Improvement of ScatSat-1 sea surface wind vector fields

Abstract

Following the success of the QuikSCAT, Oceansat-2, HY-2A, and RapidScat missions, a new Ku-band rotating pencil-beam scatterometer, ScatSat-1 from the Indian Space Research Organization (ISRO) was launched in September 2016. Scatterometer sea surface winds have been used in a wide variety of atmospheric, oceanic, and climate applications. Moreover, thanks to the near-real-time data distribution of most missions, scatterometer wind data have been successfully assimilated into numerical weather prediction models for more than two decades.

In the framework of the EUMETSAT Numerical Weather Prediction Satellite Application Facility (NWP SAF) and Ocean and Sea Ice Satellite Application Facility (OSI SAF), the Royal Netherlands Meteorological Institute (KNMI) has developed the so-called Pencil-beam Wind data Processor (PenWP), which has provided and provides near-real-time Level 2 (swath-based) sea surface wind fields for all past and current rotating pencil-beam scatterometer missions. The main components of PenWP include calibration, inversion, quality control, and ambiguity removal. Research & Development activities within the NWP SAF and OSI SAF over the past 15 years have focused on the improvement of the different algorithms of the scatterometer wind data processors, including PenWP. Recent results show that both the Ku-band forward model or geophysical model function (GMF) and the quality control (QC) can be further improved. In this paper, we focus on these two relevant aspects of the scatterometer wind data processing in order to improve the ScatSat-1 wind retrieval quality.

Recent developments on the wind GMF of Ku-band (~2 cm wavelength) scatterometers include a sea surface temperature (SST) dependent term. Moreover, it has been found that the SST effects on the radar backscatter are wind speed dependent, and are mainly relevant at radar wavelengths smaller than C-band (~5 cm wavelength). A new Ku-band GMF, NSCAT-5, has been developed based on a physical model and RapidScat radar backscatter measurements. The objective of this work is to verify the NSCAT-5 GMF at slightly different incidence angles. First, the ScatSat-1 backscatter sensitivity to sea surface wind and SST is assessed using the best available wind reference at scatterometer scales, i.e., the C-band Advanced Scatterometer (ASCAT) winds, for which plenty of collocations are available (note that during the first year of the ScatSat-1 mission, both ScatSat-1 and ASCAT were flying on a very similar orbit, which resulted in a large amount of nearly coincident measurements in space and time). Second, the approach used to derive the NSCAT-5 GMF for RapidScat is adapted to derive a SST-dependent GMF for ScatSat-1. In particular, the GMF sensitivity to quality control effects is also investigated. The new GMF will be used to consolidate the current NSCAT-5 model, and then evaluated for ScatSat-1 wind retrieval.

In the current version of PenWP, a maximum likelihood estimator (MLE-) based QC is used to discern between good- and poor-quality winds. MLE QC is generally effective in flagging rain contamination and increased sub-cell wind variability in the ocean surface wind vectors derived from Ku-band pencil-beam scatterometers. However, the MLE is not an effective quality indicator over the outer swath where the inversion is underdetermined due to the lack of azimuthal diversity (including lack of horizontal polarized measurements). Besides, it is challenging to discriminate rain contamination from “true” high winds. In this paper, recent developments for Ku-band QC are adopted for ScatSat-1 data. In addition to the MLE, two wind quality-sensitive indicators are used: the spatially averaged MLE value (MLEm), and the singularity exponent (SE) derived from an image processing technique called singularity analysis. Their sensitivities to data quality and rain are evaluated using collocated Advanced Scatterometer (ASCAT) wind data, and Global Precipitation Measurement satellite’s Microwave Imager (GMI) rain data respectively. It shows that MLEm and SE are the most effective indicators for filtering the poorest-quality winds over ScatSat-1 inner and outer swath, respectively. A simple combination of SE and MLEm thresholds is proposed to optimize ScatSat-1 wind QC. In comparison to the operational PenWP QC, the proposed method mitigates over-rejection at high winds, and improves the classification of good- and poor-quality winds.

The recently launched Ku-band scatterometer missions CFOSAT (October 2018) and HY-2B (November 2018), currently in commissioning phase, will certainly benefit from efforts like those proposed here to improve and/or validate the Ku-band forward model. Reprocessing of past missions will benefit as well. In particular, the verification of the NSCAT-5 GMF at other incidence angles will be carried out with the CFOSAT scatterometer, a rotating fan-beam system which samples the surface at varying incidence angles