session ideas

[eeholmes]

satellit book club videos https://www.youtube.com/playlist?list=PLQy8yXTKzcPIyhqfvpjqquO5nm7VCMe4A

https://www.youtube.com/playlist?list=PLJzZC8w9vPV3kIBVNmQYzZfHO6vGZeNhN
https://www.youtube.com/playlist?list=PLJzZC8w9vPV3kIBVNmQYzZfHO6vGZeNhN

https://appliedsciences.nasa.gov/get-involved/training/english/arset-biodiversity-applications-airborne-imaging-systems

https://rammb2.cira.colostate.edu/training/visit/

https://rammb2.cira.colostate.edu/training/2024_ams_satellite_short_course/

Amy Huff - NOAA Affiliate amy.huff@noaa.gov | Amy Huff - NOAA Affiliate amy.huff@noaa.gov
Thanks for reaching out about the AMS Short Course that my colleague Rebekah Esmaili (cc'd) and I taught at last year's AMS meeting. All of the Notebook files, .yml configuration file, and background info are available via Rebekah's github (STAR won't let me use github for end users): https://github.com/modern-tools-workshop/AMS-python-workshop-2023

Modern Programming Tools for Earth Science
https://github.com/modern-tools-workshop

Python for Remote Sensing: Analysis, Visualization, and Workflow for Earth Scientists
https://www.rebekahesmaili.com/workshop.html#overview

Earth's Radiation Budget
https://github.com/modern-tools-workshop/UMD-ERB-tutorial?tab=readme-ov-file

Python for Earth Sciences
https://github.com/modern-tools-workshop/AGU-python-workshop-2022

[eeholmes]

Learn Dask

https://docs.google.com/presentation/d/1HjOXgvlGcq01srJF5RD7y5sx5u8pXJzHb1E5W7s9aKc/edit#slide=id.g270e53e38ea_0_16

[eeholmes]

Hyperspectral data in R
https://www.neonscience.org/resources/learning-hub/tutorials/introduction-hyperspectral-remote-sensing-data-r
https://ourcodingclub.github.io/tutorials/spatial/

[eeholmes]

https://www.jstatsoft.org/article/view/v043i04

[eeholmes]

Go throuth this book
https://earth-env-data-science.github.io/intro.html

[eeholmes]

Sea ice
https://github.com/COSIMA/

[eeholmes]

Another earth data in cloud course with ML

https://esds-3.ncics.org/docs/module-1/mod-1-syllabus

[eeholmes]

rstac from Microsoft Planetary computer

https://planetarycomputer.microsoft.com/docs/quickstarts/reading-stac/

[eeholmes]

https://kpegion.github.io/Pangeo-at-AOES/index.html

[eeholmes]

Climatology
https://github.com/pangeo-gallery/osm2020tutorial/blob/master/AWS-notebooks/aws_mur_sst_tutorial_long.ipynb

[eeholmes]

# pak::pak("reticulate")

library(reticulate)

py_require("earthaccess")
py_require("xarray")

# xarray optional dependencies
py_require("h5netcdf")
py_require("netCDF4")
py_require("zarr")
py_require("dask")
py_require("matplotlib")

earthaccess <- import("earthaccess")
xr <- import("xarray", "xr")

matplotlib <- import("matplotlib", convert = TRUE)
matplotlib$use("Agg", force = TRUE)

plt <- import("matplotlib.pyplot", as = "plt")

auth <- earthaccess$login(strategy = "netrc")

#Access land ice height from ATLAS/ICESat-2 V005 (10.5067/ATLAS/ATL06.005), searching for data over western Greenland coast over two weeks in July 2022. The data are provided as HDF5 granules (files) that span about 1/14th of an orbit.

results <- earthaccess$search_data(
  short_name="ATL06",
  version="006",
  cloud_hosted=TRUE,
  # declare `temporal` and `bounding_box` as tuples
  temporal = tuple("2022-07-17", "2022-07-31"),
  bounding_box = tuple(-51.96423, 68.10554, -48.71969, 70.70529)
)

ds <- xr$open_mfdataset(
  earthaccess$open(results),
  group = '/gt1l/land_ice_segments'
)

ds

[eeholmes]

https://asf.alaska.edu/asf-services-open-science-lab/
https://www.earthdata.nasa.gov/data/tools/opensciencelab

[eeholmes]

virtualizarr

https://projectpythia.org/kerchunk-cookbook/README.html

pace data on open dap
https://pydap.github.io/pydap/notebooks/PACE.html

more opendap examples
https://docs.opendap.org/index.php/How-To_Guides

Nice example!
https://rabernat.github.io/research_computing_2018/xarray-tips-and-tricks.html

base_url = 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995'

files = [f'{base_url}.{year}.nc' for year in range(1948, 2019)]
files

import xarray as xr

ds = xr.open_mfdataset(files)
ds

open file by urls
zarr-developers/VirtualiZarr#121

https://www.jamstec.go.jp/ridinfo/xarray-and-opendap/

More opendap brainstorming
stuckyb/gcdl#24

Adapt this for an opendap example
https://github.com/ornldaac/daymet-python-opendap-xarray/blob/master/1_daymetv4_discovery_access_subsetting.ipynb

[eeholmes]

Cloud-Native Repositories for Big Scientific Data
https://www.authorea.com/users/372628/articles/490577-cloud-native-repositories-for-big-scientific-data

Some ideas from Rich S on fast data access to netcdfs
https://medium.com/pangeo/cloud-performant-netcdf4-hdf5-with-zarr-fsspec-and-intake-3d3a3e7cb935

[eeholmes]

Creating virtual zarr's, icechunk

background: https://icechunk.io/en/latest/icechunk-python/virtual/

https://github.com/zarr-developers/VirtualiZarr/blob/main/examples/append/noaa-cdr-sst.ipynb

[eeholmes]

https://github.com/ornldaac/daymet-python-opendap-xarray/blob/master/1_daymetv4_discovery_access_subsetting.ipynb