slide1
Download
Skip this Video
Download Presentation
Developments in the use of AMSU-A, ATMS and HIRS data at ECMWF

Loading in 2 Seconds...

play fullscreen
1 / 34

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


  • 139 Views
  • Uploaded on

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

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' Developments in the use of AMSU-A, ATMS and HIRS data at ECMWF' - eugenia-norris


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
slide1
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 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

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

slide12
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

slide13
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
slide14
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)

slide15
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
slide16
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

slide17
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

slide18
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?

slide19
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
slide21
3. AMSU-A observation errors: The Instrument

Microwave Temperature Sounder

10 Temperature sounding

channels

7 satellites: good global coverage

slide22
3. AMSU-A observation errors: The Instrument
  • Tropospheric channels 5 – 7:
  • Important for NWP
  • But cloud contamination/surface sensitive
slide23
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

slide24
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

slide25
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)

slide26
3. AMSU-A observation errors: Noise term

=

Stdev(o-b)

LWP (kg/m2)

Channel 5: 0.25 K

Channel 6 – 7: 0.20 K

slide27
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

slide28
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
slide29
3. AMSU-A observation errors: Extended coverage

Metop-B AMSU-A channel 5

Add cloud-screened data

Add data over high orography

slide30
3. AMSU-A observation errors: Results

Control vs Observation errors experiment

Geopotential 500hPa

Temperature 850hPa

degradation

improvement

Neutral Impact on forecast accuracy

slide31
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

slide32
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

slide33
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
slide34
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)
ad