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

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

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

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

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

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

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

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

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

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

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

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

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