Le sondeur infrarouge hyperspectral IASI (Interféromètre Atmosphérique de Sondage Infrarouge, développé conjointement par le CNES et EUMETSAT et embarqué à bord des satellites défilants Metop A, Metop B et très prochainement Metop C (2006, 2012 et 2018, respectivement)) apporte une très grande quantité d’informations permettant, entre autres, de décrire finement les paramètres de surface (température et émissivité sur une large gamme de longueurs d’onde). Néanmoins, les prévisions de température des surfaces continentales ne sont pas encore suffisamment réalistes pour utiliser l’information infrarouge en basse troposphère et proche de la surface au-dessus des continents car les radiances sensibles à ces régions sont fortement affectées par la variation des paramètres de surface (tels que la température, l’émissivité et l’humidité) et par la présence des nuages. Ceci peut conduire à un écart parfois important entre les observations et les simulations, conduisant à un rejet important des observations et à une mauvaise détection nuageuse. De ce fait, l’objectif principal de la thèse est l’amélioration des analyses et des prévisions par l’augmentation des observations IASI assimilées sur les continents dans le modèle à aire limitée AROME. La première partie du travail s’est focalisée sur l’identification du canal IASI le plus approprié à la restitution de la tempèrature de surface (Ts). En poursuivant les travaux de thèse de [Vincensini, 2013], cinq canaux IASI localisés entre 901.50 cm−1 et 1115.75 cm−1 ont été sélectionnés pour une meilleure prise en compte des basses couches de l’atmosphère plus particulièrement en termes de température et d’humidité. La restitution de la Ts s’est faite par inversion de l’équation du transfert radiatif [Karbou et al., 2006] en utilisant le modèle de transfert radiatif RTTOV et l’atlas d’émissivité développé par l’université de Wisconsin. Le canal IASI 1194 (943.25 cm−1) a été retenu pour la restitution des Ts suite à une série de comparaisons effectuées entre la Ts restituée à partir des différents canaux IASI sélectionnés et celle de l’ébauche. Aussi, des comparaisons ont été réalisées entre les Ts restituées à partir de IASI et celles restituées à partir de SEVIRI et de AVHRR. La seconde partie du travail a reposé sur l’étude de l’impact de l’utilisation de la Ts restituée à partir du canal IASI 1194 dans les processus de simulation et d’assimilation des canaux IASI utilisés dans les modèles de prévision numérique du temps de Météo-France (AROME et ARPEGE). La Ts restituée à partir du canal IASI 1194 a été intégrée dans le modèle RTTOV pour améliorer les simulations des autres observations IASI sensibles à la surface. L’impact sur la détection nuageuse issue de l’algorithme de [McNally and Watts, 2003] a également été évalué. Par la suite, d’autres expériences ont été menés pour étudier l’impact de l’utilisation des Ts restituées sur l’assimilation de données et sur l’amélioration de la sélection des canaux IASI sur terre dans le modèle AROME. L’impact sur les analyses et les prévisions ont été également décrits. / An essential component of the numerical weather forecast is the analysis of the atmosphere, the necessary step for the definition of the initial conditions of forecasts. This analysis uses in-situ data as well as satellite observations. The current high-spectral resolution advanced infrared sounder generation includes in particular IASI (Infrared Atmospheric Sounding Interferometer, developed by CNES / EUMETSAT) onboard polar orbiting MetOp satellites. These sounders provide a large amount of information allowing to describe accurately surface parameters (such as land surface temperature ’LST’ and surface emissivity on a wide range of wavelengths). However, the forecast of continental surface temperature is not realistic enough to use the infrared information in the lower troposphere and close to the surface over continents because radiances sensitive to these regions are strongly affected by the variation of surface parameters (e.g. LST, surface emissivity and humidity) and cloud cover. This issue could produce a large difference between the observations and the simulations, also a bad cloud detection, which prompts the system to reject the observations and limits the use of these data. This PhD work aims to improve the analyses and the forecasts by increasing the assimilation of IASI observations over land in the convectivescale AROME model of Météo-France. The first part of study was focused on the identification of the appropriate IASI surface-sensitive channel for LST retrieval. By pursuing the approach developed by [Vincensini, 2013] to find surface temperature from a combination of channels, a new channel selection over land was build, to better analyse the lower layers of the atmosphere, in particular in term of temperature and humidity. LST was extracted from IASI radiances using radiative transfer equation inversion [Karbou et al., 2006], RTTOV model and a surface emissivity atlas developed by the Space Science and Engineering Center at University of Wisconsin. IASI channel 1194 was then selected to retrieve LST as a result of several comparisons with background and other IASI, SEVIRI and AVHRR LST retrievals. The retrieved LST from this channel was then used in RTTOV model to improve the simulation of IASI surface-sensitive infrared observations. The impact on the McNally & Watts cloud detection scheme has been evaluated with more clear channels inside clear pixel with LST retrieval. Data assimilation experiments using the retrieved LST and enhancing the IASI channel selection over land were carried out in the AROME-France model. Improvement of humidity analyses and forecasts will also be described.

