Search Results
You are looking at 1 - 8 of 8 items for
- Author or Editor: Idar Barstad x
- Refine by Access: All Content x
Abstract
This paper proposes an extension of a linear theory of orographic precipitation (OP). In the original theory, cloud water is produced by forced lifting over mountains, moderated by airflow dynamics. Controlled by a time delay τc , the cloud water converts into hydrometeors, which drift and fall out as precipitation. This drift is controlled by another time delay τf . The new extension proposed here introduces vertical layers, limited to two in this study. In this way, a more realistic vertical structure is permitted. Wind and stability may change with height and different microphysical properties may be assigned to the layers. For instance, a long fallout delay in the upper layer may represent snow that, after falling through a melting layer, turns into rain that has a short delay in the lower model layer. The sensitivity to microphysical delay and wind speed has been addressed for various interface heights separating the two layers. This layered approach allows adjustment of the water vapor influx and truncation of dry descent above a crest line, which, in the context of the existing linear theory, otherwise could cancel cloud water in lower layers. The introduction of layers requires more information in the vertical, but this may be derived, to some extent, from surface information.
Abstract
This paper proposes an extension of a linear theory of orographic precipitation (OP). In the original theory, cloud water is produced by forced lifting over mountains, moderated by airflow dynamics. Controlled by a time delay τc , the cloud water converts into hydrometeors, which drift and fall out as precipitation. This drift is controlled by another time delay τf . The new extension proposed here introduces vertical layers, limited to two in this study. In this way, a more realistic vertical structure is permitted. Wind and stability may change with height and different microphysical properties may be assigned to the layers. For instance, a long fallout delay in the upper layer may represent snow that, after falling through a melting layer, turns into rain that has a short delay in the lower model layer. The sensitivity to microphysical delay and wind speed has been addressed for various interface heights separating the two layers. This layered approach allows adjustment of the water vapor influx and truncation of dry descent above a crest line, which, in the context of the existing linear theory, otherwise could cancel cloud water in lower layers. The introduction of layers requires more information in the vertical, but this may be derived, to some extent, from surface information.
Abstract
The question of whether rain gauge data from complex terrain are suitable to test physical models of orographic precipitation or to estimate free parameters is addressed. Data from three projects are considered: the Intermountain Precipitation Experiment (IPEX) and the California Land-falling Jets Experiment (CALJET), both in the United States, and the Mesoscale Alpine Programme (MAP) in the European Alps. As a prototype physical model, a new linear theory including airflow dynamics, condensed water advection, and leeside evaporation was employed. Theoretical considerations using the linear model showed sensitivity of point measurements across an ideal hill. To assist in model evaluation with real data, a new measure of “goodness of fit” was defined. This measure, “location sensitivity skill” (LSS), rewards detail as well as accuracy. For real data comparison, the linear model predictions show skill using traditional methods and the new LSS measure. The findings show that the wind direction and stability, and especially the cloud time delay (tau), are the sensitive parameters for point precipitation. The cloud time delay was the primary controller of point precipitation amplitude, and the stability tended to shift the precipitation pattern. Direct measures of tau are generally not obtainable, but this study indirectly constrained tau to 0–1000 s. The need for a denser observational network with tighter time control was revealed.
Abstract
The question of whether rain gauge data from complex terrain are suitable to test physical models of orographic precipitation or to estimate free parameters is addressed. Data from three projects are considered: the Intermountain Precipitation Experiment (IPEX) and the California Land-falling Jets Experiment (CALJET), both in the United States, and the Mesoscale Alpine Programme (MAP) in the European Alps. As a prototype physical model, a new linear theory including airflow dynamics, condensed water advection, and leeside evaporation was employed. Theoretical considerations using the linear model showed sensitivity of point measurements across an ideal hill. To assist in model evaluation with real data, a new measure of “goodness of fit” was defined. This measure, “location sensitivity skill” (LSS), rewards detail as well as accuracy. For real data comparison, the linear model predictions show skill using traditional methods and the new LSS measure. The findings show that the wind direction and stability, and especially the cloud time delay (tau), are the sensitive parameters for point precipitation. The cloud time delay was the primary controller of point precipitation amplitude, and the stability tended to shift the precipitation pattern. Direct measures of tau are generally not obtainable, but this study indirectly constrained tau to 0–1000 s. The need for a denser observational network with tighter time control was revealed.
Abstract
A linear theory of orographic precipitation is developed, including airflow dynamics, condensed water advection, and downslope evaporation. The formulation extends the widely used “upslope” model. Vertically integrated steady-state governing equations for condensed water are solved using Fourier transform techniques. Closed form expressions are derived for special cases. For more general cases, the precipitation field is computed quickly by multiplying the terrain transform by a wavenumber-dependent transfer function.
Five length scales are included in the model: mountain width, a buoyancy wave scale, the moist layer depth, and two condensed water advection distances. The efficiency of precipitation in the model is sensitive to the decay of the forced ascent through the moist layer and to the advection of condensed water downwind into the region of descent. The strong influence of horizontal scale on precipitation pattern and amount predicted by the model is discussed. The model is illustrated by applying it to the Olympic Mountains in Washington State.
