集合预报诊断分析计算ensemble
<p>[TOC]</p>
<h1>集合敏感性分析</h1>
<pre><code class="language-python">ensemble_sensitive(var_x,var_y,mean_area,sig_lev)</code></pre>
<p><font size="4"><b>参数说明</b></font></p>
<table>
<thead>
<tr>
<th style="text-align: left;">参数</th>
<th style="text-align: left;">必选</th>
<th style="text-align: left;">类型</th>
<th style="text-align: left;">说明</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align: left;">var_x</td>
<td style="text-align: left;"><font color="red">是</font></td>
<td style="text-align: left;">stda</td>
<td style="text-align: left;">敏感度自变量</td>
</tr>
<tr>
<td style="text-align: left;">var_y</td>
<td style="text-align: left;"><font color="red">是</font></td>
<td style="text-align: left;">stda</td>
<td style="text-align: left;">敏感度因变量</td>
</tr>
<tr>
<td style="text-align: left;">mean_area</td>
<td style="text-align: left;"><font color="red">是</font></td>
<td style="text-align: left;">list</td>
<td style="text-align: left;">敏感度平均区域，如：[105,125,25,35]</td>
</tr>
<tr>
<td style="text-align: left;">sig_lev</td>
<td style="text-align: left;"><font color="red">是</font></td>
<td style="text-align: left;">float</td>
<td style="text-align: left;">显著性水平，如：0.95</td>
</tr>
</tbody>
</table>
<table>
<thead>
<tr>
<th style="text-align: left;">返回值</th>
<th style="text-align: left;">说明</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align: left;">dict</td>
<td style="text-align: left;">敏感性相关矩阵和对应sig_lev显著性水平的临界相关系数</td>
</tr>
</tbody>
</table>
<p><font size="4"><b>调用示例</b></font></p>
<pre><code class="language-python"># 模式
data_name = &#039;ecmwf_ens&#039;
# 起报时间
init_time = datetime(2024,6,26,8)
# 预报时效
fhour = 24
# 敏感度平均区域
mean_area = [105,125,25,35]
hgt=metdig_io.cmadaas.get_model_grids(init_time=init_time,var_name=&#039;hgt&#039;,level=500,data_name=&#039;ecmwf_ens&#039;,fhours=[fhour], extent = [70,140,10,60])
rain=metdig_io.cmadaas.get_model_grids(init_time=init_time,var_name=&#039;rain&#039;,data_name=&#039;ecmwf_ens&#039;,fhours=[fhour], extent = [70,140,10,60])
sensitive,sig_r = metdig.cal.ensemble_sensitive(hgt,rain,mean_area=mean_area,sig_lev=0.95)
print(sensitive)
print(sig_r)</code></pre>
<p><font size="4"><b>输出信息</b></font></p>
<pre><code class="language-python">&lt;xarray.DataArray (member: 1, lat: 51, lon: 71)&gt;
array([[[-0.05641029, -0.03652296, -0.02386137, ..., -0.24727232,
-0.25757285, -0.23066381],
[-0.09596617, -0.07768409, -0.07728238, ..., -0.20457158,
-0.24136463, -0.23663574],
[-0.14247215, -0.12694309, -0.13900364, ..., -0.16469826,
-0.21014542, -0.24335117],
...,
[-0.32508798, -0.32933686, -0.32987893, ..., 0.12586257,
0.12834175, 0.13134546],
[-0.29120784, -0.29516904, -0.29522658, ..., 0.10670732,
0.1092673 , 0.11188734],
[-0.26347924, -0.26703695, -0.26827418, ..., 0.09174522,
0.09332512, 0.09553428]]])
Coordinates:
* member (member) object &#039;ecmwf_ens&#039;
dtime int32 24
time datetime64[ns] 2024-06-26T08:00:00
* lat (lat) float64 10.0 11.0 12.0 13.0 14.0 ... 56.0 57.0 58.0 59.0 60.0
* lon (lon) float64 70.0 71.0 72.0 73.0 74.0 ... 137.0 138.0 139.0 140.0
0.2756838051439881</code></pre>
<h1>tubing聚类分析</h1>
<p>part1: Central Cluster
The ensemble mean (EM) is defined as the average of the ensemble forecasts (fl) .
