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Feimin Zhang and Zhaoxia Pu

Abstract

This study examines the sensitivity of numerical simulations of near-surface atmospheric conditions to the initial surface albedo and snow depth during an observed ice fog event in the Heber Valley of northern Utah. Numerical simulation results from the mesoscale community Weather Research and Forecasting (WRF) Model are compared with observations from the Mountain Terrain Atmospheric Modeling and Observations (MATERHORN) Program fog field program. It is found that near-surface cooling during the nighttime is significantly underestimated by the WRF Model, resulting in the failure of the model to reproduce the observed fog episode. Meanwhile, the model also overestimates the temperature during the daytime. Nevertheless, these errors could be reduced by increasing the initial surface albedo and snow depth, which act to cool the near-surface atmosphere by increasing the reflection of downward shortwave radiation and decreasing the heating effects from the soil layer. Overall results indicate the important effects of snow representation on the simulation of near-surface atmospheric conditions and highlight the need for snow measurements in the cold season for improved model physics parameterizations.

Open access
Robert S. Arthur, Katherine A. Lundquist, Jeffrey D. Mirocha, and Fotini K. Chow

Abstract

Topographic effects on radiation, including both topographic shading and slope effects, are included in the Weather Research and Forecasting (WRF) Model, and here they are made compatible with the immersed boundary method (IBM). IBM is an alternative method for representing complex terrain that reduces numerical errors over sloped terrain, thus extending the range of slopes that can be represented in WRF simulations. The implementation of topographic effects on radiation is validated by comparing land surface fluxes, as well as temperature and velocity fields, between idealized WRF simulations both with and without IBM. Following validation, the topographic shading implementation is tested in a semirealistic simulation of flow over Granite Mountain, Utah, where topographic shading is known to affect downslope flow development in the evening. The horizontal grid spacing is 50 m and the vertical grid spacing is approximately 8–27 m near the surface. Such a case would fail to run in WRF with its native terrain-following coordinates because of large local slope values reaching up to 55°. Good agreement is found between modeled surface energy budget components and observations from the Mountain Terrain Atmospheric Modeling and Observations (MATERHORN) program at a location on the east slope of Granite Mountain. In addition, the model captures large spatiotemporal inhomogeneities in the surface sensible heat flux that are important for the development of thermally driven flows over complex terrain.

Open access
Raquel Lorente-Plazas and Joshua P. Hacker

Abstract

In numerical weather prediction and in reanalysis, robust approaches for observation bias correction are necessary to approach optimal data assimilation. The success of bias correction can be limited by model errors. Here, simultaneous estimation of observation and model biases, and the model state for an analysis, is explored with ensemble data assimilation and a simple model. The approach is based on parameter estimation using an augmented state in an ensemble adjustment Kalman filter. The observation biases are modeled with a linear term added to the forward operator. A bias is introduced in the forcing term of the model, leading to a model with complex errors that can be used in imperfect-model assimilation experiments.

Under a range of model forcing biases and observation biases, accurate observation bias estimation and correction are possible when the model forcing bias is simultaneously estimated and corrected. In the presence of both model error and observation biases, estimating one and ignoring the other harms the assimilation more than not estimating any errors at all, because the biases are not correctly attributed. Neglecting a large model forcing bias while estimating observation biases results in filter divergence; the observation bias parameter absorbs the model forcing bias, and recursively and incorrectly increases the increments. Neglecting observation bias results in suboptimal assimilation, but the model forcing bias parameter estimate remains stable because the model dynamics ensure covariance between the parameter and the model state.

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Matthew E. Jeglum, Sebastian W. Hoch, Derek D. Jensen, Reneta Dimitrova, and Zachariah Silver

Abstract

Large temperature fluctuations (LTFs), defined as a drop of the near-surface temperature of at least 3°C in less than 30 min followed by a recovery of at least half of the initial drop, were frequently observed during the Mountain Terrain Atmospheric Modeling and Observations (MATERHORN) program. Temperature time series at over 100 surface stations were examined in an automated fashion to identify and characterize LTFs. LTFs occur almost exclusively at night and at locations elevated 50–100 m above the basin floors, such as the east slope of the isolated Granite Mountain (GM). Temperature drops associated with LTFs were as large as 13°C and were typically greatest at heights of 4–10 m AGL. Observations and numerical simulations suggest that LTFs are the result of complex flow interactions of stably stratified flow with a mountain barrier and a leeside cold-air pool (CAP). An orographic wake forms over GM when stably stratified southwesterly nocturnal flow impinges on GM and is blocked at low levels. Warm crest-level air descends in the lee of the barrier, and the generation of baroclinic vorticity leads to periodic development of a vertically oriented vortex. Changes in the strength or location of the wake and vortex cause a displacement of the horizontal temperature gradient along the slope associated with the CAP edge, resulting in LTFs. This mechanism explains the low frequency of LTFs on the west slope of GM as well as the preference for LTFs to occur at higher elevations later at night, as the CAP depth increases.

