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Identification and Optimization of Retrieval Model in Atmosphere
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Identification and Optimization of Retrieval Model in Atmosphere

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".

Deadline for manuscript submissions: closed (19 September 2022) | Viewed by 24413

Special Issue Editors

Dr. Haijiang Wang

E-Mail Website
Guest Editor
College of Electronic Engineering, Chengdu University of Information Technology, Chengdu 610225, China
Interests: weather radar signal processing; dual-polarization Doppler weather radar data analysis and processing; wind field retrieval
Special Issues, Collections and Topics in MDPI journals
Dr. Jiafeng Zheng

E-Mail Website
Guest Editor
School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, China
Interests: cloud radar and its application; remote sensing of cloud and precipitation properties; zenithal meteorological radar and its application; Doppler wind Lidar and its application
Special Issues, Collections and Topics in MDPI journals
Dr. Hao Wu

E-Mail Website
Guest Editor
School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, China
Interests: impact by turbulence parameters on New Particle Formation (NPF) event; the diurnal variation of solar radiation (PAR) and strong aerosol nucleation radiation and interaction with boundary layer
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The complete and detailed knowledge of atmospheric physical quantity profiles and fields is of extreme importance as the first step in a wide range of meteorological applications, including diagnostic research studies, hazard warnings, nowcasting and numerical forecasting. So, the related retrieval methods and models have always been research hotspots in the field of atmospheric science. Accurate profiling and field retrieval often involves model optimization based on mathematical and physical methods, especially in situations involving sparse data and a low SNR (signal to noise ratio).

On the other hand, with the advances in atmosphere sounding technologies, the detail of the detected atmospheric data and the accuracy of retrievable atmospheric physical products have both undergone remarkable enhancement. This provides better input information for weather phenomena and hydrometeor identification but creates higher requirements in regard to identification precision. The accurate identification and tracking of some extreme atmospheric phenomena such as tornadoes, mesocyclones, and supercell storms are basic and essential parts of severe weather warning operations. Meanwhile, the excellent hydrometeor identification effect is of great significance for both cloud microphysics research and the productivity of individuals’ lives and activities.

We invite manuscripts regarding the retrieval models and algorithms of atmospheric profiles and fields and hydrometeor and weather phenomena identification based on atmospheric data and retrieval products. Relevant topics include, but are not limited to:

  1. The optimization of profile and field retrieval models and methods of atmospheric physical quantities, including wind, temperature, pressure, aerosol, and so on.
  2. Interpolation, extrapolation, and fitting algorithms for atmospheric physical quantities based on analytical and numerical methods.
  3. Hydrometeor and cloud identification with improved fuzzy logic algorithms or machine learning.
  4. The identification and tracking of extreme and severe weather events, such as tornadoes, large hails, supercell storms and heavy flood-causing precipitation.

Dr. Haijiang Wang
Dr. Jiafeng Zheng
Dr. Hao Wu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • profile and field retrieval
  • hydrometeor and cloud identification
  • extreme weather event identification and tracking
  • model optimization
  • quality control

Published Papers (15 papers)

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Research

21 pages, 16155 KiB  
Article
Research on a Clustering Forecasting Method for Short-Term Precipitation in Guangdong Based on the CMA-TRAMS Ensemble Model
by Jiawen Zheng, Pengfei Ren, Binghong Chen, Xubin Zhang, Hongke Cai and Haowen Li
Atmosphere 2023, 14(10), 1488; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos14101488 - 26 Sep 2023
Cited by 1 | Viewed by 621
Abstract
In light of the 2020–2021 flood season in Guangdong, we conducted a comprehensive assessment of short-term precipitation forecasts generated by the ensemble prediction system (EPS) based on the China Meteorological Administration Tropical Regional Atmosphere Model for the South China Sea (CMA-TRAMS). Furthermore, we [...] Read more.
