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Synergetic Remote Sensing of Clouds and Precipitation II

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

Deadline for manuscript submissions: 30 April 2024 | Viewed by 5779

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Dr. Haoran Li

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Guest Editor

Chinese Academy of Meteorological Sciences, Beijing, China
Interests: radar meteorology; cloud and precipitation physics
Special Issues, Collections and Topics in MDPI journals

Dr. Haonan Chen

E-Mail Website
Guest Editor

NOAA Earth System Research Laboratory, 325 Broadway, Boulder, CO 80305, USA
Interests: radar remote sensing; radar polarimetry; radar and satellite data fusion; precipitation microphysics; precipitation classification and quantification using multiparameter weather radar
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Observations of clouds and precipitation are essential for understanding the global hydrological cycle, assessing the Earth’s radiation budgets, and monitoring high-impact events. Remote sensing technologies provide in-depth insights into the formation and development of clouds and precipitation, thanks to the development of a wide variety of observing instruments such as radars, lidars, spectrometers and microwave radiometers (MWRs). These instruments, as carried by multiple platforms, e.g., vehicles, satellites, and ships, bring an unprecedented opportunity to observe clouds and precipitation with a synergy of observations. In recent years, an enormous variety of algorithms have been proposed and developed for synergetic retrievals, such as remote sensing and in situ, active and passive remote sensing, multifrequency radars, radar and lidar, and radar and lidar and MWR, to disentangle the complex of clouds and precipitation. Emerging artificial intelligence (AI) techniques further extend the capability of remote sensing measurements for scientific research and operational applications.

This Special Issue will focus on recent advances in the synergetic remote sensing of clouds and precipitation, including algorithm development, comparison and evaluation of multisource remote sensing data, and applications of AI in remote sensing. The topics will include, but are not limited to, research on cloud/precipitation physics, nowcasting, high-impact weather monitoring using weather/cloud/phased-array radars, lidars, spectrometers, microwave radiometers, etc.

Research articles, review articles, and short communications are welcome.

This is the second edition. For more information on the first edition, please see:

https://www.mdpi.com/journal/remotesensing/special_issues/Synergetic_RemoteSensing_Clouds_and_Precipitation.

Dr. Haoran Li
Dr. Haonan Chen
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

clouds and precipitation
artificial intelligence
synergetic remote sensing
radars
lidars
spectrometers
microwave radiometers
severe weather

Published Papers (7 papers)

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17 pages, 13321 KiB

Open AccessArticle

AIDER: Aircraft Icing Potential Area DEtection in Real-Time Using 3-Dimensional Radar and Atmospheric Variables

by Yura Kim, Bo-Young Ye and Mi-Kyung Suk

Remote Sens. 2024, 16(8), 1468; https://doi.org/10.3390/rs16081468 - 21 Apr 2024

Viewed by 314

Abstract

Aircraft icing refers to the accumulation of ice on the surface and components of an aircraft when supercooled water droplets collide with the aircraft above freezing levels (at altitudes at which the temperature is below 0 °C), which requires vigilant monitoring to avert aviation accidents attributable to icing. In response to this imperative, the Weather Radar Center (WRC) of the Korea Meteorological Administration (KMA) has developed a real-time icing detection algorithm. We utilized 3D dual-polarimetric radar variables, 3D atmospheric variables, and aircraft icing data and statistically analyzed these variables within the icing areas determined by aircraft icing data from 2018–2022. An algorithm capable of detecting icing potential areas (icing potential) was formulated by applying these characteristics. Employing this detection algorithm enabled the classification of icing potential into three stages: precipitation, icing caution, and icing warning. The algorithm was validated, demonstrating a notable performance with a probability of detection value of 0.88. The algorithm was applied to three distinct icing cases under varying environmental conditions—frontal, stratiform, and cumuliform clouds—thereby offering real-time observable icing potential across the entire Korean Peninsula. Full article

(This article belongs to the Special Issue Synergetic Remote Sensing of Clouds and Precipitation II)

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23 pages, 16578 KiB

Open AccessArticle

The Kinematic and Microphysical Characteristics of Extremely Heavy Rainfall in Zhengzhou City on 20 July 2021 Observed with Dual-Polarization Radars and Disdrometers

by Bin Wu, Shuang Du, Wenjuan Li, Yian Shen, Ling Luo, Yanfang Li, Ming Wei, Dandan Wang and Lei Xi

Remote Sens. 2023, 15(24), 5688; https://doi.org/10.3390/rs15245688 - 11 Dec 2023

