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Complex-terrain locations often have repeatable near-surface wind patterns, such as synoptic gap flows and local thermally forced flows. An example is the Columbia River Valley in east-central Oregon-Washington, a significant wind-energy-generation region and the site of the Second Wind-Forecast Improvement Project (WFIP2). Data from three Doppler lidars deployed during WFIP2 define and characterize summertime wind regimes and their large-scale contexts, and provide insight into NWP model errors by examining differences in the ability of a model [NOAA’s High-Resolution Rapid-Refresh (HRRR-version1)] to forecast wind-speed profiles for different regimes. Seven regimes were identified based on daily time series of the lidar-measured rotor-layer winds, which then suggested two broad categories. First, in three regimes the primary dynamic forcing was the large-scale pressure gradient. Second, in two regimes the dominant forcing was the diurnal heating-cooling cycle (regional sea-breeze-type dynamics), including the marine intrusion previously described, which generates strong nocturnal winds over the region. The other two included a hybrid regime and a non-conforming regime. For the large-scale pressure-gradient regimes, HRRR had wind-speed biases of ~1 m s−1 and RMSEs of 2-3 m s−1. Errors were much larger for the thermally forced regimes, owing to the premature demise of the strong nocturnal flow in HRRR. Thus, the more dominant the role of surface heating in generating the flow, the larger the errors. Major errors could result from surface heating of the atmosphere, boundary-layer responses to that heating, and associated terrain interactions. Measurement/modeling research programs should be aimed at determining which modeled processes produce the largest errors, so those processes can be improved and errors reduced.

Ground-based Doppler-lidar instrumentation provides atmospheric wind data at dramatically improved accuracies and spatial/temporal resolutions. These capabilities have provided new insights into atmospheric flow phenomena, but they also should have a strong role in NWP model improvement. Insight into the nature of model errors can be gained by studying recurrent atmospheric flows, here a regional summertime diurnal sea breeze and subsequent marine-air intrusion into the arid interior of Oregon–Washington, where these winds are an important wind-energy resource. These marine intrusions were sampled by three scanning Doppler lidars in the Columbia River basin as part of the Second Wind Forecast Improvement Project (WFIP2), using data from summer 2016. Lidar time–height cross sections of wind speed identified 8 days when the diurnal flow cycle (peak wind speeds at midnight, afternoon minima) was obvious and strong. The 8-day composite time–height cross sections of lidar wind speeds are used to validate those generated by the operational NCEP–HRRR model. HRRR simulated the diurnal wind cycle, but produced errors in the timing of onset and significant errors due to a premature nighttime demise of the intrusion flow, producing low-bias errors of 6 m s−1. Day-to-day and in the composite, whenever a marine intrusion occurred, HRRR made these same errors. The errors occurred under a range of gradient wind conditions indicating that they resulted from the misrepresentation of physical processes within a limited region around the measurement locations. Because of their generation within a limited geographical area, field measurement programs can be designed to find and address the sources of these NWP errors.

In 2015 the U.S. Department of Energy (DOE) initiated a 4-yr study, the Second Wind Forecast Improvement Project (WFIP2), to improve the representation of boundary layer physics and related processes in mesoscale models for better treatment of scales applicable to wind and wind power forecasts. This goal challenges numerical weather prediction (NWP) models in complex terrain in large part because of inherent assumptions underlying their boundary layer parameterizations. The WFIP2 effort involved the wind industry, universities, the National Oceanographic and Atmospheric Administration (NOAA), and the DOE’s national laboratories in an integrated observational and modeling study. Observations spanned 18 months to assure a full annual cycle of continuously recorded observations from remote sensing and in situ measurement systems. The study area comprised the Columbia basin of eastern Washington and Oregon, containing more than 6 GW of installed wind capacity. Nests of observational systems captured important atmospheric scales from mesoscale to NWP subgrid scale. Model improvements targeted NOAA’s High-Resolution Rapid Refresh (HRRR) model to facilitate transfer of improvements to National Weather Service (NWS) operational forecast models, and these modifications have already yielded quantitative improvements for the short-term operational forecasts. This paper describes the general WFIP2 scope and objectives, the particular scientific challenges of improving wind forecasts in complex terrain, early successes of the project, and an integrated approach to archiving observations and model output. It provides an introduction for a set of more detailedBAMSpapers addressing WFIP2 observational science, modeling challenges and solutions, incorporation of forecasting uncertainty into decision support tools for the wind industry, and advances in coupling improved mesoscale models to microscale models that can represent interactions between wind plants and the atmosphere.

