Articles | Volume 17, issue 8
https://doi.org/10.5194/amt-17-2295-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-17-2295-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Apr 2024
Research article |
| 18 Apr 2024
| 18 Apr 2024
Hailstorm events in the Central Andes of Peru: insights from historical data and radar microphysics
Jairo M. Valdivia
CORRESPONDING AUTHOR
Subdirección de Ciencias de la Atmósfera e Hidrósfera, Instituto Geofísico del Perú (IGP), Lima, Peru
Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, Colorado, USA
José Luis Flores-Rojas
Subdirección de Ciencias de la Atmósfera e Hidrósfera, Instituto Geofísico del Perú (IGP), Lima, Peru
Josep J. Prado
Dirección de Agrometeorología, Servicio Nacional de Meteorología e Hidrología del Perú, Lima, Peru
David Guizado
Subdirección de Ciencias de la Atmósfera e Hidrósfera, Instituto Geofísico del Perú (IGP), Lima, Peru
Instituto Nacional de Investigación en Glaciares y Ecosistemas de Montaña (INAIGEM), Lima, Peru
Elver Villalobos-Puma
Instituto Nacional de Investigación en Glaciares y Ecosistemas de Montaña (INAIGEM), Lima, Peru
Stephany Callañaupa
Instituto Nacional de Investigación en Glaciares y Ecosistemas de Montaña (INAIGEM), Lima, Peru
Yamina Silva-Vidal
Subdirección de Ciencias de la Atmósfera e Hidrósfera, Instituto Geofísico del Perú (IGP), Lima, Peru
Related authors
María A. Pérez-Tello, Iralmy Platero, Jairo M. Valdivia, Daniel Martinez-Castro, Elver E. Villalobos-Puma, and Fey Y. Silva
EGUsphere, https://doi.org/10.5194/egusphere-2026-2705, https://doi.org/10.5194/egusphere-2026-2705, 2026
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
Understanding complex precipitation in tropical high-mountain regions is vital for water resource management. This study evaluates measurements from a PARSIVEL2 disdrometer near a Peruvian glacier. The instrument systematically overestimated solid and mixed precipitation. We developed a methodology using velocity-diameter relationships and specific density optimization to correct these errors. The corrected data reveals wet snow and graupel dominate over pure snow in this environment.
María A. Pérez-Tello, Iralmy Platero, Jairo M. Valdivia, Daniel Martinez-Castro, Elver E. Villalobos-Puma, and Fey Y. Silva
EGUsphere, https://doi.org/10.5194/egusphere-2026-2705, https://doi.org/10.5194/egusphere-2026-2705, 2026
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
Understanding complex precipitation in tropical high-mountain regions is vital for water resource management. This study evaluates measurements from a PARSIVEL2 disdrometer near a Peruvian glacier. The instrument systematically overestimated solid and mixed precipitation. We developed a methodology using velocity-diameter relationships and specific density optimization to correct these errors. The corrected data reveals wet snow and graupel dominate over pure snow in this environment.
