A5085847352- Atmospheric aerosols and clouds
- Atmospheric chemistry and aerosols
- Atmospheric and Environmental Gas Dynamics
- Meteorological Phenomena and Simulations
- Aeolian processes and effects
- Atmospheric Ozone and Climate
- Climate variability and models
- Solar Radiation and Photovoltaics
- Remote Sensing in Agriculture
- Particle Dynamics in Fluid Flows
- Flood Risk Assessment and Management
- Air Quality Monitoring and Forecasting
- Oceanographic and Atmospheric Processes
- Fire effects on ecosystems
- Advanced Causal Inference Techniques
- Graphite, nuclear technology, radiation studies
- Marine and coastal ecosystems
- Nuclear and radioactivity studies
- Statistical Methods and Inference
- Air Traffic Management and Optimization
- Ocean Acidification Effects and Responses
- Advanced Aircraft Design and Technologies
- Precipitation Measurement and Analysis
- Health Systems, Economic Evaluations, Quality of Life
- Air Quality and Health Impacts
University of Oxford
2021-2023
University of Wisconsin–Madison
2018-2020
Aerosol-cloud interactions (ACIs) are considered to be the most uncertain driver of present-day radiative forcing due human activities. The nonlinearity cloud-state changes aerosol perturbations make it challenging attribute causality in observed relationships forcing. Using correlations infer can when meteorological variability also drives both and cloud independently. Natural anthropogenic from well-defined sources provide "opportunistic experiments" (also known as natural experiments)...
Abstract. While many studies have tried to quantify the sign and magnitude of warm marine cloud response aerosol loading, both remain uncertain, owing multitude factors that modulate microphysical thermodynamic processes within cloud. Constraining aerosol–cloud interactions using local meteorology liquid water may offer a way account for covarying influences, potentially increasing our confidence in observational estimates indirect effects. A total 4 years collocated satellite observations...
Abstract. Aerosol–cloud interactions and their resultant forcing remains one of the largest sources uncertainty in future climate scenarios. The effective radiative due to aerosol–cloud (ERFaci) is a combination two different effects, namely how aerosols modify cloud brightness (RFaci, intrinsic) extent reacts aerosol (cloud adjustments CA; extrinsic). Using satellite observations warm clouds from NASA A-Train constellation 2007 2010 along with MERRA-2 Reanalysis SPRINTARS model, we evaluate...
Abstract. The radiative effects of clouds make a large contribution to the Earth's energy balance, and changes in constitute dominant source uncertainty global warming response carbon dioxide forcing. To characterize constrain this uncertainty, cloud-controlling factor (CCF) analyses have been suggested that estimate sensitivities large-scale environmental changes, typically cloud-regime-specific multiple linear regression frameworks. Here, local cloud number controlling factors are...
One of the greatest sources uncertainty in future climate projections comes from limitations modelling clouds and understanding how different cloud types interact with system. A key first step reducing this is to accurately classify at high spatial temporal resolution. In paper, we introduce Cumulo, a benchmark dataset for training evaluating global classification models. It consists one year 1km resolution MODIS hyperspectral imagery merged pixel-width 'tracks' CloudSat labels. Bringing...
Abstract. Aerosol–cloud–precipitation interactions can lead to a myriad of responses within shallow cumulus clouds, including an invigoration response, whereby aerosol loading results in higher rain rate, more turbulence, and deepening the cloud layer. However few global studies have found direct evidence that occurs. The satellite-based report for such effects generally focus on only response. Here, we show beyond response by investigating latent heating vertical motion profiles warm rain....
Abstract. Aerosol-cloud interactions and their resultant forcing remains one of the largest sources uncertainty future climate scenarios. The effective radiative due to aerosol-cloud (ERFaci) is a combination two different effects, how aerosols modify cloud brightness (RFaci) extent reacts aerosol (CA). Using satellite observations warm clouds from NASA A-Train constellation 2007 2010 along with MERRA-2 reanalysis SPRINTARS model, we evaluate ERFaci its components, RFaci CA, while accounting...
Abstract. Aerosol-cloud interactions remain a large source of uncertainty in global climate models due to how pre-industrial clouds, aerosols, and the environment behaved. We employ three machine learning models, random forest, stochastic gradient boosting, an extreme boosting regressor derive proxy for warm cloudiness predicted using only their environmental controls. train our on boundary layer stability, relative humidity free atmosphere, upper level vertical motion, sea surface...
Abstract. Aerosol-cloud interactions (ACI) are considered to be the most uncertain driver of present-day radiative forcing due human activities. The non-linearity cloud-state changes aerosol perturbations make it challenging attribute causality in observed relationships forcing. Using correlations infer can also when meteorological variability drives both and cloud independently. Natural anthropogenic from well defined sources provide “opportunistic experiments” (also known as natural...
Abstract. While many studies have tried to quantify the sign and magnitude of warm cloud response aerosol loading, both remain uncertain owing multitude factors that modulate microphysical thermodynamic processes within cloud. Constraining aerosol-cloud interactions using local meteorology liquid water may offer a way account for covarying influences, potentially increasing our confidence in observational estimates indirect effects. Four years collocated satellite observations from NASA...
Estimating the effects of continuous-valued interventions from observational data is a critically important task for climate science, healthcare, and economics. Recent work focuses on designing neural network architectures regularization functions to allow scalable estimation average individual-level dose-response curves high-dimensional, large-sample data. Such methodologies assume ignorability (observation all confounding variables) positivity treatment levels every covariate value...
Abstract. Aerosol-cloud-precipitation interactions can lead to a myriad of responses within shallow cumulus clouds including an invigoration response, whereby aerosol loading results in higher rain rate, more turbulence, and deepening the cloud layer. However few global studies have found direct evidence that occurs. The satellite based report for such effects generally focus on only response. Here, we show beyond Using latent heating vertical motion profiles derived from CloudSat radar...
Aerosol-cloud interactions include a myriad of effects that all begin when aerosol enters cloud and acts as condensation nuclei (CCN). An increase in CCN results decrease the mean droplet size (r$_{e}$). The smaller leads to brighter, more expansive, longer lasting clouds reflect incoming sunlight, thus cooling earth. Globally, aerosol-cloud cool Earth, however strength effect is heterogeneous over different meteorological regimes. Understanding how evolve function local environment can help...
Aerosol-cloud interactions (ACI) include various effects that result from aerosols entering a cloud, and affecting cloud properties. In general, an increase in aerosol concentration results smaller droplet sizes which leads to larger, brighter, longer-lasting clouds reflect more sunlight cool the Earth. The strength of effect is however heterogeneous, meaning it depends on surrounding environment, making ACI one most uncertain our current climate models. work, we use causal machine learning...
In this contribution, a statistical learning technique is used to quantify the response of cloud radiative effects changes in large number environmental factors spatial observation data.Clouds play key role for Earth’s energy balance; however, their climatic and anthropogenic aerosol emission not clear, yet. Here, 20 years satellite observations (CRE) are analysed together with reanalysis data sets (regularised) ridge regression framework quantitatively link variability observed...
Abstract. The radiative effects of clouds make a large contribution to the Earth's energy balance, and changes in constitute dominant source uncertainty global warming response carbon dioxide forcing. To characterize constrain this uncertainty, cloud controlling factor (CCF) analyses have been suggested that estimate sensitivities large-scale environmental changes, typically cloud-regime specific multiple linear regression frameworks. Here, local number factors are estimated...