Abstract
A linear theory of orographic precipitation is developed, including airflow dynamics, condensed water advection, and downslope evaporation. The formulation extends the widely used “upslope” model. Vertically integrated steady-state governing equations for condensed water are solved using Fourier transform techniques. Closed form expressions are derived for special cases. For more general cases, the precipitation field is computed quickly by multiplying the terrain transform by a wavenumber-dependent transfer function.
Five length scales are included in the model: mountain width, a buoyancy wave scale, the moist layer depth, and two condensed water advection distances. The efficiency of precipitation in the model is sensitive to the decay of the forced ascent through the moist layer and to the advection of condensed water downwind into the region of descent. The strong influence of horizontal scale on precipitation pattern and amount predicted by the model is discussed. The model is illustrated by applying it to the Olympic Mountains in Washington State.
Abstract
Oregon’s sharp east–west climate transition was investigated using a linear model of orographic precipitation and four datasets: (a) interpolated annual rain gauge data, (b) satellite-derived precipitation proxies (vegetation and brightness temperature), (c) streamflow data for a small catchment, and (d) stable isotope analysis of water samples from streams. The success of the linear model against these datasets suggests that the main elements of the model (i.e., airflow dynamics, cloud time delays, condensed water advection, and leeside evaporation) are behaving reasonably, although the high Oregon terrain may push the linear theory beyond its range of applicability.
A key parameter in the linear model is the cloud delay time (τ), encapsulating the action of orographic cloud processes. Each dataset was examined to see if it can constrain the τ values. The statewide precipitation patterns from rain gauge and satellite constrain the τ values only within a broad range from about 500 to 5000 s. A focus on the sharp gradient on the lee slopes of the Cascades suggests that τ values in the range of 1800–2400 s are preferred. The study of the small Alsea watershed constrains τ little, as it receives a mixture of upslope and spillover precipitation. Stable isotope ratios in stream water indicate an atmospheric drying ratio of about 43%, requiring an average cloud physics delay time greater than τ = 600 s.
Abstract
Oregon’s sharp east–west climate transition was investigated using a linear model of orographic precipitation and four datasets: (a) interpolated annual rain gauge data, (b) satellite-derived precipitation proxies (vegetation and brightness temperature), (c) streamflow data for a small catchment, and (d) stable isotope analysis of water samples from streams. The success of the linear model against these datasets suggests that the main elements of the model (i.e., airflow dynamics, cloud time delays, condensed water advection, and leeside evaporation) are behaving reasonably, although the high Oregon terrain may push the linear theory beyond its range of applicability.
A key parameter in the linear model is the cloud delay time (τ), encapsulating the action of orographic cloud processes. Each dataset was examined to see if it can constrain the τ values. The statewide precipitation patterns from rain gauge and satellite constrain the τ values only within a broad range from about 500 to 5000 s. A focus on the sharp gradient on the lee slopes of the Cascades suggests that τ values in the range of 1800–2400 s are preferred. The study of the small Alsea watershed constrains τ little, as it receives a mixture of upslope and spillover precipitation. Stable isotope ratios in stream water indicate an atmospheric drying ratio of about 43%, requiring an average cloud physics delay time greater than τ = 600 s.
Abstract
With limited computational resources, there is a need for computationally frugal models. This is particularly the case for atmospheric sciences, which have long relied on either simplistic analytical solutions or computationally expensive numerical models. The simpler solutions are inadequate for many problems, while the cost of numerical models makes their use impossible for many problems, most notably high-resolution climate downscaling applications spanning large areas, long time periods, and many global climate projections. Here the Intermediate Complexity Atmospheric Research model (ICAR) is presented to provide a new step along the modeling complexity continuum. ICAR leverages an analytical solution for high-resolution perturbations to wind velocities, in conjunction with numerical physics schemes, that is, advection and cloud microphysics, to simulate the atmosphere. The focus of the initial development of ICAR is for predictions of precipitation, and eventually temperature, humidity, and radiation at the land surface. Comparisons between ICAR and the Weather Research and Forecasting (WRF) Model for simulations over an idealized mountain are presented, as well as among ICAR, WRF, and the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) observation-based product for a year-long simulation over the Colorado Rockies. In the ideal simulations, ICAR matches WRF precipitation predictions across a range of environmental conditions with a coefficient of determination r 2 of 0.92. In the Colorado Rockies, ICAR, WRF, and PRISM show very good agreement, with differences between ICAR and WRF comparable to the differences between WRF and PRISM in the cool season. For these simulations, WRF required 140–800 times more computational resources than ICAR.
