Basic matchups with PACE data

import point_collocation as pc

• Create a plan for files to use pc.plan()
• Print the plan to check it print(plan.summary())
• Do the plan and get matchups pc.matchup(plan, geometry="grid")

Read in some points

import pandas as pd
time = "2025-04-09"
lat = 30.0
lon = -89.0

df = pd.DataFrame(
    {
        "lat": [lat],
        "lon": [lon],
        "time": [time],
    }
)
df["time"] = pd.to_datetime(df["time"])

Create a plan

%%time
import point_collocation as pc
plan = pc.plan(
    df,
    data_source="earthaccess",
    source_kwargs={
        "short_name": "PACE_OCI_L3M_Rrs",
        "granule_name": "*.8D.*.4km.*",
    }
)

CPU times: user 18.7 ms, sys: 219 μs, total: 18.9 ms
Wall time: 16.6 s

Look at variables in that dataset

plan.show_variables(geometry="grid")

Dimensions : {'lat': 4320, 'lon': 8640, 'wavelength': 172, 'rgb': 3, 'eightbitcolor': 256}
Variables  : ['Rrs', 'palette']

plan.summary()

Plan: 1 points → 1 unique granule(s)
  Points with 0 matches : 0
  Points with >1 matches: 0
  Variables  : []
  Time buffer: 0 days 00:00:00

First 1 point(s):
  [0] lat=30.0000, lon=-89.0000, time=2025-04-09 00:00:00: 1 match(es)
    → https://obdaac-tea.earthdatacloud.nasa.gov/ob-cumulus-prod-public/PACE_OCI.20250407_20250414.L3m.8D.RRS.V3_1.Rrs.4km.nc

Get the matchups

For variables with a 3rd dimension, like wavelength, all variables will be shown with _3rd dim value.

res = pc.matchup(plan, geometry="grid", variables=["Rrs"])
res

	lat	lon	time	granule_id	Rrs_346	Rrs_348	Rrs_351	Rrs_353	Rrs_356	Rrs_358	...	Rrs_706	Rrs_707	Rrs_708	Rrs_709	Rrs_711	Rrs_712	Rrs_713	Rrs_714	Rrs_717	Rrs_719
0	30.0	-89.0	2025-04-09	https://obdaac-tea.earthdatacloud.nasa.gov/ob-...	0.000306	0.000488	0.00065	0.000656	0.000726	0.000932	...	0.003598	0.003496	0.003386	0.003268	0.003138	0.003004	0.00286	0.002662	0.002098	0.001644

1 rows × 176 columns

Datasets with only lat, lon

In this case, just the variable appears.

%%time
import point_collocation as pc
plan = pc.plan(
    df,
    data_source="earthaccess",
    source_kwargs={
        "short_name": "PACE_OCI_L3M_AVW",
        "granule_name": "*.DAY.*.4km.*",
    }
)
res = pc.matchup(plan, geometry="grid", variables=["avw"])
res

CPU times: user 8.32 ms, sys: 11 ms, total: 19.3 ms
Wall time: 9.02 s

Plan with many files

If you are not sure what files to use, you can use a short name without granule_name. Then look at the plan summary to see the file names. You just need to look at one file (n=1). In this example, there are 16 files that match. 2 resolutions (4km and 0.1 deg) and 8 temporal resolutions:

• R32: rolling 32 days starting every 7 days, 4 dates
• SNSP: seasonal/quarterly
• 8D: 8 day
• DAY: daily
• MO: monthly starting 1st day of each month to last

import point_collocation as pc
plan = pc.plan(
    df,
    data_source="earthaccess",
    source_kwargs={
        "short_name": "PACE_OCI_L3M_AVW",
    }
)

plan.summary(n=1)

Plan: 1 points → 16 unique granule(s)
  Points with 0 matches : 0
  Points with >1 matches: 1
  Variables  : []
  Time buffer: 0 days 00:00:00

