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

2012 ◽  
Vol 45 (1) ◽  
pp. 133-140 ◽  
Author(s):  
Akira Shibata
Keyword(s):  
Wind Speed ◽  
Retrieval Algorithm ◽  
Ocean Wind

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

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

<p>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’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.</p>

GNSS reflectometry global ocean wind speed using deep learning: Development and assessment of CyGNSSnet

2022 ◽  
Vol 269 ◽  
pp. 112801
Author(s):  
Milad Asgarimehr ◽  
Caroline Arnold ◽  
Tobias Weigel ◽  
Chris Ruf ◽  
Jens Wickert
Keyword(s):  
Deep Learning ◽  
Wind Speed ◽  
Global Ocean ◽  
Gnss Reflectometry ◽  
Learning Development ◽  
Ocean Wind

An Assessment of CYGNSS Ocean Wind Speed Products

2021 ◽  
Author(s):  
Matthew Hammond ◽  
Giuseppe Foti ◽  
Christine Gommenginger ◽  
Meric Srokosz ◽  
Nicolas Floury
Keyword(s):  
Remote Sensing ◽  
Wind Speed ◽  
Satellite System ◽  
Global Navigation Satellite Systems ◽  
Navigation Satellite ◽  
The Earth ◽  
L1 Data ◽  
Global Navigation ◽  
Global Navigation Satellite ◽  
Ocean Wind

<p>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’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.</p>

Wind Speed Retrieval Algorithm for Ku-Band Radar Onboard GPM Satellite

2021 ◽  
Author(s):  
Maria Panfilova ◽  
Vladimir Karaev ◽  
Leonid Mitnik
Keyword(s):  
Wind Speed ◽  
Retrieval Algorithm ◽  
Ku Band

scholarly journals Profiling wind and greenhouse gases by infrared-laser occultation: results from end-to-end simulations in windy air

2015 ◽  
Vol 8 (7) ◽  
pp. 2813-2825 ◽  
Author(s):  
A. Plach ◽  
V. Proschek ◽  
G. Kirchengast
Keyword(s):  
Wind Speed ◽  
Greenhouse Gases ◽  
Doppler Shift ◽  
Lower Stratosphere ◽  
Infrared Laser ◽  
Retrieval Algorithm ◽  
Upper Troposphere ◽  
Wind Retrieval ◽  
End To End ◽  
Thermodynamic Variables

Abstract. The new mission concept of microwave and infrared-laser occultation between low-Earth-orbit satellites (LMIO) is designed to provide accurate and long-term stable profiles of atmospheric thermodynamic variables, greenhouse gases (GHGs), and line-of-sight (l.o.s.) wind speed with focus on the upper troposphere and lower stratosphere (UTLS). While the unique quality of GHG retrievals enabled by LMIO over the UTLS has been recently demonstrated based on end-to-end simulations, the promise of l.o.s. wind retrieval, and of joint GHG and wind retrieval, has not yet been analyzed in any realistic simulation setting. Here we use a newly developed l.o.s. wind retrieval algorithm, which we embedded in an end-to-end simulation framework that also includes the retrieval of thermodynamic variables and GHGs, and analyze the performance of both stand-alone wind retrieval and joint wind and GHG retrieval. The wind algorithm utilizes LMIO laser signals placed on the inflection points at the wings of the highly symmetric C18OO absorption line near 4767 cm−1 and exploits transmission differences from a wind-induced Doppler shift. Based on realistic example cases for a diversity of atmospheric conditions, ranging from tropical to high-latitude winter, we find that the retrieved l.o.s. wind profiles are of high quality over the lower stratosphere under all conditions, i.e., unbiased and accurate to within about 2 m s−1 over about 15 to 35 km. The wind accuracy degrades into the upper troposphere due to the decreasing signal-to-noise ratio of the wind-induced differential transmission signals. The GHG retrieval in windy air is not vulnerable to wind speed uncertainties up to about 10 m s−1 but is found to benefit in the case of higher speeds from the integrated wind retrieval that enables correction of wind-induced Doppler shift of GHG signals. Overall both the l.o.s. wind and GHG retrieval results are strongly encouraging towards further development and implementation of a LMIO mission.

