• Arulraj, M., and A. P. Barros, 2019a: Towards a physically-based orographic precipitation retrieval correction algorithm for GPM-DPR using numerical weather prediction model simulations and ground-based observations. 2019 Fall Meeting, San Francisco, CA, Amer. Geophys. Union, Abstract H13P-1980.

    • Search Google Scholar
    • Export Citation
  • Arulraj, M., and A. P. Barros, 2019b: Improving quantitative precipitation estimates in mountainous regions by modelling low-level seeder-feeder interactions constrained by Global Precipitation Measurement Dual-frequency Precipitation Radar measurements. Remote Sens. Environ., 231, 111213, https://doi.org/10.1016/j.rse.2019.111213.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Atlas, D., R. C. Srivastava, and R. S. Sekhon, 1973: Doppler radar characteristics of precipitation at vertical incidence. Rev. Geophys., 11, 135, https://doi.org/10.1029/RG011i001p00001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barrett, B. S., R. Garreaud, and M. Falvey, 2009: Effect of the Andes cordillera on precipitation from a midlatitude cold front. Mon. Wea. Rev., 137, 30923109, https://doi.org/10.1175/2009MWR2881.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bauer-Pfunstein, M. R., 2007: Target separation and classification using cloud radar doppler-spectra. 33rd Conf. on Radar Meteorology, Cairns, Australia, Amer. Meteor. Soc., 11.B2, https://ams.confex.com/ams/pdfpapers/123456.pdf.

    • Search Google Scholar
    • Export Citation
  • Cao, Q., Y. Hong, J. J. Gourley, Y. Qi, J. Zhang, Y. Wen, and P.-E. Kirstetter, 2013: Statistical and physical analysis of the vertical structure of precipitation in the mountainous west region of the united states using 11+ years of spaceborne observations from TRMM precipitation radar. J. Appl. Meteor. Climatol., 52, 408424, https://doi.org/10.1175/JAMC-D-12-095.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cao, Q., T. H. Painter, W. R. Currier, J. D. Lundquist, and D. P. Lettenmaier, 2018: Estimation of precipitation over the OLYMPEX domain during winter 2015/16. J. Hydrometeor., 19, 143160, https://doi.org/10.1175/JHM-D-17-0076.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cardona, O. D., and Coauthors, 2012: Determinants of risk: Exposure and vulnerability. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation, C. B. Field et al., Eds., Cambridge University Press, 65108, https://doi.org/10.1017/CBO9781139177245.005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chavez, S. P., Y. Silva, and A. P. Barros, 2020: High-elevation monsoon precipitation processes in the central Andes of Peru. J. Geophys. Res. Atmos., 125, e2020JD032947, https://doi.org/10.1029/2020JD032947.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Das, S., and A. Maitra, 2016: Vertical profile of rain: Ka band radar observations at tropical locations. J. Hydrol., 534, 3141, https://doi.org/10.1016/j.jhydrol.2015.12.053.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Del Castillo-Velarde, C., S. Kumar, J. M. Valdivia-Prado, A. S. Moya-Álvarez, J. L. Flores-Rojas, E. Villalobos-Puma, D. Martínez-Castro, and Y. Silva-Vidal, 2021: 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 Syst. Environ., 5, 597619, https://doi.org/10.1007/s41748-021-00242-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Derin, Y., and Coauthors, 2019: Evaluation of GPM-era Global Satellite Precipitation products over multiple complex terrain regions. Remote Sens., 11, 2936, https://doi.org/10.3390/rs11242936.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Flores-Rojas, J. L., and Coauthors, 2021a: 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Flores-Rojas, J. L., and Coauthors, 2021b: 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Foote, G. B., and P. S. Du Toit, 1969: Terminal velocity of raindrops aloft. J. Appl. Meteor., 8, 249253, https://doi.org/10.1175/1520-0450(1969)008<0249:TVORA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Furukawa, K., K. Yamamoto, T. Kubota, R. Oki, and T. Iguchi, 2018: Scan pattern change test operations of the dual-frequency precipitation radar on the Global Precipitation Measurement core spacecraft. IGARSS 2018—2018 Int. Geoscience and Remote Sensing Symp., Valencia, Spain, IEEE, 83318334, https://doi.org/10.1109/IGARSS.2018.8518305.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gatlin, P. N., W. A. Petersen, K. R. Knupp, and L. D. Carey, 2018: Observed response of the raindrop size distribution to changes in the melting layer. Atmosphere, 9, 319, https://doi.org/10.3390/atmos9080319.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Germann, U., G. Galli, M. Boscacci, and M. Bolliger, 2006: Radar precipitation measurement in a mountainous region. Quart. J. Roy. Meteor. Soc., 132, 16691692, https://doi.org/10.1256/qj.05.190.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gunn, R., and G. D. Kinzer, 1949: The terminal velocity of fall for water droplets in stagnant air. J. Meteor., 6, 243248, https://doi.org/10.1175/1520-0469(1949)006%3C0243:TTVOFF%3E2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hildebrand, P. H., and R. S. Sekhon, 1974: Objective determination of the noise level in Doppler spectra. J. Appl. Meteor., 13, 808811, https://doi.org/10.1175/1520-0450(1974)013<0808:ODOTNL>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hou, A. Y., and Coauthors, 2014: The Global Precipitation Measurement mission. Bull. Amer. Meteor. Soc., 95, 701722, https://doi.org/10.1175/BAMS-D-13-00164.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Houze, R. A., Jr., 2012: Orographic effects on precipitating clouds. Rev. Geophys., 50, RG1001, https://doi.org/10.1029/2011RG000365.

