Wind Speed Retrieval Algorithm for the Cyclone Global Navigation Satellite System (CYGNSS) Mission

Clarizia, Maria Paola; Ruf, Christopher S.

doi:10.1109/tgrs.2016.2541343

Cited by 216 publications

(138 citation statements)

References 24 publications

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“…Among diverse observables, the BRCS computed from the signal-to-noise ratio (SNR) of the correlated power around the specular point is used to retrieve wind speed form TDS-1 measurements [3] and the average normalized BRCS around the specular point is used in the CYGNSS wind speed algorithm [27]. As a result, the rain attenuation effects on this parameter and, consequently, the derived wind speed is investigated.…”

Section: Determining Rain Attenuation On Powermentioning

confidence: 99%

Evaluating Impact of Rain Attenuation on Space-borne GNSS Reflectometry Wind Speeds

2019

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The novel space-borne Global Navigation Satellite System Reflectometry (GNSS-R) technique has recently shown promise in monitoring the ocean state and surface wind speed with high spatial coverage and unprecedented sampling rate. The L-band signals of GNSS are structurally able to provide a higher quality of observations from areas covered by dense clouds and under intense precipitation, compared to those signals at higher frequencies from conventional ocean scatterometers. As a result, studying the inner core of cyclones and improvement of severe weather forecasting and cyclone tracking have turned into the main objectives of GNSS-R satellite missions such as Cyclone Global Navigation Satellite System (CYGNSS). Nevertheless, the rain attenuation impact on GNSS-R wind speed products is not yet well documented. Evaluating the rain attenuation effects on this technique is significant since a small change in the GNSS-R can potentially cause a considerable bias in the resultant wind products at intense wind speeds. Based on both empirical evidence and theory, wind speed is inversely proportional to derived bistatic radar cross section with a natural logarithmic relation, which introduces high condition numbers (similar to ill-posed conditions) at the inversions to high wind speeds. This paper presents an evaluation of the rain signal attenuation impact on the bistatic radar cross section and the derived wind speed. This study is conducted simulating GNSS-R delay-Doppler maps at different rain rates and reflection geometries, considering that an empirical data analysis at extreme wind intensities and rain rates is impossible due to the insufficient number of observations from these severe conditions. Finally, the study demonstrates that at a wind speed of 30 m/s and incidence angle of 30 • , rain at rates of 10, 15, and 20 mm/h might cause overestimation as large as ≈0.65 m/s (2%), 1.00 m/s (3%), and 1.3 m/s (4%), respectively, which are still smaller than the CYGNSS required uncertainty threshold. The simulations are conducted in a pessimistic condition (severe continuous rainfall below the freezing height and over the entire glistening zone) and the bias is expected to be smaller in size in real environments.

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Section: Determining Rain Attenuation On Powermentioning

confidence: 99%

Evaluating Impact of Rain Attenuation on Space-borne GNSS Reflectometry Wind Speeds

2019

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“…Global Navigation Satellite Systems Reflectometry (GNSS‐R) uses reflected microwave navigation signals (L‐Band from GPS or similar systems) to characterize the reflective surface and was first proposed in 1988 (Hall & Cordey, ). This technique is at present largely used to study winds over the ocean (e.g., Clarizia & Ruf, ; Foti et al, ; Foti et al, ; Ruf & Balasubramaniam, ). However, other Earth observation applications, such as ice sheet and ocean altimetry (e.g., Cardellach et al, ; Cartwright et al, ; Clarizia et al, ), the monitoring of soil moisture (Chew et al, ), and the cryosphere (e.g., Belmonte Rivas et al, ; Gleason, ), are also being investigated.…”

Section: Introductionmentioning

confidence: 99%

Sea Ice Detection Using GNSS‐R Data From TechDemoSat‐1

2019

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A new method for the detection of sea ice using GNSS‐R (Global Navigation Satellite Systems Reflectometry) is presented and applied to 33 months of data from the U.K. TechDemoSat‐1 mission. This method of sea ice detection shows the potential for a future GNSS‐R polar mission, attaining an agreement of over 98% and 96% in the Antarctic and Arctic, respectively, when compared to the European Space Agency's Climate Change Initiative sea ice concentration product. The algorithm uses a combination of two parameters derived from the delay‐Doppler Maps to quantify the spread of power in delay and Doppler. Application of thresholds then allows sea ice to be distinguished from open water. Differences between the TechDemoSat‐1 sea ice detection and comparison data sets are explored. The results provide information on the seasonal and multiyear changes in sea ice distribution of the Arctic and Antarctic. Future potential and applications of this technique are discussed.

