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Atmospheric Precipitation Sensors

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: 30 October 2025 | Viewed by 5943

Special Issue Editors

Dr. Arianna Cauteruccio

E-Mail Website
Guest Editor
Department of Civil, Chemical and Environmental Engineering (DICCA), University of Genova, 1 Montallegro, 16145 Genova, GE, Italy
Interests: hydrology; accuracy of atmospheric precipitation measurements; fluid dynamics and environmental engineering; pluvial flooding; sustainable urban drainage
Special Issues, Collections and Topics in MDPI journals
Dr. Elisa Adirosi

E-Mail Website
Guest Editor
National Research Council of Italy—Institute of Atmospheric Sciences and Climate (CNR—ISAC), 7, 00185 Roma, Italy
Interests: ground validation studies of precipitation; disdrometers and particle size distributions; retrieval techniques from radar and in situ devices
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, Sensors dedicated a Special Issue to rain sensor technologies and applications, collecting several papers on various aspects such as sensor calibration, uncertainty assessment, standardization, and validation. The papers dealt with conventional in situ or remote sensing devices and new technologies.

The present Special Issue on “Atmospheric Precipitation Sensors” aims at continuing the collection of papers related to rain sensors while enlarging its coverage to include solid and mixed precipitation measurement instruments. The goal of this Special Issue is to provide the readership with an understanding of operating principles, accuracy assessment, state of the art, applications, and future trends of precipitation devices. Precipitation (both liquid and solid) is a key element in the water cycle, and its measurement and monitoring are crucial for the management of water resources, flood forecasting, numeric weather prediction, erosion and climate studies, etc. Furthermore, assessing the role of possible climate trends in modifying the frequency and intensity of precipitation events must be based on accurate information about the distribution and variability of precipitation at the global scale and on a long-term basis. Despite its relevance, the ability to measure precipitation, in particular snowfall and hail events, is still somewhat inadequate.

We invite contributions to this Special Issue in the form of articles reporting research about precipitation sensors technologies including measurement principles, network operation, raw data processing (i.e., rainfall or snowfall rate improvements, hydrometeors classification, hail detection, microphysical information retrievals, etc.), calibration, uncertainty assessment, standardization, and validation. Papers regarding in situ and remote sensing devices (ground based and on spaceborne or aircraft platforms) as well as new technologies or opportunistic sensors are welcome. Papers are also invited on the analysis of measured precipitation time series (either on an event or a long-term basis) and of precipitation characteristics and variability (at the local, regional or global scale).     

Dr. Arianna Cauteruccio
Dr. Elisa Adirosi
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. Sensors 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 2600 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

  • traditional atmospheric precipitation sensors
  • disdrometers
  • rain sensors
  • snow sensors
  • opportunistic sensors
  • weather radar
  • satellite-borne sensors
  • in situ and remote atmospheric precipitation measurements
  • satellite vs. ground validation studies
  • severe storms analysis, interpretation and nowcasting
  • liquid/solid precipitation microphysical parameters
  • quantitative precipitation estimation
  • data quality
  • climate records
  • urban-scale monitoring
  • sensor networking
  • artificial intelligence and multi-sensor big data
  • hail detection
  • microwave propagation
  • rain fading
  • meteorology
  • climatology
  • hydrology
  • agriculture
  • water management
  • environmental monitoring

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Published Papers (3 papers)

