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Atmosphere
Special Issues
Advances in Applications of Weather Radar Data
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Advances in Applications of Weather Radar Data

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".

Deadline for manuscript submissions: closed (31 July 2019) | Viewed by 32609

Special Issue Editors

Dr. Shinju Park

E-Mail Website
Guest Editor
Centre de Recerca Aplicada en Hidrometeorologia, Universitat Politècnica de Catalunya, Barcelona, Spain
Interests: radar hydrometeorology; precipitation; meteorological observations (surface measurements, TRMM, GPM); nowcasting; hazard and risk assessment; flash floods
Prof. Daniel Sempere Torres

E-Mail Website1 Website2
Guest Editor
Centre de Recerca Aplicada en Hidrometeorologia, Universitat Politècnica de Catalunya, Barcelona, Spain
Interests: radar hydrometeorology; hydrological modeling; hydrology; remote sensing; atmospheric physics; precipitation; water resources; rainfall runoff modeling; rainfall; extreme events; climate change

Special Issue Information

Dear Colleagues,

Weather radars have been a primary tool for monitoring and short-term forecasting precipitation, with high temporal and spatial resolutions. The advancements in technologies and radar networks have allowed to explore their uses in, not only improving weather observations and Quantitative Precipitation Estimation (QPE) and Forecasting (QPF), but also in various interdisciplinary fields and applications toward supporting decision makers for better management of hazardous weather events.

This Special Issue focuses on the recent achievements and lessons learnt in various applications using operational or research radar data; e.g., QPE, very short-term forecasting of precipitation, precipitation climatology, hydrological modeling and forecasting, natural hazard assessment, aviation, road management, and even for some applications of non-meteorological weather radar observations. We encourage contributions on the current state-of-the-art in the field, including challenges and discussions toward better utilization of radar data.

We invite manuscripts on the following topics:

  • Radar Networking
  • Radar data validation or merging with other sensors (ground/spaceborne based instruments)
  • Quantitative Precipitation Estimation
  • Nowcasting techniques
  • Assimilation of radar data in NWP
  • Hydrological applications using weather radar
  • Use of weather radar data in natural hazard assessment, agriculture, insurance, road management
  • Studies on non-meteorological radar data

Prof. Daniel Sempere Torres
Dr. Shinju Park
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. Atmosphere is an international peer-reviewed open access monthly 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 2400 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

  • Quantitative precipitation estimation
  • Quantitative precipitation forecast
  • Data assimilation in NWP
  • Hydrological applications
  • Applications in natural hazard and risk assessment
  • Non-meteorological observation

Published Papers (6 papers)

