Methodology for Aligning and Comparing Spaceborne Radar and Ground-Based Radar Observations

Bolen, S.; Chandrasekar, V.

doi:10.1175/1520-0426(2003)20<647:mfaacs>2.0.co;2

Cited by 63 publications

(50 citation statements)

References 2 publications

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“…The problem of radar alignment is also present in meteorology where space radar and ground radar observations are aligned to get a more complete picture of the weather in [30]. The scans are aligned by dividing them into smaller volumes that are matched by their respective precipitation intensities.…”

Section: Background and Relation To Slammentioning

confidence: 99%

Radar SLAM using visual features

Callmer

Törnqvist

Gustafsson

et al. 2011

EURASIP J. Adv. Signal Process.

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A vessel navigating in a critical environment such as an archipelago requires very accurate movement estimates. Intentional or unintentional jamming makes GPS unreliable as the only source of information and an additional independent supporting navigation system should be used. In this paper, we suggest estimating the vessel movements using a sequence of radar images from the preexisting body-fixed radar. Island landmarks in the radar scans are tracked between multiple scans using visual features. This provides information not only about the position of the vessel but also of its course and velocity. We present here a navigation framework that requires no additional hardware than the already existing naval radar sensor. Experiments show that visual radar features can be used to accurately estimate the vessel trajectory over an extensive data set.

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Section: Background and Relation To Slammentioning

confidence: 99%

Radar SLAM using visual features

Callmer

Törnqvist

Gustafsson

et al. 2011

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

show abstract

“…The specific differential phase at S-band can be written as [Bringi and Chandrasekar, 2001] K dp ¼ 180l…”

Section: Polarimetric Radar Observablesmentioning

confidence: 99%

“…The canting angle distribution for rain was fairly narrow, but for graupel/hail was broad to model tumbling and random orientation. These are, again, consistent with the literature [Bringi and Chandrasekar, 2001].…”

Section: Theoretical Modelmentioning

confidence: 99%

“…where s 2 is the variance in the canting angle [Bringi and Chandrasekar, 2001] and F 3 is a function of m and D o . Thus it can be seen that K dp can be reduced by the variance of canting angle distribution, or decreasing the mean axis ratio.…”

Section: Theoretical Modelmentioning

confidence: 99%

“…From experience, it is found that the correlation between space and ground radar reflectivity measurements can increase significantly when both volume matching and spatial alignment is performed. Additional details of the volume matching and alignment method is given by Bolen and Chandrasekar [2003].…”

Section: Ground and Space Radar Data Matchingmentioning

confidence: 99%

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Quantitative estimation of Tropical Rainfall Mapping Mission precipitation radar signals from ground‐based polarimetric radar observations

Bolen

Chandrasekar

2003

Radio Science

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[1] The Tropical Rainfall Mapping Mission (TRMM) is the first mission dedicated to measuring rainfall from space using radar. The precipitation radar (PR) is one of several instruments aboard the TRMM satellite that is operating in a nearly circular orbit with nominal altitude of 350 km, inclination of 35°, and period of 91.5 min. The PR is a singlefrequency K u -band instrument that is designed to yield information about the vertical storm structure so as to gain insight into the intensity and distribution of rainfall. Attenuation effects on PR measurements, however, can be significant and as high as 10-15 dB. This can seriously impair the accuracy of rain rate retrieval algorithms derived from PR signal returns. Quantitative estimation of PR attenuation is made along the PR beam via ground-based polarimetric observations to validate attenuation correction procedures used by the PR. The reflectivity (Z h ) at horizontal polarization and specific differential phase (K dp ) are found along the beam from S-band ground radar measurements, and theoretical modeling is used to determine the expected specific attenuation (k) along the space-Earth path at K u -band frequency from these measurements. A theoretical k-K dp relationship is determined for rain when K dp ! 0.5°/km, and a power law relationship, k = a Z h b , is determined for light rain and other types of hydrometers encountered along the path. After alignment and resolution volume matching is made between ground and PR measurements, the two-way path-integrated attenuation (PIA) is calculated along the PR propagation path by integrating the specific attenuation along the path. The PR reflectivity derived after removing the PIA is also compared against ground radar observations.

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Comparison of rainfall estimates from GPM dual-frequency precipitation radar and ground dual-polarization radar

Kou

Mao

Wang

et al. 2021

Preprint

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The dual-frequency precipitation radar (DPR) onboard the Global Precipitation Measurement (GPM) core satellite can provide information on drop size distribution (DSD) to improve rainfall estimation. The ground-based dual-polarization radar has great advantages for rainfall estimation, owing to the greater accessibility to information about the DSD and hydrometeor type. In this study, the three-dimensional rainfall products from DPR, with normal scan (NS), matched scan (MS), and high sensitivity scan (HS) mode, and C-band dual-polarization radar (CDP) were compared based on the volume matching algorithm and hydrometeor identification. The reliability of CDP rainfall and DSD parameter estimation for liquid samples was evaluated using rain gauge and disdrometer data. Rainfall relations for non-liquid samples for CDP were obtained via scattering simulation. An intercomparison of reflectivity revealed correlations of more than 0.8 for all three DPR scanning modes for stratiform and convective precipitation. Rainfall comparison performance of the MS mode was slightly better than that of the NS mode for liquid samples, especially for convective precipitation, which may be attributed to MS mode having the best consistency of mass-weighted mean diameter estimation. The HS mode showed good agreement, with respect to stratiform rainfall, but poor agreement, with respect to convective rainfall. For non-liquid samples, the biases were within 0.8 mm/h. The NS mode showed the best agreement, followed by the HS mode; however, the consistency was worse than that for liquid samples. Given the different physical characteristics of hydrometeors, our findings highlight the importance of rainfall estimation based on hydrometeor phases.

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Methodology for Aligning and Comparing Spaceborne Radar and Ground-Based Radar Observations

Cited by 63 publications

References 2 publications

Radar SLAM using visual features

Radar SLAM using visual features

Quantitative estimation of Tropical Rainfall Mapping Mission precipitation radar signals from ground‐based polarimetric radar observations

Comparison of rainfall estimates from GPM dual-frequency precipitation radar and ground dual-polarization radar

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