The Swedish Armed Forces setup of the Weather Research and Forecasting Model (WRF) has problems to forecast low clouds in stably stratified conditions when the ground is covered by snow. The aim of this thesis is to understand what causes this deficit. Simulations during January and February 2018 are here compared with observations from Sodankylä in northern Finland. It is revealed that neither type of planetary boundary layer parameterization chosen nor vertical or horizontal interpolation are responsible for the deficiency. Instead, our experiments show that, to first order, poor initialization of Stratocumulus (Sc) clouds from the host model, Atmospheric Model High Resolution (HRES), of the Integrated Forecast System (IFS) is the missing link. In situations when Sc clouds are missing in the IFS analysis, although they exist in reality, we use information from vertical soundings from Sodankylä. In the initialization process we used the fact that liquid potential temperature is constant in a well-mixed cloud. Initializing cloud water and cloud ice from IFS HRES and from soundings with different methods improves the model performance and the formation of very low artificial clouds at the first model level is prohibited.

L'étude porte sur l'Afrique australe et surtout l'Afrique du Sud, pays bénéficiant d'un excellent réseau d'observations. La capacité du modèle climatique régional WRF à simuler la pluviométrie et la circulation atmosphérique y est évaluée pendant le coeur de la saison des pluies d'été austral (décembre à février : DJF) au travers de trois séries de simulations. WRF est forcé toutes les 6h par les réanalyses ERA40. La résolution horizontale est de 35km. La première série détermine la sensibilité aux paramétrisations de la convection atmosphérique, de la couche limite planétaire et de la microphysique nuageuse. La saison étudiée (DJF 1993-94 : DJF94) est représentative de la climatologie des pluies sud-africaines. La géographie des cumuls saisonniers, leurs intensités et leurs caractéristiques intrasaisonnières sont surtout contrôlées par la paramétrisation de la convection atmosphérique. En Afrique du Sud, les biais saisonniers varient du simple au double en fonction des trois schémas testés (Kain- Fritsch, Betts-Miller-Janjic et Grell-Dévényi). Les schémas de couche limite et de microphysique génèrent des différences moindres, mais pouvant se cumuler avec celles liées à la convection. La deuxième série évalue l'état saisonnier moyen et les structures pluviométriques quotidiennes récurrentes sur la période 1971-1999 au travers d'une configuration physique satisfaisante sur DJF94. La climatologie de la pluviométrie sud-africaine réanalysée (ERA40) est nettement améliorée par WRF, notamment en raison d'une dépression subcontinentale plus creusée au-dessus de l'Angola. Excepté les jours faiblement pluvieux, WRF restitue les principales structures pluviométriques observées au pas de temps quotidien, malgré des décalages fréquents de l'ordre de quelques jours. La troisième série quantifie la variabilité interne à partir de deux simulations d'ensemble de 30 membres. La variabilité interne est modulée par la paramétrisation de la convection atmosphérique (Kain-Fritsch en générant plus que Grell-Dévényi). De fortes similitudes sont néanmoins trouvées. Elles concernent la géographie de la variabilité interne, maximale le long d'une large bande étendue du nord-ouest au sud-est du domaine au pas de temps quotidien. Les incertitudes concernent la morphologie et la vitesse de propagation des bandes pluvieuses synoptiques, de l'ordre de 1 000 km dans le sens zonal pour les bandes pluvieuses tropicales-tempérées. Ces incertitudes sont indicatives des limites théoriques de la prévision opérationnelle en raison de la composante chaotique de l'atmosphère sur la région.

