Regional climate downscaling with prior statistical correction of the global climate forcing

International audienceA novel climate downscaling methodology that attempts to correct climate simulation biases is proposed. By combining an advanced statistical bias correction method with a dynamical downscaling it constitutes a hybrid technique that yields nearly unbiased, high-resolution, physically consistent, three-dimensional fields that can be used for climate impact studies. The method is based on a prior statistical distribution correction of large-scale global climate model (GCM) 3-dimensional output fields to be taken as boundary forcing of a dynamical regional climate model (RCM). GCM fields are corrected using meteorological reanalyses. We evaluate this methodology over a decadal experiment. The improvement in terms of spatial and temporal variability is discussed against observations for a past period. The biases of the downscaled fields are much lower using this hybrid technique, up to a factor 4 for the mean temperature bias compared to the dynamical downscaling alone without prior bias correction. Precipitation biases are subsequently improved hence offering optimistic perspectives for climate impact studies

Air quality assessment for Portugal

According to the Air Quality Framework Directive, air pollutant concentration levels have to be assessed and reported annually by each European Union member state, taking into consideration European air quality standards. Plans and programmes should be implemented in zones and agglomerations where pollutant concentrations exceed the limit and target values. The main objective of this study is to perform a long-term air quality simulation for Portugal, using the CHIMERE chemistry-transport model, applied over Portugal, for the year 2001. The model performance was evaluated by comparing its results to air quality data from the regional monitoring networks and to data from a diffusive sampling experimental campaign. The results obtained show a modelling system able to reproduce the pollutant concentrations' temporal evolution and spatial distribution observed at the regional networks of air quality monitoring. As far as the fulfilment of the air quality targets is concerned, there are excessive values for nitrogen and sulfur dioxides, ozone also being a critical gaseous pollutant in what concerns hourly concentrations and AOT40 (Accumulated Over Threshold 40 ppb) values

Mitigating wind induced noise in outdoor microphone signals using a singular spectral subspace method

Wind induced noise is one of the major concerns of outdoor acoustic signal acquisition. It affects many field measurement and audio recording scenarios. Filtering such noise is known to be difficult due to its broadband and time varying nature. In this paper, a new method to mitigate wind induced noise in microphone signals is developed. Instead of applying filtering techniques, wind induced noise is statistically separated from wanted signals in a singular spectral subspace. The paper is presented in the context of handling microphone signals acquired outdoor for acoustic sensing and environmental noise monitoring or soundscapes sampling. The method includes two complementary stages, namely decomposition and reconstruction. The first stage decomposes mixed signals in eigen-subspaces, selects and groups the principal components according to their contributions to wind noise and wanted signals in the singular spectrum domain. The second stage reconstructs the signals in the time domain, resulting in the separation of wind noise and wanted signals. Results show that microphone wind noise is separable in the singular spectrum domain evidenced by the weighted correlation. The new method might be generalized to other outdoor sound acquisition applications. Keywords: microphone; wind noise; matrix decomposition and reconstruction; separability; weighted correlation; acoustic sensing; acoustic signals; environmental noise; monitorin

Nonlinear projective filtering in a data stream

We introduce a modified algorithm to perform nonlinear filtering of a time series by locally linear phase space projections. Unlike previous implementations, the algorithm can be used not only for a posteriori processing but includes the possibility to perform real time filtering in a data stream. The data base that represents the phase space structure generated by the data is updated dynamically. This also allows filtering of non-stationary signals and dynamic parameter adjustment. We discuss exemplary applications, including the real time extraction of the fetal electrocardiogram from abdominal recordings.Comment: 8 page

Interannual prediction of the Paraná River

Interannual‐to‐decadal predictability of the Paraná river in South America is investigated by extracting near‐cyclic components in summer‐season streamflows at Corrientes over the period 1904–1997. It is found that oscillatory components with periods of about 2–5, 8 and 17 years are accompanied by statistically significant changes in monthly streamflow. Autoregressive predictive models are constructed for each component. Cross‐validated categorical hindcasts based on the 8‐yr predicted component are found to yield some skill up to four years in advance for below‐average flows. A prediction based upon the 8‐ and 17‐yr components including data up to 1999 suggests increased probability of below‐average flows until 2006

Aerosol chemical and optical properties over the Paris area within ESQUIF project

