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2,152 results for “outputs”
WRF output. Resolution: 12 km. Domain: Eastern North America
<p>Derived products from WRF simulations at 12 km resolution. LBC: ERA-Interim. Output every 10-min. Output level: 3rd model level </p> <p>Details given in: Pryor S.C. et al. (2018): Inter-annual variability of wind climates and wind turbine annual energy production. Manuscript published in Wind Energy Science.</p> <p>5 netcdf files;</p> <p>Pryoretal_WES2018_part1.nc contains information shown in Figure 3. Included variables; lat, lng, medianWSHH, CI(medianWSHH), vectors of data in WT grid cells and in grid cells without WT.</p> <p>Pryoretal_WES2018_part2.nc contains information shown in Figure 4. Included variables; Weibull distribution fits by wind speed bin and year, and FFT of output from TX, IA, and NY.</p> <p>Pryoretal_WES2018_part3.nc contains information shown in Figure 5. Included variables; lat, lng, 50th and 90th percentile WI, AEP and difference, vectors of data in WT grid cells and in grid cells without WT.</p> <p>Pryoretal_WES2018_part4.nc contains information shown in Figure 6.Included variables; lat, lng, 50th and 90th percentile AEP and difference, vectors of data in WT grid cells and in grid cells without WT.</p> <p>Pryoretal_WES2018_part5.nc contains information shown in Figure 7. Included variables; lat, lng, time of minimum, time of maximum.</p> <p> </p>
Secondary ice production parameterization output - COSMO model
<p>Secondary ice production via processes like rime splintering, frozen droplet shattering, and breakup upon ice hydrometeor collision have been proposed to explain discrepancies between in-cloud ice crystal and ice-nucleating particle numbers. To understand the impact of this kind of additional ice number generation on surface precipitation, we present one of the first studies to implement frozen droplet shattering and ice-ice collisional breakup parameterizations in a larger-scale model. We simulate a cold frontal rainband from the Aerosol Properties, PRocesses, And InfluenceS on the Earth's Climate campaign and investigate the impact of the new parameterizations on the simulated ice crystal number concentrations (ICNC) and precipitation. Near the convective regions of the rainband, contributions to ICNC can be as large from secondary production as from primary nucleation, but ICNCs greater than 50 L-1 remain underestimated by the model. Addition of the secondary production parameterizations also clearly intensifies the differences in both accumulated precipitation and precipitation rate between the convective towers and non-convective gap regions. We suggest, then, that secondary ice production parameterizations be included in large-scale models on the basis of large hydrometeor concentration and convective activity criteria.</p>
Annex B to the technical report on the raw primary commodity (RPC) model - Summary statistics of the output data
<p><strong>The raw primary commodity model</strong>:</p> <p>Dietary exposure is typically calculated by combining food consumption data with occurrence data. EFSA’s food consumption data are stored in the Comprehensive European Food Consumption Database (Comprehensive Database). Some of these data, however, cannot be used in exposure assessments when the occurrence data are reported for the raw primary commodities (RPCs). The RPC model aims to bridge this gap by transforming the Comprehensive Database into RPC consumption data. Using the RPC model, EFSA successfully developed a new RPC Consumption Database, which contains 51 dietary surveys from 23 different countries. These surveys cover a total of 94,532 subjects and 26,573,088 RPC consumption records. The consumption data generated by the RPC model were manually checked and validated by means of case studies. These case studies demonstrated that the RPC consumption data are suitable for assessing dietary exposure to chemicals where the occurrence data are predominantly available for RPCs.</p> <p><strong>Annex B to the technical report on the raw primary commodity model:</strong></p> <p>Annex B is an excel file which presents summary statistics of the output data generated by the RPC model. The following tables are included in Annex B:</p> <p>Table B.1 :Summary statistics of chronic RPC consumption expressed in g/kg bw per day (total population)</p> <p>Table B.2 :Summary statistics of chronic RPC consumption expressed in g/day (total population)</p> <p>Table B.3 :Summary statistics of acute RPC consumption expressed in g/kg bw (consumers only)</p> <p>Table B.4 :Summary statistics of acute RPC consumption expressed in g (consumers only)</p> <p>Table B.5 :Comparison of the RPC consumption data with RPC consumption data used in EFSA's Pesticides Residues Intake Model (PRIMo)</p> <p>Table B.6 :Contribution of processed products to the average chronic RPC consumption</p>
Screw and hole detection output results dataset in an autonomous screwing operation
