The Tiled ECMWF Scheme for Surface Exchanges over Land (TESSEL) is used operationally in the Integrated Forecast System (IFS) for describing the evolution of soil, vegetation, and snow over the continents at diverse spatial resolutions. A revised land surface hydrology (H-TESSEL) is introduced in the ECMWF operational model to address shortcomings of the land surface scheme, specifically the lack of surface runoff and the choice of a global uniform soil texture. New infiltration and runoff schemes are introduced with a dependency on the soil texture and standard deviation of orography. A set of experiments in stand-alone mode is used to assess the improved prediction of soil moisture at the local scale against field site observations. Comparison with basin-scale water balance (BSWB) and Global Runoff Data Centre (GRDC) datasets indicates a consistently larger dynamical range of land water mass over large continental areas and an improved prediction of river runoff, while the effect on atmospheric fluxes is fairly small. Finally, the ECMWF data assimilation and prediction systems are used to verify the effect on surface and near-surface quantities in the atmospheric-coupled mode. A midlatitude error reduction is seen both in soil moisture and in 2-m temperature.

Abstract. Understanding the error structures of remotely sensed soil moisture observations is essential for correctly interpreting observed variations and trends in the data or assimilating them in hydrological or numerical weather prediction models. Nevertheless, a spatially coherent assessment of the quality of the various globally available datasets is often hampered by the limited availability over space and time of reliable in-situ measurements. As an alternative, this study explores the triple collocation error estimation technique for assessing the relative quality of several globally available soil moisture products from active (ASCAT) and passive (AMSR-E and SSM/I) microwave sensors. The triple collocation is a powerful statistical tool to estimate the root mean square error while simultaneously solving for systematic differences in the climatologies of a set of three linearly related data sources with independent error structures. Prerequisite for this technique is the availability of a sufficiently large number of timely corresponding observations. In addition to the active and passive satellite-based datasets, we used the ERA-Interim and GLDAS-NOAH reanalysis soil moisture datasets as a third, independent reference. The prime objective is to reveal trends in uncertainty related to different observation principles (passive versus active), the use of different frequencies (C-, X-, and Ku-band) for passive microwave observations, and the choice of the independent reference dataset (ERA-Interim versus GLDAS-NOAH).Correspondence to: W. A. Dorigo (wd@ipf.tuwien.ac.at)The results suggest that the triple collocation method provides realistic error estimates. Observed spatial trends agree well with the existing theory and studies on the performance of different observation principles and frequencies with respect to land cover and vegetation density. In addition, if all theoretical prerequisites are fulfilled (e.g. a sufficiently large number of common observations is available and errors of the different datasets are uncorrelated) the errors estimated for the remote sensing products are hardly influenced by the choice of the third independent dataset. The results obtained in this study can help us in developing adequate strategies for the combined use of various scatterometer and radiometerbased soil moisture datasets, e.g. for improved flood forecast modelling or the generation of superior multi-mission longterm soil moisture datasets.

The scatterometers onboard the European Remote Sensing satellites (ERS-1 & ERS-2) and the METeorological OPerational satellite (METOP) have been shown to be useful for surface soil moisture retrieval using the so-called TU-Wien change detection method. This paper presents an improved soil moisture retrieval algorithm based on the existing TU-Wien method but with new parameterization as well as a series of modifications. The new algorithm, WAter Retrieval Package 5 (WARP5), copes with some limitations identified in the earlier method WARP4 and provides the possibility of migrating soil moisture retrieval from ERS-SCAT to METOP-ASCAT data. The WARP5 algorithm results in a more robust and spatially uniform soil moisture product, thanks to its new processing elements, including a method for the correction of azimuthal anisotropy of backscatter, a comprehensive noise model, and new techniques for calculation of the model parameters. Cross-comparisons of WARP4 and WARP5 data sets with the Oklahoma Mesonet in situ observations and also with European Centre of Medium Range Weather Forcast (ECMWF) ReAnalysis (ERA-Interim) global modeled data show that the new algorithm has a better performance and effectively corrects retrieval errors in certain areas.

[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 Microwave Instrument flown onboard the European satellites 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.

In the last few years, research made significant progress towards operational soil moisture remote sensing which lead to the availability of several global data sets. For an optimal use of these data, an accurate estimation of the error structure is an important condition. To solve for the validation problem we introduce the triple collocation error estimation technique. The triple collocation technique is a powerful tool to estimate the root mean square error while simultaneously solving for systematic differences in the climatologies of a set of three independent data sources. We evaluate the method by applying it to a passive microwave (TRMM radiometer) derived, an active microwave (ERS‐2 scatterometer) derived and a modeled (ERA‐Interim reanalysis) soil moisture data sets. The results suggest that the method provides realistic error estimates.

[1] The lack of global soil moisture data has spurred research in the field of microwave remote sensing. Both passive (radiometers) and active (scatterometer) microwave data are very sensitive to the moisture content of the surface soil layer. To retrieve soil moisture, the effects of vegetation, surface roughness, and heterogeneous land cover must be taken into account. Field experiments have shown that passive microwave data at long wavelengths (L-band) are best suited for soil moisture retrieval. Nevertheless, the first global, multiannual soil moisture data set (1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000) has been derived from active microwave data acquired by the European Remote Sensing Satellites (ERS) ERS-1 and ERS-2 scatterometer (C-band). The retrieval algorithm is based on a change detection approach that naturally accounts for surface roughness and heterogeneous land cover. In this paper the scatterometer-derived soil moisture data are compared to gridded precipitation data and soil moisture modeled by a global vegetation and water balance model. The correlation between soil moisture and rainfall anomalies is observed to be best over areas with a dense rainfall gauge network. Also, the scatterometer-derived and modeled soil moisture agree reasonably well over tropical and temperate climates. The fact that the algorithm performs equally well for regions with summer rain and Mediterranean areas indicates that dynamic vegetation effects are correctly represented in the retrieval. More research is needed to better understand the backscattering behavior over dry (steppe, deserts) and cold (boreal zone, tundra) climatic regions. The scatterometerderived soil moisture data are available to other research groups at http:// www.ipf.tuwien.ac.at/radar/ers-scat/home.htm.

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Abstract:The objective of this study was to validate the soil moisture data derived from coarse-resolution active microwave data (50 km) from the ERS scatterometer. The retrieval technique is based on a change detection method coupled with a data-based modelling approach to account for seasonal vegetation dynamics. The technique is able to derive information about the soil moisture content corresponding to the degree of saturation of the topmost soil layer (¾5 cm). To estimate profile soil moisture contents down to 100 cm depth from the scatterometer data, a simple two-layer water balance model is used, which generates a red noise-like soil moisture spectrum. The retrieval technique had been successfully applied in the Ukraine in a previous study. In this paper, the performance of the model in a semi-arid Mediterranean environment characterized by low annual precipitation (400 mm), hot dry summers and sandy soils is investigated. To this end, field measurements from the REMEDHUS soil moisture station network in the semi-arid parts of the Duero Basin (Spain) were used. The results reveal a significant coefficient of determination R 2 D 0Ð75 for the averaged 0-100 cm soil moisture profile and a root mean square error (RMSE) of 2Ð2 vol%. The spatial arrangement of the REMEDHUS soil moisture stations also allowed us to study the influence of the small-scale variability of soil moisture within the ERS scatterometer footprint. The results show that the small-scale variability in the study area is modest and can be explained in terms of texture fraction distribution in the soil profiles.