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Ligia Bernardet, Laurie Carson, and Vijay Tallapragada


NOAA/NCEP runs a number of numerical weather prediction (NWP) modeling suites to provide operational guidance to the National Weather Service field offices and service centers. A sophisticated infrastructure, which includes a complex set of software tools, is required to facilitate running these NWP suites. This infrastructure needs to be maintained and upgraded so that continued improvements in forecast accuracy can be achieved. This contribution describes the design of a robust NWP Information Technology Environment (NITE) to support and accelerate the transition of innovations to NOAA operational modeling suites.

Through consultation with and at the request of the NOAA NCEP Environmental Modeling Center, a survey of segments of the national NWP community, and a review of selected aspects of the computational infrastructure of several modeling centers was conducted, which led to the following elements being considered as key for NITE: data management, source code management and build systems, suite definition tools, scripts, workflow management, experiment database, and documentation and training.

The design for NITE put forth by the DTC would make model development by NOAA staff and their external collaborators more effective and efficient. It should be noted that NITE was not designed to work exclusively for a certain modeling suite; instead it transcends the current operational suites and is applicable to the expected evolution in NCEP systems. NITE is particularly important for community engagement in the Next-Generation Global Prediction System, which is expected to be an Earth modeling system including several components.

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Lígia R. Bernardet and William R. Cotton


In this paper the authors address one type of severe weather: strong straight-line winds. The case of a mesoscale convective system that developed in eastern Colorado on 12–13 May 1985 was studied. The system formed in the afternoon, was active until early morning, and caused strong winds during the night.

A multiscale nonhydrostatic full physics simulation was performed to formulate a conceptual model of the main airflow branches of the system, and to gain understanding of the physical processes involved in the strong wind generation in this storm. Four telescopically nested grids covering from the synoptic-scale down to cloud-scale circulations were used. A Lagrangian model was employed to follow trajectories of parcels that took part in the updraft and downdraft, and balances of forces were computed along the trajectories.

The strong nocturnal winds were caused by downdrafts reaching the surface and by a dynamically forced horizontal pressure gradient force. The most important branch of the downdraft had an “up–down” trajectory. Parcels originated close to the ground, were lifted up by a strong upward-directed pressure gradient force, and became colder than their surroundings as they ascended in a stable environment. Then, as they went through the precipitation shaft, they sank due to negative buoyancy enhanced by condensate loading. The upward pressure gradient force was partially related to midlevel perturbation vorticity in the storm.

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Richard M. Yablonsky, Isaac Ginis, Biju Thomas, Vijay Tallapragada, Dmitry Sheinin, and Ligia Bernardet


The Princeton Ocean Model for Tropical Cyclones (POM-TC), a version of the three-dimensional primitive equation numerical ocean model known as the Princeton Ocean Model, was the ocean component of NOAA’s operational Hurricane Weather Research and Forecast Model (HWRF) from 2007 to 2013. The coupled HWRF–POM-TC system facilitates accurate tropical cyclone intensity forecasts through proper simulation of the evolving SST field under simulated tropical cyclones. In this study, the 2013 operational version of HWRF is used to analyze the POM-TC ocean temperature response in retrospective HWRF–POM-TC forecasts of Atlantic Hurricanes Earl (2010), Igor (2010), Irene (2011), Isaac (2012), and Leslie (2012) against remotely sensed and in situ SST and subsurface ocean temperature observations. The model generally underestimates the hurricane-induced upper-ocean cooling, particularly far from the storm track, as well as the upwelling and downwelling oscillation in the cold wake, compared with observations. Nonetheless, the timing of the model SST cooling is generally accurate (after accounting for along-track timing errors), and the ocean model’s vertical temperature structure is generally in good agreement with observed temperature profiles from airborne expendable bathythermographs.

