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Processing and Application of Weather Radar Data
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Processing and Application of Weather Radar Data

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmospheric Remote Sensing".

Deadline for manuscript submissions: closed (25 September 2023) | Viewed by 23407

Special Issue Editors

Dr. Youcun Qi

E-Mail Website
Guest Editor
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Interests: radar-based quantitative precipitation estimation; short-term quantitative precipitation forecast
Special Issues, Collections and Topics in MDPI journals
Dr. Zhe Zhang

E-Mail Website
Guest Editor
Shenzhen National Climate Observatory, Shenzhen, China
Interests: radar QPE methods; raindrop size distribution (DSD) characteristics; high-impact weather
Special Issues, Collections and Topics in MDPI journals
Dr. Zhanfeng Zhao

E-Mail Website
Guest Editor
Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Interests: hydrology; hydrological modeling; inverse modeling; catchment; baseflow
Special Issues, Collections and Topics in MDPI journals
Dr. Bong-Chul Seo

E-Mail Website
Guest Editor
Department of Civil, Architectural, and Environmental Engineering, Missouri University of Science and Technology, 229 Butler-Carlton Hall,1401 N. Pine St., Rolla, MO 65409, USA
Interests: radar hydrology; rainfall uncertainties
Special Issues, Collections and Topics in MDPI journals
Dr. Huiqi Li

E-Mail Website
Guest Editor
Guangzhou Institute of Tropical and Marine Meteorology, China Meteorological Administration, Guangzhou 510641, China
Interests: observational analysis of severe weather; radar data assimilation

Special Issue Information

Dear Colleagues,

In 2019, the World Meteorological Organization (WMO) pointed out the following based on statistics from 2007 to 2019: In natural disasters, 90% of losses are related to meteorology, of which heavy storms and floods account for more than 70%. Heavy precipitation plays a very important role in the early warning of meteorological, hydrological, and geological disasters. Heavy rainstorms induced by strong convection often cause serious natural disasters, such as floods, landslides, and mudslides. Accurate monitoring and early warning and forecasting of heavy rainfall induced by strong convection is the basis for improving the ability to prevent flood, landslide, and mudslide disasters.

Currently, the most powerful technique for monitoring natural hazards induced by heavy rainstorms is to use weather radars (e.g., ground based radars, profiling radars, as well as space born radars). Moreover, dual-polarization or dual-frequency radar data are used to derive water mixing ratios and number concentrations and to improve the capability of the convection-permitting numerical weather prediction (NWP) models to forecast severe storms at scales varying from a few hundred meters to kilometers. Advanced QPF products are of great assistance in short-term weather and hydrological forecasting. Associated surface in situ observations, such as from rain gauges, runoff gauges, and disdrometers, are also required for calibrating radar observational variables and products.

Although weather radars have been widely used in many fields, several valid challenges remain in radar processing and application. They can be summarized as:

  • Development of radar signal processing methods, especially for polarimetric variables;
  • Development of nonmeteorological echo identification methods and hydrometeor classification methods;
  • Assessment of observational quality of new-developed radars, e.g., phase-array radars;
  • Characterization of errors/uncertainties in remote sensing precipitation products and retrieval algorithm functions of different conditions, e.g., elevations, storm, and climatic regimes, and communicating the uncertainties for hydrogeological applications;
  • Development of more accurate ground radar- and/or satellite-based quantitative precipitation estimation (QPE) algorithms;
  • New sensing techniques and attenuation correction and calibration techniques;
  • Application of radar data in data assimilation to improve performance of NWP models;
  • Development of new analysis methods, including machine learning and data assimilation, to maximize the benefits of using extensive datasets; multi-scale remote sensing data and in situ data fusion;
  • Artificial intelligence and machine (deep) learning applications;
  • Application of radar data in disastrous weathers (e.g., heavy rain, hail, and tornado) analysis;
  • Radar observations of hydrometeorological extremes;
  • Improving the skills of quantitative precipitation forecasts (QPF);
  • Improving the monitoring and forecasting of heavy rainfall for warnings triggered by hydrometeorological hazards with radar products; improving the ability of convection-induced flood forecasting and early warning capabilities in small mountain basins and urban areas with radar products;
  • Improving the forecasting and early warning capabilities of geological disasters, such as landslides and mudslides, caused by convective precipitation with radar products.

Submission of manuscripts involving radar signal processing methods, applications in QPE, QPF, and severe weather observation, and data assimilation for NWP models is strongly encouraged.

