scholarly journals Retrieval of Ocean Surface Wind Speed Using Reflected BPSK/BOC Signals

2020 ◽  
Vol 12 (17) ◽  
pp. 2698 ◽  
Author(s):  
Hao-Yu Wang ◽  
Jyh-Ching Juang
Keyword(s):  
Remote Sensing ◽  
Wind Speed ◽  
Frequency Band ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Satellite System ◽  
Retrieval Algorithm ◽  
Binary Offset Carrier ◽  
Remote Sensing Technique ◽  
Sensing Applications

The Global Navigation Satellite System (GNSS) has become a valuable resource as a remote sensing technique. In the past decade, the use of reflected GNSS signals for sensing the Earth, also known as GNSS reflectometry (GNSS-R), has grown rapidly. On the other hand, with the continuous development of GNSS, multi-frequency multi-modulation signals have been used to enhance not only positioning performance, but also remote sensing applications. It is known that for some constellations, navigation satellites broadcast signals employing BPSK (binary phase-shift keying) modulation and BOC (binary offset carrier) modulation at the same frequency band. This paper proposes a new GNSS-R measurement, called a composite delay-Doppler map (cDDM), by utilizing the received reflected GNSS signals with different modulation techniques for the purpose of retrieving wind speed. The GNSS-R receiver can receive BPSK and BOC signals simultaneously at the same frequency band (e.g., GPS III L1 C/A and L1C or QZSS L1 C/A and L1C) and process the signals to generate GNSS-R measurements. Exploration of the observable features extracted from the composite DDM and the wind speed retrieval algorithm are also provided. The simulation verifies the proposed method under a configuration that is specified for the orbital and instrument specification of the upcoming TRITON mission.

scholarly journals A Unique Satellite-Based Sea Surface Wind Speed Algorithm and Its Application in Tropical Cyclone Intensity Analysis

2016 ◽  
Vol 33 (7) ◽  
pp. 1363-1375 ◽  
Author(s):  
Sungwook Hong ◽  
Hwa-Jeong Seo ◽  
Young-Joo Kwon
Keyword(s):  
Wind Speed ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Sea Surface ◽  
Japan Meteorological Agency ◽  
Retrieval Algorithm ◽  
Sea Surface Wind Speed ◽  
Brightness Temperatures ◽  
Sea Surface Wind ◽  
Medium Range

AbstractThis study proposes a sea surface wind speed retrieval algorithm (the Hong wind speed algorithm) for use in rainy and rain-free conditions. It uses a combination of satellite-observed microwave brightness temperatures, sea surface temperatures, and horizontally polarized surface reflectivities from the fast Radiative Transfer for TOVS (RTTOV), and surface and atmospheric profiles from the European Centre for Medium-Range Weather Forecasts (ECMWF). Regression relationships between satellite-observed brightness temperature and satellite-simulated brightness temperatures, satellite-simulated brightness temperatures, rough surface reflectivities, and between sea surface roughness and sea surface wind speed are derived from the Advanced Microwave Scanning Radiometer 2 (AMSR-2). Validation results of sea surface wind speed between the proposed algorithm and the Tropical Atmosphere Ocean (TAO) data show that the estimated bias and RMSE for AMSR-2 6.925- and 10.65-GHz bands are 0.09 and 1.13 m s−1, and −0.52 and 1.21 m s−1, respectively. Typhoon intensities such as the current intensity (CI) number, maximum wind speed, and minimum pressure level based on the proposed technique (the Hong technique) are compared with best-track data from the Japan Meteorological Agency (JMA), the Joint Typhoon Warning Center (JTWC), and the Cooperative Institute for Mesoscale Meteorological Studies (CIMSS) for 13 typhoons that occurred in the northeastern Pacific Ocean throughout 2012. Although the results show good agreement for low- and medium-range typhoon intensities, the discrepancy increases with typhoon intensity. Consequently, this study provides a useful retrieval algorithm for estimating sea surface wind speed, even during rainy conditions, and for analyzing characteristics of tropical cyclones.

scholarly journals Retrieved wind speed from the Orbiting Carbon Observatory-2

2020 ◽  
Vol 13 (12) ◽  
pp. 6889-6899
Author(s):  
Robert R. Nelson ◽  
Annmarie Eldering ◽  
David Crisp ◽  
Aronne J. Merrelli ◽  
Christopher W. O'Dell
Keyword(s):  
Wind Speed ◽  
Near Infrared ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Retrieval Algorithm ◽  
Economic Sectors ◽  
Wind Speeds ◽  
Clear Sky ◽  
Potential Benefits ◽  
Microwave Sensors

