आईआईटीएम
IITM

34. Gaikwad S., Kumar Bipin, Yadav P.P., Ambulkar R., Govardhan G., Kulkarni S.H., Kumar Rajesh, Chate D.M., Nigam N., Rao Suryachandra A., Ghude S.D., Harnessing deep learning for forecasting fire-burning locations and unveiling PM2.5 emissions, Modeling Earth Systems and Environment, Online, July 2023, DOI:10.1007/s40808-023-01831-1, 1-15 

33. Kumar Bipin, Atey K., Singh B.B., Chattopadhyay R., Acharya N., Singh Manmeet, Nanjundiah R.S., Rao Suryachandra A., On the modern deep learning approaches for precipitation downscaling, Earth Science Informatics, 16, June 2023, DOI:10.1007/s12145-023-00970-4, 1459-1472

32. Kalbande R., Kumar Bipin, Maji S., Yadav R., Atey K., Rathore D.S., Beig G., Machine learning based quantification of VOC contribution in surface ozone prediction, Chemosphere, 326: 138474, June 2023, DOI:10.1016/j.chemosphere.2023.138474, 1-8 

31. Kumar Bipin, Atey K., Singh B.B., Chattopadhyay R., Acharya N., Singh Manmeet, Nanjundiah R.S., Rao Suryachandra A., On the modern deep learning approaches for precipitation downscaling, Earth Science Informatics, Online, March 2023, DOI:10.1007/s12145-023-00970-4, 1-14

30. Singh Manmeet, Acharya N., Patel P., Jamshidi S., Yang Z-L, Kumar Bipin, Rao Suryachandra A., Gill S.S., Chattopadhyay R., Nanjundiah R.S., Niyogi D., A modified deep learning weather prediction using cubed sphere for global precipitation, Frontiers in Climate, 4: 1022624, January 2023, DOI:10.3389/fclim.2022.1022624, 1-10

29. Malap N., Prabhakaran Thara, Bera S., Kumar Bipin, Karipot A., Impact of CCN on mixing line structure over the peninsular Indian region, Journal of Earth System Science, 131: 241, November 2022, DOI:10.1007/s12040-022-01994-y, 1-12

28. Grabowski W.W., Thomas L., Kumar Bipin, Impact of cloud-base turbulence on CCN activation: Single-size CCN , Journal of the Atmospheric Sciences, 79, February 2022, DOI:10.1175/JAS-D-21-0184.1, 551–566 

27. Kumar, B., Chattopadhyay, R., Singh, M. Chaudhary, N., Kodari, K. and Barve, A. (2021), “Deep learning–based downscaling of summer monsoon rainfall data over Indian region”, Theoretical and Applied Climatology, 143, 1145–1156 (2021), https://doi.org/10.1007/s00704-020-03489-6.

26. Kumar, B. Ranjan R, Yau, M.K., Bera, S. Rao, S, A. (2021), “Impact of high- and low-vorticity turbulence on cloud–environment mixing and cloud microphysics processes”, Atmospheric Chemistry and Physics, 21, 12317–12329, https://doi.org/10.5194/acp-21-12317-2021.

25. Gharat, J., Kumar, B., Barve, A., Ragha, L., Jeelani, S.M., and Clyne, J. (2021) “Development of NCL equivalent serial and parallel Python routines for meteorological data analysis”, International Journal of High Performance Computing Applications, under review.

24. Grabowski, W.W., Thomas, L. and Kumar B. (2021), “Impact of cloud base turbulence on CCN activation: Single size CCN”, Journal of Atmospheric Science, under review.

23. Kalamkar, S.R., Kumar, V., Kumar, B.V.R., Satoh, M., Kumar, B. and Bhat, S.P., (2021), “A hybrid time-stepping approach for weather simulations using NICAM”, Atmospheric Research, under review.

22. Kumar, B., Abhishek, N., Chattopadhyay, R., George, S., Singh, B.B., Samanta, A., Patnaik, B.S.V., Gill, S.G., Nanjundiah, R.S., Singh and M., (2021), “Deep Learning Based Forecasting of Indian Summer Monsoon Rainfall”, arXiv:2107.04270v2.

21. Singh, M., Kumar, B., Niyogi, N., Rao, S. A., Gill, S.G., Chattopadhyay and Nanjundiah, R.S. (2021), “Deep learning for improved global precipitation in numerical weather prediction systems”,  arXiv:2106.12045v1

20. Kumar, B., Rehme, M., Suresh, N., Cherukuru, N., Jaroszynski, S., Li, S., Pearse, S., Scheitlin, T., Rao, S.A., Nanjundiah, R.S. (2021), “Optimization of DNS code and visualization of entrainment and mixing phenomena at cloud edges”, Parallel Computing, 107, pp. 102811.

19. Kumar, B., Nanjundiah, R.S. and Bhowmik, M. (2020), “Prospective applications of artificial intelligence/machine learning techniques in earth science”, Current Science, 119, pp. 424-425.