Dr. Youcun Qi
Dr. Zhe Zhang
Dr. Zhanfeng Zhao
Dr. Bong-Chul Seo
Dr. Huiqi Li
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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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

  • dual-pol radar
  • radar signal processing
  • radar quality control (QC)
  • radar quantitative precipitation estimations (QPE)
  • radar date assimilation (DA)
  • extreme weather events
  • artificial intelligence (AI) and machine(deep) learning
  • radar hydrology
  • extreme weather analysis
  • hydrometeorological observation
  • weather forecast

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Published Papers (16 papers)

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19 pages, 9482 KiB  
Article
Correction for the Attenuation Due to Atmospheric Gas and Stratiform Clouds in Triple-Frequency Radar Observations of the Microphysical Properties of Snowfall
by Yue Chang, Hongbin Chen, Xiaosong Huang, Yongheng Bi, Shu Duan, Pucai Wang and Jie Liu
Remote Sens. 2023, 15(19), 4843; https://doi.org/10.3390/rs15194843 - 6 Oct 2023
Viewed by 863
Abstract
For triple-frequency radar, the attenuation attributed to atmospheric gases and stratiform clouds is diverse due to different snowfall microphysical properties, particularly in regions far from the radar. When using triple-frequency ground-based radar measurements, evaluating the attenuation of the three radars at different heights [...] Read more.
For triple-frequency radar, the attenuation attributed to atmospheric gases and stratiform clouds is diverse due to different snowfall microphysical properties, particularly in regions far from the radar. When using triple-frequency ground-based radar measurements, evaluating the attenuation of the three radars at different heights is common to derive attenuation-corrected effective reflectivity. Therefore, this study proposes a novel quality-controlled approach to identify radar attenuation due to gases and stratiform clouds that can be neglected due to varying snowfall microphysical properties and assess attenuation along the radar observation path. The key issue lies in the lack of information about vertical hydrometeor and cloud distribution. Therefore, European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data are employed. The Self-Similar-Rayleigh-Gans Approximation (SSRGA) for the nonspherical scattering model in the Passive and Active Microwave TRAnsfer model 2 (PAMTRA2) is compared and analyzed against other scattering models to obtain the optimal triple-frequency radar attenuation correction strategies for stratiform cloud meteorological conditions with varying snowfall microphysical properties. This methodology paves the way for understanding differential attenuation attributed to gas and stratiform clouds with snowfall microphysical properties. Simultaneously, the bin-by-bin approximation method is used to perform the attenuation correction. The two-way attenuation correction increased up to 4.71 dB for heights above 6 km, remaining minimal for regions with heights below 6 km. These values, attributable to gases and stratiform clouds’ two-way attenuation, are nonnegligible, especially at distances far from the W-band radar at heights above 6 km. Both values are relatively small for the X- and Ka-band radars and can be neglected for the varying snowfall microphysical properties. The attenuation correction of triple-frequency radar reflectivity is validated using the cross-calibration and dual-frequency reflectivity ratios. The results show that the method is valid and feasible. Full article
(This article belongs to the Special Issue Processing and Application of Weather Radar Data)
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23 pages, 13417 KiB  
Article
Improving the Completion of Weather Radar Missing Data with Deep Learning
by Aofan Gong, Haonan Chen and Guangheng Ni
Remote Sens. 2023, 15(18), 4568; https://doi.org/10.3390/rs15184568 - 17 Sep 2023
Viewed by 1071
Abstract
Weather radars commonly suffer from the data-missing problem that limits their data quality and applications. Traditional methods for the completion of weather radar missing data, which are based on radar physics and statistics, have shown defects in various aspects. Several deep learning (DL) [...] Read more.
Weather radars commonly suffer from the data-missing problem that limits their data quality and applications. Traditional methods for the completion of weather radar missing data, which are based on radar physics and statistics, have shown defects in various aspects. Several deep learning (DL) models have been designed and applied to weather radar completion tasks but have been limited by low accuracy. This study proposes a dilated and self-attentional UNet (DSA-UNet) model to improve the completion of weather radar missing data. The model is trained and evaluated on a radar dataset built with random sector masking from the Yizhuang radar observations during the warm seasons from 2017 to 2019, which is further analyzed with two cases from the dataset. The performance of the DSA-UNet model is compared to two traditional statistical methods and a DL model. The evaluation methods consist of three quantitative metrics and three diagrams. The results show that the DL models can produce less biased and more accurate radar reflectivity values for data-missing areas than traditional statistical methods. Compared to the other DL model, the DSA-UNet model can not only produce a completion closer to the observation, especially for extreme values, but also improve the detection and reconstruction of local-scale radar echo patterns. Our study provides an effective solution for improving the completion of weather radar missing data, which is indispensable in radar quantitative applications. Full article
