La productivité primaire nette PPN des grands écosystèmes est la variable-clé pour comprendre et quantifier les intéractions biosphère / atmosphère. Le modèle d'efficience photosynthétique de Monteith mettant en relation la PPN avec la quantité de rayonnement solaire photosynthétiquement actif absorbé (ou intercepté) APAR par le couvert végétal, mesurable à partir des indices de végétation, se prête parfaitement à une approche par télédétection. Le coefficient qui lie ces deux paramètres est appelé rendement photosynthétique ou encore efficience de conversion aérienne nette notée En. L'incertitude sur la valeur de En et l'absence de valeur pour nombre de biomes montrent l'importance de l'étalonnage du modèle. On dispose de mesures de biomasse permettant de calculer la PPN pour 60 des régions écologiques du Québec (qui en compte 69), d'une longue série temporelle d'images satellitaires de Global Vegetation Index NOAA-AVHRR de 1985 à 1992 et des normales climatiques au Québec. La mise en évidence de corrélation entre les propriétés optiques de la végétation (liées à l'APAR) et sa croissance montre la faisabilité d'un suivi de cette PPN par satellite. La biomasse aérienne anhydre est de 7 kg/m2 pour l'ensemble du territoire du Québec. La PPN varie de 100 à 400 g de matière sèche aérienne/m2/an pour le domaine de la pessière du Nord, à 670 g/m2/an pour les domaines feuillus du Sud. Les paramètres qui régissent la productivité forestière ne sont pas constants sur tout le Québec. Le modèle original est donc validé par région écologique appartenant à un même sous-domaine et pour des conditions climatiques données. L'étude de la sensibilité de En à ces variables climatiques a permis d'élaborer un tableau de correspondance entre les valeurs d'APAR, variant entre 664 et 1342 MJ/m2, des variables climatiques d'une région et sa productivité. Ainsi, En varie de 0,1 g/Mégajoules pour le domaine de la pessière noire à 0,57 g/MJ pour les feuillus du sud de la forêt boréale. Pour affiner ces estimations, l'accent devra être mis sur la connaissance des phénomènes physiques qui régissent les propriétés optiques de la végétation et surtout sur une harmonisation des méthodes de calcul et de mesure.

Our research primarily focuses on the risk assessment and vulnerabilities of commercial broiler farms/houses due to severe weather events. The area of focus is counties within a 60-mile radius of broiler processing plants, as most broiler farms/houses are centrally located around these plants. Using data from the National Oceanic and Atmospheric Administration (NOAA), we calculate a risk index to evaluate the vulnerability of these counties. This study incorporates historical weather event data to identify patterns and predict future risks, providing valuable insights for disaster preparedness and mitigation strategies. Through a detailed analysis of various severe weather events, including tornadoes, hurricanes, floods, and lightning, we aim to highlight the specific vulnerabilities of broiler farms/houses and propose targeted strategies to enhance their resilience against these climatic threats. By focusing on high-risk areas, we ensure that our findings are both relevant and actionable, enabling stakeholders to implement effective risk management practices. This research fills an important gap in the existing literature. and also contributes significantly to the development of robust predictive models and comprehensive risk assessments for the poultry industry. Key Words: broiler production plants, severe weather events, risk assessment, NOAA data, vulnerability analysis, predictive modeling, climatic threats, resilience enhancement, machine learning models, Bayesian risk, geospatial analysis, hypothesis testing, nowcasting.

