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>

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>

scholarly journals Evaluation of Spaceborne GNSS-R Retrieved Ocean Surface Wind Speed with Multiple Datasets

2019 ◽  
Vol 11 (23) ◽  
pp. 2747 ◽  
Author(s):  
Zhounan Dong ◽  
Shuanggen Jin
Keyword(s):  
Wind Speed ◽  
Weather Prediction ◽  
Surface Wind ◽  
Ocean Surface ◽  
Surface Wind Speed ◽  
Retrieval Algorithm ◽  
Navigation Satellite ◽  
Ocean Surface Wind ◽  
Global Navigation ◽  
Global Navigation Satellite

Spaceborne Global Navigation Satellite Systems-Reflectometry (GNSS-R) can estimate the geophysical parameters by receiving Earth’s surface reflected signals. The CYclone Global Navigation Satellite System (CYGNSS) mission with eight microsatellites launched by NASA in December 2016, which provides an unprecedented opportunity to rapidly acquire ocean surface wind speed globally. In this paper, a refined spaceborne GNSS-R sea surface wind speed retrieval algorithm is presented and validated with the ground surface reference wind speed from numerical weather prediction (NWP) and cross-calibrated multi-platform ocean surface wind vector analysis product (CCMP), respectively. The results show that when the wind speed was less than 20 m/s, the RMS of the GNSS-R retrieved wind could achieve 1.84 m/s in the case where the NWP winds were used as the ground truth winds, while the result was better than the NWP-based retrieved wind speed with an RMS of 1.68 m/s when the CCMP winds were used. The two sets of inversion results were further evaluated by the buoy winds, and the uncertainties from the NWP-derived and CCMP-derived model prediction wind speed were 1.91 m/s and 1.87 m/s, respectively. The accuracy of inversed wind speeds for different GNSS pseudo-random noise (PRN) satellites and types was also analyzed and presented, which showed similar for different PRN satellites and different types of satellites.

scholarly journals Reviewing Wind Measurement Approaches for Fixed-Wing Unmanned Aircraft

Atmosphere ◽  
2018 ◽  
Vol 9 (11) ◽  
pp. 422 ◽  
Author(s):  
Alexander Rautenberg ◽  
Martin Graf ◽  
Norman Wildmann ◽  
Andreas Platis ◽  
Jens Bange
Keyword(s):  
Wind Speed ◽  
Unmanned Aircraft ◽  
Flow Sensor ◽  
Navigation Satellite ◽  
Wind Vector ◽  
Data Set ◽  
Vector Measurement ◽  
Wide Range ◽  
Global Navigation ◽  
Global Navigation Satellite

One of the biggest challenges in probing the atmospheric boundary layer with small unmanned aerial vehicles is the turbulent 3D wind vector measurement. Several approaches have been developed to estimate the wind vector without using multi-hole flow probes. This study compares commonly used wind speed and direction estimation algorithms with the direct 3D wind vector measurement using multi-hole probes. This was done using the data of a fully equipped system and by applying several algorithms to the same data set. To cover as many aspects as possible, a wide range of meteorological conditions and common flight patterns were considered in this comparison. The results from the five-hole probe measurements were compared to the pitot tube algorithm, which only requires a pitot-static tube and a standard inertial navigation system measuring aircraft attitude (Euler angles), while the position is measured with global navigation satellite systems. Even less complex is the so-called no-flow-sensor algorithm, which only requires a global navigation satellite system to estimate wind speed and wind direction. These algorithms require temporal averaging. Two averaging periods were applied in order to see the influence and show the limitations of each algorithm. For a window of 4 min, both simplifications work well, especially with the pitot-static tube measurement. When reducing the averaging period to 1 min and thereby increasing the temporal resolution, it becomes evident that only circular flight patterns with full racetracks inside the averaging window are applicable for the no-flow-sensor algorithm and that the additional flow information from the pitot-static tube improves precision significantly.

scholarly journals Rancang Bangun Alat Pendeteksi Kondisi Cuaca Kota Balikpapan Menggunakan Global Navigation Satellite System (GNSS) Wind Turbine

2019 ◽  
Vol 2 (2) ◽  
pp. 11-18
Author(s):  
Budiani Fitria Endrawati ◽  
Adhe Yusphie ◽  
Arum Prastiyo Putri ◽  
Azam Fadhil A ◽  
Mohammad Saiful R ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Global Navigation Satellite System ◽  
Satellite System ◽  
Navigation Satellite ◽  
East Kalimantan ◽  
Measurement Results ◽  
Global Navigation ◽  
Global Navigation Satellite ◽  
The City

Balikpapan is one of the cities in East Kalimantan with unique characteristic of the region. The characteristics of the region and the height of the area from the sea surface of the city of Balikpapan is one of the factors, therefore need to be designed a tool to detect weather especially in Balikpapan city. The device design using Global Navigation Satellite System (GNSS) Wind Turbine. The measurement results are based on temperature, humidity and wind speed. From the measurement of temperature, humidity and wind speed obtained an average of 30.97oC; 73.49% and 2.91 m/s. According to method World Meteorological Organization, temperature, humidity and wind speed data is accepted.