(This article belongs to the Special Issue Processing and Application of Weather Radar Data)
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19 pages, 7705 KiB  
Article
Spatial Variability of Raindrop Size Distribution at Beijing City Scale and Its Implications for Polarimetric Radar QPE
by Zhe Zhang, Huiqi Li, Donghuan Li and Youcun Qi
Remote Sens. 2023, 15(16), 3964; https://doi.org/10.3390/rs15163964 - 10 Aug 2023
Cited by 1 | Viewed by 982
Abstract
Understanding the characteristics of the raindrop size distribution (DSD) is crucial to improve our knowledge of the microphysical processes of precipitation and to improve the accuracy of radar quantitative precipitation estimation (QPE). In this study, the spatial variability of DSD in different regions [...] Read more.
Understanding the characteristics of the raindrop size distribution (DSD) is crucial to improve our knowledge of the microphysical processes of precipitation and to improve the accuracy of radar quantitative precipitation estimation (QPE). In this study, the spatial variability of DSD in different regions of Beijing and its influence on radar QPE are analyzed using 11 disdrometers. The DSD data are categorized into three regions: Urban, suburban, and mountainous according to their locations. The DSD exhibits evidently different characteristics in the urban, suburban, and mountain regions of Beijing. The average raindrop diameter is smaller in the urban region compared to the suburban region. The average rain rate and raindrop number concentration are lower in the mountainous region compared to both urban and suburban regions. The difference in DSD between urban and suburban regions is due to the difference in DSD for the same precipitation types, while the difference in DSD between mountain and plains (i.e., urban and suburban regions) is the combined effect of the convection/stratiform ratio and the difference of DSD for the same precipitation types. Three DSD-based polarimetric radar QPE estimators were retrieved and estimated. Among these three QPE estimators, R(ZH), R(Kdp), and R(Kdp, ZDR), R(Kdp, ZDR) performs best, followed by R(Kdp), and R(ZH) performs worst. R(Kdp) is more sensitive to the representative parameters, while R(ZH) and R(Kdp, ZDR) are more sensitive to observational error and systematic bias (i.e., calibration). Full article
(This article belongs to the Special Issue Processing and Application of Weather Radar Data)
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16 pages, 4166 KiB  
Article
Machine Learning Model-Based Retrieval of Temperature and Relative Humidity Profiles Measured by Microwave Radiometer
by Yuyan Luo, Hao Wu, Taofeng Gu, Zhenglin Wang, Haiyan Yue, Guangsheng Wu, Langfeng Zhu, Dongyang Pu, Pei Tang and Mengjiao Jiang
Remote Sens. 2023, 15(15), 3838; https://doi.org/10.3390/rs15153838 - 1 Aug 2023
Cited by 1 | Viewed by 1228
Abstract
The accuracy of temperature and relative humidity (RH) profiles retrieved by the ground-based microwave radiometer (MWR) is crucial for meteorological research. In this study, the four-year measurements of brightness temperature measured by the microwave radiometer from Huangpu meteorological station in Guangzhou, China, and [...] Read more.
The accuracy of temperature and relative humidity (RH) profiles retrieved by the ground-based microwave radiometer (MWR) is crucial for meteorological research. In this study, the four-year measurements of brightness temperature measured by the microwave radiometer from Huangpu meteorological station in Guangzhou, China, and the radiosonde data from the Qingyuan meteorological station (70 km northwest of Huangpu station) during the years from 2018 to 2021 are compared with the sonde data. To make a detailed comparison on the performance of machine learning models in retrieving the temperature and RH profiles, four machine learning algorithms, namely Deep Learning (DL), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost) and Random Forest (RF), are employed and verified. The results show that the DL model performs the best in temperature retrieval (with the root-mean-square error and the correlation coefficient of 2.36 and 0.98, respectively), while the RH of the four machine learning methods shows different excellence at different altitude levels. The integrated machine learning (ML) RH method is proposed here, in which a certain method with the minimum RMSE is selected from the four methods of DL, GBM, XGBoost and RF for a certain altitude level. Two cases on 29 January 2021 and on 10 February 2021 are used for illustration. The case on 29 January 2021 illustrates that the DL model is suitable for temperature retrieval and the ML model is suitable for RH retrieval in Guangzhou. The case on 10 February 2021 shows that the ML RH method reaches over 85% before precipitation, implying the application of the ML RH method in pre-precipitation warnings. Full article
(This article belongs to the Special Issue Processing and Application of Weather Radar Data)
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20 pages, 13198 KiB  
Article
Enhancing Spatial Variability Representation of Radar Nowcasting with Generative Adversarial Networks
by Aofan Gong, Ruidong Li, Baoxiang Pan, Haonan Chen, Guangheng Ni and Mingxuan Chen
Remote Sens. 2023, 15(13), 3306; https://doi.org/10.3390/rs15133306 - 28 Jun 2023
Cited by 6 | Viewed by 1316
Abstract
Weather radar plays an important role in accurate weather monitoring and modern weather forecasting, as it can provide timely and refined weather forecasts for the public and for decision makers. Deep learning has been applied in radar nowcasting tasks and has exhibited a [...] Read more.