Cited by 1 | Viewed by 688

Abstract

In this study, we utilized dual-polarization weather radar and disdrometer data to investigate the kinematic and microphysical characteristics of an extreme heavy rainfall event that occurred on 20 July 2021, in Zhengzhou. The results are as follows: FY-2G satellite images showed that extremely heavy rainfall mainly occurred during the merging period of medium- and small-scale convective cloud clusters. The merging of these cloud clusters enhanced the rainfall intensity. The refined three-dimensional wind field, as retrieved by the multi-Doppler radar, revealed a prominent mesoscale vortex and convergence structure at the extreme rainfall stage. This led to echo stagnation, resulting in localized extreme heavy rainfall. We explored the formation mechanism of the notable Z_DR arc feature of dual-polarization variables during this phase. It was revealed that during the record-breaking hourly rainfall event in Zhengzhou (20 July 2021, 16:00–17:00 Beijing Time), the warm rain process dominated. Effective collision–coalescence processes, producing a high concentration of medium- to large-sized raindrops, significantly contributed to heavy rainfall at the surface. From an observational perspective, it was revealed that raindrops exhibited significant collision interactions during their descent. Moreover, a conceptual model for the kinematic and microphysical characteristics of this extreme rainfall event was established, aiming to provide technical support for monitoring and early warning of similar extreme rainfall events. Full article

(This article belongs to the Special Issue Synergetic Remote Sensing of Clouds and Precipitation II)

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22 pages, 8199 KiB

Open AccessArticle

Weather Radar Parameter Estimation Based on Frequency Domain Processing: Technical Details and Performance Evaluation

by Shuai Zhang, Yubao Chen, Zhifeng Shu, Haifeng Yu, Hui Wang, Jianjun Chen and Lu Li

Remote Sens. 2023, 15(23), 5624; https://doi.org/10.3390/rs15235624 - 04 Dec 2023

Viewed by 749

Abstract

Parameter estimation is important in weather radar signal processing. Time-domain processing (TDP) and frequency-domain processing (FDP) are two basic parameter estimation methods used in the weather radar field. TDP is widely used in operational weather radars because of its high efficiency and robustness; however, it must be assumed that the received signal has a symmetrical or Gaussian power spectrum, which limits its performance. FDP does not require assumptions about its power spectrum model and has a seamless connection to spectrum analysis; however, its application performance has not been fully validated to ensure its robustness in an operational environment. In this study, we introduce several technical details in FDP, including window function selection, aliasing correction, and noise correction. Additionally, we evaluate the performance of FDP and compare the performance of FDP and TDP based on simulated and measured weather in-phase/quadrature (I/Q) data. The results show that FDP has potential for operational applications; however, further improvements are required, e.g., windowing processing for signals mixed with severe clutter. Full article

(This article belongs to the Special Issue Synergetic Remote Sensing of Clouds and Precipitation II)

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20 pages, 8035 KiB

Open AccessArticle

An Observation of Precipitation during Cooling with Ka-Band Cloud Radar in Wuhan, China

by Zhiwen Mao, Kaiming Huang, Junjie Fang, Zirui Zhang, Rang Cao and Fan Yi

Remote Sens. 2023, 15(22), 5397; https://doi.org/10.3390/rs15225397 - 17 Nov 2023

Viewed by 779

Abstract

Exploiting their sensitivity to cloud droplets and small raindrops, Ka-band cloud radar observations are used to investigate weak stratiform precipitation over Wuhan during cooling on 16–17 February 2022. During cooling, the surface temperature drops by about 8 °C with the lowest value less than 0 °C because of the strong cold air from the north. The cold air lifts the warm and humid air transported by the southeasterly and southwesterly winds, causing thick stratiform clouds and persistent weak precipitation. The Ka-band radar captures the full process of stratiform cloud occurrence; light rain and then mixed rain and snow; and the characteristics of clouds and precipitation at each stage due to its fine sensitivity to small hydrometeors, whereas the reanalysis data alone cannot capture the transition to the mixed rain and snow regime, which can cause dangerous freezing rain or sleet on the ground. Hence, a detailed analysis of cooling and cold surges and their hazards to society, and their reproduction in numerical predictions, needs to use high-sensitivity radar data as much as possible. Full article

(This article belongs to the Special Issue Synergetic Remote Sensing of Clouds and Precipitation II)

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24 pages, 8359 KiB

Open AccessArticle

Spatial Reconstruction of Quantitative Precipitation Estimates Derived from Fengyun-2G Geostationary Satellite in Northeast China

by Hao Wu, Bin Yong and Zhehui Shen

Remote Sens. 2023, 15(21), 5251; https://doi.org/10.3390/rs15215251 - 06 Nov 2023

