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Faisal Boudala, George A. Isaac, and Di Wu

Abstract

Light (LGT) to moderate (MOD) aircraft icing (AI) is frequently reported at Cold Lake, Alberta, but forecasting AI has been a big challenge. The purpose of this study is to investigate and understand the weather conditions associated with AI based on observations in order to improve the icing forecast. To achieve this goal, Environment and Climate Change Canada in cooperation with the Department of National Defence deployed a number of ground-based instruments that include a microwave radiometer, a ceilometer, disdrometers, and conventional present weather sensors at the Cold Lake airport (CYOD). A number of pilot reports (PIREPs) of icing at Cold Lake during the 2016/17 winter period and associated observation data are examined. Most of the AI events were LGT (76%) followed by MOD (20%) and occurred during landing and takeoff at relatively warm temperatures. Two AI intensity algorithms have been tested based on an ice accumulation rate (IAR) assuming a cylindrical shape moving with airspeed υ a of 60 and 89.4 m s−1, and the Canadian numerical weather prediction model forecasts. It was found that the algorithms IAR2 with υ a = 89.4 m s−1 and IAR1 with υ a = 60 m s−1 underestimated (overestimated) the LGT (MOD) icing events, respectively. The algorithm IAR2 with υ a = 60 m s−1 appeared to be more suitable for forecasting LGT icing. Over all, the hit rate score was 0.33 for the 1200 UTC model run and 0.6 for 0000 UTC run for both algorithms, but based on the individual icing intensity scores, the IAR2 did better than IAR1 for forecasting LGT icing events.

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Hong Guan, Stewart G. Cober, and George A. Isaac

Abstract

In situ measurements of temperature (Ta), horizontal wind speed (V), dewpoint (Td), total water content (TWC), and cloud and supercooled cloud water (SCW) events, made during 50 flights from three research field programs, have been compared to forecasts made with the High Resolution Model Application Project version of the Global Environmental Multiscale model. The main purpose of the comparisons was to test the accuracy of the forecasts of cloud and SCW fields. The forecast accuracy for Ta, V, and Td agreed closely with the results from radiosonde–model validation experiments, implying that the aircraft–model validation methodology was equally feasible and, therefore, potentially applicable to SCW forecast verifications (which the radiosondes could not validate).

The hit rate (HR), false alarm rate (FAR), and true skill statistic (TSS) for cloud forecasts were found to be 0.52, 0.30, and 0.22, respectively, when the model data were inferred at a horizontal resolution of 1.5 km (averaging scale of the aircraft data). The corresponding values for SCW forecasts were 0.37, 0.22, and 0.15, respectively. The HRs (FARs) for cloud and SCW events are sensitive to horizontal resolution and increase to 0.76 (0.50) and 0.66 (0.53), respectively, when a horizontal resolution of 100 km is used. The model TWC was found to agree poorly with aircraft measurements, with the model generally underestimating TWC. For cases when the forecasts and observations of cloud agreed, the SCW-forecast HR, FAR, and TSS were 0.63, 0.22, and 0.41, respectively, which implies that improvement in the model cloud fields would substantially improve the SCW forecast accuracy.

The demonstrated comparison methodology will allow a quantitative comparison between different SCW and cloud algorithms. Such a comparison will provide insight into the strengths and weaknesses of these algorithms and will allow the development of more accurate cloud and SCW forecasts.

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Laura X. Huang, George A. Isaac, and Grant Sheng

Abstract

This study addresses the issue of improving nowcasting accuracy by integrating several numerical weather prediction (NWP) model forecasts with observation data. To derive the best algorithms for generating integrated forecasts, different integration methods were applied starting with integrating the NWP models using equal weighting. Various refinements are then successively applied including dynamic weighting, variational bias correction, adjusted dynamic weighting, and constraints using current observation data. Three NWP models—the Canadian Global Environmental Multiscale (GEM) regional model, the GEM Limited Area Model (LAM), and the American Rapid Update Cycle (RUC) model—are used to generate the integrated forecasts. Verification is performed at two Canadian airport locations [Toronto International Airport (CYYZ), in Ontario, and Vancouver International Airport (CYVR), in British Columbia] over the winter and summer seasons. The results from the verification for four weather variables (temperature, relative humidity, and wind speed and gust) clearly show that the integrated models with new refinements almost always perform better than each of the NWP models individually and collectively. When the integrated model with innovative dynamic weighting and variational bias correction is further updated with the most current observation data, its performance is the best among all models, for all the selected variables regardless of location and season. The results of this study justify the use of integrated NWP forecasts for nowcasting provided they are properly integrated using appropriate and specifically designed rules and algorithms.