<p>This dataset contains a set of csv files containing the results of a screwing and hole detection developed for a screwing mobile robot. It also contains a set of images representing the inputs, the outputs and some relevant images in the hole detection algorithm.</p> <p>Thee different scenarios were built, and a total of 25 missions were executed in order to build the datasets presented in appendix 5.7. The first scenario (missions 1 to 15), represents a normal operation procedure, meaning, a screwing 6 hole screwing and 5 screws refilling. The second scenario (missions 16 to 20), represents an operation with an exception handling during the refill (the second, third and fourth screws are missing in the refill support). Lastly, the third scenario (missions 21 to 25) has another exception handling, this time on both hole and screw detections (the third, fourth and fifth holes are already screwed, and the screwdriver trigger is not pressed, so the screw is not tightened resulting in a screw detecting after the refilling phase.</p> <p>Also three different group of csv files can be found:</p> <ul> <li><em>hole_detection_mission_xx.csv</em><sup><em><a href="#sdfootnote1sym"><sup>1</sup></a></em></sup>: contains the hole detection result (detected or non detected) and the located hole coordinates (u,v), in pixels;</li> <li> <p><em>detect_screw_screwing_result_mission_xx</em>.csv: contains the screw detection result performed after a screwing to detect if the screw was screwed successfully. 1 means the screwing was ok (there is no screw on the screwdriver), 0 means the screwing was not ok (there was a screw on the screwdriver);</p> </li> <li> <p><em>detect_screw_refill_mission_xx</em>.csv: contains the screw detection result performed after a refill attempt, to check if there was any screw missing in the refill support. 0 means that no screw was missing on the refill support (there was a screw on the screwdriver), 1 means there was a screw missing on the support (there was no screw on the screwdriver).</p> </li> </ul> <p>The <em>mission_xx</em> suffix represents which mission file (rosbag) the mission was acquired in and, each csv file contains the timestamp in which it happens.</p> <p><a href="#sdfootnote1anc">1</a> xx represents the mission number</p>
WRF-Chem output. Resolution: 24 km. Domain: Eastern North America
<p>Derived products from WRF-Chem simulations at 24 km resolution.</p> <p>Details given in: Crippa P. et al. (2019): Sensitivity of simulated aerosol properties over eastern North America to WRF-Chem parameterizations. Manuscript published in the Journal of Geophysical Research.</p> <p>14 netcdf files:</p> <p>Crippaetal_JGR2019_AOD.nc contains monthly mean AOD cloud screened for each ensemble member.</p> <p>Crippaetal_JGR2019_net_radiation.nc contains monthly mean surface net radiation for each ensemble member.</p> <p>Crippaetal_JGR2019_NO3_fine.nc contains monthly mean NO3 in the fine aerosol mode for each ensemble member.</p> <p>Crippaetal_JGR2019_SO4_fine.nc contains monthly mean SO4 in the fine aerosol mode for each ensemble member.</p> <p>Crippaetal_JGR2019_PM25_daily.nc contains daily PM<sub>2.5 </sub>concentrations for each ensemble member.</p> <p>Crippaetal_JGR2019_O3_daily.nc contains daily mean surface ozone concentrations in the fine aerosol mode for each ensemble member.</p> <p>Crippaetal_JGR2019_PBLH.nc contains monthly mean planetary boundary layer height for each ensemble member regridded on the MERRA-2 resolution</p> <p>Crippaetal_JGR2019_T2.nc contains monthly mean surface temperature at 2m for each ensemble member regridded on the MERRA-2 resolution</p> <p>Crippaetal_JGR2019_QPBL.nc contains monthly mean specific humidity in the PBL for each ensemble member regridded on the MERRA-2 resolution</p> <p>Crippaetal_JGR2019_PPT.nc contains monthly total precipitation for each ensemble member regridded on the MERRA-2 resolution</p> <p>Crippaetal_JGR2019_lat_WRF-Chem.nc contains the latitude coordinates of the WRF-Chem grid</p> <p>Crippaetal_JGR2019_lon_WRF-Chem.nc contains the longitude coordinates of the WRF-Chem grid</p> <p>Crippaetal_JGR2019_lat_MERRA2.nc contains the latitude coordinates of the MERRA2 grid</p> <p>Crippaetal_JGR2019_on_MERRA2.nc contains the longitude coordinates of the MERRA2 grid</p>
Selected WRF output for Langtang catchment, years 2011, 2012, 2013
