Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.9
5.0
Journals
Remote Sensing
Special Issues
Atmospheric Radar for Severe Weather Research
remotesensing-logo
Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Atmospheric Radar for Severe Weather Research

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

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 8361

Special Issue Editor

Dr. Tomeu Rigo

E-Mail Website
Guest Editor
Servei Meteorologic de Catalunya, 08029 Barcelona, Spain
Interests: severe weather; remote sensing; nowcasting; hail; heavy rain; supercells; squall lines
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Severe weather occurs in many countries around the world, producing important damage and affectations in many human activities (agriculture, industry, airports, and many more). Recent advances in atmospheric radar, both in the technologies (e.g., mobile radar, different bands, dual polarization, Doppler and dual Doppler, phased array) and the methodologies (such as the improvement of the knowledge about the signatures in severe thunderstorms, of the algorithms for detecting storms, or of the combination of radar with other remote sensing data or other meteorological source) have been achieved thanks to a lot of different research and operational projects, which constitutes the object of interest of this Special Issue. More specifically, some of the topics of interest are:

  • Analysis of special events, because of the area affected or the magnitude of the phenomena or the damages produced, through radar (and other meteorological data, if necessary);
  • Presentation of technologies or state-of-the-art of a set of technical applications which have constituted an improvement of the field;
  • Climatology of severe weather phenomena (hail, downburst, tornado, and/or straight winds) considering atmospheric radar;
  • New techniques for detecting severe weather signatures in thunderstorms;
  • Other technologies and methodologies related to severe weather and radar.

Dr. Tomeu Rigo
Guest Editor

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.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

24 pages, 39181 KiB  
Article
Assimilating FY-4A Lightning and Radar Data for Improving Short-Term Forecasts of a High-Impact Convective Event with a Dual-Resolution Hybrid 3DEnVAR Method
by Peng Liu, Yi Yang, Anwei Lai, Yunheng Wang, Alexandre O. Fierro, Jidong Gao and Chenghai Wang
Remote Sens. 2021, 13(16), 3090; https://doi.org/10.3390/rs13163090 - 5 Aug 2021
Cited by 4 | Viewed by 2326
Abstract
A dual-resolution, hybrid, three-dimensional ensemble-variational (3DEnVAR) data assimilation method combining static and ensemble background error covariances is used to assimilate radar data, and pseudo-water vapor observations to improve short-term severe weather forecasts with the Weather Research and Forecast (WRF) model. The higher-resolution deterministic [...] Read more.
A dual-resolution, hybrid, three-dimensional ensemble-variational (3DEnVAR) data assimilation method combining static and ensemble background error covariances is used to assimilate radar data, and pseudo-water vapor observations to improve short-term severe weather forecasts with the Weather Research and Forecast (WRF) model. The higher-resolution deterministic forecast and the lower-resolution ensemble members have 3 and 9 km horizontal resolution, respectively. The water vapor pseudo-observations are derived from the combined use of total lightning data and cloud top height from the Fengyun-4A(FY-4A) geostationary satellite. First, a set of single-analysis experiments are conducted to provide a preliminary performance evaluation of the effectiveness of the hybrid method for assimilating multisource observations; second, a set of cycling analysis experiments are used to evaluate the forecast performance in convective-scale high-frequency analysis; finally, different hybrid coefficients are tested in both the single and cycling experiments. The single-analysis results show that the combined assimilation of radar data and water vapor pseudo-observations derived from the lightning data is able to generate reasonable vertical velocity, water vapor and hydrometeor adjustments, which help to trigger convection earlier in the forecast/analysis and reduce the spin-up time. The dual-resolution hybrid 3DEnVAR method is able to adjust the wind fields and hydrometeor variables with the assimilation of lightning data, which helps maintain the triggered convection longer and partially suppress spurious cells in the forecast compared with the three-dimensional variational (3DVAR) method. A cycling analysis that introduced a large number of observations with more frequent small adjustments is able to better resolve the observed convective events than a single-analysis approach. Different hybrid coefficients can affect the forecast results, either in the single deterministic or cycling analysis experiments. Overall, we found that a static coefficient of 0.4 and an ensemble coefficient of 0.6 yields the best forecast skill for this event. Full article
(This article belongs to the Special Issue Atmospheric Radar for Severe Weather Research)
►▼ Show Figures

