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Official 90-day Outlooks are issued once each month near mid-month at 3pm Eastern Time. Please consult the schedule of 30 & 90-day outlooks for exact release dates.

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Tools Used (see Discussion for explanation)
   CCA
   OCN
   CMP
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LONG-LEAD FORECAST TOOL DISCUSSION AND ANALYSIS (REVISED 05/11) 

FORECAST TOOLS:

CFS - AN ENSEMBLE MEAN FORECAST FROM A FULLY-COUPLED - ONE-TIER OCEAN-
ATMOSPHERE DYNAMICAL MODEL WITH NO OCEAN-ATMOSPHERE FLUX ADJUSTMENTS DONE IN
POST-PROCESSING.  OCEAN INITIAL CONDITIONS ARE FROM THE GLOBAL OCEAN DATA 
ASSIMILATION SYSTEM (GODAS).  FORECASTERS USE AN ENSEMBLE MEAN OF 40 FORECAST 
MEMBERS.  ALL ANOMALIES ARE WITH RESPECT TO A 1982-2003 HINDCAST CLIMATOLOGY - 
EXCEPT FOR NINO SSTS - FOR WHICH A BIAS CORRECTION WITH THE 1982-2003 AVERAGE IS 
FIRST APPLIED.  THE OBSERVED NINO CLIMATOLOGY FOR 1981-2010 IS THEN USED TO 
DEFINE THE NINO ANOMALIES.

NMME - THE NORTH AMERICAN MULTI-MODEL ENSEMBLE (NMME) IS A MULTI-MODEL SEASONAL 
FORECASTING SYSTEM CONSISTING OF FULLY COUPLED GLOBAL MODELS FROM MULTIPLE 
MODELING CENTERS IN THE U.S. AND CANADA. THE MODEL SUITE WILL OCCASIONALLY 
CHANGE AS INDIVIDUAL MODELS ARE UPDATED OR REPLACED; THE SUITE HAS FEATURED 
BETWEEN 6 AND 8 MODELS SINCE INCEPTION IN AUGUST 2011. THE SEASONAL (THREE-MONTH) 
NMME FORECASTS ARE AVAILABLE FROM 1- TO 5-MONTH LEADS; MONTHLY MEAN FORECASTS ARE 
AVAILABLE FROM 1- TO 7-MONTH LEADS. AVAILABLE FORECASTS FROM THIS SYSTEM INCLUDE 
INDIVIDUAL MODEL ENSEMBLE MEAN ANOMALIES, THE MULTI-MODEL ENSEMBLE MEAN ANOMALY, 
AND PROBABILISTIC FORECASTS BASED ON ALL ENSEMBLE MEMBERS FROM ALL MODELS (USUALLY 
APPROXIMATELY 100 ENSEMBLE MEMBERS TOTAL). ANOMALY FORECASTS ARE CORRECTED FOR 
BIAS IN THE INDIVIDUAL MODELS’ MEAN CLIMATOLOGIES; PROBABILISTIC FORECASTS ARE 
CORRECTED FOR BIAS IN THE INDIVIDUAL MODELS’ MEAN AND STANDARD DEVIATION. A 
CALIBRATED VERSION OF THE PROBABILISTIC FORECASTS IS CREATED USING THE PROBABILITY 
ANOMALY CORRELATION CALIBRATION METHOD (VAN DEN DOOL ET AL. 2016). 
THE NMME IS UPDATED MONTHLY ON THE 7TH DAY OF THE MONTH.
         .         .         .         .         .         .         .         .
CCA - CANONICAL CORRELATION ANALYSIS LINEARLY PREDICTS THE EVOLUTION OF PATTERNS
OF TEMPERATURE AND PRECIPITATION BASED UPON PATTERNS OF GLOBAL SST - 700MB 
HEIGHT - AND U.S. SURFACE TEMPERATURE AND PRECIPITATION FROM THE PAST YEAR FOR 
THE MOST RECENT FOUR NON-OVERLAPPING SEASONS.  CCA EMPHASIZES ENSO EFFECTS - BUT 
ONLY IN A LINEAR WAY - AND CAN ALSO ACCOUNT FOR TRENDS - LOW FREQUENCY 
ATMOSPHERIC MODES SUCH AS THE NORTH ATLANTIC OSCILLATION (NAO) AND OTHER LAGGED 
TELECONNECTIONS IN THE OCEAN-ATMOSPHERE SYSTEM.  CCA FORECASTS ARE AVAILABLE FOR 
ALL 13 FORECAST PERIODS FOR THE LOWER 48 STATES - HAWAII - AND ALASKA.

