1 / 22

Matthias Drusch ECMWF

Using TMI derived soil moisture to initialize numerical weather prediction models: Impact studies with ECMWF’s Integrated Forecast System. Matthias Drusch ECMWF. Acknowledgements: E.F. Wood and H. Gao (Princeton University). Outline. Motivation and introduction

moswen
Download Presentation

Matthias Drusch ECMWF

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. Using TMI derived soil moisture to initialize numerical weather prediction models: Impact studies with ECMWF’s Integrated Forecast System Matthias Drusch ECMWF Acknowledgements:E.F. Wood and H. Gao (Princeton University)

  2. Outline • Motivation and introduction • 2. Operational OI analysis vs Open Loop experiments • - Forecast impact • - Soil moisture validation against OK Mesonet • Operational OI analysis vs TMI nudging experiment • - Bias correction • - Soil moisture validation • - Forecast impact • 4. Summary and Outlook

  3. volumetric soil moisture 2 m temperatures turbulent surface fluxes fractional cloud coverage [º Celsius] [%] [W m-2] [%] ECMWF long-term forecasts (from ENSEMBLES project) (monthly averages for North America) • Soil moisture has an impact on the atmosphere and the weather forecast. • Systematic errors in the land surface scheme result in a (dramatic) dry down • with summer values close to the permanent wilting point. • The corresponding 2 m temperature forecasts show a strong warm bias • exceeding 10 K during summer and 5 K during winter. • The model has to be re-initialized with analysed soil moisture to prevent • from drifting into an unrealistic state.

  4. General introduction A well posed analysis is a better estimate of the true state than either the modelled background information or the observation data sets available. - initial state for a numerical weather forecast - reference against which to quality check other observations - pseudo observation for e.g. satellite retrieval algorithm development sequential, intermittent assimilation observations observations observations shortrange forecast shortrange forecast shortrange forecast analysis analysis analysis medium-range forecasts

  5. Data Assimilation Experiments • CTRL OI (Optimum Interpolation) based on screen level analyses • for the top three model soil layers. • OL (Open Loop) without any soil moisture analysis. • NUDGE(Nudging) experiment using the TMI Pathfinder soil • moisture product. • Common features: • Full atmospheric 4DVar analysis using ~ 106 observations / 6h • (reflecting the operational configuration). • Model version CY29R1. • T511 spectral resolution, 60 vertical levels. • ‘Early delivery’ set up with 10-day forecasts from 00 and 12 UTC. • Study period from 1 June to 31 July 2002.

  6. Soil moisture increments (CTRL OI) Accumulated root zone soil moisture increments for June 2 to July 30, 2002. [mm] Analysis increments are a sizeable part of the terrestrial water budget.

  7. Temperature at 1000 hPa Forecast skills Root-mean-square error E Significance levels grey: OI black: OL solid: North America dotted: Europe dashed: East Asia The proxy ‘observations’ are efficient in improving the turbulent surface fluxes and consequently the weather forecast on large geographical domains.

  8. Validation against OK Mesonet observations

  9. daily precipitation daily precipitation daily downward shortwave radiation model forecast (OI) model forecast (OI) observations observations total amount of rainfall: June 87.3 mm model on 19 days 87.8 mm observations on 9 days July 110. mm model on 26 days 79. mm observations on 20 days Correlation : 0.85 Bias : - 0.7 Wm-2 Validation of forcing data area averages for Oklahoma (72 stations)

  10. surface soil moisture root zone soil moisture • Too quick dry downs (model problem). • Too much precip in July (model problem). • Too little water added in wet conditions • (analysis problem). • NO water removed in dry conditions • (analysis problem). • Precipitation errors propagate to the • root zone. • Analysis constantly adds water. • The monthly trend is underestimated. model forecast (OI) model forecast (OI) model forecast (OL) model forecast (OL) observations observations Validation of soil moisture area averages for Oklahoma (72 stations) The current analysis fails to produce more accurate soil moisture estimates.