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Research

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15 pages, 4606 KiB  
Article
Effect of Adding Hydrometeor Mixing Ratios Control Variables on Assimilating Radar Observations for the Analysis and Forecast of a Typhoon
by Dongmei Xu, Feifei Shen and Jinzhong Min
Atmosphere 2019, 10(7), 415; https://doi.org/10.3390/atmos10070415 - 19 Jul 2019
Cited by 12 | Viewed by 3077
Abstract
The variational data assimilation (DA) method seeks the optimal analyses by minimizing a cost function with respect to control variables (CVs). CVs are extended in this study to include hydrometeor mixing ratios related variables besides the widely used sets of CVs (momentum fields, [...] Read more.
The variational data assimilation (DA) method seeks the optimal analyses by minimizing a cost function with respect to control variables (CVs). CVs are extended in this study to include hydrometeor mixing ratios related variables besides the widely used sets of CVs (momentum fields, surface pressure, temperature, and pseudo-relative humidity). The impacts of the extra CVs are investigated in terms of hydrometeor mixing ratios to the assimilation of radar radial velocity (Vr) and reflectivity (RF) for the analysis and prediction of Typhoon Chanthu (2010). It is found that the background error statistics of the extended CVs from the National Meteorological Center (NMC) method is reliable. The track forecast is improved significantly by including hydrometeor mixing ratios as CVs to assimilate radar Vr and RF. The DA experiments using the hydrometer CVs show much improved intensity analysis and forecast. It also improves the precipitation forecast skills to some extent. The positive impact is significant using a direct RF assimilation scheme, when Vr and RF data are applied together. It suggests that when we applying an indirect RF assimilation scheme, the fitting of more hydrometers in the cost function will tend to cause a slight degradation for other variables such as the wind and temperature. Full article
(This article belongs to the Special Issue Advances in Applications of Weather Radar Data)
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13 pages, 5823 KiB  
Article
OPERA the Radar Project
by Elena Saltikoff, Günther Haase, Laurent Delobbe, Nicolas Gaussiat, Maud Martet, Daniel Idziorek, Hidde Leijnse, Petr Novák, Maryna Lukach and Klaus Stephan
Atmosphere 2019, 10(6), 320; https://doi.org/10.3390/atmos10060320 - 12 Jun 2019
Cited by 43 | Viewed by 7975
Abstract
The Operational Program on the Exchange of Weather Radar Information (OPERA) has co-ordinated radar co-operation among national weather services in Europe for more than 20 years. It has introduced its own, manufacturer-independent data model, runs its own data center, and produces Pan-European radar [...] Read more.
The Operational Program on the Exchange of Weather Radar Information (OPERA) has co-ordinated radar co-operation among national weather services in Europe for more than 20 years. It has introduced its own, manufacturer-independent data model, runs its own data center, and produces Pan-European radar composites. The applications using this data vary from data assimilation to flood warnings and the monitoring of animal migration. It has used several approaches to provide a homogeneous combination of disparate raw data and to indicate the reliability of its products. In particular, if a pixel shows no precipitation, it is important to know if that pixel is dry or if the measurement was missing. Full article
(This article belongs to the Special Issue Advances in Applications of Weather Radar Data)
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17 pages, 3855 KiB  
Article
Impact of the Altitudinal Gradients of Precipitation on the Radar QPE Bias in the French Alps
by Dominique Faure, Guy Delrieu and Nicolas Gaussiat
Atmosphere 2019, 10(6), 306; https://doi.org/10.3390/atmos10060306 - 03 Jun 2019
Cited by 5 | Viewed by 3489
Abstract
In the French Alps the quality of the radar Quantitative Precipitation Estimation (QPE) is limited by the topography and the vertical structure of precipitation. A previous study realized in all the French Alps, has shown a general bias between values of the national [...] Read more.
In the French Alps the quality of the radar Quantitative Precipitation Estimation (QPE) is limited by the topography and the vertical structure of precipitation. A previous study realized in all the French Alps, has shown a general bias between values of the national radar QPE composite and the rain gauge measurements: a radar QPE over-estimation at low altitude (+20% at 200 m a.s.l.), and an increasing underestimation at high altitudes (until −40% at 2100 m a.s.l.). This trend has been linked to altitudinal gradients of precipitation observed at ground level. This paper analyzes relative altitudinal gradients of precipitation estimated with rain gauges measurements in 2016 for three massifs around Grenoble, and for different temporal accumulations (yearly, seasonal, monthly, daily). Comparisons of radar and rain gauge accumulations confirm the bias previously observed. The parts of the current radar data processing affecting the bias value are pointed out. The analysis shows a coherency between the relative gradient values estimated at the different temporal accumulations. Vertical profiles of precipitation detected by a research radar installed at the bottom of the valley also show how the wide horizontal variability of precipitation inside the valley can affect the gradient estimation. Full article
(This article belongs to the Special Issue Advances in Applications of Weather Radar Data)
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16 pages, 3864 KiB  
Article
Measurements and Modeling of the Full Rain Drop Size Distribution
by Merhala Thurai, Viswanathan Bringi, Patrick N. Gatlin, Walter A. Petersen and Matthew T. Wingo
Atmosphere 2019, 10(1), 39; https://doi.org/10.3390/atmos10010039 - 19 Jan 2019
Cited by 30 | Viewed by 4687
Abstract
The raindrop size distribution (DSD) is fundamental for quantitative precipitation estimation (QPE) and in numerical modeling of microphysical processes. Conventional disdrometers cannot capture the small drop end, in particular the drizzle mode which controls collisional processes as well as evaporation. To overcome this [...] Read more.
The raindrop size distribution (DSD) is fundamental for quantitative precipitation estimation (QPE) and in numerical modeling of microphysical processes. Conventional disdrometers cannot capture the small drop end, in particular the drizzle mode which controls collisional processes as well as evaporation. To overcome this limitation, the DSD measurements were made using (i) a high-resolution (50 microns) meteorological particle spectrometer to capture the small drop end, and (ii) a 2D video disdrometer for larger drops. Measurements were made in two climatically different regions, namely Greeley, Colorado, and Huntsville, Alabama. To model the DSDs, a formulation based on (a) double-moment normalization and (b) the generalized gamma (GG) model to describe the generic shape with two shape parameters was used. A total of 4550 three-minute DSDs were used to assess the size-resolved fidelity of this model by direct comparison with the measurements demonstrating the suitability of the GG distribution. The shape stability of the normalized DSD was demonstrated across different rain types and intensities. Finally, for a tropical storm case, the co-variabilities of the two main DSD parameters (normalized intercept and mass-weighted mean diameter) were compared with those derived from the dual-frequency precipitation radar onboard the global precipitation mission satellite. Full article
(This article belongs to the Special Issue Advances in Applications of Weather Radar Data)
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17 pages, 3268 KiB  
Article
Considering Rain Gauge Uncertainty Using Kriging for Uncertain Data
by Francesca Cecinati, Antonio M. Moreno-Ródenas, Miguel A. Rico-Ramirez, Marie-claire Ten Veldhuis and Jeroen G. Langeveld
Atmosphere 2018, 9(11), 446; https://doi.org/10.3390/atmos9110446 - 14 Nov 2018
Cited by 23 | Viewed by 4771
Abstract
In urban hydrological models, rainfall is the main input and one of the main sources of uncertainty. To reach sufficient spatial coverage and resolution, the integration of several rainfall data sources, including rain gauges and weather radars, is often necessary. The uncertainty associated [...] Read more.
In urban hydrological models, rainfall is the main input and one of the main sources of uncertainty. To reach sufficient spatial coverage and resolution, the integration of several rainfall data sources, including rain gauges and weather radars, is often necessary. The uncertainty associated with rain gauge measurements is dependent on rainfall intensity and on the characteristics of the devices. Common spatial interpolation methods do not account for rain gauge uncertainty variability. Kriging for Uncertain Data (KUD) allows the handling of the uncertainty of each rain gauge independently, modelling space- and time-variant errors. The applications of KUD to rain gauge interpolation and radar-gauge rainfall merging are studied and compared. First, the methodology is studied with synthetic experiments, to evaluate its performance varying rain gauge density, accuracy and rainfall field characteristics. Subsequently, the method is applied to a case study in the Dommel catchment, the Netherlands, where high-quality automatic gauges are complemented by lower-quality tipping-bucket gauges and radar composites. The case study and the synthetic experiments show that considering measurement uncertainty in rain gauge interpolation usually improves rainfall estimations, given a sufficient rain gauge density. Considering measurement uncertainty in radar-gauge merging consistently improved the estimates in the tested cases, thanks to the additional spatial information of radar rainfall data but should still be used cautiously for convective events and low-density rain gauge networks. Full article
(This article belongs to the Special Issue Advances in Applications of Weather Radar Data)
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Review