In light of the 2020–2021 flood season in Guangdong, we conducted a comprehensive assessment of short-term precipitation forecasts generated by the ensemble prediction system (EPS) based on the China Meteorological Administration Tropical Regional Atmosphere Model for the South China Sea (CMA-TRAMS). Furthermore, we applied four distinct strategies to cluster the ensemble forecast data produced by the model for precipitation, aiming to enhance our understanding of their applicability in short-term precipitation forecasting for Guangdong. Our key findings were as follows.: Precipitation during the 2020–2021 flood season in Guangdong exhibited distinct characteristics. The impacting areas of frontal and subtropical high-edge rainfall were relatively scattered, predominantly occurring in the evening and nighttime. In contrast, monsoon precipitation and return-flow precipitation were concentrated, with their impacts lasting from early morning to evening. Notably, the errors using the ensemble maximum and minimum values were large, while the errors for the ensemble mean values and medians were small. This indicated that the model’s short-term precipitation forecasts possessed a high degree of stability. The vertical shear of different types of precipitation exerted a noticeable influence on the model’s performance. The model consistently displayed a tendency to underestimate short-term precipitation in Guangdong; however, this bias decreased with longer lead times. Simultaneously, the model’s dispersion increased with longer lead times. In terms of mean absolute error (MAE) test results, there was little difference in the performance of ensemble primary forecasts under various strategies, while the “ward” strategy performed well in sub-primary cluster forecasts. This was particularly true for areas and types of precipitation where the model’s performance was poor. While the clustering approach lagged behind ensemble mean forecasts in predicting rainy conditions, it exhibited improvement in forecasting short-term heavy rainfall events. The “complete” and “single” strategies consistently delivered the most accurate forecasts for such events. Our study sheds light on the effectiveness of clustering methods in improving short-term precipitation forecasts for Guangdong, particularly in regions and conditions where the model initially struggled. These findings contribute to our understanding of precipitation forecasting during flood seasons and can inform strategies for enhancing forecast accuracy in similar contexts. Full article
(This article belongs to the Special Issue Identification and Optimization of Retrieval Model in Atmosphere)
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19 pages, 6070 KiB  
Article
Characteristics and Variations of Raindrop Size Distribution in Chengdu of the Western Sichuan Basin, China
by Tao Zhang, Wei Wei, Liying Zheng and Yangruixue Chen
Atmosphere 2023, 14(1), 76; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos14010076 - 30 Dec 2022
Cited by 2 | Viewed by 1191
Abstract
Knowledge of the microphysical characteristics of precipitation plays a significant role in meteorology, hydrology, and natural hazards management, especially in the western Sichuan Basin (WSB), which is located east of the Tibetan Plateau (TP) in southwestern China and thus has unique terrain conditions [...] Read more.
Knowledge of the microphysical characteristics of precipitation plays a significant role in meteorology, hydrology, and natural hazards management, especially in the western Sichuan Basin (WSB), which is located east of the Tibetan Plateau (TP) in southwestern China and thus has unique terrain conditions and weather systems. Nonetheless, the literature regarding raindrop size distribution (RSD) in the WSB is still very limited. This work investigates RSD characteristics and temporal variations in a site (Chengdu, CD) of the WSB by employing three years of quality-controlled RSD observation collected from a second-generation PARSIVEL disdrometer. The results show that RSD has noticeable seasonal and diurnal variations in CD. Specifically, the broadest mean raindrop spectra can be found in summer and the narrowest in winter, and the raindrop spectra of a day can be the narrowest during 1400–1500 BJT (Beijing Standard Time, UTC+8). In addition, the mass-weighted mean diameter (Dm) is lower in the daytime than in the nighttime, while the logarithm of the generalized intercept parameter (log10Nw, the unit of the Nw is m−3 mm−1) has a larger value in the daytime than in the nighttime. In addition, intercomparisons indicate that the mean Dm of convective rains in CD is smaller than in South China and it is higher than in the eastern slope of TP, East China, and North China; on the other hand, the corresponding mean log10Nw is close to the value at the middle TP. Local empirical relations of shape–slope parameters (μ–Λ) and reflectivity–rain rate (Z–R) are also presented to provide references for optimizing the RSD parameterization scheme and radar precipitation estimation in the local area. Full article
(This article belongs to the Special Issue Identification and Optimization of Retrieval Model in Atmosphere)
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17 pages, 3200 KiB  
Article
PRSOT: Precipitation Retrieval from Satellite Observations Based on Transformer
by Zhaoying Jia, Shengpeng Yang, Jinglin Zhang, Yushan Zhang, Zhipeng Yang, Ke Xue and Cong Bai
Atmosphere 2022, 13(12), 2048; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos13122048 - 07 Dec 2022
Cited by 1 | Viewed by 1649
Abstract
Precipitation with high spatial and temporal resolution can improve the defense capability of meteorological disasters and provide indispensable instruction and early warning for social public services, such as agriculture, forestry, and transportation. Therefore, a deep learning-based algorithm entitled precipitation retrieval from satellite observations [...] Read more.
