1 / 15

BACY = Basic Cycling A COSMO Data Assimilation Testbed for Research and Development

BACY = Basic Cycling A COSMO Data Assimilation Testbed for Research and Development Roland Potthast, Hendrik Reich, Christoph Schraff, Klaus Stephan, Andreas Rhodin, and many more … Deutscher Wetterdienst, Offenbach, Germany March 17, 2014.

megan
Download Presentation

BACY = Basic Cycling A COSMO Data Assimilation Testbed for Research and Development

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. BACY = Basic Cycling A COSMO Data Assimilation Testbed for Research and Development Roland Potthast, Hendrik Reich, Christoph Schraff, Klaus Stephan, Andreas Rhodin, and many more … Deutscher Wetterdienst, Offenbach, Germany March 17, 2014

  2. Full NWP System – IntegratesCycledModel and Data Assimilation Foryourresearch: Work withthefullsystem! Example: Radar Data 3d Volume Scan

  3. Full NWP System – IntegratesCycledModel and Data Assimilation } Analysis Cycle { Forecast Shooting Loop • Some Arguments: • Test modeldevelopmentsandforecasts in a realistic „small“ or „baby“-cyclingenvironment (BACY) • Model developments will stronglyinfluencethebehaviourofthecycledsystemandthecorrespondingforecasts (feedbackloops!) • Just testingchangesofforecastswhenmodeldevelopmentsarecarried out isonly a partofwhatreallyhappens • Observeandtreatrealisticdevelopmentofbiaseswhichoftenarisesbymultiplyereffectofcycling • Test theinfluenceofnewobservationsandrathereasilyintegratetheminto an NWP environement (withoutrunningthewhole DB System)

  4. Regional Model needsBoundaryConditionsfrom Global Model Global Model provides Boundary Conditions BC BC Efficient Treatment of Boundary Data gme_sub icon_sub … int2lm

  5. Take Part in DA + full NWP Development KilometerScaleEnsembleDataAssimilation Hendrik Reich Variational (3dVar)DeterministicData Assimilation for ICON Hybrid VariationalEnsemble KalmanFilter (VarEnKF) for ICON Boundary conditions (repository) Harald Anlauf Ana Fernandez, Alex Cress

  6. BACY Experiments: KENDA versus Nudging Experiments carried out by Hendrik Reich KENDA LETKF KENDA LETKF KENDA LETKF Deterministic Analysis Deterministic Analysis Deterministic Analysis { Forecast Shooting Loop Forecast Forecast 24h • COSMO-DE Domain, 2.8km resolution • Standard operational configurationof DWD • Bacy Speed 1.2 i.e. 1.2 simulationsdays per day • (6 Days Experiment in 5 days) • Four Experiments with different Setup carried out (adaptivity)

  7. BACY Experiment 4: KENDA versus Nudging EnKFbasicversion ComparablewithNudging

  8. BACY Experiment 4: KENDA versus Nudging EnKFbasicversion ComparablewithNudging

  9. BACY Experiment 4: KENDA versus Nudging EnKFbasicversion ComparablewithNudging

  10. BACY Experiment 4: KENDA versus Nudging EnKFbasicversion ComparablewithNudging

  11. BACY Experiment 4: KENDA versus Nudging EnKFbasicversion ComparablewithNudging See more in Hendrik‘s Talk in the afternoon workshop!

  12. Experiment ofHErZ LMU: KENDA versus COSMO-DE-EPS Experiments by Florian Harnisch and Christian Keil, LMU (1) 15 UTC 10 June - 00 UTC 12 June 2012: → 21-h fc at 00 UTC 11 / 12 June (2) 06 UTC 18 June – 12 UTC 19 June 2012: → 21-h fc at 12 UTC 18 June KENDA: - 3-hourly LETKF dataassimilationofconventionaldata - 3-hourly analysisensemblewith20ensemblemembers - 20 member ECMWF EPS lateral boundaryconditions (16 km) - Nophysicsparametrizationperturbations (PPP) - Multiplicative adaptive covarianceinflation KENDAppp:including 10 physicsparametrizationperturbations (PPP) KENDArtpp: relaxation-to-prior-perturbation inflation (α = 0.75 ) KENDArtps: relaxation-to-prior-spreadinflation (α= 0.95 ) KENDArtps40: 40 ensemblemembers / relaxation-to-prior-spread

  13. Ensemble rank histogram Experiments: KENDA versus COSMO-DE-EPS Experiments carried out by Florian Harnisch and Christian Keil, LMU KENDAppp KENDA +3 h forecasts of 10 m wind speed EnKFimprovedversions Can improve EPS OPER KENDArtps Verified against COSMO-DE analysis (similar results against observations)

  14. BSS: 21-h ensemble forecasts of precipitation Experiments: KENDA versus COSMO-DE-EPS EnKFimprovedversions Can improve EPS 3-21 h forecasts averaged over Germany KENDA KENDAppp KENDArtps OPER KENDA KENDAppp KENDArtps OPER BSS BSS 00 UTC 11 June 2012 00 UTC 12 June 2012 thresholds (mm / 3h) thresholds (mm / 3h) • Brier Skill Score = [resolution – reliability] / uncertainty • Accounting for model errors with PPP shows positive impact • Large impact of inflation procedure 14

  15. Over thepast 8 month ICON developmenthasstronglybenefitedfrom Basic Cycling (Bacy) ICON DA Development • Basic Cycle • ElementaryCycling; principleofsimplicity • File Basedfor Model Fields • Flexible DB/Files forObservations • Usefulfor Debugging • Basic speed check for DA components • Neededforefficient NUMEX implementationandtest

More Related