Cited articles
Allen, J. T. and Allen, E. R.: A review of severe thunderstorms in Australia, Atmos. Res., 178–179, 347–366, https://doi.org/10.1016/j.atmosres.2016.03.011, 2016. a
Allen, J. T., Tippett, M. K., Kaheil, Y., Sobel, A. H., Lepore, C., Nong, S., and Muehlbauer, A.: An Extreme Value Model for U.S. Hail Size, Mon. Weather Rev., 145, 4501–4519, https://doi.org/10.1175/MWR-D-17-0119.1, 2017. a, b
Allen, J. T., Giammanco, I. M., Kumjian, M. R., Jurgen Punge, H., Zhang, Q., Groenemeijer, P., Kunz, M., and Ortega, K.: Understanding Hail in the Earth System, Rev. Geophys., 58, 1–49, https://doi.org/10.1029/2019RG000665, 2020. a, b
Angulo-Martínez, M., Beguería, S., Latorre, B., and Fernández-Raga, M.: Comparison of precipitation measurements by OTT Parsivel2 and Thies LPM optical disdrometers, Hydrol. Earth Syst. Sci., 22, 2811–2837, https://doi.org/10.5194/hess-22-2811-2018, 2018. a
Battaglia, A., Rustemeier, E., Tokay, A., Blahak, U., and Simmer, C.: PARSIVEL snow observations: A critical assessment, J. Atmos. Ocean. Tech., 27, 333–344, https://doi.org/10.1175/2009JTECHA1332.1, 2010. a, b, c
Beal, A., Hallak, R., Martins, L. D., Martins, J. A., Biz, G., Rudke, A. P., and Tarley, C. R.: Climatology of hail in the triple border Paraná, Santa Catarina (Brazil) and Argentina, Atmos. Res., 234, 104747, https://doi.org/10.1016/j.atmosres.2019.104747, 2020. a, b, c, d
Blanchard, D. C. and Spencer, A. T.: Experiments on the Generation of Raindrop-Size Distributions by Drop Breakup, J. Atmos. Sci., 27, 101–108, https://doi.org/10.1175/1520-0469(1970)027<0101:EOTGOR>2.0.CO;2, 1970. a
Brimelow, J. C., Burrows, W. R., and Hanesiak, J. M.: The changing hail threat over North America in response to anthropogenic climate change, Nat. Clim. Change, 7, 516–522, https://doi.org/10.1038/nclimate3321, 2017. a
Bringi, V. N. and Chandrasekar, V.: Polarimetric Doppler Weather Radar: Principles and Applications, Cambridge University Press, ISBN 9780521623841, https://books.google.com.pe/books?id=KvJvfP9t5Y8C (last access: 16 July 2022), 2001. a
Brooks, H. E.: Severe thunderstorms and climate change, Atmos. Res., 123, 129–138, https://doi.org/10.1016/j.atmosres.2012.04.002, 2013. a
Changnon, S. A.: Increasing major hail losses in the U.S., Climatic Change, 96, 161–166, https://doi.org/10.1007/s10584-009-9597-z, 2009. a
Chavez, S. P., Silva, Y., and Barros, A. P.: High-Elevation Monsoon Precipitation Processes in the Central Andes of Peru, J. Geophys. Res.-Atmos., 125, e2020JD032947, https://doi.org/10.1029/2020JD032947, 2020. a
Del Castillo-Velarde, C., Kumar, S., Valdivia-Prado, J. M., Moya-Álvarez, A. S., Flores-Rojas, J. L., Villalobos-Puma, E., Martínez-Castro, D., and Silva-Vidal, Y.: Evaluation of GPM Dual-Frequency Precipitation Radar Algorithms to Estimate Drop Size Distribution Parameters, Using Ground-Based Measurement over the Central Andes of Peru, Earth Systems and Environment, 5, 597–619, https://doi.org/10.1007/s41748-021-00242-5, 2021. a
Dennis, E. J. and Kumjian, M. R.: The impact of vertical wind shear on hail growth in simulated supercells, J. Atmos. Sci., 74, 641–663, https://doi.org/10.1175/JAS-D-16-0066.1, 2017. a
Dessens, J., Berthet, C., and Sanchez, J. L.: Change in hailstone size distributions with an increase in the melting level height, Atmos. Res., 158–159, 245–253, https://doi.org/10.1016/j.atmosres.2014.07.004, 2015. a
