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ConFire Model input/output for South America

<p>ConFIRE input and output used in the submission of&nbsp; Kelley et al. &quot;Low Climatic Influence found in 2019 Amazonia Fires&quot;.</p> <p>&quot;input&quot; dir including &quot;amazon_inference_data-2002-MCDBA_obs-TERRA_M__T.csv&quot;, just contains a csv file of all common grid data in other &quot;input&quot; subdirectories, which can be used in &quot;optimise_run_model/bayesian_inference.ipynb&quot; notebook. The files in the subdirectories are then used to make gridded model output using &quot;optimise_run_model/make_model_output.ipynb&quot;.</p> <p>Output, also provided, is summerized in the files in&nbsp;&quot;outputs/sampled_posterior_ConFire_solutions/constant_post_2018_full_2002_BG2020/&quot;</p> <p>The three files contain:</p> <ul> <li>fire_summary_frequancy_of_counts.nc - Contains one spatial variable (long name &quot;firecount frequency of occurrence&quot;) is the probability of a fire count (along the model_level_number dimension) at a particular month (time dimension) according to the models full posterior, <span class="math-tex">\(P(y_j)\)</span>(see papers supplementary).&nbsp;All months start on Jan 2001.&nbsp;</li> <li>fire_summary_precentile.nc -&nbsp;&nbsp;Contains one spatial variable (long name &quot;firecount at percentile&quot;), the fire count at each percentile of the full posterior in 1% increments from 1-99% (along the&nbsp;model_level_number dimension). Note, by definition, the 0a nd 100% percentile is 0 and&nbsp;<span class="math-tex">\(\infty \)</span>&nbsp;.</li> <li>fire_summary_observed_liklihood.nc - the position ( &quot;variable_0&quot;) and p-value&nbsp; (&quot;variable&quot;) of MCD64A1 in the model posterior.</li> <li>model_summary.nc - Summary of each variable of ConFire: <ul> <li>&quot;burnt_area&quot;: burnt area or fire count summary, described as percentiles as per &quot;fire_summary_precentile&quot;, but this time just assessing parameter uncertainty, i.e&nbsp;<span class="math-tex">\(P(\beta | Y_s)\)</span>&nbsp;in supplementary of paper.</li> <li>All other variables describe different model controls in the same &quot;burnt_area&quot;. Full information on how controls are constructed can be found in Kelley et al. (2019) with updates listed in supplementary of this paper. <ul> <li>&quot;fuel_continuity&quot;, &quot;moisture_content&quot;, &quot;ignitions&quot;, &quot;suppression&quot; are the actual values of the control</li> <li>&quot;standard_&lt;&lt;control&gt;&gt;&quot; is the standard limitation imposed by the control.</li> <li>&quot;potential_&lt;&lt;control&gt;&gt; is the potential limitation</li> <li>&quot;sensitivity_&lt;&lt;control&gt;&gt; is the senstivity of fire to a particular control.<br> <br> See Kelley et al. 2019 for the definition of limitation types&nbsp;and sensitivity</li> </ul> </li> </ul> </li> </ul> <p>Kelley, D.I., Bistinas, I., Whitley, R.&nbsp;<em>et al.</em>&nbsp;How contemporary bioclimatic and human controls change global fire regimes.&nbsp;<em>Nat. Clim. Chang.</em>&nbsp;<strong>9,&nbsp;</strong>690&ndash;696 (2019) doi:10.1038/s41558-019-0540-7</p> <p>&nbsp;</p>

ShareScore

48/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship
8
Harmonization
8
Access
16
Reuse readiness
8
Engagement
8

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