Because knowledge of the melting level is critical to river forecasters and other users, an objective algorithm to detect the brightband height from profiles of radar reflectivity and Doppler vertical velocity collected with a Doppler wind profiling radar is presented. The algorithm uses vertical profiles to detect the bottom portion of the bright band, where vertical gradients of radar reflectivity and Doppler vertical velocity are negatively correlated. A search is then performed to find the peak radar reflectivity above this feature, and the brightband height is assigned to the altitude of the peak. Reflectivity profiles from the off-vertical beams produced when the radar is in the Doppler beam swinging mode provide additional brightband measurements. A consensus test is applied to subhourly values to produce a quality-controlled, hourly averaged brightband height. A comparison of radar-deduced brightband heights with melting levels derived from temperature profiles measured with rawinsondes l... Abstract Because knowledge of the melting level is critical to river forecasters and other users, an objective algorithm to detect the brightband height from profiles of radar reflectivity and Doppler vertical velocity collected with a Doppler wind profiling radar is presented. The algorithm uses vertical profiles to detect the bottom portion of the bright band, where vertical gradients of radar reflectivity and Doppler vertical velocity are negatively correlated. A search is then performed to find the peak radar reflectivity above this feature, and the brightband height is assigned to the altitude of the peak. Reflectivity profiles from the off-vertical beams produced when the radar is in the Doppler beam swinging mode provide additional brightband measurements. A consensus test is applied to subhourly values to produce a quality-controlled, hourly averaged brightband height. A comparison of radar-deduced brightband heights with melting levels derived from temperature profiles measured with rawinsondes l...

Radar and rain gauge observations collected in coastal mountains during the California Land-Falling Jets Experiment (CALJET) are used to diagnose the bulk physical properties of rainfall during a wet winter season (January–March 1998). Three rainfall types were clearly distinguishable by differences in their vertical profiles of radar reflectivity and Doppler vertical velocity: nonbright band, bright band, and hybrid (seeder–feeder). The contribution of each rainfall type to the total rainfall observed at the radar site (1841 mm) was determined by a new, objective algorithm. While hybrid rain occurred most often, nonbrightband rain (NBB rain) contributed significantly (28%) to the total. This paper focuses on characterizing NBB rain because of the need to document this key physical process and because of its impact on Weather Surveillance Radar-1988 Doppler (WSR-88D) precipitation surveillance capabilities. NBB rain is a quasi-steady, shallow rain process that does not exhibit a radar bright band... Abstract Radar and rain gauge observations collected in coastal mountains during the California Land-Falling Jets Experiment (CALJET) are used to diagnose the bulk physical properties of rainfall during a wet winter season (January–March 1998). Three rainfall types were clearly distinguishable by differences in their vertical profiles of radar reflectivity and Doppler vertical velocity: nonbright band, bright band, and hybrid (seeder–feeder). The contribution of each rainfall type to the total rainfall observed at the radar site (1841 mm) was determined by a new, objective algorithm. While hybrid rain occurred most often, nonbrightband rain (NBB rain) contributed significantly (28%) to the total. This paper focuses on characterizing NBB rain because of the need to document this key physical process and because of its impact on Weather Surveillance Radar-1988 Doppler (WSR-88D) precipitation surveillance capabilities. NBB rain is a quasi-steady, shallow rain process that does not exhibit a radar bright band...

The California Land-falling Jets Experiment (CALJET) was carried out along the California coast, and up to 1000-km offshore, during the winter of 1997/98 to study the underlying physical processes that cause flooding rains and high winds in the orographically complex coastal zone and to explore the impact of potential future observing systems on short-term (<24 h) quantitative precipitation and wind forecasts during the landfall of winter storms from the data-sparse eastern Pacific Ocean. Using the suite of experimental and operational observing systems that were available during CALJET, this study documented the mesoscale modification of an intense landfalling cyclone by the steep coastal orography on 3 February 1998. This storm heavily impacted the populous and highly vulnerable coastal zone of southern California with flooding rains, strong winds, and major beach erosion. A pair of landfalling cold-frontal zones produced most of the damaging weather, while the primary cyclone circulation remained offshore. Special attention is given to the development of blocking of the low-level flow by the steep coastal mountains of southern California and to the influence of this blocked flow on the observed nearshore frontal evolution. In particular, unique observations are presented of blocking-induced frontal splitting and frontal merging, as well as unparalleled documentation of terrain-forced frontal waves. The impact of these frontal modifications on rainfall distributions is explored. This study also provides clear observational evidence of the orographic modulation of a landfalling prefrontal low-level jet (LLJ) near the coast of southern California. This is especially important, given that LLJs efficiently transport moisture into the coastal mountains, often resulting in orographically enhanced flooding. The results described in this study have important generalized implications for understanding the complex interactions that occur between shallow blocked flows and landfalling winter storms along the mountainous west coast of North America and for understanding the impact of these interactions on rainfall, winds, and erosion in the coastal zone.

