1. Sukumaran, J., Pillai, D., Thilakan, V., Lekshmi, S., Udayakumar, G., Mathew,T A., Ravi, A., Manoj MG: How critical is the accuracy of the atmospheric transport modeling to improve the urban CO2 emission in India?—A Lagrangian-Based approach, Journal of Geophysical Research: Atmospheres/Volume 129, Issue 13/e2023JD039680, July 2024, https://doi.org/10.1029/2023JD039680

  2. Ravi, A., Pillai, D., Thilakan, V., Mathew,T A.:Methodological advancement in deriving primary productivity and ecosystem respiration fluxes across different biomes, MethodsX, 12, 102773, JUNE 2024, https://doi.org/10.1016/j.mex.2024.102773

  3. Thilakan, V., Pillai, D., Sukumaran, J., Gerbig, C., Hakkim, H., Sinha, V., Terao, Y., Naja, M. and Deshpande, M.: Potential of using CO2 observations over India in a regional carbon budget estimation by improving the modelling, ACP, 24, 5315–5335, 2024, https://doi.org/10.5194/acp-24-5315-2024

  4. Deshpande, M., Pillai, D., Krishna, V and Jain, M.: Detecting and quantifying zero tillage technology adoption in Indian smallholder systems using Sentinel-2 multi-spectral imagery, International Journal of Applied Earth Observation and Geoinformation, Volume 128, 2024, https://doi.org/10.1016/j.jag.2024.103779

  5. Deshpande, M., Kumar, N., Pillai, D., Krishna, V and Jain, M.: Greenhouse gas emissions from agricultural residue burning have increased by 75% since 2011 across India, Science of The Total Environment, Volume 904,2023, https://doi.org/10.1016/j.scitotenv.2023.166944.

  6. Thilakan, V., Pillai, D., Gerbig, C., Galkowski, M., Ravi, A., and Anna Mathew, T.: Towards monitoring the CO2 source–sink distribution over India via inverse modelling: quantifying the fine-scale spatiotemporal variability in the atmospheric CO2 mole fraction, Atmos. Chem. Phys., 22, 15287–15312, https://doi.org/10.5194/acp-22-15287-2022, 2022.

  7. Deshpande, M., Pillai, D., and Jain, M.: Agricultural burned area detection using an integrated approach utilizing multi spectral instrument based fire and vegetation indices from Sentinel-2 satellite, MethodsX 9 (2022), 101741

  8. Deshpande, M., Pillai, D., and Jain, M.: Detecting and quantifying residue burning in smallholder systems: An integrated approach using Sentinel-2 data, International Journal of Applied Earth Observation and Geoinformation, Volume 108,102761,2022

  9. Vellalassery, A., Pillai, D., Marshall, J., Gerbig, C., Buchwitz, M., Schneising, O., and Ravi, A.: Using TROPOspheric Monitoring Instrument (TROPOMI) measurements and Weather Research and Forecasting (WRF) CO modelling to understand the contribution of meteorology and emissions to an extreme air pollution event in India, Atmos. Chem. Phys., 21, 5393–5414, https://doi.org/10.5194/acp-21-5393-2021, 2021

  10. Thilakan, V., Pillai, D., Gerbig, C., Galkowski, M., Ravi, A., and Anna Mathew, T.: Towards monitoring CO2 source-sink distribution over India via inverse modelling: Quantifying the fine-scale spatiotemporal variability of atmospheric CO2 mole fraction, Atmos. Chem. Phys. Discuss, https://doi.org/10.5194/acp-2021-392, 2021.

  11. Kariyathan, T.,Pillai, D., Elias, E. and Mathew, T.A., 2020. On deriving influences of upwind agricultural and anthropogenic emissions on greenhouse gas concentrations and air quality over Delhi in India: A stochastic Lagrangian footprint approach. Journal of Earth System Science, 129(1), pp.1-15.

