Charles Ichoku

Wei, J., et al. (2024), Long-term mortality burden trends attributed to black carbon and PM2·5 from wildfire emissions across the continental USA from 2000 to 2020: a deep learning modelling study.

Thapa, L., et al. (2023), Heat flux assumptions contribute to overestimation of wildfire smoke injection into the free troposphere, Nature, doi:10.1038/s43247-022-00563-x.

Peterson, D.A., et al. (2022), Measurements from inside a Thunderstorm Driven by Wildfire: The 2019 FIREX-AQ Field Experiment, Bull. Amer. Meteor. Soc., 103, E2140-E2167, doi:10.1175/BAMS-D-21-0049.1.

Black, F.W., et al. (2021), Biomass Burning and Water Balance Dynamics in the Lake Chad Basin in Africa, Earth, 2, 340-356, doi:10.3390/earth2020020.

Wiggins, E.B., et al. (2021), Reconciling assumptions in bottom-up and top-down approaches for estimating aerosol emission rates from wildland fires using observations from FIREX-AQ, J. Geophys. Res., 126, e2021JD035692, doi:10.1029/2021JD035692.

Li, Y., et al. (2020), Ensemble PM2.5 Forecasting During the 2018 Camp Fire Event Using the HYSPLIT Transport and Dispersion Model, J. Geophys. Res., 125, e2020JD032768, doi:10.1029/2020JD032768.

Pan, X., et al. (2020), Six global biomass burning emission datasets: intercomparison and application in one global aerosol model, Atmos. Chem. Phys., 20, 969-994, doi:10.5194/acp-20-969-2020.

Brook, A., et al. (2019), Structural heterogeneity of vegetation fire ash, Land Degrad Dev., doi:10.1002/ldr.2922.

Matsui, T., et al. (2019), Impact of radiation frequency, precipitation radiative forcing, and radiation column aggregation on convection-permitting West African monsoon simulations, Clim. Dyn., 13, doi:10.1007/s00382-018-4187-2.

Pérez-Ramírez, D., et al. (2019), Precipitable water vapor over oceans from the Maritime Aerosol Network: Evaluation of global models and satellite products under clear sky conditions, Atmos. Res., 215, 294-304.

Iguchi, T., et al. (2018), NU-WRF Aerosol Transport Simulation over West Africa: Effects of Biomass Burning on Smoke Aerosol Distribution, J. Appl. Meteor. Climat., 57, 1551-1573, doi:10.1175/JAMC-D-17-0278.1.

Pan, X., et al. (2018), Connecting Indonesian Fires and Drought With the Type of El Niño and Phase of the Indian Ocean Dipole During 1979–2016, J. Geophys. Res., 123, 7974-7988, doi:10.1029/2018JD028402.

Policelli, F., et al. (2018), Lake Chad Total Surface Water Area as Derived from Land Surface Temperature and Radar Remote Sensing Data, Remote Sensing, 10, doi:10.3390/rs10020252.

Shtober-Zisu, N., et al. (2018), Fire induced rock spalls as long-term traps for ash T a,⁎ b b c d b, Catena, 162, 88-99, doi:10.1016/j.catena.2017.11.021.

Wang, J., et al. (2018), Mitigating Satellite-Based Fire Sampling Limitations in Deriving Biomass Burning Emission Rates: Application to WRF-Chem Model Over the Northern sub-Saharan African Region, J. Geophys. Res., 123, 507-528.

Dezfuli, A.K., et al. (2017), Validation of IMERG Precipitation in Africa, J. Hydrometeorology, 18, 2817-2825, doi:10.1175/JHM-D-17-0139.1.

Dezfuli, A.K., et al. (2017), Precipitation Characteristics in West and East Africa from Satellite and in Situ Observations, J. Hydrometeorology, 18, 1799-1805, doi:10.1175/JHM-D-17-0068.1.

Polivka, T.N., et al. (2017), Improving Nocturnal Fire Detection with the VIIRS Day-Night Band, Blank 1.

Smirnov, A., et al. (2017), Maritime Aerosol Network optical depth measurements and comparison with satellite retrievals from various different sensors, In Remote Sensing of Clouds and the Atmosphere XXII (, 10424, 1042402, doi:10.1117/12.2277113.

Waigl, C.F., et al. (2017), Detecting high and low-intensity fires in Alaska using VIIRS I-band data: An improved operational approach for high latitudes, Remote Sensing of Environment, 199, 389-400, doi:10.1016/j.rse.2017.07.003.

