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Jeremy S. Grams, Willam A. Gallus Jr., Steven E. Koch, Linda S. Wharton, Andrew Loughe, and Elizabeth E. Ebert

Abstract

The Ebert–McBride technique (EMT) is an entity-oriented method useful for quantitative precipitation verification. The EMT was modified to optimize its ability to identify contiguous rain areas (CRAs) during the 2002 International H2O Project (IHOP). This technique was then used to identify systematic sources of error as a function of observed convective system morphology in three 12-km model simulations run over the IHOP domain: Eta, the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5), and the Weather Research and Forecasting (WRF). The EMT was fine-tuned to optimize the pattern matching of forecasts to observations for the scales of precipitation systems observed during IHOP. To investigate several error measures provided by the EMT, a detailed morphological analysis of observed systems was performed using radar data for all CRAs identified in the IHOP domain. The modified EMT suggests that the Eta Model produced average rain rates, peak rainfall amounts, and total rain volumes that were lower than observed for almost all types of convective systems, likely because of its production of overly smoothed and low-variability quantitative precipitation forecasts. The MM5 and WRF typically produced average rain rates and peak rainfall amounts that were larger than observed in most linear convective systems. However, the rain volume for these models was too low for almost all types of convective systems, implying a sizeable underestimate in areal coverage. All three models forecast rainfall too far northwest for linear systems. The results for the WRF and MM5 are consistent with previous observations of mesoscale models run with explicit microphysics and no convective parameterization scheme, suggesting systematic problems with the prediction of mesoscale convective system cold pool dynamics.

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P. Klein, T. A. Bonin, J. F. Newman, D. D. Turner, P. B. Chilson, C. E. Wainwright, W. G. Blumberg, S. Mishra, M. Carney, E. P. Jacobsen, S. Wharton, and R. K. Newsom

Abstract

This paper presents an overview of the Lower Atmospheric Boundary Layer Experiment (LABLE), which included two measurement campaigns conducted at the Atmospheric Radiation Measurement (ARM) Program Southern Great Plains site in Oklahoma during 2012 and 2013. LABLE was conducted as a collaborative effort between the University of Oklahoma (OU), the National Severe Storms Laboratory, Lawrence Livermore National Laboratory (LLNL), and the ARM program. LABLE can be considered unique in that it was designed as a multiphase, low-cost, multiagency collaboration. Graduate students served as principal investigators and took the lead in designing and conducting experiments aimed at examining boundary layer processes.

The main objective of LABLE was to study turbulent phenomena in the lowest 2 km of the atmosphere over heterogeneous terrain using a variety of novel atmospheric profiling techniques. Several instruments from OU and LLNL were deployed to augment the suite of in situ and remote sensing instruments at the ARM site. The complementary nature of the deployed instruments with respect to resolution and height coverage provides a near-complete picture of the dynamic and thermodynamic structure of the atmospheric boundary layer. This paper provides an overview of the experiment including 1) instruments deployed, 2) sampling strategies, 3) parameters observed, and 4) student involvement. To illustrate these components, the presented results focus on one particular aspect of LABLE: namely, the study of the nocturnal boundary layer and the formation and structure of nocturnal low-level jets. During LABLE, low-level jets were frequently observed and they often interacted with mesoscale atmospheric disturbances such as frontal passages.

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Edward I. Tollerud, Brian Etherton, Zoltan Toth, Isidora Jankov, Tara L. Jensen, Huiling Yuan, Linda S. Wharton, Paula T. McCaslin, Eugene Mirvis, Bill Kuo, Barbara G. Brown, Louisa Nance, Steven E. Koch, and F. Anthony Eckel
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Irina V. Djalalova, Laura Bianco, Elena Akish, James M. Wilczak, Joseph B. Olson, Jaymes S. Kenyon, Larry K. Berg, Aditya Choukulkar, Richard Coulter, Harinda J. S. Fernando, Eric Grimit, Raghavendra Krishnamurthy, Julie K. Lundquist, Paytsar Muradyan, David D. Turner, and Sonia Wharton

Abstract

The second Wind Forecast Improvement Project (WFIP2) is a multiagency field campaign held in the Columbia Gorge area (October 2015–March 2017). The main goal of the project is to understand and improve the forecast skill of numerical weather prediction (NWP) models in complex terrain, particularly beneficial for the wind energy industry. This region is well known for its excellent wind resource. One of the biggest challenges for wind power production is the accurate forecasting of wind ramp events (large changes of generated power over short periods of time). Poor forecasting of the ramps requires large and sudden adjustments in conventional power generation, ultimately increasing the costs of power. A Ramp Tool and Metric (RT&M) was developed during the first WFIP experiment, held in the U.S. Great Plains (September 2011–August 2012). The RT&M was designed to explicitly measure the skill of NWP models at forecasting wind ramp events. Here we apply the RT&M to 80-m (turbine hub-height) wind speeds measured by 19 sodars and three lidars, and to forecasts from the High-Resolution Rapid Refresh (HRRR), 3-km, and from the High-Resolution Rapid Refresh Nest (HRRRNEST), 750-m horizontal grid spacing, models. The diurnal and seasonal distribution of ramp events are analyzed, finding a noticeable diurnal variability for spring and summer but less for fall and especially winter. Also, winter has fewer ramps compared to the other seasons. The model skill at forecasting ramp events, including the impact of the modification to the model physical parameterizations, was finally investigated.

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James M. Wilczak, Mark Stoelinga, Larry K. Berg, Justin Sharp, Caroline Draxl, Katherine McCaffrey, Robert M. Banta, Laura Bianco, Irina Djalalova, Julie K. Lundquist, Paytsar Muradyan, Aditya Choukulkar, Laura Leo, Timothy Bonin, Yelena Pichugina, Richard Eckman, Charles N. Long, Kathleen Lantz, Rochelle P. Worsnop, Jim Bickford, Nicola Bodini, Duli Chand, Andrew Clifton, Joel Cline, David R. Cook, Harindra J. S. Fernando, Katja Friedrich, Raghavendra Krishnamurthy, Melinda Marquis, Jim McCaa, Joseph B. Olson, Sebastian Otarola-Bustos, George Scott, William J. Shaw, Sonia Wharton, and Allen B. White

Abstract

The Second Wind Forecast Improvement Project (WFIP2) is a U.S. Department of Energy (DOE)- and National Oceanic and Atmospheric Administration (NOAA)-funded program, with private-sector and university partners, which aims to improve the accuracy of numerical weather prediction (NWP) model forecasts of wind speed in complex terrain for wind energy applications. A core component of WFIP2 was an 18-month field campaign that took place in the U.S. Pacific Northwest between October 2015 and March 2017. A large suite of instrumentation was deployed in a series of telescoping arrays, ranging from 500 km across to a densely instrumented 2 km × 2 km area similar in size to a high-resolution NWP model grid cell. Observations from these instruments are being used to improve our understanding of the meteorological phenomena that affect wind energy production in complex terrain and to evaluate and improve model physical parameterization schemes. We present several brief case studies using these observations to describe phenomena that are routinely difficult to forecast, including wintertime cold pools, diurnally driven gap flows, and mountain waves/wakes. Observing system and data product improvements developed during WFIP2 are also described.

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