diff --git a/IOC_in_STOFS.html b/IOC_in_STOFS.html
index 8f15c2c..9fda6e8 100644
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+++ b/IOC_in_STOFS.html
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [7]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [11]:</div>
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 <div class="highlight hl-ipython3"><pre><span></span><span class="n">plot1</span> <span class="o">=</span> <span class="n">stof2d</span><span class="o">.</span><span class="n">hvplot</span><span class="o">.</span><span class="n">points</span><span class="p">(</span>
@@ -8602,7 +8602,7 @@ <h2 id="Find-IOC-stations-in-STOFS2D/3D-output-file">Find IOC stations in STOFS2
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-    <span class="n">label</span> <span class="o">=</span> <span class="s1">'IOC stations'</span>
+    <span class="n">label</span> <span class="o">=</span> <span class="s1">'concomittent IOC/STOFS stations'</span>
 <span class="p">)</span>
 <span class="n">plot3</span> <span class="o">=</span> <span class="n">ioc_cleanup</span><span class="o">.</span><span class="n">hvplot</span><span class="o">.</span><span class="n">points</span><span class="p">(</span>
     <span class="n">x</span><span class="o">=</span><span class="s1">'lon'</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s1">'lat'</span><span class="p">,</span> 
@@ -8610,7 +8610,7 @@ <h2 id="Find-IOC-stations-in-STOFS2D/3D-output-file">Find IOC stations in STOFS2
     <span class="n">geo</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> 
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-    <span class="n">label</span> <span class="o">=</span> <span class="s1">'concomittent IOC/STOFS stations'</span>
+    <span class="n">label</span> <span class="o">=</span> <span class="s1">'IOC_cleanup'</span>
 <span class="p">)</span>
 <span class="p">(</span><span class="n">plot1</span> <span class="o">*</span> <span class="n">plot2</span> <span class="o">*</span> <span class="n">plot3</span><span class="p">)</span><span class="o">.</span><span class="n">opts</span><span class="p">(</span>
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@@ -8699,129 +8699,6 @@ <h2 id="Find-IOC-stations-in-STOFS2D/3D-output-file">Find IOC stations in STOFS2
 </div>
 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
-<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
-</div>
-<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [8]:</div>
-<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="n">keep_nodes</span><span class="o">.</span><span class="n">mesh_index</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-</div>
-</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[8]:</div>
-<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>count     71.000000
-mean     311.112676
-std      176.651032
-min        2.000000
-25%      163.500000
-50%      338.000000
-75%      472.500000
-max      543.000000
-Name: mesh_index, dtype: float64</pre>
-</div>
-</div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell-inputWrapper" tabindex="0">
-<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
-</div>
-<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
-<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="cm-editor cm-s-jupyter">
-<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
-<span class="kn">import</span> <span class="nn">glob</span>
-<span class="kn">import</span> <span class="nn">dask</span>
-
-<span class="n">filenames1</span> <span class="o">=</span> <span class="p">[]</span>
-<span class="n">filenames2</span> <span class="o">=</span> <span class="p">[]</span>
-<span class="k">for</span> <span class="n">file</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="s1">'noaa/stofs2d/202*'</span><span class="p">)):</span>
-    <span class="n">root</span><span class="p">,</span> <span class="n">filename</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">file</span><span class="p">)</span>
-    <span class="n">number</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">filename</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">"_"</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="k">if</span> <span class="n">number</span> <span class="o">&gt;</span> <span class="mi">20230107</span><span class="p">:</span>
-        <span class="n">filenames2</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">file</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span> 
-        <span class="n">filenames1</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">file</span><span class="p">)</span>
-
-<span class="n">ds1</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_mfdataset</span><span class="p">(</span><span class="n">filenames1</span><span class="p">,</span> <span class="n">combine</span><span class="o">=</span><span class="s1">'nested'</span><span class="p">,</span> <span class="n">concat_dim</span> <span class="o">=</span> <span class="s2">"time"</span><span class="p">)</span>
-<span class="n">ds2</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_mfdataset</span><span class="p">(</span><span class="n">filenames2</span><span class="p">,</span> <span class="n">combine</span><span class="o">=</span><span class="s1">'nested'</span><span class="p">,</span> <span class="n">concat_dim</span> <span class="o">=</span> <span class="s2">"time"</span><span class="p">)</span>
