NASA VEDA — NLDAS-3, CMIP, Black Marble HD¶

VEDA (NASA's Visualisation, Exploration and Data Analysis platform) is the anonymous, us-west-2 STAC for NASA-curated derived products — climate runs (CMIP6), hydrology (NLDAS-3), fire/disaster damage, vegetation indices (HLS NDVI), and high-definition Black Marble nightlights. Every VEDA collection exposes a single cog_default data asset (plus a preview), so the request shape is uniform across the catalogue.

Endpoint + the flagship picks in the curated catalog (no network)¶

In [ ]:

from earthlens.stac import Catalog

cat = Catalog()
e = cat.endpoints["veda"]
print(f"url   : {e.url}")
print(f"signer: {e.signer}  region: {e.region}")
veda_keys = sorted(k for k in cat.datasets if k.startswith("veda/"))
for k in veda_keys:
    print(f"  {k}")

from earthlens.stac import Catalog cat = Catalog() e = cat.endpoints["veda"] print(f"url : {e.url}") print(f"signer: {e.signer} region: {e.region}") veda_keys = sorted(k for k in cat.datasets if k.startswith("veda/")) for k in veda_keys: print(f" {k}")

NLDAS-3 over the US Midwest (live)¶

NLDAS-3 is the North-American Land Data Assimilation System reanalysis — a kilometre-scale, hourly land-surface state from NASA's Land Information System. Pulling one timestep over a Midwest bbox illustrates the uniform cog_default access pattern.

In [ ]:

import tempfile

from earthlens.earthlens import EarthLens

nldas = EarthLens(
    data_source="veda",
    start="2020-01-01", end="2024-12-31",
    dataset="veda/nldas3",
    variables=["cog_default"],
    aoi=[-100.0, 35.0, -90.0, 45.0],
    path=tempfile.mkdtemp(), max_items=1,
)
nldas_paths = nldas.download()
nldas_paths

import tempfile from earthlens.earthlens import EarthLens nldas = EarthLens( data_source="veda", start="2020-01-01", end="2024-12-31", dataset="veda/nldas3", variables=["cog_default"], aoi=[-100.0, 35.0, -90.0, 45.0], path=tempfile.mkdtemp(), max_items=1, ) nldas_paths = nldas.download() nldas_paths

Black Marble HD nightlights (recipe)¶

The NASA Black Marble HD nightlights are a disaster-impact product — high-resolution nighttime-light composites focused on regions affected by disasters. Same request shape.

In [ ]:

bm = EarthLens(
    data_source="veda",
    start="2020-01-01", end="2024-12-31",
    dataset="veda/delta-disasters-hd-blackmarble-nightlights",
    variables=["cog_default"],
    aoi=[-78.0, 18.0, -76.0, 19.5],   # Caribbean / disaster-impacted region
    path=tempfile.mkdtemp(), max_items=1,
)
bm_paths = bm.download()
bm_paths

bm = EarthLens( data_source="veda", start="2020-01-01", end="2024-12-31", dataset="veda/delta-disasters-hd-blackmarble-nightlights", variables=["cog_default"], aoi=[-78.0, 18.0, -76.0, 19.5], # Caribbean / disaster-impacted region path=tempfile.mkdtemp(), max_items=1, ) bm_paths = bm.download() bm_paths

CMIP6 climate-projection slices (recipe)¶

VEDA mirrors a curated set of CMIP6-derived gridded summaries (the ensemble-median seasonal anomalies). CMIP245-winter-median-pr is the SSP2-4.5 (mid-emissions) winter precipitation difference; CMIP585-winter-median-pr is the SSP5-8.5 (high-emissions) counterpart.

In [ ]:

cmip = EarthLens(
    data_source="veda",
    start="2020-01-01", end="2099-12-31",
    dataset="veda/CMIP585-winter-median-pr",
    variables=["cog_default"],
    aoi=[-130.0, 25.0, -65.0, 50.0],   # CONUS
    path=tempfile.mkdtemp(), max_items=1,
)
cmip_paths = cmip.download()
cmip_paths

cmip = EarthLens( data_source="veda", start="2020-01-01", end="2099-12-31", dataset="veda/CMIP585-winter-median-pr", variables=["cog_default"], aoi=[-130.0, 25.0, -65.0, 50.0], # CONUS path=tempfile.mkdtemp(), max_items=1, ) cmip_paths = cmip.download() cmip_paths

Inspect¶

In [ ]:

from pyramids.dataset import Dataset

for label, ps in (("NLDAS-3", nldas_paths), ("Black Marble HD", bm_paths), ("CMIP6 SSP5-8.5 winter pr", cmip_paths)):
    ds = Dataset.read_file(str(ps[0]))
    print(f"{label:30} bands={ds.band_count} epsg={ds.epsg} shape={ds.read_array().shape}")

from pyramids.dataset import Dataset for label, ps in (("NLDAS-3", nldas_paths), ("Black Marble HD", bm_paths), ("CMIP6 SSP5-8.5 winter pr", cmip_paths)): ds = Dataset.read_file(str(ps[0])) print(f"{label:30} bands={ds.band_count} epsg={ds.epsg} shape={ds.read_array().shape}")