Precipitation with high spatial and temporal resolution can improve the defense capability of meteorological disasters and provide indispensable instruction and early warning for social public services, such as agriculture, forestry, and transportation. Therefore, a deep learning-based algorithm entitled precipitation retrieval from satellite observations based on Transformer (PRSOT) is proposed to fill the observation gap of ground rain gauges and weather radars in deserts, oceans, and other regions. In this algorithm, the multispectral infrared brightness temperatures from Himawari-8, the new-generation geostationary satellite, have been used as predictor variables and the Global Precipitation Measurement (GPM) precipitation product has been employed to train the retrieval model. We utilized two data normalization schemes, area-based and pixel-based normalization, and conducted comparative experiments. Comparing the estimated results with the GPM product on the test set, PRSOT_Pixel_based model achieved a Probability Of Detection (POD) of 0.74, a False Alarm Ratio (FAR) of 0.44 and a Critical Success Index (CSI) of 0.47 for two-class metrics, and an Accuracy (ACC) of 0.75 for multi-class metrics. Pixel-based normalization is more suitable for meteorological data, highlighting the precipitation characteristics and obtaining better comprehensive retrieval performance in visualization and evaluation metrics. In conclusion, the proposed PRSOT model has made a remarkable and essential contribution to precipitation retrieval and outperforms the benchmark machine learning model Random Forests. Full article
(This article belongs to the Special Issue Identification and Optimization of Retrieval Model in Atmosphere)
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14 pages, 5323 KiB  
Article
Research on the Effectiveness of Deep Convolutional Neural Network for Electromagnetic Interference Identification Based on I/Q Data
by Jiamin Wang, Haijiang Wang and Zhaoping Sun
Atmosphere 2022, 13(11), 1785; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos13111785 - 28 Oct 2022
Cited by 3 | Viewed by 994
Abstract
With the development of wireless communication technology, the electromagnetic interference (EMI) of artificial radio to weather radar increases significantly, which has a serious impact on the quality of radar data. Most of the research on detecting and suppressing electromagnetic interference was based on [...] Read more.
With the development of wireless communication technology, the electromagnetic interference (EMI) of artificial radio to weather radar increases significantly, which has a serious impact on the quality of radar data. Most of the research on detecting and suppressing electromagnetic interference was based on the primary product of the radar. This paper researches the effectiveness of deep convolutional neural networks (DCNN) to identify and suppress electromagnetic interference based on the I/Q data output from the front end of a radar receiver. Firstly, this paper selected UNet, ResNet with UNet structure, and DeepLab V3+ for the semantic segmentation of electromagnetic interference and other signals. After semantic segmentation, this paper used the linear interpolation method to suppress EMI. Finally, this paper selected the prediction precision of the model and compared the quality of primary products before and after EMI suppression to evaluate the effectiveness of DCNN. The results showed that all three models could effectively identify the electromagnetic interference and the quality of the data were improved after suppression. It suggests that the use of DCNN on the I/Q data output from the front end of a radar receiver can play a certain effect on the identification of electromagnetic interference. Full article
(This article belongs to the Special Issue Identification and Optimization of Retrieval Model in Atmosphere)
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20 pages, 7564 KiB  
Article
Evaluation of the Dynamical–Statistical Downscaling Model for Extended Range Precipitation Forecasts in China
by Hongke Cai, Zuosen Zhao, Jiawen Zheng, Wei Luo and Huaiyu Li
Atmosphere 2022, 13(10), 1663; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos13101663 - 12 Oct 2022
Cited by 1 | Viewed by 1202
Abstract
In order to focus on pentad-scale precipitation forecasts, we investigated the coupling relationship between 500 hPa geopotential height (Z500) anomalies and precipitation anomalies using the China Meteorological Administration Global Land Surface ReAnalysis Interim (CRA40/Land) gridded precipitation dataset from 1999 to 2018 and the [...] Read more.
