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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. 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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  1. 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

  2. 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:

  3. 1. Investigating the value of HIRS

  4. 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…

  5. 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?

  6. 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

  7. 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

  8. 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

  9. 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

  10. 2. Introducing ATMS over land and sea-ice

  11. 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

  12. 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

  13. 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

  14. 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)

  15. 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

  16. 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

  17. 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

  18. 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?

  19. 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

  20. 3. AMSU-A Observation Errors

  21. 3. AMSU-A observation errors: The Instrument Microwave Temperature Sounder 10 Temperature sounding channels 7 satellites: good global coverage

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

  23. 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

  24. 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

  25. 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)

  26. 3. AMSU-A observation errors: Noise term = Stdev(o-b) LWP (kg/m2) Channel 5: 0.25 K Channel 6 – 7: 0.20 K

  27. 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

  28. 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

  29. 3. AMSU-A observation errors: Extended coverage Metop-B AMSU-A channel 5 Add cloud-screened data Add data over high orography

  30. 3. AMSU-A observation errors: Results Control vs Observation errors experiment Geopotential 500hPa Temperature 850hPa degradation improvement Neutral Impact on forecast accuracy

  31. 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

  32. 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

  33. 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

  34. 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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