In this paper, we will present results for tropical cyclone wind radii estimation (34-, 50-, 64-knot) using wind speed retrievals from ASCAT-B/C ultra-high-resolution (UHR) processing. ASCAT-UHR derived surface wind radii are compared to storms best tracks obtained from the International Best Track Archive for Climate Stewardship (IBTrACS) dataset. The results were developed using 275 ASCAT-B/C hurricane overpasses from 2022 and 2023, and spans wind speed range from 17-75 m/s. Results show that the root-mean-square-errors (RMSEs) of wind radii from ASCAT-UHR (25.05, 8.75, and 5.52 nautical miles for R34, R50, and R64 respectively) are comparable to the positional uncertainty of IBTrACS.

The OceanSat-3 scatterometer (OSCAT-3) is a Ku-band radar instrument designed specifically to measure wind vectors over the ocean surface. Utilizing a conical scanning design with dual-polarization pencil beams at incidence angles of ~49° and ~58°, OSCAT-3 scans the Earth’s surface to measure the normalized radar cross section (sigma0) and brightness temperature, covering a swath width of 1800 km. In this paper, we introduce a supervised machine learning approach for rain rates estimation from OSCAT-3 measurements. Specifically, we tested ability of the Support Vector Machine (SVM) algorithm to estimate rain rate and flag suspect wind vector retrievals by utilizing regression and classification analysis respectively. For regression analysis, the machine learning model was trained using features derived from a combination of brightness temperatures, sea surface temperature (SST) and OSCAT-3 wind speed retrievals. The training rain rate targets were obtained from Global Precipitation Mission (GPM) Microwave Imager (GMI) measurements. In the case of rain flag classification analysis, training features were derived from brightness temperature and sigma0. The SVM rain rate model application for rain rate retrievals showed strong potential with the OSCAT-3 rain rates product exhibiting a bias of -0.2 mm/hr and a standard deviation of 1.0 mm/hr when compared to GMI rain rate measurements. However, the application of the SVM algorithm for data flagging purposes resulted in an over-flagging of the data under light rain condition. Consequently, for improved accuracy the final rain flag is determined based on OSCAT-3 rain rate estimation.

In this paper, we present supervised machine learning employed with high-resolution ASCAT data to detect sea ice in the Alaska region for our ultra-high-resolution wind and sea ice ASCAT product. Two machine learning algorithms, Gaussian Naïve Bayes (GNB) and Support Vector Machine (SVM) were tested in this analysis. GNB utilizes a feature vector consisting of six ASCAT variables, while SVM employed the same inputs but with additional standardization prior to training. The training target consisted of collocated GDAS ice flag and ASMR-2 ice concentration data. The dataset was balanced and divided into training and testing sets for each model. The results showed that GNB achieved 94% accuracy, while SVM achieved 98% with reduced noise. Consequently, we chose SVM as the final algorithm for near real-time sea ice flag processing. SVM exhibited improved accuracy in identifying the sea ice edge, allowing us to enhance the usability of ultra-high-resolution ASCAT wind data in polar regions.

The NOAA near real-time operational ASCAT ocean surface wind vectors are produced at 12.5 and 25 km swath grid resolutions. To avoid land contamination due to the relatively large footprint size, the wind data in the inner most coastal regions (~15 - 25 km from the coast) are excluded from the final product. To obtain more retrievals in the coastal regions we utilize measurements containing up to 50% land contribution ratio and employ a combination of the enhanced resolution processing technique and a coastal normalized radar cross section correction method to achieve accurate wind retrievals to within ~1.0 - 2.5 km of the coast. The backscatter correction is implemented for each ASCAT coastal zone measurement. The mean and standard deviation of the land contribution for each measurement is determined, and then the land backscatter contribution is subtracted out from the actual measurement through an iteration method to estimate the ocean-only signal. An ocean calibration of the enhanced resolution backscatter was implemented before the wind retrieval step to improve the accuracy. Finally, a land contribution ratio ranking is implemented after the wind retrieval to further remove the remaining land contamination residuals. Additional quality control is also developed. The high-resolution coastal winds from ASCAT are validated against a variety of other independent wind measurements. The results are then presented and discussed.

The NOAA near real-time operational ASCAT ocean surface wind vectors are produced at 12.5 and 25 km swath grid resolutions. However, due to land contamination most coastal zones are left without any wind retrievals. In this paper, we show how combination of the enhanced resolution normalized radar backscattered cross section (sigma0) algorithm with an aggressive land contribution ratio rejection technique and a land contamination correction algorithm allows for accurate wind to be retrieved within 1-2.5 km of the coast. To achieve acceptable accuracy for operational applications, an additional calibration of the enhanced resolution sigma0 was also found necessary. This algorithm was tested within the Alaskan coastal waters. Variability in the sea ice along the Alaskan coasts also required implementation of a high-resolution ice detection algorithm, which yielded a combined wind and ice product from the ASCAT measurements. The results are presented and discussed.

