Browsing Atmospheric Sciences by Title
Now showing items 58-59 of 59
Using self-organizing maps to detail synoptic connections between climate indices and Alaska weatherSeasonal forecasts for Alaska strongly depend on the phases of Pacific Decadal Oscillation (PDO), El Niño-Southern Oscillation (ENSO), and warm water in the North Pacific called the North Pacific Mode or more popularly the "Pacific blob." The canonical descriptions of these climate indices are based on seasonal averages, and anomalies that are based on a long-term mean. The patterns highlight general geographical placement and display a sharp contrast between opposing phases, but this may be misleading since seasonal averages hide much of the synoptic variability. Self-organizing maps (SOMs) are a way of grouping daily sea level pressure (SLP) patterns, over many time realizations into a specified set of maps (e.g. 35 maps) that describe commonly occurring patterns. This study uses the SOMs in the context of climate indices to describe the range of synoptic patterns that are relevant for Alaska. This study found that the patterns common during a given phase of the PDO include subtle differences that would result in Alaska weather that is very different from what is expected from the canonical PDO description, thus providing some explanation for recent studies that find the PDO link to Alaska climate is weakening. SOMs analysis is consistent with recent studies suggesting that the pattern responsible for the 2014 Pacific warm blob is linked to tropical sea-surface temperature (SST) forcing. An analysis of the summer SLP SOMs in the context of Alaska wildland fires was also conducted. This analysis identified several commonly occurring patterns during summers with large areas burned. These patterns are characterized by low pressure in the Bering Sea, which would be consistent with increased storm activity and thus an ignition source for the fires. Identifying synoptic patterns that occur during a particular phase of a teleconnection index contributes towards understanding the mechanisms of how these indices influence the weather and climate of Alaska.
Using WRF/Chem, in-situ observations, and Calipso data to simulate smoke plume signatures on high-latitude pixelsThe transport of wildfire aerosols provides concerns to people at or near downwind propagation. Concerns include the health effects of inhalation by inhabitants of surrounding communities and fire crews, the environmental effects of the wet and dry deposition of acids and particles, and the effects on the atmosphere through the scattering and absorption of solar radiation. Therefore, as the population density increases in Arctic and sub-Arctic areas, improving wildfire detection increasingly becomes necessary. Efforts to improve wildfire detection and forecasting would be helped if additional focus was directed toward the distortion of pixel geometry that occurs near the boundaries of a geostationary satellite's field of view. At higher latitudes, resolution becomes coarse due to the curvature of the Earth, and pixels toward the boundaries of the field of view become difficult to analyze. To assess whether it is possible to detect smoke plumes in pixels at the edge of a geostationary satellite's field of view, several analyses were performed. First, a realistic, fourdimensional dataset was created from Weather Research and Forecasting model coupled with Chemistry (WRF/Chem) output. WRF/Chem output was statistically compared to ground observations through the use of skill scores. Output was also qualitatively compared to vertical backscatter and depolarization products from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite. After the quantitative and qualitative examinations deemed the model output to be realistic, synthetic pixels were constructed, appropriately sized, and used with the realistic dataset to examine the characteristic signatures of a wildfire plume. After establishing a threshold value, the synthetic pixels could distinguish between clean and smoke-polluted areas. Thus, specialized retrieval algorithms could be developed for smoke detection in strongly distorted pixels at the edge of a geostationary satellite's field of view.