GPM Refereed Publications
Alquaied, F., R. Chen, W. Linwood Jones ,
: Hot Load Temperature Correction for TRMM Microwave Imager in the Legacy Brightness Temperature. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(6), 1923-1931, doi:10.1109/JSTARS.2018.2837099.
Alvey III, G., E. Zipser, and J. Zawislak,
: How Does Hurricane Edouard (2014) Evolve toward Symmetry before Rapid Intensification? A High-Resolution Ensemble Study. J. Atmos. Sci., 77(4), 1329–1351 , doi:10.1175/JAS-D-18-0355.1.
Anand, A., A. S. Dinesh, P. K. Srivastava, S. K. Chaudhary, A. K. Varma, and P. Kumar,
: Rainfall rate estimation over India using global precipitation measurement’s microwave imager datasets and different variants of fuzzy information system. Geocarto International , 37(21), 6213-6231 , doi:10.1080/10106049.2021.1936208.
Angulo-Martinez, M., and A. P. Barros,
: Measurement uncertainty in rainfall kinetic energy and intensity relationships for soil erosion studies: An evaluation using PARSIVEL disdrometers in the Southern Appalachian Mountains. Geomorphology, 228, 28-40, doi:10.1016/j.geomorph.2014.07.036.
Aonashi, K., K. Okamoto, T. Tashima, T. Kubota, and K. Ito,
: Sampling Error Damping method for a Cloud-Resolving Model using a Dual-Scale Neighboring Ensemble Approach. Mon. Wea. Rev., 144, 4751-4770, doi:10.1175/MWR-D-15-0410.1.
Arabzadeh, A., M. R. Ehsani, B. Guan, S. Heflin, and A. Behrangi,
: Global Intercomparison of Atmospheric Rivers Precipitation in Remote Sensing and Reanalysis Products. JGR - Atmos., 125(21), e2020JD033021, doi:10.1029/2020JD033021.
Arias, I., and V. Chandrasekar,
: Cross validation of the network of ground-based radar with GPM during the Remote sensing of Electrification, Lightning, And Mesoscale/microscale Processes with Adaptive Ground Observations (RELAMPAGO) field campaign. J. Meteor. Soc. Japan, 99(6), 1423-1438, doi:10.2151/jmsj.2021-069.
Arulraj, M. and A. P. Barros,
: Automatic detection and classification of low-level orographic precipitation processes from space-borne radars using machine learning. Remote Sensing of Environment, 257, 112355, doi:10.1016/j.rse.2021.112355.
Arulraj, M. and A. P. Barros,
: Shallow Precipitation Detection and Classification Using Multifrequency Radar Observations and Model Simulations. J. Atmos. Oceanic Technol., 34, 1963-1983, doi:10.1175/JTECH-D-17-0060.1.
Arulraj, M. and A. P. Barros,
: Improving quantitative precipitation estimates in mountainous regions by modelling low-level seeder-feeder interactions constrained by Global Precipitation Measurement Dual-frequency Precipitation Radar measurements. Rem. Sens. Environ., 231, 111213, doi:10.1016/j.rse.2019.111213.
Arulraj, M., and A. P. Barros,
: Improving quantitative precipitation estimates in mountainous regions by modelling low-level seeder-feeder interactions constrained by Global Precipitation Measurement Dual-frequency Precipitation Radar measurements. Rem. Sens.of the Environ., 231, 111213, doi:10.1016/j.rse.2019.111213.
Augusto Guimaraes Santos, C., R. Moura Brasil Neto, R. Marques da Silva, and D. Correia dos Santos,
: Innovative approach for geospatial drought severity classification: a case study of Paraı ́ ba state, Brazil. Stochastic Environmental Research and Risk Assessment, , , doi:10.1007/s00477-018-1619-9.
Awaka, J., M. Le, V. Chandrasekar, N. Yoshida, T. Higashiuwatoko, T. Kubota, and T. Iguchi,
: Rain Type Classification Algorithm Module for GPM Dual-Frequency Precipitation Radar. J. Atmos. Oceanic Technol., 33, 1887-1898, doi:10.1175/BAMS-D-14-00228.1.
Ayat, H., J. P. Evans, S. Sherwood, and A. Behrangi,
: Are Storm Characteristics the Same When Viewed Using Merged Surface Radars or a Merged Satellite Product?. J. Hydrometeor., 22(1), 43–62, doi:10.1175/JHM-D-20-0187.1.
Balsamo, G., A. Agusti-Panareda, C. Albergel, G. Arduini, A. Beljaars, J. Bidlot, N. Bousserez, S. Boussetta, A. Brown, R. Buizza, C. Buontempo, F. Chevallier, M. Choulga, H. Cloke, M.F. Cronin, M. Dahoui, P. de Rosnay, P.A. Dirmeyer, M. Drusch, E. Dutra, M.B. Ek, P. Gentine, H. Hewitt, S.P. Keeley, Y. Kerr, S. Kumar, C. Lupu, J.-F. Mahfouf, J. McNorton, S. Mecklenburg, K. Mogensen, J. Muñoz-Sabater, R. Orth, F. Rabier, R. Reichle, B. Ruston, F. Pappenberger, I. Sandu, S.I. Seneviratne, S. Tietsche, I.F. Trigo, R. Uijlenhoet, N. Wedi, R.I. Woolway, ,
: Satellite and in situ observations for advancing global earth surface modelling: A review. Rem. Sens., 10, 2038, doi:10.3390/rs10122038.
Bang, S. D. and E. J. Zipser,
: Tropical Oceanic Thunderstorms Near Kwajalein and the Roles of Evolution, Organization, and Forcing in Their Electrification. JGR Atmospheres, 124(2), 544-562, doi:10.1029/2018JD029320.
Bang, S. D., and D. J. Cecil,
: Constructing a Multi-Frequency Passive Microwave Hail Retrieval and Climatology in the GPM Domain. J. Appl. Meteor. Climatol., 52, , doi:10.1175/JAMC-D-19-0042.1.
Barros A.P. and M. Arulraj,
: Remote Sensing of Orographic Precipitation. In: Levizzani V., Kidd C., Kirschbaum D., Kummerow C., Nakamura K., Turk F. (eds) Satellite Precipitation Measurement. Advances in Global Change Research, 69, , doi:10.1007/978-3-030-35798-6_6.
Barros, A. P.,
: Reply to comment by Qingyun Han on “Metrics to describe the dynamical evolution of atmospheric moisture: Intercomparison of model (NARR) and observations (ISCCP)” by Tao and Barros (2008). J. Geophys. Res., 115, D14125, doi:10.1029/2009JD013562.
Barros, A. P. and M. Arulraj,
: Automatic Detection and Classification of Orographic Precipitation using Machine Learning. ESSOAr, , , doi:10.1002/essoar.10502701.1.