Methodologies for converting NWP outputs into QPF and PQPF
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 X50, or amount where there is an equal chance of getting more or less precipitation that that number | ||
the X25, amount the forecaster thinks there is a 25% percent chance of exceeding that value. | ||
Can then use curve to set probabilities for any amount. |
MODEL OUTPUT STATISTICAL METHODS THAT CAN BE USED TO DEVELOP PROBABILITIES
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) |
Brier skill scores for six statistical methods for 3 precipitation thresholds
VERY USEFUL FOR LIGHTER THRESHOLDS BUT TREND TOWARDS CLIMATOLOGY AT LONGER RANGES
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. |
A better way of using enemble forecasts to generate probability forecasts
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? |
If ensemble forecasts are used, which more cost effective
More members with lower resolution? | |
Fewer members with higher resolution? |
For extreme events is there a better way?
Or is it? How Good Are the Forecasts?
Then how do we approach forecasting QPF , Probabilistically?
For lighter, more frequent events. | |||
MOS-type non-linear statistical approaches MAKE SENSE. | |||
Provide WELL CALIBRATED probabilities. | |||
Calibrated ensemble methods also work well | |||
may be more computationally expensive in long run. | |||
Statistical-man 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 3-7 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 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. | |||||
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. |