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Meteorological Remote Sensing Algorithm and Applications for Clouds and Precipitation

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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: closed (15 March 2023) | Viewed by 32422

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Special Issue Editors

Dr. Yang Zhang

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Guest Editor

State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Interests: weather radar; quantitative precipitation estimation; drop size distribution

Dr. Zhiqun Hu

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Guest Editor

State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Interests: radar meteorology; machine learning; severe convective weather

Dr. Yabin Gou

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Guest Editor

Hangzhou Meteorological Bureau, Hangzhou 310051, China
Interests: weather radar applications; extreme weather; precipitation microphysics

Dr. Jiafeng Zheng

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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 Hu

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CMA Earth System Modeling and Prediction Centre, China Meteorological Administration, Beijing 100081, China
Interests: satellite remote sensing; atmospheric retrieval algorithm; data assimilation

Dr. Yi-Ning Shi

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Guest Editor

CMA Earth System Modeling and Prediction Centre, China Meteorological Administration, Beijing 100081, China
Interests: satellite remote sensing; atmospheric retrieval algorithm; data assimilation

Special Issue Information

Dear Colleagues,

Data and data products are the basis of meteorological scientific research and weather forecasting. With the development of technology, a wide range of meteorological remote sensing equipment has been developed, such as polarimetric radar, phased-array radar, millimeter-wave cloud radar, Doppler wind lidar, as well as various meteorological satellites and GPM. These devices provide us with huge amounts of data, and it is critical to process these data to obtain physical information about clouds and precipitation. The retrieval of meteorological information from observational data usually relies on physics-based analysis or data statistics, or a combination of both. The physical principles involve the scattering of radar electromagnetic waves, atmospheric radiative transfer, physical characteristics of clouds and precipitation particles, and so on. According to these, theoretical retrieval algorithms can be obtained, and empirical formulas can be obtained through data statistics, which can also help in obtaining the product. The recent development of machine learning has been playing an increasingly important role in the processing of meteorological data. The product is ultimately obtained on the basis of extracting the feature information of massive data to establish the corresponding relationship between observation and product. In fact, different types of algorithms have their distinct advantages. With the continuous upgrading and improvement of equipment, the exploration of various retrieval algorithms for clouds and precipitation should be encouraged to obtain more accurate products that reflect the characteristics of clouds and precipitation.

This Special Issue focuses on recent advances in radar and satellite remote sensing algorithms for clouds and precipitation. These algorithms may include, but are not limited to, meteorological data quality control, the retrieval of clouds and precipitation properties, quantitative precipitation estimation or forecasting, and the assimilation of radar or satellite data in numerical weather prediction. Research may address the improvement of traditional algorithms and the development of new algorithms. Current machine-learning-related algorithms are also very welcome.

Dr. Yang Zhang
Dr. Zhiqun Hu
Dr. Yabin Gou
Dr. Jiafeng Zheng
Dr. Hao Hu
Dr. Yi-Ning Shi
Guest Editors

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Keywords

data quality control
retrieval of clouds and precipitation properties
quantitative precipitation estimation or forecasting
satellite cloud detection
assimilation of radar and satellite observations
atmospheric radiative transfer
observation and study of severe weather
machine learning

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19 pages, 964 KiB

Open AccessArticle

Study on the Backscatter Differential Phase Characteristics of X-Band Dual-Polarization Radar and its Processing Methods

by Fei Geng and Liping Liu

Remote Sens. 2023, 15(5), 1334; https://doi.org/10.3390/rs15051334 - 27 Feb 2023

Viewed by 1719

Abstract

The differential propagation phase (Φ_DP) of X-band dual-polarization weather radar (including X-band dual-polarization phased-array weather radar, X-PAR) is important for estimating precipitation and classifying hydrometeors. However, the measured differential propagation phase contains the backscatter differential phase (δ), which poses difficulties for the application of the differential propagation phase from X-band radars. This paper presents the following: (1) the simulation and characteristics analysis of the backscatter differential phase based on disdrometer DSD (raindrop size distribution) measurement data; (2) an improved method of the specific differential propagation phase (K_DP) estimation based on linear programming and backscatter differential phase elimination; (3) the effect of backscatter differential phase elimination on the specific differential propagation phase estimation of X-PAR. The results show the following: (1) For X-band weather radar, the raindrop equivalent diameters D > 2 mm may cause a backscatter differential phase between 0 and 20°; in particular, the backscatter differential phase varies sharply with raindrop size between 3.2 and 4.5 mm. (2) Using linear programming or smoothing filters to process the differential propagation phase could suppress the backscatter differential phase, but it is hard to completely eliminate the effect of the backscatter differential phase. (3) Backscatter differential phase correction may improve the calculation accuracy of the specific differential propagation phase, and the optimization was verified by the improved self-consistency of polarimetric variables, correlation between specific differential propagation phase estimations from S- and X-band radar and the accuracy of quantitative precipitation estimation. The X-PAR deployed in Shenzhen showed good observation performance and the potential to be used in radar mosaics with S-band weather radar. Full article

