Surface pressure and summer monsoon rainfall over India

The relationship between the all-India summer monsoon rainfall and surface pressure over the Indian region has been examined to obtain a useful predictor for the monsoon rainfall. The data series of all-India monsoon rainfall and the mean pressures of three seasons before and after the monsoon season as well as the winter-to-spring pressure tendency (MAM-DJF) at 100 stations for the period 1951-1980 have been used in the analysis. The all-India monsoon rainfall is negatively correlated with the pressure of the spring (MAM) season preceding the monsoon and winter-to-spring seasonal difference as pressure tendency (MAM-DJF), at almost all the stations in India, and significantly with the pressures over central and northwestern regions. The average mean sea level pressure of six stations (Jodhpur, Ahmedabed, Bombay, Indore, Sagar and Akola) in the Western Central Indian (WCI) region showed highly significant (at 1% level) and consistent CCs of -0.63 for MAM and -0.56 for MAM-DJF for the period 1951–1980. Thus, the pre-monsoon seasonal pressure anomalies over WCI could provide a useful parameter for the long-range forecasting scheme of the Indian monsoon rainfall.     

Stratospheric zonal wind and temperature in relation to summer monsoon rainfall over India

Summary  The interannual variability of the Indian summer monsoon (June–September) rainfall is examined in relation to the stratospheric zonal wind and temperature fluctuations at three stations, widely spaced apart. The data analyzed are for Balboa, Ascension and Singapore, equatorial stations using recent period (1964–1994) data, at each of the 10, 30 and 50 hPa levels. The 10 hPa zonal wind for Balboa and Ascension during January and the 30 hPa zonal wind for Balboa during April are found to be positively correlated with the subsequent Indian summer monsoon rainfall, whereas the temperature at 10 hPa for Ascension during May is negatively correlated with Indian summer monsoon rainfall. The relationship with stratospheric temperatures appears to be the best, and is found to be stable over the period of analysis. Stratospheric temperature is also significantly correlated with the summer monsoon rainfall over a large and coherent region, in the north-west of India. Thus, the 10 hPa temperature for Ascension in May appears to be useful for forecasting summer monsoon rainfall for not only the whole of India, but also for a smaller region lying to the north-west of India. Received July 30, 1999 Revised March 17, 2000     

Prediction of summer monsoon rainfall over India using the NCEP climate forecast system

The performance of a dynamical seasonal forecast system is evaluated for the prediction of summer monsoon rainfall over the Indian region during June to September (JJAS). The evaluation is based on the National Centre for Environmental Prediction’s (NCEP) climate forecast system (CFS) initialized during March, April and May and integrated for a period of 9 months with a 15 ensemble members for 25 years period from 1981 to 2005. The CFS’s hindcast climatology during JJAS of March (lag-3), April (lag-2) and May (lag-1) initial conditions show mostly an identical pattern of rainfall similar to that of verification climatology with the rainfall maxima (one over the west-coast of India and the other over the head Bay of Bengal region) well simulated. The pattern correlation between verification and forecast climatology over the global tropics and Indian monsoon region (IMR) bounded by 50°E–110°E and 10°S–35°N shows significant correlation coefficient (CCs). The skill of simulation of broad scale monsoon circulation index (Webster and Yang; WY index) is quite good in the CFS with highly significant CC between the observed and predicted by the CFS from the March, April and May forecasts. High skill in forecasting El Nino event is also noted for the CFS March, April and May initial conditions, whereas, the skill of the simulation of Indian Ocean Dipole is poor and is basically due to the poor skill of prediction of sea surface temperature (SST) anomalies over the eastern equatorial Indian Ocean. Over the IMR the skill of monsoon rainfall forecast during JJAS as measured by the spatial Anomaly CC between forecast rainfall anomaly and the observed rainfall anomaly during 1991, 1994, 1997 and 1998 is high (almost of the order of 0.6), whereas, during the year 1982, 1984, 1985, 1987 and 1989 the ACC is only around 0.3. By using lower and upper tropospheric forecast winds during JJAS over the regions of significant CCs as predictors for the All India Summer Monsoon Rainfall (AISMR; only the land stations of India during JJAS), the predicted mean AISMR with March, April and May initial conditions is found to be well correlated with actual AISMR and is found to provide skillful prediction. Thus, the calibrated CFS forecast could be used as a better tool for the real time prediction of AISMR.     

