![\"Binder\"](\"https://binder.openveda.cloud/badge_logo.svg\" "\\"A"){kind=icon}
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5eed6c15-4ced-4f16-8673-47a2cacda765",
+ "metadata": {},
+ "source": [
+ "### The VEDA Data Story on the effects of Dust Deposition over the Atlantic Ocean can be found [_**HERE**_](https://www.earthdata.nasa.gov/dashboard/stories/dust-deposition)\n",
+ "\n",
+ "\n",
+ "\n",
+ "# Introduction: Role of Dust in the Earth System\n",
+ "\n",
+ "The land, ocean, and atmosphere are all parts of the Earth system, and they are linked and influence each other in many ways. One important link is the atmosphere carrying dust, gases, and pollution between continents and from land to ocean. A great example of this connection are massive dust plumes that originate in the Sahara Desert of northern Africa. These dust plumes can travel thousands of kilometers across the Atlantic Ocean and often reach the Americas. Dust from these plumes eventually reaches the ground, either by falling through the air or being brought down by rain, and contains nutrients such as phosphorus and iron. Phosphorus deposited by dust nourishes rainforests in Central and South America. Over the Atlantic, iron is thought to help phytoplanktonβmicroscopic, plant-like organisms that live in the ocean and produce oxygen through photosynthesisβgrow rapidly and in large quantities. These tiny but mighty organisms form the first link in the large ocean food chain, sustaining fish and other marine life.\n",
+ "\n",
+ "\n",
+ "Recent studies suggest that dust falling into remote parts of the Tropical North Atlantic Ocean (TNAO) can affect tuna migration, and, in turn, fishery-dependent economies in nearby regions, including the United States (Map 1). However, our analysis using NASA Earth observation datasets shows that the effect of dust deposition on phytoplankton in the TNAO may be more complex than previously understood."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1aad4848-1a84-49fa-b206-849695be2d8b",
+ "metadata": {},
+ "source": [
+ "# Understanding the Way Dust Connects Land, Ocean, and Life Through NASA Earth Observing Tools\n",
+ "\n",
+ "\n",
+ "\n",
+ "A variety of NASA satellite observations and models help scientists explore the complex interactions between land and ocean that are influenced by dust. NASA's MODerate Resolution Imaging Spectroradiometer (MODIS), an Earth-observing satellite sensor, detects these changes by measuring ocean chlorophyllβa pigment that indicates the presence of phytoplankton. Additionally, NASAβs Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) mission, launched in 2024, continues to monitor ocean biology through the amount of sunlight reflected back to space that may be affected by atmospheric dust or other aerosols.\n",
+ "\n",
+ "\n",
+ "While satellite sensors allow for monitoring both dust and phytoplankton activity, meteorological and dust transport variables are needed to analyze this interconnected relationship. NASAβs Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) provides detailed, three-dimensional wind, temperature, moisture, and dust data, which is essential when determining the influences between dust transport and ocean productivity.\n",
+ "\n",
+ "\n",
+ "The different meteorological variables that may impact and affect phytoplankton across the TNAO include:\n",
+ "\n",
+ "Aerosol Optical Depth (AOD): A measure of how much sunlight is blocked or scattered by aerosols (tiny particles in the atmosphere)\n",
+ "Cloud Cover: Fraction or percentage of the sky covered by clouds\n",
+ "Sea-Surface Temperatures (SST): The temperature at the top layer of the ocean\n",
+ "Wind Speed: The rate at which the wind is moving on the ocean surface\n",
+ "Dust Concentration: The amount of dust present in a volume of the atmosphere\n",
+ "\n",
+ "\n",
+ "\n",
+ "Over the past 20 years, data has shown that chlorophyll-a levels β which is a direct indicator of phytoplankton β have been rising in the TNAO (Figure 1). The observed increase in phytoplankton compared to previous years alludes to a potential meteorological influence."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1e22faeb-f227-495e-b839-eb65c3ed156b",
+ "metadata": {},
+ "source": [
+ "# Chlorophyll-a Dataset Slider Map:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9fba7dec-3e8b-4085-8047-ad3e869d97c1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Calling in packages and finding the chlorophyll-a dataset:\n",
+ "\n",
+ "import glob\n",
+ "import os\n",
+ "import folium\n",
+ "from folium.plugins import SideBySideLayers\n",
+ "\n",
+ "# Base tile URLs\n",
+ "BASE_TILE_URL = \"https://openveda.cloud/api/raster/collections/{collection_id}/items/{item_id}/tiles/WebMercatorQuad/{{z}}/{{x}}/{{y}}.png?assets=cog_default\"\n",
+ "collection_id = \"dust_depo_chlorophyll\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1d95bc79-0537-4169-92f4-8e307d7f32af",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "''' + colorbar_caption + '''
