5 research outputs found
Increasing heavy rainfall events in south India due to changing land use and land cover
No full textThrough an analysis of land use land cover (LULC) data for the years 2005 and 2017 from the Advanced Wide Field Sensor onboard the Indian Remote Sensing satellite, we find considerable changes in the LULC in three major states of South India, namely, Tamil Nadu, Telangana, and Kerala. This change is mainly due to increasing urbanization, in addition to the change of prevalent mixed forest into deciduous needle/leaf forest in Kerala. Motivated by this finding, we study the impact of these LULC changes over a decade on the extremity of twelve heavy rainfall events in these states through several sensitivity experiments with a convection-permitting Weather Research and Forecasting model, by changing the LULC boundary conditions. We particularly focus on three representative heavy rainfall events, specifically, over (i) Chennai (December 01, 2015), (ii) Telangana (September 24, 2016), and (iii) Kerala (August 15, 2018). The simulated rainfall patterns of the three heavy rainfall events are found to be relatively better with the use of the 2017 LULC boundary conditions. The improvement is statistically significant in the case of the Chennai and Kerala events. On analysis of these simulations, and outputs from additional simulations we have conducted for nine other heavy rainfall events, we suggest that the recent LULC changes result in higher surface temperatures, sensible heat fluxes, and a deeper and moist boundary layer. This causes a relatively higher convective available potential energy and, consequently, heavier rainfall. We find the LULC changes in the three states, mainly dominated by the increasing urbanization in Telangana and Tamil Nadu, enhance the rainfall during the heavy rainfall events by 20% - 25%. This is the first extensive investigation of multiple and multi-regional cases over the Indian region.The authors thank the India Meteorological Department (IMD) for providing gridded rainfall dataset. We also acknowledge all data providers (ISRO, TRMM, APHRODITE, GPM, ERA5 and NCEP) that made their datasets available for this study. Dr. K. Srinivasarao (NRSC) support in the usage of the IRSO LULC datasets is particularly acknowledged, and Dr. K. Nagaratna (IMD, Begumpet, Hyderabad) for providing synoptic information of the Telangana event. The first author is thankful to KAUST for providing a student visiting research fellowship to carry out this research. All model simulations were carried out on the KAUST supercomputing facility
Shaheen. Also acknowledge Council of Scientific and Industrial Research (CSIR) for the Senior Research Fellowship (SRF
Assimilation of global positioning system radio occultation refractivity for the enhanced prediction of extreme rainfall events in southern India
No full textHere, we investigated the impact of assimilating the satellite-based product of Global Positioning System (GPS) radio occultation (RO) refractivity profiles data on the simulation of selected extreme rainfall events in three states of southern India: Tamil Nadu, Telangana, and Kerala. We assimilated the GPS RO data into the weather research and forecasting model using a 3DVar assimilation technique and evaluated the results against unassimilated (control) simulations. Various observations (e.g., rainfall measurements from AWS/rain-gauge) and observation-based gridded rainfall were used. The assimilation of the data yielded improved prediction of the spatial distributions of extreme rainfall regions and the amounts of rainfall. The analysis of the simulated dynamical and thermodynamic processes indicated that the assimilation of the data enabled the model to simulate significantly deep convection, high instability, and strong vertical motions. A vorticity budget analysis confirmed the marginally strengthened low-level convergence. The vertical motions because of assimilation facilitated an increased vertical advection of vorticity, which enhanced the extreme conditions in storms. Moreover, the assimilation of the data resulted in enhanced water vapor condensation and high levels of ice, cloud, and rain water in clouds, all of which contributed to extreme rainfall.We thank the IMD for providing the gridded rainfall dataset. We thank the ISRO and NCEP/NCAR for providing the LULC and GPS RO datasets. All model simulations were carried out on the KAUST supercomputing facility SHAHEEN. The Skew-T Log-P diagram analysis was carried out using NCAR Command Language. We also acknowledge the Council of Scientific and Industrial Research, Government of India for awarding a Senior Research Fellowship. The datasets used are freely available in selected publications (NCEP, 2008, 2015; Biswadip, 2014; Huffman, 2015; Reddy et al., 2007; Srinivas et al., 2018)
Projected frequency of low to high-intensity rainfall events over India using bias-corrected CORDEX models