In the current study, we have presented a systematic analysis of the diurnal cycle of rainfall over the Indian region using satellite observations, and evaluated the ability of the Weather Research and Forecasting Model (WRF) to simulate some of the salient features of the observed diurnal characteristics of rainfall. Using high resolution simulations, we also investigate the underlying mechanisms of some of the observed diurnal signatures of rainfall. Using the Tropical Rain-fall Measuring Mission (TRMM) 3-hourly, 0.25 ×0.25 degree 3B42 rainfall product for nine years (1999-2007), we extract the finer spatial structure of the diurnal scale signature of Indian summer monsoon rainfall. Using harmonic analysis, we construct a signal corresponding to diurnal and sub-diurnal variability. Subsequently, the 3-hourly time-period or the octet of rain-fall peak for this filtered signal, referred to as the “peak octet,” is estimated with care taken to eliminate spurious peaks arising out of Gibbs oscillations. Our analysis suggests that over the Bay of Bengal, there are three distinct modes of the peak octet of diurnal rainfall corresponding to 1130, 1430 and 1730 IST, from north central to south Bay. This finding could be seen to be consistent with southward propagation of the diurnal rainfall pattern reported by earlier studies. Over the Arabian sea, there is a spatially coherent pattern in the mode of the peak octet (1430 IST), in a region where it rains for more than 30% of the time. In the equatorial Indian Ocean, while most of the western part shows a late night/early morning peak, the eastern part does not show a spatially coherent pattern in the mode of the peak octet, owing to the occurrence of a dual maxima (early morning and early/late afternoon). The Himalayan foothills were found to have a mode of peak octet corresponding to 0230 IST, whereas over the Burmese mountains and the Western Ghats (west coast of India) the rainfall peaks during late afternoon/early evening (1430-1730 IST). This implies that the phase of the diurnal cycle over inland orography (e.g., Himalayas) is significantly different from coastal orography (e.g., Western Ghats). We also find that over the Gangetic plains, the peak octet is around 1430 IST, a few hours earlier compared to the typical early evening maxima over land. The second part of our study involves evaluating the ability of the Weather Research and Fore-casting Model (WRF) to simulate the observed diurnal rainfall characteristics. It also includes conducting high resolution simulations to explore the underlying physical mechanisms of the observed diurnal signatures of rainfall. The model (at 54km resolution) is integrated for the month of July 2006 since this period was particularly favourable for the study of diurnal cycle. We first evaluate the sensitivity of the model to the prescribed sea surface temperature (SST) by using two different SST datasets, namely Final Analyses (FNL) and Real-time Global (RTG). The overall performance of RTG SST was found to be better than FNL, and hence it was used for further model simulations. Next, we investigated the impact of different parameterisations (convective, microphysical, boundary layer, radiation and land surface) on the simulation of diurnal cycle of rainfall. Following this sensitivity study, we identified the suite of physical parameterisations in the model that “best” reproduces the observed diurnal characteristics of Indian monsoon rainfall. The “best” model configuration was used to conduct two nested simulations with one-way, three-level nesting (54-18-6km) over central India and Bay of Bengal. While the 54km and 18km simulations were conducted for July 2006, the 6km simulation was carried out for the period 18-24 July 2006. This period was chosen for our study since it is composed of an active period (19-21 July 2006), followed by a break period (22-24 July 2006). At 6km grid-spacing the model is able to realistically simulate the active and break phases in rainfall. During the chosen active phase, we find that the observed rainfall over central India tends to reach a maximum in the late night/early morning hours. This is in contrast to the observed climatological diurnal maxima of late evening hours. Interestingly, the 6km simulation for the active phase is able to reproduce this late night/early morning maxima. Upon further analysis, we find that this is because of the strong moisture convergence at the mid-troposphere during 2030-2330 IST, leading to the rainfall peak seen during 2330-0230 IST. Based on our analysis, we conclude that during both active and break phases of summer monsoon, mid-level moisture convergence seems to be one of the primary factors governing the phase of the diurnal cycle of rainfall. Over the Bay of Bengal, the 6km model simulation is in very good agreement with observations, particularly during the active phase. The southward propagation observed during 19-20 July 2006, which was not captured by the coarse resolution simulation (54km), is exceedingly well captured by the 6km simulation. The positive anomalies in specific humidity attain a maxima during 2030-0230 IST in the north and during 0830-1430 IST in the south. This confirms the role of moisture convergence in the southward propagation of rainfall. Equally importantly we find that while low level moisture convergence is dominant in the north Bay, it is the mid-level moisture convergence that is predominant in the south Bay.

