Cristina Lupu, Niels Bormann, Reima Eresmaa

Slides:



Advertisements
Similar presentations
The CONCORDIASI Workshop, Toulouse, March 2010 Impact study of AMSU-A/B data over land and sea-ice in the Météo-France global assimilation system.
Advertisements

ECMWF/EUMETSAT NWP-SAF Satellite data assimilation Training Course
ECMWF/EUMETSAT NWP-SAF Satellite data assimilation Training Course 1 to 4 July 2013.
Slide 1 IPWG, Beijing, October 2008 Slide 1 Assimilation of rain and cloud-affected microwave radiances at ECMWF Alan Geer, Peter Bauer, Philippe.
Satellite SST Radiance Assimilation and SST Data Impacts James Cummings Naval Research Laboratory Monterey, CA Sea Surface Temperature Science.
1 ATOVS and SSM/I assimilation at the Met Office Stephen English, Dave Jones, Andrew Smith, Fiona Hilton and Keith Whyte.
Slide 1 Evaluation of observation impact and observation error covariance retuning Cristina Lupu, Carla Cardinali, Tony McNally ECMWF, Reading, UK WWOSC.
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.
Data assimilation of polar orbiting satellites at ECMWF
Diagnosing Climate Change from Satellite Sounding Measurements – From Filter Radiometers to Spectrometers William L. Smith Sr 1,2., Elisabeth Weisz 1,
Slide 1 EUMETSAT Fellow Day, 9 March 2015 Observation Errors for AMSU-A and a first look at the FY-3C MWHS-2 instrument Heather Lawrence, second-year EUMETSAT.
Stephanie Guedj Florence Rabier Vincent Guidard Benjamin Ménétrier Observation error estimation in a convective-scale NWP system.
Data assimilation and observing systems strategies Pierre Gauthier Data Assimilation and Satellite Meteorology Division Meteorological Service of Canada.
Research and development on satellite data assimilation at the Canadian Meteorological Center L. Garand, S. K. Dutta, S. Heilliette, M. Buehner, and S.
© Crown copyright 2007 Optimal distribution of polar-orbiting sounding missions John EyreMet Office, UK CGMS-40; Lugano, Switzerland;5-9 Nov 2012.
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Image: MODIS Land Group, NASA GSFC March 2000 Infrared Temperature and.
Task 1 Definition of the AMSU+MHS measurement covariance.
1 Hyperspectral Infrared Water Vapor Radiance Assimilation James Jung Cooperative Institute for Meteorological Satellite Studies Lars Peter Riishojgaard.
Status of improving the use of MODIS, AVHRR, and VIIRS polar winds in the GDAS/GFS David Santek, Brett Hoover, Sharon Nebuda, James Jung Cooperative Institute.
Slide 1 VAISALA Award Lecture Characterising the FY-3A Microwave Temperature Sounder Using the ECMWF Model Qifeng Lu, William Bell, Peter Bauer, Niels.
AIRS Radiance and Geophysical Products: Methodology and Validation Mitch Goldberg, Larry McMillin NOAA/NESDIS Walter Wolf, Lihang Zhou, Yanni Qu and M.
MODIS Polar Winds in ECMWF’s Data Assimilation System: Long-term Performance and Recent Case Studies Lueder von Bremen, Niels Bormann and Jean-Noël Thépaut.
Global Modeling and Assimilation Office Goddard Space Flight Center National Aeronautics and Space Administration Recent Advances towards the Assimilation.
Concordiasi Satellite data assimilation at high latitudes F. Rabier, A. Bouchard, F. Karbou, V. Guidard, S. Guedj, A. Doerenbecher, E. Brun, D. Puech +
Radiative transfer in the thermal infrared and the surface source term
ITSC-1227 February-5 March 2002 Use of advanced infrared sounders in cloudy conditions Nadia Fourrié and Florence Rabier Météo France Acknowledgement G.
© Crown copyright Met Office Assimilating cloud affected infrared radiances at the Met Office Ed Pavelin and Roger Saunders, Met Office, Exeter.
A step toward operational use of AMSR-E horizontal polarized radiance in JMA global data assimilation system Masahiro Kazumori Numerical Prediction Division.
© 2014 RAL Space Study for the joint use of IASI, AMSU and MHS for OEM retrievals of temperature, humidity and ozone D. Gerber 1, R. Siddans 1, T. Hultberg.
Radiance Simulation System for OSSE  Objectives  To evaluate the impact of observing system data under the context of numerical weather analysis and.
Methane Retrievals in the Thermal Infrared from IASI AGU Fall Meeting, 14 th -18 th December, San Francisco, USA. Diane.
© Crown copyright Met Office Assimilating infra-red sounder data over land John Eyre for Ed Pavelin Met Office, UK Acknowledgements: Brett Candy DAOS-WG,
Passive Microwave Remote Sensing
Slide 1 Investigations on alternative interpretations of AMVs Kirsti Salonen and Niels Bormann 12 th International Winds Workshop, 19 th June 2014.
JMA GPRC report Arata OKUYAMA Meteorological Satellite Center,
Indirect impact of ozone assimilation using Gridpoint Statistical Interpolation (GSI) data assimilation system for regional applications Kathryn Newman1,2,
ECMWF/EUMETSAT NWP-SAF Satellite data assimilation Training Course
All-sky assimilation of microwave sounder radiances
Impact of Observations – recent studies Jean-Noël Thépaut credits: ECMWF staff, including Tony McNally, Stephen English, Alan Geer, Cristina Lupu (strong.
ECMWF/EUMETSAT NWP-SAF Satellite data assimilation Training Course
GSICS Microwave Sub Group Meeting
Paper under review for JGR-Atmospheres …
ECMWF/EUMETSAT NWP-SAF Satellite data assimilation Training Course
Plans for Met Office contribution to SMOS+STORM Evolution
Rory Gray Development of a Dynamic Infrared Land Surface Emissivity Atlas based on IASI Retrievals Rory Gray
Passive Microwave Systems & Products
Winds in the Polar Regions from MODIS: Atmospheric Considerations
Requirements for microwave inter-calibration
Impact Studies Of Ascat Winds in the ECMWF 4D-var Assimilation System
Stéphane Laroche Judy St-James Iriola Mati Réal Sarrazin
Assimilation of MWHS on FY-3B over Land
Impact of hyperspectral IR radiances on wind analyses
Intercomparison of IASI and CrIS spectra
James Cotton, Mary Forsythe IWW14, Jeju City, South Korea.
GOES-16 AMV data evaluation and algorithm assessment
Assimilation of Cloudy AMSU-A Microwave Radiances in 4D-Var
FSOI adapted for used with 4D-EnVar
In the past thirty five years NOAA, with help from NASA, has established a remote sensing capability on polar and geostationary platforms that has proven.
Exploring Application of Radio Occultation Data in Improving Analyses of T and Q in Radiosonde Sparse Regions Using WRF Ensemble Data Assimilation System.
Infrared Satellite Data Assimilation at NCAR
Item Taking into account radiosonde position in verification
Satellite Foundational Course for JPSS (SatFC-J)
New DA techniques and applications for stratospheric data sets
Development of inter-comparison method for 3.7µm channel of SLSTR-IASI
Outline Some work by colleagues are presented
Hartmut Bösch and Sarah Dance
AIRS (Atmospheric Infrared Sounder) Level 1B data
Current and future use of microwave imager radiances in NWP models
Assimilation of MW data in The C3S ERA5 Reanalysis
Session 1 – summary (1) Several new satellite data types have started to be assimilated in the last 4 years, all with positive impacts, including Metop-B.
Presentation transcript:

