Martin Stuefer

MM5 Contrail Forecasting in Alaska

The fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU– NCAR) Mesoscale Model (MM5) is being used for forecasting the atmospheric layers of aircraft condensation trail (contrail) formation. Contrail forecasts are based on a conventional algorithm describing the adiabatic mixing of aircraft exhaust with environmental air. Algorithm input data are MM5-forecasted temperature and humidity values at defined pressure or sigma levels, and an aircraft-relevant contrail factor that is derived statistically from a contrail observation database. For comparison purposes a mean overlap (MO), which is a parameter quantifying the overlap between forecasted contrail layers and contrail layers derived from radiosonde measurements, is introduced. Mean overlap values are used to test for the altitude and thickness of forecasted contrail layers. Contrail layers from Arctic MM5 and Air Force Weather Agency (AFWA) MM5 models agree well with contrail layers derived from corresponding radiosonde measurements for certain forecast periods; a steady decrease of the MO shows a decrease of contrail forecast accuracy with the increasing forecast period. Mean overlaps around 82% indicate reasonable results for the 48-h forecasts. Verification of MM5 with actual contrail observations shows a slightly better performance of Arctic MM5. A possible dry bias might occur in humidity measurements at low temperature levels due to temperature-dependence errors of the humidity sensor polymer, which might also affect forecasts of humidity of the upper troposphere or lower stratosphere. Despite this fact, this contrail verification study shows hit rates higher than 82% within forecast periods up to 36 h using Arctic MM5.

Numerical Modeling of Ice Fog in Interior Alaska Using the Weather Research and Forecasting Model

An ice microphysics parameterization scheme has been modified to better describe and understand ice fog formation. The modeling effort is based on observations in the Sub-Arctic Region of Interior Alaska, where ice fog occurs frequently during the cold season due to abundant water vapor sources and strong inversions existing near the surface at extremely low air temperatures. The microphysical characteristics of ice fog are different from those of other ice clouds, implying that the microphysical processes of ice should be changed in order to generate ice fog particles. Ice fog microphysical characteristics were derived with the NCAR Video Ice Particle Sampler during strong ice fog cases in the vicinity of Fairbanks, Alaska, in January and February 2012. To improve the prediction of ice fog in the Weather Research and Forecasting model, observational data were used to change particle size distribution properties and gravitational settling rates, as well as to implement a homogeneous freezing process. The newly implemented homogeneous freezing process compliments the existing heterogeneous freezing scheme and generates a higher number concentration of ice crystals than the original Thompson scheme. The size distribution of ice crystals is changed into a Gamma distribution with the shape factor of 2.0, using the observed size distribution. Furthermore, gravitational settling rates are reduced for the ice crystals since the crystals in ice fog do not precipitate in a similar manner when compared to the ice crystals of cirrus clouds. The slow terminal velocity plays a role in increasing the time scale for the ice crystals to settle to the surface. Sensitivity tests contribute to understanding the effects of water vapor emissions as an anthropogenic source on the formation of ice fog.

Chapter 24 – Coal-Fire Hazard Mapping in High-Latitude Coal Basins: A Case Study from Interior Alaska

In coal-bearing areas of the circumpolar North, a region rich in carbonaceous deposits, coal outcrops on south-facing slopes are particularly vulnerable to catching fire as they receive substantial amounts of solar radiation during the long summer days. In this study, we use remote sensing to map thermal anomalies associated with coal fires in a coal field in interior Alaska, following a two-step process: (1) thermal anomaly detection on individual thermal infrared (TIR) images from the Landsat satellites; and (2) persistent anomaly detection from a time series of these TIR images. In step 1, a Gaussian mixture model combining two independent normal distributions is fitted to every TIR scene. Subsequently, pixels are identified as thermally anomalous (hot spots) if their surface temperature is more than 4 standard deviations above the mode of the dominant normal distribution. After this individual analysis, the full series of TIR images is stacked and an anomaly occurrence index is calculated for each anomalous pixel. Only pixels that have valid data and are not masked out due to clouds or active fire are taken into account. Finally, the distribution of anomaly occurrence indices of the image stack is plotted over an interval between 0 and 1, and by observing the falloff of the distribution a pixel is counted as persistently anomalous if it appears in more than one-third of the scenes in which it is present. This automated processing flow yields coal fire hazard maps that are useful for fire and forest managers and commercial operators.