Search Results
Abstract
During winter 2016/17, California experienced numerous heavy precipitation events linked to land-falling atmospheric rivers (ARs) that filled reservoirs and ended a severe, multiyear drought. These events also caused floods, mudslides, and debris flows, resulting in major socioeconomic disruptions. During 2–11 February 2017, persistent heavy precipitation in the northern Sierra Nevada culminated in a rapid increase in the water level on Lake Oroville, necessitating the activation of an emergency spillway for the first time since the Oroville Dam was installed and forcing the evacuation of 188,000 people. The precipitation, which mostly fell as rain due to elevated freezing levels, was focused on the western slope of the Sierra Nevada in connection with orographic forcing linked to two successive ARs. Heavy rain fell on saturated soils and a snowpack produced by antecedent storms and thereby resulted in excessive runoff into Lake Oroville that led to a damaged spillway and complicated reservoir operations.
Abstract
During winter 2016/17, California experienced numerous heavy precipitation events linked to land-falling atmospheric rivers (ARs) that filled reservoirs and ended a severe, multiyear drought. These events also caused floods, mudslides, and debris flows, resulting in major socioeconomic disruptions. During 2–11 February 2017, persistent heavy precipitation in the northern Sierra Nevada culminated in a rapid increase in the water level on Lake Oroville, necessitating the activation of an emergency spillway for the first time since the Oroville Dam was installed and forcing the evacuation of 188,000 people. The precipitation, which mostly fell as rain due to elevated freezing levels, was focused on the western slope of the Sierra Nevada in connection with orographic forcing linked to two successive ARs. Heavy rain fell on saturated soils and a snowpack produced by antecedent storms and thereby resulted in excessive runoff into Lake Oroville that led to a damaged spillway and complicated reservoir operations.
The New England High-Resolution Temperature Program seeks to improve the accuracy of summertime 2-m temperature and dewpoint temperature forecasts in the New England region through a collaborative effort between the research and operational components of the National Oceanic and Atmospheric Administration (NOAA). The four main components of this program are 1) improved surface and boundary layer observations for model initialization, 2) special observations for the assessment and improvement of model physical process parameterization schemes, 3) using model forecast ensemble data to improve upon the operational forecasts for near-surface variables, and 4) transfering knowledge gained to commercial weather services and end users. Since 2002 this program has enhanced surface temperature observations by adding 70 new automated Cooperative Observer Program (COOP) sites, identified and collected data from over 1000 non-NOAA mesonet sites, and deployed boundary layer profilers and other special instrumentation throughout the New England region to better observe the surface energy budget. Comparisons of these special datasets with numerical model forecasts indicate that near-surface temperature errors are strongly correlated to errors in the model-predicted radiation fields. The attenuation of solar radiation by aerosols is one potential source of the model radiation bias. However, even with these model errors, results from bias-corrected ensemble forecasts are more accurate than the operational model output statistics (MOS) forecasts for 2-m temperature and dewpoint temperature, while also providing reliable forecast probabilities. Discussions with commerical weather vendors and end users have emphasized the potential economic value of these probabilistic ensemble-generated forecasts.
The New England High-Resolution Temperature Program seeks to improve the accuracy of summertime 2-m temperature and dewpoint temperature forecasts in the New England region through a collaborative effort between the research and operational components of the National Oceanic and Atmospheric Administration (NOAA). The four main components of this program are 1) improved surface and boundary layer observations for model initialization, 2) special observations for the assessment and improvement of model physical process parameterization schemes, 3) using model forecast ensemble data to improve upon the operational forecasts for near-surface variables, and 4) transfering knowledge gained to commercial weather services and end users. Since 2002 this program has enhanced surface temperature observations by adding 70 new automated Cooperative Observer Program (COOP) sites, identified and collected data from over 1000 non-NOAA mesonet sites, and deployed boundary layer profilers and other special instrumentation throughout the New England region to better observe the surface energy budget. Comparisons of these special datasets with numerical model forecasts indicate that near-surface temperature errors are strongly correlated to errors in the model-predicted radiation fields. The attenuation of solar radiation by aerosols is one potential source of the model radiation bias. However, even with these model errors, results from bias-corrected ensemble forecasts are more accurate than the operational model output statistics (MOS) forecasts for 2-m temperature and dewpoint temperature, while also providing reliable forecast probabilities. Discussions with commerical weather vendors and end users have emphasized the potential economic value of these probabilistic ensemble-generated forecasts.
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.
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.
