Reducing noise associated with the Monte Carlo Independent Column Approximation for weather forecasting models

2011 ◽  
Vol 137 (654) ◽  
pp. 219-228 ◽  
Author(s):  
P. G. Hill ◽  
J. Manners ◽  
J. C. Petch
Keyword(s):  
Monte Carlo ◽  
Weather Forecasting ◽  
Forecasting Models ◽  
Independent Column

CHAOS AND WEATHER FORECASTING: THE ROLE OF THE UNSTABLE SUBSPACE IN PREDICTABILITY AND STATE ESTIMATION PROBLEMS

2011 ◽  
Vol 21 (12) ◽  
pp. 3389-3415 ◽  
Author(s):  
ANNA TREVISAN ◽  
LUIGI PALATELLA
Keyword(s):  
Data Assimilation ◽  
Chaotic Systems ◽  
Weather Forecasting ◽  
Variational Assimilation ◽  
Forecasting Models ◽  
Operational Forecasting ◽  
Estimation Problems ◽  
Atmospheric Predictability ◽  
Unstable Subspace

In the first part of this paper, we review some important results on atmospheric predictability, from the pioneering work of Lorenz to recent results with operational forecasting models. Particular relevance is given to the connection between atmospheric predictability and the theory of Lyapunov exponents and vectors. In the second part, we briefly review the foundations of data assimilation methods and then we discuss recent results regarding the application of the tools typical of chaotic systems theory described in the first part to well established data assimilation algorithms, the Extended Kalman Filter (EKF) and Four Dimensional Variational Assimilation (4DVar). In particular, the Assimilation in the Unstable Space (AUS), specifically developed for application to chaotic systems, is described in detail.

Development of Temperature-based Weather Forecasting Models Using Neural Networks and Fuzzy Logic

2014 ◽  
Vol 9 (12) ◽  
pp. 343-366 ◽  
Author(s):  
L. Al-Matarneh ◽  
A. Sheta ◽  
S. Bani-Ahmad ◽  
J. Alshaer ◽  
I. Al-oqily
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Weather Forecasting ◽  
Forecasting Models

The Monte Carlo Independent Column Approximation’s Conditional Random Noise: Impact on Simulated Climate

2005 ◽  
Vol 18 (22) ◽  
pp. 4715-4730 ◽  
Author(s):  
P. Räisänen ◽  
H. W. Barker ◽  
J. N. S. Cole
Keyword(s):  
Monte Carlo ◽  
Global Climate ◽  
Random Noise ◽  
Radiative Heating ◽  
Time Interval ◽  
Additional Time ◽  
Time Step ◽  
Order Of Magnitude ◽  
Independent Column ◽  
The Impact

Abstract The Monte Carlo Independent Column Approximation (McICA) method for computing domain-average radiative fluxes is unbiased with respect to the full ICA, but its flux estimates contain conditional random noise. Results for five experiments are used to assess the impact of McICA-related noise on simulations of global climate made by the NCAR Community Atmosphere Model (CAM). The experiment with the least noise (an order of magnitude below that of basic McICA) is taken as the reference. Two additional experiments help demonstrate how the impact of noise depends on the time interval between calls to the radiation code. Each experiment is an ensemble of seven 15-month simulations. Experiments with very high noise levels feature significant reductions to cloudiness in the lowermost model layer over tropical oceans as well as changes in highly related quantities. This bias appears immediately, stabilizes after a couple of model days, and appears to stem from nonlinear interactions between clouds and radiative heating. Outside the Tropics, insignificant differences prevail. When McICA sampling is confined to cloudy subcolumns and when, on average, 50% more samples, relative to basic McICA, are drawn for selected spectral intervals, McICA noise is much reduced and the results of the simulation are almost statistically indistinguishable from the reference. This is true both for mean fields and for the nature of fluctuations on scales ranging from 1 day to at least 30 days. While calling the radiation code once every 3 h instead of every hour allows the CAM additional time to incorporate McICA-related noise, the impact of noise is enhanced only slightly. In contrast, changing the radiative time step by itself produces effects that generally exceed the impact of McICA’s noise.

scholarly journals A review of operational, regional-scale, chemical weather forecasting models in Europe

2012 ◽  
Vol 12 (1) ◽  
pp. 1-87 ◽  
Author(s):  
J. Kukkonen ◽  
T. Olsson ◽  
D. M. Schultz ◽  
A. Baklanov ◽  
T. Klein ◽  
...  
Keyword(s):  
Atmospheric Chemistry ◽  
Weather Forecasting ◽  
Numerical Models ◽  
Weather Prediction ◽  
Regional Scale ◽  
Chemical Species ◽  
Future Research ◽  
Physical Processes ◽  
Forecasting Models ◽  
Air Chemistry

Abstract. Numerical models that combine weather forecasting and atmospheric chemistry are here referred to as chemical weather forecasting models. Eighteen operational chemical weather forecasting models on regional and continental scales in Europe are described and compared in this article. Topics discussed in this article include how weather forecasting and atmospheric chemistry models are integrated into chemical weather forecasting systems, how physical processes are incorporated into the models through parameterization schemes, how the model architecture affects the predicted variables, and how air chemistry and aerosol processes are formulated. In addition, we discuss sensitivity analysis and evaluation of the models, user operational requirements, such as model availability and documentation, and output availability and dissemination. In this manner, this article allows for the evaluation of the relative strengths and weaknesses of the various modelling systems and modelling approaches. Finally, this article highlights the most prominent gaps of knowledge for chemical weather forecasting models and suggests potential priorities for future research directions, for the following selected focus areas: emission inventories, the integration of numerical weather prediction and atmospheric chemical transport models, boundary conditions and nesting of models, data assimilation of the various chemical species, improved understanding and parameterization of physical processes, better evaluation of models against data and the construction of model ensembles.

