Browsing by Author "Ferrare, Richard"
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ItemAdaptive Data Screening for Multi-Angle Polarimetric Aerosol and Ocean Color Remote Sensing Accelerated by Deep Learning(Frontiers, 2021-12-14) Gao, Meng; Knobelspiesse, Kirk; Franz, Bryan A.; Zhai, Peng-Wang; Martins, Vanderlei; Burton, Sharon P.; Cairns, Brian; Ferrare, Richard; Fenn, Marta A.; Hasekamp, Otto; Hu, Yongxiang; Ibrahim, Amir; Sayer, Andrew; Werdell, P. Jeremy; Xu, XiaoguangRemote sensing measurements from multi-angle polarimeters (MAPs) contain rich aerosol microphysical property information, and these sensors have been used to perform retrievals in optically complex atmosphere and ocean systems. Previous studies have concluded that, generally, five moderately separated viewing angles in each spectral band provide sufficient accuracy for aerosol property retrievals, with performance gradually saturating as angles are added above that threshold. The Hyper-Angular Rainbow Polarimeter (HARP) instruments provide high angular sampling with a total of 90–120 unique angles across four bands, a capability developed mainly for liquid cloud retrievals. In practice, not all view angles are optimal for aerosol retrievals due to impacts of clouds, sunglint, and other impediments. The many viewing angles of HARP can provide resilience to these effects, if the impacted views are screened from the dataset, as the remaining views may be sufficient for successful analysis. In this study, we discuss how the number of available viewing angles impacts aerosol and ocean color retrieval uncertainties, as applied to two versions of the HARP instrument. AirHARP is an airborne prototype that was deployed in the ACEPOL field campaign, while HARP2 is an instrument in development for the upcoming NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission. Based on synthetic data, we find that a total of 20–30 angles across all bands (i.e., five to eight viewing angles per band) are sufficient to achieve good retrieval performance. Following from this result, we develop an adaptive multi-angle polarimetric data screening (MAPDS) approach to evaluate data quality by comparing measurements with their best-fitted forward model. The FastMAPOL retrieval algorithm is used to retrieve scene geophysical values, by matching an efficient, deep learning-based, radiative transfer emulator to observations. The data screening method effectively identifies and removes viewing angles affected by thin cirrus clouds and other anomalies, improving retrieval performance. This was tested with AirHARP data, and we found agreement with the High Spectral Resolution Lidar-2 (HSRL-2) aerosol data. The data screening approach can be applied to modern satellite remote sensing missions, such as PACE, where a large amount of multi-angle, hyperspectral, polarimetric measurements will be collected. ItemEfficient multi-angle polarimetric inversion of aerosols and ocean color powered by a deep neural network forward model(Copernicus Publications, 2021-02-09) Gao, Meng; Franz, Bryan A.; Knobelspiesse, Kirk; Zhai, Peng-Wang; Martins, Vanderlei; Burton, Sharon; Cairns, Brian; Ferrare, Richard; Gales, Joel; Hasekamp, Otto; Hu, Yongxiang; Ibrahim, Amir; McBride, Brent; Puthukkudy, Anin; Werdell, P. Jeremy; Xu, XiaoguangNASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, scheduled for launch in the timeframe of 2023, will carry a hyperspectral Ocean Color Instrument (OCI) and two Multi-Angle Polarimeters (MAP): the UMBC Hyper-Angular Rainbow Polarimeter (HARP2) and the SRON Spectro-Polarimeter for Planetary EXploration one (SPEXone). The MAP measurements contain rich information on the microphysical properties of aerosols and hydrosols, and therefore can be used to retrieve accurate aerosol properties for complex atmosphere and ocean systems. Most polarimetric aerosol retrieval algorithms utilize vector radiative transfer models iteratively in an optimization approach, which leads to high computational costs that limit their usage in the operational processing of large data volumes acquired by the MAP imagers. In this work, we propose a deep neural network (NN) model to represent the radiative transfer simulation of coupled atmosphere and ocean systems, for applications to the HARP instrument. Through the evaluation of synthetic datasets for AirHARP (airborne version of HARP2), the NN model achieves a numerical accuracy smaller than the instrument uncertainties, with a running time of 0.01 s in a single CPU core or 1 ms in GPU. Using the NN as a forward model, we built an efficient joint aerosol and ocean color retrieval algorithm called FastMAPOL, evolved from the well-validated Multi-Angular Polarimetric Ocean coLor (MAPOL) algorithm. Retrievals of aerosol properties and water leaving signals were conducted on both the synthetic data and the AirHARP field measurements from the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign in 2017. From the validation with the synthetic data and the collocated High Spectral Resolution Lidar (HSRL) aerosol products, we demonstrated that the aerosol microphysical properties and water leaving signals can be retrieved efficiently and within acceptable error. The FastMAPOL algorithm can be used to operationally process the large volume of polarimetric data acquired by PACE and other future Earth observing satellite missions with similar capabilities. ItemAn Evaluation of Biomass Burning Aerosol Mass, Extinction, and Size Distribution in GEOS using Observations from CAMP2Ex(EGU, 2022-08-29) Collow, Allison; Buchard, Virginie; Colarco, Peter R.; Silva, Arlindo M. da; Govindaraju, Ravi; Nowottnick, Edward P.; Burton, Sharon; Ferrare, Richard; Hostetler, Chris; Ziemba, LukeBiomass burning aerosol impacts aspects of the atmosphere and Earth system through radiative forcing, serving as cloud condensation nuclei, and air quality. Despite its importance, the representation of biomass burning aerosol is not always 15 accurate in numerical weather prediction and climate models or reanalysis products. Using observations collected as part of the Cloud, Aerosol and Monsoon Processes Philippines Experiment (CAMP2Ex) in August through October