Can GNSS Reflectometry Detect Precipitation Over Oceans?

Asgarimehr, Milad; Zavorotny, Valery U.; Wickert, Jens; Reich, Sebastian

doi:10.1029/2018gl079708

Cited by 40 publications

(31 citation statements)

References 28 publications

(43 reference statements)

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“…The range of difference values is also similar regardless of rain rate. Asgarimehr et al (2018) found that some rain effects can be seen in GNSS-R datasets, especially at low wind speeds (<6 m s -1 ). Disaggregating Fig.…”

mentioning

confidence: 94%

In-Orbit Performance of the Constellation of CYGNSS Hurricane Satellites

Ruf

Asharaf

Balasubramaniam

et al. 2019

Bulletin of the American Meteorological Society

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The NASA Cyclone Global Navigation Satellite System (CYGNSS) constellation of eight satellites was successfully launched into low Earth orbit on 15 December 2016. Each satellite carries a radar receiver that measures GPS signals scattered from the surface. Wind speed over the ocean is determined from distortions in the signal caused by wind-driven surface roughness. GPS operates at a sufficiently low frequency to allow for propagation through all precipitation, including the extreme rain rates present in the eyewall of tropical cyclones. The spacing and orbit of the satellites were chosen to optimize frequent sampling of tropical cyclones. In this study, we characterize the CYGNSS ocean surface wind speed measurements by their uncertainty, dynamic range, sensitivity to precipitation, spatial resolution, spatial and temporal sampling, and data latency. The current status of each of these properties is examined and potential future improvements are discussed. In addition, examples are given of current science investigations that make use of the data.

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mentioning

confidence: 94%

In-Orbit Performance of the Constellation of CYGNSS Hurricane Satellites

Ruf

Asharaf

Balasubramaniam

et al. 2019

Bulletin of the American Meteorological Society

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“…According to the model clarifying the diffuse scattering at low winds [19], the scattering mechanism changes to a higherorder Bragg-like scattering rather than the expected forward quasi-specular mechanism whose magnitude is controlled by the low-pass mean square slope (MSS). The already conducted simulations based on this model, shown in [7], demonstrate how σ 0 loses its sensitivity to wind speeds lower than 2.5 m/s. A similar overestimation of low wind conditions is also reported in CYGNSS retrievals [20].…”

Section: Performance Characterizationmentioning

confidence: 79%

“…In addition to the unknown validity of the predefined form, ignored factors affecting GNSS-R observations may introduce further inaccuracies. These effects can be either geophysical, such as those from nonwind-derived waves [6] and precipitation [7], or nongeophysical effects such as differences in the level of the GNSS transmitted power. Various manufacturers have built GPS satellites in distinct blocks with different signal levels and differences in the angular distribution of the gain [8].…”

mentioning

confidence: 99%

A GNSS-R Geophysical Model Function: Machine Learning for Wind Speed Retrievals

Asgarimehr

Zhelavskaya

Foti

et al. 2020

IEEE Geosci. Remote Sensing Lett.

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A machine learning technique is implemented for retrieving space-borne Global Navigation Satellite System Reflectometry (GNSS-R) wind speed. Conventional approaches commonly fit a function in a predefined form to matchup data in a least-squares (LS) sense, mapping GNSS-R observations to wind speed. In this study, a feedforward neural network is trained for TechDemoSat-1 (TDS-1) wind speed inversion. The input variables, along with the derived bistatic radar cross-section σ 0 , are selected after investigating the wind speed dependence and the model performance. When compared to an LS-based approach, the derived model shows a significant improvement of 20% in the root mean square error (RMSE). The proposed neural network demonstrates an ability to model a variety of effects degrading the retrieval accuracy such as the different levels of the effective isotropic radiated power (EIRP) of GPS satellites. For example, the derived Mean Absolute Error (MAE) of the satellite with SVN 34 is decreased by 32% using the machine-learning-based approach.

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“…Other oceanic phenomena such as rain splash effects and swell, which might change the roughness and, consequently, affect the DDMs, are not investigated in this study. In case of interest, one can refer to [11] which discusses the rain splash effect intensively. Then, the reflection geometry and the specular point position is determined.…”

Section: Simulating Ddmsmentioning

confidence: 99%

“…They can create rings, stalks, and crowns [10] which alter the surface roughness, and consequently, the wind retrieval quality. Rain splash effect on GNSS-R observations is discussed [11] and a decrease in Bistatic Radar Cross Section (BRCS) at low wind speeds (≈<5 m/s) is demonstrated both with TDS-1 data analysis and based on model simulations. At high enough winds (≈>5 m/s), the intensity of forward quasi-specular scattering is controlled by surface gravity waves with lengths larger than several wavelengths of the reflected signal.…”

Section: Introductionmentioning

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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Can GNSS Reflectometry Detect Precipitation Over Oceans?

Cited by 40 publications

References 28 publications

In-Orbit Performance of the Constellation of CYGNSS Hurricane Satellites

In-Orbit Performance of the Constellation of CYGNSS Hurricane Satellites

A GNSS-R Geophysical Model Function: Machine Learning for Wind Speed Retrievals

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

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