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ECMWF – 1© European Centre for Medium-Range Weather Forecasts Developments in the use of AMSU-A, ATMS and HIRS data at ECMWF Heather Lawrence, first-year.

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Presentation on theme: "ECMWF – 1© European Centre for Medium-Range Weather Forecasts Developments in the use of AMSU-A, ATMS and HIRS data at ECMWF Heather Lawrence, first-year."— Presentation transcript:

1 ECMWF – 1© European Centre for Medium-Range Weather Forecasts 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 ECMWF – 2© European Centre for Medium-Range Weather Forecasts Outline 1. Investigating the value of HIRS 2. Introducing ATMS data over land and sea-ice 3. Situation-dependent observation errors for AMSU-A channels 5 - 7 3 PARTS:

3 ECMWF – 3© European Centre for Medium-Range Weather Forecasts 1. Investigating the value of HIRS

4 ECMWF – 4© European Centre for Medium-Range Weather Forecasts 1. HIRS: The Instrument IR sounder with Temperature sounding CO 2, CO 2 /N 2 O channels Water vapour channels 9 channels used… Coverage: MetOp-A, NOAA-19 …over ocean & sea-ice … and land for channel 12 HIRS 19 Channels

5 ECMWF – 5© European Centre for Medium-Range Weather Forecasts 1. HIRS: Aim & Motivation 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 AIM: Investigate the value of HIRS in the ECMWF forecasting system WHY?

6 ECMWF – 6© European Centre for Medium-Range Weather Forecasts 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: Method

7 ECMWF – 7© European Centre for Medium-Range Weather Forecasts 1. HIRS: Results DEPARTURE STATISTICS: observation – 12h forecast MHS MW humidity sounder Improved fit of MHS, IASI, AIRS to 12h humidity & temperature forecast IASI IR temperature sounder AIRS IR temperature sounder 0.5 – 1% improvement 2% improvement 0.4% improvement

8 ECMWF – 8© European Centre for Medium-Range Weather Forecasts 1. HIRS: results FORECAST SCORES : 1 – 10 day T, Z, R, VW forecast minus analysis Degraded forecast Improved forecast Lots of blue = HIRS improves (short-range) forecasts Day 2 500hPa Day 3 500hPa neutral to positive: e.g. 500hPa Geopotential

9 ECMWF – 9© European Centre for Medium-Range Weather Forecasts 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 ECMWF – 10© European Centre for Medium-Range Weather Forecasts 2. Introducing ATMS over land and sea-ice

11 ECMWF – 11© European Centre for Medium-Range Weather Forecasts 2. ATMS over land and sea-ice: The ATMS instrument Microwave Temperature/Humidity sounder (AMSU-A & MHS combination) 10 temperature sounding channels5 humidity sounding channels Temperature sounding: Humidity sounding:

12 ECMWF – 12© European Centre for Medium-Range Weather Forecasts 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 Land, sea-ice, ocean Channel 9 coverage (2 cycles) Channel 6 coverage (2 cycles) Ocean only

13 ECMWF – 13© European Centre for Medium-Range Weather Forecasts 2. ATMS over land and sea-ice: Aim & Motivation AIM: Add channels over land and sea-ice Intoducing more AMSU-A data improves forecasts Microwave data less affected by cloud than IR: has value over land/sea-ice Add data: Humidity sounding channels Surface-sensitive temperature channels MOTIVATION:

14 ECMWF – 14© European Centre for Medium-Range Weather Forecasts 2. ATMS over land and sea-ice: Method 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’ Desired values retrieved in analysis We need emissivity and skin temperature inputs Karbou et al, Di Tomaso et al (2013)

15 ECMWF – 15© European Centre for Medium-Range Weather Forecasts 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 ECMWF – 16© European Centre for Medium-Range Weather Forecasts 2. ATMS over land and sea-ice: Results Departures: 12h forecast – observation 0.5% improvement 1% improvement: sea-ice AMSU-A global standard deviation(o-b) 2x2 months Channel number MHS global MHS Nhem winter standard deviation(o-b) 2 months Improved temperature and humidity 12h forecasts fit to observations 0.05% improvement

