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K. Squires and S. Businger

Abstract

Data from the Long-Range Lightning Detection Network (LLDN), the Tropical Rainfall Measuring Mission (TRMM) satellite, and reconnaissance aircraft are used to analyze the morphology of lightning outbreaks in the eyewalls of Hurricanes Rita and Katrina, two of the strongest storms in the Atlantic hurricane record. Each hurricane produced eyewall lightning outbreaks during the period of most rapid intensification, during eyewall replacement cycles, and during the time period that encompassed the maximum intensity for each storm.

Within the effective range of the aircraft radar, maxima in eyewall strike density were collocated with maxima in radar reflectivity. High lightning strike rates were also consistently associated with TRMM low brightness temperatures and large precipitation ice concentration (PIC) values. The strike density ratio between the eyewall region and the outer rainband region was 6:1 for Hurricane Rita and 1:1 for Hurricane Katrina. This result is in contrast to those of previous remote lightning studies, which found that outer rainbands dominated the lightning distribution. The differences are shown to be at least in part the result of the more limited range of the National Lightning Detection Network (NLDN) data used in the earlier studies. Finally, implications of the results for the use of LLDN lightning data to remotely examine changes in hurricane intensity and structural evolution are discussed.

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T. Cherubini, S. Businger, C. Velden, and R. Ogasawara

Abstract

Tropospheric motions can be inferred from geostationary satellites by tracking clouds and water vapor in sequential imagery. These atmospheric motion vectors (AMV) have been operationally assimilated into global models for the past three decades, with positive forecast impacts. This paper presents results from a study to assess the impact of AMV derived from Geostationary Operational Environmental Satellite (GOES) imagery on mesoscale forecasts over the conventional data-poor central North Pacific region. These AMV are derived using the latest automated processing methodologies by the University of Wisconsin—Cooperative Institute for Meteorological Satellite Studies (CIMSS). For a test case, a poorly forecast subtropical cyclone (kona low) that occurred over Hawaii on 23–27 February 1997 was chosen. The Local Analysis and Prediction System (LAPS) was used to assimilate GOES-9 AMV data and to produce fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) initial conditions. The satellite wind assimilation is carried out on the 27-km-resolution domain covering the central Pacific area. The MM5 was run with three two-way nested domains (27, 9, and 3 km), with the innermost domain moving with the kona low. The AMV data are found to influence the cyclone’s development, improving the prediction of the cyclone’s central pressure and the track of the low’s center. Since September 2003, GOES-10 AMV data have been routinely accessed from CIMSS in real time and assimilated into the University of Hawaii (UH) LAPS, providing high-resolution initial conditions for twice-daily runs of MM5 at the Mauna Kea Weather Center collocated at the UH. It is found that the direct assimilation of AMV data into LAPS has a positive impact on the forecast accuracy of the UH LAPS/MM5 operational forecasting system when validated with observations in Hawaii. The implications of the results are discussed.

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