  • Iguchi, T., and R. Meneghini, 2017: GPM DPR Precipitation Profile L2A 1.5 hours 5 km V06. NASA Goddard Earth Sciences Data and Information Services Center, accessed March 2017, https://doi.org/10.5067/GPM/DPR/GPM/2A/06.

    • Search Google Scholar
    • Export Citation
  • Iguchi, T., and Coauthors, 2018: GPM/DPR Level-2 algorithm theoretical basis document. NASA Goddard Space Flight Center Rep., 127 pp., https://pps.gsfc.nasa.gov/Documents/ATBD_DPR_201811_with_Appendix3b.pdf.

    • Search Google Scholar
    • Export Citation
  • Joss, J., and R. Lee, 1995: The application of radar–gauge comparisons to operational precipitation profile corrections. J. Appl. Meteor., 34, 26122630, https://doi.org/10.1175/1520-0450(1995)034<2612:TAORCT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Junquas, C., K. Takahashi, T. Condom, J.-C. Espinoza, S. Chavez, J.-E. Sicart, and T. Lebel, 2018: Understanding the influence of orography on the precipitation diurnal cycle and the associated atmospheric processes in the central Andes. Climate Dyn., 50, 39954017, https://doi.org/10.1007/s00382-017-3858-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kubota, T., T. Iguchi, M. Kojima, L. Liao, T. Masaki, H. Hanado, R. Meneghini, and R. Oki, 2016: A statistical method for reducing sidelobe clutter for the Ku-band precipitation radar on board the GPM Core Observatory. J. Atmos. Oceanic Technol., 33, 14131428, https://doi.org/10.1175/JTECH-D-15-0202.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lee, G. W., A. W. Seed, and I. Zawadzki, 2007: Modeling the variability of drop size distributions in space and time. J. Appl. Meteor. Climatol., 46, 742756, https://doi.org/10.1175/JAM2505.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, H., and D. Moisseev, 2020: Two layers of melting ice particles within a single radar bright band: Interpretation and implications. Geophys. Res. Lett., 47, e2020GL087499, https://doi.org/10.1029/2020GL087499.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maahn, M., and P. Kollias, 2012: Improved Micro Rain Radar snow measurements using Doppler spectra post-processing. Atmos. Meas. Tech., 5, 26612673, https://doi.org/10.5194/amt-5-2661-2012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mantas, V. M., Z. Liu, C. Caro, and A. J. S. C. Pereira, 2015: Validation of TRMM multi-satellite precipitation analysis (TMPA) products in the Peruvian Andes. Atmos. Res., 163, 132145, https://doi.org/10.1016/j.atmosres.2014.11.012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Derin, Y., and Coauthors, 2016: Multiregional satellite precipitation products evaluation over complex terrain. J. Hydrometeor., 17, 18171836, https://doi.org/10.1175/JHM-D-15-0197.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Martínez-Castro, D., S. Kumar, J. L. Flores Rojas, A. Moya-Álvarez, J. M. Valdivia-Prado, E. Villalobos-Puma, C. D. Castillo-Velarde, and Y. Silva-Vidal, 2019: The impact of micro physics parameterization in the simulation of two convective rainfall events over the central Andes of Peru using WRF-ARW. Atmosphere, 10, 442, https://doi.org/10.3390/atmos10080442.