First 1 point(s):
  [0] lat=30.0000, lon=-89.0000, time=2025-04-09 00:00:00: 16 match(es)
    → https://obdaac-tea.earthdatacloud.nasa.gov/ob-cumulus-prod-public/PACE_OCI.20250314_20250414.L3m.R32.AVW.V3_1.avw.0p1deg.nc
    → https://obdaac-tea.earthdatacloud.nasa.gov/ob-cumulus-prod-public/PACE_OCI.20250314_20250414.L3m.R32.AVW.V3_1.avw.4km.nc
    → https://obdaac-tea.earthdatacloud.nasa.gov/ob-cumulus-prod-public/PACE_OCI.20250321_20250620.L3m.SNSP.AVW.V3_1.avw.0p1deg.nc
    → https://obdaac-tea.earthdatacloud.nasa.gov/ob-cumulus-prod-public/PACE_OCI.20250321_20250620.L3m.SNSP.AVW.V3_1.avw.4km.nc
    → https://obdaac-tea.earthdatacloud.nasa.gov/ob-cumulus-prod-public/PACE_OCI.20250322_20250422.L3m.R32.AVW.V3_1.avw.0p1deg.nc
    → https://obdaac-tea.earthdatacloud.nasa.gov/ob-cumulus-prod-public/PACE_OCI.20250322_20250422.L3m.R32.AVW.V3_1.avw.4km.nc
    → https://obdaac-tea.earthdatacloud.nasa.gov/ob-cumulus-prod-public/PACE_OCI.20250330_20250430.L3m.R32.AVW.V3_1.avw.0p1deg.nc
    → https://obdaac-tea.earthdatacloud.nasa.gov/ob-cumulus-prod-public/PACE_OCI.20250330_20250430.L3m.R32.AVW.V3_1.avw.4km.nc
    → https://obdaac-tea.earthdatacloud.nasa.gov/ob-cumulus-prod-public/PACE_OCI.20250401_20250430.L3m.MO.AVW.V3_1.avw.0p1deg.nc
    → https://obdaac-tea.earthdatacloud.nasa.gov/ob-cumulus-prod-public/PACE_OCI.20250401_20250430.L3m.MO.AVW.V3_1.avw.4km.nc
    → https://obdaac-tea.earthdatacloud.nasa.gov/ob-cumulus-prod-public/PACE_OCI.20250407_20250414.L3m.8D.AVW.V3_1.avw.4km.nc
    → https://obdaac-tea.earthdatacloud.nasa.gov/ob-cumulus-prod-public/PACE_OCI.20250407_20250414.L3m.8D.AVW.V3_1.avw.0p1deg.nc
    → https://obdaac-tea.earthdatacloud.nasa.gov/ob-cumulus-prod-public/PACE_OCI.20250407_20250508.L3m.R32.AVW.V3_1.avw.4km.nc
    → https://obdaac-tea.earthdatacloud.nasa.gov/ob-cumulus-prod-public/PACE_OCI.20250407_20250508.L3m.R32.AVW.V3_1.avw.0p1deg.nc
    → https://obdaac-tea.earthdatacloud.nasa.gov/ob-cumulus-prod-public/PACE_OCI.20250409.L3m.DAY.AVW.V3_1.avw.0p1deg.nc
    → https://obdaac-tea.earthdatacloud.nasa.gov/ob-cumulus-prod-public/PACE_OCI.20250409.L3m.DAY.AVW.V3_1.avw.4km.nc

Filter to the files you want

Once you see the files names, you can filter to the ones you want. using granule_name. For example *.SNSP.*.4km.* to get the seasonal (quarterly) values. * are wildcard values.

import point_collocation as pc
plan = pc.plan(
    df,
    data_source="earthaccess",
    source_kwargs={
        "short_name": "PACE_OCI_L3M_AVW",
        "granule_name": "*.SNSP.*.4km.*"
    }
)

plan.summary()

Plan: 1 points → 1 unique granule(s)
  Points with 0 matches : 0
  Points with >1 matches: 0
  Variables  : []
  Time buffer: 0 days 00:00:00

