Craig Anderson

Initial soil moisture retrievals from the METOP‐A Advanced Scatterometer (ASCAT)

[1] This article presents first results of deriving relative surface soil moisture from the METOP-A Advanced Scatterometer. Retrieval is based on a change detection approach which has originally been developed for the Active MicrowaveInstrument flownonboardtheEuropeansatellites ERS-1 and ERS-2. Using model parameters derived from eight years of ERS scatterometer data, first global soil moisture maps have been produced from ASCAT data. The ASCAT data were distributed by EUMETSAT for validation purposes during the ASCAT product commissioning activities. Several recent cases of drought and excessive rainfall are clearly visible in the soil moisture data. The results confirm that seamless soil moisture time series can be expected from the series of two ERS and three METOP scatterometers, providing global coverage on decadal time scales (from 1991 to about 2021). Thereby, operational, nearreal-time ASCAT soil moisture products will become available for weather prediction and hydrometeorological applications. Citation: Bartalis, Z., W. Wagner, V. Naeimi, S. Hasenauer, K. Scipal, H. Bonekamp, J. Figa, and C. Anderson (2007), Initial soil moisture retrievals from the METOP-A Advanced Scatterometer (ASCAT), Geophys. Res. Lett., 34, L20401, doi:10.1029/2007GL031088.

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.

Radiometric Calibration of the Advanced Wind Scatterometer Radar ASCAT Carried Onboard the METOP-A Satellite

The Advanced Wind Scatterometer (ASCAT) is a six-beam spaceborne radar instrument designed to measure wind fields over the oceans. An ASCAT instrument is carried by each of the three METOP satellites. The ASCAT calibration strategy is described and detailed results are presented concerning the radiometric calibration achieved.

Validation of Backscatter Measurements from the Advanced Scatterometer on MetOp-A

The Advanced Scatterometer (ASCAT) on the Meteorological Operational (MetOp) series of satellites is designed to provide data for the retrieval of ocean wind fields. Three transponders were used to give an absolute calibration and the worst-case calibration error is estimated to be 0.15‐0.25 dB. In this paper the calibrated data are validated by comparing the backscatter from a range of naturally distributed targets against models developed from European Remote Sensing Satellite (ERS) scatterometer data. For the Amazon rainforest it is found that the isotropic backscatter decreases from 26.2 to 26.8 dB over the incidence angle range. The ERS value is around 26.5 dB. All ASCAT beams are within 0.1 dB of each other. Rainforest backscatter over a3-yr period is found tobeverystablewithannualchangesof approximately0.02 dB. ASCAT ocean backscatter is compared against values from the C-band geophysical model function (CMOD-5) using ECMWF wind fields. A difference of approximately 0.2 dB below 558 incidence is found. Differences of over 1 dB above 558 are likely due to inaccuracies in CMOD-5, which has not been fully validated at large incidence angles. All beams are within 0.1 dB of each other. Backscatter from regions of stable Antarctic sea ice is found to be consistent with model backscatter except at large incidence angles where the model has not been validated. The noise in the ice backscatter indicates that the normalized standard deviation of the backscatter values Kp is around 4.5%, which is consistent with the expected value. These results agree well with the expected calibration accuracy and give confidence that the calibration has been successful and that ASCAT products are of high quality.

A Parameterized ASCAT Measurement Spatial Response Function

The Advanced SCATterometer (ASCAT) measurement spatial response function (SRF) relates the weighted contribution of every location within the measurement footprint to the measured normalized radar cross section σ°. The SRF results from a combination of the antenna response and the onboard processing and is computed during ground processing by modeling in detail the measurement geometry, as this is required for an accurate σ° estimation. However, the computed SRF is not disseminated as part of the L1B data. For some applications of the L1B data, the SRF is additionally required. For these applications, an approximate description of the SRF is often sufficiently accurate, estimated from information contained in the L1B data, rather than from a full calculation based on the measurement geometry. This paper describes a parameterized model of the ASCAT SRF for each measurement. First, an SRF reference estimate that incorporates details on the ASCAT design and onboard measurement processing is created. A parameterized model is fit to the reference estimate. The parameterized SRF is computationally less demanding than the reference estimate and as such more useful for near-real-time processing. The two estimates are validated with the computed SRF used in ground processing and with the transponder data from calibration campaigns. Finally, to validate the SRF in a simple application, the land fraction (a measure of land contamination in near-coastal ocean measurements) is computed and compared to actual data for a sample region.