One major challenge in representing the state of the atmosphere through weather forecast models, is the parametrization of sub-grid clouds. At every vertical column of grid cells within a weather forecast model, the fractional cloud cover is assumed to overlap according to a prescribed Cloud Overlap Assumption (COA). Since the total cloud cover is used in radiation schemes, the choice of COA affects e.g. radiative fluxes. High-quality weather forecasts is important for many aspects of the society, thus, the analysis of cloud parametrizations is significant. In this study, COAs for the HIRLAM ALADIN Research on mesoscale Modelling for NWP In Euromed (HARMONIE) - Application of Research to Operations at Mesoscale (AROME) model were investigated for two time-periods. Moreover, validation methods of cloud cover for HARMONIE-AROME were analyzed due to uncertainties in cloud observations. Both satellite data derived from geostationary Meteosat Second Generation (MSG) satellite and synoptic ground based observations were used to validate cloud cover in this project. It was found that HARMONIE-AROME underestimates the cloud cover during summer. Therefore, the random (RAN) COA is the preferred COA to use during time periods of mainly convective cloud processes. During the tested winter period, which is assumed to have most clouds of the stratiform type, the results regarding optimal COA were not certain. However, it was concluded that HARMONIE-AROME overestimates the cloud cover during winter, for in which case the maximum-random (MRN) COA is recommended to use. The comparative analysis of cloud cover as obtained from the COAs against observed cloud cover, was shown sensitive to the methods used to the observational data. Using a model grid of 25 km instead of 2.5 km when comparing synoptic observations to modelled cloud cover, the errors were reduced. When using binary satellite data, it was concluded that a 5x5 smoothing algorithm was the most appropriate to use since this averaging of several pixels are sufficient to represent sub-grid clouds.

As a complement to existing weather forecast products for aviation, a prototype of a new product is presented and evaluated. It shows the atmosphere in a vertical cross section along the intended route. This Vertical Route Forecast introduces the possibility to examine the vertical distribution of cloud layers, wind, precipitation, turbulence and more along the flight path. Through a market research with 166 participating Swedish pilots it was found that the demand for the product is high and that 90 % of the participants would use it if available. The Vertical Route Forecast is inspired by the existing product GRAMET by Ogimet (Ballester Valor, n.d) but instead of using forecasts from the weather prediction model GFS (Global Forecast System) at 0.5° (56 km) resolution it uses data from the 2.5 km resolution model HARMONIE-AROME. The latter is operational at SMHI (Swedish Meteor-ological and Hydrological Institute) and because of its high resolution it enables more detailed structures of the weather to be presented. The product differs further from GRAMET by showing only the lower parts of the atmosphere so as to be of more use to small aircraft pilots flying at low levels. To assess the accuracy of the forecasts, a model evaluation of HARMONIE-AROME has been conducted through a case study in which the model was verified and compared to GFS over Sweden. The two models were verified against their own analyses at four different atmospheric pressure levels in terms of bias, root mean square error, standard deviation and correlation. HARMONIE-AROME performed best for temperature while GFS had the best forecasts of relative humidity. Wind speed and direction were also evaluated with insignificantly better results for GFS. However, the weather did not vary very much during the study as the two weeks were dominated by high pressure systems. Other evaluations made of HARMONIE-AROME by e.g. the HIRLAM consortium (2016a) have shown good or adequate performance of the model. It was concluded that HARMONIE-AROME would be well suited as the forecast producing model for this Vertical Route Forecast. / För att piloter ska kunna planera en säker flygning behöver de tillgång till bra och användbara väderprognoser. Med de prognosprodukter som finns tillgängliga idag kan det dock vara svårt att få en detaljerad uppfattning om hur vädret kommer vara längs med vägen. Här presenteras och utvärderas därför ett förslag till en ny prognosprodukt som visar atmosfären i en sidovy längs en valfri sträcka. Med den kan piloten granska utbredningen av bland annat molntäcken, vind, nederbörd och turbulens i höjdled längs den planerade färdvägen. Denna vertikala ruttprognos är inspirerad av den redan befintliga produkten GRAMET från Ogimet (Ballester Valor, n.d) men visar mer detaljerade prognoser som är bättre anpassade till flygningar på låga höjder. Vid en marknadsundersökning