An Overview of the Aerosol and Clouds-Convection-Precipitation Study (A-CCP) and its Relationship to the Geostationary AC-VC

The 2017 Decadal Survey (DS) highlighted Earth System Science themes, science and application questions, and several high priority objectives that have led to the inclusion of Aerosols (A) and Clouds-Convection-Precipitation (CCP) as Designated Observables (DOs) The aerosol-related science questions outlined by the DS focus on two major themes: 1) Climate Variability and Change and 2) Weather and Air Quality. The Aerosol mission observables targeted to address these major objectives may potentially contribute to three additional themes: 3) Marine and Terrestrial Ecosystems, 4) Global Hydrological Cycle, and 5) Earth Surface and Interior; this study will examine these linkages.In response to NASA's Designated Observables Guidance for Multi-Center Study Plans released on June 1, 2018, GSFC, LaRC, JPL, MSFC, GRC and ARC submitted Study Plan to the NASA Earth Science Division for the Aerosol (A) and Cloud, Convection, and Precipitation (CCP) Pre-formulation Study (A-CCP). The DS recognized the science merit in combining the A and CCP DOs for both enhancing the ability to address a number of Most Important (MI) objectives defined by the disciplinary panels and also to provide an expanded capability to address additional objectives beyond those addressed by individual DOs. The DS also identified Integrating Themes that can also be addressed through combinations of observables including potential combinations of DOs and the PoR. The combined A+CCP portion of this study will demonstrate how the combination of A and CCP observables will enhance the objectives of A and CCP individually, while providing the ability to expand the DS objectives addressed, and will closely connect to the A and CCP studies being performed in parallel. A critical element of the A-CCP observing strategy is to make extensive use of the so-called Program-of-Record (PoR). In this regard, the Geostationary Atmospheric Composition Virtual Constellation consisting of the GEMS, TEMPO and SENTINEL-4 and other relevant geostationary assets will provide a critical foundation for A-CCP. In this talk we will discuss how the A-CCP measurements contributes to air-quality and the geostationary constellation, and conversely, how the geostationary constellation helps answering fundamental A-CCP science objectives

Retrieval, Inter-Comparison, and Validation of Above-Cloud Aerosol Optical Depth from A-train Sensors

Absorbing aerosols produced from biomass burning and dust outbreaks are often found to overlay lower level cloud decks and pose greater potentials of exerting positive radiative effects (warming) whose magnitude directly depends on the aerosol loading above cloud, optical properties of clouds and aerosols, and cloud fraction. Recent development of a 'color ratio' (CR) algorithm applied to observations made by the Aura/OMI and Aqua/MODIS constitutes a major breakthrough and has provided unprecedented maps of above-cloud aerosol optical depth (ACAOD). The CR technique employs reflectance measurements at TOA in two channels (354 and 388 nm for OMI; 470 and 860 nm for MODIS) to retrieve ACAOD in near-UV and visible regions and aerosol-corrected cloud optical depth, simultaneously. An inter-satellite comparison of ACAOD retrieved from NASA's A-train sensors reveals a good level of agreement between the passive sensors over the homogeneous cloud fields. Direct measurements of ACA such as carried out by the NASA Ames Airborne Tracking Sunphotometer (AATS) and Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research (4STAR) can be of immense help in validating ACA retrievals. We validate the ACA optical depth retrieved using the CR method applied to the MODIS cloudy-sky reflectance against the airborne AATS and 4STAR measurements. A thorough search of the historic AATS-4STAR database collected during different field campaigns revealed five events where biomass burning, dust, and wildfire-emitted aerosols were found to overlay lower level cloud decks observed during SAFARI-2000, ACE-ASIA 2001, and SEAC4RS- 2013, respectively. The co-located satellite-airborne measurements revealed a good agreement (RMSE less than 0.1 for AOD at 500 nm) with most matchups falling within the estimated uncertainties in the MODIS retrievals. An extensive validation of satellite-based ACA retrievals requires equivalent field measurements particularly over the regions where ACA are often observed from satellites, i.e., south-eastern Atlantic Ocean, tropical Atlantic Ocean, northern Arabian Sea, South-East and North-East Asia

Classification of Aerosol Retrievals from Spaceborne Polarimetry Using a Multiparameter Algorithm

In this presentation, we demonstrate application of a new aerosol classification algorithm to retrievals from the POLDER-3 polarimter on the PARASOL spacecraft. Motivation and method: Since the development of global aerosol measurements by satellites and AERONET, classification of observed aerosols into several types (e.g., urban-industrial, biomass burning, mineral dust, maritime, and various subtypes or mixtures of these) has proven useful to: understanding aerosol sources, transformations, effects, and feedback mechanisms; improving accuracy of satellite retrievals and quantifying assessments of aerosol radiative impacts on climate

Ultra-Stable Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research (5STAR)

The Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research (4STAR) combines airborne sun tracking and sky scanning with diffraction spectroscopy to improve knowledge of atmospheric constituents and their links to airpollution and climate. Direct beam hyperspectral measurement of optical depth improves retrievals of gas constituentsand determination of aerosol properties. Sky scanning enhances retrievals of aerosol type and size distribution.Hyperspectral cloud-transmitted radiance measurements enable the retrieval of cloud properties from below clouds.These measurements tighten the closure between satellite and ground-based measurements. 4STAR incorporates amodular sun-tracking sky-scanning optical head with optical fiber signal transmission to rack mounted spectrometers,permitting miniaturization of the external optical tracking head, and future detector evolution.4STAR has supported a broad range of flight experiments since it was first flown in 2010. This experience provides thebasis for a series of improvements directed toward reducing measurement uncertainty and calibration complexity, andexpanding future measurement capabilities, to be incorporated into a new 5STAR instrument. A 9-channel photodioderadiometer with AERONET-matched bandpass filters will be incorporated to improve calibration stability. A wide dynamic range tracking camera will provide a high precision solar position tracking signal as well as an image of sky conditions around the solar axis. An ultrasonic window cleaning system design will be tested. A UV spectrometer tailored for formaldehyde and SO2 gas retrievals will be added to the spectrometer enclosure. Finally, expansion capability for a 4 channel polarized radiometer to measure the Stokes polarization vector of sky light will be incorporated. This paper presents initial progress on this next-generation 5STAR instrument

Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research (4STAR) Instrument Improvements

The Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research (4STAR) combines airborne sun tracking and sky scanning with grating spectroscopy to improve knowledge of atmospheric constituents and their links to air-pollution and climate. Hyper-spectral measurements of direct-beam solar irradiance provide retrievals of gas constituents, aerosol optical depth, and aerosol and thin cloud optical properties. Sky radiance measurements in the principal and almucantar planes enhance retrievals of aerosol absorption, aerosol type, and size mode distribution. Zenith radiance measurements are used to retrieve cloud properties and phase, which in turn are used to quantify the radiative transfer below cloud layers. These airborne measurements tighten the closure between satellite and ground-based measurements. In contrast to the Ames Airborne Tracking Sunphotometer (AATS-14) predecessor instrument, new technologies for each subsystem have been incorporated into 4STAR. In particular, 4STAR utilizes a modular sun-trackingsky-scanning optical head with fiber optic signal transmission to rack mounted spectrometers, permitting miniaturization of the external optical head, and spectrometerdetector configurations that may be tailored for specific scientific objectives. This paper discusses technical challenges relating to compact optical collector design, radiometric dynamic range and stability, and broad spectral coverage at high resolution. Test results benchmarking the performance of the instrument against the AATS-14 standard and emerging science requirements are presented

Identifying chemical aerosol signatures using optical suborbital observations: how much can optical properties tell us about aerosol composition

Improvements in air quality and Earth\u27s climate predictions require improvements of the aerosol speciation in chemical transport models, using observational constraints. Aerosol speciation (e.g., organic aerosols, black carbon, sulfate, nitrate, ammonium, dust or sea salt) is typically determined using in situ instrumentation. Continuous, routine aerosol composition measurements from ground-based networks are not uniformly widespread over the globe. Satellites, on the other hand, can provide a maximum coverage of the horizontal and vertical atmosphere but observe aerosol optical properties (and not aerosol speciation) based on remote sensing instrumentation. Combinations of satellite-derived aerosol optical properties can inform on air mass aerosol types (AMTs). However, these AMTs are subjectively defined, might often be misclassified and are hard to relate to the critical parameters that need to be refined in models. In this paper, we derive AMTs that are more directly related to sources and hence to speciation. They are defined, characterized and derived using simultaneous in situ gas-phase, chemical and optical instruments on the same aircraft during the Study of Emissions and Atmospheric Composition, Clouds, and Climate Coupling by Regional Surveys (SEAC4RS, an airborne field campaign carried out over the US during the summer of 2013). We find distinct optical signatures for AMTs such as biomass burning (from agricultural or wildfires), biogenic and polluted dust. We find that all four AMTs, studied when prescribed using mostly airborne in situ gas measurements, can be successfully extracted from a few combinations of airborne in situ aerosol optical properties (e.g., extinction Ångström exponent, absorption Ångström exponent and real refractive index). However, we find that the optically based classifications for biomass burning from agricultural fires and polluted dust include a large percentage of misclassifications that limit the usefulness of results related to those classes. The technique and results presented in this study are suitable to develop a representative, robust and diverse source-based AMT database. This database could then be used for widespread retrievals of AMTs using existing and future remote sensing suborbital instruments/networks. Ultimately, it has the potential to provide a much broader observational aerosol dataset to evaluate chemical transport and air quality models than is currently available by direct in situ measurements. This study illustrates how essential it is to explore existing airborne datasets to bridge chemical and optical signatures of different AMTs, before the implementation of future spaceborne missions (e.g., the next generation of Earth Observing System (EOS) satellites addressing Aerosols, Cloud, Convection and Precipitation (ACCP) designated observables)

Spaceborne Remote Sensing of Aerosol Type: Global Distribution, Model Evaluation and Translation into Chemical Speciation