Ces dernières années, de nombreux efforts ont été entrepris pour mieux comprendre les phénomènes précipitants parfois à l'origine de crues de cours d'eau et d'inondations ravageuses. Courant 2009, un consortium auvergnat a été mis en place pour notamment surveiller et prévoir ces événements. Les travaux menés dans cette thèse visent d'une part à améliorer les estimations quantitatives des précipitations (QPE) et d'autre part à vérifier les prévisions issues de modèles numériques sur de petites zones d'étude telles qu'une agglomération. L'observation des précipitations peut être réalisée à l'aide soit d'un pluviomètre qui fournit une mesure directe et précise de la quantité de pluie tombée au sol mais ne renseigne pas sur la variabilité spatiale des pluies soit d'un RADAR météorologique qui donne une représentation détaillée de la structure spatiale des précipitations mais dont les estimations sont sujettes à diverses erreurs d'autant plus prononcées en régions montagneuses. Le premier défit de cette thèse a été de trouver la meilleure façon de combiner ces deux informations complémentaires. Deux techniques géostatistiques ont été sélectionnées pour obtenir la meilleur QPE : le krigeage avec dérive externe (KED) et la fusion conditionnée (MERG). Les performances de ces deux méthodes ont été comparées au travers de deux domaines d'étude qui présentent des résolutions spatio-temporelles différentes. La seconde partie de cette thèse est consacrée à la mise en place d'une méthodologie fiable permettant de comparer spatialement les champs de QPE alors reconstruits et les prévisions quantitatives des précipitations (QPF). L'effort fut porté sur le modèle " Weather Research et Forcasting " (WRF). Une étude préliminaire a été réalisée pour tester les capacités du modèle et plus particulièrement des schémas de microphysique à reproduire la pluie. Cette étude assure ainsi l'obtention de prévisions réalistes pour une application sur des cas réels. L'appréciation de la qualité des QPF s'est focalisée sur la quantification spatiale des erreurs de prévision en termes de structure, d'intensité et de localisation des systèmes précipitants (SAL : Wernli et al. 2008, 2009).

In this work, various topics regarding (urban) boundary simulations for a city situated in the alps will be discussed. First of all, we will present novel parameterization adopted to take into account the effect of mitigation strategies, such as rooftop Photovoltaic Panels and Green Roofs, on the urban environment, and their effect on average temperature and energy consumption by buildings. Secondly, a new turbulence closure, that adopts a diagnostic equation for dissipation rate, and then independent on mixing length scales, will be introduced. The new turbulence closure, implemented into the WRF model, has been coupled with multi-layer urban parameterization schemes and compared with high-resolution CFD and LES simulations.

No description available.

Land surfaces have changed as a result of human and natural processes, such asdeforestation, urbanization, desertification and natural disasters like wildfires. Land use and landcover change impacts local and regional climates through various bio geophysical processes acrossmany time scales. More realistic representation of land surface parameters within the land surfacemodels are essential to for climate models to accurately simulate the effects of past, current andfuture land surface processes. In this study, we evaluated the sensitivity and accuracy of theWeather Research and Forecasting (WRF) model though the default MODIS land cover data andannually updated land cover data over southeast of United States. Findings of this study indicatedthat the land surface fluxes, and moisture simulations are more sensitive to the surfacecharacteristics over the southeast US. Consequently, we evaluated the WRF temperature andprecipitation simulations with more accurate observations of land surface parameters over thestudy area. We evaluate the model performance for the default and updated land cover simulationsagainst observational datasets. Results of the study showed that updating land cover resulted insubstantial variations in surface heat fluxes and moisture balances. Despite updated land use andland cover data provided more representative land surface characteristics, the WRF simulated 2- m temperature and precipitation did not improved due to use of updated land cover data. Further,we conducted machine learning experiments to post-process the Noah-MP land surface modelsimulations to determine if post processing the model outputs can improve the land surfaceparameters. The results indicate that the Noah-MP simulations using machine learning remarkablyimproved simulation accuracy and gradient boosting, and random forest model had smaller meanerror bias values and larger coefficient of determination over the majority of stations. Moreover,the findings of the current study showed that the accuracy of surface heat flux simulations byNoah-MP are influenced by land cover and vegetation type.

The present work combines experimental and numerical analyses to improve current understanding of turbulence in stably stratified flows. An extensive literature review is presented on the mechanisms governing turbulence under stratified conditions, with a special focus on the Richardson number parameter, as it is often adopted as a switch to turn turbulence modelling on/off. Anisotropization of turbulence is investigated, as it is found to be an important mechanism for turbulence survival at any Richardson number, but usually overlooked in turbulence parameterizations. For this purpose, an experimental dataset previously collected over an Alpine glacier is used, with a focus on the anisotropy of the Reynolds stress tensor, as the scientific community has recently shown improvements in the description of the atmospheric surface layer by taking this aspect into account. Different sources leading stresses to deviate from the isotropic limit are explored, as well as energy exchanges across scales and between kinetic and potential reservoirs, in order to identify the main processes that should be included in turbulence parameterizations to properly represent anisotropic turbulence under stable conditions. High-resolution numerical simulations are then performed with the Weather Research and Forecasting (WRF) model to evaluate different PBL parameterizations in reproducing specific stable atmospheric conditions developing over complex terrain, and their influence on the local circulation. For this purpose, two wintertime case studies in a basin-like area of an Alpine valley are investigated. Both are fair-weather episodes with weak synoptic forcing and well-developed diurnal local circulations, differing by the thermal stratification in the basin. In particular, the influence of thermal stratification on the outbreak of a valley-exit wind coming from a tributary valley is investigated, and the influence of such type of flows on turbulence anisotropy in stably stratified conditions is discussed for future investigations.

