Developments in the use of AMSU-A, ATMS and HIRS data at ECMWF
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Developments in the use of AMSU-A, ATMS and HIRS data at ECMWF. Heather Lawrence , first-year EUMETSAT fellow, ECMWF Supervised by: Niels Bormann & Stephen English. Outline. Investigating the value of HIRS Introducing ATMS data over land and sea-ice

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Developments in the use of AMSU-A, ATMS and HIRS data at ECMWF

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Developments in the use of amsu a atms and hirs data at ecmwf

Developments in the use of AMSU-A, ATMS and HIRS data at ECMWF

Heather Lawrence, first-year EUMETSAT fellow, ECMWF

Supervised by: Niels Bormann & Stephen English


Outline

Outline

  • Investigating the value of HIRS

  • Introducing ATMS data over land and sea-ice

  • Situation-dependent observation errors for AMSU-A channels 5 - 7

3 PARTS:


1 investigating the value of hirs

1. Investigating the value of HIRS


1 hirs the i nstrument

1. HIRS: The Instrument

  • IR sounder with Temperature sounding CO2, CO2/N2O channels

  • Water vapour channels

Coverage: MetOp-A, NOAA-19

HIRS

19 Channels

…over ocean & sea-ice

… and land for channel 12

9 channels used…


1 hirs aim motivation

1. HIRS: Aim & Motivation

AIM: Investigate the value of HIRS in the ECMWF forecasting system

  • HIRS is an older instrument whose value in the ECMWF system has not been tested recently

  • New hyper-spectral IR sounders (AIRS, IASI) may have made HIRS redundant

WHY?


1 hirs method

1. HIRS: Method

  • Perform 2 sets of experiments: 2 x 2 months summer and winter, T511, 38R2:

  • Control: 38R2 version of ECMWF model (IR, MW sounders, scatterometers, radiosondes, etc.)

  • HIRS denial experiments: as control but take HIRS (MetOp-A and NOAA-19) out


1 hirs results

1. HIRS: Results

DEPARTURE STATISTICS: observation – 12h forecast

MHS

MW humidity sounder

IASI

IR temperature sounder

AIRS

IR temperature sounder

0.4% improvement

2% improvement

0.5 – 1% improvement

Improved fit of MHS, IASI, AIRS to 12h humidity & temperature forecast


1 hirs results1

1. HIRS: results

FORECAST SCORES: 1 – 10 day T, Z, R, VW forecast minus analysis

neutral to positive: e.g. 500hPa Geopotential

Degraded forecast

Improved forecast

Day 3 500hPa

Day 2 500hPa

Lots of blue = HIRS improves (short-range) forecasts


1 hirs conclusions and future developments

1. HIRS: Conclusions and future developments

  • HIRS improves short-range forecasts of temperature, humidity, geopotential

  • Future Developments: MetOp-B HIRS

  • Trials are underway to test the introduction of MetOp-B HIRS

So far results look promising

Improved AIRS departures


Developments in the use of amsu a atms and hirs data at ecmwf

2. Introducing ATMS over land and sea-ice


2 atms over land and sea ice the atms instrument

2. ATMS over land and sea-ice: The ATMS instrument

Microwave Temperature/Humidity sounder (AMSU-A & MHS combination)

Temperature sounding:

Humidity sounding:

10 temperature sounding channels

5 humidity sounding channels


Developments in the use of amsu a atms and hirs data at ecmwf

2. ATMS over land and sea-ice: The ATMS instrument

2011: Suomi-NPP satellite launched with ATMS on board

2012: Some ATMS data assimilated operationally at ECMWF

Channel 9 coverage (2 cycles)

Land, sea-ice,

ocean

Channel 6 coverage (2 cycles)

Ocean only


Developments in the use of amsu a atms and hirs data at ecmwf

2. ATMS over land and sea-ice: Aim & Motivation

AIM: Add channels over land and sea-ice

Add data:

Humidity sounding channels

Surface-sensitive temperature channels

MOTIVATION:

  • Intoducing more AMSU-A data improves forecasts

  • Microwave data less affected by cloud than IR: has value over land/sea-ice


Developments in the use of amsu a atms and hirs data at ecmwf

2. ATMS over land and sea-ice: Method

Desired values retrieved in analysis

)

We need emissivity and skin temperature inputs

How can we obtain skin temperature and emissivity?

  • Treat ATMS like AMSU-A and MHS:

  • Emissivity retrieved from window channel prior to assimilation

  • Skin temperature retrieved during assimilation as a ‘sink variable’

Karbou et al, Di Tomaso et al (2013)


Developments in the use of amsu a atms and hirs data at ecmwf

2. ATMS over land and sea-ice: Assimilation experiments

3 experiments, 1.5 + 3 months, 39R1 137 vertical levels

  • Control: Same as operational 39R1 at lower resolution T511 (~40km)

  • ATMS Land: Control + ATMS over land

  • ATMS Land Sea-ice: Control + ATMS over land + ATMS over sea-ice


Developments in the use of amsu a atms and hirs data at ecmwf

2. ATMS over land and sea-ice: Results

Departures: 12h forecast – observation

MHS Nhem winter

MHS global

AMSU-A global

1% improvement: sea-ice

Channel number

0.05% improvement

0.5% improvement

standard deviation(o-b)

2 months

standard deviation(o-b) 2x2 months

Improved temperature and humidity 12h forecasts fit to observations


Developments in the use of amsu a atms and hirs data at ecmwf

2. ATMS over land and sea-ice: Results

Forecast scores: 1 – 10 day forecast minus own analysis

Degraded

Forecast

Improved

Forecast

ATMS Land

ATMS Land

+ Sea-ice


Developments in the use of amsu a atms and hirs data at ecmwf

2. ATMS over land and sea-ice: Results

Day 1 Temperature 1000hPa

COLD SEA ATMS data appear to have a negative impact on TEMPERATURE

Could be because adding data makes analysis more variable?


