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

Journal Menu

Journal Browser

Remote Sensing Technologies for the Analysis and Modeling of Atmospheric Events

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 "Atmospheric Remote Sensing".

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 10977

Share This Special Issue

Special Issue Editors

Dr. Antonio Ricchi

E-Mail Website
Guest Editor

Department Physical and Chemical Sciences, Università degli Studi dell'Aquila/CETEMPS, Via Vetoio, 67100 Coppito (AQ), Italy
Interests: atmosphere dynamics; air–sea interaction; high precipitation events; numerical modeling
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Rossella Ferretti

E-Mail Website
Guest Editor

Department Physical and Chemical Sciences, Università degli Studi dell'Aquila/CETEMPS, Via Vetoio, 67100 Coppito (AQ), Italy
Interests: atmospheric physics; meteorology; numerical modelling; data assimilation; climate modelling

Prof. Dr. Frank Silvio Marzano

E-Mail Website
Guest Editor

1. Department Physical and Chemical Sciences, Università degli Studi dell'Aquila/CETEMPS, Via Vetoio, 67100 Coppito (AQ), Italy
2. Department of Information Engineering, Electronics and Telecommunications, Sapienza University of Rome, Via Eudossiana, 18, 00184 Rome, Italy
Interests: remote sensing; meteorology; geoscience; information engeenering

Dr. Mario Montopoli

E-Mail Website
Guest Editor

National Research Council of Italy - Institute of Atmospheric Sciences and Climate (CNR - ISAC), 00133 Rome, Italy
Interests: radar remote sensing; Doppler analysis and wind reconstruction; solid precipitation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Since the beginning of the "remote sensing era", the potential of environmental remote sensing, both satellite and ground-based, has been recognized as a key factor to advance the knowledge in the study of atmospheric science. Remote sensing has proved useful in bridging one of the main limits of investigation and application, that is, the lack of observations on the ground and at upper levels, at synoptic scale and with high spatial and temporal resolution. It also limits the impact of “observational holes” over the sea and remote areas of the planet, as well as the heterogeneity of some synoptic analyses. Among other remote sensing tools, ground-based radars have given a strong boost to the knowledge of convective dynamics and precipitation events, as well as the implementation of new forecasting and warning systems—particularly for severe and extreme weather events. From the satellite standpoint, the near future advent of cube-sat constellations will improve the time sampling of the most severe meteorological events, thus allowing more and more reliable assimilation into weather prediction models.

Satellite and ground-based radar remote sensing have become fundamental for the study of the physics of the sea and the atmosphere, but have also provided important aid to observation, nowcasting and warning systems and forecasting using numerical data assimilation techniques, with significant socio-economic repercussions guaranteed by their impact on the predictability of numerical models and the physical knowledge of severe and extreme events.

This Special Issue aims to document the most recent progress in the following areas (not an exhaustive list):

The observation and study of atmospheric events, marine dynamics and storms, as well as air–sea and air–land interface, from short-term to inter-annual timescales, through the use of remote sensing technologies;
The observation and analysis, through SAR data, of wind speed and direction, humidity, temperature and sea surface temperature;
Cloud and hydro-meteor analysis in extreme atmospheric events, using current satellite platforms (e.g., GPM, Cloudsat) and ground-based stations (weather/cloud radars, gauges, disdrometers);
Implementation and data assimilation (3D-Var, 4D-Var, RUC, etc.) of satellite (surface and upper layers) and radar data in numerical models and their impact on atmospheric and ocean predictability;
The use of satellite and radar data for the investigation of extreme atmospheric events for curiosity-driven and/or air safety studies.

Dr. Antonio Ricchi
Prof. Dr. Rossella Ferretti
Prof. Dr. Frank Silvio Marzano
Dr. Mario Montopoli
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

observation and study of atmospheric events
short-term to inter-annual timescale analysis
remote sensing in extreme events
observation and analysis through SAR data
data assimilation
numerical modelling validation
cloud and hydro-meteor analysis
remote sensing technologies
ground-based radars

Published Papers (5 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

20 pages, 6910 KiB

Open AccessArticle

Impact of Feature-Dependent Static Background Error Covariances for Satellite-Derived Humidity Assimilation on Analyses and Forecasts of Multiple Sea Fog Cases over the Yellow Sea

by Yue Yang, Shanhong Gao, Yongming Wang and Hao Shi

Remote Sens. 2022, 14(18), 4537; https://doi.org/10.3390/rs14184537 - 11 Sep 2022

Cited by 1 | Viewed by 1228

Abstract

Assimilation of satellite-derived humidity with a homogenous static background error covariance (B) matrix computed over the entire computational domain (Full-B) tends to overpredict sea fog coverage. A feature-dependent B (Fog-B) is proposed to address this issue. In Fog-B, the static error statistics for clear air and foggy areas are calculated separately using a feature-dependent binning method. The resultant error statistics are used simultaneously at appropriate locations guided by the satellite-derived sea fog. Diagnostics show that Full-B generally has broader horizontal and vertical length scales and larger error variances than Fog-B below ~300 m except for the vertical length scale near the surface. Experiments on three sea fog cases over the Yellow Sea are conducted to understand and examine the impact of Fog-B on sea fog analyses and forecasts. Results show that using Full-B produces greater and broader water vapor mixing ratio increments and thus predicts larger sea fog coverage than using Fog-B. Further evaluations suggest that using Fog-B has greater forecast skills in sea fog coverage and more accurate moisture conditions than using Full-B. Full article

