991 resultados para resolution prediction
Resumo:
Data assimilation provides an initial atmospheric state, called the analysis, for Numerical Weather Prediction (NWP). This analysis consists of pressure, temperature, wind, and humidity on a three-dimensional NWP model grid. Data assimilation blends meteorological observations with the NWP model in a statistically optimal way. The objective of this thesis is to describe methodological development carried out in order to allow data assimilation of ground-based measurements of the Global Positioning System (GPS) into the High Resolution Limited Area Model (HIRLAM) NWP system. Geodetic processing produces observations of tropospheric delay. These observations can be processed either for vertical columns at each GPS receiver station, or for the individual propagation paths of the microwave signals. These alternative processing methods result in Zenith Total Delay (ZTD) and Slant Delay (SD) observations, respectively. ZTD and SD observations are of use in the analysis of atmospheric humidity. A method is introduced for estimation of the horizontal error covariance of ZTD observations. The method makes use of observation minus model background (OmB) sequences of ZTD and conventional observations. It is demonstrated that the ZTD observation error covariance is relatively large in station separations shorter than 200 km, but non-zero covariances also appear at considerably larger station separations. The relatively low density of radiosonde observing stations limits the ability of the proposed estimation method to resolve the shortest length-scales of error covariance. SD observations are shown to contain a statistically significant signal on the asymmetry of the atmospheric humidity field. However, the asymmetric component of SD is found to be nearly always smaller than the standard deviation of the SD observation error. SD observation modelling is described in detail, and other issues relating to SD data assimilation are also discussed. These include the determination of error statistics, the tuning of observation quality control and allowing the taking into account of local observation error correlation. The experiments made show that the data assimilation system is able to retrieve the asymmetric information content of hypothetical SD observations at a single receiver station. Moreover, the impact of real SD observations on humidity analysis is comparable to that of other observing systems.
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Numerical models, used for atmospheric research, weather prediction and climate simulation, describe the state of the atmosphere over the heterogeneous surface of the Earth. Several fundamental properties of atmospheric models depend on orography, i.e. on the average elevation of land over a model area. The higher is the models' resolution, the more the details of orography directly influence the simulated atmospheric processes. This sets new requirements for the accuracy of the model formulations with respect to the spatially varying orography. Orography is always averaged, representing the surface elevation within the horizontal resolution of the model. In order to remove the smallest scales and steepest slopes, the continuous spectrum of orography is normally filtered (truncated) even more, typically beyond a few gridlengths of the model. This means, that in the numerical weather prediction (NWP) models, there will always be subgridscale orography effects, which cannot be explicitly resolved by numerical integration of the basic equations, but require parametrization. In the subgrid-scale, different physical processes contribute in different scales. The parametrized processes interact with the resolved-scale processes and with each other. This study contributes to building of a consistent, scale-dependent system of orography-related parametrizations for the High Resolution Limited Area Model (HIRLAM). The system comprises schemes for handling the effects of mesoscale (MSO) and small-scale (SSO) orographic effects on the simulated flow and a scheme of orographic effects on the surface-level radiation fluxes. Representation of orography, scale-dependencies of the simulated processes and interactions between the parametrized and resolved processes are discussed. From the high-resolution digital elevation data, orographic parameters are derived for both momentum and radiation flux parametrizations. Tools for diagnostics and validation are developed and presented. The parametrization schemes applied, developed and validated in this study, are currently being implemented into the reference version of HIRLAM.
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A modified linear prediction (MLP) method is proposed in which the reference sensor is optimally located on the extended line of the array. The criterion of optimality is the minimization of the prediction error power, where the prediction error is defined as the difference between the reference sensor and the weighted array outputs. It is shown that the L2-norm of the least-squares array weights attains a minimum value for the optimum spacing of the reference sensor, subject to some soft constraint on signal-to-noise ratio (SNR). How this minimum norm property can be used for finding the optimum spacing of the reference sensor is described. The performance of the MLP method is studied and compared with that of the linear prediction (LP) method using resolution, detection bias, and variance as the performance measures. The study reveals that the MLP method performs much better than the LP technique.
