Recently we've received a number of inquiries about accessing RDA datasets with ArcGIS. ArcGIS provides the capability to ingest most data formats that are used in the Atmospheric Science and Oceanography communities. These include GRIB-1, GRIB-2, NetCDF, HDF4, and HDF5. OPeNDAP access is also supported in ArcGIS. A list of related links describing this support is included below.
24 April 2015
23 April 2015
CFDDA: a global, hourly, 40 km resolution reanalysis
NCAR RDA announces the debut of The NCAR Global Climate Four-Dimensional Data Assimilation (CFDDA) Hourly 40 km Reanalysis, a 21-year climatography.
High-resolution temporal and spatial resolution data--usually created with regional-scale models—have traditionally been proprietary and costly to obtain. Freely available global model data suffers from either lower spatial or temporal resolution, or both. Low spatial resolution fails to realistically represent wind speeds in complex terrain. Low temporal resolution fails to capture the full diurnal cycle of wind behavior.
The NCAR Global Climate Four-Dimensional Data Assimilation (CFDDA) Hourly 40 km Reanalysis was developed in 2009-2010 by the Research Applications Laboratory (RAL) to provide the most accurate boundary layer wind estimates available at that time. CFDDA used 28 sigma levels, with 19 between the surface and 700 hPa, a four-fold improvement over the contemporary NWP models. The dataset spans 21 years, 1985-2005, providing hourly atmospheric parameters, including winds, on 28 vertical levels on a global 40 km grid.
Unlike most global reanalyses, CFDDA output is available hourly to fully capture the diurnal range of weather phenomena. For instance, if the land-sea breeze or mountain-valley circulation in a region peaks at around 14:00 local time, and that does not coincide with the 00, 06, 12, or 18 UTC times of the saved reanalysis, then the strength of the phenomena will be underestimated in that region.
High-resolution temporal and spatial resolution data--usually created with regional-scale models—have traditionally been proprietary and costly to obtain. Freely available global model data suffers from either lower spatial or temporal resolution, or both. Low spatial resolution fails to realistically represent wind speeds in complex terrain. Low temporal resolution fails to capture the full diurnal cycle of wind behavior.
The NCAR Global Climate Four-Dimensional Data Assimilation (CFDDA) Hourly 40 km Reanalysis was developed in 2009-2010 by the Research Applications Laboratory (RAL) to provide the most accurate boundary layer wind estimates available at that time. CFDDA used 28 sigma levels, with 19 between the surface and 700 hPa, a four-fold improvement over the contemporary NWP models. The dataset spans 21 years, 1985-2005, providing hourly atmospheric parameters, including winds, on 28 vertical levels on a global 40 km grid.
Unlike most global reanalyses, CFDDA output is available hourly to fully capture the diurnal range of weather phenomena. For instance, if the land-sea breeze or mountain-valley circulation in a region peaks at around 14:00 local time, and that does not coincide with the 00, 06, 12, or 18 UTC times of the saved reanalysis, then the strength of the phenomena will be underestimated in that region.
22 April 2015
Analysis, forecast, reanalysis--what's the difference?
[Note: An older version of this information is available under the Documentation Tab of both ds083.2 FNL and ds084.1 GFS as Analysis.pdf. This page is maintained and should be considered the definitive version.]
This extends the discussion in What's the difference between GFS and FNL? Read that, and then come back. I'll wait.
From Dee et al:
This extends the discussion in What's the difference between GFS and FNL? Read that, and then come back. I'll wait.
From Dee et al:
Reanalysis data provide a multivariate, spatially complete, and coherent record of the global atmospheric circulation. Unlike archived weather analyses from operational forecasting systems, a reanalysis is produced with a single version of a data assimilation system—including the forecast model used—and is therefore not affected by changes in method.
What's the difference between GFS and FNL?
[Note: An older version of this information is available under the Documentation Tab of both ds083.2 FNL and ds084.1 GFS as FNLvGFS.pdf. This page is maintained and should be considered the definitive version.]
GFS stands for Global Forecast System, the current NOAA NCEP global numerical weather prediction system.
FNL refers to the "Final" analysis, though it is now mainly referred to as the GDAS analysis, a part of the Global Data Assimilation System.
FNL and GFS are related, yet different products from the same data assimilation and forecast system. They share the same underlying model and data assimilation techniques. They contain the same data sources--but there is a subtle difference in the amount of "real" data assimilated into the initial conditions for GFS and FNL.
GFS stands for Global Forecast System, the current NOAA NCEP global numerical weather prediction system.
FNL refers to the "Final" analysis, though it is now mainly referred to as the GDAS analysis, a part of the Global Data Assimilation System.
FNL and GFS are related, yet different products from the same data assimilation and forecast system. They share the same underlying model and data assimilation techniques. They contain the same data sources--but there is a subtle difference in the amount of "real" data assimilated into the initial conditions for GFS and FNL.
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