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In this study, we propose a method for wind speed retrieval using a random forest (RF) algorithm for Cyclone Global Navigation Satellite System (CYGNSS) data. We first compared CYGNSS data with soil moisture active passive (SMAP) data and found a certain deviation in the CYGNSS “young sea, limited fetch” (YSLF) data product for high winds. Then, we used SMAP as the “ground truth” to train an RF model and applied it to the wind speed retrieval of CYGNSS data. The experimental results show that using the RF algorithm for wind speed retrieval can eliminate noise in the CYGNSS YSLF wind speed data and improve retrieval accuracy. In addition, we explored the impact of different input parameter combinations on model performance and found that using an 11-parameter model in CYGNSS wind speed retrieval can achieve optimal performance. This can provide a valuable reference for rapid near-real-time retrieval of tropical cyclones using CYGNSS.

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Li, F., Lozier, M. S., Bacon, S., Bower, A. S., Cunningham, S. A., de Jong, M. F., DeYoung, B., Fraser, N., Fried, N., Han, G., Holliday, N. P., Holte, J., Houpert, L., Inall, M. E., Johns, W. E., Jones, S., Johnson, C., Karstensen, J., Le Bras, I. A., P. Lherminier, X. Lin, H. Mercier, M. Oltmanns, A. Pacini, T. Petit, R. S. Pickart, D. Rayner, F. Straneo, V. Thierry, M. Visbeck, I. Yashayaev & Zhou, C. Subpolar North Atlantic western boundary density anomalies and the Meridional Overturning Circulation. Nature Communications, 12(1), (2021): 3002, https://doi.org/10.1038/s41467-021-23350-2., Changes in the Atlantic Meridional Overturning Circulation, which have the potential to drive societally-important climate impacts, have traditionally been linked to the strength of deep water formation in the subpolar North Atlantic. Yet there is neither clear observational evidence nor agreement among models about how changes in deep water formation influence overturning. Here, we use data from a trans-basin mooring array (OSNAP—Overturning in the Subpolar North Atlantic Program) to show that winter convection during 2014–2018 in the interior basin had minimal impact on density changes in the deep western boundary currents in the subpolar basins. Contrary to previous modeling studies, we find no discernable relationship between western boundary changes and subpolar overturning variability over the observational time scales. Our results require a reconsideration of the notion of deep western boundary changes representing overturning characteristics, with implications for constraining the source of overturning variability within and downstream of the subpolar region., We acknowledge funding from the Physical Oceanography Program of the U.S. National Science Foundation (OCE-1259398, OCE-1756231, OCE-1948335); the U.K. Natural Environment Research Council (NERC) National Capability programs the Extended Ellett Line and CLASS (NE/R015953/1), and NERC grants UK-OSNAP (NE/K010875/1, NE/K010875/2, NE/K010700/1) and U.K. OSNAP Decade (NE/T00858X/1, NE/T008938/1). Additional support was received from the European Union 7th Framework Program (FP7 2007-2013) under grant 308299 (NACLIM), the Horizon 2020 research and innovation program under grants 727852 (Blue-Action), 862626 (EuroSea). We also acknowledge support from the Royal Netherlands Institute for Sea Research, the Surface Water and Ocean Topography-Canada (SWOT-C), Canadian Space Agency, the Aquatic Climate Change Adaptation Services Program (ACCASP), Fisheries and Oceans Canada, an Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant, and from the China’s national key research and development projects (2016YFA0601803), the National Natural Science Foundation of China (41925025) and the Fundamental Research Funds for the Central Universities (201424001). Support for the 53°N array by the RACE program of the German Ministry BMBF is acknowledged, as is the contribution from Fisheries and Oceans Canada’s Atlantic Zone Monitoring Program.

