scholarly journals Correcting Marine Surface Winds Simulated in Atmospheric Models Using Spatially and Temporally Varying Linear Regression

2014 ◽  
Vol 29 (2) ◽  
pp. 305-330 ◽  
Author(s):  
Tom H. Durrant ◽  
Diana J. M. Greenslade ◽  
Ian Simmonds ◽  
Frank Woodcock
Keyword(s):  
Linear Regression ◽  
Atmospheric Model ◽  
Correction Method ◽  
Wind Forcing ◽  
Moving Window ◽  
Operational Environment ◽  
Atmospheric Models ◽  
Marine Surface ◽  
Spatially Varying ◽  
Self Learning

Abstract This study examines the application of three different variations of linear-regression corrections to the surface marine winds from the Australian Bureau of Meteorology’s recently implemented operational atmospheric model. A simple correction over the entire domain is found to inadequately account for geographical variation in the wind bias. This is addressed by considering corrections that vary in space. Further, these spatially varying corrections are extended to vary in time. In an operational environment, the error characteristics of the wind forcing can be expected to change over time with the evolution of the atmospheric model. This in turn requires any applied correction to be monitored and maintained. Motivated by a desire to avoid this manual maintenance, a self-learning correction method is proposed whereby spatially and temporally varying corrections are calculated in real time from a moving window of historical comparisons between observations and preceding forecasts. This technique is shown to effectively remove both global and regionally varying wind speed biases.

scholarly journals Random Material Property Fields of 3D Concrete Microstructures Based on CT Image Reconstruction

Materials ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1423
Author(s):  
George Stefanou ◽  
Dimitrios Savvas ◽  
Panagiotis Metsis
Keyword(s):  
Random Fields ◽  
Volume Fraction ◽  
Concrete Specimen ◽  
Elasticity Tensor ◽  
Statistical Characteristics ◽  
Moving Window ◽  
Window Method ◽  
Ct Image Reconstruction ◽  
Random Material Property ◽  
Spatially Varying

The purpose of this paper is to determine the random spatially varying elastic properties of concrete at various scales taking into account its highly heterogeneous microstructure. The reconstruction of concrete microstructure is based on computed tomography (CT) images of a cubic concrete specimen. The variability of the local volume fraction of the constituents (pores, cement paste and aggregates) is quantified and mesoscale random fields of the elasticity tensor are computed from a number of statistical volume elements obtained by applying the moving window method on the specimen along with computational homogenization. Based on the statistical characteristics of the mesoscale random fields, it is possible to assess the effect of randomness in microstructure on the mechanical behavior of concrete.

scholarly journals ESTUDO AVALIATIVO DA PREVISÃO NUMÉRICA DO TEMPO DE CURTO PRAZO PARA O MUNICÍPIO DE MACEIÓ/AL, UTILIZANDO O MODELO WRF

2020 ◽  
Vol 12 (6) ◽  
pp. 2121
Author(s):  
Rosiberto Salustiano Silva Junior ◽  
Bruno César Teixeira Cardoso ◽  
Hugo Cainã Ferreira Monteiro ◽  
Ewerton Hallan de Lima Silva
Keyword(s):  
Weather Forecast ◽  
Atmospheric Model ◽  
Weather Research And Forecasting ◽  
Atmospheric Models ◽  
Economic Activities ◽  
Land User ◽  
The City ◽  
Model Topography ◽  
Better Than ◽  
Weather Research

