Observational Data Used for Assimilation in the NCEP North American Regional Reanalysis
Perry Shafran1, Jack Woollen1, Wesley Ebisuzaki2, Wei Shi3, Yun Fan3, Robert Grumbine4, Michael Fennessy5
1SAIC/GSO and NCEP/EMC, 2NCEP/CPC, 3RSIS and NCEP/CPC, 4NCEP/EMC, 5Center for Land-Ocean-Atmosphere Studies
North American Regional Reanalysis Workshop, 11 January 2005, 85th AMS Annual Meeting, San Diego, CA
GAPP
Introduction
• North American Regional Reanalysis (NARR) assimilated great deal of data
• Data usage– Assimilated in analysis– Boundary conditions– Used during execution of Eta model
• Most data from NCAR/NCEP Global Reanalysis; some data from other sources
Data Used in Global Reanalysis and Regional Reanalysis
Dataset Details Source
Radiosondes Temperature, winds, moisture
NCEP/NCAR Global Reanalysis (GR)
Dropsondes Same as above GR
Pibals Wind GR
Aircraft Temp. and wind GR
Surface Pressure GR
Cloud drift winds Geostationary satellite GR
Radiosonde Data
Precipitation Data Sources
• CMAP used for Oceanic Data– CPC Merged Analysis of Precipitation– Global 2.5 deg dataset, pentads– Disaggregated using R2 precipitation weighting factors– Reliable up to about 50 deg N– Blending of CMAP influence in 15-degree zone over
oceans to eliminate discontinuities– Not reliable in areas of very heavy precipitation (<100
mm/day) or near centers of tropical storms
Precipitation Data Sources
• CONUS precipitation– From 1/8-degree grid– Several sources
• NCDC daily cooperative stations (~8000 reports/day)• River Forecast Center from CPC (~7000/day)• Hourly Precipitation Data (HPD) (~2500/day)
– Analyzed using orographic Mountain Mapper also known as PRISM
– Least-squares distance weighting schme– Daily precipitation datasets disaggregated using HPD
weighting factors
Precipitation Data Sources
• Canada and Mexico– Daily gage-based 1-degree grids– Disaggregated using R2 hourly precipitation weighting
factors– Data over Canada is very sparse; possibility of not
ingesting all available data due to timeliness• The four data sources then remapped to Eta grid• Blended together to minimize the boundaries from
different datasets
Sample Distribution of Canadian Precipitation Data
Data Added or Improved Upon for Regional ReanalysisDataset Details Source
Precipitation CONUS (with PRISM), Mexico, Canada, CMAP over oceans (<42.5 deg N)
NCEP/CPC
TOVS-1B radiances
Temperature, precipitable water over oceans
NESDIS
NCEP Surface Wind, moisture GR
TDL Surface Pres, wind, moisture NCAR
COADS, ship/buoys data
Pressure, wind, moisture, temperature
NCEP/EMC
Air Force Snow Snow depth COLA and NCEP/EMC
SST 1-degree Reynolds, with Great Lakes SSTs
NCEP/EMC, GLERL
Sea and lake ice Includes data on Canadian lakes, Great Lakes
NCEP/EMC, GLERL, Ice Service Canada
Tropical cyclones Locations used for blocking of CMAP Precipitation
Lawrence Livermore National Laboratory
Surface Data
Notes About Data
• Surface data: merge done between 2 sources of surface data for consistency and to eliminate duplicates
• Lake ice data and SSTs over lake ice are consistent with each other
• Tropical cyclones not actually assimilated but used to determine locations for CMAP blocking
Climatologies
Dataset Details Source
Green vegetation fraction
Used to initialize the vegetation, monthly
GR
Baseline snow-free albedo
Used to initialize the albedo, seasonal
GR
Input Differences Between NARR and R-CDAS
Variable NARR R-CDASCONUS Precipitation CPC analysis N-LDAS
Mexico Precipitation CPC Mexican analysis CPC US/Mexico combined analysis
Canada Precipitation CPC Canadian analysis None
Ocean Precipitation CMAP CMORPH
Sea ice Satellite data Ice Mapping System from NESDIS
Lake ice Data from Ice Service Canada
25-year climatology
Summary
• NARR assimilated a lot of data from different resources
• Data assimilated in NARR system with updated 3DVAR techniques helped to create accurate high-resolution climate data set
• Data to be made available to NARR users
More NARR Information
• NARR website: http://wwwt.ncep.noaa.gov/mmb/rreanl
