Articles written in Journal of Earth System Science
Volume 122 Issue 4 August 2013 pp 991-1004
The purpose of this study is to address prediction of the start date and the duration of breaks in the summer monsoon rains using multi-model superensemble. The availability of datasets from the ‘observing system research and predictability experiment (THORPEX)’ initiated a forecast data archive, called THORPEX interactive grand global ensemble (TIGGE), makes it possible to use forecasts from a suite of individual ensemble prediction systems (member models) and to construct multi-model superensemble forecasts that are designed to remove the collective bias errors of the suite of models. Precipitation datasets are important for this study, we have used high resolution daily gridded rainfall dataset of India Meteorological Department (IMD), in addition to rainfall estimates from tropical rainfall microwave mission (TRMM) satellite and the CPC morphing technique (CMORPH). The scientific approach of this study entails the use of a multi-model superensemble for forecast and to verify against the rainfall information during a training phase, as well as during a forecast phase. We examine the results of forecasts out to day-10 and ask how well do forecast strings of day-1 through day-10 handle the prediction of the onset and duration of the breaks in the summer monsoon rains. Our results confirm that it is possible to predict the onset of a dry spell, around week in advance from the use of the multi-model superensemble and a suite of TIGGE models.We also examine trajectories of the parcels arriving in India in such forecasts from member models and from the multi-model superensemble to validate the arrival of descending dry desert air from the Arabian region during the dry spells and its mode of transition from wet spell. Some phenological features such as a shift in the latitude of the tropical easterly jet and changes in its intensity during break periods are additional observed features that are validated from the history of multi-model superensemble forecasts. Invariably this multi-model superensemble performs better than any single model in proving the better forecasts during our experiment period.
Volume 122 Issue 5 October 2013 pp 1259-1268
In the present study, we report initial results on analysis of carbon dioxide (CO2), water vapour (H2O), and energy fluxes (sensible and latent heat flux) over teak mixed deciduous forests of Madhya Pradesh, central India, during winter (November 2011 and January 2012) and summer (February–May 2012) seasons using eddy covariance flux tower datasets. During the study period, continuous fast response measurements of CO2, H2O and heat fluxes above the canopy were carried out at 10 Hz and averaged for 30 minutes. Concurrently, slow response measurements of meteorological parameters are also being carried out. Diurnal and seasonal variations of CO2, H2O and heat fluxes were analysed and correlated with the meteorological variables. The study showed strong influence of leaf off and on scenario on the CO2, H2O and energy fluxes due to prevalence of deciduous vegetation type in the study area. Maximum amount of CO2 was sequestered for photosynthesis during winter (monthly mean of −25 𝜇 mol/m2/s) compared to summer (monthly mean of −2 𝜇 mol/m2/s). Energy flux analysis (weekly mean) showed more energy being portioned into latent heat during winter (668 W/m2) and sensible heat during summer (718 W/m2).
Volume 126 Issue 4 June 2017 Article ID 0055
Seasonal sensitivity characteristics (SSCs) were developed for Naradu, Shaune Garang, Gor Garang and Gara glaciers, Western Himalaya to quantify the changes in mean specific mass balance using monthly temperature and precipitation perturbations. The temperature sensitivities were observed high during summer (April–October) and precipitation sensitivities during winter months (November–March), respectively. The reconstructed mass balance correlates well with the field and remote sensing measurements, available between 1980 and 2014. Further, SSCs were used with the monthly mean temperatures and precipitation estimates of ERA 20CM ensemble climate reanalysis datasets to reconstruct the specific mass balance for a period of 110 years, between 1900 and 2010. Mass balance estimates suggest that the Shaune Garang, Gor-Garang and Gara glaciers have experienced both positive and negative mass balance, whereas the Naradu glacier has experienced only negative mass balance since 1900 AD. Further, a cumulative loss of −133±21.5 m.w.e was estimated for four glaciers during the observation period. This study is the first record from Indian Himalaya in evaluating the mass balance characteristics over a century scale.
Volume 127 Issue 2 March 2018 Article ID 0018
We have estimated soil moisture (SM) by using circular horizontal polarization backscattering coefficient (σoRH), differences of circular vertical and horizontal σo (σoRV− σo RH) from FRS-1 data of Radar Imaging Satellite (RISAT-1) and surface roughness in terms of RMS height (RMSheight). We examined the performance of FRS-1 in retrieving SM under wheat crop at tillering stage. Results revealed that it is possible to develop a good semi-empirical model (SEM) to estimate SM of the upper soil layer using RISAT-1 SAR data rather than using existing empirical model based on only single parameter, i.e., σo. Near surface SM measurements were related to σoRH, σoRV−σoRH derived using 5.35 GHz (C-band) image of RISAT-1 and RMSheight. The roughness component derived in terms of RMSheight showed a good positive correlation with σoRH−σoRH (R2 = 0.65). By considering all the major influencing factors (σoRH, σoRV− σoRH, and RMSheight), an SEM was developed where SM (volumetric) predicted values depend on σoRH, σoRV− σoRH, and RMSheight. This SEM showed R2 of 0.87 and adjusted R2 of 0.85, multiple R=0.94 and with standard error of 0.05 at 95% confidence level. Validation of the SM derived from semi-empirical model with observed measurement (SMObserved) showed root mean square error (RMSE) = 0.06, relative- RMSE (R-RMSE) = 0.18, mean absolute error (MAE) = 0.04, normalized RMSE (NRMSE) = 0.17, Nash–Sutcliffe efficiency (NSE) = 0.91 (≈1), index of agreement (d) = 1, coefficient of determination (R2) = 0.87, mean bias error (MBE) = 0.04, standard error of estimate (SEE) = 0.10, volume error (VE) = 0.15, variance of the distribution of differences (S2d) = 0.004. The developed SEM showed better performance in estimating SM than Topp empirical model which is based only on σo. By using the developed SEM, top soil SM can be estimated with low mean absolute percent error (MAPE) = 1.39 and can be used for operational applications.