The impact of satellite observations over land and sea-ice surfaces within the ECMWF system Cristina Lupu, Niels Bormann, Reima Eresmaa The WMO 6th Workshop on the Impact of various observing systems in NWP Shanghai, China, May 10-13, 2016

Outline Surface-sensitive MW sounder data IR sounder & imager data Use over land and sea-ice at ECMWF OSEs study impact results FSOI evaluation IR sounder & imager data Impact results and challenges Conclusions

Use of surface-sensitive MW sounder data over land/sea-ice Use of surface-sensitive radiances requires reliable estimates of surface emissivity and skin temperature. Over sea: Accurate fast emissivity model (FASTEM), Sea-surface temperature (SST) Over land + sea-ice: Use surface emissivity retrieved from window channel observations and FG (“dynamic emissivities”, Karbou et al. 2006), complemented by an atlas where required. Chosen window channel should have similar frequency as sounding channels and good surface sensitivity. Skin temperature taken from land-surface model.

OSE study: Channels considered and their use over land/sea-ice Instrument Satellites Snow-free land Snow-covered land Sea-ice AMSU-A (clear-sky) Metop-A, Metop-B, NOAA-15, NOAA-18, NOAA-19 Channels 5-7 Channels 6-7 Channel 5 over N.Hem. ATMS S-NPP Channels 6-8; 18-22 Channels 6-8 - MHS (all-sky) Metop-A, Metop-B, NOAA-18, NOAA-19 Channels 3-5 Channel 3 Channels 3-4 SSMI/S F-17 Channels 9-11 Channels 10-11 Temperature-sounding in red; Humidity-sounding in blue; Channel-dependent orography screening also applied.