Diaz, H. F. and Murnane, R. J.: Climate Extremes and Society, Cambridge University Press, ISBN 9780521870283, https://doi.org/10.1017/CBO9780511535840, 2008. a, b
Diffenbaugh, N. S., Scherer, M., and Trapp, R. J.: Robust increases in severe thunderstorm environments in response to greenhouse forcing, P. Natl. Acad. Sci. USA, 110, 16361–16366, https://doi.org/10.1073/pnas.1307758110, 2013. a
Doviak, R. J., Zrnic, D. S., and Sirmans, D. S.: Doppler Weather Radar, P. IEEE, 67, 1522–1553, https://doi.org/10.1109/PROC.1979.11511, 1979. a, b
Flores-Rojas, J. L., Moya-Alvarez, A. S., Kumar, S., Martinez-Castro, D., Villalobos-Puma, E., and Silva-Vidal, Y.: Analysis of possible triggering mechanisms of severe thunderstorms in the tropical central Andes of Peru, Mantaro Valley, Atmosphere, 10, 301, https://doi.org/10.3390/atmos10060301, 2019. a
Flores-Rojas, J. L., Moya-Álvarez, A. S., Valdivia-Prado, J. M., Piñas-Laura, M., Kumar, S., Karam, H. A., Villalobos-Puma, E., Martínez-Castro, D., and Silva, Y.: On the dynamic mechanisms of intense rainfall events in the central Andes of Peru, Mantaro valley, Atmos. Res., 248, 105188, https://doi.org/10.1016/j.atmosres.2020.105188, 2021a. a, b
Flores-Rojas, J. L., Silva, Y., Suárez-Salas, L., Estevan, R., Valdivia-Prado, J., Saavedra, M., Giraldez, L., Piñas-Laura, M., Scipión, D., Milla, M., Kumar, S., and Martinez-Castro, D.: Analysis of Extreme Meteorological Events in the Central Andes of Peru Using a Set of Specialized Instruments, Atmosphere, 12, 408, https://doi.org/10.3390/atmos12030408, 2021b. a, b
Friedrich, K., Higgins, S., Masters, F. J., and Lopez, C. R.: Articulating and Stationary PARSIVEL Disdrometer Measurements in Conditions with Strong Winds and Heavy Rainfall, J. Atmos. Ocean. Tech., 30, 2063–2080, https://doi.org/10.1175/JTECH-D-12-00254.1, 2013a. a
Friedrich, K., Kalina, E. A., Masters, F. J., and Lopez, C. R.: Drop-Size Distributions in Thunderstorms Measured by Optical Disdrometers during VORTEX2, Mon. Weather Rev., 141, 1182–1203, https://doi.org/10.1175/MWR-D-12-00116.1, 2013b. a, b, c
Giráldez, L., Silva, Y., Zubieta, R., and Sulca, J.: Change of the Rainfall Seasonality Over Central Peruvian Andes: Onset, End, Duration and Its Relationship With Large-Scale Atmospheric Circulation, Climate, 8, 23, https://doi.org/10.3390/cli8020023, 2020. a, b
Hoogewind, K. A., Baldwin, M. E., and Trapp, R. J.: The impact of climate change on hazardous convective weather in the United States: Insight from high-resolution dynamical downscaling, J. Climate, 30, 10081–10100, https://doi.org/10.1175/JCLI-D-16-0885.1, 2017. a
Houze, R. A.: Cloud dynamics, in: International Geophysics, Elsevier Science, p. 573, ISBN 9780123742667, 2014. a
Instituto Geofísico del Perú: Raindrop size distribution and precipitation over Peruvian central Andes (12.0 °S), Zenodo [data set], https://doi.org/10.5281/zenodo.3497564, 2019. a
Jaffrain, J. and Berne, A.: Experimental Quantification of the Sampling Uncertainty Associated with Measurements from PARSIVEL Disdrometers, J. Hydrometeorol., 12, 352–370, https://doi.org/10.1175/2010JHM1244.1, 2011. a
Jaffrain, J. and Berne, A.: Quantification of the small-scale spatial structure of the raindrop size distribution from a network of disdrometers, J. Appl. Meteorol. Clim., 51, 941–953, https://doi.org/10.1175/JAMC-D-11-0136.1, 2012. a