Impact of MJO on the intraseasonal variation of summer monsoon rainfall over India

The summer monsoon rainfall over India exhibits strong intraseasonal variability. Earlier studies have identified Madden Julian Oscillation (MJO) as one of the most influencing factors of the intraseasonal variability of the monsoon rainfall. In this study, using India Meteorological Department (IMD) high resolution daily gridded rainfall data and Wheeler?CHendon MJO indices, the intra-seasonal variation of daily rainfall distribution over India associated with various Phases of eastward propagating MJO life cycle was examined to understand the mechanism linking the MJO to the intraseasonal variability. During MJO Phases of 1 and 2, formation of MJO associated positive convective anomaly over the equatorial Indian Ocean activated the oceanic tropical convergence zone (OTCZ) and the resultant changes in the monsoon circulation caused break monsoon type rainfall distribution. Associated with this, negative convective anomalies over monsoon trough zone region extended eastwards to date line indicating weaker than normal northern hemisphere inter tropical convergence zone (ITCZ). The positive convective anomalies over OTCZ and negative convective anomalies over ITCZ formed a dipole like pattern. Subsequently, as the MJO propagated eastwards to west equatorial Pacific through the maritime continent, a gradual northward shift of the OTCZ was observed and negative convective anomalies started appearing over equatorial Indian Ocean. During Phase 4, while the eastwards propagating MJO linked positive convective anomalies activated the eastern part of the ITCZ, the northward propagating OTCZ merged with monsoon trough (western part of the ITCZ) and induced positive convective anomalies over the region. During Phases 5 and 6, the dipole pattern in convective anomalies was reversed compared to that during Phases 1 and 2. This resulted active monsoon type rainfall distribution over India. During the subsequent Phases (7 and 8), the convective and lower tropospheric anomaly patterns were very similar to that during Phase 1 and 2 except for above normal convective anomalies over equatorial Indian Ocean. A general decrease in the rainfall was also observed over most parts of the country. The associated dry conditions extended up to northwest Pacific. Thus the impact of the MJO on the monsoon was not limited to the Indian region. The impact was rather felt over larger spatial scale extending up to Pacific. This study also revealed that the onset of break and active events over India and the duration of these events are strongly related to the Phase and strength of the MJO. The break events were relatively better associated with the strong MJO Phases than the active events. About 83% of the break events were found to be set in during the Phases 7, 8, 1 and 2 of MJO with maximum during Phase 1 (40%). On the other hand, about 70% of the active events were set in during the MJO Phases of 3 to 6 with maximum during Phase 4 (21%). The results of this study indicate an opportunity for using the real time information and skillful prediction of MJO Phases for the prediction of break and active conditions which are very crucial for agriculture decisions.     

Relationships between circulation and rainfall over India during the southwest monsoon season Part I — surface pressure

Summary Rainfall over India during the southwest monsoon season exhibits large intraseasonal fluctuations. The surface pressure fields illustrate the important circulation changes and the general conditions of active and break monsoon situations. We have studied the relationship between these two successive fields at daily to monthly time scales using montly data, from July through September for an 11 year period (1966–1976). Lag relationships were also investigated to ascertain the nature of evolutionary patterns through which pressure affects rainfall and so assess the potential for predicting rainfall with the use of pressure fields. Finally, the relationship between pressure and rainfall (linear or non-linear) was examined with the use of quartile plots.With 7 Figures     

Prediction of all India summer monsoon rainfall using error-back-propagation neural networks

Summary In this paper, multilayered feedforward neural networks trained with the error-back-propagation (EBP) algorithm have been employed for predicting the seasonal monsoon rainfall over India. Three network models that use, respectively, 2, 3 and 10 input parameters which are known to significantly influence the Indian summer monsoon rainfall (ISMR) have been constructed and optimized. The results obtained thereby are rigorously compared with those from the statistical models. The predictions of network models indicate that they can serve as a potent tool for ISMR prediction.     

All India summer monsoon rainfall prediction using an artificial neural network

The prediction of Indian summer monsoon rainfall (ISMR) on a seasonal time scales has been attempted by various research groups using different techniques including artificial neural networks. The prediction of ISMR on monthly and seasonal time scales is not only scientifically challenging but is also important for planning and devising agricultural strategies. This article describes the artificial neural network (ANN) technique with error- back-propagation algorithm to provide prediction (hindcast) of ISMR on monthly and seasonal time scales. The ANN technique is applied to the five time series of June, July, August, September monthly means and seasonal mean (June + July + August + September) rainfall from 1871 to 1994 based on Parthasarathy data set. The previous five years values from all the five time-series were used to train the ANN to predict for the next year. The details of the models used are discussed. Various statistics are calculated to examine the performance of the models and it is found that the models could be used as a forecasting tool on seasonal and monthly time scales. It is observed by various researchers that with the passage of time the relationships between various predictors and Indian monsoon are changing, leading to changes in monsoon predictability. This issue is discussed and it is found that the monsoon system inherently has a decadal scale variation in predictability. Received: 13 March 1999 / Accepted: 31 August 1999     

