Transcript Document

NATS 101
Lecture 26
Weather Forecasting 2
Review: Key Concepts
There are several types of forecasts
Numerical Weather Prediction (NWP)
Use computer models to forecast weather
-Analysis Phase 
-Prediction Phase 
-Post-Processing Phase 
Humans modify computer forecasts
Suite of Official NWS Forecasts
CPC Predictions Page
3-Month SST Forecast
Most recent
Strong La Nina
SST forecasts for the El
Nino region of tropical
Pacific are a crucial
component of seasonal
and yearly forecasts.
Forecasts of El Nino and
La Nina show skill out to
around 12 months.
1997-98 El Nino forecast
was fairly accurate once
El Nino was established
Winter 2007-2008 Outlook
latest prediction
Winter 2004-2005 Outlook
(Issued 18 March 2004)
Winter 2004-2005 Outlook
(Issued 18 March 2004)
NCEP GFS Forecasts
ATMO GFS Link
NCEP global forecast; 4 times per day
Run on 50 km grid (approximately)
GFS gives the best 2-10 day forecasts
NCEP GFS Forecasts
ATMO NAM Link
NCEP CONUS forecast; 4 times per day
Run on 12 km grid (approximately)
NAM gives the best 24 h precip forecasts
Different Forecast Models
Ahrens 2nd Ed. Akin to Fig 9.1
• Different, but equally defensible models produce
different forecast evolutions for the same event.
• Although details of the evolutions differ, the largewaves usually evolve very similarly out to 2 days.
AVN-ETA-NGM Comparison
Forecast Evaluation:
Accuracy and Skill
• Accuracy measures the closeness of a forecast
value to a verifying observation
Accuracy can be measured by many metrics
• Skill compares the accuracy of a forecast
against the accuracy of a competing forecast
A forecast must beat simple competitors:
Persistence, Climatology, Random, etc.
If forecasts consistently beat these competitors,
then the forecasts are said to be “skillful”
How Humans Improve Forecasts
• Local geography in models is smoothed out.
• Model forecasts contain small, regional biases.
• Model surface temperatures must be adjusted,
and local rainfall probabilities must be forecast
based on experience and statistical models.
• Small-scale features, such as thunderstorms,
must be inferred from long-time experience.
• If model forecast appears systematically off,
human corrects it using current information.
Humans Improve Model Forecasts
Max Temp Accuracy
Aguado and Burt
Rainfall Skill
Forecasters perform better
than automated model
and statistical forecasts
for 24 and 48 h.
Human forecasters play an
important role in the
forecasting process,
especially during severe
weather situations that
impact public safety.
Current Skill
0-12 hrs: Can track individual severe storms
12-48 hrs: Can predict daily weather changes well,
including regions threatened by severe weather.
3-5 days: Can predict major winter storms, excessive heat
and cold snaps. Rainfall forecasts are less accurate.
6-15 days: Can predict average temp and rain over 5 day
period well, but daily changes are not forecast well.
30-90 days: Some skill for average temp but not so much for
rainfall over period. Forecasts use combination of model
forecasts and statistical relationships (e.g. El Nino).
90-360 days: “Slight” skill for SST anomalies.
Why NWP Forecasts Go Awry
• There are inherent flaws in all NWP models
that limit the accuracy and skill of forecasts
• Computer models idealize the atmosphere
Assumptions can be on target for some
situations and way off target for others
Why NWP Forecasts Go Awry
• All analyses contain errors
Regions with sparse or low quality observations
- Oceans have “poorer” data than continents
Instruments contain measurement error
- A 20oC reading does not exactly equal 20oC
Even a precise measurement at a point location
might not accurately represent the big picture
- Radiosonde ascent through isolated cumulus
Why NWP Forecasts Go Awry
• Insufficient resolution
Weather features smaller than the grid point
spacing do not exist in computer forecasts
Interactions between the resolved larger scales
and the excluded smaller scales are absent
• Inadequate representations of physical
processes such as friction and heating
Energy and moisture transfer at the earth's
surface are not precisely known
Chaos: Limits to Forecasting
• We now know that even if our models were
perfect, it would still be impossible to predict
precisely winter storms beyond 10-14 days
• There are countless, undetected small errors in
our initial analyses of the atmosphere
• These small disturbances grow with time as the
computer projects farther into the future
Lorenz posed, “Does the flap of a butterfly’s
wings in Brazil set off a tornado in Texas?”
Chaos: Limits to Forecasting
• After a few days, these initial imperfections
dominate forecasts, rendering it useless.
• Chaotic physical systems are characterized by
unpredictable behavior due to their sensitivity
to small changes in initial state.
• Evolutions of chaotic systems in nature might
appear random, but they are bounded.
Although bounded, they are unpredictable.
Chaos: Kleenex Example
• Drop a Kleenex to the floor
• Drop a 2nd Kleenex,
releasing it from the same spot
• Drop a 3rd Kleenex,
releasing it from the same spot, etc.
• Repeat procedure…1,000,000 times if you like,
even try moving closer to the floor
• Does a Kleenex ever land in the same place as a
prior drop?
Kleenex exhibits chaotic behavior!
Atmospheric Predictability
The atmosphere is like a falling Kleenex!
• The uncertainty in the initial conditions grow
during the evolution of a weather forecast.
So a point forecast made for a long time will
ultimately be worthless, no better than a guess!
• There is a limited amount of predictability,
but only for a short period of time.
Loss of predictability is an attribute of nature.
It is not an artifact of computer models.
Limits of Predictability
• What determines the limits of predictability for
the atmosphere?
• Limits dependent on many factors such as:
Flow regime
Geographic location
Spatial scale of disturbance
Weather element
Sensitivity to Initial Conditions
DAY 3 FORECAST
POSITIVE
DAY 3 FORECAST
NEGATIVE
DAY 3 FORECAST
UNPERTURBED
VERIFYING
ANALYSIS
Summary: Key Concepts
NCEP issues forecasts out to a season.
Human forecasters improve NWP forecasts.
NWP forecast go awry for several reasons:
measurement and analysis errors
insufficient model resolution
incomplete understanding of physics
chaotic behavior and predictability
Chaos always limits forecast skill.
Assignment for Next Lecture
• Topic - Thunderstorms
• Reading - Ahrens pg 257-271
• Problems –
10.1, 10.3, 10.4, 10.5, 10.6, 10.7, 10.16