  12. Pillai, D., Buchwitz, M., Gerbig, C., Koch, T., Reuter, M., Bovensmann, H., Marshall, J., and Burrows, J. P.: Tracking city CO2 emissions from space using a high resolution inverse modeling approach: A case study for Berlin, Germany, Atmos. Chem. Phys., 16, 9591–9610, doi:10.5194/acp-16-9591-2016

  13. Reuter, M., Buchwitz, M., Hilker, M., Heymann, J., Schneising, O.,Pillai, D., Bovensmann, H., Burrows, J. P., Bösch, H., Parker, R., Butz, A., Hasekamp, O., O'Dell, C. W., Yoshida, Y., Gerbig, C., Nehrkorn, T., Deutscher, N. M., Warneke, T., Notholt, J., Hase, F., Kivi, R., Sussmann, R., Machida, T., Matsueda, H., and Sawa, Y.: Satellite-inferred European carbon sink larger than expected, Atmos. Chem. Phys. Discuss., 14, 21829-21863, doi:10.5194/acpd-14-21829-2014, 2014.

  14. Buchwitz, M., Reuter, M., Bovensmann, H.,Pillai, D. , Heymann, J. et al.,: Carbon Monitoring Satellite (CarbonSat): assessment of atmospheric CO2 and CH4 retrieval errors by error parameterization, Atmos. Meas. Tech., 6, 3477-3500, doi:10.5194/amt-6-3477-2013, 2013.

  15. Buchwitz, M., Reuter, M., Bovensmann, H., Pillai, D., Heymann, J., Schneising, O., Rozanov, V.,Krings, T.,Burrows, J.P., Boesch,H.,Gerbig, C., Meijer, Y., and Loescher, A.: Carbon Monitoring Satellite (CarbonSat): assessment of scattering related atmospheric CO2 and CH4 retrieval errors and first results on implications for inferring city CO2 emissions, Atmos. Meas. Tech. Discuss., 6, 2013.

  16. Pillai, D., Gerbig, C., Kretschmer, R., Beck, V., Karstens, U., Neininger, B., and Heimann, M.: Comparing Lagrangian and Eulerian models for CO2 transport – a step towards Bayesian inverse modeling using WRF/STILT-VPRM, Atmos. Chem. Phys., 12, 8979-8991, doi:10.5194/acp-12-8979-2012, 2012.

  17. Pillai, D., Gerbig, C., Ahmadov, R., Rödenbeck, C., Kretschmer, R., Koch, T., Thompson, R., Neininger, B., and Lavrié, J. V.: High-resolution simulations of atmospheric CO2 over complex terrain – representing the Ochsenkopf mountain tall tower, Atmos. Chem. Phys., 11, 7445- 7464, doi:10.5194/acp-11-7445-2011, 2011.

  18. Pillai, D.: Mesoscale simulations and inversions of atmospheric CO2 using airborne and groundbased data, Max Planck Institute for Biogeochemistry, 22, pp.171, 2011.

  19. Beck, V., Koch, T., Kretschmer, R., Marshall, J., Ahmadov, R., Gerbig, C., Pillai, D. and Heimann, M.: The WRF Greenhouse Gas Model, Max Planck Institute for Biogeochemistry, 25, Jena, Germany, 2011.

  20. Pillai, D., Gerbig, C., Marshall, J., Ahmadov, R., Kretschmer, R., Koch, T., and Karstens, U.: High resolution modeling of CO2 over Europe: implications for representation errors of satellite retrievals, Atmos. Chem. Phys., 10, 83-94, doi:10.5194/acp-10-83-2010, 2010.

  21. Chimot, J., Breon, F., Pillai, D., Buchwitz, M., Bovensmann, H., Reuter, M., Broquet, G., Renault, E., Peyret, C., Vinuesa., J: LOGOFLUX - CarbonSat Earth Explorer 8 Candidate Mission Inverse Modelling and Mission Performance Study, Technical Note 8, European Space Agency (ESA), 400010537/12/NL/CO, 2013.

  1. Thilakan, V., Pillai, D., Gerbig, C., Marshall, J., Ravi, A., Galkowski, M., and Mathew, T. A.: Towards implementing an atmospheric observation-based modelling system for estimating the source-sink distribution of CO2 over India: an assessment of fine-scale CO2 spatiotemporal variability , EGU General Assembly 2021, 19–30 Apr 2021, EGU21-9218, https://doi.org/10.5194/egusphere-egu21-9218, 2021.

