The Use of LAPS As An Assimilation and Model Initialization Tool For Tropical Cyclones

Abstract

NOAA's Local Analysis and Prediction System (LAPS) is a meteorological assimilation system that employs a model first guess and all available observations (meteorological networks, radar, satellite, soundings, and aircraft) to generate a spatially distributed, mass consistent and balanced three-dimensional representation of atmospheric features and processes. In addition to this traditional analysis core, we are testing a second core called the Space-Time Mesoscale Analysis System (STMAS), which has the capability of doing a simultaneous fitting of the data and balancing step. These data-driven, high-resolution analyses can run on many types of computer hardware and have been ported to a number of sites worldwide. The focus of the present study is to assess and improve the tropical cyclone analyses produced by these cores. For this purpose we are adding reconnaissance aircraft dropsonde, radar, and other datasets. Areas of particular interest include the wind analysis, cloud analysis, and balancing of the various fields to produce a realistic atmospheric state suitable for model initialization. We used hurricane Dennis for this study, looking at the developmental stages of what later became a category four storm. The paper will discuss the strengths and weaknesses of both approaches and their suitability for initialization of the WRF.