This paper performs ground validation of the Ku, Ka-band dual frequency space radar observations with simultaneous dual polarized S-band ground radar products. The aim is to validate satellite observations of radar reflectivity and rainfall rate with similar observations from ground-based radar networks. Bright Band height detection which is one of the main features of the GPM Profile Classification Module is also compared with ground radar product. The ultimate goal is to evaluate and further develop dual-frequency retrieval algorithms used in GPM-DPR.
Direct comparison on a point-by-point basis can be very challenging due to differences in geometric alignment & resolution volumes between both the radar systems. In addition, the attenuation suffered at Ku (13.6 GHz) and Ka (35.5 GHz) bands can also lead to comparison errors. To mitigate these difficulties, in this paper, a volume matching methodology (Bolen and Chandrasekar 2003) has been adopted which matches the coincident DPR and GR observations in the common 3D resolution volume. For this study, multiple S-band ground radar locations in the continental United States such as NEXRAD, NASA N-POL, and CSU-CHILL radar are chosen. Cases of DPR overpasses with these ground radar have been selected with sufficient amount of precipitation with no more than 2 minutes of time difference. Next volume matching procedure is performed in such a way that minimizes error due to the difference in observation volume and frequency. Volume matched reflectivity from both the radar shows good agreement. Results are presented in terms of statistical scores such as mean biases and mean standard error normalized to the ground observations. Rainfall rate estimate from DPR product (Seto et al. 2013), after volume matching, is also made to qualitatively compare with that of the ground radar. Quantitative precipitation estimation algorithm (Chen et al. (2015) has been used to retrieve GR rainfall rate. The comparisons show good agreement. For validating melting layer heights first hydrometeor classification (Bechini and Chandrasekar 2015) is performed on ground radar observations and then cross compared with that of DPR product (Le and Chandrasekar 2013). The comparisons show good agreement and the results are presented.
Bolen, Steven M., and V. Chandrasekar 2003: "Methodology for aligning and comparing space borne radar and ground-based radar observations." Journal of Atmospheric and Oceanic Technology 20.5, 647-659.
Seto, S., T. Iguchi, T. Oki, 2013: The basic performance of a precipitation retrieval algorithm for the Global Precipitation Measurement mission’s single/dual-frequency radar measurements, Geoscience and Remote Sensing, IEEE Transactions, Vol 51, No12, 5239-5251.
Haonan Chen, V. Chandrasekar, 2015: The quantitative precipitation estimation system for Dallas–Fort Worth (DFW) urban remote sensing network, Journal of Hydrology, Volume 531, Part 2, Pages 259-271, ISSN 0022-1694, http://dx.doi.org/10.1016/j.jhydrol.2015.05.040.
Renzo Bechini and V. Chandrasekar, 2015: A Semisupervised Robust Hydrometeor Classification Method for Dual-Polarization Radar Applications. J. Atmos. Oceanic Technol., 32, 22–47.
M. Le and V. Chandrasekar, 2013: “Hydrometeor Profile Characterization Method for Dual-Frequency Precipitation Radar Onboard the GPM”, Geoscience and Remote Sensing, IEEE Transactions, Volume: 51.