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Intercomparison between IMD ground radar and TRMM PR observations using alignment methodology and artificial neural network
Journal of Earth System Science ( IF 1.9 ) Pub Date : 2021-02-05 , DOI: 10.1007/s12040-020-01540-8
Alok Sharma , Srinivasa Ramanujam Kannan

Abstract

An inter-comparison of ground radar reflectivity with space-borne TRMM’s Precipitation Radar using alignment methodology has been presented. For this purpose, reflectivity data from Dual Polarization Ground Radar (GR) maintained by the India Meteorological Department (IMD) at the IMD Delhi site is utilized. IMD Delhi has collected radar data during Continental Tropical Convergence Zone (CTCZ) programme from 2011 to 2013. The present study utilizes monsoon data collected during 4 months, namely, June, July, August, and September (JJAS) from the year 2013. The GR observables are first converted from polar coordinates to Cartesian coordinates and then spatially aligned with TRMM PR data at a near-real-time to a common volume. It was found that in all the overpass cases, IMD’s GR reflectivity has a positive bias when compared with TRMM PR. A methodology is proposed to ‘correct’ the GR reflectivity data by considering TRMM PR data as ‘truth’ using a neural network-based approach. A supervised learning algorithm based on the back-propagation neural network is used for this purpose. Ground radar reflectivity is fed as input to the network, while the TRMM PR reflectivity is the target. The trained network is then tested for its performance against data which is not used as part of the training process. The present methodology demonstrates the match up of uncalibrated ground radar measured reflectivity and a well-calibrated space-borne radar.

Highlights

  • IMD’s ground radar data from CTCZ campaign during the monsoon of 2013 is utilized for Intercomparison study with TRMM PR observations.

  • IMD’s ground radar and TRMM PR reflectivity observations are spatially aligned within minimum volume required to produce spatially coincident sample.

  • Non-parametric based approach using ANN is used to reduce the difference between the two instruments.

  • With the ANN training, the correlation coefficient (RMSE) between the observations made by the two instruments increased (decreased) from 0.45 (15.77 dBZ) to 0.79 (4.69 dBZ).



中文翻译:

使用对准方法和人工神经网络对IMD地面雷达和TRMM PR观测值进行比对

摘要

提出了使用对准方法将地面雷达反射率与星载TRMM的降水雷达进行比对的方法。为此,利用了印度气象局(IMD)在IMD德里站点维护的双极化地面雷达(GR)的反射率数据。IMD德里在2011年至2013年的大陆热带收敛带(CTCZ)计划期间收集了雷达数据。本研究利用了从2013年开始的4个月(即6月,7月,8月和9月(JJAS))收集的季风数据。首先将GR观测值从极坐标转换为笛卡尔坐标,然后与TRMM PR数据在近乎实时的空间上对齐到一个公共体积。发现在所有立交桥情况下,与TRMM PR相比,IMD的GR反射率具有正偏差。提出了一种使用基于神经网络的方法通过将TRMM PR数据视为“真相”来“校正” GR反射率数据的方法。为此,使用了基于反向传播神经网络的监督学习算法。地面雷达反射率作为网络的输入,而TRMM PR反射率是目标。然后,针对未用作训练过程一部分的数据测试经过训练的网络的性能。本方法论证明了未经校准的地面雷达测得的反射率与经过良好校准的星载雷达的匹配。地面雷达反射率作为网络的输入,而TRMM PR反射率是目标。然后,针对未用作训练过程一部分的数据测试经过训练的网络的性能。本方法论证明了未经校准的地面雷达测得的反射率与经过良好校准的星载雷达的匹配。地面雷达反射率作为网络的输入,而TRMM PR反射率是目标。然后,针对未用作训练过程一部分的数据测试经过训练的网络的性能。本方法论证明了未经校准的地面雷达测得的反射率与经过良好校准的星载雷达的匹配。

强调

  • IMD在2013年季风期间从CTCZ战役获得的地面雷达数据用于TRMM PR观测的比对研究。

  • IMD的地面雷达和TRMM PR反射率观测值在空间上对齐,以产生空间一致的样本所需的最小体积。

  • 使用基于神经网络的基于非参数的方法可减少两种仪器之间的差异。

  • 通过ANN训练,两种仪器的观测值之间的相关系数(RMSE)从0.45(15.77 dBZ)增加(减小)到0.79(4.69 dBZ)。

更新日期:2021-02-05
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