ALBERT

Description: The purposes of this study were to forecast the hourly typhoon wind velocity over the Penghu Islands, and to discuss the effects of the terrain of the Central Mountain Range (CMR) of Taiwan over the Penghu Islands based on typhoon tracks. On average, a destructive typhoon hits the Penghu Islands every 15–20 yr. As a typhoon approaches the Penghu Islands, its track and intensity are influenced by the CMR topography. Therefore, CMR complicates the wind forecast of the Penghu Islands. Six main typhoon tracks (classes I–VI) are classified based on typhoon directions, as follows: (I) the direction of direct westward movement across the CMR of Taiwan, (II) the direction of northward movement along the eastern coast of Taiwan, (III) the direction of northward movement traveling through Taiwan Strait, (IV) the direction of westward movement traveling through Luzon Strait, (V) the direction of westward movement traveling through the southern East China Sea (near northern Taiwan), and (VI) the irregular track direction. The adaptive network-based fuzzy inference system (ANFIS) and multilayer perceptron neural network (MLPNN) were used as the forecasting technique for predicting the wind velocity. A total of 49 typhoons from 2000 to 2012 were analyzed. Results showed that the ANFIS models provided high-reliability predictions for wind velocity, and the ANFIS achieved more favorable performance than did the MLPNN. In addition, a detailed discussion on the interaction of the CMR with the Penghu Islands based on various track directions is provided. For class I, the CMR is observed to have significantly influenced variations in wind speed when typhoons approached the Penghu Islands. In addition, the winds on the Penghu Islands were observed to have been influenced by the distance from the typhoon center to the Penghu Islands for all classes except class II.

Description: Convection-permitting Weather Research and Forecasting (WRF) Model forecasts with 3-km horizontal grid spacing were produced for a 50-member ensemble over a domain spanning three-quarters of the contiguous United States between 25 May and 25 June 2012. Initial conditions for the 3-km forecasts were provided by a continuously cycling ensemble Kalman filter (EnKF) analysis–forecast system with 15-km horizontal grid length. The 3-km forecasts were evaluated using both probabilistic and deterministic techniques with a focus on hourly precipitation. All 3-km ensemble members overpredicted rainfall and there was insufficient forecast precipitation spread. However, the ensemble demonstrated skill at discriminating between both light and heavy rainfall events, as measured by the area under the relative operating characteristic curve. Subensembles composed of 20–30 members usually demonstrated comparable resolution, reliability, and skill as the full 50-member ensemble. On average, deterministic forecasts initialized from mean EnKF analyses were at least as or more skillful than forecasts initialized from individual ensemble members “closest” to the mean EnKF analyses, and “patched together” forecasts composed of members closest to the ensemble mean during each forecast interval were skillful but came with caveats. The collective results underscore the need to improve convection-permitting ensemble spread and have important implications for optimizing EnKF-initialized forecasts.

Description: A new hazard index is presented to estimate and rank hurricane severity according to a storm’s damage and death toll after landfall on the continental United States. The index uses three characteristic meteorological aspects of hurricanes: wind, torrential rainfall, and storm surge, each with an individual subindex. Rainfall is identified as an important and frequently dominant hazard in terms of damage and death toll, but is not included in any current hazard scales or indices. The new rainfall subindex adopts rainfall intensity, storm rainfall area, and the forward speed of the system to estimate the rainfall hazard. The new hazard index, applied to recent U.S. hurricanes (2003–12), has better skill than existing scales in terms of ranking the severity of the events by both damage and death toll. Further, the index can provide good quantitative estimates of dollar values for damage and death toll, whereas previous models provide only a scale or ranking. The index provides a basis for improved hazard planning and emergency response, and may also be useful for insurance and risk management processes.

Description: A unified boundary layer and shallow convection parameterization based on a stochastic eddy-diffusivity/mass-flux (EDMF) approach is implemented and tested in the Navy Global Environmental Model (NAVGEM). The primary goals of this work are to improve the representation of convectively driven boundary layers and the coupling between the boundary layer and cumulus regions. Within the EDMF framework the subgrid vertical fluxes are calculated as a sum of an eddy-diffusivity part, which in the current implementation is based on the approach developed by Louis in the late 1970s, and a stochastic mass-flux parameterization. The mass-flux parameterization is a model for both dry and moist convective thermals. Dry thermals, which represent surface-forced coherent structures in a flow, provide countergradient mixing in the boundary layer and, if conditions permit, are the roots for moist thermals. Moist thermals represent shallow convective clouds. The new parameterization implemented in a single-column model (SCM) version of NAVGEM is shown to be able to realistically simulate a variety of dry and moist convective cases. The NAVGEM SCM results are validated against large-eddy-simulation results. The skill of NAVGEM as a global weather forecasting model is considerably improved with the new EDMF parameterization. The EDMF parameterization became part of the operational NAVGEM in November 2013.