18. Thomas, L., Grabowski, W. W., and Kumar, B. (2020), “Diffusional growth of cloud droplets in homogeneous isotropic turbulence: DNS, scaled-up DNS, and stochastic model”, Atmospheric Chemistry and Physics, 20, pp. 9087–9100, https://doi.org/10.5194/acp-20-9087-2020.

17. Belgaonkar, S., Bamble, B., Barve A., and Kumar, B. (2020), “Visualization of Droplet Dynamics in Cloud”, ITM Web of Conferences, 32, pp.03033-03038, https://doi.org/10.1051/itmconf/20203203033.

16. Kumar, B., Manapragada M. and Suresh, N. (2019), “High Level File System and Parallel I/O Optimization of DNS Code", Springer Nature, Communications in Computer and Information Science, 964, pp. 1-11, doi.org/10.1007/978-981-13-7729-7_2.

15. Kumar, B., Götzfried, P., Suresh, N., Schumacher, J., and Shaw, R. A. (2018), “Scale dependence of cloud microphysical response to turbulent entrainment and mixing”, Journal of Advances in Modeling Earth Systems, 10, pp. 2777-2785, doi:/10.1029/2018MS001487

14. Kumar, B., Bhowmik, M. and Ravi S. Nanjundiah (2018), “Clouds, microphysical processes and small scale simulations”, Current Science, 115(9), pp. 1636-1637.

13. Kumar, B., Bera, S., Parabhakaran, T. and Grabowski, W. W. (2017), “Cloud-Edge Mixing: Direct Numerical Simulation and Observations in Indian Monsoon Cloud”, Journal of Advancing in Modeling Earth Systems 9, pp. 332-353, doi:10.1002/2016MS000731.

12. Goetzfried, P., Kumar, B., Shaw R. A. and Schumacher, J. (2017), “Droplet Dynamics and fine-scale structure in a shearless turbulent mixing layer with phase changes”,  Journal of Fluid Mechanics, 814, p 452-483 doi:10.1017/jfm.2017.23.

11. Hoffman, F., Siebert, H., Schumacher, J., Riechelmann, T., Katzwinkel, J. Kumar, B., Götzfried, P. and Raasch, S. (2014), “Entrainment and mixing at the interface of shallow cumulus clouds: Results from a combination of observations and simulations”, Meteorologische Zeitschrift  23(4) p 349-368 DOI 10.1127/0941-2948/2014/0597. 

10. Kumar, B., Schumacher, J. and Shaw, R. A. (2014), “Lagrangian Mixing Dynamics at the Cloudy-Clear Air Interface” Journal of Atmospheric Sciences, 71 (7), p2564-2580, DOI: 10.1175/JAS-D-13-0294.1.

9. Kumar, B., Schumacher, J. and Shaw, R. A. (2013), “Cloud microphysical effects of turbulent mixing and entrainment”, Theoretical and Computational Fluid Dynamics. 27 (3), p 361-376,DOI: 10.1007/s00162-012-0272-z (Springer).

8. Kumar, B., Crane, M. and Delauré, Y. (2013), “On the Volume of Fluid Method for Multiphase Fluid Flow simulation”, International Journal of Modeling, Simulation, and Scientific Computing, 4(2) p 135002 (World Scientific Publication).

7. Kumar, B., Janetzko, F. Schumacher, J. and Shaw, R. A. (2012), “Extreme responses of a coupled scalar-particle system during turbulent mixing“, New Journal of Physics, 14, 115020, DOI:0.1088/1367-2630/14/11/115020.

6. Kumar, B.V.R., Kumar, B. (2005), “Parallel Pre-Conditioners for Heat Transfer Applications on Anu-Cluster”, Applied Mathematics and Computations, 163(3),p 1243-1263.

5. Kumar, B.V.R., Kumar, B. (2005), “On the development of Parallel Sparse CGM Solver for CFD Computations on ANU-Cluster”, Applied Mathematics and Computations, 160(3), p 739-761.

4. Kumar, B.V.R. and Kumar, B.(2004), “Parallel Computation of Natural Convection in Trapezoidal Porous Enclosures”, Mathematics and Computers in Simulation, 65, p 221-229.

3. Kumar, B.V.R., Kumar, B. and others, (2004), “Krylov Subspace Methods in Parallel Finite Element Computation of Heat Transfer Application”, Numerical Heat Transfer Part A, 45, p 479-503.

2. Kumar, B., Prasad Patnaik, Lin Ping, (2005), “Krylov Subspace Solvers for Grain Growth Simulations using Phase Field Method” (2005), Asian Mathematical Conference, NUS, Singapore, pp. 64 (http://ww1.math.nus.edu.sg/AMC/AMC2005-Proceedings.pdf). 

1. Kumar, B., Kumar, B.V.R., (2004), “Parallel Pre-Conditioners for Heat Transfer Applications on Anu-Cluster”, International Conference on Computational Methods, Dec. 15-17, 2004 pp. 66, National University of Singapore, SINGAPORE.