Progress in Time Series Soil Moisture Retrievals Using the Cyclone Global Navigation Satellite System Mission

2021 ◽  
Author(s):  
Mohammad Al-Khaldi ◽  
Joel Johnson ◽  
Scott Gleason
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Global Navigation Satellite System ◽  
Satellite System ◽  
Retrieval Algorithm ◽  
Navigation Satellite ◽  
Gps Satellites ◽  
Wide Range ◽  
Global Navigation ◽  
Global Navigation Satellite

<p>NASA's Cyclone Global Navigation Satellite System (CYGNSS) mission has continued to provide measurements of land surface specular scattering since its launch in December 2016. CYGNSS’s operates in a GNSS-R configuration in which  CYGNSS satellites together with GPS satellites form a bistatic radar geometry with GPS satellites acting as transmitters and CYGNSS satellites acting as receivers. The fundamental GNSS-R measurement obtained using the CYGNSS observatories is the delay-Doppler map (DDM), from which normalized radar cross section (NRCS) estimates are derived. The sensitivity of CYGNSS measurements to a wide range of surface properties has motivated their use for soil moisture retrievals.</p><p>This presentation reports an updated analysis of soil moisture retrieval errors using a previously reported time series soil moisture retrieval algorithm that considers a  multi-year CYGNSS dataset. The presentation also reports recent progress in which further simplifications to the proposed algorithm are introduced that limit its need for ancillary soil moisture data and promote use in an operational capacity. This is accomplished, in part, through the incorporation of a recently developed global Level-1 coherence detection methodology and the use of a soil moisture climatology.</p><p>Soil moisture is sensed using a time-series retrieval in which NRCS ratios derived from CYGNSS measurements are used to form a system of equations that can be solved for a times series of surface reflectivities. While the NRCS exhibits a dependence on a wide range of properties such as soil moisture, soil composition, vegetation cover, and surface roughness, NRCS ratios in consecutive acquisitions, at sufficiently low latency, exhibit a direct proportionality to reflectivity ratios that are a function of soil permittivity and therefore soil moisture. The dependence of NRCS ratios on reflectivity facilitates a location dependent inversion of reflectivity to soil moisture through a dielectric mixing model. The use of NRCS ratios however results in N-1 equations for the N soil moistures in the time series, thereby necessitating the incorporation of additional information typically expressed in terms of maximum and/or minimum soil moisture (or reflectivity) values over the time series when solving the system. These values can be obtained either from ancillary data from other systems or from a soil moisture climatology as incorporated in this presentation.</p><p>Retrieved moisture values from the updated algorithm are compared against observed values reported by the Soil Moisture Active Passive (SMAP) mission. The findings suggest that there exists potential for using GNSS-R systems for global soil moisture retrievals with an RMS error on the order of 0.06 cm<sup>3</sup>/cm<sup>3</sup> over varied terrain. The dependence of the algorithm’s retrieval error on land cover class, soil texture, and moisture variability trends will be reported in detail in this presentation.</p>

NOC GNSS-R Global Ocean Wind Speed and Sea-Ice Products Using Data from the TechDemoSat-1 Mission

2020 ◽  
Author(s):  
Giuseppe Foti ◽  
Matthew Lee Hammond ◽  
Christine Gommenginger ◽  
Meric Srokosz ◽  
Martin Unwin ◽  
...  
Keyword(s):  
Wind Speed ◽  
Sea Ice ◽  
Global Ocean ◽  
Using Data ◽  
Ocean Wind

scholarly journals Retrieval of sea ice thickness using global navigation satellite system reflected signals

2019 ◽  
Vol 52 (7-8) ◽  
pp. 1131-1136
Author(s):  
Hongxing Gao ◽  
Dongkai Yang ◽  
Qiang Wang
Keyword(s):  
Sea Ice ◽  
Global Navigation Satellite System ◽  
Satellite System ◽  
Ice Thickness ◽  
Navigation Satellite ◽  
Sea Ice Thickness ◽  
Direct Signal ◽  
Reflected Signal ◽  
Global Navigation ◽  
Global Navigation Satellite

The aim of this paper is to develop a model which can be used to retrieve sea ice thickness based on global navigation satellite system reflected signals at a shore-based platform. First, the method calculates the intensity ratio of the reflected signal and the direct signal of the global navigation satellite system satellite, which is the ratio of the power of the reflected signal to the power of the direct signal. Then, the information of the sea ice thickness is obtained according to the empirical model of the sea ice thickness. In order to verify the effectiveness of the method, the global navigation satellite system reflected signals were observed in the experiment in the Bayu enclosure of Liaoning Province, China. The results show that the sea ice thickness of the global navigation satellite system reflected signal is 10–20 cm, which is consistent with the synthetic-aperture radar observation.

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