of 2019, aerosol concentration and optical properties are evaluated within the Goddard Earth Observing System (GEOS) and its underlying aerosol module, GOCART. In the operational configuration, GEOS assimilates aerosol optical depth observations at 550 nm to constrain aerosol fields. Particularly for biomass burning aerosol, without the assimilation of aerosol optical depth, aerosol extinction is underestimated compared to observations collected in the Philippines region during the CAMP2 20 Ex campaign. The assimilation process adds excessive amounts of carbon to account for the underestimated extinction, resulting in positive biases in the mass of black and organic carbon, especially within the boundary layer, relative to in situ observations from the Langley Aerosol Research Group Experiment. Counteracting this, GEOS is deficient in sulphate and nitrate aerosol just above the boundary layer. Aside from aerosol mass, extinction within GEOS is a function of ambient relative humidity and an assumed 25 particle size distribution. The relationship between dry and ambient extinction in GEOS reveals that hygroscopic growth is too aggressive within the model for biomass burning aerosol. An additional concern lies in the assumed particle size distribution for GEOS, which has a mode radius that is too small for organic carbon. Variability in the observed particle size distribution for biomass burning aerosol within a single flight also illuminates the fact that a single assumed particle size distribution is not sufficient and that for a proper representation, a more advanced aerosol module with GEOS may be necessary. ItemModeling air quality in the San Joaquin valley of California during the 2013 Discover-AQ field campaign(Elsevier, 2020-01) Chena, Jianjun; Yin, Dazhong; Zhao, Zhan; Kaduwela, Ajith P.; Avise, Jeremy C.; DaMassa, John A.; Beyersdorf, Andreas; Burton, Sharon; Ferrare, Richard; Herman, Jay; Kim, Hwajin; Neuman, Andy; Nowak, John B.; Parworth, Caroline; Scarino, Amy Jo; Wisthaler, Armin; Young, Dominique E.; Zhang, QiThe San Joaquin Valley (SJV) of California has one of the nation's most severe wintertime PM₂.₅ pollution problems. The DISCOVER-AQ (Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality) field campaign took place in the SJV from January 16 to February 6, 2013. It captured two PM₂.₅ pollution episodes with peak 24-h concentrations approaching 70 μg/m³. Using meteorological fields generated from WRFv3.6, CMAQv5.0.2 was applied to simulate PM₂.₅ formation in the SJV from January 10 through February 10, 2013. Overall, the model was able to capture the observed accumulation of PM₂.₅ within the simulation period. The model was able to produce increased concentrations of ammonium nitrate and organic carbon, which are two major components of wintertime PM₂.₅ in the SJV. Comparison to measurements made by aircraft showed that there was general agreement between observed and modeled daytime vertical distributions of selected gas and particulate species, reflecting the adequacy of modeled daytime mixing layer heights. Excess ammonia predicted by the model implied that ammonium nitrate formation was limited by the availability of nitric acid, consistent with observations. Evaluation of the ammonium nitrate diurnal profile revealed that the observed morning increase of ammonium nitrate was also evident from the model. This paper demonstrates that the CMAQ model is able to simulate elevated wintertime PM₂.₅ formation observed in the SJV during the DISCOVER-AQ 2013 period, which featured both climatic (i.e., 2011–2014 California Drought) and emissions differences compared to a previous large air quality field campaign in the SJV during 1999–2000. ItemObserving atmospheric formaldehyde (HCHO) from space: validation and intercomparison of six retrievals from four satellites (OMI, GOME2A, GOME2B, OMPS) with SEAC4RS aircraft observations over the southeast US(EGU Publications, 2016-11-01) Zhu, Lei; Jacob, Daniel J.; Kim, Patrick S.; Fisher, Jenny A.; Yu, Karen; Travis, Katherine R.; Mickley, Loretta J.; Yantosca, Robert M.; Sulprizio, Melissa P.; Smedt, Isabelle De; Abad, Gonzalo González; Chance, Kelly; Li, Can; Ferrare, Richard; Fried, Alan; Hair, Johnathan W.; Hanisco, Thomas F.; Richter, Dirk; Scarino, Amy Jo; Walega, James; Weibring, Petter; Wolfe, Glenn M.Formaldehyde (HCHO) column data from satellites are widely used as a proxy for emissions of volatile organic compounds (VOCs), but validation of the data has been extremely limited. Here we use highly accurate HCHO aircraft observations from the NASA SEAC4RS (Studies of Emissions, Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys) campaign over the southeast US in August–September 2013 to validate and intercompare six retrievals of HCHO columns from four different satellite instruments (OMI, GOME2A, GOME2B and OMPS; for clarification of these and other abbreviations used in the paper, please refer to Appendix A) and three different research groups. The GEOS-Chem chemical transport model is used as a common intercomparison platform. All retrievals feature a HCHO maximum over Arkansas and Louisiana, consistent with the aircraft observations and reflecting high emissions of biogenic isoprene. The retrievals are also interconsistent in their spatial variability over the southeast US (r = 0.4–0.8 on a 0.5° × 0.5° grid) and in their day-to-day variability (r = 0.5–0.8). However, all retrievals are biased low in the mean by 20–51 %, which would lead to corresponding bias in estimates of isoprene emissions from the satellite data. The smallest bias is for OMI-BIRA, which has high corrected slant columns relative to the other retrievals and low scattering weights in its air mass factor (AMF) calculation. OMI-BIRA has systematic error in its assumed vertical HCHO shape profiles for the AMF calculation, and correcting this would eliminate its bias relative to the SEAC4RS data. Our results support the use of satellite HCHO data as a quantitative proxy for isoprene emission after correction of the low mean bias. There is no evident pattern in the bias, suggesting that a uniform correction factor may be applied to the data until better understanding is achieved.