Forecasting Of Monsoon Rainfall Research Articles

District Level Extended Range Forecast of Monsoon Rainfall Over India: Prospects and Limitations

District Level Extended Range Forecast of Monsoon Rainfall Over India: Prospects and Limitations

Hydraulic jump: The cause of heavy rainfall on the immediate lee side of the Western Ghats in the Maharashtra state of India

AbstractNorth–South‐oriented mountains (known as “The Western Ghats [WG]”) running parallel to the west coast of peninsular India separate the Maharashtra state (lying on the northwest of peninsular India) into two meteorological subdivisions (a) Konkan lying on the windward side of WG receiving high monsoon rainfall (2,390 mm) and (b) Madhya Maharashtra (MM) lying on the leeward side receiving low monsoon rainfall (579 mm). The state of Maharashtra has two more subdivisions on the leeward side of WG, viz. Marathwada and Vidarbha, which however do not lie on the immediate leeward side of WG. In this study, a subdivision of MM is further subdivided into (a) north–south strip of 80 km width on the immediate lee side (ILS) from the crest of WG and (b) area extending eastwards from ILS to 280 km, referred to as “distant lee side (DLS).” DLS contains some parts of the Marathwada subdivision also. The study indicates that the mean monsoon rainfall over ILS region is higher than DLS region (proportion is 1.4:1). Higher rainfall over ILS region has been shown due to the occurrence of the “hydraulic jump (HJ).” The dynamics of HJ are explained using Froude number and vertical velocities. The climatological high rainfall over ILS remained an unnoticed feature in the spatial monsoon variability over peninsular India. The study focuses on the unnoticed high rainfall in the ILS region and attributes it to HJ. The incorporation of HJ has the potential to impact on forecasting of monsoon rainfall over the region by numerical models.

Deterministic Seasonal Quantitative Precipitation Forecasts: Benchmark Skill with a GCM

Many applications, from agricultural planning (such as crop choice) to estimation of hydro-power and surface water availability require quantitative precipitation forecasts (QPF). While a large number of studies have addressed the problem of forecasting seasonal Indian summer monsoon (ISM) rainfall over the decades, the emphasis generally has been on simulation and forecasting of rainfall anomalies from long-period mean. However, given the trends in monsoon rainfall, the procedure of considering anomalies has inherent errors; thus reconstruction of actual rainfall from the anomalies does not necessarily provide accurate information. Most QPF have been attempted at short-range forecasts, with a variety of techniques like model output statistics. This work represents evaluation of an atmospheric Variable Resolution General Circulation Model (VRGCM) for QPF during monsoon season. The VRGCM, with variable grid resolution provides relatively high (~ 50 km) resolution over the ISM region, has been validated over all India as well as in different regions like Central and North India, South India, North East India for seasonal forecast of monsoon rainfall. VRGCM simulated QPF appears to provide comparable information as that of anomaly forecasts of monsoon over different regions in India. The forecast skill is appreciable, with significant correlation at all India, South India and North East India regions. The Root Mean Square Error of VRGCM in forecasting quantitative rainfall overall India and Central North India is very low and bit high over South and North East region of India. The performance of VRGCM in forecasting the rainfall in extreme (deficit or excess) monsoon years is also very high with phase synchronisation of 89% in the inter annual variability of rainfall during monsoon at all India scale while the value is about 57%, 76% and 55% over Central North, South and North East India region, respectively.

