The Airborne Cloud-Aerosol Transport System

The Airborne Cloud-Aerosol Transport System (ACATS) is a multi-channel Doppler lidar system recently developed at NASA Goddard Space Flight Center (GSFC). A unique aspect of the multi-channel Doppler lidar concept such as ACATS is that it is also, by its very nature, a high spectral resolution lidar (HSRL). Both the particulate and molecular scattered signal can be directly and unambiguously measured, allowing for direct retrievals of particulate extinction. ACATS is therefore capable of simultaneously resolving the backscatterextinction properties and motion of a particle from a high altitude aircraft. ACATS has flown on the NASA ER-2 during test flights over California in June 2012 and science flights during the Wallops Airborne Vegetation Experiment (WAVE) in September 2012. This paper provides an overview of the ACATS method and instrument design, describes the ACATS retrieval algorithms for cloud and aerosol properties, and demonstrates the data products that will be derived from the ACATS data using initial results from the WAVE project. The HSRL retrieval algorithms developed for ACATS have direct application to future spaceborne missions such as the Cloud-Aerosol Transport System (CATS) to be installed on the International Space Station (ISS). Furthermore, the direct extinction and particle wind velocity retrieved from the ACATS data can be used for science applications such 27 as dust or smoke transport and convective outflow in anvil cirrus clouds

Mabel Engineering Flights, 2010-2013: Flight Report

In December 2010, NASA deployed for the first time the Multiple Altimeter Beam Experimental Lidar (MABEL), an airborne simulator for Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) algorithm development. Between 2010 and 2013, engineering flights were conducted in the continental United States, to ready the instrument for deployments to Alaska and Iceland, where flight lines could be designed and flown over sea ice and grounded ice. Ultimately, MABEL engineering missions included: 1) flights based out of NASA Armstrong Flight Research Center (California, formerly Dryden Flight Research Center) in 2010, 2011, and 2012; flights based out of NASA Wallops Flight Facility (Virginia) in 2012; flights based out of NASA Langley Research Center (Virginia) in 2013; and flights based out of the Mojave Air and Space Port (California) in 2013

Cloud-Aerosol Transport System (CATS) 1064 nm calibration and validation

The Cloud-Aerosol Transport System (CATS) lidar on board the International Space Station (ISS) operated from 10 February 2015 to 30 October 2017 providing range-resolved vertical backscatter profiles of Earth's atmosphere at 1064 and 532 nm. The CATS instrument design and ISS orbit lead to a higher 1064 nm signal-to-noise ratio than previous space-based lidars, allowing for direct atmospheric calibration of the 1064 nm signals. Nighttime CATS version 3-00 data were calibrated by scaling the measured data to a model of the expected atmospheric backscatter between 22 and 26 km a.m.s.l. (above mean sea level). The CATS atmospheric model is constructed using molecular backscatter profiles derived from Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis data and aerosol scattering ratios measured by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). The nighttime normalization altitude region was chosen to simultaneously minimize aerosol loading and variability within the CATS data frame, which extends from 28 to −2 km a.m.s.l. Daytime CATS version 3-00 data were calibrated through comparisons with nighttime measurements of the layer-integrated attenuated total backscatter (iATB) from strongly scattering, rapidly attenuating opaque cirrus clouds. The CATS nighttime 1064 nm attenuated total backscatter (ATB) uncertainties for clouds and aerosols are primarily related to the uncertainties in the CATS nighttime calibration technique, which are estimated to be ∼9  %. Median CATS V3-00 1064 nm ATB relative uncertainty at night within cloud and aerosol layers is 7 %, slightly lower than these calibration uncertainty estimates. CATS median daytime 1064 nm ATB relative uncertainty is 21 % in cloud and aerosol layers, similar to the estimated 16 %–18 % uncertainty in the CATS daytime cirrus cloud calibration transfer technique. Coincident daytime comparisons between CATS and the Cloud Physics Lidar (CPL) during the CATS-CALIPSO Airborne Validation Experiment (CCAVE) project show good agreement in mean ATB profiles for clear-air regions. Eight nighttime comparisons between CATS and the PollyXT ground-based lidars also show good agreement in clear-air regions between 3 and 12 km, with CATS having a mean ATB of 19.7 % lower than PollyXT. Agreement between the two instruments (∼7 %) is even better within an aerosol layer. Six-month comparisons of nighttime ATB values between CATS and CALIOP also show that iATB comparisons of opaque cirrus clouds agree to within 19 %. Overall, CATS has demonstrated that direct calibration of the 1064 nm channel is possible from a space-based lidar using the atmospheric normalization technique

CATS Version 2 Aerosol Feature Detection and Applications for Data Assimilation

Using GEOS-5, we are developing a 1D ENS approach for assimilating CATS near real time observations of total attenuated backscatter at 1064 nm: a) After performing a 1-ENS assimilation of a cloud-free profile, the GEOS-5 analysis closely followed observed total attenuated backscatter. b) Vertical localization length scales were varied for the well-mixed PBL and the free troposphere After assimilating a cloud free segment of a CATS granule, the fine detail of a dust event was obtained in the GEOS-5 analysis for both total attenuated backscatter and extinction. Future Work: a) Explore horizontal localization and test within a cloudy aerosol layer. b) Address noisy analysis increments in the free troposphere where both CATS and GEOS-5 aerosol loadings are low. c) Develop a technique to screen CATS ground return from profiles. d) "Dynamic" lidar ratio that will evolve in conjunction with simulated aerosol mixtures

Aerosol and Cloud Detection Using Machine Learning Algorithms and Space-Based Lidar Data

Clouds and aerosols play a significant role in determining the overall atmospheric radiation budget, yet remain a key uncertainty in understanding and predicting the future climate system. In addition to their impact on the Earth’s climate system, aerosols from volcanic eruptions, wildfires, man-made pollution events and dust storms are hazardous to aviation safety and human health. Space-based lidar systems provide critical information about the vertical distributions of clouds and aerosols that greatly improve our understanding of the climate system. However, daytime data from backscatter lidars, such as the Cloud-Aerosol Transport System (CATS) on the International Space Station (ISS), must be averaged during science processing at the expense of spatial resolution to obtain sufficient signal-to-noise ratio (SNR) for accurately detecting atmospheric features. For example, 50% of all atmospheric features reported in daytime operational CATS data products require averaging to 60 km for detection. Furthermore, the single-wavelength nature of the CATS primary operation mode makes accurately typing these features challenging in complex scenes. This paper presents machine learning (ML) techniques that, when applied to CATS data, (1) increased the 1064 nm SNR by 75%, (2) increased the number of layers detected (any resolution) by 30%, and (3) enabled detection of 40% more atmospheric features during daytime operations at a horizontal resolution of 5 km compared to the 60 km horizontal resolution often required for daytime CATS operational data products. A Convolutional Neural Network (CNN) trained using CATS standard data products also demonstrated the potential for improved cloud-aerosol discrimination compared to the operational CATS algorithms for cloud edges and complex near-surface scenes during daytime