# Presentation: 232711

## NUMERICAL WEATHER PREDICTION AND

**LINKS WITH CLIMATE APPLICATIONS**

**Environmental Modeling Center**
**NOAA/NWS/NCEP**
- USA
- Acknowledgements:  Steve Lord, David Helms, Geoff DiMego, NWS/OST, John Derber
*http://wwwt.emc.ncep.noaa.gov/gmb/ens/index.html*

- Zoltan Toth

## OUTLINE

**WHAT IS WEATHER, AND WHAT IS CLIMATE?**
**GENERAL DESCRIPTION OF WEATHER FORECAST PROCESS **
- Observations
- Data Assimilation
- Numerical Modeling
- Socio-Economic Applications
**LINKS BETWEEN WEATHER AND CLIMATE FORECASTING**
- Exist in all sub-areas of forecast process
**REGIONAL APPLICATIONS OF WEATHER FORECASTS**
- Extreme events
- Seamless use of forecasts

## What is WEATHER?
Instantaneous atmospheric and related conditions, and their 
Effects on people over short (up to couple of days) periods of time

What is CLIMATE?
Statistics of weather over expanded (longer than a month) periods 

Are there SEPARATE “WEATHER” & “CLIMATE” REALITIES?
No, there is one natural process, with 
Variability on multiple spatial and temporal scales
Both weather & climate are concepts about this natural process,
Emphasizing different aspects of nature; 
Weather more concrete – you can directly experience at the moment
Climate more abstract – one needs to intellectually comprehend effect

FORECASTING weather & climate
Predicting the same reality, “weather process”
Sharing the same basic procedures
Priorities differ according to focus (on weather or climate)

- Instantaneous atmospheric and related conditions, and their
- Effects on people over short (up to couple of days) periods of time
- What is **CLIMATE?**
- Statistics of weather over expanded (longer than a month) periods
- Are there SEPARATE “WEATHER” & “CLIMATE” REALITIES?
- No, there is one natural process, with
- Variability on multiple spatial and temporal scales
- Both weather & climate are concepts about this natural process,
- Emphasizing different aspects of nature;
- Weather more concrete – you can directly experience at the moment
- Climate more abstract – one needs to intellectually comprehend effect
**FORECASTING** weather & climate
- Predicting the same reality, “weather process”
- Sharing the same basic procedures
- Priorities differ according to focus (on weather or climate)

**DEFINITION OF WEATHER & CLIMATE**

## Assess current weather situation
Before we can look into future, understand what is happening now
“Initial condition”

Digest observational information
Bring observed data into “standard” format
“Data assimilation”

Project initial state into future
Based on laws of physics
“Numerical Weather Prediction” (NWP) model forecasting

Apply weather forecast information
Statistical post-processing
“User applications”

FORECASTING IS ULTIMATE TEST OF ALL SCIENCE

- Before we can look into future, understand what is happening now
*“**Initial condition”*
- Digest observational information
- Bring observed data into “standard” format
*“**Data assimilation”*
- Project initial state into future
- Based on laws of physics
*“**Numerical Weather Prediction”* (NWP) model forecasting
- Apply weather forecast information
- Statistical post-processing
*“**User applications”*
***FORECASTING IS ULTIMATE TEST OF ALL SCIENCE***

**THE MAKINGS OF A WEATHER FORECAST –**
*WHAT WE NEED FOR PREPARING A USEFUL FORECAST?*

## OBSERVING THE CURRENT STATE –

**OBSERVING THE CURRENT STATE –**
*SURFACE-BASED SYSTEMS*

- Land surface synop station
- (In situ)

- Land-based radar
- (Remote)

- Ocean buoy
- (In situ)

*Great advances in*
*Remote sensing*

## OBSERVING THE CURRENT STATE –

*SPACE-BASED SYSTEMS*

- Enormous technological advances
- New observing platforms
- New observing instruments