In this paper we develop high wind portion of a Geophysical Model Function (GMF) for Scatsat-1 scatterometer measurements. Starting with NSCAT4 GMF the high wind portion has been modifying by utilizing measurements obtained by IWRAP instrument on board of NOAA P3 aircraft within tropical and extratropical cyclones. The Ao coefficient in NSCAT4 GMF was modeled so its slope is a linear function of a logarithm of wind speed and its first derivative of it equals to zero at saturation wind speed for particular incidence angle and measurements frequency as it was measured by IWRAP. The calibrated measured sigma0's from Scatsat-1 shows good agreement with the new GMF named NSCAT4.H at the high winds. The respective wind retrievals are higher and much better aligned with ASCAT high winds while the global winds statistic performance of ScatSat-1 winds remained unchanged. The new ScatSat-1 wind products produced by NSCAT4.H GMF has been implemented in NOAA's operational marine forecasting and warning applications and examples are presented here.

The Cyclone Global Navigation Satellite System (CYGNSS) mission provides ocean wind speed measurements using GNSS reflectometry observations from eight space-borne receivers. The various CYGNSS wind products including conventional CYGNSS L2 winds, NOAA L2 winds, and TES (Trailing Edge Slope) winds are currently available. These wind products were compared to the ECMWF model winds using 8 hours data on June 8, 2018. The initial results showed that all retrievals showed some correlations with the ECMWF model winds, but the bias and the standard deviation are quite different among the methods. Comparison of three products for larger data set will be evaluated and presented in detail.

The relationship between the total precipitable water (TPW) in the atmosphere and Global Navigation Satellite System (GNSS) Zenith Total Delay (ZTD) has been investigated. The study was performed for ZTD observations derived from GPS measurements at 5 sites across Thailand, over one-year period, July 2017 to August 2018. The ZTD was then collocated in space and time with the TPW data available from Global Data Assimilation System (GDAS) Numerical Weather Prediction (NWP) model. The result showed that the ZTD observations are highly correlated with the TPW but there are some biases between the ground base station locations largely due to the altitude differences. An empirical model was then developed to remove the altitude dependence and provide a unique relationship between the ZTD and the TPW. This model can be useful in the further TPW measurements from widely available GNSS receivers.

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.

In this paper, we present the SCATSAT-1 wind data processor developed by NOAA. The sigma0 from L1B produced by ISRO was used as an input to our processor. Ocean surface wind vector products are produced at the grid resolutions of 12.5 km and 25 km. We experimented with different objective functions and the number of solutions. The ambiguity removal method utilized in our processor is the Two-Dimensional Variational Ambiguity Removal (2DVAR). The rain flag algorithm was developed based on the Bayes' theorem by calculating the rain probability given the rain sensitive parameter threshold.Finally, we validated our Scatsat-1 wind retrievals by both statistical analyses and visual inspection of the wind field for meteorological consistency to determine which objective function produced the best results. The performance of the Scatsat-1 wind retrievals compared to the Global Data Assimilation System (GDAS) winds shows reasonable wind speed and wind direction biases and standard deviation differences.

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 [1]. CYGNSS uses a constellation of eight microsatellite observatories that can receive both the direct and reflected signals from GNSS. The CYGNSS observatories are capable of collecting up to four simultaneous reflections each, thus providing high temporal-resolution of ocean surface observations. Thus far, most CYGNSS studies have utilized simulated data from the E2ES [2]-[9]. The current setup of the E2ES assumes the surface slope variances and correlation are completely locally wind-driven, and they are calculated solely based on the local wind speed and wind direction. Analysis of the actual CYGNSS measurements during the course of the calibration and validation process indicates that this assumption is not valid over a large portion of the measurements. While the primary objective of the CYGNSS mission is measuring ocean winds in tropical cyclones, examination of collected GNSS-R signal has shown that the measured signal is a function of both winds and waves. This paper discusses CYGNSS capability in measuring both winds and waves over the ocean, and evaluates its capability in tropical cyclones.

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

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.

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.

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 winds5 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 NASA's 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 hurricane maximum wind retrieval experiment is conducted using CYGNSS (Cyclone Global Navigation Satellite System) tracks simulated over a series of tropical cyclone scenes from the Hurricane Weather Research and Forecasting model (HWRF). This experiment makes use of a forward model relating simulated CYGNSS power-vs-delay waveforms to hurricane maximum winds. The forward model uses synthetic Willoughby modeled storms as `truth' data. 1148 HWRF storm scenes from the 2010-11 hurricane seasons, from both the Atlantic and Eastern pacific basins, are used as input wind field to the forward model. Retrieved maximum winds are compared to both NHC best-track and HWRF. Results show a definite potential in using the CYGNSS data in retrieving hurricane maximum winds, although the current retrieval performance is hindered due to the symmetrical nature of the modeled Willoughby storms.

