Spatial interpolation of meteorological fields using a multilevel parametric dynamic stochastic low-order model

Lavrinenko, A. V.; Moldovanova, E.A.; Mymrina, Dina F.; Popova, A. I.; Popova, Kseniya; Popov, Y.B.

doi:10.1016/j.jastp.2018.10.009

Cited by 3 publications

(13 citation statements)

References 16 publications

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“…Let us briefly state the problem considered earlier in [14,15] in general form. Assume that (s + 1) of spatially separated points with known coordinates (x i , y i ), where i = 0, .…”

Section: General Problem Statement and Description Of The Modelmentioning

confidence: 99%

“…As compared to the model [21,22], based on the two-dimensional mesoscale diffusion equation, the proposed model [14,15] includes a new element that is responsible for vertical correlation, as shown in Equation (3). Meteorological variables at the interpolation point (x 0 , y 0 ) are unknown and need to be estimated.…”

Section: General Problem Statement and Description Of The Modelmentioning

confidence: 99%

“…Our previous studies [14,15] proposed a simplified four-dimensional model that represents the evolution of meteorological variables in time and space for a mesometeorological scale. The dynamic stochastic model considers spatial and temporal changes in meteorological fields as random processes with known correlation properties.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we use a low-order parametric dynamic stochastic model with a vertical component based on a four-dimensional differential equation. It should be noted that based on this model, researchers have developed two filtering algorithms that ensure the interpolation of meteorological fields at the mesoscale [14,15]. These algorithms are characterized by different representations of the initial forecast model in state and observation space equations in terms of the Kalman filter.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, the aim of this paper is to consider the structural differences between the two algorithms [14,15], and to assess the effects of these differences on the accuracy of interpolation of meteorological fields.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

et al. 2019

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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.

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“…Let us briefly state the problem considered earlier in [14,15] in general form. Assume that (s + 1) of spatially separated points with known coordinates (x i , y i ), where i = 0, .…”

Section: General Problem Statement and Description Of The Modelmentioning

confidence: 99%

Section: General Problem Statement and Description Of The Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

et al. 2019

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Kriging based sequence interpolation and probability distribution correction for gaussian wind field data reconstruction

Lin

2020

Journal of Wind Engineering and Industrial Aerodynamics

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Safe Path Correction Method for Ambulance Aircraft Based on Low-Altitude Wind Prediction

Zhang

2020

IEEE Access

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The flight height of ambulance aircraft during aviation emergency rescue is low. Low-altitude wind fields and complex terrains may threaten the flight safety of rescue aircraft and thus limit rescue efficiency. The path planning of ambulance aircraft with consideration of low-altitude wind fields and terrains was presented in this study. First, a new wind field interpolation method was proposed to address the shortages of traditional reverse-distance wind field interpolation methods that neglect the effects of mountain slopes on wind vectors. This new interpolation method comprehensively considers the effects of terrain elevation, topographical slope, underlying surface roughness, and distance on wind vectors. The wind field interpolation results could accurately reflect the influence of topographic slopes on wind vectors and were close to practical results. Second, the effects of wind fields of different wind directions on flight paths were analyzed in view of the effects of low-altitude wind fields and restrictions of terrain obstacles. On the basis of the results, a method for correcting corresponding path nodes given aircraft performance constraints was proposed to ensure path safety. The connection matrix was solved by combining the A * algorithm and a 3D meshing technique. The safe path planning method for low-altitude rescue was concluded by combining a path-solving algorithm and the correction method for path nodes. Finally, the topographical features from Wenchuan and Chengdu to Mianyang during the Wenchuan earthquake were simulated with a constructed digital equation model. In the simulation environment, the safety of the path plan of the ambulance aircraft was verified under the influence of low-altitude wind fields. INDEX TERMS Path planning, wind field interpolation method, topographic slope, A * algorithm and a 3D meshing algorithm, digital equation model.

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Spatial interpolation of meteorological fields using a multilevel parametric dynamic stochastic low-order model

Cited by 3 publications

References 16 publications

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

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

Kriging based sequence interpolation and probability distribution correction for gaussian wind field data reconstruction

Safe Path Correction Method for Ambulance Aircraft Based on Low-Altitude Wind Prediction

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