CMAQ PM2.5 forecast improvements to a Kalman-filter Analog hybrid post-processing scheme

From our previous research an automated method for post-processing surface PM2.5 predictions from the NOAA Community Multiscale Air Quality (CMAQ) forecasting system was developed, delivered to the National Centers for Environmental Prediction (NCEP), used in the developmental mode for testing. The bias correction scheme implemented is one that first creates a time history of analog PM2.5 forecasts, formed as the weighted average of the PM2.5 observations that corresponded to the 10 closest historical analog forecasts (AN bias correction), and then applies the Kalman filter to this time-series of analog forecasts (KFAN bias correction). A significant number of model parameters are used in the search for analogs, including PM2.5, wind speed and direction, surface temperature, and solar radiation. The biases of the selected analogs at each observation site are then used to compute the bias to be applied to the current forecast. It is important to note that the time series of any bias-correction procedure, either AN or KFAN, is formed from the same forecast hour lead time, so the time series data are based on 24-hour scale. The new KFAN-hybrid technique is implemented. It uses the continued hourly time series of the data, both observed and predicted (CMAQ and AN or KFAN) from the past several days, usually 24, 48 or 72 hours, prior to the forecasting time to spread the most recent bias of the best possible prediction and observation over the new forecast cycle. The major statistics in terms of BIAS, MAE and CORRELATION and their improvements of the KFAN-hybrid method compared to AN and KFAN bias-correction techniques are computed and discussed.