (P < 0.001) after inclusion of interaction term, the likelihood of favorable outcome for a GCC team decreased (P < 0.001) when playing against a stronger opponent. Finally, the temperature and wet bulb globe temperature approximation were found as better indicators of the effect of environmental conditions than absolute and relative humidity or heat index on match outcomes. CONCLUSIONS: In GCC region, higher temperature increased the likelihood of a favorable outcome when playing against non-GCC teams. However, international ranking should be considered because an opponent with a higher rank reduced, but did not eliminate, the likelihood of a favorable outcome.

The relationship between Lamb weather types and long-term changes in flood frequency, River Eden, UK

Research has found that both flood magnitude and frequency in the UK may have increased over the last five decades. However, evaluating whether or not this is a systematic trend is difficult because of the lack of longer records. Here we compile and consider an extreme flood record that extends back to 1770. Since 1770, there have been 137 recorded extreme floods. However, over this period, there is not a unidirectional trend of rising extreme flood risk over time. Instead, there are clear flood-rich and flood-poor periods. Three main flood-rich periods were identified: 18731904, 19231933, and 1994 onwards. To provide a first analysis of what is driving these periods, and given the paucity of more sophisticated datasets that extend back to the 18th century, objective Lamb weather types were used. Of the 27 objective Lamb weather types, only 11 could be associated with the extreme floods during the gauged period, and only 5 of these accounted for > 80% of recorded extreme floods The importance of these five weather types over a longer timescale for flood risk in Carlisle was assessed, through calculating the proportion of each hydrological year classified as being associated with these flood-generating weather types. Two periods clearly had more than the average proportions of the year classified as one of the flood causing weather types; 19001940 and 19832007; and these two periods both contained flood-rich hydrological records. Thus, the analysis suggests that systematic organisation of the North Atlantic climate system may be manifest as periods of elevated and reduced flood risk, an observation that has major implications for analyses that assume that climatic drivers of flood risk can be either statistically stationary or are following a simple trend. Copyright (c) 2011 Royal Meteorological Society