Seubson Soisuvarn

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.

Retrieval of Total Precipitable Water (TPW) using ground-based Global Navigation Satellite System (GNSS) observations is a challenging task due to its real‐time and high temporal resolution. In this paper, we present a method for establishing an analytic model for retrieving the total precipitable water (TPW) based on Global Navigation Satellite System (GNSS) observations over one-year period from 12 distributed stations across Thailand. The derived zenith total delay (ZTD) at all stations agrees well with the TPW data available from Global Data Assimilation System (GDAS) Numerical Weather Prediction (NWP) model. At first, a unique relationship between the ZTD and the TPW was established by taking into account of the variation of station altitudes. In addition, the bias correction technique using probability distribution function (PDF) matching was also applied to improve the final model. The inversion model of TPW from ZTD is then easily obtained using a numerical technique. The performance of our method has been successfully evaluated on an independent test data. This model can be useful in the further near real-time TPW measurements from widely available GNSS receivers.

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.

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. 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.

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.

A cyclone global navigation satellite system (CYGNSS)calibration analysis is presented using version 2.0 of the Level 1 dataset available on PO.DAAC. Three separate analyzes are conducted, namely, an examination of the specular bin location (in delay) andrelationship, an investigation of the impact of recently improved characterizations of the GPS effective isotropically radiated power on CYGNSS, and an intersatellitecalibration analysis. We first noted a correlation between the specular delay bin location and, where an increase in the specular delay bin resulted in an increase inregardless of the wind speed level; a specular delay bin location ranging from 4.75 to 5.00 and from 7.00 to 7.25 resulted in a 14.74 and 17.72 dB median, respectively. Noticeable improvements in the medianwere present in the version 2.0 dataset, when separating the data by GPS block type: for blocks IIR, IIF, and IIR-M, medianwere 15.39, 15.42, and 15.10 dB, respectively (compared to 19.38, 20.53, and 21.38 dB in version 1.1). Finally, an unexpected correlation between the instrument noise floor andwas observed for all eight observatories while conducting the intersatellitecalibration analysis. Approximately 0–1 dB absolutedifference biases (with up to1 dB standard deviation) between spacecrafts were observed. A report of this analysis was presented to CYGNSS scientists and engineers, who eventually found an issue with the D/A DDM scaling algorithm. We expect better statistical performances in future releases of the data.

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.