Winter '18 / '19 - Gains and Losses: 10-NOV-18

Minor snow event over extreme northern portions of the forecast area today forced by a lifting 5H LOW centered @12z over SE Canada and 2ndry SFC LOW occluding INVOF the Gulf of ME.

Gains:  Blue
Losses:  Reds

Image courtesy NOHRSC

Winter '18 / '19 - Snow Advance Index and Season-total Snowfall in the NE and Mid-Atlantic

The Snow Advance Index (SAI) measures the mean daily rate-of-change in Eurasia's areal snow cover at latitudes equatorward of 60°N during OCT.  Published research suggests rapid increases in Eurasia/s OCT areal snow cover are associated with increased season-total snowfall (STP) in the eastern U.S.

Does the SAI provide useful guidance for season-total snowfall at NEWxSFC forecast stations?

Meh ... me thinks.

This analysis looks at the relationship between the SAI and NEWxSFC stations' STP for the winters between '70 / '71 and '17' / '18 (n = 48).

PCT increase in areal coverage is proxy for the mean rate of change of snow cover extent (SCE) from daily snow cover data in the above referenced study. (Cohen and Jones 2011).

AVG PCT increase in weekly Eurasian areal snow cover between Week 40 and Week 44 (OCT):  416% (ORANGE LINE)

OCTs ... where SAI > AVG:  20

Accordingly ... if OCT/s SAI is above AVG ... then STP at NEWxSFC forecast station should also be > AVG.

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AVG 'season-total' snowfall (STP) for NEWxSFC forecast stations:  1,042" (GREEN LINE)
Winters ... where STP > AVG:  23

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The question the analysis wants to answer:
Does the SAI-proxy - PCT increase in Eurasia/s OCT snow cover - predict whether NEWxSFC forecast stations' STP > AVG.

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Years ... where SAI > AVG & Years ... where NEWxSFC stations' STP > AVG:  11 (RED DOTS)
Recall ... there were 20 years when OCT/s SAI > AVG which means nine years the STP < AVG.

Probability of  OCT SAI > AVG:  42% (20/48)
For any given OCT ... there's a 42% chance of SAI > AVG.

Probability NEWxSFC stations STP > AVG:  48% (23/48)
For any given year ... there's a 48% chance of STP > AVG.

What's the probability given SAI > AVG ... the STP will also be > AVG?
Cumulative probability for NEWxSFC stations STP > AVG ... given SAI > AVG:  42%

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BOTTOM LINE:  In any given year ... if the SAI > AVG ... there's a 42% chance the 'season-total' snowfall ... for all NEWxSFC forecast stations ... will be above the period of study's AVG 'season-total' snowfall.

Looking at it another way ... there's a 58% chance the 'season-total' snowfall ... for all NEWxSFC forecast stations ... will _not_ be above the period of study's AVG 'season-total' snowfall.  This isn't to say SAI doesn't offer useful guidance for individual NEWxSFC forecast stations.

OCT-18/s PCT snow cover increase is just under 500% or ~80 PCT-points > AVG; therefore ... there/s a mere 42% chance this season the STP from NEWxSFC/s forecast stations will be > AVG.

Winter '18 / '19 - Predictive Value of NW Atlantic Ocean's SST Anomalies for Winter's Phase of the North Atlantic Oscillation

Up until a few years ago ... the UKMET office issued a long-lead forecast for the phase of the upcoming winter's North Atlantic Oscillation (NAO).  The forecast was based on a statistically significant correlation between May's sea-surface temperature anomalies (SSTA) in the western Atlantic ocean (depicted in the image below left) and NAO's future D-J-F phase (as depicted in the Winter Z500 pattern image below right).

To apply this technique for Winter '18 / '19; take note of the SSTA pattern in the NW Atlantic at the end of MAY-18 shown below.

- Positive anomalies off the mid-Atlantic and NE coasts.
- Negative anomalies in the Labrador Sea off Greenland's southern coast.
- Positive anomalies north of Iceland in the Norwegian Sea.

https://www.ospo.noaa.gov/data/sst/anomaly/2018/anomnight.5.31.2018.gif

Compare these anomalies with the 'May SST pattern' map.
See how well they align?

MET Office research concluded if May's SSTA aligns generally with the prediction pattern in the western Atlantic ocean ... then the expected dominant NAO state would be positive during the upcoming winter.

