Effects Of Different Model Lower Boundary Conditions In The Simulation Of An Orographic...

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: jcmt@ua.pt

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