Laser disdrometers measure the particle size distribution (PSD) of hydrometeors through a small cross-sectional (tens of square centimeters) surface. Such a limited area induces a sampling effect in the estimates of the PSD, which translates to error in the reflectivity–rain-rate (Z–R) relationship used for ground radar estimates of rainfall, estimates of kinetic energy of precipitation, and any other hydrometeorological application relying on particle size information. Here, the results of a dedicated experiment to estimate the extent of the effect of limited area sampling of rainfall are presented. Using 14 Parsivel, version 1 (Parsivel-1), disdrometers placed within 6 m2, it was found that the combined area of at least seven disdrometers is required for the estimates to start converging to a stable value. The results can be used to quantify the degree of over-/underestimation of precipitation parameters for a single instrument due to the limited collecting area effect. It has been found that a single disdrometer may underestimate instantaneous rain rate by 70%.

HPG Aligner applies suffix arrays for DNA read mapping. This implementation produces a highly sensitive and extremely fast mapping of DNA reads that scales up almost linearly with read length. The approach presented here is faster (over 20× for long reads) and more sensitive (over 98% in a wide range of read lengths) than the current state-of-the-art mappers. HPG Aligner is not only an optimal alternative for current sequencers but also the only solution available to cope with longer reads and growing throughputs produced by forthcoming sequencing technologies. Availability and implementation:https://github.com/opencb/hpg-aligner. Contact:jdopazo@cipf.es or imedina@ebi.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

ESMs (Earth system models) are important tools that help scientists understand the complexities of the Earth's climate. Advances in computing power have permitted the development of increasingly complex ESMs and the introduction of better, more accurate parameterizations of processes that are too complex to be described in detail. One of the least well-controlled parameterizations involves human activities and their direct impact at local and regional scales. In order to improve the direct representation of human activities and climate, we have developed a simple, scalable approach that we have named the POPEM module (POpulation Parameterization for Earth Models). This module computes monthly fossil fuel emissions at grid-point scale using the modeled population projections. This paper shows how integrating POPEM parameterization into the CESM (Community Earth System Model) enhances the realism of global climate modeling, improving this beyond simpler approaches. The results show that it is indeed advantageous to model CO₂ emissions and pollutants directly at model grid points rather than using the same mean value globally. A major bonus of this approach is the increased capacity to understand the potential effects of localized pollutant emissions on long-term global climate statistics, thus assisting adaptation and mitigation policies.

One of the applications of satellite‐derived precipitation datasets, such as Global Precipitation Climatology Project (GPCP) or Climate Prediction Centre Merged Analysis of Precipitation (CMAP), is to validate output from numerical models, either numerical weather prediction (NWP) models, Regional Climate Models (RCMs), Global Climate Models (GCMs) or Earth System Models (ESMs). A qualitative comparison of total annual precipitation and climatology is the first step in detecting model deficiencies and thus improving our understanding of the world's climate. However, spatial (or association) analysis of the precipitation fields offers new insights into model performance and their ability to provide realistic predictions of rain and snowfall in both current and future climates. Here we analyse the spatial structure of precipitation according to 40 GCMs for 20 years (January 1980 to December 1999), quantitatively comparing the modelled precipitation against five observational datasets: three land‐only (CRU, PRECL and GPCC) and two global (GPCP and CMAP). We found discrepancies between the GCMs' predictions and the observational datasets and noted that satellite‐derived datasets are essential for pinpointing areas that require attention. The analyses also revealed a consistent trend towards less spatially correlated fields. This trend is not apparent in aggregated, traditional validation exercises but arises when spatial association indices are applied. So long as the trend is not an artefact in the observational datasets, then we suggest the tendency could be attributed to changes in stratiform/convective partitioning or the result of increasingly intense convection.

HidroMORE software was developed in the Remote Sensing and Geographic Information Systems (GIS) section from the University of Castilla-la Mancha to extend the Evapotranspiration assessment to a regional scale, implementing the FAO-56 methodology and the assimilation of the basal crop coefficient from Normalized Difference Vegetation Index (NDVI) images calculated from satellite images. However, when this software deals with high dimension images, the performance greatly decays. Currently, HidroMORE is being required for carring out calculations that result unapproachable in its current state. In this work HidroMORE 2 is presented where a High Performance Computing approach has been considered to manage the complexity of HidroMORE software. The work presented here takes into account two main aspects in order to improve the performance: improvements on input/output operations, that is, a better manage of hard disk operations; and on the other hand the use of Parallel Computing by exploiting current computer architectures, in particular, multicore architectures.