Dynamics and data assimilation

Transcription

1 Buys Ballot Medal Symposium 23 June 2014 Dynamics and data assimilation A selective presentation of history and a sample of results from reanalysis Adrian Simmons European Centre for Medium-Range Weather Forecasts Reanalysis is partly funded by the European Union s Seventh Framework Programme

2 Data assimilation Data assimilation blends information from: - observations - a short background model forecast - estimates of observational and background errors - the dynamics built into the model and the representation of background errors to produce an estimate of the atmospheric state 4D variational assimilation as used in ERA-Interim The model carries information from earlier observations forward in time and spreads it in space Information is spread from one variable to another by the model and by background-error relationships

3 Buys Ballot s law 'Staat men met de rug in de richting van de wind dan is op het noordelijk halfrond de luchtdruk aan de linkerhand lager dan aan de rechterhand; op het zuidelijk halfrond is het juist andersom.' (as quoted by F. van Lunteren, 12 mei 2011, H On setting up a volunteer observing network: Ook stelde hij geen eisen aan de gebruikte instrumenten of aan de nauwkeurigheid van de waarnemingen. Google Translate, refined by Dick Dee: He also made no demands on the instruments used, nor on the accuracy of the observations. L M. Buys-Ballot, "Note sur le rapport de l'intensité et de la direction du vent avec les écarts simultanés du baromètre", Académie des sciences (France). Comptes rendus hebdomadaires, 1857.

4 Vilhelm Bjerknes first task of theoretical meteorology Vilhelm Bjerknes s 1904 paper: Das Problem der Wettervorhersage, betrachtet vom Standpunkte der Mechanik und der Physik enunciated the basic principles of numerical weather prediction identified the first (analyse the observations) and second (make the forecast) tasks of theoretical meteorology Based on the observations made, the first task of theoretical meteorology will then be to derive the clearest possible picture of the physical and dynamical state of the atmosphere at the time of the observations. [ ] From the directly observable quantities we must calculate as comprehensively as possible all accessible data on the non-observable ones. For that purpose one has to utilize the relationships between the different quantities. Thus [ ] one must extensively use dynamical-physical methods Translated from the original German by Esther Volken and Stefan Brönnimann

5 von Neumann Fjörtoft Charney In 1950, Charney, Fjörtoft, von Neumann and colleagues make first numerical forecast, exploiting the advent of the electronic computer and a rational approximation of the governing equations They took values from a standard, subjectively-analysed 500hPa chart but concluded in hindsight that it would have been preferable to use an objective analysis Data assimilation was subsequently proposed by Gilchrist and Cressman (1954) and Smagorinsky (unpublished), and first demonstrated by Bergthórsson and Döös (1955)

6 But the second task of theoretical meteorology remained paramount for some time Emphasis continued to be placed largely on model development though global forecasting became operational in the USA in 1974 ECMWF was conceived around 1970 as the EMCC the European Meteorological Computing Centre concentrating on running the forecast model importing the global analysis from another institution Good sense prevailed, but when ECMWF started operations in 1979 its modellers outnumbered its assimilaters by 2:1 a single 10-day global forecast dominated daily operational computer usage data assimilation was generally a minor and often neglected sub-discipline of numerical weather prediction (Daley, 1996) Tony Hollingsworth ( )

7 Diagnosis of analyses for from the FGGE year and subsequent ECMWF operations One of the encouragements to ECMWF to begin a programme of reanalysis in the 1990s

8 Global balance of data assimilation systems ERA-40 (and its users!) suffered from the effects of global imbalance stemming from a basic mis-match between the climate of the assimilating model and the observations driving an erroneous stratospheric Brewer-Dobson circulation which KNMI quickly discovered gave poor simulations of chemical transfer ERA-Interim is better, but Drying over sea peaks in mid 2000s from misuse of SSMI humidity data and switch to cooler SST analyses Satellite radiance data repeatedly warm the background over sea Background is biased warm compared with radiosonde data over land

9 The time series of precipitation rate are averages over the 1 O grid squares (illustrated) where the Global Precipitation Climatology Centre (GPCC, hosted by DWD) has data from at least one station for every month from 1979 to 2013 Comparison of ERA-Interim precipitation with gridded gauge data from GPCC

10 Examples from the development of 4D-Var and chemical data assimilation Florence Rabier and Philippe Courtier Antje Inness Using 4D-Variational assimilation to reconstruct an idealised baroclinic wave development from partial data Comparing ozone analyses with mean ozone profile from 371 ascents or descents of MOZAIC aircraft at Frankfurt

11 ERA-CLIM reanalyses ERA-CLIM provides reanalyses based on historical observations for: 1900 to 2010 assimilating only surface pressure and marine wind observations (ERA-20C) 1939 to 1957 adding upper-air observations Building on and extending work by Compo et al. (2011) Its uses: standard observational databases, plus upper-air data recovered in ERA-CLIM Met Office datasets on sea surface-temperature and ice-cover a low-resolution version of the ECMWF system with 24-hour cycling other adjustments, including to the ensemble approach, to account for sparse and changing data coverage Acknowledgements: Dick Dee, Hans Hersbach, Paul Poli and colleagues

12 Locations of observations assimilated between 0901 UTC 4 June and 0900 UTC 5 June 1944

13 12 UTC ERA-CLIM and 13 UTC Stagg analyses for 3 and 4 June 1944

14 Equivalent to two-day forecast ERA-CLIM H+60 forecasts from 00 UTC and 13 UTC Stagg analyses for 5 and 6 June 1944 Equivalent to two-day forecast

15 500 hpa height analyses 4-6 June 1944 Mean-sea-level pressure analysis 7 June 1944 Contour interval: 60m Contour interval: 200ft Copies of original Met Office charts from Karl Johannessen, Amer. Met. Soc. (1984) Johannessen: What did we know about development, and how did the upper-air maps help us tell what was going to happen near the surface? The answers are very, very little.

16 00UTC 500 hpa height analyses June 1944

17 00UTC 330K PV analyses June 1944

18 00UTC surface analyses for 16 to 23 June 1944

19 Cloud cover, 10m wind and waves from short-range high-resolution forecasts Extract from a US Naval Report: the velocity of the wind increased to 30 knots, with wave action of eight to ten feet From Bates (2010): Off Omaha the USS Augusta [reported] maximum wave heights of 12 feet

20 The ECMWF Council Chamber and ERA-CLIM analyses for 00UTC 1 February 1953

21 Analyses, and 3- and 5-day forecasts for 00 UTC 1 February 1953, with and without upper-air data

22 315K PV analyses 26 January to 2 February 1953

23 Objective verification

24 The challenge of reproducing the long-term temperature record ERA-20CM, a model run with prescribed sea-surface temperatures, fits the CRUTEM4 surface air temperature record over land with reasonable fidelity on the annual time scale and upwards. ERA-20C (assimilating surface-pressure and marine-wind data only) and NOAA/CIRES 20CR (assimilating surface-pressure data only) are both poorer than ERA-20CM on long time scales but better on monthly time scale. Narrower, darker bars denote CRUTEM4