Environmental and Climate research at Brookhaven National Lab is focused on aerosol chemistry and microphysics, aerosol related infrastructure, climate and process modeling, cloud processes, data management and software engineering, terrestrial ecosystems, meteorological services, and tracer technologies.
Aerosol Chemistry & Microphysics
Focused on improving process-level understanding of aerosol formation and evolution mechanisms, aerosol absorption, and the direct and indirect influences that aerosols have on clouds, precipitation and climate.
Aerosol Related Infrastructure
Provides measurement capabilities to the DOE Atmospheric Radiation Measurement (ARM) program for long-term measurements of aerosols and their precursors across a global network of ground- and aircraft-based locations.
Climate and Process Modeling
Uses multi-scale process modeling and observational analyses to understand the processes essential to clouds, precipitation, land-atmosphere interactions, and urban impacts.
Cloud Processes
Seeks to improve understanding of microphysical and dynamical processes that impact the lifecycle of clouds to improve their representation in climate models.
Technology Development & Applications and Meteorological Services
Responsible for the maintenance, calibration, data collection and data archiving for the weather instrumentation network associated with BNL's atmospheric dispersion concerns.
Terrestrial Ecosystem Science & Technology
Seeks to improve the representation of ecosystem processes in Earth System Models in order to increase our ability to understand and project global change.
Tracer Technologies
The Tracer Technology Group uses perfluorocarbon tracers as a tool for understanding the processes that transport air, heat, water, and pollutants.
Funding Agencies
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FEB
27
Thursday
Environmental & Climate Sciences Department Seminar
"Developing Storm Outage Prediction Models"
Presented by Steven Quiring, Ohio State University, Canada
11 am, Large Conference Room, Bldg. 490
Thursday, February 27, 2020, 11:00 am
Hosted by: Mike Jensen
Weather can cause significant damage to the electrical power system, leading to prolonged power interruptions to a large number of customers. The estimated annual cost to the U.S. economy from storm-related power outages is >$20 billion. The number of weather-related outages has increased significantly in recent years. One approach to deal with this problem is to develop predictive techniques for forecasting how storms will impact the power grid hours to days in advance. This information can help utilities, first responders, and emergency managers to better prepare for the outages and more quickly restore power. This presentation summarizes the data-driven power outage models that we have developed for the U.S. Department of Energy and a number of investor-owned electrical utilities in the United States. These models are used to support decision making for near-term events (e.g., pre-storm preparation) and longer-term planning. The development and validation of our models will be presented and our approach for quantifying uncertainty will also be discussed. The talk will also highlight the challenges and successes from recent applications for American Electric Power, FirstEnergy, Southern Company and Southern California Edison.
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MAR
9
Monday
Environmental & Climate Sciences Department Seminar
"Use of In-situ Cloud Microphysical Observations for Quantifying Ice Cloud Microphysical Properties and Processes, and their Uncertainties"
Presented by Greg McFarquhar, University of Oklahoma
11 am, Large Conference Room, Bldg. 490
Monday, March 9, 2020, 11:00 am
Hosted by: Mike Jensen
Ice clouds currently reflect ~17 W m-2 of shortwave radiation and trap ~22 W m-2 of longwave radiation on global average. However, if the distribution of cloud heights and microphysical properties changes in response to increases in greenhouse gases and aerosols, associated changes in the radiative impact of clouds could feed back on Earth's climate. Representations of ice particle density, scattering and sedimentation are needed for global and regional climate models that predict these effects. Parameterizations of other processes, such as riming, aggregation, sedimentation and evaporation, are also needed for numerical weather models that predict the destructive impact and quantitative precipitation forecasts for winter storms, hurricanes, mesoscale convective systems and other events. Further, algorithms retrieving cloud properties from ground- and satellite-based sensors require assumptions about ice crystal properties. To develop such parameterizations, accurate observations of ice particle sizes, shapes, phases and concentrations are needed. Techniques measuring these ice crystal properties are reviewed. Sources of uncertainty, related to statistical counting, variability in cloud properties for similar environmental conditions, and errors induced by the processing of data and the instruments themselves are discussed using data collected over Alaska, Australia, and the continental United States. It is shown that although there are still uncertainties in in-situ observations of small ice crystals due to potential shattering of large particles on probe tips and the limited resolution of state-of-the-art cloud particle imagers, progress on characterizing small crystals has been made. The use of instrumental and statistical uncertainties in the development of stochastic cloud parameterizations is then introduced. A specific application to the representation of mass-dimensional (m-D) relationships m=aDb is shown, where (a,b) are given as surf
The Environmental & Climate Sciences Department is part of the Environment, Biology, Nuclear Science & Nonproliferation Directorate at Brookhaven National Laboratory.