<p>Includes selected variables for the Langtang catchment of the WRF run used in <em>Contrasting meteorological drivers of the glacier mass balance between the Karakoram and central Himalaya, </em>by PNJ Bonekamp, RJ de Kok, E Collier and WW Immerzeel. </p> <p>WRF version 3.8.1 is used for the simulations. The outer domain is forced with 6-hourly ERA-Interim data (0.75 o x0.75o), and grid analysis nudging is applied to the horizontal wind, water vapour mixing ratio and potential temperature fields in the highest 15 vertical levels. In all domains, the land cover dataset is updated using the climate change initiative dataset (CCI, Defourny et al. (2017)) of the European Space Agency (ESA), which has a spatial resolution of 300 m. The soil moisture, soil temperatures and skin temperatures is initialized with the GLDAS dataset (0.25x0.25, Rodell et al. 2004).</p> <p>Important parameter settings are:<br> - One way nesting<br> - Model top pressure<br> - Morrison microphysics<br> -YSU planetary boundary layer scheme wit topo_wind option=1<br> - Noah-MP scheme<br> -RRTGM radiation scheme<br> -MM5 Similarity Scheme<br> - Rayleigh damping at the top boundary<br> - Diffusion is calculated in physical space<br> <br> Included variables [unit']:<br> Time [hours from 1 December 2010], total simulation time is till 1 January 2014. All data are hourly variables. <br> ALBEDO [-]<br> GLW [Wm-2], longwave downward radiation<br> GRAUPELNC [m], non convective graupel<br> GRDFLX [Wm-2], ground heat flux <br> HFX [Wm-2], Sensible heat flux<br> HGT [m], altitude above sea level<br> LH [Wm-2], latent heat flux<br> LU_INDEX [-] #24 categories<br> LWDNB [Wm-2], longwave downward radiation<br> LWUPB [Wm-2], longwave upward radiation<br> RAINC [m], convective rainfall<br> RAINNC [m], non convective rainfall<br> SNOWC [-], snow cover<br> SNOWNC [-], non convective snowfall<br> SWDOWN [Wm-2], shortwave downwards radiations<br> T2 [K], 2-meter temperature<br> TSK [K], surface temperature<br> U10 [ms-1], wind in U-direction at 10-m<br> V10 [ms-1], wind in V direction at 10-m</p> <p>See for the full description of the variables Chapter 5 of the WRF user guide</p>
Selected WRF output for Shimshal catchment, years 2011, 2012, 2013
<p>Includes selected variables for the Langtang catchment of the WRF run used in <em>Contrasting meteorological drivers of the glacier mass balance between the Karakoram and central Himalaya, </em>by PNJ Bonekamp, RJ de Kok, E Collier and WW Immerzeel. </p> <p>WRF version 3.8.1 is used for the simulations. The outer domain is forced with 6-hourly ERA-Interim data (0.75 o x0.75o), and grid analysis nudging is applied to the horizontal wind, water vapour mixing ratio and potential temperature fields in the highest 15 vertical levels. In all domains, the land cover dataset is updated using the climate change initiative dataset (CCI, Defourny et al. (2017)) of the European Space Agency (ESA), which has a spatial resolution of 300 m. The soil moisture, soil temperatures and skin temperatures is initialized with the GLDAS dataset (0.25x0.25, Rodell et al. 2004).</p> <p>Important parameter settings are:<br> - One way nesting<br> - Model top pressure<br> - Morrison microphysics<br> -YSU planetary boundary layer scheme wit topo_wind option=1<br> - Noah-MP scheme<br> -RRTGM radiation scheme<br> -MM5 Similarity Scheme<br> - Rayleigh damping at the top boundary<br> - Diffusion is calculated in physical space<br> <br> Included variables [unit']:<br> Time [hours from 1 December 2010], total simulation time is till 1 January 2014. All data are hourly variables. <br> ALBEDO [-]<br> GLW [Wm-2], longwave downward radiation<br> GRAUPELNC [m], non convective graupel<br> GRDFLX [Wm-2], ground heat flux <br> HFX [Wm-2], Sensible heat flux<br> HGT [m], altitude above sea level<br> LH [Wm-2], latent heat flux<br> LU_INDEX [-] #24 categories<br> LWDNB [Wm-2], longwave downward radiation<br> LWUPB [Wm-2], longwave upward radiation<br> RAINC [m], convective rainfall<br> RAINNC [m], non convective rainfall<br> SNOWC [-], snow cover<br> SNOWNC [-], non convective snowfall<br> SWDOWN [Wm-2], shortwave downwards radiations<br> T2 [K], 2-meter temperature<br> TSK [K], surface temperature<br> U10 [ms-1], wind in U-direction at 10-m<br> V10 [ms-1], wind in V direction at 10-m</p> <p>See for the full description of the variables Chapter 5 of the WRF user guide</p>
Results of TIMES model & inputs and outputs of the multicriteria and portfolio analysis
<p>These datasets contain the underlying data for the following publication: <strong>Energy efficiency promotion in Greece in light of risk: Evaluating policies as portfolio assets, Energy, https://doi.org/10.1016/j.energy.2018.12.180</strong></p>
MOM6 output from a zonally reentrant Southern Ocean-like channel
<p>This dataset contains output from MOM6 output from a zonally reentrant Southern Ocean-like channel used for investigating the relative role of the baroclinic and barotropic dynamics in establishing eddy-saturated states.</p>
A pooled analysis of the duration of chemoprophylaxis against malaria after treatment with artesunate-amodiaquine and artemether-lumefantrine: data & analysis output
<p>Data files and analysis output associated with the medRxiv preprint article: Bretscher et al. 2019 A pooled analysis of the duration of chemoprophylaxis against malaria after treatment with artesunate-amodiaquine and artemether-lumefantrine. </p>
Bahamas National Hazard Analysis. Data Inputs and Outputs for the InVEST Coastal Vulnerability Model.