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18 pages, 5472 KiB  
Review
Improvements in Forecasting Intense Rainfall: Results from the FRANC (Forecasting Rainfall Exploiting New Data Assimilation Techniques and Novel Observations of Convection) Project
by Sarah L. Dance, Susan P. Ballard, Ross N. Bannister, Peter Clark, Hannah L. Cloke, Timothy Darlington, David L. A. Flack, Suzanne L. Gray, Lee Hawkness-Smith, Nawal Husnoo, Anthony J. Illingworth, Graeme A. Kelly, Humphrey W. Lean, Dingmin Li, Nancy K. Nichols, John C. Nicol, Andrew Oxley, Robert S. Plant, Nigel M. Roberts, Ian Roulstone, David Simonin, Robert J. Thompson and Joanne A. Walleradd Show full author list remove Hide full author list
Atmosphere 2019, 10(3), 125; https://doi.org/10.3390/atmos10030125 - 07 Mar 2019
Cited by 24 | Viewed by 7829
Abstract
The FRANC project (Forecasting Rainfall exploiting new data Assimilation techniques and Novel observations of Convection) has researched improvements in numerical weather prediction of convective rainfall via the reduction of initial condition uncertainty. This article provides an overview of the project’s achievements. We highlight [...] Read more.
The FRANC project (Forecasting Rainfall exploiting new data Assimilation techniques and Novel observations of Convection) has researched improvements in numerical weather prediction of convective rainfall via the reduction of initial condition uncertainty. This article provides an overview of the project’s achievements. We highlight new radar techniques: correcting for attenuation of the radar return; correction for beams that are over 90% blocked by trees or towers close to the radar; and direct assimilation of radar reflectivity and refractivity. We discuss the treatment of uncertainty in data assimilation: new methods for estimation of observation uncertainties with novel applications to Doppler radar winds, Atmospheric Motion Vectors, and satellite radiances; a new algorithm for implementation of spatially-correlated observation error statistics in operational data assimilation; and innovative treatment of moist processes in the background error covariance model. We present results indicating a link between the spatial predictability of convection and convective regimes, with potential to allow improved forecast interpretation. The research was carried out as a partnership between University researchers and the Met Office (UK). We discuss the benefits of this approach and the impact of our research, which has helped to improve operational forecasts for convective rainfall events. Full article
(This article belongs to the Special Issue Advances in Applications of Weather Radar Data)
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