OSEs for surface-sensitive MW sounder data over land/sea-ice Experiments over 8 months: 2 June – 30 Sept 2014; 2 Dec 2014 – 31 March 2015 Base: No surface-sensitive MW sounder data over land and sea-ice Base + MW sea-ice: Add surface-sensitive MW sounders over sea-ice (i.e., 5 x AMSU-A, MHS, SSMI/S) Base + MW sea-ice + MW WV land: Add MW humidity sounders over land (i.e., ATMS, 4 x MHS, SSMI/S) Base + MW sea-ice + MW land: Add surface-sensitive MW temperature sounders over land (i.e., data usage as in operations)

Impact over sea-ice and land

Impact of surface-sensitive MW sounders over sea-ice and land Base + MW sea-ice + MW land Base + MW sea-ice vs Base, normalised difference in RMSE for Z 500 hPa, verification against own analysis Bad Good

Impact of surface-sensitive MW sounders over sea-ice and land Normalised change in Stdev error of vector wind, Base + MW sea-ice + MW land vs Base, Strong impact over extra-tropics/higher latitudes. Modest impact in tropics. Good Bad -0.04 -0.02 0.00 0.02 0.04

Short-range forecast impact against other observations Stdev(o-b) normalised by Base, global statistics, 8 months Good Bad Good Bad TEMP-T TEMP-q CONV-VW IASI H2O O3 Window/ lower T

Seasonal dependence of impact of surface-sensitive MW data over sea-ice and land Base + MW sea-ice + MW land Base + MW sea-ice vs Base, normalised difference in RMSE for Z 500 hPa, verification against own analysis June – Sept 2014 Dec 2014 – March 2015

Seasonal dependence of impact of surface-sensitive MW data over land and sea-ice Base + MW sea-ice + MW land Base + MW sea-ice vs Base, normalised difference in RMSE for Z 500 hPa, verification against own analysis. June – Sept 2014 Larger sea-ice extent Dec 2014 – March 2015

Seasonal dependence of impact of surface-sensitive MW data over sea-ice and land Base + MW sea-ice + MW land Base + MW sea-ice vs Base, normalised difference in RMSE for Z 500 hPa, verification against own analysis. June – Sept 2014 Less sea-ice Dec 2014 – March 2015

Seasonal dependence of impact of surface-sensitive MW data over sea-ice and land Base + MW sea-ice + MW land Base + MW sea-ice vs Base, normalised difference in RMSE for Z 500 hPa, verification against own analysis. June – Sept 2014 Less impact in winter: Difficulties due to snow? Sampling? Dec 2014 – March 2015

Seasonal dependence of impact of surface-sensitive MW data over sea-ice and land Normalised change in Stdev error of Z 500 hPa Base + MW sea-ice + MW land vs Base T+48 h 0.15 0.10 0.05 June – Sept 2014 0.00 -0.05 Dec 2014 – March 2015 -0.10 -0.15

Seasonal dependence of impact of surface-sensitive MW data over sea-ice and land Data coverage, MHS channel 4 (Metop-B) Base Base + MW sea-ice + MW land Aug 2014 Difference in sea-ice extent Not used over snow in winter Feb 2015

Impact from temperature and humidity-sounding channels

Impact of surface-sensitive MW humidity and temperature sounders over land Base + MW sea-ice + MW land Base + MW sea-ice + MW WV land vs Base + MW sea-ice, normalised difference in RMSE for Z 500 hPa. Bad Good

Short-range forecast impact against other observations Stdev(o-b) normalised by Base, global statistics, 8 months Good Bad Good Bad TEMP-T TEMP-q CONV-VW IASI H2O O3 Window/ lower T

Seasonal dependence of impact of MW humidity and temperature sounders over land Base + MW sea-ice + MW land Base + MW sea-ice + MW WV land vs Base + MW sea-ice Dec 2014 – March 2015 June – Sept 2014 Bad Good

FSOI: surface-sensitive MW sounders over land and sea-ice 1.53% MW land 1.59% MW seaice 2.27% MW land 0.87% MW seaice

Forecast sensitivity to observations impact (FSOI) - Globe Instr. FSOI %,Land %, Sea-ice AMSU-A 2.39 % 1.22 % ATMS 1.45 % 0.37% T sounding 1.07% q sounding - MHS 1.32 % 1.07 % SSMI/S 0.22 % 0.17 % Total 5.38 % 2.76% T sounding 2.61% q sounding 2.46 %

Conclusions Surface-sensitive MW sounder data over land and sea-ice provide significant positive forecast impact in the operational ECMWF system. Very significant positive impact from data over sea-ice over S. Hemis. Comparable impact from temperature and humidity-sounding channels over land. Results suggest significant seasonal dependence of the forecast impact, most likely due to changes in surface characteristics. N.Hemisphere impact much weaker over winter. Most likely due to limitations in surface emissivity or skin temperature estimation over snow, leading to a more restricted/sub-optimal use. S.Hemisphere impact appears to be linked to sea-ice extent. FSOI evaluation results are well in agreement with the OSEs impact study results.

