Documentary sources to investigate multidecadal variability of droughts

Droughts are probably the natural hazard with the highest socioeconomic impact. Simultaneously, they are a very complex phenomenon; they are triggered by a diversity of physical factors and occur at a variety of time scales. Consequently, the instrumental record currently available is too short and the characterization of its multidecadal variability requires the use of natural proxies (tree rings, sedimentary records) or documentary sources. In this paper we analyse three documentary sources with potential to analyse the long-term variability of droughts: chapter acts, logbooks and chronicles. The chapter acts recorded discussions and decisions made during the assemblies of the local authorities and provide continuous and direct evidence on drought impacts. They are especially useful to study droughts between the 15th and the 19th centuries in Europe and the 17th to 18th in the former colonies. Logbooks recorded the meteorological conditions and the incidents occurred during navigation. They provide indirect information through the circulation indices that can be very helpful to understand the mechanisms and teleconnections associated to droughts. Finally, the chronicles are historiographical documents describing political and social events. They are secondary sources and the references to climatic events are discontinuous, thus their analysis must be extremely careful, but they are especially useful to study specific drought events especially prior to 15th century when no other sources are available.

Numerical Simulations of Fog Events in Southern Portugal

This work aims at improving the knowledge on fog formation and its evolution in the Alentejo region (Portugal). For this purpose, brief regional fog climatology, essentially based on information from the Beja Air Base meteorological station, was produced and several numerical high resolution simulations were performed using the Meso-NH. The ECOCLIMAP database used to generate the model physiography was improved to include the Alqueva reservoir (~250 km2), filled in 2003. The model results were compared with surface and satellite observations, showing good agreement in terms of fog occurrence and persistence. Various forcing mechanisms for formation, development, and dissipation of fog were identified, confirming the influence of two small mountains that block the moist air from the Atlantic Ocean, preventing the fog from reaching innermost regions. The introduction of the Alqueva large reservoir induces changes in the landscape and environment. The effects of the water vapour addition and of the changes in mass and energy surface fluxes on fog formation and evolution were studied. It was found that the reservoir may have a direct impact on fog formation over the lake and its vicinity. Depending on the large scale meteorological conditions, their influence can be both positive and negative, in terms of spatial coverage and temporal persistence.

Statistical analysis and modelling of weather radar beam propagation conditions in the Po Valley (Italy)

Ground clutter caused by anomalous propagation (anaprop) can affect seriously radar rain rate estimates, particularly in fully automatic radar processing systems, and, if not filtered, can produce frequent false alarms. A statistical study of anomalous propagation detected from two operational C-band radars in the northern Italian region of Emilia Romagna is discussed, paying particular attention to its diurnal and seasonal variability. The analysis shows a high incidence of anaprop in summer, mainly in the morning and evening, due to the humid and hot summer climate of the Po Valley, particularly in the coastal zone. Thereafter, a comparison between different techniques and datasets to retrieve the vertical profile of the refractive index gradient in the boundary layer is also presented. In particular, their capability to detect anomalous propagation conditions is compared. Furthermore, beam path trajectories are simulated using a multilayer ray-tracing model and the influence of the propagation conditions on the beam trajectory and shape is examined. High resolution radiosounding data are identified as the best available dataset to reproduce accurately the local propagation conditions, while lower resolution standard TEMP data suffers from interpolation degradation and Numerical Weather Prediction model data (Lokal Model) are able to retrieve a tendency to superrefraction but not to detect ducting conditions. Observing the ray tracing of the centre, lower and upper limits of the radar antenna 3-dB half-power main beam lobe it is concluded that ducting layers produce a change in the measured volume and in the power distribution that can lead to an additional error in the reflectivity estimate and, subsequently, in the estimated rainfall rate.

Effects of anomalous propagation conditions on weather radar observations

In this chapter our objective is to provide an overview of the effects of anomalous propagation conditions on weather radar observations, based mostly on studies performed by the authors during the last decade, summarizing results from recent publications, presentations, or unpublished material. We believe this chapter may be useful as an introductory text for graduate students, or researchers and practitioners dealing with this topic. Throughout the text a spherical symmetric atmosphere is assumed and the focus is on the occurrence of ground and sea clutter and subsequent problems for weather radar applications. Other related topics such as long-path, over-the-horizon propagation and detection of radar targets (either clutter or weather systems) at long ranges is not considered here; however readers should be aware of the potential problems these phenomena may have as range aliasing may cause these echoes appear nearer than they are ¿ for more details see the discussion about second trip echoes by Zrnic, this volume.

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.