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mourre, L., T. Condom, C. Junquas, T. Lebel, J. E. Sicart, R. Figueroa, and A. Cochachin, 2016: Spatio-temporal assessment of WRF, TRMM and in situ precipitation data in a tropical mountain environment (Cordillera Blanca, Peru). Hydrol. Earth Syst. Sci., 20, 125141, https://doi.org/10.5194/hess-20-125-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Moya-Álvarez, A. S., J. Gálvez, A. Holguín, R. Estevan, S. Kumar, E. Villalobos, D. Martínez-Castro, and Y. Silva, 2018a: Extreme rainfall forecast with the WRF-ARW Model in the central Andes of Peru. Atmosphere, 9, 362, https://doi.org/10.3390/atmos9090362.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Moya-Álvarez, A. S., D. Martínez-Castro, J. L. Flores, and Y. Silva, 2018b: Sensitivity study on the influence of parameterization schemes in WRF-ARW Model on short- and medium-range precipitation forecasts in the central Andes of Peru. Adv. Meteor., 2018, 1381092, https://doi.org/10.1155/2018/1381092.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Moya-Álvarez, A. S., D. Martínez-Castro, S. Kumar, R. Estevan, and Y. Silva, 2019: Response of the WRF model to different resolutions in the rainfall forecast over the complex Peruvian orography. Theor. Appl. Climatol. 137, 29933007, https://doi.org/10.1007/s00704-019-02782-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peel, M. C., B. L. Finlayson, and T. A. McMahon, 2007: Updated world map of the Köppen-Geiger climate classification. Hydrol. Earth Syst. Sci., 11, 16331644, https://doi.org/10.5194/hess-11-1633-2007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peters, G., B. Fischer, and T. Andersson, 2002: Rain observations with a vertically looking Micro Rain Radar (MRR). Boreal Environ. Res., 7, 353362.

    • Search Google Scholar
    • Export Citation
  • Peters, G., B. Fischer, H. Münster, M. Clemens, and A. Wagner, 2005: Profiles of raindrop size distributions as retrieved by microrain radars. J. Appl. Meteor., 44, 19301949, https://doi.org/10.1175/JAM2316.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peters, G., B. Fischer, and M. Clemens, 2010: Rain attenuation of radar echoes considering finite range resolution and using drop size distributions. J. Atmos. Oceanic Technol., 27, 829842, https://doi.org/10.1175/2009JTECHA1342.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Poveda, G., J. C. Espinoza, M. D. Zuluaga, S. A. Solman, R. Garreaud, and P. J. van Oevelen, 2020: High impact weather events in the Andes. Front. Earth Sci., 8, 162, https://doi.org/10.3389/feart.2020.00162.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ralph, F. M., 1995: Using radar-measured radial vertical velocities to distinguish precipitation scattering from clear-air scattering. J. Atmos. Oceanic Technol., 12, 257267, https://doi.org/10.1175/1520-0426(1995)012%3C0257:URMRVV%3E2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rao, T. N., D. N. Rao, and S. Raghavan, 1999: Tropical precipitating systems observed with Indian MST radar. Radio Sci., 34, 11251139, https://doi.org/10.1029/1999RS900054.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roe, G. H., 2005: Orographic precipitation. Annu. Rev. Earth Planet. Sci., 33, 645671, https://doi.org/10.1146/annurev.earth.33.092203.122541.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Russchenberg, H., 1991: Doppler-polarimetric radar measurements of the melting layer of precipitation. 1991 Seventh Int. Conf. on Antennas and Propagation, York, United Kingdom, IEEE, 7679.