Above normal SST in the offshore waters of the NE coast ... below normal water temperatures SW of Iceland ... and positive anomalies to the northeast point to a +NAO.  When the pattern is reversed a -NAO is forecast.

Based on May-18 SSTA ... the dominant NAO state prediction for Winter '18 / '19 is positive.

There's even empirical evidence the D-J-F NAO will average above zero if NOV's NAO index is greater than zero.

Winter-season forecasters hitching their collective wagons to the +ENSO state might could want to stop-and-consider the effect of the Northern Annual Modes on their outlooks.

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Earlier post about UKMET NAO predictive research here.

Winter '18 / '19 - VCP32: Just Not The Same

Winter '18 / '19 - How Much For Philly?

A weak-to-moderate El Niño continues to be the consensus forecast for Winter '18 / '19.

Weak-to-moderate El Niño conditions -- defined generally as sea surface temperature anomalies at least 0.5°C above normal near the International Dateline in the tropical Pacific ocean -- correlates with more than a few more inches of snow than average in and around Philly.

One problem; average isn't always the best metric to describe what's typical or most likely to occur.  That honor frequently falls to the median.

As such ... the median snowfall is four inches less than the 23" 51-year average suggesting this winter's season-total snowfall in Philly could very be up to seven inches more than most other winters.

Winter '17 / '18 - Correlation of Eurasia's OCT Snow Cover and Season-total Snowfall in NE and M-A Regions

REPOST from OCT-17 (lightly edited for clarity)
Updated with Winter '17 / '18 verifications for RIC and NYC and their outlooks for Winter '18 / '19

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The correlation between the areal coverage of Eurasia/s OCT snow cover and season-total snowfall has become broad-brushed conventional wisdom (CW) following the innovative research by AER climatologist Dr. Judah Cohen.

But ... just how well does the CW hold up for NEWxSFC/s forecast stations across New England (NE) and the mid-Atlantic (M-A) regions?

To find out ... monthly period-of-record areal snow cover data for Eurasia from Rutgers Global Snow Lab were correlated with season-total snowfall data for the 27 NEWxSFC/s stations.

A positive and statistically significant correlation means the greater the areal snow cover over Eurasia in OCT ... the greater the season-total snowfall for the following winter.

An Excel radar chart shown below depicts the results of the analysis.

DISCUSSION:  Stations between the inner and outer rings have a positive correlation coefficient statistically different than zero.  The coefficients range between 0.289 (CAR) and 0.424 (ORH).   Correlation values in this range are classified generally as 'low' (moderate:  >= 0.5 - 0.7; strong:  >= 0.7 - 0.9).  Even though the correlations are weak ... they can still provide useful information for seasonal snowfall forecasts.

Translation:  greater season-total snowfall over select stations in the NE and M-A is correlated with greater OCT areal snow cover in Eurasia.

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The analysis showed other significant correlations of interest.

- RIC/s season-total snowfall has a positive correlation with Eurasia/s AUG areal snow cover.
Eurasia/s AUG-17 snow cover was well below normal ==> lower season-total snowfall @RIC this winter.

VERIFICATION Winter '17 / '18:  RIC STP 12.4"  (AVG:  13.2")
OUTLOOK Winter '18 / '19:  Eurasia's AUG snow cover below average ==> STP below AVG

- NYC/s season-total snowfall has a negative correlation with Eurasia's JUN areal snow cover.
Eurasia/s JUN-17 snow cover was above normal ==> lower season-total snowfall @ NYC this winter.

VERIFICATION Winter '17 / '18:  NYC STP 35.4"  (AVG:  26.1")
OUTLOOK Winter '18 / '19:  Eurasia's JUN snow cover below average ==> STP above AVG

GREEN (RED):  positive (negative) correlation between monthly Eurasian areal snow cover and season-total snowfall.

VERIFICATION Winter '17 / '18
Eurasia's OCT-17 areal snow cover was greater than average (12,051,667 km^2 v 10,261,134 km^2).
Positive correlations for the stations listed below suggest Winter '17 / '18 STP would be above average

STN:  STP" / AVG"
ABE:  46 / 31.4
BOS:  58.6 / 41.6
BTV:  81.5 / 69.8
PWM:  91.1 / 63.8

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FINDINGS:  data analysis supports the CW for NE forecast stations but not so much across the M-A.