Abstract
With limited computational resources, there is a need for computationally frugal models. This is particularly the case for atmospheric sciences, which have long relied on either simplistic analytical solutions or computationally expensive numerical models. The simpler solutions are inadequate for many problems, while the cost of numerical models makes their use impossible for many problems, most notably high-resolution climate downscaling applications spanning large areas, long time periods, and many global climate projections. Here the Intermediate Complexity Atmospheric Research model (ICAR) is presented to provide a new step along the modeling complexity continuum. ICAR leverages an analytical solution for high-resolution perturbations to wind velocities, in conjunction with numerical physics schemes, that is, advection and cloud microphysics, to simulate the atmosphere. The focus of the initial development of ICAR is for predictions of precipitation, and eventually temperature, humidity, and radiation at the land surface. Comparisons between ICAR and the Weather Research and Forecasting (WRF) Model for simulations over an idealized mountain are presented, as well as among ICAR, WRF, and the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) observation-based product for a year-long simulation over the Colorado Rockies. In the ideal simulations, ICAR matches WRF precipitation predictions across a range of environmental conditions with a coefficient of determination r 2 of 0.92. In the Colorado Rockies, ICAR, WRF, and PRISM show very good agreement, with differences between ICAR and WRF comparable to the differences between WRF and PRISM in the cool season. For these simulations, WRF required 140–800 times more computational resources than ICAR.
Abstract
A linear model of orographic precipitation that includes airflow dynamics, condensed water advection, and downslope evaporation is adapted for Iceland. The model is driven using coarse-resolution 40-yr reanalysis data from the European Centre for Medium-Range Weather Forecasts (ERA-40) over the period 1958–2002. The simulated precipitation is in good agreement with precipitation observations accumulated over various time scales, both in terms of magnitude and distribution. The results suggest that the model captures the main physical processes governing orographic generation of precipitation in the mountains of Iceland. The approach presented in this paper offers a credible method to obtain a detailed estimate of the distribution of precipitation in mountainous terrain for various conditions involving orographic generation of precipitation. It appears to be of great practical value to the hydrologists, glaciologists, meteorologists, and climatologists.
Abstract
A linear model of orographic precipitation that includes airflow dynamics, condensed water advection, and downslope evaporation is adapted for Iceland. The model is driven using coarse-resolution 40-yr reanalysis data from the European Centre for Medium-Range Weather Forecasts (ERA-40) over the period 1958–2002. The simulated precipitation is in good agreement with precipitation observations accumulated over various time scales, both in terms of magnitude and distribution. The results suggest that the model captures the main physical processes governing orographic generation of precipitation in the mountains of Iceland. The approach presented in this paper offers a credible method to obtain a detailed estimate of the distribution of precipitation in mountainous terrain for various conditions involving orographic generation of precipitation. It appears to be of great practical value to the hydrologists, glaciologists, meteorologists, and climatologists.
Abstract
A new wind farm parameterization has been developed for the mesoscale numerical weather prediction model, the Weather Research and Forecasting model (WRF). The effects of wind turbines are represented by imposing a momentum sink on the mean flow; transferring kinetic energy into electricity and turbulent kinetic energy (TKE). The parameterization improves upon previous models, basing the atmospheric drag of turbines on the thrust coefficient of a modern commercial turbine. In addition, the source of TKE varies with wind speed, reflecting the amount of energy extracted from the atmosphere by the turbines that does not produce electrical energy.
Analyses of idealized simulations of a large offshore wind farm are presented to highlight the perturbation induced by the wind farm and its interaction with the atmospheric boundary layer (BL). A wind speed deficit extended throughout the depth of the neutral boundary layer, above and downstream from the farm, with a long wake of 60-km e-folding distance. Within the farm the wind speed deficit reached a maximum reduction of 16%. A maximum increase of TKE, by nearly a factor of 7, was located within the farm. The increase in TKE extended to the top of the BL above the farm due to vertical transport and wind shear, significantly enhancing turbulent momentum fluxes. The TKE increased by a factor of 2 near the surface within the farm. Near-surface winds accelerated by up to 11%. These results are consistent with the few results available from observations and large-eddy simulations, indicating this parameterization provides a reasonable means of exploring potential downwind impacts of large wind farms.
Abstract
A new wind farm parameterization has been developed for the mesoscale numerical weather prediction model, the Weather Research and Forecasting model (WRF). The effects of wind turbines are represented by imposing a momentum sink on the mean flow; transferring kinetic energy into electricity and turbulent kinetic energy (TKE). The parameterization improves upon previous models, basing the atmospheric drag of turbines on the thrust coefficient of a modern commercial turbine. In addition, the source of TKE varies with wind speed, reflecting the amount of energy extracted from the atmosphere by the turbines that does not produce electrical energy.
Analyses of idealized simulations of a large offshore wind farm are presented to highlight the perturbation induced by the wind farm and its interaction with the atmospheric boundary layer (BL). A wind speed deficit extended throughout the depth of the neutral boundary layer, above and downstream from the farm, with a long wake of 60-km e-folding distance. Within the farm the wind speed deficit reached a maximum reduction of 16%. A maximum increase of TKE, by nearly a factor of 7, was located within the farm. The increase in TKE extended to the top of the BL above the farm due to vertical transport and wind shear, significantly enhancing turbulent momentum fluxes. The TKE increased by a factor of 2 near the surface within the farm. Near-surface winds accelerated by up to 11%. These results are consistent with the few results available from observations and large-eddy simulations, indicating this parameterization provides a reasonable means of exploring potential downwind impacts of large wind farms.