Periodicity in intraseasonal variation of summer monsoon rainfall over Orissa, India in relation to synoptic disturbances

Summary The summer monsoon rainfall over Orissa, a state of eastern India, shows characteristic intraseasonal and interannual variability, due to interaction of basic westerly flow with orography and the synoptic scale monsoon disturbances including low-pressure systems and cyclonic circulations extending upto mid-tropospheric level (LPSC). These systems normally develop over the north Bay of Bengal and move west-northwestwards along the monsoon trough. The essence of this study is to find out the main features of the intraseasonal variability of daily monsoon rainfall over Orissa in relation to synoptic systems like LPSC and its implication on the interannual variation of rainfall. For this purpose, the actual and mean daily rainfall data of 31 uniformly distributed stations, six homogeneous regions and Orissa as a whole during monsoon season (June–September) over a period of 20 years (1980–1999) are subjected to auto-correlation and power spectrum analyses. The actual and average daily scores of significant EOFs and actual daily occurrence along with daily probability of occurrence of the LPSC influencing rainfall over Orissa during the same period are also subjected to auto-correlation and power spectrum analyses. The intraseasonal variation of monsoon rainfall over Orissa and different homogeneous regions is dominated by the synoptic mode (3–9 days) of variation due to the similar mode of variation in the occurrence of LPSC influencing the rainfall. The seasonal rainfall and hence the interannual variation depends on the intraseasonal variation of rainfall modulated with the synoptic mode of variation in the occurrence of the LPSC. The occurrence of LPSC over the northwest (NW) Bay/NW and adjoining northeast (NE) Bay and its subsequent movement and persistence over Orissa and east Madhya Pradesh & Chhattisgarh in synoptic mode (3–6 days) alongwith absence of similar mode in the occurrence of the LPSC over NE Bay, Gangetic West Bengal (GWB) in the north and west central (WC) Bay to the south leads to excess rainfall over different homogeneous regions and Orissa as a whole. The reverse is the case in deficient years over Orissa and all homogeneous regions except southwest Orissa. The occurrence of the LPSC over GWB in synoptic mode (about 5 days) alongwith absence of synoptic mode in the occurrence of the LPSC over NW Bay leads to deficient rainfall year over southwest Orissa. Correspondence: U. C. Mohanty, Centre for Atmospheric Sciences, Indian Institute of Technology, Delhi Hauz Khas, New Delhi 110016, India     

The interdecadal variations of the summer monsoon rainfall over South China

Summary This paper is to promote a further understanding of the interdecadal variations of the summer monsoon rainfall over South China (SCMR). With this focus, we will specifically aim at better understanding possible mechanism responsible for such an interdecadal variation relationship between the SCMR and El Ni?o/Southern Oscillation (ENSO). In many of the previous studies on precipitation, the datasets used are satellite observations or gridded reanalyzed data due to the lack of long-term reliable observations over the marginal seas of the Asian continent. Such an approach could lead to possible errors in the results. In this work, several representative stations with long-term rain-gauge observations are chosen to reduce such uncertainty. The study of the interdecadal variabilities of SCMR indicates that there is a strong linkage between SCMR and ENSO on the interdecadal variations. These results agree well with those from previous studies that the Pacific Decadal Oscillation (PDO) and ENSO are not independent of each other, the interannual and interdecadal variations of tropical Pacific Sea Surface temperatures (SSTs) could affect the interdecadal variations of the SCMR, and the incorporating information on the PDO/ENSO could improve the long-term prediction of the SCMR.     

Northeast monsoon rainfall variability over south peninsular India and its teleconnections

Rainfall over south peninsular India during the northeast (NE) monsoon season (Oct–Dec) shows significant interannual variation. In the present study, we relate the northeast monsoon rainfall (NEMR) over south peninsular India with the major oscillations like El Ni?o Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), and Equatorial Indian Ocean Oscillation (EQUINOO) in the Indian and Pacific Oceans. For establishing the teleconnections, sea surface temperature, outgoing long wave radiation, and circulation data have been used. The present study reveals that the positive phase of ENSO, IOD, and EQUINOO favor the NEMR to be normal or above normal over southern peninsular India. The study reveals that the variability of NEMR over south peninsula can be well explained by its relationship with positive phase of ENSO, IOD, and EQUINOO.     

The Interannual variability of rainfall over homogeneous regions of Indian summer monsoon

Theoretical and Applied Climatology - The Indian subcontinent, due to its enormous variety of geographical features, is associated with inhomogeneity. Hence, in the present study, we have...     