<p>The following folders contain the model inputs and outputs for the InVEST Coastal Vulnerability model that were used in the analysis discussed in:</p> <p>Silver JM, Arkema KK, Griffin RM, Lashley B, Lemay M, Maldonado S,<br> Moultrie SH, Ruckelshaus M, Schill S, Thomas A, Wyatt K and Verutes G<br> (2019) Advancing Coastal Risk Reduction Science and Implementation by<br> Accounting for Climate, Ecosystems, and People. Front. Mar. Sci. 6:556.<br> doi: 10.3389/fmars.2019.00556</p> <p>The readme.txt file contains information about data layers.</p>
Annexes to the external scientific report on the cumulative dietary exposure assessment of pesticides that have acute effects on the nervous system using MCRA software - Input and output data sets
<p>Retrospective dietary exposure assessments were conducted for two groups of pesticides that have acute effects on the nervous system: </p> <ol> <li>brain and/or erythrocyte acetylcholinesterase inhibition (CAG-NAN);</li> <li>functional alterations of the motor division (CAG-NAM).</li> </ol> <p>The pesticides considered in this assessment were identified and characterised in the scientific report on the establishment of cumulative assessment groups of pesticides for their effects on the nervous system (<a href="https://doi.org/10.2903/j.efsa.2019.5800">here</a>).</p> <p>The exposure calculations used monitoring data collected by Member States under their official pesticide monitoring programmes in 2014, 2015 and 2016 and individual food consumption data from ten populations of consumers from different countries and from different age groups. Regarding the selection of relevant food commodities, the assessment included water, foods for infants and young children and 30 raw primary commodities of plant origin that are widely consumed within Europe.</p> <p>Exposure estimates were obtained with Monte Carlo Risk Assessment (MCRA) software using a 2-dimensional Monte Carlo simulation, which is composed of an inner-loop execution and an outer-loop execution. Variability within the population is modelled through the inner-loop execution and is expressed as a percentile of the exposure distribution. The outer-loop execution is used to derive 95% confidence intervals around those percentiles (reflecting the sampling uncertainty of the input data).</p> <p>Furthermore, calculations were carried out according to a tiered approach. While the first-tier calculations (Tier I) use very conservative assumptions for an efficient screening of the exposure with low risk for underestimation, the second-tier assessment (Tier II) includes assumptions that are more refined but still conservative. For each scenario, exposure estimates were obtained for different percentiles of the exposure distribution and the total margin of exposure (MOET, i.e. the ratio of the toxicological reference dose to the estimated exposure) was calculated at each percentile.</p> <p>The input and output data for the exposure assessment are reported in the following annexes:</p> <ul> <li>Annex A.1 – Input data for the exposure assessment of CAG-NAN</li> <li>Annex A.2 – Input data for the exposure assessment of CAG-NAM</li> <li>Annex B.1 – Output data from the Tier II exposure assessment of CAG-NAN</li> <li>Annex B.2 – Output data from the Tier II exposure assessment of CAG-NAM</li> </ul> <p>Further information on the data, methodologies and interpretation of the results are provided in the external scientific report on the cumulative dietary exposure assessment of pesticides that have acute effects on the nervous system using MCRA software (<a href="https://doi.org/10.2903/sp.efsa.2019.en-1708">here</a>).</p> <p>The results reported in this assessment only refer to the exposure and are not an estimation of the actual risks. These exposure estimates should therefore be considered as documentation for the final scientific report on the cumulative risk assessment of dietary exposure to pesticides for their effects on the nervous system (<a href="https://www.efsa.europa.eu/en/consultations/call/public-consultation-scientific-report-cumulative">here</a>). The latter combines the hazard assessment and exposure assessment into a consolidated risk characterisation, including all related uncertainties.</p>
Annexes to the external scientific report on the cumulative dietary exposure assessment of pesticides that have chronic effects on the thyroid using MCRA software - Input and output data sets
<p>Retrospective dietary exposure assessments were conducted for two groups of pesticides that have chronic effects on the thyroid: </p> <ol> <li>hypertrophy, hyperplasia and neoplasia of C-cells, i.e. affecting the parafollicular cells or the calcitonin system of the thyroid (CAG-TCP);</li> <li>hypothyroidism, i.e. affecting the follicular cells and/or the hormone system of the thyroid (CAG-TCF).</li> </ol> <p>The pesticides considered in this assessment were identified and characterised in the scientific report on the establishment of cumulative assessment groups of pesticides for their effects on the thyroid (<a href="https://doi.org/10.2903/j.efsa.2019.5801">here</a>).</p> <p>The exposure calculations used monitoring data collected by Member States under their official pesticide monitoring programmes in 2014, 2015 and 2016 and individual food consumption data from ten populations of consumers from different countries and from different age groups. Regarding the selection of relevant food commodities, the assessment included water, foods for infants and young children and 30 raw primary commodities of plant origin that are widely consumed within Europe.</p> <p>Exposure estimates were obtained with Monte Carlo Risk Assessment (MCRA) software using a 2-dimensional probabilistic method, which is composed of an inner-loop execution and an outer-loop execution. Variability within the population is modelled through the inner-loop execution and is expressed as a percentile of the exposure distribution. The outer-loop execution is used to derive 95% confidence intervals around those percentiles (reflecting the sampling uncertainty of the input data).