First 1 point(s):
  [0] lat=30.0000, lon=-89.0000, time=2025-04-09 00:00:00: 1 match(es)
    → https://obdaac-tea.earthdatacloud.nasa.gov/ob-cumulus-prod-public/PACE_OCI.20250321_20250620.L3m.SNSP.AVW.V3_1.avw.4km.nc

Try many points

from pathlib import Path
import earthaccess
import point_collocation as pc
import pandas as pd

HERE = Path.cwd()
POINTS_CSV = HERE / "fixtures" / "points.csv"
df_points = pd.read_csv(POINTS_CSV)  # lat, lon, date columns
print(len(df_points))
df_points.head()

595

	lat	lon	date
0	27.3835	-82.7375	2024-06-13
1	27.1190	-82.7125	2024-06-14
2	26.9435	-82.8170	2024-06-14
3	26.6875	-82.8065	2024-06-14
4	26.6675	-82.6455	2024-06-14

Get a plan for matchups from PACE data

%%time
import earthaccess
import point_collocation as pc

earthaccess.login()

plan = pc.plan(
    df_points[0:100],
    data_source="earthaccess",
    source_kwargs={
        "short_name": "PACE_OCI_L3M_AVW",
        "granule_name": "*.DAY.*.4km.*",
    }
)

CPU times: user 89.4 ms, sys: 937 μs, total: 90.3 ms
Wall time: 1.87 s

plan.summary(n=0)

Plan: 100 points → 131 unique granule(s)
  Points with 0 matches : 0
  Points with >1 matches: 0
  Variables  : []
  Time buffer: 0 days 00:00:00

Get 100 matchups using that plan

%%time
# 739Mb
res = pc.matchup(plan[0:100], geometry="grid", variables = ["avw"], batch_size=1000)

granules 1-18 of 18 processed, 100 points matched, 00:00:12
CPU times: user 3.85 s, sys: 496 ms, total: 4.35 s
Wall time: 12.4 s

%%time
# 630Mb
res = pc.matchup(plan[0:100], geometry="grid", variables = ["avw"], batch_size=1)

granules 1-1 of 18 processed, 1 points matched, 00:00:04
granules 2-2 of 18 processed, 9 points matched, 00:00:04
granules 3-3 of 18 processed, 8 points matched, 00:00:05
granules 4-4 of 18 processed, 8 points matched, 00:00:05
granules 5-5 of 18 processed, 10 points matched, 00:00:06
granules 6-6 of 18 processed, 1 points matched, 00:00:06
granules 7-7 of 18 processed, 2 points matched, 00:00:06
granules 8-8 of 18 processed, 1 points matched, 00:00:07
granules 9-9 of 18 processed, 1 points matched, 00:00:07
granules 10-10 of 18 processed, 9 points matched, 00:00:08
granules 11-11 of 18 processed, 8 points matched, 00:00:08
granules 12-12 of 18 processed, 8 points matched, 00:00:09
granules 13-13 of 18 processed, 6 points matched, 00:00:09
granules 14-14 of 18 processed, 3 points matched, 00:00:10
granules 15-15 of 18 processed, 9 points matched, 00:00:10
granules 16-16 of 18 processed, 9 points matched, 00:00:11
granules 17-17 of 18 processed, 6 points matched, 00:00:11
granules 18-18 of 18 processed, 1 points matched, 00:00:12
CPU times: user 3.88 s, sys: 468 ms, total: 4.35 s
Wall time: 12.1 s

