Temporal variability of GNSS-Reflectometry ocean wind speed retrieval performance during the UK TechDemoSat-1 mission

Global Navigation Satellite System-Reflectometry (GNSS-R) is an innovative and rapidly developing approach to Earth Observation that makes use of signals of opportunity from Global Navigation Satellite Systems, which have been reflected off the Earth&#8217;s surface. CYGNSS is a constellation of 8 satellites launched in 2016 which use GNSS-R technology for the remote sensing of ocean wind speed. The ESA ECOLOGY project aims to evaluate CYGNSS data which has recently undergone a series of improvements in the calibration approach. Using CYGNSS collections above the ocean surface, an assessment of Level-1 calibration is presented, alongside a performance evaluation of Level-2 wind speed products. L1 data collected by the individual satellites are shown to be generally well inter-calibrated and remarkably stable over time, a significant improvement over previous versions. However, some geographical biases are found, which appear to be linked to a number of factors including the transmitter-receiver pair considered, viewing geometry, and surface elevation. These findings provide a basis for further improvement of CYGNSS products and have wider applicability to improving calibration of GNSS-R sensors for remote sensing of the Earth.

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Ocean Wind Speed Retrieval Algorithm using the frequency 36GHz Vertical/Horizontal and 6GHz Horizontal Data of the Advanced Microwave Scanning Radiometer (AMSR)

European Journal of Remote Sensing ◽

10.5721/eujrs20124513 ◽

2012 ◽

Vol 45 (1) ◽

pp. 133-140 ◽

Cited By ~ 1

Author(s):

Akira Shibata

Keyword(s):

Wind Speed ◽

Retrieval Algorithm ◽

Ocean Wind

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Calibration and Cross Validation of Global Ocean Wind Speed Based on Scatterometer Observations

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-19-0119.1 ◽

2020 ◽

Vol 37 (2) ◽

pp. 279-297 ◽

Cited By ~ 7

Author(s):

Agustinus Ribal ◽

Ian R. Young

Keyword(s):

Wind Speed ◽

Cross Validation ◽

Regression Line ◽

Total Duration ◽

Global Ocean ◽

High Wind ◽

Wind Speeds ◽

Buoy Data ◽

Ocean Wind

AbstractGlobal ocean wind speed observed from seven different scatterometers, namely, ERS-1, ERS-2, QuikSCAT, MetOp-A, OceanSat-2, MetOp-B, and Rapid Scatterometer (RapidScat) were calibrated against National Data Buoy Center (NDBC) data to form a consistent long-term database of wind speed and direction. Each scatterometer was calibrated independently against NDBC buoy data and then cross validation between scatterometers was performed. The total duration of all scatterometer data is approximately 27 years, from 1992 until 2018. For calibration purposes, only buoys that are greater than 50 km offshore were used. Moreover, only scatterometer data within 50 km of the buoy and for which the overpass occurred within 30 min of the buoy recording data were considered as a “matchup.” To carry out the calibration, reduced major axis (RMA) regression has been applied where the regression minimizes the size of the triangle formed by the vertical and horizontal offsets of the data point from the regression line and the line itself. Differences between scatterometer and buoy data as a function of time were investigated for long-term stability. In addition, cross validation between scatterometers and independent altimeters was also performed for consistency. The performance of the scatterometers at high wind speeds was examined against buoy and platform measurements using quantile–quantile (Q–Q) plots. Where necessary, corrections were applied to ensure scatterometer data agreed with the in situ wind speed for high wind speeds. The resulting combined dataset is believed to be unique, representing the first long-duration multimission scatterometer dataset consistently calibrated, validated and quality controlled.

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An investigation into the mechanical damping characteristics of catenary contact wires and their effect on aerodynamic galloping instability

Proceedings of the Institution of Mechanical Engineers Part F Journal of Rail and Rapid Transit ◽

10.1243/095440903765762814 ◽

2003 ◽

Vol 217 (2) ◽

pp. 63-71 ◽

Cited By ~ 16

Author(s):

M. T. Stickland ◽

T. J. Scanlon ◽

I. A. Craighead ◽

J Fernandez

Keyword(s):

Wind Speed ◽

East Coast ◽

Damping Ratio ◽

Friction Damper ◽

Wind Speeds ◽

Damping Characteristics ◽

Damped Oscillation ◽

Mechanical Damping ◽

The Uk ◽

Wire System

Measurement of the damped oscillation of a section of the UK East Coast Main Line (ECML) catenary/contact wire system was undertaken, and the natural frequency and mechanical damping were found to be 1.4Hz and 0.05 respectively. This information was used to assess the effect of increasing the mechanical damping ratio on the susceptibility of the system to an aerodynamic galloping instability. The section of line tested was known to gallop at wind speeds of approximately 40 mile/h, and theoretical and experimental work verified this. A friction damper arm was designed and three units were fitted to the section of line affected. The introduction of increased mechanical damping was found to raise the mechanical damping coefficient of the line to between 0.095 and 0.18, and the mathematical analysis produced a theoretical wind speed for galloping oscillation of between 75 and 141 mile/h respectively. For over a year since the units were fitted, no problems with galloping instability have been observed.