Figure 1

24 pages, 8810 KiB  
Article
Assimilation of Polarimetric Radar Data in Simulation of a Supercell Storm with a Variational Approach and the WRF Model
by Muyun Du, Jidong Gao, Guifu Zhang, Yunheng Wang, Pamela L. Heiselman and Chunguang Cui
Remote Sens. 2021, 13(16), 3060; https://doi.org/10.3390/rs13163060 - 4 Aug 2021
Cited by 3 | Viewed by 1858
Abstract
Polarimetric radar data (PRD) have potential to be used in numerical weather prediction (NWP) models to improve convective-scale weather forecasts. However, thus far only a few studies have been undertaken in this research direction. To assimilate PRD in NWP models, a forward operator, [...] Read more.
Polarimetric radar data (PRD) have potential to be used in numerical weather prediction (NWP) models to improve convective-scale weather forecasts. However, thus far only a few studies have been undertaken in this research direction. To assimilate PRD in NWP models, a forward operator, also called a PRD simulator, is needed to establish the relation between model physics parameters and polarimetric radar variables. Such a forward operator needs to be accurate enough to make quantitative comparisons between radar observations and model output feasible, and to be computationally efficient so that these observations can be easily incorporated into a data assimilation (DA) scheme. To address this concern, a set of parameterized PRD simulators for the horizontal reflectivity, differential reflectivity, specific differential phase, and cross-correlation coefficient were developed. In this study, we have tested the performance of these new operators in a variational DA system. Firstly, the tangent linear and adjoint (TL/AD) models for these PRD simulators have been developed and checked for the validity. Then, both the forward operator and its adjoint model have been built into the three-dimensional variational (3DVAR) system. Finally, some preliminary DA experiments have been performed with an idealized supercell storm. It is found that the assimilation of PRD, including differential reflectivity and specific differential phase, in addition to radar radial velocity and horizontal reflectivity, can enhance the accuracy of both initial conditions for model hydrometer state variables and ensuing model forecasts. The usefulness of the cross-correlation coefficient is very limited in terms of improving convective-scale data analysis and NWP. Full article
(This article belongs to the Special Issue Atmospheric Radar for Severe Weather Research)
►▼ Show Figures

Figure 1

18 pages, 4300 KiB  
Article
Climatology of Convective Storms in Estonia from Radar Data and Severe Convective Environments
by Tanel Voormansik, Tuule Müürsepp and Piia Post
Remote Sens. 2021, 13(11), 2178; https://doi.org/10.3390/rs13112178 - 2 Jun 2021
Cited by 4 | Viewed by 2873
Abstract
Data from the C-band weather radar located in central Estonia in conjunction with the latest reanalysis of the European Centre for Medium-Range Weather Forecasts (ECMWF), ERA5, and Nordic Lightning Information System (NORDLIS) lightning location system data are used to investigate the climatology of [...] Read more.
Data from the C-band weather radar located in central Estonia in conjunction with the latest reanalysis of the European Centre for Medium-Range Weather Forecasts (ECMWF), ERA5, and Nordic Lightning Information System (NORDLIS) lightning location system data are used to investigate the climatology of convective storms for nine summer periods (2010–2019, 2017 excluded). First, an automated 35-dBZ reflectivity threshold-based storm area detection algorithm is used to derive initial individual convective cells from the base level radar reflectivity. Those detected cells are used as a basis combined with convective available potential energy (CAPE) values from ERA5 reanalysis to find thresholds for a severe convective storm in Estonia. A severe convective storm is defined as an area with radar reflectivity at least 51 dBZ and CAPE at least 80 J/kg. Verification of those severe convective storm areas with lightning data reveals a good correlation on various temporal scales from hourly to yearly distributions. The probability of a severe convective storm day in the study area during the summer period is 45%, and the probability of a thunderstorm day is 54%. Jenkinson Collison’ circulation types are calculated from ERA5 reanalysis to find the probability of a severe convective storm depending on the circulation direction and the representativeness of the investigated period by comparing it against 1979–2019. The prevailing airflow direction is from SW and W, whereas the probability of the convective storm to be severe is in the case of SE and S airflow. Finally, the spatial distribution of the severe convective storms shows that the yearly mean number of severe convective days for the 100 km2 grid cell is mostly between 3 and 8 in the distance up to 150 km from radar. Severe convective storms are most frequent in W and SW parts of continental Estonia. Full article
(This article belongs to the Special Issue Atmospheric Radar for Severe Weather Research)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop