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… Hurricane forecasting… “Snowstorms along the Northeastern U.S. Coast of the United States: ” Kocin & Uccellini 1990 AMS Monograph Major snowstorms….

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

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?

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…..

A sobering perspective… “…it would take order 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.

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?

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

An exploratory study 2 Historical archive: 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

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

hPa 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

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

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

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:

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 % NWP cost? No forecast skill Forecast skill Climatology Forecast length (days) MAE

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

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 8in each of the available years

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

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

51 years of Analog Selection: The DNA of atmospheric recurrence? PercentPercent

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?

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

Trying to understand abruptly changing analog selection patterns: A dataset explanation Approx. Daily # Obs (Log) LandRawinsondesAircraft SatellitesRadiances Year

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

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 = MAE CLIMO - MAE ANALOG

Forecast Skill Benchmarks

Forecast Skill Benchmarks: Climatology

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

Forecast Skill Benchmarks: Detrended climatology

Analog Forecast Skill: 51 year mean

Skill to 8.5 months Skill to 25 months Skill to 12 months

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?

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) = MAE CLIMO – MAE ANALOG : [Red: Skill > 2m ] 2

The importance of matching globally January 1997 Obs12 month forecast January 1996 Obs12 month forecast January 1952 Obs12 month forecast

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

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 forecasts were unusually poor. Evidently, Pinatubo produced a global pattern unlike any observed in the 54-year period

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?

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… 

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?

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

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

Current Analog ENSO Forecasts

Jan 2002 Forecast of Extended 2002 El Niño

2003 Forecast: Initialized Dec. 2002

Forecast: Initialized Jan. 2003

Forecast: Initialized Feb. 2003