Trends of mean and extreme temperature indices since 1874 at low-elevation sites in the Southern Alps

We describe the recovery of three daily meteorological records for the southern Alps (Domodossola, Riva del Garda, and Rovereto), all starting in the second half of the nineteenth century. We use these new data, along with additional records, to study regional changes in the mean temperature and extreme indices of heat waves and cold spells frequency and duration over the period 1874–2015. The records are homogenized using subdaily cloud cover observations as a constraint for the statistical model, an approach that has never been applied before in the literature. A case study based on a record of parallel observations between a traditional meteorological window and a modern screen shows that the use of cloud cover can reduce the root-mean-square error of the homogenization by up to 30% in comparison to an unaided statistical correction. We find that mean temperature in the southern Alps has increased by 1.4°C per century over the analyzed period, with larger increases in daily minimum temperatures than maximum temperatures. The number of hot days in summer has more than tripled, and a similar increase is observed in duration of heat waves. Cold days in winter have dropped at a similar rate. These trends are mainly caused by climate change over the last few decades.

A Bayesian approach to estimating the regression coefficients of a multinomial logit model

A Bayesian approach to estimation of the regression coefficients of a multinominal logit model with ordinal scale response categories is presented. A Monte Carlo method is used to construct the posterior distribution of the link function. The link function is treated as an arbitrary scalar function. Then the Gauss-Markov theorem is used to determine a function of the link which produces a random vector of coefficients. The posterior distribution of the random vector of coefficients is used to estimate the regression coefficients. The method described is referred to as a Bayesian generalized least square (BGLS) analysis. Two cases involving multinominal logit models are described. Case I involves a cumulative logit model and Case II involves a proportional-odds model. All inferences about the coefficients for both cases are described in terms of the posterior distribution of the regression coefficients. The results from the BGLS method are compared to maximum likelihood estimates of the regression coefficients. The BGLS method avoids the nonlinear problems encountered when estimating the regression coefficients of a generalized linear model. The method is not complex or computationally intensive. The BGLS method offers several advantages over Bayesian approaches. ^

ERA-interim reanalysis debiased at FLUXNET sites

(preliminary) Exchanges of carbon, water and energy between the land surface and the atmosphere are monitored by eddy covariance technique at the ecosystem level. Currently, the FLUXNET database contains more than 500 sites registered and up to 250 of them sharing data (Free Fair Use dataset). Many modelling groups use the FLUXNET dataset for evaluating ecosystem model's performances but it requires uninterrupted time series for the meteorological variables used as input. Because original in-situ data often contain gaps, from very short (few hours) up to relatively long (some months), we develop a new and robust method for filling the gaps in meteorological data measured at site level. Our approach has the benefit of making use of continuous data available globally (ERA-interim) and high temporal resolution spanning from 1989 to today. These data are however not measured at site level and for this reason a method to downscale and correct the ERA-interim data is needed. We apply this method on the level 4 data (L4) from the LaThuile collection, freely available after registration under a Fair-Use policy. The performances of the developed method vary across sites and are also function of the meteorological variable. On average overall sites, the bias correction leads to cancel from 10% to 36% of the initial mismatch between in-situ and ERA-interim data, depending of the meteorological variable considered. In comparison to the internal variability of the in-situ data, the root mean square error (RMSE) between the in-situ data and the un-biased ERA-I data remains relatively large (on average overall sites, from 27% to 76% of the standard deviation of in-situ data, depending of the meteorological variable considered). The performance of the method remains low for the Wind Speed field, in particular regarding its capacity to conserve a standard deviation similar to the one measured at FLUXNET stations.

Impact studies from different factors on root development from Stevia rebaudiana bertoni

The demand for an alternative and a high potency sweetener to substitute sugar increases year in year out, more so as a high percentage of the world population becomes increasingly diabetic. The alternative natural sweetener at hand has been Stevia rebaudiana Bertoni, a plant species, native to Paraguay and a member of the family compositae. Stevia is usually propagated by stem cuttings due to low percentage (10 %) seed germination, thus limiting large scales cultivation. To cultivate this crop en mass therefore, there is need to evolve efficient rooting techniques. Influences of irradiation from light, and hormones on rooting have been reported. The rooting efficacy in stem cuttings of this crop under varying light wavelengths, dark and hormone factors was investigated. Evaluated parameters include- (i) day of root emergent, (ii) percentage of rooted cuttings, (iii) average number, (iv) length and (v) width, of roots. Analysis of variance at p<.05 revealed that the number, length and width, of roots differed significantly in each case at p<0.000. Light irradiation was highly effective and a necessary factor for rooting in stems cuttings of this crop. The red light-IBA combined factors served best in stem micro-cutting practice and facilitation of effective mass cultivation in stevia crop.