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Land surface temperature (LST) is an important variable in climate, hydrologic, ecological, biophysical and biochemical studies (Mildrexler et al., 2011). The most effective way to obtain LST measurements is through satellites. Presently, LST from moderate resolution imaging spectroradiometer (MODIS) sensor is applied in various fields due to its high spatial and temporal availability over the globe, but quite difficult to provide observations in cloudy conditions. This study evolves of prediction of LST under clear and cloudy conditions using microwave vegetation indices (MVIs), elevation, latitude, longitude and Julian day as inputs employing an artificial neural network (ANN) model. MVIs can be obtained even under cloudy condition, since microwave radiation has an ability to penetrate through clouds. In this study LST and MVIs data of the year 2010 for the Cauvery basin on a daily basis were obtained from MODIS and advanced microwave scanning radiometer (AMSR-E) sensors of aqua satellite respectively. Separate ANN models were trained and tested for the grid cells for which both LST and MVI were available. The performance of the models was evaluated based on standard evaluation measures. The best performing model was used to predict LST where MVIs were available. Results revealed that predictions of LST using ANN are in good agreement with the observed values. The ANN approach presented in this study promises to be useful for predicting LST using satellite observations even in cloudy conditions. (C) 2015 The Authors. Published by Elsevier B.V.
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A new transition prediction model is introduced, which couples the intermittency effect into the turbulence transport equations and takes the characteristics of fluid transition into consideration to mimic the exact process of transition. Test cases include a two-dimensional incompressible plate and a two-dimensional NACA0012 airfoil. Performance of this transition model for incompressible flows is studied, with numerical results consistent to experimental data. The requirement of grid resolution for this transition model is also studied.
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This paper proposes a method for extracting reliable architectural characteristics from complex porous structures using micro-computed tomography (μCT) images. The work focuses on a highly porous material composed of a network of fibres bonded together. The segmentation process, allowing separation of the fibres from the remainder of the image, is the most critical step in constructing an accurate representation of the network architecture. Segmentation methods, based on local and global thresholding, were investigated and evaluated by a quantitative comparison of the architectural parameters they yielded, such as the fibre orientation and segment length (sections between joints) distributions and the number of inter-fibre crossings. To improve segmentation accuracy, a deconvolution algorithm was proposed to restore the original images. The efficacy of the proposed method was verified by comparing μCT network architectural characteristics with those obtained using high resolution CT scans (nanoCT). The results indicate that this approach resolves the architecture of these complex networks and produces results approaching the quality of nanoCT scans. The extracted architectural parameters were used in conjunction with an affine analytical model to predict the axial and transverse stiffnesses of the fibre network. Transverse stiffness predictions were compared with experimentally measured values obtained by vibration testing. © 2011 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.
Resumo:
A new scalable Monotonically Integrated Large Eddy Simulation (MILES) method based on the Compact Accurately Boundary-Adjusting high-REsolution Technique (CABARET) has been applied for the simulation of unsteady flow around NACA0012 airfoil at Re = 400,000 and M = 0.058. The flow solution is coupled with the Ffowcs Williams-Hawkings formulation for far-field noise prediction. The computational modeling results are presented for several computational grid resolutions: 8, 16, and 32 million grid cells and compared with the experimental data available.
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The present study reports an application of the searching combination moving window partial least squares (SCMWPLS) algorithm to the determination of ethenzamide and acetoaminophen in quaternary powdered samples by near infrared (NIR) spectroscopy. Another purpose of the study was to examine the instrumentation effects of spectral resolution and signal-to-noise ratio of the Buchi NIRLab N-200 FT-NIR spectrometer equipped with an InGaAs detector. The informative spectral intervals of NIR spectra of a series of quaternary powdered mixture samples were first located for ethenzamide and acetoaminophen by use of moving window partial least squares regression (MWPLSR). Then, these located spectral intervals were further optimised by SCMWPLS for subsequent partial least squares (PLS) model development. The improved results are attributed to both the less complex PLS models and to higher accuracy of predicted concentrations of ethenzamide and acetoaminophen in the optimised informative spectral intervals that are featured by NIR bands. At the same time, SCMWPLS is also demonstrated as a viable route for wavelength selection.
