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.     

Pre-monsoon surface pressure and summer monsoon rainfall over India

Summary The relationship between the surface air pressure field during the pre-monsoon months and the Indian summer monsoon rainfall is analysed using climate data from 105 stations situated in Eurasia between 0°–60° N and 20°–100° E. Moreover, grid-point data for the whole northern hemisphere are used. Pressure during April over an area around 50° N and 35° E is found to show a significant negative correlation with the subsequent monsoon rainfall. During May the pressure over a large part of the study area south of 40° N shows a significant correlation with its highest value in the heat low region over Pakistan. It is assumed that monitoring of pressure variations over this region may be useful in predicting monsoon rainfall, particularly the rainfall during the first half of the season. Certain limitations of the climate data in this region are also discussed.With 5 Figures     

基于RClimDex模型的江西省极端降水时空变化研究

基于赣江流域1964-2013年13个水文站的日降水资料,采用AM抽样和POT抽样相结合的方法,对极端降水序列,选定日最大降水量（RX1）、极端降水量（R95）、极端降水天数（RD95）和极端降水强度（RI95）四个指标,利用Mann-Kendall趋势分析方法、Pettitt变点检验法分别对赣江流域极端降水进行时间变化趋势和突变的分析,并利用普通克里金插值,对各指标进行空间分布的分析。研究结果表明,时间变化上,赣江流域RX1、R95和RD95均表现出一定的增加趋势,但RI95变化不大,各指标在过去50 a没有发生显著突变;空间分布上,RX1、R95和RI95沿着赣江流向从西南向东北增加,而RD95的空间分布没有明显的变化规律,存在多个极大极小值中心。

     

On the link between extreme floods and excess monsoon epochs in South Asia

This paper provides a synoptic view of extreme monsoon floods on all the nine large rivers of South Asia and their association with the excess (above-normal) monsoon rainfall periods. Annual maximum flood series for 18 gauging stations spread over four countries (India, Pakistan, Bangladesh and Nepal) and long-term monsoon rainfall data were analyzed to ascertain whether the extreme floods were clustered in time and whether they coincided with multi-decade excess monsoon rainfall epochs at the basin level. Simple techniques, such as the Cramer’s t-test, regression and Mann–Kendall (MK) tests and Hurst method were used to evaluate the trends and patterns of the flood and rainfall series. MK test reveals absence of any long-term tendency in all the series. However, the Cramer’s t test and Hurst-Mandelbrot rescaled range statistic provide evidence that both rainfall and flood time series are persistent. Using the Cramer’s t-test the excess monsoon epochs for each basin were identified. The excess monsoon periods for different basins were found to be highly asynchronous with respect to duration as well as the beginning and end. Three main conclusions readily emerge from the analyses. Extreme floods (>90th percentile) in South Asia show a tendency to cluster in time. About three-fourth of the extreme floods have occurred during the excess monsoon periods between ~1840 and 2000 AD, implying a noteworthy link between the two. The frequency of large floods was higher during the post-1940 period in general and during three decades (1940s, 1950s and 1980s) in particular.     

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     

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.     

A further note on extreme monsoonal rainfall deficits in India

Summary In a previous paper, maps were provided of the extreme monsoonal rainfall deficits likely to occur somewhere within individual Standard Meteorological Sub-Divisions in India. The present paper extends this study to similar evaluations for areas of radius 150 km around each of the 306 stations used in the previous study. Maps are presented of percentage rainfall deficits (or percentage of average values) with return periods of 2, 5, 10 and 50 years, and of the spatial pattern of return periods for deficits of 40%, 50%, 60% and 70% below the average. In addition, there are graphs for 25 stations displaying the relationships between deficit intensity and return period.

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Zusammenfassung In einer vorhergehenden Arbeit wurden Karten über extreme Monsonregendefizite vorgelegt, wie sie wahrscheinlich innerhalb der einzelnen meteorologischen Untereinheiten Indiens vorkommen. Diese Arbeit präzisiert diese Studien auf Gebiete im Umkreis von 150 km jeder der 306 schon früher verwendeten Meßstationen. Hierbei werden Karten vorgelegt, die sowohl Prozentsätze der Regendefizite (bzw. der Durchschnittswerte) für Wiederholungszeiten von 2, 5, 10 und 50 Jahren als auch räumliche Muster der Wiederholungszeiten für Defizite von 40, 50, 60 und 70% unter dem Durchschnittswert angeben. Dem sind Grafiken von 25 Stationen beigefügt, die die Zusammenhänge von Defizitintensität und Wiederholungszeit zeigen.

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With 4 Figures     

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     

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.     

