Search

Quality control (QC) is an essential part of the scatterometer wind retrieval. In the current pencil-beam scatterometer wind processor (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 subcell wind variability in the ocean surface wind vectors derived from Ku-band pencil-beam scatterometers, such as the RapidScat (RSCAT) installed on the International Space Station. 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. This paper reviews several wind quality-sensitive indicators derived from the RSCAT data, such as MLE and its spatially averaged value (MLEm), and the singularity exponents (SE) derived from an image processing technique, called singularity analysis. Their sensitivities to data quality and rain are evaluated using collocated Advanced Scatterometer wind data, and global precipitation measurement satellite’s microwave imager rain data, respectively. It shows that MLEm and SE are the most effective indicators for filtering the poorest-quality winds over RSCAT inner and outer swath, respectively. A simple combination of SE and MLEm thresholds is proposed to optimize RSCAT wind QC. Comparing to the operational PenWP QC, the proposed method mitigates over-rejection at high winds, and improves the classification of good- and poor-quality winds. [ABSTRACT FROM PUBLISHER]

The two-dimensional variational ambiguity removal (2DVAR) method provides a spatial analysis of the sampled ocean vector winds to resolve the local Advanced Scatterometer (ASCAT) dual wind vector ambiguity. Like other variational meteorological data assimilation systems in numerical weather prediction (NWP), 2DVAR combines ASCAT observations with prior NWP background information, in this case from the European Centre for Medium-range Weather Forecasts (ECMWF). Although 2DVAR is generally effective, it may select the wrong ambiguity under certain conditions, e.g. when the background mislocates frontal areas or low-pressure centres, or when it misses convective systems. The relative influence of the ASCAT and ECMWF wind fields in the resulting 2DVAR analysis field can be controlled by adjusting the background error spatial correlation structure, and the background and/or observation error variances. In this article an adaptive 2DVAR approach is proposed to improve ASCAT ambiguity removal, using background error spatial correlations estimated from the autocorrelation of observed scatterometer wind components minus ECMWF forecasts, and using observation and background errors estimated from triple collocation analysis on collocated buoy, ASCAT and ECMWF data. The triple collocations are segregated into several categories according to the ASCAT-derived parameters that have proven to be effective in detecting the correct position of frontal lines and low-pressure centres. Verification using a typical cyclone case and collocated ASCAT and buoy winds shows that the 2DVAR analysis as well as the ASCAT ambiguity removal is improved significantly by putting more weight on the ASCAT observations using empirically determined spatial background error structure functions and situation-dependent observation/background error variances. [ABSTRACT FROM AUTHOR]

The Advanced Scatterometer (ASCAT) onboard the Metop-A satellite became operational shortly after launch in 2006, and an absolute calibration using three transponders was achieved in November 2008. In this paper, we describe how the CMOD5.n ocean backscatter geophysical model function (GMF), which was derived using data from previous scatterometers onboard the European Remote Sensing 1 and 2 satellites (ERS-1 and ERS-2), was used to derive backscatter bias correction factors. The purpose is to remove the bias between ASCAT backscatter data and the CMOD5.n GMF output which allows these data to be used in place of ERS data in existing wind processing algorithms. The ASCAT Wind Data Processor, developed at the Royal Netherlands Meteorological Institute (KNMI), applies the bias correction factors to ASCAT data and uses CMOD5.n to retrieve wind vectors in order to produce an operational wind product. This resulted in a stable and high-quality ASCAT wind product since February 2007. We validate this product by comparing it to the European Centre for Medium-range Weather Forecasts (ECMWF) winds and buoy measurements. The bias correction factors indicate that ASCAT data and the GMF differ by roughly 0.3 dB below 55° and up to 0.8 dB above 55°. A possible explanation lies in CMOD5.n which has been poorly validated in this incidence angle regime. Validation of ASCAT data using the ocean calibration method confirms this result and also indicates that bias-corrected data are everywhere within 0.3 dB of CMOD5.n. The wind product validation shows an rms error of 1.3 m ⋅ s-1 in wind speed and 16° in wind direction when compared to ECMWF winds. This is better than the results achieved using ERS scatterometer data. Against buoy winds, we find an rms error wind component error of approximately 1.8 m ⋅ s-1. These results show that the ASCAT wind product is of high quality and satisfies its wind component accuracy requirement of 2 m ⋅ s-1. [ABSTRACT FROM AUTHOR]

