Seubson Soisuvarn

The GNSS Reflectometry Response to the Ocean Surface Winds and Waves

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 <;6 m/s. Additionally, a small correlation with SST is observed in which the slope of the SGR-ReSI measurements is positive for winds <;5 m/s, and reverses 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.

Performance Assessment of Simulated CYGNSS Measurements in the Tropical Cyclone Environment

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.

Validation of NOAA's Near Real-Time Ascat Ocean Vector Winds

The ASCAT, launched on board MetOp-A satellite on October 19th 2006, is a C-band scatterometer operating at 5.255 GHz using fan-beam antennae to measure near surface vector wind over the worlds ocean. NOAA produces near-real-time ASCAT wind product at 50 and 25 km resolutions. These wind data are validated against global wind field model and satellite observation from QuikSCAT. The results show ASCAT wind speed retrievals perform well for low to moderate wind speed under most weather conditions, but are underestimated for wind speeds ¿ 15 m/s. The standard deviation wind direction errors are well below 20 degrees for wind speed ¿ 5 m/s.

Calibration and validation of the cygnss level 1 data products

This presentation will include an overview of the recently launched NASA CYGNSS mission Level 1 calibration algorithms and their on-orbit validation [1], [2]. The validation of the Level 1 calibration will be performed in several steps, including a) a detailed noise floor analysis to assess the observed on-orbit noise power levels over the open ocean, b) multiple consistency checks using a forward model and co-located ocean wind and wave truth reference data and c) a term by term error analysis of all the non-ocean corrections applied to the final sigma0 estimates. An outline of the Level 1a (calibration from raw Level 0 instrument counts to units of watts for the received power) and the Level 1b (calibration from watts to bistatic scattering cross section) algorithms are each shown below. Three key components of the Level 1a calibration will be presented, namely, an analysis of the instrument (alone) and antenna noise characteristics over the ocean, a study of the range of received power levels from the surface, and comparisons with a forward model. The key components of the Level 1b calibration presented here will include validation of the main corrections applied to arrive at a surface sigma0 estimate, including receiver antenna gain, GPS transmitter and scattering area corrections.

The development of a C-band Advanced Scatterometer (ASCAT) geophysical model function at NOAA/NESDIS

Validation of the ASCAT wind vectors show that the ASCAT wind speed errors are within 2 m/s RMS error for wind speeds up to 15 m/s, however they exhibit and increasing low bias beyond 15 m/s. An examination of the ASCAT σ0 revealed some additional sensitivity at the higher wind speeds that was not adequately represented by the current geophysical model function (GMF). A revised GMF is empirically derived using a near-real-time QuikSCAT as a surface truth. A new DC term in the GMF is derived and replaced in the operational CMOD5.5 GMF. Validation of the revised GMF shows that the wind speed retrievals are closer to QuikSCAT for wind speeds > 15 m/s than the operational retrievals, while wind direction retrievals remain the same for all wind speeds as expected.

Evaluation of cygnss gnss-r signal sensitivity to ocean parameters and wind retrieval assesment

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 [3], [4]. CYGNSS will use a constellation of eight microsatellite observatories that can receive both the direct and reflected signals from GNSS. These observatories are capable of collecting four simultaneous reflections each, thus providing high temporal-resolution wind speed retrievals within TCs.

An overview of NOAA's GCOM-W1/AMSR-2 product processing and utilization

Passive microwave radiometry is a special application of microwave communications technology for the purpose of collecting Earths electromagnetic radiation. With the use of radiometers onboard earth orbiting satellites, scientists are able to monitor the Earths environment and climate system on both short- and long-term temporal scales with near global coverage.

The GNSS Reflectometry response to the ocean surface

We investigate 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 are characterized. The effects of sea surface temperature, wind direction, and rain are also investigated. The SGR-ReSI measurements exhibited sensitivity through the entire range of wind speeds sampled in this dataset, up to 35 m/s. A significant dependence on the larger waves was observed for winds <; 6 m/s. Additionally, an interesting dependence on SST was observed where the slope of the SGR-ReSI measurements is positive for winds <; 5 m/s and reverses for winds > 5 m/s. There appeared to be very little wind direction signal, and investigation of the rain impacts found no apparent sensitivity in the data. These results are shown through the analysis of global statistics and examination of a few case studies. This released SGR-ReSI dataset provided the first opportunity to comprehensively investigate the sensitivity of satellite-based GNSS-R measurements to various ocean surface parameters. The upcoming NASAs Cyclone Global Navigation Satellite System (CYGNSS) satellite constellation will utilize a similar receiver to SGI-ReSI and thus this data provides valuable pre-launch knowledge.

A revised geophysical model function for the advanced scatterometer (ASCAT) at NOAA/NESDIS

The current ASCAT winds retrieval is based on the CMOD5.n geophysical model function (GMF) with the ASCAT wind data processor developed at the Royal Netherlands Meteorological Institute (KNMI). Recent validation of ASCAT wind retrieval reveals that high wind retrievals were underestimated as being compared to the operational QuikSCAT scatterometer. The goal in this paper is to improve ASCAT wind retrievals at high winds. In this paper we map the radar backscatter (σ0) as a function of extreme wind conditions as measured by an airborne scatterometer and adjusted the isotropic term in CMOD5.n to follow the aircraft GMF trend. The geophysical model QuikSCAT wind inputs are improved for σ0 that calculated from QuikSCAT wind inputs are improved for σ0 approximately > −15 dB and in very good agreement with the ASCAT σ0 measurement. The wind retrieval validations show wind speed rms error is improved at approximately wind speed > 12 m/s and example mean wind composite from two winter seasons shows significant in detection of storm-force winds.

A geophysical model function for windsat polarimetric radiometer wind retrievals using linear polarizations

In this paper, we develop a novel geophysical model function (GMF) for the WindSat radiometer relating the vertically (V-pol) and horizontally (H-pol) polarized brightness temperature (TB) to the ocean surface wind field. The brightness temperature data from the 10,18 and 37 GHz channels were used in this analysis. The brightness temperature combination of the form (AV-H) is found to be mostly independent of the atmospheric variations, where A is a constant number for each frequency. The GMF was developed empirically using the collocated wind vectors from the QuikSCAT scatterometer retrieval as truth and the WindSats (AV-H) TBs. We examined the characteristic of the GMF and explored the opportunity to improve our current WindSat wind direction retrieval that utilizes only 3rd and 4th Stokes measurements, by integrating this GMF. The strength of the wind directional signals is encouraging for moderate wind speed at 8 m/s and higher.