Sensitivity of initial‐condition and cloud microphysics to the forecasting of monsoon rainfall in South Asia

The objective of this study is to assess the impact of using different initialization techniques and cloud microphysics of a numerical atmospheric model to improve the forecasting of Indian summer monsoon rainfall (ISMR). A total of six intense precipitation events over the Ganges–Brahmaputra–Meghna and Indus River basins were tested to identify the most suitable combination of parameterization and initialization techniques. The global forecast system (GFS)‐based numerical weather prediction (NWP) forecast fields were dynamically downscaled by the mesoscale model of weather research and forecasting (WRF). The performance of four types of initial conditions with three cloud microphysics was assessed using a model resolution of up to 9 km. A main conclusion is that the model initialized using hot start in the study involves more uncertainty, probably due to poor‐quality data assimilation, and it cannot exceed the performance of cold‐start initialization. The study findings provide evidence that the finer resolution initial condition is promising in higher resolution models. In the case of cloud microphysics, the performance of WRF single moment 5 class (WSM5) was sufficient for South Asian monsoon systems within this scale of the model resolution. The findings provide a general guideline for flood forecasters for the WRF model set‐up for forecasting the ISMR from publicly available GFS‐based NWP forecast fields.

Verification of operational rainfall forecast over eastern India during southwest monsoon season

The Indian summer monsoon rainfall forecast and its verification has a direct impact on various sectors of public interest besides economy of the country. The present study highlights the verification of distribution forecast of synoptic method issued daily for six met subdivisions, comprising of five states of eastern India namely West Bengal, Sikkim, Bihar, Jharkhand and Odisha. Three years monsoon season rainfall data from 2011 through 2013 are used for the study area. The distribution-oriented verification is done for different rainfall classes like dry, isolated, scattered, fairly widespread and widespread to understand the usefulness of the synoptic method. Statistics are presented for both combined classes of Percentage Correct (PC) and Heidke Skill Score (HSS) of the met subdivision wise forecast and PC, POD and CSI for individual classes. It has been observed that among the met subdivision the efficiency of the method is highest in Sub Himalayan West Bengal (SHWB) & Sikkim followed by Gangetic West Bengal (GWB), Odisha, Jharkhand and Bihar.

Predictability of global monsoon rainfall in NCEP CFSv2

This study evaluates the actual and potential prediction skill of the global monsoon rainfall using hindcast simulations by NCEP CFSv2 at zero to three lead forecast months (L0–L3). It is shown that the model has moderate skill in global monsoon rainfall (GMR) prediction, where the boreal summer monsoon rainfall forecast is more skillful than that of the austral summer. In general, the prediction skill of the GMR (actual and potential) increases with the decrease in lead forecast time, which is true for the all major regional monsoons, except the Australian monsoon. Over the Australian monsoon region, both actual and potential prediction skills in rainfall increase with increase in lead forecast. The forecast skill of tropical SST during austral summer is a maximum at 3 months lead forecast (i.e. July initial conditions) and that is associated with spring predictability barrier. Using partial least square (PLS) regression method, it is shown that the major predictor (first latent vector) of the boreal and austral summer monsoon rainfall variability is ENSO, and the influence of ENSO on rainfall variability is much stronger in the model as compared to the observation. The second PLS regression mode is associated with the non-ENSO variability like tropical Atlantic, Indian, subtropical northwest Pacific Ocean variability, midlatitude interactions etc. However, the model has very poor skill in reproducing the second mode, particularly during the boreal summer monsoon season. It is also shown that a significant part of the Indian summer monsoon rainfall variability is controlled by other than ENSO variability and the model has limited success in capturing that.

A hybrid model based on latest version of NCEP CFS coupled model for Indian monsoon rainfall forecast

AbstractThe forecast skill of all India summer monsoon rainfall (AISMR) during June to September (JJAS) in the new version of the National Centers for Environmental Prediction (NCEP)'s Climate Forecast System version 2 (CFSv2) is analyzed by considering 28 years (1982–2009) retrospective forecasts. The spatial patterns of JJAS mean rainfall and its interannual variability is more realistic over the Indian monsoon region in CFSv2 as compared to previous version of NCEP's CFS. A hybrid (dynamical–empirical) model based on the forecast variables of CFSv2 is developed for AISMR, which shows correlation that is highly significant with observed AISMR. The hybrid model correctly predicted the observed AISMR departure of 2013.