(Table 3) Air temperatures, and snow and ice characteristics of fresh and brackish water lakes in the Northwest Territories, Canada

The algorithms designed to estimate snow water equivalent (SWE) using passive microwave measurements falter in lake-rich high-latitude environments due to the emission properties of ice covered lakes on low frequency measurements. Microwave emission models have been used to simulate brightness temperatures (Tbs) for snowpack characteristics in terrestrial environments but cannot be applied to snow on lakes because of the differing subsurface emissivities and scattering matrices present in ice. This paper examines the performance of a modified version of the Helsinki University of Technology (HUT) snow emission model that incorporates microwave emission from lake ice and sub-ice water. Inputs to the HUT model include measurements collected over brackish and freshwater lakes north of Inuvik, Northwest Territories, Canada in April 2008, consisting of snowpack (depth, density, and snow water equivalent) and lake ice (thickness and ice type). Coincident airborne radiometer measurements at a resolution of 80x100 m were used as ground-truth to evaluate the simulations. The results indicate that subsurface media are simulated best when utilizing a modeled effective grain size and a 1 mm RMS surface roughness at the ice/water interface compared to using measured grain size and a flat Fresnel reflective surface as input. Simulations at 37 GHz (vertical polarization) produce the best results compared to airborne Tbs, with a Root Mean Square Error (RMSE) of 6.2 K and 7.9 K, as well as Mean Bias Errors (MBEs) of -8.4 K and -8.8 K for brackish and freshwater sites respectively. Freshwater simulations at 6.9 and 19 GHz H exhibited low RMSE (10.53 and 6.15 K respectively) and MBE (-5.37 and 8.36 K respectively) but did not accurately simulate Tb variability (R= -0.15 and 0.01 respectively). Over brackish water, 6.9 GHz simulations had poor agreement with airborne Tbs, while 19 GHz V exhibited a low RMSE (6.15 K), MBE (-4.52 K) and improved relative agreement to airborne measurements (R = 0.47). Salinity considerations reduced 6.9 GHz errors substantially, with a drop in RMSE from 51.48 K and 57.18 K for H and V polarizations respectively, to 26.2 K and 31.6 K, although Tb variability was not well simulated. With best results at 37 GHz, HUT simulations exhibit the potential to track Tb evolution, and therefore SWE through the winter season.

Mineraly of oceanic crust from the Kane Transform, Mid-Atlantic Ridge

Gabbroic cumulates drilled south of the Kane Transform Fault on the slow-spread Mid-Atlantic Ridge preserve up to three discrete magnetization components. Here we use absolute age constraints derived from the paleomagnetic data to develop a model for the magmatic construction of this section of the lower oceanic crust. By comparing the paleomagnetic data with mineral compositions, and based on thermal models of local reheating, we infer that magmas that began crystallizing in the upper mantle intruded into the lower oceanic crust and formed meter-scale sills. Some of these magmas were crystal-laden and the subsequent expulsion of interstitial liquid from them produced '"cumulus" sills. These small-scale magmatic injections took place over at least 210 000 years and at distances of ~3 km from the ridge axis and may have formed much of the lower crust. This model explains many of the complexities described in this area and can be used to help understand the general formation of oceanic crust at slow-spread ridges.

GPS raw data (control points and ground control points) from the Liguria Region, Borghetto Santo Spirito, Italy

Monitoring the impact of sea storms on coastal areas is fundamental to study beach evolution and the vulnerability of low-lying coasts to erosion and flooding. Modelling wave runup on a beach is possible, but it requires accurate topographic data and model tuning, that can be done comparing observed and modeled runup. In this study we collected aerial photos using an Unmanned Aerial Vehicle after two different swells on the same study area. We merged the point cloud obtained with photogrammetry with multibeam data, in order to obtain a complete beach topography. Then, on each set of rectified and georeferenced UAV orthophotos, we identified the maximum wave runup for both events recognizing the wet area left by the waves. We then used our topography and numerical models to simulate the wave runup and compare the model results to observed values during the two events. Our results highlight the potential of the methodology presented, which integrates UAV platforms, photogrammetry and Geographic Information Systems to provide faster and cheaper information on beach topography and geomorphology compared with traditional techniques without losing in accuracy. We use the results obtained from this technique as a topographic base for a model that calculates runup for the two swells. The observed and modeled runups are consistent, and open new directions for future research.