utförd med 166 medverkande svenska piloter stod det klart att efterfrågan på produkten är hög och 90 % av de medverkande påstod att de skulle använda den om den fanns tillgänglig. För att bedöma prognosernas precision har en utvärdering gjorts av den prognosmodell som använts till produkten. Modellen används annars hos SMHI (Sveriges Meteorologiska och Hydrologiska Institut) och kallas HARMONIE-AROME. I en fallstudie jämfördes den med modellen GFS som skapar prognoserna för GRAMET. Studien täckte Sverige och sträckte sig över 14 dagar i början av februari, 2017. HARMONIE-AROME visade bäst resultat för temperatur medan GFS gjorde de bästa fuktighetsprognoserna. Vindhastighet och vindriktning undersöktes också och för dem var modellerna ungefär lika bra. Vädret varierade dock inte så mycket under tvåveckorsperioden som dominerades av högtryck. Andra utvärderingar som gjorts av HARMONIE-AROME togs också i beaktande och modellen verkar generellt sett göra bra prognoser. Från samtliga resultat drogs slutsatsen att prognos-produkten skulle underlätta för småplanspiloter samt att HARMONIE-AROME är en lämplig modell att använda för att skapa dess prognoser.

Temperaturprognoser är av stor betydelse för många i dagens samhälle, både privatpersoner och diverse olika sektorer. Förväntan på att prognoserna håller hög träffsäkerhet är stor och god kvalitet på dessa är viktigt av många olika aspekter. De numeriska vädermodellerna, som används för att göra väderprognoser, har brister som i stort sätt alltid leder till systematiska fel i prognoserna. Bristerna beror exempelvis på dålig representation av atmosfärens fysikaliska processer och för att korrigera och reducera dessa fel efterbehandlas prognoserna med olika metoder, så kallad post-processing. För att minimera de systematiska felen och öka träffsäkerheten för prognoserna pågår ständigt en utveckling och förbättring av både modellerna och post-processingmetodiken. Uppföljning och utvärdering av prognoser är av stor nytta för denna utveckling som ska leda till minimering av prognosfel och optimering av modell och metodik. I denna studie har temperaturprognosdata, med prognoslängd 0-12 timmar, från Sveriges Meteorologiska och Hydrologiska Institut (SMHI) och norska Meteorologisk institutt (met.no) jämförts med uppmätta värden för 2 m-temperatur. Observerad temperaturdata från 22 olika synoptiska väderstationer på platser utspridda över hela Sverige har använts i studien och perioden som studien är baserad på är 20 februari till 31 maj 2018. Statistiska mått, med mest fokus på korrelationskoefficient och bias, har analyserats och jämförts för att undersöka likheter och skillnader i temperaturprognoserna från de två olika väderinstituten. Resultaten av studien visar att temperaturprognoserna från met.no generellt sett har något högre träffsäkerhet än SMHI:s för de allra flesta av de 22 geografiska platserna. Båda institutens prognoser har för flertalet av stationerna i fjällen samt norra Sverige generellt sett lägre träffsäkerhet för februari än för mars, april och maj. / Temperature forecasts are of great importance for many different reasons in today's society, both for private individuals and various sectors. The expectations that the forecasts maintain high accuracy and good quality is important in many different aspects. The weather models, which are used to make the forecasts, have deficiencies which in large part always lead to systematic errors in the forecasts. The deficiencies are for example, due to poor representation of the physical processes of the atmosphere and to correct and reduce these errors, the forecasts are post-processed by various methods. To minimize the systematic errors and increase the accuracy of the forecasts, there is an ongoing development and improvement of both the models and the post-processing methods. Evaluation of forecasts is of great benefit to this development, which will lead to minimization of forecast errors and optimization of the model and methodology. In this study, temperature forecast data, with a forecast length of 0-12 hours, from the Swedish Meteorological and Hydrological Institute (SMHI) and the Norwegian Meteorological Institute (met.no) were compared with measured 2 m-temperature values. Observed temperature data from 22 different weather stations in locations scattered all over Sweden have been used in the study and the period on which the study is based is from the 20th of February to 31st of May, 2018. Different statistical measures have been analyzed and compared to examine similarities and differences in temperature forecasts from the two different weather institutes. The results of the study show that met.no's temperature forecasts generally have slightly higher accuracy than SMHI's for most of the 22 locations. For any of the stations in the mountains and northern Sweden forecasts from both institutes generally have lower accuracy for February than March, April and May.

