Wind Speed Estimation From CYGNSS Using Artificial Neural Networks

Reynolds, Jennifer; Clarizia, Maria Paola; Santi, Emanuele

doi:10.1109/jstars.2020.2968156

Cited by 62 publications

(48 citation statements)

References 36 publications

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“…The first type of input data includes DDM and SRS parameters [22]. The second type of input data includes DDM feature (such as DDMA and LES) manually extracted from raw DDM data and SRS parameters [23] [24]. Though there are more than 30 SRS parameters, the previous research works, no matter the first or the second type, only use less than 10 SRS parameters to compose the input data.…”

Section: Introductionmentioning

confidence: 99%

“…The recent study [22] on GNSS-R ocean wind speed retrieve also shows a similar result since the features automatically extracted from the raw DDM contains more useful information for retrieve than the manually extracted features. For the retrieval using the manfully extracted DDM features and SRS parameters, machine learning model only plays the role of regression [23] [24]. For the retrieval using the DDM and SRS parameters, the machine learning model has to fulfill feature extraction, data fusion, as well as regression.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multimodal Deep Learning for Heterogeneous GNSS-R Data Fusion and Ocean Wind Speed Retrieval

Chu

Song

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The comprehensiveness of the raw input data and the effectiveness of feature engineering are two key factors affecting the performance of machine learning (ML). To improve the data comprehensiveness for Global Navigation Satellite System Reflectometry (GNSS-R) ocean wind speed retrieval, this paper introduces a new input data structure, which is composed of Delay-Doppler Maps (DDM) and all satellite receiver status (SRS) parameters. Then, to overcome the difficulty of handcrafted feature engineering and effectively fusion the information of DDM and SRS, we presented a heterogeneous multimodal deep learning (HMDL) method to retrieve the wind speed according to the heterogeneity of the input data. The proposed model is verified by the performance evaluation of realistic data sets obtained from TDS-1. The new input data structure improves the prediction accuracy at 13.5% to 30.7% on mean absolute error (MAE) at 10.6% to 29.5% on the root mean square error (RMSE). The HMDL improves the prediction accuracy at 7.7% on MAE and 7.1% on RMSE. The whole proposed solution improves the prediction accuracy at 36.3% on MAE and 36.8% on RMSE, comparing with the traditional neural network (NN) based solution. The results clearly show that both the introduction of the new input data structure and HMDL effectively improve the accuracy and robustness of GNSS-R wind speed retrieval.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multimodal Deep Learning for Heterogeneous GNSS-R Data Fusion and Ocean Wind Speed Retrieval

Chu

Song

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…However, in the case of high wind speed (the wind speed is greater than 20 m/s), the wind speed estimation errors became larger for the three models generally, but Model-3 had a certain improvement compared with the other two models. The error sources of the model estimation of wind speed mainly include: (1) The time resolutions of CYGNSS and ECMWF reanalysis data are not exactly the same. The matching of some sample instants is based on a close approximation of time, which may lead to differences in wind speed.…”

Section: Double-parameter (Model-1) and Triple-parameter (Model-2) Performance Assessment Based On Les Observationsmentioning

confidence: 99%

Developing and Testing Models for Sea Surface Wind Speed Estimation with GNSS-R Delay Doppler Maps and Delay Waveforms

Zhu

et al. 2020

Remote Sensing

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This paper focuses on sea surface wind speed estimation based on cyclone global navigation satellite system reflectometry (GNSS-R) data. In order to extract useful information from delay-Doppler map (DDM) data, three delay waveforms are presented for wind speed estimation. The delay waveform without Doppler shift is defined as central delay waveform (CDW), and the integral of the delay waveforms with different Doppler shift values is defined as integral delay waveform (IDW), while the difference between normalized IDW (NIDW) and normalized CDW (NCDW) is defined as differential delay waveform (DDW). We first propose a data filtering method based on threshold setting for data quality control. This method can select good-quality DDM data by adjusting the root mean square (RMS) threshold of cleaned DDW. Then, the normalized bistatic radar scattering cross section (NBRCS) and the leading edge slope (LES) of IDW are calculated using clean DDM data. Wind speed estimation models based on NBRCS and LES observations are then developed, respectively, and on this basis, a combination wind speed estimation model based on determination coefficient is further proposed. The CYGNSS data and ECMWF reanalysis data collected from 12 May 2020 to 12 August 2020 are used, excluding data collected on land, to evaluate the proposed models. The evaluation results show that the wind speed estimation accuracy of the piecewise function model based on NBRCS is 2.3 m/s in terms of root mean square error (RMSE), while that of the double-parameter and triple-parameter models is 2.6 and 2.7 m/s, respectively. The wind speed estimation accuracy of the double-parameter and triple-parameter models based on LES is 3.3 and 2.5 m/s. The results also demonstrate that the RMSE of the combination method is 2.1 m/s, and the coefficient of determination is 0.906, achieving a considerable performance gain compared with the individual NBRCS- and LES-based methods.

