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D Winker

Publications and source records attributed to D Winker.

9 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Progress and Challenges in Quantifying Wildfire Smoke Emissions, Their Properties, Transport, and Atmospheric Impacts

Wildfire is a natural and integral ecosystem process that is necessary to maintain species composition, structure, and ecosystem function. Extreme fires have been increasing over the last decades, which have a substantial impact on air quality, human health, the environment, and climate systems. Smoke aerosols can be transported over large distances, acting as pollutants that affect adjacent and distant downwind communities and environments. Fire emissions are a complicated mixture of trace gases and aerosols, many of which are short‐lived and chemically reactive, and this mixture affects atmospheric composition in complex ways that are not completely understood. We present a review of the current state of knowledge of smoke aerosol emissions originating from wildfires. Satellite observations, from both passive and active instruments, are critical to providing the ability to view the large‐scale influence of fire, smoke, and their impacts. Progress in the development of fire emission estimates to regional and global chemical transport models has advanced, although significant challenges remain, such as connecting ecosystems and fuels burned with dependent atmospheric chemistry. Knowledge of the impact of smoke on radiation, clouds, and precipitation has progressed and is an essential topical research area. However, current measurements and parameterizations are not adequate to describe the impacts on clouds of smoke particles (e.g., CNN, INP) from fire emissions in the range of representative environmental conditions necessary to advance science or modeling. We conclude by providing recommendations to the community that we believe will advance the science and understanding of the impact of fire smoke emissions on human and environmental health, as well as feedback with climate system.

I N Sokolik↗
NASA NTRS2016

TPSAS-NF1676L-23428-DND

Recent theoretical advances now enable accurate characterization of both the single scattering and multiple scattering contributions to the lidar backscatter signals obtained from opaque water clouds (Hu et al., 2006). As a consequence, lidar measurements of opaque water clouds have increasingly broad applications, especially for space-based polarization-sensitive lidars such as CALIOP. Among the most prominent and useful of these are (1) calibration and assessments of calibration accuracy (e.g., O'Connor et al., 2004; Hu et al., 2006); (2) accurate estimates of extrinsic (e.g., optical depths) and intrinsic (e.g., extinction-to-backscatter ratios) optical properties of clouds and aerosol layers lying above opaque water clouds (Hu et al., 2007; Liu et al., 2015); and (3) retrievals of water cloud microphysical properties such as cloud droplet number concentrations (Hu et al., 2007; Li et al., 2011; Zeng et al., 2014). In the first part of this presentation we give an overview of the recent advances in this subject area. The second part introduces several new studies of water clouds using the multi-wavelength depolarization measurement capabilities of NASA's airborne high spectral resolution lidars (HSRL). We use these measurements to assess existing theory, validate the measurement concept and explore several new application concepts. The third part discusses changes in Arctic water clouds using CALIOP measurements. The HSRL water cloud study is supported by NASA's atmospheric composition program.

Y Hu↗
NASA NTRS2010

TPSAS-NF1676L-11609-DND

China and much of East Asia experienced a drought in the spring of 2010 that is said to be the worst in the past century. Strong winds (wind speed > 7 m/s) occurred in spring this year ~40% more than average in recent years. MODIS imagery indicates numerous major dust storms occurring in the Taklimakan and Gobi deserts. Intense, persistent dust was subsequently observed over North America by space-based, airborne and ground-based lidars during April 2010. Using CALIPSO lidar (CALIOP) measurements and air parcel back trajectories, we track the dust measured over North America back to East Asian source regions (mainly in the Tarim Basin). We also interpret the CALIOP and other A-Train observations using results from a 3D chemical transport model (GEOS-Chem) and investigate the meteorological context that gave rise to these dust storms.

Z Liu↗
NASA NTRSDate not supplied

TPSAS-NF1676L-20108-DND

This presentation describes several enhancements planned for the version 4 aerosol subtyping and lidar ratio selection algorithms of the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. The CALIOP subtyping algorithm determines the most likely aerosol type from CALIOP measurements (attenuated backscatter, estimated particulate depolarization ratios de, layer altitude), and surface type. The aerosol type, so determined, is associated with a lidar ratio (LR) from a discrete set of values. In the version 3 algorithms, there are 6 pairs of 532 and 1064 nm lidar ratios. Some of these lidar ratios will be updated in the version 4 algorithms. In particular, the dust and polluted dust will be adjusted to reflect the latest measurements and model studies of these types. The algorithms are being updated to eliminate the occasional confusion between smoke and clean marine aerosols seen in version 3 by modifications to the elevated layer flag definitions that are used to determine the presence of smoke aerosols over the ocean. In the subtyping algorithms pure dust is determined by high estimated particulate depolarization ratios [de > 0.20]. Mixtures of dust and other aerosol types are determined by intermediate values of the estimated depolarization ratio [0.075< de <0.2]. The version 3 algorithms are limited to mixtures of dust and smoke, the so-called polluted dust aerosol type. To differentiate between mixtures of dust and smoke, and dust and marine aerosols, a new aerosol type will be added in the version 4 data products. In the revised classification algorithms, polluted dust will still defined as dust + smoke/pollution but in the marine boundary layer instances of moderate depolarization will be typed as dusty marine aerosols with a lower lidar ratio [LR = 35 sr] than polluted dust [currently LR = 55 sr]. The dusty marine type introduced in version 4 is modeled as a mixture of dust + marine aerosol. In the v3 algorithms the frequency of dust and polluted dust aerosols at daytime is higher than at nighttime. We present possible reasons for this and present the v4 distributions resulting from both improved background slope corrections of the daytime depolarization ratios and changes to the daytime thresholds for the polluted dust and dusty marine types of version 4. To gauge the impact of the enhancements, we contrast the following between versions 3 and 4: aerosol type, parameter distributions of each type, layer heights of maximum frequency, and distributions of smoke in biomass burning regions. To illustrate specific impacts the presentation shows case studies of version 3 and version 4 vertical feature masks of the aerosol subtypes, where appropriate, for the above enhancements.

A Omar↗
NASA NTRSDate not supplied

Using CALIPSO's New Ocean Derived Column Optical Depths

CALIPSO’s Version 4.51 Level 2 data release introduces an all-new group of science data sets containing estimates of total column two-way transmittances and effective optical depths derived from CALIOP ocean surface backscatter measurements and MERRA-2 reanalysis wind speed data. These estimates use data from the standard CALIOP lidar signal but in a passive sensor-like way, thus creating a unique link to passive instrument measurements. These new retrievals are provided for the entire mission, day and night, at 532nm, and are reported at single shot, 1km, and 5km resolutions for all profiles in which a valid lidar ocean surface return is detected. The addition of a total column optical depth constraint on subsequent retrievals of cloud and aerosol optical properties opens the door for many exciting new ways to leverage the already rich and versatile CALIPSO dataset. Following a brief review of the retrieval technique, this talk will focus on quality assurance assessments, estimated uncertainties, and data usage scenarios. We will conclude with examples highlighting some of the exciting work already being done using this new addition to CALIPSO’s already rich data record.

R Ryan↗