Dr. Govindan Kutty M
Professor
- R.215,D2
- govind [at] iist.ac.in
-
+91-471-2568517
Research interests
Education
- Postdoctoral Research Associate, University of Oklahoma, USA (2011 – 2014)
- Ph.D. in Atmospheric Science, IIT Kharagpur (2006- 2010)
- M.Sc. in Meteorology, Cochin University of Science and Technology (2003-2005)
Area of Interest
Improving the understanding of the predictability of extreme weather events through data assimilation and ensemble approaches
- Atmospheric Thermodynamics
- Planetary Atmospheres
- Boundary Layer Meteorology
Student Name | Title of Thesis & Year of completion | Current Position |
Dr. Rekha Bharali Gogoi
| Impact of Ensemble Derived Flow-dependent Background
Error Covariance in a Data Assimilation System for
Regional-scale NWP model
Year: 2021 |
Scientist – F NESAC,
DoS, Umiam |
Dr. Arpita Munsi
(Co-guided with Dr. Amit P Kesarkar,
NARL)
|
Understanding the helical evolution of tropical cyclones and
their interaction with the upper ocean
Year: 2022 |
Research Associate,
NARL |
Dr. Babitha George
|
Predictability and Dynamics of Extreme Weather Events
over the Indian Subcontinent using Ensemble Sensitivity
Analysis in EnKF Data Assimilation System
Year: 2023 | Earth
Sciences department,
Vrije Universiteit
Amsterdam,
Netherlands |
Year |
Title of Project |
Funding Agency |
Project cost |
2023 – 2025
|
Implementation of Ensemble Forecast
Sensitivity Approach to Estimate the Impact of
Observations in IMD GFS forecast
|
Monsoon Mission
(Ministry of Earth
Science) |
58.0 Lakhs |
2022 – 2024 | Improving the Prediction of Thunderstorms
using Dual – Resolution Hybrid Ensemble –
Variational Data Assimilation System in WRF
model
|
Ministry of Earth
Science (MoES) |
75.0 Lakhs |
2018 – 2019
|
Implementing 4DVAR Data Assimilation in
SASE forecasts |
SASE, DRDO |
10.0 Lakhs |
2014 – 2017
|
Use of Hybrid Ensemble Data Assimilation
system in NARL operational forecasts
|
ASRG |
8.0 Lakhs |
A probabilistic method has been developed to identify regions of Initial Condition uncertainty in the NWP model
Forecast errors can be reduced if observations are assimilated in regions with the largest percentage of analysis error growth. In our recent study, the regions where additional observations will impact the forecasts over the Indian subcontinent during the summer monsoon season have been identified using the ensemble approach.
You may read the details here: https://assets.researchsquare.com/files/rs-2893417/v1/74e2a094-565d-41f1-b004-08d742468068.pdf?c=1683814947