%%time
res = pc.matchup(plan[0:100], geometry="grid", variables = ["avw"], batch_size=1, spatial_method="xoak")

granules 1-1 of 18 processed, 1 points matched, 00:00:06
granules 2-2 of 18 processed, 9 points matched, 00:00:07
granules 3-3 of 18 processed, 8 points matched, 00:00:08
granules 4-4 of 18 processed, 8 points matched, 00:00:08
granules 5-5 of 18 processed, 10 points matched, 00:00:09
granules 6-6 of 18 processed, 1 points matched, 00:00:09
granules 7-7 of 18 processed, 2 points matched, 00:00:10
granules 8-8 of 18 processed, 1 points matched, 00:00:10
granules 9-9 of 18 processed, 1 points matched, 00:00:11
granules 10-10 of 18 processed, 9 points matched, 00:00:12
granules 11-11 of 18 processed, 8 points matched, 00:00:12
granules 12-12 of 18 processed, 8 points matched, 00:00:13
granules 13-13 of 18 processed, 6 points matched, 00:00:13
granules 14-14 of 18 processed, 3 points matched, 00:00:14
granules 15-15 of 18 processed, 9 points matched, 00:00:15
granules 16-16 of 18 processed, 9 points matched, 00:00:15
granules 17-17 of 18 processed, 6 points matched, 00:00:16
granules 18-18 of 18 processed, 1 points matched, 00:00:16
CPU times: user 4.66 s, sys: 483 ms, total: 5.14 s
Wall time: 17.5 s

res.head()

	lat	lon	time	granule_id	avw
0	27.3835	-82.7375	2024-06-13 12:00:00	https://obdaac-tea.earthdatacloud.nasa.gov/ob-...	NaN
1	27.1190	-82.7125	2024-06-14 12:00:00	https://obdaac-tea.earthdatacloud.nasa.gov/ob-...	NaN
2	26.9435	-82.8170	2024-06-14 12:00:00	https://obdaac-tea.earthdatacloud.nasa.gov/ob-...	NaN
3	26.6875	-82.8065	2024-06-14 12:00:00	https://obdaac-tea.earthdatacloud.nasa.gov/ob-...	NaN
4	26.6675	-82.6455	2024-06-14 12:00:00	https://obdaac-tea.earthdatacloud.nasa.gov/ob-...	NaN

Try lots of products

Pick a recent data point so NRT works. Not all products have files.

import pandas as pd
time = "2026-01-09"
lat = 30.0
lon = -89.0

df = pd.DataFrame(
    {
        "lat": [lat],
        "lon": [lon],
        "time": [time],
    }
)
df["time"] = pd.to_datetime(df["time"])

import earthaccess
results = earthaccess.search_datasets(instrument="oci")

short_names = [
    item.summary()["short-name"]
    for item in results
    if "L3M" in item.summary()["short-name"]
]

print(short_names)

['PACE_OCI_L3M_UVAI_UAA_NRT', 'PACE_OCI_L3M_UVAI_UAA', 'PACE_OCI_L3M_AER_UAA_NRT', 'PACE_OCI_L3M_AER_UAA', 'PACE_OCI_L3M_AOT_NRT', 'PACE_OCI_L3M_AOT', 'PACE_OCI_L3M_AVW_NRT', 'PACE_OCI_L3M_AVW', 'PACE_OCI_L3M_CHL_NRT', 'PACE_OCI_L3M_CHL', 'PACE_OCI_L3M_CLOUD_MASK_NRT', 'PACE_OCI_L3M_CLOUD_MASK', 'PACE_OCI_L3M_CLOUD_NRT', 'PACE_OCI_L3M_CLOUD', 'PACE_OCI_L3M_KD_NRT', 'PACE_OCI_L3M_KD', 'PACE_OCI_L3M_FLH_NRT', 'PACE_OCI_L3M_FLH', 'PACE_OCI_L3M_LANDVI_NRT', 'PACE_OCI_L3M_LANDVI', 'PACE_OCI_L3M_IOP_NRT', 'PACE_OCI_L3M_IOP', 'PACE_OCI_L3M_PIC_NRT', 'PACE_OCI_L3M_PIC', 'PACE_OCI_L3M_POC_NRT', 'PACE_OCI_L3M_POC', 'PACE_OCI_L3M_PAR_NRT', 'PACE_OCI_L3M_PAR', 'PACE_OCI_L3M_CARBON', 'PACE_OCI_L3M_CARBON_NRT', 'PACE_OCI_L3M_RRS_NRT', 'PACE_OCI_L3M_RRS', 'PACE_OCI_L3M_SFREFL_NRT', 'PACE_OCI_L3M_SFREFL', 'PACE_OCI_L3M_TRGAS_NRT', 'PACE_OCI_L3M_TRGAS']