Land surface models (LSMs) implemented in numerical weather prediction (NWP) models use several parameters to suitably describe the surface and its interaction with the atmosphere, whose determination is often affected by many uncertainties, strongly influencing simulation results. However, the sensitivity of meteorological model results to these parameters has not yet been studied systematically, especially in complex terrain, where uncertainty is expected to be even larger. This work aims at identifying critical LSM parameters influencing the results of NWP models, focusing in particular on the simulation of thermally-driven circulations over complex terrain. While previous sensitivity analyses employed offline LSM simulations to evaluate the sensitivity to surface parameters, this study adopts an online coupled approach, utilizing the Noah-MP LSM within the Weather Research and Forecasting (WRF) model. To overcome computational constraints, a novel tool, Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool (ML-AMPSIT), is developed and tested. This tool allows users to explore the sensitivity of the results to model parameters using supervised machine learning regression algorithms, including Random Forest, CART, XGBoost, SVM, LASSO, Gaussian Process Regression, and Bayesian Ridge Regression. These algorithms serve as fast surrogate models, greatly accelerating sensitivity analyses while maintaining a high level of accuracy. The versatility and effectiveness of ML-AMPSIT enable the fast implementation of advanced sensitivity methods, such as the Sobol method, overcoming the computational limitations encountered in expensive models like WRF. The suitability of this tool to assess model’s sensitivity to the variation of specific parameters is first tested in an idealized sea breeze case study where six surface parameters are varied. Then, the analysis focuses on the evaluation of the sensitivity to surface parameters in the simulation of thermally-driven circulations in a mountain valley. Specifically, an idealized three-dimensional topography consisting of a valley-plain system is adopted, analyzing a complete diurnal cycle of valley and slope winds. The analysis focuses on all the key surface parameters governing the interactions between NoahMP and WRF. The proposed approach, novel in the context of LSM-NWP model coupling, draws from established applications of machine learning in various Earth science disciplines, underscoring its potential to improve the estimation of parameter sensitivities in NWP models.

