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hot007/CAWCR wave hindcast remote access point data demo.ipynb

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CAWCR wave hindcast remote access point data demo.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## CAWCR Wave Hindcast extract timeseries data at a point using OPeNDAP remote file access with python's xarray\n",
"\n",
"This notebook demonstrates how to find files available on THREDDS in a given catalogue using the `siphon` tool, and pass them as a list to `xarray` to open as a \"multifile dataset\". \n",
"\n",
"Xarray \"lazily loads\" the data, which means it gets the metadata from each file to construct a virtual file, but no data is loaded in this process. On a filesystem this is a quick process, but via THREDDS accessing so many files takes quite some time.\n",
"\n",
"We then demonstrate extracting data for a variable (`hs`) at a particular point location for all time using the `.load()` fuction, which relies on `dask` to parallelise the task of loading in the data.\n",
"\n",
"Finally we make a quick plot of the data to demonstrate it's probably done the right thing.\n",
"\n",
"**NOTES :**\n",
"- **Regarding OPeNDAP**: The THREDDS server has a transaction limit which by default is I think 500MB per request. Thus this method works okay for point data extraction but is not appropriate for areas\n",
"- **Not the Aggregate collection**: The CAWCR wave hindcast is available from 1979-present, however a new version of wavewatchIII was used after ~2013 (documented in DAP). In this change, some variables changed names, and the netCDF chunks changed. Therefore it is not possible to use `xarray.open_mfdataset` on the aggregate collection, instead if using this method, each hindcast folder will need to be opened independently and the timeseries concatenated after that.\n",
"\n",
"**Libaries required for this notebook:** `xarray`, `dask`, `matplotlib` (and their dependencies); and `siphon` (not an anaconda standard library, most people will need to install this themselves)."
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
"import xarray as xr\n",
"from siphon.catalog import TDSCatalog\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"ww3.aus_10m.199201.nc\n",
"ww3.aus_10m.199112.nc\n",
"ww3.aus_10m.199111.nc\n",
"ww3.aus_10m.199110.nc\n",
"ww3.aus_10m.199109.nc\n",
"ww3.aus_10m.199108.nc\n",
"ww3.aus_10m.199107.nc\n",
"ww3.aus_10m.199106.nc\n",
"ww3.aus_10m.199105.nc\n",
"ww3.aus_10m.199104.nc\n",
"ww3.aus_10m.199103.nc\n",
"ww3.aus_10m.199102.nc\n",
"ww3.aus_10m.199101.nc\n",
"ww3.aus_10m.199012.nc\n",
"ww3.aus_10m.199011.nc\n",
"ww3.aus_10m.199010.nc\n",
"ww3.aus_10m.199009.nc\n",
"ww3.aus_10m.199008.nc\n",
"ww3.aus_10m.199007.nc\n",
"ww3.aus_10m.199006.nc\n",
"ww3.aus_10m.199005.nc\n",
"ww3.aus_10m.199004.nc\n",
"ww3.aus_10m.199003.nc\n",
"ww3.aus_10m.199002.nc\n",
"ww3.aus_10m.199001.nc\n",
"ww3.aus_10m.198912.nc\n",
"ww3.aus_10m.198911.nc\n",
"ww3.aus_10m.198910.nc\n",
"ww3.aus_10m.198909.nc\n",
"ww3.aus_10m.198908.nc\n",
"ww3.aus_10m.198907.nc\n",
"ww3.aus_10m.198906.nc\n",
"ww3.aus_10m.198905.nc\n",
"ww3.aus_10m.198904.nc\n",
"ww3.aus_10m.198903.nc\n",
"ww3.aus_10m.198902.nc\n",
"ww3.aus_10m.198901.nc\n",
"ww3.aus_10m.198812.nc\n",
"ww3.aus_10m.198811.nc\n",
"ww3.aus_10m.198810.nc\n",
"ww3.aus_10m.198809.nc\n",
"ww3.aus_10m.198808.nc\n",
"ww3.aus_10m.198807.nc\n",
"ww3.aus_10m.198806.nc\n",
"ww3.aus_10m.198805.nc\n",
"ww3.aus_10m.198804.nc\n",
"ww3.aus_10m.198803.nc\n",
"ww3.aus_10m.198802.nc\n",
"ww3.aus_10m.198801.nc\n",
"ww3.aus_10m.198712.nc\n",
"ww3.aus_10m.198711.nc\n",
"ww3.aus_10m.198710.nc\n",
"ww3.aus_10m.198709.nc\n",
"ww3.aus_10m.198708.nc\n",
