Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Menu

Journal Browser

Remote Sensing of Clouds and Precipitation at Multiple Scales II

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

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

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 15539

Share This Special Issue

Special Issue Editors

Dr. Min Min

E-Mail Website
Guest Editor

School of Atmospheric Sciences, Sun Yat-Sen University (Zhuhai Campus), Haiqin Building #2, A271, Xiangzhou District, Zhuhai 519082, China
Interests: cloud; precipitation; convection; aerosol remote sensing using LEO and GEO satellites
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Chao Liu

E-Mail Website
Guest Editor

School of Atmospheric Physics, Nanjing University of Information Science and Technology, Ningliu Road 219, Nanjing 210044, China
Interests: atmospheric radiative transfer and remote sensing of clouds
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Clouds and precipitation play an essential role in global weather and climate systems because of their impact on the distribution of atmospheric energy. Many well developed remote sensing techniques have greatly assisted our ability to characterize the inter-decadal, inter-annual, and diurnal variability of clouds and precipitation, connect clouds and precipitation to large-scale circulation patterns, and understand the impacts of clouds and precipitation on the Earth’s atmosphere. However, some recent advances and innovations in terms of active/passive sensors for detecting clouds and precipitation have successfully been launched. We invite studies using these or other new observational data to help further understand the internal processes and dynamics of clouds and precipitation, spanning global to regional scales.

The previous Special Issue 'Remote Sensing of Clouds and Precipitation at Multiple Scales' was a great success. The second volume aims at collecting new developments and methodologies, best practices, and applications of remote sensing for clouds and precipitation at multiple scales. We welcome submissions that provide the community with the most recent advancements in all aspects of cloud and precipitation remote sensing, including, but not limited to, the following:

Active and passive detection of cloud and precipitation;
Cloud remote sensing;
Precipitation remote sensing;
Convection remote sensing;
Multi-instruments;
Cloud and precipitation detections for weather, climatic, and environment studies.

Authors are required to check and follow the specific Instructions to Authors; please see: https://www.mdpi.com/journal/remotesensing/instructions

Dr. Min Min
Prof. Dr. Chao Liu
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

active and passive detection of cloud and precipitation
cloud remote sensing
precipitation remote sensing
convection remote sensing
multi-instruments
cloud and precipitation detections for weather, climatic, and environment studies

Published Papers (10 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Other

19 pages, 3589 KiB

Open AccessArticle

Evaluation of the RF-MEP Method for Merging Multiple Gridded Precipitation Products in the Chongqing City, China

by Yongming Shi, Cheng Chen, Jun Chen, Babak Mohammadi, Majid Cheraghalizadeh, Mohammed Abdallah, Okan Mert Katipoğlu, Haotian Li and Zheng Duan

Remote Sens. 2023, 15(17), 4230; https://doi.org/10.3390/rs15174230 - 28 Aug 2023

Cited by 2 | Viewed by 1024

Abstract

Precipitation is a major component of the water cycle. Accurate and reliable estimation of precipitation is essential for various applications. Generally, there are three main types of precipitation products: satellite based, reanalysis, and ground measurements from rain gauge stations. Each type has its advantages and disadvantages. Recent efforts have been made to develop various merging methods to improve precipitation estimates by combining multiple precipitation products. This study evaluated for the first time the performance of the random forest-based merging procedure (RF-MEP) method in enhancing the accuracy of daily precipitation estimates in Chongqing city, China with a complex terrain and sparse observational data. The RF-MEP method was used to merge three widely used gridded precipitation products (CHIRPS, ERA5-Land, and GPM IMERG) with ground measurements from a limited number of rain gauge stations to produce the merged precipitation dataset. Eight stations (approximately 70% of the available stations) were used to train the RF-MEP approach, while four stations (30%) were used for independent testing. Various statistical metrics were employed to assess the performance of the merged precipitation dataset and the three existing precipitation products against the ground measurements. Our results demonstrated that the RF-MEP approach significantly enhances the accuracy of daily precipitation estimates, surpassing the performance of the individual precipitation products and two other merging methods (the simple linear regression model and the simple averaging). Among the three existing products, ERA5-Land exhibited the best performance in capturing daily precipitation, followed by GPM IMERG, while CHIRPS performed the worst. Regarding precipitation intensity, all three existing products and the RF-MEP merged dataset performed well in capturing light precipitation events with an intensity of less than 1 mm/day, which accounts for the majority (more than 70%) of occurrences. However, all datasets showed rather poor capability in capturing precipitation events beyond 1 mm/day, with the worst performance observed for extreme heavy precipitation events exceeding 50 mm/day. The RF-MEP approach significantly improves the detection ability for all precipitation intensities, except for the most extreme intensity (>50 mm/day), where only marginal improvement is observed. Analysis of the spatial pattern of precipitation estimates and the temporal bias of daily precipitation estimates further confirms the superior performance of the RF-MEP merged precipitation dataset over the three existing products. Full article