Meteorological subdivisional-level extended range forecast over India during southwest monsoon 2012

The real-time multi-model ensemble (MME)-based extended range (up to 3 weeks) forecast of monsoon rainfall over India during the 2012 monsoon season is analyzed using the outputs of European Centre for Medium Range Weather Forecasts (ECMWF) monthly forecast coupled model, National Centre for Environmental Prediction (NCEP) Climate Forecast System version 2 coupled model and Japan Meteorological Agency (JMA)’ ensemble prediction system. Although the individual models show useful skill in predicting the extended range forecast of monsoon, the MME forecast is found to be superior compared to these. For the country as a whole, the correlation coefficient (CC) between the observed and MME forecast rainfall departure is found to be statistically significant (99 % level) at least for 2 weeks (up to 18 days). Over the four homogeneous regions of India, the CC is found to be significant (above 95 % level) up to 2 weeks except in case of northeast India, which shows significant CC for week 1 (days 5–11) only. On the meteorological subdivision level (India is divided into 36 meteorological subdivisions) the mean percentage of correct forecast is found to be much higher than the climatology forecast. Considering the complex problem of forecasting of monsoon in the extended range timescales, the MME-based predictions for 2–3 weeks provide skillful results and useful guidance for application in agriculture and other sectors in India.

Comparison of intra-seasonal forecast of Indian summer monsoon between two versions of NCEP coupled models

The real-time forecasting of monsoon activity over India on extended range time scale (about 3 weeks) is analyzed for the monsoon season of 2012 during June to September (JJAS) by using the outputs from latest (CFSv2 [Climate Forecast System version 2]) and previous version (CFSv1 [Climate Forecast System version 1]) of NCEP coupled modeling system. The skill of monsoon rainfall forecast is found to be much better in CFSv2 than CFSv1. For the country as a whole the correlation coefficient (CC) between weekly observed and forecast rainfall departure was found to be statistically significant (99 % level) at least for 2 weeks (up to 18 days) and also having positive CC during week 3 (days 19–25) in CFSv2. The other skill scores like the mean absolute error (MAE) and the root mean square error (RMSE) also had better performance in CFSv2 compared to that of CFSv1. Over the four homogeneous regions of India the forecast skill is found to be better in CFSv2 with almost all four regions with CC significant at 95 % level up to 2 weeks, whereas the CFSv1 forecast had significant CC only over northwest India during week 1 (days 5–11) forecast. The improvement in CFSv2 was very prominent over central India and northwest India compared to other two regions. On the meteorological subdivision level (India is divided into 36 meteorological subdivisions) the percentage of correct category forecast was found to be much higher than the climatology normal forecast in CFSv2 as well as in CFSv1, with CFSv2 being 8–10 % higher in the category of correct to partially correct (one category out) forecast compared to that in CFSv1. Thus, it is concluded that the latest version of CFS coupled model has higher skill in predicting Indian monsoon rainfall on extended range time scale up to about 25 days.

Meta-heuristic ant colony optimization technique to forecast the amount of summer monsoon rainfall: skill comparison with Markov chain model

Forecasting summer monsoon rainfall with precision becomes crucial for the farmers to plan for harvesting in a country like India where the national economy is mostly based on regional agriculture. The forecast of monsoon rainfall based on artificial neural network is a well-researched problem. In the present study, the meta-heuristic ant colony optimization (ACO) technique is implemented to forecast the amount of summer monsoon rainfall for the next day over Kolkata (22.6°N, 88.4°E), India. The ACO technique belongs to swarm intelligence and simulates the decision-making processes of ant colony similar to other adaptive learning techniques. ACO technique takes inspiration from the foraging behaviour of some ant species. The ants deposit pheromone on the ground in order to mark a favourable path that should be followed by other members of the colony. A range of rainfall amount replicating the pheromone concentration is evaluated during the summer monsoon season. The maximum amount of rainfall during summer monsoon season (June—September) is observed to be within the range of 7.5–35 mm during the period from 1998 to 2007, which is in the range 4 category set by the India Meteorological Department (IMD). The result reveals that the accuracy in forecasting the amount of rainfall for the next day during the summer monsoon season using ACO technique is 95 % where as the forecast accuracy is 83 % with Markov chain model (MCM). The forecast through ACO and MCM are compared with other existing models and validated with IMD observations from 2008 to 2012.