</p> <p>Furthermore, calculations were carried out according to a tiered approach. While the first-tier calculations (Tier I) use very conservative assumptions for an efficient screening of the exposure with low risk for underestimation, the second-tier assessment (Tier II) includes assumptions that are more refined but still conservative. For each scenario, exposure estimates were obtained for different percentiles of the exposure distribution and the total margin of exposure (MOET, i.e. the ratio of the toxicological reference dose to the estimated exposure) was calculated at each percentile.</p> <p>The input and output data for the exposure assessment are reported in the following annexes:</p> <ul> <li>Annex A.1 – Input data for the exposure assessment of CAG-TCP</li> <li>Annex A.2 – Input data for the exposure assessment of CAG-TCF</li> <li>Annex B.1 – Output data from the Tier II exposure assessment of CAG-TCP</li> <li>Annex B.2 – Output data from the Tier II exposure assessment of CAG-TCF</li> </ul> <p>Further information on the data, methodologies and interpretation of the results are provided in the external scientific report on the cumulative dietary exposure assessment of pesticides that have chronic effects on the thyroid using MCRA software (<a href="https://doi.org/10.2903/sp.efsa.2019.en-1707">here</a>).</p> <p>The results reported in this assessment only refer to the exposure and are not an estimation of the actual risks. These exposure estimates should therefore be considered as documentation for the final scientific report on the cumulative risk assessment of dietary exposure to pesticides for their effects on the thyroid (<a href="https://www.efsa.europa.eu/en/consultations/call/public-consultation-scientific-report-cumulative">here</a>). The latter combines the hazard assessment and exposure assessment into a consolidated risk characterisation, including all related uncertainties.</p>
Annexes to the scientific report on the cumulative dietary exposure assessment of pesticides that have chronic effects on the thyroid using SAS® software - Input and output data sets
<p>Retrospective dietary exposure assessments were conducted for two groups of pesticides that have chronic effects on the thyroid: </p> <ol> <li>hypertrophy, hyperplasia and neoplasia of C-cells, i.e. affecting the parafollicular cells or the calcitonin system of the thyroid (CAG-TCP);</li> <li>hypothyroidism, i.e. affecting the follicular cells and/or the hormone system of the thyroid (CAG-TCF).</li> </ol> <p>The pesticides considered in this assessment were identified and characterised in the scientific report on the establishment of cumulative assessment groups of pesticides for their effects on the thyroid (<a href="https://doi.org/10.2903/j.efsa.2019.5801">here</a>).</p> <p>The exposure calculations used monitoring data collected by Member States under their official pesticide monitoring programmes in 2014, 2015 and 2016 and individual food consumption data from ten populations of consumers from different countries and from different age groups. Regarding the selection of relevant food commodities, the assessment included water, foods for infants and young children and 30 raw primary commodities of plant origin that are widely consumed within Europe.</p> <p>Exposure estimates were obtained with SAS<sup>®</sup> software using a 2-dimensional probabilistic method, which is composed of an inner-loop execution and an outer-loop execution. Variability within the population is modelled through the inner-loop execution and is expressed as a percentile of the exposure distribution. The outer-loop execution is used to derive 95% confidence intervals around those percentiles (reflecting the sampling uncertainty of the input data).</p> <p>Furthermore, calculations were carried out according to a tiered approach. While the first-tier calculations (Tier I) use very conservative assumptions for an efficient screening of the exposure with low risk for underestimation, the second-tier assessment (Tier II) includes assumptions that are more refined but still conservative. For each scenario, exposure estimates were obtained for different percentiles of the exposure distribution and the total margin of exposure (MOET, i.e. the ratio of the toxicological reference dose to the estimated exposure) was calculated at each percentile.</p> <p>The input and output data for the exposure assessment are reported in the following annexes:</p> <ul> <li>Annex A.1 – Input data for the exposure assessment of CAG-TCP</li> <li>Annex A.2 – Input data for the exposure assessment of CAG-TCF</li> <li>Annex B.1 – Output data from the Tier I exposure assessment of CAG-TCP</li> <li>Annex B.2 – Output data from the Tier I exposure assessment of CAG-TCF</li> <li>Annex C.1 – Output data from the Tier II exposure assessment of CAG-TCP</li> <li>Annex C.2 – Output data from the Tier II exposure assessment of CAG-TCF</li> </ul> <p>Further information on the data, methodologies and interpretation of the results are provided in the scientific report on the cumulative dietary exposure assessment of pesticides that have chronic effects on the thyroid using SAS<sup>®</sup> software (<a href="https://doi.org/10.2903/j.efsa.2019.5763">here</a>).</p> <p>The results reported in this assessment only refer to the exposure and are not an estimation of the actual risks. These exposure estimates should therefore be considered as documentation for the final scientific report on the cumulative risk assessment of dietary exposure to pesticides for their effects on the thyroid (<a href="https://www.efsa.europa.eu/en/consultations/call/public-consultation-scientific-report-cumulative">here</a>). The latter combines the hazard assessment and exposure assessment into a consolidated risk characterisation, including all related uncertainties.</p>
