Merge pull request #13 from nilssonj/master

06_Gridding_Nilsson

Author: dshean

.DS_Store

@@ -765,21 +765,21 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 2",
    "language": "python",
-   "name": "python3"
+   "name": "python2"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 3
+    "version": 2
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "pygments_lexer": "ipython2",
+   "version": "2.7.15"
   }
  },
  "nbformat": 4,

04_HDF5AndICESat2Data_Paolo/.DS_Store

@@ -0,0 +1,12 @@
+## Gridding and Filtering of Elevation Change Observations 
+
+Instructor: Johan Nilsson (johan.nilsson@jpl.nasa.gov)
+
+This tutorial focuses on the usefulness of different interpolation techniques and filtering approaches used in satellite altimetry to derive gridded products of elevation change. These products will then used to demonstrate how one can derive the integrated volume change of the dataset or region in question. 
+
+### Learning Objectives
+- General understanding of why gridded datasets are usefull in altimetry
+- Understand the impact of different gridding and filtering strategies 
+- How to derive integrated volume change estimates of gridded datasets
+
+The algorithms used in this tutorial are downscaled versions of a python altimetry package, currently being devloped at JPL, providing a range of algorithms for common tasks in altimetry data processing. It will soon become avalibale on GitHub as the “captoolkit” for public usage.