Application of Remote Sensing Techniques for Estimation and Nowcasting of Heavy Rainfall

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 December 2022) | Viewed by 5578

Special Issue Editors

Faculty of Building Services, Hydro and Environmental Engineering, Warsaw University of Technology, 00-661 Warsaw, Poland
Interests: remote sensing; rainfall-runoff modeling; soil moisture; climate change; hydrology; spatial analysis and statistics; sustainable development; GIS
Special Issues, Collections and Topics in MDPI journals
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,

Remote sensing techniques are one of the most efficient methods for the estimation and nowcasting of precipitation. They can provide reliable information on the distribution of precipitation in space and time, in contrast to rain gauge data, which cover only point location. Accurate observations of precipitation are especially important for heavy rainfall, which may cause flash floods. Recent advances in remote sensing techniques related to improving algorithms applied in weather radars and introducing new satellite missions dedicated to precipitation estimations (i.a., GPM mission) take these data to the next level in terms of their reliability. Even though a significant improvement has been made, there is still a considerable dose of uncertainty in these estimates, which requires investigation. In conjunction with hydrological models, accurately estimated precipitation from remote sensing techniques can be a great tool to model discharge in river bodies and flood extent.

This Special Issue invites contributions of ground and satellite remote sensing techniques used to estimate and forecast heavy rainfall. Submissions are encouraged to cover a broad range of remote sensing science, which may include, but is not limited to, the following activities:

  • Improvement of algorithms used in remote sensing techniques for estimation of heavy rainfall;
  • Investigation of uncertainties in acquired precipitation data;
  • Assimilation of auxiliary data to improve the quality of precipitation estimates;
  • Application of remote sensing precipitation data for hydrological modeling of flash flood events.

Dr. Paweł Gilewski
Dr. Jan Szturc
Guest Editors

Manuscript Submission Information

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Keywords

  •  precipitation
  •  precipitation nowcasting
  •  remote sensing
  •  heavy rainfall
  •  hydrological modeling

Published Papers (4 papers)