Jaffrain, J., Studzinski, A., and Berne, A.: A network of disdrometers to quantify the small-scale variability of the raindrop size distribution, Water Resour. Res., 47, 1–8, https://doi.org/10.1029/2010WR009872, 2011. a
Jia, X., Liu, Y., Ding, D., Ma, X., Chen, Y., Bi, K., Tian, P., Lu, C., and Quan, J.: Combining disdrometer, microscopic photography, and cloud radar to study distributions of hydrometeor types, size and fall velocity, Atmos. Res., 228, 176–185, https://doi.org/10.1016/j.atmosres.2019.05.025, 2019. a, b
Kneifel, S., Maahn, M., Peters, G., and Simmer, C.: Observation of snowfall with a low-power FM-CW K-band radar (Micro Rain Radar), Meteorol. Atmos. Phys., 113, 75–87, https://doi.org/10.1007/s00703-011-0142-z, 2011. a
Knight, C. A. and Knight, N. C.: Hailstorms, in: Severe Convective Storms, American Meteorological Society, Boston, MA, 223–254, https://doi.org/10.1007/978-1-935704-06-5_6, 2001. a
Knight, N. C.: The Climatology of Hailstone Embryos, J. Appl. Meteorol., 20, 750–755, https://doi.org/10.1175/1520-0450(1981)020<0750:TCOHE>2.0.CO;2, 1981. a, b, c
Kumar, S., Del Castillo-Velarde, C., Prado, J. M., Rojas, J. L. F., Gutierrez, S. M., Alvarez, A. S., Martine-Castro, D., and Silva, Y.: Rainfall characteristics in the mantaro basin over tropical andes from a vertically pointed profile rain radar and in-situ field campaign, Atmosphere, 11, 248, https://doi.org/10.3390/atmos11030248, 2020. a, b
Kunz, M., Sander, J., and Kottmeier, C.: Recent trends of thunderstorm and hailstorm frequency and their relation to atmospheric characteristics in southwest Germany, Int. J. Climatol., 29, 2283–2297, https://doi.org/10.1002/joc.1865, 2009. a
Kunz, M., Blahak, U., Handwerker, J., Schmidberger, M., Punge, H. J., Mohr, S., Fluck, E., and Bedka, K. M.: The severe hailstorm in southwest Germany on 28 July 2013: characteristics, impacts and meteorological conditions, Q. J. Roy. Meteor. Soc., 144, 231–250, https://doi.org/10.1002/qj.3197, 2018. a
Lamb, D. and Verlinde, J.: Physics and Chemistry of Clouds, Cambridge University Press, ISBN 9780521899109, https://doi.org/10.1017/CBO9780511976377, 2011. a
Löffler-Mang, M. and Joss, J.: An optical disdrometer for measuring size and velocity of hydrometeors, J. Atmos. Ocean. Tech., 17, 130–139, https://doi.org/10.1175/1520-0426(2000)017<0130:AODFMS>2.0.CO;2, 2000. a
Ludlam, F. H.: The hail problem, Unione nazionale antigrandine, https://books.google.com/books?id=s4NvnQEACAAJ (last access: 7 July 2023), 1958. a
Maahn, M. and Kollias, P.: Improved Micro Rain Radar snow measurements using Doppler spectra post-processing, Atmos. Meas. Tech., 5, 2661–2673, https://doi.org/10.5194/amt-5-2661-2012, 2012. a, b
Mahoney, K., Alexander, M. A., Thompson, G., Barsugli, J. J., and Scott, J. D.: Changes in hail and flood risk in high-resolution simulations over Colorado's mountains, Nat. Clim. Change, 2, 125–131, https://doi.org/10.1038/nclimate1344, 2012. a
Martins, J. A., Brand, V. S., Capucim, M. N., Felix, R. R., Martins, L. D., Freitas, E. D., Gonçalves, F. L., Hallak, R., Dias, M. A. F. S., and Cecil, D. J.: Climatology of destructive hailstorms in Brazil, Atmos. Res., 184, 126–138, https://doi.org/10.1016/j.atmosres.2016.10.012, 2017. a