(This article belongs to the Special Issue Remote Sensing Technologies for the Analysis and Modeling of Atmospheric Events)

► Show Figures

Figure 1

24 pages, 8992 KiB

Open AccessArticle

Combined Assimilation of Doppler Wind Lidar and Tail Doppler Radar Data over a Hurricane Inner Core for Improved Hurricane Prediction with the NCEP Regional HWRF System

by Xin Li, Zhaoxia Pu, Jun A. Zhang and George David Emmitt

Remote Sens. 2022, 14(10), 2367; https://doi.org/10.3390/rs14102367 - 13 May 2022

Cited by 2 | Viewed by 1844

Abstract

Accurate specification of hurricane inner-core structure is critical to predicting the evolution of a hurricane. However, observations over hurricane inner cores are generally lacking. Previous studies have emphasized Tail Doppler radar (TDR) data assimilation to improve hurricane inner-core representation. Recently, Doppler wind lidar (DWL) has been used as an observing system to sample hurricane inner-core and environmental conditions. The NOAA P3 Hurricane Hunter aircraft has DWL installed and can obtain wind data over a hurricane’s inner core when the aircraft passes through the hurricane. In this study, we examine the impact of assimilating DWL winds and TDR radial winds on the prediction of Hurricane Earl (2016) with the NCEP operational Hurricane Weather Research and Forecasting (HWRF) system. A series of data assimilation experiments are conducted with the Gridpoint Statistical Interpolation (GSI)-based ensemble-3DVAR hybrid system to identify the best way to assimilate TDR and DWL data into the HWRF forecast system. The results show a positive impact of DWL data on hurricane analysis and prediction. Compared with the assimilation of u and v components, assimilation of DWL wind speed provides better hurricane track and intensity forecasts. Proper choices of data thinning distances (e.g., 5 km horizontal thinning and 70 hPa vertical thinning for DWL) can help achieve better analysis in terms of hurricane vortex representation and forecasts. In the analysis and forecast cycles, the combined TDR and DWL assimilation (DWL wind speed and TDR radial wind, along with other conventional data, e.g., NCEP Automated Data Processing (ADP) data) offsets the downgrade analysis from the absence of DWL observations in an analysis cycle and outperforms assimilation of a single type of data (either TDR or DWL) and leads to improved forecasts of hurricane track, intensity, and structure. Overall, assimilation of DWL observations has been beneficial for analysis and forecasts in most cases. The outcomes from this study demonstrate the great potential of including DWL wind profiles in the operational HWRF system for hurricane forecast improvement. Full article

(This article belongs to the Special Issue Remote Sensing Technologies for the Analysis and Modeling of Atmospheric Events)

► Show Figures

Graphical abstract

30 pages, 7053 KiB

Open AccessArticle

A Network of X-Band Meteorological Radars to Support the Motorway System (Campania Region Meteorological Radar Network Project)

by Vincenzo Capozzi, Vincenzo Mazzarella, Carmela De Vivo, Clizia Annella, Alberto Greco, Giannetta Fusco and Giorgio Budillon

Remote Sens. 2022, 14(9), 2221; https://doi.org/10.3390/rs14092221 - 06 May 2022

Cited by 3 | Viewed by 2403

Abstract

The transport sector and road infrastructures are very sensitive to the issues connected to the atmospheric conditions. The latter constitute a source of relevant risk, especially for roads running in mountainous areas, where a wide spectrum of meteorological phenomena, such as rain showers, snow, hail, wind gusts and ice, threatens drivers’ safety. In such contexts, to face out critical situations it is essential to develop a monitoring system that is able to capillary surveil specific sectors or very small basins, providing real time information that may be crucial to preserve lives and assets. In this work, we present the results of the “Campania Region Meteorological Radar Network”, which is focused on the development of X-band radar-based meteorological products that can support highway traffic management and maintenance. The X-band measurements provided by two single-polarization systems, properly integrated with the observations supplied by disdrometers and conventional automatic weather stations, were involved in the following main tasks: (i) the development of a radar composite product; (ii) the devise of a probability of hail index; (iii) the real time discrimination of precipitation type (rain, mixed and snow); (iv) the development of a snowfall rate estimator. The performance of these products was assessed for two case studies, related to a relevant summer hailstorm (which occurred on 1 August 2020) and to a winter precipitation event (which occurred on 13 February 2021). In both cases, the X-band radar-based tools proved to be useful for the stakeholders involved in the management of highway traffic, providing a reliable characterization of precipitation events and of the fast-changing vertical structure of convective cells. Full article

(This article belongs to the Special Issue Remote Sensing Technologies for the Analysis and Modeling of Atmospheric Events)

