Arlindo da Silva
Organization:
NASA Goddard Space Flight Center
Email:
Business Address:
Global Modeling and Assimilation Office
Code 610.1
Greenbelt, MD 20771
United StatesCo-Authored Publications:
- Kahn, R., et al. (2023), Reducing Aerosol Forcing Uncertainty by Combining Models With Satellite and Within-The-Atmosphere Observations: A Three-Way Street, Rev. Geophys., 61, e2022RG000796, doi:10.1029/2022RG000796.
- Marshak, A., et al. (2023), Aerosol Properties in Cloudy Environments from Remote Sensing Observations, Bull. Am. Meteorol. Soc., 102, E2177-E2197, doi:10.1175/BAMS-D-20-0225.1.
- Zhou, M., et al. (2023), Enhancement of Nighttime Fire Detection and Combustion Efficiency Characterization Using Suomi-NPP and NOAA-20 VIIRS Instruments, IEEE Trans. Geosci. Remote Sens., 61, 4402420, doi:10.1109/TGRS.2023.3261664.
- LeBlanc, S., et al. (2022), Airborne observations during KORUS-AQ show that aerosol optical depths are more spatially self-consistent than aerosol intensive properties, Atmos. Chem. Phys., doi:10.5194/acp-22-11275-2022.
- Doherty, S., et al. (2021), Modeled and observed properties related to the direct aerosol radiative effect of biomass burning aerosol over the Southeast Atlantic, Atmos. Chem. Phys., doi:10.5194/acp-2021-333 (submitted).
- Pistone, K., et al. (2021), Exploring the elevated water vapor signal associated with the free-tropospheric biomass burning plume over the southeast Atlantic Ocean, Atmos. Chem. Phys., doi:10.5194/acp-2020-1322 (submitted).
- Pistone, K., et al. (2021), Exploring the elevated water vapor signal associated with the free tropospheric biomass burning plume over the southeast Atlantic Ocean, Atmos. Chem. Phys., 21, 9643-9668, doi:10.5194/acp-21-9643-2021.
- Xu, F., et al. (2021), A Combined Lidar-Polarimeter Inversion Approach for Aerosol Remote Sensing Over Ocean, Front. Remote Sens., 2, 1-24, doi:10.3389/frsen.2021.620871.
- Carter, T. S., et al. (2020), How emissions uncertainty influences the distribution and radiative impacts of smoke from fires in North America, Atmos. Chem. Phys., 20, 2073-2097, doi:10.5194/acp-20-2073-2020.
- Shinozuka, Y., et al. (2020), Modeling the smoky troposphere of the southeast Atlantic: a comparison to ORACLES airborne observations from September of 2016, Atmos. Chem. Phys., 20, 11491-11526, doi:10.5194/acp-20-11491-2020.
- Wang, J., et al. (2020), Detecting nighttime fire combustion phase by hybrid application of visible T and infrared radiation from Suomi NPP VIIRS, Remote Sensing of Environment, 237, 111466, doi:10.1016/j.rse.2019.111466.
- Bian, H., et al. (2019), Observationally constrained analysis of sea salt aerosol in the marine atmosphere, Atmos. Chem. Phys., 19, 10773-10785, doi:10.5194/acp-19-10773-2019.
- Pérez-Ramírez, D., et al. (2019), Retrievals of aerosol single scattering albedo by multiwavelength lidar T measurements: Evaluations with NASA Langley HSRL-2 during discover-AQ field campaigns ⁎, Remote Sensing of Environment, 222, 144-164, doi:10.1016/j.rse.2018.12.022.
- shinozuka, et al. (2019), Modeling the smoky troposphere of the southeast Atlantic: a comparison to ORACLES airborne observations from September of 2016, Atmos. Chem. Phys. Discuss., doi: https://doi.org/10.5194/acp-2019-678 (submitted).
- Liu, F., et al. (2018), A new global anthropogenic SO2 emission inventory for the last decade: a mosaic of satellite-derived and bottom-up emissions, Atmos. Chem. Phys., 18, 16571-16586, doi:10.5194/acp-18-16571-2018.
- Veselovskii, I., et al. (2018), Characterization of smoke and dust episode over West Africa: comparison of MERRA-2 modeling with multiwavelength Mie–Raman lidar observations, Atmos. Meas. Tech., 11, 949-969, doi:10.5194/amt-11-949-2018.
