Articles written in Journal of Earth System Science
Volume 119 Issue 3 June 2010 pp 229-247
In this paper, we suggest criteria for the identification of active and break events of the Indian summer monsoon on the basis of recently derived high resolution daily gridded rainfall dataset over India (1951–2007). Active and break events are defined as periods during the peak monsoon months of July and August, in which the normalized anomaly of the rainfall over a critical area, called the monsoon core zone exceeds 1 or is less than −1.0 respectively, provided the criterion is satisfied for at least three consecutive days. We elucidate the major features of these events. We consider very briefly the relationship of the intraseasonal fluctuations between these events and the interannual variation of the summer monsoon rainfall.
We find that breaks tend to have a longer life-span than active spells. While, almost 80% of the active spells lasted 3–4 days, only 40% of the break spells were of such short duration. A small fraction (9%) of active spells and 32% of break spells lasted for a week or longer. While active events occurred almost every year, not a single break occurred in 26% of the years considered. On an average, there are 7 days of active and break events from July through August. There are no significant trends in either the days of active or break events. We have shown that there is a major difference between weak spells and long intense breaks. While weak spells are characterized by weak moist convective regimes, long intense break events have a heat trough type circulation which is similar to the circulation over the Indian subcontinent before the onset of the monsoon.
The space-time evolution of the rainfall composite patterns suggests that the revival from breaks occurs primarily from northward propagations of the convective cloud zone. There are important differences between the spatial patterns of the active/break spells and those characteristic of interannual variation, particularly those associated with the link to ENSO. Hence, the interannual variation of the Indian monsoon cannot be considered as primarily arising from the interannual variation of intraseasonal variation. However, the signature over the eastern equatorial Indian Ocean on intraseasonal time scales is similar to that on the interannual time scales.
Volume 121 Issue 2 April 2012 pp 355-371
A prediction model based on the perfect prognosis method was developed to predict the probability of lightning and probable time of its occurrence over the south-east Indian region. In the perfect prognosis method, statistical relationships are established using past observed data. For real time applications, the predictors are derived from a numerical weather prediction model. In the present study, we have developed the statistical model based on Binary Logistic Regression technique. For developing the statistical model, 115 cases of lightning that occurred over the south-east Indian region during the period 2006–2009 were considered. The probability of lightning (yes or no) occurring during the 12-hour period 0900–2100 UTC over the region was considered as the predictand. The thermodynamic and dynamic variables derived from the NCEP Final Analysis were used as the predictors. A three-stage strategy based on Spearman Rank Correlation, Cumulative Probability Distribution and Principal Component Analysis was used to objectively select the model predictors from a pool of 61 potential predictors considered for the analysis. The final list of six predictors used in the model consists of the parameters representing atmospheric instability, total moisture content in the atmosphere, low level moisture convergence and lower tropospheric temperature advection. For the independent verifications, the probabilistic model was tested for 92 days during the months of May, June and August 2010. The six predictors were derived from the 24-h predictions using a high resolution Weather Research and Forecasting model initialized with 00 UTC conditions. During the independent period, the probabilistic model showed a probability of detection of 77% with a false alarm rate of 35%. The Brier Skill Score during the independent period was 0.233, suggesting that the prediction scheme is skillful in predicting the lightning probability over the south-east region with a reasonable accuracy.
Volume 122 Issue 3 June 2013 pp 573-588
Daily rainfall datasets of 10 years (1998–2007) of Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) version 6 and India Meteorological Department (IMD) gridded rain gauge have been compared over the Indian landmass, both in large and small spatial scales. On the larger spatial scale, the pattern correlation between the two datasets on daily scales during individual years of the study period is ranging from 0.4 to 0.7. The correlation improved significantly (∼0.9) when the study was confined to specific wet and dry spells each of about 5–8 days. Wavelet analysis of intraseasonal oscillations (ISO) of the southwest monsoon rainfall show the percentage contribution of the major two modes (30–50 days and 10–20 days), to be ranging respectively between ∼30–40% and 5–10% for the various years. Analysis of inter-annual variability shows the satellite data to be underestimating seasonal rainfall by ∼110 mm during southwest monsoon and overestimating by ∼150 mm during northeast monsoon season.
At high spatio-temporal scales, viz., 1° × 1° grid, TMPA data do not correspond to ground truth. We have proposed here a new analysis procedure to assess the minimum spatial scale at which the two datasets are compatible with each other. This has been done by studying the contribution to total seasonal rainfall from different rainfall rate windows (at 1 mm intervals) on different spatial scales (at daily time scale). The compatibility spatial scale is seen to be beyond 5° × 5° average spatial scale over the Indian landmass. This will help to decide the usability of TMPA products, if averaged at appropriate spatial scales, for specific process studies, e.g., cloud scale, meso scale or synoptic scale.