Annexes to the scientific report on the cumulative dietary exposure assessment of pesticides that have acute effects on the nervous system using SAS® software - Input and output data sets
<p>Retrospective dietary exposure assessments were conducted for two groups of pesticides that have acute effects on the nervous system: </p> <ol> <li>brain and/or erythrocyte acetylcholinesterase inhibition (CAG-NAN);</li> <li>functional alterations of the motor division (CAG-NAM).</li> </ol> <p>The pesticides considered in this assessment were identified and characterised in the scientific report on the establishment of cumulative assessment groups of pesticides for their effects on the nervous system (<a href="https://doi.org/10.2903/j.efsa.2019.5800">here</a>).</p> <p>The exposure calculations used monitoring data collected by Member States under their official pesticide monitoring programmes in 2014, 2015 and 2016 and individual food consumption data from ten populations of consumers from different countries and from different age groups. Regarding the selection of relevant food commodities, the assessment included water, foods for infants and young children and 30 raw primary commodities of plant origin that are widely consumed within Europe.</p> <p>Exposure estimates were obtained with SAS<sup>®</sup> software using a 2-dimensional Monte Carlo simulation, which is composed of an inner-loop execution and an outer-loop execution. Variability within the population is modelled through the inner-loop execution and is expressed as a percentile of the exposure distribution. The outer-loop execution is used to derive 95% confidence intervals around those percentiles (reflecting the sampling uncertainty of the input data).</p> <p>Furthermore, calculations were carried out according to a tiered approach. While the first-tier calculations (Tier I) use very conservative assumptions for an efficient screening of the exposure with low risk for underestimation, the second-tier assessment (Tier II) includes assumptions that are more refined but still conservative. For each scenario, exposure estimates were obtained for different percentiles of the exposure distribution and the total margin of exposure (MOET, i.e. the ratio of the toxicological reference dose to the estimated exposure) was calculated at each percentile.</p> <p>The input and output data for the exposure assessment are reported in the following annexes:</p> <ul> <li>Annex A.1 – Input data for the exposure assessment of CAG-NAN</li> <li>Annex A.2 – Input data for the exposure assessment of CAG-NAM</li> <li>Annex B.1 – Output data from the Tier I exposure assessment of CAG-NAN</li> <li>Annex B.2 – Output data from the Tier I exposure assessment of CAG-NAM</li> <li>Annex C.1 – Output data from the Tier II exposure assessment of CAG-NAN</li> <li>Annex C.2 – Output data from the Tier II exposure assessment of CAG-NAM</li> </ul> <p>Further information on the data, methodologies and interpretation of the results are provided in the scientific report on the cumulative dietary exposure assessment of pesticides that have acute effects on the nervous system using SAS<sup>®</sup> software (<a href="https://doi.org/10.2903/j.efsa.2019.5764">here</a>).</p> <p>The results reported in this assessment only refer to the exposure and are not an estimation of the actual risks. These exposure estimates should therefore be considered as documentation for the final scientific report on the cumulative risk assessment of dietary exposure to pesticides for their effects on the nervous system (<a href="https://www.efsa.europa.eu/en/consultations/call/public-consultation-scientific-report-cumulative">here</a>). The latter combines the hazard assessment and exposure assessment into a consolidated risk characterisation, including all related uncertainties.</p>
Shaping photovoltaic array output to align with changing wholesale electricity price profiles
<p>This repository includes python scripts and input/output data associated with the following publication:</p> <p>[1] Brown, P.R.; O'Sullivan, F. "Shaping photovoltaic array output to align with changing wholesale electricity price profiles." Applied Energy 2019. <a href="http://doi.org/10.1016/j.apenergy.2019.113734">https://doi.org/10.1016/j.apenergy.2019.113734</a></p> <p>Please cite reference [1] for full documentation if the contents of this repository are used for subsequent work.</p> <p>Some of the scripts and data are also used in the following working paper:</p> <p>[2] Brown, P.R.; O'Sullivan, F. "Spatial and temporal variation in the value of solar power across United States electricity markets". Working Paper, MIT Center for Energy and Environmental Policy Research. 2019. <a href="http://ceepr.mit.edu/publications/working-papers/705">http://ceepr.mit.edu/publications/working-papers/705</a></p> <p>All code is in python 3 and relies on a number of dependencies that can be installed using pip or conda.</p> <p><strong>Contents</strong></p> <ul> <li>pvvm.zip : Python module with functions for modeling PV generation, calculating PV revenues and capacity factors, and optimizing PV orientation.</li> <li>notebooks.zip : Jupyter notebooks, including: <ul> <li>pvvm-pvtos-data.ipynb: Example scripts used to download and clean input LMP data, determine LMP node locations, and reproduce some figures in reference [1]</li> <li>pvvm-pvtos-analysis.ipynb: Example scripts used to perform the calculations and reproduce some figures in reference [1]</li> <li>pvvm-pvtos-plots.ipynb: Scripts used to produce additional figures in reference [1]</li> <li>pvvm-example-generation.ipynb: Example scripts demonstrating the usage of the PV generation model and orientation optimization</li> </ul> </li> <li>html.zip : Static images of the above Jupyter notebooks for viewing without a python kernel</li> <li>data.zip : Day-ahead and real-time nodal locational marginal prices (LMPs) for CAISO, ERCOT, MISO, NYISO, and ISONE. <ul> <li>At the time of publication of this repository, permission had not been received from PJM to republish their LMP data. If permission is received in the future, a new version of this repository will linked here with the complete dataset.