scholarly journals A New Diffusion Scheme for Numerical Models Based on Full Irreversibility

2009 ◽  
Vol 24 (2) ◽  
pp. 595-600 ◽  
Author(s):  
C. Liu ◽  
Y. Liu ◽  
H. Xu
Keyword(s):  
Numerical Models ◽  
Weather Prediction ◽  
Forecast Accuracy ◽  
Second Law Of Thermodynamics ◽  
New Physics ◽  
Numerical Weather Prediction Model ◽  
One Dimensional ◽  
Diffusion Scheme ◽  
The One ◽  
Mean Square Errors

Abstract In this work, the forecast accuracy of a numerical weather prediction model is improved by emulating physical dissipation as suggested by the second law of thermodynamics, which controls the irreversible evolutionary direction of a many-body system like the atmosphere. The ability of the new physics-based scheme to improve model accuracy is demonstrated via the case of the one-dimensional viscous Burgers equation and the one-dimensional diffusion equation, as well as a series of numerical simulations of the well-known 1998 successive torrential rains along the Yangtze River valley and 365 continuous 24-h simulations during 2005–06 with decreased root-mean-square errors and improved forecasts in all of the simulations.

scholarly journals Anomalous Diffusion with an Apparently Negative Diffusion Coefficient in a One-Dimensional Quantum Molecular Chain Model

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 506
Author(s):  
Sho Nakade ◽  
Kazuki Kanki ◽  
Satoshi Tanaka ◽  
Tomio Petrosky
Keyword(s):  
Diffusion Coefficient ◽  
Diffusion Constant ◽  
Mean Square Displacement ◽  
Second Law Of Thermodynamics ◽  
Molecular Chain ◽  
Chain Model ◽  
Phase Mixing ◽  
One Dimensional ◽  
The One ◽  
Negative Diffusion

An interesting anomaly in the diffusion process with an apparently negative diffusion coefficient defined through the mean-square displacement in a one-dimensional quantum molecular chain model is shown. Nevertheless, the system satisfies the H-theorem so that the second law of thermodynamics is satisfied. The reason why the “diffusion constant” becomes negative is due to the effect of the phase mixing process, which is a characteristic result of the one-dimensionality of the system. We illustrate the situation where this negative “diffusion constant” appears.

scholarly journals Mechanics and thermodynamics of a new minimal model of the atmosphere

2020 ◽  
Vol 135 (10) ◽  
Author(s):  
Gabriele Vissio ◽  
Valerio Lucarini
Keyword(s):  
Numerical Models ◽  
Preliminary Evidence ◽  
Chaotic Motions ◽  
One Dimensional ◽  
Fundamental Properties ◽  
Multiscale Dynamics ◽  
Energy Cycle ◽  
The One ◽  
Lorenz 96 ◽  
Model Features

AbstractThe understanding of the fundamental properties of the climate system has long benefitted from the use of simple numerical models able to parsimoniously represent the essential ingredients of its processes. Here, we introduce a new model for the atmosphere that is constructed by supplementing the now-classic Lorenz ’96 one-dimensional lattice model with temperature-like variables. The model features an energy cycle that allows for energy to be converted between the kinetic form and the potential form and for introducing a notion of efficiency. The model’s evolution is controlled by two contributions—a quasi-symplectic and a gradient one, which resemble (yet not conforming to) a metriplectic structure. After investigating the linear stability of the symmetric fixed point, we perform a systematic parametric investigation that allows us to define regions in the parameters space where at steady-state stationary, quasi-periodic, and chaotic motions are realised, and study how the terms responsible for defining the energy budget of the system depend on the external forcing injecting energy in the kinetic and in the potential energy reservoirs. Finally, we find preliminary evidence that the model features extensive chaos. We also introduce a more complex version of the model that is able to accommodate for multiscale dynamics and that features an energy cycle that more closely mimics the one of the Earth’s atmosphere.

scholarly journals On the effect of model parameters on forecast objects

2018 ◽  
Vol 11 (4) ◽  
pp. 1577-1590 ◽  
Author(s):  
Caren Marzban ◽  
Corinne Jones ◽  
Ning Li ◽  
Scott Sandgathe
Keyword(s):  
Forest Fires ◽  
Eddy Currents ◽  
Numerical Models ◽  
Weather Prediction ◽  
Clustering Algorithms ◽  
Latin Hypercube Sampling ◽  
Numerical Weather Prediction Model ◽  
Model Parameters ◽  
Multiple Model ◽  
The Impact

