ITSC-X111, Sainte Adele, Canada © Crown copyright

Use Of AMSU data in the UK MesoscaleModel

Brett Candy

Steven English, Richard Renshaw & Bruce Macpherson

Satellite Applications & Data Assimilation,

Met Office

ITSC X111 Slide 2

Talk Outline

Background and Motivation

Limited Area Models at the Met Office

Data Usage in the Mesoscale model

– Source of observations

– Data screening

Bias Correction

Impact Assessment

– Method

– Some results

Future Work

ITSC X111 Slide 3

Background

Contribution of ATOVS in global NWP is very important

To date effort has focused on assimilating satellite data in global NWP

– Some data types are currently precluded by timelinessInitial tests of assimilating radiance data in the UK Mesencouraging

– Information retained in the short-rangeBut…..objectives are different.

– Key forecast parameters cloud cover, precip and surface temp

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UK Mesoscale Model 1 Model Domain

(Grid resolution=12km)

Model Domain

(Grid resolution=12km)

Background: Full Resolution AMSUB Imagery 89 GHz

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UK Mesoscale Model 2

Assimilation system:

– incremental 3D-Var

– assimilation window ±1½ hours

– 2 hour data cutoff

Observations:

– radiosondes, air reps, wind profilers

– land station reps, including visibility

– satellite winds from Meteosat

Additionally cloud cover and surface rainrate information is assimilated via a different route (i.e. outside of Var)

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Data Acquisition

Local PassesHRPT

West Freugh

AAPP AAPP

NESDIS

1b data

Washington

1d data on HIRS grid

1d data on HIRS grid

Comparisons for quality monitoring

UK MesNWP

Global NWP

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ATOVS Data Use

HIRS data not used – calibration problems associated with partial super swath

AMSU data– Remapped to HIRS grid (allows use of same code as global)– AMSUB 183 GHz channels over sea only

AMSU data screening– Liquid water test in AAPP → reject channels 4,5 & 20– Ice test on 183 GHz channels → reject channels 19,20– Rain test in AAPP → reject channels 4-8 & 18-20

Data Thinning– 1 observation every 40 km. More weight given to clear &

microwave clear scenes.

ITSC X111 Slide 8

AMSU channels

Tuning AMSU Observation Errors

AMSUB humidity

errors

Reduced to 2K

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

4 5 6 7 8 9 10 11 18 19 20

RM

S (o

-b) K

NOAA17 NOAA16 Global R Matrix

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Mid-lat Cyclone Case StudyAVHRR IR image AMSU data screening

green: lwp yellow: precip

red: AMSUB cirrus

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Bias Correction 1

Airmass dependent predictors (Eyre, 1992)

– problem in a LAM is to sample enough representative synoptic systems

– could monitor departures over a year, assuming negligibleinstrument drift

Current solution is to use global bias correction coefficients

– assumes global and LAM NWP are unbiased

– monitoring with sondes confirms this, at least for the troposphere

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Bias Correction 2AMSU channels Mean O-B Difference (K) over Mesoscale Domain

Uncorrected

Radiances

Corrected

Radiances

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Strategy for Assessing Impact

Case study for poor operational forecast.– Convection over S.W. Britain– Rain forecasts compared to radar

Set of cases containing range of weather situations observed over UK.– Chosen by forecaster– Subjective verification from station reports of 6 hour precip, surface

temp & cloud cover– NOAA15 & 16 assimilated

Extended Trial.– Ran for 1 month– Avoids spin-up problems– Near Real Time to get operational boundary conditions– Forecasts assessed by forecaster– NOAA16 & 17 assimilated

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Convective Event 1Observations Used

Channel 19 Channel 20

Situation: Warm moist air moving northwards, mixing with cooler air at higher latitudes

dark colours indicate

Negative o-b model too dry

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Convective Event 2Integrated Water Vapour Analysis

region > 30kgm-2-2

AMSU No AMSU

T+6 rainrate forecast

Radar AMSU No AMSU

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Verification of Case Studies

6 cases improved, 6 cases worsened due to inclusion of

AMSU

Worse case highlights difficulties of using sparse verification sites for reporting precipitation

Control +AMSU

Hourly Precip, 0z 26th August 2001 T+6

Verifying Radar

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AMSU Impact on Cloud

Validation: vis image

Clear

low

medium

high

AMSU

No AMSU

T+6 Cloud Cover

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Conclusions

Operational in Mesoscale model from May 2003.– NRT trial positive for cloud & visibility. – Including a significant fog clearance case.

Similar approach adopted for European model.Future Work– AMSUB at full resolution.

» Issues for qc & bias correction.» Extend number of channels

– Assimilation in regions of significant LWP. » Total humidity control variable. » 1D Var» 3D Var

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Additional Slides

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Local – Global BT DifferenceChannel 15 Channel 16

~0.5 K

HIR

S

Channel 6Channel 5

~0.02 K

AM

SU

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Initialisation of the Mesoscale Model: Weights given to Var & MOPs data

T-3 T-2 T-1 T+0 T+1 T+2 T+3

VAR incs

T-3 T-2 T-1 T+0 T+1 T+2 T+3

Cloud incs

T-3 T-2 T-1 T+0 T+1 T+2 T+3

RainRate incs -from hourly fields

International TOVS Study Conference, 13th

, TOVS-13, Sainte Adele, Quebec, Canada, 29

October-4 November 2003. Madison, WI, University of Wisconsin-Madison, Space Science and

Engineering Center, Cooperative Institute for Meteorological Satellite Studies, 2003.