Resumo:
Monolithic catalysts are widely used as structured catalysts, especially in the abatement of pollutants. Probing what happens inside these monoliths during operation is, therefore, vital for modelling and prediction of the catalyst behavior. SpaciMS is a spatially resolved capillary-inlet mass spectroscopy system allowing for the generation of spatially resolved maps of the reactions within monoliths. In this study SpaciMS results combined with 3D CFD modelling demonstrate that SpaciMS is a highly sensitive and minimally invasive technique that can provide reaction maps as well as catalytic temporal behavior. Herein we illustrate this by examining kinetic oscillations during a CO oxidation reaction over a Pt/Rh on alumina catalyst supported on a cordierite monolith. These oscillations were only observed within the monolith by SpaciMS between 30 and 90% CO conversion. Equivalent experiments performed in a plug-flow reactor using this catalyst in a crushed form over a similar range of reaction conditions did not display any oscillations demonstrating the importance of intra monolith analysis. This work demonstrates that the SpaciMS offers an accurate and comprehensive picture of structured catalysts under operation.
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La compréhension de processus biologiques complexes requiert des approches expérimentales et informatiques sophistiquées. Les récents progrès dans le domaine des stratégies génomiques fonctionnelles mettent dorénavant à notre disposition de puissants outils de collecte de données sur l’interconnectivité des gènes, des protéines et des petites molécules, dans le but d’étudier les principes organisationnels de leurs réseaux cellulaires. L’intégration de ces connaissances au sein d’un cadre de référence en biologie systémique permettrait la prédiction de nouvelles fonctions de gènes qui demeurent non caractérisées à ce jour. Afin de réaliser de telles prédictions à l’échelle génomique chez la levure Saccharomyces cerevisiae, nous avons développé une stratégie innovatrice qui combine le criblage interactomique à haut débit des interactions protéines-protéines, la prédiction de la fonction des gènes in silico ainsi que la validation de ces prédictions avec la lipidomique à haut débit. D’abord, nous avons exécuté un dépistage à grande échelle des interactions protéines-protéines à l’aide de la complémentation de fragments protéiques. Cette méthode a permis de déceler des interactions in vivo entre les protéines exprimées par leurs promoteurs naturels. De plus, aucun biais lié aux interactions des membranes n’a pu être mis en évidence avec cette méthode, comparativement aux autres techniques existantes qui décèlent les interactions protéines-protéines. Conséquemment, nous avons découvert plusieurs nouvelles interactions et nous avons augmenté la couverture d’un interactome d’homéostasie lipidique dont la compréhension demeure encore incomplète à ce jour. Par la suite, nous avons appliqué un algorithme d’apprentissage afin d’identifier huit gènes non caractérisés ayant un rôle potentiel dans le métabolisme des lipides. Finalement, nous avons étudié si ces gènes et un groupe de régulateurs transcriptionnels distincts, non préalablement impliqués avec les lipides, avaient un rôle dans l’homéostasie des lipides. Dans ce but, nous avons analysé les lipidomes des délétions mutantes de gènes sélectionnés. Afin d’examiner une grande quantité de souches, nous avons développé une plateforme à haut débit pour le criblage lipidomique à contenu élevé des bibliothèques de levures mutantes. Cette plateforme consiste en la spectrométrie de masse à haute resolution Orbitrap et en un cadre de traitement des données dédié et supportant le phénotypage des lipides de centaines de mutations de Saccharomyces cerevisiae. Les méthodes expérimentales en lipidomiques ont confirmé les prédictions fonctionnelles en démontrant certaines différences au sein des phénotypes métaboliques lipidiques des délétions mutantes ayant une absence des gènes YBR141C et YJR015W, connus pour leur implication dans le métabolisme des lipides. Une altération du phénotype lipidique a également été observé pour une délétion mutante du facteur de transcription KAR4 qui n’avait pas été auparavant lié au métabolisme lipidique. Tous ces résultats démontrent qu’un processus qui intègre l’acquisition de nouvelles interactions moléculaires, la prédiction informatique des fonctions des gènes et une plateforme lipidomique innovatrice à haut débit , constitue un ajout important aux méthodologies existantes en biologie systémique. Les développements en méthodologies génomiques fonctionnelles et en technologies lipidomiques fournissent donc de nouveaux moyens pour étudier les réseaux biologiques des eucaryotes supérieurs, incluant les mammifères. Par conséquent, le stratégie présenté ici détient un potentiel d’application au sein d’organismes plus complexes.