Results are presented from the 2021 test run of active fire detection using the Visual Infrared Imaging Radiometer Suite (VIIRS) instrument, that is currently onboard the polar satellites Suomi-NPP and NOAA-20. The test is performed by the Swedish Civil Contingencies Agency and the Swedish Meteorological and Hydrological Institute, in cooperation with local fire departments in Sweden. The aim of this report was to study the impact of clouds on the ability of active fire detection, as well as to identify objects that potentially can cause commission errors in the VIIRS 375 m active fire algorithm (false positive notifications). Also, the study aimed to investigate what may cause omission errors in the algorithm, and to show to what extent the detections can be used to represent the true time development of the wildfire front and burned area. Using a cloudmask and a cloudtype classification product from Nowcasting Satellite Application Facilities (NWC SAF), the impact of clouds was anlalyzed by comparing the cloud data with the obtained fire notifications from the satellites. Active fires and newly burned areas were also studied using Sentinel-2 imagery, specifically the False Color Urban and the Short Wave Infrared (SWIR) RGB composites, as well as images from the 842 nm band, making use of the relatively high spatial resolution as well as the spectral signatures of fire and newly burned vegetation. Detection of active fires occurred in both cloud free and completely cloud covered conditions. How-ever, roughly 70% of the detected vegetation fire pixels were obtained in conditions with 20% clouds or less. / I rapporten presenteras resultat från 2021 års test av satellitdetektering av skogs- och vegetationsbränder, ett test som genomförs av MSB och SMHI i samverkan med kommunala räddningstjänster. De två satelliter som ingår i testet (Suomi-NPP och NOAA-20) går i polära omloppsbanor och är utrustade med instrumentet Visible Infrared Imaging Radiometer Suite (VIIRS). Detta projekt syftade till att undersöka hur molnighet påverkar möjligheten till detektion med satellit, vilka objekt som potentiellt kan ge upphov till falska detektioner samt vad som kan orsaka uteblivna satellitdetektioner. Ytterligare ett mål med rapporten var att genom fallstudier av större bränder undersöka i vilken utsträckning satellitdetektionerna kan användas för att representera brandfrontens utveckling med tiden och brandens faktiska utbredning. Vid studierna av molnighet analyserades en molnmask och en molnklassificeringsprodukt från Nowcasting Satellite Application Facilities (NWC SAF). I utvärderingen användes även data från Sentinel-2 för att studera pågående bränder och avbränd yta, som syns tydligt i RGB-kompositerna False Color Urban och Short Wave Infrared (SWIR) och i 842 nm-bandet, tack vare den relativt höga bildupplösningen och den nyligen avbrända ytans spektralsignatur. Brand detekterades i både molnfria och helt molntäckta förhållanden. Drygt 70 % av detektionerna vid vegetationsbrand kom emellertid i förhållanden med 20 % moln eller mindre.

Seagrass habitats in South Florida are exceptionally valuable. They play an important ecological role in the coastal environment by stabilizing sediment, providing habitat for other species and supporting a whole food web. The availability of light and nutrients in aquatic ecosystems are the driving factors behind seagrass distribution. Water quality has been known to influence the abundance, distribution and composition of seagrass beds. South Florida has extensive diverse coastal communities. Throughout its human development dramatic changes have occurred in its natural ecosystems. In South Florida, many examples of seagrass habitat loss are documented, with a variety of contributing factors. The present research investigates the spatial and temporal patterns in benthic vegetation of the North Biscayne Bay marine basin, located just south of the heavily urbanized Port of Miami. The area has been altered significantly through dredging projects to widen and deepen the channels around the port facilities in order to accommodate larger vessels. This study focuses primarily on environmental and physical conditions that are likely to alter the distribution of seagrass. The availability of light and nutrients in aquatic ecosystems are the driving factors behind seagrass distribution and therefore one may expect seagrass degradation if any drastic changes occurred in these parameters. Project data used were collected from the South Florida Fish and Invertebrate Assessment Network project (FIAN), an element of the greater Everglades Restoration Program. Additional Environmental and physical data were obtained from the South Florida Water Management District (SFWMD) and the National Ocean and Atmospheric Administration (NOAA). The FIAN Port of Miami (POM) study location is dominated by three species of seagrass: Thalassia testudinum, Syringodium filiforme, and Halodule wrightii. Analysis has shown that over the seven-year period, 2005 - 2011 the state of the seagrass has been fairly stable with minor perturbances (p > 0.05). There are some seasonal fluctuations evident in seagrass cover-densities, but minimal change was observed between the spring and fall (p > 0.05). Syringodium is the dominant species, followed by Thalassia and Halodule within the POM. Environmental and physical conditions from FIAN (salinity, temperature, sediment depth, turbidity, etc.) varied between years and seasons; however, most measurements remained in the ideal range for seagrass growth. Water depth, sediment depth, and turbidity were significant predictors of seagrass occurrence in the POM; however, water depth was the only major predictor of seagrass cover-density. The available environmental and physical data from the SFWMD showed minimal changes in the environmental and physical measurements across available sample years and are in the ideal range for seagrass. Turbidity has improved since the completion of the port construction and major weather disturbances (hurricanes) in 2005. Minimal changes were detected during the seven year study period (2005-2011) within the seagrass habitat of the heavily urbanized region of POM.

