Special Issue "Radar Hydrology and QPE Uncertainties"

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

Deadline for manuscript submissions: 31 May 2020.

Special Issue Editors

Dr. Youcun Qi
E-Mail
Guest Editor
Institute of Geographic Sciences and Natural Resources Research, CAS
Special Issues and Collections in MDPI journals
Dr. Bong-Chul Seo
E-Mail Website
Guest Editor
University of Iowa, IIHR-Hydroscience and Engineering, Iowa City, United States
Dr. Gyuwon Lee
E-Mail
Guest Editor
Kyungpook National University

Special Issue Information

Dear Colleagues,

Precipitation is a main driver of numerous hydrologic processes, and the collection of reliable precipitation information with a proper space and time scale is a key element of water resources management. For several decades, researchers have used weather radars for weather monitoring, which has provided quantitative rainfall information with the enhanced space and time resolutions required for hydrologic and meteorological applications. These quantitative precipitation estimates (QPE) have become a primary dataset that helps to represent the complexities associated with the space and time variability of hydrologic processes through distributed modeling procedures. However, radar QPE often leads to undesirable errors in hydrologic simulation/prediction because of the uncertainty associated with radar sampling geometry and many other factors.

The goal of this Special Issue is to embrace and connect a variety of established and ongoing yet scattered research activities regarding precipitation estimation and subsequent hydrologic applications, as well as the uncertainty characterization of precipitation estimates and its propagation through hydrologic modeling procedures. We encourage contributions from the following topics:

  • Radar-based precipitation estimation and analysis;
  • Radar-based hydrologic modeling and forecasting;
  • Analysis of precipitation estimation uncertainties associated with (space and time) scale-dependent variability;
  • Spatiotemporal modeling of precipitation estimation uncertainties;
  • Radar-based short-term precipitation forecasting and data assimilation;
  • Radar-based analysis of extreme events;
  • Urban hydrologic applications using weather radar;
  • Probabilistic (e.g., ensemble) approaches in radar-based hydrologic applications;
  • Data-intensive techniques (e.g., machine learning) in precipitation estimation and hydrologic applications;
  • Time and space variability of precipitation and hydrologic processes

Dr. Youcun Qi
Dr. Bong-Chul Seo
Dr. Gyuwon Lee
Guest Editors

Manuscript Submission Information

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Keywords

  • precipitation
  • weather radar
  • QPE
  • hydrology
  • distributed model
  • flood forecasting
  • uncertainty modeling
  • scale

Published Papers (4 papers)