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Jeffrey D. Massey, W. James Steenburgh, Sebastian W. Hoch, and Derek D. Jensen

Abstract

Weather Research and Forecasting (WRF) Model simulations of the autumn 2012 and spring 2013 Mountain Terrain Atmospheric Modeling and Observations Program (MATERHORN) field campaigns are validated against observations of components of the surface energy balance (SEB) collected over contrasting desert-shrub and playa land surfaces of the Great Salt Lake Desert in northwestern Utah. Over the desert shrub, a large underprediction of sensible heat flux and an overprediction of ground heat flux occurred during the autumn campaign when the model-analyzed soil moisture was considerably higher than the measured soil moisture. Simulations that incorporate in situ measurements of soil moisture into the land surface analyses and use a modified parameterization for soil thermal conductivity greatly reduce these errors over the desert shrub but exacerbate the overprediction of latent heat flux over the playa. The Noah land surface model coupled to WRF does not capture the many unusual playa land surface processes, and simulations that incorporate satellite-derived albedo and reduce the saturation vapor pressure over the playa only marginally improve the forecasts of the SEB components. Nevertheless, the forecast of the 2-m temperature difference between the playa and desert shrub improves, which increases the strength of the daytime off-playa breeze. The stronger off-playa breeze, however, does not substantially reduce the mean absolute errors in overall 10-m wind speed and direction. This work highlights some deficiencies of the Noah land surface model over two common arid land surfaces and demonstrates the importance of accurate land surface analyses over a dryland region.

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Jeffrey D. Massey, W. James Steenburgh, Jason C. Knievel, and William Y. Y. Cheng

Abstract

Operational Weather Research and Forecasting (WRF) Model forecasts run over Dugway Proving Ground (DPG) in northwest Utah, produced by the U.S. Army Test and Evaluation Command Four-Dimensional Weather System (4DWX), underpredict the amplitude of the diurnal temperature cycle during September and October. Mean afternoon [2000 UTC (1300 LST)] and early morning [1100 UTC (0400 LST)] 2-m temperature bias errors evaluated against 195 surface stations using 6- and 12-h forecasts are –1.37° and 1.66°C, respectively. Bias errors relative to soundings and 4DWX-DPG analyses illustrate that the afternoon cold bias extends from the surface to above the top of the planetary boundary layer, whereas the early morning warm bias develops in the lowest model levels and is confined to valleys and basins. These biases are largest during mostly clear conditions and are caused primarily by a regional overestimation of near-surface soil moisture in operational land surface analyses, which do not currently assimilate in situ soil moisture observations. Bias correction of these soil moisture analyses using data from 42 North American Soil Moisture Database stations throughout the Intermountain West reduces both the afternoon and early morning bias errors and improves forecasts of upper-level temperature and stability. These results illustrate that the assimilation of in situ and remotely sensed soil moisture observations, including those from the recently launched NASA Soil Moisture Active Passive (SMAP) mission, have the potential to greatly improve land surface analyses and near-surface temperature forecasts over arid regions.

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Sean M. Wile, Joshua P. Hacker, and Kenneth H. Chilcoat

Abstract

Expansion in the availability of relocatable near-surface atmospheric observing sensors introduces the question of where placement maximizes gain in forecast accuracy. As one possible method of addressing observation placement, the performance of ensemble sensitivity analysis (ESA) is examined for high-resolution (Δx = 4 km) predictions in complex terrain and during weak flow. ESA can be inaccurate when the underlying assumptions of linear dynamics (and Gaussian statistics) are violated, or when the sensitivity cannot be robustly sampled. A case study of a fog event at Salt Lake City International Airport (KSLC) in Utah provides a useful basis for examining these issues, with the additional influence of complex terrain. A realistic upper-air observing network is used in perfect-model ensemble data assimilation experiments, providing the statistics for ESA. Results show that water vapor mixing ratios over KSLC are sensitive to potential temperature on the first model layer tens of kilometers away, 6 h prior to verification and prior to the onset of fog. Potential temperatures indicate inversion strength in the Salt Lake basin; the ESA predicts southerly flow and strengthened inversions will increase water vapor over KSLC. Linearity tests show that the nonlinear response is about twice the expected response. Experiments with smaller ensembles show that qualitatively similar conclusions about the sensitivity pattern can be reached with ensembles as small as 48 members, but smaller ensembles do not produce accurate sensitivity estimates. Taken together, the results motivate a closer look at the fundamental characteristics of ESA when dynamics (and therefore correlations) are weak.

