Calibration Method for Standard Scattering Plate Calibration System Used in Calibrating Visibility Meter

Jian, 张健 Zhang; Guoyu, 张国玉 Zhang; Gaofei, 孙高飞 Sun; Shi, 苏拾 Su; Jianliang, 张建良 Zhang

doi:10.3788/gzxb20174603.0312003

Cited by 4 publications

(4 citation statements)

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“…Therefore, based on the length of the chamber, the baseline x was 20 m. Meanwhile, the transmissivity accuracy of the reference transmissometer was controlled to 0.2%, which was verified by the attenuation filters calibrated against the national standards of the National Institute of Metrology, China. Therefore, the relative visibility error dMOR MOR can be calculated at any MOR point using equation (8). The FSM indoor calibration method was limited to 1500 m; thus, a relative visibility error dMOR MOR of 5% could be achieved.…”

Section: Accuracy Of the Reference Transmissometermentioning

confidence: 99%

“…They were first located close together, with transverse and longitudinal spacings of 1.0 m and 1.5 m, respectively, and the height of the sampling area was 1.6 m above-ground. Before the test, four black extinction cones were placed near each sampling area to reduce the interference, and all FSMs were calibrated with the scatter meter calibration unit (SCU) [8]. First, the chamber was filled with fog until the visibility reported by the transmissometer in the chamber was well below 10 m to ensure that the fog reached a supersaturated state.…”

Section: Accuracy Of the Reference Transmissometermentioning

confidence: 99%

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A method for calibrating forward scatter meters indoors

et al. 2020

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In this study, we developed a method for calibrating forward scatter meters (FSMs) against transmissometers for measuring visibility under laboratory conditions, and we designed and constructed a novel fog chamber for calibrating FSMs indoors. The fog chamber was used to simulate a controlled visibility environment, and a transmissometer was installed on both ends of the fog chamber as the standard. The developed method involved a comparison of the FSMs and transmissometer in a group of fog events. FSMs were installed on both sides of the optical path between the transmitter and receiver of the transmissometer. The chamber door was closed, and, after creating fog to simulate low-visibility conditions, measurements were collected from the FSMs and transmissometer every minute while the fog dissipated very slowly. The measurement errors of the FSM could then be calculated. To evaluate this method, tests were conducted in the fog chamber, and the results show that the fog chamber could simulate visibility from 10 m to 10 000 m, while the FSMs could only be calibrated below a visibility of 1500 m due to the baseline of the transmissometer. The fog homogeneity was controlled within 1.2% or less, and the stability was limited to 5% per 30 min. These parameters are reasonably homogeneous and stable for FSM-transmissometer comparison. Three PWD22 FSMs were calibrated using this method, and the calibration uncertainties were estimated to be in the range of 7.8% to 9.2%.

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Section: Accuracy Of the Reference Transmissometermentioning

confidence: 99%

Section: Accuracy Of the Reference Transmissometermentioning

confidence: 99%

A method for calibrating forward scatter meters indoors

et al. 2020

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“…Furthermore, this method is inefficient and irreproducible, and the observation of visibility is also limited by the time between the observations and the environmental changes. The visibility meter approach includes the forward scattering method and the back-scattering method [7]. In general, based on the cost and performance considerations, most of visibility meters use the forward scattering method.…”

Section: Introductionmentioning

confidence: 99%

A Transfer Learning Method for Meteorological Visibility Estimation Based on Feature Fusion Method

Hong³

et al. 2021

Applied Sciences

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Meteorological visibility is an important meteorological observation indicator to measure the weather transparency which is important for the transport safety. It is a challenging problem to estimate the visibilities accurately from the image characteristics. This paper proposes a transfer learning method for the meteorological visibility estimation based on image feature fusion. Different from the existing methods, the proposed method estimates the visibility based on the data processing and features’ extraction in the selected subregions of the whole image and therefore it had less computation load and higher efficiency. All the database images were gray-averaged firstly for the selection of effective subregions and features extraction. Effective subregions are extracted for static landmark objects which can provide useful information for visibility estimation. Four different feature extraction methods (Densest, ResNet50, Vgg16, and Vgg19) were used for the feature extraction of the subregions. The features extracted by the neural network were then imported into the proposed support vector regression (SVR) regression model, which derives the estimated visibilities of the subregions. Finally, based on the weight fusion of the visibility estimates from the subregion models, an overall comprehensive visibility was estimated for the whole image. Experimental results show that the visibility estimation accuracy is more than 90%. This method can estimate the visibility of the image, with high robustness and effectiveness.

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“…Another popular visibility measurement method is the visibility meter method in which the meter estimates the visible distance by measurement of atmospheric transmittance or extinction coefficient [5]. The forward scattering and back-scattering method [7] are among the most popular metering methods while most visibility meter employs the forward scattering method as it can give reasonable accuracy and performance with lower cost. However, forward scattering visibility meter with high accuracy is very expensive and requires specialized installation and calibration skills and usually this type of meter could give good accuracy with relatively short range only.…”

Section: Introductionmentioning

confidence: 99%

Experimental Evaluation of PSO Based Transfer Learning Method for Meteorological Visibility Estimation

Chung

Hong

2021

Atmosphere

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Estimation of Meteorological visibility from image characteristics is a challenging problem in the research of meteorological parameters estimation. Meteorological visibility can be used to indicate the weather transparency and this indicator is important for transport safety. This paper summarizes the outcomes of the experimental evaluation of a Particle Swarm Optimization (PSO) based transfer learning method for meteorological visibility estimation method. This paper proposes a modified approach of the transfer learning method for visibility estimation by using PSO feature selection. Image data are collected at fixed location with fixed viewing angle. The database images were gone through a pre-processing step of gray-averaging so as to provide information of static landmark objects for automatic extraction of effective regions from images. Effective regions are then extracted from image database and the image features are then extracted from the Neural Network. Subset of Image features are selected based on the Particle Swarming Optimization (PSO) methods to obtain the image feature vectors for each effective sub-region. The image feature vectors are then used to estimate the visibilities of the images by using the Multiple Support Vector Regression (SVR) models. Experimental results show that the proposed method can give an accuracy more than 90% for visibility estimation and the proposed method is effective and robust.

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Calibration Method for Standard Scattering Plate Calibration System Used in Calibrating Visibility Meter

Cited by 4 publications

References 0 publications

A method for calibrating forward scatter meters indoors

A method for calibrating forward scatter meters indoors

A Transfer Learning Method for Meteorological Visibility Estimation Based on Feature Fusion Method

Experimental Evaluation of PSO Based Transfer Learning Method for Meteorological Visibility Estimation

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