  11. July 2nd, 1999 0 5 10 15 20 25 30 35 40 45 (%) TMI Pathfinder Data Set Data set produced by: Dept. Civil and Environmental Engineering, Princeton University, NJ Basis: brightness temperatures at 10.65 GHz horizontal polarization Method: physical retrieval based on land surface microwave emission model and auxiliary data sets from the North American Land Data Assimilation Study project Output: surface soil moisture [cm3 cm-3], (Gao et al. 2006)

  12. Corrected TMI soil moisture volumetric surface soil moisture [%] for 06/06/2004 the modelled first guess original TMI Pathfinder data corrected TMI data set (bias correction through CDF matching)

  13. 00 03 06 09 12 15 18 21 00 03 06 09 12 15 18 21 00 Nudging set up Delayed cut-off atmospheric 4D-Var (12 h) AN AN AN FC FC FC TMI sampling period (daily) soil moisture analysis 1/4 2/4 1/4 2/4 10-day forecasts

  14. surface soil moisture root zone soil moisture • Nudging / satellite data remove water • effectively and produce a realistic dry • down. • Nudging the satellite results in the most • accurate surface soil moisture estimate. • The information introduced at the • surface propagates to the root zone. • The monthly trend is well reproduced • using the nudging scheme. Validation of soil moisture area averages for Oklahoma Satellite derived soil moisture improves the soil moisture analysis and results in the most accurate estimate.

  15. Nudging OI OL rH T Forecast skill correlation (observation / fc) bias rH The impact of the satellite data on the forecast quality (of screen level variables) is neutral (correlation). The biases obtained from the nudging experiment are slightly higher when compared against the OI and lower when compared against the OL. T

  16. Forecast – observation differences CTRL Open Loop NUDGE RH2m [%] T2m [%] The nudging experiment performs best in the south-western and central parts of the study area, which are characterized by ‘low vegetation’ (short grass) and ~ 15 % of bare soil.

  17. Impact on weather parameters CTRL Open Loop NUDGE surface soil moisture [%] at 18 June, 12 UTC latent heat flux [Wm-2] mean over 18 June 12 UTC to 00 UTC sensible heat flux [Wm-2] mean over 18 June 12 UTC to 00 UTC planetary boundary layer height [m] at 19 June 00 UTC total cloud coverage [0-1] at 19 June 00 UTC

  18. Soil moisture increments accumulated increments over June and July 2002 [mm] Optimal Interpolation (2 m T and RH) Nudging (TMI soil moisture)

  19. Summary The OI analysis using 2 m temperature and precipitation is efficient in Improving the turbulent fluxes and consequently the weather forecast on large geographical domains. The quality of the resulting soil moisture profile is not improved. The OI analysis is not able to correct for the underestimation of the seasonal cycle in root zone soil moisture and for the effects of erroneous precipitation forecasts. However, it prevents the system from drifting into a too dry state. Surface soil moisture is a strong constraint for the NWP model. The surface scheme is able to propagate the information introduced in the top layer to the root zone. Soil moisture analysed from the satellite data is most accurate. There is a clear impact of soil moisture on weather parameters. The forecast skill is neutral (rms) to slightly negative (rH bias).

  20. Summary (continued) The best soil moisture product does not necessarily result in the best NWP forecast. New (satellite) observations help to identify model errors and to improve physical models. In the end, the forecast will benefit from a better soil moisture product. In-situ observation are of fundamental importance for the development of model / data assimilation systems. It is important to observe soil moisture AND fluxes, screen level variables and meteorological forcings.

  21. Oklahoma data sets 2002

  22. transfer funcion 03/2002-10/2002 • CDF matching reduces systematic errors: • The bias has been removed and the dynamic • range has been adjusted. • The random error may increase. r2 = 0.66 r2 = 0.69 r2 = 0.01 r2 = 0.18 x‘-x Bias: -0.35 % x Bias: -11.67 % TMI soil moisture transformation / bias correction

More Related