Potential New Directions for CMAQ Post-Processing: Probabilistic AQ Forecasts Jim Wilczak and Irina Djalalova NOAA/Earth System Research Laboratory/Physical Sciences Division
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Potential New Directions for CMAQ Post-Processing: Probabilistic AQ Forecasts
Jim Wilczak and Irina DjalalovaNOAA/Earth System Research Laboratory/Physical Sciences Division
At each AirNOW observation site x
Forecast Fx Find Analog Forecasts
Find corresponding AirNOW Observations
Interpolate biases from AirNOW sites to every CMAQ gridpoint
Calculate weighted ensemble mean of observations, ANENS
Over entire CMAQ grid
Add interpolated bias to rawCMAQ forecast
Apply KF to ANENS, KFAN Calculate bias (Fx – KFAN)Apply Large ForecastError correction
Current KFAN Post-Processing System:
Operational later this autumn for PM2.5 and ozone
Ideal
Analogs
Obs
CMAQ
KFAN
Time
Ozo
ne, P
M2.
5
Ozo
ne, P
M2.
5
Large Forecast Error Correction
Ideal situation
Algorithm works because it can find good analogs
Analogs
Obs
CMAQ
KFAN
Time
Ozo
ne
KFAN-NewO
zone
, PM
2.5
Large Forecast Error Correction
Extreme event
Problem occurs because we are always working with short training data sets
Reason why the correction works is because CMAQ has some skill at predicting these extreme (forest fire) events
KFAN_new analog method
KFAN old analog method
Large Forecast Error Correction
At each AirNOW observation site x
Forecast Fx Find Analog Forecasts
Find corresponding AirNOW Observations
Interpolate biases from AirNOW sites to every CMAQ gridpoint
Calculate weighted ensemble mean of observations, ANENS
Over entire CMAQ grid
Add interpolated bias to rawCMAQ forecast
Apply KF to ANENS, KFAN Calculate bias (Fx – KFAN)Apply Large ForecastError Algorithm
Apply Large ForecastError Algorithm
Calculateprobabilityforecast, n/10
Modifications for Probabilistic Forecasts:
Interpolate biases from to CMAQ grid and add to raw CMAQ forecast
10 Member Ensemble, Ozone
Ozone Exceedance ProbabilityMaps
O3 > 50 ppbv
O3 > 70 ppbv
CMAQ OBSERVATION ANENS_probabilistic
Regional Probability Ozone Exceedance MapO3 > 70 ppbv
ANENS
Reliability Diagrams
July-August 2017
Spread-SkillCorrelations
PM2.5 Exceedance ProbabilityMaps
Summary
• New Large Forecast Error Correction scheme adds skill to all ranges of PM2.5 and ozone forecasts, but most importantly for high concentration events
• Probabilistic forecasts for ozone (and PM2.5) can be made from the existing analog ensemble
• These forecasts have skill as shown by reliability diagrams
• The spread of the ensemble members is moderately correlated with forecast skill, allowing for time-series of point or regional forecasts of forecast uncertainty
• Would these forecasts be useful?
Spread-Skill Relationship
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