Resumo:
Tropical cyclones have been investigated in a T159 version of the MPI ECHAM5 climate model using a novel technique to diagnose the evolution of the 3-dimensional vorticity structure of tropical cyclones, including their full life cycle from weak initial vortex to their possible extra-tropical transition. Results have been compared with reanalyses (ERA40 and JRA25) and observed tropical storms during the period 1978-1999 for the Northern Hemisphere. There is no indication of any trend in the number or intensity of tropical storms during this period in ECHAM5 or in re-analyses but there are distinct inter-annual variations. The storms simulated by ECHAM5 are realistic both in space and time, but the model and even more so the re-analyses, underestimate the intensities of the most intense storms (in terms of their maximum wind speeds). There is an indication of a response to ENSO with a smaller number of Atlantic storms during El Niño in agreement with previous studies. The global divergence circulation responds to El Niño by setting up a large-scale convergence flow, with the center over the central Pacific with enhanced subsidence over the tropical Atlantic. At the same time there is an increase in the vertical wind shear in the region of the tropical Atlantic where tropical storms normally develop. There is a good correspondence between the model and ERA40 except that the divergence circulation is somewhat stronger in the model. The model underestimates storms in the Atlantic but tends to overestimate them in the Western Pacific and in the North Indian Ocean. It is suggested that the overestimation of storms in the Pacific by the model is related to an overly strong response to the tropical Pacific SST anomalies. The overestimation in 2 the North Indian Ocean is likely to be due to an over prediction in the intensity of monsoon depressions, which are then classified as intense tropical storms. Nevertheless, overall results are encouraging and will further contribute to increased confidence in simulating intense tropical storms with high-resolution climate models.
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A fast radiative transfer model (RTM) to compute emitted infrared radiances for a very high resolution radiometer (VHRR), onboard the operational Indian geostationary satellite Kalpana has been developed and verified. This work is a step towards the assimilation of Kalpana water vapor (WV) radiances into numerical weather prediction models. The fast RTM uses a regression‐based approach to parameterize channel‐specific convolved level to space transmittances. A comparison between the fast RTM and the line‐by‐line RTM demonstrated that the fast RTM can simulate line‐by‐line radiances for the Kalpana WV channel to an accuracy better than the instrument noise, while offering more rapid radiance calculations. A comparison of clear sky radiances of the Kalpana WV channel with the ECMWF model first guess radiances is also presented, aiming to demonstrate the fast RTM performance with the real observations. In order to assimilate the radiances from Kalpana, a simple scheme for bias correction has been suggested.