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Mrinal K. Biswas, Jun A. Zhang, Evelyn Grell, Evan Kalina, Kathryn Newman, Ligia Bernardet, Laurie Carson, James Frimel, and Georg Grell


The Developmental Testbed Center (DTC) tested two convective parameterization schemes in the Hurricane Weather Research and Forecasting (HWRF) Model and compared them in terms of performance of forecasting tropical cyclones (TCs). Several TC forecasts were conducted with the scale-aware Simplified Arakawa Schubert (SAS) and Grell–Freitas (GF) convective schemes over the Atlantic basin. For this sample of over 100 cases, the storm track and intensity forecasts were superior for the GF scheme compared to SAS. A case study showed improved storm structure for GF when compared with radar observations. The GF run had increased inflow in the boundary layer, which resulted in higher angular momentum. An angular momentum budget analysis shows that the difference in the contribution of the eddy transport to the total angular momentum tendency is small between the two forecasts. The main difference is in the mean transport term, especially in the boundary layer. The temperature tendencies indicate higher contribution from the microphysics and cumulus heating above the boundary layer in the GF run. A temperature budget analysis indicated that both the temperature advection and diabatic heating were the dominant terms and they were larger near the storm center in the GF run than in the SAS run. The above results support the superior performance of the GF scheme for TC intensity forecast.

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Lígia Bernardet, Louisa Nance, Meral Demirtas, Steve Koch, Edward Szoke, Tressa Fowler, Andrew Loughe, Jennifer Luppens Mahoney, Hui-Ya Chuang, Matthew Pyle, and Robert Gall

The Weather Research and Forecasting (WRF) Developmental Testbed Center (DTC) was formed to promote exchanges between the development and operational communities in the field of Numerical Weather Prediction (NWP). The WRF DTC serves to accelerate the transfer of NWP technology from research to operations and to support a subset of the current WRF operational configurations to the general community. This article describes the mission and recent activities of the WRF DTC, including a detailed discussion about one of its recent projects, the WRF DTC Winter Forecasting Experiment (DWFE).

DWFE was planned and executed by the WRF DTC in collaboration with forecasters and model developers. The real-time phase of the experiment took place in the winter of 2004/05, with two dynamic cores of the WRF model being run once per day out to 48 h. The models were configured with 5-km grid spacing over the entire continental United States to ascertain the value of high-resolution numerical guidance for winter weather prediction. Forecasts were distributed to many National Weather Service Weather Forecast Offices to allow forecasters both to familiarize themselves with WRF capabilities prior to WRF becoming operational at the National Centers for Environmental Prediction (NCEP) in the North American Mesoscale Model (NAM) application, and to provide feedback about the model to its developers. This paper presents the experiment's configuration, the results of objective forecast verification, including uncertainty measures, a case study to illustrate the potential use of DWFE products in the forecasting process, and a discussion about the importance and challenges of real-time experiments involving forecaster participation.

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James D. Doyle, Saša Gaberšek, Qingfang Jiang, Ligia Bernardet, John M. Brown, Andreas Dörnbrack, Elmar Filaus, Vanda Grubišić, Daniel J. Kirshbaum, Oswald Knoth, Steven Koch, Juerg Schmidli, Ivana Stiperski, Simon Vosper, and Shiyuan Zhong


Numerical simulations of flow over steep terrain using 11 different nonhydrostatic numerical models are compared and analyzed. A basic benchmark and five other test cases are simulated in a two-dimensional framework using the same initial state, which is based on conditions during Intensive Observation Period (IOP) 6 of the Terrain-Induced Rotor Experiment (T-REX), in which intense mountain-wave activity was observed. All of the models use an identical horizontal resolution of 1 km and the same vertical resolution. The six simulated test cases use various terrain heights: a 100-m bell-shaped hill, a 1000-m idealized ridge that is steeper on the lee slope, a 2500-m ridge with the same terrain shape, and a cross-Sierra terrain profile. The models are tested with both free-slip and no-slip lower boundary conditions.

The results indicate a surprisingly diverse spectrum of simulated mountain-wave characteristics including lee waves, hydraulic-like jump features, and gravity wave breaking. The vertical velocity standard deviation is twice as large in the free-slip experiments relative to the no-slip simulations. Nevertheless, the no-slip simulations also exhibit considerable variations in the wave characteristics. The results imply relatively low predictability of key characteristics of topographically forced flows such as the strength of downslope winds and stratospheric wave breaking. The vertical flux of horizontal momentum, which is a domain-integrated quantity, exhibits considerable spread among the models, particularly for the experiments with the 2500-m ridge and Sierra terrain. The differences among the various model simulations, all initialized with identical initial states, suggest that model dynamical cores may be an important component of diversity for the design of mesoscale ensemble systems for topographically forced flows. The intermodel differences are significantly larger than sensitivity experiments within a single modeling system.

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