A retrieval algorithm is presented for the Level 2 ocean surface wind speed data product of the Cyclone Global Navigation Satellite System (CYGNSS) mission. The algorithm is based on the approach described by Clarizia et al ., 2014. The approach is applied to the specific orbital measurement geometry, antenna, and receiver hardware characteristics of the CYGNSS mission. Several additional processing steps have also been added to improve the performance. A best weighted estimator is used to optimally combine two different partially correlated estimates of the winds by taking their weighted average. The optimal weighting dynamically adjusts for variations in the signal-to-noise ratio of the observations that result from changes in the measurement geometry. Variations in the incidence angle of the measurements are accounted for by the use of a 2-D geophysical model function that depends on both wind speed and incidence angle. Variations in the propagation time and signal Doppler shift at different measurement geometries affect the instantaneous spatial resolution of the measurements, and these effects are compensated by a variable temporal integration of the data. In addition to a detailed description of the algorithm itself, the root-mean-square wind speed retrieval error is characterized as a function of the measurement geometry and the wind speed using a detailed mission end-to-end simulator.

Wind Speed Retrieval Algorithm for the Cyclone Global Navigation Satellite System (CYGNSS) Mission

A Minimum Variance (MV) wind speed estimator for Global Navigation Satellite System-Reflectometry (GNSS-R) is presented. The MV estimator is a composite of wind estimates obtained from five different observables derived from GNSS-R Delay-Doppler Maps (DDMs). Regression-based wind retrievals are developed for each individual observable using empirical geophysical model functions that are derived from NDBC buoy wind matchups with collocated overpass measurements made by the GNSS-R sensor on the United Kingdom-Disaster Monitoring Constellation (UK-DMC) satellite. The MV estimator exploits the partial decorrelation that is present between residual errors in the five individual wind retrievals. In particular, the RMS error in the MV estimator, at 1.65 m/s, is lower than that of each of the individual retrievals. Although they are derived from the same DDM, the partial decorrelation between their retrieval errors demonstrates that there is some unique information contained in them. The MV estimator is applied here to UK-DMC data, but it can be easily adapted to retrieve wind speed for forthcoming GNSS-R missions, including the UK's TechDemoSat-1 (TDS-1) and NASA's Cyclone Global Navigation Satellite System (CYGNSS).

Spaceborne GNSS-R Minimum Variance Wind Speed Estimator

Data from the CYGNSS mission, originally conceived to monitor tropical cyclones, are being investigated here for land applications as well. In this paper, a methodology for soil moisture (SM) retrieval from CYGNSS data is presented. The approach derives Level 3 gridded daily SM estimations, over the latitudinal band covered by CYGNSS, at a resolution of 36 km × 36 km, using the CYGNSS reflectivity over land, coupled with ancillary vegetation and roughness information from the SMAP mission. The results are compared globally with SM measurements from SMAP, which are assumed to be ground truth, showing a good agreement, and a global root-mean-square difference of 0.07 cm3/cm3. A more extensive comparison is performed over two test regions—Texas in the United States and New South Wales in Australia—where reference data from SMAP are complemented with measurements from the SMOS mission. The results over both regions are generally consistent with the global results, and a good agreement is observed between CYGNSS and reference SM measurements from SMAP and SMOS. The study demonstrates that SM can be successfully retrieved from the CYGNSS mission on a global scale and using ancillary information about the overlying vegetation and the characteristics of the soil. The results open up further future perspectives for global, high-resolution SM products from spaceborne Global Navigation Satellite System-Reflectometry data.

Analysis of CYGNSS Data for Soil Moisture Retrieval

Geophysical model functions (GMFs) are developed which map the Level 1 observables made by the Cyclone Global Navigation Satellite System (CYGNSS) radar receivers to ocean surface wind speed. The observables are: 1) the normalized bistatic radar cross section ( σo ) of the ocean surface; and 2) the slope of the leading edge of the radar return pulse scattered by the ocean surface. GMFs are empirically derived from measurements by CYGNSS which are nearly coincident with independent estimates of the 10-m-referenced ocean surface wind speed ( u 10). Two different sources of “ground truth” wind speed are considered: numerical weather prediction model outputs and measurements by the NOAA P-3 hurricane hunter during eyewall penetrations of major hurricanes. The GMFs derived in each case have significant differences that are believed to result from differences in the state of development of the long wave portion of the ocean surface height spectrum that result from characteristic differences in wave age and fetch length near versus far from a hurricane.