Weather radar plays an important role in accurate weather monitoring and modern weather forecasting, as it can provide timely and refined weather forecasts for the public and for decision makers. Deep learning has been applied in radar nowcasting tasks and has exhibited a better performance than traditional radar echo extrapolation methods. However, current deep learning-based radar nowcasting models are found to suffer from a spatial “blurry” effect that can be attributed to a deficiency in spatial variability representation. This study proposes a Spatial Variability Representation Enhancement (SVRE) loss function and an effective nowcasting model, named the Attentional Generative Adversarial Network (AGAN), to alleviate this blurry effect by enhancing the spatial variability representation of radar nowcasting. An ablation experiment and a comparison experiment were implemented to assess the effect of the generative adversarial (GA) training strategy and the SVRE loss, as well as to compare the performance of the AGAN and SVRE loss function with the current advanced radar nowcasting models. The performances of the models were validated on the whole test set and inspected in two storm cases. The results showed that both the GA strategy and SVRE loss function could alleviate the blurry effect by enhancing the spatial variability representation, which helps the AGAN to achieve better nowcasting performance than the other competitor models. Our study provides a feasible solution for high-precision radar nowcasting applications. Full article
(This article belongs to the Special Issue Processing and Application of Weather Radar Data)
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20 pages, 10226 KiB  
Article
A Novel Optimization Strategy of Sidelobe Suppression for Pulse Compression Weather Radar
by Jiaqi Hu, Xichao Dong, Weiming Tian, Cheng Hu, Kai Feng and Jun Lu
Remote Sens. 2023, 15(12), 3188; https://doi.org/10.3390/rs15123188 - 19 Jun 2023
Cited by 2 | Viewed by 1363
Abstract
The solid-state transmitters are widely adopted for weather radars, where pulse compression is operated to provide the required sensitivity and range resolution. Therefore, effective sidelobe suppression strategies must be employed, especially for weather observation. Currently, many methods can suppress the sidelobe to a [...] Read more.
The solid-state transmitters are widely adopted for weather radars, where pulse compression is operated to provide the required sensitivity and range resolution. Therefore, effective sidelobe suppression strategies must be employed, especially for weather observation. Currently, many methods can suppress the sidelobe to a very low level in the case of point targets or uniformly distributed targets. However, in strong convection weather process, the weather echo amplitude lies in a wide dynamic range and the main lobe of weak target is prone to being contaminated by the sidelobe of strong target, causing the degradation of weather fundamental data estimation, even generating artifacts and affecting the quantitative precipitation evaluation. In this paper, we propose a novel strategy which is the further processing of a general pulse compression radar to mitigate the effects of sidelobes. The proposed method is called the predominant component extraction (PCE), in which the re-weighting processing is operated after pulse compression, and then the echo of each bin is optimized and its energy will approach the real targets in each bin. It can improve the estimation of weak signals or even eliminate the artifact at the edge of the scene. Numerical simulation experiments and real-data verifications are implemented to validate the feasibility and superiority. It is noted that the proposed method has no requirement on the transmitted waveform and can be realized only by adding a step after pulse compression in the actual system. Full article
(This article belongs to the Special Issue Processing and Application of Weather Radar Data)
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19 pages, 12131 KiB  
Article
Technical Evaluation of Precipitation Forecast by Blending Weather Radar Based on New Spatial Test Method
by Junchao Wang, Zhibin Wang, Jintao Ye, Anwei Lai, Hedi Ma and Wen Zhang
Remote Sens. 2023, 15(12), 3134; https://doi.org/10.3390/rs15123134 - 15 Jun 2023
Viewed by 809
Abstract
The Fourier–Merlin transform method, multi-scale optical flow method, and Weibull distribution are used to integrate the GRAPES_3 km model and Radar Extrapolation Forecast (REF) both developed independently by China. Taking GRAPES_3 km, Wuhan Rapid Update Cycle (WHRUC), and the REF as examples, the [...] Read more.
The Fourier–Merlin transform method, multi-scale optical flow method, and Weibull distribution are used to integrate the GRAPES_3 km model and Radar Extrapolation Forecast (REF) both developed independently by China. Taking GRAPES_3 km, Wuhan Rapid Update Cycle (WHRUC), and the REF as examples, the prediction performance of the Blending forecast is evaluated comprehensively by the traditional point-to-point method. A new spatial test method is introduced to evaluate the applicability and difference of high-resolution model evaluation. The area, position, shape, and intensity of the precipitation area are matched through the target object test method. The potential forecast information of the spatial field is obtained and the related results are compared and analyzed. The results show that: (1) the comprehensive application of various evaluation methods can evaluate the convective storm forecast more comprehensively. The