The estimation of tropical cyclone (TC) intensity using Ku-band scatterometer data is challenging due to rain perturbation and signal saturation in the radar backscatter measurements. In this paper, an alternative approach to directly taking the maximum scatterometer-derived wind speed is proposed to assess the TC intensity. First, the TC center location is identified based on the unique characteristics of wind stress divergence/curl near the TC core. Then the radial extent of 17-m/s winds (i.e., R17) is calculated using the wind field data from the Haiyang-2B (HY-2B) scatterometer (HSCAT). The feasibility of HSCAT wind radii in determining TC intensity is evaluated using the maximum sustained wind speed (MSW) in the China Meteorological Administration best-track database. It shows that the HSCAT R17 value generally better correlates with the best-track MSW than the HSCAT maximum wind speed, therefore indicating the potential of using the HSCAT data to improve the TC nowcasting capabilities. [ABSTRACT FROM AUTHOR]

Wind retrieval from scatterometer backscatter measurements is not trivial. A good assessment of the different measurement uncertainties inherent in scatterometer systems is very important for successful wind retrieval and quality control. One source of these uncertainties, i.e., geophysical noise, is dominated by the subcell wind variability. Although the latter is known to dominate the total measurement noise at low winds, no attempt to fully model such effect has yet been performed. In this paper, a simple method to derive a model of geophysical noise for the European Remote Sensing Satellite (ERS) scatterometer is proposed. It is assumed that this noise is mainly due to the spatial distribution of the backscatter footprints and the wind variability within the wind vector cell. In a simulation experiment these parameters were varied, and the values for which the simulation compares best to real data in the three-dimensional measurement space were selected. The resulting geophysical noise model is dependent on wind speed and across subsatellite track location. The empirical method presented here is straightforward and could be applied to other scatterometer systems. [ABSTRACT FROM AUTHOR]

In a quest for a generic unbiased scatterometer wind inversion method, the different inversion procedures currently in use are revisited in this paper. A careful examination of both the errors in the wind and in the measurement domain, combined with the nonlinear shape of the geophysical model function (GMF), leads to a generic and novel Bayesian wind retrieval approach in the measurement domain. In this approach the shape of the GMF solution manifold in measurement space is more important than the specified noise. This shape is related to the system wind direction sensitivity, and when this sensitivity is uniform, realistic and precise wind direction distributions are retrieved, even when measurements lie far from the GMF manifold. A simplified measurement space transformation that produces such uniform sensitivity for the European Remote Sensing Satellite (ERS) scatterometer is presented and shown to have reduced wind direction bias compared to the more traditional (measurement-noise normalized) inversion for ERS. Moreover, the simplified wind inversion reveals a similar performance to the current operational ERS wind inversion, but is potentially more generally applicable. The simplified method is then applied to SeaWinds but is ineffective. In this case the instrument geometry results in a low sensitivity to wind direction at a few specific directions. As a consequence, certain wind direction solutions remain favored in the SeaWinds inversion. [ABSTRACT FROM AUTHOR]

The Ku-band scatterometer onboard China France Oceanography Satellite (CFOSAT) observes the sea surface with two conically scanning fan beams. Compared to the prior Ku-band pencil beam scatterometers, this innovative observing mechanism provides more independent backscatter measurements at varying incidence and azimuth angles, as such it brings challenges for the sea surface wind inversion, particularly under rainy conditions. In this paper, the rain effects on the CFOSAT SCATterometer (CSCAT) are investigated using the collocated numerical weather prediction (NWP) wind data and the Global Precipitation Measurement (GPM) microwave imager (GMI) rain data. Similar to the prior Ku-band or C-band scatterometers, the sensitivity of CSCAT radar backscatter to rain substantially varies with wind speed, radar polarization and incidence angle. However, due to the complex observation geometries, rain effects on the CSCAT retrieved winds is more complex than that of prior scatterometers, which may lead to a remarkable underestimation of CSCAT wind speed at high winds and heavy rain conditions. A simple simulation method is used to clarify the relation between the retrieved wind speed and the dependency of radar rain effects on the incidence angle. It is found that the backscatter measurements at low incidence angles, which are generally underestimated at high winds and heavy rainy conditions, have a larger influence on the wind inversion minimization, leading to much lower retrieved wind speeds than those of ECMWF and the pencil beam scatterometer (e.g., Haiyang-2B scattometer). Under low and moderate rain conditions though, a more compensated effect between low and high incidence angle measurements is found, leading to generally unbiased CSCAT high winds, in contrast to the generally underestimated pencil-beam scatterometer winds. • High winds under heavy rain are underestimated by the CFOSAT scatterometer (CSCAT). • The dependency of rain effect on the incidence angle is explored for CSCAT. • Rain effect on the CSCAT σ0is more complex than that of prior scatteormeters. • A simulation method is used to investigate the rain impact on the wind retrieval. [ABSTRACT FROM AUTHOR]