scholarly journals Comparative Evaluation of Algorithms for Spatial Interpolation of Atmospheric State Parameters Based on a Dynamic Stochastic Model Taking into Account the Vertical Variation of a Meteorological Field

Symmetry ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1207
Author(s):  
Popov ◽  
Lavrinenko ◽  
Krasnenko ◽  
Popova ◽  
Popova ◽  
...  
Keyword(s):  
Stochastic Model ◽  
Spatial Interpolation ◽  
Forecast Model ◽  
Wind Fields ◽  
Vertical Variation ◽  
Meteorological Field ◽  
Dynamic Stochastic ◽  
Atmospheric State ◽  
Observation Space

The paper presents a comparative analysis of two algorithms for the spatial interpolation of meteorological fields. Both algorithms are based on a four-dimensional low-order parametric dynamic stochastic model, taking into account the vertical variation of a meteorological field. The algorithms are characterized by different representations of the forecast model in state and observation space equations for the Kalman filter. The authors studied the accuracy of the spatial interpolation of temperature and wind fields for the developed algorithms. The results of the study are presented in this paper. Numerical simulation was conducted using long-term upper-air observations obtained for a typical mesometeorological range. The results of the study demonstrate that the accuracy of interpolation for the two considered algorithms is comparable.

scholarly journals Spatial mapping of ground-based observations of total ozone

2015 ◽  
Vol 8 (10) ◽  
pp. 4487-4505 ◽  
Author(s):  
K.-L. Chang ◽  
S. Guillas ◽  
V. E. Fioletov
Keyword(s):  
Spatial Interpolation ◽  
Stochastic Partial Differential Equation ◽  
Positive Impact ◽  
The Novel ◽  
Total Column Ozone ◽  
Column Ozone ◽  
Source Of Information ◽  
Total Column ◽  
Ozone Levels

Abstract. Total column ozone variations estimated using ground-based stations provide important independent source of information in addition to satellite-based estimates. This estimation has been vigorously challenged by data inhomogeneity in time and by the irregularity of the spatial distribution of stations, as well as by interruptions in observation records. Furthermore, some stations have calibration issues and thus observations may drift. In this paper we compare the spatial interpolation of ozone levels using the novel stochastic partial differential equation (SPDE) approach with the covariance-based kriging. We show how these new spatial predictions are more accurate, less uncertain and more robust. We construct long-term zonal means to investigate the robustness against the absence of measurements at some stations as well as instruments drifts. We conclude that time series analyzes can benefit from the SPDE approach compared to the covariance-based kriging when stations are missing, but the positive impact of the technique is less pronounced in the case of drifts.

scholarly journals Does afforestation deteriorate haze pollution in Beijing–Tianjin–Hebei (BTH), China?

2018 ◽  
Author(s):  
Xin Long ◽  
Naifang Bei ◽  
Jiarui Wu ◽  
Xia Li ◽  
Tian Feng ◽  
...  
Keyword(s):  
Forest Cover ◽  
Fine Particles ◽  
Control Strategies ◽  
Surface Wind ◽  
Forecast Model ◽  
Surface Wind Speed ◽  
Satellite Measurements ◽  
Haze Pollution ◽  
The Impact

Abstract. Although aggressive emission control strategies have been implemented recently in the Beijing–Tianjin–Hebei area (BTH), China, pervasive and persistent haze still frequently engulfs the region during wintertime. Afforestation in BTH, primarily concentrated in the Taihang and Yanshan Mountains, has constituted one of the controversial factors exacerbating the haze pollution due to its slowdown of the surface wind speed. We report here an increasing trend of forest cover in BTH during 2001–2013 based on long-term satellite measurements and the impact of the afforestation on the fine particles (PM2.5) level. Simulations using the Weather Research and Forecast model with chemistry reveal that the afforestation in BTH since 2001 generally deteriorates the haze pollution in BTH to some degree, enhancing PM2.5 concentrations by up to 6 % on average. Complete afforestation or deforestation in the Taihang and Yanshan Mountains would increase or decrease the PM2.5 level within 15 % in BTH. Our model results also suggest that implementing a large ventilation corridor system would not be effective or beneficial to mitigate the haze pollution in Beijing.

scholarly journals A Stochastic Model for Virus Growth in a Cell Population

2014 ◽  
Vol 51 (3) ◽  
pp. 599-612 ◽  
Author(s):  
J. E. Björnberg ◽  
T. Britton ◽  
E. I. Broman ◽  
E. Natan
Keyword(s):  
Stochastic Model ◽  
Host Cell ◽  
Cell Population ◽  
Branching Process ◽  
Virus Population ◽  
Virus Growth ◽  
Optimal Balance ◽  
Large Numbers ◽  
A Cell

