No abstract

No abstract

Localized Aviation MOS Program (LAMP) forecasts of ceiling height, visibility, wind, and other weather elements of interest to the aviation community have been produced and put into the National Digital Guidance Database (NDGD) since 2006. The High Resolution Rapid Refresh (HRRR) model is now producing explicit forecasts of ceiling height and visibility computed by algorithms based on variables directly forecasted by the HRRR. The Meteorological Development Laboratory has improved the LAMP ceiling and visibility forecasts by combining these two sources of information into a LAMP–HRRR MELD. The new forecasts show improvement over the original LAMP and particularly over the HRRR and persistence in terms of bias, threat score, and the Gerrity score. This paper explains how the MELD is produced and shows selected verification and example forecasts. A new guidance product based on this work will be made available to partners and customers.

The Hydrometeorological Prediction Center (HPC) at the NCEP has produced a suite of deterministic quantitative precipitation forecasts (QPFs) for over 40 yr. While the operational forecasts have proven to be useful in their present form, they offer no information concerning the uncertainties of individual forecasts. The purpose of this study is to develop a methodology to quantify the uncertainty in manually produced 6-h HPC QPFs (HQPFs) using NCEP short-range ensemble forecasts (SREFs). Results presented herein show the SREFs can predict the uncertainty of HQPFs. The correlation between HQPF absolute error (AE) and ensemble QPF spread (SP) is greater than 0.5 at 90.5% of grid points in the continental United States, exceeding 0.8 at 10% of these, for the 6-h forecast in winter. On the basis of the high correlation, the linear regression equations of AE on SP are derived at each point on a grid covering the United States. In addition, the regression equations for data categorized according to the observed and forecasted precipitation amounts are obtained and evaluated. Using the regression model equation parameters for 15 categorized ranges of HQPF at each horizontal grid point for each season and individual forecast lead time, an AE associated with an individual SP is predicted, as is the 95% confidence interval (CI) of the AE. Based on the AE CI forecast and the HQPF itself, the 95% CI of the HQPF is predicted as well. This study introduces an efficient and advanced method, providing an estimate of the uncertainty in the deterministic HQPF. Verification demonstrates the usefulness of the CI forecasts for a variety of classifications, such as season, CI range, HQPF, and forecast lead time.