Previous studies, such as van der Veen (2012) and White et al. (2017), have demonstrated the potential of using measurement-based cloud data to improve Numerical Weather Prediction (NWP) based cloud forecasts. This can be done through cloud initialization; a process of injecting cloud data after the regular data assimilation in an NWP model. The purpose of this study was to use cloud data from the Mesoscale Analysis system MESAN to investigate cloud initialization in the HARMONIE-AROME model system for improving short-range cloud forecasts. The cloud initialization method that was used was similar to a method used by van der Veen (2012), where specific humidities, temperatures, and hydrometeor concentrations were altered using information on cloud fractions, cloud base heights and cloud top heights. MESAN input data analyses as well as cloud initialization investigations were carried out. MESAN input data analyses revealed significant differences in cloud fractions between MESAN and the background model field in MESAN. Overestimations of cloud fractions in MESAN over sea were caused by satellite data, particularly due to the inclusion of the fractional cloud category. Underestimations of cloud fractions over land were caused by limitations of the synoptic weather (SYNOP) stations in measuring clouds. Furthermore, larger differences between MESAN and SYNOP were found over Sweden and Finland compared to Norway, which may be tied to Norway having mostly manual SYNOP stations, and Sweden and Finland having mostly automatic stations. Shortcomings were found in the investigated cloud initialization method. Such shortcomings involved a limit check on the specific humidity change, the cloud initialization being repeated for an unnecessarily large amount of iterations, and the use of a sub-optimal profile of critical relative humidity. Using a one-dimensional vertical column version of HARMONIE-AROME, named MUSC, to integrate forward in time revealed a large sensitivity to the use of forcing profiles and forcing time scales in MUSC. Alterations made through cloud initialization were found to last over 12 h, with varying effects depending on the investigated height. A reasonably good agreement between MUSC results and results from the three-dimensional version of HARMONIE-AROME was found. Findings in this thesis point at potential to further enhance the HARMONIE-AROME cloud initialization technique. These enhancements concern a revised MESAN cloud product and taking care of some flaws in the cloud initialization method. / I en operationell vädermodell inkluderas olika mätdata, såsom temperatur och atmosfärstryck, i ett regelbundet intervall. Molnighet är inte vanligtvis en del av dessa cykler; istället bildas molnen av modellen utifrån balanser i de andra fysikaliska fälten. Detta projekt gick ut på att direkt införa molnmätningar från väderanalyssystemet MESAN i vädermodellsystemet HARMONIE-AROME genom en metod som kallas molninitialisering. Specifikt förbättringar för korttidsprognoser var av i ntresse. MESAN är ett system vars produkter är en sammanslagning av ett bakgrundsfält från en vädermodellkörning med olika mätdata. I MESAN kommer molndata från tre källor: bakgrundsfältet, satellitdata och synoptisk väderstationsdata (SYNOP-data). Undersökningar av indata till MESAN samt molninitialiseringsmetoden har utförts. Analyser av indata till MESAN visade på överskattningar av moln i satellitdata över hav och underskattningar av moln i SYNOP-data över land. För satellitdatat berodde detta på medtagande av moln på liten skala eller väldigt tunna moln, medan det för SYNOP berodde på begränsningar i mätmetoderna. Det fanns även en skillnad i kvalitet i SYNOP-data i Sverige och Finland gentemot Norge, vilket kan bero på att de flesta mätstationer i Norge är manuella medan de flesta i Sverige och Finland är automatiska. Molninitialiseringsmetoden bestod i att extrahera data om molnbashöjd och molntopphöjd från MESAN, och sedan modifiera fuktighet, temperatur och hydrometeorer (såsom molndroppar och iskristaller) i HARMONIE-AROME utifrån molnens position. Brister i metoden hittades. Initialiseringsprocessen upprepades ett suboptimalt antal gånger. En begränsning i hur mycket fuktigheten tillåts modifieras förändras under initialiseringsprocessen och fungerade inte som avsett. Dessutom, jämförese med radiosonddata pekar på att relativa fuktighetsgränserna för villket moln bildas inledningsvis inte ansattes korrekt. Effekterna av metoden kunde vara i över 12 timmar, men denna studie pekar på ytterligare troliga förbättringsmöjligheter i HARMONIE-AROME genom införande av reviderad version av metoden samt förbättrade satellitprodukter.