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H. J. S. Fernando, E. R. Pardyjak, S. Di Sabatino, F. K. Chow, S. F. J. De Wekker, S. W. Hoch, J. Hacker, J. C. Pace, T. Pratt, Z. Pu, W. J. Steenburgh, C. D. Whiteman, Y. Wang, D. Zajic, B. Balsley, R. Dimitrova, G. D. Emmitt, C. W. Higgins, J. C. R. Hunt, J. C. Knievel, D. Lawrence, Y. Liu, D. F. Nadeau, E. Kit, B. W. Blomquist, P. Conry, R. S. Coppersmith, E. Creegan, M. Felton, A. Grachev, N. Gunawardena, C. Hang, C. M. Hocut, G. Huynh, M. E. Jeglum, D. Jensen, V. Kulandaivelu, M. Lehner, L. S. Leo, D. Liberzon, J. D. Massey, K. McEnerney, S. Pal, T. Price, M. Sghiatti, Z. Silver, M. Thompson, H. Zhang, and T. Zsedrovits

Abstract

Emerging application areas such as air pollution in megacities, wind energy, urban security, and operation of unmanned aerial vehicles have intensified scientific and societal interest in mountain meteorology. To address scientific needs and help improve the prediction of mountain weather, the U.S. Department of Defense has funded a research effort—the Mountain Terrain Atmospheric Modeling and Observations (MATERHORN) Program—that draws the expertise of a multidisciplinary, multi-institutional, and multinational group of researchers. The program has four principal thrusts, encompassing modeling, experimental, technology, and parameterization components, directed at diagnosing model deficiencies and critical knowledge gaps, conducting experimental studies, and developing tools for model improvements. The access to the Granite Mountain Atmospheric Sciences Testbed of the U.S. Army Dugway Proving Ground, as well as to a suite of conventional and novel high-end airborne and surface measurement platforms, has provided an unprecedented opportunity to investigate phenomena of time scales from a few seconds to a few days, covering spatial extents of tens of kilometers down to millimeters. This article provides an overview of the MATERHORN and a glimpse at its initial findings. Orographic forcing creates a multitude of time-dependent submesoscale phenomena that contribute to the variability of mountain weather at mesoscale. The nexus of predictions by mesoscale model ensembles and observations are described, identifying opportunities for further improvements in mountain weather forecasting.

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Joshua P. Hacker and Lili Lei

Abstract

Ensemble sensitivities have proven a useful alternative to adjoint sensitivities for large-scale dynamics, but their performance in multiscale flows has not been thoroughly examined. When computing sensitivities, the analysis covariance is usually approximated with the corresponding diagonal matrix, leading to a simple univariate regression problem rather than a more general multivariate regression problem. Sensitivity estimates are affected by sampling error arising from a finite ensemble and can lead to an overestimated response to an analysis perturbation. When forecasts depend on many details of an analysis, it is reasonable to expect that the diagonal approximation is too severe. Because spurious covariances are more likely when correlations are weak, computing the sensitivity with a multivariate regression that retains the full analysis covariance may increase the need for sampling error mitigation. The purpose of this work is to clarify the effects of the diagonal approximation, and investigate the need for mitigating spurious covariances arising from sampling error. A two-scale model with realistic spatial covariances is the basis for experimentation. For most problems, an efficient matrix inversion is possible by finding a minimum-norm solution, and employing appropriate matrix factorization. A published hierarchical approach for estimating regression factors for tapering (localizing) covariances is used to measure the effects of sampling error. Compared to univariate regressions in the diagonal approximation, skill in predicting a nonlinear response from the linear sensitivities is superior when localized multivariate sensitivities are used, particularly when fast scales are present, model error is present, and the observing network is sparse.

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Manuela Lehner, C. David Whiteman, Sebastian W. Hoch, Derek Jensen, Eric R. Pardyjak, Laura S. Leo, Silvana Di Sabatino, and Harindra J. S. Fernando

Abstract

Observations were taken on an east-facing sidewall at the foot of a desert mountain that borders a large valley, as part of the Mountain Terrain Atmospheric Modeling and Observations (MATERHORN) field program at Dugway Proving Ground in Utah. A case study of nocturnal boundary layer development is presented for a night in mid-May when tethered-balloon measurements were taken to supplement other MATERHORN field measurements. The boundary layer development over the slope could be divided into three distinct phases during this night: 1) The evening transition from daytime upslope/up-valley winds to nighttime downslope winds was governed by the propagation of the shadow front. Because of the combination of complex topography at the site and the solar angle at this time of year, the shadow moved down the sidewall from approximately northwest to southeast, with the flow transition closely following the shadow front. 2) The flow transition was followed by a 3–4-h period of almost steady-state boundary layer conditions, with a shallow slope-parallel surface inversion and a pronounced downslope flow with a jet maximum located within the surface-based inversion. The shallow slope boundary layer was very sensitive to ambient flows, resulting in several small disturbances. 3) After approximately 2300 mountain standard time, the inversion that had formed over the adjacent valley repeatedly sloshed up the mountain sidewall, disturbing local downslope flows and causing rapid temperature drops.

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