Download Satellite Observation, 10 km resolution

Will HPC Ever Meet the Demand of
Weather and Climate Forecasting
REACH-2010
IIT-Kanpur
P Goswami
Centre for Mathematical Modelling and Computer Simulation
Bengaluru
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?
The Grandest Challenge in
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
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
Forecasting Weather and Climate
The Measure of our Understanding is our
Ability to Forecast
The ability to forecast depends on power to
compute
Forecasting Weather and Climate
The Route to Forecasting
The Technology: A Generic Structure of Dynamical Forecasting
Identification of
Scales
Mathematical Representations
Mapping of small
scales to large scales
Variables and
Relations
Simplifying Assumptions
Parameterization Schemes
Numerical Representation
Code development
Computing Platform
Post Processing
Error Management
Simulation
Initial and Boundary Data
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
The Orissa Super Cyclone: A Case Study
ic: 26 Oct
Wind Vector and Surface Pressure on 27th Oct
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.
Track Forecast Error
Bay of Bengal (15 cases: 1980-2000)
Lead -1
Forecast Time (hour)
Forecast Time (hour)
Lead 0
Lead +1
Multi-scale Forecasting: Heavy Rainfall Events
Mumbai Heavy Rainfall on 26th July 2005
Forecast GCM (40km Resolution)
(Satellite Observation, 10 km resolution)
Multi-scale Forecasting: Heavy Rainfall Events
BANGALORE
Heavy Rainfall on 24th October 2005
CHENNAI
Heavy Rainfall on 27th October 2005
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
Initial Data
Boundary data
Model Configuration
H
P
C
False limits on predictability
Reducible Errors
Resolution
Optimum Model Configuration
Intrinsic limits on predictability
Nature is subtle; Reaching irreducible error configuration
may require more computing than we can afford!
Lower Boundary Forcing may change depending
on resolution
Monsoon and Extreme Rainfall Events:
A Case of Tail Wagging the Dog?
Daily Rainfall
(Satellite) at
10 KM Resolution
Weekly average time series of rainfall (red line) and number of ERE
(blue line) >mm/day) both average over the region (70-85E; 530N). 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)
Simulation of Weather and Climate
Challenges 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
• Inter-model
Multi-lead/Multi-grid Ensemble
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
Large-ensemble Short Runs
•Climate Simulations
•Impact Assessment
• ……………………….
•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 Forecasting
Summary
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!)
Looking ahead
To 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
Light Years of Computing before we stop;
Happy Computing!