1. Statistical vs. Physical Adaptation
T2m Nowcasting:
Statistical vs. Physical Adaptation
T. HAIDEN, S. GREILBERGER, A. SCHMALWIESER
ZAMG, Vienna, Austria
Cloudiness
Problem
Hourly updated forecasts
Error characteristics
Evening cooling
Process-oriented approach
Advection
T2m
Wind speed
Soil temperature
May 2003
SWSA 2003 – Vienna

2. Problem Customer request (power industry):
High precision, hourly, T2m point forecasts for the next 4 hours (12 hours),
during winter half year, at location Vienna Hohe-Warte.
May 2003
SWSA 2003 – Vienna

3. Adjusted LAM skill > Climatology skill > LAM DMO skill
T2m nowcasting error
Adjusted LAM skill > Climatology skill > LAM DMO skill
May 2003
SWSA 2003 – Vienna

4. T2m error distribution during the first forecast hours
Error mostly between –2 and +2 K
Occasional outliers with error of 3-6 K (non-Gaussian)
May 2003
SWSA 2003 – Vienna

5. Error characteristics
Air mass change (frontal passage): timing problem
Amount/speed of evening cooling overestimated
May 2003
SWSA 2003 – Vienna

6. Error characteristics (contd.)
Improvement by statistical adaptation (MOS, Kalman, etc)
limited by NWP forecast quality
May 2003
SWSA 2003 – Vienna

7. Evening cooling cooling little/no cooling cooling May 2003
SWSA 2003 – Vienna

8. Reduced leeside cooling
3-d high resolution (1 km) model necessary?
Statistical correction?
May 2003
SWSA 2003 – Vienna

9. Low stratus MODEL OBS Temperature inversion too smooth
Inversion base too warm  cloudiness underestimated
Underestimated cloudiness  PBL cooling too weak
May 2003
SWSA 2003 – Vienna

10. Low stratus 1-d experiments
Experiment I: Vertical diffusion + subsidence throughout PBL
00 UTC obs
12 UTC obs
12 UTC forecast
May 2003
SWSA 2003 – Vienna

11. Low stratus 1-d experiments
Experiment II: Vertical diffusion + subsidence above PBL
00 UTC obs
12 UTC obs
12 UTC forecast
May 2003
SWSA 2003 – Vienna

12. Low stratus 1-d experiments
Experiment III: Vertical diffusion + subsidence above PBL + `cloud-top cooling`
00 UTC obs
12 UTC obs
12 UTC forecast
May 2003
SWSA 2003 – Vienna

13. Process-oriented approach
Cloudiness
Advection
T2m
Wind speed
Soil
1-d model: radiation fluxes, turbulent fluxes, surface exchange
Run every hour, use adapted model sounding as initial condition
Cloudiness: extrapolate observed trend (+ trajectories)
Advection: apply trajectories to observed temperature distribution
Wind speed: weighted combination of model and observation
Soil: use observed near-surface temperatures, soil conditions
! perform separate verification of individual modules
May 2003
SWSA 2003 – Vienna