We describe sensitivity studies on the remote sensing of cirrus cloud optical thickness and effective particle size using the National Polar-orbiting Operational Environmental Satellite System Visible/Infrared Imager Radiometer Suite 0.67-, 1.24-, 1.61-, and 2.25-microm reflectances and thermal IR 3.70- and 10.76-microm radiances. To investigate the accuracy and precision of the solar and IR retrieval methods subject to instrument noise and uncertainties in environmental parameters, we carried out signal-to-noise ratio tests as well as the error budget study, where we used the University of California at Los Angeles line-by-line equivalent radiative transfer model to generate radiance tables for synthetic retrievals. The methodology and results of these error analyses are discussed.

No abstract

Sensor Web observing systems may have the potential to significantly improve our ability to monitor, understand, and predict the evolution of rapidly evolving, transient, or variable environmental features and events. This improvement will come about by integrating novel data collection techniques, new improved instruments, emerging communications technologies, and interoperable planning and scheduling systems. In contrast with today's observing systems, "event-driven" sensor webs will synthesize near-real time measurements and information from other platforms and then reconfigure themselves to invoke new measurement modes and adaptive observation strategies. Meteorological prediction models may also serve to initiate new measurement modes (e.g., higher spatial, temporal resolution) or to target observations to specific regions. These "model-driven" sensor webs will complement event-driven measurements. Platforms will be tasked to target measurements within specific areas where sensitivity to initial conditions may cause ensemble forecasts to diverge when predicting the future state of atmospheric features (e.g., hurricane track) or when discriminating subtle yet critical differences in atmospheric states (e.g., winter precipitation type and location). The targeted measurements would then be assimilated to establish new initial conditions. This operations concept could contribute to reducing forecast model error growth, and concomitantly, forecast uncertainty. The sensor web concept contrasts with today's data collection techniques and observing system operations concepts. Although the technologies and capabilities of our space-, atmospheric-, and surface-based platforms and instruments have evolved significantly during the past four decades, operations concepts for present day observing systems remain essentially unchanged: independent platforms and instruments characterize today's "distributed data collection" systems. Information sharing between platforms and instruments, and interoperable planning and scheduling systems needed to coordinate and facilitate multiplatform measurements and sensor data fusion, are essentially non-existent. Sensor web observing systems, using closed loop controls between platforms and data assimilation and modeling processes, are expected to contribute to improving 10-14 day predictive weather forecast skill. However, investing in the design, implementation, and deployment of such a large, complex observing system would be very costly and almost certainly involve a great amount of risk. An analytical tool is needed to provide engineers and scientists with the ability to define, model, and objectively assess alternative sensor web system designs and to be able to quantitatively measure any improvement in predictive forecast skill. In this paper we describe a software architecture and the salient characteristics of a meteorological sensor web simulator. We believe the simulator could serve as a valuable tool to perform trade studies that: evaluate the impact of selecting different ty...

No abstract

Over the next 10-50 years, policy makers in the southwestern United States are faced with complex planning and policy issues associated with increasing water and energy demand as a result of warmer temperatures and reduced availability of water, compounded by continued rapid population growth and economic development. This study uses a top-down, end-to-end approach consisting of dynamical downscaling, a novel bias-correction technique, and custom-developed decision-aid tools to assess regional climate changes in the Southwest and to derive decision aids that are based on direct communication with the planners at four military installations in the region. Dynamical downscaling is performed with the Weather Research and Forecasting model driven by the National Centers for Environmental Prediction reanalysis and the Max Planck Institute for Meteorology's ECHAM5 general circulation model for two time periods: current (2000s) and future (2030s). A unique twostage bias correction is developed to adjust current and future hourly temperature and precipitation to be consistent with historical reference data. The authors' assessment of regional climate change, which is based on downscaled bias-corrected fields, points to a dryer and warmer future climate in the Southwest. The energyusage modeling produced a statistically significant increase in natural gas consumption and a possible decrease in electricity usage in two military installations in Colorado, which is a direct consequence of decrease/increase in heating/cooling degree-days resulting from warmer temperatures in the future. In addition, the results indicate an increasing number of oppressive heat days in the future, which may impact long-term planning practices with respect to heat-stress control and heat-casualty management.