  2. Ravi P, A., K Pillai, D., Gerbig, C., Marshall, J., and Jha, C. S.: Towards improved estimation of the atmosphere - terrestrial biosphere CO2 exchange over India using a diagnostic satellite based model, EGU General Assembly 2021, 19–30 Apr 2021, EGU21-7019, https://doi.org/10.5194/egusphere-egu21-7019, 2021.

  3. Pillai, D., Deshpande, M., Marshall, J., Gerbig, C., Schneising, O., and Buchwitz, M.: Satellite-derived Indian methane emission sources with TROPOMI retrievals and a high-resolution modelling framework: Initial comparison with WRF-GHG model results, EGU General Assembly 2020, 4–8 May 2020, EGU2020-12559, https://doi.org/10.5194/egusphere-egu2020-12559, 2020

  4. Inferring Source and Sink of Atmospheric CO2 at High-resolution from Space: a Mesoscale Modeling Approach using Inverse Technique, Geophysical Research Abstracts, Vol. 15, 2013

  5. Towards Top-down Constraints on Regional Sources and Sinks of CO2 Imposed by Column Observations: a High-resolution Inverse Modeling Approach, American Geophysical Union, abstract A33F-0241, 2015

  6. Estimating the Regional CO2 Budget Using Observational Constraints From Flux And Mixing-ratio Measurements, American Geophysical Union, abstract A31B-0067, 2011

  7. Scaling C fluxes from point to region using observational constraints from flux and mixing ratio measurements, Geophysical Research Abstracts, Vol. 10, 2008

  8. High Resolution CO2 Simulations: Scaling C- fluxes From Point to Region, American Geophysical Union, abstract A33B-0228, 2008

  1. EGU Conference, “Satellite-derived Indian methane emission sources with TROPOMI retrievals and high-resolution modelling framework: Initial comparison with WRF-GHG model results." 06 May 2020, 08:30-10.15(CET)

  2. International Workshop on Greenhouse Gas Measurements from Space organized by ESA, “Quantifying the sources and sinks of CO2 over India using WRF-Chem - Retrieval algorithms and uncertainty quantification”, EUMETSAT, and ECMWF June 2-5, 2020

  3. MPI-BGC Sys. research seminar, “WRF-GHG implementation and inverse optimization”, MPI-BGC, Germany, 2nd May, 2019

  4. Monitoring the city breathe: Need and Challenges (Indian perspective), 1st ICOS workshop on strategies to monitor greenhouse gases in urban environments (invited), Helsinki, Finland, July 1 -4, 2019.

  5. Towards designing a high-resolution atmospheric CO2 modeling system over India- Mesoscale variability using WRF-Chem, European WRF-Chem Workshop, LMU, Munich, Germany, May 7-8, 2019.

  6. WRF-GHG implementation and inverse optimization, MPI-BGC Sys. research seminar, MPI-BGC, Germany, May 2, 2019

  7. Carbon “Weather”: Why do we need data-model fusion system at high-resolution?, Stakeholders’ workshop, UKEIRI program on Climate and Air quality Stress on Agriculture sector in India, IIT Delhi, April 15-16, 2019.

  8. Tracing the future with an observationally driven approach, Collaborative research between IISERB and NRSC, ISRO, NRSC Hyderabad, March 26-28, 2019.

  9. Greenhouse gas modeling and applications- Overview, 7th Meeting of Heads of Max-Planck Partner Groups, Hotel Taj Land Ends, Mumbai, March 15-16, 2019.

  10. On quantifying Indian carbon balance: Integrating observations and models at different scales, MPI-BGC Sys. research seminar, MPI-BGC, Germany, December 4, 2018.

