Precipitation downscaling improves the coarse resolution and poor representation of precipitation in global climate models and helps end users to assess the likely hydrological impacts of climate change. This paper integrates perspectives from meteorologists, climatologists, statisticians, and hydrologists to identify generic end user (in particular, impact modeler) needs and to discuss downscaling capabilities and gaps. End users need a reliable representation of precipitation intensities and temporal and spatial variability, as well as physical consistency, independent of region and season. In addition to presenting dynamical downscaling, we review perfect prognosis statistical downscaling, model output statistics, and weather generators, focusing on recent developments to improve the representation of space-time variability. Furthermore, evaluation techniques to assess downscaling skill are presented. Downscaling adds considerable value to projections from global climate models. Remaining gaps are uncertainties arising from sparse data; representation of extreme summer precipitation, subdaily precipitation, and full precipitation fields on fine scales; capturing changes in small-scale processes and their feedback on large scales; and errors inherited from the driving global climate model.

/pdf/precipitation-downscaling-under-climate-change-recent-1vqa3tzzno.pdf

Precipitation downscaling under climate change: Recent developments to bridge the gap between dynamical models and the end user

The focus of this article is to review the recent advances in proteome analysis of human body fluids, including plasma/serum, urine, cerebrospinal fluid, saliva, bronchoalveolar lavage fluid, synovial fluid, nipple aspirate fluid, tear fluid, and amniotic fluid, as well as its applications to human disease biomarker discovery. We aim to summarize the proteomics technologies currently used for global identification and quantification of body fluid proteins, and elaborate the putative biomarkers discovered for a variety of human diseases through human body fluid proteome (HBFP) analysis. Some critical concerns and perspectives in this emerging field are also discussed. With the advances made in proteomics technologies, the impact of HBFP analysis in the search for clinically relevant disease biomarkers would be realized in the future.

/pdf/human-body-fluid-proteome-analysis-5avybowf0d.pdf

Human body fluid proteome analysis

The recent statements of both the European Union and the US Presidency pushed in the direction of using renewable forms of energy, in order to act against climate changes induced by the growing concentration of carbon dioxide in the atmosphere. In this paper, a survey regarding methods and tools presently available to determine potential and exploitable energy in the most important renewable sectors (i.e., solar, wind, wave, biomass and geothermal energy) is presented. Moreover, challenges for each renewable resource are highlighted as well as the available tools that can help in evaluating the use of a mix of different sources.

/pdf/methods-and-tools-to-evaluate-the-availability-of-renewable-1zptx7dh9x.pdf

Methods and tools to evaluate the availability of renewable energy sources

This paper presents results from an investigation of global wave energy resources derived from analysis of wave climate predictions generated by the WAVEWATCH-III (NWW3) wind-wave model (Tolman, 2002) spanning the 10 year period from 1997 to 2006. The methodology that was followed to obtain these new results is described in detail. The spatial and temporal variations of the global wave energy resource are presented and described. Several parameters to describe and quantify the temporal variation of wave energy resources are presented and discussed. The new results are also validated through comparisons with energy estimates from buoy data and previous studies.

A global wave energy resource assessment

https://dkfz.de/funct_genome/pdf-files/drugdiscovtoday-10-2005-503.pdf

Progress in protein and antibody microarray technology.

Exceedances over high thresholds are often modeled by fitting a generalized Pareto distribution (GPD) on R+. It is difficult to select the threshold, above which the GPD assumption is enough solid and enough data is available for inference. We suggest a new dynamically weighted mixture model, where one term of the mixture is the GPD, and the other is a light-tailed density distribution. The weight function varies on R+ in such a way that for large values the GPD component is predominant and thus takes the role of threshold selection. The full data set is used for inference on the parameters present in the two component distributions and in the weight function. Maximum likelihood provides estimates with approximate standard deviations. Our approach has been successfully applied to simulated data and to the (previously studied) Danish fire loss data set. We compare the new dynamic mixture method to Dupuis' robust thresholding approach in peaks-over-threshold inference. We discuss robustness with respect to the choice of the light-tailed component and the form of the weight function. We present encouraging simulation results that indicate that the new approach can be useful in unsupervised tail estimation, especially in heavy tailed situations and for small percentiles.

A Dynamic Mixture Model for Unsupervised Tail Estimation without Threshold Selection

We describe a novel approach for high-throughput analysis of the immune response in cancer patients using phage-based microarray technology. The recombinant phages used for fabricating phage arrays were initially selected via the use of random peptide phage libraries and breast cancer patient serum antibodies. The peptides displayed by the phages retained their ability to be recognized by serum antibodies after immobilization. The recombinant phage microarrays were screened against either breast cancer or healthy donor serum antibodies. A model-based statistical method is proposed to estimate significant differences in serum antibody reactivity between patients and normals. A significant tumor effect was found with most of the selected phage-displayed peptides, suggesting that recombinant phage microarrays can serve as a tool in monitoring humoral responses towards phage-displayed peptides.

/pdf/analysis-of-the-humoral-immune-response-to-immunoselected-4ewv9ikb4u.pdf

Analysis of the humoral immune response to immunoselected phage‐displayed peptides by a microarray‐based method

Climate change will affect the insurance industry. We develop a Bayesian hierarchical statistical approach to explain and predict insurance losses due to weather events at a local geographic scale. The number of weather-related insurance claims is modelled by combining generalized linear models with spatially smoothed variable selection. Using Gibbs sampling and reversible jump Markov chain Monte Carlo methods, this model is fitted on daily weather and insurance data from each of the 319 municipalities which constitute southern and central Norway for the period 1997–2006. Precise out-of-sample predictions validate the model. Our results show interesting regional patterns in the effect of different weather covariates. In addition to being useful for insurance pricing, our model can be used for short-term predictions based on weather forecasts and for long-term predictions based on downscaled climate models.

A Bayesian hierarchical model with spatial variable selection: the effect of weather on insurance claims

Satellite Altimeter Data in Wave Energy Studies

In order to assess the potential of regional climate models to be used to project future weather events, a first step is to study the regional model forced by actual weather, or more precisely by reanalysis of weather data. In this paper we investigate how well the Norwegian regional model HIRHAM, forced by ERA-40 reanalysis data, compares to observed precipitation data from the Norwegian Meteorological Institute over Norwegian mainland. This paper aims to show how standard methods of statistical testing may be used to assess dynamic downscaling. Methods considered are the Kolmogorov–Smirnov two-sample test, a Fisher exact test for equality of quantiles, an Extreme Value Theory test, where equality of the 1-yr return levels are tested, and equality of wet-day frequency. All tests are performed seasonally. The regional model is skillful in describing the lower quartile of the precipitation distribution, but underestimates higher levels of precipitation. Our results indicate that the regional model has too many but too small rain events for all seasons.

Ola Haug

Papers

A Dynamic Mixture Model for Unsupervised Tail Estimation without Threshold Selection

Analysis of the humoral immune response to immunoselected phage‐displayed peptides by a microarray‐based method

A Bayesian hierarchical model with spatial variable selection: the effect of weather on insurance claims

Satellite Altimeter Data in Wave Energy Studies

Evaluation of a dynamic downscaling of precipitation over the Norwegian mainland