Abstract. Many physics-based numerical models produce a gridded, spatial field of forecasts, e.g., a temperature map. The field for some quantities generally consists of spatially coherent and disconnected objects. Such objects arise in many problems, including precipitation forecasts in atmospheric models, eddy currents in ocean models, and models of forest fires. Certain features of these objects (e.g., location, size, intensity, and shape) are generally of interest. Here, a methodology is developed for assessing the impact of model parameters on the features of forecast objects. The main ingredients of the methodology include the use of (1) Latin hypercube sampling for varying the values of the model parameters, (2) statistical clustering algorithms for identifying objects, (3) multivariate multiple regression for assessing the impact of multiple model parameters on the distribution (across the forecast domain) of object features, and (4) methods for reducing the number of hypothesis tests and controlling the resulting errors. The final output of the methodology is a series of box plots and confidence intervals that visually display the sensitivities. The methodology is demonstrated on precipitation forecasts from a mesoscale numerical weather prediction model.

Impact of aerosols on the forecast accuracy of solar irradiance calculated by a numerical weather prediction model

2014 ◽  
Vol 223 (12) ◽  
pp. 2621-2630 ◽  
Author(s):  
Ken-ichi Shimose ◽  
Hideaki Ohtake ◽  
Joao Gari da Silva Fonseca ◽  
Takumi Takashima ◽  
Takashi Oozeki ◽  
...  
Keyword(s):  
Prediction Model ◽  
Numerical Weather Prediction ◽  
Solar Irradiance ◽  
Weather Prediction ◽  
Forecast Accuracy ◽  
Numerical Weather Prediction Model ◽  
Numerical Weather

scholarly journals Nonequilibrium Time Reversibility with Maps and Walks

Entropy ◽  
2022 ◽  
Vol 24 (1) ◽  
pp. 78
Author(s):  
William Graham Hoover ◽  
Carol Griswold Hoover ◽  
Edward Ronald Smith
Keyword(s):  
Piecewise Linear ◽  
Second Law Of Thermodynamics ◽  
Model Systems ◽  
Computational Time ◽  
Nonequilibrium Systems ◽  
Time Reversibility ◽  
One Dimensional ◽  
Fractal Properties ◽  
Dynamical Simulations ◽  
The One

Time-reversible dynamical simulations of nonequilibrium systems exemplify both Loschmidt’s and Zermélo’s paradoxes. That is, computational time-reversible simulations invariably produce solutions consistent with the irreversible Second Law of Thermodynamics (Loschmidt’s) as well as periodic in the time (Zermélo’s, illustrating Poincaré recurrence). Understanding these paradoxical aspects of time-reversible systems is enhanced here by studying the simplest pair of such model systems. The first is time-reversible, but nevertheless dissipative and periodic, the piecewise-linear compressible Baker Map. The fractal properties of that two-dimensional map are mirrored by an even simpler example, the one-dimensional random walk, confined to the unit interval. As a further puzzle the two models yield ambiguities in determining the fractals’ information dimensions. These puzzles, including the classical paradoxes, are reviewed and explored here.

scholarly journals Sensitivity of Glacier Runoff to Winter Snow Thickness Investigated for Vatnajökull Ice Cap, Iceland, Using Numerical Models and Observations

Atmosphere ◽  
2018 ◽  
Vol 9 (11) ◽  
pp. 450 ◽  
Author(s):  
Louise Schmidt ◽  
Peter Langen ◽  
Guðfinna Aðalgeirsdóttir ◽  
Finnur Pálsson ◽  
Sverrir Guðmundsson ◽  
...  
Keyword(s):  
Climate Model ◽  
Numerical Models ◽  
Weather Prediction ◽  
Regional Climate ◽  
Numerical Weather Prediction Model ◽  
Ice Cap ◽  
Winter Snow ◽  
Surface Climate ◽  
Glacier Runoff ◽  
Snow Thickness

