Observing

System

Performance

in the

CFS Reanalysis

JACK WOOLLEN

SAIC/EMC/NCEP

Upper Air

TEMP/PILOT

PROFLR

VADWIND

Aircraft

PIREP

AIREP

ACARS

Surface

TEMP/PILOT

SYNOP/METAR

MARINE

BOGUS

Space based

SATOBS

SSMI/ERS

SBUV

These data report information using conventional meteorological units (p,q,t,u,v)either directly or indirectly observed and converted.

Traditionally these data are transmitted from the field over the GTS for access by operational NWP centers for the purpose of making real time weather forecasts.

Many overlapping archives of these data now exist internationally. It is necessary to collate many sources and standardize the datasets in order to create a compatible dataset for retrospective reanalysis.

RAOB/PIBAL Reprocessed ON29/NCAR + MARS/ECMWFAIRCFT MARS/ECMWFSATOB JMA Reprocessed GMS/NCARMARINE Reprocessed COADS/NCARMARINE ESA Reprocessed ERS-1, ERS-2MARINE SSMI Sensor Data Records/NCDCSYNOP Reprocessed ON124/NCARSH BOGUS Reprocessed PAOBS/NCAR/JRA25/ERA40OZONE Version 8 SBUV retrievals

TOVS

HIRS2-MSU-SSU

ATOVS

HIRS3-AMSU-A-B

JPL/AQUA

AIRS

AMSUA

AMSRE

GOES

SNDR/IMGR

CHAMP/COSMIC

GPS RADIO OCCULTATION

ESA/METOP

IASI-HIRS4

AMSU-MHS

GOME-ASCAT

These data report information in unconventional units such as IR/MW waveband brightness (radiance) or refractive bending angle from radio occultation.

The data are managed in real time by organizations such as NESDIS, NASA, and ESA/EUMETSAT for access by operational NWP centers. Some important datasets have been reprocessed from the archives for reanalysis, i.e. MSU and GPSRO, and more are in progress. The volume of this data is many orders of magnitude greater than the conventional observations.

1980 1990 2000 2010

ssu_tirosnssu_n06ssu_n07ssu_n08ssu_n09ssu_n11ssu_n14

amsua_aquaamsua_metop-a

amsua_n15amsua_n16amsua_n18amsub_n15amsub_n16amsub_n17msu_tirosn

msu_n06msu_n07msu_n08msu_n09msu_n10msu_n11msu_n12msu_n14

hirs2_tirosnhirs2_n06hirs2_n07hirs2_n08hirs2_n09hirs2_n10hirs2_n11hirs2_n12hirs2_n14hirs3_n15hirs3_n16hirs3_n17

hirs4_metop-aairs_aqua

mhs_metop-amhs_n18

sndrD1_g11sndrD1_g12sndrD2_g11sndrD2_g12sndrD3_g11sndrD3_g12sndrD4_g11sndrD4_g12

sndr_g08sndr_g09sndr_g10sndr_g11sndr_g12

1980 1990 2000 2010

TEMP/PILOT

SYNOP

AIRCRAFT

MARINE

SATOB

PAOB

OSBUV8

HIRS

MSU

SSU

SSMIWND

VADWND

PROFILR

METARACARS

QKSWND

MSONET

HIRS3

AMSUA

AMSUB

Rep ESA/ERS

AIRS

GOESND1X1

AMSRE

HIRS4

MHS

WNDSATRep JMA/GMS

MTIASI

GOESND

AIRSEV GOESFV

AMSRE

GOESND VADWND PROFLR METAR

MSONET

GPSRO QUIKSCAT

AIRS

RMS all ob-fg

RMS acc ob-fg

RMS acc ob-anl

Mean values Tot count

Acc count

Monthly values

00z OB counts

Seasonal biases

Some thingsto look into

This data is not assimilated but is a

fairly good fit to first guess

Seasonal biases

This data is not assimilated but is a fairly

good fit to first guess

Very good fits to the first

guess

Starts with new 1997 decoders

Not assimilated but fairly good fit to the

first guess

Some seasonal bias

Begins with new

decoders in 1997

Not assimilated but fairly good fit to the

first guess

Begins with new

decoders in 1997

COADS data hassome QC applied

No big differences between COADS and

NCEP OPS marine winds

Reprocessed PAOBS begin in

1985

Some benefit possible from assimilating

PAOBS 1999-2002

Noisy first couple years of

reprocessed data

Otherwise fairly consistent with large

impact in analysis

ERS-1 ERS-2ERS-2 off line

OPS soon began to thin

the data

NCDC archive available from Aug 1993 when we obtained the data

SSM/I data now available in

NOAA/CLASS back to July 1991

Data starts in 1987

Data quality increases in mid-1990

Not sure what happens here

Steady improvement in fits over 30

years

Reprocessed GMS winds from JMA

Missing about 2 years of this

data

May be an off-time data issue; need to check

Missing one year due to a

processing problem

Hi resolution data

implemented in 1998

Temperature bias improves over the

30 years

New GSI VAR QC does a good job controlling

noise in the assimilation

Conventional data counts vary quite a bit over the 30 years, both by season and decade

Could look at assimilating SYNOP and METAR temp and wind

data, next time

Specially re-processed datasets GMS, ERS,

PAOB, ON29, ON124 perform well

The performance charts highlight a number of

cases which need looking into

The quality of the first guess steadily improves in time as measured by

fits to data

It would be useful to apply VAR QC to

observations being monitored but not

assimilated

With some exceptions the quality of the input datasets is very high, as

measured by QC rejection rates

For CFSR data access at NCDC:

http://nomads.ncdc.noaa.gov/NOAAReanalysis/cfsrr