scholarly journals Avaliação da dependência dos parâmetros de ajuste de um modelo E − l com a estratificação térmica estável

2018 ◽  
Vol 40 ◽  
pp. 245
Author(s):  
Arlindo Dutra Carvalho Junior ◽  
Pablo E.S. de Oliveira ◽  
Daniel Michelon dos Santos ◽  
Felipe Denardin Costa
Keyword(s):  
Weather Forecasting ◽  
Great Influence ◽  
Atmospheric Model ◽  
The Other ◽  
Turbulent Diffusivity ◽  
Physical Description ◽  
Forecasting Models ◽  
Dissipation Term ◽  
Real Value

One of the main challenges of the atmospheric model is the proper  determination of the turbulent diffusivity. In this sense, variousboundary layer parametrization have been developed along of the years. For the same closure order, many times, the bigger differences between them, are concentrated in the adjustment parameters. From the adequate physical description, to find the real value of each constant is the great challenge of the models. Them, the present work compare three different parametrization for the non-dimensional relation u2 ∗=E, that is used as a constant in the momentum diffusion coefficient in the E - l models. In the comparison with the GABLS experiment, the results show that the constant does not have a great influence over the windcomponents and over the temperature. On the other hand, the constant have a fundamental role in the behavior of the turbulence kinetic energy. This is due the fact of the constant is also present in the turbulence viscous dissipation term. Finally, it is important to stress that this is a work that is in its beginning and it aims the construction of a boundary layer parameterization for climate and weather forecasting models.

Evaluating the performance of Gulf of Alaska walleye pollock (Theragra chalcogramma) recruitment forecasting models using a Monte Carlo resampling strategy

2009 ◽  
Vol 66 (3) ◽  
pp. 367-381 ◽  
Author(s):  
Yong-Woo Lee ◽  
Bernard A. Megrey ◽  
S. Allen Macklin
Keyword(s):  
Time Series ◽  
Monte Carlo ◽  
Gulf Of Alaska ◽  
Data Set ◽  
Forecasting Models ◽  
Walleye Pollock Theragra Chalcogramma ◽  
Sample Data ◽  
Out Of Sample ◽  
Data Points ◽  
Recruitment Model

Multiple linear regressions (MLRs), generalized additive models (GAMs), and artificial neural networks (ANNs) were compared as methods to forecast recruitment of Gulf of Alaska walleye pollock ( Theragra chalcogramma ). Each model, based on a conceptual model, was applied to a 41-year time series of recruitment, spawner biomass, and environmental covariates. A subset of the available time series, an in-sample data set consisting of 35 of the 41 data points, was used to fit an environment-dependent recruitment model. Influential covariates were identified through statistical variable selection methods to build the best explanatory recruitment model. An out-of-sample set of six data points was retained for model validation. We tested each model’s ability to forecast recruitment by applying them to an out-of-sample data set. For a more robust evaluation of forecast accuracy, models were tested with Monte Carlo resampling trials. The ANNs outperformed the other techniques during the model fitting process. For forecasting, the ANNs were not statistically different from MLRs or GAMs. The results indicated that more complex models tend to be more susceptible to an overparameterization problem. The procedures described in this study show promise for building and testing recruitment forecasting models for other fish species.

scholarly journals Tests of Monte Carlo Independent Column Approximation in the ECHAM5 Atmospheric GCM

2007 ◽  
Vol 20 (19) ◽  
pp. 4995-5011 ◽  
Author(s):  
P. Räisänen ◽  
S. Järvenoja ◽  
H. Järvinen ◽  
M. Giorgetta ◽  
E. Roeckner ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Water Content ◽  
Beta Distribution ◽  
Cloud Fraction ◽  
Total Water Content ◽  
Subgrid Scale ◽  
Total Water ◽  
Distribution Scheme ◽  
Independent Column ◽  
The Impact

Abstract The Monte Carlo Independent Column Approximation (McICA) method for computing domain-average radiative fluxes allows a flexible treatment of unresolved cloud structure, and it is unbiased with respect to the full ICA, but its flux estimates contain conditional random noise. Here, tests of McICA in the ECHAM5 atmospheric GCM are reported. ECHAM5 provides an interesting test bed for McICA because it carries prognostic variables for the subgrid-scale probability distribution of total water content, which allows us to determine subgrid-scale cloud variability directly from the resolved-scale model variables. Three experiments with differing levels of radiative noise, each consisting of ten 6-yr runs, are performed to estimate the impact of McICA noise on simulated climate. In an experiment that attempted to deliberately maximize McICA noise, a systematic reduction in low cloud fraction occurred. For a more reasonable implementation of McICA, the impact of noise is very small, although statistically discernible. In terms of the impacts of noise, McICA appears to be a viable approach for use in ECHAM5. However, to improve the simulation of cloud radiative effects, realistic representation of both unresolved and resolved cloud structures is needed, which remains a challenging problem. Comparison of ECHAM5 data with a global cloud system–resolving model dataset and with International Satellite Cloud Climatology Project data suggested two problems related to unresolved cloud structures. First, ECHAM5 appears to underestimate subgrid-scale cloud variability. This problem seems partly related to the use of the beta distribution scheme for total water content in ECHAM5: in its current form, the scheme is unable to generate highly inhomogeneous clouds (relative standard deviation of condensate amount >1). Second, it appears that in ECHAM5, overcast cloud layers occur too frequently and partially cloudy layers too rarely. This problem is not unique to the beta distribution scheme; in fact, it is more pronounced when using an alternative, relative humidity–based cloud fraction scheme.

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