It is essential to evaluate and refine aerosol classification methods applied to passive satellite remote sensing. We have developed an aerosol classification algorithm (called Specified Clustering and Mahalanobis Classification, SCMC) that assigns an aerosol type to multi-parameter retrievals by spaceborne, airborne or ground-based passive remote sensing instruments [1]. The aerosol types identified by our scheme are pure dust, polluted dust, urban-industrialdeveloped economy, urban-industrialdeveloping economy, dark biomass smoke, light biomass smoke and pure marine. We apply the SCMC method to inversions from the ground-based AErosol RObotic NETwork (AERONET [2]) and retrievals from the space-borne Polarization and Directionality of Earths Reflectances instrument (POLDER, [3]). The POLDER retrievals that we use differ from the standard POLDER retrievals [4] as they make full use of multi-angle, multispectral polarimetric data [5]. We analyze agreement in the aerosol types inferred from both AERONET and POLDER and evaluate GEOS-Chem [6] simulations over the globe. Finally, we use in-situ observations from the SEAC4RS airborne field experiment to bridge the gap between remote sensing-inferred qualitative SCMC aerosol types and their corresponding quantitative chemical speciation. We apply the SCMC method to airborne in-situ observations from the NASA Langley Aerosol Research Group Experiment (LARGE, [7]) and the Differential Aerosol Sizing and Hygroscopicity Spectrometer Probe (DASH-SP, [8]) instruments; we then relate each coarsely defined SCMC type to a sum of percentage of individual aerosol species, using in-situ observations from the Particle Analysis by Laser Mass Spectrometry (PALMS, [9]), the Soluble Acidic Gases and Aerosol (SAGA, [10]), and the High - Resolution Time - of - Flight Aerosol Mass Spectrometer (HR ToF AMS, [11])

Remote Sensing of Above Cloud Aerosols

The direct and indirect radiative effects of aerosols suspended in the atmosphere above clouds (ACA) are a highly uncertain component of both regional and global climate. Much of this uncertainty is observational in nature most orbital remote sensing algorithms were not designed to simultaneously retrieve aerosol and cloud optical properties in the same vertical column. Thus the climate modeling community has limited data to inform model development efforts, which encapsulate the current understanding of climate. Furthermore, field measurements have identified regions with consistent ACA, and regional simulations show that the radiative forcing may be significant. For this reason, there has been a recent push to develop the ability to determine ACA distribution, optical properties and cloud interactions, while also providing a means to validate models. Several algorithms have been created that utilize existing instruments for these purposes. However, the observational data sources, algorithm characteristics, geophysical assumptions and retrieved products from these methods are highly variable. This chapter is a review of these techniques, their uncertainties, and the associated validation efforts. We will also discuss the future of ACA remote sensing, both with regards to new instruments and the potential for new algorithms

Biomass burning aerosol heating rates from the ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) 2016 and 2017 experiments

Aerosol heating due to shortwave absorption has implications for local atmospheric stability and regional dynamics. The derivation of heating rate profiles from space-based observations is challenging because it requires the vertical profile of relevant properties such as the aerosol extinction coefficient and single-scattering albedo (SSA). In the southeastern Atlantic, this challenge is amplified by the presence of stratocumulus clouds below the biomass burning plume advected from Africa, since the cloud properties affect the magnitude of the aerosol heating aloft, which may in turn lead to changes in the cloud properties and life cycle. The combination of spaceborne lidar data with passive imagers shows promise for future derivations of heating rate profiles and curtains, but new algorithms require careful testing with data from aircraft experiments where measurements of radiation, aerosol, and cloud parameters are better colocated and readily available. In this study, we derive heating rate profiles and vertical cross sections (curtains) from aircraft measurements during the NASA ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) project in the southeastern Atlantic. Spectrally resolved irradiance measurements and the derived column absorption allow for the separation of total heating rates into aerosol and gas (primarily water vapor) absorption. The nine cases we analyzed capture some of the co-variability of heating rate profiles and their primary drivers, leading to the development of a new concept: the heating rate efficiency (HRE; the heating rate per unit aerosol extinction). HRE, which accounts for the overall aerosol loading as well as vertical distribution of the aerosol layer, varies little with altitude as opposed to the standard heating rate. The large case-to-case variability for ORACLES is significantly reduced after converting from heating rate to HRE, allowing us to quantify its dependence on SSA, cloud albedo, and solar zenith angle

Aerosol analysis and forecast in the ECMWF integrated forecast system : evaluation by means of case studies

A near real-time assimilation and forecast system of aerosols has been developed by integration in the ECMWF IFS code within the GEMS project. The GEMS aerosol modeling system is novel as it is the first aerosol model fully coupled to a NWP model with data assimilation. Aerosol optical depth (AOD) data of the MODIS instrument on Terra and Aqua satellites was assimilated. The performance of the aerosol model was evaluated by the means of case studies. The assimilation of MODIS AOD improved the subsequent aerosol predictions when compared with observations, in particular concerning correlations and AOD peak values. The assimilation is less effective in correcting a positive or a negative bias