TopoSCALE v.1.0: Downscaling gridded climate data in complex terrain

Simulation of land surface processes is problematic in heterogeneous terrain due to the the high resolution required of model grids to capture strong lateral variability caused by, for example, topography, and the lack of accurate meteorological forcing data at the site or scale it is required. Gridded data products produced by atmospheric models can fill this gap, however, often not at an appropriate spatial resolution to drive land-surface simulations. In this study we describe a method that uses the well-resolved description of the atmospheric column provided by climate models, together with high-resolution digital elevation models (DEMs), to downscale coarse-grid climate variables to a fine-scale subgrid. The main aim of this approach is to provide high-resolution driving data for a land-surface model (LSM). The method makes use of an interpolation of pressure-level data according to topographic height of the subgrid. An elevation and topography correction is used to downscale short-wave radiation. Long-wave radiation is downscaled by deriving a cloud-component of all-sky emissivity at grid level and using downscaled temperature and relative humidity fields to describe variability with elevation. Precipitation is downscaled with a simple non-linear lapse and optionally disaggregated using a climatology approach. We test the method in comparison with unscaled grid-level data and a set of reference methods, against a large evaluation dataset (up to 210 stations per variable) in the Swiss Alps. We demonstrate that the method can be used to derive meteorological inputs in complex terrain, with most significant improvements (with respect to reference methods) seen in variables derived from pressure levels: air temperature, relative humidity, wind speed and incoming long-wave radiation. This method may be of use in improving inputs to numerical simulations in heterogeneous and/or remote terrain, especially when statisti

Improving permafrost physics in the coupled Canadian Land Surface Scheme (v.3.6.2) and Canadian Terrestrial Ecosystem Model (v.2.1) (CLASS-CTEM)

The Canadian Land Surface Scheme and Canadian Terrestrial Ecosystem Model (CLASS-CTEM) together form the land surface component of the Canadian Earth System Model (CanESM). Here, we investigate the impact of changes to CLASS-CTEM that are designed to improve the simulation of permafrost physics. Overall, 18 tests were performed, including changing the model configuration (number and depth of ground layers, different soil permeable depth datasets, adding a surface moss layer), and investigating alternative parameterizations of soil hydrology, soil thermal conductivity, and snow properties. To evaluate these changes, CLASS-CTEM outputs were compared to 1570 active layer thickness (ALT) measurements from 97 observation sites that are part of the Global Terrestrial Network fo

GlobSim (v1.0): Deriving meteorological time series for point locations from multiple global reanalyses

Simulations of land-surface processes and phenomena often require driving time series of meteorological variables. Corresponding observations, however, are unavailable in most locations, even more so, when considering the duration, continuity and data quality required. Atmospheric reanalyses provide glo

REDCAPP (v1.0): Parameterizing valley inversions in air temperature data downscaled from reanalyses

In mountain areas, the use of coarse-grid reanalysis data for driving fine-scale models requires downscaling of near-surface (e.g., 2 m high) air temperature. Existing approaches describe lapse rates well but differ in how they include surface effects, i.e., the difference between the simulated 2 m and upper-air temperatures. We show that different treatment of surface effects result in some methods making better predictions in valleys while others are better in summit areas. We propose the downscaling method REDCAPP (REanalysis Downscaling Cold Air Pooling Parameterization) with a spatially variable magnitude of surface effects. Results are evaluated with observations (395 stations) from two mountain regions and compared with three reference methods. Our findings suggest that the difference between near-surface air temperature and pressure-level temperature (ΔT) is a good proxy of surface effects. It can be used with a spatially variable land-surface correction factor (LSCF) for

The ERA5-Land soil temperature bias in permafrost regions

ERA5-Land (ERA5L) is a reanalysis product derived by running the land component of ERA5 at increased resolution. This study evaluates ERA5L soil temperature in permafrost regions based on observations and published permafrost products. We find that ERA5L overestimates soil temperature in northern Canada and Alaska but underestimates it in mid-low latitudes, leading to an average bias of -0.08 ĝC. The warm bias of ERA5L soil is stronger in winter than in other seasons. As calculated from its soil temperature, ERA5L overestimates active-layer thickness and underestimates near-surface (<1.89

