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Gamma, lognormal and Weibull distributions are most commonly used in modeling asymmetric data coming from the areas of life testing and reliability engineering. In this study, we deal with the prob...

Portugal is recurrently affected by large wildfire events that have serious impacts at the socio-economic and environmental levels and dramatic consequences associated with the loss of lives and the destruction of the landscape. Accordingly, seasonal forecasts are required to assist fire managers, thus contributing to alter the historically based purely reactive response. In this context, we present and discuss a statistical model to estimate the probability that the total burned area during summer will exceed a given threshold. The statistical model uses meteorological information that rates the accumulation of thermal and vegetation stress. Outlooks for the 39-year study period (1980–2018) show that, when the statistical model is applied from 26 May to 30 June, out of the six severe years, only one year is not anticipated as potentially severe and, out of the six weak years, only one is not anticipated as potentially weak. The availability of outlooks of wildfire potential with an anticipation of up to 1 month before the starting of the fire season, such as the one proposed here, may serve to provide clear directions for the fire community when planning prevention and combating fire events.

Assessing the exposure of an individual or population is a challenging statistical task. Borrowing from the concept of the genome in genetics, recently the concept of an exposome has emerged to help explain the complexities of exposure assessment and guide research in this area. According to the Centers for Disease Control and Prevention, “The exposome can be defined as the measure of all the exposures of an individual in a lifetime and how those exposures relate to health.” The simplest algorithm to estimate concentration at an unmonitored locations is to use the measured value for the nearest monitoring location. The estimated nearest-neighbor concentration surface is then piecewice constant over Voronoi polygons around the monitors. This is a special case of K-nearest neighbor (KNN) interpolation. Inverse distance weighting (also known as kernel smoothing) is an extension to KNN that weights locations according to their distance from the prediction location.

Across the globe, human activities have been gaining importance relatively to climate and ecology as the main controls on fire regimes and consequently human activity became an important driver of the frequency, extent and intensity of vegetation burning worldwide. Our objective in the present study is to look for weekly cycles in vegetation fire activity at global scale as evidence of human agency, relying on the original MODIS active fire detections at 1 km spatial resolution (MCD14ML) and using novel statistical methodologies to detect significant periodicities in time series data. We tested the hypotheses that global fire activity displays weekly cycles and that the weekday with the fewest fires is Sunday. We also assessed the effect of land use and land cover on weekly fire cycle significance by testing those hypotheses separately for the Villages, Settlements, Croplands, Rangelands, Seminatural, and Wildlands anthromes. Based on a preliminary data analysis of the daily global active fire counts periodogram, we developed an harmonic regression model for the mean function of daily fire activity and assumed a linear model for the de-seasonalized time series. For inference purposes, we used a Bayesian methodology and constructed a simultaneous 95% credible band for the mean function. The hypothesis of a Sunday weekly minimum was directly investigated by computing the probabilities that the mean functions of every weekday (Monday to Saturday) are inside the credible band corresponding to mean Sunday fire activity. Since these probabilities are small, there is statistical evidence of significantly fewer fires on Sunday than on the other days of the week. Cropland, rangeland, and seminatural anthromes, which cover 70% of the global land area and account for 94% of the active fires analysed, display weekly cycles in fire activity. Due to lower land management intensity and less strict control over fire size and duration, weekly cycles in Rangelands and Seminatural anthromes, which jointly account for 53.46% of all fires, although statistically significant are weaker than those detected in Croplands.