</li> </ul> </li> <li>results.zip : Simulation results associated with reference [1] above, including modeled revenue, capacity factor, and optimized orientations for PV systems at all LMP nodes</li> </ul> <p><strong>Data terms and usage notes</strong></p> <ul> <li>ISO LMP data are used with permission from the different ISOs. Adapting the MIT License (<a href="http://opensource.org/licenses/MIT">https://opensource.org/licenses/MIT</a>), "The data are provided 'as is', without warranty of any kind, express or implied, including but not limited to the warranties of merchantibility, fitness for a particular purpose and noninfringement. In no event shall the authors or sources be liable for any claim, damages or other liability, whether in an action of contract, tort or otherwise, arising from, out of or in connection with the data or other dealings with the data." Copyright and usage permissions for the LMP data are available on the ISO websites, linked below.</li> <li>ISO-specific notes: <ul> <li>CAISO data from <a href="http://oasis.caiso.com/mrioasis/logon.do">http://oasis.caiso.com/mrioasis/logon.do</a> are used pursuant to the terms at <a href="http://www.caiso.com/Pages/PrivacyPolicy.aspx#TermsOfUse">http://www.caiso.com/Pages/PrivacyPolicy.aspx#TermsOfUse</a>.</li> <li>ERCOT data are from <a href="http://www.ercot.com/mktinfo/prices">http://www.ercot.com/mktinfo/prices</a>.</li> <li>MISO data are from <a href="http://www.misoenergy.org/markets-and-operations/real-time--market-data/market-reports/">https://www.misoenergy.org/markets-and-operations/real-time--market-data/market-reports/</a> and <a href="https://www.misoenergy.org/markets-and-operations/real-time--market-data/market-reports/market-report-archives/">https://www.misoenergy.org/markets-and-operations/real-time--market-data/market-reports/market-report-archives/</a>.</li> <li>PJM data were originally downloaded from <a href="https://www.pjm.com/markets-and-operations/energy/day-ahead/lmpda.aspx">https://www.pjm.com/markets-and-operations/energy/day-ahead/lmpda.aspx</a> and <a href="https://www.pjm.com/markets-and-operations/energy/real-time/lmp.aspx">https://www.pjm.com/markets-and-operations/energy/real-time/lmp.aspx</a>. At the time of this writing these data are currently hosted at <a href="https://dataminer2.pjm.com/feed/da_hrl_lmps">https://dataminer2.pjm.com/feed/da_hrl_lmps</a> and <a href="https://dataminer2.pjm.com/feed/rt_hrl_lmps">https://dataminer2.pjm.com/feed/rt_hrl_lmps</a>.</li> <li>NYISO data from <a href="http://mis.nyiso.com/public/">http://mis.nyiso.com/public/</a> are used subject to the disclaimer at <a href="https://www.nyiso.com/legal-notice">https://www.nyiso.com/legal-notice</a>.</li> <li>ISONE data are from <a href="https://www.iso-ne.com/isoexpress/web/reports/pricing/-/tree/lmps-da-hourly">https://www.iso-ne.com/isoexpress/web/reports/pricing/-/tree/lmps-da-hourly</a> and <a href="https://www.iso-ne.com/isoexpress/web/reports/pricing/-/tree/lmps-rt-hourly-final">https://www.iso-ne.com/isoexpress/web/reports/pricing/-/tree/lmps-rt-hourly-final</a>. The Material is provided on an "as is" basis. ISO New England Inc., to the fullest extent permitted by law, disclaims all warranties, either express or implied, statutory or otherwise, including but not limited to the implied warranties of merchantability, non-infringement of third parties' rights, and fitness for particular purpose. Without limiting the foregoing, ISO New England Inc. makes no representations or warranties about the accuracy, reliability, completeness, date, or timeliness of the Material. ISO New England Inc. shall have no liability to you, your employer or any other third party based on your use of or reliance on the Material.</li> </ul> </li> <li>Data workup: LMP data were downloaded directly from the ISOs using scripts similar to the pvvm.data.download_lmps() function (see below for caveats), then repackaged into single-node single-year files using the pvvm.data.nodalize() function. These single-node single-year files were then combined into the dataframes included in this repository, using the procedure shown in the pvvm-pvtos-data.ipynb notebook for MISO. We provide these yearly dataframes, rather than the long-form data, to minimize file size and number. These dataframes can be unpacked into the single-node files used in the analysis using the pvvm.data.copylmps() function.</li> </ul> <p><strong>Code license and usage notes</strong></p> <ul> <li>Code (*.py and *.ipynb files) is provided under the <a href="https://opensource.org/licenses/MIT">MIT License</a>, as specified in the pvvm/LICENSE file.</li> <li>Updates to the code, if any, will be posted in the non-static repository at <a href="https://github.com/patrickbrown4/pvvm_pvtos">https://github.com/patrickbrown4/pvvm_pvtos</a>. The code in the present repository has the following version-specific dependencies: <ul> <li>matplotlib: 3.0.3</li> <li>numpy: 1.16.2</li> <li>pandas: 0.24.2</li> <li>pvlib: 0.6.1</li> <li>scipy: 1.2.1</li> <li>tqdm: 4.31.1</li> </ul> </li> <li>To use the NSRDB download functions, modify the "settings.py" file to insert a valid NSRDB API key, which can be requested from <a href="https://developer.nrel.gov/signup/">https://developer.nrel.gov/signup/</a>. Locations can be specified by passing latitude, longitude floats to pvvm.data.downloadNSRDBfile(), or by passing a string googlemaps query to pvvm.io.queryNSRDBfile(). To use the googlemaps functionality, request a googlemaps API key (<a href="https://developers.google.com/maps/documentation/javascript/get-api-key">https://developers.google.com/maps/documentation/javascript/get-api-key</a>) and insert it in the "settings.py" file.</li> <li>Note that many of the ISO websites have changed in the time since the functions in the pvvm.data module were written and the LMP data used in the above papers were downloaded. As such, the pvvm.data.download_lmps() function no longer works for all ISOs and years. We provide this function to illustrate the general procedure used, and do not intend to maintain it or keep it up to date with the changing ISO websites. For up-to-date functions for accessing ISO data, the following repository (no connection to the present work) may be helpful: <a href="https://github.com/catalyst-cooperative/pudl">https://github.com/catalyst-cooperative/pudl</a>.</li> </ul>