(This article belongs to the Special Issue Meteorological Remote Sensing Algorithm and Applications for Clouds and Precipitation)

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22 pages, 5580 KiB

Open AccessArticle

Study on Attenuation Correction for the Reflectivity of X-Band Dual-Polarization Phased-Array Weather Radar Based on a Network with S-Band Weather Radar

by Fei Geng and Liping Liu

Remote Sens. 2023, 15(5), 1333; https://doi.org/10.3390/rs15051333 - 27 Feb 2023

Cited by 1 | Viewed by 1776

Abstract

X-band dual-polarization phased-array weather radars (X-PARs) have been used in South China extensively. Eliminating the attenuation and system bias of X-band radar data is the key to utilizing the advantage of X-PAR networks. In this paper, the disdrometer raindrop-size distribution (DSD) measurements are used to calculate the radar polarimetric variables and analyze the characteristics of precipitation attenuation. Furthermore, based on the network of S-band dual-polarization Doppler weather radar (S-POL) and X-PARs, an attenuation-correction method for X-PAR reflectivity is proposed with S-POL constraints in view of the radar-mosaic requirements of a multi-radar network. Linear programming is used to calculate the attenuation-correction parameters of different rainfall areas, which realizes the attenuation correction for X-PAR. The results show that the attenuation-correction parameters simulated based on the disdrometer DSD vary with different precipitation classification; the attenuation-corrected reflectivity of X-PARs is consistent with S-POL and can realize a more precise observation of the evolution of the convective system. Compared with previous attenuation-correction methods with constant correction parameters, the improved method can reduce the deviation between X-PAR reflectivity and that of S-POL in heavy rainfall areas and areas of strong attenuation. The method proposed in this paper is stable and effective. After effective quality control, it is found that the X-PAR network deployed in South China observes data accurately and is consistent with S-POL; thus, it is expected to achieve high temporal–spatial resolution within a radar mosaic. Full article

(This article belongs to the Special Issue Meteorological Remote Sensing Algorithm and Applications for Clouds and Precipitation)

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23 pages, 6632 KiB

Open AccessArticle

Characteristics of Raindrop Size Distributions in the Southwest Mountain Areas of China According to Seasonal Variation and Rain Types

by Haopeng Wu, Shengjie Niu, Yue Zhou, Jing Sun, Jingjing Lv and Yixiao He

Remote Sens. 2023, 15(5), 1246; https://doi.org/10.3390/rs15051246 - 24 Feb 2023

Cited by 3 | Viewed by 1842

Abstract

The precipitation and raindrop size distribution (RSD) characteristics of the four seasons and different rain types were studied using a PARSIVEL² raindrop disdrometer set in the southwest mountain areas of China from 2019 to 2021. The seasonal precipitation in the southwest mountain areas was mainly stratiform rain. The peaks of the RSD were about 1–2 orders of magnitude higher than those in the plains. The convective rain in spring and autumn was very close to the ocean-like convective mass. The local shape–slope (μ–Λ), radar reflectivity–rain rate (Z–R), and kinetic energy–rain rate (KE–R) relationships were further derived, and the diversity of these relationships was mainly due to the variability of the RSDs. In addition, the differences in the RSD characteristics between the top and the foot of the mountain during a typical precipitation process in the summer of 2020 were further compared. It was found that the number density of the small particles at the top of the mountain was higher than that at the foot of the mountain due to the broken large raindrops caused by the high wind speed, while the high evaporation rate, strong convective available potential energy (CPAE), and water vapor content at the foot of the mountain could strengthen the RSD, making the number density of the large raindrops at the foot of the mountain higher than that at the top. Full article

