[1] The real-time forecasts of ozone (O 3 ) from seven air quality forecast models (AQFMs) are statistically evaluated against observations collected during July and August of 2004 (53 days) through the Aerometric Information Retrieval Now (AIRNow) network at roughly 340 monitoring stations throughout the eastern United States and southern Canada. One of the first ever real-time ensemble O 3 forecasts, created by combining the seven separate forecasts with equal weighting, is also evaluated in terms of standard statistical measures, threshold statistics, and variance analysis. The ensemble based on the mean of the seven models and the ensemble based on the median are found to have significantly more temporal correlation to the observed daily maximum 1-hour average and maximum 8-hour average O 3 concentrations than any individual model. However, root-mean-square errors (RMSE) and skill scores show that the usefulness of the uncorrected ensembles is limited by positive O 3 biases in all of the AQFMs. The ensembles and AQFM statistical measures are reevaluated using two simple bias correction algorithms for forecasts at each monitor location: subtraction of the mean bias and a multiplicative ratio adjustment, where corrections are based on the full 53 days of available comparisons. The impact the two bias correction techniques have on RMSE, threshold statistics, and temporal variance is presented. For the threshold statistics a preferred bias correction technique is found to be model dependent and related to whether the model overpredicts or underpredicts observed temporal O 3 variance. All statistical measures of the ensemble mean forecast, and particularly the bias-corrected ensemble forecast, are found to be insensitive to the results of any particular model. The higher correlation coefficients, low RMSE, and better threshold statistics for the ensembles compared to any individual model point to their preference as a real-time O 3 forecast.

[1] Real-time forecasts of PM 2.5 aerosol mass from seven air quality forecast models (AQFMs) are statistically evaluated against observations collected in the northeastern United States and southeastern Canada from two surface networks and aircraft data during the summer of 2004 International Consortium for Atmospheric Research on Transport and Transformation (ICARTT)/New England Air Quality Study (NEAQS) field campaign. The AIRNOW surface network is used to evaluate PM 2.5 aerosol mass, the U.S. EPA STN network is used for PM 2.5 aerosol composition comparisons, and aerosol size distribution and composition measured from the NOAA P-3 aircraft are also compared. Statistics based on midday 8-hour averages, as well as 24-hour averages are evaluated against the AIRNOW surface network. When the 8-hour average PM 2.5 statistics are compared against equivalent ozone statistics for each model, the analysis shows that PM 2.5 forecasts possess nearly equivalent correlation, less bias, and better skill relative to the corresponding ozone forecasts. An analysis of the diurnal variability shows that most models do not reproduce the observed diurnal cycle at urban and suburban monitor locations, particularly during the nighttime to early morning transition. While observations show median rural PM 2.5 levels similar to urban and suburban values, the models display noticeably smaller rural/urban PM 2.5 ratios. The ensemble PM 2.5 forecast, created by combining six separate forecasts with equal weighting, is also evaluated and shown to yield the best possible forecast in terms of the statistical measures considered. The comparisons of PM 2.5 composition with NOAA P-3 aircraft data reveals two important features: (1) The organic component of PM 2.5 is significantly underpredicted by all the AQFMs and (2) those models that include aqueous phase oxidation of SO 2 to sulfate in clouds overpredict sulfate levels while those AQFMs that do not include this transformation mechanism underpredict sulfate. Errors in PM 2.5 ammonium levels tend to correlate directly with errors in sulfate. Comparisons of PM 2.5 composition with the U.S. EPA STN network for three of the AQFMs show that sulfate biases are consistently lower at the surface than aloft. Recommendations for further research and analysis to help improve PM 2.5 forecasts are also provided.

One year of observations from a network of five 915-MHz boundary-layer radar wind profilers equipped with radio acoustic sounding systems located in California's Central Valley are used to investigate the annual variability of convective boundary-layer depth and its correlation to meteorological parameters and conditions. Results from the analysis show that at four of the sites, the boundary-layer height reaches its maximum in the late-spring months then surprisingly decreases during the summer months, with mean July depths almost identical to those for December. The temporal decrease in boundary-layer depth, as well as its spatial variation, is found to be consistent with the nocturnal low-level lapse rate observed at each site. Multiple forcing mechanisms that could explain the unexpected seasonal behaviour of boundary-layer depth are investigated, including solar radiation, precipitation, boundary-layer mesoscale convergence, low-level cold-air advection, local surface characteristics and irrigation patterns and synoptic-scale subsidence. Variations in solar radiation, precipitation and synoptic-scale subsidence do not explain the shallow summertime convective boundary-layer depths observed. Topographically forced cold-air advection and local land-use characteristics can help explain the shallow CBL depths at the four sites, while topographically forced low-level convergence helps maintain larger CBL depths at the fifth site near the southern end of the valley.

The primary goal of the Second Wind Forecast Improvement Project (WFIP2) is to advance the state-of-the-art of wind energy forecasting in complex terrain. To achieve this goal, a comprehensive 18-month field measurement campaign was conducted in the region of the Columbia River basin. The observations were used to diagnose and quantify systematic forecast errors in the operational High-Resolution Rapid Refresh (HRRR) model during weather events of particular concern to wind energy forecasting. Examples of such events are cold pools, gap flows, thermal troughs/marine pushes, mountain waves, and topographic wakes. WFIP2 model development has focused on the boundary layer and surface-layer schemes, cloud–radiation interaction, the representation of drag associated with subgrid-scale topography, and the representation of wind farms in the HRRR. Additionally, refinements to numerical methods have helped to improve some of the common forecast error modes, especially the high wind speed biases associated with early erosion of mountain–valley cold pools. This study describes the model development and testing undertaken during WFIP2 and demonstrates forecast improvements. Specifically, WFIP2 found that mean absolute errors in rotor-layer wind speed forecasts could be reduced by 5%–20% in winter by improving the turbulent mixing lengths, horizontal diffusion, and gravity wave drag. The model improvements made in WFIP2 are also shown to be applicable to regions outside of complex terrain. Ongoing and future challenges in model development will also be discussed.

