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This study demonstrates improvements in ensemble wind forecasts at hub height due to bias correction strategies and their impact on wind energy forecasts at the Alta II wind farm in southern California. The ensemble consists of twenty members that differ in physics schemes used. Ensemble wind forecasts are produced for three months. Hub-height sodar wind observations are used to evaluate forecast performance. Time-dependent bias correction (TBC) and probability bias correction (PBC) are proposed to calibrate hub-height ensemble wind forecasts. The root mean square errors (RMSEs) and biases of forecasted hub-height winds are significantly reduced using both bias correction methods. RMSEs were reduced by 20% and 15% for TBC and PBC, respectively. When evaluating forecast performance from a reliability perspective, PBC better corrects both high and low forecast probabilities for high-wind thresholds. The penalty associated with deviation of the wind energy forecasts from observed values is reduced by 8.7% and 8.0% for TBC and PBC, respectively. It is notable that PBC produces a greater penalty reduction during the high wind bias forecast period (6 p.m.–8 a.m. local standard time); during this time period, PBC reduces the penalty by 25.8% while TBC reduces it by just 19.2%.

Accurate air quality forecasts can allow for mitigation of the health risks associated with high levels of air pollution. During September 2003, a team of NASA, NOAA, and EPA researchers demonstrated a prototype tool for improving fine particulate matter (PM2.5) air quality forecasts using satellite aerosol observations. Daily forecast products were generated from a near-real-time fusion of multiple input data products, including aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS)/Earth Observing System (EOS) instrument on the NASA Terra satellite, PM2.5 concentration from over 300 state/local/national surface monitoring stations, meteorological fields from the NOAA/NCEP Eta Model, and fire locations from the NOAA/National Environmental Satellite, Data, and Information Service (NESDIS) Geostationary Operational Environmental Satellite (GOES) Wildfire Automated Biomass Burning Algorithm (WF_ABBA) product. The products were disseminated via a Web interface to a small g...

[1] Methane mixing ratios measured at a tall tower are compared to model predictions to estimate surface emissions of CH4 in Central California for October–December 2007 using an inverse technique. Predicted CH4 mixing ratios are calculated based on spatially resolved a priori CH4 emissions and simulated atmospheric trajectories. The atmospheric trajectories, along with surface footprints, are computed using the Weather Research and Forecast (WRF) coupled to the Stochastic Time-Inverted Lagrangian Transport (STILT) model. An uncertainty analysis is performed to provide quantitative uncertainties in estimated CH4 emissions. Three inverse model estimates of CH4 emissions are reported. First, linear regressions of modeled and measured CH4 mixing ratios obtain slopes of 0.73 ± 0.11 and 1.09 ± 0.14 using California-specific and Edgar 3.2 emission maps, respectively, suggesting that actual CH4 emissions were about 37 ± 21% higher than California-specific inventory estimates. Second, a Bayesian “source” analysis suggests that livestock emissions are 63 ± 22% higher than the a priori estimates. Third, a Bayesian “region” analysis is carried out for CH4 emissions from 13 subregions, which shows that inventory CH4 emissions from the Central Valley are underestimated and uncertainties in CH4 emissions are reduced for subregions near the tower site, yielding best estimates of flux from those regions consistent with “source” analysis results. The uncertainty reductions for regions near the tower indicate that a regional network of measurements will be necessary to provide accurate estimates of surface CH4 emissions for multiple regions.