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<title>specMACS cloud geometry — How to EUREC⁴A</title>
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<h1>specMACS cloud geometry</h1>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#project-points-to-specmacs-measurements">Project points to specMACS measurements</a></li>
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<section class="tex2jax_ignore mathjax_ignore" id="specmacs-cloud-geometry">
<h1>specMACS cloud geometry<a class="headerlink" href="#specmacs-cloud-geometry" title="Link to this heading">#</a></h1>
<p>In the following, an example of how to use the specMACS cloud geometry dataset will be shown.
The cloud geometry is retrieved by a stereographic feature matching method described by <span id="id1">Kölling <em>et al.</em> (<a class="reference internal" href="references.html#id15" title="T. Kölling, T. Zinner, and B. Mayer. Aircraft-based stereographic reconstruction of 3-d cloud geometry. Atmospheric Measurement Techniques, 12(2):1155–1166, 2019. URL: https://amt.copernicus.org/articles/12/1155/2019/, doi:10.5194/amt-12-1155-2019.">2019</a>)</span>.
The algorithm is applied to the data of the two polarization resolving cameras of the instrument covering a large combined field of view of about <span class="math notranslate nohighlight">\(\pm 45° \times \pm 59°\)</span> (along track <span class="math notranslate nohighlight">\(\times\)</span> across track) <span id="id2">(<a class="reference internal" href="references.html#id16" title="V. Pörtge, T. Kölling, A. Weber, L. Volkmer, C. Emde, T. Zinner, L. Forster, and B. Mayer. High-spatial-resolution retrieval of cloud droplet size distribution from polarized observations of the cloudbow. Atmospheric Measurement Techniques, 16(3):645–667, 2023. URL: https://amt.copernicus.org/articles/16/645/2023/, doi:10.5194/amt-16-645-2023.">Pörtge <em>et al.</em>, 2023</a>)</span>. It is important to mention that no points found does not necessarily mean that no cloud has been observed as this can also happen if the cloud lacks in contrasts or due to a bad tracking of features.</p>
<section id="open-dataset">
<h2>Open dataset<a class="headerlink" href="#open-dataset" title="Link to this heading">#</a></h2>
<p>The cloud geometry data were calculated for each of the flight segments flown by HALO seperately, thus data access will be per <code class="docutils literal notranslate"><span class="pre">segment_id</span></code>.
The flight segmentation can be found in the respective <a class="reference external" href="https://github.com/eurec4a/flight-phase-separation/">github repository</a>.
In this example, we’ll have a look at the cloud geometry of the straight leg segment <code class="docutils literal notranslate"><span class="pre">HALO-0205_sl1</span></code> flown on 2020-02-05.
If you don’t know your favourite flight segment already or want to query or loop over multiple segments, please have a look at <a class="reference internal" href="flight-phase-operations.html"><span class="std std-doc">flight segmentation usage</span></a>.</p>
<div class="cell docutils container">
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">eurec4a</span>
<span class="n">segment_id</span> <span class="o">=</span> <span class="s2">"HALO-0205_sl1"</span>
<span class="n">cat</span> <span class="o">=</span> <span class="n">eurec4a</span><span class="o">.</span><span class="n">get_intake_catalog</span><span class="p">()</span>
<span class="n">ds</span> <span class="o">=</span> <span class="n">cat</span><span class="o">.</span><span class="n">HALO</span><span class="o">.</span><span class="n">specMACS</span><span class="o">.</span><span class="n">cloud_geometry</span><span class="p">[</span><span class="n">segment_id</span><span class="p">]</span><span class="o">.</span><span class="n">to_dask</span><span class="p">()</span>
<span class="n">ds</span>
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<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/usr/share/miniconda3/envs/how_to_eurec4a/lib/python3.12/site-packages/intake_xarray/base.py:21: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
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</style><pre class='xr-text-repr-fallback'><xarray.Dataset> Size: 220MB
Dimensions: (sample: 2392497, v: 3)
Dimensions without coordinates: sample, v
Data variables:
time (sample) datetime64[ns] 19MB ...
lat (sample) float64 19MB ...
lon (sample) float64 19MB ...
height (sample) float64 19MB ...
height_above_geoid (sample) float64 19MB ...
loc (sample, v) float64 57MB ...
viewpoint (sample, v) float64 57MB ...
max_misspointing (sample) float32 10MB ...