Blending forecast effect is obviously better than those of other models by using the point-to-point scoring method, especially in the heavy precipitation forecast. The shorter the prediction time is, the better the effect is. (2) The new spatial test method can evaluate the prediction effect of convective storm characteristics, and the target recognition hit rate of the Blending forecast is highest. The scores of target area, position, shape, and median intensity of precipitation are better than those of other forecasts. The variation in the east–west direction is less than that in the north–south direction, which is basically consistent with the actual observation. The variation range of the forecast grid before and after translation is the closest to the reality. (3) The Blending forecast method combines the advantages and disadvantages of the numerical model and REF, which can not only grasp the precipitation area but also improve the prediction ability of rainfall intensity. The traditional point-to-point scoring method and the new spatial test method have the same conclusion as the convective storm forecast of the high-resolution model, which has a certain reference value, and the new spatial test method can provide more detailed evaluation information. Full article
(This article belongs to the Special Issue Processing and Application of Weather Radar Data)
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19 pages, 7143 KiB  
Article
Microphysical Characteristics of Raindrop Size Distribution and Implications for Dual-Polarization Radar Quantitative Precipitation Estimations in the Tianshan Mountains, China
by Yong Zeng, Jiangang Li, Lianmei Yang, Haoyang Li, Xiaomeng Li, Zepeng Tong, Yufei Jiang, Jing Liu, Jinru Zhang and Yushu Zhou
Remote Sens. 2023, 15(10), 2668; https://doi.org/10.3390/rs15102668 - 20 May 2023
Cited by 2 | Viewed by 2031
Abstract
In order to improve the understanding of the microphysical characteristics of raindrop size distribution (DSD) under different rainfall rates (R) classes, and broaden the knowledge of the impact of radar wavelengths and R classes on the QPE of dual-polarization radars in [...] Read more.
In order to improve the understanding of the microphysical characteristics of raindrop size distribution (DSD) under different rainfall rates (R) classes, and broaden the knowledge of the impact of radar wavelengths and R classes on the QPE of dual-polarization radars in the Tianshan Mountains, a typical arid area in China, we investigated the microphysical characteristics of DSD across R classes and dual-polarimetric radar QPE relationships across radar wavelengths and R classes, based on the DSD data from a PARSIVEL2 disdrometer at Zhaosu in the Tianshan Mountains during the summers of 2020 and 2021. As the R class increased, the DSD became wider and flatter. The mean value of the mass-weighted mean diameters (Dm) increased, while the mean value of logarithm normalized intercept parameters (log10 Nw) decreased after increasing from C1 to C3, as the R class increased. The largest contributions to R and the radar reflectivity factor from large raindrops (diameter > 3 mm) accounted for approximately 50% and 97%, respectively, while 84% of the total raindrops were small raindrops (diameter < 1 mm). Dual-polarization radars—horizontal polarization reflectivity (Zh), differential reflectivity (Zdr), and specific differential phase (Kdp)—were retrieved based on the DSD data using the T-matrix scattering method. The DSD-based polarimetric radar QPE relations of a single-parameter (R(Zh), R(Kdp)), and double-parameters (R(Zh,Zdr), R(Kdp,Zdr)) on the S-, C-, and X-bands were derived and evaluated. Overall, the performance of the R(Kdp) (R(Kdp,Zdr)) scheme was better than that of R(Zh) (R(Zh,Zdr)) for the QPE in the three bands. Furthermore, we have for the first time confirmed and quantified the performance differences in the QPE relationship of dual-polarization radars under different schemes, radar wavelengths, and R classes in typical arid areas of China. Therefore, selecting an appropriate dual-polarization radar band and QPE scheme for different R classes is necessary to improve the QPE ability compared with an independent scheme under all R classes. Full article
(This article belongs to the Special Issue Processing and Application of Weather Radar Data)
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11 pages, 2991 KiB  
Communication
Statistical Analysis of Mesovortices during the First Rainy Season in Guangdong, South China
by Ying Tang, Xin Xu, Yuanyuan Ju, Zhenyu Wu, Shushi Zhang, Xunlai Chen and Qi Xu
Remote Sens. 2023, 15(8), 2176; https://doi.org/10.3390/rs15082176 - 20 Apr 2023
Viewed by 1252
Abstract
Based on Doppler radar observation and reanalysis data, the statistical characteristics of mesovortices (MVs) during the first rainy season (April–June) in Guangdong, South China, from 2017 to 2019 are studied, including their spatiotemporal distributions, structural features and favorable environmental conditions. The results show [...] Read more.
