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Weather Radar for Hydrological Modelling
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Weather Radar for Hydrological Modelling

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 July 2021) | Viewed by 25730

Special Issue Editors

Dr. Zbyněk Sokol

E-Mail Website
Guest Editor
Institute of Atmospheric Physics, Czech Academy of Sciences, Prague, Czech Republic
Interests: precipitation; precipitation nowcasting; numerical weather prediction models; radar; lightning
Special Issues, Collections and Topics in MDPI journals
Dr. Jan Szturc

E-Mail Website
Guest Editor
Institute of Meteorology and Water Management – National Research Institute, Warsaw, Poland
Interests: weather radar; remote sensing; precipitation; nowcasting
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Weather radars are one of the basic data sources for analysis and forecast of precipitation. They provide measurements of high spatial and temporal resolution, and in conjunction with rain gauge measurements and others they provide a sufficiently accurate estimate of areal precipitation, which is essential to hydrological rainfall-runoff modelling. Weather radar data are irreplaceable especially in the case of precipitation nowcasting, which is based on extrapolation of the current state into the near future. The nowcasts are also valuable as next precipitation input to hydrological models. Moreover, weather radar data and mainly the radar-derived estimates of areal accumulated precipitation are crucial to verification of forecasts given by numerical weather prediction (NWP) models. Last but not least, radar data are used to prepare initial conditions for the models and are also assimilated into NWP models to improve their predictions. Radars measure also the Doppler velocity and often other polarimetric parameters, which makes it possible to determine the movement and the type of hydrometeor. This information can be used in NWP models. Thus, the aim of this special issue is to map the current state and the progress of the use of radar data in both meteorological and hydrological forecasting and modelling.

Dr. Zbyňek Sokol
Dr. Jan Szturc
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.

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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

  • weather radar
  • precipitation
  • precipitation nowcasting
  • hydrological modelling
  • numerical weather forecast

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Published Papers (4 papers)

