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10-day forecasts for Melbourne, initialized on Tuesday 12 UTC

10-day forecasts for Melbourne, initialized on Tuesday 12 UTC. Cloud Cover Precipitation Wind Speed Temperature. Melbourne, UK Melbourne, Australia Melbourne, Tennessee. Impact of Rainfall Observations in ECMWF 4D-Var Data Assimilation System

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10-day forecasts for Melbourne, initialized on Tuesday 12 UTC

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  1. 10-day forecasts for Melbourne, initialized on Tuesday 12 UTC Cloud Cover Precipitation Wind Speed Temperature Melbourne, UK Melbourne, Australia Melbourne, Tennessee

  2. Impact of Rainfall Observations in ECMWF 4D-Var Data Assimilation System Peter Bauer, Philippe Lopez, Alan Geer, and Deborah Salmond European Centre for Medium-Range Weather Forecasts (ECMWF) Shinfield Park, Reading, Berkshire, RG2 9AX, UK peter.bauer@ecmwf.int

  3. forward inverse xa, A AN state xb, B FG state, error xi State update y(xb.i), F Moist physics-RT model xJ Adjoint: Moist physics-RT model yJ yo, E Satellite data (1D+) 4D-Var Assimilation

  4. Low-level stratus Thin cirrus Typhoon Matsa (04/08/2005 00 UTC) Used SSM/I Clear-sky Channel 3 (22 GHz) Used SSM/I 1D-Var (GMS IR, DMSP F13-15) (Courtesy G. Kelly)

  5. Model too dry Model too moist Typhoon Matsa (04/08/2005 00 UTC) TCWV FG-Departures (GMS IR, DMSP F13-15) (Courtesy G. Kelly)

  6. Typhoon Matsa (04/08/2005 00 UTC) 4D-Var moisture increments with rain assimilation (colors in %), 900 hPa wind increments (white arrows), surface pressure (isolines). MTSAT Infrared image of typhoon MATSA approaching Taiwanese and Chinese coast on August 4, 2005, 00 UTC.

  7. Global operational systems: ECMWF Mean TCWV Analysis/Forecast Difference 08-10/2004 Rain – No Rain Analysis Day 2 (48h) Day 3 (72h) Day 5 (120h) [kg m-2] [kg m-2] [kg m-2] [kg m-2]

  8. Global operational systems: ECMWF Mean MSLP Analysis Difference 08-10/2004 Rain – No Rain Rain Assimilation produces larger MSLP than control Rain Assimilation produces lower MSLP than control [hPa]

  9. Global operational systems: ECMWF Mean 850 hPa Divergent Wind Analysis Difference 08-10/2004 Rain – No Rain Divergence Convergence

  10. Global operational systems: ECMWF Mean Relative Humidity Forecast RMSE Difference 08-10/2004 Rain – No Rain 90% Statistical significance Improvement Deterioration

  11. Case studies: South Atlantic 12Z, 14/08/2005 MSLP TCWV B A Meteosat VIS channel imagery

  12. Case A: Stratiform Precipitation, high TCWV Rain/snow Cloud water/ice Cloud cover temperature dewpoint Analysis First guess 3D model

  13. Case B: Convective Precipitation, low TCWV Rain/snow Cloud water/ice Cloud cover dewpoint temperature Analysis First guess 3D model

  14. Example: 1st cycle ITCZ East Pacific: Rain FG-Departures AN-Differences CY30R2/CY31R1: Moisture Analysis Rain – No Rain Mean August 2004 TCWV difference: Rain FG-Departures AN-Differences unbiased net drying

  15. TCWV increments 1-cycle: 2005080100 1D+4D-Var Assimilation system (operational since June 2005) Direct 4D-Var Assimilation system (experimental) [kg m-2]

  16. RMSE Z1000 N.AMER (21 cases, own ana) RMSE Z200 N.ATL (21 cases, own ana) Global experimental systems: ECMWF Assimilation over Land: NCEP Stage IV rain accumulations • Observations: Combined hourly rain-gauge and NEXRAD radar precipitation accumulation. • ~1200 observations over continental US per 12-hour cycle. • Available in near real-time incl. quality indicators. • Assimilation experiments: • T511L60, CY29R2, 20/05-20/06 2005;1D+4D-Var, 1D-Var uses only moist physics in observation operator. • Add Stage IV observations to operational data set:

  17. noqUS+StageIV – noqUS noqUS – control Global experimental systems: ECMWF 4D-Var Data Denial Experiment control: all observations (incl. SSM/I rain assimilation over oceans); Experiments noqUS: withdraw TEMP-q, RH2m, HIRS, AMSU-B, SSM/I, AIRS, GOES-WV over US; noqUS+StageIV: as noqUS but add Stage IV data. Mean differences of TCWV analyses at 00UTC

  18. Global experimental systems: ECMWF Assimilation over Land: TRMM 2A12 Rain Rates Mean TCWV analysis increments 1-25/07/2006 at 00UTC

  19. Global experimental systems: ECMWF 24-hour Forecast Differences: Rain – No Rain Rain TCWV RR24h Rain - No Rain TCWV RR24h

  20. Summary Analysis system: General:1st global operational 4D-Var system (since June 2005)! Introduces first satellite observations in cloud/rain affected areas. Incremental 4D-Var efficient and well behaved. 1D+4D-Var: Advantage: - additional quality control, - safe first implementation, Disadvantage: - background fields are used twice, - only moisture increments provided to 4D-Var, - loss of vertical information, - only instantaneous measurements are assimilated. Observations: - Radiance measurements provide sensitivity in both clear and cloudy conditions (compared to rain rate observations). - Microwave radiances very accurately measured, stable calibration, continuous availability from operational satellites. - Combined microphysics + microwave radiative transfer operator well behaved and not too non-linear. Errors: - Observation + modelling errors can only be indirectly derived (but realistic). - Background errors require improved specification inside clouds/precipitation. Control variable: - Desirable: Control variable that includes water vapour and condensates.

  21. Future • Employ 1D+4D-Var system with microwave sounder/imager observations over land surfaces. • Fully implement direct 4D-Var radiance assimilation system: • - make use of quality control lessons learned from 1D+4D-Var system. • - allow variable use of different imager/sounder channel combinations as in clear-sky applications. • - extend to infra-red observations from space. • Requires: • - Background error formulation that is more cloud/precipitation specific. • - Developments towards total water control variable. • - Continuous efforts towards improved moist physics parameterizations. • Combined efforts of physics/data assimilation/satellite sections at ECMWF • Programmatic involvement: • - Global Precipitation Measuring (GPM) mission, NASA & JAXA, 2013+. • - Post-EPS programme, Eumetsat, 2020+. • - Earth Explorer programme, ESA, uncertain. • - WMO International Precipitation Working Group (IPWG).

  22. AMSR-E+ AMSR-E+ AMSR-E+ AMSR-E+ Definition of passive microwave imager specs for post-EPS • Eumetsat Polar System (EPS) follow-on, 2020+ • Study on dedicated specifications for clouds and precipitation: channel selection • Channel identification and hydrometeor retrieval accuracy estimation (also with AMSR-E as baseline) Rain Snow ocean land (Channel priorities for land/ocean surfaces, global profile datasets, optimal estimation theory, x-axis: mean entropy reduction)