Annually and seasonally averaged wind profiles from three Doppler lidars were obtained from sites in the Columbia River basin of east-central Oregon and Washington, a major region of wind-energy production, for the Second Wind Forecast Improvement Project (WFIP2) experiment. The profile data are used to quantify the spatial variability of wind flows in this area of complex terrain, to assess the HRRR–NCEP model’s ability to capture spatial and temporal variability of wind profiles, and to evaluate model errors. Annually averaged measured wind speed differences over the 70-km extent of the lidar measurements reached 1 m s−1 within the wind-turbine rotor layer, and 2 m s−1 for 200–500 m AGL. Stronger wind speeds in the lowest 500 m occurred at sites higher in elevation, farther from the river, and farther west—closer to the Cascade Mountain barrier. Validating against the lidar data, the HRRR model underestimated strong wind speeds (>12 m s−1) and, consequently, their frequency of occurrence, especially at the two lowest-elevation sites, producing annual low biases in rotor-layer wind speed of 0.5 m s−1. The RMSE between measured and modeled winds at all sites was about 3 m s−1 and did not degrade significantly with forecast lead time. The nature of the model errors was different for different seasons. Moreover, although the three sites were located in the same basin terrain, the nature of the model errors was different at each site. Thus, if only one of the sites had been instrumented, different conclusions would have been drawn as to the major sources of model error, depending on where the measurements were made.

Large mountains can modify the weather downstream of the terrain. In particular, when stably stratified air ascends a mountain barrier, buoyancy perturbations develop. These perturbations can trigger mountain waves downstream of the mountains that can reach deep into the atmospheric boundary layer where wind turbines operate. Several such cases of mountain waves occurred during the Second Wind Forecast Improvement Project (WFIP2) in the Columbia Basin in the lee of the Cascade Mountains bounding the states of Washington and Oregon in the Pacific Northwest of the United States. Signals from the mountain waves appear in boundary-layer sodar and lidar observations as well as in nacelle wind speeds and power observations from wind plants. Weather Research and Forecasting model simulations also produce mountain waves. Even small oscillations in wind speed caused by mountain waves can induce oscillations between full rated power of a wind farm and half of the power output, depending on the position of the mountain wave's crests and troughs. This paper aims at understanding how mountain waves form in the complex terrain of the Columbia Basin, subsequently affect wind energy production, and impact aspects of operational forecasting, wind power plant layout, and integration of power into the electrical grid.

We describe an open source evaluation framework for solar forecasting to support the DOE Solar Forecasting 2 program and the broader solar forecast community. The framework enables evaluations of solar irradiance, solar power, and net-load forecasts that are impartial, repeatable and auditable. First, we define the use cases of the framework. The use cases, developed from the project’s initial stakeholder engagement sessions, include comparisons to reference data sets, private forecast trials, evaluation of probabilistic forecast skill, and examinations of forecast errors during critical periods. We discuss the framework’s data validation toolkit, reference data sources, and data privacy protocols. We describe the framework’s benchmark forecast capabilities for intra-hour and day ahead forecast horizons. Finally, we summarize the reports and metrics that communicate the relative merits of the test and benchmark forecasts. The reports are created from standardized templates and include graphics for quantitatively evaluating deterministic and probabilistic forecasts and standard metrics for quantitatively evaluating forecasts.

The wind-energy (WE) industry relies on numerical weather prediction (NWP) forecast models as foundational or base models for many purposes, including wind-resource assessment and wind-power forecasting. During the Second Wind Forecast Improvement Project (WFIP2) in the Columbia River Basin of Oregon and Washington, a significant effort was made to improve NWP forecasts through focused model development, to include experimental refinements to the High Resolution Rapid Refresh (HRRR) model physics and horizontal grid spacing. In this study, the performance of an experimental version of HRRR that includes these refinements is tested against a control version, which corresponds to that of the operational HRRR run by National Oceanic and Atmospheric Administration/National Centers for Environmental Protection at the outset of WFIP2. The effects of horizontal grid resolution were also tested by comparing wind forecasts from the HRRR (with 3-km grid spacing) with those from a finer-resolution HRRR nest with 750-m grid spacing. Model forecasts are validated against accurate wind-profile measurements by three scanning, pulsed Doppler lidars at sites separated by a total distance of 71 km. Model skill and improvements in model skill, attributable to physics refinements and improved horizontal grid resolution, varied by season, by site, and during periods of atmospheric phenomena relevant to WE. In general, model errors were the largest below 150 m above ground level (AGL). Experimental HRRR refinements tended to reduce the mean absolute error (MAE) and other error metrics for many conditions, but degradation in skill (increased MAE) was noted below 150 m AGL at the two lowest-elevation sites at night. Finer resolution was found to produce the most significant reductions in the error metrics.

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.

The rapid deployment of wind and solar energy generation systems has resulted in a need to better understand, predict, and manage variable generation. The uncertainty around wind and solar power forecasts is still viewed by the power industry as being quite high, and many barriers to forecast adoption by power system operators still remain. In response, the U.S. Department of Energy has sponsored, in partnership with the National Oceanic and Atmospheric Administration, public, private, and academic organizations, two projects to advance wind and solar power forecasts. Additionally, several utilities and grid operators have recognized the value of adopting variable generation forecasting and have taken great strides to enhance their usage of forecasting. In parallel, power system markets and operations are evolving to integrate greater amounts of variable generation. This paper will discuss the recent trends in wind and solar power forecasting technologies in the U.S., the role of forecasting in an evolving power system framework, and the benefits to intended forecast users.

