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Single column experiments at ECMWF, status of work, and plans for 2003

Single column experiments at ECMWF, status of work, and plans for 2003. Pedro Viterbo and Gisela Seuffert European Centre for Medium-Range Weather Forecasts. Thanks to: J.F. Mahfouf, H. Wilker, M. Drusch, and J.-C. Calvet. ELDAS 2 nd Progress Meeting INM, Madrid, 10-11 December 2003. Layout.

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Single column experiments at ECMWF, status of work, and plans for 2003

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  1. Single column experiments at ECMWF, status of work, and plans for 2003 Pedro Viterbo and Gisela Seuffert European Centre for Medium-Range Weather Forecasts Thanks to: J.F. Mahfouf, H. Wilker, M. Drusch, and J.-C. Calvet ELDAS 2nd Progress Meeting INM, Madrid, 10-11 December 2003

  2. Layout • Introduction • Assimilation (OI vs. KF and synthetic mw Tb) • Assimilation of observed mw Tb • Additional experiments • Development of production system • Remaining work • (Scientific) conclusions

  3. Layout • Introduction • Assimilation (OI vs. KF and synthetic mw Tb) • Assimilation of observed mw Tb • Additional experiments • Development of production system • Remaining work • (Scientific) conclusions

  4. Goals • Build a soil moisture analysis system assimilating • 2m temperature and relative humidity • Thermal IR heating rates • MW brightness temperature • Forced by observation based estimates of • Precipitation and radiation • Study its properties and compare to OI in a controlled environment • Build the production system

  5. Action: Completed using SCM Action: Building … Action: SCM test runs • Action: 2 papers • published in GRL (T,RH,Tb) • Accepted at JHM (OI, EKF) Action: Still pending Plans ( ELDAS 1st progress meeting) Assimilation aspects: • Minimize the combined errors in prediction of soil moisture, latent heat flux and screen level observations • Further mw-Tb assimilation experiments (viewing angle, times) • Assimilation of heating rates Reports: • Paper(s) focusing on the - new features of assimilation method - assimilation of mw-Tb - (assimilation of heating rates) Technical aspects: • Summer 2003: Build production system for the annual data base • End of 2003: Start production

  6. Soil moisture analysis systems Optimal Interpolation: • Used in the operational ECMWF-forecast since 1999 (Douville et al., 2000) • Fixed statistically derived forecast errors • Criteria for the applicability of the method - atmospheric and soil exceptions - corrections when T and RH error are negatively correlated • Extended Kalman Filter: • Used in the operational DWD- • forecast since 2000 (Hess, 2001) * • Updated forecast errors • Criteria for the applicability of the method • - no ‘direct’ atmospheric exceptions • - soil exceptions still to be tested • * Changes: • - Assimilation of 2m- T and RH, mw-Tb • Model forecast operator accounts for water transfer between soil layers • Test adaptive EKF

  7. SCM experiment Design Atm. initial conditions + dynamics forcing from ECMWF reanalysis (ERA40) Single-column model of the ECMWF NWP model + microwave emissivity model Observation of precipitation + radiation Increments (daily) First guess: T2m,RH2m,HR(?) Soil moisture analysis scheme OI or Extended Kalman Filter Observations: T2m,RH2m,HR Soil moisture Background error

  8. Observations Murex: • 1.6 – 9.10.1997 (1995- 1998) • Forcing: SW , (unbiased) LW , precipitation • Validation: Soil Moisture, Rnet, H, G, LE=Rnet-H-G, Ts • Assimilation/Validation: T2m, RH2m, synthetic mw-Tb SGP 97: • 15.6 – 19.7.1997 • Little Washita site (2) (Central Facility site(3)) • Forcing: SW , (simulated) LW , precipitation • Validation:Soil Moisture, Rnet, H, G, LE, Ts • Assimilation/Validation: T2m, RH2m, mw-Tb

  9. Layout • Introduction • Assimilation (OI vs. KF and synthetic mw Tb) • Assimilation of observed mw Tb • Additional experiments • Development of production system • Remaining work • (Scientific) conclusions

  10. 0.0020 OI EKF 0.0015 Frobenius norm 0.0010 0.0005 0.0000 160 180 200 220 240 260 280 julian day OI vs KF: Gain matrix (FIFE) • Both systems distinguish between periods off strong and weak influence of soil moisture on screen-level variables. • OI does that thanks to carefully selected thresholds; EKF has built-in dynamic dependency • In clear-sky, the FN of EKF is slightly larger than that of OI: EKF has a slight preference for the obs, rather than the bg.

