Causal inference and forescasting methods for climate data nalysis

To advance time series forecasting we need to progress on multiple fronts. In this thesis, we develop algorithms to identify causal relations which allow to identify the driving processes containing useful information for the prediction of the process of interest. Complementing this, machine learning algorithms allow to exploit such information to build data-driven forecast models, and to correct the prediction of dynamical models. The identification from time series analysis of reliable indicators of causal relationships, is essential for many disciplines. Main challenges are distinguishing correlation from causality and discriminating between direct and indirect interactions. Over the years, many methods for data-driven causal inference have been proposed; however, their success largely depends on the characteristics of the system under investigation. Often, their data requirements, computational cost or number of parameters, limit their applicability. In this thesis, we propose a computationally efficient measure for causality testing, with the goal of overcoming the limitations of information-theoretic measures, due their high computational cost. The proposed metric is useful when causality networks need to be inferred from the analysis of a large number of relatively short time series. It can also be very useful for the selection of the inputs for the machine learning algorithms; in fact, it allows to identify those processes which contain useful information for the prediction of a given process. This is particularly useful for systems composed of a large number of processes, whose interactions are poorly understood. On the other hand, the socioeconomic impact of weather extremes draws the attention of researchers to the development of novel methodologies to make more accurate weather predictions. The Madden-Julian Oscillation (MJO), which is the dominant mode of variability in the tropical atmosphere on sub-seasonal time scales, is particularly important because it can promote or enhance extreme events in both, the tropics and the extratropics. Currently, the prediction skill of MJO is receiving a lot of attention, and in this thesis we take two machine learning approaches; first we use machine learning as a stand-alone technique to analyze observed data, showing that two artificial neural networks, a feed-forward neural network and a recurrent neural network, allow a competitive prediction, yet not exceeding the skill of the state-of-art dynamical models. Then, we combine dynamical models with machine learning, which allows to improve the predictions of the best dynamical model. In particular, machine learning allows to improve the prediction of the MJO intensity and geographical localizationPara avanzar en el pronóstico de series temporales, es necesario avanzar en múltiples frentes. En esta tesis, desarrollamos algoritmos para descubrir relaciones causales que identifican los procesos que actúan como fuentes de información y pueden ayudar a mejorar la predicción del proceso de interés. Por otro lado, los algoritmos de aprendizaje automático permiten explotar dicha información para mejorar la predicción de los modelos dinámicos. La identificación de relaciones de causalidad a partir de series temporales es esencial en muchas disciplinas. Los desafíos en este ámbito son distinguir la correlación de la causalidad, así como diferenciar entre las interacciones directas e indirectas. A lo largo de los años se han propuesto numerosos métodos de inferencia causal basados en la observación de datos. No obstante, su éxito depende de las características del sistema a investigar. A menudo, el coste computacional o el número de parámetros limitan su aplicabilidad. En esta tesis se propone una medida computacionalmente eficiente para el testeo de causalidad. La métrica que se propone resulta util cuando es necesario inferir causalidad a partir de análisis de un gran número de series temporales relativamente cortas. También puede resultar muy útil en la selección de entradas en los algoritmos de aprendizaje automático. De hecho, permite identificar aquellos procesos que contienen información útil en la predicción de cierto proceso dado. Por otro lado, el impacto socioeconómico de fenómenos meteorológicos extremos requiere el desarrollo de nuevas metodologías con el objetivo de obtener predicciones meteorológicas más precisas. La Oscilación de Madden-Julian (MJO) es el modo dominante de variabilidad en la atmósfera tropical en escalas temporales subestacionales, y puede promover o aumentar eventos extremos tanto en el trópico como el extratrópico. Actualmente, la prediccion de la MJO genera mucho interés. Por esta razon, en esta tesis se han escogido dos metodologías diferentes de aprendizaje automático. Primero, se han utilizado dos redes neuronales artificiales para analizar datos observacionales, una red neuronal feed-forward y una red neuronal recurrente. Se muestra que esto permite una predicción competitiva, pero sin superar la capacidad de los modelos dinámicos actuales. Por este motivo, en un segundo estudio se han combinado modelos dinámicos con aprendizaje automático, que permiten mejorar las predicciones del mejor modelo dinámico. En particular, el aprendizaje automático permite mejorar la predicción de la intensidad de MJO y su localización geográficaPostprint (published version

Approaching the isoperimetric problem in HCmH^m_{\mathbb{C}} via the hyperbolic log-convex density conjecture

