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Henk Eskes, William Lahoz, ESTEC, 20 Jan 2004 The role of data assimilation in atmospheric composition monitoring and forecasting Henk Eskes, William Lahoz.

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Presentation on theme: "Henk Eskes, William Lahoz, ESTEC, 20 Jan 2004 The role of data assimilation in atmospheric composition monitoring and forecasting Henk Eskes, William Lahoz."— Presentation transcript:

1 Henk Eskes, William Lahoz, ESTEC, 20 Jan 2004 The role of data assimilation in atmospheric composition monitoring and forecasting Henk Eskes, William Lahoz 1. Royal Netherlands Meteorological Institute, De Bilt, The Netherlands 2. Data Assimilation Research Centre, University of Reading, UK

2 Henk Eskes, William Lahoz, ESTEC, 20 Jan ) From sequential sets of data points to synoptic global fields Complementarity: Measurements - Snapshots of the atmospheric state Model - Describes the evolution of the atmosphere Data assimilation works best for long-lived tracers (or slowly-varying emissions) Value adding: Easy to use synoptic 3D fields

3 Henk Eskes, William Lahoz, ESTEC, 20 Jan 2004 "from gaps to maps" MIPAS ozone analysis 10 hPa 22 Sep 2002 Courtesy: Alan Geer, DARC

4 Henk Eskes, William Lahoz, ESTEC, 20 Jan ) Propagation of information to data-poor regions and unobserved variables, unobserved chemical species Examples: Atmospheric chemistry: effective number of degrees of freedom smaller than number of species. Measurement information transferred to unobserved species Tracer transport: the wind will carry information from observed to unobserved regions, e.g. the dark winter pole NWP and ozone: ozone observations contain information on the wind field Depends critically on the quality of the model and observations

5 Henk Eskes, William Lahoz, ESTEC, 20 Jan 2004 Impact of ozone on NWP Wind increments due to TOVS ozone observations ECMWF model Courtesy: Elias Holm ECMWF Wind increments ~ 0.5 m/s

6 Henk Eskes, William Lahoz, ESTEC, 20 Jan 2004 Unobserved species Elbern, Schmidt JGR 104, (1999) Impact of ozone observations on NO, NO 2 in 4D-Var Forecast Analysis, truth observation

7 Henk Eskes, William Lahoz, ESTEC, 20 Jan ) Confronting models with data, data with analyses Detailed feedback on: quality of the model - understanding quality of the observations Observation minus forecast statistics Validation with data assimilation

8 Henk Eskes, William Lahoz, ESTEC, 20 Jan 2004 Feedback on retrieval Sciamachy ozone column retrieval at KNMI Observation minus forecast vs. Solar zenith angle

9 Henk Eskes, William Lahoz, ESTEC, 20 Jan ) Use of complex observations and heterogeneous data sets Complex observations: Satellite (remote sensing) observations have complicated relation with atmospheric composition Described by averaging kernels, which complicates interpretation Use of averagingkernel in data assimilation straightforward (in principle): Observation operator = averaging kernel Heterogeneous data sets: Combine satellite observations (different geometries, techniques) and routine surface observations, e.g. NWP

10 Henk Eskes, William Lahoz, ESTEC, 20 Jan 2004 Kernels for total column observations Examples: - TOMS O 3 - GOME NO 2, H 2 CO - MOPITT CO

11 Henk Eskes, William Lahoz, ESTEC, 20 Jan ) Emission estimates based on satellite observations Data assimilation and inverse modelling involve the same technique Assimilation: state analysis Inverse modelling: source/sink estimates Assimilation is more general and combined 3D field and source/sink analysis logical extension of state analysis Example: 4D-Var source/state approach, applied to CH 4 from Sciamachy

12 Henk Eskes, William Lahoz, ESTEC, 20 Jan 2004 CH4 emission analyses based on Sciamachy observations Courtesy: Jan Fokke Meirink KNMI Analysis minus forecast emissions

13 Henk Eskes, William Lahoz, ESTEC, 20 Jan ) Monitoring of the environment, trends Re-analysis based on available satellite and ground-based observations Combination of different data sets with a model allows a detailed bias correction to be determined and applied to the various data sets to account for differences between instruments, techniques, drifts. Trend analysis: Very tricky! Examples: ECMWF temperature data set from ERA-40 GOME ozone assimilation data set,

