NATS 101

Lecture 18Weather Forecasting

Review: ET CyclonesIngredients for Intensification

• Strong Temperature Contrast

• Jet Stream Overhead• S/W Trough to West• UL Divergence over

Surface Low• If UL Divergence

exceeds LL Inflow, Cyclone Deepens

• Similar Life CyclesAhrens, Meteorology Today, 5th Ed.

filling

deepening

Reasons to Forecast Weather & Climate

• Should I bring my umbrella to work today?• Should Miami be evacuated for a hurricane?• How much heating oil should a refinery process

for the upcoming winter? • Will the average temperature change if CO2

levels double during the next 100 years?• How much to charge for flood insurance?• How much water will be available for

agriculture & population in 30 yearsThese questions require weather-climate forecasts

for today, a few days, months, years, decades

Forecasting Questions

• How are weather forecasts made?• How accurate are current weather forecasts?• How accurate can weather forecasts be?

Types of Forecasts

Persistence - forecast the future atmospheric state to be the same as current state

-Raining today, so forecast rain tomorrow

-Useful for few hours to couple days

Types of Forecasts

Trend - add past change to current condition to obtain forecast for the future state

-Useful for few hours to couple days

10 am 11 am 12 pm

59 F 63 F 67 F

Past Now Future

Types of Forecasts

Analog - find past state that is most similar to current state, then forecast same evolution

-Difficulty is that no two states exactly alike

-Useful for forecasts up to one or two days

Types of Forecasts

Climatology - forecast future state to be same as climatology or average of past weather for date

-Forecast July 4th MAX for Tucson to be 100 F

-Most accurate for long forecast projections, forecasts longer that 30 days

Types of Forecasts

Numerical Weather Prediction (NWP) - use mathematical models of physics principles to forecast future state from current conditions.

Process involves three major phases

1. Analysis Phase (estimate present conditions)

2. Prediction Phase (computer modeling)

3. Post-Processing Phase (use of products)

To justify NWP cost, it must beat forecasts of persistence, trend, analog and climatology

Analysis Phase• Purpose: Estimate the current weather

conditions to use to initialize the weather forecast

• Implementation: Because observations are always incomplete, the Analysis is accomplished by combining observations and the most recent forecast

Analysis Phase

• Current weather conditions are observed around the global (surface data, radar, weather balloons, satellites, aircraft).

• Millions of observations are transmitted via the Global Telecommunication System (GTS) to the various weather centers.

• U.S. center is in D.C. and is named National Centers for Environmental Prediction (NCEP)

Analysis Phase

• The operational weather centers sort, archive, and quality control the observations.

• Computers then analyze the data and draw maps to help us interpret weather patterns.

Procedure is called Objective Analysis.

Final chart is referred to as an Analysis.• Computer models at weather centers make

global or national weather forecast maps

Courtesy ECMWF

Sparse data over oceans and Southern Hemisphere

Surface Data

Courtesy ECMWF

Some buoy data over Southern Hemisphere

Surface Buoy Reports

Courtesy ECMWF

Little data over oceans and Southern Hemisphere

Radiosonde Coverage

Aircraft Reports

Courtesy ECMWF

Little data over oceans and Southern Hemisphere

Weather Satellites

Geostationary

Polar Orbit

Satellite observations fill data void regions

Geostationary SatellitesHigh temporal samplingLow spatial resolutionPolar Orbiting SatellitesLow temporal samplingHigh spatial resolution

Ahrens, Figs. 9.5 & 9.6

Courtesy ECMWF

Obs from Geostationary Satellites

Temperature from Polar Satellites

Courtesy ECMWF

Operational ECMWF system September to December 2008. Averaged over all model layers and entire global atmosphere. % contribution of different observations to

reduction in forecast error.

GPS RO has significant impact (ranked #5 amongall observing systems) in reducing forecast errors, despite the small number of soundings.

Courtesy: Carla Cardinaliand Sean Healy, ECMWF22 Oct. 2009

Forecast error contribution (%)

Atmospheric Models

• Weather models are based on mathematical equations that retain the most important aspects of atmospheric behavior- Newton's 2nd Law (density, press, wind)- Conservation of mass (density, wind)- Conservation of energy (temp, wind)- Equation of state (density, press, temp)

• Governing equations relate time changes of fields to spatial distributions of the fieldse.g. warm to south + southerly winds warming

Prediction Phase

• Analysis of the current atmospheric state (wind, temp, press, moisture) are used to start the model equations running forward in time

• Equations are solved for a short time period (~5 minutes) over a large number (107 to 108) of discrete locations called grid points

• Grid spacing is 2 km to 50 km horizontally and 100 m to 500 m vertically

Model Grid Boxes

10-20 km

100-

500

m

“A Lot Happens Inside a Grid Box”(Tom Hamill, CDC/NOAA)

Approximate Size of One Grid Box for NCEP Global Ensemble Model

Note Variability in Elevation, Ground

Cover, Land Use

Source: www.aaccessmaps.co

Rocky Mountains

Denver50 km

13 km Model

Terrain

Big mountain ranges, like the Sierra Nevada Range, are resolved.

But isolated peaks, like the Catalinas,

are not evident!

100 m contour

Post-Processing Phase• Computer draws maps of projected state to help

humans interpret weather forecast• Observations, analyses and forecasts are

disseminated to private and public agencies, such as the local NWS Forecast Office and UA

• Forecasters use the computer maps, along with knowledge of local weather phenomena and model performance to issue regional forecasts

• News media broadcast these forecasts to public

Suite of Official NWS Forecasts

CPC Predictions Page

Summary: Key Concepts

Forecasts are needed by many usersThere are several types of forecastsNumerical Weather Prediction (NWP)

Use computer models to forecast weather-Analysis Phase-Prediction Phase-Post-Processing Phase

Humans modify computer forecasts

Summary: Key Concepts

National Centers for Environment Prediction (NCEP) issues operational forecasts for

El Nino tropical SST anomalies

Seasonal outlooks

10 to 15 day weather forecasts

2 to 3 day fine scale forecasts

NATS 101

Weather Forecasting 2

3-Month SST Forecast (Issued 6 April 2004)

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 somewhat accurate once the El Nino was established

Weak El Nino

Winter 2004-2005 Outlook(Issued 20 October 2005)

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: Slight skill for average temp and 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 - Weather Forecasting Part II

• Reading - Ahrens pg 249-260

• Problems - 9.11, 9.15, 9.18

• Topic - Thunderstorms

• Reading - Ahrens pg 263-276

• Problems –

10.1, 10.3, 10.4, 10.5, 10.6, 10.7, 10.16