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.

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 σ0that calculated from QuikSCAT wind inputs are improved for σ0approximately > -15 dB and in very good agreement with the ASCAT σ0measurement. 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.

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 ¿0revealed 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.

The ASCAT, launched on board MetOp-A satellite on October 19th2006, is a C-band scatterometer operating at 5.255 GHz using fan-beam antennae to measure near surface vector wind over the world's 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.

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 WindSat’s (AV-H) TB’s. We examined the characteristic of the GMF and explored the opportunity to improve our current WindSat wind direction retrieval that utilizes only 3rdand 4thStokes measurements, by integrating this GMF. The strength of the wind directional signals is encouraging for moderate wind speed at 8 m/s and higher.

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.

The WindSat microwave polarimetric radiometer consists of 22 channels of polarized brightness temperatures operating at five frequencies: 6.8, 10.7, 18.7, 23.8, and 37.0 GHz. The 10.7-, 18.7-, and 37.0-GHz channels are fully polarimetric (vertical/horizontal, /spl plusmn/45/spl deg/ and left-hand and right-hand circularly polarized) to measure the four Stokes radiometric parameters. The principal objective of this Naval Research Laboratory experiment, which flys on the USAF Coriolis satellite, is to provide the proof of concept of the first passive measurement of ocean surface wind vector from space. This paper presents details of the on-orbit absolute radiometric calibration procedure, which was performed during of a series of satellite pitch maneuvers. During these special tests, the satellite pitch was slowly ramped to +45/spl deg/ (and -45/spl deg/), which caused the WindSat conical spinning antenna to view deep space during the forward (or aft portion) of the azimuth scan. When viewing the homogeneous and isotropic brightness of space (2.73 K) through both the main reflector and the cold-load calibration reflector, it is possible to determine the absolute calibration of the individual channels and the relative calibration bias between polarimetric channels. Results demonstrate consistent and stable channel calibrations (with very small brightness biases) that exceed the mission radiometric calibration requirements.

Conventional "multiple-azimuth-look" space-borne scatterometers have been used to retrieve ocean surface wind vector for over three decades. However, the requirement for the antenna to view both forward and aft has made the scatterometer a difficult instrument to accommodate on remote sensing satellites with multiple earth-viewing sensors. In this paper, we present a new technique for ocean surface wind vector measurement using a "single-azimuth-look" combined active and passive microwave sensing from space. We demonstrate this methodology by using actual measurements of the ocean normalized radar cross section (sigma-0) and the radiometer-inferred wind magnitude respectively from the SeaWinds scatterometer and the Advanced Microwave Scanning Radiometer (AMSR) on-board the Advanced Earth Observing Satellite (ADEOS-II). For the scatterometer measurements, only the forward look, in the same direction as the AMSR measurements, is used. Owing to the biharmonic nature of the scatterometer anisotropic Geophysical Model Function (GMF), the retrieved wind direction consists of as many as four possible wind directions (aliases) at each wind vector cell (WVC). Alias selection is presented and performance is measured by comparison of the selected to the standard wind vector product from SeaWinds. Statistical analyses are performed and results show that it is feasible to measure ocean surface wind vector using single look geometry. © 2005 IEEE.

A new ocean surface wind vector measurement has been developed using combined active and passive microwave measurements from the TRMM satellite. In this method, collocated ocean normalized radar backscatter from the Precipitation Radar (PR) and retrieved wind speeds from the TRMM Microwave Imager (TMI) are used to derive ocean wind direction. Since PR provides only a single azimuth look, multiple wind direction solutions exists; but we compare the "closest" retrieved wind direction with near-simultaneous surface truth from three ocean buoy networks, namely; National Data Buoy Center (NDBC), Tropical Atmosphere Ocean (TAO), and Pilot Research moored Array in the Tropical Atlantic (PIRATA). Comparisons are also presented for QuikSCAT wind vector retrievals.

This paper presents a new ocean wind vector measurement technique that uses the combined passive and active microwave measurements respectively from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and the Precipitation Radar (PR). The wind speed is inferred by TMI over a wide swath that includes the narrower PR swath. The PR scans cross-talk /spl plusmn/18/spl deg/; and near the swath edges, where the radar backscatter responds to both the magnitude and direction of the surface wind, we use the microwave radiometer estimate of wind speed and the measured sigma-0 at incidence angles greater than 15 degrees to derive wind direction. Because the PR provides only a single azimuth look, multiple possible wind direction solutions exist. The ability to select the proper (single) direction is beyond the scope of this paper; but comparisons are presented between the "closest" retrieved TRMM wind vectors and near-simultaneous wind vectors measured by the QuikSCAT satellite scatterometer to demonstrate the potential for measuring ocean surface vector winds.