Search

This paper describes use of Advanced Scatterometer (ASCAT) C-band scatterometer data for determination of the sea ice edge. The variation in backscatter with measurement geometry is different for the sea ice surface compared to the open water surface. Utilizing the ASCAT antenna configuration with three different look angles for the same surface spot, a new ASCAT sea ice parameter has been defined. One year of ASCAT measurements has been collocated with background sea ice information to derive probability distributions for the ASCAT sea ice parameter given the known ice condition. The result can be used in an inverse methodology, a Bayesian approach, to calculate the probability of sea ice from the ASCAT measurements. The method has been tested for a full year and validated against high-resolution satellite images and ice charts from an operational Ice Service. It reveals a realistic ice edge result, however, with weather-induced noise problems in terms of “false sea ice.” For automatic ice edge detection, ancillary information is needed to remove this noise. The paper shows how the method also can be used for ice edge detection with passive microwave data from SSM/I, and how it can be extended to utilize ASCAT together with SSM/I in a multisensor approach. [ABSTRACT FROM AUTHOR]

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 Mission's (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. [ABSTRACT FROM AUTHOR]

The C-band EUMETSAT Advanced Scatterometer (ASCAT) was launched in 2007, and provides backscatter measurements with excellent azimuth and incidence angle diversity in polar regions. C-band backscatter measurements can be used to retrieve near-surface snow/firn properties such as grain size and accumulation rate, however the Antarctic Ice Sheet (AIS) exhibits strong microwave backscatter anisotropy as a function of both incidence and azimuth angles, and this anisotropy must be well understood before such physical parameter retrieval can be accurately performed. This paper presents a detailed comparison of several different parameterizations of both the azimuth and the incidence anisotropy. Parameterizations used here result in residuals similar to the radiometric performance of ASCAT, indicating accurate characterization of the anisotropy. The analysis of these parameterizations provides an insight into the physical mechanisms taking place near the surface of the AIS. 30-day composite maps of several parameters are presented, and discussed in the context of near-surface glaciology. This anisotropy characterization will form the basis of a technique for extraction of physical parameters of the AIS. [ABSTRACT FROM PUBLISHER]

Spatial and temporal variations of soil moisture strongly affect flooding, erosion, solute transport, and vegetation productivity. Their characterization offers numerous possibilities for the improvement of our understanding of complex land-surface-atmosphere interactions. In this paper, soil moisture dynamics at the soil's surface (the first centimeters) and in its root zone (at depths down to 1 m) are investigated using 25 × 25 km2 scale data (Advanced Scatterometer (ASCAT)/METorological OPerational (METOP) scatterometer), for a semiarid region in North Africa. Our study highlights the quality of the surface and root-zone soil moisture products, derived from ASCAT data recorded over a two-year period. Surface soil moisture tends to be highly variable because it is strongly influenced by atmospheric conditions (rain and evaporation). On the other hand, root-zone moisture is considerably less variable. A statistical drought-monitoring index, referred to as the “moisture anomaly index,” is derived from ASCAT and European Remote Sensing (ERS) time series. This index was tested with ERS and ASCAT products during the 1991-2010 study period. A strong correlation is found between the proposed index and the standardized precipitation index. [ABSTRACT FROM AUTHOR]

Since December 2008, the European Organisation for the Exploitation of Meteorological Satellites has been operationally distributing a global 25-km surface soil moisture product derived from the Advanced Scatterometer (ASCAT) onboard the meteorological operational platform (METOP) satellite METOP-A. Soil moisture is retrieved by using the semiempirical change detection method originally developed by the Vienna University of Technology (TU Wien) for the Active Microwave Instrument (AMI) flown on the European Remote Sensing (ERS) satellites ERS-1 and ERS-2. With the launch of the first of the three Meteorological Operational Platforms (METOP-A) in October 2006, ASCAT onboard METOP-A inherits and continues the role of his predecessor AMI. The original soil moisture retrieval algorithm (TU Wien model) was expected to be almost directly applicable for ASCAT with only minor changes, since the configuration and technical design is similar to the ERS scatterometers. Since the TU Wien model requires a robust historic long-term reference of scattering parameters, the initial near real-time METOP ASCAT soil moisture product had to rely on the model parameters derived from over 15 years of ERS-1/2. However, the combination of ASCAT backscatter measurements and ERS-1/2 historic long-term reference introduced some artifacts in the soil moisture product. The objectives of this paper were to analyze and investigate the impact of the ERS-1/2 historic long-term reference on the soil moisture retrieval. An error model has been developed to quantify the effects of the two main error sources: differences in spatial resolution and absolute calibration. The results of the study show that a simple model is able to describe the artifacts in the initial near real-time METOP ASCAT soil moisture product, which frequently occur in areas characterized by sharp backscatter contrasts. The expected overestimation of soil moisture using ERS-1/2 model parameters due to a calibration bias between AMI and ASCAT could be modeled as well. [ABSTRACT FROM AUTHOR]