(This article belongs to the Special Issue Meteorological Remote Sensing Algorithm and Applications for Clouds and Precipitation)

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21 pages, 25257 KiB

Open AccessArticle

Evaluation of Hourly Precipitation Characteristics from a Global Reanalysis and Variable-Resolution Global Model over the Tibetan Plateau by Using a Satellite-Gauge Merged Rainfall Product

by Tianru Chen, Jian Li, Yi Zhang, Haoming Chen, Puxi Li and Huizheng Che

Remote Sens. 2023, 15(4), 1013; https://doi.org/10.3390/rs15041013 - 12 Feb 2023

Cited by 9 | Viewed by 2555

Abstract

High-resolution meteorological datasets are urgently needed for understanding the hydrological cycle of the Tibetan Plateau (TP), where ground-based meteorological stations are sparse. Rapid advances in remote sensing create possibilities to represent spatiotemporal properties of precipitation at a high resolution. In this study, the hourly precipitation characteristics over the TP from two gridded precipitation products, one from global reanalysis (the fifth generation of the European Center for Medium-Range Weather Forecasts atmospheric reanalysis of the global climate; ERA5) and the other is simulated by Global-to-Regional Integrated forecast SysTem (GRIST) global nonhydrostatic model, are compared against satellite-gauge merged precipitation analysis (China Merged Precipitation Analysis; CMPA) from 27 July to 31 August 2014, and a satellite-retrieved precipitation estimate from the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) is also evolved. Two aspects are mainly focused on: the spatial distribution and the elevation dependence of hourly precipitation characteristics (including precipitation amount, frequency, intensity, diurnal variations, and frequency–intensity structure). Results indicate that: (1) The precipitation amount, frequency, and intensity of CMPA and IMERG decrease with altitude in the Yarlung Tsangpo river valley (YTRV), but increase at first and then decrease with altitude (except for intensity) in the eastern periphery of TP (EPTP). ERA5 performed well on the variation of precipitation amount with altitude (especially in EPTP), but poorly on the frequency and intensity. GRIST is the antithesis of ERA5, but they all overestimate (underestimate) the frequency (intensity) at all heights; (2) With increasing altitude, the diurnal phase of precipitation of CMPA and IMERG shifted from night to evening in the two sub-regions. IMERG’s diurnal phase is 1 to 3 h earlier than CMPA’s, and the discrepancy decreases (increases) as the altitude increases in YTRV (EPTP). The diurnal phase of precipitation amount and frequency in ERA5 and GRIST is significantly earlier than CMPA, and the frequency peaks around midday except in the basin. GRIST’s simulation of the diurnal variation in intensity at various altitudes is consistent with CMPA; (3) ERA5 and GRIST overestimate (underestimate) the frequency of weak (intense) precipitation, with ERA5’s deviance being the most severe. The deviations increased with altitude. These findings provide intensive metrics to evaluate precipitation in complex terrain and are helpful for deepening the understanding of simulated biases for further improving performance in high-resolution simulation. Full article

(This article belongs to the Special Issue Meteorological Remote Sensing Algorithm and Applications for Clouds and Precipitation)

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18 pages, 8116 KiB

Open AccessArticle

X-Band Radar Attenuation Correction Method Based on LightGBM Algorithm

by Qiang Yang, Yan Feng, Li Guan, Wenyu Wu, Sichen Wang and Qiangyu Li

Remote Sens. 2023, 15(3), 864; https://doi.org/10.3390/rs15030864 - 3 Feb 2023

Cited by 2 | Viewed by 2678

Abstract

X-band weather radar can provide high spatial and temporal resolution data, which is essential to precipitation observation and prediction of mesoscale and microscale weather. However, X-band weather radar is susceptible to precipitation attenuation. This paper presents an X-band attenuation correction method based on the light gradient machine (LightGBM) algorithm (the XACL method), then compares it with the Z_H correction method and the Z_H-K_DP comprehensive correction method. The XACL method was validated using observations from two radars in July 2021, the X-band dual-polarization weather radar at the Shouxian National Climatology Observatory of China (SNCOC), and the S-band dual-polarization weather radar at Hefei. During the rainfall cases on July 2021, the results of the attenuation correction were used for precipitation estimation and verified with the rainfall data from 1204 automatic ground-based meteorological network stations in Anhui Province, China. We found that the XACL method produced a significant correction effect and reduced the anomalous correction phenomenon of the comparison methods. The results show that the average error in precipitation estimation by the XACL method was reduced by 39.88% over 1204 meteorological stations, which is better than the effect of the other two correction methods. Thus, the XACL method proved good local adaptability and provided a new X-band attenuation correction scheme. Full article

