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MAPP Webinar

Date/Time Title
September 25, 2012
1:00 PM – 2:00 PM ET
Evaluation of Reanalysis Products
  Speakers and Topics: Edmund Chang (Stony Brook University)
Northern Hemisphere cyclone trends in Reanalysis Data
Michela Biasutti (Columbia University)
Climatology and variability of rainfall in the 20th Century Reanalysis
Ron Lindsay (University of Washington)
Evaluation of seven different atmospheric reanalysis products in the Arctic
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Edmund Chang — Northern Hemisphere cyclone trends in Reanalysis Data — Previous studies based mainly on NCEP-NCAR reanalysis data have suggested that both the Pacific and Atlantic storm tracks in the Northern Hemisphere have intensified over the second half of the 20th century. Our previous study examined upper tropospheric storm track activity in terms of 300 hPa meridional velocity variance statistics, and suggested that the trend in storm track activity derived from 20th Century Reanalysis data is most
consistent with that derived directly from rawinsonde observations. Our results also suggested that the trend derived from NCEP-NCAR reanlaysis data is clearly biased high, especially in the Pacific. In this study, we examine sea level pressure data from 20th Century Reanalysis, ERA40, and NCEP-NCAR reanalysis. Both variance and cyclone track statistics will be compared, and
interannual variability and trend in near surface storm track activity will be assessed.
Michela Biasutti — Climatology and variability of rainfall in the 20th Century Reanalysis — We validate the climatology and variability of rainfall in the 20th Century reanalysis, version 2 (20CRv2, Compo et al., 2010) in the tropics and the northern mid-latitudes. We document biases in seasonal mean rainfall patterns in comparison to satellite-based records for the most recent past and we contrast the ensemble-mean reanalyzed century-long time-series of rainfall variability in key land areas with gridded observations.
To gauge the importance of the SST boundary conditions, the assimilation of surface pressure observations, and the lack of assimilation of other surface and upper-level observations, we contrast rainfall variability in the 20CRv2 reanalysis against an ensemble integration of an atmospheric GCM forced by historical SST and against the ERA-Interim and NCEP-DOE reanalysis products. We find that the assimilation of surface pressure dramatically improves the agreement with observations (compared to only having knowledge of SST) in the midlatitudes, but offers more modest improvements in the tropics. The assimilation of a wide range of additional observations is not a surefire way to improve the simulation of rainfall: while ERA-interim matches observed rainfall time series better than 20CRv2, NCEP-DOE shows a better match in some mid-latitude regions, but a worse one in the tropics. Agreement between 20CRv2 and observations grows through the 20th century, but not in a dramatic fashion, indicating that even a sparse network can be sufficient to constrain the seasonal means.
Ron Lindsay — Evaluation of seven different atmospheric reanalysis products in the Arctic — Atmospheric reanalyses depend on a mix of observations and model forecasts.  In data-sparse regions such as the Arctic the solution will be more dependent on the model structure and assumptions and data assimilation methods than in data-rich regions.  State variables that are not directly observed are also expected to show more variability across models than those subject to observation.  We present comparative analysis in the Arctic regions of seven different atmospheric reanalysis data sets:  NCEP-R1, NCEP-R2, 20CR, CFSR, MERRA, ERA-Interim, and JRA-25.  While basic parameters such as near-surface air temperature and the heights of pressure levels are considered, emphasis is placed on variables not directly observed such as surface fluxes, precipitation, and trends.  The monthly averaged surface temperature, radiative fluxes, and precipitation are also compared with observed values from a limited number of available shore and drifting stations to see how well the different data sets capture the seasonal cycles.  The 30-year period from 1980 to 2009 is analyzed.

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