1. 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

2. 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

3. 5 different lower boundary datasets were used TopographyLand use – GTOPO 30 Resolution = 30” Year = 1996 – SRTM Resolution = 3” Year = 2005 – ASTER Resolution = 1” Year = 2006 – USGS Land Use Resolution = 30” Year = 1993 Categories = 25 – CORINE Land Cover Resolution = 100 m Year = 2006 Catgories = 44 Default in WRF Recategorisation according to Pineda et al. (2004) in order to be compatible with WRF Introduction

4. → 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

5. → Triple domain with two-way nesting Model Configuration d01d02d03 Horizonta Res. (km) 2551 Time step (s) 150306 Method

6. → Observed data location → ● Portuguese Meteorological Institute → ○ Madeira's Regional Laboratory of Civil Engineering Method

7. 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 ~ S obs Bias 2 << E 2 E < S obs E UB < S obs

8. 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

9. 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. 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 Difference CTL Results

11. 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

12. USGS land use CORINE land use Results

13. 10 m mean wind intensity differenceTotal accumulated precipitation difference CORINE – CTL differences → There are only small differences for this particular event – specially for precipitation → Topography gives greater differences Results

14. Componente uComponente 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

15. Taylor DiagramSkill 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

16. 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

17. → 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