- Vast increase in number of observations

*Logaritmic scale!*

## OBSERVING THE CURRENT STATE –

*REMOTELY SENSED “IMAGES”, INSTEAD OF “DATA POINTS”*

*Satellite imagery*

*Wind speed*
*(Radar)*

*Precipitation type*
*(Radar derived)*

## OBSERVING THE CURRENT STATE –

**OBSERVING THE CURRENT STATE –**
*HOW LARGE AN AREA WE NEED TO OBSERVE?*

- Coherent weather systems (fronts, cyclones)
- Travel with relatively low speed (<50 km/hr)
- Influence of observations spreads through “downstream development”
- Can advance at speed of upper level jet stream (~150 km/hr)
*For extended-range prediction, large areas must be observed*

- Target area

- 1-day
- ahead

- 2-day
- ahead

- 3-day
- ahead

## HOW OBSERVATIONS ARE USED?

*DATA MUST BE MOLDED INTO STANDARD FORMAT*
- ENORMOUS TECHNOLOGICAL EVOLUTION

*“**Weather factory” of the past:*
*Manual analysis*

*Computing machines (1950s)*

*Supercomputers*

## Global Observations 12 UTC 6 hour window

- Polar Satellite Radiances (2 sat)

- Satellite Winds

- DMSP Imager – Sfc winds/PW

## HOW OBSERVATIONS ARE USED?

**HOW OBSERVATIONS ARE USED?**
*DATA MUST BE MOLDED INTO “MODEL” FORMAT*
- Data assimilation combines observed & model forecast data

***Raw data***
- Intermittent
- Noisy
- Not suitable for numerical model

***Assimilated data:***
- Continuous
- Smooth
- Provides model initial state

## HOW CURRENT STATE GETS PROJECTED INTO FUTURE?

*NUMERICAL WEATHER PREDICTION*

*Use Newton’s laws of physics, plus thermodynamics*

*Synoptic forecasting of past*

*Numerical model calculations on 3-dimensional grids*

## STATUS OF WEATHER FORECASTING

- Peak Performance Trend

***NO LIMITS TO WEATHER FORECASTING?***

*1) Observing techniques improve*

*2) Computing power keeps multiplying*

*Logaritmic scale!*

## Initial state is imperfect

- Problems with observations and data coverage
- Problems with assimilating the data
- Imperfect statistical and numerical forecast methods
- Random (and systematic) errors
**Numerical model is imperfect**
- Limited resolution
- Processes represented in model must be truncated
- Spatially
- Temporally
- Physically
- Systematic (and random) errors
**Atmosphere is chaotic**
- Small errors amplify rapidly
- Forecasts lose skill with increasing lead time
- Loss of skill is case specific
*THOUGH SKILL IN FORECASTS EVER INCREASES &*
*LIMITS PUSHED FURTHER OUT IN TIME*
***LIMITS REMAIN - NEED PROBABILISTIC APPROACH***

**LIMITS IN WEATHER FORECASTING**

*More predictable*

*Less predictable*

*Initial state*

*Buizza et al.*

## FORECASTING IN A CHAOTIC ENVIRONMENT - 3DETERMINISTIC APPROACH - PROBABILISTIC FORMAT

**MONTE CARLO APPROACH** –* ****ENSEMBLE FORECASTING***
* ****IDEA*****:****	****	****Sample sources of forecast error**
- Generate initial ensemble perturbations
- Represent model related uncertainty
* ****PRACTICE***:	**Run multiple NWP model integrations**
- Advantage of perfect parallelization
- Use lower spatial resolution if short on resources
-  ***USAGE*****:**		**Construct forecast pdf based on finite sample**
- Ready to be used in real world applications
- Verification of forecasts
- Statistical post-processing (remove bias in 1st, 2nd, higher moments)
***CAPTURES FLOW DEPENDENT VARIATIONS***
***	******	******	******	******	******	****** ******IN FORECAST UNCERTAINTY***

## No matter what / how sophisticated forecast methods we use

- Forecast skill limited
- Skill varies from case to case
**Forecast uncertainty must be assessed by meteorologists**