Matrosov, S. Y., Reinking, R. F., Kropfli, R. A., and Bartram, B. W.: Estimation of Ice Hydrometeor Types and Shapes from Radar Polarization Measurements, J. Atmos. Ocean. Tech., 13, 85–96, https://doi.org/10.1175/1520-0426(1996)013<0085:EOIHTA>2.0.CO;2, 1996. a
Matrosov, S. Y., Reinking, R. F., Kropfli, R. A., Martner, B. E., and Bartram, B. W.: On the Use of Radar Depolarization Ratios for Estimating Shapes of Ice Hydrometeors in Winter Clouds, J. Appl. Meteorol., 40, 479–490, https://doi.org/10.1175/1520-0450(2001)040<0479:OTUORD>2.0.CO;2, 2001. a
Matthews, J. H. and Mason, B. J.: Electrification produced by the rupture of large water drops in an electric field, Q. J. Roy. Meteor. Soc., 90, 275–286, https://doi.org/10.1002/qj.49709038506, 1964. a
Mezher, R. N., Doyle, M., and Barros, V.: Climatology of hail in Argentina, Atmos. Res., 114–115, 70–82, https://doi.org/10.1016/j.atmosres.2012.05.020, 2012. a
Mróz, K., Battaglia, A., Kneifel, S., von Terzi, L., Karrer, M., and Ori, D.: Linking rain into ice microphysics across the melting layer in stratiform rain: a closure study, Atmos. Meas. Tech., 14, 511–529, https://doi.org/10.5194/amt-14-511-2021, 2021. a, b
OTT: Operating Instructions Manual: OTT Parsivel2, appendix D, Table 4680, OTT, https://www.ott.com/ download/operating-instructions-present-weather-sensor-ott-parsivel2-without-screen-heating-1/ (last access: 7 July 2023), 2016. a, b
Oue, M., Kumjian, M. R., Lu, Y., Jiang, Z., Clothiaux, E. E., Verlinde, J., and Aydin, K.: X-Band Polarimetric and Ka-Band Doppler Spectral Radar Observations of a Graupel-Producing Arctic Mixed-Phase Cloud, J. Appl. Meteorol. Clim., 54, 1335–1351, https://doi.org/10.1175/JAMC-D-14-0315.1, 2015a. a, b
Oue, M., Kumjian, M. R., Lu, Y., Verlinde, J., Aydin, K., and Clothiaux, E. E.: Linear depolarization ratios of columnar ice crystals in a deep precipitating system over the arctic observed by zenith-pointing Ka-band doppler radar, J. Appl. Meteorol. Clim., 54, 1060–1068, https://doi.org/10.1175/JAMC-D-15-0012.1, 2015b. a, b, c
Park, S. G., Kim, H. L., Ham, Y. W., and Jung, S. H.: Comparative evaluation of the OTT PARSIVEL2using a collocated two-dimensional video disdrometer, J. Atmos. Ocean. Tech., 34, 2059–2082, https://doi.org/10.1175/JTECH-D-16-0256.1, 2017. a, b, c
Peters, G., Fischer, B., and Clemens, M.: Rain attenuation of radar echoes considering finite-range resolution and using drop size distributions, J. Atmos. Ocean. Tech., 27, 829–842, https://doi.org/10.1175/2009JTECHA1342.1, 2010. a
Prein, A. F. and Heymsfield, A. J.: Increased melting level height impacts surface precipitation phase and intensity, Nat. Clim. Change, 10, 771–776, https://doi.org/10.1038/s41558-020-0825-x, 2020. a
Prein, A. F. and Holland, G. J.: Global estimates of damaging hail hazard, Weather and Climate Extremes, 22, 10–23, https://doi.org/10.1016/j.wace.2018.10.004, 2018. a
Prieto, R., Gimeno, L., García, R., Herrera, R., Hernández, E., and Ribera, P.: Interannual variability of hail-days in the Andes region since 1885, Earth Planet. Sc. Lett., 171, 503–509, https://doi.org/10.1016/S0012-821X(99)00170-3, 1999. a, b
Pruppacher, H. and Klett, J.: Microphysics of Clouds and Precipitation, vol. 18 of Atmospheric and Oceanographic Sciences Library, Springer Netherlands, Dordrecht, 2nd edn., ISBN 978-0-7923-4211-3, https://doi.org/10.1007/978-0-306-48100-0, 2010. a, b
