


Norman (Wes) Junker 

National Centers for Environmental Prediction 

Hydrometeorological Prediction Center 

Camp Springs, MD 





Is there one best way to do PQPF? 

Keep man in the loop? 

Statistical methods applied to model output 

Calibrated probabilities from Enembles? 

Point versus the probability of occurrence
within some area? 





Relies on calibration of subjective forecasts of
probability. 

Need rain/no rain probability 

Uses conditional exceedence fractiles: 

The X_{50}, or amount where there is an
equal chance of getting more or less precipitation that that number 

the X_{25}, amount the forecaster thinks there is a 25% percent chance of
exceeding that value. 

Can then use curve to set probabilities for any
amount. 




LINEAR REGRESSION (MDL MOS) 

LOGISTIC REGRESSION (NON LINEAR) USES SAME TYPE
OF FUNCTIONAL RELATIONSHIPS AS NEURAL NETWORKS BUT HAS NO HIDDEN LAYERS 

DISCRIMINANT ANALYSIS 

NEURAL NETWORKS (NON LINEAR) 

CLASSIFIER SYSTEM (IF THEN STATEMENTS, SURVIVAL
OF THE FITTEST) 










Another way to assess probabilities for various
thresholds. 

Provides a method to take into account the
predictability of the pattern. 

Need to perturb initial conditions and model
physics. 

You assume each member has equal skill 

this
might be an incorrect assumption, if you are not careful how you perturb
the physics. 







Develop rank histograms based on the
precipitation forecast by each of the members. 



















however the shape of the histograms change significantly based on
the variability of the ensemble members. 

So separate histograms need to be developed for
high, medium and low variability cases. 

How do you handle the heaviest 10%, the extreme
rainfall events? 





More members with lower resolution? 

Fewer members with higher resolution? 












For lighter, more frequent events. 

MOStype nonlinear statistical approaches MAKE
SENSE. 

Provide WELL CALIBRATED probabilities. 

Calibrated ensemble methods also work well 

may be more computationally expensive in long
run. 

Statisticalman mix may be an option. Since a
forecaster might be able to take into account the predictability of the
pattern. 

a person
might be able to combine information from ensemble and statistical methods
to adjust POPS (this is already being done at HPC in the 37 day range). 








point probabilities may not always make
sense. Point probabilities will
always be very low, possibly too
low for emergency managers to act. 

Other methods need to be explored. 

One possibility develop probabilities of
various thresholds within a circle of some radius. Such forecasts might be useful to
emergency managers helping them decide when to put their staffs on alert 

Ensemble forecasts, combined with statistical
methods might be able to provide such probabilities and might be used to
determine the size of the circle.
or 

A single nonhydrostatic model run might provide
enough guidance to develop such probabilities if the radius of the circle
is based on error characteristics of the model. 

The phase error helps determine size of circle. 

the magnitude of the precipitation forecast be
used to help determine probabilities of occurrence within the circle for
various thresholds. 