We prove that geodesic balls centered at some base point are isoperimetric in the real hyperbolic space $H_{\mathbb R}^n$ endowed with a smooth, radial, strictly log-convex density on the volume and perimeter. This is an analogue of the result by G. R. Chambers for log-convex densities on $\mathbb R^n$. As an application we prove that in any rank one symmetric space of non-compact type, geodesic balls are isoperimetric in a class of sets enjoying a suitable notion of radial symmetry.Comment: 17 pages, 5 figures. Added references. Generalized Definition 1.2 to the octonionic case, and simplified the argument in Section

Quantitative C1C^1-stability of spheres in rank one symmetric spaces of non-compact type

We prove that in any rank one symmetric space of non-compact type $M\in\{\mathbb{R} H^n,\mathbb{C} H^m,\mathbb{H} H^m,\mathbb{O} H^2\}$, geodesic spheres are uniformly quantitatively stable with respect to small $C^1$-volume preserving perturbations. We quantify the gain of perimeter in terms of the $W^{1,2}$-norm of the perturbation, taking advantage of the explicit spectral gap of the Laplacian on geodesic spheres in $M$. As a consequence, we give a quantitative proof that for small volumes, geodesic spheres are isoperimetric regions among all sets of finite perimeter.Comment: 24 page

Causal inference and forescasting methods for climate data nalysis

To advance time series forecasting we need to progress on multiple fronts. In this thesis, we develop algorithms to identify causal relations which allow to identify the driving processes containing useful information for the prediction of the process of interest. Complementing this, machine learning algorithms allow to exploit such information to build data-driven forecast models, and to correct the prediction of dynamical models. The identification from time series analysis of reliable indicators of causal relationships, is essential for many disciplines. Main challenges are distinguishing correlation from causality and discriminating between direct and indirect interactions. Over the years, many methods for data-driven causal inference have been proposed; however, their success largely depends on the characteristics of the system under investigation. Often, their data requirements, computational cost or number of parameters, limit their applicability. In this thesis, we propose a computationally efficient measure for causality testing, with the goal of overcoming the limitations of information-theoretic measures, due their high computational cost. The proposed metric is useful when causality networks need to be inferred from the analysis of a large number of relatively short time series. It can also be very useful for the selection of the inputs for the machine learning algorithms; in fact, it allows to identify those processes which contain useful information for the prediction of a given process. This is particularly useful for systems composed of a large number of processes, whose interactions are poorly understood. On the other hand, the socioeconomic impact of weather extremes draws the attention of researchers to the development of novel methodologies to make more accurate weather predictions. The Madden-Julian Oscillation (MJO), which is the dominant mode of variability in the tropical atmosphere on sub-seasonal time scales, is particularly important because it can promote or enhance extreme events in both, the tropics and the extratropics. Currently, the prediction skill of MJO is receiving a lot of attention, and in this thesis we take two machine learning approaches; first we use machine learning as a stand-alone technique to analyze observed data, showing that two artificial neural networks, a feed-forward neural network and a recurrent neural network, allow a competitive prediction, yet not exceeding the skill of the state-of-art dynamical models. Then, we combine dynamical models with machine learning, which allows to improve the predictions of the best dynamical model. In particular, machine learning allows to improve the prediction of the MJO intensity and geographical localizationPara avanzar en el pronóstico de series temporales, es necesario avanzar en múltiples frentes. En esta tesis, desarrollamos algoritmos para descubrir relaciones causales que identifican los procesos que actúan como fuentes de información y pueden ayudar a mejorar la predicción del proceso de interés. Por otro lado, los algoritmos de aprendizaje automático permiten explotar dicha información para mejorar la predicción de los modelos dinámicos. La identificación de relaciones de causalidad a partir de series temporales es esencial en muchas disciplinas. Los desafíos en este ámbito son distinguir la correlación de la causalidad, así como diferenciar entre las interacciones directas e indirectas. A lo largo de los años se han propuesto numerosos métodos de inferencia causal basados en la observación de datos. No obstante, su éxito depende de las características del sistema a investigar. A menudo, el coste computacional o el número de parámetros limitan su aplicabilidad. En esta tesis se propone una medida computacionalmente eficiente para el testeo de causalidad. La métrica que se propone resulta util cuando es necesario inferir causalidad a partir de análisis de un gran número de series temporales relativamente cortas. También puede resultar muy útil en la selección de entradas en los algoritmos de aprendizaje automático. De hecho, permite identificar aquellos procesos que contienen información útil en la predicción de cierto proceso dado. Por otro lado, el impacto socioeconómico de fenómenos meteorológicos extremos requiere el desarrollo de nuevas metodologías con el objetivo de obtener predicciones meteorológicas más precisas. La Oscilación de Madden-Julian (MJO) es el modo dominante de variabilidad en la atmósfera tropical en escalas temporales subestacionales, y puede promover o aumentar eventos extremos tanto en el trópico como el extratrópico. Actualmente, la prediccion de la MJO genera mucho interés. Por esta razon, en esta tesis se han escogido dos metodologías diferentes de aprendizaje automático. Primero, se han utilizado dos redes neuronales artificiales para analizar datos observacionales, una red neuronal feed-forward y una red neuronal recurrente. Se muestra que esto permite una predicción competitiva, pero sin superar la capacidad de los modelos dinámicos actuales. Por este motivo, en un segundo estudio se han combinado modelos dinámicos con aprendizaje automático, que permiten mejorar las predicciones del mejor modelo dinámico. En particular, el aprendizaje automático permite mejorar la predicción de la intensidad de MJO y su localización geográficaFísica computacional i aplicad