Abstract. Satellite measurements of surface wind speed over the ocean inform a wide variety of scientific pursuits. While both active and passive microwave sensors are traditionally used to detect surface wind speed over water surfaces, measurements of reflected sunlight in the near-infrared made by the Orbiting Carbon Observatory-2 (OCO-2) are also sensitive to the wind speed. In this work, retrieved wind speeds from OCO-2 glint measurements are validated against the Advanced Microwave Scanning Radiometer-2 (AMSR2). Both sensors are in the international Afternoon Constellation (A-Train), allowing for a large number of co-located observations. Several different OCO-2 retrieval algorithm modifications are tested, with the most successful being a single-band Cox–Munk-only model. Using this, we find excellent agreement between the two sensors, with OCO-2 having a small mean bias against AMSR2 of −0.22 m s−1, an RMSD of 0.75 m s−1, and a correlation coefficient of 0.94. Although OCO-2 is restricted to clear-sky measurements, potential benefits of its higher spatial resolution relative to microwave instruments include the study of coastal wind processes, which may be able to inform certain economic sectors.

scholarly journals CYGNSS Ocean Surface Wind Validation in the Tropics

2020 ◽  
Author(s):  
Shakeel Asharaf ◽  
Duane E. Waliser ◽  
Derek J. Posselt ◽  
Christopher S. Ruf ◽  
Chidong Zhang ◽  
...  
Keyword(s):  
Wind Speed ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Satellite System ◽  
Cold Pool ◽  
Tropical Ocean ◽  
Near Surface ◽  
Wind Speeds ◽  
Low Wind Speeds ◽  
Global Navigation Satellite

AbstractSurface wind plays a crucial role in many local/regional weather and climate processes, especially through the exchanges of energy, mass and momentum across the Earth’s surface. However, there is a lack of consistent observations with continuous coverage over the global tropical ocean. To fill this gap, the NASA Cyclone Global Navigation Satellite System (CYGNSS) mission was launched in December 2016, consisting of a constellation of eight small spacecrafts that remotely sense near surface wind speed over the tropical and sub-tropical oceans with relatively high sampling rates both temporally and spatially. This current study uses data obtained from the Tropical Moored Buoy Arrays to quantitatively characterize and validate the CYGNSS derived winds over the tropical Indian, Pacific, and Atlantic Oceans. The validation results show that the uncertainty in CYGNSS wind speed, as compared with these tropical buoy data, is less than 2 m s-1 root mean squared difference, meeting the NASA science mission Level-1 uncertainty requirement for wind speeds below 20 m s-1. The quality of the CYGNSS wind is further assessed under different precipitation conditions, and in convective cold-pool events, identified using buoy rain and temperature data. Results show that CYGNSS winds compare fairly well with buoy observations in the presence of rain, though at low wind speeds the presence of rain appears to cause a slight positive wind speed bias in the CYGNSS data. The comparison indicates the potential utility of the CYGNSS surface wind product, which in turn may help to unravel the complexities of air-sea interaction in regions that are relatively under-sampled by other observing platforms.

A Coupled-Pixel Model (CPM) Atmospheric Retrieval Algorithm for High-Resolution Imagers

2015 ◽  
Vol 32 (10) ◽  
pp. 1866-1879 ◽  
Author(s):  
Mary Morris ◽  
Christopher S. Ruf
Keyword(s):  
Wind Speed ◽  
Rain Rate ◽  
Microwave Radiometer ◽  
Surface Wind ◽  
Low Frequency ◽  
Surface Wind Speed ◽  
Retrieval Algorithm ◽  
Single Column ◽  
Simplifying Assumptions ◽  
Cross Track

AbstractLow-frequency passive microwave observations allow for oceanic remote sensing of surface wind speed and rain rate from spaceborne and airborne platforms. For most instruments, the modeling of contributions of rain absorption and reemission in a particular field of view is simplified by the observing geometry. However, the simplifying assumptions that can be applied in most applications are not always valid for the scenes that the airborne Hurricane Imaging Radiometer (HIRAD) regularly observes. Collocated Stepped Frequency Microwave Radiometer (SFMR) and HIRAD observations of Hurricane Earl (2010) indicate that retrieval algorithms based on the usual simplified model, referred to here as the decoupled-pixel model (DPM), are not able to resolve two neighboring rainbands at the edge of HIRAD’s swath. The DPM does not allow for the possibility that a single column of atmosphere can affect the observations at multiple cross-track positions. This motivates the development of a coupled-pixel model (CPM) that is developed and tested in this paper. Simulated observations as well as HIRAD’s observations of Hurricane Earl (2010) are used to test the CPM algorithm. Key to the performance of the CPM algorithm is its ability to deconvolve the cross-track scene, as well as unscramble the signatures of surface wind speed and rain rate in HIRAD’s observations. While the CPM approach was developed specifically for HIRAD, other sensors could employ this method in similar complicated observing scenarios.