Operational use of IR sounder data over land and sea-ice IR sounder radiances are currently more exploited over sea than over land: Over sea: ISEM sea-surface emissivity model; Over land: Limited number of channels insensitive to surface emission (emissivity is assigned default value 0.98 - independent of the wavenumber or SZA); a background error of 5K is assumed for the Tskin. Over sea-ice: Limited to channels in the long-wave CO2 absorption band; Instrument Satellites Sea Land Sea-ice IASI Metop-A/B 188 channels No data 162 channels AIRS Aqua 136 channels 48 stratospheric channels 88 channels CrIS S-NPP 77 channels 30 stratospheric channels 52 channels

Impact of operational IR radiances assimilated over land and sea-ice Experiments over 4 months: 2 June – 30 July 2014; 2 Dec 2014 – 23 January 2015 Base + IR seaice + land Base + IR seaice vs Base, normalised difference in RMSE for Z 500 hPa, verification against own analysis Bad Good

Trials to extend the use of advanced IR sounders data over land The set-up over land is just the same as over sea, but not over high orography Instrument Satellites Sea Land Sea-ice IASI Metop-A/B 188 channels 162 channels AIRS Aqua 136 channels 88 channels CrIS S-NPP 77 channels 52 channels Mixed impact on forecast scores (e.g., RMSE Z 500 hPa) and on the background fit to other observations (e.g., MW observations) Bad Good

Way forward The future use of infrared radiances over land is likely to rely on fundamentally different cloud detection and improved handling of skin temperature for satellite data assimilation.

Impact of emissivity modelling on BT simulations emis=atlas emis=0.98 BT differences between IASI observations and simulations RTTOV IR UWiremis land emissivity atlas (Borbas et al., 2007) Window ch. 901.5 cm-1 Assuming constant emissivity, the simulated IASI spectra in the window regions significantly deviate from measurements. Need good a-priori information of emissivity over land !

Surface skin temperature at ECMWF - issues Tskin is not independently observed; Highly reactive in space and time; Polar orbiting satellites have a very biased diurnal sampling of the skin temperature (2 passes per day) Complex to maintain, is changing during minimisation It is an independent variable, separate to the atmospheric temperature and skin temperature at other locations Atmospheric information aliases into Tskin and is “lost” Does not influence the model forecast of skin temperature; Strategy: Evaluate the possibility of moving away from sink variable to a full field control variable. Better characterize the background errors for Tskin.

Strategy for cloud detection over land Large uncertainties in surface emission make the current cloud detection for IR radiances unfit for use over land Cloud detection land using observed TB only So far experimenting with IASI only Initial attempts rely on four super-channels, each super-channel ~10-12 channels (1) Channels in wavenumber range 706—715 cm-1 (2) Channels in wavenumber range 722—740 cm-1 (3) Channels around wavenumber 875 cm-1 (4) Channels around wavenumber 962.5 cm-1 Two checks for presence of cloud in the field-of-view Sounding-channel check using super-channels (1) and (2) Check passed if data falls between these lines Window-channel check using super-channels (3) and (4) Check passed if data falls above these lines Clear Cloudy Clear Cloudy

Locations of clear data on a window channel Sea Snow or ice surface Desert Vegetation Tune the scheme for data over vegetation, desert and snow False alarms: Canada and tropical rainforests? FG-departure clear data (shown over vegetation) Non-gaussianity, cloud-contaminated data getting through?

Conclusions Surface-sensitive MW sounder data over land and sea-ice provide significant positive forecast impact in the operational ECMWF system. Very significant positive impact from data over sea-ice over S. Hemis. Comparable impact from temperature and humidity-sounding channels over land. Results suggest significant seasonal dependence of the forecast impact, most likely due to changes in surface characteristics. N.Hemisphere impact much weaker over winter. Most likely due to limitations in surface emissivity or skin temperature estimation over snow, leading to a more restricted/sub-optimal use. S.Hemisphere impact appears to be linked to sea-ice extent. IR sounder data over land and sea-ice – some progress has been made, but significant challenges persist. Tskin and the cloud detection are the main limiting factors in using more IR satellite sounding data over land.

Seasonal dependence of impact of surface-sensitive MW data over sea-ice and land Data coverage, AMSU-A channel 6 (Metop-B) Base Base + MW sea-ice + MW land Aug 2014 Difference in sea-ice extent Reduced use over snow in winter Feb 2015

Seasonal dependence of impact of surface-sensitive MW data over sea-ice and land Data coverage, AMSU-A channel 6 (Metop-B) Base Base + MW sea-ice + MW land Aug 2014 Some data rejections over Sahara, probably due to skin temperature issues Feb 2015