(This article belongs to the Special Issue Meteorological Remote Sensing Algorithm and Applications for Clouds and Precipitation)

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27 pages, 4854 KiB

Open AccessArticle

Integrated Convective Characteristic Extraction Algorithm for Dual Polarization Radar: Description and Application to a Convective System

by Chao Wang, Chong Wu and Liping Liu

Remote Sens. 2023, 15(3), 808; https://doi.org/10.3390/rs15030808 - 31 Jan 2023

Cited by 2 | Viewed by 1852

Abstract

To further enhance the application of dual-polarization radar in hail nowcasting, we develop an integrated convective characteristic extraction (ICCE) algorithm based on the storm cell identification and tracking (SCIT) algorithm using dual-polarization radar data and its secondary products (hydrometeor classification data and mesocyclone data). The ICCE identifies and tracks not storm cells but convective systems, and it adds other storm characteristics, such as storm microphysics (hail- and graupel-related) and storm dynamics (mesocyclone-related), to the original storm characteristics, such as storm structure (reflectivity-related) and storm tracking (motion-related). The data of four mesocyclonic hailstorms observed by the two S-band dual-polarization radars in Guangdong Province, China, are utilized, from which we draw the following conclusions: (1) ICCE excels in identifying, characterizing, matching, and tracking convective systems; and (2) the newly added storm microphysics and dynamics characteristics can more accurately quantify the relationship between mesocyclone development, hail growth, and convective system enhancement throughout the evolution of the convective system. Full article

(This article belongs to the Special Issue Meteorological Remote Sensing Algorithm and Applications for Clouds and Precipitation)

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18 pages, 4974 KiB

Open AccessArticle

QuickOSSE Research on the Impact of Airship-Borne Doppler Radar Radial Winds to Predict the Track and Intensity of a Tropical Cyclone

by Jianing Feng, Yihong Duan, Xudong Liang, Wei Sun, Tao Liu and Qian Wang

Remote Sens. 2023, 15(1), 191; https://doi.org/10.3390/rs15010191 - 29 Dec 2022

Viewed by 1616

Abstract

Different from the aircraft TC reconnaissance flight missions before, a tropical cyclone (TC) field campaign project with a Doppler radar equipped on an airship that could hang over on the top of a TC (about 20 km) has been recently carried out in China. To understand the impact of airship-borne radar radial wind observations in TC forecasting, this work conducted quick observation simulation system experiments (QuickOSSE) by assimilating simulated airship-borne Doppler radar radial winds with an Ensemble Kalman Filter (EnKF) algorithm. The results show that airship-borne radial winds assimilation reproduces the forecasted track and minimum sea level pressure of the nature run. The forecast of dynamic and thermodynamic TC structures, such as tangential wind, secondary circulation, and warm core, are also improved. In addition, two determining factors, the radar depression angle (D-ang) and the distance from the airship to the TC center (DIS), are found to primarily affect the forecast of the TC track and intensity, respectively. Benefiting from a larger horizontal coverage, observations under a smaller D-ang improved the track more significantly. Meanwhile, the intensity forecast error with a DIS around the radius of the maximum wind is the smallest among several sensitive experiments, which may because the peak-velocity winds representing the TC’s intensity could be observed by radar. The results are expected to help establish an observational strategy for upcoming airship flight missions in practice. Full article

(This article belongs to the Special Issue Meteorological Remote Sensing Algorithm and Applications for Clouds and Precipitation)

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21 pages, 7322 KiB

Open AccessArticle

A Cloud Detection Method Based on Spectral and Gradient Features for SDGSAT-1 Multispectral Images