No full textHeavy rainfall events and associated floods have emerged as one of the great threats to society that mainly manifested due the climate change. The Indian summer monsoon (ISM) contributes 80 % of annual rainfall and is characterized mainly by high-intensity rainfall events (HiREs) in the recent era. We investigated the spatiotemporal variability of HiREs from a climate change perspective by accessing the India Meteorological Department's (IMD) observed daily gridded rainfall dataset (0.25° × 0.25°) from 1961 to 2020 during the ISM season. Our observational analysis shows that the ISM total and the frequency of low- to high-intensity rainfall events have significantly decreased mostly over the central northeastern, Jammu and Kashmir, and some places in the northeastern and central parts of India. However, they have significantly increased over Gujarat, the northwestern, the Western Ghats, and the southern parts of India during the present climate period (1991–2020) compared to the past climate period (1961–1990). Furthermore, we explored the fidelity of five Coordinated Regional Climate Downscaling Experiments (CORDEX) Regional Climate Models (RCMs) in simulating the spatiotemporal variability of ISM total rainfall and the frequency of low- to high-intensity rainfall events over India during the historical (1976–2005) and future periods (2006–2100). All CORDEX RCMs overestimate the ISM total rainfall over India's heavy rainfall zones during the historical period by ∼10–30 % compared to IMD observations. To improve CORDEX RCM's skills in simulating the frequency of low- to high-intensity rainfall events, we employed a percentile-based bias correction technique. Compared to non-bias-corrected outputs from the RCMs, the quantile-bias-corrected method significantly enhanced the probability of detection rate (hit rate) in all studied models for extreme, heavy, and moderate rainfall events, excluding light rainfall events. Interestingly, the improvement is greater for extreme events, followed by heavy and moderate rainfall events. The composite hit rate of all the models shows 381 %, 146 %, and 44 % improvement for extreme, heavy, and moderate events, respectively. It is noticed that the CCCMA model performed better than the other four CORDEX models in capturing the spatial patterns of ISM total rainfall and the frequency of total extreme and heavy rainfall events over higher rainfall zones in India. Additionally, this study suggests that there will likely be no significant changes in ISM total rainfall over India in the future, but the frequency of total extreme and heavy rainfall events will most likely increase, while the frequency of moderate rainfall events will likely decrease mostly over southern parts of India in future projections.The first author is thankful to the India Meteorological Department (IMD) and the Centre for Climate Change Research (CCCR) at the Indian Institute of Tropical Meteorology (IITM) for providing the observed and CORDEX model's gridded daily rainfall datasets. The first author is also thankful to the Centre for Development of Advanced Computing (C-DAC) for providing funding support to carry out this research (CORP: DG:3170) under the National Supercomputing Mission (NSM) program, Govt. of India. Additionally, the first author is grateful to Dr. Madhusmitha Swain from the Indian Institute of Technology Bhubaneswar (IITB) for her technical assistance with the Matlab code. The Indian Institute of Technology Bhubaneswar is also acknowledged for providing the necessary infrastructure to carry out this research. We gratefully acknowledge the anonymous reviewers for their valuable comments and appreciation of the work
Assessment of the WRF model configuration optimization in predicting the heavy rainfall over urban city Bhubaneswar, India
No full textAbstract Bhubaneswar, Odisha, experiences an increasing trend of heavy rainfall events (HREs). This study aims to configure the WRF mesoscale model configuration at a hectometre scale and undertakes numerical experiments at a 0.5 km grid spacing. The experiments simulate HREs and assess the various physical parameterization schemes to identify suitable combinations for the region. Sensitivity experiments with various physical parametrization options identified the top eight combinations based on rainfall statistics. Their performance was further evaluated by simulating an additional four HREs over Bhubaneswar. A novel rank analysis approach based on statistical techniques to determine the rank of each configuration. The Noah-MP; Ferrier; Multi-Scale Kain-Fritsch (MFS), Noah-MP;Ferrier; Kain-Fritsch (MFK), as well as Noah; Lin;No cumulus (NLN), and Noah; Ferrier; No cumulus (NFN) emerged as the top performers in simulating precipitation. The study also tested eight parameterization combinations for simulating air temperature, relative humidity, and wind speed. The top configurations change when a different variable is used as a reference. However, a broad choice of MFS, MFK, and Noah-MP; Ferrier; No cumulus (MFN) merged as the top configurations in simulating HRE characteristics. These model configurations were independently tested and yielded good performance in simulating the atmospheric pre-storm environment and storm characteristics. Broadly stated the choice of Noah-MP instead of the Noah land model, with Ferrier and Multi-Scale Kain-Fritsch schemes could yield good results- though there is no singular best potential. These findings help establish the computational framework for studying and improving the understanding of heavy rainfall, enhance weather hazard preparedness, and offer an optimized WRF model for forecasting HRE in cities