**HOW TO DEAL WITH FORECAST UNCERTAINTY?**

***THE PROBABILISTIC APPROACH***

## SOCIO-ECONOMIC BENEFITS OFSEAMLESS WEATHER/CLIMATE FORECAST SUITE

- Minutes

- Days

- Hours

- Years

- Seasons

- Months

***Protection of Life/Property***

***Flood mitigation***
***Navigation***

***Transportation***
***Fire weather***

***Hydropower***
***Agriculture***

***Ecosystem***
***Health***

***Commerce***
***Energy***

***Initial Condition Sensitivity***

***Boundary Condition Sensitivity***

**SOCIO-ECONOMIC BENEFITS OF****SEAMLESS WEATHER/CLIMATE FORECAST SUITE**

***Reservoir control***
***Recreation***

## Assess current weather situation
Before we can look into future, understand what is happening now
“Initial condition”

Digest observational information
Bring observed data into “standard” format
“Data assimilation”

Project initial state into future
Based on laws of physics
“Numerical Weather Prediction” (NWP) model forecasting

Apply weather forecast information
Statistical post-processing
“User applications”

REPRESENT FORECAST UNCERTAINTY – PROBABILISTIC FORMAT

- Before we can look into future, understand what is happening now
*“**Initial condition”*
- Digest observational information
- Bring observed data into “standard” format
*“**Data assimilation”*
- Project initial state into future
- Based on laws of physics
*“**Numerical Weather Prediction”* (NWP) model forecasting
- Apply weather forecast information
- Statistical post-processing
*“**User applications”*
***REPRESENT FORECAST UNCERTAINTY – PROBABILISTIC FORMAT***

**THE MAKINGS OF A WEATHER FORECAST –**
*EVER IMPROVING, BUT ALWAYS IMPERFECT*

## Slide 19

## Slide 20

## Contemporaneous weather/climate forecast procedures have common basis
Based on same scientific principles, yet
Developed from somewhat different origins, by different scientific communities
Somewhat different priorities due to difference in applications

Distinction is made depending on “lead time” of forecast:
The same natural process is forecast
Shorter lead time - “weather” – up to a week
Longer lead time - “climate” – over a month

Separate streams of weather and climate forecast procedures

Short range weather prediction, up to a few days:
Dominated by initial condition of atmosphere

Climate prediction, beyond 60 days
Dominated by boundary conditions of atmosphere, ie –
Ocean initial conditions
Land surface initial conditions

- Based on same scientific principles, yet
- Developed from somewhat different origins, by different scientific communities
- Somewhat different priorities due to difference in applications
- Distinction is made depending on “lead time” of forecast:
- The same natural process is forecast
- Shorter lead time - “weather” – up to a week
- Longer lead time - “climate” – over a month
- Separate streams of weather and climate forecast procedures
- Short range weather prediction, up to a few days:
- Dominated by initial condition of atmosphere
- Climate prediction, beyond 60 days
- Dominated by boundary conditions of atmosphere, ie –
- Ocean initial conditions
- Land surface initial conditions

**LINKS BETWEEN WEATHER & CLIMATE FORECASTING**

## Slide 22

## Slide 23

## Prediction for 10-60 day period - No-person’s land
Affected by both atmospheric and oceanic/land initial conditions
Great challenge technically,
How to realistically capture sensitivities to both constraints at same time?

What is predictable on different time scales?
Individual events for short range
Statistics of individual events at longer ranges

Probabilistic approach allows for seamless suite of products
Across different time ranges

How to bridge the gap between weather and climate forecasting?

Review links between weather and climate forecast procedures
Observing System
Data Assimilation
Numerical Forecasting
Socio-Economic Applications

- Affected by both atmospheric and oceanic/land initial conditions
- Great challenge technically,
- How to realistically capture sensitivities to both constraints at same time?
- What is predictable on different time scales?
- Individual events for short range
- Statistics of individual events at longer ranges
- Probabilistic approach allows for seamless suite of products
- Across different time ranges
- How to bridge the gap between weather and climate forecasting?
- Review links between weather and climate forecast procedures
- Observing System
- Data Assimilation
- Numerical Forecasting
- Socio-Economic Applications

**LINKS BETWEEN WEATHER & CLIMATE FORECASTING - 2**

## Slide 25

## Improved skill for intermediate ranges:
10-60 days, affected by both atmospheric & ocean initial conditions 

Shared scientific knowledge:
“Cross fertilization”, exchange/use of ideas from other discipline