BACKGROUND Human parechovirus (HPeV) causes central nervous system (CNS) infection in infants. To further understand HPeV CNS infection, we describe its clinical, laboratory and epidemiologic characteristics from a Midwestern US tertiary care center. Because HPeV CNS infections have appeared clinically and seasonally similar to enterovirus (EV) infections, we retrospectively compared characteristics of young infants undergoing sepsis evaluations in whom HPeV, EV or neither were detected in cerebrospinal fluid (CSF). METHODS HPeV real-time reverse-transcription polymerase chain reaction (RT-PCR) assay was performed on frozen nucleic acid extracts of CSF specimens submitted for EV RT-PCR assay from children seen at our hospital in 2009. HPeV genotyping was performed by sequencing of the viral protein 1 region. Clinical data were abstracted from medical records retrospectively for EV-positive, HPeV-positive and age-matched controls in whom neither virus was detected from CSF testing. RESULTS HPeV was detected in 66 of the 388 (17%) CSF specimens whereas EV was detected in 54 of the 388 (14%) from June through October 2009. Genotyping identified HPeV3 in 51 of the 66 (77%) positive CSF specimens. Males predominated (61%) with the most common presenting symptoms (91%) being fever and irritability. All HPeV-positive patients were

Advances in atmospheric science are critical to increased deployment of variable renewable energy (VRE) sources. For VRE sources, such as wind and solar, to reach high penetration levels in the nation's electric grid, electric system operators and VRE operators need better atmospheric observations, models, and forecasts. Improved meteorological observations through a deep layer of the atmosphere are needed for assimilation into numerical weather prediction (NWP) models. The need for improved operational NWP forecasts that can be used as inputs to power prediction models in the 0–36-h time frame is particularly urgent and more accurate predictions of rapid changes in VRE generation (ramp events) in the very short range (0–6 h) are crucial. We describe several recent studies that investigate the feasibility of generating 20% or more of the nation's electricity from weather-dependent VRE. Next, we describe key advances in atmospheric science needed for effective development of wind energy and approaches to achieving these improvements. The financial benefit to the nation of improved wind forecasts is potentially in the billions of dollars per year. Obtaining the necessary meteorological and climatological observations and predictions is a major undertaking, requiring collaboration from the government, private, and academic sectors. We describe a field project that will begin in 2011 to improve short-term wind forecasts, which demonstrates such a collaboration, and which falls under a recent memorandum of understanding between the Office of Energy Efficiency and Renewable Energy at the Department of Energy and the Department of Commerce/National Oceanic and Atmospheric Administration.

This paper quantifies the impact of the Columbia Gorge on the weather and climate within and downstream of this mesoscale gap and examines the influence of synoptic-scale flow on gorge weather. Easterly winds occur more frequently and are stronger at stations such as Portland International Airport (KPDX) that are close to the western terminus of the gorge than at other lowland stations west of the Cascades. In the cool season, there is a strong correlation between east winds at KPDX and cooler temperatures in the Columbia Basin, within the gorge, and over the northern Willamette River valley. At least 56% of the annual snowfall, 70% of days with snowfall, and 90% of days with freezing rain at KPDX coincide with easterly gorge flow. Synoptic composites were created to identify the large-scale patterns leading to strong winds, snowfall, and freezing rain in the gorge. These composites showed that all gorge gap flow events are associated with a high-amplitude 500-mb ridge upstream of the Pacific Northwest, colder than normal 850-mb temperatures over the study region, and a substantial offshore sea level pressure gradient force between the interior and the northwest coast. However, the synoptic evolution varies for different kinds of gorge weather events. For example, the composite of the 500-mb field for freezing rain events features a split developing in the upstream ridge with zonal flow at midlatitudes, while for easterly gap winds accompanied by snowfall, there is an amplification of the ridge.

We detected a 3% prevalence rate for human parechovirus (HPeV) in 720 respiratory specimens collected from 637 children seen in our hospital in 2009. Fifteen of 20 were HPeV-3 and two were HPeV-1. Only nonspecific, modest respiratory symptoms were evident in patients with detectable HPeV in respiratory specimens. Seven patients had concurrent respiratory and central nervous system (CNS) HPeV-3 infection, suggesting a possible respiratory route of acquisition.

International audience; Electricity systems around the world are decarbonizing, driven by reductions in the cost of renewable energy and encouraged by supportive regulatory policy. Electricity market designs are increasingly being tested to ensure that the bulk power system can deliver reliable, cost-effective energy to all consumers.

The modes and frequencies of demagnetised and magnetised planar disc, triangular, wye and hexagonal resonators have already been described in the literature. Another resonator which has the required symmetry is the ring resonator. Here we give the resonant frequencies of this type of gyromagnetic resonator.