{"notebook_content":"\n\n\n\n\nNotebook\n\n\n\n\n\n\n\n\n\n\n\n\n
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Access ACTRIS Cloud Remote Sensing data\u00b6

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The goal of this notebook is to provide guide on how to access cloud remote sensing data.

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Let's get started!

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Import libraries\u00b6

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In\u00a0[4]:
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from pathlib import Path\nimport datetime\n\nimport requests\nimport netCDF4\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom ipywidgets import IntProgress\nfrom IPython.display import display\n
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Download\u00b6

We're going to use CLU data portal API to search and download netCDF4 files. For more information, see https://docs.cloudnet.fmi.fi.

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Let's download all classification products from Hyyti\u00e4l\u00e4 in 2022. These products contain CLU classification of atmospheric scatterers detected by radar and lidar.

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Documentation on the CLU file formats (NetCDF) is available here: https://cloudnet.fmi.fi/products/

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In\u00a0[5]:
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outdir = Path(\"static/clu-data\")\noutdir.mkdir(exist_ok=True)\nr = requests.get(\"https://cloudnet.fmi.fi/api/files\", \n                 params={\"site\": \"hyytiala\",\n                         \"product\": \"classification\",\n                         \"dateFrom\": \"2022-01-01\",\n                         \"dateTo\": \"2022-12-31\"})\nr.raise_for_status()\nfiles = r.json()\nprogress = IntProgress(min=0, max=len(files), description=\"Download:\")\ndisplay(progress)\nfor file in files:\n    r = requests.get(file[\"downloadUrl\"])\n    r.raise_for_status()\n    (outdir / file[\"filename\"]).write_bytes(r.content)\n    progress.value += 1\n
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IntProgress(value=0, description='Download:', max=361)
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Analyse\u00b6

Count and visualise number of pixels containing insects relative to the total number of pixels. Insect are indicated by classification class 9 (insects, no cloud or precipitation) and class 10 (aerosol coexisting with insects, no cloud or precipitation).

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In\u00a0[14]:
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dates = []\ninsects = []\nfor p in sorted(outdir.iterdir()):\n    with netCDF4.Dataset(p) as nc:\n        n_total = np.prod(nc[\"target_classification\"].shape)\n        n_insects = np.count_nonzero(nc[\"target_classification\"][:] == 9)\n        n_aerosols_and_insects = np.count_nonzero(nc[\"target_classification\"][:] == 10)\n        dates.append(datetime.date(int(nc.year), int(nc.month), int(nc.day)))\n        insects.append((n_insects + n_aerosols_and_insects) / n_total)\nplt.plot(dates, insects)\nplt.ylabel(\"Insect occurrence fraction (\u2212)\")\nplt.xlabel(\"Date\")\ntick_dates = [d for i, d in enumerate(dates) if i == 0 or d.month != dates[i - 1].month]\ntick_labels = [d.strftime(\"%b %Y\") for d in tick_dates]\nplt.xticks(tick_dates, tick_labels, rotation=45, ha=\"right\")\n\nplt.title(\"Insect occurrence fraction in Hyyti\u00e4l\u00e4 in 2022\")\n\nplt.tight_layout()\nplt.show()\n
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