  23. Publications Andersson, E., P. Bauer, A. Beljaars, F. Chevallier, E. Hólm, M. Janisková, P. Kallberg, G. Kelly, P. Lopez, A. McNally, E. Moreau, A. Simmons and J.-N. Thépaut, 2005: Assimilation and Modelling of the Hydrological Cycle. Bull. Amer. Meteor. Soc., 86, 387-402. Andersson, E., E. Hólm, P. Bauer, A. Beljaars, G.A. Kelly, A.P. McNally, A.J. Simmons, and J.-N. Thépaut, 2006: Analysis and forecast impact of the main humidity observing system. Q. J. Roy. Meteor. Soc., submitted. Bauer, P., J.-F. Mahfouf, S. di Michele, F.S. Marzano, W.S. Olson, 2002: Errors in TMI rainfall estimates over ocean for variational data assimilation. Q. J. Roy. Meteor. Soc., 128, 2129-2144. Bauer, P., E. Moreau, F. Chevallier, and U. O'Keefe, 2006: Multiple-scattering microwave radiative transfer for data assimilation applications. Q. J. Roy. Meteor. Soc., 132, 1259-1281. Bauer, P., P. Lopez, A. Benedetti, D. Salmond, and E. Moreau, 2006: Implementation of 1D+4D-Var assimilation of precipitation affected microwave radiances at ECMWF, Part I: 1D-Var. Q. J. Roy. Meteor. Soc., in press. Bauer, P., P. Lopez, A. Benedetti, D. Salmond, S. Saarinen and M. Bonazzola, 2006: Implementation of 1D+4D-Var assimilation of precipitation affected microwave radiances at ECMWF, Part II: 4D-Var. Q. J. Roy. Meteor. Soc., in press. Benedetti, A., P. Lopez, P. Bauer, and E. Moreau, 2005: Experimental use of TRMM Precipitation Radar observations in 1D+4D-Var assimilation. Q. J. Roy. Meteor. Soc., 131, 2473-2495. Benedetti, A., P. Lopez, E. Moreau, P. Bauer and V. Venugopal. 2005: Verification of TMI-Adjusted Rainfall Analyses of Tropical Cyclones at ECMWF Using TRMM Precipitation Radar. J. Appl. Meteor., 44, 1677-1690. Chevallier, F., P. Bauer, J.-F. Mahfouf, and J.-J. Morcrette, 2002: Variational retrieval of cloud profiles from ATOVS observations., Q. J. Roy. Meteor. Soc., 128, 2511-2525. Chevallier, F. and P. Bauer, 2003: Model rain and clouds over oceans: Comparison with SSM/I observations. Mon. Wea. Rev., 131, 1240-1255. Errico, R., P. Bauer, and J.-F. Mahfouf, 2006: Assimilation of cloud and precipitation data: Current issues and future prospects. J. Atmos. Sci., submitted. Lopez, P., and E. Moreau, 2005: A convection scheme for data assimilation: Description and initial tests. Q. J. Roy. Meteor. Soc., 131, 409--436. Lopez, P., A. Benedetti, P. Bauer, M. Janisková, M. and M. Köhler, 2006: Experimental 2D-Var assimilation of ARM cloud and precipitation observations, Q. J. Roy. Meteor. Soc., 132, 1325-1347. Mahfouf, J.-F., P. Bauer, and V. Marécal, 2003: The comparative impact of the assimilation of SSM/I and TMI rainfall rates in the ECMWF 4D-Var system, Q. J. Roy. Meteor. Soc., 131, 437-458. Marécal V. and J.-F. Mahfouf, 2000: Variational retrieval of temperature and humidity profiles from TRMM precipitation data. Mon. Wea. Rev., 128, 3853-3866. Marécal, V., J.-F. Mahfouf, and P. Bauer, 2002: Comparison of TMI rainfall estimates and their impact on four-dimensional variational rainfall assimilation. Q. J. Roy. Meteor. Soc., 128, 2737-2758. Marécal V. and J.-F. Mahfouf, 2002: Four-dimensional variational assimilation of total column water vapour in rainy areas. Mon. Wea. Rev., 130, 43-58. Marécal V. and J.-F. Mahfouf, 2003: Experiments on 4D-Var assimilation of rainfall data using an incremental formulation. Quart. J. Roy. Meteor. Soc., 129, 3137-3160 Moreau, E., P. Bauer, and F. Chevallier, 2002: Variational retrieval of rain profiles from spaceborne passive microwave radiance observations. J. Geophys. Res., 203, D16, 4521, doi: 10.1029/2002JD003315, ACL11-1-ACL11-18. Moreau, E., P. Lopez, P. Bauer, A. Tompkins, M. Janisková, and F. Chevallier, 2003: Rainfall vs. microwave brightness temperature assimilation: A comparison of 1D-Var results using TMI and SSM/I observations. Q. J. Roy. Meteor. Soc., 130, 827-852. O'Dell, C.W., P. Bauer, and R. Bennartz, 2006: A fast cloud overlap parameterization for microwave radiance assimilation. J. Atmos. Sci., submitted.

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