Rohit Chakraborty

Abstract: Summary Nowcasting of heavy rain events using microwave radiometer has been carried out at Kolkata (22.65°N, 88.45°E), a tropical location. Microwave radiometer can produce the temperature and humidity profiles of the atmosphere with fairly good accuracy. Definite changes are observed in temperature and humidity profiles before and at the onset of heavy rain events. Concurrent changes in the brightness temperatures (BT) at 22 GHz and 58 GHz are found to be suitable to nowcast rain. The time derivatives of brightness temperatures at 22 GHz and 58 GHz are used as inputs to the proposed nowcasting model. In addition, the standard deviation of the product of these time derivatives is also considered. The model has been developed using the data of 2011 and validated for rain events of 2012–2013 showing a prediction efficiency of about 90% with alarm generated about 25 min in advance.

Abstract: In the present study, the effectiveness of nowcasting convective activities using a microwave radiometer has been examined for Kolkata (22.65° N, 88.45° E), a tropical location. It has been found that the standard deviation of brightness temperature (BT) at 22 GHz and instability indices like Lifting Index (LI), K Index (KI) and Humidity Index (HI) has shown definite changes before convective events. It is also seen that combination of standard deviation of BT at 22 GHz and LI can be most effective in predicting convection. A nowcasting algorithm is prepared using 18 isolated convective events of 2011 and in all cases, a marked variation of these parameters has been seen an hour before the event. Accordingly, a prediction model is developed and tested on convective events of 2012 and 2013. It is seen that the model gives reasonable success in predicting convective rain about 7075 min in advance with a prediction efficiency of 80%.

Abstract: The behaviors of various meteorological parameters during 1981–2010 are investigated to obtain any asymmetric variability of summertime near surface wind over Indian coastal boundaries. No significant changes were obtained in the trends of surface pressure, surface relative humidity, 2-metre temperature and surface precipitation; although, near surface wind speed is found to have significantly declined on the eastern coast with respect to the western coast during this period. Summertime surface wind speed on the eastern coast have decreased from 3.5 to 2.5 m s − 1 (7 to 5 knots) whereas 4.5 to 4 m s − 1 (9 to 8 knots) during the last three decades (statistical significance level ~ 95%). A decrease in the atmospheric instability may serve as the potential reason for the suppression of severe convective occurrences manifested by a parallel decrease in surface wind speeds over these regions. The local heating up of middle atmosphere (300–500 hPa pressure level) due to increased humidity and the difference in net heat flux over Arabian Sea and Bay of Bengal due to the variance of temperature gradient (1000–925 hPa) along the coastal boundaries might be responsible for this climatic disparity between the coastal regions of India since the last three decades. Summertime near surface wind speed projections for Indian sub-continent based on 7 best climate models, for RCP8.5 scenarios, has been calculated to show a mean increase by ~ 10–15% on the eastern coast (Eastern Ghats), ~ 1–2% on the western coasts (Western Ghats), ~ 1–5% decrease in the Indo-Gangetic Basin and ~ 3% decrease in the Gangetic West Bengal and adjoining Bangladesh.

Abstract: Automatic nowcasting of convective initiation and thunderstorms has potential applications in several sectors including aviation planning and disaster management. In this paper, random forest based machine learning algorithm is tested for nowcasting of convective rain with a ground based radiometer. Brightness temperatures measured at 14 frequencies (7 frequencies in 22–31 GHz band and 7 frequencies in 51–58 GHz bands) are utilized as the inputs of the model. The lower frequency band is associated to the water vapor absorption whereas the upper frequency band relates to the oxygen absorption and hence, provide information on the temperature and humidity of the atmosphere. Synthetic minority over-sampling technique is used to balance the data set and 10-fold cross validation is used to assess the performance of the model. Results indicate that random forest algorithm with fixed alarm generation time of 30 min and 60 min performs quite well (probability of detection of all types of weather condition ∼90%) with low false alarms. It is, however, also observed that reducing the alarm generation time improves the threat score significantly and also decreases false alarms. The proposed model is found to be very sensitive to the boundary layer instability as indicated by the variable importance measure. The study shows the suitability of a random forest algorithm for nowcasting application utilizing a large number of input parameters from diverse sources and can be utilized in other forecasting problems.

Abstract: This study examines the response of the tropical atmospheric and oceanic circulation to increasing greenhouse gases using a coordinated set of twenty-first-century climate model experiments performed for the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). The strength of the atmospheric overturning circulation decreases as the climate warms in all IPCC AR4 models, in a manner consistent with the thermodynamic scaling arguments of Held and Soden. The weakening occurs preferentially in the zonally asymmetric (i.e., Walker) rather than zonal-mean (i.e., Hadley) component of the tropical circulation and is shown to induce substantial changes to the thermal structure and circulation of the tropical oceans. Evidence suggests that the overall circulation weakens by decreasing the frequency of strong updrafts and increasing the frequency of weak updrafts, although the robustness of this behavior across all models cannot be confirmed because of the lack of data. As the cli...

