1 / 63

Reanalysis: Data assimilation aspects

ECMWF Meteorological Training Course Numerical Weather Prediction Data Assimilation and Use of Satellite Data (NWP-DA). Reanalysis: Data assimilation aspects. Paul Poli paul.poli@ecmwf.int. Reanalysis course outline. Why reanalysis? (example 1). (a) ECMWF Operations. (b) ERA-15.

allegra-hodges
Download Presentation

Reanalysis: Data assimilation aspects

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. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. ECMWF Meteorological Training Course Numerical Weather Prediction Data Assimilation and Use of Satellite Data (NWP-DA) Reanalysis:Data assimilation aspects Paul Polipaul.poli@ecmwf.int

  2. Reanalysis course outline ECMWF NWP-DA Reanalysis

  3. Why reanalysis? (example 1) (a) ECMWF Operations (b) ERA-15 Latitude 1979 1994 1994 1979 Zonal mean vertical velocity at 500 hPa ECMWF NWP-DA Reanalysis

  4. Why reanalysis? (example 2) (a) ECMWF Operations Northern Hemisphere SouthernHemisphere (b) ERA-40 and ERA-Interim Anomaly correlation of 500 hPa Z Forecasts ECMWF NWP-DA Reanalysis

  5. Reanalysis sales pitch ECMWF NWP-DA Reanalysis Products are consistent … (in Time) across Atmospheric Parameters in the Horizontal in the Vertical

  6. Approach differences with “observations-only” gridded datasets ECMWF NWP-DA Reanalysis

  7. Reanalysis course outline ECMWF NWP-DA Reanalysis

  8. Changes in operational NWP systems and input that affect product quality (usually for the better) Changes that cannot be avoided Changes that can be avoided Common “change mitigation” trade-off issues: Products rely on a priori science(model) Changes are difficult to avoid ECMWF NWP-DA Reanalysis

  9. Reanalysis simple as 1-2-3Part 1: NWP forecast model ECMWF NWP-DA Reanalysis

  10. Reanalysis simple as 1-2-3Part 2: Data assimilation ECMWF NWP-DA Reanalysis

  11. Reanalysis simple as 1-2-3Part 3: Observation and forcing data ECMWF NWP-DA Reanalysis

  12. Background forecast p Observations Analysis Analysis Analysis Analysis Analysis t 00UTC 12UTC 00UTC 12UTC 21 March 2010 22 March 2010 12-hourly 4D-Var assimilation observation constraint background constraint simulates the observations The ubiquitous data assimilation slide – applied to reanalysis • Data assimilation combines information from • Observations • A short-range background forecast, carrying forward the information extracted from prior observations • Error statistics • Dynamical and physical relationships 4DVAR This produces the “most probable” atmospheric state (maximum-likelihood estimate)*** ***if background and observation errors are Gaussian, unbiased, uncorrelated with each other; all error covariances are correctly specified; model errors are negligible within the 12-h analysis window ECMWF NWP-DA Reanalysis

  13. 4D-Var CONTROL 4D-Var 3D-Var Illustration: why an advanced DA scheme is helpful in reanalysis 15 February 2005 00 UTC All observations (surface, radiosondes, satellite etc…) Surface pressure observations only Advances in data assimilation can help extract more information from historic data that could ever be thought possible at the time the observations were collected ECMWF NWP-DA Reanalysis

  14. Background forecast p Observations Analysis Analysis Analysis Analysis Analysis t 00UTC 12UTC 00UTC 12UTC 21 March 2010 22 March 2010 12-hourly 4D-Var assimilation observation constraint background constraint simulates the observations The ubiquitous data assimilation slide – applied to reanalysis • Data assimilation combines information from • Observations • A short-range “background” forecast that carries forward the information extracted from prior observations • Error statistics • Dynamical and physical relationships 4DVAR ***if background and observation errors are Gaussian, unbiased, uncorrelated with each other; all error covariances are correctly specified; model errors are negligible within the 12-h analysis window This produces the “most probable” atmospheric state (maximum-likelihood estimate)*** ***if background and observation errors are Gaussian, unbiased, uncorrelated with each other; all error covariances are correctly specified; model errors are negligible within the 12-h analysis window ECMWF NWP-DA Reanalysis

  15. Various observation bias correctionsExample: in ERA-Interim ECMWF NWP-DA Reanalysis

  16. Minimise Minimise Variational bias correction First proposed and implemented by Derber and Wu, 1998 • Solve for analysis and bias parameters at the same time The bias parameters: b The bias model: b(x,b) : Typically a linear combination of bias parameters with robust predictors to characterize air mass or observation geometry • The aim is to correct for observation and observation operator (radiative transfer) error bias – altogether • Assuming that these biases are constant for the duration of the analysis window ECMWF NWP-DA Reanalysis