  11. OI vs KF: Average increments (FIFE)

  12. OI vs KF: Time series of increments (FIFE) • EKF increments are at the same order of magnitude of OI, and come at the same time • OI has soil moisture increments similar across the 3 layers. • EKF puts more weight on the deeper layers

  13. (Synthetic) microwave Tb assimilation (MUREX) Soil moisture Sensible Heat flux

  14. Daytime fit to observations (MUREX) 2 metre temperature • The control simulation has a cold and wet bias, but hardly any bias on sensible heat flux, (and a wet bias in root zone moisture). • The assimilation of screen-level parameters tends to reduce the cold/wet bias, reducing soil moisture (moving away from observations), and giving too much sensible heat. • EKF tends to follow mainly 2T information, in detriment of 2RH. • The assimilation of mw Tb moves the root zone moisture closer to the (ground-based) observations 2 metre relative humidity

  15. Soil moisture, Teff, mw-Tb at 6 LT Surface soil moisture • Assimilation of mw Tb (on its own or combined with screen level parameters) brings the simulated Tb and the top soil moisture closer to the observations. Microwave Tb

  16. Layout • Introduction • Assimilation (OI vs. KF and synthetic mw Tb) • Assimilation of observed mw Tb • Additional experiments • Development of production system • Remaining work • (Scientific) conclusions

  17. Assimilation of mw Tb: Performance of 2T/RH 2T SGP97 2RH

  18. Surface soil moisture and Tb (SGP97) Tb • The control simulation (indeed, all simulations) are too warm and too dry. Model day-to-day variability of humidity exceeds observations. • Top soil moisture in the control simulation compares well with observations. • Assimilation of screen-level parameters decrease the warm/dry bias by 30-40%, but deteriorate the fit to top soil moisture. • Assimilation of mw Tb, on top of screen-level parameters, improves again the top soil moisture but deteriorates the fit to screen-level observations. Top soil moisture

  19. Root zone soil moisture (SGP97) • Results for root zone soil moisture are similar to those of the top layer. • The assimilation scheme responds correctly to a (very large) imposed error in the precipitation. No precipitation simulation

  20. Evaporative fraction (SGP97) • Evaporative fraction [EF=LE/(H+LE)], the relevant quantity for the surface impact on the atmosphere, is underestimated by the control simulation (cf. dry/warm bias). • EF is clearly improved when screen-level parameters are used. • And deteriorated again when mw Tb is added …

  21. EKF assimilation of microwave Tb • Assimilation of mw Tb: • Transports surface soil moisture signal from 1st layer to deeper root zone • Improves simulated soil moisture, surface energy fluxes, T,RH • Best results for atmosphere, when T,RH and Tb are assimilated • Assimilation of Tb needs better background: • Different soil types • More soil layers • Removal of soil temperature bias necessary (results not shown here)

  22. Layout • Introduction • Assimilation (OI vs. KF and synthetic mw Tb) • Assimilation of observed mw Tb • Additional experiments • Development of production system • Remaining work • (Scientific) conclusions

  23. Assimilation of satellite heating rate Days when SHR is available (50% data missing, 25% cloudy) Soil moisture • Variable SHR observation error depends on cloud fraction flag (how many hours are • cloud free): • Cloud fraction flag of neighbouring pixels • Cloud fraction flag of pixel • Assimilating SHR: • Low data coverage does not allow for real conclusions • When T, RH are available no extra information • SHR error difficult to define

  24. 97/98 1.4.98 1.10.97 Winter simulations Soil moisture • Without any additional flags EKF-system computes rather large soil moisture increments in winter • Flags necessary: • a) Low radiation (zenith angle) • b) Freezing

  25. Soil temperature analysis • Soil temperature analysis • 2m-T is assimilated at 3 and 6 LT (zenith angle dependent) • Soil temperature increments of similar magnitude to OI • Combining soil moisture (SMA) and soil temperature (STA) analysis • Tests about the order of the SMA + STA analysis + avoidance of SCM-runs • Cannot be based on the same background run • Almost no difference when SMA or STA is performed first • Better performance when final trajectory is calculated (expensive)