Solar radiation use efficiency by soybean under field conditions in the Amazon region

The objective of this work was to evaluate the efficiency of soybean (Glycine max) in intercepting and using solar radiation under natural field conditions, in the Amazon region, Brazil. The meteorological data and the values of soybean growth and leaf area were obtained from an agrometeorological experiment carried out in Paragominas, Pará state, during 2007 and 2008. The radiation use efficiency (RUE) was obtained from the ratio between the above-ground biomass production and the intercepted photosynthetically active radiation (PAR) accumulated to 99 and 95 days after sowing, in 2007 and 2008, respectively. Climatic conditions during the experiment were very distinct, with reduction in rainfall in 2007, which began during the soybean mid-cycle, due to the El Niño phenomenon. An important reduction in the leaf area index and biomass production was observed during 2007. Under natural field conditions in the Amazon region, the values of RUE were 1.46 and 1.99 g MJ-1 PAR in the 2007 and 2008 experiments, respectively. The probable reason for the differences found between these years might be associated to the water restriction in 2007 coupled with the higher air temperature and vapor pressure deficit, and also to the increase in the fraction of diffuse radiation that reached the land surface in 2008.

Simulation of soybean growth and yield under northeastern Amazon climatic conditions

The objective of this work was to parameterize, calibrate, and validate a new version of the soybean growth and yield model developed by Sinclair, under natural field conditions in northeastern Amazon. The meteorological data and the values of soybean growth and leaf area were obtained from an agrometeorological experiment carried out in Paragominas, PA, Brazil, from 2006 to 2009. The climatic conditions during the experiment were very distinct, with a slight reduction in rainfall in 2007, due to the El Niño phenomenon. There was a reduction in the leaf area index (LAI) and in biomass production during this year, which was reproduced by the model. The simulation of the LAI had root mean square error (RMSE) of 0.55 to 0.82 m² m-2, from 2006 to 2009. The simulation of soybean yield for independent data showed a RMSE of 198 kg ha-1, i.e., an overestimation of 3%. The model was calibrated and validated for Amazonian climatic conditions, and can contribute positively to the improvement of the simulations of the impacts of land use change in the Amazon region. The modified version of the Sinclair model is able to adequately simulate leaf area formation, total biomass, and soybean yield, under northeastern Amazon climatic conditions.

Climate variations in greenhouse cultivated with gerbera and relationship with external conditions

Black meshes used in greenhouses provide shade to plants, affecting photosynthesis and presenting certain properties that change the microclimatic conditions in these environments. The objective of this study was to evaluate the variation in climate elements in greenhouse cultivated with gerbera (Gerbera jamesonii, Vr. Rambo) in relation to external conditions and the reference evapotranspiration (ETo) at Teresina, State of Piauí, Brazil. The measurements were obtained from July to October 2007 by an automatic data acquisition system installed inside and outside the greenhouse. The global solar radiation, evapotranspiration, precipitation, temperature, relative humidity, and wind speed were estimated. The results showed that major effect of the shading occurred on the mean air temperature during the 120 days, making it higher than the external environment. Inside the greenhouse, mean values of relative air humidity, reference evapotranspiração, global solar radiation and wind speed were lower compared to those outside the greenhouse.

A Study on the Transport and Distribution of Atmospheric Aerosols and its Influence on Regional Meteorological Parameters over the Indian Subcontinent

This doctoral thesis addresses the growing concern about the significant changes in the climatic and weather patterns due to the aerosol loading that have taken place in the Indo Gangetic Plain(IGP)which includes most of the Northern Indian region. The study region comprises of major industrial cities in India (New Delhi, Kanpur, Allahabad, Jamshedpur and Kolkata). Northern and central parts of India are one of the most thickly populated areas in the world and have the most intensely farmed areas. Rapid increase in population and urbanization has resulted in an abrupt increase in aerosol concentrations in recent years. The IGP has a major source of coal; therefore most of the industries including numerous thermal power plants that run on coal are located around this region. They inject copious amount of aerosols into the atmosphere. Moreover, the transport of dust aerosols from arid locations is prevalent during the dry months which increase the aerosol loading in theatmosphere. The topography of the place is also ideal for the congregation of aerosols. It is bounded by the Himalayas in the north, Thar Desert in the west, the Vindhyan range in the south and Brahmaputra ridge in the east. During the non‐monsoon months (October to May) the weather in the location is dry with very little rainfall. Surface winds are weak during most of the time in this dry season. The aerosols that reach the location by means of long distance transport and from regional sources get accumulated under these favourable conditions. The increase in aerosol concentration due to the complex combination of aerosol transport and anthropogenic factors mixed with the contribution from the natural sources alters the optical properties and the life time of clouds in the region. The associated perturbations in radiative balance have a significant impact on the meteorological parameters and this in turn determines the precipitation forming process. Therefore, any change in weather which disturbs the normal hydrological pattern is alarming in the socio‐economic point of view. Hence, the main focus of this work is to determine the variation in transport and distribution of aerosols in the region and to understand the interaction of these aerosols with meteorological parameters and cloud properties.

