Experimental Seasonal Forecast of Monsoon 2005 Using T170L42 AGCM on PARAM Padma

As a part of the Experimental Extended Range Monsoon Prediction Experiment, ensemble mode seasonal runs for the monsoon season of 2005 were made using the National Centre for Environmental Prediction (NCEP), T170L42 AGCM. The seasonal runs were made using six initial atmospheric conditions based on the NCEP operational analysis and with forecast monthly sea-surface temperature (SST) of the NCEP Coupled forecast system (CFS). These simulations were carried out on the PARAM Padma supercomputer of Centre for Development of Advanced Computing (C-DAC), India. The model climatology was prepared by integrating the model for ten years using climatological SST as the lower boundary. The climatology of the model compares well with the observed, in terms of the spatial distribution of rainfall over the Indian land mass. The model-simulated rainfall compares well with the Tropical Rainfall Measuring Mission (TRMM) estimates for the 2005 monsoon season. Compared to the model climatology (7.81 mm/day), the model had simulated a normal rainfall (7.75 mm/day) for the year 2005 which is in agreement with the observations (99% of long-term mean). However, the model could not capture the observed increase in September rainfall from that of a low value in August 2005. The circulation patterns simulated by the model are also comparable to the observed patterns. The ensemble mean onset is found to be nearer to the observed onset date within one pentad.     

Impact of variational assimilation technique on simulation of a heavy rainfall event over Pune,India

Prediction of heavy rainfall events due to severe convective storms in terms of their spatial and temporal scales is a challenging task for an operational forecaster. The present study is about a record-breaking heavy rainfall event observed in Pune (18°31′N, 73°55′E) on October 4, 2010. The day witnessed highest 24-h accumulated precipitation of 181.3 mm and caused flash floods in the city. The WRF model-based real-time weather system, operating daily at Centre for Development of Advanced Computing using PARAM Yuva supercomputer showed the signature of this convective event 4-h before, but failed to capture the actual peak rainfall and its location with reference to the city’s observational network. To investigate further, five numerical experiments were conducted to check the impact of assimilation of observations in the WRF model forecast. First, a control experiment was conducted with initialization using National Centre for Environmental Prediction (NCEP)’s Global Forecast System 0.5° data, while surface observational data from NCEP Prepbufr system were assimilated in the second experiment (VARSFC). In the third experiment (VARAMV), NCEP Prepbufr atmospheric motion vectors were assimilated. Fourth experiment (VARPRO) was assimilated with conventional soundings data, and all the available NCEP Prepbufr observations were assimilated in the fifth experiment (VARALL). Model runs were compared with observations from automated weather stations (AWS), synoptic charts of Indian Meteorological Department (IMD). Comparison of 24-h accumulated rainfall with IMD AWS 24-h gridded data showed that the fifth experiment (VARALL) produced better picture of heavy rainfall, maximum up to 251 mm/day toward the southern side, 31 km away from Pune’s IMD observatory. It was noticed that the effect of soundings observations experiment (VARPRO) caused heavy precipitation of 210 mm toward the southern side 49 km away from Pune. The wind analysis at 850 and 200 hPa indicated that the surface and atmospheric motion vector observations (VARAMV) helped in shifting its peak rainfall toward Pune, IMD observatory by 18 km, though VARALL over-predicted rainfall by 60 mm than the observed.     

Simulation of the Indian monsoon using the RegCM3–ROMS regional coupled model

A regional coupled atmosphere–ocean model was developed to study the role of air–sea interactions in the simulation of the Indian summer monsoon. The coupled model includes the regional climate model (RegCM3) as atmospheric component and the regional ocean modeling system (ROMS) as oceanic component. The two-way coupled model system exchanges sea surface temperature (SST) from the ocean to the atmospheric model and surface wind stress and energy fluxes from the atmosphere to the ocean model. The coupled model is run for four years 1997, 1998, 2002 and 2003 and the results are compared with observations and atmosphere-only model runs employing Reynolds SSTs as lower boundary condition. It is found that the coupled model captures the main features of the Indian monsoon and simulates a substantially more realistic spatial and temporal distribution of monsoon rainfall compared to the uncoupled atmosphere-only model. The intraseasonal oscillations are also better simulated in the coupled model compared to the atmosphere-only model. These improvements are due to a better representation of the feedbacks between the SST and convection and highlight the importance of air–sea coupling in the simulation of the Indian monsoon.     

Combining the FCM Classifier with Various Kernels to Handle Non-linearity of Class Boundaries

This article presents the use of kernel functions in fuzzy classifiers for an efficient land use/land cover mapping. It focuses on handling mixed pixels obtained from a remote sensing image by considering non-linearity between class boundaries. It uses kernel functions combined with the conventional fuzzy c-means (FCM) classifier. Kernel-based fuzzy c-mean classifiers were applied to classify AWiFS and LISS-III images from Resourcesat-1 and Resourcesat-2 satellites. Optimal kernels were obtained from eight single kernel functions. Fractional images generated from high resolution LISS-IV image were used as reference data. Classification accuracy of the FCM classifier increased with 12.93%. Improvement in overall accuracy shows that non-linearity in the dataset was handled adequately. The inverse multiquadratic kernel and the Gaussian kernel with the Euclidean norm were identified as optimal kernels. The study showed that overall classification accuracy of the FCM classifier improved if kernel functions were included.