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

► Show Figures

Figure 1

22 pages, 7641 KiB

Open AccessArticle

Diurnal Variation Characteristics of Clouds and Precipitation during the Summer Season in Two Typical Climate Regions of the Tibetan Plateau

by Renran Zhou, Gaili Wang, Kun Zhao, Liping Liu and Jisong Sun

Remote Sens. 2023, 15(15), 3731; https://doi.org/10.3390/rs15153731 - 27 Jul 2023

Viewed by 959

Abstract

Mêdog and Nagqu are two typical climate regions of the Tibetan Plateau, with different atmospheric conditions and local orography. This may lead to different diurnal variation patterns of clouds and precipitation. This paper investigates the diurnal variations of clouds and precipitation in Mêdog and Nagqu, using ground-based measurements from Ka-band cloud radar and a Particle Size and Velocity (PARSIVEL) disdrometer. High frequencies of cloud cover and precipitation occur from 23:00 local solar time (LST) to 05:00 LST in Mêdog, while low frequencies appear from 11:00 LST to 17:00 LST. The occurrence frequencies in Nagqu maintain high values from 13:00 LST to 21:00 LST. In terms of mean rain rate, heavier rainfall appears in the evening and at night in Mêdog, with peaks at 00:00 LST and 18:00 LST, respectively. In Nagqu, the heaviest rainfall occurs at 12:00 LST. In addition, the afternoon convective rainfall in Nagqu is characterized by a much higher concentration of large drops, which can be classified as continental-like. The morning rainfall has the lowest concentration of large drops and can be classified as maritime-like. Finally, the mechanisms of diurnal variations in the two regions are discussed. The diurnal cycle of clouds and precipitation in Mêdog may be associated with the nocturnal convergence of moisture flux and mountain–valley wind circulation. Diurnal variations in Nagqu have a high correlation with the diurnal cycle of solar radiation. The high nocturnal frequency of clouds and precipitation in the two regions at night is closely related to the convergence of moisture flux. Full article

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

► Show Figures

Figure 1

22 pages, 7128 KiB

Open AccessArticle

Regional Variability of Raindrop Size Distribution from a Network of Disdrometers over Complex Terrain in Southern China

by Asi Zhang, Chao Chen and Lin Wu

Remote Sens. 2023, 15(10), 2678; https://doi.org/10.3390/rs15102678 - 21 May 2023

Cited by 1 | Viewed by 1181

Abstract

Raindrop size distribution (DSD) over the complex terrain of Guangdong Province, southern China, was studied using six disdrometers operated by the Guangdong Meteorology Service during the period 1 March 2018 to 30 August 2022 (~5 years). To analyze the long-term DSD characteristics over complex topography in southern China, three stations on the windward side, Haifeng, Enping and Qingyuan, and three stations on the leeward side, Meixian, Luoding and Xuwen, were utilized. The median mass-weighted diameter (D_m) value was higher on the windward than on the leeward side, and the windward-side stations also showed greater D_m variability. With regard to the median generalized intercept (log₁₀N_w) value, the log₁₀N_w values decreased from coastal to mountainous areas. Although there were some differences in D_m, log₁₀N_w and liquid water content (LWC) frequency between the six stations, there were still some similarities, with the D_m, log₁₀N_w and LWC frequency all showing a single-peak curve. In addition, the diurnal variation of the mean log₁₀N_w had a negative relationship with D_m diurnal variation although the inverse relationship was not particularly evident at the Haifeng site. The diurnal mean rainfall rate also peaked in the afternoon and exceeded the maximum at night which indicated that strong land heating in the daytime significantly influenced the local DSD variation. What is more, the number concentration of drops, N(D), showed an exponential shape which decreased monotonically for all rainfall rate types at the six observation sites, and an increase in diameter caused by increases in the rainfall rate was also noticeable. As the rainfall rate increased, the N(D) for sites on the windward side (i.e., Haifeng, Enping and Qingyuan) were higher than for the sites on the leeward side (i.e., Meixian, Luoding and Xuwen), and the difference between them also became distinct. The abovementioned DSD characteristic differences also showed appreciable variability in convective precipitation between stations on the leeward side (i.e., Meixian, Luoding and Xuwen) and those on the windward side (Haifeng and Enping, but not Qingyuan). This study enhances the precision of numerical weather forecast models in predicting precipitation and verifies the accuracy of measuring precipitation through remote sensing instruments, including weather radars located on the ground. Full article