Developments in the use of amsu a atms and hirs data at ecmwf

2. ATMS over land and sea-ice: Conclusions

  • ATMS temperature and humidity sounding data was introduced over land and sea-ice

  • Departure statistics were improved for AMSU-A and MHS

  • Forecast scores were neutral to positive for ATMS over land data

  • Geopotential Forecast scores were neutral for ATMS over sea-ice

  • Short-range Temperature forecasts appeared degraded over Southern Ocean when sea-ice data introduced


Developments in the use of amsu a atms and hirs data at ecmwf

3. AMSU-A Observation Errors


Developments in the use of amsu a atms and hirs data at ecmwf

3. AMSU-A observation errors: The Instrument

Microwave Temperature Sounder

10 Temperature sounding

channels

7 satellites: good global coverage


Developments in the use of amsu a atms and hirs data at ecmwf

3. AMSU-A observation errors: The Instrument

  • Tropospheric channels 5 – 7:

  • Important for NWP

  • But cloud contamination/surface sensitive


Developments in the use of amsu a atms and hirs data at ecmwf

3. AMSU-A observation errors: Aim & Motivation

Channels 5 – 7 observation errors should contain:

=

Observation error = surface term + cloud term + noise

constant

Situation-dependent

stdev(o-b) MetOp-A AMSU-A channel 5: ALL DATA

NOT CONSTANT

AIM: Develop situation-dependent observation errors


Developments in the use of amsu a atms and hirs data at ecmwf

3. AMSU-A observation errors: Surface term

=

(S. English 2008)

Do not include skin temperature term:

skin temperature retrieved as sink variable in analysis

Include emissivity term


Developments in the use of amsu a atms and hirs data at ecmwf

3. AMSU-A observation errors: Liquid water path term

Data screened for cloud but may still have some contamination…

Channel 5:

Channel 6:

Stdev(o-b)

Channel 7:

LWP (kg/m2)


Developments in the use of amsu a atms and hirs data at ecmwf

3. AMSU-A observation errors: Noise term

=

Stdev(o-b)

LWP (kg/m2)

Channel 5: 0.25 K

Channel 6 – 7: 0.20 K


Developments in the use of amsu a atms and hirs data at ecmwf

3. AMSU-A observation errors: New Observation Errors

=

Metop-B AMSU-A channel 5 observation errors: used data

Nadir angles have higher values

High lwp = higher value


Developments in the use of amsu a atms and hirs data at ecmwf

3. AMSU-A observation errors: Assimilation Trials

  • Situation- dependent observation errors:

  • Weight data differently

  • Allows the introduction of more data in ‘difficult’ areas: cloudy, high orography

  • Assimilation trials (2 months):

  • Control: version 40R1 with some 40R2 contributions at T511 (40km) resolution, 137 vertical levels

  • New observation errors: Control + new observation errors

  • Extended coverage over cloud: Control + new observation errors + relaxed cloud screening

  • Extended coverage over high orography: control + new observation errors + relaxed orography screening


Developments in the use of amsu a atms and hirs data at ecmwf

3. AMSU-A observation errors: Extended coverage

Metop-B AMSU-A channel 5

Add cloud-screened data

Add data over high orography


Developments in the use of amsu a atms and hirs data at ecmwf

3. AMSU-A observation errors: Results

Control vs Observation errors experiment

Geopotential 500hPa

Temperature 850hPa

degradation

improvement

Neutral Impact on forecast accuracy


Developments in the use of amsu a atms and hirs data at ecmwf

3. AMSU-A observation errors: Results

Control vs Extended coverage in cloudy regions

Improved fit to ATMS, neutral forecast scores: results encouraging

ATMS over sea

Observation - 12h forecast

Ctrl – obs error

Ctrl – ext. cloud

Ctrl – obs error

Ctrl – ext. cloud

degradation

0.4% improvement

improvement


Developments in the use of amsu a atms and hirs data at ecmwf

3. AMSU-A observation errors: Results

Control vs Extended coverage in high topography

Mixed positive/negative results

3 day geopotential fc - an

Blue= Improved forecast

Red/green= degraded forecast

Mixed positive/negative

Over Antarctica

Positive impact in northern hemisphere

3 day temperature fc - an


Developments in the use of amsu a atms and hirs data at ecmwf

3. AMSU-A observation errors: Conclusions

  • Situation-dependent observation errors were derived for AMSU-A channels 5 -7

  • This gave neutral results with screening as-is

  • Introducing data previously screened for clouds improved fit to ATMS instrument

  • Introducing data over high orography had mixed positive/negative results

  • Work is ongoing


Developments in the use of amsu a atms and hirs data at ecmwf

Summary of Findings

  • The HIRS instrument has a small positive impact on short-term T, Z, R forecasts

  • Introduction of ATMS data over land improves temperature/humidity forecast accuracy

  • Introduction of ATMS data over sea-ice has mixed results – further investigation needed

  • Situation-dependent observation errors for AMSU-A channels 5 – 7 have the potential to improve forecasts by introducing more data (work ongoing)


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