ECCA - UTILIZES THE CCA (CANONICAL CORRELATION ANALYSIS) METHOD OF PROJECTING
LOADING PATTERNS ONTO PREDICTOR FIELDS TO MAKE A LINEAR PREDICTION OF
TEMPERATURE AND PRECIPITATION. THESE LOADING PATTERNS ARE STATISTICALLY
DETERMINED BY MAXIMIZING THE CORRELATION BETWEEN THE PREDICTORS AND PREDICTANDS
(FORECAST FIELDS) USING DATA GOING BACK TO 1953.  THE ENSEMBLE IS CREATED BY
MAKING FORECASTS USING VARIOUS PREDICTOR VARIABLES TO MAKE FORECASTS, THEN
AVERAGING THE FORECASTS WITH EQUAL WEIGHTS.  THE POOL OF POSSIBLE PREDICTORS
USED ARE 200MB GLOBAL VELOCITY POTENTIAL, GLOBAL SEA SURFACE TEMPERATURES, SEA
LEVEL PRESSURE (NORTH OF 40N), AND US SOIL MOISTURE

ENSO COMPOSITES - AVERAGES OF OBSERVATIONAL DATA STRATIFIED BY EL NINO - LA NINA 
OR ENSO-NEUTRAL CONDITIONS PROVIDE GUIDANCE FOR U.S. EL NINO AND LA NINA EFFECTS 
BY SUPPLYING HISTORICAL FREQUENCIES OF THE THREE FORECAST CLASSES IN PAST YEARS 
WHEN (FOR THE PARTICULAR FORECAST SEASON) THE CENTRAL EQUATORIAL PACIFIC WAS 
CHARACTERIZED BY MODERATE OR STRONG LA NINA OR EL NINO CONDITIONS.  REGIONS 
INFLUENCED BY ENSO ARE DEFINED BY HISTORICAL FREQUENCIES THAT DIFFER 
SIGNIFICANTLY FROM CLIMATOLOGY.  PROBABILITY ANOMALIES ARE ESTIMATED BY THE USE 
OF HISTORICAL FREQUENCIES TEMPERED BY THE DEGREE OF CONFIDENCE THAT EITHER WARM 
OR COLD ENSO CONDITIONS WILL BE IN PLACE IN A GIVEN TARGET SEASON.  VERSIONS OF 
THE MAPS OF THE HISTORICAL FREQUENCIES USED TO MAKE THE FORECASTS CAN BE VIEWED 
UNDER "U.S. EL NINO/LA NINA IMPACTS" ON THE CPC WEBSITE LOCATED AT - 
http://www.cpc.ncep.noaa.gov/products/precip/CWlink/MJO/enso.shtml#composite.  

OCN - THE OPTIMAL CLIMATE NORMALS METHOD PREDICTS T AND P ON THE BASIS OF 
YEAR-TO-YEAR PERSISTENCE OF THE OBSERVED AVERAGE ANOMALIES FOR A GIVEN SEASON 
DURING THE LAST 10 YEARS FOR T - AND THE LAST 15 YEARS FOR P.  OCN EMPHASIZES 
LONG-TERM TRENDS AND MULTI-YEAR REGIME EFFECTS.  OCN FORECASTS ARE AVAILABLE FOR 
ALL 13 FORECAST PERIODS - BUT ARE NOT YET AVAILABLE FOR HAWAII.

