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CYGNSS: Enabling the Future of Hurricane Prediction [Remote Sensing Satellites]

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Abstract

The CYGNSS mission introduces a new paradigm in low-cost Earth science missions that employs a constellation of science-based microsats to fill a gap in capabilities of existing large systems at a fraction of the cost. The CYGNSS observatories will make frequent wind observations, and wind observations in precipitating conditions, using GPS reflectometry to observe the TC inner core ocean surface. These efforts will result in unprecedented coverage of windswithin a TC throughout its life cycle and thus provide critical data necessary for advancing the forecast of TC intensification.
... The operating GNSS-R missions include the Techdemosat-1 (TDS-1) [6], launched by UK in 2014, the Cyclone Global Navigation Satellite System (CYGNSS) [7], launched by NASA in 2016, and Bufeng-1, launched by China in 2019 [35]. The GNSS-R data are collected in the form of Delay Doppler Maps (DDMs), which have been applied to the retrieval of ocean wind speed [36], sea ice thickness [37], and monitoring the wetland changes [38]. ...
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In this paper, we provide updates on our recent work on the theory of microwave remote sensing for applications in remote sensing of soil moisture and snow water equivalent (SWE). The three topics are the following. (i) For the effects of forests and vegetation, we developed the hybrid method of NMM3D full-wave simulations over the vegetation field and forest canopies. In the hybrid method, we combined the use of commercial off-the-shelf software and wave multiple scattering theory (W-MST). The results showed much larger transmission than classical radiative transfer theory. (ii) In signals of opportunity at L-band and P-band, which are radar bistatic scattering in the vicinity of the specular direction, we developed the Analytical Kirchhoff solution (AKS) and Numerical Kirchhoff approach (NKA) in the calculations of coherent waves and incoherent waves. We also took into account of the effects of topographical elevations and slopes which have strong influences. (iii) In rough surface radar backscattering, we used the volume integral equation approach for NMM3D full-wave simulations for soil surfaces with kh up to 15. The simulations were calculated for the X-band and Ku-band and the results showed saturation effects. The simulation results can be applied to microwave remote sensing of SWE at these two frequencies.
... The present study extends the methodology of Allende-Alba and Thoelert (2020a) for s-pattern reconstruction by introducing a multi-station approach, which makes it suitable for GPS satellites as well, thus, adding up to recent efforts for the on-orbit characterization of gain patterns of GPS antennas. These include studies focusing on the improvement of the calibration of Level 1B data (Wang et al., 2019a(Wang et al., , 2019b for the Cyclone Global Navigation Satellite System (CYGNSS) mission (Ruf et al., 2013) as well as on the analysis of the space service volume for missions in the geostationary and highly elliptical orbits (Donaldson et al., 2020). Likewise, Marquis and Reigh (2015) and Marquis (2016) analyzed the performance of antennas of GPS IIR satellites using observations from a high-gain antenna. ...
Conference Paper
The CYclone Global Navigation Satellite System (CYGNSS) End-to-End Simulator (E2ES) is updated with a new module capable of accurately simulating coherent forward scattering from inland water bodies. This module uses a Global Navigation Satellite Systems Reflectometry (GNSS-R) model that can account for both coherent and incoherent scattering due to small scale surface roughness. The model is developed based on the Huygens-Kirchhoff principle. One principle application is to support the development of wetland extent retrieval algorithms over heterogenous scenes. In this abstract, a water mask corresponding to a lake surrounded by mountains and vegetation is considered. A modeled time series of reflected power is generated and results are compared to actual CYGNSS raw IF data. The final goal is to support scientific studies related to the capabilities of CYGNSS for inland water body monitoring.
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This paper focuses on sea surface wind speed estimation using L1B level v3.1 data of reflected GNSS signals from the Cyclone GNSS (CYGNSS) mission and European Centre for Medium-range Weather Forecast Reanalysis (ECMWF) wind speed data. Seven machine learning methods are applied for wind speed retrieval, i.e., Regression trees (Binary Tree (BT), Ensembles of Trees (ET), XGBoost (XGB), LightGBM (LGBM)), ANN (Artificial neural network), Stepwise Linear Regression (SLR), and Gaussian Support Vector Machine (GSVM), and a comparison of their performance is made. The wind speed is divided into two different ranges to study the suitability of the different algorithms. A total of 10 observation variables are considered as input parameters to study the importance of individual variables or combinations thereof. The results show that the LGBM model performs the best with an RMSE of 1.419 and a correlation coefficient of 0.849 in the low wind speed interval (0–15 m/s), while the ET model performs the best with an RMSE of 1.100 and a correlation coefficient of 0.767 in the high wind speed interval (15–30 m/s). The effects of the variables used in wind speed retrieval models are investigated using the XGBoost importance metric, showing that a number of variables play a very significant role in wind speed retrieval. It is expected that these results will provide a useful reference for the development of advanced wind speed retrieval algorithms in the future.
