How the cloud top temperature field is derived ---------------------------------------------- The cloud top temperature (CTT) field is derived by matching GOES-8 infrared satellite data to each RUC model grid point. The infrared satellite data has a resolution of approximately 4 km in the horizontal, while the RUC model has a resolution of approximately 60 km in the horizontal. Typically, about 200 infrared temperature data points are matched to each RUC model grid point. To determine the CTT value at each RUC grid point, we must first determine whether or not each satellite data point is providing temperature data from a cloud, rather than from the ground, a water body or snow cover. This yes/no cloud field is determined through a complicated combination of satellite retrieved fields, and is described by Thompson et al. (1997a,b). Their technique does miss some clouds, however, especially in the -25 to -35C range, and is aided here by combining surface observations of overcast cloud cover with satellite infrared temperature. Once it is determined that a particular satellite grid point is giving information about a cloud, the infrared temperature from that data point is stored. Values are stored for all valid grid points, providing a distribution of CTTs for the given RUC grid point. The CTT assigned to the RUC grid point is chosen by cycling through the distribution from the coldest temperatures to warmest temperatures, and selecting the temperature that is 5% of the way through the distribution. This essentially sets the CTT equal to the coldest values found for that RUC model grid point. It is important to note that this technique will only assign a value to a RUC grid point for which at least 10 satellite data points indicated the existence of cloud. Typically, satellite CTTs will be fairly uniform across the area covered by a particular RUC grid point, making the five percent value quite representative of the CTTs across the grid point. At times, however, significant gradients in CTT occur within a given RUC grid point. In this case, the five percent value will represent the colder end of the spectrum, possibly (but not always) resulting in an underestimate of the icing potential at some levels within the column. Thus, we are erring on the side of conservatism with this field at this time. In future upgrades, we hope to make better use of the spectrum of CTTs indicated for a given RUC grid point. Compare this field to the latest satellite IR image. Both are available on this page. IR imagery is courtesy of Greg Thompson. How the CTT field is used in the integrated algorithm ----------------------------------------------------------------------- The CTT field is used as an indicator of the likelihood that the clouds matched to a given RUC grid point contain ice crystals, if not high concentrations of snow. When cloud tops are quite warm (> -12 C or so), the cloud deck does not typically contain many ice crystals since ice processes are relatively inefficient in that temperature range. When cloud tops become colder than about -20C, the ice process becomes much more efficient, and the clouds are likely to contain more ice crystals. Typically, the coldest temperatures within a given cloud deck will be at or near cloud top (except when a very strong inversion is present). The CTT provides a good indication of the maximum likelihood that ice crystals will be present near cloud top and subsequently fall through the entire depth of the cloud deck. Thus, the CTT provides a good measure of the likelihood that ice crystals will exist through the entire cloud deck, from cloud top to the highest freezing level. With this in mind, the CTT field is used to create an interest field which is maximized when CTTs are high (> -12C) and minimized when they are low (< -50 C). This information is applied in a variety of SINGLE CLOUD DECK scenarios to decrease the chance of icing when cloud tops are cold (since ice crystals are likely to scavenge out most of the supercooled liquid water in the cloud) and maximize the chance of icing when cloud tops are warm (since the cloud is likely to be dominated by liquid processes). When more then one cloud deck is present (see the information about multiple cloud deck determination), the satellite CTT is only applied to the upper cloud deck. A second, model-derived CTT that is representative of the lower cloud deck is applied to the lower deck. When a classical freezing rain structure (a warm nose) is present, the cloud top interest field is only applied above the warm nose. This is done because the ice should completely or partially melt while falling through the warm nose, leaving only liquid drops below the melting level. See the information about the warm nose for more details. REFERENCES ---------- Thompson, G., T.F. Lee, R.T. Bruintjes, and R. Bullock, 1997: Using satellite data to reduce area extent of diagnosed icing. Weather and Forecasting, 12, 185-190. Thompson, G., T.F. Lee and J. Vivekanandan, 1997: Comparisons of satellite- based aircraft icing diagnoses. Preprints, 7th Conf. on Aviation, Range and Aerospace Meteorology, Long Beach, CA, 2-7 February. Amer. Meteor. Soc., Boston, 132-137.