Characterization of Ocean Wind Vector Retrievals using ERS-2 High-Resolution Long-term Dataset and Buoy Measurements
Polverari, Federica1; Talone, Marco2; Crapolicchio, Raffaele2; Levy, Gad3; Marzano, Frank1
1Sapienza University of Rome, ITALY; 2SERCO, ITALY; 3NWRA, UNITED STATES
Remote sensing has become more and more important in the last decades, playing currently a key role in Earth Observation. The advantages of satellite observation are well-known, and are basically related to the possibility to monitor remote areas with a relatively small revisit time and accessible operating costs. On the other hand remote measurements often does not resolve the required spatial resolution or accuracy necessary for certain applications. In-situ measurements are crucial to complement this weak point of remote sensing, those in fact permit to achieve higher spatial-temporal resolution and accuracy than remote sensing in particular areas of interest. In addition, in situ measurements are direct measurements of a geophysical quantity, while remote sensing is often based on indirect measurements, and thus involves geophysical modelling. The refinement and improvement of the geophysical models is crucial and grounded on in situ observations. The European Remote-sensing Satellite (ERS)-2 was launched in July 1995 as the follow-on mission to ERS-1. It embarks six different instruments, namely a radar altimeter working in the Ku-band (13.8 GHz), the along-track scanning radiometer (infrared and microwave), an ultraviolet and visible spectrometer called global ozone monitoring experiment, a microwave radiometer (acquiring at 23.8 and 36.5 GHz), an active microwave instrument (AMI) working at 5.3 GHz (C-band). AMI can be operated in three different acquisition modes, the wind scatterometer mode providing measurements of radar backscatter from the sea surface. The ERS-2 wind scatterometer consists of three different antennas looking at 45° forward, sideways, and 45° afterward with respect to the satellite's flight direction. The resulting swath is 500 km wide and is centered 450 km at the right of the satellite's nadir; the nominal spatial resolution of the ERS-2 wind scatterometer is 50 km, each resolved point at the Earth is called node.
The ERS-2 scatterometer measures the so-called radar cross-section ó0 of the Earth surface, which is, on the sea, directly connected to the sea roughness. The sea roughness is coupled with the surface wind speed (it increases when the wind speed increases). The most widely used forward models relating ó0 to the wind speed are empirical and are periodically updated and improved based on real satellite measurements. ERS-2 ocean wind vector retrievals are available at nominal (linear) resolution of about 50 km and at high resolution of about 25 km. One of the largest and longest database of in situ wind speed over the ocean is provided by the Prediction and Research Moored Array in the Atlantic (PIRATA). PIRATA is a program designed to study ocean-atmosphere interactions in the tropical Atlantic that affect regional climate variability on seasonal, interannual and longer time scales. The array was originally developed in the mid-1990s and has undergone expansions and enhancements since 2005 to improve its utility for describing, understanding, and predicting socially relevant climate fluctuations. PIRATA has been implemented through multi-national cooperation in support of CLIVAR, GOOS, GCOS, and GEOSS.
In this work high-resolution ERS-2 retrievals (called ASPS2.0_H as derived from Advanced Scatterometer Processing System 2.0) are considered within a long-term time frame (1995-2005) over the whole Earth globe. A comparative analysis has been carried out for the period 1997-2003, including: i) long-term characterization of the wind field, according to the satellite and the in situ reference measurements; ii) statistical analysis of both the datasets and of their difference; iii) Comparative analysis of extreme events, if any. Since scatterometers respond to changes in the water surface and not directly to changes in the wind speed due to atmospheric stratification, ERS-2 retrievals have been calibrated to equivalent neutral speeds rather than wind speeds. This analysis has been also evaluated by considering the effect of the interpolation and data discrimination techniques. The long-term ERS-2 dataset has been also characterized in terms of behaviours of predicted and estimated CMOD5N geophysical forward model coefficients estimated for buoy wind speed for different wind speeds. Summary of statistical comparisons between buoy and ERS-2 wind data during the considered period will be reported. Wind speed histograms, estimated from ERS-2 observations and collocated ECMWF data, will be illustrated with respect to season variability and geographical area i.e., over global ocean, north oceans (30° N-60° N), tropical oceans (10° S-10° N), and south oceans (30° S-60° S). Standard deviation of ocean-wind speed and direction difference between ERS-2 and buoys and ECMWF data will be also discussed in terms of buoy wind speed ranges.