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George A. Isaac, Terry Bullock, Jennifer Beale, and Steven Beale

Abstract

As several review papers have concluded, marine fog is imperfectly characterized, and quantitative visibility forecasts are difficult to produce accurately. Some unique measurements have been made offshore Newfoundland and Labrador of the climatology of occurrence and the microphysical characteristics of marine, or open-ocean, fog. Based on measurements made at an offshore installation over 21 years, the percent of time with visibilities less than 0.5 n mi or approximately 1 km (1 n mi ≈ 1.85 km) reaches 45% in July, with a low of about 5% during the winter. The occurrence of fog is mainly due to warm air advection, with the highest frequency occurring with wind directions from over the warm Gulf Stream, and with air temperatures about 2°C warmer than the sea surface temperature. There is no diurnal variation in the frequency of occurrence of fog. The microphysical properties of the fog have been documented in the summer time frame, with over 550 h of in situ measurements made offshore with fog liquid water content greater than 0.005 g m−3. The fog droplet number concentration spectra peaks near 6 μm, with a secondary peak near 25–40 μm, which typically contains most of the liquid water content. The median droplet concentration is approximately 70–100 cm−3. The microphysical spectra have been used to develop a new NWP visibility parameterization scheme, and this scheme is compared with other parameterizations currently in use.

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Hong Guan, Stewart G. Cober, George A. Isaac, André Tremblay, and André Méthot

Abstract

In situ aircraft measurements, collected during three research projects, are used to compare forecasts from three explicit cloud schemes. These schemes include the Canadian operational Sundqvist (SUND) scheme, the Tremblay mixed-phase (MIX) cloud scheme, and the Kong and Yau (KY) cloud scheme. The supercooled liquid water forecast accuracy is also determined for the MIX and KY schemes. For the entire in situ dataset, the three cloud forecast schemes show a similar skill in detecting the presence of clouds, with a true skill statistic ranging between 0.27 and 0.34. Quantitative comparisons of total cloud water content (TWC), supercooled liquid water content (SLWC), and ice water content (IWC) suggest that adjustments for autoconversion thresholds for precipitation formation within the different cloud microphysical schemes would improve forecasts of SLWC, IWC, and TWC.

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Julie M. Thériault, Roy Rasmussen, Trevor Smith, Ruping Mo, Jason A. Milbrandt, Melinda M. Brugman, Paul Joe, George A. Isaac, Jocelyn Mailhot, and Bertrand Denis

Abstract

Accurate forecasting of precipitation phase and intensity was critical information for many of the Olympic venue managers during the Vancouver 2010 Olympic and Paralympic Winter Games. Precipitation forecasting was complicated because of the complex terrain and warm coastal weather conditions in the Whistler area of British Columbia, Canada. The goal of this study is to analyze the processes impacting precipitation phase and intensity during a winter weather storm associated with rain and snow over complex terrain. The storm occurred during the second day of the Olympics when the downhill ski event was scheduled. At 0000 UTC 14 February, 2 h after the onset of precipitation, a rapid cooling was observed at the surface instrumentation sites. Precipitation was reported for 8 h, which coincided with the creation of a nearly 0°C isothermal layer, as well as a shift of the valley flow from up valley to down valley. Widespread snow was reported on Whistler Mountain with periods of rain at the mountain base despite the expectation derived from synoptic-scale models (15-km grid spacing) that the strong warm advection would maintain temperatures above freezing. Various model predictions are compared with observations, and the processes influencing the temperature, wind, and precipitation types are discussed. Overall, this case study provided a well-observed scenario of winter storms associated with rain and snow over complex terrain.

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