1 / 72

Seasonal Analog Forecasting: Breathing New Life into an Old Friend

This study explores the feasibility of analog forecasting using NCEP reanalyses data from 1948-2002. It delves into the strengths and weaknesses of the analog approach, methodology, historical archive, and more.

nhogan
Download Presentation

Seasonal Analog Forecasting: Breathing New Life into an Old Friend

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Breathing new life into an old friend: Objective seasonal analog forecasting using NCEP reanalyses 4 February 2003 Robert Hart1 Jeremy Ross1 Mike Fritsch1 Charles Hosler1 Richard Grumm2 1Penn State University 2NWS State College, PA

  2. 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?

  3. 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.

  4. We have decided not to wait, and instead have drastically reduced our expectations. • We are not looking for an exact replication of patterns • We do want to determine on which side of climatology we are most likely to reside. • We do not need to forecast all the time: Only when confidence measures allow. • With these lesser expectations: Is 50 years of archive sufficient for skillful seasonal analog forecasts?

  5. Analogs • Previous work illustrates mixed success of analog approach and its complexities: • Lorenz 1969 van Den Dool 1987 • Radinovic 1975 Ruosteenoja 1988 • Barnett and Preisendorfer 1978 Barnston & Livezey 1989 • Bergen and Harnack 1982 Toth 1989, 1991 • Kruizinga and Murphy 1983 Barnston et al. 1994 • Gustzler and Shukla 1984 Livezey 1994 • Livezey et al. 1984 van Den Dool 1994 • Currently have the benefits of a longer historical archive, more accurate global analyses, and greater computer power than was present during most of the previous studies • Focus on tropical forecasts where timescales of forcing are more slowly evolving and thus more susceptible to seasonal forecasting

  6. 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 • Results are preliminary • We desire further guidance & collaboration

  7. 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

  8. 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

  9. 1000-500hPa Thickness as Global Pattern Descriptor • Fewer degrees of freedom than other atmospheric variables (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) • Pattern matching performed using MAE

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

  11. Analogs: How to pattern match? • 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) • For example, a monthly mean forecast for June 2003:  t = 5 months L = 1 month

  12. Initial experiment:Pattern matching instantaneous analyses • Initial tests matched instantaneous thickness analyses  Lead to forecast skill out to 8 days. No forecast skill MAE Climatology Forecast skill 5 10 15 20 25 30 35 Forecast length (days)

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

  14. 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

  15. 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

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

  17. 51 years of Analog Selection: The DNA of atmospheric recurrence? P e r c e n t

  18. Trying to understand changing analog selection patterns Annual Mean Thickness NH Globe SH

  19. 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. • Tropical (20°S-20°N) monthly mean thickness forecast is evaluated • Skill here = MAECLIMO - MAEANALOG

  20. Forecast Skill Benchmarks

  21. Forecast Skill Benchmarks

  22. Forecast Skill Benchmarks

  23. Forecast Skill Benchmarks

  24. Forecast Skill Benchmarks

  25. Forecast Skill Benchmarks

  26. Forecast Skill Benchmarks

  27. Forecast Skill Benchmarks

  28. Adjust climatology for long-term trend… Annual mean thickness NH Globe SH Adjusted climatology for skill benchmark

  29. Forecast Skill Benchmarks

  30. Analog Forecast Skill: 51 year mean

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

  32. 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 • 8.5 months of forecast skill against trend-adjusted climatology means we are able to forecast seasonal thickness pattern evolution in the tropics • How does the forecast skill vary from year to year?

  33. Skill (shaded) = MAECLIMO – MAEANALOG: [Red: Skill > 2m ] Winter/spring 1997 Forecast of 1998 El Nino Pinatubo hinders analog matching Spring 1986 prediction of 1987 El Nino Spring 1982 prediction of 1983 El Nino Successful forecast of a non-ENSO anomaly 2

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

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

  36. Analog Forecast confidence • It is possible to define a measure of forecast confidence, C C = climo  / analog ensemble  In any given case this appears schematically as: High confidence: C > 1 Low confidence: C  1 Extremes Climo  Standard Deviation Analog  Analog  Climo  Extremes Init Init Forecast length Forecast length

  37. Analog Forecast Confidence CMEAN • 51-year mean C (CMEAN) is a function of forecast length • Compare any given forecast C to CMEAN to arrive at a measure of relative confidence (CR): CR = C – CMEAN • Determines whether confidence is currently above (CR > 0) or below (CR < 0) average for given forecast length

  38. Analog Forecast Confidence:High Confidence Example: 1982

  39. Analog Forecast Confidence:Low Confidence Example: Pinatubo’s Effects in 1993

  40. Can the prototype analog system predict surface parameters such as temperature and precipitation?

  41. What do we need to do this? • 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)

  42. For our first experiment, we decided to test a station correlated to ENSO events

  43. 52-Year Temporal Correlation of Monthly MEI and Precipitation

  44. Choosing a test site • Long-term records of daily surface data • Global Climate Observing System (GCOS) Surface Network (GSN Data) • Established in 1992 by four international organizations to provide long-term historical records of surface data for monitoring the global climate

  45. Available GSN Station Data at NCDC Acquisition of Surface Data?

  46. MEI and Precipitation CorrelationWith Available GSN Data

  47. MEI and Precipitation CorrelationWith Available GSN Data

More Related