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Hui-Ya Chuang and Peter J. Sousounis

Abstract

A new idealized initialization technique has been developed for the Mesoscale Model version 5 modeling system. The technique allows the specification of baroclinic disturbances that feature vertical variations of the height, temperature, and wind fields in terms of phase lag, wavelength, and phase speed. The technique involves specifying a sounding profile at some reference point, generating the desired height fields using an analytic formulation, constructing the wind fields to be in geostrophic balance, and generating temperature fields using the hydrostatic relationship.

A distinct advantage of this technique over existing ones is that the boundary conditions are not restricted to being specified as periodic. The flexibility means that 1) users do not have to specify a domain whose size is equal to an integer number of wavelengths of the specified flow; 2) users can specify a flow that consists of different wavelengths at different heights, as is typically observed; and 3) any responses that are generated orographically or thermally in the domain and which leave the eastern boundary will not reenter the western boundary. This last item is particularly advantageous because it allows users to study the effects of a preconditioned environment on subsequent development of a featured disturbance rather than studying the repetitive effects of the same forcing mechanism on the same disturbance.

Examples of simulations using initial conditions that are generated with this technique are shown for 1) zonal flow and 2) continuous sinusoidal waves. Flat terrain was adopted for both examples. In example 1, the boundary layer parameterization scheme and surface fluxes were turned off for a simplified zonal flow situation to demonstrate the stability of this technique. During the simulations, the flow remained zonal, exactly as specified, even after 48 h. In example 2, a situation consisting of continuous sinusoidal waves moving across an array of four warm circular lakes was created to demonstrate the utility of the technique for examining how disturbances may be affected by the Great Lakes. Realistic-looking highs, lows, and fronts, along with individual and lake-aggregate enhancements developed by 48 h. Good stability and lack of distortion throughout the domain in both examples add credibility to the technique.

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Hui-Ya Chuang and Peter J. Sousounis

Abstract

The effects of a group (aggregate) of relatively warm circular meso-β-scale lakes on different flow regimes were investigated by conducting a series of idealized numerical experiments. This investigation was motivated by the observed behavior of synoptic-scale cyclones moving through the Great Lakes region during winter. Three with-lake (WL) and three corresponding no-lake (NL) simulations were initialized with 1) zonal flow, 2) a solitary trough, and 3) continuous sinusoidal waves, respectively. The WL experiments were intercompared to examine the importance of a preexisting disturbance and preconditioning. The NL simulations were compared to the corresponding WL simulations to study the contributions of the lake aggregate. The simulation results suggest that the lake aggregate induced or enhanced warm fronts when there were preexisting disturbances. They also suggest that a perturbation mesoscale aggregate vortex was generated in each of the three different flow scenarios even though the lake aggregate alone could only generate a weak meso-α-scale trough.

To identify the physical processes that were altered by the lake aggregate to enhance cyclone development, surface pressure tendency diagnosis using the extended Zwack–Okossi (ZO) equation was applied to the simulation results. The results of the ZO surface pressure (PSFC) tendency diagnosis indicated that the preconditioning from the preceding ridge contributed to the further development of the lake-aggregate–enhanced cyclones. The results also indicated that the lake aggregate not only reduced the PSFC locally through surface sensible heating but also and, more importantly, contributed to large-scale surface pressure deepening by enhancing the surface warm front.

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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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Tanya L. Otte, George Pouliot, Jonathan E. Pleim, Jeffrey O. Young, Kenneth L. Schere, David C. Wong, Pius C. S. Lee, Marina Tsidulko, Jeffery T. McQueen, Paula Davidson, Rohit Mathur, Hui-Ya Chuang, Geoff DiMego, and Nelson L. Seaman

Abstract

NOAA and the U.S. Environmental Protection Agency (EPA) have developed a national air quality forecasting (AQF) system that is based on numerical models for meteorology, emissions, and chemistry. The AQF system generates gridded model forecasts of ground-level ozone (O3) that can help air quality forecasters to predict and alert the public of the onset, severity, and duration of poor air quality conditions. Although AQF efforts have existed in metropolitan centers for many years, this AQF system provides a national numerical guidance product and the first-ever air quality forecasts for many (predominantly rural) areas of the United States. The AQF system is currently based on NCEP’s Eta Model and the EPA’s Community Multiscale Air Quality (CMAQ) modeling system. The AQF system, which was implemented into operations at the National Weather Service in September of 2004, currently generates twice-daily forecasts of O3 for the northeastern United States at 12-km horizontal grid spacing. Preoperational testing to support the 2003 and 2004 O3 forecast seasons showed that the AQF system provided valuable guidance that could be used in the air quality forecast process. The AQF system will be expanded over the next several years to include a nationwide domain, a capability for forecasting fine particle pollution, and a longer forecast period. State and local agencies will now issue air quality forecasts that are based, in part, on guidance from the AQF system. This note describes the process and software components used to link the Eta Model and CMAQ for the national AQF system, discusses several technical and logistical issues that were considered, and provides examples of O3 forecasts from the AQF system.