  26. Layout • Introduction • Assimilation (OI vs. KF and synthetic mw Tb) • Assimilation of observed mw Tb • Additional experiments • Development of production system • Remaining work • (Scientific) conclusions

  27. Production system for ELDAS • Requirements: • Annual database (1.10.1999–31.12.2000) of soil moisture for Europe • 0.2 x 0.2 regular lat/lon grid (15W-38E,35N-72N) • Computer time (cost efficiency) • Starting point: • Experiments based on Single Column version of the ECMWF’s NWP model (SCM) • Solutions: • Add 1: Run n x n SCMs over Europe (each SCM runs independently) • Add 2: • Run SCMs only for land points (about 25 000 SCMs) • I/O consideration • Open MP • Add 3: Supervisor Monitor Scheduler (SMS)

  28. Progress of work • Changes to the SCM +SMA source code • SCM structure has been changed to run n x n SCMs in one run • I/O netcdf  I/O grib • OpenMPI parallelization (up to 8 processes on one thread) • Forcing data • Composition of forcing data changed from one point to n x n points • Output netcdf  Output grib • Control Structure • First SMS layout

  29. Get forcing data from Mars archive • Prepare data for SCM INPUT 1) Background run • Get forcing data from Mars archive • Prepare data for SCM INPUT • Soil moisture perturbation • Soil temperature perturbation 1) Soil moisture analysis • Final (soil moisture) trajectory • Check success of SMA (Costfunctions) • Soil temperature analysis • Final (soil temperature) trajectory • Check success of STA (costfunctions) • Forecast run

  30. Layout • Introduction • Assimilation (OI vs. KF and synthetic mw Tb) • Assimilation of observed mw Tb • Additional experiments • Development of production system • Remaining work • (Scientific) conclusions

  31. Future work (1) • Run validation points first in SCM mode • Satisfies the validation group needs • Early warning on system performance • Learning exercise for Janneke • But entails: • Delays on start of production • Extra work on software development (e.g., era40 forcing, validation diagnostics) • Production system • What is still missing? • Interpolation from gaussian grid to reg. 0.2 x 0.2 lat/lon grid • Incorporation of ELDAS maps (e.g. land cover) • Incorporation of ELDAS forcing data (precipitation, radiation) • Archiving of output in MARS • Observation (Re-analysis) data of 2mT and 2mRH for SMA +STA • Post-processing routines for parameters especially asked for by ELDAS validation • ECMWF orography problems (LW) • Final tests

  32. Production system • Estimated Production Time • Analysis for one day • One SCM run for 1000 pixels needs 5 min on 8 nodes  ~ 2 hours for 25000 pixels • 5 x SCMs are needed  10 hours for 25000 pixels • Approx. 5-6 months for annual database • Further parallelization needed • Splitting Europe into boxes • MPI, distributed memory • Run the system at t511 (resolution 39 km) • Expected Start of production • Under normal circumstances • 6 weeks required to include missing bits and pieces • 2 weeks final tests ?

  33. Layout • Introduction • Assimilation (OI vs. KF and synthetic mw Tb) • Assimilation of observed mw Tb • Additional experiments • Development of production system • Remaining work • (Scientific) conclusions

  34. Conclusions • An E(xtended)KF was developed for land data assimilation, for the assimilation of observations of screen-level T/RH, mw Tb, and SHR, a flexible introduction of new observation types, and usage of observed radiation and precipitation. • The properties of such a system were systematically explored in a controlled environment (the atmosphere acts as a buffer, but the system does not feed back to the atmosphere), and confronted to the OI system operational at ECMWF, using the Single Column Model. • “The devil is in the details”: The ratios sigma_o/sigma_b for the different observation types determines the response of the assimilations system. • Screen-level parameters and mw Tb contain independent, and often contradictory, information on soil moisture, with possible contradictory impacts on surface fluxes. NWP centres tend to tune the assimilation to fit the evaporative fraction, since that is the quantity impacting on the atmosphere. • The assimilation system will face biases (in both model and observations), mismatches of soil/vegetation parameters between model and real world, …

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