scholarly journals A Marine Radar Wind Sensor

2007 ◽  
Vol 24 (9) ◽  
pp. 1629-1642 ◽  
Author(s):  
Heiko Dankert ◽  
Jochen Horstmann
Keyword(s):  
Standard Deviation ◽  
Correlation Coefficient ◽  
Wind Direction ◽  
Ocean Surface ◽  
Image Sequences ◽  
Wind Vector ◽  
Sea State ◽  
Wind Speeds ◽  
X Band ◽  
Wind Measurements

Abstract A new method for retrieving the wind vector from radar-image sequences is presented. This method, called WiRAR, uses a marine X-band radar to analyze the backscatter of the ocean surface in space and time with respect to surface winds. Wind direction is found using wind-induced streaks, which are very well aligned with the mean surface wind direction and have a typical spacing above 50 m. Wind speeds are derived using a neural network by parameterizing the relationship between the wind vector and the normalized radar cross section (NRCS). To improve performance, it is also considered how the NRCS depends on sea state and atmospheric parameters such as air–sea temperature and humidity. Since the signal-to-noise ratio in the radar sequences is directly related to the significant wave height, this ratio is used to obtain sea state parameters. All radar datasets were acquired in the German Bight of the North Sea from the research platform FINO-I, which provides environmental data such as wind measurements at different heights, sea state, air–sea temperatures, humidity, and other meteorological and oceanographic parameters. The radar-image sequences were recorded by a marine X-band radar installed aboard FINO-I, which operates at grazing incidence and horizontal polarization in transmit and receive. For validation WiRAR is applied to the radar data and compared to the in situ wind measurements from FINO-I. The comparison of wind directions resulted in a correlation coefficient of 0.99 with a standard deviation of 12.8°, and that of wind speeds resulted in a correlation coefficient of 0.99 with a standard deviation of 0.41 m s−1. In contrast to traditional offshore wind sensors, the retrieval of the wind vector from the NRCS of the ocean surface makes the system independent of the sensors’ motion and installation height as well as the effects due to platform-induced turbulence.

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.

scholarly journals Evaluation of the Ocean Surface Wind Speed Change following the Super Typhoon from Space-Borne GNSS-Reflectometry

2020 ◽  
Vol 12 (12) ◽  
pp. 2034 ◽  
Author(s):  
Hongsu Liu ◽  
Shuanggen Jin ◽  
Qingyun Yan
Keyword(s):  
Wind Speed ◽  
Correlation Coefficient ◽  
Surface Wind ◽  
Ocean Surface ◽  
Surface Wind Speed ◽  
Wind Speeds ◽  
Mean Error ◽  
Super Typhoon ◽  
The Mean ◽  
Ocean Surface Wind

Ocean surface wind speed is an essential parameter for typhoon monitoring and forecasting. However, traditional satellite and buoy observations are difficult to monitor the typhoon due to high cost and low temporal-spatial resolution. With the development of spaceborne GNSS-R technology, the cyclone global navigation satellite system (CYGNSS) with eight satellites in low-earth orbit provides an opportunity to measure the ocean surface wind speed of typhoons. Though observations are made at the extremely efficient spatial and temporal resolution, its accuracy and reliability are unclear in an actual super typhoon case. In this study, the wind speed variations over the life cycle of the 2018 Typhoon Mangkhut from CYGNSS observations were evaluated and compared with European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis-5 (ERA-5). The results show that the overall root-mean-square error (RMSE) of CYGNSS versus ECMWF was 4.12 m/s, the mean error was 1.36 m/s, and the correlation coefficient was 0.96. For wind speeds lower and greater than 15 m/s, the RMSE of CYGNSS versus ECMWF were 1.02 and 4.36 m/s, the mean errors were 0.05 and 1.61 m/s, the correlation coefficients were 0.91 and 0.90, and the average relative errors were 9.8% and 11.6%, respectively. When the typhoon reached a strong typhoon or super typhoon, the RMSE of CYGNSS with respect to ERA-5 from ECMWF was 5.07 m/s; the mean error was 3.57 m/s; the correlation coefficient was 0.52 and the average relative error was 11.0%. The CYGNSS estimation had higher precision for wind speeds below 15 m/s, but degraded when the wind speed was above 15 m/s.

scholarly journals Inter-comparison of wind measurements in the atmospheric boundary layer with Aeolus and a ground-based coherent Doppler lidar network over China

2021 ◽  
Author(s):  
Songhua Wu ◽  
Kangwen Sun ◽  
Guangyao Dai ◽  
Xiaoye Wang ◽  
Xiaoying Liu ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Standard Deviation ◽  
Atmospheric Boundary Layer ◽  
Correlation Coefficient ◽  
Vertical Velocity ◽  
European Space Agency ◽  
Wind Data ◽  
Horizontal Line ◽  
Comparison Results ◽  
Wind Measurements

