SRINIVASA RAMANUJAM KANNAN
Articles written in Journal of Earth System Science
Volume 130 All articles Published: 5 February 2021 Article ID 0020 Research article
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.
$\bullet$ IMD’s ground radar data from CTCZ campaign during the monsoon of 2013 is utilized for Intercomparison study with TRMM PR observations.
$\bullet$ IMD’s ground radar and TRMM PR reflectivity observations are spatially aligned within minimum volume required to produce spatially coincident sample.
$\bullet$ Non-parametric based approach using ANN is used to reduce the difference between the two instruments.
$\bullet$ 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).
Volume 131 All articles Published: 5 February 2022 Article ID 0046 Research article
The present study mainly focuses on the effect of temperature enhancement on local weather due to the heat emitted from anthropogenic sources with a numerical weather prediction model. In this study, anthropogenic heat (AH) flux is mainly considered as heat generated due to industrial action in the urban area. Angul district located between 20.41–21.80°N latitude and 84.55–85.30°E longitude in the Odisha state of India is chosen as the study region. In this location, a heavy rain event on 16 August 2008, and a light rain event on 22 March 2008 were identified. In the first part of this study, numerical simulations are performed using the mesoscale weather research and forecasting (WRF) model for both the rain events, based on which the near-surface rain rate is simulated. The simulated rainfall is compared against tropical rainfall measuring mission (TRMM) precipitation radar observations qualitatively for validation purposes. The comparative study throws a lot of insight based on different physics options available in the WRF model. The study found that the WRF double moment, 6-class microphysics scheme is better in capturing both the rain events in 2008. The TRMM validated WRF simulation now constitutes the control run against which comparisons for other cases are made. In the second part, a numerical experiment is performed to understand the effect of AH on local weather for the same region. The temperature at the surface level is perturbed by increasing it by 10K near the industrial site and exponentially decreasing with a height up to the atmospheric boundary layer. The design of the numerical experiment is such that the sensible heat, latent heat and moisture parameters are affected by changing the temperature parameter alone. The result shows that the rainfall rate increases locally for both the events due to the increase in temperature at the industrial site. The rate of increase in heavy rain event is nearly twice whereas, in light rain, it was found to increase by 1.7 times. In the third and final part of the study, the flow pattern at the near-surface level is studied in and around the industrial zone, and the same is then compared with the perturbed case for both the rain events. In the perturbed cases, the difference in temperature in and around the region causes pressure differential leading to the formation of stronger wind.
Volume 131, 2022
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