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

► Show Figures

Figure 1

19 pages, 9993 KiB

Open AccessArticle

Representativeness of Two Global Gridded Precipitation Data Sets in the Intensity of Surface Short-Term Precipitation over China

by Xiaocheng Wei, Yu Yu, Bo Li and Zijing Liu

Remote Sens. 2023, 15(7), 1856; https://doi.org/10.3390/rs15071856 - 30 Mar 2023

Viewed by 1260

Abstract

This study evaluates the representativeness of two widely used next-generation global satellite precipitation estimates data for short-term precipitation over China, namely the satellite data from the Climate Prediction Center morphing (CMORPH) and the satellite data from the Global Precipitation Measurement (GPM) mission. These two satellite precipitation data sets were compared with the hourly liquid in-situ precipitation from China national surface stations from 2016 to 2020. The results showed that the GPM precipitation data has better representativeness of surface short-term precipitation than that of the CMORPH data, and these two quantitative precipitation estimate (QPE) data sets underestimated extreme precipitation. Moreover, we analyzed the influence of the error between two QPE data sets and the in-situ precipitation on the classification of short-term precipitation intensity. China uses 8.1–16 mm/h as the definition of heavy precipitation, but the accuracy of the satellite QPE product was different due to the different lowest threshold of heavy rain (more than 8.1 mm/h or more than 16 mm/h). Increasing the threshold value of the QPE data for short-term strong precipitation resulted in lower accuracy for detecting such events, but higher accuracy for detecting moderate intensity rainfall. When studying short-term strong precipitation over China using precipitation grade, selecting an appropriate threshold was important to ensure accurate judgments. Additionally, it is important to account for errors caused by QPE data, which can significantly affect the accuracy of precipitation grading. Full article

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

► Show Figures

Figure 1

12 pages, 3146 KiB

Open AccessCommunication

Tropical Tropopause Layer Cloud Properties from Spaceborne Active Observations

by Siliang Lei, Xijuan Zhu, Yuxiang Ling, Shiwen Teng and Bin Yao

Remote Sens. 2023, 15(5), 1223; https://doi.org/10.3390/rs15051223 - 22 Feb 2023

Cited by 1 | Viewed by 1247

Abstract

A significant part of clouds in the tropics appears over the tropopause due to intense convections and in situ condensation activity. These tropical tropopause layer (TTL) clouds not only play an important role in the radiation budget over the tropics, but also in water vapor and other chemical material transport from the troposphere to the stratosphere. This study quantifies and analyzes the properties of TTL clouds based on spaceborne active observations, which provide one of the most reliable sources of information on cloud vertical distributions. We use four years (2007–2010) of observations from the joint Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and CloudSat and consider all cloudy pixels with top height above the tropopause as TTL clouds. The occurrence frequency of TTL clouds during the nighttime is found to be almost 13% and can reach ~50–60% in areas with frequent convections. The annual averages of tropical tropopause height, tropopause temperature, and cloud top height are 16.2 km, −80.7 °C, and 16.6 km, respectively, and the average cloud top exceeds tropopause by approximately 500 m. More importantly, the presence of TTL clouds causes tropopause temperature to be ~3–4 °C colder than in the all-sky condition. It also lifts the tropopause heights ~160 m during the nighttime and lowers the heights ~84 m during the daytime. From a cloud type aspect, ~91% and ~4% of the TTL clouds are high clouds and altostratus, and only ~5% of them are associated with convections (i.e., nimbostratus and deep convective clouds). Approximately 30% of the TTL clouds are single-layer clouds, and multi-layer clouds are dominated by those with 2–3 separated layers. Full article

