High resolution spectroscopy of the BCD galaxy Haro 15: II. Chemodynamics

We present a detailed study of the physical properties of the nebular material in four star-forming knots of the blue compact dwarf galaxy Haro 15. Using long-slit and echelle spectroscopy obtained at Las Campanas Observatory, we study the physical conditions (electron density and temperatures), ionic and total chemical abundances of several atoms, reddening and ionization structure, for the global flux and for the different kinematical components. The latter was derived by comparing the oxygen and sulphur ionic ratios to their corresponding observed emission line ratios (the $\eta$ and $\eta$' plots) in different regions of the galaxy. Applying the direct method or empirical relationships for abundance determination, we perform a comparative analysis between these regions. The similarities found in the ionization structure of the different kinematical components implies that the effective temperatures of the ionizing radiation fields are very similar in spite of some small differences in the ionization state of the different elements. Therefore the different gaseous kinematical components identified in each star forming knot are probably ionized by the same star cluster. However, the difference in the ionizing structure of the two knots with knot A showing a lower effective temperature than knot B, suggests a different evolutionary stage for them consistent with the presence of an older and more evolved stellar population in the first.Comment: 21 pages, 6 figures, 8 tables, accepted by MNRA

The metal abundance of circumnuclear star forming regions in early type spirals. Spectrophotometric observations

We have obtained long-slit observations in the optical and near infrared of 12 circumnuclear HII regions (CNSFR) in the early type spiral galaxies NGC 2903, NGC 3351 and NGC 3504 with the aim of deriving their chemical abundances. Only for one of the regions, the [SIII] $\lambda$ 6312 \AA was detected providing, together with the nebular [SIII] lines at $\lambda\lambda$ 9069, 9532 \AA, a value of the electron temperature of T$_e$([SIII])= 8400$^{+ 4650}_{-1250}$K. A semi-empirical method for the derivation of abundances in the high metallicity regime is presented. We obtain abundances which are comparable to those found in high metallicity disc HII regions from direct measurements of electron temperatures and consistent with solar values within the errors. The region with the highest oxygen abundance is R3+R4 in NGC 3504, 12+log(O/H) = 8.85, about 1.5 solar if the solar oxygen abundance is set at the value derived by Asplund et al. (2005), 12+log(O/H)$_{\odot}$ = 8.66$\pm$0.05. Region R7 in NGC 3351 has the lowest oxygen abundance of the sample, about 0.6 times solar. In all the observed CNSFR the O/H abundance is dominated by the O$^+$/H$^+$ contribution, as is also the case for high metallicity disc HII regions. For our observed regions, however, also the S$^+$/S$^{2+}$ ratio is larger than one, contrary to what is found in high metallicity disc HII regions for which, in general, the sulphur abundances are dominated by S$^{2+}$/H$^+$...Comment: 24 pages, 19 figures, accepted by MNRA

Spacecraft sun sensors, NASA space vehicle design criteria /guidance and control/

Design criteria and performance specifications for spacecraft sun sensor

Analysis of UV protection requirements and testing of candidate attenuators for the Haloe optical instrument

Results of calculations are presented which simulate photolytic processes occurring in HALOE gas calibration cells exposed to extra-terrestrial solar ultraviolet photons. These calculations indicate that significant photolysis takes place in two of the sapphire-enclosed cells over the exposure periods of the proposed mission. A subsequent laboratory investigation is also described in which a high-voltage discharge hydrogen light source is used in conjunction with a vacuum ultraviolet spectrograph. The UV emission from this lamp was used to expose two candidate UV attenuators (ZnSe and coated Ge) to ascertain their suitability as UV filters while maintaining original infrared optical properties. Both materials were found to be effectively opaque to vacuum UV radiaton and suffered no adverse effects regarding their infrared transmissivity

