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Dataset: E3SMv2.1-SMYLE_NOV.TS.climoIC.nc
Catalog: /thredds/catalog/files/d010074/data/catalog.html
dataFormatNetCDF
authorityedu.ucar.gdex
featureTypeGRID
dataSize212368922
idfiles/d010074/data/E3SMv2.1-SMYLE_NOV.TS.climoIC.nc
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OpenDAP Data Access Access dataset through OPeNDAP using the DAP2 protocol.
DAP4 Data Access Access dataset using the DAP4 protocol.
NetcdfSubset Data Access A web service for subsetting CDM scientific grid datasets.
CdmRemote Data Access Provides index subsetting on remote CDM datasets, using ncstream.
CdmrFeature Data Access Provides coordinate subsetting on remote CDM Feature Datasets, using ncstream.
WCS Data Access Supports access to geospatial data as 'coverages'.
WMS Data Access Supports access to georegistered map images from geoscience datasets.
HTTPServer Data Access HTTP file download.
ISO Metadata Provide ISO 19115 metadata representation of a dataset's structure and metadata.
NCML Metadata Provide NCML representation of a dataset.
UDDC Metadata An evaluation of how well the metadata contained in the dataset conforms to the NetCDF Attribute Convention for Data Discovery (NACDD)

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Godiva3 Browser
default_viewer.ipynb Jupyter Notebook The TDS default viewer attempts to plot any Variable contained in the Dataset.
Documentation
Dates
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Description:

  • Rights: Freely Available
  • summary: Ensemble Earth system model predictions initialized from states close to observations generally drift away from observed climatology and towards a biased model climatology over timescales from days to years. Estimation of drift, the change in forecast climatology with lead time, is essential for computing forecast anomalies that can be meaningfully compared with observed anomalies from the past and used with confidence to credibly anticipate weather pattern changes from weeks to decades in advance. Conventional methods for estimating drift rely on the availability of a large sample of reforecasts spanning at least two decades, but generating such comprehensive reforecast sets requires a significant investment of both human and computer resources. We show here that subseasonal to decadal forecast drift can be well estimated using minimal reforecast methods that target a predetermined climatological window, yielding forecast anomaly and skill verification metrics that closely match those obtained using standard (much more expensive) methods. Efficient and accurate forecast drift quantification facilitates prediction system experimentation with greatly reduced overhead.
  • NCAR GDEX - Efficient Drift Correction of Initialized Earth System Predictions(d010074)

Dates:

  • modified : 2025-07-24T19:19:36.801Z

Creators:

  • UCAR/NCAR/CGD

Publishers:

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