History and Current Status of the Localized Aviation MOS Program (LAMP) Statistical Post-processing System for Short-term Weather Forecast Guidance (Invited Presentation)

The Meteorological Development Laboratory (MDL) of the National Weather Service (NWS) has been statistically post-processing Numerical Weather Prediction (NWP) models to add value and aid in interpretation of NWP output as it relates to sensible weather for the past 50 years. Producing skillful, value-added guidance for sensible weather requires overcoming different challenges when forecasting for the short term (first 36 hours) compared to the medium and long term. The need to develop a short-range forecast guidance system led MDL to develop LAMP in the 1980’s (then called the Local Automation of Field Operations and Services [AFOS] MOS Program). While the LAMP acronym and the basic underlying techniques employed have stayed the same for the last 40 years, the acronym definition has changed as have the details of the methodology and the output products in response to new operational systems, new communication mechanisms, new users, new guidance requirements, recognition of new hazards for which to provide guidance, and new NWP models and observational datasets. The LAMP system has come a long way in 30 years, from the first test in Topeka KS in 1989 to now when it provides guidance for over 2000 stations in the contiguous United States, Alaska, Hawaii, and Puerto Rico, runs every 15 minutes in NWS operations, and provides skillful gridded guidance of very challenging forecast elements such as ceiling height, visibility, convection, and lightning.

In this talk, the history of the LAMP system will be presented. Unique challenges in maintaining the system, adapting to changing NWP models, and responding to evolving user requirements will be discussed. The presentation will include an overview of the current status of LAMP and how it has evolved to meet the growing requirements for probabilistic forecast guidance to aid in decision support services.