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Norman (Wes) Junker |
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National Centers for Environmental Prediction |
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Hydrometeorological Prediction Center |
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Camp Springs, MD |
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Is there one best way to do PQPF? |
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Keep man in the loop? |
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Statistical methods applied to model output |
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Calibrated probabilities from Enembles? |
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Point versus the probability of occurrence
within some area? |
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Relies on calibration of subjective forecasts of
probability. |
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Need rain/no rain probability |
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Uses conditional exceedence fractiles: |
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The X50, or amount where there is an
equal chance of getting more or less precipitation that that number |
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the X25, amount the forecaster thinks there is a 25% percent chance of
exceeding that value. |
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Can then use curve to set probabilities for any
amount. |
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LINEAR REGRESSION (MDL MOS) |
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LOGISTIC REGRESSION (NON LINEAR) USES SAME TYPE
OF FUNCTIONAL RELATIONSHIPS AS NEURAL NETWORKS BUT HAS NO HIDDEN LAYERS |
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DISCRIMINANT ANALYSIS |
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NEURAL NETWORKS (NON LINEAR) |
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CLASSIFIER SYSTEM (IF THEN STATEMENTS, SURVIVAL
OF THE FITTEST) |
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Another way to assess probabilities for various
thresholds. |
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Provides a method to take into account the
predictability of the pattern. |
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Need to perturb initial conditions and model
physics. |
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You assume each member has equal skill |
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this
might be an incorrect assumption, if you are not careful how you perturb
the physics. |
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Develop rank histograms based on the
precipitation forecast by each of the members. |
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however the shape of the histograms change significantly based on
the variability of the ensemble members. |
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So separate histograms need to be developed for
high, medium and low variability cases. |
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How do you handle the heaviest 10%, the extreme
rainfall events? |
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More members with lower resolution? |
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Fewer members with higher resolution? |
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For lighter, more frequent events. |
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MOS-type non-linear statistical approaches MAKE
SENSE. |
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Provide WELL CALIBRATED probabilities. |
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Calibrated ensemble methods also work well |
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may be more computationally expensive in long
run. |
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Statistical-man mix may be an option. Since a
forecaster might be able to take into account the predictability of the
pattern. |
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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 3-7 day range). |
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point probabilities may not always make
sense. Point probabilities will
always be very low, possibly too
low for emergency managers to act. |
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Other methods need to be explored. |
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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 |
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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 |
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A single non-hydrostatic model run might provide
enough guidance to develop such probabilities if the radius of the circle
is based on error characteristics of the model. |
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The phase error helps determine size of circle. |
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the magnitude of the precipitation forecast be
used to help determine probabilities of occurrence within the circle for
various thresholds. |
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