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Impact of AMDAR/RS Modelling at the SAWS

Warren Tennant Impact of AMDAR/RS Modelling at the SAWS Weather Forecast Modelling at the SAWS UK Met Office Unified Modelling system running operationally at the SAWS since September 2006 Installed under an operational licence with Met Office that includes:

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Impact of AMDAR/RS Modelling at the SAWS

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  1. Warren Tennant Impact of AMDAR/RS Modelling at the SAWS

  2. Weather Forecast Modelling at the SAWS • UK Met Office Unified Modelling system running operationally at the SAWS since September 2006 • Installed under anoperational licence with Met Office that includes: • using the model for operational forecasting responsibilities • Includes limited commercial use of model output • Real-time feed of model input data (initial conditions and observations) from Met Office • Software support and upgrades • Scientist exchange and visits

  3. SAWS National Responsibility • SAWS is responsible to the public of South Africa to provide: • national forecasts of basic weather conditions up to three days ahead • advisories of high-impact weather conditions up to 48-hours ahead and longer where possible, and • warnings of imminent severe weather, to • safeguard lives and property and mitigate the impact of weather on South Africans

  4. SAWS SADC Responsibility • SAWS serves as a WMO Regional Specialised Meteorological Centre (RSMC) for the Southern Africa Development Community (SADC) • Plays a backup role in the event of national disasters, e.g. Tropical Cyclone in Mozambique • Provides NWP guidance products to SADC as part of the WMO/CBS Severe Weather Forecasting Demonstration Project (SWFDP) • Process to role-out UM SA12 forecasts (images and data) to SADC countries has been started

  5. SA12 Regional Model(based on Met Office NAE model) • Reconfigure global input for 48hr fcst at 00Z • 3DVAR at 06 & 12Z from above => 48hr fcst at 12Z • Continuous 3DVAR 6-hourly cycle => 48hr fcst at 00Z

  6. Observations in southern Africa • SAWS has a moderate upper-air network • 8 GPS stations • 2 Island stations • QC and availability good • Rest of southern Africa is a rawindsonde void

  7. Global AMDAR Availability 00Z

  8. Global AMDAR Availability 06Z

  9. Global AMDAR Availability 12Z

  10. Global AMDAR Availability 18Z

  11. SAA-AMDAR Coverage

  12. OSE Experiment Design • Continuous 3dVAR 6-hourly cycle • Control: All observations • Experiment: No AMDAR or Rawindsondes • Independent: Interpolated global model 4dVAR initial conditions (no LAM DA) • Winter case: 1 May to 8 Jul 2007 • Summer case: 24 Oct 2006 to 18 Jan 2007 • Verification:~2000 rainfall stations and ~10 rawindsonde stations in South Africa

  13. Wind-speed verified against Rawindsondes • Obvious impact on analyses • Significant impact at 24 hours • Little impact at 48 hours (slight degradation at 250hPa) • Forecast without DA better scores!

  14. Spatial ACC of wind-speed :: MJJ • Impact of AMDAR/RS on wind speed is positive throughout • True even if using global 4dVAR analysis as verification standard • Run with no-DA sometimes better than DA run – especially after 48 hours

  15. Temperature temporal correlation to Radiosondes • Mostly positive impact • Some cases where no-DA works better • possibly because 4dVAR initial conditions from global model better • Inconsistency at LBCs from LAM DA initial conditions

  16. Spatial ACC of temperature forecasts • Similar to wind speed results • Positive impact throughout • Less dependency of impact on forecast lead-time

  17. Rainfall forecast verification :: BIAS • Bias expressed as a percentage of the observed rainfall • Summer Case: • No strong impact on forecast day 1 • On day 2 more positive impact, except very light rain

  18. Rainfall forecast verification :: BIAS • Winter Case: • Bias similar on forecast day 1 • Slight negative impact for light rain amounts on day 1 • On day 2 not a positive impact as with summer case

  19. Rainfall forecast verification :: RMSE • Summer Case: • No strong impact on forecast day 1 • On day 2 positive impact for all thresholds • DA runs better than no-DA run except for heavy rain

  20. Summary and Conclusions • AMDAR/RS have a definite positive impact on regional model 3dVAR forecasts in southern Africa • Impact decreases with forecast lead-time :: signal probably influenced by LBCs • Best impact found with wind in mid-upper troposphere in tropics • Data assimilation plays an important role in rainfall initialisation • Impact on rainfall best seen in day 2 forecasts and with heavy rainfall in winter

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