Diagnosing a Colorado Heavy Snow Event with a Nonhydrostatic Mesoscale Numerical Model Structured for Operational Use

Abstract

State-of-the-art data sources, such as Doppler radar, automated surface observations, wind profiler, digital satellite, and aircraft reports, are for the first time providing the capability to generate real-time, operational three-dimensional gridded datasets with sufficient spatial and temporal resolutions to diagnose the structure and evolution of mesoscale systems. A prototype data assimilation system of this type, called the Local Analysis and Prediction System (LAPS), is being developed at the National Oceanic and Atmospheric Administration's Forecast Systems Laboratory. The investigation uses the three-dimensional LAPS analyses for initialization of the nonhydrostatic Regional Atmospheric Modeling System (RAMS) model developed at Colorado State University to create a system capable of generating operational mesoscale predictions. The LAPS/RAMS system structured for operational use can add significant value to existing operational model output and provide an improved scientific understanding of mesoscale weather events. The results are presented through a case study analysis, the 7 January 1992 northeast Colorado blizzard. The case is ideal for this investigation because of the significant mesoscale variation observed in the precipitation and flow structure. The case study results demonstrate the ability to successfully detect and predict mesoscale features using a mesoscale numerical model initialized with high-resolution (10-km horizontal grid interval), nonhomogeneous data. A conceptual model of the snow-storm is developed by using the RAMS model output in combination with observations and other larger domain model simulations. The LAPS/RAMS system demonstrates the ability to operationally forecast and display mesoscale systems in the local weather office.