    • Search Google Scholar
    • Export Citation
  • Scheel, M. L. M., M. Rohrer, C. Huggel, D. Santos Villar, E. Silvestre, and G. J. Huffman, 2011: Evaluation of TRMM Multi-satellite Precipitation Analysis (TMPA) performance in the central Andes region and its dependency on spatial and temporal resolution. Hydrol. Earth Syst. Sci., 15, 26492663, https://doi.org/10.5194/hess-15-2649-2011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Skofronick-Jackson, G., D. Kirschbaum, W. Petersen, G. Huffman, C. Kidd, E. Stocker, and R. Kakar, 2018: The Global Precipitation Measurement (GPM) mission’s scientific achievements and societal contributions: Reviewing four years of advanced rain and snow observations. Quart. J. Roy. Meteor. Soc., 144, 2748, https://doi.org/10.1002/qj.3313.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Speirs, P., M. Gabella, and A. Berne, 2017: A comparison between the GPM dual-frequency precipitation radar and ground-based radar precipitation rate estimates in the Swiss Alps and Plateau. J. Hydrometeor., 18, 12471269, https://doi.org/10.1175/JHM-D-16-0085.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Taylor, G. I., 1938: The spectrum of turbulence. Proc. Roy. Soc., 164A, 476490, https://doi.org/10.1098/rspa.1938.0032.

  • Valdivia, J. M., K. Contreras, D. Martinez-Castro, E. Villalobos-Puma, L. F. Suarez-Salas, and Y. Silva, 2020a: 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 Brief, 29, 105215, https://doi.org/10.1016/j.dib.2020.105215.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Valdivia, J. M., D. E. Scipión, M. Milla, and Y. Silva, 2020b: Multi-instrument rainfall-rate estimation in the Peruvian central Andes. J. Atmos. Oceanic Technol., 37, 18111826, https://doi.org/10.1175/JTECH-D-19-0105.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Viale, M., and F. A. Norte, 2009: Strong cross-barrier flow under stable conditions producing intense winter orographic precipitation: A case study over the subtropical central Andes. Wea. Forecasting, 24, 10091031, https://doi.org/10.1175/2009WAF2222168.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Villalobos, E. E., D. Martinez-castro, S. Kumar, Y. Silva, and O. Fashe, 2019: Estudio de tormentas convectivas sobre los Andes centrales del Perú usando los radares PR-TRMM (Study of convective storms in the Peruvian central Andes using the PR-TRMM radar). Rev. Cubana Meteor., 25, 5975, http://opn.to/a/tG8c1.

    • Search Google Scholar
    • Export Citation
  • Welle, T., and J. Birkmann, 2015: The world risk index—An approach to assess risk and vulnerability on a global scale. J. Extreme Events, 2, 1550003, https://doi.org/10.1142/S2345737615500037.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wen, Y., P. Kirstetter, Y. Hong, J. J. Gourley, Q. Cao, J. Zhang, Z. Flamig, and X. Xue, 2016: Evaluation of a method to enhance real-time, ground radar–based rainfall estimates using climatological profiles of reflectivity from space. J. Hydrometeor., 17, 761775, https://doi.org/10.1175/JHM-D-15-0062.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zawadzki, I. I., 1973: Statistical properties of precipitation patterns. J. Appl. Meteor., 12, 459472, https://doi.org/10.1175/1520-0450(1973)012%3C0459:SPOPP%3E2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 527 522 43
Full Text Views 94 91 3
PDF Downloads 116 112 5

The GPM-DPR Blind Zone Effect on Satellite-Based Radar Estimation of Precipitation over the Andes from a Ground-Based Ka-band Profiler Perspective

Jairo M. ValdiviaaInstituto Geofísco del Perú, Lima, Peru

Search for other papers by Jairo M. Valdivia in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0003-0709-1163
,
Patrick N. GatlinbNASA Marshall Space Flight Center, Huntsville, Alabama

Search for other papers by Patrick N. Gatlin in
Current site
Google Scholar
PubMed
Close
,
Shailendra KumaraInstituto Geofísco del Perú, Lima, Peru
cDepartment of Environmental Science, SRM University-AP, Neerukonda, Andhra Pradesh, India