Abstract. After the successful launch of Aeolus which is the first spaceborne wind lidar developed by the European Space Agency (ESA) on 22 August 2018, we deployed several ground-based coherent Doppler wind lidars (CDLs) to verify the wind observations from Aeolus. By the simultaneous wind measurements with CDLs at 17 stations over China, the Rayleigh-clear and Mie-cloudy horizontal-line-of-sight (HLOS) wind velocities from Aeolus in the atmospheric boundary layer are compared with that from CDLs. To ensure the quality of the measurement data from CDL and Aeolus, strict quality controls are applied in this study. Overall, 52 simultaneous Mie-cloudy comparison pairs and 387 Rayleigh-clear comparison pairs from this campaign are acquired. All of the Aeolus-produced L2B Mie-cloudy HLOS, Rayleigh-clear HLOS and CDL-produced HLOS are compared individually. For the inter-comparison result of Mie-cloudy HLOS wind and CDL-produced HLOS wind, the correlation coefficient, the standard deviation, the scaled MAD and the bias are 0.83, 3.15 m/s, 2.64 m/s and −0.25 m/s respectively, while the "y = ax" slope, the "y = ax + b" slope and the "y = ax + b" intercept are 0.93, 0.92 and −0.33 m/s. For the Rayleigh-clear HLOS wind, the correlation coefficient, the standard deviation, the scaled MAD and the bias are 0.62, 7.07 m/s, 5.77 m/s and −1.15 m/s respectively, while the "y = ax" slope, the "y = ax + b" slope and the "y = ax + b" intercept are 1.00, 0.96 and −1.2 m/s. It is found that the standard deviation, the scaled MAD and the bias on ascending tracks are slightly better than that on descending tracks. Moreover, to evaluate the accuracy of Aeolus HLOS wind measurements under different product baselines, the Aeolus L2B Mie-cloudy HLOS wind data and L2B Rayleigh-clear HLOS wind data under Baselines 07/08, Baselines 09/10, and Baseline 11 are compared against the CDL-retrieved HLOS wind data separately. From the comparison results, marked misfits between the wind data from Aeolus Baselines 07/08 and wind data from CDL in planetary boundary layer are found. With the continuous calibration and validation and product processor updates, the performances of Aeolus wind measurements under Baselines 09/10 and Baseline 11 are improved significantly. Considering the influence of turbulence and convection in the planetary boundary layers, higher values for the vertical velocity are common in this region. Hence, as a special note, the vertical velocity could impact the HLOS wind velocity retrieval from Aeolus.

scholarly journals Real-Time Ocean Wind Vector Retrieval from Marine Radar Image Sequences Acquired at Grazing Angle

2013 ◽  
Vol 30 (1) ◽  
pp. 127-139 ◽  
Author(s):  
Raul Vicen-Bueno ◽  
Jochen Horstmann ◽  
Eric Terril ◽  
Tony de Paolo ◽  
Jens Dannenberg
Keyword(s):  
Wind Speed ◽  
Cross Section ◽  
Image Sequences ◽  
Sea Surface ◽  
Intensity Level ◽  
Wind Vector ◽  
Wind Speeds ◽  
Marine Radar ◽  
Radar Measurements ◽  
Ocean Wind

Abstract This paper proposes a novel algorithm for retrieving the ocean wind vector from marine radar image sequences in real time. It is presented as an alternative to mitigate anemometer problems, such as blockage, shadowing, and turbulence. Since wind modifies the sea surface, the proposed algorithm is based on the dependence of the sea surface backscatter on wind direction and speed. This algorithm retrieves the wind vector using radar measurements in the range of 200–1500 m. Wind directions are retrieved from radar images integrated over time and smoothed (averaged) in space by searching for the maximum radar cross section in azimuth as the radar cross section is largest for upwind directions. Wind speeds are retrieved by an empirical third-order polynomial geophysical model function (GMF), which depends on the range distance in the upwind direction to a preselected intensity level and the intensity level. This GMF is approximated from a dataset of collocated in situ wind speed and radar measurements (~31 000 measurements, ~56 h). The algorithm is validated utilizing wind and radar measurements acquired on the Research Platform (R/P) FLIP (for Floating Instrumentation Platform) during the 13-day Office of Naval Research experiment on High-Resolution Air–Sea Interaction (HiRes) in June 2010. Wind speeds ranged from 4 to 22 m s−1. Once the proposed algorithm is tuned, standard deviations and biases of 14° and −1° for wind directions and of 0.8 and −0.1 m s−1 for wind speeds are observed, respectively. Additional studies of uncertainty and error of the retrieved wind speed are also reported.

scholarly journals Improved Wind and Turbulence Measurements Using a Low-Cost 3-D Sonic Anemometer at a Low-Wind Site

2008 ◽  
Vol 2 (1) ◽  
pp. 131-138 ◽  
Author(s):  
Brent M. Bowen
Keyword(s):  
Standard Deviation ◽  
Wind Direction ◽  
Sonic Anemometer ◽  
Vertical Wind ◽  
Wind Speeds ◽  
Low Wind Speeds ◽  
Mechanical Sensors ◽  
Wind Vane ◽  
Cup Anemometer ◽  
Propeller Anemometer

A year of data from sonic anemometer and mechanical wind sensors was analyzed and compared at a low-wind site. Results indicate that 15-minute average and peak 1-second wind speeds (u) from the sonic agree well with data derived from a co-located cup anemometer over a wide range of speeds. Wind direction data derived from the sonic also agree closely with those from a wind vane except for very low wind speeds. Values of standard deviation of longitudinal wind speed (σu) and wind direction fluctuations (σø) from the sonic and mechanical sensors agree well for times with u > 2 ms-1 but show significant differences with lower u values. The most significant differences are associated with the standard deviation of vertical wind fluctuations (σw): the co-located vertical propeller anemometer yields values increasingly less than those measured by the sonic anemometer as u decreases from 2.5 approaching 0 ms-1. The combination of u over-estimation and under-estimation of σw from the mechanical sensors at low wind speeds causes considerable underestimation of the standard deviation of vertical wind angle fluctuations (σø), an indicator of vertical dispersion. Calculations of σø from sonic anemometer measurements are typically 5° to 10° greater than from the mechanical sensors when the mechanical instruments indicate that σø < 5° or so. The errors with the propeller anemometer, cup anemometer and wind vane, caused by their inability to respond to higher frequency (smaller scale) turbulent fluctuations, can therefore lead to large (factors of 2 to 10 or more) errors in both the vertical and horizontal dispersion during stable conditions with light winds. The sonic anemometer clearly provides more accurate and reliable wind data than the mechanical wind sensor with u < 2.5 ms-1

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