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Transcript of ECMWF – 1© European Centre for Medium-Range Weather Forecasts Developments in the use of AMSU-A,...
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
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:
ECMWF – 3 © European Centre for Medium-Range Weather Forecasts
1. Investigating the value of HIRS
ECMWF – 4 © European Centre for Medium-Range Weather Forecasts
1. HIRS: The Instrument IR sounder with Temperature sounding CO2, CO2/N2O channels Water vapour channels
9 channels used…
Coverage: MetOp-A, NOAA-19
…over ocean & sea-ice… and land for channel 12
HIRS19 Channels
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?
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
ECMWF – 7 © European Centre for Medium-Range Weather Forecasts
1. HIRS: Results
DEPARTURE STATISTICS: observation – 12h forecast
MHSMW humidity sounder
Improved fit of MHS, IASI, AIRS to 12h humidity & temperature forecast
IASIIR temperature sounder
AIRSIR temperature sounder
0.5 – 1% improvement 2% improvement
0.4% improvement
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
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
ECMWF – 10 © European Centre for Medium-Range Weather Forecasts
2. Introducing ATMS over land and sea-ice
ECMWF – 11 © European Centre for Medium-Range Weather Forecasts
2. ATMS over land and sea-ice: The ATMS instrumentMicrowave Temperature/Humidity sounder (AMSU-A & MHS combination)
10 temperature sounding channels 5 humidity sounding channels
Temperature sounding: Humidity sounding:
ECMWF – 12 © European Centre for Medium-Range Weather Forecasts
2. ATMS over land and sea-ice: The ATMS instrument2011: 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
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 channelsSurface-sensitive temperature channels
MOTIVATION:
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)
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
ECMWF – 16 © European Centre for Medium-Range Weather Forecasts
2. ATMS over land and sea-ice: ResultsDepartures: 12h forecast – observation
0.5% improvement
1% improvement: sea-ice
AMSU-A global
standard deviation(o-b) 2x2 months
Cha
nnel
num
ber
MHS global MHS Nhem winter
standard deviation(o-b) 2 months
Improved temperature and humidity 12h forecasts fit to observations
0.05% improvement
ECMWF – 17 © European Centre for Medium-Range Weather Forecasts
2. ATMS over land and sea-ice: ResultsForecast scores: 1 – 10 day forecast minus own analysis
DegradedForecast
ImprovedForecast
ATMS Land
ATMS Land+ Sea-ice
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
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
ECMWF – 20 © European Centre for Medium-Range Weather Forecasts
3. AMSU-A Observation Errors
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
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
ECMWF – 23 © European Centre for Medium-Range Weather Forecasts
3. AMSU-A observation errors: Aim & MotivationChannels 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
ECMWF – 24 © European Centre for Medium-Range Weather Forecasts
3. AMSU-A observation errors: Surface term
𝝈𝒔𝒖𝒓𝒇𝒂𝒄𝒆𝟐=(𝑻 ¿¿ 𝒔𝚪𝟐)𝟐𝝈𝒆𝒎𝒊𝒔𝒔𝒊𝒗𝒊𝒕𝒚
𝟐+(𝜺𝚪)𝟐𝝈 𝒔𝒌𝒊𝒏𝒕𝒆𝒎𝒑𝒆𝒓𝒂𝒕𝒖𝒓𝒆𝟐 ¿
Do not include skin temperature term: skin temperature retrieved as sink variable
in analysis
Include emissivity term
Surface type
Sea 0.015
Sea-ice 0.050
Snow-free land 0.022
Snow-covered land 0.050
=
(S. English 2008)
ECMWF – 25 © European Centre for Medium-Range Weather Forecasts
3. AMSU-A observation errors: Liquid water path term
𝝈𝒄𝒍𝒐𝒖𝒅= 𝒇𝒖𝒏𝒄𝒕𝒊𝒐𝒏(𝒍𝒊𝒒𝒖𝒊𝒅𝒘𝒂𝒕𝒆𝒓 𝒑𝒂𝒕𝒉)
𝝈𝒄𝒍𝒐𝒖𝒅=𝟐 .𝟎𝟎𝒍𝒘𝒑𝟐+𝟎 .𝟕𝟗 𝒍𝒘𝒑
𝝈𝒄𝒍𝒐𝒖𝒅=𝟎 .𝟓𝟒𝒍𝒘𝒑𝟐+𝟎 .𝟑𝟎𝒍𝒘𝒑
𝝈𝒄𝒍𝒐𝒖𝒅=𝟎 .𝟐𝟎𝒍𝒘𝒑
Channel 5:
Channel 6:
Channel 7:
LWP (kg/m2)
Std
ev(o
-b)
Data screened for cloud but may still have some contamination…
ECMWF – 26 © European Centre for Medium-Range Weather Forecasts
3. AMSU-A observation errors: Noise term
LWP (kg/m2)
Std
ev(o
-b)
Channel 5: 0.25 KChannel 6 – 7: 0.20 K
=
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 valuesHigh lwp = higher value
=
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
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
ECMWF – 30 © European Centre for Medium-Range Weather Forecasts
3. AMSU-A observation errors: ResultsControl vs Observation errors experiment
Neutral Impact on forecast accuracy
degradation
Temperature 850hPa Geopotential 500hPa
improvement
ECMWF – 31 © European Centre for Medium-Range Weather Forecasts
Ctrl – obs errorCtrl – ext. cloud
3. AMSU-A observation errors: ResultsControl 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 errorCtrl – ext. cloud
ECMWF – 32 © European Centre for Medium-Range Weather Forecasts
3. AMSU-A observation errors: ResultsControl vs Extended coverage in high topography
3 day geopotential fc - an
3 day temperature fc - an
Blue= Improved forecastRed/green= degraded forecast
Positive impact in northern hemisphere
Mixed positive/negativeOver Antarctica
Mixed positive/negative results
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
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)