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.

A case study is carried out for the 29 July-3 August 2000 episode of the Central California Ozone Study (CCOS), a typical summertime high-ozone event in the Central Valley of California. The focus of the study is on the low-level winds that control the transport and dispersion of pollutants in the Central Valley. An analysis of surface and wind profiler observations from the CCOS field experiment indicates a number of important low-level flows in the Central Valley: 1) the incoming low-level marine airflow through the Carquinez Strait into the Sacramento River delta, 2) the diurnal cycle of upslope-downslope flows, 3) the up-and down-valley flow in the Sacramento Valley, 4) the nocturnal low-level jet in the San Joaquin Valley, and 5) the orographically induced mesoscale eddies (the Fresno and Schultz eddies). A numerical simulation using the advanced research version of the Weather Research and Forecasting Model (WRF) reproduces the overall pattern of the observed low-level flows. The physical reasons behind the quantitative differences between the observed and simulated low-level winds are also analyzed and discussed, although not enough observations are available to diagnose thoroughly the model-error sources. In particular, hodograph analysis is applied to provide physical insight into the impact of the large-scale, upper-level winds on the locally forced low-level winds. It is found that the diurnal rotation of the observed and simulated hodographs of the local winds varies spatially in the Central Valley, resulting from the combining effect of topographically induced local forcing and the interaction between the upper-level winds and the aforementioned low-level flows. The trajectory analysis not only further confirms that WRF reproduces the observed low-level transport processes reasonably well but also shows that the simulated upper-level winds have noticeable errors. The results from this study strongly suggest that the errors in the WRF-simulated low-level winds are related not only to the errors in the model's surface conditions and atmospheric boundary layer physics but also to the errors in the upper-level forcing mostly prescribed in the model's lateral boundary conditions.

The Wind Forecast Improvement Project (WFIP) is a public–private research program, the goal of which is to improve the accuracy of short-term (0–6 h) wind power forecasts for the wind energy industry. WFIP was sponsored by the U.S. Department of Energy (DOE), with partners that included the National Oceanic and Atmospheric Administration (NOAA), private forecasting companies (WindLogics and AWS Truepower), DOE national laboratories, grid operators, and universities. WFIP employed two avenues for improving wind power forecasts: first, through the collection of special observations to be assimilated into forecast models and, second, by upgrading NWP forecast models and ensembles. The new observations were collected during concurrent year-long field campaigns in two high wind energy resource areas of the United States (the upper Great Plains and Texas) and included 12 wind profiling radars, 12 sodars, several lidars and surface flux stations, 184 instrumented tall towers, and over 400 nacelle anemometers. Results demonstrate that a substantial reduction (12%–5% for forecast hours 1–12) in power RMSE was achieved from the combination of improved numerical weather prediction models and assimilation of new observations, equivalent to the previous decade’s worth of improvements found for low-level winds in NOAA/National Weather Service (NWS) operational weather forecast models. Data-denial experiments run over select periods of time demonstrate that up to a 6% improvement came from the new observations. Ensemble forecasts developed by the private sector partners also produced significant improvements in power production and ramp prediction. Based on the success of WFIP, DOE is planning follow-on field programs.

In situ samples of cloud droplets by aircraft in Oklahoma in 1997, the Surface Heat Budget of the Arctic Ocean (SHEBA)/First ISCCP Regional Experiment (FIRE)-Arctic Cloud Experiment (ACE) in 1998, and various other locations around the world were used to evaluate a ground-based remote sensing technique for retrieving profiles of cloud droplet effective radius. The technique is based on vertically pointing measurements from highsensitivity millimeter-wavelength radar and produces height-resolved estimates of cloud particle effective radius. Although most meteorological radars lack the sensitivity to detect small cloud droplets, millimeter-wavelength cloud radars provide opportunities for remotely monitoring the properties of nonprecipitating clouds. These highsensitivity radars reveal detailed reflectivity structure of most clouds that are within several kilometers range. In order to turn reflectivity into usable microphysical quantities, relationships between the measured quantities and the desired quantities must be developed. This can be done through theoretical analysis, modeling, or empirical measurements. Then the uncertainty of each procedure must be determined in order to know which ones to use. In this study, two related techniques are examined for the retrieval of the effective radius. One method uses both radar reflectivity and integrated liquid water through the clouds obtained from a microwave radiometer; the second uses the radar reflectivity and an assumption that continental stratus clouds have a concentration of 200 drops per cubic centimeter and marine stratus 100 cm Ϫ3. Using in situ measurements of marine and continental stratus, the error analysis herein shows that the error in these techniques would be about 15%. In comparing the techniques with in situ aircraft measurements of effective radius, it is found that the radar radiometer retrieval was not quite as good as the technique using radar reflectivity alone. The radar reflectivity alone gave a 13% standard deviation with the in situ comparison, while the radar-radiometer retrieval gave a 19% standard deviation.