Knowledge Creation as a Square Dance on the Hilbert Cube

This paper presents a micro-model of knowledge creation and transfer in a small group of people. Our model incorporates two key aspects of the cooperative process of knowledge creation: (i) heterogeneity of people in their state of knowledge is essential for successful cooperation in the joint creation of new ideas, while (ii) the very process of cooperative knowledge creation a¤ects the heterogeneity of people through the accumulation of knowledge in common. The model features myopic agents in a pure externality model of interaction. In the two person case, we show that the equilibrium process tends to result in the accumulation of too much knowledge in common compared to the most productive state. Unlike the two-person case, in the four person case we show that the equilibrium process of knowledge creation may converge to the most productive state. Equilibrium paths are found analytically, and they are a discontinuous function of initial heterogeneity.

Aplicación de técnicas de inteligencia artificial a la predicción de contaminantes atmosféricos

Abstract Air pollution is a big threat and a phenomenon that has a specific impact on human health, in addition, changes that occur in the chemical composition of the atmosphere can change the weather and cause acid rain or ozone destruction. Those are phenomena of global importance. The World Health Organization (WHO) considerates air pollution as one of the most important global priorities. Salamanca, Gto., Mexico has been ranked as one of the most polluted cities in this country. The industry of the area led to a major economic development and rapid population growth in the second half of the twentieth century. The impact in the air quality is important and significant efforts have been made to measure the concentrations of pollutants. The main pollution sources are locally based plants in the chemical and power generation sectors. The registered concerning pollutants are Sulphur Dioxide (SO2) and particles on the order of ∼10 micrometers or less (PM10). The prediction in the concentration of those pollutants can be a powerful tool in order to take preventive measures such as the reduction of emissions and alerting the affected population. In this PhD thesis we propose a model to predict concentrations of pollutants SO2 and PM10 for each monitoring booth in the Atmospheric Monitoring Network Salamanca (REDMAS - for its spanish acronym). The proposed models consider the use of meteorological variables as factors influencing the concentration of pollutants. The information used along this work is the current real data from REDMAS. In the proposed model, Artificial Neural Networks (ANN) combined with clustering algorithms are used. The type of ANN used is the Multilayer Perceptron with a hidden layer, using separate structures for the prediction of each pollutant. The meteorological variables used for prediction were: Wind Direction (WD), wind speed (WS), Temperature (T) and relative humidity (RH). Clustering algorithms, K-means and Fuzzy C-means, are used to find relationships between air pollutants and weather variables under consideration, which are added as input of the RNA. Those relationships provide information to the ANN in order to obtain the prediction of the pollutants. The results of the model proposed in this work are compared with the results of a multivariate linear regression and multilayer perceptron neural network. The evaluation of the prediction is calculated with the mean absolute error, the root mean square error, the correlation coefficient and the index of agreement. The results show the importance of meteorological variables in the prediction of the concentration of the pollutants SO2 and PM10 in the city of Salamanca, Gto., Mexico. The results show that the proposed model perform better than multivariate linear regression and multilayer perceptron neural network. The models implemented for each monitoring booth have the ability to make predictions of air quality that can be used in a system of real-time forecasting and human health impact analysis. Among the main results of the development of this thesis we can cite: A model based on artificial neural network combined with clustering algorithms for prediction with a hour ahead of the concentration of each pollutant (SO2 and PM10) is proposed. A different model was designed for each pollutant and for each of the three monitoring booths of the REDMAS. A model to predict the average of pollutant concentration in the next 24 hours of pollutants SO2 and PM10 is proposed, based on artificial neural network combined with clustering algorithms. Model was designed for each booth of the REDMAS and each pollutant separately. Resumen La contaminación atmosférica es una amenaza aguda, constituye un fenómeno que tiene particular incidencia sobre la salud del hombre. Los cambios que se producen en la composición química de la atmósfera pueden cambiar el clima, producir lluvia ácida o destruir el ozono, fenómenos todos ellos de una gran importancia global. La Organización Mundial de la Salud (OMS) considera la contaminación atmosférica como una de las más importantes prioridades mundiales. Salamanca, Gto., México; ha sido catalogada como una de las ciudades más contaminadas en este país. La industria de la zona propició un importante