by Kaiqiang Ge, Jiayin Liu, Feng Wang, Bo Chen and Yuxin Hu

Remote Sens. 2023, 15(1), 24; https://doi.org/10.3390/rs15010024 - 21 Dec 2022

Cited by 5 | Viewed by 2487

Abstract

Due to the limited penetration of visible bands, optical remote sensing images are inevitably contaminated by clouds. Therefore, cloud detection or cloud mask products for optical image processing is a very important step. Compared with conventional optical remote sensing satellites (such as Landsat series and Sentinel-2), sustainable development science Satellite-1 (SDGSAT-1) multi-spectral imager (MII) lacks a short-wave infrared (SWIR) band that can be used to effectively distinguish cloud and snow. To solve the above problems, a cloud detection method based on spectral and gradient features (SGF) for SDGSAT-1 multispectral images is proposed in this paper. According to the differences in spectral features between cloud and other ground objects, the method combines four features, namely, brightness, normalized difference water index (NDWI), normalized difference vegetation index (NDVI), and haze-optimized transformation (HOT) to distinguish cloud and most ground objects. Meanwhile, in order to adapt to different environments, the dynamic threshold using Otsu’s method is adopted. In addition, it is worth mentioning that gradient features are used to distinguish cloud and snow in this paper. With the test of SDGSAT-1 multispectral images and comparison experiments, the results show that SGF has excellent performance. The overall accuracy of images with snow surface can reach 90.80%, and the overall accuracy of images with other surfaces is above 94%. Full article

(This article belongs to the Special Issue Meteorological Remote Sensing Algorithm and Applications for Clouds and Precipitation)

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17 pages, 9709 KiB

Open AccessArticle

A Method for Retrieving Cloud-Top Height Based on a Machine Learning Model Using the Himawari-8 Combined with Near Infrared Data

by Yan Dong, Xuejin Sun and Qinghui Li

Remote Sens. 2022, 14(24), 6367; https://doi.org/10.3390/rs14246367 - 16 Dec 2022

Cited by 4 | Viewed by 2444

Abstract

Different cloud-top heights (CTHs) have different degrees of atmospheric heating, which is an important factor for weather forecasting and aviation safety. AHIs (Advanced Himawari Imagers) on the Himawari-8 satellite are a new generation of visible and infrared imaging spectrometers characterized by a wide observation range and a high temporal resolution. In this paper, a cloud-top height retrieval algorithm based on XGBoost is proposed. The algorithm comprehensively utilizes AHI L1 multi-channel radiance data and calculates the input parameters of the generated model according to the characteristics of the cloud phase, texture, and the local brightness temperature change of the cloud. In addition, the latitude, longitude, solar zenith angle and satellite zenith angle are input into the model to further constrain the influence of the geographical and spatial factors such as the sea and land location, on CTH. Compared with the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) cloud-top height data (CTH_CAL), the results show that: the algorithm retrieved the cloud-top height (CTH_XGB) with a mean error (ME) of 0.3 km, a standard deviation (Std) of 1.72 km, and a root mean square error (RMSE) of 1.74 km. Additionally, it improves the problem of the large systematic deviation in the cloud-top height products released by the Japan Meteorological Agency (CTH_JMA), especially for ice clouds and multi-layer clouds with ice clouds on the top layer. For water clouds below 2 km and multi-layer clouds with water clouds at the top, the algorithm solves the systematically serious CTH_JMA problem. XGBoost can effectively distinguish between different cloud scenarios within the model, which is robust and suitable for CTH retrieval. Full article

(This article belongs to the Special Issue Meteorological Remote Sensing Algorithm and Applications for Clouds and Precipitation)

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20 pages, 11701 KiB

Open AccessArticle

Characteristics of Summer Hailstorms Observed by Radar and Himawari-8 in Beijing, China

by Yingying Jing, Yichen Chen, Xincheng Ma, Jianli Ma, Xia Li, Ningkun Ma and Kai Bi

Remote Sens. 2022, 14(22), 5843; https://doi.org/10.3390/rs14225843 - 18 Nov 2022

Cited by 1 | Viewed by 1933

Abstract

Severe hailstorms frequently occurred in Beijing between May and August 2021, leading to extensive hail damage. These hailstorms were observed by radar and satellite data, and reported by surface observers. In this study, the spectral and cloud microphysical characteristics of typical Beijing events in 2021 were analyzed using Himawari-8 satellite products and ground-based S-band weather radar data obtained from the Beijing Meteorological Bureau. The relationship between Himawari-8 brightness temperature differences (BTD) and radar reflectivity was also investigated. The results revealed that the significant spectral depression of brightness temperatures (BTs) in hail clouds was observed by a satellite. Furthermore, the stronger the radar reflectivity was, the more rapidly BTD decreased, with a nonlinear relationship between them. The results of cloud physical characteristics show that, for cloud-top heights above 10 km, the cloud effective radius was about 25 μm, with a cloud-top temperature of 225 K during these hail events. By means of Gaussian fitting, the BT threshold value (11.2 μm) was determined by satellite at 230 K, with a BTD focused on 1.9 K when hailstorms occurred. These results will help us better understand the characteristics of hailstorms, while also providing information for future hail suppression in Beijing. Full article

