Aerosol-Cloud interactions : a new perspective in precipitation enhancement

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Earth, Atmospheric, and Planetary Sciences, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 171-184).Increased industrialization and human activity modified the atmospheric aerosol composition and size-distribution during the last several decades. This has affected the structure and evolution of clouds, and precipitation from them. The processes and mechanisms by which clouds and precipitation are modified by changes in aerosol composition and size-distribution are very intricate. The objective of this thesis is to improve the understanding of the processes and mechanisms through which the changes in aerosol concentrations impact the evolution of deep convective clouds and precipitation formation. We develop a new coupled model in which a very detailed model of aerosol activation is coupled to a three-dimensional cloud resolving model. This coupled model can accurately represent different kinds of aerosol populations. This coupled model is used to investigate the impact of changing aerosol concentrations on the dynamics, microphysical evolution and precipitation formation in deep convective clouds. We examine the theories of aerosol activation, and the representation of aerosol activation in cloud models. The limitations of the extant methods of representation of aerosol activation in cloud models are evaluated. Then we descibe the components of the coupled model - Modified Eulerian and Lagrangian Aerosol Model (MELAM) and the Cloud Resolving Model (CRM). The features of these two component models with respect to aersol activation and cloud formation are discussed. The evaluation of the coupled model by simulation of a deep convertive event observed during the INDian Ocean EXperiment (INDOEX) by statistcal comparison of observed and simulated cloud fields shows that the coupled model can simulate deep convective events reasonably well. We present a study of the senstivity of the model to initial thermodynamic conditions (CAPE). Different initial thermodynamic conditons sampled during the INDOEX are used to initialize the coupled model and, the structure and evolution of the deep convective event are discussed. The study sheds new light on the respone of deep convection to CAPE. It is found that when the atmosphere has moderate CAPE, the precipitation forming processes are very active and when the CAPE is (cont.) low or high, they are comparatively less efficient.As the most important part of our study, we examine the response of deep convection to changing initial aerosol concentration. Different aerosol concentrations from those representing pristine to polluted atmospheres are considered. We look at the buoyancy of the cloud and the microphysical evolution. It is found that the dynamics and microphysics are tightly coupled and we infer that to understand aerosol-cloud interactions in deep convective clouds, both - dynamics and microphysics - and their interaction have to be taken into consideration. Our results show that the response of a deep convective cloud to changing aerosol concentration is very different from the much well understood reponse of shallow clouds or small cumulus clouds. In general, increase in aerosol concentratin is seen to invigorate convection and lead to greater condensate. Although the cloud droplet size decreases, collision-coalescence is not completely inefficient. The precipitation in high aerosol regime is seen to occure in short spells of intense rain. A very interesting anomalous response of deep convection to initial aerosol concentration is observed at intermediate aerosol concentrations. The cloud lifetime, and precipitation are seen to increase in this regime. A possible mechanism to explain this anomalous behavior is proposed and the available circumstantial support for the mechanism from extant observations is presented. It is proposed that the efficient collection of rain and cloud droplets by ice and graupel particles in the middle troposphere is primarily responsible for this increased cloud lifetime and precipitation.by Udaya Bhaskar Gunturu.Ph.D

Characterization of wind power resource in the United States

Wind resource in the continental and offshore United States has been reconstructed and characterized using metrics that describe, apart from abundance, its availability, persistence and intermittency. The Modern Era Retrospective-Analysis for Research and Applications (MERRA) boundary layer flux data has been used to construct wind profile at 50 m, 80 m, 100 m, 120 m turbine hub heights. The wind power density (WPD) estimates at 50 m are qualitatively similar to those in the US wind atlas developed by the National Renewable Energy Laboratory (NREL), but quantitatively a class less in some regions, but are within the limits of uncertainty. The wind speeds at 80 m were quantitatively and qualitatively close to the NREL wind map. The possible reasons for overestimation by NREL have been discussed. For long tailed distributions like those of the WPD, the mean is an overestimation and median is suggested for summary representation of the wind resource. The impact of raising the wind turbine hub height on metrics of abundance, persistence, variability and intermittency is analyzed. There is a general increase in availability and abundance of wind resource but there is an increase in intermittency in terms of level crossing rate in low resource regions.Massachusetts Institute of Technology. Joint Program on the Science & Policy of Global Chang

