सीएसआईआर-राष्ट्रीय डेटा विज्ञान एवं कृत्रिम बुद्धिमत्ता संस्थान

by K. C. Gouda, S. Nahak & P. Goswami

Many applications, from agricultural planning (such as crop choice) to estimation of hydro-power and surface water availability require quantitative precipitation forecasts (QPF). While a large number of studies have addressed the problem of forecasting seasonal Indian summer monsoon (ISM) rainfall over the decades, the emphasis generally has been on simulation and forecasting of rainfall anomalies from long-period mean. However, given the trends in monsoon rainfall, the procedure of considering anomalies has inherent errors; thus reconstruction of actual rainfall from the anomalies does not necessarily provide accurate information. Most QPF have been attempted at short-range forecasts, with a variety of techniques like model output statistics. This work represents evaluation of an atmospheric Variable Resolution General Circulation Model (VRGCM) for QPF during monsoon season. The VRGCM, with variable grid resolution provides relatively high (~ 50 km) resolution over the ISM region, has been validated over all India as well as in different regions like Central and North India, South India, North East India for seasonal forecast of monsoon rainfall. VRGCM simulated QPF appears to provide comparable information as that of anomaly forecasts of monsoon over different regions in India. The forecast skill is appreciable, with significant correlation at all India, South India and North East India regions. The Root Mean Square Error of VRGCM in forecasting quantitative rainfall overall India and Central North India is very low and bit high over South and North East region of India. The performance of VRGCM in forecasting the rainfall in extreme (deficit or excess) monsoon years is also very high with phase synchronisation of 89% in the inter annual variability of rainfall during monsoon at all India scale while the value is about 57%, 76% and 55% over Central North, South and North East India region, respectively.

Source: https://link.springer.com/article/10.1007%2Fs00024-020-02463-7 

by M K Sharada, C Kalyani Devasena and P S Swathi

There have been many studies on iron as a limiting nutrient for productivity in the World Ocean, but only a few studies have been done in the Arabian Sea on iron limitation. Sensitivity of primary productivity, chlorophyll, nitrate, pCO2 and carbon Cux in the Arabian Sea to one of the parameters related to iron limitation has been investigated using a three-dimensional coupled biogeochemical model (TOPAZ) embedded in Modular Ocean Model (MOM) in the global domain, for climatology and interannual variability. Initially, the model results are evaluated for many of the biogeochemical components using data from World Ocean Atlas, satellites and cruises in the Arabian Sea. It is noticed that model results capture spatial and temporal variations of some of the biogeochemical components and Cuxes in the north Indian Ocean. Subsequently, it is shown that micronutrient iron plays a significant role on the growth of phytoplankton, utilization of nutrients, pCO2 in the ocean and carbon Cux across the air–sea interface in north and north-west regions of the Arabian Sea.

Source: https://link.springer.com/article/10.1007%2Fs12040-020-1361-9

by A. K. Nekrasova, V. G. Kossobokov, I. A. Parvez and X. Tao 

Abstract

The distribution of the number of seismic events by magnitudes—the Gutenberg–Richter frequency–magnitude relation—is of paramount importance for seismic hazard assessment of a territory. The generalization of the Gutenberg–Richter relation—the Unified Scaling Law for Earthquakes (USLE) proposed in 1988 makes it possible to take into account the pattern of epicentral distribution of seismic events when changing the spatial scale of the analysis. This is extremely important for adequate downscaling of the frequency of occurrence into a smaller area within the territory under study (e.g., in the megalopolis). In 2002, Per Buck suggested a dual formulation of USLE where, instead of the number of earthquakes over a certain period of time, the reciprocal of their frequency of occurrence—the time between seismic events—is used. The same year, the Institute of Earthquake Prediction Theory and Mathematical Geophysics of the Russian Academy of Sciences developed a modified algorithm for robust estimation of USLE parameters referred to as Scaling Coefficients Estimation (SCE) for producing seismic hazard maps of territories prone to seismic effects. This brief review is focused on the use of the USLE approach to the assessment of seismic hazard and associated risk.

Source: https://link.springer.com/article/10.1134/S1069351320010097

by S K Sahoo, Ajilesh P P, K C Gouda & Himesh S

Abstract

This study is about the impact assessment of different land-use data sets on the simulation of an Extreme Rainfall Event (ERE) which is one of the unusually rare events that occurred between 14th to 18th of June 2013 over Uttarakhand in India. In this work, high-resolution (2-km), time ensemble simulations are carried out using Weather Research and Forecasting model (WRFV3.5) with a 3-nest configuration. The sensitivity analysis of the model in simulating rainfall to different land-use data i.e. USGS-24 category (1992–93), ISRO (2004–05) and (2012–13) are carried out. Comparison of simulated rainfall which is averaged over the study region with that of IMD observed station data (averaged over 23 stations) showed that the simulations based on ISRO land-use data are comparatively more accurate with lesser simulation error when compared to simulations with USGS land-use data. The percentage of error in rainfall for the 3 simulations was found to be 24% (USGS), 9.5% (ISRO-2005) and 10% (ISRO-2013) with respect to the IMD observation. During the initial stage, the results have shown maximum convergence and vorticity with a strong updraft. The strong updraft, however, persisted throughout the simulation period. The increasing tendency of positive vorticity both in the simulation and observation suggests an intensification of cyclonic circulation in a vertical direction and hence creates instability in the boundary layer causing ERE over Uttarakhand. This study shows that ISRO land-use data is a relatively more realistic representation of the study region than the USGS data, and found to be useful in reducing the model error in the simulation of such rare events over this kind of mountainous region.

Source: https://link.springer.com/article/10.1007/s00704-020-03129-z