CAS - CONSTRUCTED ANALOG ON SOIL MOISTURE IS BASED ON EMPIRICAL ORTHOGONAL 
FUNCTIONS (EOF) FROM DATA OVER THE LOWER 48 STATES BEGINNING IN 1932.  THIS TOOL 
CONSTRUCTS A SOIL MOISTURE ANALOG FROM A WEIGHTED MEAN OF PAST YEARS.  THE 
WEIGHTS ARE DETERMINED FROM THE SIMILARITY OF SOIL MOISTURE CONDITIONS IN PRIOR 
YEARS TO A COMBINATION OF RECENTLY SOIL MOISTURE OBSERVATIONS AND A MEDIUM RANGE 
FORECAST OF SOIL MOISTURE OUT TO 14 DAYS BASED ON MRF TEMPERATURE AND 
PRECIPITATION FORECASTS. THEN THE TEMPERATURE AND PRECIPITATION OBSERVED IN 
SUBSEQUENT SEASONS IN THOSE PAST YEARS ARE WEIGHTED IN THE SAME PROPORTION TO 
PRODUCE A FORECAST THAT IS CONSISTENT WITH CURRENT SOIL MOISTURE CONDITIONS.  
ALTHOUGH AVAILABLE THROUGHOUT THE YEAR - THE CAS IS USED ONLY DURING THE WARM 
HALF OF THE YEAR FROM APRIL TO SEPTEMBER AND FOR THE SHORTER LEADS WHEN THEIR 
EFFECTS ARE THE MOST PRONOUNCED AND SKILLFUL.

SMLR - SCREENING MULTIPLE LINEAR REGRESSION TOOL IS USED TO EXTRACT INFORMATION 
FROM A VARIETY OF SOURCES TO PRODUCE A FORECAST FOR SEASONAL AND MONTHLY 
TEMPERATURE AND PRECIPITATION.  SMLR USES THE SAME PREDICTOR FIELDS AS FOR CCA 
BUT IS APPLIED TO SINGLE STATIONS RATHER THAN MULTI-STATION ANOMALY PATTERNS AS 
IS DONE IN CCA.  ADDITIONALLY - SMLR USES THE TWO WEEK MRF-BASED SOIL MOISTURE 
FORECAST AS A PREDICTOR.    

FORECAST SKILL:

PREDICTIVE ACCURACY IN THE LOWER 48 STATES FOR TEMPERATURE PEAKS IN THE LATE 
WINTER WITH A SECONDARY PEAK IN THE LATE SUMMER - AND IS LOWEST IN THE LATE 
SPRING AND LATE FALL.  ALASKAN TEMPERATURE SKILL IS HIGHEST IN THE EARLY WINTER 
AND ALSO GOOD IN EARLY SUMMER AND IS LOWEST IN EARLY FALL FOR CCA.    

FOR ALL MODELS PRECIPITATION FORECASTS ARE GENERALLY LESS SKILLFUL THAN 
TEMPERATURE -- WITH MARGINAL SKILL FOR ALL TOOLS EVEN IN THEIR BEST SEASONS AND 
LOCATIONS UNDER NORMAL CIRCUMSTANCES.  HOWEVER WHEN STRONG EL NINO OR LA NINA 
CONDITIONS ARE PRESENT - PRECIPITATION SKILL CAN BE AS HIGH AS TEMPERATURE SKILL 
FOR COOL SEASON FORECASTS FOR A NUMBER OF AREAS OF THE U.S. - INCLUDING THE 
SOUTHERN THIRD - THE NORTHERN ROCKIES - THE HIGH PLAINS AND THE OHIO VALLEY.  
STRONG LA NINA CONDITIONS IMPLY THE POSSIBILITY OF MODERATE PRECIPITATION SKILL 
FOR SOME PARTS OF THE WARM SEASON AS WELL. 

24 YEARS OF HINDCASTS ARE RUN EACH MONTH FOR USE IN DEFINING THE CLIMATOLOGY AND
SKILL CHARACTERISTICS OF THE CFS.  A SKILL MASK IS CONSTRUCTED FROM THIS DATA
AND MASKED FORECASTS ARE PROVIDED TO THE FORECASTER. 

SKILL OF CFS NINO 3.4 FORECASTS EQUALS OR EXCEEDS THAT OF THE STATISTICAL 
FORECAST MODELS AT LEADS OUT TO NINE MONTHS. 

THE CONSTRUCTED ANALOG FORECAST FROM SOIL MOISTURE (CAS) GIVES HIGHEST SKILL FOR 
TEMPERATURE FROM APRIL THROUGH SEPTEMBER - WITH PEAK SKILL IN EARLY SUMMER.  THE 
MOST SKILLFUL SEASONS FOR PRECIPITATION FORECASTS ARE SON THROUGH JFM FOR OCN 
PREDICTIONS - AND THE LATE WINTER FOR THE OTHER TOOLS.  ALASKAN SKILL PEAKS IN 
THE LATE FALL FOR BOTH CCA AND THE CMP.  