This paper demonstrates the capability and performance of sea surface wind speed retrieval in coastal regions (within 200 km away from the coastline) using spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) data from NASA's Cyclone GNSS (CYGNSS) mission. The wind speed retrieval is based on the Artificial Neural Network (ANN). A feedforward neural network is trained with the collocated CYGNSS Level 1B (version 2.1) observables and the wind speed from European Centre for Medium-range Weather Forecast Reanalysis 5th Generation (ECMWF ERA5) data in coastal regions. An ANN model with five hidden layers and 200 neurons in each layer has been constructed and applied to the validation set for wind speed retrieval. The proposed ANN model achieves good wind speed retrieval performance in coastal regions with a bias of −0.03 m/s and a RMSE of 1.58 m/s, corresponding to an improvement of 24.4% compared to the CYGNSS Level 2 (version 2.1) wind speed product. The ANN based retrievals are also compared to the ground truth measurements from the National Data Buoy Center (NDBC) buoys, which shows a bias of −0.44 m/s and a RMSE of 1.86 m/s. Moreover, the sensitivities of the wind speed retrieval performance to different input parameters have been analyzed. Among others, the geolocation of the specular point and the swell height can provide significant contribution to the wind speed retrieval, which can provide useful reference for more generic GNSS-R wind speed retrieval algorithms in coastal regions.

The National Aeronautics and Space Administration (NASA) Cyclone Global Navigation Satellite System (CyGNSS) mission was launched in December 2016, which can remotely sense sea surface wind with a relatively high spatio-temporal resolution for tracking tropical cyclones. In recent years, with the gradual development of the geophysical model function (GMF) for CyGNSS wind retrieval, different versions of CyGNSS Level 2 products have been released and their performance has gradually improved. This paper presents a comprehensive evaluation of CyGNSS wind product v1.1 produced by the National Oceanic and Atmospheric Administration (NOAA). The Cross-Calibrated Multi-Platform (CCMP) analysis wind (v02.0 and v02.1 near real time) products produced by Remote Sensing Systems (RSS) were used as the reference. Data pairs between the NOAA CyGNSS and RSS CCMP products were processed and evaluated by the bias and standard deviation SD. The CyGNSS dataset covers the period between May 2017 and December 2020. The statistical comparisons show that the bias and SD of CyGNSS relative to CCMP-nonzero collocations when the flag of CCMP winds is nonzero are –0.05 m/s and 1.19 m/s, respectively. The probability density function (PDF) of the CyGNSS winds coincides with that of CCMP-nonzero. Furthermore, the average monthly bias and SD show that CyGNSS wind is consistent and reliable generally. We found that negative deviation mainly appears at high latitudes in both hemispheres. Positive deviation appears in the China Sea, the Arabian Sea, and the west of Africa and South America. Spatial–temporal analysis demonstrates the geographical anomalies in the bias and SD of the CyGNSS winds, confirming that the wind speed bias shows a temporal dependency. The verification and comparison show that the remotely sensed wind speed measurements from NOAA CyGNSS wind product v1.1 are in good agreement with CCMP winds.

While the dynamics of narrow fjords, i.e. narrow with respect to their internal Rossby radius, have been widely studied, it is only recently that interest sparked in studying the physics of broad fjords due to their importance in glacial ice melting (in Greenland, especially). Here, we present a comprehensive set of data collected in Fortune Bay, a broad, mid-latitude fjord located on the Northwest Atlantic shores. Aside from being wide (15–25 km width) and deep (600 m at its deepest), Fortune Bay also has the characteristics of having steep slopes, weak tides and of being strongly stratified from spring to fall. Thus, and since strong along-shore winds also characterise the region, this system is prone to interesting dynamics, generally taking the form of transient upwelling and downwelling travelling along its shores, similar to processes encountered in broad fjords of higher latitudes. The dataset collected to study those dynamics consists of water column physical parameters (temperature, salinity, currents and water level) and atmospheric forcing (wind speed and direction, atmospheric pressure, air temperature and solar radiation) taken at several points around the fjord using oceanographic moorings and land-based stations. The program lasted 2 full years and achieved a good data return of 90 %, providing a comprehensive dataset not only for Fortune Bay studies but also for the field of broad fjord studies. The data are available publically from the SEANOE repository (http://www.seanoe.org/data/00511/62314/; Donnet and Lazure, 2020).