%%time
import point_collocation as pc
for short_name in short_names:
    print(f"\n===== {short_name} =====")

    try:
        plan = pc.plan(
            df,
            data_source="earthaccess",
            source_kwargs={
                "short_name": short_name,
                "granule_name":"*.DAY.*",
             }
        )
        plan.show_variables(geometry="grid")
    except Exception as e:
        print("Failed:", e)

===== PACE_OCI_L3M_UVAI_UAA_NRT =====
Failed: No granules in plan — cannot show variables.

===== PACE_OCI_L3M_UVAI_UAA =====
Failed: No granules in plan — cannot show variables.

===== PACE_OCI_L3M_AER_UAA_NRT =====
Dimensions : {'lat': 180, 'lon': 360}
Variables  : ['Aerosol_Optical_Depth_354', 'Aerosol_Optical_Depth_388', 'Aerosol_Optical_Depth_480', 'Aerosol_Optical_Depth_550', 'Aerosol_Optical_Depth_670', 'Aerosol_Optical_Depth_870', 'Aerosol_Optical_Depth_1240', 'Aerosol_Optical_Depth_2200', 'Optical_Depth_Ratio_Small_Ocean_used', 'NUV_AerosolCorrCloudOpticalDepth', 'NUV_AerosolOpticalDepthOverCloud_354', 'NUV_AerosolOpticalDepthOverCloud_388', 'NUV_AerosolOpticalDepthOverCloud_550', 'NUV_AerosolIndex', 'NUV_CloudOpticalDepth', 'AAOD_354', 'AAOD_388', 'AAOD_550']

===== PACE_OCI_L3M_AER_UAA =====
Dimensions : {'lat': 180, 'lon': 360}
Variables  : ['Aerosol_Optical_Depth_354', 'Aerosol_Optical_Depth_388', 'Aerosol_Optical_Depth_480', 'Aerosol_Optical_Depth_550', 'Aerosol_Optical_Depth_670', 'Aerosol_Optical_Depth_870', 'Aerosol_Optical_Depth_1240', 'Aerosol_Optical_Depth_2200', 'Optical_Depth_Ratio_Small_Ocean_used', 'NUV_AerosolCorrCloudOpticalDepth', 'NUV_AerosolOpticalDepthOverCloud_354', 'NUV_AerosolOpticalDepthOverCloud_388', 'NUV_AerosolOpticalDepthOverCloud_550', 'NUV_AerosolIndex', 'NUV_CloudOpticalDepth', 'AAOD_354', 'AAOD_388', 'AAOD_550']

===== PACE_OCI_L3M_AOT_NRT =====
Failed: No granules in plan — cannot show variables.

===== PACE_OCI_L3M_AOT =====
Failed: No granules in plan — cannot show variables.

===== PACE_OCI_L3M_AVW_NRT =====
Dimensions : {'lat': 1800, 'lon': 3600, 'rgb': 3, 'eightbitcolor': 256}
Variables  : ['avw', 'palette']

===== PACE_OCI_L3M_AVW =====
Dimensions : {'lat': 1800, 'lon': 3600, 'rgb': 3, 'eightbitcolor': 256}
Variables  : ['avw', 'palette']

===== PACE_OCI_L3M_CHL_NRT =====
Dimensions : {'lat': 1800, 'lon': 3600, 'rgb': 3, 'eightbitcolor': 256}
Variables  : ['chlor_a', 'palette']

===== PACE_OCI_L3M_CHL =====
Dimensions : {'lat': 1800, 'lon': 3600, 'rgb': 3, 'eightbitcolor': 256}
Variables  : ['chlor_a', 'palette']

===== PACE_OCI_L3M_CLOUD_MASK_NRT =====
Failed: No granules in plan — cannot show variables.

