excercise_baltic_bloom.ipynb

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    "# Excercise: Calculate seasonal variation of SST in the Baltic\n",
    "\n",
    "For this excersise, use glider data from VOTO to calculate seasonal vartiation of SST"
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    "import matplotlib.pyplot as plt\n",
    "import xarray as xr\n",
    "import datetime\n",
    "import numpy as np\n",
    "import cartopy\n",
    "from erddapy import ERDDAP"
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    "1. Setup the ERDDAP connection to VOTO gliders ERDDAP \n",
    "\n",
    "https://erddap.observations.voiceoftheocean.org/erddap/index.html"
   ]
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    "e = ERDDAP(\n",
    "    server=\"your server here\", \n",
    "    protocol=\"tabledap\", # use griddap for gridded datasets\n",
    ")"
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    "2. Search for gliders in the Baltic proper (east of 18 degrees to avoid including the Kattegat)\n",
    "\n",
    "\n",
    "**hint:** download the `allDatasets` dataset and keep only glider missions within geographical bounds"
   ]
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    "ioos_datasets"
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    "3. Download some datasets\n",
    "\n",
    "**hint** we are only interested in SST, so try specifying a depth range, as demonstrated in notebook [02_glider_analysis section 4](02_glider_analysis.ipynb#4.-Download-the-full-datasets) "
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    "e.constraints = {}\n",
    "baltic_datasets = {}\n",
    "for ds_name in datasets_to_download:\n",
    "    baltic_datasets[ds_name] = e."
   ]
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   "source": [
    "4. Start analysis"
   ]
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    "### Expansion ideas\n",
    "\n",
    "- Compare SST trends of different areas on the Baltic, you can find a heat map of VOTO glider missions on the [stats page](https://observations.voiceoftheocean.org/stats)\n",
    "- Look for trends below the mixed layer\n",
    "- Find data from other Baltic providers to use here. e.g. the argo program (see the [argopy docs](https://argopy.readthedocs.io/en/latest/data_fetching.html))\n",
    "- Reuse your code with a different ERDDAP server (e.g. IOOS) to compute SST trends from another region"
   ]
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