Development of the CYGNSS Geophysical Model Function for Wind Speed

The bistatic radar equation currently used for simulating surface-reflected waveforms or delay-Doppler maps (DDMs), produced by signals of opportunity from global navigation satellites system (GNSS) or communication satellites, was previously derived under some limiting assumptions. One of them was the use of the Kirchhoff approximation in a geometric optics limit that assumes strong diffuse (noncoherent) scattering typical for very rough surfaces. This equation would produce an incorrect result for the case of weak diffuse scattering, or in the presence of coherent reflection. In this paper, it is shown that the assumption of strong diffuse scattering is not necessary in deriving such an equation. The derivation of a generalized bistatic radar equation is now based only on the assumption of roughness statistics being spatially homogeneous, and thus this equation is applicable for a much wider range of surface conditions and scattering geometries. This approach allows to correctly describe the transition from partially coherent scattering to completely noncoherent, strong diffuse scattering. It is demonstrated for the case of the GNSS-R DDMs simulated for a wide range of surface winds, and their transitional behavior is discussed.

Bistatic Radar Equation for Signals of Opportunity Revisited

This paper investigates the global navigation satellite system-reflectometry (GNSS-R) measurements collected by the space GNSS receiver-remote sensing instrument (SGR-ReSI) on board the TechDemoSat-1 (TDS-1) satellite. The sensitivity of the SGR-ReSI measurements to the ocean surface winds and waves is characterized. The correlation with sea surface temperature (SST), wind direction, and rain is also investigated. The SGR-ReSI measurements exhibit clear sensitivity to wind speeds up to 20 m/s. There is also apparent sensitivity to 35 m/s wind speeds although the collocation dataset becomes sparser. A dependence on the swell is also observed for winds 5 m/s. A weak wind direction signal was also observed, and an investigation of rain impacts did not conclusively confirm any influence on the data. These results are shown through an analysis of global statistics as well as an analysis of several case studies. This publicly released SGR-ReSI dataset provided a first opportunity to comprehensively investigate the sensitivity of GNSS-R measurements to various ocean surface parameters. The upcoming NASA cyclone global navigation satellite system will utilize a similar receiver to the SGI-ReSI; therefore, this data provides a valuable prelaunch insight.

The GNSS Reflectometry Response to the Ocean Surface Winds and Waves

The Advanced Scatterometer (ASCAT) on the MetOp-A satellite is a radar instrument designed specifically to retrieve the ocean surface wind speed and direction. The ASCAT wind vector products are produced and utilized operationally in support of the National Oceanic and Atmospheric Administration (NOAA)'s weather forecasting and warning mission. The standard ASCAT winds at NOAA are produced using the ASCAT wind data processor developed at the Royal Netherlands Meteorological Institute (KNMI) utilizing the CMOD5.n geophysical model function (GMF). Recent validation of the ASCAT wind retrievals revealed a low bias at high wind speeds when compared to both the QuikSCAT winds and the National Centers for Environmental Prediction numerical weather prediction (NWP) model winds. The goal of this paper is to investigate the ASCAT high-wind-speed performance and to modify, as appropriate, the high-wind-speed portion of CMOD5.n GMF. This effort would potentially improve the utility of ASCAT wind retrievals in supporting wind warning and analysis and thus better mitigate the loss of QuikSCAT data products. Traditionally, the GMF is developed empirically by collocating scatterometer measurements and other truth data such as buoy and NWP model winds. However, NWP models are known to underestimate the intensity of higher wind speeds, and data sources such as ship-based or buoy-based observations provide an inadequate quantity of measurements for empirical GMF development. In this paper, a method utilizing aircraft-based scatterometer measurements in the high-wind-speed regimes is used in conjunction with satellite scatterometer measurements to refine the satellite GMF. As a result of this paper, a high wind C-band satellite GMF, CMOD5.h, was developed and implemented in NOAA's ASCAT processor. The validation comparison of the high wind and standard ASCAT wind products revealed 0.6-m/s reduction in the wind speed bias for winds greater than 15 m/s with respect to QuikSCAT, WindSat, and Step Frequency Microwave Radiometer high wind measurements.