In this work we introduce a stochastic model for the spread of a virus in a cell population where the virus has two ways of spreading: either by allowing its host cell to live and duplicate, or by multiplying in large numbers within the host cell, causing the host cell to burst and thereby let the virus enter new uninfected cells. The model is a kind of interacting Markov branching process. We focus in particular on the probability that the virus population survives and how this depends on a certain parameter λ which quantifies the ‘aggressiveness’ of the virus. Our main goal is to determine the optimal balance between aggressive growth and long-term success. Our analysis shows that the optimal strategy of the virus (in terms of survival) is obtained when the virus has no effect on the host cell's life cycle, corresponding to λ = 0. This is in agreement with experimental data about real viruses.

scholarly journals Forecast of Waterway Cargo Turnover Volume Based on Genetic Algorithm to Optimize Neural Network Parameters

2021 ◽  
Vol 2083 (3) ◽  
pp. 032010
Author(s):  
Rong Ma
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Forecast Model ◽  
Network Parameters ◽  
The Neural Network ◽  
Target Effect ◽  
The Empirical Analysis ◽  
Better Than

Abstract The traditional BP neural network is difficult to achieve the target effect in the prediction of waterway cargo turnover. In order to improve the accuracy of waterway cargo turnover forecast, a waterway cargo turnover forecast model was created based on genetic algorithm to optimize neural network parameters. The genetic algorithm overcomes the trap that the general iterative method easily falls into, that is, the “endless loop” phenomenon that occurs when the local minimum is small, and the calculation time is small, and the robustness is high. Using genetic algorithm optimized BP neural network to predict waterway cargo turnover, and the empirical analysis of the waterway cargo turnover forecast is carried out. The results obtained show that the neural network waterway optimized by genetic algorithm has a higher accuracy than the traditional BP neural network for predicting waterway cargo turnover, and the optimization model can long-term analysis of the characteristics of waterway cargo turnover changes shows that the prediction effect is far better than traditional neural networks.

A stability analysis on a smoking model with stochastic perturbation

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Anwar Zeb ◽  
Sunil Kumar ◽  
Almaz Tesfay ◽  
Anil Kumar
Keyword(s):  
Stochastic Model ◽  
Stochastic System ◽  
Existence And Uniqueness ◽  
Deterministic Model ◽  
Sufficient Conditions ◽  
Reproductive Number ◽  
Content Type ◽  
Model Form ◽  
The World ◽  
Dynamic Stochastic

Purpose The purpose of this paper is to investigate the effects of irregular unsettling on the smoking model in form of the stochastic model as in the deterministic model these effects are neglected for simplicity. Design/methodology/approach In this research, the authors investigate a stochastic smoking system in which the contact rate is perturbed by Lévy noise to control the trend of smoking. First, present the formulation of the stochastic model and study the dynamics of the deterministic model. Then the global positive solution of the stochastic system is discussed. Further, extinction and the persistence of the proposed system are presented on the base of the reproductive number. Findings The authors discuss the dynamics of the deterministic smoking model form and further present the existence and uniqueness of non-negative global solutions for the stochastic system. Some previous study’s mentioned in the Introduction can be improved with the help of obtaining results, graphically present in this manuscript. In this regard, the authors present the sufficient conditions for the extinction of smoking for reproductive number is less than 1. Research limitations/implications In this work, the authors investigated the dynamic stochastic smoking model with non-Gaussian noise. The authors discussed the dynamics of the deterministic smoking model form and further showed for the stochastic system the existence and uniqueness of the non-negative global solution. Some previous study’s mentioned in the Introduction can be improved with the help of obtained results, clearly shown graphically in this manuscript. In this regard, the authors presented the sufficient conditions for the extinction of smoking, if <1, which can help in the control of smoking. Motivated from this research soon, the authors will extent the results to propose new mathematical models for the smoking epidemic in the form of fractional stochastic modeling. Especially, will investigate the effective strategies for control smoking throughout the world. Originality/value This study is helpful in the control of smoking throughout the world.

Long-Term Rainfall Forecast Model Based on The TabNet and LightGbm Algorithm

2020 ◽  
Author(s):  
Tianyu Xu ◽  
Yongchuan Yu ◽  
Jianzhuo Yan ◽  
Hongxia Xu
Keyword(s):  
Prediction Model ◽  
Feature Fusion ◽  
Forecast Model ◽  
Data Sets ◽  
Good Prediction ◽  
Data Set ◽  
Rainfall Prediction ◽  
Improve Model ◽  
Probability Prediction

Abstract Due to the problems of unbalanced data sets and distribution differences in long-term rainfall prediction, the current rainfall prediction model had poor generalization performance and could not achieve good prediction results in real scenarios. This study uses multiple atmospheric parameters (such as temperature, humidity, atmospheric pressure, etc.) to establish a TabNet-LightGbm rainfall probability prediction model. This research uses feature engineering (such as generating descriptive statistical features, feature fusion) to improve model accuracy, Borderline Smote algorithm to improve data set imbalance, and confrontation verification to improve distribution differences. The experiment uses 5 years of precipitation data from 26 stations in the Beijing-Tianjin-Hebei region of China to verify the proposed rainfall prediction model. The test set is to predict the rainfall of each station in one month. The experimental results shows that the model has good performance with AUC larger than 92%. The method proposed in this study further improves the accuracy of rainfall prediction, and provides a reference for data mining tasks.

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