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Data-mining methods are applied to numerical weather prediction (NWP) output and satellite data to develop automated algorithms for the diagnosis of cloud ceiling height in regions where no local observations are available at analysis time. A database of hourly records that include Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) output, satellite data, and ground truth observations [aviation routine weather reports (METAR)] has been created. Data were collected over a 2.5-yr period for specific locations in California. Data-mining techniques have been applied to the database to determine relationships in the collected physical parameters that best estimate cloud ceiling conditions, with an emphasis on low ceiling heights. Algorithm development resulted in a three-step approach: 1) determine if a cloud ceiling exists, 2) if a cloud ceiling is determined to exist, determine if the ceiling is high or low (below 1 000 m), and 3) if the cloud ceiling is determined to be low, compute ceiling height. A sample of the performance evaluation indicates an average absolute height error of 120.6 m with a 0.76 correlation and a root-mean-square error of 168.0 m for the low-cloud-ceiling testing set. These results are a significant improvement over the ceiling-height estimations generated by an operational translation algorithm applied to COAMPS output.
An expert system (MEDEX) for predicting the gale-force onset, continuation, and cessation of seven major wind types within the Mediterranean basin has been designed, developed, and tested. The six wind types consist of the bora (flowing through both the Adriatic and Aegean Seas), etesian, levante, mistral, sirocco and westerly (poniente and vendaval
Accurate cloud-ceiling-height forecasts derived from numerical weather prediction (NWP) model data are useful for aviation and other interests where low cloud ceilings have an impact on operations. A demonstration of the usefulness of data-mining methods in developing cloud-ceiling forecast algorithms from NWP model output is provided here. Rapid Update Cycle (RUC) 1-h forecast data were made available for nearly every hour in 2004. Various model variables were extracted from these data and stored in a database of hourly records for routine aviation weather report (METAR) station KJFK at John F. Kennedy International Airport along with other single-station locations. Using KJFK cloud-ceiling observations as ground truth, algorithms were derived for 1-, 3-, 6-, and 12-h forecasts through a data-mining process. Performance of these cloud-ceiling forecast algorithms, as evaluated through cross-validation testing, is compared with persistence and Global Forecast System (GFS) model output statistics (MOS) performance (6 and 12 h only) over the entire year. The 1-h algorithms were also compared with the RUC model cloud-ceiling (or cloud base) height translation algorithms. The cloud-ceiling algorithms developed through data mining outperformed these RUC model translation algorithms, showed slightly better skill and accuracy than persistence at 3 h, and outperformed persistence at 6 and 12 h. Comparisons to GFS MOS (which uses observations in addition to model data for algorithm derivation) at 6 h demonstrated similar performance between the two methods with the cloud-ceiling algorithm derived through data mining demonstrating more skill at 12 h.
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