(This article belongs to the Special Issue Meteorological Remote Sensing Algorithm and Applications for Clouds and Precipitation)

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18 pages, 5792 KiB

Open AccessArticle

Classification of Ground-Based Cloud Images by Contrastive Self-Supervised Learning

by Qi Lv, Qian Li, Kai Chen, Yao Lu and Liwen Wang

Remote Sens. 2022, 14(22), 5821; https://doi.org/10.3390/rs14225821 - 17 Nov 2022

Cited by 4 | Viewed by 4037

Abstract

Clouds have an enormous influence on the hydrological cycle, Earth’s radiation budget, and climate changes. Accurate automatic recognition of cloud shape based on ground-based cloud images is beneficial to analyze the atmospheric motion state and water vapor content, and then to predict weather trends and identify severe weather processes. Cloud type classification remains challenging due to the variable and diverse appearance of clouds. Deep learning-based methods have improved the feature extraction ability and the accuracy of cloud type classification, but face the problem of lack of labeled samples. In this paper, we proposed a novel classification approach of ground-based cloud images based on contrastive self-supervised learning (CSSL) to reduce the dependence on the number of labeled samples. First, data augmentation is applied to the input data to obtain augmented samples. Then contrastive self-supervised learning is used to pre-train the deep model with a contrastive loss and a momentum update-based optimization. After pre-training, a supervised fine-tuning procedure is adopted on labeled data to classify ground-based cloud images. Experimental results have confirmed the effectiveness of the proposed method. This study can provide inspiration and technical reference for the analysis and processing of other types of meteorological remote sensing data under the scenario of insufficient labeled samples. Full article

(This article belongs to the Special Issue Meteorological Remote Sensing Algorithm and Applications for Clouds and Precipitation)

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16 pages, 6974 KiB

Open AccessArticle

Analysis of the Microphysical Structure and Evolution Characteristics of a Typical Sea Fog Weather Event in the Eastern Sea of China

by Jianxin He, Xinyue Ren, Hao Wang, Zhao Shi, Fugui Zhang, Lijun Hu, Qiangyu Zeng and Xin Jin

Remote Sens. 2022, 14(21), 5604; https://doi.org/10.3390/rs14215604 - 6 Nov 2022

Cited by 5 | Viewed by 2333

Abstract

This study is the first to use the observation data of a fog monitor, a visibility meter, and an automatic weather station to carry out a comprehensive observation experiment from the perspective of microphysics on a severe sea fog process in Beilun District, China, from 14 to 15 June 2021. The results show the following: (1) Temperature is closely related to nucleation, condensation growth, and other processes. The decrease (increase) in temperature is the main reason for the enhancement (weakening) of nucleation and the growth of condensation (evaporation of droplets), which leads to an increase (or decrease) in microphysical quantities, such as droplet number concentration and liquid water content. (2) The average droplet number spectral distribution roughly conforms to the Gamma distribution, and the spectral distribution of the fog process presents a ”multi-peak” structure, with peak diameters of 6 μm, 12 μm, 16 μm, 24 μm, and 44 μm. Droplets with a diameter of less than 16 μm account for 75% of the droplet size distribution. (3) During this sea fog process, three microphysical parameters, namely, number concentration, liquid water content, and average diameter, are all positively correlated in pairs, but the positive correlation between the number concentration and the average diameter is weak. This shows that the condensation nucleation and the condensation growth of droplets are the main processes in this sea fog process and that the collision process occurs but is not the dominant process. The sea fog comprehensive observation experiment provides an important demonstration of the microphysics research of sea fog in the eastern coastal areas of China and provides more reference information for sea fog research and equipment comparisons between different regions. At the same time, it also provides an essential scientific basis for the short-term forecast of sea fog in the future and for the optimization of the microphysical parameters of related models. Full article

(This article belongs to the Special Issue Meteorological Remote Sensing Algorithm and Applications for Clouds and Precipitation)