Pour les distributeurs d’eau, l’utilisation de chlore permet d’assurer la qualité bactériologique de l’eau, de l’usine de traitement au robinet du consommateur. Cependant, la flaveur chlore constitue une des plaintes les plus importantes adressée à l’encontre de l’eau du robinet et constitue donc un enjeu majeur de satisfaction des consommateurs. Dans ce contexte, l’objectif des travaux engagés dans cette thèse a été de mettre en évidence des moyens potentiels de neutralisation sensorielle de la flaveur chlore dans l’eau. Néanmoins, les mécanismes impliqués dans la perception de cette flaveur étant largement méconnu, notre première étape a consisté à préciser la nature de ces mécanismes. Nos résultats ont ainsi mis en évidence que l’acide hypochloreux sous sa forme associée, qui est le stimulus supposé de la flaveur chlore dans l’eau, active le système olfactif à faibles concentrations et le système trigéminal à partir de 4 mg/L. De plus, nous avons observé que la consommation d’eau du robinet ne semble pas liée à la sensibilité au chlore mais plutôt à la représentation qu’ont les consommateurs de l’eau du robinet. Dans un deuxième temps, nous avons exploré le rôle de la matrice de l’eau dans la perception de la flaveur chlore. Nous avons d’abord montré que les variations de molarité et de composition en cations de l’eau modulent le goût de l’eau. Nous avons ensuite mis en évidence une modulation de l’intensité chlorée en fonction de la matrice minérale de l’eau. Cependant, nos données suggèrent l’existence de mécanismes multiples, physicochimiques, physiologiques en bouche et sensoriels, susceptibles de moduler la perception de la flaveur chlore. Enfin, nous avons exploré les interactions perceptives entre un arôme supposé neutralisant et ajouté à l’eau de boisson et la flaveur chlore. Nos résultats montrent que l’ajout d’un arôme à un niveau péri-liminaire augmente la perception de la flaveur chlore et diminue l’acceptabilité des consommateurs. A plus forte concentrations, certains arômes semblent capables de diminuer la perception du chlore, mais ces conditions sont incompatibles avec les contraintes liées à l’eau de distribution / For water suppliers, using chlorine is necessary to ensure water bacteriological quality from the treatment plant to the consumers’ tap. However, chlorine flavour is one of the most common reasons advocated for choosing tap water alternatives as drinking water. As a consequence, the putative link between chlorine flavour perception and tap water consumption is an issue in drinking water habits studies. Since the sensory mechanisms involved in chlorine flavour perception remained largely unknown, the main objective of this thesis work was to first highlight those mechanisms and then to identify potential lever chlorine flavour sensory neutralisation.In a first step, we demonstrated that hypochlorous acid associated, which is likelyresponsible of chlorine flavour in tap water, could activate the olfactory system at low concentrations and the trigeminal system for concentrations up to 4 mg/L Cl2. Additionally, our results suggested that tap water consumption does not seem to be related to sensitivity to chlorine flavour but rather to consumers’ tap water representation.In a second stage, we explored the impact of water mineral matrix on chlorine flavour perception. We demonstrated first that water molarity and cationic content variations modulate drinking water taste. We also evidenced that chlorine flavour intensity is modulated according to water composition. Nevertheless, our data suggest that physico-chemical, in- mouth physiological and sensory mechanisms are likely involved in such modulation.In the last part of the Thesis work, we investigate the putative influence of aroma perceptionon chlorine flavour. Our results showed that beyond chemical reactions between hypochlorous acid and odorants, aromas at peri-threshold concentration enhance chlorine flavour and decrease tap water acceptability