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

► Show Figures

Figure 1

17 pages, 4766 KiB

Open AccessArticle

Retrieval of Atmospheric Water Vapor Content in the Environment from AHI/H8 Using Both Physical and Random Forest Methods—A Case Study for Typhoon Maria (201808)

by Linyan Zhu, Ronglian Zhou, Di Di, Wenguang Bai and Zijing Liu

Remote Sens. 2023, 15(2), 498; https://doi.org/10.3390/rs15020498 - 13 Jan 2023

Cited by 3 | Viewed by 1380

Abstract

The advanced imagers onboard the new generation of geostationary satellites could provide multilayer atmospheric moisture information with unprecedented high spatial and temporal resolutions, while the physical retrieval algorithm (One-Dimensional Variational, 1DVAR) is performed for operational atmospheric water vapor products with reduced resolutions, which is due to the limited computational efficiency of the physical retrieval algorithm. In this study, a typical cost-efficient machine learning (Random Forecast, RF) algorithm is adopted and compared with the physical retrieval algorithm for retrieving the atmospheric moisture from the measurements of Advance Himawari Imager (AHI) onboard the Himawari-8 satellite during the typhoon Maria (201808). It is found that the accuracy of the RF-based algorithm has much high computational efficiency and provides moisture retrievals with accuracy 35–45% better than that of 1DVAR, which means the retrieval process can be conducted at full spatial resolution for potential operational application. Both the Global Forecast System (GFS) forecasts and the AHI measurements are necessary information for moisture retrievals; they provide added value for each other. Full article

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

► Show Figures

Figure 1

12 pages, 3612 KiB

Open AccessArticle

Evaluating Latent-Heat-Nudging Schemes and Radar forward Operator Settings for a Convective Summer Period over Germany Using the ICON-KENDA System

by Yuefei Zeng, Yuxuan Feng, Alberto de Lozar, Klaus Stephan, Leonhard Scheck, Kobra Khosravianghadikolaei and Ulrich Blahak

Remote Sens. 2022, 14(21), 5295; https://doi.org/10.3390/rs14215295 - 22 Oct 2022

Cited by 1 | Viewed by 2121

Abstract

Radar data assimilation has been operational at the Deutscher Wetterdienst for several years and is essential for generating accurate precipitation forecasts. The current work attempts to further enhance the radar data assimilation by improving the latent heat nudging (LHN) scheme and by reducing the observation error (OE) caused by the representation error of the efficient modular volume radar operator (EMVORADO). First of all, a series of hindcasts for a one-month convective period over Germany are performed. Compared with radar reflectivity and satellite observations, it is found that the LHN scheme that implicitly adjusts temperature performs better, and the beam broadening effect and the choice of the scattering schemes in EMVORADO are important. Moreover, the Mie scheme with the new parameterization to reduce the brightband effect not only proves to be the best in hindcasts but also that it results in the smallest standard deviations and the shortest horizontal correlation length scales of the OE in data assimilation experiments. Full article

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

► Show Figures

Figure 1

24 pages, 7060 KiB

Open AccessArticle

Multiscale Ground Validation of Satellite and Reanalysis Precipitation Products over Diverse Climatic and Topographic Conditions

by Muhammad Umer Nadeem, Abdulnoor A. J. Ghanim, Muhammad Naveed Anjum, Donghui Shangguan, Ghulam Rasool, Muhammad Irfan, Usama Muhammad Niazi and Sharjeel Hassan

Remote Sens. 2022, 14(18), 4680; https://doi.org/10.3390/rs14184680 - 19 Sep 2022

Cited by 12 | Viewed by 2100

Abstract

The validity of two reanalysis (ERA5 and MEERA2) and seven satellite-based (CHIRPS, IMERG, PERSIANN-CCS, PERSIANN-CDR, PERSIANN-PDIR, PERSIANN, and TRMM) precipitation products was assessed in relation to the observations of in situ weather stations installed in different topographical and climatic regions of Pakistan. From 2010 to 2018, all precipitation products were evaluated on daily, monthly, seasonal, and annual bases at a point-to-pixel scale and over the entire spatial domain. The accuracy of the products was evaluated using commonly used evaluation and categorical indices, including Root Mean Square Error (RMSE), Correlation Coefficient (CC), Bias, Relative Bias (rBias), Critical Success Index (CSI), Success Ratio (SR) Probability of Detection (POD), and False Alarm Ratio (FAR). The results show that: (1) Over the entire country, the spatio-temporal distribution of observed precipitation could be represented by IMERG and TRMM products. (2) All products (reanalysis and SPPs) demonstrated good agreement with the reference data at the monthly scale compared to the daily data (CC > 0.7 at monthly scale). (3) All other products were outperformed by IMERG and TRMM in terms of their capacity to detect precipitation events throughout the year, regardless of the season (i.e., winter, spring, summer, and autumn). Furthermore, both products (IMERG and TRMM) consistently depicted the incidence of precipitation events across Pakistan’s various topography and climatic regimes. (4) Generally, CHIRPS and ERA5 products showed moderate performances in the plan areas. PERSIANN, PERSIANN-CCS, PDIR, PERSIANN-CDR, and MEERA2 products were uncertain to detect the occurrence and precipitation over the higher intensities and altitudes. Considering the finding of this assessment, we recommend the use of daily and monthly estimates of the IMERG product for hydro climatic studies in Pakistan. Full article