Hydroclimate variability and long-lead forecasting of rainfall over Thailand by large-scale atmospheric variables

The development of statistical relationships between local hydroclimates and large-scale atmospheric variables enhances the understanding of hydroclimate variability. The rainfall in the study basin (the Upper Chao Phraya River Basin, Thailand) is influenced by the Indian Ocean and tropical Pacific Ocean atmospheric circulation. Using correlation analysis and cross-validated multiple regression, the large-scale atmospheric variables, such as temperature, pressure and wind, over given regions are identified. The forecasting models using atmospheric predictors show the capability of long-lead forecasting. The modified k-nearest neighbour (k-nn) model, which is developed using the identified predictors to forecast rainfall, and evaluated by likelihood function, shows a long-lead forecast of monsoon rainfall at 7–9 months. The decreasing performance in forecasting dry-season rainfall is found for both short and long lead times. The developed model also presents better performance in forecasting pre-monsoon season rainfall in dry years compared to wet years, and vice versa for monsoon season rainfall. Editor Z.W. Kundzewicz Citation Singhrattna, N., Babel, M.S. and Perret, S.R., 2012. Hydroclimate variability and long-lead forecasting of rainfall over Thailand by large-scale atmospheric variables. Hydrological Sciences Journal, 57 (1), 26–41.

Comparative Evaluation of Two Ensembles for Long-Range Forecasting of Monsoon Rainfall

Abstract It is now well known that changes in initial conditions can give rise to substantial changes in the forecasts even at the long range. Ensemble averaging of forecasts from different initial conditions provides an efficient way of assessing and reducing uncertainties in the forecasts due to inherent uncertainties in the initial conditions. However, the procedure for generating the ensemble of forecasts has to be based on careful consideration. Although there now exist several well-tested frameworks for ensemble forecasting at the short range, the procedure for and impact of ensemble forecasting on long-range forecasting of monsoons remain relatively less explored. In particular, the procedure for the choice of the ensemble for long-range forecasting of monsoons needs special considerations. The Indian summer monsoon is characterized by a number of intraseasonal oscillations (ISOs) whose phases and amplitudes can significantly affect the monsoon forecast and that can be adequately sampled only using initial states spread over time scales comparable to the characteristic time scales of these ISOs. It is shown that use of initial states spread over a longer period (such as 1 April–1 May) results in a better ensemble average for long-range forecasting of Indian summer monsoon than that from an ensemble of closely packed states with a shorter lead. An optimized configuration for long-range forecasting of monsoons using a variable resolution general circulation model is adopted. The climatological monthly mean SST field is used to assess realizable skill, as use of observed SST provides only potential skill. Then five-member wide-lead (1 April–1 May) ensemble average forecasts are compared with five-member compact-lead (27 April–1 May) ensemble average forecasts for 24 (1980–2003) hindcasts; it is shown that the skill of the wide-lead ensemble average is superior to that of the compact-lead ensemble at different spatial and temporal scales in spite of the longer lead of the former.

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.

El Nino of 1997-1998 and Indian Monsoon

El-Nino of 1997-1998 was accompanied by severe global weather anomalies, which generated widespread interest at all levels in the world. As a result, United Nations General Assembly passed a resolution (52 / 200) urging International co-operation to reduce the adverse impact of El-Nino on human society and Environment. The El-Nino (Warm Phase) commenced around April – May 1997, reached peak intensity around December 1997 and ended around May 1998. La-Nina (Cold Phase) started around this time, reached its peak in January 1999, weakened around June - July 1999 and has continued in its weak phase at the time of writing, August 1999.
 