- Buchard-Marchant, V. J., et al. (2017), The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part II: Evaluation and Case Studies, J. Climate, 30, 6851-6872, doi:10.1175/JCLI-D-16-0613.1.
- Hughes, E. J., et al. (2016), Using CATS near-real-time lidar observations to monitor and constrain volcanic sulfur dioxide (SO2) forecasts, Geophys. Res. Lett., 43, 11,089-11,097, doi:10.1002/2016GL070119.
- Yu, P., et al. (2016), Surface dimming by the 2013 Rim Fire simulated by a sectional aerosol model, J. Geophys. Res., 121, 7079-7087, doi:10.1002/2015JD024702.
- Buchard, V., et al. (2015), Using the OMI aerosol index and absorption aerosol optical depth to evaluate the NASA MERRA Aerosol Reanalysis, Atmos. Chem. Phys., 15, 5743-5760, doi:10.5194/acp-15-5743-2015.
- Saide Peralta, et al. (2015), Revealing important nocturnal and day-to-day variations in fire smoke emissions through a multiplatform inversion, Geophys. Res. Lett., 42, 3609-3618, doi:10.1002/2015GL063737.
- Matsui, T., et al. (2014), Current And Future Perspectives Of Aerosol Research At Nasa Goddard Space Flight Center, Bull. Am. Meteorol. Soc., 1-5, doi:10.1175/BAMS-D-13-00153.1.
- Reale, O., et al. (2014), Impact of assimilated and interactive aerosol on tropical cyclogenesis, Geophys. Res. Lett., 41, 3282-3288, doi:10.1002/2014GL059918.
- Zhang, F., et al. (2014), Sensitivity of mesoscale modeling of smoke direct radiative effect to the emission inventory: A case study in northern sub-Saharan African region, Environmental Research Letter, 9, 075002, doi:10.1088/1748-9326/9/7/075002.
- Bian, H., et al. (2013), Source attributions of pollution to the Western Arctic during the NASA ARCTAS field campaign, Atmos. Chem. Phys., 13, 4707-4721, doi:10.5194/acp-13-4707-2013.
- Randles, C., P. R. Colarco, and A. da Silva (2013), Direct and semi-direct aerosol effects in the NASA GEOS-5 AGCM: aerosol-climate interactions due to prognostic versus prescribed aerosols, J. Geophys. Res., 118, 149-169, doi:10.1029/2012JD018388.
- Choi, S., et al. (2012), Analysis of satellite-derived Arctic tropospheric BrO columns in conjunction with aircraft measurements during ARCTAS and ARCPAC, Atmos. Chem. Phys., 12, 1255-1285, doi:10.5194/acp-12-1255-2012.
- Liang, Q., et al. (2011), Reactive nitrogen, ozone and ozone production in the Arctic troposphere and the impact of stratosphere-troposphere exchange, Atmos. Chem. Phys., 11, 13181-13199, doi:10.5194/acp-11-13181-2011.
- Witte, J. C., et al. (2011), NASA A-Train and Terra observations of the 2010 Russian wildfires, Atmos. Chem. Phys., 11, 9287-9301, doi:10.5194/acp-11-9287-2011.
- Colarco, P. R., et al. (2010), Online simulations of global aerosol distributions in the NASA GEOS‐4 model and comparisons to satellite and ground‐based aerosol optical depth, J. Geophys. Res., 115, D14207, doi:10.1029/2009JD012820.
- Salawitch, R., et al. (2010), A new interpretation of total column BrO during Arctic spring, Geophys. Res. Lett., 37, L21805, doi:10.1029/2010GL043798.
- Wong, S., et al. (2008), Long-term variability in Saharan dust transport and its link to North Atlantic sea surface temperature, Geophys. Res. Lett., 35, L07812, doi:10.1029/2007GL032297.
- Joiner, J., et al. (2007), Effects of data selection and error specification on the assimilation of AIRS data, Q. J. R. Meteorol. Soc., 133, 181-196, doi:10.1002/qj.8.
- Weaver, C., et al. (2007), Direct Insertion of MODIS Radiances in a Global Aerosol Transport Model, J. Atmos. Sci., 64, 808-826, doi:10.1175/JAS3838.1.