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Evaluation of the Latent heat nudging scheme for the rainfall assimilation at the meso-gamma scale

Evaluation of the Latent heat nudging scheme for the rainfall assimilation at the meso-gamma scale. Andrea Rossa* and Daniel Leuenberger MeteoS wiss *current affiliation: ARPA Veneto , Centro Meteo Teolo. Motivation. QPF poorest performance area of NWP, especially in summer (convection)

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Evaluation of the Latent heat nudging scheme for the rainfall assimilation at the meso-gamma scale

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  1. Evaluation of the Latent heat nudging scheme for the rainfall assimilation at the meso-gamma scale Andrea Rossa* and Daniel Leuenberger MeteoSwiss *current affiliation: ARPA Veneto , Centro Meteo Teolo

  2. Motivation • QPF poorest performance area of NWP, especially in summer (convection) • Rainfall assimilation to mitigate spinup effect • Radar observations for high-resolution NWP models • Characteristics of LHN • Buoyancy driven • Computational efficiency • 4DDA, timely assimilation of high-frequency observations • This study: reevaluation of Latent Heat Nudging (LHN) within high-res model using idealised and real radar data arossa@arpa.veneto.it WSN05 Toulouse, 5.-9. September 2005

  3. Idealised Experiments: Setup and Strategy • Model: Local Model (LM) in idealised configuration • Unstable environment supportive for supercell storms • Reference simulation • Surface precipitation for assimilation • 4D fields for validation • Assimilation simulations • Proof of concept with identical twin simulation (CTRL) • Sensitivity to uncertainty in observations and environment arossa@arpa.veneto.it WSN05 Toulouse, 5.-9. September 2005

  4. y z y CTRL + 5 % x x x Reference and CTRL simulation y z y REF x x x RH, W and qe z , W and cold pool total sfc rain arossa@arpa.veneto.it WSN05 Toulouse, 5.-9. September 2005

  5. Delay: 12 min Extrema of w arossa@arpa.veneto.it WSN05 Toulouse, 5.-9. September 2005

  6. Transient phase: 45 min Assimilation increments arossa@arpa.veneto.it WSN05 Toulouse, 5.-9. September 2005

  7. Observation Uncertainty • Amplitude Error • Factor 0.5 (2) results in 88% (133%) of total precipitation • Environment and model dynamics damps error • Temporal resolution • Combined amplitude and structure error • Strong sensitivity • Non-Rain Echoes • Potential strong sensitivity arossa@arpa.veneto.it WSN05 Toulouse, 5.-9. September 2005

  8. Non-Rain Echoes: convective conditions 6h Sum of Model Precipitation 6h Sum of Radar y y arossa@arpa.veneto.it WSN05 Toulouse, 5.-9. September 2005 x x

  9. Non-Rain Echoes: AP conditions Vertical mixing due to strong, spurious updrafts w z arossa@arpa.veneto.it WSN05 Toulouse, 5.-9. September 2005 t x

  10. Environment Uncertainty • Perfect observations, degraded environment • Low-level Humidity • -4% (4%) error in PBL humidity  -14% (11%) precip. deviation • For slightly drier conditions: convection suppressed • For slightly moister conditions: multi-cell development • Environmental Wind • Can lead to distorted system dynamics arossa@arpa.veneto.it WSN05 Toulouse, 5.-9. September 2005

  11. Uncertainty in the Environment: Error in Wind Bias in environmental wind: +2m/s; assimilation only z y y x x x arossa@arpa.veneto.it WSN05 Toulouse, 5.-9. September 2005 Surface precipitation DTLHN, cloud, precip z , W and cold pool

  12. 200 km Case study of 8. May 2003 21 UTC arossa@arpa.veneto.it WSN05 Toulouse, 5.-9. September 2005

  13. CTRL LHN RADAR 17-18 UTC Analysis 21-22 UTC 4h Forecast Simulations with Dx = 2.2km, no CPS arossa@arpa.veneto.it WSN05 Toulouse, 5.-9. September 2005

  14. RADAR RADAR LHN Analysis 1h Forecast 8. May 2003 Storm arossa@arpa.veneto.it WSN05 Toulouse, 5.-9. September 2005

  15. Findings • LHN has good potential for triggering convection • Excellent results in identical twin simulation • Scheme sensitive to observation and environment errors • Non-rain echoes pose serious problem  Data quality • Case study of real storm • LHN triggers storm at right location and intensity • Radar data clearly improves QPF in the first hours • Outflow located too far downstream  position error • Storm kept in model for more than 4h • Storm environment important, particularly PBL! Use mesoscale data assimilation to complement rainfall assimilation (AWS, VAD/radial winds, GPS tomography, PBL profilers) arossa@arpa.veneto.it WSN05 Toulouse, 5.-9. September 2005

  16. Thank you for your attention ! arossa@arpa.veneto.it WSN05 Toulouse, 5.-9. September 2005

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