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22 pages, 26744 KiB

Open AccessArticle

Spatial Patterns of Errors in GPM IMERG Summer Precipitation Estimates and Their Connections to Geographical Features in Complex Topographical Area

by Rui Li, Shunxian Tang, Zhao Shi, Jianxin He, Wenjing Shi and Xuehua Li

Remote Sens. 2022, 14(19), 4789; https://doi.org/10.3390/rs14194789 - 25 Sep 2022

Cited by 4 | Viewed by 1650

Abstract

Error evaluation is essential for the improvement and application of the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) Version 06 daily precipitation estimates, including early-, late-, and final-run products (IMERG-DE, IMERG-DL, and IMERG-DF, respectively), especially for summer precipitation in complex topographical areas. However, many existing works mainly focus on comparing the error statistical metrics of precipitation estimates, but few further analyze the internal relationships between these error statistics and geographical features. Therefore, taking Sichuan Province of China as a case study of the complex topographic and mountainous area, we adopt statistical metrics, error decomposition schemes, systematic and random error separation models, and regression methods to analyze the relationships between the spatial distribution of IMERG summer precipitation error metrics and geographical features. These features include longitude, latitude, distance from Sichuan Basin edge (DFBE), digital elevation model (DEM), normalized difference vegetation index (NDVI), slope, aspect, and topographic position index (TPI). The results show that: (1) DEM and DFBE are the two most important geographical features affecting the spatial distribution of error metrics, while both aspect and TPI have negligible effects on these metrics; (2) the variations in DEM, DFBE, and latitude have the negative relationships with error metrics; (3) longitude and DFBE do not have a direct impact on the errors, but indirectly affect the precipitation errors through the changing DEM; (4) slope shows a strong negative correlation with hit bias, and its increase significantly amplifies the sensitivity of systematic errors of hit bias from IMERG-DE and DL; and (5) the high detection probability and small missed precipitation error of the three IMERG estimates are virtually unaffected by changes in geographical features. Full article

(This article belongs to the Special Issue Meteorological Remote Sensing Algorithm and Applications for Clouds and Precipitation)

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20 pages, 8018 KiB

Open AccessArticle

Confidence and Error Analyses of the Radiosonde and Ka-Wavelength Cloud Radar for Detecting the Cloud Vertical Structure

by Yun Yuan, Huige Di, Yuanyuan Liu, Danmin Cheng, Ning Chen, Qing Yan and Dengxin Hua

Remote Sens. 2022, 14(18), 4462; https://doi.org/10.3390/rs14184462 - 7 Sep 2022

Cited by 3 | Viewed by 1696

Abstract

A macro-vertical structure is closely related to weather evolution and the energy budget balance of the atmospheric system of the Earth. In this study, radiosonde data were used to identify a cloud vertical structure (CVS) using the adjusted relative humidity threshold method. To evaluate the reliability and stability of this method, the results obtained based on the spatiotemporal matching criteria established in this study were compared with Ka-band millimetre-wave cloud radar (MMCR) observation data. This comparison showed that both devices exhibit high consistency in low-level cloud detection. With the increase in the cloud height, the frequency of the cloud appearance detection by the radiosonde became higher than that by the MMCR. In spring, the results of the CVS detection by the two devices were in good agreement. Specifically, the determination coefficients of the modified degrees of freedom (adjusted R-square) of the cloud base height (CBH) and cloud top height (CTH) detected by the two devices were 0.934 and 0.879, respectively. The horizontal drift of the radiosonde was the smallest in summer, and the adj. R-square values of the CBH and CTH were 0.814 and 0.852, respectively. The CVS observation results by the radiosonde and the MMCR were significantly different in autumn (the adj. R-Square values of the CBH and CTH were 0.715 and 0.629, respectively). In winter, the adj. R-Square values of the CBH and CTH observed by the radiosonde and the MMCR were 0.958 and 0.710, respectively. The statistics and analysis of the results of the distribution characteristics of the CVSs using radiosonde data from 2019 to 2021 from Xi’an showed that the average CTH and CBH were at 7–10 km and 3–5 km, respectively. The frequencies of the cloud absence, rainfall, and two- and three-layer clouds were the highest in the winter (34.36%), autumn (12.99%), and summer, respectively. Full article

(This article belongs to the Special Issue Meteorological Remote Sensing Algorithm and Applications for Clouds and Precipitation)

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Meteorological Remote Sensing Algorithm and Applications for Clouds and Precipitation

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