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Research

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21 pages, 10245 KiB  
Article
Utilization of Weather Radar Data for the Flash Flood Indicator Application in the Czech Republic
by Petr Novák, Hana Kyznarová, Martin Pecha, Petr Šercl, Vojtěch Svoboda and Ondřej Ledvinka
Remote Sens. 2021, 13(16), 3184; https://doi.org/10.3390/rs13163184 - 11 Aug 2021
Cited by 3 | Viewed by 2638
Abstract
In the past few years, demands on flash flood forecasting have grown. The Flash Flood Indicator (FFI) is a system used at the Czech Hydrometeorological Institute for the evaluation of the risk of possible occurrence of flash floods over the whole Czech Republic. [...] Read more.
In the past few years, demands on flash flood forecasting have grown. The Flash Flood Indicator (FFI) is a system used at the Czech Hydrometeorological Institute for the evaluation of the risk of possible occurrence of flash floods over the whole Czech Republic. The FFI calculation is based on the current soil saturation, the physical-geographical characteristics of every considered area, and radar-based quantitative precipitation estimates (QPEs) and forecasts (QPFs). For higher reliability of the flash flood risk assessment, calculations of QPEs and QPFs are crucial, particularly when very high intensities of rainfall are reached or expected. QPEs and QPFs entering the FFI computations are the products of the Czech Weather Radar Network. The QPF is based on the COTREC extrapolation method. The radar-rain gauge-combining method MERGE2 is used to improve radar-only QPEs and QPFs. It generates a combined radar-rain gauge QPE based on the kriging with an external drift algorithm, and, also, an adjustment coefficient applicable to radar-only QPEs and QPFs. The adjustment coefficient is applied in situations when corresponding rain gauge measurements are not yet available. A new adjustment coefficient scheme was developed and tested to improve the performance of adjusted radar QPEs and QPFs in the FFI. Full article
(This article belongs to the Special Issue Weather Radar for Hydrological Modelling)
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18 pages, 4318 KiB  
Article
Assessment of Native Radar Reflectivity and Radar Rainfall Estimates for Discharge Forecasting in Mountain Catchments with a Random Forest Model
by Johanna Orellana-Alvear, Rolando Célleri, Rütger Rollenbeck, Paul Muñoz, Pablo Contreras and Jörg Bendix
Remote Sens. 2020, 12(12), 1986; https://doi.org/10.3390/rs12121986 - 20 Jun 2020
Cited by 15 | Viewed by 3225
Abstract
Discharge forecasting is a key component for early warning systems and extremely useful for decision makers. Forecasting models require accurate rainfall estimations of high spatial resolution and other geomorphological characteristics of the catchment, which are rarely available in remote mountain regions such as [...] Read more.
Discharge forecasting is a key component for early warning systems and extremely useful for decision makers. Forecasting models require accurate rainfall estimations of high spatial resolution and other geomorphological characteristics of the catchment, which are rarely available in remote mountain regions such as the Andean highlands. While radar data is available in some mountain areas, the absence of a well distributed rain gauge network makes it hard to obtain accurate rainfall maps. Thus, this study explored a Random Forest model and its ability to leverage native radar data (i.e., reflectivity) by providing a simplified but efficient discharge forecasting model for a representative mountain catchment in the southern Andes of Ecuador. This model was compared with another that used as input derived radar rainfall (i.e., rainfall depth), obtained after the transformation from reflectivity to rainfall rate by using a local Z-R relation and a rain gauge-based bias adjustment. In addition, the influence of a soil moisture proxy was evaluated. Radar and runoff data from April 2015 to June 2017 were used. Results showed that (i) model performance was similar by using either native or derived radar data as inputs (0.66 < NSE < 0.75; 0.72 < KGE < 0.78). Thus, exhaustive pre-processing for obtaining radar rainfall estimates can be avoided for discharge forecasting. (ii) Soil moisture representation as input of the model did not significantly improve model performance (i.e., NSE increased from 0.66 to 0.68). Finally, this native radar data-based model constitutes a promising alternative for discharge forecasting in remote mountain regions where ground monitoring is scarce and hardly available. Full article
(This article belongs to the Special Issue Weather Radar for Hydrological Modelling)
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24 pages, 5533 KiB  
Article
Quality-Based Combination of Multi-Source Precipitation Data
by Anna Jurczyk, Jan Szturc, Irena Otop, Katarzyna Ośródka and Piotr Struzik
Remote Sens. 2020, 12(11), 1709; https://doi.org/10.3390/rs12111709 - 27 May 2020
Cited by 23 | Viewed by 3898
Abstract
A quantitative precipitation estimate (QPE) provides basic information for the modelling of many kinds of hydro-meteorological processes, e.g., as input to rainfall-runoff models for flash flood forecasting. Weather radar observations are crucial in order to meet the requirements, because of their very high [...] Read more.
A quantitative precipitation estimate (QPE) provides basic information for the modelling of many kinds of hydro-meteorological processes, e.g., as input to rainfall-runoff models for flash flood forecasting. Weather radar observations are crucial in order to meet the requirements, because of their very high temporal and spatial resolution. Other sources of precipitation data, such as telemetric rain gauges and satellite observations, are also included in the QPE. All of the used data are characterized by different temporal and spatial error structures. Therefore, a combination of the data should be based on quality information quantitatively determined for each input to take advantage of a particular source of precipitation measurement. The presented work on multi-source QPE, being implemented as the RainGRS system, has been carried out in the Polish national meteorological and hydrological service for new nowcasting and hydrological platforms in Poland. For each of the three data sources, different quality algorithms have been designed: (i) rain gauge data is quality controlled and, on this basis, spatial interpolation and estimation of quality field is performed, (ii) radar data are quality controlled by RADVOL-QC software that corrects errors identified in the data and characterizes its final quality, (iii) NWC SAF (Satellite Application Facility on support to Nowcasting and Very Short Range Forecasting) products for both visible and infrared channels are combined and the relevant quality field is determined from empirical relationships that are based on analyses of the product performance. Subsequently, the quality-based QPE is generated with a 1-km spatial resolution every 10 minutes (corresponding to radar data). The basis for the combination is a conditional merging technique that is enhanced by involving detailed quality information that is assigned to individual input data. The validation of the RainGRS estimates was performed taking account of season and kind of precipitation. Full article
(This article belongs to the Special Issue Weather Radar for Hydrological Modelling)
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Review

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37 pages, 2904 KiB  
Review
The Role of Weather Radar in Rainfall Estimation and Its Application in Meteorological and Hydrological Modelling—A Review
by Zbyněk Sokol, Jan Szturc, Johanna Orellana-Alvear, Jana Popová, Anna Jurczyk and Rolando Célleri
Remote Sens. 2021, 13(3), 351; https://doi.org/10.3390/rs13030351 - 20 Jan 2021
Cited by 88 | Viewed by 14509
Abstract
Radar-based rainfall information has been widely used in hydrological and meteorological applications, as it provides data with a high spatial and temporal resolution that improve rainfall representation. However, the broad diversity of studies makes it difficult to gather a condensed overview of the [...] Read more.
Radar-based rainfall information has been widely used in hydrological and meteorological applications, as it provides data with a high spatial and temporal resolution that improve rainfall representation. However, the broad diversity of studies makes it difficult to gather a condensed overview of the usefulness and limitations of radar technology and its application in particular situations. In this paper, a comprehensive review through a categorization of radar-related topics aims to provide a general picture of the current state of radar research. First, the importance and impact of the high temporal resolution of weather radar is discussed, followed by the description of quantitative precipitation estimation strategies. Afterwards, the use of radar data in rainfall nowcasting as well as its role in preparation of initial conditions for numerical weather predictions by assimilation is reviewed. Furthermore, the value of radar data in rainfall-runoff models with a focus on flash flood forecasting is documented. Finally, based on this review, conclusions of the most relevant challenges that need to be addressed and recommendations for further research are presented. This review paper supports the exploitation of radar data in its full capacity by providing key insights regarding the possibilities of including radar data in hydrological and meteorological applications. Full article
(This article belongs to the Special Issue Weather Radar for Hydrological Modelling)
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