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

► Show Figures

Graphical abstract

18 pages, 5355 KiB

Open AccessArticle

Convective Entrainment Rate over the Tibetan Plateau and Its Adjacent Regions in the Boreal Summer Using SNPP-VIIRS

by Junjun Li, Zhiguo Yue, Chunsong Lu, Jinghua Chen, Xiaoqing Wu, Xiaoqi Xu, Shi Luo, Lei Zhu, Shiying Wu, Fan Wang and Xin He

Remote Sens. 2022, 14(9), 2073; https://doi.org/10.3390/rs14092073 - 26 Apr 2022

Cited by 4 | Viewed by 1810

Abstract

The entrainment rate (λ) is difficult to estimate, and its uncertainties cause a significant error in convection parameterization and precipitation simulation, especially over the Tibetan Plateau, where observations are scarce. The λ over the Tibetan Plateau, and its adjacent regions, is estimated for the first time using five-year satellite data and a reanalysis dataset. The λ and cloud base environmental relative humidity (RH) decrease with an increase in terrain height. Quantitatively, the correlation between λ and RH changes from positive at low terrain heights to negative at high terrain heights, and the underlying mechanisms are here interpreted. When the terrain height is below 1 km, large RH decreases the difference in moist static energy (MSE) between the clouds and the environment and increases λ. When the terrain height is above 1 km, the correlation between λ and RH is related to the difference between MSE turning point and cloud base, because of decreases in specific humidity near the surface with increasing terrain height. These results enhance the theoretical understanding of the factors affecting λ and pave the way for improving the parameterization of λ. Full article

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

► Show Figures

Graphical abstract

Other

Jump to: Research

15 pages, 10902 KiB

Open AccessTechnical Note

Spatiotemporal Variation of Snow Cover Frequency in the Qilian Mountains (Northwestern China) during 2000–2020 and Associated Circulation Mechanisms

by Wentao Du, Shichang Kang, Libing Qian, Youyan Jiang, Wenxuan Sun, Jizu Chen, Zhilong Xu, Weijun Sun, Xiang Qin and Xian Chai

Remote Sens. 2022, 14(12), 2823; https://doi.org/10.3390/rs14122823 - 12 Jun 2022

Cited by 5 | Viewed by 1480

Abstract

Linking snow cover frequency (SCF) and atmospheric circulation is vital for comprehension of hemispheric-scale change mechanisms and for accurate forecasting. This study combined MODIS imagery with meteorological observations to investigate the variation of annual SCFs in the Qilian Mountains. Results indicated that more than 80% of annual SCF is distributed at high elevations and mostly on northern slopes, and that SCF is greater in the west than in the east. Abrupt change in the increase in annual SCF was not detected; however, significant (0.05 confidence level) variation with quasi-3-year and quasi-5-year periods indicated potential connection with monsoons. Topographically, SCF increased at high elevations and decreased in valleys. Moreover, SCF increased significantly with a rise in slope below 23° and then decreased between 23° and 45°, and it decreased with a change in aspect from 70° to 200° and then increased from 200° to 310°. Annual SCF variation in the Qilian Mountains is dominated by precipitation rather than by temperature. In the years with high SCFs, southeasterly winds associated with an anticyclone over southeastern China and southwesterly winds associated with the cyclone over the Iranian Plateau brought warm moisture across northwestern China, favoring snowfall in the Qilian Mountains. Meanwhile, cold moisture outbreaks from the Arctic into the mid-latitudes are conducive to maintaining snow cover. However, in the years with low SCFs, the cold air might be difficultly transporting out of the Arctic region due to the strengthening polar vortex. Moreover, the water vapor was less than that of the mean state and divergence over the Qilian Mountains, which difficultly conduced snowfall over the Qilian Mountains. Full article

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

► Show Figures

Journal Menu

Journal Browser

Remote Sensing of Clouds and Precipitation at Multiple Scales II

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (10 papers)

Research

Other

Further Information

Guidelines

MDPI Initiatives

Follow MDPI