Polivka, T.N., et al. (2016), Improving Nocturnal Fire Detection with the VIIRS Day-Night Band, IEEE Transactions on Geoscience &amp, Remote Sensing, 9, 5503-5519.

Polivka, T.N., et al. (2016), Improving Nocturnal Fire Detection with the VIIRS Day-Night Band, IEEE Transactions on Geoscience &amp, Remote Sensing, 9, 5503-5519.

Engelbrecht, F., et al. (2015), Projections of rapidly rising surface temperatures over Africa under, Environ. Res. Lett., 10, 085004, doi:10.1088/1748-9326/10/8/085004.

Gatebe, C.K., et al. (2014), Surface albedo darkening from wildfires in northern sub-Saharan Africa, Environ. Res. Lett., 9, 065003, doi:10.1088/1748-9326/9/6/065003.

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.

Schroeder, W., et al. (2014), Integrated Active Fire Retrievals and Biomass Burning Emissions Using Complementary Near-Coincident Ground, Airborne, and Spaceborne Sensor data, Remote Sensing of Environment, 140, 719-730.

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.

Anderson, J.C., et al. (2013), Long-term statistical assessment of Aqua-MODIS aerosol optical depth over coastal regions: bias characteristics and uncertainty sources, Tellus, 65, 20805.

Peterson, D.A., et al. (2013), A sub-pixel-based calculation of fire radiative power from MODIS observations: 1 Algorithm development and initial assessment, Remote Sensing of Environment, 129, 262-279, doi:10.1016/j.rse.2012.10.036.

Petrenko, ., and C. Ichoku (2013), Coherent uncertainty analysis of aerosol measurements from multiple satellite sensors, Atmos. Chem. Phys., 13, 6777-6805, doi:10.5194/acp-13-6777-2013.

Yang, Z., et al. (2013), Mesoscale modeling and satellite observation of transport and mixing of smoke and dust particles over northern sub-Saharan African region, J. Geophys. Res., 118, 12139-12157, doi:10.1002/2013JD020644.

Gatebe, C.K., et al. (2012), Taking the pulse of pyrocumulus clouds, Atmos. Environ., 52, 121-130, doi:10.1016/j.atmosenv.2012.01.045.

Val Martin, ., et al. (2012), Space-based observational constraints for 1-D fire smoke plume-rise models, J. Geophys. Res., 117, D22204, doi:10.1029/2012JD018370.

Petrenko, ., et al. (2012), Multi-sensor Aerosol Products Sampling System (MAPSS), Atmos. Meas. Tech., 5, 913-926, doi:10.5194/amt-5-913-2012.

Henderson, S.B., et al. (2010), International Journal of Wildland Fire 2010, 19, 844–852 www.publish.csiro.au/journals/ijwf The validity and utility of MODIS data for simple estimation of area burned and aerosols emitted by wildfire events, www.publish.csiro.au/journals/ijwf, 19, 844-852.

Koukouli, M.E., et al. (2010), Signs of a negative trend in the MODIS aerosol optical depth over the Southern Balkans, Atmos. Environ., 44, 1219-1228, doi:10.1016/j.atmosenv.2009.11.024.

Levy, R.C., et al. (2010), Global evaluation of the Collection 5 MODIS dark-target aerosol products over land, Atmos. Chem. Phys., 10, 10399-10420, doi:10.5194/acp-10-10399-2010.

Peterson, D., et al. (2010), Effects of lightning and other meteorological factors on fire activity in the North American boreal forest: implications for fire weather forecasting, Atmos. Chem. Phys., 10, 6873-6888, doi:10.5194/acp-10-6873-2010.

El-Askary, H., et al. (2009), Transport of dust and anthropogenic aerosols across Alexandria, Egypt, Ann. Geophys., 27, 2869-2879.

Zerefos, C.S., et al. (2009), Solar dimming and brightening over Thessaloniki, Greece, and Beijing, China, Tellus, doi:10.1111/j.1600-0889.2009.00425.x.

Remer, L.A., et al. (2008), Global aerosol climatology from the MODIS satellite sensors, J. Geophys. Res., 113, D14S07, doi:10.1029/2007JD009661.

Levy, ., et al. (2003), Evaluation of the MODIS retrievals of dust aerosol over the ocean during PRIDE, J. Geophys. Res., 108, D19, doi:10.1029/2002JD002460.