"ww3.aus_10m.198707.nc\n",
"ww3.aus_10m.198706.nc\n",
"ww3.aus_10m.198705.nc\n",
"ww3.aus_10m.198704.nc\n",
"ww3.aus_10m.198703.nc\n",
"ww3.aus_10m.198702.nc\n",
"ww3.aus_10m.198701.nc\n",
"ww3.aus_10m.198612.nc\n",
"ww3.aus_10m.198611.nc\n",
"ww3.aus_10m.198610.nc\n",
"ww3.aus_10m.198609.nc\n",
"ww3.aus_10m.198608.nc\n",
"ww3.aus_10m.198607.nc\n",
"ww3.aus_10m.198606.nc\n",
"ww3.aus_10m.198605.nc\n",
"ww3.aus_10m.198604.nc\n",
"ww3.aus_10m.198603.nc\n",
"ww3.aus_10m.198602.nc\n",
"ww3.aus_10m.198601.nc\n",
"ww3.aus_10m.198512.nc\n",
"ww3.aus_10m.198511.nc\n",
"ww3.aus_10m.198510.nc\n",
"ww3.aus_10m.198509.nc\n",
"ww3.aus_10m.198508.nc\n",
"ww3.aus_10m.198507.nc\n",
"ww3.aus_10m.198506.nc\n",
"ww3.aus_10m.198505.nc\n",
"ww3.aus_10m.198504.nc\n",
"ww3.aus_10m.198503.nc\n",
"ww3.aus_10m.198502.nc\n",
"ww3.aus_10m.198501.nc\n",
"ww3.aus_10m.198412.nc\n",
"ww3.aus_10m.198411.nc\n",
"ww3.aus_10m.198410.nc\n",
"ww3.aus_10m.198409.nc\n",
"ww3.aus_10m.198408.nc\n",
"ww3.aus_10m.198407.nc\n",
"ww3.aus_10m.198406.nc\n",
"ww3.aus_10m.198405.nc\n",
"ww3.aus_10m.198404.nc\n",
"ww3.aus_10m.198403.nc\n",
"ww3.aus_10m.198402.nc\n",
"ww3.aus_10m.198401.nc\n",
"ww3.aus_10m.198312.nc\n",
"ww3.aus_10m.198311.nc\n",
"ww3.aus_10m.198310.nc\n",
"ww3.aus_10m.198309.nc\n",
"ww3.aus_10m.198308.nc\n",
"ww3.aus_10m.198307.nc\n",
"ww3.aus_10m.198306.nc\n",
"ww3.aus_10m.198305.nc\n",
"ww3.aus_10m.198304.nc\n",
"ww3.aus_10m.198303.nc\n",
"ww3.aus_10m.198302.nc\n",
"ww3.aus_10m.198301.nc\n",
"ww3.aus_10m.198212.nc\n",
"ww3.aus_10m.198211.nc\n",
"ww3.aus_10m.198210.nc\n",
"ww3.aus_10m.198209.nc\n",
"ww3.aus_10m.198208.nc\n",
"ww3.aus_10m.198207.nc\n",
"ww3.aus_10m.198206.nc\n",
"ww3.aus_10m.198205.nc\n",
"ww3.aus_10m.198204.nc\n",
"ww3.aus_10m.198203.nc\n",
"ww3.aus_10m.198202.nc\n",
"ww3.aus_10m.198201.nc\n",
"ww3.aus_10m.198112.nc\n",
"ww3.aus_10m.198111.nc\n",
"ww3.aus_10m.198110.nc\n",
"ww3.aus_10m.198109.nc\n",
"ww3.aus_10m.198108.nc\n",
"ww3.aus_10m.198107.nc\n",
"ww3.aus_10m.198106.nc\n",
"ww3.aus_10m.198105.nc\n",
"ww3.aus_10m.198104.nc\n",
"ww3.aus_10m.198103.nc\n",
"ww3.aus_10m.198102.nc\n",
"ww3.aus_10m.198101.nc\n",
"ww3.aus_10m.198012.nc\n",
"ww3.aus_10m.198011.nc\n",
"ww3.aus_10m.198010.nc\n",
"ww3.aus_10m.198009.nc\n",
"ww3.aus_10m.198008.nc\n",
"ww3.aus_10m.198007.nc\n",
"ww3.aus_10m.198006.nc\n",
"ww3.aus_10m.198005.nc\n",
"ww3.aus_10m.198004.nc\n",
"ww3.aus_10m.198003.nc\n",
"ww3.aus_10m.198002.nc\n",
"ww3.aus_10m.198001.nc\n",
"ww3.aus_10m.197912.nc\n",
"ww3.aus_10m.197911.nc\n",
"ww3.aus_10m.197910.nc\n",
"ww3.aus_10m.197909.nc\n",
"ww3.aus_10m.197908.nc\n",
"ww3.aus_10m.197907.nc\n",
"ww3.aus_10m.197906.nc\n",
"ww3.aus_10m.197905.nc\n",
"ww3.aus_10m.197904.nc\n",
"ww3.aus_10m.197903.nc\n",
"ww3.aus_10m.197902.nc\n",
"ww3.aus_10m.197901.nc\n"
]
}
],
"source": [
"#cat = TDSCatalog(\"http://data-cbr.csiro.au/thredds/catalog/catch_all/CMAR_CAWCR-Wave_archive/CAWCR_Wave_Hindcast_aggregate/gridded/catalog.xml\")\n",
"cat = TDSCatalog(\"http://data-cbr.csiro.au/thredds/catalog/catch_all/CMAR_CAWCR-Wave_archive/CAWCR_Wave_Hindcast_1979-2010/gridded/catalog.xml\")\n",
"print(\"\\n\".join(cat.datasets.keys()))"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"filelist=[x for x in cat.datasets if x.startswith('ww3.aus_4m.')]\n",
"#DAProot='http://data-cbr.csiro.au/thredds/dodsC/catch_all/CMAR_CAWCR-Wave_archive/CAWCR_Wave_Hindcast_aggregate/gridded/'\n",
"DAProot='http://data-cbr.csiro.au/thredds/dodsC/catch_all/CMAR_CAWCR-Wave_archive/CAWCR_Wave_Hindcast_1979-2010/gridded/'\n",
"path = [ DAProot+f for f in filelist]"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"path.sort()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (latitude: 796, longitude: 1126, time: 280512)\n",
