https://johniitr.github.io/tags/rainfall/RainfallHugo Blox Builder (https://hugoblox.com)en-usTue, 17 Jan 2023 00:00:00 +0000https://johniitr.github.io/media/icon_hu_982c5d63a71b2961.pnghttps://johniitr.github.io/tags/rainfall/https://johniitr.github.io/publications/monsoon-cordex-tac-2023/Tue, 17 Jan 2023 00:00:00 +0000https://johniitr.github.io/publications/monsoon-cordex-tac-2023/<h2 id="abstract">Abstract</h2> <p>In this study, the performance of the latest high-resolution CORDEX-CORE model simulations is assessed with respect to the corresponding gridded Indian Meteorological Department (IMD) and ERA5 observations in representing the monsoon rainfall over northeast India during the historical period (1979–2005). Three different RCM model simulations (COSMO, RegCM4.7, and REMO) downscaled from the global data over the South Asian CORDEX domain are used in this study. Their corresponding RCM ensembles using boundary conditions from ERA-Interim re-analysis and CMIP5 GCMs and a combined ensemble of all the RCMs were also evaluated to assess their performance. The analysis shows that the COSMO model experiments resemble closer to both the observations while comparing with the other two RCMs. With respect to IMD and ERA5 observations, the COSMO model experiments show moderate wet bias across the study area. In contrast, the RegCM model experiments show very wet bias and the REMO model experiments show more dry bias across a significant part of the study region. Quantitatively, in comparison to IMD, the COSMO suite of models shows a slight overestimation ranging between 7 and 13%, and with ERA5, an underestimation of about 14 to 18% is observed. In comparison to both observations, the REMO model experiments underestimate (15 to 50%), whereas the RegCM model experiments overestimate (15 to 80%) the monsoon rainfall. Overall, these model experiments replicate the monsoon rainfall over the study region but with biases that differ spatially.</p>https://johniitr.github.io/publications/rainfall-trends-mandi-sjce-2017/Tue, 03 Oct 2017 00:00:00 +0000https://johniitr.github.io/publications/rainfall-trends-mandi-sjce-2017/<h2 id="abstract">Abstract</h2> <p>Climate variability, particularly, that of the annual air temperature and precipitation, has received a great deal of attention worldwide. The magnitude of the variability of the factors changes according to the locations. The present study focuses on detecting the trends and variability in the annual temperature and rainfall for the district of Mandi in Himachal Pradesh, India. This study used annual and monsoon time series data for the time period 1981-2010 and modified the Mann-Kendall test and Sen&rsquo;s slope estimator in analyzing the problem. The results of the analysis indicate that the annual maximum temperature (TMX) and annual minimum temperature (TMN) for the period of 30 years have shown an increasing trend, whereas the monsoon’s maximum and minimum temperatures have shown a decreasing trend, although it is statistically not significant. The amount of annual rainfall does not show any significant trend, but the monsoonal rainfall has shown an increasing trend that is also statistically not significant. The resulting Mann-Kendall test statistic (Z) and Sen’s slope estimate (Q) of all the parameters studied indicate that changes are occurring in the magnitude and timing of the precipitation and temperature events at the Mandi station.</p>