Relative wind vorticity ω (hereafter vorticity) is a crucial parameter to understand the spatial features of the wind field. In the Mediterranean Sea, which is the area where this study is focused, these are particularly interesting because they are often the effects of the interaction between the airflow and the orography. Wind vorticity has been derived from both Quick Scatterometer (QuikSCAT) and Advanced Scatterometer (ASCAT) 12.5-km scatterometer data downloaded from the National Aeronautics and Space Administration Physical Oceanography Distributed Active Archive Center data archive, and compared in the period from March to November 2009. The monthly mean fields of the vorticity ω̅ show discrepancies which need to be understood. This paper thus aims to understand the nature of these differences, to make the two vorticity data sets compatible and usable as a common data set. Results have been provided in terms of the relative bias in vorticity 〈Δ̅ω̅〉, which is the mean difference between the ASCAT ω̅A and QuikSCAT ω̅Q monthly mean vorticities averaged over the entire Mediterranean Basin and the entire study period. This difference 〈Δ̅ω̅〉 = 0.093 ·10-5 ±0.05 ·10-5s-1) is mainly due to a relative vorticity bias in the cyclonic component of ω̅, rather than in the anticyclonic component, whose bias is four times smaller. This bias does not depend significantly on the variable accuracy of the wind speed and direction across the QuikSCAT swath. This study led us to define and analyze the so-called vorticity noise, which is present particularly in the QuikSCAT-derived vorticity, to understand if, and how, it can contribute to the relative bias in vorticity. The contribution of this kind of noise on ω̅ has been found relevant only for the cyclonic vorticity of ω̅Q. By applying a cyclonic denoising to each swath of QuikSCAT, 〈Δ̅ω̅〉 = -0.016 ·10-5 ±0.05 ·10-5s-1 is obtained, drastically reduced with respect to the initial value. This may be considered the typical bias over the Mediterranean Sea between ω̅A and (ω̅Q derived from the 12.5-km data, after applying the cyclonic denoising to QuikSCAT vorticity fields. [ABSTRACT FROM AUTHOR]

Nowadays, the availability of soil moisture estimates from satellite sensors offers a great chance to improve real-time flood forecasting through data assimilation. In this paper, two real data and two synthetic experiments have been carried out to assess the effects of assimilating soil moisture estimates into a two-layer rainfall-runoff model. By using the ensemble Kalman filter, both the surface- and root-zone soil moisture (RZSM) products derived by the Advanced SCATterometer (ASCAT) have been assimilated and the model performance on flood estimation is analyzed. RZSM estimates are obtained through the application of an exponential filter. Hourly rainfall-runoff observations for the period 1994-2010 collected in the Niccone catchment (137 km2), Central Italy, are employed as case study. The ASCAT soil moisture products are found to be in good agreement with the modeled soil moisture data for both the surface layer (correlation coefficient (R) of 0.78) and the root zone (R = 0.94). In the real data experiment, the assimilation of the RZSM product has a significant impact on runoff simulation that provides a clear improvement in the discharge modeling performance. On the other hand, the assimilation of the surface soil moisture product has a small effect. The same findings are also confirmed by the synthetic twin experiments. Even though the obtained results are model dependent and site specific, the possibility to efficiently employ coarse resolution satellite soil moisture products for improving flood prediction is proven, mainly if RZSM data are assimilated into the hydrological model. [ABSTRACT FROM AUTHOR]