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CMOD5.H—A High Wind Geophysical Model Function for C-Band Vertically Polarized Satellite Scatterometer Measurements

Surface wind vector measurements over the oceans are vital for scientists and forecasters to understand the Earth's global weather and climate. In the last two decades, operational measurements of global ocean wind speeds were obtained from passive microwave radiometers (Special Sensor Microwave/ Imagers); and over this period, full ocean surface wind vector data were obtained from several National Aeronautics and Space Administration and European Space Agency scatterometry missions. However, since SeaSat-A in 1978, there have not been other combined active and passive wind measurements on the same satellite until the launch of Japan Aerospace Exploration Agency's Advanced Earth Observing Satellite-II in 2002. This mission provided a unique data set of coincident measurements between the SeaWinds scatterometer and the Advanced Microwave Scanning Radiometer (AMSR). The AMSR instrument measured linearly polarized brightness temperatures (TB) over the ocean. Although these measurements contained wind direction information, the overlying atmospheric influence obscured this signal and made wind direction retrievals not feasible. However, for radiometer channels between 10 and 37 GHz, a certain linear combination of vertical and horizontal brightness temperatures causes the atmospheric dependence to cancel and surface parameters such as wind speed and direction and sea surface temperature to dominate the resulting signal. In this paper, an empirical relationship between AMSR TB's (specifically A . TBV - TBH) and surface wind vectors (inferred from SeaWinds' retrievals) is established for three microwave frequencies: 10, 18, and 37 GHz. This newly developed wind vector model function for microwave radiometers can serve as a basis for wind vector retrievals either separately or in combination with active scatterometer measurements.

An Ocean Surface Wind Vector Model Function for a Spaceborne Microwave Radiometer

The capability of the cyclone global navigation satellite system (CYGNSS) to observe winds within tropical cyclones (TCs) is assessed by using simulated CYGNSS observations over 43 cyclones from 2010 to 2011. The CYGNSS end-to-end simulator (E2ES) is utilized to generate delay-Doppler maps from which wind speeds are then retrieved. These wind speeds are first compared to the high-resolution model winds input into the E2ES. For range corrected gain (RCG) values greater than 20, the CYGNSS winds have a standard deviation of 0.57 m/s relative to these model winds. An error probability lookup table is developed to improve performance for RCG values lower than 20, as well as minimizing data loss (up to 4.4%). Since actual TCs are utilized in this study, the CYGNSS winds are also compared to winds from the advanced SCATterometer (ASCAT), the Oceansat-II scatterometer, GPS dropsondes, the stepped frequency microwave radiometer, and the H*wind model analysis. The CYGNSS winds compared best to the ASCAT winds with an overall bias around $-$ 0.4 m/s, and a standard deviation less than 1.54 m/s. GPS dropsonde winds compared less favorably with a standard deviation around 7.36 m/s, which can be partially attributed to spatial and temporal sampling differences. The CYGNSS ability to retrieve the TC maximum wind is also evaluated using the national hurricane center best track maximum winds. Although the CYGNSS overall bias varied from season to season (from $-$ 4.7 m/s in the Atlantic basin-2011 to $-$ 13.0 m/s in the Atlantic basin-2010), the standard deviation remained fairly consistent.

Performance Assessment of Simulated CYGNSS Measurements in the Tropical Cyclone Environment

The cyclone global navigation satellite system (CYGNSS), launched on December 15 2016, represents the first dedicated GNSS-R satellite mission specifically designed to retrieve ocean surface wind speeds in the Tropical Cyclone (TC) environment. The baseline wind retrieval algorithm for the CYGNSS mission makes use of two observables (the normalized bi-static radar cross section and the leading edge slope) to retrieve the average wind speed within a 25 km resolution cell. The premise of the algorithm is that these two observables are only a function of wind speed and incidence angle. Analysis of actual CYGNSS measurements during the course of the calibration and validation process, indicates that collected GNSS-R signals show a dependence on both winds and waves. This paper will present an alternative method in retrieving the wind speed from CYGNSS data, which will include the use of a geophysical model function dependent on both wind and wave data.

Seubson Soisuvarn

Papers

The GNSS Reflectometry Response to the Ocean Surface Winds and Waves

CMOD5.H—A High Wind Geophysical Model Function for C-Band Vertically Polarized Satellite Scatterometer Measurements

An Ocean Surface Wind Vector Model Function for a Spaceborne Microwave Radiometer

Performance Assessment of Simulated CYGNSS Measurements in the Tropical Cyclone Environment

A ‘Track-Wise’ Wind Retrieval Algorithm for the CYGNSS Mission