Púcik, T., Castellano, C., Groenemeijer, P., Kühne, T., Rädler, A. T., Antonescu, B., and Faust, E.: Large hail incidence and its economic and societal impacts across Europe, Mon. Weather Rev., 147, 3901–3916, https://doi.org/10.1175/MWR-D-19-0204.1, 2019. a
Rasmussen, K. L., Prein, A. F., Rasmussen, R. M., Ikeda, K., and Liu, C.: Changes in the convective population and thermodynamic environments in convection-permitting regional climate simulations over the United States, Clim. Dynam., 55, 383–408, https://doi.org/10.1007/s00382-017-4000-7, 2020. a
Rasmussen, R. M. and Heymsfield, A. J.: Melting and Shedding of Graupel and Hail. Part II: Sensitivity Study, J. Atmos. Sci., 44, 2764–2782, https://doi.org/10.1175/1520-0469(1987)044<2764:MASOGA>2.0.CO;2, 1987. a
Raupach, T. H. and Berne, A.: Correction of raindrop size distributions measured by Parsivel disdrometers, using a two-dimensional video disdrometer as a reference, Atmos. Meas. Tech., 8, 343–365, https://doi.org/10.5194/amt-8-343-2015, 2015. a
Raupach, T. H., Martius, O., Allen, J. T., Kunz, M., Lasher-Trapp, S., Mohr, S., Rasmussen, K. L., Trapp, R. J., and Zhang, Q.: The effects of climate change on hailstorms, Nature Reviews Earth and Environment, 2, 213–226, https://doi.org/10.1038/s43017-020-00133-9, 2021. a, b, c, d
Reinking, R. F., Matrosov, S. Y., Kropfli, R. A., and Bartram, B. W.: Evaluation of a 45° slant quasi-linear radar polarization state for distinguishing drizzle droplets, pristine ice crystals, and less regular ice particles, J. Atmos. Ocean. Tech., 19, 296–321, https://doi.org/10.1175/1520-0426-19.3.296, 2002. a
Schmit, T. J., Griffith, P., Gunshor, M. M., Daniels, J. M., Goodman, S. J., and Lebair, W. J.: A Closer Look at the ABI on the GOES-R Series, B. Am. Meteorol. Soc., 98, 681–698, https://doi.org/10.1175/BAMS-D-15-00230.1, 2017. a
Silva, Y., Takahashi, K., and Chávez, R.: Dry and wet rainy seasons in the Mantaro river basin (Central Peruvian Andes), Adv. Geosci., 14, 261–264, https://doi.org/10.5194/adgeo-14-261-2008, 2008. a
Sokol, Z., Minářová, J., and Novák, P.: Classification of hydrometeors using measurements of the ka-band cloud radar installed at the Milešovka Mountain (Central Europe), Remote Sens., 10, 1674, https://doi.org/10.3390/rs10111674, 2018. a, b
Tokay, A., Wolff, D. B., and Petersen, W. A.: Evaluation of the new version of the laser-optical disdrometer, OTT parsivel, J. Atmos. Ocean. Tech., 31, 1276–1288, https://doi.org/10.1175/JTECH-D-13-00174.1, 2014. a, b, c, d
Tokay, A., D'Adderio, L. P., Wolff, D. B., and Petersen, W. A.: A field study of pixel-scale variability of raindrop size distribution in the Mid-Atlantic region, J. Hydrometeorol., 17, 1855–1868, https://doi.org/10.1175/JHM-D-15-0159.1, 2016. a
Trapp, R. J., Diffenbaugh, N. S., Brooks, H. E., Baldwin, M. E., Robinson, E. D., and Pal, J. S.: Changes in severe thunderstorm environment frequency during the 21st century caused by anthropogenically enhanced global radiative forcing, P. Natl. Acad. Sci. USA, 104, 19719–19723, https://doi.org/10.1073/pnas.0705494104, 2007. a
Trapp, R. J., Hoogewind, K. A., and Lasher-Trapp, S.: Future changes in hail occurrence in the United States determined through convection-permitting dynamical downscaling, J. Climate, 32, 5493–5509, https://doi.org/10.1175/JCLI-D-18-0740.1, 2019. a
