Search

To predict atmospheric conditions in an urban environment, the land surface processes must be accurately described through the use of detailed land use (LU) and land cover (LC) data. Use of the U.S. Geological Survey (USGS) 25-category data, currently in the Fifth-generation Mesoscale Model (MM5), with the Noah land surface model (LSM) and MRF (medium-range forecast) planetary boundary layer (PBL) schemes resulted in the over-prediction of daytime temperatures in the Houston downtown area due to the inaccurate representation as a completely impervious surface. This bias could be corrected with the addition of canopy water in the urban areas from the evapotranspiration effects of urban vegetation. A more fundamental approach would be to utilize an LULC dataset that represents land surface features accurately. The Texas Forest Service (TFS) LULC dataset established with the LANDSAT satellite imagery correctly represents the Houston–Galveston–Brazoria (HGB) area as mixtures of urban, residential, grass, and forest LULC types. This paper describes how the Noah LSM and PBL schemes in the MM5 were modified to accommodate the TFS-LULC data. Comparisons with various meteorological measurements show that the MM5 simulation made with the high resolution LULC data improves the boundary layer mixing conditions and local wind patterns in the Houston Ship Channel, which is a critically important anthropogenic emission area affecting the HGB air pollution problems. In particular, when the synoptic flows are weak, the improved LULC data simulates the asymmetrically elongated Houston heat island convergence zone influencing the location of the afternoon Gulf of Mexico sea-breeze front and the Galveston Bay breeze flows. This paper is part I of a two-part study and focuses on the meteorological simulation. In part II, effects of using the different meteorological inputs on air quality simulations are discussed. [Copyright &y& Elsevier]

The mission operations directorate (MOD) at the Johnson Space Center (JSC) is responsible for the planning and operation of human space flight missions. MOD is being challenged with sustaining and developing new operations capabilities to support increasingly demanding requirements and to improve its processes to accomplish these missions at higher levels of safety, mission success, and effectiveness. Automation is being considered as an enabling technology to meet the aforementioned challenges. The synergistic combination of flight controllers and intelligent software providing the function of ‘operations assistants’ (OA) is being pursued as the key implementation of this technology in the Mission Control Center (MCC).The flight control team (FCT) assesses the condition and operability of the major systems such as electrical power, thermal control, life support, communications, altitude control, and data handling at MCC. OA assist the flight controllers with their tasks of monitoring the status and health of the flight systems. They also help maintain the flight controller''s awareness of the operations being performed during the mission and help assure that operational objectives are being met. The station duty officer (SDO) performs the lead operations role for the International Space Station (ISS) during quiescent times when FCT and the flight director are off-duty. SDOs assess the condition and operability of the major ISS systems. This assessment involves monitoring and controlling several periodic processes on systems such as the electrical power, thermal control, life support, communications, altitude control, and data handling systems. The SDO is also responsible for coordinating operations with the Russian FCT. The OA will help the SDO maintain an awareness of all the processes performed on board and will assist with the responses to anomalous conditions. The OA for this position will support the concept of reduced control center staffing during quiescent times. The purpose of this paper is twofold: (1) to present a unique two-stage specification methodology that combines data flow diagrams and petri nets and (2) to apply the proposed methodology in a complex space station system. [Copyright &y& Elsevier]