Smart models to improve agrometeorological estimations and predictions

La población mundial, en continuo crecimiento, alcanzará de forma estimada los 9,7 mil millones de habitantes en el 2050. Este incremento, combinado con el aumento en los estándares de vida y la situación de emergencia climática (aumento de la temperatura, intensificación del ciclo del agua, etc.) nos enfrentan al enorme desafío de gestionar de forma sostenible los cada vez más escasos recursos disponibles. El sector agrícola tiene que afrontar retos tan importantes como la mejora en la gestión de los recursos naturales, la reducción de la degradación medioambiental o la seguridad alimentaria y nutricional. Todo ello condicionado por la escasez de agua y las condiciones de aridez: factores limitantes en la producción de cultivos. Para garantizar una producción agrícola sostenible bajo estas condiciones, es necesario que todas las decisiones que se tomen estén basadas en el conocimiento, la innovación y la digitalización de la agricultura de forma que se garantice la resiliencia de los agroecosistemas, especialmente en entornos áridos, semi-áridos y secos sub-húmedos en los que el déficit de agua es estructural. Por todo esto, el presente trabajo se centra en la mejora de la precisión de los actuales modelos agrometeorológicos, aplicando técnicas de inteligencia artificial. Estos modelos pueden proporcionar estimaciones y predicciones precisas de variables clave como la precipitación, la radiación solar y la evapotranspiración de referencia. A partir de ellas, es posible favorecer estrategias agrícolas más sostenibles, gracias a la posibilidad de reducir el consumo de agua y energía, por ejemplo. Además, se han reducido el número de mediciones requeridas como parámetros de entrada para estos modelos, haciéndolos más accesibles y aplicables en áreas rurales y países en desarrollo que no pueden permitirse el alto costo de la instalación, calibración y mantenimiento de estaciones meteorológicas automáticas completas. Este enfoque puede ayudar a proporcionar información valiosa a los técnicos, agricultores, gestores y responsables políticos de la planificación hídrica y agraria en zonas clave. Esta tesis doctoral ha desarrollado y validado nuevas metodologías basadas en inteligencia artificial que han ser vido para mejorar la precision de variables cruciales en al ámbito agrometeorológico: precipitación, radiación solar y evapotranspiración de referencia. En particular, se han modelado sistemas de predicción y rellenado de huecos de precipitación a diferentes escalas utilizando redes neuronales. También se han desarrollado modelos de estimación de radiación solar utilizando exclusivamente parámetros térmicos y validados en zonas con características climáticas similares a lugar de entrenamiento, sin necesidad de estar geográficamente en la misma región o país. Analógamente, se han desarrollado modelos de estimación y predicción de evapotranspiración de referencia a nivel local y regional utilizando también solamente datos de temperatura para todo el proceso: regionalización, entrenamiento y validación. Y finalmente, se ha creado una librería de Python de código abierto a nivel internacional (AgroML) que facilita el proceso de desarrollo y aplicación de modelos de inteligencia artificial, no solo enfocadas al sector agrometeorológico, sino también a cualquier modelo supervisado que mejore la toma de decisiones en otras áreas de interés.The world population, which is constantly growing, is estimated to reach 9.7 billion people in 2050. This increase, combined with the rise in living standards and the climate emergency situation (increase in temperature, intensification of the water cycle, etc.), presents us with the enormous challenge of managing increasingly scarce resources in a sustainable way. The agricultural sector must face important challenges such as improving natural resource management, reducing environmental degradation, and ensuring food and nutritional security. All of this is conditioned by water scarcity and aridity, limiting factors in crop production. To guarantee sustainable agricultural production under these conditions, it is necessary to based all the decision made on knowledge, innovation, and the digitization of agriculture to ensure the resilience of agroecosystems, especially in arid, semi-arid, and sub-humid dry environments where water deficit is structural. Therefore, this work focuses on improving the precision of current agrometeorological models by applying artificial intelligence techniques. These models can provide accurate estimates and predictions of key variables such as precipitation, solar radiation, and reference evapotranspiration. This way, it is possible to promote more sustainable agricultural strategies by reducing water and energy consumption, for example. In addition, the number of measurements required as input parameters for these models has been reduced, making them more accessible and applicable in rural areas and developing countries that cannot afford the high cost of installing, calibrating, and maintaining complete automatic weather stations. This approach can help provide valuable information to technicians, farmers, managers, and policy makers in key wáter and agricultural planning areas. This doctoral thesis has developed and validated new methodologies based on artificial intelligence that have been used to improve the precision of crucial variables in the agrometeorological field: precipitation, solar radiation, and reference evapotranspiration. Specifically, prediction systems and gap-filling models for precipitation at different scales have been modeled using neural networks. Models for estimating solar radiation using only thermal parameters have also been developed and validated in areas with similar climatic characteristics to the training location, without the need to be geographically in the same region or country. Similarly, models for estimating and predicting reference evapotranspiration at the local and regional level have been developed using only temperature data for the entire process: regionalization, training, and validation. Finally, an internationally open-source Python library (AgroML) has been created to facilitate the development and application of artificial intelligence models, not only focused on the agrometeorological sector but also on any supervised model that improves decision-making in other areas of interest

A dataset of future daily weather data for crop modelling over Europe derived from climate change scenarios

Coupled atmosphere-ocean general circulation models (AOGCMs, or just GCMs for short) simulate different realizations of possible future climates at global scale under contrasting scenarios of greenhouse gases emissions. While these datasets provide several meteorological variables as output, but two of the most important ones are air temperature at the Earth's surface and daily precipitation. GCMs outputs are spatially downscaled using different methodologies, but it is accepted that such data require further processing to be used in impact models, and particularly for crop simulation models. Daily values of solar radiation, wind, air humidity, and, at times, rainfall may have values which are not realistic, and/or the daily record of data may contain values of meteorological variables which are totally uncorrelated. Crop models are deterministic, but they are typicallyrun in a stochastic fashion by using a sample of possible weather time series that can be generated using stochastic weather generators. With their random variability, these multiple years of weather data can represent the time horizon of interest. GCMs estimate climate dynamics, hence providing unique time series for a given emission scenario; the multiplicity of years to evaluate a given time horizon is consequently not available from such outputs. Furthermore, if the time horizons of interest are very close (e.g. 2020 and 2030), averaging only the non-overlapping years of the GCM weather variables time series may not adequately represent the time horizon; this may lead to apparent inversions of trends, creating artefacts also in the impact model simulations. This paper presents a database of consolidated and coherent future daily weather data covering Europe with a 25 km grid, which is adequate for crop modelling in the near-future. Climate data are derived from the ENSEMBLES downscaling of the HadCM3, ECHAM5, and ETHZ realizations of the IPCC A1B emission scenario, using for HadCM3 two different regional models for downscaling. Solar radiation, wind and relative air humidity weather variables where either estimated or collected from historical series, and derived variables reference evapotranspiration and vapour pressure deficit were estimated from other variables, ensuring consistency within daily records. Synthetic time series data were also generated using the weather generator ClimGen. All data are made available upon request to the European Commission Joint Research Centre's MARS unit.JRC.H.7-Climate Risk Managemen