Based on Doppler radar observation and reanalysis data, the statistical characteristics of mesovortices (MVs) during the first rainy season (April–June) in Guangdong, South China, from 2017 to 2019 are studied, including their spatiotemporal distributions, structural features and favorable environmental conditions. The results show that the MVs usually exhibit short lifetimes; about 70% last for less than 30 min. The intensity and horizontal scale of the MVs are proportional to their lifetime. Long-lived MVs have larger horizontal scales and stronger intensities than short-lived ones. The MVs are mainly observed over the Pearl River Delta region, followed by western Guangdong Province, but relatively fewer in both eastern and northern Guangdong Province. The uneven spatial distribution of the MVs is closely related to the differences in the topographical features and the environment conditions over South China. MVs are prone to form over flat regions. The Pearl River Delta and eastern Guangdong regions are relatively flat compared to the more mountainous western and northern Guangdong regions. Moreover, the monsoonal south-westerlies, water vapor flux, atmospheric instability and vertical wind shear over southwest Guangdong are significantly larger than those in other regions and are favorable for the formation of MVs. The occurrence frequencies of MVs in central and southern parts of Guangdong display similar diurnal variations, reaching the peak during the late afternoon and early evening while dropping to the minimum overnight. However, the situation is opposite in northern Guangdong, with the peak overnight and the minimum during the late afternoon and early evening. The regional differences in diurnal variation are likely related to the moving direction of mesoscale convective systems (MCSs) in Guangdong. Full article
(This article belongs to the Special Issue Processing and Application of Weather Radar Data)
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18 pages, 109292 KiB  
Article
Study on the Vertical Structure and the Evolution of Precipitation Particle Spectrum Parameters of Stratocumulus Clouds over North China Based on Aircraft Observation
by Jingyuan Xiong, Xiaoli Liu and Jing Wang
Remote Sens. 2023, 15(8), 2168; https://doi.org/10.3390/rs15082168 - 20 Apr 2023
Cited by 1 | Viewed by 879
Abstract
The understanding of the macro- and micro-structure, particle spectrum parameters, and their evolutions in different parts of stratocumulus clouds based on aircraft observation data, is important basic data for the development of cloud microphysical parameterization schemes and the quantitative retrieval of cloud-precipitation by [...] Read more.
The understanding of the macro- and micro-structure, particle spectrum parameters, and their evolutions in different parts of stratocumulus clouds based on aircraft observation data, is important basic data for the development of cloud microphysical parameterization schemes and the quantitative retrieval of cloud-precipitation by radar and satellite detections. In this study, a total of ten vertical measurements during three aircraft observations were selected to analyze the vertical distribution of cloud microphysical properties in different parts of stratocumulus clouds in Hebei, North China. It was found that the downdraft in the cumulus cloud area was stronger than that in the stratiform cloud area, with the temperature at the same height higher than that in the stratiform cloud area, and the height of the 0 °C layers was correspondingly higher. In terms of particle spectrum parameters, the intercept and slope parameters of particle spectrum below melting levels in the cumulus part were higher than those in stratiform clouds area in the same weather process. In different vertical detection, it was found that the ice particles have begun to melt in the negative temperature layer near 0 °C level, and there might be sublimation, fragmentation, and aggregation in the melting process of ice phase particles. In addition, the melting process changed the spectral parameters greatly and also changed the correlation between the intercept and slope of the particle spectrum. The slope below the 0 °C level increased with the increase of intercept, which was greater than that above the 0 °C level. The relationship obtained between the intercept parameter of the particle’s spectrum and temperature, and the correlation between the maximum diameter and slope parameter of the particle spectrum, have certain reference significance for cloud physical parameterization and the quantitative retrieval of cloud precipitation by radar and satellite in North China and similar climate background areas. Full article
(This article belongs to the Special Issue Processing and Application of Weather Radar Data)
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18 pages, 8465 KiB  
Article
Correction of Dual-PRF Velocity for Operational S-Band Doppler Weather Radar
by So-Yeon Park, Sung-Hwa Jung and Kwang-Ho Kim
Remote Sens. 2023, 15(7), 1920; https://doi.org/10.3390/rs15071920 - 3 Apr 2023
Viewed by 1516
Abstract
The Weather Radar Center (WRC) of the Korea Meteorological Administration (KMA) has been providing three-dimensional radar wind fields based on the “WInd Synthesis System using Doppler Measurements (WISSDOM)” in real time since February 2019. Its accuracy is significantly affected by the quality of [...] Read more.
The Weather Radar Center (WRC) of the Korea Meteorological Administration (KMA) has been providing three-dimensional radar wind fields based on the “WInd Synthesis System using Doppler Measurements (WISSDOM)” in real time since February 2019. Its accuracy is significantly affected by the quality of the Doppler velocity, such as velocity aliasing. For the de-aliasing of Doppler velocity, the dual-pulse repetition frequency (dual-PRF) technique is commonly utilized for commercial Doppler weather radar. The Doppler weather radars of the KMA have extended their Nyquist velocity up to 132 m s−1 using a dual PRF of 5:4. However, the dual-PRF technique produces significant noise and loss of radial velocity. Therefore, we developed a technique for noise cancelation and recovery of radial velocity to improve the quality of WISSDOM wind fields. The proposed approach identifies and removes speckles of abnormal radial velocity by comparing the sign of the median radial velocity with the surrounding radar bins. We then recovered the eliminated radial velocity using median interpolation. To recover the losses of radial velocity over a wide area using the dual-PRF technique, we used the Velocity Azimuth Display curve-fitting technique. These techniques are straightforward, preserve spatial gradients, and suppress local extrema. We tested this technique, verified its performance, and applied it to the operational radar quality control system of the WRC from August 2021. We concluded that the process helps improve the quality of the radial velocity and the accuracy of the WISSDOM wind fields. Full article
