Analogs: Or How I Learned to Stop Worrying and Love the Past… 10 April 2003 Robert Hart Penn State...
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Transcript of Analogs: Or How I Learned to Stop Worrying and Love the Past… 10 April 2003 Robert Hart Penn State...
![Page 1: Analogs: Or How I Learned to Stop Worrying and Love the Past… 10 April 2003 Robert Hart Penn State University Jeremy Ross, PSU Mike Fritsch, PSU Charles.](https://reader033.fdocuments.us/reader033/viewer/2022051618/56649ceb5503460f949b6e05/html5/thumbnails/1.jpg)
Analogs: Or How I Learned to Stop Worrying and Love the Past…
10 April 2003
Robert Hart
Penn State University
Jeremy Ross, PSU
Mike Fritsch, PSU
Charles Hosler, PSU
Richard Grumm, SOO/NWS CTP
Richard James, PSU
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As meteorologists we may be somewhat familiar with analogs…
Hurricane forecasting…
“Snowstorms along the Northeastern U.S. Coast of
the United States: 1955-1985”
Kocin & Uccellini 1990
AMS Monograph
Major snowstorms….
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Analogs• Looking for patterns in historical meteorological data
that are similar to those occurring today.
• Also used extensively in other areas with relatively low predictability:
– Stock Market– Species evolution & extinction– Sports– Planetary evolution – Politics– War– History in general
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Analog forecasting
• The oldest forecasting method?
• Compare historical cases to existing conditions
• Subjectively: Memory• Analog forecast skill a function of human age…?
• Objectively: Objective pattern comparison• Analog forecast skill a function of dataset length?
How long of a dataset is required?
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As with most things in life, great insight is provided by “The Simpsons”
1996, Episode “Hurricane Neddy” “The Simpsons” provide insight on the perils of analog forecasting:
Homer Simpson:“Oh Lisa! There's no record of a hurricane ever hitting Springfield.“
Lisa Simpson: “Yes, but the records only go back to 1978 when theHall of Records was mysteriously blown away!”
Simpsons argue 20 years not enough…..
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A sobering perspective…
“…it would take order 1030 years to find analogues that match over the entire Northern Hemisphere 500mb height field to within current observational error.”
From: Searching for analogues, how long must we wait?
Van Den Dool, 1994, Tellus.
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We have decided not to wait, and instead have
drastically reduced our expectations.• We are not looking for an exact replication of patterns
• We want to determine on which side of climatology we are most likely to reside.
• We do not need to forecast departures from climatology all the time: Only when confidence measures allow.
• With these lesser expectations: Is 50 years of archive sufficient for skillful seasonal analog forecasts?
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An exploratory study
• Goal: To test feasibility of analog approach using longest continuous global datasets
• Methods will be improved with additional work
• Many parameter choices probably not ideal, but based upon physical insight
• Limit forecasts to tropics where seasonal forecast skill is more easily obtained
• Results are preliminary
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An exploratory study 2• Historical archive:
1948-2002 NCEP/NCAR Reanalysis Dataset
– Consistent method of data assimilation
– Incorporates majority of available observations
– Global, 2.5°x2.5°, 6-hourly resolution
– Dynamically grows in time: updates daily
– Areal weighting for pattern matching & skill evaluation
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An exploratory study 3• Strengths of analog approach
– Forecasts confined to what has occurred– Quick compared to NWP– Do not need to understand cause/effect– Can predict any variable for which historical data is available
• Weaknesses:– Forecasts confined to what has occurred– Do not need to understand cause/effect– Requires lengthy archive
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1000-500hPa Thickness as Global Pattern Descriptor
• Fewer degrees of freedom than other atmospheric variables
• Great integrator of:– Long wave pattern– Global temperature pattern– Global lower tropospheric moisture pattern
• Large inertia: Not greatly influenced by transient fluctuations (e.g. short-lived convection)
• Pattern matching performed using MAE of global thickness pattern comparison
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Matching instantaneous thickness analysis
MRF Thickness Analysis at 00Z 19 Jan 2003
#1 Analog: 12Z 10 Jan 1981
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Analogs: How to pattern match?
• Instantaneous (unfiltered) thickness analyses?
• Filtered thickness analyses?– Spatial? [EOF]– Temporal?
• Choice likely depends on desired forecast length– Short term forecast: compare instantaneous analyses– Long term forecast: compare filtered analyses
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Analog Forecast• For any given initialization, the closest matching N
members are chosen – Leads to an ensemble of analog matches with spread
– Significant difference from most current analog methods which use constructed analog approaches
• Their ensemble mean evolutions are used to produce the analog forecast thickness anomaly:
)(1
)()(1
ttZN
ttZttF a
Na
aANOMCLIMO
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Initial experiment:Pattern matching instantaneous analyses
• Initial tests matched instantaneous thickness analyses Lead to forecast skill out to 8 days.
We can reproduce current NWP range with 0.00001% NWP cost?
No forecast skill
Forecast skill
Climatology
5 10 15 20 25 30 35Forecast length (days)
MAE
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Method
)(125
1)(
30
iZtZti
daystiSMOOTH
• Since our goal is seasonal forecasting, we next matched the 31-day lagged mean smoothed thickness fields
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Method • Global pattern matching of smoothed thickness
• Allow analog matches to occur within 2-week window about initialization date/time to increase variety of available analogs.
e.g. analogs for July 1 come fromJune 24 – July 8 in each of the
available years
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Matching Window for July 1
J D1998
J D1997
J D1996
J D1948
J D1949
J D
J D
J D
J D
J DMatch exact time/date # = 51
Match within 2 wk window # 3000
J D
J D
J D
J D
J DMatch allowed over entire year # 75000
1998
1997
1996
1948
1949
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Method • For each 6-hour initialization time in 1948-1998, the top
200 analogs were selected from the available 3000 (about 6%).