► Show Figures

Graphical abstract

21 pages, 6486 KiB

Open AccessArticle

Modification of Temperature Lapse Rates and Cloud Properties during a Spatiotemporally Extended Dust Aerosol Episode (16–18 June 2016) over the Mediterranean Basin Based on Satellite and Reanalysis Data

by Maria Gavrouzou, Nikolaos Hatzianastassiou, Christos J. Lolis, Marios-Bruno Korras-Carraca and Nikolaos Mihalopoulos

Remote Sens. 2022, 14(3), 679; https://doi.org/10.3390/rs14030679 - 31 Jan 2022

Cited by 2 | Viewed by 1862

Abstract

A spatiotemporally extended dust aerosol episode that occurred over the Mediterranean Basin (MB) from 16 to 18 June 2016 is investigated using observational satellite and reanalysis data, focusing on the effects of high dust loads on cloud formation and temperature fields, including the creation of temperature inversions. The atmospheric conditions before and during the 3-day dust aerosol episode case (DAEC) are also analyzed. The dust episode, which is identified using a contemporary satellite algorithm, consists of long-range transport of African dust to the western and central MB. The day to day, before and during the DAEC, atmospheric circulation, dust-cloud interactions, and dust effect on temperature are examined using a variety of Moderate Resolution Imaging Spectroradiometer (MODIS) Level-3 Collection 6.1 satellite and Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis data. According to the obtained results, the dust export from N. Africa, which occurs under the prevalence of a trough over the western MB, and a ridge over the central MB, extends from southwest to northeast along two axes, one in the western and another in the central Mediterranean, covering remote areas up to the coasts of southern Europe, including the Balearic and Tyrrhenian Seas, the Italian peninsula, the Ionian and Adriatic Seas, and the Balkan peninsula. The analysis provides evidence of the formation of mixed-phase clouds, with high cloud-top heights (CTH higher than 10 km) and low cloud-top temperatures (CTT as low as 230 K), which spatiotemporally coincide with the high dust loadings that provide the necessary CCN and IN. Dust aerosols are transported either in the boundary layer (within the first 1–2 km) of areas close to the North African dust source areas or in the free troposphere over the Mediterranean Sea and the Italian and Balkan peninsulas (between 2 and 8 km). Distinct and extended layers of remarkable temperature inversions (up to 20 K/km) are created below the exported dust layers in the boundary layer of Mediterranean Sea areas, while weak/reduced lapse rates are formed over continental areas of MB undergoing the dust transport. Such modifications of temperature fields are important for the dynamics of the atmosphere of MB. Full article

(This article belongs to the Special Issue Remote Sensing Technologies for the Analysis and Modeling of Atmospheric Events)

► Show Figures

Graphical abstract

Other

Jump to: Research

16 pages, 1519 KiB

Open AccessTechnical Note

Study on Sensitivity of Observation Error Statistics of Doppler Radars to the Radar forward Operator in Convective-Scale Data Assimilation

by Yuefei Zeng, Hong Li, Yuxuan Feng, Ulrich Blahak, Alberto de Lozar, Jingyao Luo and Jinzhong Min

Remote Sens. 2022, 14(15), 3685; https://doi.org/10.3390/rs14153685 - 01 Aug 2022

Cited by 2 | Viewed by 2334

Abstract

In the present work, we investigate the impacts on the observation error (OE) statistics due to different types of errors in the forward operator (FE) for both radar reflectivity and radial wind data, in the context of convective-scale data assimilation in the summertime. A series of sensitivity experiments were conducted with the Efficient Modular VOlume RADar Operator (EMVORADO), using the operational data assimilation system of the Deutscher Wetterdienst (DWD, German Weather Service). The investigated FEs are versatile, including errors caused by neglecting the terminal fall speed of hydrometeor, the reflectivity weighting, and the beam broadening and attenuation effects, as well as errors caused by different scattering schemes and formulations for melting particles. For reflectivity, it is found that accounting for the beam broadening effect evidently reduces the standard deviations, especially at higher altitudes. However, it does not shorten the horizontal or along-beam correlation length scales. In comparison between the Rayleigh and the Mie schemes (with specific configurations), the former one results in much smaller standard deviations for heights up to 4 km, and aloft, slightly larger standard deviations. Imposing the attenuation to the Mie scheme slightly reduces the standard deviations at lower altitudes; however, it largely increases the standard deviations at higher altitudes and it also leads to longer correlation length scales. For radial wind, positive impacts of considering the beam broadening effect on standard deviations and neutral impacts on correlations are observed. For both reflectivity and radial wind, taking the terminal fall speed of hydrometeor and the reflectivity weighting into account does not make remarkable differences in the estimated OE statistics. Full article

(This article belongs to the Special Issue Remote Sensing Technologies for the Analysis and Modeling of Atmospheric Events)

► Show Figures

Journal Menu

Journal Browser

Remote Sensing Technologies for the Analysis and Modeling of Atmospheric Events

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (5 papers)

Research

Other

Further Information

Guidelines

MDPI Initiatives

Follow MDPI