(This article belongs to the Special Issue Processing and Application of Weather Radar Data)
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21 pages, 9305 KiB  
Article
Statistical Bias Correction of Precipitation Forecasts Based on Quantile Mapping on the Sub-Seasonal to Seasonal Scale
by Xiaomeng Li, Huan Wu, Nergui Nanding, Sirong Chen, Ying Hu and Lingfeng Li
Remote Sens. 2023, 15(7), 1743; https://doi.org/10.3390/rs15071743 - 24 Mar 2023
Cited by 3 | Viewed by 2276
Abstract
Accurate precipitation forecasting is challenging, especially on the sub-seasonal to seasonal scale (14–90 days) which mandates the bias correction. Quantile mapping (QM) has been employed as a universal method of precipitation bias correction as it is effective in correcting the distribution attributes of [...] Read more.
Accurate precipitation forecasting is challenging, especially on the sub-seasonal to seasonal scale (14–90 days) which mandates the bias correction. Quantile mapping (QM) has been employed as a universal method of precipitation bias correction as it is effective in correcting the distribution attributes of mean and variance, but neglects the correlation between the model and observation data and has computing inefficiency in large-scale applications. In this study, a quantile mapping of matching precipitation threshold by time series (MPTT-QM) method was proposed to tackle these problems. The MPTT-QM method was applied to correct the FGOALS precipitation forecasts on the 14-day to 90-day lead times for the Pearl River Basin (PRB), taking the IMERG-final product as the observation. MPTT-QM was justified by comparing it with the original QM method in terms of precipitation accumulation and hydrological simulations. The results show that MPTT-QM not only improves the spatial distribution of precipitation but also effectively preserves the temporal change, with a better precipitation detection ability. Moreover, the MPTT-QM-corrected hydrological modeling has better performance in runoff simulations than the QM-corrected modeling, with significantly increased KGE metrics ranging from 0.050 to 0.693. MPTT-QM shows promising values in improving the hydrological utilities of various lead time precipitation forecasts. Full article
(This article belongs to the Special Issue Processing and Application of Weather Radar Data)
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22 pages, 12411 KiB  
Article
Evaluating Simulated Microphysics of Stratiform and Convective Precipitation in a Squall Line Event Using Polarimetric Radar Observations
by Yuting Sun, Zhimin Zhou, Qingjiu Gao, Hongli Li and Minghuan Wang
Remote Sens. 2023, 15(6), 1507; https://doi.org/10.3390/rs15061507 - 9 Mar 2023
Cited by 2 | Viewed by 1546
Abstract
Recent upgrades to China’s radar network now allow for polarimetric measurements of convective systems in central China, providing an effective data set with which to evaluate the microphysics schemes employed in local squall line simulations. We compared polarimetric radar variables derived by Weather [...] Read more.
Recent upgrades to China’s radar network now allow for polarimetric measurements of convective systems in central China, providing an effective data set with which to evaluate the microphysics schemes employed in local squall line simulations. We compared polarimetric radar variables derived by Weather Research and Forecasting (WRF) and radar forward models and the corresponding hydrometeor species with radar observations and retrievals for a severe squall line observed over central China on 16 March 2022. Two microphysics schemes were tested and were able to accurately depict the contrast between convective and stratiform regions in terms of the drop size distribution (DSD) and reproduce the classical polarimetric signatures of the observed differential reflectivity (ZDR) and specific differential phase (KDP) columns. However, for the convective region, the simulated DSDs in both schemes exhibited lower proportions of large drops and lower liquid water content; by contrast, for the stratiform region, the proportion of large drops was found to be too high in the Morrison (MORR) scheme. The underprediction of ice-phase processes in the convective region, particularly the riming processes associated with graupel and hail, was likely responsible for the bias toward large raindrops at low levels. In the stratiform region, raindrop evaporation in the WRF Double-Moment 6-Class (WDM6) scheme, which partially offsets the overestimation of ice-phase processes, produced ground DSDs that more closely matched the observational data, and did not exhibit the overly strong warm-rain collisional growth processes of MORR. Full article
(This article belongs to the Special Issue Processing and Application of Weather Radar Data)
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18 pages, 8844 KiB  
Article
A Nonlinear Grid Transformation Method for Extrapolating and Predicting the Convective Echo of Weather Radar
