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Abstract
Persistent midwinter cold air pools produce multiday periods of cold, dreary weather in basins and valleys. Persistent stable stratification leads to the buildup of pollutants and moisture in the pool. Because the pool sometimes has temperatures below freezing while the air above is warmer, freezing precipitation often occurs, with consequent effects on transportation and safety. Forecasting the buildup and breakdown of these cold pools is difficult because the interacting physical mechanisms leading to their formation, maintenance, and destruction have received little study.
In this paper, persistent wintertime cold pools in the Columbia River basin of eastern Washington are studied. First a succinct meteorological definition of a cold pool is provided and then a 10-yr database is used to develop a cold pool climatology. This is followed by a detailed examination of two cold pool episodes that were accompanied by fog and stratus using remote and in situ temperature and wind sounding data. The two episodes illustrate many of the physical mechanisms that affect cold pool evolution. In one case, the cold pool was formed by warm air advection above the basin and was destroyed by downslope winds that descended into the southern edge of the basin and progressively displaced the cold air in the basin. In the second case, the cold pool began with a basin temperature inversion on a clear night and strengthened when warm air was advected above the basin by a westerly flow that descended from the Cascade Mountains. The cold pool was nearly destroyed one afternoon by cold air advection aloft and by the growth of a convective boundary layer (CBL) following the partial breakup of the basin stratus. The cold pool restrengthened, however, with nighttime cooling and was destroyed the next afternoon by a growing CBL.
Abstract
Persistent midwinter cold air pools produce multiday periods of cold, dreary weather in basins and valleys. Persistent stable stratification leads to the buildup of pollutants and moisture in the pool. Because the pool sometimes has temperatures below freezing while the air above is warmer, freezing precipitation often occurs, with consequent effects on transportation and safety. Forecasting the buildup and breakdown of these cold pools is difficult because the interacting physical mechanisms leading to their formation, maintenance, and destruction have received little study.
In this paper, persistent wintertime cold pools in the Columbia River basin of eastern Washington are studied. First a succinct meteorological definition of a cold pool is provided and then a 10-yr database is used to develop a cold pool climatology. This is followed by a detailed examination of two cold pool episodes that were accompanied by fog and stratus using remote and in situ temperature and wind sounding data. The two episodes illustrate many of the physical mechanisms that affect cold pool evolution. In one case, the cold pool was formed by warm air advection above the basin and was destroyed by downslope winds that descended into the southern edge of the basin and progressively displaced the cold air in the basin. In the second case, the cold pool began with a basin temperature inversion on a clear night and strengthened when warm air was advected above the basin by a westerly flow that descended from the Cascade Mountains. The cold pool was nearly destroyed one afternoon by cold air advection aloft and by the growth of a convective boundary layer (CBL) following the partial breakup of the basin stratus. The cold pool restrengthened, however, with nighttime cooling and was destroyed the next afternoon by a growing CBL.
Abstract
Model improvement efforts involve an evaluation of changes in model skill in response to changes in model physics and parameterization. When using wind measurements from various remote sensors to determine model forecast accuracy, it is important to understand the effects of measurement-uncertainty differences among the sensors resulting from differences in the methods of measurement, the vertical and temporal resolution of the measurements, and the spatial variability of these differences. Here we quantify instrument measurement variability in 80-m wind speed during WFIP2 and its impact on the calculated errors and the change in error from one model version to another. The model versions tested involved updates in model physics from HRRRv1 to HRRRv4, and reductions in grid interval from 3 km to 750 m. Model errors were found to be 2–3 m s−1. Differences in errors as determined by various instruments at each site amounted to about 10% of this value, or 0.2–0.3 m s−1. Changes in model skill due to physics or grid-resolution updates also differed depending on the instrument used to determine the errors; most of the instrument-to-instrument differences were ∼0.1 m s−1, but some reached 0.3 m s−1. All instruments at a given site mostly showed consistency in the sign of the change in error. In two examples, though, the sign changed, illustrating a consequence of differences in measurements: errors determined using one instrument may show improvement in model skill, whereas errors determined using another instrument may indicate degradation. This possibility underscores the importance of having accurate measurements to determine the model error.
Significance Statement
To evaluate model forecast accuracy using remote sensing instruments, it is important to understand the effects of measurement uncertainties due to differences in the methods of measurement and data processing techniques, the vertical and temporal resolution of the measurements, and the spatial variability of these differences. In this study, three types of collocated remote sensing systems are used to quantify the impact of measurement variability on the magnitude of calculated errors and the change in error from one model version to another. The model versions tested involved updates in model physics from HRRRv1 to HRRRv4, and reductions in grid interval from 3 km to 750 m.
Abstract
Model improvement efforts involve an evaluation of changes in model skill in response to changes in model physics and parameterization. When using wind measurements from various remote sensors to determine model forecast accuracy, it is important to understand the effects of measurement-uncertainty differences among the sensors resulting from differences in the methods of measurement, the vertical and temporal resolution of the measurements, and the spatial variability of these differences. Here we quantify instrument measurement variability in 80-m wind speed during WFIP2 and its impact on the calculated errors and the change in error from one model version to another. The model versions tested involved updates in model physics from HRRRv1 to HRRRv4, and reductions in grid interval from 3 km to 750 m. Model errors were found to be 2–3 m s−1. Differences in errors as determined by various instruments at each site amounted to about 10% of this value, or 0.2–0.3 m s−1. Changes in model skill due to physics or grid-resolution updates also differed depending on the instrument used to determine the errors; most of the instrument-to-instrument differences were ∼0.1 m s−1, but some reached 0.3 m s−1. All instruments at a given site mostly showed consistency in the sign of the change in error. In two examples, though, the sign changed, illustrating a consequence of differences in measurements: errors determined using one instrument may show improvement in model skill, whereas errors determined using another instrument may indicate degradation. This possibility underscores the importance of having accurate measurements to determine the model error.
Significance Statement
To evaluate model forecast accuracy using remote sensing instruments, it is important to understand the effects of measurement uncertainties due to differences in the methods of measurement and data processing techniques, the vertical and temporal resolution of the measurements, and the spatial variability of these differences. In this study, three types of collocated remote sensing systems are used to quantify the impact of measurement variability on the magnitude of calculated errors and the change in error from one model version to another. The model versions tested involved updates in model physics from HRRRv1 to HRRRv4, and reductions in grid interval from 3 km to 750 m.