Valdivia, J. and Silva, Y.: Estadística de ocurrencia de tormentas en el Observatorio de Huancayo, Tech. Rep. October, Insitituto Geofísico del Perú, https://doi.org/10.13140/RG.2.2.22792.37121, 2016. a
Valdivia, J. M.: JValdivia23/parsivel2: OTT Parsivel2 toolbox (v1.1), Zenodo [code], https://doi.org/10.5281/zenodo.3471885, 2019. a
Valdivia, J. M., Contreras, K., Martinez-Castro, D., Villalobos-Puma, E., Suarez-Salas, L. F., and Silva, Y.: Dataset on raindrop size distribution, raindrop fall velocity and precipitation data measured by disdrometers and rain gauges over Peruvian central Andes (12.0° S), Data in Brief, 29, 105215, https://doi.org/10.1016/j.dib.2020.105215, 2020a. a
Valdivia, J. M., Scipión, D. E., Milla, M., and Silva, Y.: Multi-Instrument Rainfall-Rate Estimation in the Peruvian Central Andes, J. Atmos. Ocean. Tech., 37, 1811–1826, https://doi.org/10.1175/jtech-d-19-0105.1, 2020b. a, b, c, d
Villalobos-Puma, E., Martinez-Castro, D., Flores-Rojas, J. L., Saavedra-Huanca, M., and Silva-Vidal, Y.: Diurnal Cycle of Raindrops Size Distribution in a Valley of the Peruvian Central Andes, Atmosphere, 11, 38, https://doi.org/10.3390/atmos11010038, 2019. a, b
Villermaux, E. and Bossa, B.: Single-drop fragmentation determines size distribution of raindrops, Nat. Phys., 5, 697–702, https://doi.org/10.1038/nphys1340, 2009. a
Weisman, M. L. and Klemp, J. B.: The structure and classification of numerically simulated convective storms in directionally varying wind shears, Mon. Weather Rev., 112, 2479–2498, https://doi.org/10.1175/1520-0493(1984)112<2479:TSACON>2.0.CO;2, 1984. a
Williams, C. R., Ecklund, W. L., and Gage, K. S.: Classification of Precipitating Clouds in the Tropics Using 915-MHz Wind Profilers, J. Atmos. Ocean. Tech., 12, 996–1012, https://doi.org/10.1175/1520-0426(1995)012<0996:COPCIT>2.0.CO;2, 1995. a
Williams, C. R., Maahn, M., Hardin, J. C., and de Boer, G.: Clutter mitigation, multiple peaks, and high-order spectral moments in 35 GHz vertically pointing radar velocity spectra, Atmos. Meas. Tech., 11, 4963–4980, https://doi.org/10.5194/amt-11-4963-2018, 2018. a, b
WMO: International Cloud Atlas: Manual on the Observation of Clouds and Other Meteors, World Meteorological Organization, ISBN 9263104077, 2017. a
Xie, B., Zhang, Q., and Wang, Y.: Trends in hail in China during 1960–2005, Geophys. Res. Lett., 35, L13801, https://doi.org/10.1029/2008GL034067, 2008. a, b, c
Xue, M., Droegemeier, K. K., and Wong, V.: The Advanced Regional Prediction System (ARPS) – A multi-scale nonhydrostatic atmospheric simulation and prediction model. Part I: Model dynamics and verification, Meteorol. Atmos. Phys., 75, 161–193, https://doi.org/10.1007/s007030070003, 2000. a
Short summary
In this study, we explored hailstorms in the Central Andes of Peru. We used historical records and radar measurements to understand the frequency, timing, and characteristics of these hail events. Our research found a trend of decreasing hail frequency, probably due to anthropogenic climate change. Understanding these weather patterns is critical for local communities, as it can help improve weather forecasts and manage risks related to these potentially destructive events.
In this study, we explored hailstorms in the Central Andes of Peru. We used historical records...