Lakes are key indicators of environmental change and major repositories of biodiversity and ecosystem services. However, studies of lake response to drivers of change at a pan-European scale are exceptionally rare. The need for such studies has been given renewed impetus by concerns over climate change and because of international policyrelated schemes, such as the EU Water Framework Directive that has made it legal requirement to repeatedly assess and monitor the ecological status of European lakes toward their effective management and sustainable use. This has introduced the need for methods that can be widely applied across large spatial and temporal scales and produce comparable results. Remote sensing is a promising method for providing such information, but the spatial transferability and temporal repeatability of methods and relationships observed remains untested. In this project, an extensive dataset of field measurements was compiled covering temperature, chlorophyll a and Secchi disk depth in 23 European lakes spanning the last 30 years. The characteristics of these lake systems were explored and similarities in their ecological behavior identified, thus providing the basis for their grouping. Then the potential of remote sensing for estimating and monitoring lake water quality at wide spatial and temporal scales was assessed and thus the long remote sensing archive at the NEODAAS DSRS was fundamental for the purposes of this project. Using NOAA AVHRR, Terra/Aqua MODIS and field data from lakes that represented three main lake groups, the spatial and temporal reliability of 26 existing water quality estimation algorithms was assessed. Following this, the best performing algorithms were applied to all study sites and the effect of scale and spatial resolution upon reliable estimation of key water quality parameters was evaluated. It was demonstrated that the NOAA AVHRR and Terra/Aqua MODIS were both capable of producing highly accurate (R2 > 0.9) lake surface temperature estimates in lakes with variable characteristics and a variety of thermal spatial features, and longterm patterns within the study sites could be studied with NOAA AVHRR data despite the relatively coarse spatial resolution of the sensor. Restricting factors to the latter were the size and shape of lakes and the frequency of cloud cover. By contrast, the development of a universal Terra/Aqua MODIS algorithm for the estimation of chlorophyll a and Secchi disk depth in variable lakes was more challenging due to the optical complexity of Case II waters. Terra/Aqua MODIS data showed a potential, but the use of a different technique (e.g. multivariate regression or neural networks) and/or a different sensor (e.g. Envisat MERIS) could potentially improve the predictive accuracy of the algorithms.

Snow indicates the potential stored water volume that is an important source of water supply, which has been the most valuable and indispensable natural resource throughout the history of the world. Euphrates and Tigris, having the biggest dams of Turkey, are the two largest trans-boundary rivers that originate in Turkey and pass throughout the water deficit nations Syria, Iran, Iraq and Saudi Arabia bringing life as well as water all their way. Snowmelt runoff originating from the mountains of Eastern Turkey accounts for 60 to 70 % of total annual discharge observed in Euphrates and Tigris. For an optimum operation of the dams, maximizing energy production, mitigation of floods and satisfying water rights, hydrological models which can both simulate and forecast the river discharges of Euphrates and Tigris are needed. In this study a hydrological model, snowmelt runoff model (SRM), is used in conjunction with remote sensing and geographic information systems to forecast the river discharges in the headwaters of Euphrates River, Upper Euphrates Basin. NOAA and MODIS satellite images were used to derive the snow covered area (SCA) information required by SRM. Linear reduction methodologies based on accumulated air temperature, with constant or varying gradient, were developed to get the continuous daily SCA values from the discrete daily satellite images. Temperature and precipitation forecasts were gathered from two different numerical weather prediction models, namely European Center for Medium Range Weather Forecasts (ECMWF) and Mesoscale Model Version 5 (MM5) from Turkish State Meteorological Services. These data sets provided t+24 hour forecasts of both temperature and precipitation. Temperature, precipitation and SCA information are fed into SRM. Discharge forecasts obtained from the model outputs are compared with the observed values. The overall performance of the model was seen as promising. Possible reasons of the mismatches between the forecasted and observed values are searched. Experiences gained throughout the study are summarized and recommendations on further forecast studies are mentioned.