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“…For example, TDS-1 SGR-ReSI measurements have been used to characterize ocean winds [6,7], sea surface height [8], soil moisture and vegetation [9,10], wetland inundation [11,12], sea ice detection and concentration [13][14][15][16], sea ice altimetry [17], and sea ice type classification [18]. CYGNSS measurements have been used to observe ocean wind speeds [19][20][21][22], soil moisture [23,24], wetlands inundation characterization and dynamics [25][26][27], and hurricane/tsunami-driven flooding [28,29].…”

Section: Introductionmentioning

confidence: 99%

The Use of SMAP-Reflectometry in Science Applications: Calibration and Capabilities

et al. 2019

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The Soil Moisture Active Passive (SMAP) mission became one of the newest spaceborne Global Navigation Satellite System–Reflectometry (GNSS-R) missions collecting Global Positioning System (GPS) bistatic radar measurements when the band-pass center frequency of its radar receiver was switched to the GPS L2C band. SMAP-Reflectometry (SMAP-R) brings a set of unique capabilities, such as polarimetry and improved spatial resolution, that allow for the exploration of scientific applications that other GNSS-R missions cannot address. In order to leverage SMAP-R for scientific applications, a calibration must be performed to account for the characteristics of the SMAP radar receiver and each GPS transmitter. In this study, we analyze the unique characteristics of SMAP-R, as compared to other GNSS-R missions, and present a calibration method for the SMAP-R signals that enables the standardized use of these signals by the scientific community. There are two key calibration parameters that need to be corrected: The first is the GPS transmitted power and GPS antenna gain at the incidence angle of the measured reflections and the second is the convolution of the SMAP high gain antenna pattern and the glistening zone (Earth surface area from where GPS signals scatter). To account for the GPS transmitter variability, GPS instrument properties—transmitted power and antenna gain—are collocated with information collected from the CYclone Global Navigation Satellite System (CYGNSS) at SMAP’s range of incidence angles (37.3° to 42.7°). To account for the convolutional effect of the SMAP antenna gain, both the scattering area of the reflected GPS signal and the SMAP antenna footprint are mapped on the surface. We account for the size of the scattering area corresponding to each delay and Doppler bin of the SMAP-R measurements based off the SMAP antenna pattern, and normalize according to the size of a measurement representative to one obtained with an omnidirectional antenna. We have validated these calibration methods through an analysis of the coherency of the reflected signal over an extensive area of old sea ice having constant surface characteristics over a period of 3 months. By selecting a vicarious scattering surface with high coherency, we eliminated scene variability and complexity in order to avoid scene dependent aliases in the calibration. The calibration method reduced the dependence on the GPS transmitter power and gain from ~1.08 dB/dB to a residual error of about −0.2 dB/dB. Results also showed that the calibration method eliminates the effect of the high gain antenna filtering effect, thus reducing errors as high as 10 dB on angles furthest from SMAP’s constant 40° incidence angle.

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Wind Speed Retrieval Algorithm for the Cyclone Global Navigation Satellite System (CYGNSS) Mission

Cited by 216 publications

References 24 publications

Evaluating Impact of Rain Attenuation on Space-borne GNSS Reflectometry Wind Speeds

Evaluating Impact of Rain Attenuation on Space-borne GNSS Reflectometry Wind Speeds

Sea Ice Detection Using GNSS‐R Data From TechDemoSat‐1

The Use of SMAP-Reflectometry in Science Applications: Calibration and Capabilities

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