 Development and decay of the El-Nino are illustrated through SST,SOI and sea-water temperature below the sea-surface. Features during peak period of El-Nino are illustrated through SST, sea-level pressure, surface wind, OLR, and Walker Circulation. There is clear evidence of west-to-east propagation of OLR anomaly, 850 hPa zonal wind anomaly and sea-level pressure anomaly. SST anomaly pattern did not give strong evidence of this type of zonal progression.
 
 El-Nino is global in nature.
 
 El-Nino / La-Nina years during the 120-year period 1871-1990 are tabulated along with All India Summer Monsoon Rainfall (AISMR) anomalies. There is evidence of El-Nino years tending to become years of deficit rainfall and La-Nina years being years of excess rainfall over India. El-Nino / La-Nina events, which can be predicted 6-12 months in advance, can be used and are being used as part of the prediction formulae, in the issue of official monsoon rainfall forecast by India Meteorological Department. Based on El-Nino considerations alone, it has been feared, in some quarters, that 1997 might become a year of extreme deficit summer monsoon rainfall. However, the
 actual rainfall over India during June – September 1997 was 2 % above normal. India Meteorological Department had predicted "normal" rainfall (+-10% of the rainfall).

Breaks in the Asian Monsoon: The Influence of Southern Hemisphere Weather Systems

Abstract Atmospheric model results suggest that chaotic weather systems in the Southern Hemisphere midlatitudes can trigger “breaks” in the Indian monsoon rainfall. Indeed, the mechanism may be able to trigger a more general break of the entire Asian monsoon. The mechanism proposed involves the injection of dry, high negative potential vorticity air from the Southern Hemisphere midlatitudes into the low-level monsoon inflow. Observations from the 1994 monsoon season tend to support this mechanism and, if true, it may imply some predictive skill for shorter-range forecasting. However, the mechanism proposed may also imply that an accurate seasonal forecast of monsoon rainfall is an impossible objective, with important consequences for the agricultural economies of the region. Results are presented from both an idealized model and a full general circulation model.

Recent models for long range forecasting of Southwest monsoon rainfall in India

India has a long tradition of scientific work or, long range forecasting of the southwest monsoon ever since the times of Blanford and Walker In the early Parts of this century, the recent decades have witnessed increased research in regard to the development of new long range forecast models in the India Meteorological Department which have, given correct long range (seasonal) forecasts of southwest monsoon rainfall, over the country as a whole, during, the successive four year~, 1988 to 1991, Presently, four models namely, Para-metric Power Regression, Dynamic Stochastic Transfer and Improved Multiple Regression models are being used for formulating the seasonal forecast of monsoon rainfall over the country as a whole, The forecast is issued in two stages, In the first stage a tentative inference which is qualitative in nature is issued before the middle of April based mainly on the Parametric model which utilizes signals from 16 regional and global parameters that are related to land, ocean and atmospheric forcing and show physical linkages with monsoon. In the second stage, a firm quantitative forecast is issued towards the end of the May and is based on the remaining three models mentioned above, although higher weight age is given to the Power Regression Model which has shown encouraging performance during the last four years. In this paper, these recently developed models and the scientific basis underlying these are discussed, Data on validation of these Operational models, used for the long range forecast during the past four years (1988-91) are also presented.

A POWER REGRESSION MODEL FOR LONG RANGE FORECAST OF SOUTHWEST MONSOON RAINFALL OVER INDIA

A detailed analysis of southwest monsoon (June to September) rainfall over India of several decades vis-a-vis the regional and global antecedent signals in numerous permutations and combinations has led the authors to conclude that a long range forecast based on one, two, three or four parameters as attempted by several workers in the past, cannot be reliable on all occasions as indeed has proved to be the case. The parametric and power regression models utilizing 16 parameters, described in the present paper, suggest that it is a tapestry of several parameters and interactive nature of the regional and global climatic forcings that govern the quality and quantity of the monsoon. A detailed analysis of non-linear interactions among the antecedent climatic conditions and the monsoon has led the authors to introduce the concept of proportionate weightage to the signals of different parameters. This has led to the development of a power regression model, which is able to quantify the effect of each parameter. Details of the model are presented, Based on the model, the India Meteorological Department has been issuing the operational long range forecast of monsoon rainfall over India as a whole during the past 3 years, 1988 to 1990, and these forecasts have proved to be correct.

