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Effects Of Different Model Lower Boundary Conditions In The Simulation Of An Orographic Precipitation Extreme
Event
J. Teixeira, A. C. Carvalho, T. Luna and A. Rocha
Physics Department – University of Aveiro
Correspond to: [email protected]
Topography forced processes are difficult to simulate accurately
Atmospheric Models are sensible to lower boundary conditions
→ Topography driven precipitation
→ Wind flow paths
It is expected → Better description of the lower boundary → Better results
Introduction
5 different lower boundary datasets were used
Topography Land use
– GTOPO 30Resolution = 30”Year = 1996
– SRTMResolution = 3”Year = 2005
– ASTERResolution = 1”Year = 2006
– USGS Land UseResolution = 30”Year = 1993Categories = 25
– CORINE Land CoverResolution = 100 mYear = 2006Catgories = 44
Default in WRF
Recategorisation according to Pineda et al. (2004) in order to be compatible with WRF
Introduction
→ Study WRF model sensitivity to different lower boundary conditions in an extreme orographic precipitation event
Case Study → Extreme precipitation over Madeira island – 20 de February de 2010
Objectives
→ Triple domain with two-way nesting
Model Configuration
d01 d02 d03
Horizonta Res. (km) 25 5 1
Time step (s) 150 30 6
Method
→ Observed data location → ● Portuguese Meteorological Institute→ ○ Madeira's Regional Laboratory of Civil Engineering
Method
It is considered that the model has skill when:
– Modelled standard deviation approximate to the observed
– Model root mean squared error smaller than the observed standard deviation
– Bias squared less than the error squared
Method
• S ~ Sobs • Bias2 << E2
• E < Sobs • EUB < Sobs
Sea level pressure (hPa) Precipitable water (mm)
→ Quick transition from a hight to a low pressure system
→ Large amount of precipitable water available over Madeira – Atmospheric river
Sinoptic Setting – 20 February at 1200 UTC
Method
SRTM – GTOPO30
Topography differences (SRTM – GTOPO30) – WRF 1 km (d03)
→ Higher summits and deeper valleys → GTOPO30 topography is smother
→ Better representation of areas with steep slopes (ex: Ponta do Parco – West)
→ Similar differences for ASTER – GTOPO30
GTOPO30
Results
10 m wind intensity difference (SRTM – CTL)
→ Main differences are located over the island
→ High correlation with topography differences (~ 0.6 – SRTM e ASTER)
→ Small differences at leeward
Mean DifferenceCTL
Results
SRTM – CTL
Total accumulated precipitation difference (SRTM – CTL)
→ Large differences in Madeira's mountainous region→ More precipitation in the summits→ Less precipitation in the valleys
→ Correlation with the topography difference of 0.36 – SRTM and 0.46 – ASTER
→ Similar differences for ASTER simulation
CTL
Results
USGS land use
CORINE land use
Results
10 m mean wind intensity difference Total accumulated precipitation difference
CORINE – CTL differences
→ There are only small differences for this particular event – specially for precipitation
→ Topography gives greater differences
Results
Componente u Componente v
→ Low skill simulating wind
→ Better model performance when the new boundary is used for v wind component
→ Worse model performance when the new boundary is used for u wind component
Taylor Diagrams – Wind
Results
Taylor Diagram Skill Diagram
→ There is skill in simulating precipitation
→ Similar skill results between different simulations
→ Worse skill when the new boundary condition is used
Skill Diagrams – Precipitation
Results
Skill Diagrams – Regions
v wind component – Windward
→ 4 distinct regions have been defined:– Mountainous – Coastal– Windward – Leeward
Windward / Leeward
→ Worse skill result fot precipitation and better for wind at Leeward
→ Better skill result for precipitation and worse for wind at Windward
Precipitation – Leeward
→ Better model skill for the Coastal region – Wind and Precipitation.Particular case for SRTM
Results
→ Large differences between the new boundary and default model datasets
→ There is a change in modelled results – Precipitation and Wind
→ There is a local enhancement of model skill in simulating this extreme precipitation event
Concluding Remarks
However dependent on the representativeness of the location of the observations