Attributes:
_NCProperties: version=2,netcdf=4.7.3,hdf5=1.10.4
title: Stereographic retrieved cloud geometry b...
institution: Meteorological Institut, Ludwig-Maximili...
source: specMACS polcameras
history: Created 2023-05-13T18:57:20
references: Kölling, T., Zinner, T., and Mayer, B.: ...
comment: This dataset contains stereographically ...
contact: Lea Volkmer, L.Volkmer@physik.uni-muench...
DODS_EXTRA.Unlimited_Dimension: sample</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-e9b80fb6-450d-4427-a7cd-047b05e362b7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e9b80fb6-450d-4427-a7cd-047b05e362b7' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>sample</span>: 2392497</li><li><span>v</span>: 3</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-26939d24-f1f8-4bbb-b4cf-55c3447640bd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-26939d24-f1f8-4bbb-b4cf-55c3447640bd' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-7cd822b7-b8b1-4c88-ac21-f82538c1ae3b' class='xr-section-summary-in' type='checkbox' checked><label for='section-7cd822b7-b8b1-4c88-ac21-f82538c1ae3b' class='xr-section-summary' >Data variables: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7bd9e36c-e92a-4994-91a0-ec1a58e72dd2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7bd9e36c-e92a-4994-91a0-ec1a58e72dd2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da5b18d2-0872-46b7-ab9e-a3f2627cf30c' class='xr-var-data-in' type='checkbox'><label for='data-da5b18d2-0872-46b7-ab9e-a3f2627cf30c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>_ChunkSizes :</span></dt><dd>512</dd></dl></div><div class='xr-var-data'><pre>[2392497 values with dtype=datetime64[ns]]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d381d292-3fec-4288-9b3f-3af155ed6d93' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d381d292-3fec-4288-9b3f-3af155ed6d93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2cdebe2-d041-4de6-a9f6-0bee0104eca6' class='xr-var-data-in' type='checkbox'><label for='data-d2cdebe2-d041-4de6-a9f6-0bee0104eca6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_north</dd><dt><span>long_name :</span></dt><dd>latitude in WGS84</dd><dt><span>_ChunkSizes :</span></dt><dd>512</dd></dl></div><div class='xr-var-data'><pre>[2392497 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9c54c5ed-6a05-42a9-a107-38f9b7b6f3f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9c54c5ed-6a05-42a9-a107-38f9b7b6f3f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-febc941d-d262-42fb-afd2-a323001435f9' class='xr-var-data-in' type='checkbox'><label for='data-febc941d-d262-42fb-afd2-a323001435f9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_east</dd><dt><span>long_name :</span></dt><dd>longitude in WGS84</dd><dt><span>_ChunkSizes :</span></dt><dd>512</dd></dl></div><div class='xr-var-data'><pre>[2392497 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>height</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-da25f42e-4b37-4d1e-8aaf-753c5ad41730' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-da25f42e-4b37-4d1e-8aaf-753c5ad41730' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a740814-9b04-44bc-87ea-1a767cd71d1d' class='xr-var-data-in' type='checkbox'><label for='data-9a740814-9b04-44bc-87ea-1a767cd71d1d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>height above WGS84</dd><dt><span>_ChunkSizes :</span></dt><dd>512</dd></dl></div><div class='xr-var-data'><pre>[2392497 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>height_above_geoid</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ef70e5dd-87b6-4228-bf67-65d7c0f014a4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ef70e5dd-87b6-4228-bf67-65d7c0f014a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4915aed-1a72-4516-a3cc-1ad2ee8445f5' class='xr-var-data-in' type='checkbox'><label for='data-f4915aed-1a72-4516-a3cc-1ad2ee8445f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>height above geoid/seasurface in meter</dd><dt><span>_ChunkSizes :</span></dt><dd>512</dd></dl></div><div class='xr-var-data'><pre>[2392497 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>loc</span></div><div class='xr-var-dims'>(sample, v)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4cffa7d4-eb3e-4dd1-9e53-72ff5106aea0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4cffa7d4-eb3e-4dd1-9e53-72ff5106aea0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f6b19fd-ba20-41b1-a697-40bb7391de70' class='xr-var-data-in' type='checkbox'><label for='data-6f6b19fd-ba20-41b1-a697-40bb7391de70' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>location in cartesian reference frame at 