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Research

Open AccessArticle
Radar-Based Precipitation Climatology in Germany—Developments, Uncertainties and Potentials
Atmosphere 2020, 11(2), 217; https://doi.org/10.3390/atmos11020217 - 21 Feb 2020
Abstract
Precipitation is a crucial driver for many environmental processes and weather radars are capable of providing precipitation information with high spatial and temporal resolution. However, radar-based quantitative precipitation estimates (QPE) are also subject to various potential uncertainties. This study explored the development, uncertainties [...] Read more.
Precipitation is a crucial driver for many environmental processes and weather radars are capable of providing precipitation information with high spatial and temporal resolution. However, radar-based quantitative precipitation estimates (QPE) are also subject to various potential uncertainties. This study explored the development, uncertainties and potentials of the hourly operational German radar-based and gauge-adjusted QPE called RADOLAN and its reanalyzed radar climatology dataset named RADKLIM in comparison to ground-truth rain gauge data. The precipitation datasets were statistically analyzed across various time scales ranging from annual and seasonal aggregations to hourly rainfall intensities in regard to their capability to map long-term precipitation distribution, to detect low intensity rainfall and to capture heavy rainfall. Moreover, the impacts of season, orography and distance from the radar on long-term precipitation sums were examined in order to evaluate dataset performance and to describe inherent biases. Results revealed that both radar products tend to underestimate total precipitation sums and particularly high intensity rainfall. However, our analyses also showed significant improvements throughout the RADOLAN time series as well as major advances through the climatologic reanalysis regarding the correction of typical radar artefacts, orographic and winter precipitation as well as range-dependent attenuation. Full article
(This article belongs to the Special Issue Radar Hydrology and QPE Uncertainties)
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Open AccessArticle
Assimilation of Radar Data, Pseudo Water Vapor, and Potential Temperature in a 3DVAR Framework for Improving Precipitation Forecast of Severe Weather Events
Atmosphere 2020, 11(2), 182; https://doi.org/10.3390/atmos11020182 - 09 Feb 2020
Abstract
An improved approach to derive pseudo water vapor mass mixing ratio and in- cloud potential temperature was developed in this paper to better initialize numerical weather prediction (NWP) and build convective-scale predictions of severe weather events. The process included several steps. The first [...] Read more.
An improved approach to derive pseudo water vapor mass mixing ratio and in- cloud potential temperature was developed in this paper to better initialize numerical weather prediction (NWP) and build convective-scale predictions of severe weather events. The process included several steps. The first was to identify areas of deep moist convection, utilizing Vertically Integrated Liquid water (VIL) derived from a mosaicked 3D radar reflectivity field. Then, pseudo- water vapor and pseudo- in- cloud potential temperature observations were derived based on the VIL. For potential temperature, the latent heat initialization for stratiform cloud and moist adiabatic initialization for deep moist convection were used based on a cloud analysis method. The third step was to assimilate the derived pseudo- water vapor and potential temperature observations, together with radar radial velocity and reflectivity into a convective-scale NWP model during data assimilation cycles spanning several hours. Finally, 3-h forecasts were launched each hour during the data assimilation period. The effects of radar data and pseudo- observation assimilation on the prediction of rainfall associated with convective systems surrounding the Meiyu front in 2018 were explored using two real cases. Two sets of experiments, each including several experiments in each real case, were designed to compare the effects of assimilation radar and pseudo- observations on the ensuing forecasts. Relative to the control experiment without data assimilation and radar experiment, the analyses and forecasts of convections were found to be improved for the two Meiyu front cases after pseudo- water vapor and potential temperature information was assimilated. Full article
(This article belongs to the Special Issue Radar Hydrology and QPE Uncertainties)
Open AccessArticle
The Development of a Quantitative Precipitation Forecast Correction Technique Based on Machine Learning for Hydrological Applications
Atmosphere 2020, 11(1), 111; https://doi.org/10.3390/atmos11010111 - 16 Jan 2020
Abstract
This study aimed to enhance the accuracy of extreme rainfall forecast, using a machine learning technique for forecasting hydrological impact. In this study, machine learning with XGBoost technique was applied for correcting the quantitative precipitation forecast (QPF) provided by the Korea Meteorological Administration [...] Read more.
This study aimed to enhance the accuracy of extreme rainfall forecast, using a machine learning technique for forecasting hydrological impact. In this study, machine learning with XGBoost technique was applied for correcting the quantitative precipitation forecast (QPF) provided by the Korea Meteorological Administration (KMA) to develop a hydrological quantitative precipitation forecast (HQPF) for flood inundation modeling. The performance of machine learning techniques for HQPF production was evaluated with a focus on two cases: one for heavy rainfall events in Seoul and the other for heavy rainfall accompanied by Typhoon Kong-rey (1825). This study calculated the well-known statistical metrics to compare the error derived from QPF-based rainfall and HQPF-based rainfall against the observational data from the four sites. For the heavy rainfall case in Seoul, the mean absolute errors (MAE) of the four sites, i.e., Nowon, Jungnang, Dobong, and Gangnam, were 18.6 mm/3 h, 19.4 mm/3 h, 48.7 mm/3 h, and 19.1 mm/3 h for QPF and 13.6 mm/3 h, 14.2 mm/3 h, 33.3 mm/3 h, and 12.0 mm/3 h for HQPF, respectively. These results clearly indicate that the machine learning technique is able to improve the forecasting performance for localized rainfall. In addition, the HQPF-based rainfall shows better performance in capturing the peak rainfall amount and spatial pattern. Therefore, it is considered that the HQPF can be helpful to improve the accuracy of intense rainfall forecast, which is subsequently beneficial for forecasting floods and their hydrological impacts. Full article
(This article belongs to the Special Issue Radar Hydrology and QPE Uncertainties)
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Open AccessArticle
Relationship between Rainfall Variability and the Predictability of Radar Rainfall Nowcasting Models
Atmosphere 2019, 10(8), 458; https://doi.org/10.3390/atmos10080458 - 12 Aug 2019
Abstract
Radar rainfall nowcasts are subject to many sources of uncertainty and these uncertainties change with the characteristics of a storm. The predictive skill of a radar rainfall nowcasting model can be difficult to understand as sometimes it appears to be perfect but at [...] Read more.
Radar rainfall nowcasts are subject to many sources of uncertainty and these uncertainties change with the characteristics of a storm. The predictive skill of a radar rainfall nowcasting model can be difficult to understand as sometimes it appears to be perfect but at other times it is highly inaccurate. This hinders the decision making required for the early warning of natural hazards caused by rainfall. In this study we define radar spatial and temporal rainfall variability and relate them to the predictive skill of a nowcasting model. The short-term ensemble prediction system model is configured to predict 731 events with lead times of one, two, and three hours. The nowcasting skill is expressed in terms of six well-known indicators. The results show that the quality of radar rainfall nowcasts increases with the rainfall autocorrelation and decreases with the rainfall variability coefficient. The uncertainty of radar rainfall nowcasts also shows a positive connection with rainfall variability. In addition, the spatial variability is more important than the temporal variability. Based on these results, we recommend that the lead time for radar rainfall nowcasting models should change depending on the storm and that it should be determined according to the rainfall variability. Such measures could improve trust in the rainfall nowcast products that are used for hydrological and meteorological applications. Full article
(This article belongs to the Special Issue Radar Hydrology and QPE Uncertainties)
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