In this thesis, we present the design and implementation of a CubeSat receiver system using the Universal Software Radio Peripheral (USRP) and GNU Radio. The goal of this project is to develop a low-cost and flexible ground station capable of receiving telemetry and payload data from CubeSats in real time. The CubeSat receiver operates in the UHF frequency range with a center frequency of 435 MHz and uses a software-defined radio (SDR) approach to provide wideband signal processing and demodulation capabilities. The satellite transceiver transmits an Ax.25 Transciever packet every 1 second using the Pumpkin CubeSat kit programmed in MPLab. To achieve this goal, we discuss the design considerations for the receiver system, including the selection of suitable hardware components and the development of custom software blocks in GNU Radio. We also developed the GFSK-based transmitter and receiver in GNU Radio, as well as a tracking system for the satellite. To decode the Ax.25 radio packet transmitted by the Pumpkin CubeSat kit, we developed an Ax.25 deframer in GNU Radio to decode the received signal. Our results demonstrate that the CubeSat receiver is capable of receiving and demodulating AX.25 formatted radio signals from Transciever. Additionally, we show that the receiver system is scalable and can be easily adapted for use with other CubeSat missions. Overall, our work provides a practical solution for CubeSat communication and lays the groundwork for future developments in low-cost CubeSat ground station technology.

El objetivo general del presente trabajo de investigación fue analizar la variabilidad de la temperatura satelital superficial del mar (TSM) y su efecto en las principales pesquerías que operan sobre un sector de la Plataforma Continental Argentina, que abarca desde el Golfo San Jorge (GSJ) hasta el Talud Continental (TC) adyacente. Esta zona es estratégica dentro del Proyecto Nacional Pampa Azul por su importancia socio-ambiental. Para abordar dicho objetivo se elaboró, por primera vez, una matriz de puntos equidistante que permitió ensamblar productos satelitales derivados de los sensores NOAA/AVHRR y Aqua-MODIS. Esta matriz de puntos posibilitó compararlos (período de solapamiento 2003-2006) y analizar la factibilidad de utilizar una combinación de ambos productos para construir series de tiempo más extensas y confiables. El estudio comparativo de la TSM demostró un alto grado de correlación entre ambos sensores, evidenciando que los productos satelitales son similares entre sí y que pueden ser utilizados de manera conjunta. A partir de los resultados obtenidos se construyó una serie satelital de 35 años de información radiométrica de la TSM proveniente de los sensores NOAA/AVHRR y Aqua-MODIS, la cual permitió realizar un análisis de la variabilidad de este parámetro y sus correspondientes anomalías. Además, se analizaron sus fluctuaciones con el fin de identificar tendencias de cambio térmico en el área de estudio. Para ello, y dadas las características batimétricas y oceanográficas diversas que actúan sobre el área de estudio, se dividió al mismo en tres sectores: Zona 1: Golfo San Jorge; Zona 2: Plataforma Continental (PC) y Zona 3: Talud continental. La señal de la TSM evidenció en todo el sector una variabilidad significativa intra-estacional, anual e interanual (28 meses). Sin embargo, se identificaron señales con diferentes rangos de periodicidad dependiendo de la Zona (1, 2 o 3) estudiada. Posteriormente, se examinó la variabilidad de la anomalía de la TSM (ATSM) y sus posibles conexiones con los siguientes índices climáticos: Modo Anular del Sur (SAM), Índice de la Oscilación del Sur (SOI) y Índice Niño Oceánico (ONI). El análisis permitió establecer que las variaciones atmosféricas remotas influyen en la ATSM local. Si bien se ratifica el efecto del SAM sobre el área, se destacó el efecto del ONI-SOI durante las últimas décadas. El vínculo entre las variaciones de la TSM y las capturas del Abadejo (Genypterus blacodes), Calamar argentino (Illex argentinus), Langostino (Pleoticus muelleri) y Merluza común (Merluccius hubbsii) en combinación con las presiones de origen antrópico (valor de la especie en el mercado y el valor del combustible) permitió determinar que la variación de las capturas de ciertas especies como el Abadejo y el Calamar argentino están relacionadas de manera significativa con la variación de la TSM. El análisis demostró que los registros de las capturas por unidad de esfuerzo (CPUE) del Abadejo se asociaron a meses en que la TSM estuvo por encima de valor promedio. Para el Calamar argentino, el aumento de la CPUE fue posterior a los registros de ATSM positivos. En cambio, para el caso del Langostino y la Merluza, los resultados permitieron determinar que los valores de CPUE no mostraron relación con la variación de la temperatura superficial del mar. Asimismo, posibilitó entender que los forzantes antrópicos ejercen presión sobre el aumento de las capturas ya que en las cuatro pesquerías se observó una correlación significativa entre el valor de la especie y el valor del combustible. Para la pesquería del Abadejo, el Calamar y la Merluza esta relación fue positiva, indicado que los precios de estas especies han ido aumentando a lo largo del período analizado, reflejando un equilibrio entre el costo y el beneficio. En cambio, para el Langostino está correlación fue negativa, evidenciando un comportamiento inverso en la relación costo-beneficio de dicha pesquería. Los resultados obtenidos permitieron determinar el grado de complejidad que conlleva entender las relaciones atmósfera-océano-sociedad, sino que también sentó las bases para comprender que los factores que influyen