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Winter Season Forecasting Using the Winter Disruptiveness Index


A numeric scale: Higher values denote cold and snowy winter seasons ... Predicted a normal to slightly mild winter for 2002-03 ... – PowerPoint PPT presentation

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Title: Winter Season Forecasting Using the Winter Disruptiveness Index

Winter Season Forecasting Using the Winter
Disruptiveness Index
  • Methodology and results of the 2002/2003 Winter
    season forecast.

Dan Swank Meteo 497 Long Range Forecasting
The Winter Disruptiveness Index (WDI)
  • A quantitative measure of winter season severity.
  • Defined over the time period November through
  • Designed to be applicable everywhere
  • A numeric scale Higher values denote cold and
    snowy winter seasons
  • Negative values for mild winters
  • Values are the sum of seven components

WDI Value scale
The 7 WDI Components What can be forecasted
with WDI?
  • Average NDJFM Temperature
  • Total NDJFM Snowfall
  • NDJFM days with gt 1 snowcover
  • Abnormally cold days
  • Abnormal daily snowfall
  • Abnormal daily rainfall
  • Ice storms

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How can we forecast the next winters WDI?
  • Use Analog forecasting. Currently two
    experimental methods.
  • Compare global 500 mb height anomalies during
    summer/autumn months for years when the WDI is
    between a certain range (i.e. gt 12)
  • Correlate average monthly values of
    oceanic-atmospheric indices, before November, to
    that seasons WDI value.

Averaged 500 Mb Height anomaliesAnalog
forecasting method
  • This method can gives a general idea of the
    likely outcome of the coming winter, but does not
    give an exact value for the WDI
  • Take the average height anomaly over 3 months,
    such as August, September, and October. Average
    them over all years where the WDI is within a
    given range
  • Maps shown on the next slide are composites of 4
    years where the WDI was between a set range.
  • These maps would be different if WDI value at
    another location are used
  • Interestingly, teleconnection nodes tend to show
    u in the averaged 500 mb analyses

WDI gt12
6 to 12
0 - 6
2002 August to October (ASO) 500 mb height
500 mb analog method
  • Often anomaly comparisons may be inconclusive.
    Unless patterns similar to the extreme cases are
    present, go near normal.
  • The 2002/03 pattern best matched the harsh (6 to
    12) regime. Although vaguely.
  • This method can also be applied with any of the 7
    WDI components, to forecast likely temperature
    and precip trends.
  • Other month ranges (besides ASO) can be used,
    however months closer to November will probably
    be more reliable.

Oceanic Atmospheric Indices Analog forecasting
  • Correlate the WDI to averaged monthly values of
    Ocean/Atmospheric indices such as the NAO, SOI,
    and PNA using various lag/span computations.
  • For example Each years average April through
    August NAO correlated with the value of WDI for
    the following winter, starting in November.
  • Much more complicated then the 500 mb method, but
    gives an exact forecast value for the WDI.
  • Used to make the 2002/03 forecast

Oceanic Atmospheric Indices method
  • Must use a computer program to calculate the
    millions of correlation possibilities.
  • Output from the program can be accessed via a web
    form http//
  • Use the best 4 predictors that can be found. The
    values of indices must be taken over months
    before the winter occurs, in order to be useful
    for forecasting.

Correlation coefficients
  • The WDI correlation calculations were done with
    the Pearson Correlation Coefficient (R).
  • 1 for a perfect correlation, 0 for no
    relationship what-so-ever.
  • gt 0.6 indicate a good relationship exists between
    the two datasets
  • 0.2 to 0.6 represents a weak relationship.
  • The best WDI correlations fall between 0.4 and 0.6

1 Find the best predictors - Example EPO
WDI correlations in State College
EPO is undefined in August and Sept.
For the 2002/03 winter forecast, the following
predictors were used
Next steps
  • Make a table of values, listing the WDI, PNA,
    EPO, AO, and SOI values for each year where data
    is available.
  • Obtain the index values for the current year.
  • Make a list of analog years where the current
    index values match previous years
  • Also keep track of how many indices each analog
    year matched
  • Take a weighted average of the analog years WDI

Analog Years
2002-03 Analog years
Listing of analog years, which matched one
(single) index two (double) atmospheric/oceanic
Taking the weighted average of each analog
winters WDI (the double match years are double
weighted), gives the value of roughly WDI
-0.5 Rounded to the nearest 0.5
Oceanic/Atmospheric index method summary
  • More specific and calculation intensive then the
    500 mb method
  • Correlation values may be too low to be
  • Predicted a normal to slightly mild winter for
  • The two methods should be compared to see if they
  • Other methods not involving the WDI should be
    incorporated into the final forecast.

The 2002/03 winter forecast
  • WDI forecasted to be from 0 to 2, after
    considering other techniques
  • When the WDI is in this range, the typical
    conditions usually occur, typical of an average
    winter season in this area
  • -0.8 to 0.7 degree departure from average NDJFM
  • 34-43 inches of snowfall
  • 32 to 44 days with 1 snowcover
  • 1 major (12) snowstorm, or 2 moderate snowfalls
  • 1 storm with minor ice accumulation

STC Verification
  • WDI 8.0
  • Components, statistics and averages
  • Tmean 1.37 (30.7 F, AVG 32.8 )
  • Smean 2.39 (75.1, AVG 41)
  • SCmean 1.61 (60 days SCgt1, AVG 36 days)
  • Tdaily 0.20 (16 DCDs, AVG of 15)
  • Sdaily 2.5
  • Rdaily 0 Idaily 0
  • Winter as much colder and snowier then expected.
    Total snowfall was nearly twice the average.
  • However, the predication did not indicate a mild
    winter, which is what most people are adjusted to
    because of the past few years

Insight explanations
  • Analog forecasts are subject to error because of
    the relatively short period of record of existing
    weather data
  • The WDI definition was changed since the forecast
    was made, values were amplified
  • Weak correlation values
  • Perhaps a component-wise analog method would be
    more accurate, this would also give more insight
    into the temperature and precipitation breakdowns
  • Weather patterns can change drastically over the
    period of 5 months
  • A few more methods should be developed which use
    the WDI to make a seasonal forecast. 2 methods
    may not be enough