Download ppt - Alpine Weather Forecasting Neil Stringfellow CSCS Swiss National Supercomputing Centre

Alpine Weather Forecasting

Neil Stringfellow

CSCS Swiss National Supercomputing Centre

CSCS – Swiss National Supercomputing Centre

National Supercomputing Centre since 1992 Provides compute facilities and scientific support

to Swiss research community– Federal High Schools, Federal research institutes,

Universities and University of Applied Sciences

Switzerland is currently planning its national strategy in HPC

CSCS also provides facilities to MeteoSwiss for operational weather forecasting

HPC User Forum – Tucson - 9th Sept 2008

CSCS User Base

Scientists drawn from a large number of disciplines

Climate research is a major research field


Climate Modelling at CSCS

One of CSCS “ALPS” projects awarded to model hydrological cycle in Alpine Environment

Various software packages are run at CSCS– Echam5 & Echam5-HAM (Atmosphere & Aerosol)– CCSM & CSM with Carbon Cycle– COSMO climate model (regional and local)

No non-coupled ocean modelling


Economic Importance of Climate Modelling

Tourism– Important to know long-term effects for planning

where to locate ski resorts

Agriculture– Swiss agriculture is expected to benefit from modest

temperature increases (up to 2°C)

Electricity generation– Hydro power requires precipitation– Nuclear power plants require cooling


Water and Electricity Generation

Swiss electricity generation is carbon neutral!– Approx 60% from hydroelectric power plants– Most of the rest is Nuclear

Need to know precipitation levels for electricity generation

Cooling of nuclear power plants relies on water, and the temperature of that water– During the 2003 summer heatwave, electricity

production from nuclear was reduced by 25%


Future Climate Scenarios

Current prediction is for higher temperatures and lower precipitation

Glacial melt will increase in near future but water available for hydro-generation will reduce from present levels by 2050

Warmer water will reduce cooling capacity for nuclear reactors

There is a need for research, and in particular numerical simulation


MeteoSwiss and CSCS

MeteoSwiss is the Swiss federal weather office MeteoSwiss run operational weather forecast

model at CSCS MeteoSwiss runs the COSMO model from the

COSMO consortium– This is a local (not global) model

CSCS provides compute resources and technical and scientific services


High Resolution Forecasting

European Windstorms Lothar and Martin caused destruction and loss of life in 1999

Not detected by national weather services Demands for improved forecasting Additional requirements for accurate forecasting from Nuclear Power

Plant operators


Destruction in black forest due to Windstorm Lothar

European Windstorms - background

Windstorms occur in Winter, typically December to February– Sometimes called “Winter storms” or Orkan

Naming system similar to hurricanes– Names issued by Free University of Berlin

Actually all high and low pressures are named

Historically have caused major loss of life– Mainly due to dyke breaches in Netherlands

Occasionally missed by national weather services– 1987 Storm in United Kingdom– “Lothar” in 1990 by Germany (and others inc. Switzerland)


Features of European Windstorms

Don’t dissipate quickly over land– They sometimes intensify over land

Often occur in clusters of 2 or more– Daria & Herta (Jan 1990)– Vivan & Wiebke (Feb 1990)– Désirée, Esther, Fanny, Hetty (Jan 1998)– Lothar & Martin (Dec 1999)

Wind speeds, insurance losses and fatalities are similar to U.S. hurricanes– No massive loss of life in modern times to compare with

Hurricanes Jeanne and Katrina


Swiss Topography

High mountains and deep valleys lead to extreme winds during storms– 225 km/h on Aetsch Glacier for Kyrill– 285 km/h at Jungfraujoch for Wiebke


Insurance Losses

European Windstorms are the second highest cause of insurance losses– Highest losses are caused by U.S. Hurricanes

Average annual loss is around $2 Billion 5 of top 20 biggest ever insurance losses are

due to European Windstorms


Losses of Big Storms

Position in top 40 all time losses

Wind Storm

Year Loss in US $Billion

Fatalities

11 Daria 1990 7.4 95

12 Lothar * 1999 7.2 110

13 Kyrill * 2007 6.1 54

14 “1987 Storm”

1987 5.7 22

16 Vivian * 1990 5.0 64

27 Martin * 1999 3.0 45

35 Anatol 1999 2.4 20


* affected Switzerland

Combined Lothar/Martin (25th & 27th Dec. 1999) would be 8th largest loss

Source: Swiss Re

Lothar/Martin – December 1999

Storm Lothar crossed France, Germany and Switzerland on 24th & 25th December 1999

