1. The Available NCEP Reanalyses Wesley Ebisuzaki Climate Prediction Center National Centers for Environmental Prediction NWS/NOAA Maryland, USA

2. Topics Introduction R1, R2, NARR, CFSR
Grib 1 and 2 and various utilities
Getting the data by Nomads
Will try to cover these topics from a user perspective. Some overlap with NOMADS and GrADS presentations.

3. Download links http://www. cpc. noaa. gov/products/ wesley/ams2010_sc
Download links wesley/ams2010_sc.html Documentation package includes a pdf version. The pdf has more details and covers more material.

4. Are Reanalyses Truth? Convenient Run through quality control programs
Consistent (ex. winds, temperature, heights)
Almost every variable that you would ever want
Better than interpolation of observations

5. But Need observations Some fields depend on the model physics
Some regions are poorly observed
Some levels are poorly observed
Some fields depend on the model physics
Clear sky radiation is pretty good
Clouds and other moist process are bad
BL is somewhere in between

6. Reanalysis Errors Best for winds, temperature and heights Humidity
Observations, consistency relationships
Near surface more dependent on BL physics
Humidity
Fewer observations, model physics important
Other fields
More model physics dependent

7. Not all observations are equal
Sondes and aircraft data are good quality
Surface pressure is good quality
Surface winds, temperatures, humidities
Hard to assimilate, elevation, representativeness
Satellite data
Retrievals=old, Radiances=new
Hard to get a consistent record with historical data
A concern for trend analyses

8. Analysis Uncertainties Factors to Consider
Observation density
Sensitivity to model parameterizations
Dependent on lesser quality observations
Model and data assimilation system

9. Are Observations Truth?
Data assimilation could fit the observations exactly but produce a worse forecast!
Error by equipment manufacturer
Representativeness error
Observation in an air parcel is not the same as average value in the grid cell

10. Finally a plot spread from an ensemble of opn analyses and reanalyses simple way to get a error estimate

11. Averaged over a year

12. Monthly Means

13. Analysis Uncertainties
Not simple
Uncertainty for a day: synoptic and data
Averaged over a year: data
Monthly/seasonal means have smaller uncertainties than daily field, biases

14. NCEP Reanalyses NCEP/NCAR Reanalysis (R1, CDAS)
Mid 1990s, 1947-present, 2.5 degree grid, global
NCEP/DOE Reanalyses (R2)
Late 1990s, 1979-present, 2.5 degree grid, global
North American Regional Reanalysis (NARR)
Early 2000's, 1979-present, 32 km grid
Climate Forecast System Reanalysis (CFSR)
2010, 1979-present, 0.5 degree grid, global

15. Others Reanalyses ECMWF: ERA-15, ERA-40, ERA-interim
JMA, CRIEPI: JRA-25/JCDAS
NASA/GSFC: MERRA
ERSL (different approach)
Check for robust signal

16. Support of NCEP Reanalyses
1) Journal articles (BAMS)
2) Web: NCEP, NCAR, ERSL, NCDC
3) Questions to provider of data
4) NCDC, NCAR, ERSL will forward tough questions to NCEP
5) At NCEP: R1, R2, NARR:
CFSR: to be determined

17. Nuts and Bolts: data formats
grib1, grib2
grib is a WMO standard and national meteorological centers use WMO standards for day-to-day operations
Reanalyses run at highest resolution possible
Large portion of supercomputer, massive tape storage
grib files are smaller than netcdf
Netcdf
NCAR and ERSL often translate into netcdf and redistribute the NCEP reanalyses

18. My grib1 toolbox wgrib: inventory, get values, database tool
GrADS: plots, some computations
copygb: convert to different grid
Calculations often easier on lat-lon grid
Save space
ggrib and lcgrib: subset of lat-lon, lambert- conformal grids, faster than copygb
C/fortran programs: ieee -> grib

19. My grib2 tool box wgrib2: inventory, contents, database, encode
GrADS: plots and some computations
copygb2: convert to a different grid, computations are often easier on lat-lon grid
cnvgrib: convert between grib1 and grib2
ggrib and lcgrib functionality in wgrib2
wgrib2: ieee -> grib2

20. grib2 to grib1: cnvgrib
grib2 is new, many people use cnvgrib to convert from grib2 to grib1
Long term solution? NO!
NCO dropped support for cnvgrib (5 years)
New variables in grib2 are not in grib1
grib2 files are compressed, easier to use
New features in grib1 utilities?