Aerosol chemical and optical properties are extensively investigated for the first time over the Paris Basin in July 2000 within the ESQUIF project. The measurement campaign offers an exceptional framework to evaluate the performances of the chemistry-transport model CHIMERE in simulating concentrations of gaseous and aerosol pollutants, as well as the aerosol-size distribution and composition in polluted urban environments against ground-based and airborne measurements. A detailed comparison of measured and simulated variables during the second half of July with particular focus on 19 and 31 pollution episodes reveals an overall good agreement for gas-species and aerosol components both at the ground level and along flight trajectories, and the absence of systematic biases in simulated meteorological variables such as wind speed, relative humidity and boundary layer height as computed by the MM5 model. A good consistency in ozone and NO concentrations demonstrates the ability of the model to reproduce the plume structure and location fairly well both on 19 and 31 July, despite an underestimation of the amplitude of ozone concentrations on 31 July. The spatial and vertical aerosol distributions are also examined by comparing simulated and observed lidar vertical profiles along flight trajectories on 31 July and confirm the model capacity to simulate the plume characteristics. The comparison of observed and modeled aerosol components in the southwest suburb of Paris during the second half of July indicates that the aerosol composition is rather correctly reproduced, although the total aerosol mass is underestimated by about 20%. The simulated Parisian aerosol is dominated by primary particulate matter that accounts for anthropogenic and biogenic primary particles (40%), and inorganic aerosol fraction (40%) including nitrate (8%), sulfate (22%) and ammonium (10%). The secondary organic aerosols (SOA) represent 12% of the total aerosol mass, while the mineral dust accounts for 8%. The comparison demonstrates the absence of systematic errors in the simulated sulfate, ammonium and nitrates total concentrations. However, for nitrates the observed partition between fine and coarse mode is not reproduced. In CHIMERE there is a clear lack of coarse-mode nitrates. This calls for additional parameterizations in order to account for the heterogeneous formation of nitrate onto dust particles. Larger discrepancies are obtained for the secondary organic aerosols due to both inconsistencies in the SOA formation processes in the model leading to an underestimation of their mass and large uncertainties in the determination of the measured aerosol organic fraction. The observed mass distribution of aerosols is not well reproduced, although no clear explanation can be given

An RCM multi-physics ensemble over Europe: Multi-variable evaluation to avoid error compensation

ABSTRACT:Regional Climate Models (RCMs) are widely used tools to add detail to the coarse resolution of global simulations. However, these are known to be affected by biases. Usually, published model evaluations use a reduced number of variables, frequently precipitation and temperature. Due to the complexity of the models, this may not be enough to assess their physical realism (e.g. to enable a fair comparison when weighting ensemble members). Furthermore, looking at only a few variables makes difficult to trace model errors. Thus, in many previous studies, these biases are de- scribed but their underlying causes and mechanisms are often left unknown. In this work the ability of a multi-physics ensemble in reproducing the observed climatologies of any variables over Europe is analysed. These are temperature, precipitation, cloud cover, ra- diative fluxes and total soil moisture content. It is found that, during winter, the model suffers a significant cold bias over snow covered regions. This is shown to be re- lated with a poor representation of the snow-atmosphere interaction, and is amplified by an albedo feedback. It is shown how two members of the ensemble are able to alleviate this bias, but by generating a too large cloud cover. During summer, a large sensitivity to the cumulus parameterization is found, related to large differences in the cloud cover and short wave radiation flux. Results also show that small errors in one variable are sometimes a result of error compensation, so the high dimensionality of the model evaluation problem cannot be disregarded.This work was partially supported by Projects EXTREMBLES (CGL2010-21869) and CORWES (CGL2010-22158-C02), funded by the Spanish R&D Programme. WRF4G (CGL2011-28864) provided the framework to run the model; this Spanish R&D project is co-funded by the European Regional Development Fund (ERDF). Partial support from the 7th European Framework Programme (FP7) through Grant 308291 (EUPORIAS) is also acknowledged

Near Real-Time Data Labeling Using a Depth Sensor for EMG Based Prosthetic Arms

Recognizing sEMG (Surface Electromyography) signals belonging to a particular action (e.g., lateral arm raise) automatically is a challenging task as EMG signals themselves have a lot of variation even for the same action due to several factors. To overcome this issue, there should be a proper separation which indicates similar patterns repetitively for a particular action in raw signals. A repetitive pattern is not always matched because the same action can be carried out with different time duration. Thus, a depth sensor (Kinect) was used for pattern identification where three joint angles were recording continuously which is clearly separable for a particular action while recording sEMG signals. To Segment out a repetitive pattern in angle data, MDTW (Moving Dynamic Time Warping) approach is introduced. This technique is allowed to retrieve suspected motion of interest from raw signals. MDTW based on DTW algorithm, but it will be moving through the whole dataset in a pre-defined manner which is capable of picking up almost all the suspected segments inside a given dataset an optimal way. Elevated bicep curl and lateral arm raise movements are taken as motions of interest to show how the proposed technique can be employed to achieve auto identification and labelling. The full implementation is available at https://github.com/GPrathap/OpenBCIPytho

Acclimatization across space and time in the effects of temperature on mortality: a time-series analysis

Background: Climate change has increased the days of unseasonal temperature. Although many studies have examined the association between temperature and mortality, few have examined the timing of exposure where whether this association varies depending on the exposure month even at the same temperature. Therefore, we investigated monthly differences in the effects of temperature on mortality in a study comprising a wide range of weather and years, and we also investigated heterogeneity among regions. Methods: We analyzed 38,005,616 deaths from 148 cities in the U.S. from 1973 through 2006. We fit city specific Poisson regressions to examine the effect of temperature on mortality separately for each month of the year, using penalized splines. We used cluster analysis to group cities with similar weather patterns, and combined results across cities within clusters using meta-smoothing. Results: There was substantial variation in the effects of the same temperature by month. Heat effects were larger in the spring and early summer and cold effects were larger in late fall. In addition, heat effects were larger in clusters where high temperatures were less common, and vice versa for cold effects. Conclusions: The effects of a given temperature on mortality vary spatially and temporally based on how unusual it is for that time and location. This suggests changes in variability of temperature may be more important for health as climate changes than changes of mean temperature. More emphasis should be placed on warnings targeted to early heat/cold temperature for the season or month rather than focusing only on the extremes. Electronic supplementary material The online version of this article (doi:10.1186/1476-069X-13-89) contains supplementary material, which is available to authorized users