Storm Martin followed a similar path on 26th & 27th December

Many fatalities, billions of dollars of damage Not predicted by National Weather Services


Advances in Prediction

Study of prediction of Lothar/Martin (Walser et. al) looked at 3 aspects– Moist Singular Vectors

Different approach to calculate initial perturbations for ensemble forecasts

– Increased Resolution– Ensembles

Showed great potential for improved forecasts


opr SVs, x~80 km

Forecast storm Lothar: max. wind gusts t+(42-66) (1)

Configuration:

• opr SVs, 80 km

• opr SVs, 10 km, 80 km topo

• moist SVs, 10 km,80 km topo

• moist SVs, 10 km

• moist SVs, 10 km,10 members

Configuration:

• opr SVs, 80 km





opr SVs, x~10 km, x topography ~ 80 km


Configuration:

• opr SVs, 80 km





moist SVs, x~10 km, x topography ~ 80 km


Configuration:

• opr SVs, 80 km





moist SVs, x~10 km


Configuration:

• opr SVs, 80 km





moist SVs, x~10 km, 10 members


Going from 80km to 10km


ECMWF EPS (80 km) COSMO-LEPS (10 km)

Current Situation of MeteoSwiss

Forecast runs on a 896 core Cray XT4

Runs 8 times per day for ~ 30 mins



Need for High Resolution

The forecast simulation resolves Switzerland using a two-grid refinement– coarse 6.6km spacing between grid points

385 x 325 grid, 60 atmospheric levels over Western Europe, 72 second time step with numerical leapfrog scheme

– fine simulation uses 2.2km spacing 520 x 350 grid, 60 atmospheric levels over “Alpine Arc”, 20

second time step with Runge-Kutta numerical scheme

Many features in Switzerland were not resolved at the older 7km resolution– Few valleys are resolved at this resolution

Resolution change 6.6km to 2.2km


COSMO-7 (6.6 km) COSMO-2 (2.2 km)


Example - Magadino Plain

Magadino Plain is the lowest part of Switzerland– Lowest point is on shore of

Lago Maggiore Plane is surrounded by

mountains At 6.6km resn it resolves to

be a 1km high plateau At 2.2km resn it has a

valley floor at 200m height

Parameterisation v Direct Simulation

At low resolution many features cannot be directly modelled - have to be parameterised

Higher resolutions allow more physics 6.6km -> 2.2km deep convection is computed

explicitly Higher resolution also allows modelling of

valley winds


Full Suite

7 components– Interpolation, assimilation and 24 hour forecast on coarse grid– Interpolation and assimilation on fine grid– Interpolation and 24 hour forecast on fine grid

All components have to complete in ~20 minutes– To allow for data post-processing to complete within 30

minutes of start

Suite runs every 3 hours Twice per day a 72 hour coarse grid forecast is added


Model Heirarchy


ECMWF IFSECMWF IFS (global)

• 25km, 91 levels• 2 x 240h per day

+ 2 x 78h per day

COSMO-7 (regional)

• 6.6km, 60 levels• 2 x 72h per day

COSMO-7

COSMO-2 (local)

• 2.2km, 60 levels• 8 x 24h per day

COSMO-2

Full Suite Timeline


12 15 18 21 00 03 06 09 12 15 18

COSMO-7 COSMO-2

Time UTC

.. +72h

.. +72h

Full Suite Forecast

Example of Improvement - Wind

South of Zurich Lake

Wind field at 6.6km and 2.2km resolution

Features only resolved at high resolution


Other Extreme Events in Switzerland

Summer Flooding– Summer floods over central Europe

in 2005– 38th largest insurance loss 1970-

2007 (Swiss Re)

Summer Heatwaves– European heatwave of 2003

responsible for 35,000 deaths 8th largest number of deaths from natural

catastrophe 1970-2007

Others, e.g. hailstorms halted Tour de Suisse in 2007


HPC Issues in Climate/Weather

What is typical high-end Climate HPC work? Future Modelling in Climate/Weather

– Higher resolution– More physics– Ensembles

Very complex and large codes– Not likely to be an early adopter or new languages– No compact kernel for accelerators


I/O Rate and Storage

Many codes use proprietary formats– Grib format in European codes

No widespread adoption of parallel I/O– often I/O is done on one or a few processes

Increasing amounts of data being generated– reluctance to delete data– two-thirds of CSCS archive is used for Climate

and Weather data


Acknowledgements

Great many thanks go to Andre Walser and Daniel Leuenberger of MeteoSwiss for providing slides and answering questions