Several simulations of the surface climate and energy balance of Vatnajökull ice cap, Iceland, are used to estimate the glacier runoff for the period 1980–2015 and the sensitivity of runoff to the spring conditions (e.g., snow thickness). The simulations are calculated using the snow pack scheme from the regional climate model HIRHAM5, forced with incoming mass and energy fluxes from the numerical weather prediction model HARMONIE-AROME. The modeled runoff is compared to available observations from two outlet glaciers to assess the quality of the simulations. To test the sensitivity of the runoff to spring conditions, simulations are repeated for the spring conditions of each of the years 1980–2015, followed by the weather of all summers in the same period. We find that for the whole ice cap, the variability in runoff as a function of varying spring conditions was on average 31% of the variability due to changing summer weather. However, some outlet glaciers are very sensitive to the amount of snow in the spring, as e.g., the variation in runoff from Brúarjökull due to changing spring conditions was on average 50% of the variability due to varying summer weather.

scholarly journals On the Effect of Model Parameters on Forecast Objects

2017 ◽  
Author(s):  
Caren Marzban ◽  
Corinne Jones ◽  
Ning Li ◽  
Scott Sandgathe
Keyword(s):  
Prediction Model ◽  
Numerical Weather Prediction ◽  
Numerical Models ◽  
Weather Prediction ◽  
Clustering Algorithms ◽  
Numerical Weather Prediction Model ◽  
Model Parameters ◽  
Numerical Weather ◽  
The Impact ◽  
Mesoscale Numerical Weather Prediction

Abstract. Many physics-based numerical models produce a gridded, spatial field of forecasts, e.g., a temperature "map". However, the field for some quantities such as precipitation generally consists of spatially coherent and disconnected "objects". Certain features of these objects (e.g., number, size, and intensity) are generally of interest. Here, a methodology is developed for assessing the impact of model parameters on features of forecast objects. Although, in principle, the objects can be defined by any means, here they are identified via clustering algorithms. The methodology is demonstrated on precipitation forecasts from a mesoscale numerical weather prediction model.

scholarly journals Experience of using the Kalman filter to correct numerical forecasts of surface air temperature

2020 ◽  
Vol 4 ◽  
pp. 28-42
Author(s):  
Yu.V. Alferov . ◽  
◽  
E.G. Klimova ◽  
Keyword(s):  
Kalman Filter ◽  
Air Temperature ◽  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Weather Prediction ◽  
Surface Air Temperature ◽  
One Dimensional ◽  
Numerical Weather ◽  
Statistical Correction ◽  
The One

A possibility of using the one-dimensional Kalman filter to improve the forecast of surface air temperature at an irregular grid of point is studied. This mechanism is tested using the forecasts obtained from different configurations of two different numerical weather prediction models. An algorithm for the statistical correction of numerical forecasts of surface air temperature based on the one-dimensional Kalman filter is constructed. Two methods are proposed for estimating the bias noise dispersion. The series of experiments demonstrated the effectiveness of the algorithm for the bias compensation.The most significantresults are achieved for the models with large bias or for long-range forecasts. At the same time, the use of the algorithm has little effect on the root-meansquare error of the forecast. Keywords: hydrodynamic model of the atmosphere, numerical weather prediction, statistical correction of numerical forecasts, Kalman filter

Drying Rate of Wood Particles With Longitudinal Mass Transfer

1985 ◽  
Vol 107 (1) ◽  
pp. 12-18 ◽  
Author(s):  
B. Dorri ◽  
A. F. Emery ◽  
P. C. Malte
Keyword(s):  
Numerical Models ◽  
Spherical Particles ◽  
Drying Rate ◽  
Evaporation Front ◽  
Dimensional Model ◽  
One Dimensional ◽  
Wood Particles ◽  
Small Wood ◽  
The One ◽  
Rectangular Model

The drying of small wood particles of shape L:W:t = 3 to 5:2:1 is examined by three numerical models, and results are compared to measurements. (i) A one-dimensional rectangular model has liquid water concentrated in the center of a particle, and this is removed as an evaporation front propagates into the liquid. (ii) The one-dimensional model is also treated by the volume-averaged, or “smeared” approach, for which the moisture at any point is a distribution of liquid and vapor. For the simple rectangular geometry, the frontal and smeared models give similar results. (iii) Equivalent spherical particles are analyzed by a smeared model which includes capillarity. Reasonable agreement is obtained between the spherical results and the measurements, though an overprediction in drying rate occurs for slender particles.

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