Abstract: Commercial success of big data has led to speculation that big-data-like reasoning could partly replace theory-based approaches in science. Big data typically has been applied to ‘small problems’, which are well-structured cases characterized by repeated evaluation of predictions. Here, we show that in climate research, intermediate categories exist between classical domain science and big data, and that big-data elements have also been applied without the possibility of repeated evaluation. Big-data elements can be useful for climate research beyond small problems if combined with more traditional approaches based on domain-specific knowledge. The biggest potential for big-data elements, we argue, lies in socioeconomic climate research. Big data is increasingly popular in many research domains. This Perspective discusses where elements of big data approaches have been employed in climate research and where combining big data with theory-driven research can be most fruitful.

Abstract: Due to the importance of climatic variability, an assessment detecting the changes and trends has been carried out over different time series of major climatic variables from the records of meteorological stations over Bangladesh from 1988–2017. Linear regression, the Mann-Kendall test, and Sen's slope method were used to analyze the significant trends and magnitude of the variables' changes, while the Pearson and Spearman rho correlation test have been applied to correlate between the variables. The results show that the average monthly maximum temperature (Tmax) and minimum temperature (Tmin) have increased significantly by 0.35 °C/decade and 0.16 °C/decade, respectively. However, the increase in Tmax is comparatively higher than Tmin and caused significant increases in the monthly temperature range (MTR) at a higher rate in winter than in the monsoon season. The trend patterns of Tmax, Tmin, and MTR reveal that most of the regions of the country (especially the south-eastern and north-eastern) have been colder during winter and hotter during the monsoon. In contrast, the wind speed (WS) has decreased significantly all over the country and decreased by a higher rate in the north-western (NW) region (monsoon, 0.60 and annual, 0.51 kt/decade) than other regions, while the monsoonal and annual precipitation have decreased by 87.35 mm/decade and 107 mm/decade, respectively. The monsoonal Tmax and Tmin (0.47 °C/decade and 0.38 °C/decade, respectively) have increased significantly in the NW; consequently, this region has been warmed by 0.27 °C/decade. The increase in temperature and decrease in WS may cause a decrease in rainfall in the NW region. Humidity changes are not significant except in the monsoon season across the country. Precipitation, WS, and humidity are negatively correlated with the temperature variables. The declination of WS may influence the rising trend in temperature and the falling trend in precipitation and humidity, suggesting the need for further advanced study on the negative effects of climate change in Bangladesh.

Abstract: Summary Nowcasting of heavy rain events using microwave radiometer has been carried out at Kolkata (22.65°N, 88.45°E), a tropical location. Microwave radiometer can produce the temperature and humidity profiles of the atmosphere with fairly good accuracy. Definite changes are observed in temperature and humidity profiles before and at the onset of heavy rain events. Concurrent changes in the brightness temperatures (BT) at 22 GHz and 58 GHz are found to be suitable to nowcast rain. The time derivatives of brightness temperatures at 22 GHz and 58 GHz are used as inputs to the proposed nowcasting model. In addition, the standard deviation of the product of these time derivatives is also considered. The model has been developed using the data of 2011 and validated for rain events of 2012–2013 showing a prediction efficiency of about 90% with alarm generated about 25 min in advance.

Abstract: Grasslands are an important component of terrestrial ecosystems that play a crucial role in the carbon cycle and climate change. In this study, we collected aboveground biomass (AGB) data from 223 grassland quadrats distributed across the Loess Plateau from 2011 to 2013 and predicted the spatial distribution of the grassland AGB at a 100-m resolution from both meteorological station and remote sensing data (TM and MODIS) using a Random Forest (RF) algorithm. The results showed that the predicted grassland AGB on the Loess Plateau decreased from east to west. Vegetation indexes were positively correlated with grassland AGB, and the normalized difference vegetation index (NDVI) acquired from TM data was the most important predictive factor. Tussock and shrub tussock had the highest AGB, and desert steppe had the lowest. Rainfall higher than 400 m might have benefitted the grassland AGB. Compared with those obtained for the bagging, mboost and the support vector machine (SVM) models, higher values for the mean Pearson coefficient (R) and the symmetric index of agreement (λ) were obtained for the RF model, indicating that this RF model could reasonably estimate the grassland AGB (65.01%) on the Loess Plateau.