Application of the NCEP Regional Spectral Model to Improve Mesoscale Weather Forecasts in Hawaii

Abstract

The operational implementation of the National Centers for Environmental Prediction (NCEP) Regional Spectral Model (RSM) in Hawaii is the first application of a mesoscale model to improve weather forecasts in the Pacific region. The primary model guidance for the National Weather Service Pacific region has been provided by the NCEP Aviation (AVN) run of the Global Spectral Model (GSM). In this paper, three recent synopticscale disturbances that affected the Hawaiian Islands are selected to demonstrate the potential utility of model guidance produced by the RSM and contrast it qualitatively with that from the AVN. NCEP RSM simulations, with enhanced grid resolution, can resolve convective rainbands and the interaction between the environmental airflow and the complex island topography, features the GSM cannot capture.RSM model performance in reproducing mesoscale structures associated with the synoptic-scale systems is encouraging. For the first simulation, a kona low case on 3 November 1995, the RSM predicted a northeastsouthwest-oriented rainband that closely matched a convective cloud band in the satellite imagery and maximum rainfall over Kauai. The second RSM simulation, a cyclogenesis event on 3 March 1996, shows remarkable agreement with observations. Important features such as the heavy rains and high winds over portions of Maui and Hawaii are accurately forecast. The third RSM simulation, a heavy rain event on 13 November 1996, is associated with convergence along a trailing cold-frontal trough. In this case the RSM correctly forecast the timing and distribution of heavy rainfall on the island of Oahu. Subjective comparisons between RSM output and observations demonstrate the potential utility of the model guidance for local weather forecasts in Hawaii.

Numerical Simulations of Island Effects on Airflow and Weather during the Summer over the Island of Oahu

“…Recently, Zhang et al (2005a) used a nonhydrostatic mesoscale spectral model (MSM; Juang 2000) to simulate weather conditions at three major airport stations on Oahu, and found that an advanced land surface model (LSM) with adequate descriptions of vegetation and ground cover over Oahu was required to better simulate the diurnal variations of the island's surface airflow and weather. They compared results of the regional spectral model (RSM) at a horizontal resolution of 10 km (Wang et al 1998) with that of the high-resolution (1.5 km) MSM coupled with the Noah LSM, and determined that the MSM/LSM produced much better simulations than the RSM alone.…”

The Ka‘ storm (November 2000): Imaging precipitable water using GPS

“…The NCEP global spectral model, which, with about 1‐degree grid spacing, does not include topography for the Hawaiian Islands, failed to predict any of the subsequent rainfall. The more detailed regional spectral model (10‐km grid) run operationally by NWS/University of Hawaii Department of Meteorology [ Wang et al , 1998], which incorporates coarse topography, was able to predict some rainfall, but it was both grossly underestimated and mislocated. Only with the inclusion of detailed topography in a 3‐km grid mesoscale spectral model (MSM) ( Zhang et al [2000]; http://www.soest.hawaii.edu/~rsm), run after the event, was a more realistic model of the actual rainfall generated, although even then the predicted rainfall was only 50–60% of the actual measured values.…”

Estimation of Fire Danger in Hawai'i Using Limited Weather Data and Simulation

“…Prior work has shown the sensitivity of fire spread simulations using FARSITE to weather data resolution . The availability of multiple weather stations and high-resolution gridded weather predicted by the Mesoscale Spectral Model ( Juang 1992, Wang et al 1998 afforded us the opportunity to further examine the effect of spatial resolution of weather data on simulated fire size and corresponding risk. The Mesoscale Spectral Model is a ''set of fully compressible nonhydrostatic equations governing a broad spectrum of atmospheric motion'' ( Juang 1992:75).…”