NATS 101

Lecture 26Weather 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

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

Strong La Nina

Winter 2007-2008 Outlooklatest prediction

Winter 2004-2005 Outlook(Issued 18 March 2004)

Winter 2004-2005 Outlook(Issued 18 March 2004)

NCEP GFS ForecastsNCEP 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 ForecastsNCEP 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 ModelsDifferent Forecast Models

• Different, but equally defensible models produce different forecast evolutions for the same event.

• Although details of the evolutions differ, the large-waves usually evolve very similarly out to 2 days.

Ahrens 2nd Ed. Akin to Fig 9.1

AVN-ETA-NGM Comparison

Forecast Evaluation:Accuracy and Skill

• Accuracy measures the closeness of a forecast value to a verifying observationAccuracy can be measured by many metrics

• Skill compares the accuracy of a forecast against the accuracy of a competing forecastA 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

Aguado and Burt

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.

Max Temp AccuracyMax Temp Accuracy

Rainfall SkillRainfall Skill

Current Skill

0-12 hrs: Can track individual severe storms12-48 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

VERIFYING ANALYSIS

DAY 3 FORECAST POSITIVE

DAY 3 FORECAST NEGATIVE

DAY 3 FORECAST UNPERTURBED

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 errorsinsufficient model resolutionincomplete understanding of physicschaotic 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