Winter '17 / '18 - Arctic Oscillation (AO) Analog Verification

The analog forecasting technique seeks similarities to the AO state in the run-up to the coming winter with AO run-up states of winters past.  Presented here is the verification of AO analogs for the '17 /'18 winter.

The NEWxSFC method ranks analog years by their the sum of square errors (SSE) statistic.
Lower SSE errors ==> stronger analog

Constraining the number of analog winters for analysis to five is arbitrary.

Arctic Oscillation (AO) Index Analog Forecast Verification

In the run-up to Winter '17 / '18 ... '73 / '74 was the leading analog followed by '08 / '09 ... '54 / '55 ... '95 / '96 ... and '75 / '76.  Winter '17 / '18 AO started weakly negative and remained weakly negative until meteorological winter's end.  MAR-18 AO (not shown) crashed to -0.941.

 

A qualitative assessment of the forecast's accuracy would rate all analogs except '73 / '74 as 'poor' ... IOW ... useless.

 

OTOH ... analog #1 mimicked the observed behavior of the AO associated with a weak La Nina; whereas ... .  strong La Nina conditions prevailed during the winters of '73 / '74.

 

CONCLUSION:  The analog forecasting technique provided useful guidance for Winter '17 / '18.

 

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An alternative forecasting technique looks at the AO/s 'sign' (i.e., positive or negative) for any calendar-year's month preceding the pending winter as a potential leading indicator of the AO's sign for upcoming D-J-F period.

Results from a chi-square 'test for independence' infers a statistically significant relationship ... at the 95% confidence level and a p-value < 0.05 ... between NOV's AO sign and AO's sign of the upcoming D-J-F period.

IOW ... if NOV's AO is negative (positive) ... then the average AO state during the upcoming winter will also be negative (positive); although the classification model is stronger ... i.e., lower false alarm rate ... for the predictor's month with negative signs than positive.  This is opposite of the relationship found for the NAO.

BOTTOM LINE:  If NOV's AO is negative ... chances are good the AO state will average negative during the D-J-F period.

 

NOV '17 AO/s sign was negative (-0.078).  The 2x2 contingency technique predicted correctly AO state for Winter '17 / '18 would average less than zero.  AO for the D-J-F period averaged (0-.076).

Winter '17 / '18 - North Atlantic Oscillation (NAO) Analog Verification

The analog forecasting technique seeks similarities to the NAO state in the run-up to the coming winter with NAO run-up states of winters past.  Presented here is the verification of NAO analogs for the '17 /'18 winter.

The NEWxSFC method ranks analog years by their the sum of square errors (SSE) statistic.
Lower SSE errors ==> stronger analog

Constraining the number of analog winters for analysis to five is arbitrary.

North Atlantic Oscillation (NAO) Index Analog Forecast Verification

In the run-up to Winter '17 / '18 ... '90 / '91 was the leading analog followed by '94 / '95 ... '54 / '55 ... '04 / '05 ... and '02 / '03.  Winter '17 / '18 NAO started strongly positive ... strengthened through the heart of the season then crashed below zero following meteorological winter's end

 

A qualitative assessment of the forecast's accuracy would rate the analogs #2 ... #3 ... and #4 as 'poor' ... IOW ... useless.

 

OTOH ... analogs #1 and #5 mimicked the observed behavior of the NAO associated with a weak La Nina.  Moderate El Niño conditions prevailed during the winters of '94 / '95 and '02 / '03.

 

CONCLUSION:  The analog forecasting technique provided useful guidance for Winter '17 / '18.

  

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An alternative forecasting technique looks at the NAO/s 'sign' (i.e., positive or negative) for any calendar-year's month preceding the pending winter as a potential leading indicator of the NAO's sign for upcoming D-J-F period.

Results from a chi-square 'test for independence' infers a statistically significant relationship ... at the 95% confidence level and a p-value < 0.05 ... between NOV's NAO sign and NAO's sign of the upcoming D-J-F period.

IOW ... if NOV's NAO is negative (positive) ... then the average NAO state during the upcoming winter will also be negative (positive); although the classification model is stronger ... i.e., lower false alarm rate ... for the predictor's month with positive signs than negative.  This is opposite of the relationship found for the Arctic Oscillation (AO).

 

BOTTOM LINE:  If NOV's NAO is positive ... chances are good the NAO state will average positive during the D-J-F period.