===== PACE_OCI_L3M_CLOUD_MASK =====
Failed: No granules in plan — cannot show variables.

===== PACE_OCI_L3M_CLOUD_NRT =====
Dimensions : {'lat': 180, 'lon': 360}
Variables  : ['cloud_fraction', 'ice_cloud_fraction', 'water_cloud_fraction', 'ctt', 'ctp', 'cth', 'cth_cot', 'cth_alb', 'ctt_water', 'ctp_water', 'cth_water', 'cth_cot_water', 'cth_alb_water', 'ctt_ice', 'ctp_ice', 'cth_ice', 'cth_cot_ice', 'cth_alb_ice', 'cer_16', 'cot_16', 'cwp_16', 'cer_16_water', 'cot_16_water', 'cwp_16_water', 'cer_16_ice', 'cot_16_ice', 'cwp_16_ice', 'cer_21', 'cot_21', 'cwp_21', 'cer_21_water', 'cot_21_water', 'cwp_21_water', 'cer_21_ice', 'cot_21_ice', 'cwp_21_ice', 'cer_22', 'cot_22', 'cwp_22', 'cer_22_water', 'cot_22_water', 'cwp_22_water', 'cer_22_ice', 'cot_22_ice', 'cwp_22_ice']

===== PACE_OCI_L3M_CLOUD =====
Dimensions : {'lat': 180, 'lon': 360}
Variables  : ['cloud_fraction', 'ice_cloud_fraction', 'water_cloud_fraction', 'ctt', 'ctp', 'cth', 'cth_cot', 'cth_alb', 'ctt_water', 'ctp_water', 'cth_water', 'cth_cot_water', 'cth_alb_water', 'ctt_ice', 'ctp_ice', 'cth_ice', 'cth_cot_ice', 'cth_alb_ice', 'cer_16', 'cot_16', 'cwp_16', 'cer_16_water', 'cot_16_water', 'cwp_16_water', 'cer_16_ice', 'cot_16_ice', 'cwp_16_ice', 'cer_21', 'cot_21', 'cwp_21', 'cer_21_water', 'cot_21_water', 'cwp_21_water', 'cer_21_ice', 'cot_21_ice', 'cwp_21_ice', 'cer_22', 'cot_22', 'cwp_22', 'cer_22_water', 'cot_22_water', 'cwp_22_water', 'cer_22_ice', 'cot_22_ice', 'cwp_22_ice']

===== PACE_OCI_L3M_KD_NRT =====
Dimensions : {'lat': 1800, 'lon': 3600, 'wavelength': 17, 'rgb': 3, 'eightbitcolor': 256}
Variables  : ['Kd', 'palette']

===== PACE_OCI_L3M_KD =====
Dimensions : {'lat': 1800, 'lon': 3600, 'wavelength': 17, 'rgb': 3, 'eightbitcolor': 256}
Variables  : ['Kd', 'palette']

===== PACE_OCI_L3M_FLH_NRT =====
Dimensions : {'lat': 1800, 'lon': 3600, 'rgb': 3, 'eightbitcolor': 256}
Variables  : ['nflh', 'palette']

===== PACE_OCI_L3M_FLH =====
Dimensions : {'lat': 1800, 'lon': 3600, 'rgb': 3, 'eightbitcolor': 256}
Variables  : ['nflh', 'palette']

===== PACE_OCI_L3M_LANDVI_NRT =====
Dimensions : {'lat': 1800, 'lon': 3600, 'rgb': 3, 'eightbitcolor': 256}
Variables  : ['ndvi', 'evi', 'ndwi', 'ndii', 'cci', 'ndsi', 'pri', 'cire', 'car', 'mari', 'palette']