-
-<span class="k">def</span> <span class="nf">keep_first_timeindex</span><span class="p">(</span><span class="n">ds</span><span class="p">):</span>
-    <span class="n">mask</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">time</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">()</span><span class="o">.</span><span class="n">duplicated</span><span class="p">(</span><span class="s2">"last"</span><span class="p">)</span><span class="o">.</span><span class="n">values</span>
-    <span class="n">data_vars</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="n">ds</span><span class="o">.</span><span class="n">data_vars</span><span class="p">):</span>
-        <span class="k">with</span> <span class="n">dask</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="o">**</span><span class="p">{</span><span class="s2">"array.slicing.split_large_chunks"</span><span class="p">:</span> <span class="kc">True</span><span class="p">}):</span>
-            <span class="n">data_vars</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="n">var</span><span class="p">][</span><span class="o">~</span><span class="n">mask</span><span class="p">])</span>
-    <span class="k">return</span> <span class="n">xr</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">data_vars</span><span class="p">)</span>
-
-
-<span class="n">ds1</span> <span class="o">=</span> <span class="n">keep_first_timeindex</span><span class="p">(</span><span class="n">ds1</span><span class="p">)</span>
-<span class="n">ds2</span> <span class="o">=</span> <span class="n">keep_first_timeindex</span><span class="p">(</span><span class="n">ds2</span><span class="p">)</span>
-<span class="n">ds1</span>
-</pre></div>
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-<div class="highlight hl-ipython3"><pre><span></span><span class="k">for</span> <span class="n">it</span><span class="p">,</span> <span class="n">tmp</span> <span class="ow">in</span> <span class="n">keep_nodes</span><span class="o">.</span><span class="n">iterrows</span><span class="p">():</span>
-    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Station </span><span class="si">{</span><span class="n">tmp</span><span class="o">.</span><span class="n">ioc_code</span><span class="si">}</span><span class="s1"> at </span><span class="si">{</span><span class="n">tmp</span><span class="o">.</span><span class="n">lon</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">, </span><span class="si">{</span><span class="n">tmp</span><span class="o">.</span><span class="n">lat</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
-
-    <span class="n">ds1_subset</span> <span class="o">=</span> <span class="n">ds1</span><span class="o">.</span><span class="n">isel</span><span class="p">(</span><span class="n">station</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">tmp</span><span class="o">.</span><span class="n">mesh_index</span><span class="p">))</span>
-    <span class="n">ds2_subset</span> <span class="o">=</span> <span class="n">ds2</span><span class="o">.</span><span class="n">isel</span><span class="p">(</span><span class="n">station</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">tmp</span><span class="o">.</span><span class="n">mesh_index</span><span class="p">))</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">ds1_subset</span><span class="p">)</span>
-    <span class="n">ds_subset</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">ds1_subset</span><span class="p">,</span> <span class="n">ds2_subset</span><span class="p">],</span> <span class="n">dim</span><span class="o">=</span><span class="s2">"time"</span><span class="p">)</span>
-    <span class="n">df</span> <span class="o">=</span> <span class="n">ds_subset</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">()</span>
-    <span class="n">df</span><span class="o">.</span><span class="n">to_parquet</span><span class="p">(</span><span class="sa">f</span><span class="s1">'01_obs/stofs2d/</span><span class="si">{</span><span class="n">tmp</span><span class="o">.</span><span class="n">ioc_code</span><span class="si">}</span><span class="s1">.parquet'</span><span class="p">)</span>
-    <span class="k">break</span>
-</pre></div>
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-<pre>Station abed at -2.08, 57.14
-&lt;xarray.Dataset&gt; Size: 315kB
-Dimensions:       (time: 3840)
-Coordinates:
-  * time          (time) datetime64[ns] 31kB 2022-01-01T06:06:00 ... 2023-01-...
-Data variables:
-    station_name  (time) |S50 192kB dask.array&lt;chunksize=(60,), meta=np.ndarray&gt;
-    x             (time) float64 31kB dask.array&lt;chunksize=(60,), meta=np.ndarray&gt;
-    y             (time) float64 31kB dask.array&lt;chunksize=(60,), meta=np.ndarray&gt;
-    zeta          (time) float64 31kB dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;
-Attributes:
-    long_name:  station name
-</pre>
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-</div>
-</div>
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