Topics

1. The Available NCEP ReanalysesWesley EbisuzakiClimate Prediction CenterNational Centers for Environmental PredictionNWS/NOAAMaryland, USAwesley.ebisuzaki@noaa.gov

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 linkshttp://www.cpc.noaa.gov/products/wesley/ams2010_sc.htmlDocumentation 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 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 • 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 plotspread from an ensemble of opn analyses and reanalysessimple 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. ECMWF: ERA-15, ERA-40, ERA-interim JMA, CRIEPI: JRA-25/JCDAS NASA/GSFC: MERRA ERSL (different approach) Check for robust signal Others Reanalyses

16. 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: wesley.ebisuzaki@noaa.gov CFSR: to be determined Support of NCEP Reanalyses

17. Nuts and Bolts: data formats • 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 • Save space • 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. -sh-3.00$ wgrib -s narr.t09z.awip32.merged 1:0:d=09102809:MSLET:MSL:anl:NAve=0 2:166602:d=09102809:PRMSL:MSL:anl:NAve=0 3:333204:d=09102809: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 Short Grib1 Inventories

22. --sh-3.00$ wgrib2 pgblnl.gdas.2007010100.grb2 -s 1:4:d=2007010100:HGT:1 mb:anl: 2:16552:d=2007010100:TMP:1 mb:anl: 3:22064:d=2007010100: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 Short Grib2 Inventories

23. 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 Names

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. 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 (run grads) Making plots with GrADS: grib1

32. 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 (run grads) Unified options, -b option is working Making plots with GrADS: grib2

33. See Jennifer's presentations More GrADS

34. 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). Getting data the Nomads way

35. 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 Downloading Methods

36. 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 Downloading Methods

37. 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 Downloading Methods

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