Sendo as diferentes atividades econômicas fortemente influenciadas pela condição do tempo, faz-se necessário antever com dias de antecedência a situação meteorológica favorável ou não para o cotidiano da sociedade. E os modelos atmosféricos são ferramentas amplamente utilizados para avaliar o estado futuro da atmosfera, neste contexto, avaliar a precisão das previsões realizadas por estas ferramentas, tem sido cada fez mais recorrente. Neste trabalho foi utilizado o modelo atmosférico WRF (Weather Research and Forecasting) para realizar previsões diárias com duração de 72h, durante o período de 10 a 19 de julho de 2017 para a cidade de Maceió/AL. Para validar as previsões foram utilizados os dados observados da estação meteorológica automática do INMET (Instituto Nacional de Meteorologia). Para este estudo também foi proposto a atualização da topografia e uso do solo da área de estudo em questão, que gerou melhorias nas comparações realizadas para todas as variáveis analisadas, em destaque a previsão da variável pressão atmosférica, quando atualizada a topografia houve sensíveis melhorias nos indicadores estatísticos em comparação aos demais testes que não contaram com mesma atualização. Além disso, as análises estatísticas e os gráficos apresentados comprovam que o modelo previu melhor para 24h do que para 48h e nesta sequência melhor que 72h, ou seja, existiu a depreciação das previsões com o aumento da duração das previsões. Study of the Efficiency of the Short-Term Numerical Forecast for the City of Maceió / Al, Using the WRF ModelA B S T R A C TThe different economic activities are strongly influenced by the condition of the weather, it is necessary to forecast with days in advance the meteorological situation favorable or not for the daily life of the society. The atmospheric models are tools widely used to assess the future state of the atmosphere, in this context, assess the accuracy of the forecasts made by these tools, has been each made more recurrent. In this work the atmospheric model WRF (Weather Research and Forecasting) was used to make daily forecasts with a duration of 72h during the period from July 10 to 19, 2017 for the city of Maceió / AL, to validate the forecasts were used the observed data of the INMET (National Meteorological Institute) automatic weather station. For this study it was also proposed to update the topography and land user of the study area, which generated improvements in the comparisons made for all variables analyzed, in particular the prediction of the variable atmospheric pressure, when updated the topography there were sensible improvements in statistical indicators compared to the other tests that did not have the same update. In addition, the statistical analyzes and the graphs presented show that the model predicted better for 24h than for 48h and in this sequence better than 72h, that is, there was depreciation of the forecasts with the increase of the forecast duration.Keywords: Weather Forecast, Atmospheric Model, Topography, Land User.

Pollution gas correction method based on linear regression

2020 ◽  
Vol 5 (1) ◽  
pp. 46-52
Author(s):  
Jiewen Wei ◽  
Siewei Wang
Keyword(s):  
Linear Regression ◽  
Correction Method

scholarly journals Modeling the Infrared Magnesium and Hydrogen Lines from Quiet and Active Solar Regions

1994 ◽  
Vol 154 ◽  
pp. 323-339
Author(s):  
E. H. Avrett ◽  
E. S. Chang ◽  
R. Loeser
Keyword(s):  
Cross Sections ◽  
Line Emission ◽  
Atmospheric Model ◽  
Atmospheric Temperature ◽  
Atomic Data ◽  
Atmospheric Models ◽  
Spatial Features ◽  
Highly Sensitive ◽  
Hydrogen Lines ◽  
The Continuum

The emission lines of Mg I at 7.4, 12.2, and 12.3 μm are now known to be formed in the upper photosphere; the line emission is due to collisional coupling of higher levels with the continuum together with radiative depopulation of lower levels. These combined effects cause the line source functions of high-lying transitions to exceed the corresponding Planck functions. However, there are uncertainties in a) the relevant atomic data, particularly the collisional rates and ultraviolet photoionization rates, and b) the sensitivity of the calculated results to changes in atmospheric temperature and density. These uncertainties are examined by comparing twelve calculated Mg I line profiles in the range 2.1-12.3 μm with ATMOS satellite observations. We show results based on different rates, and using different atmospheric models representing a range of dark and bright spatial features. The calculated Mg profiles are found to be relatively insensitive to atmospheric model changes, and to depend critically on the choice of collisional and photoionization rates. We find better agreement with the observations using collision rates from van Regemorter (1962) rather than from Seaton (1962). We also compare twelve calculated hydrogen profiles in the range 2.2-12.4 μm with ATMOS observations. The available rates and cross sections for hydrogen seem adequate to account for the observed profiles, while the calculated lines are highly sensitive to atmospheric model changes. These lines are perhaps the best available diagnostics of the temperature and density structure of the photosphere and low chromosphere. Further calculations based on these infrared hydrogen lines should lead to greatly improved models of the solar atmosphere.

Real-Time, High-Resolution, Space–Time Analysis of Sea Surface Temperatures from Multiple Platforms

2007 ◽  
Vol 135 (9) ◽  
pp. 3158-3173 ◽  
Author(s):  
Steven M. Lazarus ◽  
Corey G. Calvert ◽  
Michael E. Splitt ◽  
Pablo Santos ◽  
David W. Sharp ◽  
...  
Keyword(s):  
Bias Correction ◽  
Atmospheric Model ◽  
Current Data ◽  
Sea Surface ◽  
Loop Current ◽  
Background Error ◽  
Analysis System ◽  
Spatially Varying ◽  
Cloud Mask ◽  
The Impact