Sensitivity of tropospheric forecasts to stratospheric initial conditions

A number of recent papers in the atmospheric science literature have suggested that a dynamical link exists between the stratosphere and troposphere. Numerical modelling studies have shown that the troposphere has a time-mean response to changes to the stratospheric climatological state. In this study the response of the troposphere to an imposed transient stratospheric change is examined. The study uses a high horizontal and vertical resolution numerical weather-prediction model. Experiments compare the tropospheric forecasts of two medium-range forecast ensembles which have identical tropospheric initial conditions and different stratospheric initial conditions. In three case studies described here, stratospheric initial conditions have a statistically significant impact on the tropospheric flow. The mechanism for this change involves, in its most basic step, a change to tropospheric synoptic-scale systems. A consistent change to the tropospheric synoptic-scale systems occurs in response to the stratospheric initial conditions. The aggregated impact of changes to individual synoptic systems maps strongly onto the structure of the Arctic Oscillation, particularly over the North Atlantic storm track. The relationship between the stratosphere and troposphere, while apparent in Arctic Oscillation diagnostics, does not occur on coherent, hemispheric scales.

Layers of nocturnal insect migrants at high-altitude: the influence of atmospheric conditions on their formation

1 Radar studies of nocturnal insect migration have often found that the migrants tend to form well-defined horizontal layers at a particular altitude. 2 In previous short-term studies, nocturnal layers were usually observed to occur at the same altitude as certain meteorological features, most notably at the altitudes of temperature inversions or nocturnal wind jets. 3 Statistical analyses are presented of four years’ data that compared the presence, sharpness and duration of nocturnal layer profiles (observed using continuously-operating entomological radar) with meteorological variables at typical layer altitudes over the UK. 4 Analysis of these large datasets demonstrated that temperature was the foremost meteorological factor persistently associated with the presence and formation of longer-lasting and sharper layers of migrating insects over southern UK.

Emergence of Chenopodium album and Stellaria media of different origins under different climatic conditions

The emergence behaviour of weed species in relation to cultural and meteorological events was studied. Dissimilarities between populations in dormancy and germination ecology, between-year maturation conditions and seed quality and burial site climate all contribute to potentially unpredictable variability. Therefore, a weed emergence data set was produced for weed seeds of Stellaria media and Chenopodium album matured and collected from three populations (Italy, Sweden and UK). The seeds were collected in two consecutive seasons (1999 and 2000) and subsequently buried in the autumn of the same year of maturation in eight contrasting climatic locations throughout Europe and the USA. The experiment sought to explore and explain differences between the three populations in their emergence behaviour. Evidence was demonstrated of synchrony in the timing of the emergence of different populations of a species at a given burial site. The relative magnitudes of emergence from the three populations at a given burial site in a given year were generally similar across all the burial sites in the study. The resulting data set was also used to construct a simple weed emergence model, which was tested for its application to the range of different burial environments and populations. The study demonstrated the possibility of using a simple thermal time-based model to describe part of the emergence behaviour across different burial sites, seed populations and seasons, and a simple winter chilling relationship to adjust for the magnitude of the flush of emergence at a given burial site. This study demonstrates the possibility of developing robust generic models for simple predictions of emergence timing across populations.

Wind generation's contribution to supporting peak electricity demand: meteorological insights

Wind generation’s contribution to meeting extreme peaks in electricity demand is a key concern for the integration of wind power. In Great Britain (GB), robustly assessing this contribution directly from power system data (i.e. metered wind-supply and electricity demand) is difficult as extreme peaks occur infrequently (by definition) and measurement records are both short and inhomogeneous. Atmospheric circulation-typing combined with meteorological reanalysis data is proposed as a means to address some of these difficulties, motivated by a case study of the extreme peak demand events in January 2010. A preliminary investigation of the physical and statistical properties of these circulation types suggests that they can be used to identify the conditions that are most likely to be associated with extreme peak demand events. Three broad cases are highlighted as requiring further investigation. The high-over-Britain anticyclone is found to be generally associated with very low winds but relatively moderate temperatures (and therefore moderate peak demands, somewhat in contrast to the classic low-wind cold snap that is sometimes apparent in the literature). In contrast, both longitudinally extended blocking over Scotland/Scandinavia and latitudinally extended troughs over western Europe appear to be more closely linked to the very cold GB temperatures (usually associated with extreme peak demands). In both of these latter situations, wind resource averaged across GB appears to be more moderate.