 

NOV '17 NAO/s sign was zero (0) for the 1st time in the index's 68 year record.  The 2x2 contingency technique offered no useful guidance.

Winter '17 / '18 - 19th Annual 'Regular Season' Snowfall Forecast Contest: Season Summary and Historical Perspective

FINAL results here.

EIGHT contest-worthy storms this season ... one more than average.
The storm count tied for 6th place with four other Winters ('07 / '08 ... '08 / '09 ... '10 / '11 ... and '13 / '14).

Contest-worthy Storm Count by Month
DEC - 1 [25-DEC-17]
JAN - 2 [04-JAN-18; 17-JAN-18]
FEB - 2 [07-FEB-18; 17-FEB-18]
MAR - 3 [07-MAR-18; 12-MAR-18; 21-MAR-18]
TOT - 8

 

 

Verified forecasts

http://www.newx-forecasts.com/NEWxSFC_19/storms/storm1_verifications_25Dec17.htm

http://www.newx-forecasts.com/NEWxSFC_19/storms/storm2_verifications_04Jan18.htm
http://www.newx-forecasts.com/NEWxSFC_19/storms/storm3_verifications_17Jan18.htm
http://www.newx-forecasts.com/NEWxSFC_19/storms/storm4_verifications_07Feb18.htm
http://www.newx-forecasts.com/NEWxSFC_19/storms/storm5_verifications_17Feb18.htm
http://www.newx-forecasts.com/NEWxSFC_19/storms/storm6_verifications_07Mar18.htm
http://www.newx-forecasts.com/NEWxSFC_19/storms/storm7_verifications_12Mar18.htm
http://www.newx-forecasts.com/NEWxSFC_19/storms/storm8_verifications_21Mar18.htm

 

FINAL Results and Storm Summaries

http://www.newx-forecasts.com/NEWxSFC_19/storms/storm1_summary_25Dec17.htm

http://www.newx-forecasts.com/NEWxSFC_19/storms/storm2_summary_04Jan18.htm
http://www.newx-forecasts.com/NEWxSFC_19/storms/storm3_summary_17Jan18.htm
http://www.newx-forecasts.com/NEWxSFC_19/storms/storm4_summary_07Feb18.htm
http://www.newx-forecasts.com/NEWxSFC_19/storms/storm5_summary_17Feb18.htm
http://www.newx-forecasts.com/NEWxSFC_19/storms/storm6_summary_07Mar18.htm
http://www.newx-forecasts.com/NEWxSFC_19/storms/storm7_summary_12Mar18.htm
http://www.newx-forecasts.com/NEWxSFC_19/storms/storm8_summary_21Mar18.htm

Interim Standings
http://www.newx-forecasts.com/interim_19/interim1.htm
http://www.newx-forecasts.com/interim_19/interim2.htm
http://www.newx-forecasts.com/interim_19/interim3.htm
http://www.newx-forecasts.com/interim_19/interim4.htm
http://www.newx-forecasts.com/interim_19/interim5.htm
http://www.newx-forecasts.com/interim_19/interim6.htm

 

 

Winter '17 / '18 - 19th Annual 'Regular Season' Snowfall Forecast Contest: FINAL Results

Under the ‘two-thirds’ rule … forecasters who entered at least SIX forecasts are included in this season's FINAL standings.

Full table with all other error statistics at the Contest/s web site here (direct link).
Individual forecaster's storm statistics here (direct link).

 

 

Best Forecasts by Storm

 

Top 10 Forecasts (Error statistic:  SUMSQ Z)

SUMSQ Error Z is the primary measure of forecaster skill (lower the better).
Accounts for the magnitude and distribution of the storm-total snowfall for all stations.

Top 10 Forecasts (Error statistic:  Total Absolute Error Z - TAE)

TAE Error Z is the secondary measure of forecaster skill (lower the better).
Accounts for the magnitude of snowfall at each station.
 

Top 10 Forecasts (Error statistic:  RSQ Z)

R-squared (RSQ Z) is a supplementary measure of forecaster skill (higher the better).

Accounts for how well the variability of the observed snowfall was accounted for by the forecast.

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Eighteen unique forecasters submitted a total of 2,582 stations forecasts.
Eight forecasters entered all 8 contests.
Three forecasters entered 7 contests.
The remainder entered fewer than six

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Hope to see y'all again next winter.