THE SCREENING MULTIPLE LINEAR REGRESSION (SMLR) TOOL HAS SKILL CHARACTERISTICS 
SOMEWHAT SIMILAR TO CCA - BUT SINCE IT IS DESIGNED TO PREDICT FOR INDIVIDUAL 
STATIONS AND REGIONAL CLIMATE DIVISIONS IT MAY DO BETTER THAN CCA IN SMALLER 
REGIONS HAVING UNIQUE RELATIONSHIPS SUCH AS THOSE CAUSED BY LOCAL TERRAIN - 
ADJACENT WATER BODIES - OR DEVELOPING URBAN HEAT ISLANDS. 

FORECAST FORMAT: 

FORECASTS ARE EXPRESSED AS THE PROBABILITIES OF THE OBSERVATION OF MEAN 
TEMPERATURE (TOTAL PRECIPITATION) FALLING INTO THE MOST LIKELY OF THREE CLASSES 
- EITHER ABOVE - NEAR - OR BELOW NORMAL (MEDIAN).  CLASSES ARE DEFINED BY LIMITS 
THAT DIVIDE THE 1981-2010 CLIMATOLOGICAL DISTRIBUTION INTO THIRDS.  THUS EACH 
CLASS HAS A CLIMATOLOGICAL CHANCE OF OCCURANCE OF 33.3%. 

A FORECAST PROBABILITY OF EITHER ABOVE OR BELOW NORMAL IN THE THREE-
CLASS SYSTEM IMPLIES A CORRESPONDING REDUCTION IN THE PROBABILITY OF THE 
OPPOSITE CLASS AND A FIXED PROBABILITY (AT 33.3%) OF THE NEAR NORMAL CLASS FOR 
PROBABILITY ANOMALIES UP TO 30%.  FOR PROBABILITY ANOMALIES GREATER THAN 30% OF 
ABOVE OR BELOW NORMAL THE PROBABILITY OF THE OPPOSITE CLASS IS FIXED AT 3.3% 
(A -30% ANOMALY) AND THE PROBABILITY OF THE NEAR NORMAL CLASS IS REDUCED BY THE 
EXCESS FORECAST PROBABILITY ANOMALY OVER 30%.  NOTE THAT THIS IS ONLY A CRUDE 
APPROXIMATION OF THE TRUE PROBABILITY OF THE NON-SPECIFIED CLASSES AND IS 
GENERALLY LESS ACCURATE FOR EXTREME SHIFTS (20% OR MORE) IN THE PROBABILITY 
ANOMALY OF THE MOST LIKELY CLASS. 

EXAMPLES:  FORECAST PROBABILITY ANOMALIES OF 20%, 30% AND 40% FOR ABOVE NORMAL 
IMPLY PROBABILITIES FOR ALL THREE CLASSES (ABOVE - NEAR - BELOW) OF 53.3% - 33.3% 
- 13.3% --- 63.3% - 33.3% - 3.3% AND 73.3% - 23.3% - 3.3% RESPECTIVELY.

OCCASIONALLY THE FORECAST CALLS FOR AN INCREASED CHANCE OF THE OBSERVATION 
FALLING IN THE MIDDLE CLASS.  WHEN THIS OCCURS - HALF OF THE INCREASED 
PROBABILITY OF THE MIDDLE CLASS IS SUBTRACTED FROM EACH OF THE EXTREMES. 

FOR USERS WHO PREFER A 2-CLASS SYSTEM TO THE CURRENT 3-CLASS SYSTEM - CONVERSION 
TO A 2-CLASS SYSTEM CAN BE DONE VERY SIMPLY BY ALTERING 50-50 CLIMATOLOGICAL 
PROBABILITIES FOR THE BELOW VERSUS ABOVE NORMAL TWO CLASS CATEGORIES BY THE 
PROBABILITY ANOMALY SEEN ON OUR MAPS.  FOR EXAMPLE -- A 20% ANOMALY TOWARD ABOVE 
NORMAL W0ULD CONVERT TO AN 70% CHANCE FOR ABOVE AND A 30% CHANCE FOR BELOW IN A 
2-CLASS SYSTEM - A 30% ANOMALY TO 80 AND 20% - AND A 40% TO 85 AND 15%. 

FUTURE REVISIONS OF THIS MESSAGE WILL BE ISSUED WHENEVER SIGNIFICANT CHANGES IN 
THE AVAILABLE FORECAST TOOLS ARE MADE.

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