Ad Stoffelen

Scatterometer data interpretation: Estimation and validation of the transfer function CMOD4

In this paper we estimate the 18 coefficients of the CMOD4 σ0-to-wind transfer function using a maximum likelihood estimation (MLE) method in order to improve the prelaunch function. We show that a MLE method has to be used with caution when dealing with a nonlinear relationship or with measurement errors that depend on the measured values. In the transfer function estimation it is crucial to use the components of the wind, rather than wind speed and direction, to use σ0 in logarithmic units rather than physical ones, and to use well-sampled input data. In Stoffelen and Anderson [1997a] we showed that the triplets of measured backscatter are very coherent and, when plotted in a three-dimensional measurement space, they lie on a well-defined conical surface. Here we propose a strategy for validation of a transfer function, the first step of which is to test the ability of a transfer function to fit this conical surface. We derive an objective measure to compute the average fit of the transfer function surface to the distribution of measured σ0 triplets. The transfer function CMOD4, derived in the first part of this paper, is shown to fit the cone surface to within the observed scatter normal to the cone, i.e., within roughly 0.2 dB, equivalent to a root-mean-square wind vector error of ∼0.5 m s−1 The second step in the validation strategy is the verification of retrieved scatterometer winds at each position on the cone surface. Scatterometer winds computed from CMOD4 compare better to the European Centre for Medium-Range Weather Forecasts model winds than real-time conventional surface wind data (ship, buoy, or island reports) with the root-mean-square wind vector difference typically 3.0 m s−1. This surprising result can be explained by the so-called representativeness error. We further show that no significant spatial wind error correlation is present in scatterometer data and therefore conclude that the ERS 1 scatterometer provides winds useful for weather forecasting and climate studies.

Toward the true near-surface wind speed: Error modeling and calibration using triple collocation

Wind is a very important geophysical variable to accurately measure. However, a statistical phenomenon important for the validation or calibration of winds is the small dynamic range relative to the typical measurement uncertainty, i.e., the generally small signal-to-noise ra- tio. In such cases, pseudobiases may occur when standard validation or calibration methods are applied, such as regression or bin-average analyses. Moreover, nonlinear translbrmation of ran- dom error, for instance, between wind components and speed and direction, may give rise to substantial pseudobiases. In fact, validation or calibration can only be done properly when the full error characteristics of the data are hown. In practice, the problem is that prior howledge on the error characteristics is seldom available. In this paper we show that simultaneous eTor modeling and calibration can be achieved by using triple collocations. This is a fundamental fincling that is generally relevant to all geophysical validation. To illustrate the statistical analysis using triple collocations, in sire, ERS scatterometer, and Ibrecast model winds are used. Wind component error analysis is shown to be more convenient than wind speed and direction error analysis. The anemometer winds from the National Oceanic and Atmospheric Administration (NOAA) buoys are shown to have the largest error variance, followed by the scatterometer and the National Centers Ibr Enviromental Prediction (NCEP) forecast model winds proved the most accurate. When using the in situ winds as a reference, the scatterometer wind components are biased low by -4%. The NCEP forecast model winds are found to be biased high by -6%. After applying a higher-order calibration procedure an improved ERS scatterometer wind re- trieval is proposed. The systematic and random error analysis is relevant for the use of near- surface winds to compute Iluxes of momentum, humidity, or heat or to drive ocean wave or cir- culation mtxlel s.

The atmospheric dynamics mission for global wind field measurement

The prime aim of the Atmospheric Dynamics Mission is to demonstrate measurements of vertical wind profiles from space. Extensive studies conducted by the European Space Agency over the past 15 years have culminated in the selection of a high-performance Doppler wind lidar based on direct-detection interferometric techniques. Such a system, with a pulsed laser operating at 355-nm wavelength, would utilize both Rayleigh scattering from molecules and Mie scattering from thin cloud and aerosol particles; measurement of the residual Doppler shift from successive levels in the atmosphere provides the vertical wind profiles. The lidar would be accommodated on a satellite flying in a sun-synchronous orbit, at an altitude of ~400 km, providing near-global coverage; target date for launch is in 2007. Processing of the backscatter signals will provide about 3000 globally distributed wind profiles per day, above thick clouds or down to the surface in clear air, at typically 200-km separation along the satellite track...

