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Assimilation of Cloudy AMSU-A Microwave Radiances in 4D-Var

Una O’Keeffe

Thanks to Martin Sharpe and Stephen English

IPWG Workshop, Melbourne

October 2006

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Overview

Motivation

AMSU-A 23GHz and 31Ghz

Cloud liquid water incrementing operator

Assimilation set up

Assimilation results

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Motivation

Cloud liquid water has large impact on microwave radiances

Currently low peaking AMSU-A channels are not assimilated if significant water is present

Significant data gaps due to cloud

AMSU-A window channels contain information on liquid water which is not currently exploited

Step towards assimilation of AMSR high resolution cloud and precipitation-affected radiances

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Information on cloud liquid water

NOAA-16 ObsRTTOV8 with clw emission

RTTOV8 without clw emission23GHz

31GHz

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O-B Stats for IRclear RTTOV – 31GHz

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O-B Stats for MWcloudy RTTOV – 31GHz

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Cloud Incrementing Operator

Total moisture analysis variable used in 4D-Var

Need cloud incrementing operator that relates liquid water and specific humidity to the total water control variable

Cx+ = Cx + KCw’

Cx = model state (q,qcl,qcf,cf)

Cw’ = analysis increment (T’,p’,qT’)K = incremental transform variable between control variable

space and model parameter space (uses linearised physics).Sharpe,2005

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1D-Var Preprocessor

Currently formulated with full field total water

Up to 8% of solutions are rejected in 1D-Var with this approach

Data volume in 3D-Var is not reduced but is biased away from cloudy areas, giving negative impact

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Assimilation Experiment Set Up

Configuration: 3DVar, Dec05 four week period

10 day run to generate clear air bias corrections

Cloudy obs 23+31GHz assimilation trialassimilate NOAA-16 AMSU-A 23GHz and 31GHz

extra-tropics sea onlyfor all cloud conditions except for where rain flag is on

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Analysis Increments

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Analysis Increments

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Impact on large scale fields fit to analysis

NH | TROPICS | SH

50hPa height

500hPa and 250hPa temp

Most fields improved in SH

850hPa humidity

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Fit to observations 31GHz

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Fit to observations 31GHz

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Bias Correction of Cloudy Data…???

For this test, used N16 HIRS to define ‘clear air’ and bias corrected clear air data

Operationally, also want to use N15, N17, N18

Options:Bias correct clear air data only – ignores large cloudy biases

Bias correct all data – may degrade clear air assimilation

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Current Status

Testing different bias corrections

Investigations of 1D-Var rejections indicated issue with high retrieved LWP on the first iteration causing failures. A fix is now in place

Operational implementation planned for early 2007

PlansSSMI/SSMISAMSRAMSU-B + ice incrementing operator

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Summary

Assimilation of cloudy AMSU-A 23GHz and 31GHz data gives consistent positive impacts in SH and tropics

Some significant changes to lower level humidity cf analysis

Cloud fields improved

Unresolved issues with bias correction

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Questions?