Abstract A sea surface temperature (SST) analysis system designed to initialize short-term atmospheric model forecasts is evaluated for a month-long, relatively clear period in May 2004. System inputs include retrieved SSTs from the Geostationary Operational Environmental Satellite (GOES)-East and the Moderate Resolution Imaging Spectroradiometer (MODIS). The GOES SSTs are processed via a sequence of quality control and bias correction steps and are then composited. The MODIS SSTs are bias corrected and checked against the background field (GOES composites) prior to assimilation. Buoy data, withheld from the analyses, are used to bias correct the MODIS and GOES SSTs and to evaluate both the composites and analyses. The bias correction improves the identification of residual cloud-contaminated MODIS SSTs. The largest analysis system improvements are obtained from the adjustments associated with the creation of the GOES composites (i.e., a reduction in buoy/GOES composite rmse on the order of 0.3°–0.5°C). A total of 120 analyses (80 night and 40 day) are repeated for different experimental configurations designed to test the impact of the GOES composites, MODIS cloud mask, spatially varying background error covariance and decorrelation length scales, data reduction, and anisotropy. For the May 2004 period, the nighttime MODIS cloud mask is too conservative, at times removing good SST data and degrading the analyses. Nocturnal error variance estimates are approximately half that of the daytime and are relatively spatially homogeneous, indicating that the nighttime composites are, in general, superior. A 30-day climatological SST gradient is used to create anisotropic weights and a spatially varying length scale. The former improve the analyses in regions with significant SST gradients and sufficient data while the latter reduces the analysis rmse in regions where the innovations tend to be well correlated with distinct and persistent SST gradients (e.g., Loop Current). Data thinning reduces the rmse by expediting analysis convergence while simultaneously enhancing the computational efficiency of the analysis system. Based on these findings, an operational analysis configuration is proposed.

A Guiding Principles for Choosing Numerical Precision in Atmospheric Model based on CESM

2020 ◽  
Author(s):  
Jiayi Lai
Keyword(s):  
Climate Models ◽  
Spatial Scales ◽  
Atmospheric Model ◽  
Model Complexity ◽  
Double Precision ◽  
Atmospheric Models ◽  
Weather Model ◽  
Mixed Precision ◽  
Numerical Precision ◽  
Discrimination Method

<p><span>The next generation of weather and climate models will have an unprecedented level of resolution and model complexity, while also increasing the requirements for calculation and memory speed. Reducing the accuracy of certain variables and using mixed precision methods in atmospheric models can greatly improve Computing and memory speed. However, in order to ensure the accuracy of the results, most models have over-designed numerical accuracy, which results in that occupied resources have being much larger than the required resources. Previous studies have shown that the necessary precision for an accurate weather model has clear scale dependence, with large spatial scales requiring higher precision than small scales. Even at large scales the necessary precision is far below that of double precision. However, it is difficult to find a guided method to assign different precisions to different variables, so that it can save unnecessary waste. This paper will take CESM1.2.1 as a research object to conduct a large number of tests to reduce accuracy, and propose a new discrimination method similar to the CFL criterion. This method can realize the correlation verification of a single variable, thereby determining which variables can use a lower level of precision without degrading the accuracy of the results.</span></p>

Optimizing COSMO_REA6 reanalysis radiation flux for a high resolution coastal ocean model

2020 ◽  
Author(s):  
Martin Vodopivec ◽  
Matjaž Ličer
Keyword(s):  
High Resolution ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Atmospheric Model ◽  
Short Wave ◽  
Ocean Model ◽  
Sea Surface ◽  
Atmospheric Models ◽  
Fine Grid ◽  
Wave Flux

<p>When modelling coastal areas in high spatial resolution, it is also essential to obtain atmospheric forcing with suitably fine grid. The complex coastline and coastal orography exert strong influence on atmospheric fields, wind in particular, and the east Adriatic coast with numerous islands and coastal mountain ridges is a fine example. We decided to use a high resolution COSMO atmospheric reanalysis for our long term ROMS_AGRIF hindcasts, but in our initial experiments we found out that the atmospheric model significantly underestimates the short wave flux over the Mediterranean Sea, probably due to overestimation of high clouds formation and erroneous sea surface temperature used as a boundary condition. We explore different atmospheric models and different combinations of fluxes - direct, diffuse and clear sky solar radiation and combinations of fluxes from different atmospheric models (eg. ERA5). We compare them with solar irradiance observations at a coastal meteorological station and run year-long simulations to compare model sea surface temperature (SST) with satellite observations obtained from Coprenicus Marine Environment Monitoring Service.</p>

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