PLANT GROWTH PROMOTING AND ANTAGONISTIC TRAITS OF INDIGENOUS FLUORESCENT PSEUDOMONAS SPP. ISOLATED FROM WHEAT RHIZOSPHERE AND A. HALIMUS ENDOSPHERE

Fluorescent Pseudomonas spp. are an important group of plant growth promoting rhizobacteria (PGPR). They increase the growth of their host plant directly or indirectly. In this study, 3 Fluorescent pseudomonads were isolated from the wheat rhizosphere and one from the endophyte of the halophyte Atriplex halimus. Based on biochemical, physiological reactions and 16S rRNA gene sequences, the isolates were identified as Pseudomonas putida AF2, P. aeruginosa RB5, P. fluorescens RB13 and P. aeruginosa EH4. These strains and P. fluorescens CHA0 were screened for their PGPR activities. All the strains solubilized phosphate with a maximum of 187.9 μg / ml. P. fluorescens CHA0 produced a significant amount (88.37μg/ml) of IAA. The siderophores production by all the strains was proved and the percent of production varied from 38 to 46. The strains produced HCN, protease and amylase. Mycelial growth of F. oxysporum and A. alternata was strongly reduced in the presence of antagonistic fluorescent Pseudomonas spp., with the inhibition rate varying between 25 to 38% and 17 to 27%, respectively. On the basis of excellent growth promoter, biocontrol activities, the fluorescent Pseudomonas spp. tested could be applied as inoculants of wheat for sustainable agriculture in salty soils

PLANT GROWTH PROMOTING AND ANTAGONISTIC TRAITS OF INDIGENOUS FLUORESCENT PSEUDOMONAS SPP. ISOLATED FROM WHEAT RHIZOSPHERE AND A. HALIMUS ENDOSPHERE

Fluorescent Pseudomonas spp. are an important group of plant growth promoting rhizobacteria (PGPR). They increase the growth of their host plant directly or indirectly. In this study, 3 Fluorescent pseudomonads were isolated from the wheat rhizosphere and one from the endophyte of the halophyte Atriplex halimus. Based on biochemical, physiological reactions and 16S rRNA gene sequences, the isolates were identified as Pseudomonas putida AF2, P. aeruginosa RB5, P. fluorescens RB13 and P. aeruginosa EH4. These strains and P. fluorescens CHA0 were screened for their PGPR activities. All the strains solubilized phosphate with a maximum of 187.9 μg / ml. P. fluorescens CHA0 produced a significant amount (88.37μg/ml) of IAA. The siderophores production by all the strains was proved and the percent of production varied from 38 to 46. The strains produced HCN, protease and amylase. Mycelial growth of F. oxysporum and A. alternata was strongly reduced in the presence of antagonistic fluorescent Pseudomonas spp., with the inhibition rate varying between 25 to 38% and 17 to 27%, respectively. On the basis of excellent growth promoter, biocontrol activities, the fluorescent Pseudomonas spp. tested could be applied as inoculants of wheat for sustainable agriculture in salty soils

Machine learning prediction of the Madden-Julian oscillation

The socioeconomic impact of weather extremes draws the attention of researchers to the development of novel methodologies to make more accurate weather predictions. The Madden–Julian oscillation (MJO) is the dominant mode of variability in the tropical atmosphere on sub-seasonal time scales, and can promote or enhance extreme events in both, the tropics and the extratropics. Forecasting extreme events on the sub-seasonal time scale (from 10 days to about 3 months) is very challenging due to a poor understanding of the phenomena that can increase predictability on this time scale. Here we show that two artificial neural networks (ANNs), a feed-forward neural network and a recurrent neural network, allow a very competitive MJO prediction. While our average prediction skill is about 26–27 days (which competes with that obtained with most computationally demanding state-of-the-art climate models), for some initial phases and seasons the ANNs have a prediction skill of 60 days or longer. Furthermore, we show that the ANNs have a good ability to predict the MJO phase, but the amplitude is underestimated