equations

Author: EddyCMWF

_config.yml

@@ -30,4 +30,10 @@ launch_buttons:
   binderhub_url: "https://mybinder.org"  # The URL for your BinderHub (e.g., https://mybinder.org)
 
 exclude_patterns:
-  - developer/*
+  - developer/*
+
+parse:
+  myst_enable_extensions:
+    # don't forget to list any other extensions you want enabled,
+    # including those that are enabled by default!
+    - amsmath

lai-fapar-mean-with-inverse-variance-weights.ipynb

@@ -10,29 +10,19 @@
     "\n",
     "Time series of unsmoothed satellite derived LAI and fAPAR can show noise and natural fluctuations. By computing a monthly average these can be reduced. Each grid cell comes with a retrieval uncertainty which can be used to give appropriate weight to retrievals with different uncertainty in the average computation. Here, we use inverse variance weights, which is the optimal estimator of the average of $N$ uncorrelated measurements in the sense that the estimated average has the least variance among all the weighted averages.\n",
     "\n",
+    "<a id='formulae'></a>\n",
     "## Inverse Variance Weights\n",
     "Given a sequence of independent observations $y_i$ with variances $\\sigma_{i}^2$, the inverse-variance weighted average is given by\n",
-    "\n",
     "\\begin{equation}\n",
-    "\n",
     "\\hat{y} = \\frac{\\sum_i y_i/\\sigma_{i}^2}{\\sum_i 1/\\sigma_{i}^2}.\n",
-    "\n",
     "\\end{equation}\n",
-    "\n",
     "The variance of $\\hat{y}$ is\n",
-    "\n",
     "\\begin{equation}\n",
-    "\n",
     "Var(\\hat{y}) = \\frac{1}{\\sum_i 1/\\sigma_{i}^2}.\n",
-    "\n",
     "\\end{equation}\n",
-    "\n",
     "Computing $Var(\\hat{y})$ first, we can use it to save some operations:\n",
-    "\n",
     "\\begin{equation}\n",
-    "\n",
     "\\hat{y} = Var(\\hat{y}) \\sum_i y_i/\\sigma_{i}^2\n",
-    "\n",
     "\\end{equation}\n",
     "\n",
     "## Data\n",