In order to focus on pentad-scale precipitation forecasts, we investigated the coupling relationship between 500 hPa geopotential height (Z500) anomalies and precipitation anomalies using the China Meteorological Administration Global Land Surface ReAnalysis Interim (CRA40/Land) gridded precipitation dataset from 1999 to 2018 and the National Centers for Environmental Prediction 1 reanalysis dataset for Z500. We obtained a dynamical–statistical downscaling model (DSDM) on the pentad scale and used the daily Z500 forecast product for sub-seasonal to seasonal forecasts (15–60 days) of the FGOALS-f2 model as the predictor. Our results showed that pentad-scale prediction of precipitation is the key to bridging the current deficiencies in sub-seasonal forecasts. Compared with the FGOALS-f2 model, the pentad DSDM had a higher skill for prediction of precipitation in China at lead times longer than four pentads throughout the year and of two pentads in the summer months. FGOALS-f2 had excellent precipitation predictability at lead times less than three pentads (15 days), so the proposed pentad DSDM could not perform better than FGOALS-f2 in this period. However, at lead times greater than four pentads, the precipitation prediction scores (such as the anomaly correlation coefficient (ACC), the temporal correlation coefficient (TCC) and the mean square skill score (MSSS)) of the pentad DSDM for the whole of China were higher than those of the FGOALS-f2 model. With the rate of increase ranging from 76% to 520%, the mean ACC scores of pentad DSDM were basically greater than 0.04 after a lead time of five pentads, whereas those of the FGOALS-f2 were less than 0.04. An analysis of the Zhengzhou “720” super heavy rainstorm event showed that the pentad DSDM also had better predictability for the distribution of precipitation at lead times of three pentads than the FGOALS-f2 model for the extreme precipitation event. Full article
(This article belongs to the Special Issue Identification and Optimization of Retrieval Model in Atmosphere)
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14 pages, 3706 KiB  
Article
Analysis of Microtopography Atmospheric Precipitable Water Vapour over the Northeastern Margin of the Qinghai–Tibet Plateau
by Zhiliang Shu, Tao Tao, Dongyang Pu, Hao Wu, Tong Lin, Haoran Zhu, Yanqiao Sun, Jianren Sang and Yong Yue
Atmosphere 2022, 13(10), 1635; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos13101635 - 07 Oct 2022
Viewed by 983
Abstract
The Liupan Mountain (LPM) area is located on the northeast margin of the Qinghai–Tibet Plateau, which is the western part of the second ladder of China’s terrain. It is also an intersection area of two air currents, which are caused by the combined [...] Read more.
The Liupan Mountain (LPM) area is located on the northeast margin of the Qinghai–Tibet Plateau, which is the western part of the second ladder of China’s terrain. It is also an intersection area of two air currents, which are caused by the combined action of the Qinghai–Tibet Plateau, the middle and lower levels of the westerly belt and the edge of the monsoon area. LPM is one of the main air water vapour transport pathways in Northwest China as well as a main water conservation area for nearly ten million people. Research on atmospheric precipitable water vapour (PWV) variation characteristics in LPM is beneficial for understanding the mechanisms of orographic precipitation and improving the effects of weather modification. Based on the data from 10 Global Navigation Satellite System Meteorology (GNSS/MET) stations for 6 years and the data of automatic weather stations in the LPM, the temporal and spatial variation characteristics of PWV in the LPM were analysed, and the differences in PWV in the 24 h before and after precipitation were compared in this study. The results showed that the hourly, monthly and seasonal variations in PWV displayed obvious patterns. PWV increased rapidly 10 h ahead of precipitation, while it decreased rapidly within 10 h after precipitation, which was slower than that before precipitation. In terms of spatial distribution, PWV was larger in the south than in the north and larger in the east than in the west. Although the precipitation on the LPM peak was the highest in the whole LPM area, its PWV was always the lowest, indicating that the PWV was obviously affected by the air temperature. This showed that under the same water vapour condition, precipitation was more likely to form in the area with low temperature, and the precipitation was larger, which also provided a train of thought for improving the method of artificial precipitation enhancement by using the condensation catalyst. Full article
(This article belongs to the Special Issue Identification and Optimization of Retrieval Model in Atmosphere)
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24 pages, 8466 KiB  
Article
Detailed Evolution Characteristics of an Inclined Structure Hailstorm Observed by Polarimetric Radar over the South China Coast
by Honghao Zhang, Xiaona Rao, Zeyong Guo, Xiantong Liu, Xiaoding Yu, Xingdeng Chen, Huiqi Li, Jingjing Zhang, Guangyu Zeng and Shidong Chen
Atmosphere 2022, 13(10), 1564; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos13101564 - 25 Sep 2022
Cited by 3 | Viewed by 1555
Abstract
A hailstorm with an inclined structure occurred in the western part of the South China coast on 27 March 2020. This study investigates the detailed evolution characteristics of this inclined structure using the Doppler radar data assimilation system (VDRAS) and the improved fuzzy [...] Read more.
