Download D1-Barnston.pps

IRI Experience in Forecasting
and Applications for the
Mercosur Countries
Purely
Empirical
(observational)
approach
Mason & Goddard, 2001, Bull.Amer.Meteor.Soc.
Prediction Systems:
statistical vs. dynamical system
ADVANTAGES
DISADVANTAGES
Based on actual, real-world
Depends on quality and
observed data. Knowledge of
length of observed data
Stati- physical processes not needed.
stical
Does not fully account
Many climate relationships
for climate change, or
quasi-linear, quasi-Gaussian
new climate situations.
------- ------------------------------------ -----------------------------Some physical laws must
Uses proven laws of physics.
be abbreviated or statisDyna- Quality observational data not
tically estimated, leading
mical required (but helpful for valto errors and biases.
idation). Can handle cases
Computer intensive.
that have never occurred.
Dynamical Prediction System:
2-tiered vs. 1-tiered forecast system
ADVANTAGES
DISADVANTAGES
Two-way air-sea interaction,
Model biases amplify
as in real world (required
(drift); flux corrections
1-tier Where fluxes are as important as
large scale ocean dynamics)
Computationally
expensive
------- ------------------------------------- -----------------------------More stable, reliable SST in
Flawed (1-way) physics,
the prediction; lack of drift
especially unacceptable
2-tier that can appear in 1-tier system in tropical Atlantic and
Indian oceans (monsoon)
Reasonably effective for regions
impacted most directly by ENSO
IRI DYNAMICAL CLIMATE FORECAST SYSTEM
2-tiered
OCEAN
ATMOSPHERE
PERSISTED
GLOBAL
SST
GLOBAL
ATMOSPHERIC
MODELS
ANOMALY
ECPC(Scripps)
10
24
10
Persisted
SST
Ensembles
3 Mo. lead
ECHAM4.5(MPI)
FORECAST SST
TROP. PACIFIC: THREE
(multi-models, dynamical
and statistical)
TROP. ATL, INDIAN
(ONE statistical)
EXTRATROPICAL
(damped persistence)
CCM3.6(NCAR)
NCEP(MRF9)
NSIPP(NASA)
COLA2
GFDL
10
24
12
30
12
30
30
Forecast
SST
Ensembles
3/6 Mo. lead
POST
PROCESSING
MULTIMODEL
ENSEMBLING
Method of Forming 3 SST Predictions for Climate Predictions
damped
persistence
0.25
--------------------------------------------------
IRI CCA
(1) NCEP CFS
(2) LDEO
(3) CPC Constr Analog
CPTEC
CCA
-------------------------------------------------damped persistence
Method of Forming 4th SST Prediction for Climate Predictions
(4)Anomaly
persistence
from most
recently
observed
month
(all oceans)
Collaboration on Input to Forecast Production
Sources of the Global Sea Surface Temperature Forecasts
Tropical Pacific
Tropical Atlantic Indian Ocean
Extratropical Oceans
NCEP Coupled
LDEO Coupled
Constr Analogue
CPTEC Statistical
Damped Persistence
IRI Statistical
Atmospheric General Circulation Models Used in the IRI's Seasonal Forecasts,
for Superensembles
Name
Where Model Was Developed
Where Model Is Run
NCEP MRF-9 NCEP, Washington, DC
QDNR, Queensland, Australia
ECHAM 4.5
MPI, Hamburg, Germany
IRI, Palisades, New York
NSIPP
NASA/GSFC, Greenbelt, MD
NASA/GSFC, Greenbelt, MD
COLA
COLA, Calverton, MD
COLA, Calverton, MD
ECPC
SIO, La Jolla, CA
SIO, La Jolla, CA
CCM3.6
NCAR, Boulder, CO
IRI, Palisades, New York
GFDL
GFDL, Princeton, NJ
GFDL, Princeton, NJ
Forecasts of the climate
The tercile-based category system:
Below, near, and above normal
Probability: 33%
33%
Below| Near |
Below| Near |
Below| Near |
33%
Above
Data: | || ||| ||||.| || | | || | | | . | | | | |
| | | |

|
0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200
Rainfall Amount (mm)
(30 years of historical data for a particular location & season)
Monthly issued probability forecasts of
seasonal global precipitation and temperature
Four lead times - example:
JUL | Aug-Sep-Oct*
Sep-Oct-Nov
Oct-Nov-Dec
Nov-Dec-Jan
*probabilities of extreme (low and high) 15% issued also
very
good
skill
OND 1997
good
skill
OND 1998
somewhat
negative
skill
fairly
good
skill
OND 1999
OND 2000
Neutral
(zero)
skill
OND 2001
good
skill
OND 2002
negative
skill
OND 2003
neutral
(zero)
skill
OND 2004
Historical skill using actually observed SST
Ensemble mean (10 members)
Historical skill using actually observed SST
Ensemble mean (24 members)
OND
JFM
AMJ
JAS
Indonesia
Australia/
Indonesia
Europe
South
America
North America
Tropical
Americas
OND
Precip
19502000
Some Themes in Customer Demands:
1. Higher skills in existing products:
-Tighter range of possibilities
2. More usable and easily understood product
3. Expansion of repertoire of products
beyond seasonally averaged climate
Specifically, users consistently request:
More concrete forecast format
Direct use of physical units (inches, degr C)
Best guess quantity, with comparison to average
More flexibility in probabilistic forecast format
More temporal detail than 3 month averages
Outlooks for single months, even at long lead
Rainy season (or monsoon) onset time
Weather features within season (e.g. dry spells)
Chance of extreme event (storm, flood, drought)
Climate change nowcast (the new “normal”)
Almost all users want reliable probabilities
(probabilities that would match observed frequencies
of occurrence over a large sample of identical forecasts).
Even if reliable, users for some applications are not
impressed with slight changes from the climatological
precipitation probabilities. They are interested in
forecasts having probabilities that deviate strongly
from the neutral, climatological probabilities.
OND 1997
Ranked Probability Skill
Score (RPSS)
3
prob
prob
RPSfcst =  (Fcsticat – Obsicat)2
icat=1
icat ranges from 1 (below normal) to 3 (above normal)
icat is cumulative, from 1 to icat
RPSS = 1 - (RPSfcst / RPSclim)
RPSclim is RPS of forecasts of
climatology (33%,33%,33%)
Real-time
Forecast
Skill
(from Goddard
et al. 2003,
BAMS, p1761)
Real-time
Forecast
Skill
(from Goddard
et al. 2003,
BAMS, p1761)
Real-time
Forecast
Skill
(from Goddard
et al. 2003,
BAMS, p1761
1998-2001 Reliability Diagram
from Barnston et al. 2003, BAMS, p1783
Reliability Diagram
longer “AMIP”
period
from Goddard
et al. 2003
(EGS-AGU-EUG)
IRI Forecast Information Pages on the Web
IRI Home Page: http://iri.columbia.edu/
ENSO Quick Look:
http://iri.columbia.edu/climate/ENSO/currentinfo/QuickLook.html
IRI Probabilistic ENSO Forecast:
http://iri.columbia.edu/climate/ENSO/currentinfo/figure3.html
Current Individual Numerical Model Climate Forecasts
http://iri.columbia.edu/forecast/climate/prec03.html
Individual Numerical Model Hindcast Skill Maps
http://iri.columbia.edu/forecast/climate/skill/SkillMap.html
Current Multi-model Climate Forecasts
http://iri.columbia.edu/forecast/climate/multi_model2003.html