"Coordinates:\n",
" * longitude (longitude) float32 100.0 100.066666 ... 174.9337 175.00038\n",
" * latitude (latitude) float32 -50.0 -49.933334 ... 2.933598 3.000265\n",
" * time (time) datetime64[ns] 1979-01-01 ... 2010-12-31T23:00:00.000013440\n",
"Data variables:\n",
" MAPSTA (time, latitude, longitude) int16 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" U10 (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" V10 (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" CI (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" hs (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" wl (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" t02 (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" t (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" tm0m1 (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" CgE (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" fp (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" dir (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" spr (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" dp (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" hs0 (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" hs1 (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" hs2 (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" hs3 (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" tp0 (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" tp1 (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" tp2 (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" tp3 (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" lp0 (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" lp1 (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" lp2 (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" lp3 (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" th0 (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" th1 (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" th2 (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" th3 (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" si0 (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" si1 (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" si2 (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" si3 (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" ws0 (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" ws1 (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" ws2 (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" ws3 (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" wsf (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" pnr (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" dtd (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" uust (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" vust (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" cha (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" faw (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" utaw (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" vtaw (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" utwa (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" vtwa (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" wcc (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" Sxx (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" Syy (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" Sxy (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" utwo (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" vtwo (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" uuss (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
" vuss (time, latitude, longitude) float32 dask.array<shape=(280512, 796, 1126), chunksize=(744, 796, 1126)>\n",
"Attributes:\n",
" product_name: ww3.aus_4m.197901.nc\n",
" area: 4 min Australian grid\n",
" latitude_resolution: 0.0666667\n",
" longitude_resolution: 0.0666667\n",
" southernmost_latitude: -50.0000000\n",
" northernmost_latitude: 3.0002649\n",
" westernmost_longitude: 100.0000000\n",
" easternmost_longitude: 175.0003815\n",
" minimum_altitude: -12000 m\n",
" maximum_altitude: 9000 m\n",
" altitude_resolution: n/a\n",
" start_date: 1979-01-01T00:00:00Z\n",
" stop_date: 1979-01-31T23:00:00Z\n",
" date_created: 2012-09-10T09:40:00Z\n",
" author: Durrant, Hemer, Trenham and Greenslade\n",
" contact: Mark.Hemer@csiro.au,T.Durrant@bom.gov.au\n",
" DOI: http://dx.doi.org/10.4225/08/523168703DCC5\n",
" related_materials: Centre for Australian Weather and Climat...\n",
" creation_method: The hindcast was performed using the WAV...\n",
" license: Please note that the licensee is require...\n",
" DODS_EXTRA.Unlimited_Dimension: time\n",
" EXTRA_DIMENSION.level: 1"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = xr.open_mfdataset(path, combine='by_coords',parallel=True)#,chunks={\"time\": 744}) #This bit is slooooow! Start it and wander off for a long while (20min-2hrs?).\n",
"data"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<Client: 'tcp://127.0.0.1:45609' processes=5 threads=20, memory=134.95 GB>\n"
]
}
],
"source": [
"from dask.distributed import Client\n",
"client = Client()\n",
"print(client)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"distributed.comm.tcp - WARNING - Closing dangling stream in <TCP local=tcp://127.0.0.1:52088 remote=tcp://127.0.0.1:45609>\n",
"distributed.comm.tcp - WARNING - Closing dangling stream in <TCP local=tcp://127.0.0.1:39656 remote=tcp://127.0.0.1:45609>\n"
]
}
],
"source": [
"hs=data.hs.sel(latitude=-35.023,longitude=138.46,method='nearest').load(scheduler='processes')"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.DataArray 'hs' (time: 280512)>\n",
"array([0. , 0.046, 0.072, ..., 0.166, 0.152, 0.142], dtype=float32)\n",
"Coordinates:\n",
" longitude float32 138.46686\n",
" latitude float32 -34.999924\n",
" * time (time) datetime64[ns] 1979-01-01 ... 2010-12-31T23:00:00.000013440\n",
"Attributes:\n",
" long_name: significant height of wind and swell waves\n",
" standard_name: sea_surface_wave_significant_height\n",
" globwave_name: significant_wave_height\n",
" units: m\n",
" valid_min: 0\n",
" valid_max: 32000\n",
" _ChunkSizes: [ 1 796 1126]"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hs"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x2aaaf4e668d0>]"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"hs.plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.7.2",
"language": "python",
"name": "python-3.7.2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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hot007 commented Apr 28, 2021 •
edited
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Note that the input netCDF data contains a "scale factor", which for significant wave height hs is 0.002. Xarray is netCDF-aware and knows what to do with this, which is why when we plot the data it comes out in the range 0-2.5m.
It may be possible to do area extraction not only point, I recommend point here to avoid hitting THREDDS server limits. It's fine to experiment with area extraction but unintuitive errors may result.
Although I ran this on an HPC interactive node, this code could be executed on a laptop and it shouldn't take much longer - we're not taking advantage of the HPC here.

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