The Available NCEP Reanalyses

Wesley EbisuzakiClimate Prediction Center

National Centers for Environmental PredictionNWS/NOAA

Maryland, USAwesley.ebisuzaki@noaa.gov

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.

Download links

http://www.cpc.noaa.gov/products/wesley/ams2010_sc.html

Documentation package includes a pdf version. The pdf has more details and covers more material.

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

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

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

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

Analysis Uncertainties Factors to Consider

Observation density Sensitivity to model parameterizations Dependent on lesser quality observations Model and data assimilation system

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

Finally a plotspread from an ensemble of opn analyses and reanalyses

simple way to get a error estimate

Averaged over a year

Monthly Means

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

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

Others Reanalyses

ECMWF: ERA-15, ERA-40, ERA-interim

JMA, CRIEPI: JRA-25/JCDAS

NASA/GSFC: MERRA

ERSL (different approach)

Check for robust signal

Support of NCEP Reanalyses

1) Journal articles (BAMS)2) Web: NCEP, NCAR, ERSL, NCDC3) Questions to provider of data4) NCDC, NCAR, ERSL will forward tough

questions to NCEP5) At NCEP: R1, R2, NARR: wesley.ebisuzaki@noaa.gov CFSR: to be determined

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

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

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

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?

Short Grib1 Inventories-sh-3.00$ wgrib -s narr.t09z.awip32.merged1:0:d=09102809:MSLET:MSL:anl:NAve=02:166602:d=09102809:PRMSL:MSL:anl:NAve=03:333204:d=09102809:PRES:hybrid lev 1:anl:NAve=0..-s is the short inventorycolumn 1 = message (record) numbercolumn 2 = byte location starting from 0column 3 = analysis time or initial time of the forecastcolumn 4 = variable namecolumn 5 = level/layercolumn 6 = timing information,

anl=analysis, acc=accumulation, ave=averagecolumn 7 = number of fields used to make an ave/acc

Short Grib2 Inventories

--sh-3.00$ wgrib2 pgblnl.gdas.2007010100.grb2 -s1:4: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 numbercolumn 2: the byte location of the grib messagecolumn 3: the analysis or start of forecast time,

use -T to see the minutes and secondscolumn 4: variable namecolumn 5: levelcolumn 6 = timing information, anl=analysis, acc=accumulation, ave=average, fcst = forecast

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

Grid information: grib

grib1/grib2 support many different grids

internally grib stores data in different orders,8 in grib1, 16 in grib2up to software to figure it out

wgrib -Vwgrib2 -grid

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

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

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)

NARR Output Grid

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

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

Making plots with GrADS: grib1

Analyses grib2ctl.pl grib_file >ctl_file

gribmap -0 -i ctl_file(run grads)

Forecastsgrib2ctl.pl -verf grib_file >ctl_filegribmap -i ctl_file(run grads)

Making plots with GrADS: grib2Analysesg2ctl.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

More GrADS

See Jennifer's presentations

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).

Downloading Methods

Partial http downloading:download the fields that you wantselect by field/time/leveldata is compressed (grib2) or packed

(grib1)efficient for the server (support many

clients)easy to script (not point and click)example in the documentation packagegood for large downloads

Downloading Methods

g2subset (grib-filter):download the fields that you wantselect by field/time/levelselect an optional regional subsetgrib2 onlypoint and click to learn or a few filesmoderately easy to scriptexample in the documentation packagemore server resources, less data

transferred

Downloading MethodsOPeNDAP:

standard protocol (text based)select field/time/level/regiondata is read by the serverinterpolated to a lat-lon grid if neededsent to the client by a standard protocol

advantages: supported by softwareeasy to use .. can even use a browser

disadvantages: server overheaddata may not be compressed

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

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.

Typical Nomads front page

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