desarrollo económico y un crecimiento acelerado de la población en la segunda mitad del siglo XX. Las afectaciones en el aire son graves y se han hecho importantes esfuerzos por medir las concentraciones de los contaminantes. Las principales fuentes de contaminación son fuentes fijas como industrias químicas y de generación eléctrica. Los contaminantes que se han registrado como preocupantes son el Bióxido de Azufre (SO2) y las Partículas Menores a 10 micrómetros (PM10). La predicción de las concentraciones de estos contaminantes puede ser una potente herramienta que permita tomar medidas preventivas como reducción de emisiones a la atmósfera y alertar a la población afectada. En la presente tesis doctoral se propone un modelo de predicción de concentraci ón de los contaminantes más críticos SO2 y PM10 para cada caseta de monitorización de la Red de Monitorización Atmosférica de Salamanca (REDMAS). Los modelos propuestos plantean el uso de las variables meteorol ógicas como factores que influyen en la concentración de los contaminantes. La información utilizada durante el desarrollo de este trabajo corresponde a datos reales obtenidos de la REDMAS. En el Modelo Propuesto (MP) se aplican Redes Neuronales Artificiales (RNA) combinadas con algoritmos de agrupamiento. La RNA utilizada es el Perceptrón Multicapa con una capa oculta, utilizando estructuras independientes para la predicción de cada contaminante. Las variables meteorológicas disponibles para realizar la predicción fueron: Dirección de Viento (DV), Velocidad de Viento (VV), Temperatura (T) y Humedad Relativa (HR). Los algoritmos de agrupamiento K-means y Fuzzy C-means son utilizados para encontrar relaciones existentes entre los contaminantes atmosféricos en estudio y las variables meteorológicas. Dichas relaciones aportan información a las RNA para obtener la predicción de los contaminantes, la cual es agregada como entrada de las RNA. Los resultados del modelo propuesto en este trabajo son comparados con los resultados de una Regresión Lineal Multivariable (RLM) y un Perceptrón Multicapa (MLP). La evaluación de la predicción se realiza con el Error Medio Absoluto, la Raíz del Error Cuadrático Medio, el coeficiente de correlación y el índice de acuerdo. Los resultados obtenidos muestran la importancia de las variables meteorológicas en la predicción de la concentración de los contaminantes SO2 y PM10 en la ciudad de Salamanca, Gto., México. Los resultados muestran que el MP predice mejor la concentración de los contaminantes SO2 y PM10 que los modelos RLM y MLP. Los modelos implementados para cada caseta de monitorizaci ón tienen la capacidad para realizar predicciones de calidad del aire, estos modelos pueden ser implementados en un sistema que permita realizar la predicción en tiempo real y analizar el impacto en la salud de la población. Entre los principales resultados obtenidos del desarrollo de esta tesis podemos citar: Se propone un modelo basado en una red neuronal artificial combinado con algoritmos de agrupamiento para la predicción con una hora de anticipaci ón de la concentración de cada contaminante (SO2 y PM10). Se diseñó un modelo diferente para cada contaminante y para cada una de las tres casetas de monitorización de la REDMAS. Se propone un modelo de predicción del promedio de la concentración de las próximas 24 horas de los contaminantes SO2 y PM10, basado en una red neuronal artificial combinado con algoritmos de agrupamiento. Se diseñó un modelo para cada caseta de monitorización de la REDMAS y para cada contaminante por separado.

Distribution of indole-3-acetic acid in Petunia hybrida shoot tip cuttings and relationship between auxin transport, carbohydrate metabolism and adventitious root formation.

To determine the contribution of polar auxin transport (PAT) to auxin accumulation and to adventitious root (AR) formation in the stem base of Petunia hybrida shoot tip cuttings, the level of indole-3-acetic acid (IAA) was monitored in non-treated cuttings and cuttings treated with the auxin transport blocker naphthylphthalamic acid (NPA) and was complemented with precise anatomical studies. The temporal course of carbohydrates, amino acids and activities of controlling enzymes was also investigated. Analysis of initial spatial IAA distribution in the cuttings revealed that approximately 40 and 10% of the total IAA pool was present in the leaves and the stem base as rooting zone, respectively. A negative correlation existed between leaf size and IAA concentration. After excision of cuttings, IAA showed an early increase in the stem base with two peaks at 2 and 24h post excision and, thereafter, a decline to low levels. This was mirrored by the expression pattern of the auxin-responsive GH3 gene. NPA treatment completely suppressed the 24-h peak of IAA and severely inhibited root formation. It also reduced activities of cell wall and vacuolar invertases in the early phase of AR formation and inhibited the rise of activities of glucose-6-phosphate dehydrogenase and phosphofructokinase during later stages. We propose a model in which spontaneous AR formation in Petunia cuttings is dependent on PAT and on the resulting 24-h peak of IAA in the rooting zone, where it induces early cellular events and also stimulates sink establishment. Subsequent root development stimulates glycolysis and the pentosephosphate pathway