The snow level, or altitude in the atmosphere where snow melts to rain, is an important variable for hydrometeorological prediction in mountainous watersheds; yet, there is no operational performance measure associated with snow-level forecasts in the United States. To establish a performance measure, it is first necessary to establish the baseline performance associated with snow-level forecasts. Using data collected by vertically pointing Doppler radars, an automated algorithm has been developed to detect the altitude of maximum radar reflectivity in the radar bright band that results from the precipitation melting process. This altitude can be used as a proxy for the snow level, partly because it always exists below the freezing level, which is defined as the altitude of the 0°C isotherm. The skill of freezing-level forecasts produced by the California–Nevada River Forecast Center (CNRFC) is evaluated by comparing spatially interpolated and forecaster-adjusted numerical model freezing-level forecasts with observed freezing levels estimated by radars operating at 2875 MHz (S band). The freezing level was chosen instead of the snow level as the comparison parameter because the radar algorithm and the CNRFC have different interpretations of the snow level. The evaluation occurred at two sites: one in the coastal mountains north of San Francisco and the other in the Sierra Nevada. The evaluation was conducted for forecasts made during the winter wet season of 2005/06. Although the overall mean freezing-level forecast bias is small enough not to be hydrologically significant, about 15% of the forecasts had biases greater than 300 m (forecast too low). The largest forecast biases were associated with freezing levels above 2.3 km that were underforecasted by as much as 900 m. These high freezing-level events were accompanied by the heaviest precipitation intensities, exacerbating the flood threat and making the forecast even more challenging.

During Northern Hemisphere winters, the West Coast of North America is battered by extratropical storms. The impact of these storms is of paramount concern to California, where aging water supply and flood protection infrastructures are challenged by increased standards for urban flood protection, an unusually variable weather regime, and projections of climate change. Additionally, there are inherent conflicts between releasing water to provide flood protection and storing water to meet requirements for the water supply, water quality, hydropower generation, water temperature and flow for at-risk species, and recreation. To improve reservoir management and meet the increasing demands on water, improved forecasts of precipitation, especially during extreme events, are required. Here, the authors describe how California is addressing their most important and costliest environmental issue—water management—in part, by installing a state-of-the-art observing system to better track the area’s most severe wintertime storms.

The partitioning of precipitation into frozen and liquid components influences snow-derived water resources and flood hazards in mountain environments. We used a 915-MHz Doppler radar wind profiler upstream of the northern Sierra Nevada to estimate the hourly elevation where snow melts to rain, or the snow level, during winter (December–February) precipitation events spanning water years (WY) 2008–2017. During this ten-year period, a Mann-Kendall test indicated a significant (p < 0.001) positive trend in snow level with a Thiel-Sen slope of 72 m year⁻¹. We estimated total precipitation falling as snow (snow fraction) between WY1951 and 2017 using nine daily mid-elevation (1200–2000 m) climate stations and two hourly stations spanning WY2008–2017. The climate-station-based snow fraction estimates agreed well with snow-level radar values (R² = 0.95, p < 0.01), indicating that snow fractions represent a reasonable method to estimate changes in frozen precipitation. Snow fraction significantly (p < 0.001) declined during WY2008–2017 at a rate of 0.035 (3.5%) year⁻¹. Single-point correlations between detrended snow fraction and sea-surface temperatures (SST) suggested that positive SST anomalies along the California coast favor liquid phase precipitation during winter. Reanalysis-derived integrated moisture transported upstream of the northern Sierra Nevada was negatively correlated with snow fraction (R² = 0.90, p < 0.01), with atmospheric rivers representing the likely circulation mechanism producing low-snow-fraction storms.