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time
timestamp[ns]date
2018-01-23 00:00:01
2022-02-22 00:59:59
z
float64
750
18.6k
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2018-01-01 00:00:00
2022-02-01 00:00:00
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2018-01-23T00:00:01.332000
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2018-01-23T00:00:01.352000
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2018-01-23T00:00:01.372000
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2018-01-23T00:00:01.852000
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2018-01-23T00:00:01.892000
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2018-01-23T00:00:02.232000
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2018012300
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2018-01-23T00:00:02.272000
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2018-01-23T00:00:02.292000
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2018-01-23T00:00:02.832000
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2018-01-23T00:00:02.952000
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2018-01-23T00:00:02.972000
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2018-01-23T00:00:04.632000
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2018-01-23T00:00:04.652000
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2018-01-23T00:00:04.772000
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2018-01-23T00:00:05.352000
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2018012300
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2018-01-23T00:00:05.732000
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2018012300
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2018-01-23T00:00:05.752000
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2018-01-23T00:00:05.832000
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2018012300
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2018-01-23T00:00:06.272000
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2018012300
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2018-01-23T00:00:07.942000
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2018012300
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2018-01-23T00:00:07.962000
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2018012300
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2018-01
liquid
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2018-01-23T00:00:08.382000
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2018012300
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2018-01-23T00:00:08.842000
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2018012300
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2018-01
liquid
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2018-01-23T00:00:08.862000
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2018012300
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2018-01-23T00:00:08.902000
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2018012300
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liquid
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2018-01-23T00:00:09.402000
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2018012300
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2018-01-23T00:00:11.882000
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2018012300
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2018-01-23T00:00:11.942000
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2018012300
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2018-01
liquid
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2018-01-23T00:00:11.962000
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2018012300
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2018-01-23T00:00:12.322000
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2018012300
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liquid
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2018-01-23T00:00:12.342000
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2018012300
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2018-01-23T00:00:12.362000
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2018012300
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2018-01-23T00:00:12.382000
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2018012300
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2018-01-23T00:00:12.422000
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2018012300
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2018-01-23T00:00:15.012000
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2018012300
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2018-01-23T00:00:15.032000
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2018012300
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2018-01-23T00:00:15.072000
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2018012300
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2018-01-23T00:00:15.112000
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2018012300
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2018-01-23T00:00:15.232000
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2018012300
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2018-01-23T00:00:15.252000
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2018012300
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2018-01-23T00:00:15.292000
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2018012300
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2018-01-23T00:00:15.312000
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2018012300
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2018-01-23T00:00:15.352000
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2018012300
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2018-01-23T00:00:15.372000
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2018012300
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2018012300
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2018-01-23T00:00:15.492000
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2018012300
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2018012300
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2018-01
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2018-01-23T00:00:18.012000
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2018012300
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2018-01
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2018-01-23T00:00:18.492000
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2018012300
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2018-01
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2018-01-23T00:00:18.532000
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End of preview. Expand in Data Studio

CERES row

CERES SSF Aqua-FM3-MODIS Edition4A cloud-layer rows for training a MessiahMaybe PINN, split from the full holdout in QueenPeppa/messiahmaybe-external-holdout (ceres_ssf). Each cloud-layer row is assigned on its own.

Seed 42. Seventy percent of rows form a train pool and 30% a test pool. Five percent of all rows are then taken from each pool to make validation:

  • train: 65%

  • val: 10%

  • test: 25%

  • train: 2,330,522 rows

  • val: 358,542 rows

  • test: 896,354 rows

SLF is a layer-mean mass ratio, f_liq * LWP / (f_liq * LWP + (1 - f_liq) * IWP), and q_total depends on hypsometric layer thickness. The columns match the EarthCARE filtered table, including pinn_nan_policy in catalog.json.

splits/train.npz, splits/val.npz, and splits/test.npz are the arrays read by pinn_architecture.load_filtered_splits(splits_dir, split=...). Pass splits_dir pointing at the splits folder to train_keep_ladder.

The Hub splits CERES train row, CERES val row, and CERES test row are the Parquet shards.

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