A hailstorm with an inclined structure occurred in the western part of the South China coast on 27 March 2020. This study investigates the detailed evolution characteristics of this inclined structure using the Doppler radar data assimilation system (VDRAS) and the improved fuzzy logic hydrometeor classification algorithm (HCA). Obvious differential reflectivity (often referred to as ZDR) arc characteristics, ZDR column characteristics, and the specific differential phase (often referred to as KDP) of the column are observed using dual-polarization radar prior to hailfall. Both the ZDR column and KDP column reached their strongest intensities during the hailfall phase, with their heights exceeding the height of the −20 °C layer (7.997 km above ground level), displaying a cross-correlation coefficient (CC) valley during this phase. Meanwhile, two centers of strong reflectivity were found, with one (C1) being located at 2–4 km, and the other (C2) being located at 6–8 km. The maximum horizontal distance between the two centers is 8 km, suggesting a strongly inclined structure. This inclined structure was closely related to the interaction between upper-level divergent outflows and ambient horizontal winds. The updraft on the front edge of the hailstorm continued to increase, keeping C2 at the upper level. At the same time, large raindrops at the lower part of C2 are continuously lifted, leading to ice formation. These ice particles then fell obliquely from their high altitude, merging with C1. Full article
(This article belongs to the Special Issue Identification and Optimization of Retrieval Model in Atmosphere)
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16 pages, 5132 KiB  
Article
Variation Characteristics and Source Analysis of Cloud Condensation Nuclei at the Ridge of Liupan Mountain Located in Western China
by Tong Lin, Zhiliang Shu, Hao Wu, Tao Tao, Ning Cao, Haoran Zhu, Chenxi Liu, Jianhua Mu and Lei Tian
Atmosphere 2022, 13(9), 1483; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos13091483 - 13 Sep 2022
Cited by 1 | Viewed by 1093
Abstract
Two years of data on cloud condensation nuclei (CCN) measured at the Liupan Mountain (LPS) Meteorological Station from August 2020 to November 2021 were analyzed in this study. The results show that the mean annual CCN concentration was 851 cm−3 and that [...] Read more.
Two years of data on cloud condensation nuclei (CCN) measured at the Liupan Mountain (LPS) Meteorological Station from August 2020 to November 2021 were analyzed in this study. The results show that the mean annual CCN concentration was 851 cm−3 and that the mean concentration of CCN increases with the supersaturation degree. The curves of the diurnal variation in CCN concentration show one peak and one valley, which correspond to the diurnal variation in the mixed-layer height and valley wind. Regarding seasonal variations, the CCN concentration, as well as the degree of internal mixing, is higher in the spring and winter, while the degree of external mixing is higher in the summer and autumn. The transport of CCN is closely related to the wind transport evolution, and the southeast and southwest sides of the LPS station contribute more to the CCN concentration in the spring and winter due to central heating in the wintertime. Though correlations between CCN concentration and pressure are scarce, the CCN concentration and temperature (or humidity) are positively (or negatively) correlated, especially in the spring. Furthermore, the 48-h backward trajectory analysis indicates that the sources in the northwest direction are a major contributor to the CCN concentration. The pollutants mainly came from the northwest and southwest sides, according to the analysis of potential sources using the PSCF and CWT approach. The study of CCN evolution and contribution area is beneficial for further research on the physical properties of cloud droplets, the influence of mountains on CCN changes and the role of CCN in terrain cloud precipitation, which are significant for the improvement of weather modification techniques. Full article
(This article belongs to the Special Issue Identification and Optimization of Retrieval Model in Atmosphere)
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13 pages, 56854 KiB  
Article
Numerical Modeling of the Radio Wave Over-the-Horizon Propagation in the Troposphere
by Min Xu, Melad Olaimat, Tao Tang, Omar M. Ramahi, Maged Aldhaeebi, Zhu Jin and Ming Zhu
Atmosphere 2022, 13(8), 1184; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos13081184 - 27 Jul 2022
Cited by 1 | Viewed by 1917
Abstract
Using atmospheric data, which include pressure, temperature, relative humidity and water vapor pressure, the actual refractive index of a specific segment of the atmosphere has been modeled. Based on the refractive index, a numerical method is presented to quickly estimate the propagation path [...] Read more.
