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During August and September 2012, a study was conducted to determine the sources of PM2.5 adjacent to the I-710 Long Beach Freeway. The site is directly affected by the emissions from heavy diesel traffic flowing from major container ports about 10 km south of the sampling site. Hourly average data were obtained for particulate species including PM2.5, black carbon and UV absorbing carbon, EC, fine particulate nonvolatile and semi-volatile organic material (NVOM and SVOM), sulfate, nitrate, chloride, ammonium ion, and Na ion, and for related factors including O3, CO, NOX, SO2, and total traffic flow on the I-710. A total of 520 hourly averaged data sets with 15 measured variables were analyzed by EPA-PMF v5.0. The data were best described by a 10-factor solution. Based on the composition and diurnal patterns of the factors, they were assigned to three diesel-related factors (two of which appeared to represent traffic from the ports and one general freeway diesel factor), a light-duty, spark-ignition vehicle-related factor, three secondary factors (one of which was associated with O3 formation processes), and three factors dominated by sulfate, SO2, and chloride, respectively. The diurnal patterns for these last three factors are strongly correlated. Meteorological and refinery upset data indicate that they are associated with emissions from a nearby refinery. The results of the PMF analysis were combined with nephelometer light scattering, corrected for coarse particle scattering and estimated aerosol water content in a multilinear regression analysis to identify visibility degradation sources. Major contributors were the aerosol water content, and the secondary PMF factors associated with either Nitrate and NVOM or NVOM and SVOM. The use of hourly average data made possible the identification of factors associated with gasoline vehicle emissions and both port and non-port diesel emissions.Implications: Hourly averaged data were obtained for PM2.5, its components and factors related to primary emissions and the formation of secondary material at a near freeway sampling location adjacent to the I-710 freeway just south of the Long Beach Boulevard entrance and 10 km north of the Ports of Long Beach and Los Angeles. The major objective of the study was to determine the impact of traffic from the ports at the monitoring site. This manuscript reports on the PMF analysis of the data set. Factors related to both diesel traffic originating from the ports and diesel traffic from non-port origins were identified. The diesel traffic originating from the ports was responsible for 9% of the total traffic and 95% of the BC measured at the sampling site. The non-port diesel traffic was responsible for 15% of the total traffic and 5% of the BC. While the Port 1 diesel traffic coming from the ports contributed a large fraction of the BC, this source contributed only 2% of the CO and 5% of the NOX at the sampling site. The impact of these traffic sources on light scattering was also small. Analysis of sources of sulfate and SO2 at the sampling site indicated that these species did not come from port activities of ships at or approaching the port, but rather from upset flare events at a nearby oil refinery.

Much of the electric system is weather dependent; thus, our ability to forecast the weather contributes to its efficient and economical operation. Climatological forecasts of meteorological variables are used for long-term planning, capturing changing frequencies of extreme events, such as cold and hot periods, and identifying suitable locations for deploying new resources. Planning for fuel delivery and maintenance relies on subseasonal to seasonal forecasts. On shorter timescales of days, the weather affects both energy demand and supply. Electrical load depends critically on weather because electricity is used for heating and cooling. As more renewable energy is deployed, it becomes increasingly important to understand how these energy sources vary with atmospheric conditions; thus, predictions are necessary for planning unit commitments. On the scales of minutes to hours, shortterm nowcasts aid in the real-time grid integration of these variable energy resources (VERs).