The Portuguese National Statistical Institute is responsible for estimating and publishing quarterly labour market figures at national level for both NUTS I and NUTS II regions. Over recent years it has become increasinglyimportant to identify these figures at more disaggregated levels. However, based on the established direct estimation method, it is not possible to produce satisfactorily precise estimates at higher spatial resolutions. From the 4th quarter of 2014 onwards, all the sampling units, namely the residential buildings, of the Portuguese Labour Force Survey (PLFS) were georeferenced. To take full advantage of this information, Pereira et al. (2019) proposed applying a spatial marked point process approach to unemployment estimation, in which the estimation of the unemployment intensity becomes the focal point. There, the sampling units were assumed to be a realization of a spatial point process, specifically a log Gaussian Cox process, with the number of unemployed in each unit being their respective marks. Recently, further information on the geo-referenced locations of all units of the population, namely all residential buildings in the national territory, became available. Consequently, it is no longer necessary to model the spatial configuration of the units of the population. Thus, we propose a new point referenced model for the marks based on the sampled units and extrapolate this to all units of the population. As expected, this extra information, and as a consequence the new model itself, produce estimates with higher precision.

In this work we reassess the impacts of blocking patterns on precipitation regimes in the Iberian Peninsula, distinguishing between north Atlantic and European blocking. For this we take full advantage of the recently developed high-resolution datasets for the Iberian countries. Precipitation anomalies during blocking events obtained with this dataset allow a much finer regional characterization of the impacts in both average and extreme daily precipitation, particularly when compared to widely used low-resolution reanalysis datasets. Blocked patterns induce a negative-positive dipole of precipitation anomalies from northwest to southeast Iberia. Increases are widespread during Atlantic blocks and pronounced in southern and eastern areas of Iberia, while during European blocks they are more spatially restricted, with increases above 50 % in coastal Mediterranean areas, which represents a considerable fraction of the annual precipitation. Blocking impacts in precipitation are nearly opposite to those found during strong zonal flow situations, but there are also some asymmetries in the precipitation responses. A significant increase in cyclones and cut-off lows frequency southwards of blocking structures is related to precipitation excesses over southern and eastern areas, where dynamical factors and local processes play a crucial role. On the contrary, precipitation deficits in northwest Iberia during blocking episodes are better explained by a reduction in north Atlantic frontal activity and simultaneous decreases in large-scale moisture advection towards northern Iberia. We show that these anomalies during blocking result from changes in precipitation amount rather than from increases in rainy days, pointing to more extreme rainfall regimes, particularly in southeastern Iberia. Finally, an Extreme Value Analysis was performed, fitting Generalized Pareto Distributions to precipitation extremes. Results show that the different extreme precipitation regimes of northwest and Mediterranean regions are partially determined by opposite anomalies of the zonal flow. Thus, heavy precipitation events in Mediterranean areas are usually short-lived and frequently associated with blocking conditions, while in northwest Iberia the total accumulations during rainfall episodes are more important for triggering extreme events and they are mainly related to strong westerly flows.

From the fourth quarter of 2014, the Portuguese Labour Force Survey (PLFS) started geo-referencing the sampling units, namely the dwellings, in which the surveys are carried out. This opens new possibilities in analysing and estimating unemployment and its spatial distribution across any region by employing point referenced methods and models. According to a preestablished sampling criteria, the labour force survey selects a certain number of dwellings from across the nation to study, and establishes the number of unemployed people in each dwelling. Based on this survey, the National Statistical Institute of Portugal presently uses direct estimation methods to estimate the national unemployment figures. Recently however, there has been increased interest in estimating these figures in smaller areas. Due to reduced sampling sizes in small areas, direct estimation methods tend to produce fairly large sampling variations. Therefore, model based methods should be favoured as these tend to borrow strength from area to area by making use of the areal dependence. These model based methods tend to use areal counting processes as models and typically introduce spatial dependence through the model parameters using a latent random effect. In this paper, we suggest using point referenced models as an alternative to the traditional small area estimation methods for unemployment estimation. Specifically, we model the spatial distribution of residential buildings across Portugal using a log Gaussian Cox process, and the number of unemployed people per residential unit as a mark attached to these random points. Thus, the main focus of the study is to model the spatial intensity function of this marked point process. The number of unemployed people in any region can then be estimated using a proper functional of this marked point process. The principal objective of this point referenced method for unemployment estimation is to produce reliable estimates at higher spatial resolutions, and at the same time to incorporate into the model any available auxiliary information of the residential units, such as mean age or education level as compared to areal unit averages used in small area estimation.