Using atmospheric data, which include pressure, temperature, relative humidity and water vapor pressure, the actual refractive index of a specific segment of the atmosphere has been modeled. Based on the refractive index, a numerical method is presented to quickly estimate the propagation path of the radio wave in the troposphere. Utilizing the terrain and the surface medium model of the propagation area and the parabolic equation (PE) method, an image of the electric field distribution of radio waves in the troposphere is obtained. A comparison of propagation paths between the numerical method and the PE model is presented. Additionally, the effects of the antenna’s elevation angle have been studied. Physical measurements provide a reference for the accuracy of the simulation results obtained using the method presented in this work. Full article
(This article belongs to the Special Issue Identification and Optimization of Retrieval Model in Atmosphere)
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12 pages, 4148 KiB  
Article
Fast Dual-LiDAR Reconstruction for Dynamic Wind Field Retrieval
by Yong Bao, Chao Tan and Jiabin Jia
Atmosphere 2022, 13(6), 905; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos13060905 - 02 Jun 2022
Viewed by 1508
Abstract
With the advantages of high accuracy, high spatial resolution, and long measurement range, LiDAR is considered as the most suitable measurement technique to deliver quantitative imaging of wind fields. However, for complex wind fields, such as monitoring wind turbine wakes where both the [...] Read more.
With the advantages of high accuracy, high spatial resolution, and long measurement range, LiDAR is considered as the most suitable measurement technique to deliver quantitative imaging of wind fields. However, for complex wind fields, such as monitoring wind turbine wakes where both the temporal resolution and reconstruction speed are of great significance, the conventional LiDAR system lacks the temporal resolution to capture the fast changes of wind turbine wake fields. In this paper, a novel dynamic wind retrieval method is developed to improve temporal resolution using the unsynchronised dual-LiDAR scanning scheme. By exploiting the temporal redundancy information of the LiDAR Line-of-Sight (LoS) data in successive frames, a reduced number of LiDAR scanning points is required for the 2D horizontal wind field retrieval with the help of unsynchronised dual-LiDAR wind scanning scheme, low-rank data up-sampling and a divergence-free regularised wind retrieval algorithm. Numerical simulation is performed to validate the proposed method. Results show that the temporal resolution of LiDAR wind retrieval can be improved by a factor of 2 to 8 and provide acceptable results with good spatial resolution. Full article
(This article belongs to the Special Issue Identification and Optimization of Retrieval Model in Atmosphere)
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28 pages, 19156 KiB  
Article
Uncertainty Quantification of WRF Model for Rainfall Prediction over the Sichuan Basin, China
by Yu Du, Ting Xu, Yuzhang Che, Bifeng Yang, Shaojie Chen, Zhikun Su, Lianxia Su, Yangruixue Chen and Jiafeng Zheng
Atmosphere 2022, 13(5), 838; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos13050838 - 20 May 2022
Cited by 5 | Viewed by 2454
Abstract
The mesoscale Weather Research and Forecasting (WRF) model has been widely employed to forecast day-ahead rainfalls. However, the deterministic predictions from the WRF model incorporate relatively large errors due to numerical discretization, inaccuracies in initial/boundary conditions and parameterizations, etc. Among them, the uncertainties [...] Read more.
