Retrieving wind speed from rain-contaminated X-band nautical radar images

2015 ◽  
Author(s):  
Weimin Huang ◽  
Eric W. Gill
Keyword(s):  
Wind Speed ◽  
Radar Images ◽  
X Band

scholarly journals Visibility of River Plume Fronts with an X-Band Radar

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Satoshi Takewaka
Keyword(s):  
Wind Speed ◽  
Water Level ◽  
River Plume ◽  
Field Observations ◽  
Local Wind ◽  
Radar Images ◽  
Optical Images ◽  
X Band ◽  
Wave Heights ◽  
Radar Measurements

A land-based X-band radar was employed to observe river plume fronts at the mouth of the Tenryu River, Japan. Time-averaged radar images captured fronts extending offshore from the river’s mouth as bright streaks. Comparisons between satellite optical images and radar images confirm that streaky features in the radar image represent color river plume fronts. Further corroboration comes from field observations of water temperature, salinity, and turbidity conducted simultaneously with the radar measurements. When a survey ship crossed the front, the measured properties varied discontinuously, suggesting that water from the river and sea converged there and also that a downwards current was present. Variation of visibility of the fronts was assessed and compared with the rate of variation of water level and the wind speed and direction. The radar is able to image fronts when the water level is decreasing during ebb tide and the wind speed is over 3 m/s along shore. Surface ripple waves are generated by the local wind, and if they propagate across the front, wave heights increase, causing higher backscatter of the emitted radar beam. This observation gives further evidence on the imaging mechanism of river plume fronts with X-band radars in relation to wind direction.

Wind speed determination from X-band nautical radar images

2017 ◽  
Author(s):  
Xinlong Liu ◽  
Weimin Huang ◽  
Eric W. Gill
Keyword(s):  
Wind Speed ◽  
Radar Images ◽  
X Band

scholarly journals Signature of Arctic first-year ice melt pond fraction in X-band SAR imagery

2017 ◽  
Vol 11 (2) ◽  
pp. 755-771 ◽  
Author(s):  
Ane S. Fors ◽  
Dmitry V. Divine ◽  
Anthony P. Doulgeris ◽  
Angelika H. H. Renner ◽  
Sebastian Gerland
Keyword(s):  
Wind Speed ◽  
Correlation Coefficient ◽  
First Year ◽  
Backscatter Signal ◽  
Noise Floor ◽  
Ice Melt ◽  
Wind Speeds ◽  
Melt Pond ◽  
Melt Ponds ◽  
X Band

Abstract. In this paper we investigate the potential of melt pond fraction retrieval from X-band polarimetric synthetic aperture radar (SAR) on drifting first-year sea ice. Melt pond fractions retrieved from a helicopter-borne camera system were compared to polarimetric features extracted from four dual-polarimetric X-band SAR scenes, revealing significant relationships. The correlations were strongly dependent on wind speed and SAR incidence angle. Co-polarisation ratio was found to be the most promising SAR feature for melt pond fraction estimation at intermediate wind speeds (6. 2 m s−1), with a Spearman's correlation coefficient of 0. 46. At low wind speeds (0. 6 m s−1), this relation disappeared due to low backscatter from the melt ponds, and backscatter VV-polarisation intensity had the strongest relationship to melt pond fraction with a correlation coefficient of −0. 53. To further investigate these relations, regression fits were made both for the intermediate (R2fit = 0. 21) and low (R2fit = 0. 26) wind case, and the fits were tested on the satellite scenes in the study. The regression fits gave good estimates of mean melt pond fraction for the full satellite scenes, with less than 4 % from a similar statistics derived from analysis of low-altitude imagery captured during helicopter ice-survey flights in the study area. A smoothing window of 51 × 51 pixels gave the best reproduction of the width of the melt pond fraction distribution. A considerable part of the backscatter signal was below the noise floor at SAR incidence angles above  ∼  40°, restricting the information gain from polarimetric features above this threshold. Compared to previous studies in C-band, limitations concerning wind speed and noise floor set stricter constraints on melt pond fraction retrieval in X-band. Despite this, our findings suggest new possibilities in melt pond fraction estimation from X-band SAR, opening for expanded monitoring of melt ponds during melt season in the future.

Evaluation of micro physical products of GPM/DPR with X-band radar network data

2021 ◽  
Author(s):  
Nobuhiro Takahashi ◽  
Takeharu Kouketsu
Keyword(s):  
Wind Speed ◽  
Three Dimensional ◽  
Vertical Wind ◽  
Network Data ◽  
Dual Frequency ◽  
Convective Storm ◽  
Radar Rainfall ◽  
Radar Network ◽  
X Band ◽  
Vertical Wind Speed

<p>One of the major characteristics of dual-frequency precipitation radar (DPR) onboard Global Precipitation Measurement (GPM) core satellite, is estimation of cloud physical properties of precipitation such as drop size distribution (DSD), existence of hail/graupel particles and possibly the mixed phase region above freezing height.  In this study, ground-based X-band radar network data are utilized for evaluate the cloud physical products from GPM/DPR.  The X-band radar network, composed of 39 X-band dual polarimetric radars developed by the Ministry of Land, Infrastructure, Transport and Tourism (MLIT) of Japan, called XRAIN[1] is utilized for the evaluation.  The XRAIN radar completes volume scan up to the elevation angle of 20 degrees in 5 minutes.  By using multiple radars, three dimensional wind field is estimated by using the dual-Doppler analysis technique.　In this analysis DSD parameter from DPR (which is called epsilon in DPR product) and dual frequency ratio (DFR) that correlate well median diameter of DSD are compared with ZDR and KDP from XRAIN data.  The vertical wind data from XRAIN is utilized to characterize the Z of DPR. The case on August 27, 2018, on which GPM satellite flew over a hail producing convective storm around Tokyo, is analyzed.  Comparison of three dimensional structure of the storm between KuPR (Ku-band radar of DPR) and XRAIN from multiple radar observations shows that both observations are quite similar each other except for the KuPR observation show rather larger volume because of the larger footprint size.  At the rain region (below freezing height), the DSD parameter of DPR (epsilon) and DFR correlate well with ZDR and KDP from XRAIN, respectively.  This result indicates the DPR algorithm works well to estimate the DSD information of rain.  The comparison of Z with vertical wind speed indicates that the higher Z is characterized as higher variance of vertical wind speed. Above the freezing height, the relationship between both observations are complicated.  This result indicates that the various types of precipitation particles not only solid particles but also liquid/mixed phase particle can exist in the severe convective storm.  The hydrometeor type classification from XRAIN by using the method by Kouketsu et al. (2015) [2] confirms that the various types of precipitation exist in this case.</p><p>References</p><p>[1] Tsuchiya, S., M. Kawasaki, H. Godo, 2015: Improvement of the radar rainfall accuracy of XRAIN by modifying of rainfall attenuation correction and compositing radar rainfall, Journal of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering), 2015, Volume 71, Issue 4, pp. I_457-I_462 (in Japanese with English abstract).</p><p>[2] Kouketsu, T., Uyeda, H., Ohigashi, T., Oue, M., Takeuchi, H., Shinoda, T., Tsuboki, K., Kubo, M., and Muramoto, K., 2015: A Hydrometeor Classification Method for X-Band Polarimetric Radar: Construction and Validation Focusing on Solid Hydrometeors under Moist Environments, Journal of Atmospheric and Oceanic Technology, 32(11), 2052-2074.</p>

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