Viewed by 855

Abstract

With the development of the Chinese Fengyun satellite series, Fengyun-2G (FY-2G) quantitative precipitation estimates (QPE) can provide real-time and high-quality precipitation data over East Asia. However, FY-2G QPE cannot offer precipitation information beyond the latitude band of 50°N due to the limitation of the observation coverage of the FY-2G-based satellite-borne sensor. To this end, a precipitation space reconstruction using the geographically weighted regression (GWR) coupled with a geographical differential analysis (GDA) (PSR2G) algorithm was developed, based on the land surface variables related to precipitation, including vegetational cover, land surface temperature, geographical location, and topographic characteristics. This study used the PSR2G-based reconstructed model to estimate the FY-2G QPE over Northeast China (the latitude band beyond 50°N) from December 2015 to November 2019 with a spatiotemporal resolution of 0.1°/month. The PSR2G-based reconstructed results were validated with the ground observations of 80 rain gauges, and also compared to the reconstructed results using random forest (RF) and GWR. The results show that the spatio-temporal pattern of PSR2G QPE is closer to ground observations than those of RF and GWR, which indicates that the PSR2G QPE is more competent to capture the spatio-temporal variation of rainfall over Northeast China than other two reconstruction methods. In addition, the reconstructed precipitation dataset using PSR2G has higher accuracy over study area than the FY-2G QPE below the band of 50°N. It suggested that PSR2G reconstruction precipitation strategies do not lose the precision of the original satellite precipitation data. Full article

(This article belongs to the Special Issue Synergetic Remote Sensing of Clouds and Precipitation II)

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20 pages, 14164 KiB

Open AccessArticle

The Use of Regional Data Assimilation to Improve Numerical Simulations of Diurnal Characteristics of Precipitation during an Active Madden–Julian Oscillation Event over the Maritime Continent

by Zhiqiang Cui and Zhaoxia Pu

Remote Sens. 2023, 15(9), 2405; https://doi.org/10.3390/rs15092405 - 04 May 2023

Viewed by 1093

Abstract

This study examines the impact of regional data assimilation on diurnal characteristics of precipitation and winds over the Maritime Continent (MC) using a set of cloud-permitting-scale (~3 km) numerical simulations with the mesoscale community Weather Research and Forecasting (WRF) model and the NCEP Gridpoint Statistical Interpolation (GSI)-based ensemble-3DVAR hybrid data assimilation system. Numerical experiments focus on January 2018, when a well-defined, active Madden–Julian Oscillation (MJO) propagated through the MC region. Available conventional and satellite data are assimilated. Results show that simulated precipitation with data assimilation generally agrees better with satellite-derived rainfall than the control simulation without data assimilation. Simulations with data assimilation also reproduce the diurnal cycle of precipitation better, especially for the timing of the precipitation peak. Data assimilation modulates the overstrong (overweak) diurnal forcing over the land (ocean) in the control simulation. The vertical phase shift of the thermodynamic environment, associated with the timing of vertical motion transition along with low-level water vapor supplies, results in maximum precipitation occurring later, especially over land. To further demonstrate the impact of data assimilation, an additional experiment assimilates NASA Cyclone Global Navigation Satellite System (CYGNSS)-derived ocean surface winds. The results indicate that the assimilation of CYGNSS data exhibits an evident impact on the diurnal variation of surface variables and a similar shift in the diurnal cycle of precipitation. Overall, this study highlights the importance of regional data assimilation in improving the representation of precipitation over the MC, paving the way for a better understanding of the interactions of local diurnal convective precipitation cycles with MJO. Full article

(This article belongs to the Special Issue Synergetic Remote Sensing of Clouds and Precipitation II)

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13 pages, 6128 KiB

Open AccessTechnical Note

Windshear Detection in Rain Using a 30 km Radius Coherent Doppler Wind Lidar at Mega Airport in Plateau

by Haiyun Xia, Yixiang Chen, Jinlong Yuan, Lian Su, Zhu Yuan, Shengjun Huang and Dexian Zhao

Remote Sens. 2024, 16(5), 924; https://doi.org/10.3390/rs16050924 - 06 Mar 2024

Viewed by 593

Abstract

Convective weather is often accompanied by precipitation and windshear, seriously endangering the safety of aircraft during takeoff and landing. However, under rainfall conditions, conventional wind lidars have a limited detection range due to significant signal attenuation. To solve this problem, a 200 mm temperature-controlled telescope coated with a hydrophobic film is applied in the coherent Doppler wind lidar system to improve the detection capability in rain. The maximum detection range of the lidar is extended to 30 km and demonstrated at Kunming Changshui International Airport at an altitude of 2102 m. Firstly, the detection accuracy and maximum detection range of the lidar are verified. Through the analysis of the horizontal wind field under two typical convective weather conditions, it is found that convective weather often accompanies low-level convergence and divergence structures, leading to headwind shear and crosswind shear on the airport runway. From the vertical profile, it is shown that the triggering of convective weather is accompanied by low-level southwest winds and high-altitude northeastern winds. According to the statistics of wind speed and direction on clear and rainy days over 9 months, rainy days are usually caused by the invasion of cold air from Northeast China, resulting in airport windshear. In summary, the enhanced lidar can effectively identify and analyze windshear during rainy days, which is very useful for aviation safety, especially for takeoff and landing in all weather conditions. Full article

(This article belongs to the Special Issue Synergetic Remote Sensing of Clouds and Precipitation II)

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