The mesoscale Weather Research and Forecasting (WRF) model has been widely employed to forecast day-ahead rainfalls. However, the deterministic predictions from the WRF model incorporate relatively large errors due to numerical discretization, inaccuracies in initial/boundary conditions and parameterizations, etc. Among them, the uncertainties in parameterization schemes have a huge impact on the forecasting skill of rainfalls, especially over the Sichuan Basin which is located east of the Tibetan Plateau in southwestern China. To figure out the impact of various parameterization schemes and their interactions on rainfall predictions over the Sichuan Basin, the Global Forecast System data are chosen as the initial/boundary conditions for the WRF model and 48 ensemble tests have been conducted based on different combinations of four microphysical (MP) schemes, four planetary boundary layer (PBL) schemes, and three cumulus (CU) schemes, for four rainfall cases in summer. Compared to the observations obtained from the Chinese ground-based and encrypted stations, it is found that the Goddard MP scheme together with the asymmetric convective model version 2 PBL scheme outperforms other combinations. Next, as the first step to explore further improvement of the WRF physical schemes, the polynomial chaos expansion (PCE) approach is then adopted to quantify the impacts of several empirical parameters with uncertainties in the WRF Single Moment 6-class (WSM6) MP scheme, the Yonsei University (YSU) PBL scheme and the Kain-Fritsch CU scheme on WRF rainfall predictions. The PCE statistics show that the uncertainty of the scaling factor applied to ice fall velocity in the WSM6 scheme and the profile shape exponent in the YSU scheme affects more dominantly the rainfall predictions in comparison with other parameters, which sheds a light on the importance of these schemes for the rainfall predictions over the Sichuan Basin and suggests the next step to further improve the physical schemes. Full article
(This article belongs to the Special Issue Identification and Optimization of Retrieval Model in Atmosphere)
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20 pages, 6948 KiB  
Article
Weather Radar Echo Extrapolation Method Based on Deep Learning
by Fugui Zhang, Can Lai and Wanjun Chen
Atmosphere 2022, 13(5), 815; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos13050815 - 16 May 2022
Cited by 3 | Viewed by 2831
Abstract
In order to forecast some high intensity and rapidly changing phenomena, such as thunderstorms, heavy rain, and hail within 2 h, and reduce the influence brought by destructive weathers, this paper proposes a weather radar echo extrapolation method based on deep learning. The [...] Read more.
In order to forecast some high intensity and rapidly changing phenomena, such as thunderstorms, heavy rain, and hail within 2 h, and reduce the influence brought by destructive weathers, this paper proposes a weather radar echo extrapolation method based on deep learning. The proposed method includes the design and combination of the data preprocessing, convolutional long short-term memory (Conv-LSTM) neuron and encoder–decoder model. We collect eleven thousand weather radar echo data in high spatiotemporal resolution, these data are then preprocessed before they enter the neural network for training to improve the data’s quality and make the training better. Next, the neuron integrates the structure and the advantages of convolutional neural network (CNN) and long short-term memory (LSTM), called Conv-LSTM, is applied to solve the problem that the full-connection LSTM (FC-LSTM) cannot extract the spatial information of input data. This operation replaced the full-connection structure in the input-to-state and state-to-state parts so that the Conv-LSTM can extract the information from other dimensions. Meanwhile, the encoder–decoder model is adopted due to the size difference of the input and output data to combine with the Conv-LSTM neuron. In the neural network training, mean square error (MSE) loss function weighted according to the rate of rainfall is added. Finally, the matrix “point-to-point” test method, including the probability of detection (POD), critical success index (CSI), false alarm ratio (FAR) and spatial test method contiguous rain areas (CRA), is used to examine the radar echo extrapolation’s results. Under the threshold of 30 dBZ, at the time of 1 h, we achieved 0.60 (POD), 0.42 (CSI) and 0.51 (FAR), compared with 0.42, 0.28 and 0.58 for the CTREC algorithm, and 0.30, 0.24 and 0.71 for the TITAN algorithm. Meanwhile, at the time of 1 h, we achieved 1.35 (total MSE ) compared with 3.26 for the CTREC algorithm and 3.05 for the TITAN algorithm. The results demonstrate that the radar echo extrapolation method based on deep learning is obviously more accurate and stable than traditional radar echo extrapolation methods in near weather forecasting. Full article
(This article belongs to the Special Issue Identification and Optimization of Retrieval Model in Atmosphere)
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19 pages, 8854 KiB  
Article
A Quality Control Method and Implementation Process of Wind Profiler Radar Data
by Yang Qi and Yong Guo
Atmosphere 2022, 13(5), 796; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos13050796 - 13 May 2022
Cited by 1 | Viewed by 1707
Abstract
Wind profiler radar (WPR) is used for all-weather atmospheric wind-field monitoring. However, the reliability of these observations reduces significantly when there is electromagnetic interference echo, generally caused by ground objects, birds, or rain. Therefore, to optimize the data reliability of WPR, we proposed [...] Read more.
