Research

Remote Sensing

Radiative Transfer

Aerosol-Cloud-Precipitation-Radiation Interactions

Improved retrievals of fresh and aged smoke properties from GOES, funded by NOAA: The increasing importance of biomass burning emissions to air quality, climate, and human health has spurred numerous laboratory, field, and modeling efforts aimed at improving understanding of smoke properties, and how these vary with factors such as fuel type, combustion efficiency, and atmospheric processing. Smoke plumes evolve physically and chemically as they are transported downwind, manifested as changes in their optical properties. Observations from geostationary satellites offer unprecedented opportunity to track and better understand smoke plumes in the atmosphere. In this project, we will develop a new, machine learning based aerosol retrieval method for GOES observations and characterize spatial and temporal variability of smoke plumes near-field and far-field. This will provide powerful constraints for regional and global models that represent biomass burning emissions and evolutions, necessary information for evaluating the impact of smoke plumes on climate.

Near-cloud changes in CALIOP and MODIS/VIIRS aerosol observations, funded by NASA: Aerosol observations in partly cloudy regions play a crucial role in understanding aerosol-cloud interactions and contribute significantly to uncertainties in the Earth radiation budget. Aerosol properties vary in response to nearby clouds and cloud-related processes, but the exact causes and radiative effects of these variations have remained elusive. In this project, we will study the properties and radiative impacts of near-cloud aerosols using satellite measurements from passive (MODIS and VIIRS) and active (Caliop) sensors. We will extend a newly developed retrival method to better understand the effects of clouds and cloud-related processes on aerosol properties.

A 3D Ensemble-based retrieval method for highly heterogeneous clouds, funded by NASA: Clouds are a key element that determines the Earth’s radiative and hydrological budgets and influences the forcing and sensitivity of our climate system. This profound significance of clouds renders satellite-based cloud observations of crucial importance. In the past, cloud properties, such as cloud optical depth and effective radius, are retrieved largely from shortwave reflectance. The retrieval theory commonly uses one-dimensional radiative transfer and assumes that clouds are homogeneous in the pixels of interest. This assumption ignores the three-dimensional (3D) nature of clouds and radiation, and therefore, introduces significant biases and errors. We propose to develop a new cloud retrieval method that directly confronts the issue of 3D clouds and radiation. This new method extracts 3D cloud structure information by maximizing the synergy between A-Train radar, lidar and shortwave measurements. Specifically, we will focus clouds over the Southeast Atlantic and evaluate our retireval against in-situ measurements from the recent NASA and UK field campaigns.

Constraining microphysical processes of warm rain formation using advanced spectral separations, an ensemble retrieval framework and machine learning techniques, to be funded by Department of Energy: Drizzle, common in maritime warm clouds, plays a crucial role in determining cloud lifecycle and thus has a significant impact on Earth’s energy budget. Yet, many models struggle to produce drizzle in the right amount with the right frequency, making it difficult to determine the response of clouds to a warmer climate. This calls for a need for improved understanding and model representation of drizzle formation. In this project we will exploit ARM measurements from the Azores and create concurrent cloud and drizzle properties and vertical profiles, critical observables for determining autoconversion and accretion rates. This dataset will be the first direct vertical profile estimates of the process rates. Given our present understanding, this will be a huge step forward, creating an unprecedented opportunity for the ARM users and the wider community to address key science questions in drizzle initiation and formation, and their interactions with aerosols, cloud, radiation and dynamics.

Assessing Secondary Ice Production in Continental Clouds Based on AMF Synergistic Remote Sensing Observations, funded by Department of Energy: Ice crystals play an important role in radiation and precipitation formation. However, the prediction of ice number concentration has proven to be problematic, because the predicted primary ice number concentration is often smaller than the observed by several orders of magnitude. The difference between the observed and the predicted number concentration has been explained by so-called secondary ice production (SIP), which includes hypothesized mechanisms like rime splintering, shattering of large frozen drop, and ice-ice collision breakup. In the past, these mechanisms were assessed largely by airborne in-situ measurements from field campaigns. While these intensive field observations have generally supported the hypothesized mechanisms, it remains unclear which mechanism dominates; what the trigger requirements are; what their typical time scale is; how their production rate depends on ambient atmospheric conditions; and importantly, how they influence the subsequent precipitation and radiation. We will use multiple-wavelength, dual-polarization, and Doppler information makes it possible to address those questions.

3D shortwave radiative kernels of marine boundary-layer clouds using scanning radar/lidar and array spectroradiometer, funded by Department of Energy: The response of global mean surface temperature to emissions of greenhouse gases from human activities remains highly uncertain. One of the primary causes for this uncertainty is cloud feedback, to what extent changes in low-altitude cloud cover and properties will amplify or dampen climate change. To better quantify cloud feedback, the radiative kernel approach has been increasingly used to compare model simulations across a range of climate change scenarios. The objective of this proposal is for the first time to develop fully 3D, observationally-based and spectrally-resolved radiative kernels at both the surface and the top of the atmosphere, which is necessary in tackling the uncertainty in cloud feedback. To do this, we propose to develop a novel 3D cloud retrieval method that uses ARM measurements synergistically by adapting an ensemble Kalman Filter approach. The retrieved cloud fields will be used to compute 3D kernels with help from a newly developed radiative transfer scheme. Finally, we will exploit this new knowledge of the observed 3D cloud fields to assess the inaccuracies in model-based 1D kernel calculations and impacts upon the estimated cloud feedback magnitude.

Dynamics-aerosol-chemistry-cloud interactions in West Africa (DACCIWA, funded by the European Commission): The DACCIWA project will conduct extensive fieldwork in southern West Africa to collect high-quality observations, spanning the entire process chain from surface-based natural and anthropogenic emissions to impacts on health, ecosystems and climate. Combining the resulting benchmark dataset with a wide range of modelling activities will allow (a) to assess all relevant physical and chemical processes, (b) to improve the monitoring of climate and compositional parameters from space and (c) to develop the next generation of weather and climate models capable of representing coupled cloud-aerosol interactions, which will ultimately lead to reduced uncertainties in climate predictions.