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While hurricane track forecasts have improved in accuracy by ~50% since 1990, there has been essentially no improvement in the accuracy of intensity prediction. This lack of progress is thought to be caused by inadequate observations and modeling of the inner core due to two causes: 1) much of the inner core ocean surface is obscured from conventional remote sensing instruments by intense precipitation in the inner rain bands and 2) the rapidly evolving stages of the tropical cyclone (TC) life cycle are poorly sampled in time by conventional polar-orbiting, wide-swath surface wind imagers. NASA's most recently awarded Earth science mission, the NASA EV-2 Cyclone Global Navigation Satellite System (CYGNSS) has been designed to address these deficiencies by combining the all-weather performance of GNSS bistatic ocean surface scatterometry with the sampling properties of a satellite constellation. This paper provides an overview of the CYGNSS flight segment requirements, implementation, and concept of operations for the CYGNSS constellation; consisting of 8 microsatellite-class spacecraft (
Conference Paper
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Hurricane track forecasts have improved in accuracy by ~50% since 1990, while in that same period there has been essentially no improvement in the accuracy of intensity prediction. One of the main problems in addressing intensity occurs because the rapidly evolving stages of the tropical cyclone (TC) life cycle are poorly sampled in time by conventional polar-orbiting, wide-swath surface wind imagers. NASA's most recently awarded Earth science mission, the NASA EV-2 Cyclone Global Navigation Satellite System (CYGNSS) has been designed to address this deficiency by using a constellation of micro-satellite-class Observatories designed to provide improved sampling of the TC during its life cycle. Managing a constellation of Observatories has classically resulted in an increased load on the ground operations team as they work to create and maintain schedules and command loads for multiple Observatories. Using modern tools and technologies at the Mission Operations Center (MOC) in conjunction with key components implemented in the flight system and an innovative strategy for pass execution coordinated with the ground network operator, the CYGNSS mission reduces the burden of constellation operations to a level commensurate with the low-cost mission concept. This paper focuses on the concept of operations for the CYGNSS constellation as planned for implementation at the CYGNSS MOC in conjunction with the selected ground network operator.
Conference Paper
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As part of the EV-2 Cyclone Global Navigation Satellite System (CYGNSS) mission team, Surrey will be providing the Delay Doppler Mapping Instrument (DDMI) for eight Observatories designed and built by the University of Michigan and Southwest Research Institute (SwRI). Following the success of the GPS Reflectometry Experiment on the UK-DMC 1 satellite launched in 2003, Surrey has developed the SGR-ReSI as a move towards operational reflectometry and other applications. The Space GPS Receiver Remote Sensing Instrument (SGR-ReSI) is a COTS-electronics based GNSS receiver which can support up to eight programmable front-ends. It allows collection of raw sampled data but also is capable of processing the reflections into Delay Doppler Maps in real time. The first flight of the SGR-ReSI will be on the UK TechDemoSat-1 to prove the instrument and its various applications. The SGR-ReSI on CYGNSS has a different configuration to that on TechDemoSat-1 which is needed to focus on the requirements for operational cyclone sensing.
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The NASA EV-2 Cyclone Global Navigation Satellite System (CYGNSS) is a spaceborne mission focused on tropical cyclone (TC) inner core process studies. CYGNSS attempts to resolve the principle deficiencies with current TC intensity forecasts, which lies in inadequate observations and modeling of the inner core. The inadequacy in observations results from two causes: 1) Much of the inner core ocean surface is obscured from conventional remote sensing instruments by intense precipitation in the eye wall and inner rain bands. 2) The rapidly evolving (genesis and intensification) stages of the TC life cycle are poorly sampled in time by conventional polar-orbiting, wide-swath surface wind imagers. CYGNSS is specifically designed to address these two limitations by combining the all-weather performance of GNSS bistatic ocean surface scatterometry with the sampling properties of a constellation of satellites. The use of a dense constellation of nanosatellite results in spatial and temporal sampling properties that are markedly different from conventional imagers. Simulation studies will be presented which examine the sampling as functions of various orbit parameters of the constellation. Historical records of actual TC storm tracks are overlaid onto a simulated time series of the surface wind sampling enabled by the constellation. For comparison purposes, a similar analysis is conducted using the sampling properties of several past and present conventional spaceborne ocean wind scatterometers. Differences in the ability of the sensors to resolve the evolution of the TC inner core are examined. The spacecraft and constellation mission are described. The signal-to-noise ratio of the measured scattered signal and the resulting uncertainty in retrieved surface wind speed are also examined.