The Potential Wind Power Resource in Australia: A New Perspective

Australia’s wind resource is considered to be very good, and the utilization of this renewable energy resource is increasing rapidly: wind power installed capacity increased by 35% from 2006 to 2011 and is predicted to account for over 12% of Australia’s electricity generation in 2030. Due to this growth in the utilization of the wind resource and the increasing importance of wind power in Australia’s energy mix, this study sets out to analyze and interpret the nature of Australia’s wind resources using robust metrics of the abundance, variability and intermittency of wind power density, and analyzes the variation of these characteristics with current and potential wind turbine hub heights. We also assess the extent to which wind intermittency, on hourly or greater timescales, can potentially be mitigated by the aggregation of geographically dispersed wind farms, and in so doing, lessen the severe impact on wind power economic viability of long lulls in wind and power generated. Our results suggest that over much of Australia, areas that have high wind intermittency coincide with large expanses in which the aggregation of turbine output does not mitigate variability. These areas are also geographically remote, some are disconnected from the east coast’s electricity grid and large population centers, which are factors that could decrease the potential economic viability of wind farms in these locations. However, on the eastern seaboard, even though the wind resource is weaker, it is less variable, much closer to large population centers, and there exists more potential to mitigate it’s intermittency through aggregation. This study forms a necessary precursor to the analysis of the impact of large-scale circulations and oscillations on the wind resource at the mesoscale.Massachusetts Institute of Technology. Joint Program on the Science & Policy of Global Chang

The Potential Wind Power Resource in Australia: A New Perspective

Australia is considered to have very good wind resources, and the utilization of this renewable energy resource is increasing. Wind power installed capacity increased by 35% from 2006 to 2011 and is predicted to account for over 12% of Australia’s electricity generation in 2030. This study uses a recently published methodology to address the limitations of previous wind resource analyses, and frames the nature of Australia’s wind resources from the perspective of economic viability, using robust metrics of the abundance, variability and intermittency of wind power density, and analyzes whether these differ with higher wind turbine hub heights. We also assess the extent to which wind intermittency can potentially be mitigated by the aggregation of geographically dispersed wind farms. Our results suggest that over much of Australia, areas that have high wind intermittency coincide with large expanses in which the aggregation of turbine output does not mitigate variability. These areas are also geographically remote, some are disconnected from the east coast’s electricity grid and large population centers, and often are not connected or located near enough to high capacity electricity infrastructure, all of which would decrease the potential economic viability of wind farms in these locations. However, on the eastern seaboard, even though the wind resource is weaker, it is less variable, much closer to large population centers, and there exists more potential to mitigate its intermittency through aggregation.The authors gratefully acknowledge the financial support for this work provided by the MIT Joint Program on the Science and Policy of Global Change through a number of federal agencies and industrial sponsors including US Department of Energy grant DE-FG02-94ER61937

Comparison of the range of values (m/s) in many areas of the 80 m wind speed map constructed from MERRA data to the one produced by the Australian Government.

<p>The first region encompasses much of the East coast, and includes southeast and northeast QLD, and Tasmania.</p

Anticoincidence (left) and Null-anticoincidence (right) of wind power density, at 50 m.

<p>Units indicate the number of grid points in a ∼1000×1000 km box surrounding the gridpoint in question which are anticoincident to the central gridpoint, which is when the hourly time series of WPD is greater than 200 W m-2 at one of the two points, but not both, for 50% of the total length of the time series.</p

Measures of abundance.

<p>(a) The mean WPD at 50 m, (b) the change in the mean from 50 m to 80 m and from (c) 50 m to 150 m, (d) median wind power density at 50 m, (e) the change in the median from 50 m to 80 m and from (f) 50 m to 150 m. All units are W m⁻².</p