Norwegian Climate Prediction Model reanalysis with assimilation of SST anomaly : 1950-2010 ...

The Norwegian Climate Prediction model combines the Norwegian Earth System Model version 1 (medium resolution) with the Ensemble Kalman Filter data assimilation method. netcdf 4 format. Technical details: Production machine: Cray XE6 in Bergen (hexagon) Model source: projectEPOCASA-3: atmosphere=CAM...

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Bibliographic Details
Main Authors: Counillon, Francois, Keenlyside, Noel, Wang, Yiguo, Nansen Environmental And Remote Sensing Center, Geophysical Institute, University Of Bergen, Uni Research Climate, Bjerknes Centre For Climate Research, Bethke, Ingo
Format: Dataset
Language:unknown
Published: Norstore 2016
Subjects:
Online Access:https://dx.doi.org/10.11582/2016.00002
https://archive.norstore.no/pages/public/datasetDetail.jsf?id=10.11582/2016.00002
id ftdatacite:10.11582/2016.00002
record_format openpolar
spelling ftdatacite:10.11582/2016.00002 2023-08-27T04:11:54+02:00 Norwegian Climate Prediction Model reanalysis with assimilation of SST anomaly : 1950-2010 ... Counillon, Francois Keenlyside, Noel Wang, Yiguo Nansen Environmental And Remote Sensing Center Geophysical Institute, University Of Bergen Uni Research Climate Bjerknes Centre For Climate Research Bethke, Ingo 2016 https://dx.doi.org/10.11582/2016.00002 https://archive.norstore.no/pages/public/datasetDetail.jsf?id=10.11582/2016.00002 unknown Norstore Natural sciences FOS Natural sciences Earth science Geophysics FOS Earth and related environmental sciences dataset Model, Raw Dataset 2016 ftdatacite https://doi.org/10.11582/2016.00002 2023-08-07T14:24:23Z The Norwegian Climate Prediction model combines the Norwegian Earth System Model version 1 (medium resolution) with the Ensemble Kalman Filter data assimilation method. netcdf 4 format. Technical details: Production machine: Cray XE6 in Bergen (hexagon) Model source: projectEPOCASA-3: atmosphere=CAM4; ocean=MICOM; land=CLM; sea ice=CICE Horizontal resolution: atmosphere/land=1.9x2.5 degree; ocean/sea ice=~1 degree Output frequency: monthly + yearly run on 160 mpi tasks. Use : hybrid start from historical simulation 30 members (N20TREXTAERCN COMPSET) at 1950-01-15; which are initiazed from preindustrial run (N1850; starting date 1500:10:1790). Library: craype-barcelona, craype/2.2.1, cray-libsci/12.1.3, cray-mpich/6.0.2, cray-netcdf/4.3.2, cray-hdf5/1.8.13, pcp, coreutils-cnl). For the assimilation, we use the Detereministic Ensemble Kalman Filter (EnKF-MPI-TOPAZ_Yiguo_no_copy), with 30 members, localisation radius of 1 grid cell with no tapering (STEP); rfactor=2 and kfactor=2, inflation=1, ... : 69564 Hierarchical Data Format (version 5) data, totaling ...... 549.68 GB ... Dataset Sea ice DataCite Metadata Store (German National Library of Science and Technology) Bergen
institution Open Polar
collection DataCite Metadata Store (German National Library of Science and Technology)
op_collection_id ftdatacite
language unknown
topic Natural sciences
FOS Natural sciences
Earth science
Geophysics
FOS Earth and related environmental sciences
spellingShingle Natural sciences
FOS Natural sciences
Earth science
Geophysics
FOS Earth and related environmental sciences
Counillon, Francois
Keenlyside, Noel
Wang, Yiguo
Nansen Environmental And Remote Sensing Center
Geophysical Institute, University Of Bergen
Uni Research Climate
Bjerknes Centre For Climate Research
Bethke, Ingo
Norwegian Climate Prediction Model reanalysis with assimilation of SST anomaly : 1950-2010 ...
topic_facet Natural sciences
FOS Natural sciences
Earth science
Geophysics
FOS Earth and related environmental sciences
description The Norwegian Climate Prediction model combines the Norwegian Earth System Model version 1 (medium resolution) with the Ensemble Kalman Filter data assimilation method. netcdf 4 format. Technical details: Production machine: Cray XE6 in Bergen (hexagon) Model source: projectEPOCASA-3: atmosphere=CAM4; ocean=MICOM; land=CLM; sea ice=CICE Horizontal resolution: atmosphere/land=1.9x2.5 degree; ocean/sea ice=~1 degree Output frequency: monthly + yearly run on 160 mpi tasks. Use : hybrid start from historical simulation 30 members (N20TREXTAERCN COMPSET) at 1950-01-15; which are initiazed from preindustrial run (N1850; starting date 1500:10:1790). Library: craype-barcelona, craype/2.2.1, cray-libsci/12.1.3, cray-mpich/6.0.2, cray-netcdf/4.3.2, cray-hdf5/1.8.13, pcp, coreutils-cnl). For the assimilation, we use the Detereministic Ensemble Kalman Filter (EnKF-MPI-TOPAZ_Yiguo_no_copy), with 30 members, localisation radius of 1 grid cell with no tapering (STEP); rfactor=2 and kfactor=2, inflation=1, ... : 69564 Hierarchical Data Format (version 5) data, totaling ...... 549.68 GB ...
format Dataset
author Counillon, Francois
Keenlyside, Noel
Wang, Yiguo
Nansen Environmental And Remote Sensing Center
Geophysical Institute, University Of Bergen
Uni Research Climate
Bjerknes Centre For Climate Research
Bethke, Ingo
author_facet Counillon, Francois
Keenlyside, Noel
Wang, Yiguo
Nansen Environmental And Remote Sensing Center
Geophysical Institute, University Of Bergen
Uni Research Climate
Bjerknes Centre For Climate Research
Bethke, Ingo
author_sort Counillon, Francois
title Norwegian Climate Prediction Model reanalysis with assimilation of SST anomaly : 1950-2010 ...
title_short Norwegian Climate Prediction Model reanalysis with assimilation of SST anomaly : 1950-2010 ...
title_full Norwegian Climate Prediction Model reanalysis with assimilation of SST anomaly : 1950-2010 ...
title_fullStr Norwegian Climate Prediction Model reanalysis with assimilation of SST anomaly : 1950-2010 ...
title_full_unstemmed Norwegian Climate Prediction Model reanalysis with assimilation of SST anomaly : 1950-2010 ...
title_sort norwegian climate prediction model reanalysis with assimilation of sst anomaly : 1950-2010 ...
publisher Norstore
publishDate 2016
url https://dx.doi.org/10.11582/2016.00002
https://archive.norstore.no/pages/public/datasetDetail.jsf?id=10.11582/2016.00002
geographic Bergen
geographic_facet Bergen
genre Sea ice
genre_facet Sea ice
op_doi https://doi.org/10.11582/2016.00002
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