Validation and Calibration of ASCAT Using CMOD5.n

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.

ERS scatterometer wind data impact on ECMWF's tropical cyclone forecasts

This paper describes the positive impact of ERS scatterometer data on tropical cyclone analyses and forecasts at the European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, U.K. ERS scatterometer data is especially valuable because sparse genesis regions of tropical cyclones are available in the data, and they are available in cloudy and rainy conditions. In November 1997, ECMWF introduced a four-dimensional variational assimilation system (4D-Var) in operational use. This system benefits from a better utilization of ERS scatterometer wind data, ECMWF is using ERS-2 scatterometer wind data in the daily operational assimilation system. In order to understand and investigate the impact of ERS scatterometer wind data, assimilations with and without the use of scatterometer data have been performed for the most intense part of the 1995 Atlantic hurricane season. A comparison with the 1995 operational ECMWF optimum interpolation (OI) assimilation systems performance has also been done. Both intensity and positional errors of tropical cyclones are investigated for analyses and forecasts. The 4D-Var assimilation system shows great improvements compared to the previous OI assimilation system, and the best results are obtained when ERS scatterometer data are used in 4D-Var.

Extended triple collocation: Estimating errors and correlation coefficients with respect to an unknown target

Calibration and validation of geophysical measurement systems typically require knowledge of the true value of the target variable. However, the data considered to represent the true values often include their own measurement errors, biasing calibration, and validation results. Triple collocation (TC) can be used to estimate the root-mean-square-error (RMSE), using observations from three mutually independent, error-prone measurement systems. Here, we introduce Extended Triple Collocation (ETC): using exactly the same assumptions as TC, we derive an additional performance metric, the correlation coefficient of the measurement system with respect to the unknown target, rho(t,Xi). We demonstrate that rho(2)(t,Xi) is the scaled, unbiased signal-to-noise ratio and provides a complementary perspective compared to the RMSE. We apply it to three collocated wind data sets. Since ETC is as easy to implement as TC, requires no additional assumptions, and provides an extra performance metric, it may be of interest in a wide range of geophysical disciplines.

On Scatterometer Ocean Stress

Abstract Scatterometers estimate the relative atmosphere–ocean motion at spatially high resolution and provide accurate inertial-scale ocean wind forcing information, which is crucial for many ocean, atmosphere, and climate applications. An empirical scatterometer ocean stress (SOS) product is estimated and validated using available statistical information. A triple collocation dataset of scatterometer, and moored buoy and numerical weather prediction (NWP) observations together with two commonly used surface layer (SL) models are used to characterize the SOS. First, a comparison between the two SL models is performed. Although their roughness length and the stability parameterizations differ somewhat, the two models show little differences in terms of stress estimation. Second, a triple collocation exercise is conducted to assess the true and error variances explained by the observations and the SL models. The results show that the uncertainty in the NWP dataset is generally larger than in the buoy and s...

Scatterometer Backscatter Uncertainty Due to Wind Variability

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

On Bayesian scatterometer wind inversion

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.

Rain Effects on ASCAT-Retrieved Winds: Toward an Improved Quality Control

The quality of the Ku-band scatterometer-derived winds is known to be degraded by the presence of rain. Little work has been done in characterizing the impact of rain on C-band scatterometer winds, such as those from the Advanced Scatterometer (ASCAT) onboard Metop-A. In this paper, the rain impact on the ASCAT operational level 2 quality control (QC) and retrieved winds is investigated using the European Centre for Medium-range Weather Forecasts (ECMWF) model winds, the Tropical Rainfall Measuring Missions (TRMM) Microwave Imager (TMI) rain data, and tropical buoy wind and precipitation data as reference. In contrast to Ku-band, it is shown that C-band is much less affected by direct rain effects, such as ocean splash, but effects of increased wind variability appear to dominate ASCAT wind retrieval. ECMWF winds do not well resolve the airflow under rainy conditions. ASCAT winds do but also show artifacts in both the wind speed and wind direction distributions for high rain rates (RRs). The operational QC proves to be effective in screening these artifacts but at the expense of many valuable winds. An image-processing method, known as singularity analysis, is proposed in this paper to complement the current QC, and its potential is illustrated. QC at higher resolution is also expected to result in improved screening of high RRs.