The Wind Forecast Improvement Project (WFIP) is a public–private research program, the goal of which is to improve the accuracy of short-term (0–6 h) wind power forecasts for the wind energy industry. WFIP was sponsored by the U.S. Department of Energy (DOE), with partners that included the National Oceanic and Atmospheric Administration (NOAA), private forecasting companies (WindLogics and AWS Truepower), DOE national laboratories, grid operators, and universities. WFIP employed two avenues for improving wind power forecasts: first, through the collection of special observations to be assimilated into forecast models and, second, by upgrading NWP forecast models and ensembles. The new observations were collected during concurrent year-long field campaigns in two high wind energy resource areas of the United States (the upper Great Plains and Texas) and included 12 wind profiling radars, 12 sodars, several lidars and surface flux stations, 184 instrumented tall towers, and over 400 nacelle anemometers. Results demonstrate that a substantial reduction (12%–5% for forecast hours 1–12) in power RMSE was achieved from the combination of improved numerical weather prediction models and assimilation of new observations, equivalent to the previous decade’s worth of improvements found for low-level winds in NOAA/National Weather Service (NWS) operational weather forecast models. Data-denial experiments run over select periods of time demonstrate that up to a 6% improvement came from the new observations. Ensemble forecasts developed by the private sector partners also produced significant improvements in power production and ramp prediction. Based on the success of WFIP, DOE is planning follow-on field programs.

Policy makers, responsible for the economic well-being of regions and states, have sought a variety of tools for predicting global change. One set of tools for simulating climate change, at a global scale, are the general circulation models (GCMs). Current versions of GCMs are limited to global studies, which are spatially crude and have output that is of minimal use to decision-makers accustomed to day-to-day, synoptic-scale changes in weather events. This paper discusses the use of a mesoscale model to “downscale” GCM output to the regional and local scales desired by policy makers. It presents a “nested” dynamical modeling approach. The paper also addresses several issues associated with the dynamical linking of GCMs and mesoscale models. Issues having to do with the choice of an atmospheric system, a variable as the best indicator of climate change, or having to do with validation of mesoscale simulations, which were based on GCM output, are considered.

This paper describes the capabilities and operational utility of a version of the Mesoscale Atmospheric Simulation System (MASS) that has been developed to support operational weather forecasting at the Kennedy Space Center (KSC) and Cape Canaveral Air Station (CCAS). The implementation of local, mesoscale modeling systems at KSC/CCAS is designed to provide detailed short-range (less than 24 h) forecasts of winds, clouds, and hazardous weather such as thunderstorms. Short-range forecasting is a challenge for daily operations, and manned and unmanned launches since KSC/CCAS is located in central Florida where the weather during the warm season is dominated by mesoscale circulations like the sea breeze. For this application, MASS has been modified to run on a Stardent 3000 workstation. Workstation-based, real-time numerical modeling requires a compromise between the requirement to run the system fast enough so that the output can be used before expiration balanced against the desire to improve the simulations by increasing resolution and using more detailed physical parameterizations. It is now feasible to run high-resolution mesoscale models such as MASS on local workstations to provide timely forecasts at a fraction of the cost required to run these models on mainframe supercomputers. MASS has been running in the Applied Meteorology Unit (AMU) at KSC/CCAS since January 1994 for the purpose of system evaluation. In March 1995, the AMU began sending real-time MASS output to the forecasters and meteorologists at CCAS, Spaceflight Meteorology Group (Johnson Space Center, Houston, Texas), and the National Weather Service (Melbourne, Florida). However, MASS is not yet an operational system. The final decision whether to transition MASS for operational use will depend on a combination of forecaster feedback, the AMU's final evaluation results, and the life-cycle costs of the operational system.

The International Geosphere-Biosphere Program — Biological Aspects of the Hydrological Cycle (IGBP-BAHC) is concerned with the role of the biosphere in the hydrological cycle over a range of space and time scales. One crucial emphasis in this exercise involves ‘upscaling’ or aggregating the processes in the soil-vegetation-atmosphere interface from the patch scale of ecology to the mesoscale, larger regional scales and the continental scale. In time the upscaling must move from hours to decades and even longer time periods. A second emphasis involves the ‘downscaling’ of low-resolution climatic and meteorological data to high-resolution grids that are suitable for ecological and hydrological research. These two emphases are described along with prototype models for upscaling and downscaling. Other issues such as the incorporation of land surface heterogeneity in upscaling and the classification of atmospheric circulation patterns in downscaling are discussed along with other aspects related to these emphases are presented in greater detail. In particular the discussion of the downscaling component in BAHC summarizes the results of the most recent workshops (BAHC, 1993a and b).