13.1°N, -57.4°E</dd><dt><span>_ChunkSizes :</span></dt><dd>[1 3]</dd></dl></div><div class='xr-var-data'><pre>[7177491 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>viewpoint</span></div><div class='xr-var-dims'>(sample, v)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cebd827a-5242-424d-bc05-ff588832a4eb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cebd827a-5242-424d-bc05-ff588832a4eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f5be3a5-c5fd-4eb7-a480-4677a6cbe4ef' class='xr-var-data-in' type='checkbox'><label for='data-3f5be3a5-c5fd-4eb7-a480-4677a6cbe4ef' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>camera location in cartesian reference frame at 13.1°N, -57.4°E</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>_ChunkSizes :</span></dt><dd>[1 3]</dd></dl></div><div class='xr-var-data'><pre>[7177491 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>max_misspointing</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d68d29f9-0101-4350-b139-22f8ee8a4877' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d68d29f9-0101-4350-b139-22f8ee8a4877' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b6937bd-1243-4457-922c-71e4ae5f89e5' class='xr-var-data-in' type='checkbox'><label for='data-2b6937bd-1243-4457-922c-71e4ae5f89e5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>maximum misspointing from stereographic reconstruction</dd><dt><span>_ChunkSizes :</span></dt><dd>1024</dd></dl></div><div class='xr-var-data'><pre>[2392497 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f161588a-c2f7-4576-aae9-9c8fbbb1b7ad' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f161588a-c2f7-4576-aae9-9c8fbbb1b7ad' class='xr-section-summary' title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-25ba5df1-699c-477f-b862-fdf9c6981a64' class='xr-section-summary-in' type='checkbox' checked><label for='section-25ba5df1-699c-477f-b862-fdf9c6981a64' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>_NCProperties :</span></dt><dd>version=2,netcdf=4.7.3,hdf5=1.10.4</dd><dt><span>title :</span></dt><dd>Stereographic retrieved cloud geometry based on Kölling et al 2019.</dd><dt><span>institution :</span></dt><dd>Meteorological Institut, Ludwig-Maximilians-University Munich</dd><dt><span>source :</span></dt><dd>specMACS polcameras</dd><dt><span>history :</span></dt><dd>Created 2023-05-13T18:57:20</dd><dt><span>references :</span></dt><dd>Kölling, T., Zinner, T., and Mayer, B.: Aircraft-based stereographic reconstruction of 3-D cloud geometry, Atmos. Meas. Tech., 12, 1155–1166, https://doi.org/10.5194/amt-12-1155-2019, 2019.</dd><dt><span>comment :</span></dt><dd>This dataset contains stereographically derived points on the cloud surfaces observed by the two polarization resolving cameras of specMACS. As the algorithm is based on feature matching due to contrast gradients, it does not have to mean that there is no cloud if no points are identified. This dataset is based on the camera calibration and orientation defined in https://gitlab.lrz.de/ls-mayer/mounttrees, commit b'00e719084b3e33c727046e835d2a59872752a39e'</dd><dt><span>contact :</span></dt><dd>Lea Volkmer, L.Volkmer@physik.uni-muenchen.de</dd><dt><span>DODS_EXTRA.Unlimited_Dimension :</span></dt><dd>sample</dd></dl></div></li></ul></div></div></div></div>
</div>
</section>
<section id="simple-plots">
<h2>Simple plots<a class="headerlink" href="#simple-plots" title="Link to this heading">#</a></h2>
<p>We will start to do some first simple scatter plots of the cloud top heights (CTH). There are two CTH variables in the dataset: A normal ‘height’ variable and a variable called ‘height_above_geoid’. The first is defined with respect to the WGS84 ellipsoid and the second with respect to the EGM2008 geoid. Although there is not a large difference between both in the EUREC4A area, we will exemplarily plot both.</p>
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">mpl_toolkits.axes_grid1</span> <span class="kn">import</span> <span class="n">make_axes_locatable</span>
</pre></div>
</div>
</div>
</div>
<p>We’ll select only every 100th data point for a quick overview.</p>
<div class="admonition important">
<p class="admonition-title">Important</p>
<p>Depending on the clouds there might be a lot of points in the dataset or not such that this step might be useful or not.</p>
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ds_sel</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">isel</span><span class="p">(</span><span class="n">sample</span><span class="o">=</span><span class="nb">slice</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">100</span><span class="p">))</span>