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

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

Winter 2007-2008 Outlook
latest prediction

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


	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.

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 ÒskillfulÓ

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


	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: ÒSlightÓ 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 ÒpoorerÓ data than continents
	Instruments contain measurement error
	- A 20^oC reading does not exactly equal 20^oC
	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, ÒDoes the flap of a butterflyÕs wings in Brazil set off a tornado in Texas?Ó

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 2^nd Kleenex, releasing it from the same spot
	Drop a 3^rd Kleenex, releasing it from the same spot, etc.
	Repeat procedureÉ1,000,000 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

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