A universal spectrum for interannual variability of monsoon rainfall over India

Continuous periodogram analyses of 115 years (1871-1985) summer monsoon rainfall over the Indian region show that the power spectra follow the universal and unique inverse power law form of the statistical normal distribution with the percentage contribution to total variance representing the eddy probability corresponding to the normalized standard deviation equal to [(log L/log T50) – 1] where L is the period length in years and T50 the period up to which the cumulative percentage contribution to total variance is equal to 50. The above results are con-sistent with a recently developed non-deterministic cell dynamical model for atmospheric flows. The implications of the above result for prediction of interannual variability of rainfall is discussed.     

Surface and upper air temperatures over India in relation to monsoon rainfall

Summary The relationship between the all-India summer monsoon rainfall and surface/upper air (850, 700, 500 and 200 mb levels) temperatures over the Indian region and its spatial and temporal characteristics have been examined to obtain a useful predictor for the monsoon rainfall. The data series of all-India and subdivisional summer monsoon rainfall and various seasonal air temperatures at 73 surface observatories and 9 radiosonde stations (1951–1980) have been used in the analysis. The Correlation Coefficients (CCs) between all-India monsoon rainfall and seasonal surface air temperatures with different lags relative to the monsoon season indicate a systematic relationship.The CCs between the monsoon rainfall and surface-air temperature of the preceding MAM (pre-monsoon spring) season are positive over many parts of India and highly significant over central and northwestern regions. The average surface air temperature of six stations i.e., Jodhpur, Ahmedabad, Bombay, Indore, Sagar and Akola in this region (Western Central India, WCI) showed a highly significant CC of 0.60 during the period 1951–1980. This relationship is also found to be consistently significant for the period from 1950 to present, though decreasing in magnitude after 1975. WCI MAM surface air temperature has shown significant CCs with the monsoon rainfall over eleven sub-divisions mainly in northwestern India, i.e., north of 15 °N and west of 80 °E.Upper air temperatures of the MAM season at almost all the stations and all levels considered show positive CCs with the subsequent monsoon rainfall. These correlations are significant at some central and north Indian stations for the lower and middle tropospheric temperatures.The simple regression equation developed for the period 1951–1980 isy = – 183.20 + 8.83x, wherey is the all-India monsoon rainfall in cm andx is the WCI average surface air temperature of MAM season in °C. This equation is significant at 0.1% level. The suitability of this parameter for inclusion in a predictive regression model along with five other global and regional parameters has been discussed. Multiple regression analysis for the long-range prediction of monsoon rainfall, using several combinations of these parameters indicates that the improvement of predictive skill considerably depends upon the selection of the predictors.With 9 Figures     

Experimental 15-day-lead statistical forecast of intraseasonal summer monsoon rainfall over Eastern China

1996年长江中下游、2002年华中南部以及2006年华南沿海地区出现了显著洪涝,并均伴随强季节内降水活动。在30–60天和10–20天这两个时间尺度上,季节内降水异常均与亚洲季风区大气季节内振荡密切相关。本文选取亚洲季风区大气季节内振荡的关键要素作为预报因子,应用贝叶斯小波频段方案对季节内降水进行了预报时效为15天的延伸期预测。该方案对这三年的季节内降水中心的预测水平均较好,预测与观测的季节内降水相关系数均在0.6以上。     

New statistical models for long-range forecasting of southwest monsoon rainfall over India

The India Meteorological Department (IMD) has been issuing long-range forecasts (LRF) based on statistical methods for the southwest monsoon rainfall over India (ISMR) for more than 100 years. Many statistical and dynamical models including the operational models of IMD failed to predict the recent deficient monsoon years of 2002 and 2004. In this paper, we report the improved results of new experimental statistical models developed for LRF of southwest monsoon seasonal (June–September) rainfall. These models were developed to facilitate the IMD’s present two-stage operational forecast strategy. Models based on the ensemble multiple linear regression (EMR) and projection pursuit regression (PPR) techniques were developed to forecast the ISMR. These models used new methods of predictor selection and model development. After carrying out a detailed analysis of various global climate data sets; two predictor sets, each consisting of six predictors were selected. Our model performance was evaluated for the period from 1981 to 2004 by sliding the model training period with a window length of 23 years. The new models showed better performance in their hindcast, compared to the model based on climatology. The Heidke scores for the three category forecasts during the verification period by the first stage models based on EMR and PPR methods were 0.5 and 0.44, respectively, and those of June models were 0.63 and 0.38, respectively. Root mean square error of these models during the verification period (1981–2004) varied between 4.56 and 6.75% from long period average (LPA) as against 10.0% from the LPA of the model based on climatology alone. These models were able to provide correct forecasts of the recent two deficient monsoon rainfall events (2002 and 2004). The experimental forecasts for the 2005 southwest monsoon season based on these models were also found to be accurate.