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_sources/chapters/homework/homework1.ipynb

@@ -86,7 +86,7 @@
    "source": [
     "## Q2: Fit the GEV distribution \n",
     "\n",
-    "For the driest season you identified in Q1, find the seasonal maximum rainfall deficit based on the 30-day moving average rainfall deficit. This will result in a data set of 40 values, one value for each year. Fit two GEV distributions of seasonal maximum rainfall deficit using data from the first 20 years (1981-2000) and the last 20 years (2001-2020) separately. To do this, estimate the GEV parameters using (i) Maximum Likelihood, and (ii) L-Moments, respectively. (Details on fitting a GEV distribution can be found in the [Scipy tutorial](https://xiaoganghe.github.io/python-climate-visuals/chapters/data-analytics/scipy-basic.html)). (Hint: The rainfall deficit can be obtained by subtracting the 30-day moving average rainfall from the mean rainfall calculated in Q1) (40 marks)"
+    "For the driest season you identified in Q1, find the seasonal maximum rainfall deficit based on the 30-day moving average rainfall deficit (please use centered moving average). This will result in a data set of 40 values, one value for each year. Fit two GEV distributions of seasonal maximum rainfall deficit using data from the first 20 years (1981-2000) and the last 20 years (2001-2020) separately. To do this, estimate the GEV parameters using (i) Maximum Likelihood, and (ii) L-Moments, respectively. (Details on fitting a GEV distribution can be found in the [Scipy tutorial](https://xiaoganghe.github.io/python-climate-visuals/chapters/data-analytics/scipy-basic.html)). (Hint: The rainfall deficit can be obtained by subtracting the 30-day moving average rainfall from the mean rainfall calculated in Q1) (40 marks)"
    ]
   },
   {

chapters/homework/homework1.html

@@ -460,7 +460,7 @@ <h2>Q1: Calculate extreme rainfall statisitics<a class="headerlink" href="#q1-ca
 </section>
 <section id="q2-fit-the-gev-distribution">
 <h2>Q2: Fit the GEV distribution<a class="headerlink" href="#q2-fit-the-gev-distribution" title="Permalink to this heading">#</a></h2>
-<p>For the driest season you identified in Q1, find the seasonal maximum rainfall deficit based on the 30-day moving average rainfall deficit. This will result in a data set of 40 values, one value for each year. Fit two GEV distributions of seasonal maximum rainfall deficit using data from the first 20 years (1981-2000) and the last 20 years (2001-2020) separately. To do this, estimate the GEV parameters using (i) Maximum Likelihood, and (ii) L-Moments, respectively. (Details on fitting a GEV distribution can be found in the <a class="reference external" href="https://xiaoganghe.github.io/python-climate-visuals/chapters/data-analytics/scipy-basic.html">Scipy tutorial</a>). (Hint: The rainfall deficit can be obtained by subtracting the 30-day moving average rainfall from the mean rainfall calculated in Q1) (40 marks)</p>
+<p>For the driest season you identified in Q1, find the seasonal maximum rainfall deficit based on the 30-day moving average rainfall deficit (please use centered moving average). This will result in a data set of 40 values, one value for each year. Fit two GEV distributions of seasonal maximum rainfall deficit using data from the first 20 years (1981-2000) and the last 20 years (2001-2020) separately. To do this, estimate the GEV parameters using (i) Maximum Likelihood, and (ii) L-Moments, respectively. (Details on fitting a GEV distribution can be found in the <a class="reference external" href="https://xiaoganghe.github.io/python-climate-visuals/chapters/data-analytics/scipy-basic.html">Scipy tutorial</a>). (Hint: The rainfall deficit can be obtained by subtracting the 30-day moving average rainfall from the mean rainfall calculated in Q1) (40 marks)</p>
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Your solutions go here.</span>