{ "cells": [ { "cell_type": "markdown", "id": "65f79301-6b74-4df9-91a3-d8ab060bbe9e", "metadata": {}, "source": [ "# TEEHR Evaluation Example 3 \n", "## Hourly NWM Retrospective 3.0, CAMELS Subset (648)" ] }, { "cell_type": "markdown", "id": "26909a06-6c1f-4abf-bf34-861c1dae5ead", "metadata": {}, "source": [ "### 1. Get the data from S3\n", "For the sake of time, we prepared the individual datasets in advance and are simply copying to your 2i2c home directory. After running the cell below to copy the example_2 data." ] }, { "cell_type": "code", "execution_count": null, "id": "26dfc5fb-d4cb-4744-8716-58925b20b330", "metadata": {}, "outputs": [], "source": [ "!rm -rf ~/teehr/example-3/*\n", "!aws s3 cp --recursive --no-sign-request s3://ciroh-rti-public-data/teehr-workshop-devcon-2024/workshop-data/example-3 ~/teehr/example-3" ] }, { "cell_type": "code", "execution_count": null, "id": "eb119a6c-197e-4c88-823b-a4d12b2aa0b4", "metadata": {}, "outputs": [], "source": [ "!tree ~/teehr/example-3/" ] }, { "cell_type": "markdown", "id": "91f899b9-5de5-45d2-8697-cd42825fc89b", "metadata": {}, "source": [ "### Evaluate Model Output\n", "This notebook we will demonstrate how to use TEEHR to calculate metrics from a previously created joined TEEHR database containing hourly NWM3.0 Retrospective simulations and USGS observations from 1981-2022, using a range of different options for grouping and filtering. We will then create some common graphics based on the results (the same as Example 2)\n", "\n", "\n", "#### In this notebook we will perform the following steps:\n", "