Shared infrastructure and technology:
Use same numerical forecast procedures

Computational savings
Run forecast system once
Optimized jointly for weather/climate forecasting
Avoid extra costs from running two separate systems

Seamless product suite:
Easier to achieve if forecast system is unified

- 10-60 days, affected by both atmospheric & ocean initial conditions
- Shared scientific knowledge:
- “Cross fertilization”, exchange/use of ideas from other discipline
- Shared infrastructure and technology:
- Use same numerical forecast procedures
- Computational savings
- Run forecast system once
- Optimized jointly for weather/climate forecasting
- Avoid extra costs from running two separate systems
- Seamless product suite:
- Easier to achieve if forecast system is unified

**ADVANTAGES OF**
**UNIFIED WEATHER-CLIMATE FORECAST SYSTEM**

## What is important for weather & climate prediction?
Set performance measures for both applications
For assessing impact of observations

What are the observational needs of weather & climate forecasting?
Evaluate in common framework
Observing System Experiments (OSE)
Observing System Simulation Experiments (OSSE)
Assess priorities for both applications

Design future observing system that takes advantage of synergies, eg:
Adaptive observational strategy may be useful for both
Weather – optimized for short-range forecasting
Climate – optimized for detection of extreme events

- Set performance measures for both applications
- For assessing impact of observations
- What are the observational needs of weather & climate forecasting?
- Evaluate in common framework
- Observing System Experiments (OSE)
- Observing System Simulation Experiments (OSSE)
- Assess priorities for both applications
- Design future observing system that takes advantage of synergies, eg:
- Adaptive observational strategy may be useful for both
- Weather – optimized for short-range forecasting
- Climate – optimized for detection of extreme events

**OBSERVING SYSTEM -**
**SYNERGY BETWEEN WEATHER & CLIMATE COMPONENTS**

## Real-time data access
Critical for atmospheric data 
Ocean data must be made available similarly in real time

Initialization of coupled system
Current practice – treat atmosphere and ocean separately
Challenge related to coupling of atmospheric and ocean models
Technical issue, instabilities related to coupling procedure...

Ensemble perturbation techniques
“Coupled” initial perturbations needed
Model perturbations for describing model-related forecast errors

- Critical for atmospheric data
- Ocean data must be made available similarly in real time
- Initialization of coupled system
- Current practice – treat atmosphere and ocean separately
- Challenge related to coupling of atmospheric and ocean models
- Technical issue, instabilities related to coupling procedure...
- Ensemble perturbation techniques
- “Coupled” initial perturbations needed
- Model perturbations for describing model-related forecast errors

**DATA ASSIMILATION -**
**SYNERGY BETWEEN WEATHER & CLIMATE COMPONENTS**

## Test use of cascadingly lower resolution models
Start with very high resolution, expensive model for details at short range
Truncate after some time, continue with lower resolution, cheaper model

Use of Limited Area Models (LAM) for downscaling?
Originates from weather forecast practice
“Forecast” information is from coupled ocean-atmosphere-land model
LAM specifies regional conditions consistent with global forecast

Test use of mixed-layer ocean model as intermediate solution
Avoid problems with full coupling
Improve extended-range weather forecasts

Study models’ ability to simulate/forecast intra-seasonal variability
Unified approach potentially most beneficial for 10-60 day range

- Start with very high resolution, expensive model for details at short range
- Truncate after some time, continue with lower resolution, cheaper model
- Use of Limited Area Models (LAM) for downscaling?
- Originates from weather forecast practice
- “Forecast” information is from coupled ocean-atmosphere-land model
- LAM specifies regional conditions consistent with global forecast
- Test use of mixed-layer ocean model as intermediate solution
- Avoid problems with full coupling
- Improve extended-range weather forecasts
- Study models’ ability to simulate/forecast intra-seasonal variability
- Unified approach potentially most beneficial for 10-60 day range

**NUMERICAL MODELING -**
**SYNERGY BETWEEN WEATHER & CLIMATE COMPONENTS**

## Study and compare weather and climate forecast applications
Shorter lead times (1-14 days)
Intermediate lead times (10-90 days)
Longer lead times (60+ days)
Exploit experience/knowledge accumulated in climate applications (eg, at IRI) for shorter ranges