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51 years of Analog
Selection:
The DNA of atmospheric recurrence?
P e r c e n t
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The “1976 Fracture”• Cause of abrupt change in pattern matching after 1976:
– Data changes• Observation network change?• Buoys, satellite availability?
– Rapid Surface condition changes• Deforestation?• Ocean conveyor & salinity changes?
– Long-term global change?• Global warming?• Frequency of ENSO events changed?
– Global seasonal pattern change?• Actual synoptic to long-wave patterns have changed?
• Why abrupt and not smooth change?
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Trying to understand abruptly changing analog selection patterns: A meteorological explanation
Annual Mean Thickness
NH
SH
Globe
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Trying to understand abruptly changing analog selection patterns: A dataset explanation
1950 1960 1970 1980 1990
Approx.Daily # Obs (Log)
Land Rawinsondes Aircraft Satellites Radiances108
106
104
Year
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What area to forecast for?• Tropical (20°S-20°N) monthly mean thickness forecast is
evaluated
• Not a signal to noise ratio as some have feared!• Tropical thickness responds to changes in magnitude of
sustained convection
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How to measure skill?• Persistence, anomaly persistence?
• Convention for seasonal forecasting: Climatology. – 54-year mean? 10-year mean?– 30-year mean? Previous year?
• Skill measured here against 54-year mean. The impact of climatology period choice will be shown.
• Skill here = MAECLIMO - MAEANALOG
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Forecast Skill Benchmarks
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Forecast Skill Benchmarks
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Forecast Skill Benchmarks
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Forecast Skill Benchmarks: Climatology
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Forecast Skill Benchmarks: Climatology
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Forecast Skill Benchmarks: Climatology
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Forecast Skill Benchmarks: Climatology
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Forecast Skill Benchmarks: Climatology
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Harshest competition: Adjust climatology linearly for long-term trend…
Annual mean thickness
Adjusted climatology for skill benchmark
NH
SH
Globe
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Forecast Skill Benchmarks: Detrended climatology
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Analog Forecast Skill: 51 year mean
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Analog Forecast Skill: 51 year mean
Skill to 8.5 months
Skill to 25 months
Skill to 12 months
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Analog Forecast Skill: 51 year mean• Forecast skill extends to:
– 25 months against 54-year climatology– 12 months against previous 10-year climatology– 8.5 months against a trend-adjusted climatology
• This argues analog forecast skill is a combination of:– Correctly forecasting seasonal pattern (majority of skill)– Correctly forecasting mean pattern: global trend
• The latter two skill results argues we are able to forecast seasonal thickness pattern evolution in the tropics
• How does the forecast skill vary from year to year?
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Winter/spring 1997 Forecast of 1998 El Niño
Pinatubo hinders analog matching
Spring 1986 prediction of 1987 El Niño
Spring 1982 prediction of 1983 El Niño
Successful forecast of a non-ENSO anomaly
Skill (shaded) = MAECLIMO – MAEANALOG: [Red: Skill > 2m ]
2
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The importance of matching globally
January 1997 Obs 12 month forecast
January 1996 Obs 12 month forecast
January 1952 Obs 12 month forecast
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Implications:There may be signs of an upcoming ENSO event 12 months in advance
outside the tropics?
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Summary• Highest skill and longest lead times occur for large
tropical thickness anomalies (e.g. ENSO)
• 5-12 month lead on ENSO events often precedes infamous “April” barrier
• Forecast skill exists during non-ENSO anomalies
• 1992-1994 forecasts were unusually poor. Evidently, Pinatubo produced a global pattern unlike any observed in the 54-year period
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Future Work: Many unanswered questions…
• How does analog forecast skill vary with filtering of thickness in time and space
• How does de-trending the raw dataset impact analog selection (and forecast skill)?
Lost analog potential b/c of climate change?
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Future Work: Many unanswered questions…
• How does trajectory matching rather than single analysis impact skill? – Match thickness evolution (trajectory) through Jan 1-31 rather
than Jan 1-31 mean?
• But the current approach views them as the same…
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Many unanswered questions…• What is the impact of using another reanalysis
dataset (ECMWF, JMS)?
• Where outside the tropics do ENSO indications lie?
• How can mid-latitude forecast skill outside ENSO (NAO/PNA predictability?) be obtained? [NCEP/CDC/CPC: It can’t]
• Is skill possible in surface parameters?
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52-Year Temporal Correlation of Monthly MEI and PrecipitationTeleconnection pattern between ENSO and Global Precip
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Acknowledgments
• Resources:– Penn State University– NCEP & NCAR through CDC: Reanalysis
• Insightful discussion & guidance:– Jenni Evans, PSU– Paul Knight, PSU– Robert Livezey, NOAA/CDC– Huug Vandendool, NCEP/CPC– Chris Landsea, HRD/AOML
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Current Analog ENSO Forecasts
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Jan 2002 Forecast of Extended 2002 El Niño
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2003 Forecast: Initialized Dec. 2002
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2003-4 Forecast: Initialized Jan. 2003
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2003-4 Forecast: Initialized Feb. 2003