en una determinada pesquería no necesariamente se aplican a otras. / ABSTRACT The purpose of the present research work was to analyze the variability of satellite sea surface temperature (SST) and its effect on the main fisheries operating on a sector of the Argentine Continental Shelf (ACS), which extends from the San Jorge Gulf (SJG) to the adjacent Continental Slope (CS). This area is strategic within the Pampa Azul National Project due to its socio-environmental importance. For the first time, an equidistant grid was developed, which allowed the assembly of satellite products derived from NOAA/AVHRR and Aqua-MODIS sensors. This grid made it possible to compare them (overlap period 2003-2006) and analyze the feasibility of using both products to build longer and more reliable time series. The SST comparative study showed a high degree of correlation between both sensors, evidencing that the satellite products are similar and can be used jointly. Based on the obtained results, a satellite series of 35 years of SST radiometric information from NOAA/AVHRR and Aqua-MODIS sensors was constructed, which allowed an analysis of the variability of this parameter and its corresponding anomalies. In addition, its fluctuations were analyzed to identify thermal change trends in the study area. For this purpose, and given the different bathymetric and oceanographic characteristics acting on the study area, it was divided into three sectors: Zone 1: SJG; Zone 2: ACS and Zone 3: CS. The SST signal showed significant intra-seasonal, annual and interannual (28 months) variability throughout the area. However, signals with different periodicity ranges were identified depending on the Zone (1, 2 or 3) studied. Subsequently, the variability of the SST anomaly (SSTA) and its possible connections with the Southern Annular Mode (SAM), the Southern Oscillation Index (SOI) and the Oceanic Niño Index (ONI) were examined. The analysis allowed establishing that remote atmospheric variations influence the local SSTA. Even though the effect of SAM on the area was confirmed, the effect of ONI-SOI during the last decades was highlighted. The link between SST variations and catches of pollock (Genypterus blacodes), Argentine squid (Illex argentinus), shrimp (Pleoticus muelleri) and common hake (Merluccius hubbsii) in combination with pressures of anthropic origin (the market value of the species and the value of fuel) allowed determining that the variation in catches of certain species such as pollock and Argentine squid are significantly related to SST variation. The analysis showed that catch per unit effort (CPUE) records for pollock were associated with months when SST was above the average value. For Argentine squid, the increase in CPUE followed positive SST records. On the other hand, in the case of shrimp and hake, the results allowed us to determine that CPUE values showed no relationship with SST variation. Likewise, it was possible to understand that anthropic forcing exerts pressure on the increase in catches since in the four fisheries, a significant correlation was observed between the value of the species and the value of the fuel. For the pollock, squid and hake fisheries, this relationship was positive, which indicates that the prices of these species have been increasing throughout the period analyzed, reflecting a balance between cost and benefit. On the other hand, this correlation was negative for shrimp, showing an inverse behavior in the cost-benefit relationship of this fishery. The obtained results made it possible to determine the degree of complexity involved in understanding the atmosphere- ocean-society relationships and laid the foundations for understanding that the factors that influence a particular fishery do not necessarily apply to others. / TEXTO PARCIAL en período de teletrabajo

The history of mitigation programs to contain vector-borne diseases is a story of successes and failures. Due to the complex interplay among multiple factors that determine disease dynamics, the general principles for timely and specific intervention for incidence reduction or eradication of life-threatening diseases has yet to be determined. This research discusses computational methods developed to assist in the understanding of complex relationships affecting vector-borne disease dynamics. A computational framework to assist public health practitioners with exploring the dynamics of vector-borne diseases, such as malaria and dengue in homogenous and heterogeneous populations, has been conceived, designed, and implemented. The framework integrates a stochastic computational model of interactions to simulate horizontal disease transmission. The intent of the computational modeling has been the integration of stochasticity during simulation of the disease progression while reducing the number of necessary interactions to simulate a disease outbreak. While there are improvements in the computational time reducing the number of interactions needed for simulating disease dynamics, the realization of interactions can remain computationally expensive. Using multi-threading technology to improve performance upon the original computational model, multi-threading experimental results have been tested and reported. In addition, to the contact model, the modeling of biological processes specific to the corresponding pathogen-carrier vector to increase the specificity of the vector-borne disease has been integrated. Last, automation for requesting, retrieving, parsing, and storing specific weather data and geospatial information from federal agencies to study the differences between homogenous and heterogeneous populations has been implemented.