21. Short Grib1 Inventories
-sh-3.00$ wgrib -s narr.t09z.awip32.merged
1:0:d= :MSLET:MSL:anl:NAve=0
2:166602:d= :PRMSL:MSL:anl:NAve=0
3:333204:d= :PRES:hybrid lev 1:anl:NAve=0 ..
-s is the short inventory
column 1 = message (record) number
column 2 = byte location starting from 0
column 3 = analysis time or initial time of the forecast
column 4 = variable name
column 5 = level/layer
column 6 = timing information,
anl=analysis, acc=accumulation, ave=average
column 7 = number of fields used to make an ave/acc

22. Short Grib2 Inventories
--sh-3.00$ wgrib2 pgblnl.gdas grb2 -s
1:4:d= :HGT:1 mb:anl:
2:16552:d= :TMP:1 mb:anl:
3:22064:d= :RH:1 mb:anl: ..
column 1: message or message.submessage number
column 2: the byte location of the grib message
column 3: the analysis or start of forecast time,
use -T to see the minutes and seconds
column 4: variable name
column 5: level
column 6 = timing information, anl=analysis, acc=accumulation, ave=average, fcst = forecast

23. Names HGT = geopotential height (m) TMP = temperature (K)
UGRD = zonal wind (m/s)
VGRD = meridional wind (m/s)
see NCEP tables on web (see pdf file)
or use -v option in wgrib/wgrib2

24. Grid information: grib
grib1/grib2 support many different grids
internally grib stores data in different orders,
8 in grib1, 16 in grib2
up to software to figure it out
wgrib -V
wgrib2 -grid

25. Values at specified locations
Grib1: understand the grid
R1, R2: WE:NS storage
NARR: WE:SN storage
Global – easy to figure out the lat-lon of points
NARR – need file with the lat-lon of the points
rr-fixed.grb is with other course files
On your own

26. Values at specified locations
Grib2: harder to understand and easier to use
16 storage orders (3 in common use)
wgrib2 convert data to WE:SN order by default
Can use old way to pick up data (i.e. get n-th point)
Can use wgrib2 to get the data (-lon option)
See documentation for wgrib2 examples

27. Winds and other vectors
Winds have two orientations
North can point to the north pole (earth relative)
North can point to the north grid point (grid relative)
Non-staggered grid: (ix,iy) -> (ix,iy+1)

28. NARR Output Grid

29. Winds and other vectors
NCEP convention is grid relative
Lat-lon, Gaussian, Mercator is not an issue
Lambert-conformal, polar stereographic an issue
NARR is earth relative (anti-NCEP convention)
Good for new users
Bad for users of other NCEP regional products

30. GrADS and grib Widely used, open source, visualization + more
Data model
GrADS x,y,z,t and ensemble
Grib time is more complicated
forecast verification time
Start of forecast time (or analysis time)
average from 6-12 hours into the forecast
Monthly average of one of the above
Need to map grib times into GrADS time

31. Making plots with GrADS: grib1
Analyses
grib2ctl.pl grib_file >ctl_file
gribmap -0 -i ctl_file
(run grads)
Forecasts
grib2ctl.pl -verf grib_file >ctl_file
gribmap -i ctl_file

32. Making plots with GrADS: grib2
Analyses
g2ctl.pl -0 grib_file >ctl_file
gribmap -0 -i ctl_file
(run grads)
Forecasts
g2ctl.pl grib_file >ctl_file
gribmap -i ctl_file
Unified options, -b option is working

33. See Jennifer's presentations
More GrADS
See Jennifer's presentations

34. Getting data the Nomads way
On-line is easiest way – if downloading time ok
Big jobs: spend more time optimizing the transfer and reduce the amount of data transferred
Small jobs: ease of use is important, ex. plots, OpeNDAP (lat-lon grid, text output).

35. Downloading Methods Partial http downloading:
download the fields that you want
select by field/time/level
data is compressed (grib2) or packed (grib1)
efficient for the server (support many clients)
easy to script (not point and click)
example in the documentation package
good for large downloads

36. Downloading Methods g2subset (grib-filter):
download the fields that you want
select by field/time/level
select an optional regional subset
grib2 only
point and click to learn or a few files
moderately easy to script
example in the documentation package
more server resources, less data transferred

37. Downloading Methods OPeNDAP: standard protocol (text based)
select field/time/level/region
data is read by the server
interpolated to a lat-lon grid if needed
sent to the client by a standard protocol
advantages: supported by software
easy to use .. can even use a browser
disadvantages: server overhead
data may not be compressed

38. Downloading Methods Plots: Part of the development nomads at NCEP
Point and click
Designed for casual use of the data
Not designed to do everything
Research: download and do own plots
For some people, this all that they need

39. Downloading Methods Full File:
Can use browser, etc to download full file from http server, get the directory listing and right click.
Easy
Good to get a sample file to help plan the download.

40. Typical Nomads front page

41. Summary Reanalyses are not truth Observations are not truth
Tools for grib1 and grib2
Introduction to Nomads
Save time in downloading data
Select fields that you will need
Can alway get other fields later
Select access method