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Bianco, Laura

Publications and source records attributed to Bianco, Laura.

5 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

The Short Life of Upvalley Wind in a High‐Altitude Valley in the Colorado Rocky Mountains

Thermally driven upvalley (UV) wind in the upper East River Valley in the Colorado Rocky Mountains often unexpectedly stops in midmorning and reverses back to downvalley (DV) wind. We use a comprehensive observational data set for a nearly two‐year long period to analyze the wind system and boundary layer evolution in this high‐altitude valley and determine the reason for this early wind reversal. Days with short UV wind predominantly occur during the warm season when the valley floor is free of snow and the convective boundary layer (CBL) grows well above the height of the surrounding ridges. UV wind persists throughout the day only on a few days during the warm season. We link differences in valley wind evolution to wind direction at upper levels at and above ridge height and propose forced channeling mechanisms to describe coupling between valley and upper‐level wind when the CBL grows above ridge height. The frequency distribution of upper‐level wind direction is such that channeling in the DV direction is favored, which explains the predominance of days with short UV wind. The deep CBL is supported by the presence of a deep weakly stably stratified residual layer with high aerosol content, which is regularly present over the mountain range during the warm season. On days when the CBL does not grow above ridge height, for example, when the valley floor is covered by snow, thermally driven UV wind is able to persist throughout the day independent of upper‐level wind direction.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Sensitivity of thermodynamic profiles retrieved from ground-based microwave and infrared observations to additional input data from active remote sensing instruments and numerical weather prediction models

Accurate and continuous estimates of the thermodynamic structure of the lower atmosphere are highly beneficial to meteorological process understanding and its applications, such as weather forecasting. In this study, the Tropospheric Remotely Observed Profiling via Optimal Estimation (TROPoe) physical retrieval is used to retrieve temperature and humidity profiles from various combinations of input data collected by passive and active remote sensing instruments, in situ surface platforms, and numerical weather prediction models. Among the employed instruments are microwave radiometers (MWRs), infrared spectrometers (IRSs), radio acoustic sounding systems (RASSs), ceilometers, and surface sensors. TROPoe uses brightness temperatures and/or radiances from MWRs and IRSs, as well as other observational inputs (virtual temperature from the RASS, cloud-base height from the ceilometer, pressure, temperature, and humidity from the surface sensors) in a physical iterative retrieval approach. This starts from a climatologically reasonable profile of temperature and water vapor, with the radiative transfer model iteratively adjusting the assumed temperature and humidity profiles until the derived brightness temperatures and radiances match those observed by the MWR and/or IRS instruments within a specified uncertainty, as well as within the uncertainties of the other observations, if used as input. In this study, due to the uniqueness of the dataset that includes all the above-mentioned sensors, TROPoe is tested with different observational input combinations, some of which also include information higher than 4 km above ground level (a.g.l.) from the operational Rapid Refresh numerical weather prediction model. These temperature and humidity retrievals are assessed against independent collocated radiosonde profiles under non-cloudy conditions to assess the sensitivity of the TROPoe retrievals to different input combinations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Supporting Advancement in Weather and Water Prediction in the Upper Colorado River Basin: The SPLASH Campaign

Water is a critical resource that causes significant challenges to inhabitants of the western United States. These challenges are likely to intensify as the result of expanding population and climate-related changes that act to reduce runoff in areas of complex terrain. To better understand the physical processes that drive the transition of mountain precipitation to streamflow, the National Oceanic and Atmospheric Administration has deployed suites of environmental sensors throughout the East River watershed of Colorado as part of the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH). This includes surface-based sensors over a network of five different observing sites, airborne platforms, and sophisticated remote sensors to provide detailed information on spatiotemporal variability of key parameters. With a 2-yr deployment, these sensors offer detailed insight into precipitation, the lower atmosphere, and the surface, and support the development of datasets targeting improved prediction of weather and water. Initial datasets have been published and are laying a foundation for improved characterization of physical processes and their interactions driving mountain hydrology, evaluation and improvement of numerical prediction tools, and educational activities. SPLASH observations contain a depth and breadth of information that enables a variety of atmospheric and hydrological science analyses over the coming years that leverage collaborations between national laboratories, academia, and stakeholders, including industry.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Impact of Seasonal Snow-Cover Change on the Observed and Simulated State of the Atmospheric Boundary Layer in a High-Altitude Mountain Valley

The structure and evolution of the atmospheric boundary layer (ABL) under clear-sky fair weather conditions over mountainous terrain is dominated by the diurnal cycle of the surface energy balance and thus strongly depends on surface snow cover. We use data from three passive ground-based infrared spectrometers deployed in the East River Valley in Colorado's Rocky Mountains to investigate the response of the thermal ABL structure to changes in surface energy balance during the seasonal transition from low to high snow cover. Temperature profiles were retrieved from the infrared radiances using the optimal estimation physical retrieval Tropospheric Remotely Observed Profiling via Optimal Estimation. A nocturnal surface inversion formed in the valley during clear-sky days, which was subsequently mixed out during daytime with the development of a convective boundary layer when snow cover was low. Over high snow cover, a very shallow convective boundary layer formed, above which the inversion persisted through the daytime hours. We compare these observations to NOAA's operational High-Resolution-Rapid-Refresh model and find large warm biases on clear-sky days resulting from the model's inability to form strong nocturnal inversions and to maintain the stable stratification in the valley during daytime when there was snow on the ground. We suggest several factors contributing to the large model errors. These are (a) the inability of the model to represent well-developed thermally driven flows likely due to the too coarse horizontal grid spacing (3 km), (b) too much convective mixing during daytime, and (c) too strong vertical coupling between the valley atmosphere and the free troposphere.

54 ENVIRONMENTAL SCIENCES↗