L'important increment de la capacitat de càlcul computacional que s'ha donat en els últims anys ha fet que els models numèrics meteorològics hagin pogut assolir resolucions de treball molt fines. Ara bé, aquest increment en la resolució, per si sol, no és suficient per millorar-ne les prestacions. Cal introduir algorismes més sofisticats per afinar en la simulació de la dinàmica atmosfèrica, i també cal millorar la caracterització física de la superfície. En aquest sentit, els valors climàtics globals que es fan servir actualment no siguin prou precisos i calguin valors característics de cada regió. Malgrat que a través de la teledetecció no es pot fer una mesura real dels diferents paràmetres de la superfície terrestre, les imatges de satèl·lit són el millor recurs per estimar-los. La periodicitat en l'adquisició d'imatges d'una mateixa zona fa que es puguin fer estimacions estacionals o mensuals, podent així fer un seguiment de la variació d'aquests paràmetres al llarg del temps.En aquest treball s'ha establert una metodologia operacional per a l'obtenció periòdica de paràmetres de superfície, a partir d'imatges del sensor satel·litari NOAA/AVHRR, per Catalunya i les regions que l'envolten. La revisió bibliogràfica de les diverses metodologies existents ha permès seleccionar les més idònies per aquesta zona. A nivell dels resultats, s'ha obtingut una sèrie mensual de diferents paràmetres, que cobreix els mesos de març a octubre de l'any 2000. Aquests paràmetres de superfície són els següents:· Albedo· Emissivitat· Índex de vegetació NDVI· Temperatura de superfície diürna i nocturna.· Inèrcia tèrmica Els resultats obtinguts amb dades de satèl·lit són espacials, són imatges que ens donen valors quantitatius per a cada cel·la de la superfície estudiada. A l'hora de fer-ne un tractament estadístic, aquestes imatges es sintetitzen a través de mapes d'usos del sòl, obtenint resultats per a les categories dels mapes. Com tota simplificació, aquesta comporta una pèrdua d'informació; i en aquest punt cal ser força crític a l'hora de triar una classificació d'usos del sòl de la regió de treball. Aquest aspecte és un dels punts on s'ha volgut aprofundir en la discussió de resultats. S'ha treballat amb dos mapes d'usos del sòl, el del USGS (Servei Geològic dels EEUU) i el CORINE (Agència Europea del Medi Ambient). El primer és d'abast global, té 24 categories i és el que fa servir el model meteorològic MM5 per la caracterització de la superfície en les simulacions. El segon és més actual i cobreix gran part d'Europa. El major nombre de categories (44), i la metodologia emprada en l'elaboració, pensada per els usos del sòl que dominen el nostre continent, fan que aquest mapa sigui més adequat per tal de caracteritzar els usos del sòl de Catalunya i els seus voltants.Els paràmetres geofísics de superfície obtinguts s'han fet servir per inicialitzar un model meteorològic de mesoescala, amb la finalitat de millorar els pronòstics. Les simulacions s'han fet amb el model de mesoescala MM5 (PSU/NCAR). Els canvis introduïts en els paràmetres de superfície, a través del canvi del mapa d'usos del sòl i de la introducció dels valors calculats amb AVHRR per l'any 2000, han estat prou importants com per afectar els resultats de les simulacions. Cal destacar les diferències en el balanç hídric, que provoquen simulacions diferents de les masses nuvoloses i el patrons de precipitació. Aquestes diferències en la nuvolositat també modifiquen el balanç radiatiu, que alhora afecta l'evolució diària de la temperatura a nivells baixos. A nivell meteorològic, les variacions en el desenvolupament de núvols en situacions de domini mesoescalar és important per a la correcta simulació de desenvolupament de tempestes locals durant èpoques estivals. Finalment, també s'han observat variacions significatives en el camp de vent de superfície, aspecte important quan s'utilitza el model MM5 per a la simulació de la dispersió de contaminants atmosfèrics.Els resultats obtinguts per l'any 2000 no es poden considerar climàticament significatius. En aquest sentit, una de les vies de continuïtat d' aquest treball és l'obtenció de resultats per una sèrie més llarga de dades, que permeti conèixer millor les