Description: The development of a Great Lakes wave forecasting system at NOAA’s National Centers for Environmental Prediction (NCEP) is described. The system is an implementation of the WAVEWATCH III model, forced with atmospheric data from NCEP’s regional Weather Research and Forecasting (WRF) Model [the North American Mesoscale Model (NAM)] and the National Digital Forecast Database (NDFD). Reviews are made of previous Great Lakes wave modeling efforts. The development history of NCEP’s Great Lakes wave forecasting system is presented. A performance assessment is made of model wind speeds, as well as wave heights and periods, relative to National Data Buoy Center (NDBC) measurements. Performance comparisons are made relative to NOAA’s Great Lakes Environmental Research Laboratory (GLERL) wave prediction system. Results show that 1- and 2-day forecasts from NCEP have good skill in predicting wave heights and periods. NCEP’s system provides a better representation of measured wave periods, relative to the GLERL model in most conditions. Wave heights during storms, however, are consistently underestimated by NCEP’s current operational system, whereas the GLERL model provides close agreement with observations. Research efforts to develop new wave-growth parameterizations and overcome this limitation have led to upgrades to the WAVEWATCH III model, scheduled to become operational at NCEP in 2013. Results are presented from numerical experiments made with the new wave-model physics, showing significant improvements to the skill of NCEP’s Great Lakes wave forecasting system in predicting storm wave heights.

Description: On 8–9 February 2013, the northeastern United States experienced a historic winter weather event ranking among the top five worst blizzards in the region. Heavy snowfall and blizzard conditions occurred from northern New Jersey, inland to New York, and northward through Maine. Storm-total snow accumulations of 30–61 cm were common, with maximum accumulations up to 102 cm and snowfall rates exceeding 15 cm h−1. Dual-polarization radar measurements collected for this winter event provide valuable insights into storm microphysical processes. In this study, polarimetric data from the Weather Surveillance Radar-1988 Doppler (WSR-88D) in Upton, New York (KOKX), are investigated alongside thermodynamic analyses from the 13-km Rapid Refresh model and surface precipitation type observations from both Meteorological Phenomena Identification Near the Ground (mPING) and the National Weather Service (NWS) Forecast Office in Upton, New York, for interpretation of polarimetric signatures. The storm exhibited unique polarimetric signatures, some of which have never before been documented for a winter system. Reflectivity values were unusually large, reaching magnitudes 〉50 dBZ in shallow regions of heavy wet snow near the surface. The 0°C transition line was exceptionally distinct in the polarimetric imagery, providing detail that was often unmatched by the numerical model output. Other features include differential attenuation of magnitudes typical of melting hail, depolarization streaks that provide evidence of electrification, nonuniform beamfilling, a “snow flare” signature, and localized downward excursions of the melting-layer bright band collocated with observed transitions in surface precipitation types. In agreement with previous studies, widespread elevated depositional growth layers, located at temperatures near the model-predicted −15°C isotherm, appear to be correlated with increased snowfall and large reflectivity factors ZH near the surface.

Description: A protocol for the collection and analysis of high-resolution (≤300 m) temperature and humidity data along a transect using an unmodified passenger vehicle is described. A case is investigated of a weak dissipating cold front that became disconnected from its upper-tropospheric support and interacted with a developing Gulf of Mexico return flow. The changing relationships between the thermal and moisture gradients are described through cross-section analyses of the mobile, surface synoptic, and radiosonde data, extending from Texas to Alabama. The mobile transect data facilitated description of subsynoptic-scale airmass transition zones in the vicinity of the frontal remnant, as well as other variations within air masses. Similarities and differences are noted relative to previous studies of cold fronts, drylines, and coastal fronts.

Description: Wind is one of the parameters best predicted by numerical weather models, as it can be directly calculated from the physical equations of pressure that govern its movement. However, local winds are considerably affected by topography, which global numerical weather models, due to their limited resolution, are not able to reproduce. To improve the skill of numerical weather models, statistical and data analysis methods can be used. Machine learning techniques can be applied to train a model with data coming from both the model and observations in the area of interest. In this paper, a new method based on nonparametric multivariate locally weighted regression is studied for improving the forecasted wind speed of a numerical weather model. Wind direction data are used to build different regression models, as a way of accounting for the effect of surrounding topography. The use of this technique offers similar levels of accuracy for wind speed forecasts compared with other machine learning algorithms with the advantage of being more intuitive and easy to interpret.