Many physical, chemical and biological processes taking place at the land surface are strongly influenced by the amount of water stored within the upper soil layers. Therefore, many scientific disciplines require soil moisture observations for developing, evaluating and improving their models. One of these disciplines is meteorology where soil moisture is important due to its control on the exchange of heat and water between the soil and the lower atmosphere. Soil moisture observations may thus help to improve the forecasts of air temperature, air humidity and precipitation. However, until recently, soil moisture observations had only been available over a limited number of regional soil moisture networks. This has hampered scientific progress as regards the characterisation of land surface processes not just in meteorology but many other scientific disciplines as well. Fortunately, in recent years, satellite soil moisture data have increasingly become available. One of the freely available global soil moisture data sets is derived from the backscatter measurements acquired by the Advanced Scatterometer (ASCAT) that is a C-band active microwave remote sensing instrument flown on board of the Meteorological Operational (METOP) satellite series. ASCAT was designed to observe wind speed and direction over the oceans and was initially not foreseen for monitoring soil moisture over land. Yet, as argued in this review paper, the characteristics of the ASCAT instrument, most importantly its wavelength (5.7 cm), its high radiometric accuracy, and its multiple-viewing capabilities make it an attractive sensor for measuring soil moisture. Moreover, given the operational status of ASCAT, and its promising long-term prospects, many geoscientific applications might benefit from using ASCAT soil moisture data. Nonetheless, the ASCAT soil moisture product is relatively complex, requiring a good understanding of its properties before it can be successfully used in applications. To provide a comprehensive overview of the major characteristics and caveats of the ASCAT soil moisture product, this paper describes the ASCAT instrument and the soil moisture processor and near-real-time distribution service implemented by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). A review of the most recent validation studies shows that the quality of ASCAT soil moisture product is-with the exception of arid environments-comparable to, and over some regions (e.g. Europe) even better than currently available soil moisture data derived from passive microwave sensors. Further, a review of applications studies shows that the use of the ASCAT soil moisture product is particularly advanced in the fields of numerical weather prediction and hydrologic modelling. But also in other application areas such as yield monitoring, epidemiologic modelling, or societal risks assessment some first progress can be noted. Considering the generally positive evaluation results, it is expected that the ASCAT soil moisture product ...

We introduce the Nondeterministic Strong Exponential Time Hypothesis (NSETH) as a natural extension of the Strong Exponential Time Hypothesis (SETH). We show that both refuting and proving NSETH would have interesting consequences. In particular we show that disproving NSETH would give new nontrivial circuit lower bounds. On the other hand, NSETH implies non-reducibility results, i.e. the absence of (deterministic) fine-grained reductions from SAT to a number of problems. As a consequence we conclude that unless this hypothesis fails, problems such as 3-sum, APSP and model checking of a large class of first-order graph properties cannot be shown to be SETH-hard using deterministic or zero-error probabilistic reductions.

A mesoscale data analysis method for meteorological station reports is presented. Irregularly distributed measured values are combined with measurement-independent a priori information about the modification of analysis fields due to topographic forcing. As a physical constraint to a thin-plate spline interpolation, the so-called "fingerprint method" recognizes patterns of topographic impact in the data and allows for the transfer of information to data-sparse areas. The results of the method are small-scale interpolation fields on a regular grid including topographically induced patterns that are not resolved by the station network. Presently, the fingerprint method is designed for the analysis of scalar meteorological variables like reduced pressure or air temperature. The principles for the fingerprint technique are based on idealized influence fields. They are calculated for thermal and dynamic surface forcing. For the former, the effects of reduced air volumes in valleys, the elevated heat sources, and the stability of the valley atmosphere are taken into account. The increase of temperature under ideal conditions in comparison to flat terrain is determined on a 1-km grid using height and surface geometry information. For the latter, a perturbation of an originally constant cross-Alpine temperature gradient is calculated by a topographical weighting. As a result, the gradient is steep where the mountain range is high and steep. If, during the interpolation process, some signal of the idealized patterns is found in the station data, it is used to downscale the analysis. It is shown by a cross validation of a case study that the interpolation of a mean sea level pressure field over the Alpine region is improved objectively by the method. Thermally induced mesoscale patterns are visible in the interpolated pressure field.

We give a nontrivial algorithm for the satisfiability problem for threshold circuits of depth two with a linear number of wires which improves over exhaustive search by an exponential factor. The independently interesting problem of the feasibility of sparse 0-1 integer linear programs is a special case. To our knowledge, our algorithm is the first to achieve constant savings even for the special case of Integer Linear Programming. The key idea is to reduce the satisfiability problem to the Vector Domination problem, the problem of checking whether there are two vectors in a given collection of vectors such that one dominates the other component-wise.Our result generalizes to formulas of arbitrary constant depth. We also provide a satisfiability algorithm with constant savings for depth two circuits with symmetric gates where the total weighted fan-in is at most linear in the number of variables.One of our motivations is proving strong lower bounds for TC0 circuits, exploiting the connection (established by Williams) between satisfiability algorithms and lower bounds. Our second motivation is to explore the connection between the expressive power of the circuits and the complexity of the corresponding circuit satisfiability problem.

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Governments, aid organizations and researchers are struggling with the complexity of detecting and monitoring drought events, which leads to weaknesses regarding the translation of early warnings into action. Embedded in an advanced decision-support framework for Doctors without Borders (Médecins sans Frontières), this study focuses on identifying the added-value of combining different satellite-derived datasets for drought monitoring and forecasting in Ethiopia. The core of the study is the improvement of an existing drought index via methodical adaptations and the integration of various satellite-derived datasets. The resulting Enhanced Combined Drought Index (ECDI) links four input datasets (rainfall, soil moisture, land surface temperature and vegetation status). The respective weight of each input dataset is calculated for every grid point at a spatial resolution of 0.25 degrees (roughly 28 kilometers). In the case of data gaps in one input dataset, the weights are automatically redistributed to other available variables. Ranking the years 1992 to 2014 according to the ECDI-based warning levels allows for the identification of all large-scale drought events in Ethiopia. Our results also indicate a good match between the ECDI-based drought warning levels and reported drought impacts for both the start and the end of the season.

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In this study, remotely sensed soil moisture data from the Advanced Scatterometer (ASCAT) on board the Meteorological Operational (MetOp) series of satellites are assimilated in the regional forecasting model, Aire Limit ee Adaptation Dynamique D eveloppement International (ALADIN-Austria), using a simplified extended Kalman filter. A pointwise bias correction method is applied to the ASCAT data as well as quality flags prepared by the data provider. The ASCAT assimilation case study is performed over central Europe during a 1-month period in July 2009. Forecasts of those assimilation experiments are compared to the control run provided by the operational ALADIN version of the Austrian Met Service, Zentralanstalt f€ ur Meteorologie und Geodynamik (ZAMG). Forecasts are furthermore verified versus in situ data. For a single-day case study the ability of the approach to improve precipitation forecast quality in the presence of high impact weather is demonstrated. Results show that 1) based on a one station in situ data evaluation, soil moisture analysis is improved, compared to the operational analysis, when ASCAT soil moisture data is assimilated; 2) pointwise bias correction of the satellite data is beneficial for forecast quality; 3) screen level parameter forecasts can be slightly improved as a result of this approach; and 4) convective precipitation forecast is improved over flatland for the investigation period while over mountainous regions the impact is neutral.