Makarand A. Kulkarni

Probabilistic prediction of Indian summer monsoon rainfall using global climate models

Probabilistic seasonal predictions of rainfall that incorporate proper uncertainties are essential for climate risk management. In this study, three different multi-model ensemble (MME) approaches are used to generate probabilistic seasonal hindcasts of the Indian summer monsoon rainfall based on a set of eight global climate models for the 1982–2009 period. The three MME approaches differ in their calculation of spread of the forecast distribution, treated as a Gaussian, while all three use the simple multi-model subdivision average to define the mean of the forecast distribution. The first two approaches use the within-ensemble spread and error residuals of ensemble mean hindcasts, respectively, to compute the variance of the forecast distribution. The third approach makes use of the correlation between the ensemble mean hindcasts and the observations to define the spread using a signal-to-noise ratio. Hindcasts are verified against high-resolution gridded rainfall data from India Meteorological Department in terms of meteorological subdivision spatial averages. The use of correlation for calculating the spread provides better skill than the other two methods in terms of rank probability skill score. In order to further improve the skill, an additional method has been used to generate multi-model probabilistic predictions based on simple averaging of tercile category probabilities from individual models. It is also noted that when such a method is used, skill of probabilistic forecasts is improved as compared with using the multi-model ensemble mean to define the mean of the forecast distribution and then probabilities are estimated. However, skill of the probabilistic predictions of the Indian monsoon rainfall is too low.

A neurocomputing approach to predict monsoon rainfall in monthly scale using SST anomaly as a predictor

A relationship between summer monsoon rainfall and sea surface temperature anomalies was investigated with the aim of predicting the monthly scale rainfall during the summer monsoon period over a section (80°–90°E, 14°–24°N) of eastern India that depends heavily upon the rainfall during the summer monsoon months for its agricultural practices. The association between area-averaged rainfall of June over the study zone and global sea surface temperature (SST) anomalies for the period 1982–2008 was examined and the variability of rainfall in monthly scale was calculated. With a view to significant variability in the rainfall in the monthly scale, it was decided to implement the artificial neural network (ANN) for forecasting the monthly scale rainfall using the SST anomalies as a predictor. Finally, the potential of ANN in this prediction has been assessed.

Effect of spatial correlation on regional trends in rain events over India

The regional trends are evaluated in the frequency of various rain events using the daily gridded (1° × 1°) rainfall dataset for the time period 1901–2004, prepared by the India Meteorological Department (IMD). In terms of intensity, the events are classified as low, moderate, heavy and extreme heavy, while short and long spells are classified on the basis of duration of rainfall. The analytical (parametric) and the empirical (bootstrap) techniques were used to incorporate the impact of spatial correlation in regional trends. It is observed that, consideration of spatial correlation reduces the significance level of the trends and the effective number of grid points falling under each category. Especially, the noticeable cross-correlation have reduced the significance of the trends in moderate and long spell rain events to a large extent, while the significance of trends in the extreme heavy and short spell events is not highly affected because of small cross-correlation.

Simulation of Tropical Cyclones Over Indian Seas: Data Impact Study Using WRF-Var Assimilation System

The track and intensity prediction of TCs require accurate representation of the vortex in the model initial conditions. The sparsity of observations, both near the vortex and in the surrounding environment, causes either undetectability in standard analyses or poor analysis with ill-defined centers and locations. So, much emphasis over the years has been laid on improving the initial conditions of NWP models, particularly high-resolution mesoscale models in a number of ways. The initial errors obviously have a major impact on the forecast of cyclone tracks using numerical models. One way of overcoming the above difficulty is by improving the initial analysis with the assimilation of conventional and nonconventional observations, which include the development and testing of a range of assimilation methods in the numerical weather prediction (NWP) model. Unfortunately, conventional measurements used to initialize forecast models are unavailable over vast areas of the tropical oceans. So, the high-resolution data required for numerical prediction of TC can be derived by tracking cloud features in the satellite imageries, which provide a large amount of data over data-void regions of the oceans. These derived winds can be used to improve the initialization of the model for the TC forecast. The ability to provide high-density wind coverage over large regions of the tropics makes satellite winds particularly useful for studying TCs (Velden et al. 1998).

Comparative Evaluation of Performances of Two Versions of NCEP Climate Forecast System in Predicting Indian Summer Monsoon Rainfall

The operational prediction of climatic variables in monthly-to-seasonal scales has been issued by National Centers for Environmental Prediction (NCEP) through Climate Forecast System model (CFSv1) since 2004. After incorporating significant changes, a new version of this model (CFSv2) was released in 2011. The present study is based on the comparative evaluation of performances of CFSv2 and CFSv1 for the southwest monsoon season (June-July-August-September, JJAS) over India with May initial condition during 1982–2009. It was observed that CFSv2 has improved over CFSv1 in simulating the observed monsoon rainfall climatology and inter annual variability. The movement of the cell of Walker circulation in years of excessive and deficient rainfall is better captured in CFSv2, as well. The observed teleconnection pattern between ISMR-sea surface temperature (SST) is also better captured in CFSv2. The overall results suggest that the changes incorporated in CFSv1 through the development of CFSv2 have resulted in an improved prediction of ISMR.21

A Comparative Study on Performance of Analysis Nudging and 3DVAR in Simulation of a Heavy Rainfall Event Using WRF Modeling System

The present study focuses on the performance-based comparison of simulations carried out using nudging (NUD) technique and three-dimensional variational (3DVAR) data assimilation system (3DV) of a heavy rainfall event occurred during 25–28 June 2005 along the west coast of India. The Indian conventional and nonconventional observations are used in the 3DV experiment. Three numerical experiments are conducted using WRF modeling system, the model is integrated upto 54 hours from the initial time 0000 UTC of 25 June 2005. It is noticed that the meteorological parameters are improved in the resulting high-resolution analyses prepared by NUD and 3DV compared to without data assimilation experiment (i.e., called CNTL experiment). However, after the successful inclusion of observations using the 3DVAR data assimilation technique, the model is able to simulate better structure of the convective organization as well as prominent synoptic features associated with the mid-tropospheric cyclones (MTC) than the NUD experiment and well correlated with the observations. The simulated location and intensity of rainfall is also improved in 3DV simulation as compared with other experiments. Similar results are noticed in the root mean squar errors, correlation coefficients, and Equitable Threat Scores between TRMM and model simulated rainfall for all the three experiments.