P GoswamiCentre for Mathematical Modelling and Computer Simulation

Bengaluru

Will HPC Ever Meet the Demand of Weather and Climate Forecasting

REACH-2010IIT-Kanpur

Why doubt the power of computing?!

By 2050 the cost of computing comparable to 1 Billion Human brains will be US$ 1000

By 2050 each human being will want customized personal forecast!

What will such demand mean for computing?

Atmosphere: A thermally active (water in three phases, with phase transition) mechanical system with interacting and dynamic boundary conditions

External Persistent Forcing: Solar Radiation, Lower Boundary

Random Forcing: Volcanoes, Forest Fire etc.

Anthropogenic Forcing: Emissions, Land Use

The Grandest Challenge in Computing

Sl No System Cahracteristics Scales Extreme Scales

(km)

Resolution Reqd

Spatial

(Kms)

Temporal

(hours)

Largest Smallest Spatial

(Km)

Temporal

(minutes)

1. Extreme Weather 10 0.25 Global <1 <1 <1

2. Tropical Cyclone 1000 1 Global <1 <1 <1

3. Monsoon 10,000 1 ≥Global <1 <1 <1

4. Regional Climate 10,000 1 ≥Global <1 <1 <1

5. Global Climate 10,000 1 ≥Global <1 <1 <1

6. Geo-Dynamics 105 ? ≥Global ? ? <1

7. Solar systems and Space weather

1010 ? 1010 ? ? ?

8. Stellar Evolution ? ? 1015 ? ? ?

9. Cluster Dynamics ? ? 1018 ? ? ?

10. Galactic Evolution ? ? 1020 ? ? ?

These are Interacting Scales

Forecast of Weather and Climate: The Wish List

On-Demand Forecast (Location, time, variable, resolution)

Projections Backward and Forward in time: Paleo-climate and climate forecast

Reliability: 90%, No False Positive, No False Negative

Forecast (Hindcast) Period: Hour to decades

Range of Forecast: Hours to decades and beyond

Spatial Coverage: Station to global, and beyond

The Measure of our Understanding is our Ability to Forecast

Forecasting Weather and Climate

The ability to forecast depends on power to compute

The Route to Forecasting

Forecasting Weather and Climate

Mathematical RepresentationsVariables and

Relations

Numerical RepresentationParameterization Schemes

Post Processing Initial and Boundary Data

Simplifying Assumptions

Code development

Computing Platform

Error Management

Simulation

Mapping of small scales to large scales

Identification of Scales

The Technology: A Generic Structure of Dynamical Forecasting

Tropical Precip forecast made: 1Apr2006

India

The Promise of Weather Forecasting

NOAA NCEP CPC CAMS_OPI V0208 ANOMALY PRCP JUN-AUG 2007

Model JJA Rainfall Anomaly

Vector wind (m/s) over the Bay of Bengal region on 27 Oct 1999, 00 hour. The left panels represent ECMWF Analysis while the right panels represent model forecasts.The panels represent data for 925mb, 850mb and 200mb, respectively.

Surface Pressure (hPa) over the Bay of Bengal region on 27 Oct 1999. The left panels represent ECMWF Analysis while the right panels represent model forecasts.

The Orissa Super Cyclone: A Case Study

ic: 26 OctWind Vector and Surface Pressure on 27th Oct

Track Forecast Error Bay of Bengal (15 cases: 1980-2000)

Lead -1

Lead 0Forecast Time (hour)

Forecast Time (hour)

Lead +1

Multi-scale Forecasting: Heavy Rainfall Events

Forecast GCM (40km Resolution) (Satellite Observation, 10 km resolution)

Mumbai Heavy Rainfall on 26th July 2005

BANGALOREHeavy Rainfall on 24th October 2005

CHENNAIHeavy Rainfall on 27th October 2005

Multi-scale Forecasting: Heavy Rainfall Events

The circled areas indicate observed locations of heavy rainfall

Satellite observations at 10 Km resolution

Satellite observations at 10 Km resolution

Compromise with Computing

• Are we doing it right?

