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