From c17c5a1b16923ac5ee79232aa85c573049e7a61f Mon Sep 17 00:00:00 2001 From: NiuZhixiao Date: Wed, 25 Oct 2023 12:51:36 +0000 Subject: [PATCH] deploy: a4515a300031e6e70445ccab7a072913eb849f02 --- _sources/chapters/homework/homework1.ipynb | 2 +- chapters/homework/homework1.html | 2 +- searchindex.js | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/_sources/chapters/homework/homework1.ipynb b/_sources/chapters/homework/homework1.ipynb index b652eb7..da4ca18 100644 --- a/_sources/chapters/homework/homework1.ipynb +++ b/_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)" ] }, { diff --git a/chapters/homework/homework1.html b/chapters/homework/homework1.html index 8541ad0..2bb2f33 100644 --- a/chapters/homework/homework1.html +++ b/chapters/homework/homework1.html @@ -460,7 +460,7 @@

Q1: Calculate extreme rainfall statisitics

Q2: Fit the GEV distribution#

-

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). (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). (Hint: The rainfall deficit can be obtained by subtracting the 30-day moving average rainfall from the mean rainfall calculated in Q1) (40 marks)

# Your solutions go here.
diff --git a/searchindex.js b/searchindex.js
index 1364191..7044b50 100644
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