Parametric and power regression models: New approach to long range forecasting of monsoon rainfall in India

Monsoon is a complex system. As such it is not yet possible to develop a deterministic model for long range forecasting of monsoon rainfall. An attempt h!1.s been made here to develop a parametric and a power regression model to predict monsoon rainfall by utilising signals from 15 parameters known to be related with the Indian summer monsoon rainfall. Some of the parameters are global and others are regional in nature. These parameters also have physical links with the Indian summer monsoon. Some of these parameters are inter-related. The parametric model, which analyses the monsoon rainfall vis-à-vis the 15 parameters for the last 37 years (1951.-1987), shows encouraging results. It is observed that whenever more than 70% parameters showed favourable signals, the monsoon rainfall in India was not only normal (percentage departure of rainfall -+10% of normal), but it was towards the positive side of the normal. In .1988, a large number (87%) of parameters were favourable. Based on this analysis the long range forecast for monsoon 1988 was estimated towards the positive side of the normal and actually the monsoon rainfall in 1988 was above normal (+19% of the normal) .
 
 This parametric model is purely a qualitative decision making tool where equal weightage has been given to all the 15 parameters. However, the relationship of monsoon rainfall with individual predictors exhibits a nonlinear relationship. To take care of this non-linearity, a curvilinear relationship has been determined by fitting the equation of different degrees. The best fit degree of relationship with all the 15 parameters has been combined and a power regression model has been developed. The forecasts from this power regression model have been found encouraging both during the sample period (1958-1980) on which the model is developed, as well as during the independent test period (1981-1988). The power regression model has correctly predicted the drought and large excess rainfall during the sample and independent test periods.
 
 The performance of the power regression model has been compared with that of the multiple linear regression model, formed by utilising exactly the same set of data which was used for formulating the power model. Comparison of two models shows that the performance of power regression model is better than that of the multiple regression model

Year-in-advance Forecasting of the Indian Monsoon Rainfall

The paper starts out by discussing factors which influence the Indian monsoon rainfall—including Northern Hemispheric temperature changes, long-period soli–lunar tides, and the tidelike phenomena associated with the wobble of the Earth relative to its spin-axis. When the effects of these factors on precipitation are quantified and integrated into a thermodynamical–statistical model, long-range climatic forecasts can be made that are better than chance or than predicting ‘normal’ up to two or more years in advance.Climatic changes have had profound effects on the peoples of India through the controlling influence of the monsoon on Indian agriculture. We now have a technological tool, never before available, that might lessen the impact of variations of the monsoon by forecasting these variations one to two years in advance. The two-years-in-advance 1982 monsoon rainfall forecast for June and July, using the same station models that produced the excellent 1981 forecast, indicates that there is a two-to-one chance that the 1982 Indian monsoon rainfall will be less, in general, in June and July, than it was in 1981. The question which we wish to ask the reader is: How can this new technology be used? For example, how can this type of information be used for planning and policy-making in order to ensure less uncertainty about food supplies in the future than in the past?This region cannot depend upon the monsoon to bring plentiful rain every year, and, therefore, it is essential that we should continue to improve our forecasting capability and that others learn to make optimum use of these forecasts.

Climatic teleconnections from the equatorial Pacific to the Indian monsson — Analysis and implications

The statistical technique used for seasonal forecasting of monsoon rainfall over India has been closely examined in the light of recent studies on climatic teleconnections. Such an analysis provides a potential for seasonal foreshadowing of large-scale atmospheric behaviour in tropics.