Stewart G. Cober

THE FOG REMOTE SENSING AND MODELING FIELD PROJECT

The main purpose of this work is to describe a major field project on fog and summarize the preliminary results. Three field phases of the Fog Remote Sensing and Modeling (FRAM) project were conducted over the following two regions of Canada: 1) the Center for Atmospheric Research Experiments (CARE), in Toronto, Ontario (FRAM-C), during the winter of 2005/06, and 2) Lunenburg, Nova Scotia (FRAM-L), during June 2006 and June 2007. Fog conditions observed during FRAM-C were continental in nature, while those conditions observed during FRAM-L were of marine origin. The main objectives of the project were to attain 1) a better description of fog environments, 2) the development of microphysical parameterizations for model applications, 3) the development of remote sensing methods for fog nowcasting/forecasting, 4) an understanding of issues related to instrument capabilities and improvement of the analysis, and 5) an integration of model data with observations to predict and detect fog areas and particle phas...

Thermodynamic Atmospheric Profiling During the 2010 Winter Olympics Using Ground-Based Microwave Radiometry

Ground-based microwave radiometer profilers in the 20-60-GHz range operate continuously at numerous sites in different climate regions. Recent work suggests that a 1-D variational (1-DVAR) technique, coupling radiometric observations with outputs from a numerical weather prediction model, may outperform traditional retrieval methods for temperature and humidity profiling. The 1-DVAR technique is applied here to observations from a commercially available microwave radiometer deployed at Whistler, British Columbia, which was operated by Environment Canada to support nowcasting and short-term weather forecasting during the Vancouver 2010 Winter Olympic and Paralympic Winter Games. The analysis period included rain, sleet, and snow events (~235-mm total accumulation and rates up to 18 mm/h). The 1-DVAR method is applied “quasi-operationally,” i.e., as it could have been applied in real time, as no data were culled. The 1-DVAR-achieved accuracy has been evaluated by using simultaneous radiosonde and ceilometer observations as reference. For atmospheric profiling from the surface to 10 km, we obtain retrieval errors within 1.5 K for temperature and 0.5 g/m3 for water vapor density. The retrieval accuracy for column-integrated water vapor is 0.8 kg\m2, with small bias (-0.1 kg\m2) and excellent correlation (0.96). The retrieval of cloud properties shows a high probability of detection of cloud/no cloud (0.8/0.9, respectively), low false-alarm ratio (0.1), and cloud-base height estimate error within ~0.60 km.

Single-scattering properties of mixed-phase Arctic Clouds at solar wavelengths: Impacts on radiative transfer

In situ observations of the sizes, shapes, and phases of Arctic clouds were obtained during the First International Satellite Cloud Climatology Project Regional Experiment (FIRE) Arctic Clouds Experiment (ACE). These particle distributions were then combined with a library of single-scattering properties, calculated using Mie theory and improved geometric ray optics, to determine the corresponding single-scattering properties (single-scattering albedo v 0, phase function, and asymmetry parameter g) at solar wavelengths. During FIRE-ACE, mixed-phase clouds, where both water and ice were detected in 30 s of flight track, corresponding to 3.0-km horizontal extent, were observed in 33% of clouds. Because supercooled water drops generally dominate mass contents of these mixed-phase clouds, there is no statistically significant difference in the distributions of single-scattering properties of mixed-phase clouds compared to liquid-phase clouds, whereas those of ice crystals differ significantly. The average g for all mixed-phase clouds at visible wavelengths is 0.8556.005, similar to 0.8636.007 computed for water clouds, but higher than 0.7676.007 computed for ice clouds. Differences in g and v 0 between mixedand ice-phase clouds for near-infrared bands are also noted, whereas they are similar for mixed- and liquidphase clouds. Single-scattering properties computed using observations of mixed-phase clouds differ by more than 10% on average from those computed using a parameterization that describes the average fraction of water and ice in mixed-phase clouds. Simulations using a plane-parallel radiative transfer model show that these differences can cause top of the atmosphere albedos to vary between 6% and 100% depending on wavelength. However, when single-scattering properties are computed from observations over all phases (mixed, ice, and liquid), and average albedos are compared against those determined using the parameterized scattering properties, there is a difference of only 2% at visible wavelengths. Since observations show that the occurrence of phases is clustered, largescale averages may not be representative of mixed-phase cloud climatic effects.

The Fog Remote Sensing and Modeling (FRAM) field project: visibility analysis and remote sensing of fog

The main objective of this work is to describe a research project on fog and visibility, and to summarize the results. The Fog Remote Sensing and Modeling (FRAM) project was designed to focus on 1) development of microphysical parameterizations for model applications, 2) development of remote sensing methods for fog nowcasting/forecasting, 3) understanding of issues related to instrument capabilities and improvement of the analysis, and 4) integration of model data with observations. The FRAM was conducted over three regions of Canada and US. These locations were: 1) Center for Atmospheric Research Experiments (CARE), Egbert, Ontario 2005-2006, 2) Lunenburg, Nova Scotia June of 2006 and 2007, and 3) U.S. Department Of Energy (DOE) ARM Climate Research Facility at Barrow, Alaska, US during the Indirect and Semi-Direct Aerosol Campaign (ISDAC) field program April of 2008; FRAM C, FRAM-L, and ISDAC-FRAM-B, respectively. FRAM-C was undertaken in a continental fog environment while FRAM-L was in a marine environment. The FRAM-B was undertaken to study ice fog conditions. During the project, numerous in-situ measurements were obtained, including droplet and aerosol spectra, precipitation, and visibility. Analysis of satellite microphysical retrievals and visibility parameterizations suggested that improved scientific understanding of fog formation can lead to better forecasting/nowcasting skills benefiting both aviation and public forecasting applications.

Decision Making Regarding Aircraft De-Icing and In-Flight Icing Using the Canadian Airport Nowcasting System (CAN-Now)

The Canadian Airport Nowcasting Project (CAN-Now) has developed an advanced prototype all-season weather forecasting and nowcasting system that can be used at major airports. This system uses numerical model data, pilot reports, ground in-situ sensor observations (precipitation, icing, ceiling, visibility, winds, etc), on-site remote sensing (such as vertically pointing radar and microwave radiometer) and off-site remote sensing (satellite and radar) information to provide detailed nowcasts out to approximately 6 hours. The nowcasts, or short term weather forecasts, should allow decision makers at airports such as pilots, dispatchers, deicing crews, ground personnel or air traffic controllers to make plans with increased margins of safety and improved efficiency. The system is being developed and tested at Toronto Pearson International Airport (CYYZ) and Vancouver International Airport (CYVR). A Situation Chart has been developed to allow users to have a high glance value product which identifies significant weather related problems at the airport. Some new products combining observations and numerical model output into nowcasts are being tested. This talk will describe the uses of the system for decisions regarding aircraft de-icing at the ground and in-flight icing over the airport. Some statistical verifications of forecast products regarding precipitation amount, precipitation type, in-flight icing, etc, will be given.