Forecasting quantitative rainfall over India using multi-model ensemble technique

A new approach to ensemble forecasting of rainfall over India based on daily outputs of four operational numerical weather prediction (NWP) models in the medium-range timescale (up to 5 days) is proposed in this study. Four global models, namely ECMWF, JMA, GFS and UKMO available on real-time basis at India Meteorological Department, New Delhi, are used simultaneously with adequate weights to obtain a multi-model ensemble (MME) technique. In this technique, weights for each NWP model at each grid point are assigned on the basis of unbiased mean absolute error between the bias-corrected forecast and observed rainfall time series of 366 daily data of 3 consecutive southwest monsoon periods (JJAS) of 2008, 2009 and 2010. Apart from MME, a simple ensemble mean (ENSM) forecast is also generated and experimented. The prediction skill of MME is examined against observed and corresponding outputs of each constituent model during monsoon 2011. The inter-comparison reveals that MME is able to provide more realistic forecast of rainfall over Indian monsoon region by taking the strength of each constituent model. It has been further found that the weighted MME technique has higher skill in predicting daily rainfall compared to ENSM and individual member models. RMSE is found to be lowest in MME forecasts both in magnitude and area coverage. This indicates that fluctuations of day-to-day errors are relatively less in the MME forecast. The inter-comparison of domain-averaged skill scores for different rainfall thresholds further clearly demonstrates that the MME algorithm improves slightly above the ENSM and member models.     

Influence of Eurasian snow on Indian summer monsoon in NCEP CFSv2 freerun

The latest version of the state-of-the-art global land–atmosphere–ocean coupled climate forecast system of NCEP has shown considerable improvement in various aspects of the Indian summer monsoon. However, climatological mean dry bias over the Indian sub-continent is further increased as compared to the previous version. Here we have attempted to link this dry bias with climatological mean bias in the Eurasian winter/spring snow, which is one of the important predictors of the Indian summer monsoon rainfall (ISMR). Simulation of interannual variability of the Eurasian snow and its teleconnection with the ISMR are quite reasonable in the model. Using composite analysis it is shown that a positive snow anomaly, which is comparable to the systematic bias in the model, results into significant decrease in the summer monsoon rainfall over the central India and part of the Equatorial Indian Ocean. Decrease in the summer monsoon rainfall is also found to be linked with weaker northward propagation of intraseasonal oscillation (ISO). A barotropic stationary wave triggered by positive snow anomaly over west Eurasia weakens the upper level monsoon circulation, which in turn reduces the zonal wind shear and hence, weakens the northward propagation of summer monsoon ISOs. A sensitivity experiment by reducing snow fall over Eurasian region causes decrease in winter and spring snow depth, which in turn leads to decrease in Indian summer monsoon rainfall. Results from the sensitivity experiment corroborate with those of composite analysis based on long free run. This study suggests that further improvements in the snow parametrization schemes as well as Arctic sea ice are needed to reduce the Eurasian snow bias during winter/spring, which may reduce the dry bias over Indian sub-continent and hence predictability aspect of the model.     

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.     

Intercomparison of the performance of MM5/WRF with and without satellite data assimilation in short-range forecast applications over the Indian region

The present study is conducted to verify the short-range forecasts from mesoscale model version5 (MM5)/weather research and forecasting (WRF) model over the Indian region and to examine the impact of assimilation of quick scatterometer (QSCAT) near surface winds, spectral sensor microwave imager (SSM/I) wind speed and total precipitable water (TPW) on the forecasts by these models using their three-dimensional variational (3D-Var) data assimilation scheme for a 1-month period during July 2006. The control (without satellite data assimilation) as well as 3D-Var sensitivity experiments (with assimilating satellite data) using MM5/WRF were made for 48 h starting daily at 0000 UTC July 2006. The control run is analyzed for the intercomparison of MM5/WRF short-range forecasts and is also used as a baseline for assessing the MM5/WRF 3D-Var satellite data sensitivity experiments. As compared to the observation, the MM5 (WRF) control simulations strengthened (weakened) the cross equatorial flow over southern Arabian sea near peninsular India. The forecasts from MM5 and WRF showed a warm and moist bias at lower and upper levels with a cold bias at the middle level, which shows that the convective schemes of these models may be too active during the simulation. The forecast errors in predicted wind, temperature and humidity at different levels are lesser in WRF as compared to MM5, except the temperature prediction at lower level. The rainfall pattern and prediction skill from day 1 and day 2 forecasts by WRF is superior to MM5. The spatial distribution of forecast impact for wind, temperature, and humidity from 1-month assimilation experiments during July 2006 demonstrated that on average, for 24 and 48-h forecasts, the satellite data improved the MM5/WRF initial condition, so that model errors in predicted meteorological fields got reduced. Among the experiments, MM5/WRF wind speed prediction is most benefited from QSCAT surface wind and SSM/I TPW assimilation while temperature and humidity prediction is mostly improved due to latter. The largest improvement in MM5/WRF rainfall prediction is due to the assimilation of SSM/I TPW. The assimilation of SSM/I wind speed alone in MM5/WRF degraded the humidity and rainfall prediction. In summary the assimilation of satellite data showed similar impact on MM5/WRF prediction; largest improvement due to SSM/I TPW and degradation due to SSM/I wind speed.     