17 ECMWF – 17© European Centre for Medium-Range Weather Forecasts 2. ATMS over land and sea-ice: Results Forecast scores: 1 – 10 day forecast minus own analysis Degraded Forecast Improved Forecast ATMS Land + Sea-ice

18 ECMWF – 18© European Centre for Medium-Range Weather Forecasts 2. ATMS over land and sea-ice: Results COLD SEA ATMS data appear to have a negative impact on TEMPERATURE Could be because adding data makes analysis more variable? Day 1 Temperature 1000hPa

19 ECMWF – 19© European Centre for Medium-Range Weather Forecasts 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 ECMWF – 20© European Centre for Medium-Range Weather Forecasts 3. AMSU-A Observation Errors

21 ECMWF – 21© European Centre for Medium-Range Weather Forecasts 3. AMSU-A observation errors: The Instrument 10 Temperature sounding channels 7 satellites: good global coverage Microwave Temperature Sounder

22 ECMWF – 22© European Centre for Medium-Range Weather Forecasts Tropospheric channels 5 – 7: Important for NWP But cloud contamination/surface sensitive 3. AMSU-A observation errors: The Instrument

23 ECMWF – 23© European Centre for Medium-Range Weather Forecasts 3. AMSU-A observation errors: Aim & Motivation Channels 5 – 7 observation errors should contain: AIM: Develop situation- dependent observation errors Observation error = surface term + cloud term + noise Situation-dependent constant stdev(o-b) MetOp-A AMSU-A channel 5: ALL DATA NOT CONSTANT

24 ECMWF – 24© European Centre for Medium-Range Weather Forecasts 3. AMSU-A observation errors: Surface term Do not include skin temperature term: skin temperature retrieved as sink variable in analysis Include emissivity term Surface type Sea0.015 Sea-ice0.050 Snow-free land0.022 Snow-covered land0.050 (S. English 2008)

25 ECMWF – 25© European Centre for Medium-Range Weather Forecasts 3. AMSU-A observation errors: Liquid water path term Channel 5: Channel 6: Channel 7: LWP (kg/m 2 ) Data screened for cloud but may still have some contamination…

26 ECMWF – 26© European Centre for Medium-Range Weather Forecasts 3. AMSU-A observation errors: Noise term LWP (kg/m 2 ) Channel 5: 0.25 K Channel 6 – 7: 0.20 K

27 ECMWF – 27© European Centre for Medium-Range Weather Forecasts 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 ECMWF – 28© European Centre for Medium-Range Weather Forecasts 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 ECMWF – 29© European Centre for Medium-Range Weather Forecasts 3. AMSU-A observation errors: Extended coverage Add cloud-screened data Metop-B AMSU-A channel 5 Add data over high orography

30 ECMWF – 30© European Centre for Medium-Range Weather Forecasts 3. AMSU-A observation errors: Results Control vs Observation errors experiment Neutral Impact on forecast accuracy degradation Temperature 850hPa Geopotential 500hPa improvement

31 ECMWF – 31© European Centre for Medium-Range Weather Forecasts Ctrl – obs error Ctrl – ext. cloud 3. AMSU-A observation errors: Results Control vs Extended coverage in cloudy regions ATMS over sea Observation - 12h forecast 0.4% improvement Improved fit to ATMS, neutral forecast scores: results encouraging degradation improvement Ctrl – obs error Ctrl – ext. cloud

32 ECMWF – 32© European Centre for Medium-Range Weather Forecasts 3. AMSU-A observation errors: Results Control vs Extended coverage in high topography 3 day geopotential fc - an 3 day temperature fc - an Blue= Improved forecast Red/green= degraded forecast Positive impact in northern hemisphere Mixed positive/negative Over Antarctica Mixed positive/negative results

33 ECMWF – 33© European Centre for Medium-Range Weather Forecasts 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 ECMWF – 34© European Centre for Medium-Range Weather Forecasts 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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