  17. Bias predictors in ERA-Interim for radiances ECMWF NWP-DA Reanalysis

  18. Satellite AMSU-A channel number Example 1: AMSU-A Scan-angle bias Latitudes 20S-20N, ocean only 1998 2010 After METOP-A recalib. Before METOP-A recalibration Note the asymmetric shapes ECMWF NWP-DA Reanalysis

  19. Satellite orbital drift, as estimated from the data at 1-month intervals 1989 2010 ECMWF NWP-DA Reanalysis

  20. Example 1 (cont): AMSU-A biases, after variational bias correction ECMWF NWP-DA Reanalysis

  21. Example 2: AMSU-A METOP-A recalibration after launch Mean global bias correction (K) for channel 9 NOAA-15 NOAA-16 EOS-Aqua NOAA-18 METOP-A Jan 2007 Jan 2008 Jan 2009 Data count ECMWF NWP-DA Reanalysis

  22. Variational bias estimates for NOAA-14 Actual warm-target temperatures on board NOAA-14 (Grody et al. 2004) Example 3: Reference blackbody calibration fluctuations MSU NOAA-14 channel 2 Dee and Uppala, 2009 ECMWF NWP-DA Reanalysis

  23. After bias correction Before bias correction Mean departures (K) in the Tropics for radiosondes, 60—40 hPa Background Analysis Example 4: Mt Pinatubo eruption Mean obs-background departures (K) in the Tropics for MSU channel 4 2006 1990 1992 1994 ECMWF NWP-DA Reanalysis

  24. Mean bias correction (K) in the Arctic for AMSU-A channel 10 Mean observed Bright. T. (K) in the Arctic for AMSU-A channel 10 Mean observed temperature (K) in the Arctic radiosondes 40—25 hPa Example 5: Sudden stratospheric warming events 1999 2009 ECMWF NWP-DA Reanalysis

  25. Example 6: Sensitivity to changes in the “anchoring” obs. system: GPSRO Observing System Experiment, in which GPSRO data are *not* assimilated Introduction of GPSRO COSMIC ECMWF NWP-DA Reanalysis

  26. ERA-Interim ERA-Interim without GPSRO assimil. Example 6 (cont.): Sensitivity to changes in the “anchoring” obs. system: GPSRO Bias correction estimates (global means) Observing System Experiment, in which GPSRO data are *not* assimilated ECMWF NWP-DA Reanalysis

  27. Summary: Variational bias correction ECMWF NWP-DA Reanalysis

  28. Binary decisions that affect reanalysis quality: data usage • Usage / no usage of data, besides variational QC and first-guess checks, is controlled using simple switches: • Data extraction • Thinning/sub-sampling • Blacklisting/whitelisting • Each of these steps can be a source of error in a reanalysis which concentrates many varied sources of information, whose observation characteristics sometimes even vary over time Observation monitoring is critical in a reanalysis: it is there to try to catch all the possible things that can go wrong with data usage ECMWF NWP-DA Reanalysis

  29. Observations assimilated in ERA-Interim 4D-Var Total number of observations assimilated over 20 years: exceeds 30x109 Reanalyses have to deal with very large numbers of observations, whose quantity vary over time ECMWF NWP-DA Reanalysis

  30. 1957: Radiosonde network enhanced in Southern Hemisphere for International Geophysical Year 1973: NOAA-2 – First operational sounding of temperature and humidity from polar-orbiting satellite NOAA-2 15 Oct 1972 Today: Additional satellite, aircraft and buoy data. Poorer radiosonde coverage, but better quality (systematically probe higher, network upgrades to more reliable sensors) Key changes to the observing system 1979: Improved sounding from polar orbiters Winds from geostationary orbit More data from commercial aircraft Drifting buoys ECMWF NWP-DA Reanalysis

  31. Time coverage of in situ surface data 1989 ECMWF NWP-DA Reanalysis

  32. M A I B C P J D Q K E V H N U Radiosonde coverage for 1958 Ships maintaining fixed locations Average number of soundings per day: 1609 ECMWF NWP-DA Reanalysis

  33. M A I B C P J D Q K E V H N U Radiosonde coverage for 1979 Average number of soundings per day: 1626 ECMWF NWP-DA Reanalysis

  34. Radiosonde coverage for 2001 Average number of soundings per day: 1189 ECMWF NWP-DA Reanalysis

  35. Time coverage of Atmospheric Motion Vector (AMV) data 1989 2009 ECMWF NWP-DA Reanalysis

  36. Example of improved data coverage: Reprocessed Atmospheric Motion Vectors from Meteosat Early 1980s Expanded Low-resolution Winds ECMWF NWP-DA Reanalysis

  37. 1989 2009 Time coverage of radiance data Comparatively many more sources … Comparatively fewer sources ECMWF NWP-DA Reanalysis