===== PACE_OCI_L3M_LANDVI =====
Dimensions : {'lat': 1800, 'lon': 3600, 'rgb': 3, 'eightbitcolor': 256}
Variables  : ['ndvi', 'evi', 'ndwi', 'ndii', 'cci', 'ndsi', 'pri', 'cire', 'car', 'mari', 'palette']

===== PACE_OCI_L3M_IOP_NRT =====
Dimensions : {'lat': 1800, 'lon': 3600, 'rgb': 3, 'eightbitcolor': 256}
Variables  : ['adg_s', 'palette']

===== PACE_OCI_L3M_IOP =====
Dimensions : {'lat': 1800, 'lon': 3600, 'rgb': 3, 'eightbitcolor': 256}
Variables  : ['adg_442', 'palette']

===== PACE_OCI_L3M_PIC_NRT =====
Failed: No granules in plan — cannot show variables.

===== PACE_OCI_L3M_PIC =====
Failed: No granules in plan — cannot show variables.

===== PACE_OCI_L3M_POC_NRT =====
Dimensions : {'lat': 1800, 'lon': 3600, 'rgb': 3, 'eightbitcolor': 256}
Variables  : ['poc', 'palette']

===== PACE_OCI_L3M_POC =====
Dimensions : {'lat': 1800, 'lon': 3600, 'rgb': 3, 'eightbitcolor': 256}
Variables  : ['poc', 'palette']

===== PACE_OCI_L3M_PAR_NRT =====
Dimensions : {'lat': 1800, 'lon': 3600, 'rgb': 3, 'eightbitcolor': 256}
Variables  : ['par_day_scalar_below', 'par_day_planar_above', 'par_day_planar_below', 'ipar_planar_above', 'ipar_planar_below', 'ipar_scalar_below', 'palette']

===== PACE_OCI_L3M_PAR =====
Dimensions : {'lat': 1800, 'lon': 3600, 'rgb': 3, 'eightbitcolor': 256}
Variables  : ['par_day_scalar_below', 'par_day_planar_above', 'par_day_planar_below', 'ipar_planar_above', 'ipar_planar_below', 'ipar_scalar_below', 'palette']

===== PACE_OCI_L3M_CARBON =====
Dimensions : {'lat': 1800, 'lon': 3600, 'rgb': 3, 'eightbitcolor': 256}
Variables  : ['carbon_phyto', 'palette']

===== PACE_OCI_L3M_CARBON_NRT =====
Dimensions : {'lat': 1800, 'lon': 3600, 'rgb': 3, 'eightbitcolor': 256}
Variables  : ['carbon_phyto', 'palette']

===== PACE_OCI_L3M_RRS_NRT =====
Dimensions : {'lat': 1800, 'lon': 3600, 'wavelength': 172, 'rgb': 3, 'eightbitcolor': 256}
Variables  : ['Rrs', 'palette']

===== PACE_OCI_L3M_RRS =====
Dimensions : {'lat': 1800, 'lon': 3600, 'wavelength': 172, 'rgb': 3, 'eightbitcolor': 256}
Variables  : ['Rrs', 'palette']

===== PACE_OCI_L3M_SFREFL_NRT =====
Dimensions : {'lat': 4320, 'lon': 8640, 'wavelength': 122, 'rgb': 3, 'eightbitcolor': 256}
Variables  : ['rhos', 'palette']

===== PACE_OCI_L3M_SFREFL =====
Dimensions : {'lat': 1800, 'lon': 3600, 'wavelength': 122, 'rgb': 3, 'eightbitcolor': 256}
Variables  : ['rhos', 'palette']

===== PACE_OCI_L3M_TRGAS_NRT =====
Dimensions : {'lat': 1800, 'lon': 3600}
Variables  : ['total_column_o3', 'total_column_no2']

===== PACE_OCI_L3M_TRGAS =====
Dimensions : {'lat': 1800, 'lon': 3600}
Variables  : ['total_column_o3', 'total_column_no2']
CPU times: user 2.96 s, sys: 279 ms, total: 3.24 s
Wall time: 55.6 s