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Analogs: Or How I Learned to Stop Worrying and Love the Past…

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. As meteorologists we may be somewhat familiar with analogs….

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Analogs: Or How I Learned to Stop Worrying and Love the Past…

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  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 Hosler, PSU Richard Grumm, SOO/NWS CTP Richard James, PSU

  2. As meteorologists we may be somewhat familiar with analogs… Hurricane forecasting… Major snowstorms…. “Snowstorms along the Northeastern U.S. Coast of the United States: 1955-1985” Kocin & Uccellini 1990 AMS Monograph

  3. 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

  4. 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?

  5. 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 the Hall of Records was mysteriously blown away!”  Simpsons argue 20 years not enough…..

  6. 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.

  7. 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?

  8. 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

  9. 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

  10. 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

  11. 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

  12. Matching instantaneous thickness analysis MRF Thickness Analysis at 00Z 19 Jan 2003 #1 Analog: 12Z 10 Jan 1981

  13. 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

  14. 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:

  15. 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 MAE Climatology Forecast skill 5 10 15 20 25 30 35 Forecast length (days)

  16. Method • Since our goal is seasonal forecasting, we next matched the 31-day lagged mean smoothed thickness fields

  17. 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 from June 24 – July 8 in each of the available years

  18. Matching Window for July 1 J D J D J D 1998 1998 J D J D J D 1997 1997 J D J D J D 1996 1996 J D J D J D 1949 1949 J D J D J D 1948 1948 Match exact time/date # = 51 Match within 2 wk window #  3000 Match allowed over entire year #  75000

  19. Method • For each 6-hour initialization time in 1948-1998, the top 200 analogs were selected from the available 3000 (about 6%).

  20. 51 years of Analog Selection: The DNA of atmospheric recurrence? P e r c e n t

  21. 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?

  22. Trying to understand abruptly changing analog selection patterns: A meteorological explanation Annual Mean Thickness NH Globe SH

  23. Trying to understand abruptly changing analog selection patterns: A dataset explanation Land Rawinsondes Aircraft Satellites Radiances 108 106 104 Approx.Daily # Obs (Log) 1950 1960 1970 1980 1990 Year

  24. 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

  25. 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

  26. Forecast Skill Benchmarks

  27. Forecast Skill Benchmarks

  28. Forecast Skill Benchmarks

  29. Forecast Skill Benchmarks: Climatology

  30. Forecast Skill Benchmarks: Climatology

  31. Forecast Skill Benchmarks: Climatology

  32. Forecast Skill Benchmarks: Climatology

  33. Forecast Skill Benchmarks: Climatology

  34. Harshest competition: Adjust climatology linearly for long-term trend… Annual mean thickness NH Globe SH Adjusted climatology for skill benchmark

  35. Forecast Skill Benchmarks: Detrended climatology

  36. Analog Forecast Skill: 51 year mean

  37. Analog Forecast Skill: 51 year mean Skill to 25 months Skill to 12 months Skill to 8.5 months

  38. 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?

  39. Skill (shaded) = MAECLIMO – MAEANALOG: [Red: Skill > 2m ] 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 2

  40. The importance of matching globally January 1997 Obs 12 month forecast January 1996 Obs 12 month forecast January 1952 Obs 12 month forecast

  41. Implications:There may be signs of an upcoming ENSO event 12 months in advance outside the tropics?

  42. 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

  43. 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?

  44. 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… 

  45. 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?

  46. 52-Year Temporal Correlation of Monthly MEI and PrecipitationTeleconnection pattern between ENSO and Global Precip

  47. 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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