14 Henk Eskes, William Lahoz, ESTEC, 20 Jan 2004 ECMWF ERA-40 reanalysis temperature trend Red: ERA-40 (EU final report, nov 2003) Blue: Jones & Moberg, J. Climate, 16 (2003)

15 Henk Eskes, William Lahoz, ESTEC, 20 Jan 2004 Re-analysis of GOME ozone EU GOA Project Based on GOME GDP v3 columns

16 Henk Eskes, William Lahoz, ESTEC, 20 Jan ) Quantify benefit for future missions: OSSE Observing system simulation experiment: Approach: Draw synthetic observations from reference run Assimilate these in a model run with perturbed initial conditions / emissions / model parameters Quantify the impact of these synthetic observations Examples of OSSE: Impact of SWIFT stratospheric winds on NWP W. Lahoz et al, QJRMS submitted, 2003 Impact of ozone observations on wind field Impact of Sciamachy CH4 column observations

17 Henk Eskes, William Lahoz, ESTEC, 20 Jan 2004 OSSE: Impact of TOVS ozone column on NWP winds A. Peuch et al, QJRMS 126, 1641, 2000 Zonal wind Merid. wind NHTRSH

18 Henk Eskes, William Lahoz, ESTEC, 20 Jan 2004 Courtesy: Jan Fokke Meirink KNMI Simulated observations OSSE: Impact of Sciamachy CH4 for emission estimates

19 Henk Eskes, William Lahoz, ESTEC, 20 Jan 2004 Courtesy: Jan Fokke Meirink KNMI Simulated observation errors OSSE: Impact of Sciamachy CH4 for emission estimates

20 Henk Eskes, William Lahoz, ESTEC, 20 Jan 2004 Courtesy: Jan Fokke Meirink KNMI Changes in the methane field OSSE: Impact of Sciamachy CH4 for emission estimates

21 Henk Eskes, William Lahoz, ESTEC, 20 Jan 2004 Courtesy: Jan Fokke Meirink KNMI Analysis minus forecast emissions OSSE: Impact of Sciamachy CH4 for emission estimates

22 Henk Eskes, William Lahoz, ESTEC, 20 Jan 2004 OSSE: Impact of Sciamachy CH4 for emission estimates Courtesy: Jan Fokke Meirink, KNMI EXPERIMENTR default0.21 all pixels cloudfree0.61 perfect obs.0.88 correlation between emissions0.65 R = RMS reduction factor

23 Henk Eskes, William Lahoz, ESTEC, 20 Jan ) Atmospheric composition forecasts - chemical weather Assimilation analysis to initialise a chemical forecast: Examples: Stratospheric ozone forecast BASCOE

24 Henk Eskes, William Lahoz, ESTEC, 20 Jan 2004 Analysis based on GOME ozone column observations 15 April 2001

25 Henk Eskes, William Lahoz, ESTEC, 20 Jan 2004 TOMS 15 April 2001

26 Henk Eskes, William Lahoz, ESTEC, 20 Jan 2004 Anomaly correlation

27 Henk Eskes, William Lahoz, ESTEC, 20 Jan 2004 Ozone hole breakup, September 2002 Analysis based on GOME

28 Henk Eskes, William Lahoz, ESTEC, 20 Jan 2004 Ozone hole breakup, September day forecast

29 Henk Eskes, William Lahoz, ESTEC, 20 Jan 2004 Ozone hole breakup, September day forecast

30 Henk Eskes, William Lahoz, ESTEC, 20 Jan 2004 Stratospheric chemical forecasts based on MIPAS observations Courtesy: Dominique Fonteyn

31 Henk Eskes, William Lahoz, ESTEC, 20 Jan 2004 Summary Data assimilation as value-adding instrument: Fill gaps in data records Propagation of information: data-poor regions, unobserved variables and chemicals, emissions Confronting models with data (understanding), observations with models (validation) Use of complex data: heterogeneous data sets, remote sensing Sources and sinks as part of the analysis Long-term monitoring, trends, climate change Quantify benefit of future missions: OSSE studies Forecasts of atmospheric composition: "chemical weather"


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