Analysis of ASCAT Ocean Backscatter Measurement Noise

The Advanced Scatterometer (ASCAT) level 1b products provide spatially averaged calibrated backscatter measurements along with their Kp values which are estimates of the normalized standard deviation of the backscatter values. The Kp values can be regarded as a measure of the error in the mean backscatter caused by speckle noise, instrument characteristics, data processing, and spatial inhomogeneities of the target. This information assists in the retrieval of wind vectors and allows their error characteristics to be determined. This paper describes the algorithm used to calculate Kp. The algorithm considers both the correlations that occur in ASCAT data due to onboard processing and the Hamming weights used in the spatial averaging in the ground processing. ASCAT Kp values over the open ocean are investigated, and we demonstrate that their behavior is as expected and meets requirements. This indicates that the ASCAT instrument is operating as intended and is providing good quality ocean backscatter data. The behavior of Kp over Arctic sea ice is also examined, and we show that it contains geophysical information that may be useful for various applications such as ice-type classification or sea ice tracking.

Cone Metrics: A New Tool for the Intercomparison of Scatterometer Records

With an eye on the generation of a long-term climate record of ocean winds, soil moisture, and sea ice extents across the C-band ERS and ASCAT scatterometer spans, a new calibration tool termed cone metrics has been developed. The new method is based on monitoring changes in the location and shape of the surface of maximum density of ocean backscatter measurements, also known as “the wind cone.” The cone metrics technique complements established calibration approaches, such as rain forest and NWP ocean calibration, through the characterization of linear as well as nonlinear beam offsets, the latter via wind cone deformations. Given instrument evolution, proven stability, and the monitoring by transponders, we take ASCAT-A data over 2013 as absolute calibration reference. This paper describes the new method and its application as intercalibration tool in the context of the reprocessing activities for ERS-1 and ERS-2. Cone metrics succeeds at establishing the linear and nonlinear corrections necessary to homogenize the ASCAT and ERS C-band records down to 0.05 dB.

Validation of Coarse Resolution Microwave Soil Moisture Products

The strong relationship between soil moisture content and the soil dielectric constant offers a direct way of measuring soil moisture with microwave sensors. Global coarse-resolution soil moisture datasets are currently retrieved from active microwave spaceborne instruments (AMI onboard ERS and ASCAT on Metop-A) and in the near future from dedicated passive microwave satellite sensors (SMOS). This article summarizes recent soil moisture research activities and briefly discusses the strategies for validation and cross-comparison of remotely sensed soil moisture datasets. Special attention is given to the first validation of the soil moisture data from the ASCAT instrument on Metop-A in relation to the absolute and relative calibrations of the instrument. A first assessment of the quality of the ASCAT surface soil moisture is given by studies of stable targets and the spatial and temporal extent of recent extreme drought and rainfall events.

Analysis of the noise scenario measured by ASCAT

The Advanced Scatterometer (ASCAT) is a radar system carried on board the ESA/EUMETSAT METOP series of satellites. Its main scientific objective is the retrieval of wind fields over oceans. It also provides information on soil moisture content. Although ASCAT uses a linear frequency modulated pulse with a centre frequency of 5.255 GHz (C Band), it is subject to Radio Frequency Interference (RFI) which causes, as it is shown in this paper, an increase of the number of noise outliers and an increase of the noise background level over specific land areas. This suggests that the outliers are not a natural occurrence, but are due to RFI from ground based equipments.

Radiometric performance of the Advanced Wind Scatterometer radar ASCAT

The Advanced Wind Scatterometer (ASCAT) instrument [1 & 2] is one of the instruments carried by the ESA / EUMETSAT METOP satellites (METOP A, B & C). The ASCAT is a six-beam radar instrument designed to measure wind fields over the oceans; the instrument also provides useful data for ice and land applications. The radiometric performance of the ASCAT carried by METOP-A is estimated and discussed.