The n distances dm(i) from each ensemble forecast to the EM are computed (Euclidean Distance).
The ensemble forecast that is closest to the EM, min[dm(i)], becomes the first member of the central cluster. This is iterated until the central cluster limits are reached.
Spread-dependent configuration: the limitation depends on the actual ensemble spread. For example, the central cluster variance is limited to 50% of the total ensemble variance.
The distance from the last ensemble forecast classified in the central cluster to the EM becomes the radius r of the classification.</p>
<p>part2: Tubes
The matrix d of ensemble forecasts interdistances is computed.
The ensemble forecast that is farthest from the ensemble mean, max[dm(i)], becomes the extreme of the first tube.
For each ensemble forecasts member(i) that has not been classified in the central cluster, that is, not a tube extreme, and whose distance dm(i) to the ensemble mean is maximum, if the two following operations are achieved for each existing tube extreme, member(i) is a member of the tube j
1、the projection of dm(i) on the of the axis of the exist tube j defined as dd(i, j)>0
2、the distance of member(i) to the axis of tube j defined as dx(i, j) < r </p>
<pre><code class="language-python">tubing(stda, extent_tube_based=None, threshold_tube=0.5)</code></pre>
<p><font size="4"><b>参数说明</b></font></p>
<table>
<thead>
<tr>
<th style="text-align: left;">参数</th>
<th style="text-align: left;">必选</th>
<th style="text-align: left;">类型</th>
<th style="text-align: left;">说明</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align: left;">stda</td>
<td style="text-align: left;"><font color="red">是</font></td>
<td style="text-align: left;">stda</td>
<td style="text-align: left;">被分析的要素</td>
</tr>
<tr>
<td style="text-align: left;">extent_tube_based</td>
<td style="text-align: left;">否</td>
<td style="text-align: left;">list, optional</td>
<td style="text-align: left;">tube区域, 例如 [70., 136., 10., 55.]</td>
</tr>
<tr>
<td style="text-align: left;">threshold_tube</td>
<td style="text-align: left;">否</td>
<td style="text-align: left;">float, optional</td>
<td style="text-align: left;">tube阈值，默认0.5</td>
</tr>
</tbody>
</table>
<table>
<thead>
<tr>
<th style="text-align: left;">返回值</th>
<th style="text-align: left;">说明</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align: left;">dict</td>
<td style="text-align: left;">以字典形式返回聚类结果</td>
</tr>
</tbody>
</table>
<p><font size="4"><b>调用示例</b></font></p>
<pre><code class="language-python">dic = metdig.cal.tubing(hgt)
print(dic)</code></pre>
<p><font size="4"><b>输出信息</b></font></p>
<pre><code class="language-python">{&#039;tubes_index&#039;: {1: [2, 27, 24], 2: [4], 3: [34], 4: [50], 5: [16]}, &#039;member_central&#039;: array([29, 13, 35, 3, 26, 41, 44, 23, 17, 0, 15, 11, 40, 45, 49, 28, 10,
25, 33, 8, 20, 48, 38, 43, 21, 30, 1, 9, 42, 46, 18, 5, 19, 12,
32, 39, 47, 7, 37, 31, 22, 6, 36, 14]), &#039;vari_total&#039;: &lt;xarray.DataArray &#039;hgt&#039; ()&gt;
array(1946.0272, dtype=float32)
Coordinates:
dtime int32 24
time datetime64[ns] 2024-06-26T08:00:00
level int32 500, &#039;r&#039;: array(252.34698, dtype=float32)}
</code></pre>