Using a causal smoothing to improve the performance of an on-line neural network glucose prediction algorithm

This work evaluates a spline-based smoothing method applied to the output of a glucose predictor. Methods:Our on-line prediction algorithm is based on a neural network model (NNM). We trained/validated the NNM with a prediction horizon of 30 minutes using 39/54 profiles of patients monitored with the Guardian® Real-Time continuous glucose monitoring system The NNM output is smoothed by fitting a causal cubic spline. The assessment parameters are the error (RMSE), mean delay (MD) and the high-frequency noise (HFCrms). The HFCrms is the root-mean-square values of the high-frequency components isolated with a zero-delay non-causal filter. HFCrms is 2.90±1.37 (mg/dl) for the original profiles.

Improvement of root architecture under abiotic stress through control of auxin homeostasis in Arabidopsis and Brassica crops

Auxin plays an important role in many aspects of plant development including stress responses. Here we briefly summarize how auxin is involved in salt stress, drought (i.e. mostly osmotic stress), waterlogging and nutrient deficiency in Brassica plants. In addition, some mechanisms to control auxin levels and signaling in relation to root formation (under stress) will be reviewed. Molecular studies are mainly described for the model plant Arabidopsis thaliana, but we also like to demonstrate how this knowledge can be transferred to agriculturally important Brassica species, such as Brassica rapa, Brassica napus and Brassica campestris. Moreover, beneficial fungi could play a role in the adaptation response of Brassica roots to abiotic stresses. Therefore, the possible influence of Piriformospora indica will also be covered since the growth promoting response of plants colonized by P. indica is also linked to plant hormones, among them auxin.

Dual pathways for regulation of root branching by nitrate

Root development is extremely sensitive to variations in nutrient supply, but the mechanisms are poorly understood. We have investigated the processes by which nitrate (NO3−), depending on its availability and distribution, can have both positive and negative effects on the development and growth of lateral roots. When Arabidopsis roots were exposed to a locally concentrated supply of NO3− there was no increase in lateral root numbers within the NO3−-rich zone, but there was a localized 2-fold increase in the mean rate of lateral root elongation, which was attributable to a corresponding increase in the rate of cell production in the lateral root meristem. Localized applications of other N sources did not stimulate lateral root elongation, consistent with previous evidence that the NO3− ion is acting as a signal rather than a nutrient. The axr4 auxin-resistant mutant was insensitive to the stimulatory effect of NO3−, suggesting an overlap between the NO3− and auxin response pathways. High rates of NO3− supply to the roots had a systemic inhibitory effect on lateral root development that acted specifically at the stage when the laterals had just emerged from the primary root, apparently delaying final activation of the lateral root meristem. A nitrate reductase-deficient mutant showed increased sensitivity to this systemic inhibitory effect, suggesting that tissue NO3− levels may play a role in generating the inhibitory signal. We present a model in which root branching is modulated by opposing signals from the plant’s internal N status and the external supply of NO3−.

Expressed Sequence Tags from a Root-Hair-Enriched Medicago truncatula cDNA Library1

The root hair is a specialized cell type involved in water and nutrient uptake in plants. In legumes the root hair is also the primary site of recognition and infection by symbiotic nitrogen-fixing Rhizobium bacteria. We have studied the root hairs of Medicago truncatula, which is emerging as an increasingly important model legume for studies of symbiotic nodulation. However, only 27 genes from M. truncatula were represented in GenBank/EMBL as of October, 1997. We report here the construction of a root-hair-enriched cDNA library and single-pass sequencing of randomly selected clones. Expressed sequence tags (899 total, 603 of which have homology to known genes) were generated and made available on the Internet. We believe that the database and the associated DNA materials will provide a useful resource to the community of scientists studying the biology of roots, root tips, root hairs, and nodulation.