Long Range Forecasting:Spring 2004

Paul Knight & Rich Grumm

PSU/NWS

Univ Park/State College PA

Introduction

• The Great Tragedy of Science - the slaying of a beautiful hypothesis by an ugly fact

– T.H. Huxley (1825-95)

• Define Terms

• Overview of the Material

• Developing the Discipline

Defining Terms

• Extended Forecasting– a prediction of weather conditions for a period

extending beyond more than 3 days from issuance

• Medium Range Forecasting– a prediction of weather conditions for a period

extending from 3 to 7 days from issuance

• Long Range Forecasting– a prediction of weather conditions for a period

extending beyond more than 7 days from issuance - there is no limit how far beyond

AMS Statement on Extended Forecasting

• 1991– 6 to 10 days: some skill in mean temp and

precip relative to climatology (T better than P)– Monthly/Seasonal forecasts: slight skill in

mean temperature and precipitation departure; but no skill in day to day forecasts

• 2001– dramatic improvement in 1-2 seasons in

advance forecasts of temp and precip– still no day-to-day skill beyond 1-2 weeks

Long Range Forecasting Class

Ensembles from Lagged AveragedForecasts

converging solutions

Forecast Length

Long Range Forecasting Class

NWS-State College

PDO SST Examples

Figure 2 The warm phase (left) and cold phase (right) of the PDO. The warm phase is also known as a high NPO while the cold phase is also known as a low NPO. Similar to an El Nino event, note the warm water in the eastern Pacific.

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PDO values 1900-2000

Long Range Forecasting Class

NIÑO SST regions

21

34

ENSO Neutral

Long Range Forecasting Class

The 3D view of MJO

• Axis of coupledconvection/suppressedconvection usuallybetween 5S-5N

• SST feedback couldbe sensitive enough toeither trigger or helppropagate the wave

Long Range Forecasting Class

NWS-State College

Negative PNA-J anuaryhttp:/ / www.cdc.noaa.gov/ USclimate/ pna.html

Long Range Forecasting Class

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NAO-Negative Phase

Long Range Forecasting Class

NWS-State College

Low NAO Storm Example

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The Little Iceage begins• Colder weather began to dominate in 12th

century– 1309/ 10 Thames river freezes cold and dry weather

• tree rings were narrow suggesting cold and dry

• NAO < 0

– 1312 very wet failed wheat crops (NAO>0)– 1315 very wet for next 3 years

• extraordinary tree ring growth in Iceland and Ireland

• very wet

• lead to crop failures and the Great Famine– some countries lost 5-10% of population in large towns

– 1320 NAO went negative and ended famine periods andwetness-Record cold set in

Long Range Forecasting Class

Autocorrelation J A-FE-MA 01correlation point 40N/ 75W

PA Node

EuropeanNode

PacificNode

AntiNode

Long Range Forecasting Class

2002 Model Forecasts

l Sc ripps

l Plank

GCM Model Matters

l Grid Spac ing• dependent on

coordinatesystem for globe

• dependent oncomputer space

l Time Step• dependent on

resolution andlength of forecst

l Terrain andOcean Mapping• generally rough

with little detail

l Parameterize• solar radiation• convection• heat flux• wind stress

Long Range Forecasting Class

Annular ModeAnnular Mode

nn NAM averaged forNAM averaged forJan-Feb-Mar (high)Jan-Feb-Mar (high)

nn NAM averaged forNAM averaged forJan-Feb-Mar (low)Jan-Feb-Mar (low)

Blocking PatternsBlocking Patterns

nn Example of 500 Example of 500 mb mb flowflow

Long Range Forecasting Class

Secrets Revealed

• The Tropics (oceans) drive the changes in seasonal and annual conditions in the middle latitudes

• The myth of the perfect analog– While an enormous number of cases are

needed for ‘the perfect analog’, a substantial amount of useful information is available from a carefully selected few.

• The start of desktop LRF research (for regional/local connections)

Developing the Discipline

• Recent Past– What has been the trend?– Why has the trend changed?

• Current conditions– What are the most salient features?– Why is it happening?

• Forecast conditions– What do the dynamic and statistical models

show and why?

Past Conditions

Fall (Sept-Nov) 2003 Temperatures

Past Conditions

500 hPa anomalies for last 35 days

Past Conditions

Current Conditions

Current Conditions

Future Conditions

Future Conditions

The Master Forecaster

• Seeks to learn what is going on– Diagnosis leads to understanding– Use remotely sensed and model data

• Seeks to understand what will happen– Medium range analysis tools integrating data

sets– Model tools to forecast at long range

Forecast Funnel Theory(traditional view)

• represents the scales of interaction: hemispheric, synoptic, mesoscale and local that influence the onset of and changes in weather events for a particular forecast area. These scale interactions establish a context for demonstrating and establishing essential forecasting skills.

• Forecasters spend more time on details near bottom of the funnel the local scale

COMET’s Funnel-Pyramid

(Updated) Forecast Funnel

• Consider forecast length (time) as well as scale

• Consider tools for both the scale and the time

• Forecaster time will be focused more on the details as the weather gets more interesting

• Sensible weather is local

Scales of Prediction

EnsemblesModels andClimatic Anomalies

Mesoscale Models and ensembles

Satellites

Satellites

Analysis

diagnosis

Radar

Analysis

S

C

A

L

E

Event TimeWeeks Days Hours

EnsemblesClimate (PNA/NAO)Climatic Anomalies

Mesoscale Models

References

Buroughs, W.J., 1992: Weather Cycles: Real or Imaginary? Cambridge University Press. ISBN 0 521 47869 3

Brooks, H.E, C. A. Doswell III, and R.A. Maddox, 1993: On the Use of Mesoscale and Cloud-Scale Models in Operational Forecasting. Wea. Fore.7, 7, 120-132.

URL’s:http://grads.iges.org/ellfb/Dec02/Pierce/fig1.gif

http://www.cdc.noaa.gov/HistData/http://opwx.db.erau.edu/~herbster/wx427/fcst_process.html

http://meted.ucar.edu/mesoprim/mesodefn/print.htm

http://www.atmos.colostate.edu/ao/Figures/Thompson_Wallace_Science2001/index.html

http://www.cpc.ncep.noaa.gov/products/tanal/accesspage.html

http://ingrid.ldeo.columbia.edu/maproom/Global

http://www.rap.ucar.edu/weather/surface/us_AFsnow.gif