For the construction of a wind farm, one needs to assess the wind resources of the considered site location. Using reference time series from numerical weather prediction models, global assimilation databases or observations close to the area considered, the on-site measured wind speeds and wind directions are corrected in order to represent the actual long-term wind conditions. This long-term correction (LTC) is in the typical case performed by making use of the linear regression within the Measure-Correlate-Predict (MCP) method. This method and two other methods, Sector-Bin (SB) and Synthetic Time Series (ST), respectively, are used for the determination of the uncertainties that are associated with LTC.The test area that has been chosen in this work, is located in the region of the North Sea, using 22 quality controlled meteorological (met) station observations from offshore or nearby shore locations in Denmark, Norway and Sweden. The time series that has been used cover the eight year period from 2002 to 2009 and the year with the largest variability in the wind speeds, 2007, is used as the short-term measurement period. The long-term reference datasets that have been used are the Weather Research and Forecast model, based on both ECMWF Interim Re-Analysis (ERA-Interim) and National Centers for Environmental Prediction Final Analysis (NCEP/FNL), respectively and additional reference datasets of Modern Era Re-Analysis (MERRA) and QuikSCAT satellite observations. The long-term period for all of the reference datasets despite QuikSCAT, correspond to the one of stations observations. The QuikSCAT period of observations used cover the period from November 1st, 1999 until October 31st, 2009.The analysis is divided into three parts. Initially, the uncertainty connected to the corresponding reference dataset, when used in LTC method, is investigated. Thereafter the uncertainty due to the concurrent length of the on-site measurements and reference dataset is analyzed. Finally, the uncertainty is approached using a re-sampling method of the Non-Parametric Bootstrap. The uncertainty of the LTC method SB, for a fixed concurrent length of the datasets is assessed by this methodology, in an effort to create a generic model for the estimation of uncertainty in the predicted values for SB.The results show that LTC with WRF model datasets based on NCEP/FNL and ERA-Interim, respectively, is slightly different, but does not deviate considerably in comparison when comparing with met station observations. The results also suggest the use of MERRA reference dataset in connection with long-term correction methods. However, the datasets of QuikSCAT does not provide much information regarding the overall quality of long-term correction, and a different approach than using station coordinates for the withdrawal of QuikSCAT time series is preferred. Additionally, the LTC model of Sector-Bin is found to be robust against variation in the correlation coefficient between the concurrent datasets. For the uncertainty dependence of concurrent time, the results show that an on-site measurement period of one consistent year or more, gives the lowest uncertainties compared to measurements of shorter time. An additional observation is that the standard deviation of long-term corrected means decreases with concurrent time. Despite the efforts of using the re-sampling method of Non-Parametric Bootstrap the estimation of the uncertainties is not fully determined. However, it does give promising results that are suggested for investigation in further work. / För att bygga en vindkraftspark är man i behov av att kartlägga vindresurserna i det aktuella området. Med hjälp av tidsserier från numeriska vädermodeller (NWP), globala assimileringsdatabaser och intilliggande observationer korrigeras de uppmätta vindhastigheterna och vindriktningarna för att motsvara långtidsvärdena av vindförhållandena. Dessa långtidskorrigeringsmetoder (LTC) genomförs generellt sett med hjälp av linjär regression i Mät-korrelera-predikera-metoden (MCP). Denna metod, och två andra metoder, Sektor-bin (SB) och Syntetiska tidsserier (ST), används i denna rapport för att utreda de osäkerheter som är knutna till långtidskorrigering.Det testområde som är valt för analys i denna rapport omfattas av Nordsjöregionen, med 22 meteorologiska väderobservationsstationer i Danmark, Norge och Sverige. Dessa stationer är till största del belägna till havs eller vid kusten. Tidsserierna som används täcker åttaårsperioden från 2002 till 2009, där det året med högst variabilitet i uppmätt vindhastighet, år 2007, används som den korta mätperiod som blir föremål för långtidskorrigeringen. De långa referensdataseten som använts är väderprediktionsmodellen WRF ( Weather Research and Forecast Model), baserad både på data från NCEP/FNL (National Centers for Environmental Prediciton Final Analysis) och ERA-Interim (ECMWF Interim Re-analysis). Dessutom används även data från MERRA (Modern Era Re-Analysis) och satellitobservationer från QuikSCAT. Långtidsperioden för alla dataset utom QuikSCAT omfattar samma period som observationsstationerna. QuikSCAT-datat som använts omfattar perioden 1 november 1999 till 31 oktober 2009.Analysen är indelad i tre delar. Inledningsvis behandlas osäkerheten som är kopplad till referensdatans ingående i långtidskorrigeringsmetoderna. Därefter analyseras osäkerhetens beroende av längden på den samtidiga datan i referens- och observationsdataseten. Slutligen utreds osäkerheten med hjälp av en icke-parametrisk metod, en s.k. Bootstrap: Osäkerheten i SB-metoden för en fast samtidig längd av tidsserierna från observationer och referensdatat uppskattas genom att skapa en generell modell som estimerar osäkerheten i estimatet.Resultatet visar att skillnaden när man använder WRF-modellen baserad både på NCEP/FNL och ERA-Interim i långtidskorrigeringen är marginell och avviker inte markant i förhållande till stationsobservationerna. Resultatet pekar också på att MERRA-datat kan användas som långtidsreferensdataset i långtidsdkorrigeringsmetoderna. Däremot ger inte QuikSCAT-datasetet tillräckligt med information för att avgöra om det går att använda i långtidskorrigeringsmetoderna. Därför föreslås ett annat tillvägagångssätt än stationsspecifika koordinater vid val av koordinater lämpliga för långtidskorrigering. Ytterligare ett resultat vid analys av långtidskorrigeringsmetoden SB, visar att metoden är robust mot variation i korrelationskoefficienten.Rörande osäkerhetens beroende av längden på samtidig data visar resultaten att en sammanhängande mätperiod på ett år eller mer ger den lägsta osäkerheten i årsmedelvindsestimatet, i förhållande till mätningar av kortare slag. Man kan även se att standardavvikelsen av de långtidskorrigerade medelvärdena avtar med längden på det samtidiga datat. Den implementerade ickeparametriska metoden Bootstrap, som innefattar sampling med återläggning, kan inte estimera osäkerheten till fullo. Däremot ger den lovande resultat som föreslås för vidare arbete.