Ocean circulation during the last nine interglacials inferred from carbon 13 isotopes - model outputs
<p>This dataset contains the model output corresponding to the paper entitled "Ocean circulation during the last nine interglacials inferred from carbon 13 isotopes" submitted to Paleoceanography. For the description of the model and simulations we refer to this article.</p> <p>Model outputs:</p> <p>The outputs of two series of simulations can be found in the files.</p> <p>1) MIS experiments:</p> <p>- Atmospheric CO<sub>2</sub> values (ppm) for each interglacial are in: iloveclim_CO2_MIS.txt</p> <p>- Oceanic ð<sup>13</sup>C values (permil) are in:</p> <p>iloveclim_PI_CC.nc for the pre-industrial</p> <p>iloveclim_MISXX.nc for MISXX (XX being 1,5,7,9,11,13,17 or 19)</p> <p> </p> <p>2) Sensitivity experiments</p> <p>- Atmospheric CO<sub>2</sub> values (ppm) are in: iloveclim_CO2_sensitivity_expe.txt</p> <p>- Streamfunction values (Sv) are in:</p> <p>iloveclim_PI-CC_stream.nc for the pre-industrial</p> <p>iloveclim_MIS17_CC_stream.nc for the standard MIS17 simulation</p> <p>iloveclim_MIS17-CC-hosing0.2Sv_stream.nc for the hosing simulation with 0.2sv</p> <p>iloveclim_MIS17-CC-hosing-0.2Sv_stream.nc for the hosing simulations with -0.2Sv</p> <p>iloveclim_MIS17-CC-brines0.4_stream.nc for the simulation with the sinking of brines</p> <p>- Oceanic ð<sup>13</sup>C values (permil) are in:</p> <p>iloveclim_MIS17-CC.nc for the standard MIS17 simulation</p> <p>iloveclim_MIS17-CC-hosing0.2Sv.nc for the hosing simulation with 0.2sv</p> <p>iloveclim_MIS17-CC-hosing-0.2Sv.nc for the hosing simulations with -0.2Sv</p> <p>iloveclim_MIS17-CC-brines0.4.nc for the simulation with the sinking of brines</p>
Benchmarking data and outputs for CLASSIC v. 1.0
<p><strong>CLASSIC v. 1.0 model inputs and outputs for benchmarking </strong></p> <p>This dataset is used by scripts in the <a href="https://zenodo.org/record/3522407">CLASSIC codebase</a> along with the <a href="https://zenodo.org/record/3525249">CLASSIC Singularity software container</a>.<strong> Please ensure you obtain them prior to using this dataset. Instructions are provided on the CLASSIC <a href="https://cccma.gitlab.io/classic_pages/info/get_started/">Quick Start Guide</a>.</strong></p> <p>This dataset contains <a href="https://fluxnet.fluxdata.org/data/fluxnet2015-dataset/">FLUXNET2015</a> data that is used to benchmark the <a href="https://cccma.gitlab.io/classic_pages/">Canadian Land Surface Scheme including Biogeochemical Cycles </a>(CLASSIC) v. 1.0. All model inputs required for the (31 for version 1.0) <a href="https://cccma.gitlab.io/classic_pages/benchmarking/">FLUXNET sites</a> are provided along with example outputs that benchmark CLASSIC v. 1.0. The model outputs include raw model outputs, plots of select variables and benchmarking results from the Automated Model Benchmarking (<a href="https://cran.r-project.org/package=amber">AMBER</a>) package. Following the the CLASSIC <a href="https://cccma.gitlab.io/classic_pages/info/get_started/">Quick Start Guide</a> will generate all outputs on the user's own machine.</p> <p>This work used eddy covariance data acquired and shared by the FLUXNET community, including these networks: AmeriFlux, AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada, GreenGrass, ICOS, KoFlux, LBA, NECC, OzFlux-TERN, TCOS-Siberia, and USCCC. The ERA-Interim reanalysis data are provided by ECMWF and processed by LSCE. The FLUXNET eddy covariance data processing and harmonization was carried out by the European Fluxes Database Cluster, AmeriFlux Management Project, and Fluxdata project of FLUXNET, with the support of CDIAC and ICOS Ecosystem Thematic Center, and the OzFlux, ChinaFlux and AsiaFlux offices.</p> <p>We thank C. Le Quéré for allowing us to distribute her CO<sub>2</sub> record that was originally made for the TRENDY project.</p> <p> </p> <p> </p> <p> </p> <p> </p>
IPSL-CM5A2. An Earth System Model designed for multi-millennial climate simulations: Boundary conditions and outputs.
<p>Inputs, boundary conditions, and ouputs files of the experiments described in Sepulchre et al. manuscript "<em>IPSL-CM5A2. An Earth System Model designed for multi-millennial climate simulations</em>" submitted for publication to Geoscientific Model Development:</p> <p><a href="https://www.geosci-model-dev-discuss.net/gmd-2019-332"><strong>https://www.geosci-model-dev-discuss.net/gmd-2019-332</strong></a></p> <p>The 90Ma_IPSLCM5A2_inputs.tar tarball contains the input and boundary files used to run the 3,000-year Cretaceous experiment.</p> <p>The output_files.tar tarball contains the netcdf output files of the preindustrial, historical and Cretaceous simulations analyzed in the manuscript. Diagnoses are presented through a Jupyter notebook that can be retrieved and played interactively <strong><a href="https://doi.org/10.5281/zenodo.3549652"><strong>here</strong></a>.</strong></p> <p><strong> </strong></p>
A linearized Navier-Stokes input output model
<p>Matrices for the experiments reported in DOI:10.1137/140980016</p>
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