# Presentation: 511435

## 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

## 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?

## 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.*

## 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?

## 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

## 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**

## 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

## 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

## 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

## 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?
- 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

## Initial experiment:Pattern matching instantaneous analyses

- Initial tests matched instantaneous thickness analyses
- 		Lead to forecast skill out to 8 days.

- No forecast skill

- Forecast skill

- Climatology

-   5	10	15	20	25	30	35

- 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 8	in each of the				available years

## Matching Window for July 1 

- J

- D

- 1998

- J

- D

- 1997

- J

- D

- 1996

- J

- D

- 1948

- J

- D

- 1949

- J

- D

- J

- D

- J

- D

- J

- D

- J

- D

- Match exact time/date      # = 51

- Match within 2 wk window #  3000

- J

- D

- J

- D

- J

- D

- J

- D

- J

- D

- Match allowed over entire year #  75000

- 1998

- 1997

- 1996

- 1948

- 1949

## Method 

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

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

- P e  r  c  e n  t

## Trying to understand changing analog selection patterns

- Annual Mean Thickness

- NH

- SH

- Globe

## 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

## Forecast Skill Benchmarks

## Forecast Skill Benchmarks

## Forecast Skill Benchmarks

## Forecast Skill Benchmarks

## Forecast Skill Benchmarks

## Forecast Skill Benchmarks

## Forecast Skill Benchmarks

## Forecast Skill Benchmarks

## Adjust climatology for long-term trend...

- Annual mean thickness

- Adjusted climatology for skill benchmark

- NH

- SH

- Globe

## Forecast Skill Benchmarks

## Analog Forecast Skill:  51 year mean

## Analog Forecast Skill:  51 year mean

- _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
- 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?

## 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

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

- 2

## Slide 34

## Slide 35

## The importance of matching globally

- January 1997 Obs			12 month forecast

- January 1996 Obs			12 month forecast

- January 1952 Obs			12 month forecast

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

## 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:

- Forecast length

- Standard Deviation

- High confidence: C > 1

- Low confidence: C  1

- Forecast length

- Extremes

- Extremes

- Climo 

- Climo 

- Init

- Init

- Analog 

- Analog 

## Analog Forecast Confidence

- 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

- CMEAN

## Analog Forecast Confidence:High Confidence Example: 1982

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

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

## 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)

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

## 52-Year Temporal Correlation of Monthly MEI and Precipitation

## 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

## Available GSN Station Data at NCDC

- Acquisition of Surface Data?

## MEI and Precipitation CorrelationWith Available GSN Data

- MEI and Precipitation CorrelationWith Available GSN Data

## MEI and Precipitation CorrelationWith Available GSN Data

- MEI and Precipitation CorrelationWith Available GSN Data

## Procedure

- Analog forecasts based on global thickness matches made every 6 hours from 1948-1998 (Approximately 77,000 forecasts)
- 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)

## Examples

- 	Precipitation forecasts initialized winter/spring/summer/fall of 1982 and 1997 in San Diego
- 	Forecasts dependent upon the strong El Nino conditions of the tropical Pacific.

## 1983 El Nino

- 1998 El Nino

## Seasonal Forecast For San Diego Initialized 1982

## Seasonal Forecast For San Diego Initialized 1997

## Summary

- Skillful tropical thickness forecasts out to:
- 25 months when compared against 54-year climatology
- 12 months when compared against previous 10-yr climatology
- 8.5 months when compared against adjusted climatology
- 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

## Summary

- 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.
- Ensemble spread at initialization time provides an objective indication of forecast uncertainty
- Skillful precipitation forecasts in San Diego when a strong El Nino is predicted

## A flood of 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)?
- 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 be enhanced?
- Is skill possible in other [surface] parameters?

## Concluding remarks

- The prototype analog system presented here shows promise, and efforts to improve it should continue.
- The results demonstrate the value of the freely-available NCEP/NCAR reanalysis dataset. It is fervently hoped that the reanalysis effort will be continued.

## Current State of ENSO

## Forecast Initialized Dec. 2002

## Forecast Initialized Jan. 2003

## Acknowledgments

- Resources:
- Penn State University
- NCEP & NCAR through CDC: Reanalysis
- Insightful discussion & guidance:
- Richard James
- Jenni Evans
- Paul Knight