  38. Observations available to ERA-Interim 4D-Var “Conventional” “Satellite” TOTAL Radiances Radiosondes Surface Atmospheric motion vectors Aircraft Scatterometers Radiosondes (wind only) GPS radio occultation Drifting buoys Surface pressure pseudo-observations from Australian Bureau of Meteorology (PAOB) ECMWF NWP-DA Reanalysis

  39. Percentage of data assimilated by ERA-Interim Sat Conv Sat Conv Sat Conv Conv Conv Sat A large number of satellite data are still not assimilated – these represent as many yet untapped resources  for future reanalyses of the present ECMWF NWP-DA Reanalysis

  40. Reanalysis course outline ECMWF NWP-DA Reanalysis

  41. Atmospheric reanalysis: starting points • Has its origins in datasets produced for the Global Weather Experiment (FGGE) • Widely used to compare model performances • Proposed by Roger Daley in 1983 for monitoring the impact of forecasting system changes on the accuracy of forecasts • Adrian Simmons (personal communication) • Proposed for climate-change studies in two journal articles: • Bengtsson and Shukla (1988) • Trenberthand Olson (1988) ECMWF NWP-DA Reanalysis

  42. Atmospheric reanalysis: Global products • Three centres took initiative in mid 1990s: first generation • ERA-15 (1979 - 1993) from ECMWF – with significant funding from USA • NASA/DAO (1980 - 1993) from USA • NCEP/NCAR (1948 - present) from USA • Second generation of reanalyses followed • ERA-40 (1958 - 2001) from ECMWF – with significant funding from EU FP5 • JRA-25 (1979 - 2004) from Japan • NCEP/DOE (1979 - present) from USA • Now in third generation of comprehensive global reanalysis • ERA-Interim (1989 - present) from ECMWF • JRA-55 (1958 - 2012) from Japan • NASA/GMAO-MERRA (1979 - present) from USA • NCEP-CFSRR (1979 - 2008) from USA ECMWF NWP-DA Reanalysis

  43. Atmospheric reanalysis: The user base • Many users: • 12000 registered users of ERA public data server • ≳5M fields retrieved daily by ECMWF and Member-State users • National mirror sites for ERA in several countries • And many citations: • Paper on NCEP/NCAR reanalysis is most cited paper in geosciences • Paper on ERA-40 is most cited recently in the geosciences • Many references in IPCC Fourth Assessment report (AR4) ECMWF NWP-DA Reanalysis

  44. Regional reanalysis and down-scaling from global reanalysis Long-term reanalysis using only surface-pressure observations Short-term reanalysis for chemistry& aerosols Atmospheric reanalysis: becoming more diverse North American Regional Reanalysis Maximum gusts 26 December 1999 2m temperature 6UTC, 1 January 1999 ECMWF NWP-DA Reanalysis

  45. Applications of atmospheric reanalysis • Monitoring of the observing system • Providing feedback on observational quality, bias corrections and a basis for homogenization studies of long data records that were not assimilated • Development of climate models • Providing data for verification, diagnosis, calibrating output,, … • Driving data for users’ models/applications • For smaller-scales (global→regional; regional→local), ocean circulation, chemical transport, nuclear dispersion, crop yield, health warnings, … • Providing climatologies for direct applications • Ocean waves, resources for wind and solar power generation, … • Study of short-term atmospheric processes and influences • Process of drying of air entering stratosphere, bird migration, … • Study of longer-term climate variability/trends • Preferably used with caution in conjunction with observational studies ECMWF NWP-DA Reanalysis

  46. ERA-Interim: after ERA-40, and before the next reanalyses ECMWF NWP-DA Reanalysis

  47. Under the hood of ERA-Interim • Grab observations from archive • Basic pre-processing • Ingest observations to assimilation database • Run 4D-Var (atmosphere) and surface/sea/snow DA • Run the forecast model Generate monitoring and diagnostics plots ECMWF NWP-DA Reanalysis

  48. Reanalysis and climate monitoring Benefits of reanalysis products as a proxy for observations are well established Reanalysis for climate monitoring: physically consistent Essential Climate Variables In the climate community, reanalysis is still regarded as unsuitable for trend estimation (IPCC) This view evolves as reanalyses become more consistent and rely on more observations ECMWF NWP-DA Reanalysis

  49. Global water cycle ERA-40: Excessive precipitation over tropical oceans, exacerbated by Pinatubo eruption (“Atmospheric reservoir”) Pinatubo eruption ECMWF NWP-DA Reanalysis

  50. Globally averaged bias estimates for AMSU-A radiances AQUA Drifts in AMSU-A tropospheric radiance data • There are clear inconsistencies among the AMSU-A observations -> some instrument issues • Decreasing bias estimates: Reanalysis trend > AMSU-A trend • This is not a drift toward the model climate (model has a cold bias in the troposphere) • Then why is the reanalysis getting warmer rather than colder? ECMWF NWP-DA Reanalysis

More Related