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Suranjana Saha, Shrinivas Moorthi, Xingren Wu, Jiande Wang, Sudhir Nadiga, Patrick Tripp, David Behringer, Yu-Tai Hou, Hui-ya Chuang, Mark Iredell, Michael Ek, Jesse Meng, Rongqian Yang, Malaquías Peña Mendez, Huug van den Dool, Qin Zhang, Wanqiu Wang, Mingyue Chen, and Emily Becker

Abstract

The second version of the NCEP Climate Forecast System (CFSv2) was made operational at NCEP in March 2011. This version has upgrades to nearly all aspects of the data assimilation and forecast model components of the system. A coupled reanalysis was made over a 32-yr period (1979–2010), which provided the initial conditions to carry out a comprehensive reforecast over 29 years (1982–2010). This was done to obtain consistent and stable calibrations, as well as skill estimates for the operational subseasonal and seasonal predictions at NCEP with CFSv2. The operational implementation of the full system ensures a continuity of the climate record and provides a valuable up-to-date dataset to study many aspects of predictability on the seasonal and subseasonal scales. Evaluation of the reforecasts show that the CFSv2 increases the length of skillful MJO forecasts from 6 to 17 days (dramatically improving subseasonal forecasts), nearly doubles the skill of seasonal forecasts of 2-m temperatures over the United States, and significantly improves global SST forecasts over its predecessor. The CFSv2 not only provides greatly improved guidance at these time scales but also creates many more products for subseasonal and seasonal forecasting with an extensive set of retrospective forecasts for users to calibrate their forecast products. These retrospective and real-time operational forecasts will be used by a wide community of users in their decision making processes in areas such as water management for rivers and agriculture, transportation, energy use by utilities, wind and other sustainable energy, and seasonal prediction of the hurricane season.

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Suranjana Saha, Shrinivas Moorthi, Hua-Lu Pan, Xingren Wu, Jiande Wang, Sudhir Nadiga, Patrick Tripp, Robert Kistler, John Woollen, David Behringer, Haixia Liu, Diane Stokes, Robert Grumbine, George Gayno, Jun Wang, Yu-Tai Hou, Hui-ya Chuang, Hann-Ming H. Juang, Joe Sela, Mark Iredell, Russ Treadon, Daryl Kleist, Paul Van Delst, Dennis Keyser, John Derber, Michael Ek, Jesse Meng, Helin Wei, Rongqian Yang, Stephen Lord, Huug van den Dool, Arun Kumar, Wanqiu Wang, Craig Long, Muthuvel Chelliah, Yan Xue, Boyin Huang, Jae-Kyung Schemm, Wesley Ebisuzaki, Roger Lin, Pingping Xie, Mingyue Chen, Shuntai Zhou, Wayne Higgins, Cheng-Zhi Zou, Quanhua Liu, Yong Chen, Yong Han, Lidia Cucurull, Richard W. Reynolds, Glenn Rutledge, and Mitch Goldberg

The NCEP Climate Forecast System Reanalysis (CFSR) was completed for the 31-yr period from 1979 to 2009, in January 2010. The CFSR was designed and executed as a global, high-resolution coupled atmosphere–ocean–land surface–sea ice system to provide the best estimate of the state of these coupled domains over this period. The current CFSR will be extended as an operational, real-time product into the future. New features of the CFSR include 1) coupling of the atmosphere and ocean during the generation of the 6-h guess field, 2) an interactive sea ice model, and 3) assimilation of satellite radiances by the Gridpoint Statistical Interpolation (GSI) scheme over the entire period. The CFSR global atmosphere resolution is ~38 km (T382) with 64 levels extending from the surface to 0.26 hPa. The global ocean's latitudinal spacing is 0.25° at the equator, extending to a global 0.5° beyond the tropics, with 40 levels to a depth of 4737 m. The global land surface model has four soil levels and the global sea ice model has three layers. The CFSR atmospheric model has observed variations in carbon dioxide (CO2) over the 1979–2009 period, together with changes in aerosols and other trace gases and solar variations. Most available in situ and satellite observations were included in the CFSR. Satellite observations were used in radiance form, rather than retrieved values, and were bias corrected with “spin up” runs at full resolution, taking into account variable CO2 concentrations. This procedure enabled the smooth transitions of the climate record resulting from evolutionary changes in the satellite observing system.

CFSR atmospheric, oceanic, and land surface output products are available at an hourly time resolution and a horizontal resolution of 0.5° latitude × 0.5° longitude. The CFSR data will be distributed by the National Climatic Data Center (NCDC) and NCAR. This reanalysis will serve many purposes, including providing the basis for most of the NCEP Climate Prediction Center's operational climate products by defining the mean states of the atmosphere, ocean, land surface, and sea ice over the next 30-yr climate normal (1981–2010); providing initial conditions for historical forecasts that are required to calibrate operational NCEP climate forecasts (from week 2 to 9 months); and providing estimates and diagnoses of the Earth's climate state over the satellite data period for community climate research.

Preliminary analysis of the CFSR output indicates a product that is far superior in most respects to the reanalysis of the mid-1990s. The previous NCEP–NCAR reanalyses have been among the most used NCEP products in history; there is every reason to believe the CFSR will supersede these older products both in scope and quality, because it is higher in time and space resolution, covers the atmosphere, ocean, sea ice, and land, and was executed in a coupled mode with a more modern data assimilation system and forecast model.

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