In-Orbit Performance of the Constellation of CYGNSS Hurricane Satellites

2019 ◽  
Vol 100 (10) ◽  
pp. 2009-2023 ◽  
Author(s):  
Christopher Ruf ◽  
Shakeel Asharaf ◽  
Rajeswari Balasubramaniam ◽  
Scott Gleason ◽  
Timothy Lang ◽  
...  
Keyword(s):  
Wind Speed ◽  
Tropical Cyclones ◽  
Dynamic Range ◽  
Surface Wind ◽  
Low Frequency ◽  
Surface Wind Speed ◽  
Satellite System ◽  
Low Earth Orbit ◽  
Current Status ◽  
Temporal Sampling

AbstractThe 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.

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 Sea Surface Wind Speed Retrieval from the First Chinese GNSS-R Mission: Technique and Preliminary Results

2019 ◽  
Vol 11 (24) ◽  
pp. 3013 ◽  
Author(s):  
Cheng Jing ◽  
Xinliang Niu ◽  
Chongdi Duan ◽  
Feng Lu ◽  
Guodong Di ◽  
...  
Keyword(s):  
Wind Speed ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Satellite System ◽  
Sea Surface ◽  
Satellite Mission ◽  
Preliminary Results ◽  
Sea Surface Wind Speed ◽  
Wind Speeds ◽  
Sea Surface Wind

Launched on 5 June 2019, the BuFeng-1 A/B twin satellites were part of the first Chinese global navigation satellite system reflectometry (GNSS-R) satellite mission. In this letter, a brief introduction of the BF-1 mission and its preliminary results of sea surface wind retrieval are presented. Empirical fully developed sea (FDS) geophysical model functions (GMFs) relating the normalized bistatic radar cross-section to the sea surface wind speed are proposed for the BF-1 GNSS-R instruments. The FDS GMFs are derived from the collocated BF-1 observations, the European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis data, and the advanced scatterometer (ASCAT) satellite observations. The preliminary tests reveal that the root-mean-square error (RMSE) between the derived wind speed and the reanalysis is 2.63 m/s for wind speeds in the range of 0.5–40.5 m/s. Further comparisons with the ASCAT observations and mooring buoys show that the RMSEs are 2.04 m/s and 1.77 m/s, respectively, at low-to-moderate wind speeds. This study demonstrates the effectiveness of BF-1 and provides a basis for the future GMF development of the BF-1 A/B mission.

Impact of Assimilating CYGNSS Data on Tropical Cyclone Analyses and Forecasts in a Regional OSSE Framework

2017 ◽  
Vol 51 (1) ◽  
pp. 7-15 ◽  
Author(s):  
Brian McNoldy ◽  
Bachir Annane ◽  
Sharanya Majumdar ◽  
Javier Delgado ◽  
Lisa Bucci ◽  
...  
Keyword(s):  
Wind Speed ◽  
Large Scale ◽  
Surface Wind ◽  
Ocean Surface ◽  
Surface Wind Speed ◽  
Satellite System ◽  
Directional Information ◽  
Wind Speed Data ◽  
Ocean Surface Wind ◽  
The Impact

AbstractThe impact of assimilating ocean surface wind observations from the Cyclone Global Navigation Satellite System (CYGNSS) is examined in a high-resolution Observing System Simulation Experiment (OSSE) framework for tropical cyclones (TCs). CYGNSS is a planned National Aeronautics and Space Administration constellation of microsatellites that utilizes existing GNSS satellites to retrieve surface wind speed. In the OSSE, CYGNSS wind speed data are simulated using output from a “nature run” as truth. In a case study using the regional Hurricane Weather Research and Forecasting modeling system and the Gridpoint Statistical Interpolation data assimilation scheme, analyses of TC position, structure, and intensity, together with large-scale variables, are improved due to the assimilation of the additional surface wind data. These results indicate the potential importance of CYGNSS ocean surface wind speed data and furthermore that the assimilation of directional information would add further value to TC analyses and forecasts.

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