Search for other papers by Shailendra Kumar in
Current site
Google Scholar
PubMed
Close
,
Danny ScipiónaInstituto Geofísco del Perú, Lima, Peru

Search for other papers by Danny Scipión in
Current site
Google Scholar
PubMed
Close
,
Yamina SilvaaInstituto Geofísco del Perú, Lima, Peru
dInstituto Nacional de Investigación en Glaciares y Ecosistemas de Montaña, Lima, Peru

Search for other papers by Yamina Silva in
Current site
Google Scholar
PubMed
Close
, and
Walter A. PetersenbNASA Marshall Space Flight Center, Huntsville, Alabama

Search for other papers by Walter A. Petersen in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

A vertically pointing Ka-band radar (Metek MIRA-35C) installed at the Instituto Geofísico del Perú, Atmospheric Microphysics and Radiation Laboratory (LAMAR) Huancayo Observatory, which is located at an elevation of 3.3 km MSL in the Andes Mountains of Peru, is used to investigate the effects of terrain on satellite-based precipitation measurement in the Andes. We compare the vertical structure of precipitation observed by the MIRA-35C with Ka-band radar measurements from the Dual-Frequency Precipitation Radar (DPR) on board the Global Precipitation Measurement (GPM) mission core satellite using an approach based on Taylor’s hypothesis of frozen turbulence that attempts to reduce the impact of spatiotemporal offsets between these two radar measurements. From 3 April 2014 to 20 May 2018, the DPR measured precipitation near LAMAR during 15 of its 157 coincident overpasses. There were six simultaneous observations with MIRA-35C. We found that the average of the DPR’s lowest clutter-free bin is 1.62 km AGL, but the presence of precipitation worsens the situation, causing a 0.4-km-deeper algorithm-detected blind zone for the DPR at the Huancayo Observatory. In the study area, the depth of the clutter layer observed with DPR often extends above the melting layer but can be highly variable, extending even as high as 5 km AGL. These results suggest that DPR estimates of stratiform precipitation over the Andes Mountains are likely underestimated because of the terrain effects on the satellite measurements and problems in its blind zone detection algorithms, highlighting the difficulty in estimating precipitation in mountainous terrain from spaceborne radar.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

This article is included in the Global Precipitation Measurement (GPM): Science and Applications Special Collection.

Corresponding author: Jairo M. Valdivia, valdiviaprado.ing@gmail.com

Abstract

A vertically pointing Ka-band radar (Metek MIRA-35C) installed at the Instituto Geofísico del Perú, Atmospheric Microphysics and Radiation Laboratory (LAMAR) Huancayo Observatory, which is located at an elevation of 3.3 km MSL in the Andes Mountains of Peru, is used to investigate the effects of terrain on satellite-based precipitation measurement in the Andes. We compare the vertical structure of precipitation observed by the MIRA-35C with Ka-band radar measurements from the Dual-Frequency Precipitation Radar (DPR) on board the Global Precipitation Measurement (GPM) mission core satellite using an approach based on Taylor’s hypothesis of frozen turbulence that attempts to reduce the impact of spatiotemporal offsets between these two radar measurements. From 3 April 2014 to 20 May 2018, the DPR measured precipitation near LAMAR during 15 of its 157 coincident overpasses. There were six simultaneous observations with MIRA-35C. We found that the average of the DPR’s lowest clutter-free bin is 1.62 km AGL, but the presence of precipitation worsens the situation, causing a 0.4-km-deeper algorithm-detected blind zone for the DPR at the Huancayo Observatory. In the study area, the depth of the clutter layer observed with DPR often extends above the melting layer but can be highly variable, extending even as high as 5 km AGL. These results suggest that DPR estimates of stratiform precipitation over the Andes Mountains are likely underestimated because of the terrain effects on the satellite measurements and problems in its blind zone detection algorithms, highlighting the difficulty in estimating precipitation in mountainous terrain from spaceborne radar.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

This article is included in the Global Precipitation Measurement (GPM): Science and Applications Special Collection.

Corresponding author: Jairo M. Valdivia, valdiviaprado.ing@gmail.com
Save