by Yue Sun, Hui Xiao, Ye Tian and Huiling Yang
Remote Sens. 2023, 15(5), 1406; https://doi.org/10.3390/rs15051406 - 2 Mar 2023
Viewed by 1363
Abstract
A nonlinear grid transformation (NGT) method is proposed for weather radar convective echo extrapolation prediction. The change in continuous echo images is regarded as a nonlinear transformation process of the grid. This process can be reproduced by defining and solving a 2 × [...] Read more.
A nonlinear grid transformation (NGT) method is proposed for weather radar convective echo extrapolation prediction. The change in continuous echo images is regarded as a nonlinear transformation process of the grid. This process can be reproduced by defining and solving a 2 × 6 transformation matrix, and this approach can be applied to image prediction. In ideal experiments with numerical and path changes of the target, NGT produces a prediction result closer to the target than does a conventional optical flow (OF) method. In the presence of convection lines in real cases, NGT is superior to OF: the critical success index (CSI) for 40 dBZ of the echo prediction at 60 min is approximately 0.2 higher. This is due to the better estimation of the movement of the whole cloud system in the NGT results since it reflects the continuous change in the historical images. For the case with a mesoscale convective complex, the NGT results are better than the OF results, and a deep learning result is cited from a previous study for the same case for 20 and 30 dBZ. However, the result is the opposite for 40 dBZ, where the deep learning method may produce an overestimation of the stronger echo. Full article
(This article belongs to the Special Issue Processing and Application of Weather Radar Data)
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23 pages, 9041 KiB  
Article
A Uniformity Index for Precipitation Particle Axis Ratios Derived from Radar Polarimetric Parameters for the Identification and Analysis of Raindrop Areas
by Yue Sun, Hui Xiao, Huiling Yang, Haonan Chen, Liang Feng, Weixi Shu and Han Yao
Remote Sens. 2023, 15(2), 534; https://doi.org/10.3390/rs15020534 - 16 Jan 2023
Cited by 2 | Viewed by 1541
Abstract
A uniformity index for the axis ratios (Uar) derived from dual polarization weather radar data is proposed for raindrop area identification and analysis. The derivation of this new parameter is based on radar scattering simulations and assumptions. Uar is [...] Read more.
A uniformity index for the axis ratios (Uar) derived from dual polarization weather radar data is proposed for raindrop area identification and analysis. The derivation of this new parameter is based on radar scattering simulations and assumptions. Uar is between 0 and 1 and can be calculated from the differential reflectivity (ZDR) and the copolar correlation coefficient (ρhv), which reflects the uniformity of the axis ratio (r) of the particle group. For raindrops, Uar is close to 1 under ideal conditions, but is clearly different from that of ice particles whose value is close to 0. Studies conducted during two convective weather events observed by X-band and S-band radar are presented to show the Uar features. In convective areas, high Uar presents a U-shaped vertical structure. One branch corresponds to the ZDR column, while the other branch is located at the rear of the convective cloud zone and is lower in altitude, representing the process of ice particles melting into raindrops and then being transported upward by a strong updraft. In stratiform cloud areas, a more than 95% overall identification ratio is obtained when the threshold of Uar is set to 0.2~0.3 for discriminating rain layers. Full article
(This article belongs to the Special Issue Processing and Application of Weather Radar Data)
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15 pages, 6114 KiB  
Technical Note
Multiscale Representation of Radar Echo Data Retrieved through Deep Learning from Numerical Model Simulations and Satellite Images
by Mingming Zhu, Qi Liao, Lin Wu, Si Zhang, Zifa Wang, Xiaole Pan, Qizhong Wu, Yangang Wang and Debin Su
Remote Sens. 2023, 15(14), 3466; https://doi.org/10.3390/rs15143466 - 9 Jul 2023
Cited by 1 | Viewed by 1260
Abstract
Radar reflectivity data snapshot fine-grained atmospheric variations that cannot be represented well by numerical weather prediction models or satellites, which poses a limit for nowcasts based on model–data fusion techniques. Here, we reveal a multiscale representation (MSR) of the atmosphere by reconstructing the [...] Read more.
Radar reflectivity data snapshot fine-grained atmospheric variations that cannot be represented well by numerical weather prediction models or satellites, which poses a limit for nowcasts based on model–data fusion techniques. Here, we reveal a multiscale representation (MSR) of the atmosphere by reconstructing the radar echoes from the Weather Research and Forecasting (WRF) model simulations and the Himawari-8 satellite products using U-Net deep networks. Our reconstructions generated the echoes well in terms of patterns, locations, and intensities with a root mean square error (RMSE) of 5.38 dBZ. We find stratified features in this MSR, with small-scale patterns such as echo intensities sensitive to the WRF-simulated dynamic and thermodynamic variables and with larger-scale information about shapes and locations mainly captured from satellite images. Such MSRs with physical interpretations may inspire innovative model–data fusion methods that could overcome the conventional limits of nowcasting. Full article
(This article belongs to the Special Issue Processing and Application of Weather Radar Data)
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