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Research

27 pages, 15447 KiB  
Article
High-Resolution Rainfall Estimation Using Ensemble Learning Techniques and Multisensor Data Integration
by Maulana Putra, Mohammad Syamsu Rosid and Djati Handoko
Sensors 2024, 24(15), 5030; https://doi.org/10.3390/s24155030 - 3 Aug 2024
Viewed by 1649
Abstract
In Indonesia, the monitoring of rainfall requires an estimation system with a high resolution and wide spatial coverage because of the complexities of the rainfall patterns. This study built a rainfall estimation model for Indonesia through the integration of data from various instruments, [...] Read more.
In Indonesia, the monitoring of rainfall requires an estimation system with a high resolution and wide spatial coverage because of the complexities of the rainfall patterns. This study built a rainfall estimation model for Indonesia through the integration of data from various instruments, namely, rain gauges, weather radars, and weather satellites. An ensemble learning technique, specifically, extreme gradient boosting (XGBoost), was applied to overcome the sparse data due to the limited number of rain gauge points, limited weather radar coverage, and imbalanced rain data. The model includes bias correction of the satellite data to increase the estimation accuracy. In addition, the data from several weather radars installed in Indonesia were also combined. This research handled rainfall estimates in various rain patterns in Indonesia, such as seasonal, equatorial, and local patterns, with a high temporal resolution, close to real time. The validation was carried out at six points, namely, Bandar Lampung, Banjarmasin, Pontianak, Deli Serdang, Gorontalo, and Biak. The research results show good estimation accuracy, with respective values of 0.89, 0.91, 0.89, 0.9, 0.92, and 0.9, and root mean square error (RMSE) values of 2.75 mm/h, 2.57 mm/h, 3.08 mm/h, 2.64 mm/h, 1.85 mm/h, and 2.48 mm/h. Our research highlights the potential of this model to accurately capture diverse rainfall patterns in Indonesia at high spatial and temporal scales. Full article
(This article belongs to the Special Issue Atmospheric Precipitation Sensors)
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24 pages, 9844 KiB  
Article
Rainfall Observation Leveraging Raindrop Sounds Acquired Using Waterproof Enclosure: Exploring Optimal Length of Sounds for Frequency Analysis
by Seunghyun Hwang, Changhyun Jun, Carlo De Michele, Hyeon-Joon Kim and Jinwook Lee
Sensors 2024, 24(13), 4281; https://doi.org/10.3390/s24134281 - 1 Jul 2024
Cited by 1 | Viewed by 1757
Abstract
This paper proposes a novel method to estimate rainfall intensity by analyzing the sound of raindrops. An innovative device for collecting acoustic data was designed, capable of blocking ambient noise in rainy environments. The device was deployed in real rainfall conditions during both [...] Read more.
This paper proposes a novel method to estimate rainfall intensity by analyzing the sound of raindrops. An innovative device for collecting acoustic data was designed, capable of blocking ambient noise in rainy environments. The device was deployed in real rainfall conditions during both the monsoon season and non-monsoon season to record raindrop sounds. The collected raindrop sounds were divided into 1 s, 10 s, and 1 min intervals, and the performance of rainfall intensity estimation for each segment length was compared. First, the rainfall occurrence was determined based on four extracted frequency domain features (average of dB, frequency-weighted average of dB, standard deviation of dB, and highest frequency), followed by a quantitative estimation of the rainfall intensity for the periods in which rainfall occurred. The results indicated that the best estimation performance was achieved when using 10 s segments, corresponding to the following metrics: accuracy: 0.909, false alarm ratio: 0.099, critical success index: 0.753, precision: 0.901, recall: 0.821, and F1 score: 0.859 for rainfall occurrence classification; and root mean square error: 1.675 mm/h, R2: 0.798, and mean absolute error: 0.493 mm/h for quantitative rainfall intensity estimation. The proposed small and lightweight device is convenient to install and manage and is remarkably cost-effective compared with traditional rainfall observation equipment. Additionally, this compact rainfall acoustic collection device can facilitate the collection of detailed rainfall information over vast areas. Full article
(This article belongs to the Special Issue Atmospheric Precipitation Sensors)
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21 pages, 4278 KiB  
Article
Performance of the Thies Clima 3D Stereo Disdrometer: Evaluation during Rain and Snow Events
by Sabina Angeloni, Elisa Adirosi, Alessandro Bracci, Mario Montopoli and Luca Baldini
Sensors 2024, 24(5), 1562; https://doi.org/10.3390/s24051562 - 28 Feb 2024
Viewed by 1749
Abstract
Imaging disdrometers are widely used in field campaigns to provide information on the shape of hydrometeors, together with the diameter and the fall velocity, which can be used to derive information on the shape–size relations of hydrometeors. However, due to their higher price [...] Read more.
Imaging disdrometers are widely used in field campaigns to provide information on the shape of hydrometeors, together with the diameter and the fall velocity, which can be used to derive information on the shape–size relations of hydrometeors. However, due to their higher price compared to laser disdrometers, their use is limited to scientific research purposes. The 3D stereo (3DS) is a commercial imaging disdrometer recently made available by Thies Clima and on which there are currently no scientific studies in the literature. The most innovative feature of the 3DS is its ability in capturing images of the particles passing through the measurement volume, crucial to provide an accurate classification of hydrometeors based on information about their shape, especially in the case of solid precipitation. In this paper. the performance of the new device is analyzed by comparing 3DS with the Laser Precipitation Monitor (LPM) from the same manufacturer, which is a known laser disdrometer used in many research works. The data used in this paper were obtained from measurements of the two instruments carried out at the Casale Calore site in L’Aquila during the CORE-LAQ (Combined Observations of Radar Experiments in L’Aquila) campaign. The objective of the comparison analysis is to analyze the differences between the two disdrometers in terms of hydrometeor classification, number and falling speed of particles, precipitation intensity, and total cumulative precipitation on an event basis. As regards the classification of precipitation, the two instruments are in excellent agreement in identifying rain and snow; greater differences are observed in the case of particles in mixed phase (rain and snow) or frozen phase (hail). Due to the different measurement area of the two disdrometers, the 3DS generally detects more particles than the LPM. The performance differences also depend on the size of the hydrometeors and are more significant in the case of small particles, i.e., D < 1 mm. In the case of rain events, the two instruments are in agreement with respect to the terminal velocity in still air predicted by the Gunn and Kinzer model for drops with a diameter of less than 3 mm, while, for larger particles, terminal velocity is underestimated by both the disdrometers. The agreement between the two instruments in terms of total cumulative precipitation per event is very good. Regarding the 3DS ability to capture images of hydrometeors, the raw data provide, each minute, from one to four images of single particles and information on their size and type. Their number and coarse resolution make them suitable to support only qualitative analysis of the shape of precipitating particles. Full article
(This article belongs to the Special Issue Atmospheric Precipitation Sensors)
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