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

2020 ◽  
Vol 37 (2) ◽  
pp. 279-297 ◽  
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.

scholarly journals A Unique Satellite-Based Sea Surface Wind Speed Algorithm and Its Application in Tropical Cyclone Intensity Analysis

2016 ◽  
Vol 33 (7) ◽  
pp. 1363-1375 ◽  
Author(s):  
Sungwook Hong ◽  
Hwa-Jeong Seo ◽  
Young-Joo Kwon
Keyword(s):  
Wind Speed ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Sea Surface ◽  
Japan Meteorological Agency ◽  
Retrieval Algorithm ◽  
Sea Surface Wind Speed ◽  
Brightness Temperatures ◽  
Sea Surface Wind ◽  
Medium Range

AbstractThis study proposes a sea surface wind speed retrieval algorithm (the Hong wind speed algorithm) for use in rainy and rain-free conditions. It uses a combination of satellite-observed microwave brightness temperatures, sea surface temperatures, and horizontally polarized surface reflectivities from the fast Radiative Transfer for TOVS (RTTOV), and surface and atmospheric profiles from the European Centre for Medium-Range Weather Forecasts (ECMWF). Regression relationships between satellite-observed brightness temperature and satellite-simulated brightness temperatures, satellite-simulated brightness temperatures, rough surface reflectivities, and between sea surface roughness and sea surface wind speed are derived from the Advanced Microwave Scanning Radiometer 2 (AMSR-2). Validation results of sea surface wind speed between the proposed algorithm and the Tropical Atmosphere Ocean (TAO) data show that the estimated bias and RMSE for AMSR-2 6.925- and 10.65-GHz bands are 0.09 and 1.13 m s−1, and −0.52 and 1.21 m s−1, respectively. Typhoon intensities such as the current intensity (CI) number, maximum wind speed, and minimum pressure level based on the proposed technique (the Hong technique) are compared with best-track data from the Japan Meteorological Agency (JMA), the Joint Typhoon Warning Center (JTWC), and the Cooperative Institute for Mesoscale Meteorological Studies (CIMSS) for 13 typhoons that occurred in the northeastern Pacific Ocean throughout 2012. Although the results show good agreement for low- and medium-range typhoon intensities, the discrepancy increases with typhoon intensity. Consequently, this study provides a useful retrieval algorithm for estimating sea surface wind speed, even during rainy conditions, and for analyzing characteristics of tropical cyclones.

Calibration and validation processing for the CYGNSS wind speed retrieval algorithm

2017 ◽  
Author(s):  
Rajeswari Balasubramaniam ◽  
Christopher S. Ruf ◽  
Darren McKague ◽  
Maria Paola Clarizia ◽  
Scott Gleason
Keyword(s):  
Wind Speed ◽  
Retrieval Algorithm ◽  
Calibration And Validation

scholarly journals Wind Speed Retrieval Algorithm Using Ku-Band Radar Onboard GPM Satellite

2021 ◽  
Vol 13 (22) ◽  
pp. 4565
Author(s):  
Maria Panfilova ◽  
Vladimir Karaev
Keyword(s):  
Wind Speed ◽  
Good Accuracy ◽  
Retrieval Algorithm ◽  
Dual Frequency ◽  
Precipitation Radar ◽  
Precipitation Measurement ◽  
Global Precipitation Measurement ◽  
Global Precipitation ◽  
Ku Band ◽  
Wide Swath

The algorithm to retrieve wind speed in a wide swath from the normalized radar cross section (NRCS) was developed for the data of Dual Frequency Precipitation Radar (DPR) operating in scanning mode installed onboard a Global Precipitation Measurement (GPM) satellite. The data for Ku-band radar were used. Equivalent NRCS values at nadir were estimated in a wide swath under the geometrical optics approximation from off-nadir observations. Using these equivalent NRCS nadir values and the sea buoys data, the new parameterization of dependence between NRCS at nadir and the wind speed was obtained. The algorithm was validated using ASCAT (Advanced Scatterometer) data and revealed good accuracy. DPR data are promising for determining wind speed in coastal areas.

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