+ "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0b0c26b2-a344-4b0c-924c-3fee3960a6b7",
+ "metadata": {},
+ "source": [
+ "## Environment Setup"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "73d80ca5-39e1-4b97-b1cd-50aabea1f311",
+ "metadata": {},
+ "source": [
+ "If running this notebook outside of the VEDA JupyterHub, install the following packages:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "c5281e69-5900-4204-a249-b05510df0576",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#!pip install -q earthaccess pandas xarray fsspec requests branca pystac_client matplotlib"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8dcbd07a-e677-4484-8ed8-b48c4e8cfd67",
+ "metadata": {},
+ "source": [
+ "Import the necessary libraries used to run this notebook."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "fbad9700-ce5d-4210-9c58-f9204917b7ab",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Load libraries\n",
+ "\n",
+ "import glob\n",
+ "import os\n",
+ "import requests\n",
+ "import matplotlib as mpl\n",
+ "import matplotlib.pyplot as plt\n",
+ "import plotutils as putils\n",
+ "from pystac_client import Client\n",
+ "import folium"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "8fc9234c-975b-485d-bfb3-d9100ff3b12d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# For retrieving data already catalogued in VEDA STAC\n",
+ "STAC_API_URL = \"https://openveda.cloud/api/stac\"\n",
+ "RASTER_API_URL = \"https://openveda.cloud/api/raster\"\n",
+ "\n",
+ "# Open STAC client designed for interacting with SpatioTemporal Asset Catalog (STAC) APIs and Catalogs\n",
+ "client_STAC = Client.open(STAC_API_URL)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6b9c9d7d-f5cc-49e7-b58a-8c7bb5b9d751",
+ "metadata": {},
+ "source": [
+ "#### The VEDA Data Story on the 2024 Hyperactive Tornado Season can be found [_**HERE**_](https://www.earthdata.nasa.gov/dashboard/stories/2024-tornadoes)\n",
+ "\n",
+ "## Overview\n",
+ "\n",
+ "The Spring 2024 tornado season was one of the most active on record in the United States. As of May 31st, there were 1,176 tornadoes confirmed in the United States. The vast majority of these occurred in the meteorological spring months of March-May, placing this period within the 90th percentile of activity compared to climatological averages. This three-month period was responsible for 36 fatalities, hundreds of injuries, and was the second most prolific tornado season since at least 1950βsecond only to the deadly 2011 tornado season. Through the end of May, severe thunderstorms alone accounted for nearly 42 billion dollars (USD) in damage across the United States, with four tornado outbreaks during the spring attributing over $1 billion in damage each. Four tornadoes were rated violent EF-4s on the Enhanced Fujita Scale with wind speeds from 166-200 mph, and 27 were rated as intense EF-3s with wind speeds from 136-165 mph.\n",
+ "\n",
+ "The National Weather Service (NWS) issued 1,728 tornado warnings across the United States from March to May, with several of these being upgraded to Particularly Dangerous Situation (PDS) Tornado Warnings if confirmed to be large and actively damaging structures. An even smaller subset was upgraded further from PDS to Tornado Emergencies if the tornado was confirmed to be large and was directly threatening a larger population center. As a result, millions of Americans were impacted by tornado warnings during the Spring of 2024, with the vast majority of counties in the Midwest, Southeast, and Southern Great Plains seeing a warning at least once during the three-month period. Compared to average spring severe weather seasons, the Southeast United States saw a less active season, while the Great Plains and Midwest saw a more active season."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4d361505-c25b-4bd8-b85c-8b8ecf817710",
+ "metadata": {},
+ "source": [
+ "# Example: NWS DAT Tornado Tracks (March-May 2024)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "72e56206-4827-4ac2-bbaf-62d8bf6bd234",
+ "metadata": {},
+ "source": [
+ "This example pulls tornado paths from March-May 2024 via the VEDA STAC catalog and visualizes them. These tornado tracks were rasterized from the NWS Damage Assessment Toolkit (DAT), which is a geographic information system (GIS)-hosted dataset where post-storm damage survey results are uploaded and referenced to the location where each description of damage occurred. This dataset includes center lines of all tornado tracks, polygons that break down the EF rating at each point along the path, descriptions of the damage at each survey location, and ground damage pictures of the damage at most entry points.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9851b652-08f7-48e2-9cf7-46cd0a255550",
+ "metadata": {},
+ "source": [
+ "## Processing steps:\n",
+ "1.) Choose collection ID from the STAC catalog and date for analysis