Towards Activity Context using Software Sensors

Service-Oriented Computing delivers the promise of configuring and reconfiguring software systems to address user's needs in a dynamic way. Context-aware computing promises to capture the user's needs and hence the requirements they have on systems. The marriage of both can deliver ad-hoc software solutions relevant to the user in the most current fashion. However, here it is a key to gather information on the users' activity (that is what they are doing). Traditionally any context sensing was conducted with hardware sensors. However, software can also play the same role and in some situations will be more useful to sense the activity of the user. Furthermore they can make use of the fact that Service-oriented systems exchange information through standard protocols. In this paper we discuss our proposed approach to sense the activity of the user making use of software

Schizophrenia as a disorder of disconnectivity

Schizophrenia is considered as a neurodevelopmental disorder with genetic and environmental factors playing a role. Animal models show that developmental hippocampal lesions are causing disconnectivity of the prefrontal cortex. Magnetic resonance imaging and postmortem investigations revealed deficits in the temporoprefrontal neuronal circuit. Decreased oligodendrocyte numbers and expression of oligodendrocyte genes and synaptic proteins may contribute to disturbances of micro- and macro-circuitry in the pathophysiology of the disease. Functional connectivity between cortical areas can be investigated with high temporal resolution using transcranial magnetic stimulation (TMS), electroencephalography (EEG), and magnetoencephalography (MEG). In this review, disconnectivity between different cortical areas in schizophrenia patients is described. The specificity and the neurobiological origin of these connectivity deficits and the relation to the symptom complex of schizophrenia and the glutamatergic and GABAergic system are discussed

RNAalifold: improved consensus structure prediction for RNA alignments

<p>Abstract</p> <p>Background</p> <p>The prediction of a consensus structure for a set of related RNAs is an important first step for subsequent analyses. RNAalifold, which computes the minimum energy structure that is simultaneously formed by a set of aligned sequences, is one of the oldest and most widely used tools for this task. In recent years, several alternative approaches have been advocated, pointing to several shortcomings of the original RNAalifold approach.</p> <p>Results</p> <p>We show that the accuracy of RNAalifold predictions can be improved substantially by introducing a different, more rational handling of alignment gaps, and by replacing the rather simplistic model of covariance scoring with more sophisticated RIBOSUM-like scoring matrices. These improvements are achieved without compromising the computational efficiency of the algorithm. We show here that the new version of RNAalifold not only outperforms the old one, but also several other tools recently developed, on different datasets.</p> <p>Conclusion</p> <p>The new version of RNAalifold not only can replace the old one for almost any application but it is also competitive with other approaches including those based on SCFGs, maximum expected accuracy, or hierarchical nearest neighbor classifiers.</p

Influence of different digital terrain models (DTMs)on alpine permafrost modeling

The thawing of alpine permafrost due to changes in atmospheric conditions can have a severe impact, e.g., on the stability of rock walls. The energy balance model, PERMEBAL, was developed in order to simulate the changes and distribution of ground surface temperature (GST) in complex high-mountain topography. In such environments, the occurrence of permafrost depends greatly on the topography, and thus, the digital terrain model (DTM) is an important input of PERMEBAL. This study investigates the influence of the DTM on the modeling of the GST. For this purpose, PERMEBAL was run with six different DTMs. Five of the six DTMs are based on the same base data, but were generated using different interpolators. To ensure that only the topographic effect on the GST is calculated, the snow module was turned off and uniform conditions were assumed for the whole test area. The analyses showed that the majority of the deviations between the different model outputs related to a reference DTM had only small differences of up to 1 K, and only a few pixels deviated more than 1 K. However, we also observed that the use of different interpolators for the generation of a DTM can result in large deviations of the model output. These deviations were mainly found at topographically complex locations such as ridges and foot of slopes