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During the past 15 years, a number of initiatives have been undertaken at national level to develop ocean forecasting systems operating at regional and/or global scales. The co-ordination between these efforts has been organized internationally through the Global Ocean Data Assimilation Experiment (GODAE). The French MERCATOR project is one of the leading participants in GODAE. The MERCATOR systems routinely assimilate a variety of observations such as multi-satellite altimeter data, sea-surface temperature and in situ temperature and salinity profiles, focusing on high-resolution scales of the ocean dynamics. The assimilation strategy in MERCATOR is based on a hierarchy of methods of increasing sophistication including optimal interpolation, Kalman filtering and variational methods, which are progressively deployed through the Syst`eme d’Assimilation MERCATOR (SAM) series. SAM-1 is based on a reduced-order optimal interpolation which can be operated using ‘altimetry-only’ or ‘multi-data’ set-ups; it relies on the concept of separability, assuming that the correlations can be separated into a product of horizontal and vertical contributions. The second release, SAM-2, is being developed to include new features from the singular evolutive extended Kalman (SEEK) filter, such as three-dimensional, multivariate error modes and adaptivity schemes. The third one, SAM-3, considers variational methods such as the incremental four-dimensional variational algorithm. Most operational forecasting systems evaluated during GODAE are based on least-squares statistical estimation assuming Gaussian errors. In the framework of the EU MERSEA (Marine EnviRonment and Security for the European Area) project, research is being conducted to prepare the next-generation operational ocean monitoring and forecasting systems. The research effort will explore nonlinear assimilation formulations to overcome limitations of the current systems. This paper provides an overview of the developments conducted in MERSEA with the SEEK filter, the Ensemble Kalman filter and the sequential importance re-sampling filter.
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In this paper the meteorological processes responsible for transporting tracer during the second ETEX (European Tracer EXperiment) release are determined using the UK Met Office Unified Model (UM). The UM predicted distribution of tracer is also compared with observations from the ETEX campaign. The dominant meteorological process is a warm conveyor belt which transports large amounts of tracer away from the surface up to a height of 4 km over a 36 h period. Convection is also an important process, transporting tracer to heights of up to 8 km. Potential sources of error when using an operational numerical weather prediction model to forecast air quality are also investigated. These potential sources of error include model dynamics, model resolution and model physics. In the UM a semi-Lagrangian monotonic advection scheme is used with cubic polynomial interpolation. This can predict unrealistic negative values of tracer which are subsequently set to zero, and hence results in an overprediction of tracer concentrations. In order to conserve mass in the UM tracer simulations it was necessary to include a flux corrected transport method. Model resolution can also affect the accuracy of predicted tracer distributions. Low resolution simulations (50 km grid length) were unable to resolve a change in wind direction observed during ETEX 2, this led to an error in the transport direction and hence an error in tracer distribution. High resolution simulations (12 km grid length) captured the change in wind direction and hence produced a tracer distribution that compared better with the observations. The representation of convective mixing was found to have a large effect on the vertical transport of tracer. Turning off the convective mixing parameterisation in the UM significantly reduced the vertical transport of tracer. Finally, air quality forecasts were found to be sensitive to the timing of synoptic scale features. Errors in the position of the cold front relative to the tracer release location of only 1 h resulted in changes in the predicted tracer concentrations that were of the same order of magnitude as the absolute tracer concentrations.
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The atmospheric component of the United Kingdom’s new High-resolution Global Environmental Model (HiGEM) has been run with interactive aerosol schemes that include biomass burning and mineral dust. Dust emission, transport, and deposition are parameterized within the model using six particle size divisions, which are treated independently. The biomass is modeled in three nonindependent modes, and emissions are prescribed from an external dataset. The model is shown to produce realistic horizontal and vertical distributions of these aerosols for each season when compared with available satellite- and ground-based observations and with other models. Combined aerosol optical depths off the coast of North Africa exceed 0.5 both in boreal winter, when biomass is the main contributor, and also in summer, when the dust dominates. The model is capable of resolving smaller-scale features, such as dust storms emanating from the Bode´ le´ and Saharan regions of North Africa and the wintertime Bode´ le´ low-level jet. This is illustrated by February and July case studies, in which the diurnal cycles of model variables in relation to dust emission and transport are examined. The top-of-atmosphere annual mean radiative forcing of the dust is calculated and found to be globally quite small but locally very large, exceeding 20 W m22 over the Sahara, where inclusion of dust aerosol is shown to improve the model radiative balance. This work extends previous aerosol studies by combining complexity with increased global resolution and represents a step toward the next generation of models to investigate aerosol–climate interactions. 1. Introduction Accurate modeling of mineral dust is known to be important because of its radiative impact in both numerical weather prediction models (Milton et al. 2008; Haywood et