Merging WSR-88D stage III radar rainfall data with rain gauge measurements using wavelet analysis

Kalinga, Oscar Anthony; Gan, Thian Yew

doi:10.1080/01431161.2010.550641

Cited by 5 publications

(2 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For this method, the OK interpolation results of ground measurements and radar rainfall are respectively used as the precipitation trend component and error component, and the superposition of the two is used as the analysis result. Based on the idea of image processing, Kalinga [163] used wavelet decomposition and reconstruction to realize the merging of radar and gauge rainfall.…”

Section: Ma Et Al (2018) [32] Pdmmentioning

confidence: 99%

Rainfall Spatial Estimations: A Review from Spatial Interpolation to Multi-Source Data Merging

Wang

et al. 2019

Water

105

View full text Add to dashboard Cite

Rainfall is one of the most basic meteorological and hydrological elements. Quantitative rainfall estimation has always been a common concern in many fields of research and practice, such as meteorology, hydrology, and environment, as well as being one of the most important research hotspots in various fields nowadays. Due to the development of space observation technology and statistics, progress has been made in rainfall quantitative spatial estimation, which has continuously deepened our understanding of the water cycle across different space-time scales. In light of the information sources used in rainfall spatial estimation, this paper summarized the research progress in traditional spatial interpolation, remote sensing retrieval, atmospheric reanalysis rainfall, and multi-source rainfall merging since 2000. However, because of the extremely complex spatiotemporal variability and physical mechanism of rainfall, it is still quite challenging to obtain rainfall spatial distribution with high quality and resolution. Therefore, we present existing problems that require further exploration, including the improvement of interpolation and merging methods, the comprehensive evaluation of remote sensing, and the reanalysis of rainfall data and in-depth application of non-gauge based rainfall data.

show abstract

Section: Ma Et Al (2018) [32] Pdmmentioning

confidence: 99%

Rainfall Spatial Estimations: A Review from Spatial Interpolation to Multi-Source Data Merging

Wang

et al. 2019

Water

105

View full text Add to dashboard Cite

show abstract

“…Especially in radar meteorology, as the past decades have witnessed the rapid development of the polarimetric weather radar technique, rain rate estimation from radar polarization parameters has been investigated extensively and intensively [3][4][5]. The radar polarization parameters, such as differential reflectivity (Z DR ), linear reflectivity difference (Z DP ), as well as specific differential phase (K DP ), for improving rain rate estimation can be successfully measured [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

GNSS Measurement of Rain Rate by Polarimetric Phase Shift: Theoretical Analysis

Yan

Huang

et al. 2016

Atmosphere

View full text Add to dashboard Cite

Abstract:In this paper, a novel method for rain rate estimation is researched by polarimetric phase shift of the Global Navigation Satellite System (GNSS). The physical process of GNSS signals propagating through rain-filled medium is investigated, by which the cause of polarimetric phase shift is explored. Then, a theoretical model between polarimetric phase shift ∆φ and rain rate R is established and simulated, which is based on the oblate spheroid raindrop model, four different popular raindrop size distribution models and raindrop canting angle distribution across the Space-Earth rain path. Additionally, effects of raindrop size distribution, rain path length, raindrop canting angle and temperature on the ∆φ-R relation are discussed systematically. Other factors in the slant path such as ice crystals, melting particles and ionosphere are also researched preliminarily. The results show that polarimetric phase shift of GNSS signals, which has a strong correlation with rain rate, can be used to estimate the rain rate, and these influencing factors, raindrop size distribution, rain path length, raindrop canting angle and temperature, are quite important in the process of rain rate measurement. It can be also found that the effect of ice crystals can be negligible, while that of melting particles should be considered, and though ionosphere effects are not obvious, the ionospheric anomalies cannot be neglected in future experiments. This method has potential applications in real-time, continuous, extreme precipitation reconnaissance and numerical weather prediction.

show abstract

Improving radar rainfall estimation by merging point rainfall measurements within a model combination framework

Hasan

Sharma

Mariethoz

et al. 2016

Advances in Water Resources

View full text Add to dashboard Cite

Merging WSR-88D stage III radar rainfall data with rain gauge measurements using wavelet analysis

Cited by 5 publications

References 35 publications

Rainfall Spatial Estimations: A Review from Spatial Interpolation to Multi-Source Data Merging

Rainfall Spatial Estimations: A Review from Spatial Interpolation to Multi-Source Data Merging

GNSS Measurement of Rain Rate by Polarimetric Phase Shift: Theoretical Analysis

Improving radar rainfall estimation by merging point rainfall measurements within a model combination framework

Contact Info

Product

Resources

About