Develop application methods viable at all lead times
Common forecast format – Probabilistic information
Seamless suite of products - Digital database
Spatio-temporal variations differ:
High at short,
Low at longer lead times
One-stop shopping for weather and climate information is needed as
Society becomes more sensitive to atmospheric, hydrologic, and oceanic conditions

Demonstrate joint weather-climate forecast applications
Joint Demonstration projects
How weather/climate forecast can be used in everyday decision making process
Different sectors of society
Different regions of the globe
Positive results should be distributed among potential users
THORPEX research program (out to 14 days)
Global Interactive Forecast System (GIFS)
Link with climate research

- Shorter lead times (1-14 days)
- Intermediate lead times (10-90 days)
- Longer lead times (60+ days)
- Exploit experience/knowledge accumulated in climate applications (eg, at IRI) for shorter ranges
- Develop application methods viable at all lead times
- Common forecast format – Probabilistic information
- Seamless suite of products - Digital database
- Spatio-temporal variations differ:
- High at short,
- Low at longer lead times
- One-stop shopping for weather and climate information is needed as
- Society becomes more sensitive to atmospheric, hydrologic, and oceanic conditions
- Demonstrate joint weather-climate forecast applications
- Joint Demonstration projects
- How weather/climate forecast can be used in everyday decision making process
- Different sectors of society
- Different regions of the globe
- Positive results should be distributed among potential users
- THORPEX research program (out to 14 days)
- Global Interactive Forecast System (GIFS)
- Link with climate research

**SEAMLESS APPLICATIONS -**
**SYNERGY BETWEEN WEATHER & CLIMATE COMPONENTS**

## Day 11

*Day 11*

*Day 10.5*

*Day 11.5*

*Day 2*

*Day 1.5*

*Day 2.5*

## Day 8

*Day 8*

*Day 9*

*Day 10*

*Verification*

## Slide 33

## Slide 34

## Slide 35

## STRIKE PROBABILITY

**ENSEMBLE FORECASTING - QUANTIFYING UNCERTAINTY**

*STRIKE PROBABILITY*
*At any point, how many members of ensemble had a storm within 65 nm radius*

* **Forecast track*
* **Observed track*

*Strike probability =>*

## HURRICANE WILMA

**HURRICANE WILMA**
*STRIKE PROBABILITY*
*Probability of storm within 65 nm vicinity of any point on map*

* **Forecast track*
* **Observed track*

*Strike probability =>*

## FORECAST PROBABILITY VS. OBSERVED FREQUENCY

**FORECAST PROBABILITY VS. OBSERVED FREQUENCY**
*When strike probability forecast is eg. 30%,*
*storm observed in 30% of all cases*

*Reliable probabilistic forecasts*

## HURRICANE WILMA

**HURRICANE WILMA**
*Probabilistic heavy (greater than an inch in 24 hrs) precipitation forecast*
*Valid Nov 12-26*
*1-day lead time*
*Proxy for observations*

## HURRICANE WILMA

**HURRICANE WILMA**
*Probabilistic heavy (greater than an inch in 24 hrs) precipitation forecast*
*Valid Nov 12-26*
*1-, 2-, ..., 8-day lead time*

## HURRICANE BETA

**HURRICANE BETA**
*STRIKE PROBABILITY*
*Probability of storm within 65 nm vicinity of any point on map*

* **Forecast track*
* **Observed track*

*Strike probability =>*

## PROBABILISTIC EVALUATION 

**PROBABILISTIC EVALUATION **
***ECONOMIC VALUE FOR INDIA***

## SUMMARY

**WHAT IS WEATHER, AND WHAT IS CLIMATE?**
**SAME REALITY, DIFFERENT TIME SCALES**
**GENERAL DESCRIPTION OF WEATHER FORECAST PROCESS **
- Observations
- Data Assimilation
- Numerical Modeling
- Socio-Economic Applications
**LINKS BETWEEN WEATHER AND CLIMATE FORECASTING**
- Exist in all sub-areas of forecast process
**REGIONAL APPLICATIONS OF WEATHER FORECASTS**
- Extreme events
- Seamless use of forecasts