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     

Recent changes in dry spell and extreme rainfall events in Ethiopia

Summary This paper assesses recent changes in extremes of seasonal rainfall in Ethiopia based on daily rainfall data for 11 key stations over the period 1965–2002. The seasons considered are Kiremt (‘main rains’, June–September) and Belg (‘small rains’, February/March–May). The Mann-Kendall and linear regression trend tests show decreasing trends in the Kiremt and the Belg extreme intensity and maximum consecutive 5-day rains over eastern, southwestern and southern parts of Ethiopia whereas no trends are found in the remaining part of Ethiopia. In general, no trends are found in the yearly maximum length of Kiremt and Belg dry spells (days with rainfall below 1 mm) over Ethiopia.     

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.     

Probabilities and distribution of monsoon rainfall in normal, flood and drought years over India

Summary The paper deals with the variability of summer-monsoon rainfall during normal, flood and drought years over India. During flood years the monsoon rainfall increases mostly all over parts of the country and large area less than 100 cm isohytel covers Orissa and adjoining Madhya Pradesh. During drought years the rainfall amount decreases over the entire country and isohytel of 100 cm shrinks to almost a point. The variability of monsoon rainfall from flood to normal to drought years depends upon the number of depression/low-pressure area which form over the North Bay and move inland. To understand the intraseasonal and interannual variability of the monsoon rainfall, daily and seasonal anomalies has been performed by using the Empirical Orthogonal Function analysis. Further Empirical Orthogonal Function (EOF) analysis is carried out on these data to find out the nature of rainfall distribution in different monsoon categories namely normal, flood and drought years. This technique thus serves to identify spatial and temporal patterns characteristics of possible physical significance. Received July 25, 2000/Revised September 26, 2000     

Sub-seasonal interannual variability associated with the excess and deficit Indian winter monsoon over the Western Himalayas

During the winter season (Dec., Jan., and Feb.; DJF) the western Himalaya (WH) receives one-third of its annual precipitation due to Indian winter monsoon (IWM). The IWM is characterized by eastward-moving synoptic weather systems called western disturbances. Seasonal interannual precipitation variability is positively correlated with monthly interannual variabilities. However, it was found that the monthly interannual variabilities differ. The interannual variability for Jan. is negatively correlated with that for Dec. and Feb. Because the entire seasonal interannual variability is in phase with the El Niño Southern Oscillation, it is interesting to investigate such contrasting behavior. Composite analysis based on extreme wet and dry seasons indicates that Dec. and Feb. precipitation variabilities have a high positive (low negative) correlation with eastern (western) equatorial Pacific warming (cooling), whereas Jan. precipitation variability exhibits negligible correlations. Seasonal mid/upper tropospheric cooling over the Himalayas enhances anomalous cyclonic circulation, which along with suppressed convection over the western equatorial Pacific, shifts the 200-hPa subtropical westerly jet southward over the Himalayas. Due to the upper tropospheric anomalous cyclonic circulation, mass transfer favors anticyclone formation at the mid/lower troposphere, which is enhanced in Jan. due to a warmer mid troposphere and hence decreases precipitation compared with Dec. and Feb. Additionally, a weakening of meridional moisture flux transport from the equatorial Indian Ocean to WH is observed in Jan. Further analysis reveals that mid-tropospheric and surface temperatures over WH also play dominant roles, acting as local forcing where the preceding month’s surface temperature controls the succeeding month’s precipitation.     

Long-term changes and variability of monthly extreme temperatures in India

Extreme temperatures are changing worldwide together with changes in the mean temperatures. This study investigates the long-term trends and variations of the monthly maximum and minimum temperatures and their effects on seasonal fluctuations in various climatological regions in India. The magnitude of the trends and their statistical significance were determined by parametric ordinary least square regression techniques and the variations were determined by the respective coefficient of variations. The results showed that the monthly maximum temperature increased, though unevenly, over the last century. Minimum temperature changes were more variable than maximum temperature changes, both temporally and spatially, with results of lesser significance. The results of this study are good indicators of Indian climate variability and its changes over the last century.     

Increasing trend of northeast monsoon rainfall over the equatorial Indian Ocean and peninsular India

Peninsular India and Sri Lanka receive major part of their annual rainfall during the northeast monsoon season (October–December). The long-term trend in the northeast monsoon rainfall over the Indian Ocean and peninsular India is examined in the vicinity of global warming scenario using the Global Precipitation Climatology Project (GPCP) dataset available for the period 1979–2010. The result shows a significant increasing trend in rainfall rate of about 0.5 mm day^?1 decade^?1 over a large region bounded by 10 °S–10 °N and 55 °E–100 °E. The interannual variability of seasonal rainfall rate over peninsular India using conventional rain gauge data is also investigated in conjunction to the Indian Ocean dipole. The homogeneous rain gauge data developed by Indian Institute of Tropical Meteorology over peninsular India also exhibit the considerable upward rainfall trend of about 0.4 mm day^?1 decade^?1 during this period. The associated outgoing longwave radiation shows coherent decrease in the order of 2 W?m^?2 decade^?1 over the rainfall increase region.