Wind profiler radar (WPR) is used for all-weather atmospheric wind-field monitoring. However, the reliability of these observations reduces significantly when there is electromagnetic interference echo, generally caused by ground objects, birds, or rain. Therefore, to optimize the data reliability of WPR, we proposed a synthetic data quality control process. The process included the application of a minimum connection method, judgment rule, and median test optimization algorithm for optimizing clutter suppression, spectral peak symmetry detection, and radial speed, respectively. We collected the base data from a radiosonde and multiple radars and conducted an experiment using these data and algorithms. The results indicated that the quality control method: (1) had good adaptability to multiple WPRs both in clear sky and precipitation; (2) was useful for suppressing ground clutter and (3) was superior to those of the manufacturer as a whole. Thus, the data quality control method proposed in this study can improve the accuracy and reliability of WPR products and multiple types of WPR, even when they function under vastly different weather conditions. Full article
(This article belongs to the Special Issue Identification and Optimization of Retrieval Model in Atmosphere)
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19 pages, 7333 KiB  
Article
Automated Recognition of Macro Downburst Using Doppler Weather Radar
by Xu Wang, Hailong Wang, Jianxin He, Zhao Shi and Chenghua Xie
Atmosphere 2022, 13(5), 672; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos13050672 - 22 Apr 2022
Cited by 1 | Viewed by 1834
Abstract
In light of the macro downburst’s ground divergent flow field characteristics and high reflectivity, this paper proposes an algorithm for identifying the downburst area using a Doppler weather radar low-level radial velocity and reflectivity factor (abbreviated as reflectivity, the same below). To binarize [...] Read more.
In light of the macro downburst’s ground divergent flow field characteristics and high reflectivity, this paper proposes an algorithm for identifying the downburst area using a Doppler weather radar low-level radial velocity and reflectivity factor (abbreviated as reflectivity, the same below). To binarize the radial velocity, perform quality control on the radial velocity and reflectivity, then combine the reflectivity and the radial velocity threshold. Following that, use the Eight-Neighborhood method to retrieve the positive and negative velocity connected regions and perform the connected regions. The positive and negative velocity pairs are then matched, and the zero Doppler velocity line between the positive and negative velocity pairs is extracted, followed by the center recognition of the positive and negative velocity downburst areas. The data of downbursts detected by Doppler radar in Jinan, Shandong Province, are used for algorithm verification in this paper. The results show that the proposed algorithm can detect the macro downburst area and identify the downburst center. Full article
(This article belongs to the Special Issue Identification and Optimization of Retrieval Model in Atmosphere)
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11 pages, 4001 KiB  
Article
Black Carbon Evolution at WMO/GAW Station Mt. Waliguan China and Contribution Area from 1994 to 2017
by Dongyang Pu, Rongqian Meng, Hao Wu and Fudong Zhen
Atmosphere 2022, 13(4), 534; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos13040534 - 28 Mar 2022
Cited by 2 | Viewed by 1515
Abstract
Black carbon (BC) aerosol measured at the WMO/GAW Station Mt. Waliguan from 1994 to 2017 has been analyzed. The 24 years long-term results showed that the average annual concentration ranges from 1.9 × 102 ng m−3 to 5.1 × 102 [...] Read more.
Black carbon (BC) aerosol measured at the WMO/GAW Station Mt. Waliguan from 1994 to 2017 has been analyzed. The 24 years long-term results showed that the average annual concentration ranges from 1.9 × 102 ng m−3 to 5.1 × 102 ng m−3 from 2001 to 2012, with a growth rate of 29%. However, the concentration of black carbon decreased from 2012 to 2016, with a decline rate of 64%. The monthly average concentration over the 24 years ranged from 90 ng m−3 to 7.0 × 102 ng m−3, with the peak value occurring in April and the lowest value occurring in November. The diurnal variation presented two peak types in different seasons, the first occurred at 20:00 a.m.~23:00 a.m. in the evening, and another around 06:00 a.m.~08:00 a.m. In addition, we found that the transport of black carbon aerosol is closely related to wind transport. The annual maximum black carbon concentration occurred in the east-northeast (ENE) wind direction, with a value of 4.6 × 102 ng m−3, and the second peak value occurred in the E wind direction, with a value of 3.9 × 102 ng m−3. The black carbon concentration of Waliguan was relatively high under the three wind directions of Northeast (NE), ENE, and east (E), which represented the influence of black carbon aerosol generated by human activities located on the east of the station. The 96-h backward trajectory analysis indicated that the sources in the southwest direction made a greater contribution to the black carbon concentration. the pollutants mainly came from the northwest and west sides according to the analysis of potential sources using the CWT approach. The study of black carbon evolution and contribution area is of great significance to further improve the capacity and level of global climate change research and prediction. Full article
(This article belongs to the Special Issue Identification and Optimization of Retrieval Model in Atmosphere)
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