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9 results for “Contrails”
Impact of host climate model on contrail cirrus effective radiative forcing estimates
<p>Data to reproduce the figures in the article 'Impact of host climate model on contrail cirrus effective radiative forcing estimates'.</p>
" Description and evaluation of a new contrail cirrus 2 parameterization in the ARPEGE-Climat atmospheric 3 model " datasets
<p>This repository contains the data files used for the analyses presented in the paper. The dataset includes variables of interest for the two main simulations (CONTFREE and CONTNUDGED) for the year 2019. </p> <ul> <li>"totcon" variable represents the integrated contrail cirrus coverage.</li> <li>"rst", respectively "rstcotra", represent the net downward shortwave radiation at the top of the atmosphere for the radiative call with contrails perturbation, respectively without perturbation. The difference between these two variables provides the contrail cirrus net downward shortwave radiation contribution. </li> <li>"rlut", respectively "rlutcotra", represent the net upward longwave radiation at the top of the atmosphere for the radiative call with contrails perturbation, respectively without perturbation. The difference between these two variables provides the contrail cirrus net upward longwave radiation contribution. </li> <li>"pissr" represents the probability of the gridbox being ice supersaturated. This variable is provided for pressure levels 200,225, and 250hPa.</li> <li>"clhcalipso" represents the integrated coverage of "high clouds" (>400hPa). </li> </ul>
figures_and_data_for_manuscript_contrail_formation_within_cirrus_Verma_and_Burkhardt_07032022
<p>This data set includes figures and data used in the revised manuscript 'Contrail formation within cirrus'.</p>
Contrail altitude estimation using GOES-16 ABI data and deep learning: Dataset of contrails collocated with CALIOP satellite measurements
Open the record for dataset details and reuse information.
The effect of uncertainty in humidity and model parameters on the prediction of contrail energy forcing
<p>Previous work has shown that while the net effect of aircraft condensation trails (contrails) on the<br>climate is warming, the exact magnitude of the energy forcing per meter of contrail remains uncertain.<br>In this paper, we explore the skill of a Lagrangian contrail model (CoCiP) in identifying flight<br>segments with high contrail energy forcing. We find that skill is greater than climatological<br>predictions alone, even accounting for uncertainty in weather fields and model parameters.</p> <p>We estimate the uncertainty in weather by using the ensemble ERA5 weather reanalysis from the European<br>Centre for Medium-Range Weather Forecasts (ECMWF) as Monte Carlo inputs to CoCiP. We unbias and correct<br>under-dispersion on the ERA5 humidity data by forcing a match to the distribution of in situ humidity<br>measurements taken at cruising altitude. We set aside CoCiP energy forcing estimates calculated using<br>one of the ensemble members as a proxy for ground truth, and report the skill of CoCiP in identifying<br>segments with large positive proxy energy forcing. We further estimate the uncertainty in the model<br>parameters in CoCiP by performing Monte Carlo simulations with CoCiP model parameters drawn from<br>uncertainty distributions consistent with the literature.</p> <p>When CoCiP outputs are averaged over seasons to form climatological predictions, the skill in<br>predicting the proxy is 44%, while the skill of per-flight CoCiP outputs is 84%. If these results carry<br>over to the true (unknown) contrail EF, they indicate that per-flight energy forcing predictions can<br>reduce the number of potential contrail avoidance route adjustments by 2x, hence reducing both the cost<br>and fuel impact of contrail avoidance.</p>
Natural-Color-Cloud-and-Contrail-Image-Dataset
<p>The natural color cloud and contrail image (NCCI) dataset is a dataset for satellite cloud image super resolution. The NCCI dataset is generated based on Himawari-8 satellite data. The NCCI dataset contains 1100 satellite images, including 1000 natural color images and 100 contrail images. It is worth noting that contrails have consideration in our dataset.</p>
Aviation contrail cirrus and radiative forcing over Europe for six months in 2020 during COVID-19 compared with 2019: Observations and model results
<p>This file contains supporting information for a manuscript submitted for publication.</p> <p>Aviation contrail cirrus and radiative forcing over Europe for six months in 2020 during COVID-19 compared with 2019: Observations and model results </p> <p>U. Schumann, L. Bugliaro, and C. Voigt</p> <p>Corresponding author: Ulrich Schumann (Ulrich.schumann@dlr.de)</p> <p>For details see the README.txt</p> <p> </p> <p> </p>
data_set_for_revision_of_manuscript_contrail_formation_within_cirrus_Verma_Burkhardt_26112021
<p>This data set has new figures used in the revision of the manuscript 'contrail formation within cirrus'. and data relevent to paper is in the below given DOI.</p> <pre><strong>https://doi.org/10.5281/zenodo.5744985</strong></pre> <p> </p>
data_for_paper_revision_contrail_formation_within_cirrus_30112021
<p>this is the data set for paper revision</p>
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