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Research

18 pages, 6887 KiB  
Article
Feasibility of Downscaling Satellite-Based Precipitation Estimates Using Soil Moisture Derived from Land Surface Temperature
Atmosphere 2023, 14(3), 435; https://doi.org/10.3390/atmos14030435 - 22 Feb 2023
Cited by 2 | Viewed by 864
Abstract
For many areas, satellite-based precipitation products or reanalysis model data represent the only available precipitation information. Unfortunately, the resolution of these datasets is generally too coarse for many applications. A very promising downscaling approach is to use soil moisture due to its clear [...] Read more.
For many areas, satellite-based precipitation products or reanalysis model data represent the only available precipitation information. Unfortunately, the resolution of these datasets is generally too coarse for many applications. A very promising downscaling approach is to use soil moisture due to its clear physical connection to precipitation. We investigate the feasibility of using soil moisture derived from land surface temperature in this context. These data are more widely available in the required resolution compared to other soil moisture data. Rain gauge-adjusted radar data from Namoi serves as a spatial reference dataset for two objectives: to identify the most suitable globally available precipitation dataset and to explore the precipitation information contained in the soil moisture data. The results show that these soil moisture data cannot be used to downscale satellite-based precipitation data to a high resolution because of cloud cover interference. Therefore, the Integrated Multi-satellitE Retrievals for GPM (IMERG) late data represents the best precipitation dataset for many areas in Australia that require timely precipitation information, according to this study. Full article
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31 pages, 9352 KiB  
Article
Harmonic Analysis of the Relationship between GNSS Precipitable Water Vapor and Heavy Rainfall over the Northwest Equatorial Coast, Andes, and Amazon Regions
Atmosphere 2022, 13(11), 1809; https://doi.org/10.3390/atmos13111809 - 31 Oct 2022
Cited by 2 | Viewed by 1422
Abstract
This study finds the relationship between increases in precipitable water vapor (PWV), and intense rainfall events in four different climatological regions of South America’s equatorial northwest: the coast, Andes valley, high mountains, and Amazon. First, the PWV was derived from tropospheric zenith delay [...] Read more.
This study finds the relationship between increases in precipitable water vapor (PWV), and intense rainfall events in four different climatological regions of South America’s equatorial northwest: the coast, Andes valley, high mountains, and Amazon. First, the PWV was derived from tropospheric zenith delay measured by Global Navigation Satellite System (GNSS) instrumentation located near meteorological stations within the regions of interest using hourly data from the year 2014. A harmonic analysis approach through continuous wavelet cross-spectrum and coherence, as well as discrete wavelets, was used to determine a measure of the lags found between PWV and specific heavy rain events and then compared with satellite IR images and meteorological anomalies. The link between PWV peaks and rainfall was the most evident on the coast, and less discernible in the other stations possibly due to local dynamic factors. The results showed a lag of 11 h between the preceding PWV increase and an intense rainfall event. This was apparent in all of the stations, except in Amazon where it was 6 h, with the highest precision at the coast and with the largest dispersion in the high mountains. The interpretation of this lag for each region is also discussed. Full article
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14 pages, 2377 KiB  
Article
Application of Global Environmental Multiscale (GEM) Numerical Weather Prediction (NWP) Model for Hydrological Modeling in Mountainous Environment
Atmosphere 2022, 13(9), 1348; https://doi.org/10.3390/atmos13091348 - 24 Aug 2022
Cited by 3 | Viewed by 1203
Abstract
As the world is changing, mainly due to climate change, extreme events such as floods and droughts are becoming more frequent and severe. Considering this, the predictive modeling of flow in small mountain catchments that are particularly vulnerable to flooding is critical. Rainfall [...] Read more.
As the world is changing, mainly due to climate change, extreme events such as floods and droughts are becoming more frequent and severe. Considering this, the predictive modeling of flow in small mountain catchments that are particularly vulnerable to flooding is critical. Rainfall data sources such as rain gauges, meteorological radars, and satellites provide data to the hydrological model with a lag. Only numerical weather predictions can achieve this in advance, but their estimates are often subject to considerable uncertainty. This article aims to verify whether Global Environmental Multiscale numerical precipitation prediction can be successfully applied for event-based rainfall–runoff hydrological modeling. These data were verified for use in two aspects: the flow modeling and determination of antecedent moisture conditions. The results indicate that GEM data can be satisfactorily used for hydrological modeling, and particularly good simulation results are obtained when significant rainfall occurs. In addition, these data can be used to correctly estimate the AMC groups for each sub-catchment in advance, which is one of the key elements flowing into the amount of projected outflow in the catchment. It is worth noting that, according to the literature review conducted by the article’s author, this is the first published attempt to use GEM data directly in applied hydrological applications. Full article
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28 pages, 7190 KiB  
Article
Modeling the Interdependence Structure between Rain and Radar Variables Using Copulas: Applications to Heavy Rainfall Estimation by Weather Radar
Atmosphere 2022, 13(8), 1298; https://doi.org/10.3390/atmos13081298 - 15 Aug 2022
Cited by 1 | Viewed by 1384
Abstract
In radar quantitative precipitation estimates (QPE), the progressive evolution of rainfall algorithms has been guided by attempts to reduce the uncertainties in rainfall retrieval. However, because most of the algorithms are based on the linear dependence between radar and rain variables and designed [...] Read more.
In radar quantitative precipitation estimates (QPE), the progressive evolution of rainfall algorithms has been guided by attempts to reduce the uncertainties in rainfall retrieval. However, because most of the algorithms are based on the linear dependence between radar and rain variables and designed for rain rates ranging from light to moderate rainfall, they result in misleading estimations of intense or strong rainfall rates. In this paper, based on extensive data gathered during the AMMA and Megha-Tropiques data campaigns, we provided a way to improve the estimation of intense rainfall rates from radar measurements. To this end, we designed a formulation of the QPE algorithm that accounts for the co-dependency between radar observables and rainfall rate using copula simulation synthetic datasets and using the quantile regression features for a more complete picture of covariate effects. The results show a clear improvement in heavy rainfall retrieval from radar data using copula-based R(KDP) algorithms derived from a realistic simulated dataset. For a better performance, Gaussian copula-derived algorithms require a 0.8 percentile distribution to be considered. Conversely, lower percentiles are better for Student’s, Gumbel and HRT copula estimators when retrieving heavy rainfall rates (R > 30). This highlights the need to investigate the entire conditional distribution to determine the performance of radar rainfall estimators. Full article
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