Objective Digital Analog Forecasting “Is The Future In The Past?”

1. Objective Digital Analog Forecasting“Is The Future In The Past?”

2. We’re Going Back ….. Back to the Future

3. Pattern Recognition • Important to recognize the shape and influence of patterns and teleconnection indices. • Teleconnections: • AO, NAO, NAM, PNA, AAO, EA, WP, EP, NP, EAWR, SCA, POL, PT, SZ, ASU, PDO, • El Nino/La Nina • MEI, SOI, Nino1, Nino2, Nino3, Nino4, Nino3.4 • Complex interactions in the mid and high latitudes makes forecasting most teleconnection indices difficult beyond a week or two.

4. 55-yr Monthly Temporal Correlation of AO and 1000-500 mb Thickness

5. 55-yr Monthly Temporal Correlation of AO and Precipitation

6. 55-yr Monthly Temporal Correlation of AO and 500 mb Zonal Wind

7. 55-yr Monthly Temporal Correlation of NAO and Surface Temperature

8. 55-yr Monthly Temporal Correlation of PNA and 1000-500 mb Thickness

10. 55-yr Monthly Temporal Correlation of MEI and 1000-500 mb Thickness

11. 55-yr Monthly Temporal Correlation of MEI and Precipitation

12. Analog Motivation • Monthly/seasonal pattern evolution affected by? • Sea surface temperature anomalies • ENSO • Snow Cover / Icepack • Solar cycle • Phytoplankton • Vegetation • Atmospheric Chemistry • Stratospheric Phenomena

13. Analog forecasting • The oldest forecasting method? • Compare historical cases to existing conditions • Previous analog forecasting research yielded limited success • New digital age of analog forecasting • Dataset availability • 55 Year NCEP Reanalysis • 40 Year ECMWF Reanalysis • 109 Year Climate Division Data • Etc • Computational resources – statistical forecasting - ensembling

14. 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.

15. Goals • Not seeking exact replication of patterns • Instead, determine sign of the climatological departure using an analog ensemble (on a weekly to monthly time scale) • Analogs require keys • keys to matching • keys to extracting • Statistically extracting information relevant to current patterns and removing noise.

16. Analog Components • Data • Dataset length, frequency, area, variables, filtering • Matching Method • Parameters, region, search window, threshold method (MAE, anomaly correlation, RMSE, etc), statically or dynamically • Ensemble Configuration • Match/date selection, top (1,10,100,1000 matches), ensemble of single match analysis / ensemble of match analyses / both • Forecast • Forecasts made from dates acquired from matching • Integrate historical dates forward in time to generate ensemble forecast – mean, probabilistic distributions

17. Example Analog Forecasts • Seasonal tropical thickness forecasts • Seasonal San Diego precipitation forecasts • 2-4 week mid-latitude forecasts

18. Seasonal Tropical (20N-20S) Analog Thickness Forecasts

19. 1000-500hPa Thickness as Pattern Descriptor • Fewer degrees of freedom (Radinovic 1975) • 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)

20. Matching Method? • Instantaneous (unfiltered) thickness analyses? • Filtered thickness analyses? • Choice likely depends on desired forecast length • Short term forecast: compare instantaneous analyses • Long term forecast: compare filtered analyses • Optimal Filtering F = f(t,L) t = forecast length (lead time) L = verification increment (hour, month, season)

21. Filtering • Seasonal forecasting • 30-day lagged mean smoothed thickness

22. Matching Window for July 1 J D J D J D 2003 2003 J D J D J D 2002 2002 J D J D J D 2001 2001 J D J D J D 1949 1949 J D J D J D 1948 1948 Match exact time/date # = 55 Match within 2 wk window #  3000 Match allowed over entire year #  80000

23. Analog selection for 00 UTC 12 January 2001. Choose the top 200 (out of 3000 possible or 6%) matches from a 2-week window around the initialization date. Exclude matching between the year before and after the initialization Consensus forecast made for each 6-hour initialization time in 1948-1998, approx 80,000 forecasts.

24. 51 years of Analog Selection: The DNA of atmospheric recurrence? P e r c e n t

25. Skill? • Persistence, anomaly persistence? • Convention for seasonal forecasting: Climatology. • 54-year mean? 10-year mean? • 30-year mean? Previous year? • Tropical (20°S-20°N) monthly mean thickness forecast is evaluated • Skill = MAECLIMO - MAEANALOG

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

27. Skill (shaded) = MAECLIMO – MAEANALOG: [Red: Skill > 2m ] Winter/spring 1997 Forecast of 1998 El Nino Pinatubo hinders analog matching Spring 1982 prediction of 1983 El Nino 2

29. Seasonal Precipitation Forecasts

30. “Dependent” Analog Forecasts • Analogs allow for forecasts of any dependent variable which has a historical record, regardless of what is matched. • Forecasts of dependent variables requires some relationship to the matching parameter • For example – electrical usage – long term record of electrical usage could be determined from dates provided by thickness matching, thanks to the dependence of electricity on temperature, and temperature on thickness.

31. Precipitation Forecasts • Need an analog ensemble of matching dates • Acquired from global thickness matching • Daily historical records of surface parameters with a period as long as that from which the analogs matches were extracted • 51 years (1948-1998)

32. MEI and Precipitation CorrelationWith Available GSN Data

33. Method • San Diego precipitation forecasts • Global thickness matching dates • Surface precipitation observations • Forecast length (1- 365) days • Forecasts averaged over the length of period which is to be forecast • e.g., a seasonal (3 month) forecast is composed of an average of 3 months of 6 hourly forecast initializations (~360 forecasts)

34. 1983 El Nino 1998 El Nino

35. Seasonal Precipitation Forecast For San Diego Initialized 1982

40. Seasonal Precipitation Forecast For San Diego Initialized 1997

45. Mid-Latitude 2-4 Week Thickness Forecasts

46. Method • Technique similar to seasonal tropical forecasts with the following exceptions: • 1-day filtered thickness analyses • NH matching • Matching window - 4 weeks • Forecast length 1-30 days

47. Analog Ensemble Size Observed Analyses 00Z14MAR1993 Analog Ensemble Consensus Top (1,10,100,500 analogs)00Z14MAR1993

48. Optimal Analog Ensemble Size at Analysis