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Statistical Learning Approach for Wind Speed Distribution Mapping: The UK as a Case Study

Lecture Notes in Geoinformation and Cartography - AGILE 2015 ◽

10.1007/978-3-319-16787-9_10 ◽

2015 ◽

pp. 165-180 ◽

Cited By ~ 2

Author(s):

Fabio Veronesi ◽

Stefano Grassi ◽

Martin Raubal ◽

Lorenz Hurni

Keyword(s):

Wind Speed ◽

Statistical Learning ◽

Learning Approach ◽

Speed Distribution ◽

Wind Speed Distribution ◽

Distribution Mapping ◽

The Uk

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Impact of weather regimes on wind power variability in western Europe

10.5194/egusphere-egu2020-18797 ◽

2020 ◽

Author(s):

Ricardo García-Herrera ◽

Jose M. Garrido-Perez ◽

Carlos Ordóñez ◽

David Barriopedro ◽

Daniel Paredes

Keyword(s):

High Resolution ◽

Wind Speed ◽

Wind Energy ◽

Wind Power ◽

Large Scale ◽

Western Europe ◽

Power Production ◽

Large Scale Circulation ◽

Weather Regimes ◽

The Uk

We have examined the applicability of a new set of 8 tailored weather regimes (WRs) to reproduce wind power variability in Western Europe. These WRs have been defined using a substantially smaller domain than those traditionally used to derive WRs for the North Atlantic-European sector, in order to maximize the large-scale circulation signal on wind power in the region of study. Wind power is characterized here by wind capacity factors (CFs) from a meteorological reanalysis dataset and from high-resolution data simulated by the Weather Research and Forecasting (WRF) model. We first show that WRs capture effectively year-round onshore wind power production variability across Europe, especially over northwestern / central Europe and Iberia. Since the influence of the large-scale circulation on wind energy production is regionally dependent, we have then examined the high-resolution CF data interpolated to the location of more than 100 wind farms in two regions with different orography and climatological features, the UK and the Iberian Peninsula. The use of WRs allows discriminating situations with varied wind speed distributions and power production in both regions. In addition, the use of their monthly frequencies of occurrence as predictors in a multi-linear regression model allows explaining up to two thirds of the month-to-month CF variability for most seasons and sub-regions. These results outperform those previously reported based on Euro-Atlantic modes of atmospheric circulation. The improvement achieved by the spatial adaptation of WRs to a relatively small domain seems to compensate for the reduction in explained variance that may occur when using yearly as compared to monthly or seasonal WR classifications. In addition, our annual WR classification has the advantage that it allows applying a consistent group of WRs to reproduce day-to-day wind speed variability during extreme events regardless of the time of the year. As an illustration, we have applied these WRs to two recent periods such as the wind energy deficit of summer 2018 in the UK and the surplus of March 2018 in Iberia, which can be explained consistently by the different combinations of WRs.

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NOC GNSS-R Global Ocean Wind Speed and Sea-Ice Products

10.5194/egusphere-egu2020-18916 ◽

2020 ◽

Author(s):

Matthew Hammond ◽

Giuseppe Foti ◽

Christine Gommenginger ◽

Meric Srokosz ◽

Martin Unwin ◽

...

Keyword(s):

Wind Speed ◽

Sea Ice ◽

Global Ocean ◽

Retrieval Algorithm ◽

Navigation Satellite ◽

Validation Data ◽

Global Navigation ◽

Ice Detection ◽

Global Navigation Satellite ◽

Ocean Wind

Global Navigation Satellite System-Reflectometry (GNSS-R) is an innovative and rapidly developing approach to Earth Observation that makes use of signals of opportunity from Global Navigation Satellite Systems, which have been reflected off the Earth&#8217;s surface. Using GNSS-R data collected by the UK TechDemoSat-1 (TDS-1) between 2014 and 2018, the National Oceanography Centre (NOC) has developed a GNSS-R wind speed retrieval algorithm called the Calibrated Bistatic Radar Equation (C-BRE), which now features updated data quality control mechanisms including flagging of radio frequency interference (RFI) and sea-ice detection based on the GNSS-R waveform. Here we present an assessment of the performance of the latest NOC GNSS-R ocean wind speed and sea-ice products (NOC C-BRE v1.0) using validation data from the ECMWF ERA-5 re-analysis model output. Results show the capability of spaceborne GNSS-R sensors for accurate wind speed retrieval and sea-ice detection. Additionally, ground-processed Galileo returns collected by TDS-1 are examined and the geophysical sensitivity of reflected Galileo data to surface parameters is investigated. Preliminary results demonstrate the feasibility of spaceborne GNSS-R instruments receiving a combination of GNSS signals transmitted by multiple navigation systems, which offers the opportunity for frequent, high-quality ocean wind and sea-ice retrievals at low relative cost. Other advancements in GNSS-R technology are represented by future mission concepts such as HydroGNSS, a proposed ESA Scout mission opportunity by SSTL offering support for enhanced retrieval capabilities exploiting dual polarisation, dual frequency, and coherent reflected signal reception.

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