## REFERENCES

- http://wwwt.emc.ncep.noaa.gov/gmb/ens/index.html

- Forecast examples, other training material:
- http://wwwt.emc.ncep.noaa.gov/gmb/ens/training.html

- Spanish training material:
- _[http://www.hpc.ncep.noaa.gov/international/intl2.shtml](http://www.hpc.ncep.noaa.gov/international/intl2.shtml)_

- Central American real-time images:
- _[http://](http://eyewall.met.psu.edu/ensembles)__[eyewall.met.psu.edu](http://eyewall.met.psu.edu/ensembles)__[/ensembles](http://eyewall.met.psu.edu/ensembles)_

- Asia examples:
- _[http://](http://eyewall.met.psu.edu/test/rich/Asia/)__[eyewall.met.psu.edu](http://eyewall.met.psu.edu/test/rich/Asia/)__[/test/rich/Asia/](http://eyewall.met.psu.edu/test/rich/Asia/)_

- Weather-Climate connection – See section 9:
- http://www.wmo.int/thorpex/pdf/CD_ROM_implementation_plan_v1.pdf

- Weather forecast process – See section 2.2:
- http://www.mmm.ucar.edu/uswrp/thorpex/NATHORPEX/NOAAplan.pdf

- Ensemble forecasting references:
- http://wwwt.emc.ncep.noaa.gov/gmb/ens/ens_info.html

- Extreme weather events:
- http://wwwt.emc.ncep.noaa.gov/gmb/ens/target/ens/albapr/albapr.html

**REFERENCES**

## BACKGROUND

**USER REQUIREMENTS:****PROBABILISTIC FORECAST INFORMATION IS CRITICAL**

## NCEP GLOBAL ENSEMBLE FORECAST SYSTEM

- CURRENT CONFIGURATION
- MARCH 2003

- Breeding method
- 24 hours breeding cycle
- 4 cycles per day
- 10 m for each cycle
- Ens. Ctl at t00z only
- Total 45 m in 24 hours
- 4 different resolutions
- 16-day forecasts

**NEW**

## MOTIVATION FOR ENSEMBLE FORECASTING

***FORECASTS ARE NOT PERFECT - *****IMPLICATIONS FOR:**
**USERS:**
- Need to know how often / by how much forecasts fail
- Economically optimal behavior depends on
- Forecast error characteristics
- User specific application
- Cost of weather related adaptive action
- Expected loss if no action taken
- EXAMPLE: Protect or not your crop against possible frost
- Cost = 10k, Potential Loss = 100k => Will protect if P(frost) > Cost/Loss=0.1
***NEED FOR PROBABILISTIC FORECAST INFORMATION***
**DEVELOPERS:**
- Need to improve performance   - 	*Reduce error in estimate of first moment*
- Traditional NWP activities (I.e., model, data assimilation development)
- Need to account for uncertainty -	*Estimate higher moments*
- New aspect – How to do this?
-  Forecast is incomplete without information on forecast uncertainty
***NEED TO USE PROBABILISTIC FORECAST FORMAT***

## EXAMPLE – PROBABILISTIC FORECASTS 

**EXAMPLE – PROBABILISTIC FORECASTS **
**RELIABILITY:**
- Forecast probabilities for given event match observed frequencies of that event (with given probabilistic forecast)
**RESOLUTION:**
- Many forecasts fall into classes corresponding to high or low observed frequency of given event
- (Occurrence and non-occurrence of event is *well resolved* by forecast system)

## Slide 50

## Slide 51

## Slide 52

## Slide 53

## Slide 54

## EXAMPLE WHERE MODEL MAY HAVE FAILED  D. Hou, Y. Zhu

- Day 6, 2 members

- Day 5, several members

- Day 4, still large uncertainty

- Day 7.5, 1 member?

- Day 7, 0 member

- Day 7, overconfidence?

**EXAMPLE WHERE MODEL MAY HAVE FAILED  **D. Hou, Y. Zhu

- STOCHASTIC PERTURBATIONS NEEDED TO:
- Increase growth of spread;                     2)	Avoid problems like below

## Slide 56

## Slide 57

## Slide 58