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“…The delay-Doppler map (DDM), generated by cross-correlating the received signal with a replica of the transmitted signal over a range of delays and Doppler frequencies, is the fundamental physical GNSS-R measurement. Many algorithms have been developed to retrieve ocean surface wind speed and other observables from the DDM (Clarizia et al 2009;Clarizia and Ruf, 2016;2020;Rodriguez-Alvarez and Garrison, 2016;Huang et al, 2019a;Reynolds et al, 2020). Under nominal operations, CYGNSS generates DDMs of 17 time delays × 11 Doppler frequencies in arbitrary units of "counts".…”

Section: Introductionmentioning

confidence: 99%

“…Many algorithms have been developed to retrieve ocean surface wind speed and other observables from the DDM (Clarizia et al . 2009; 2014; 2018; Clarizia and Ruf, 2016; 2017; 2020; Rodriguez‐Alvarez and Garrison, 2016; Huang et al ., 2019a; Reynolds et al ., 2020). Under nominal operations, CYGNSS generates DDMs of 17 time delays × 11 Doppler frequencies in arbitrary units of “counts”.…”

Section: Introductionmentioning

confidence: 99%

Assimilation of GNSS reflectometry delay‐Doppler maps with a two‐dimensional variational analysis of global ocean surface winds

Huang

Garrison

Leidner

et al. 2021

Quart J Royal Meteoro Soc

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Direct remote-sensing observations (e.g., radar backscatter, radiometer brightness temperature, or radio occultation bending angle) are often more effective for use in data assimilation (DA) than the corresponding geophysical retrievals (e.g., ocean surface winds, soil moisture, or atmospheric water vapor). In the particular case of Global Navigation Satellite System Reflectometry (GNSS-R), the lower-level delay-Doppler map (DDM) observable shows a complicated relationship with the ocean surface wind field. Prior studies have demonstrated DA using GNSS-R wind retrievals inferred from DDMs. The complexity of the DDM dependence on winds, however, suggests that the alternative approach of ingesting DDM observables directly into DA systems, without performing a wind retrieval, may be beneficial. We demonstrate assimilation of DDM observables from the NASA Cyclone Global Navigation Satellite System (CYGNSS) mission into global ocean surface wind analyses using a two-dimensional variational analysis method. Bias correction and quality-control methods are described.Several models for the required observation-error covariance matrix are developed and evaluated, with the conclusion that a diagonal matrix performs as well as a fully populated matrix empirically tuned to a large ensemble of CYGNSS observation data. The 10-m surface winds from the European Centre for Medium-Range Weather Forecasts (ECMWF) operational forecast are used as the background (i.e., prior in the variational analysis). Results are compared with independent scatterometer (the advanced scatterometer (ASCAT), the oceansat-2 Scatterometer (OSCAT)) winds. For one month (June 2017) of data, the root-mean-square difference (RMSD) was reduced from 1.17 to 1.07 m⋅s −1 and bias from −0.14 to −0.08 m⋅s −1 for the wind speed at the specular point. Within a 150-km wide swath along the specular point track, the RMSD was reduced from 1.20 to 1.13 m⋅s −1 . These RMSD and bias statistics are smaller than other CYGNSS wind products available at this time.

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Wind Speed Estimation From CYGNSS Using Artificial Neural Networks

Cited by 62 publications

References 36 publications

Multimodal Deep Learning for Heterogeneous GNSS-R Data Fusion and Ocean Wind Speed Retrieval

Multimodal Deep Learning for Heterogeneous GNSS-R Data Fusion and Ocean Wind Speed Retrieval

Developing and Testing Models for Sea Surface Wind Speed Estimation with GNSS-R Delay Doppler Maps and Delay Waveforms

Assimilation of GNSS reflectometry delay‐Doppler maps with a two‐dimensional variational analysis of global ocean surface winds

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