Models are metaphors; need to use them carefully

Irreducible Model Error and Predictability

Boundary data

Optimum Model Configuration

Reducible ErrorsResolution

Initial Data

Model Configuration

Intrinsic limits on predictabilityFalse limits on predictability

HPC

Nature is subtle; Reaching irreducible error configuration may require more computing than we can afford!

Lower Boundary Forcing may change depending on resolution

Weekly average time series of rainfall (red line) and number of ERE (blue line) >mm/day) both average over the region (70-85E; 5-30N). The CC between the weekly rainfall and ERE counts for each year is given in the respective panel. The blue dots represent distribution of daily counts of ERE. (Goswami and Ramesh, 2006)

Monsoon and Extreme Rainfall Events: Monsoon and Extreme Rainfall Events: A Case of Tail Wagging the Dog?A Case of Tail Wagging the Dog?

Daily Rainfall Daily Rainfall (Satellite) (Satellite)

at at 10 KM 10 KM

ResolutionResolution

Simulation of Weather and ClimateChallenges for Computing and Modelling

• Resolving small scales in a global environment: Resolution

• Removing Forecast uncertainties: Probabilistic Forecasts

• Utilization of Observations: Data Assimilation

• Customization: Sensitivity Experiments

• Industrialization: Location-specific Forecast

• Project with EID Parry: Forecast over sugar cane fields• Project with Govt. Karnataka: Hobli-level Forecast

Science and Cost of Customization

Customization An extremely computing-intensive proposition

Sensitivity of limited area simulations to model domains

Spatial distribution of 30 Hr Accumulated ensemble mean rainfall (cm) for different Domains of 90km resolution

Reducing and Managing Forecast Uncertainty

• The Problem of Forecast Dispersion

• Intra-model Multi-lead/Multi-grid Ensemble• Inter-model Multi-model Ensemble (MI-ERMP)

• Forecast dispersion may be addressed through ensemble forecasting => more computing

Ensemble Forecasting: Instead of classical initial point to final point, initial neighborhood to final neighborhood

An effective ensemble forecast may require hundreds of simulations for a given forecast!

What Type of Computing

Small-ensemble Long Runs

•Climate Simulations•Impact Assessment• ……………………….

Large-ensemble Short Runs

•Short-range Weather Forecasts•Probabilistic Forecasts•……………………….

Parallel Computing Simultaneous Multi-tasking

We may need more than one type of computing architecture to generate

the best forecast in an optimum configuration

Computational Requirement: An Example

• Creation of Monsoon Climatology

Integration Length: 6 months

Number of Time steps: 104

Resolution: 20 km

Number of Horizontal Grid Points: 105

Number of Vertical levels: 50

Ensemble Size: 100

Approximate Computing Time Required on ALTIX 3750 (SP):

100*6*10 = 6000 days !With 30 processor multi-tasking, it is still 200 days of dedicated computing.

Simulation of Weather and Climate

• A Cosmic Problem

Forecast Without Frontier

• Habitat Planning (Location for Sustainability and Health)

• Space Weather (Space Tourism and Freight Services)

• Solar Flares (Satellite and terrestrial blackout Warnings)

• Arctic Weather (Eco-Tourism and Habitat)

Martian Weather (For precision landings and future colonies)

Geo-Cosmological Computations

Beyond Earth Simulator: Cosmo Simulator

and beyond (Stars like Dust)

The Sky is not the limit!!

HPC in Weather and Climate ForecastingSummary

As HPC grows, demand grows:

•Higher Precision: Higher Resolution (larger grid)

•Higher Reliability: Ensemble Forecasts (larger number of forecasts)

• Customized Forecast: Larger Number of Simulations

• Coverage: Earth, Solar system and Beyond (domain size)

• Longer Outlook: Increase in integration time (days to centuries)

• Archival: Cumulative (New unit beyond petabytes!)

Light Years of Computing before we stop;

Happy Computing!

Looking aheadTo simulate the Galaxy at a resolution of cyclonic vortex!

Size: 1028

Number of Grid Points: 1028/103

Integration Time: Millions of years Time step: Decade