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A study of the retrieval of sea surface roughness using Global Navigation Satellite System-Reflectometry (GNSS-R) from satellite is presented. Delay-Doppler Maps (DDMs) from the SSTL UK-DMC satellite are analyzed to retrieve directional Mean Square Slopes (MSSs). Results are compared to theoretically-derived MSSs and in situ measurements from co-located buoys of the National Data Buoy Center (NDBC), showing good agreement in most cases. Here, the whole DDM, a more complete source of information, is exploited for the first time using satellite GNSS-R data. These are potentially able to provide high spatial and temporal sampling, and therefore offer an improved way to observe wind and waves by means of a very modest instrument.
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Using GPS signals reflected from the ocean surface is developing into a simple technique for measuring sea-state and inferring surface wind speeds. Theoretical models have been developed which are considered valid to approximately 24 meters per second. The GPS reflection technique has an obvious extension to extremely high sea states, cyclones and extra-tropical storms. In October of 2000 a GPS system mounted in a NOAA Hurricane Hunter research aircraft, was flown into Hurricane Michael off the South Carolina coast. The first acquisition of GPS signals reflected from the sea surface inside tropical cyclones was accomplished. This paper presents some examples of the data sets as well as early wind speed retrieval results using direct extensions of current models. Data from the GPS wind speed retrievals as well as from direct aircraft measurements are compared and discussed.
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Since 1996 when GPS reflected signals were purposefully acquired, an effort to assess the utility of these signals has been under way. It was early determined that the reflected GPS signal can be related to ocean surface wind dependent slope probability densities. Quantifying that relationship has resulted in considerable data taken at wind speeds below those associated with tropical storms. During the 2005 hurricane season, data were taken in high wind speed conditions that have been used to compare with the U.S. Navy's COAMPS model. The gridded data were used to develop a calibration for high wind speeds which also represents a measurement of apparent ocean surface slopes at L-Band. This paper presents the results of this GPS surface reflection calibration for winds up to 35 meters per second. In addition a simple function is developed that models the mean square slope variation with surface wind speed and includes winds above 35 meters per second. This model is applied to data from Hurricanes Dennis and Isabel to demonstrate the ability of surface reflected GPS signals to yield good retrieval performance for winds at and above tropical storm strength.
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Sampling patterns and sampling errors from various scatterometer datasets are examined. Four single and two tandem scatterometer mission scenarios are considered. The single scatterometer missions are ERS (with a single, narrow swath), NSCAT and ASCAT (dual swaths), and QuikSCAT (a single, broad swath obtained from the SeaWinds instrument). The two tandem scenarios are combinations of the broad-swath SeaWinds scatterometer with ASCAT and QuikSCAT. The dense, nearly uniform distribution of measurements within swaths, combined with the relatively sparse, nonuniform placement of the swaths themselves create complicated space–time sampling patterns. The temporal sampling of all of the missions is characterized by bursts of closely spaced samples separated by longer gaps and is highly variable in both latitude and longitude. Sampling errors are quantified by the expected squared bias of particular linear estimates of component winds. Modifications to a previous method that allow more efficient expecte...
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ABSTRACT A spaceborne,GNSS reflectometry,experiment,is to be undertaken,by Surrey Satellite Technology,Limited with support,from,the British National Space Centre (BNSC)
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We present a new approach to the retrieval of sea surface roughness using GNSS-R. The steps through the simulation of the whole end-to-end microwave scattering of GNSS signals from the sea surface are explained, with emphasis on how to generate a linear sea surface and to implement the Kirchhoff Approximation (KA), as the large-scale part of the full scattering model. We illustrate some examples of radar cross sections calculated using the Kirchhoff scattering model, and how they change with respect to different polarizations. Their variations with geometry, sea state and spatial resolution are investigated and discussed.