  11. Greenhouse gas budgets from atmospheric fingerprints: High-resolution forward and inverse modeling technique combining measurements and simulations, Centre for Atmospheric and Oceanic Sciences (CAOS), Indian Institute of Science, Bangalore, October 14-15, 2015

  12. CarbonSat Earth Explorer 8 Candidate Mission (LOGOFLUX Study) – Inverse Modelling and Mission Performance - Local Performance Assessment, European Space Agency (ESA) meeting, Bremen, Germany, April 24, 2014

  13. Inferring city CO2 emissions from space – Berlin City Emissions, WRF Users Workshop, IASS Potsdam, November 8, 2013

  14. Inferring Source and Sink of Atmospheric CO2 at High-resolution from Space: a Mesoscale Modeling Approach using Inverse Technique, EGU General Assembly 2013,Vienna, Austria, April 7-12, 2013

  15. A Mesoscale Modeling Approach using Inverse Technique, COSPAR Assembly -2012, Mysore, India, July 14-22, 2012

  16. Estimating the Regional CO2 Budget Using Observational Constraints From Flux And Mixing ratio Measurements, AGU Fall meeting, San Francisco, December 5-9, 2011

  17. Towards regional carbon budgeting using WRF/STILT-VPRM: A comparison of Lagrangian and Eulerian models for atmospheric CO2 transport, AGU Chapman Conference on Advances in Lagrangian Modeling of the Atmosphere, Grindelwald, Switzerland, October 10-14, 2011

  18. Tightening Atmospheric Constraints on GHG budgets (Chorus talk with Christoph Gerbig), Max Planck Institute Retreat 2011, Oberhof, Germany, January 17-20, 2011

  19. Mesoscale simulations of atmospheric CO2: Can we represent measurements over complex terrain?(Invited talk),Centre for Atmospheric and Oceanic Sciences, Indian Institute of Science (IISc), Bangalore, India, May 24, 2011

  20. High-resolution modeling of atmospheric CO2: a top-down approach (Invited talk), IUP, University of Bremen, Germany, March 10, 2011

  21. Mesoscale Simulations and Inversions of Atmospheric CO2 using airborne and ground based data, MPI-BGC-Systems Department Retreat, Jena, Germany, December 3, 2010

  22. Airborne trace gas measurements and mesoscale modeling: High resolution modeling of CO2 over Europe: implications for representation errors of satellite retrievals, Max Planck Institute's Retreat 2009, Bad Sulza, Germany, 2 - 5 November 2009

  23. High resolution modeling of CO2 over Europe: Implications for remote sensing, 8-th International Carbon Dioxide Conference (ICDC08), Jena, Germany, September 13-19, 2009

  24. Regional CO2 Modeling: Evaluation of models with tower measurements, 8th International Carbon Dioxide Conference (ICDC08), Jena, Germany, September 13-19, 2009

  25. High Resolution CO2 Simulations: Scaling C- fluxes From Point to Region, American Geophysical Union Fall Meeting, San Francisco, USA, December 15-19, 2008

  26. Accounting for Small scale Variability on Regional Flux Estimates: An Inverse Technique Approach, 6th Annual CarboEurope-IP Meeting, Jena, Germany, September 29 - 3 October 2008

  27. Airborne trace gas measurements and mesoscale modeling: Mesoscale transport of CO2 by Lagrangian model framework WRF-STILT-VPRM, Max Planck Institute's Retreat 2008, Suhl, Germany, June 16-19, 2008

  28. Mesoscale transport of CO2 by Lagrangian model framework WRF-STILT-VPRM, Transom workshop program, Utrecht, Netherlands, June 2-5, 2008

  29. Scaling C fluxes from point to region using observational constraints from flux and mixing ratio measurements, European Geophysical Union General Assembly, Vienna, Austria, 13 - 18 April 2008

  30. Assessment of modeling and observational tools - Linking point measurements with models, MPI-BGC-Systems Department Retreat, Erfurt, Germany, January 10-11, 2008

  31. Mesoscale Simulations for the Ochsenkopf tall tower: combining WRF with MODIS data, University Colloquium, Friedrich-Schiller-University, Jena, Germany, 1 November 2007

  32. Mesoscale Simulations and Inversions of Atmospheric CO2 using airborne and ground based data: Plans for Implementing WRF-VPRM-STILT Modeling Framework, Advanced Study Program summer colloquium, National Center for Atmospheric Research (NCAR), Boulder, USA, 3-15 June 2007

  33. Mesoscale simulations and inversions using tall tower data: plans for implementing the WRF-VPRM-STILT modeling framework, Max Planck Institute Retreat, Oberhof, Germany, February 12-14, 2007