característiques geofísiques de la superfície estudiada i millorar les simulacions meteorològiques. / Mesoscale models, with grid resolution higher than synoptic models, and with advanced physical parameterizations, have been an important tool for meteorological research over the past twenty years. Important improvements on mesoscale models have occurred in the last decade. The availability of high-performance workstations at affordable prices; the sharing of mesoscale models within the community; and finally the real-time accessibility of forecast data from the operational runs; have allowed using mesoscale models for real-time numerical weather prediction (NWP) at high resolutions (~1 km).As mesoscale models continue to increase in spatial resolution, correctly treating the land surface processes is becoming increasingly important for the model to be able to capture local mesoscale circulations induced by land surface forcing. Mesoscale models are incorporating progressively advanced land surface modules in order to properly initialize the state of the ground.Physical model improvements should be complemented with more accurate surface properties initiation data. The present work is focused in this point. An operational methodology has been developed, in order to calculate, from satellite imagery, the surface properties for Catalonia, in the NE of Spain. Satellite observations constitute the only available means for global or regional repetitive monitoring of the surface properties at homogeneous resolution.Prior to calculations, a bibliographical research has been done, in order to choose the most adequate methodology according to the remote sensing data available and the studied region. Monthly mean surface parameters have been calculated for the working region from an AVHRR data set of year 2000. Besides the resulting images, surface parameters have also been calculated for the land-use categories in the region. Calculated parameters are:· Albedo· Emissivity · Normalized Difference vegetation Index (NDVI)· Surface temperature· Thermal inertiaIn order to test the obtained parameters, two simulations have been done with the MM5 mesoscale model. The Fifth-Generation NCAR / Penn State Mesoscale Model (MM5) is a limited-area, non-hydrostatic, terrain-following sigma-coordinate model designed to simulate or predict mesoscale and regional-scale atmospheric circulation.A first simulation, using MM5 default values, has been compared with a second simulation where the local physical parameters have been introduced. Besides the change of the surface parameters, the default MM5 land-use map has also been changed, using a more recent land-use map of the region. Results have shown that differences in surface parameters basically rely on thermal inertia. Besides, the land-use maps comparison had shown important differences between classifications that also affect the final composition of surface parameters that get into the model. Modifications on the second simulation have been sufficiently significant to produce variations in the performance of the model. The cloud development differs basically in the location and dimensions of the clouds, that drives to a different superficial radiative budget affecting the evolution of air temperature at low levels. The different results in cumulus simulation produced important differences in the surface wind field and the updrafts. The changes introduced are sufficiently significant to obtain also slight variations in the pattern of accumulated precipitation for the simulated period. Comparisons with ground measurements of wind and temperature have been done in the test regions. Similar errors are obtained with the two land-use maps and physical parameters, without a clear improvement in the performance of the meteorological model.The simulations done in this work contributes evidence to the high influence of surface scheme applications of mesoscale models at high spatial resolution. In the context of dialogue between remote sensing scientists and numerical climate modelers, it is expected that more research should be done to investigate the sensibility of the mesoscale models to improvements in the surface properties characterization.