Seasonal prediction of Indian summer monsoon in NCEP coupled and uncoupled model

This study has identified probable factors that govern ISMR predictability. Furthermore, extensive analysis has been performed to evaluate factors leading to the predictability aspect of Indian Summer Monsoon Rainfall (ISMR) using uncoupled and coupled version of National Centers for Environmental Prediction Coupled Forecast System (CFS). It has been found that the coupled version (CFS) has outperformed the uncoupled version [Global Forecast System (GFS)] of the model in terms of prediction of rainfall over Indian land points. Even the spatial distribution of rainfall is much better represented in the CFS as compared to that of GFS. Even though these model skills are inadequate for the reliable forecasting of monsoon, it imparts the capacious knowledge about the model fidelity. The mean monsoon features and its evolution in terms of rainfall and large-scale circulation along with the zonal and meridional shear of winds, which govern the strength of the monsoon, are relatively closer to the observation in the CFS as compared to the GFS. Furthermore, sea surface temperature–rainfall relation is fairly realistic and intense in the coupled version of the model (CFS). It is found that the CFS is able to capture El Niño Southern Oscillation ISMR (ENSO-ISMR) teleconnections much strongly as compared to GFS; however, in the case of Indian Ocean Dipole ISMR teleconnections, GFS has the larger say. Coupled models have to be fine-tuned for the prediction of the transition of El Niño as well as the strength of the mature phase has to be improved. Thus, to sum up, CFS tends to have better predictive skill on account of following three factors: (a) better ability to replicate mean features, (b) comparatively better representation of air–sea interactions, and (c) much better portrayal of ENSO-ISMR teleconnections. This study clearly brings out that coupled model is the only way forward for improving the ISMR prediction skill. However, coupled model’s spurious representation of SST variability and mean model bias are detrimental in seasonal prediction.     

Assessment of Data Assimilation Approaches for the Simulation of a Monsoon Depression Over the Indian Monsoon Region

A low pressure system that formed on 21 September 2006 over eastern India/Bay of Bengal intensified into a monsoon depression resulting in copious rainfall over north-eastern and central parts of India. Four numerical experiments are performed to examine the performance of assimilation schemes in simulating this monsoon depression using the Fifth Generation Mesoscale Model (MM5). Forecasts from a base simulation (with no data assimilation), a four-dimensional data assimilation (FDDA) system, a simple surface data assimilation (SDA) system coupled with FDDA, and a flux-adjusting SDA system (FASDAS) coupled with FDDA are compared with each other and with observations. The model is initialized with Global Forecast System (GFS) forecast fields starting from 19 September 2006, with assimilation being done for the first 24 hours using conventional observations, sounding and surface data of temperature and moisture from Advanced TIROS Operational Vertical Sounder satellite and surface wind data over the ocean from QuikSCAT. Forecasts are then made from these assimilated states. In general, results indicate that the FASDAS forecast provides more realistic prognostic fields as compared to the other three forecasts. When compared with other forecasts, results indicate that the FASDAS forecast yielded lower root-mean-square (r.m.s.) errors for the pressure field and improved simulations of surface/near-surface temperature, moisture, sensible and latent heat fluxes, and potential vorticity. Heat and moisture budget analyses to assess the simulation of convection revealed that the two forecasts with the surface data assimilation (SDA and FASDAS) are superior to the base and FDDA forecasts. An important conclusion is that, even though monsoon depressions are large synoptic systems, mesoscale features including rainfall are affected by surface processes. Enhanced representation of land-surface processes provides a significant improvement in the model performance even under active monsoon conditions where the synoptic forcings are expected to be dominant.     

Objective determination of onset,advancement and withdrawal of the summer monsoon using large-scale forecast fields of a global spectral model over India

Summary In this study, a detailed examination on the evolution of summer monsoon onset over southern tip of the Indian peninsula, its advancement and withdrawal over the Indian sub-continent is carried out by utilizing the analysis/forecast fields of a global spectral model for Monsoon-1995. The data base used in this study is derived from the archives of global data assimilation and forecasting system of NCMRWF, India, valid for 00UTC at 1.5° latitude/longitude resolution for the summer monsoon period of 1995. By utilizing the analyses and forecast fields, and the established knowledge of the Indian monsoon, objective criteria are employed in this study for determining the onset, advancement, and withdrawal of the monsoon.It is found that all the major characteristics of Monsoon-1995 are captured well by the analysis-forecast system even though the criteria adopted in this study are more objective and different in nature as compared to the conventional procedures. The onset date of monsoon over the southern tip of the Indian peninsula as determined by the dynamical onset procedure is found to be matching well with the realized date. Further, the evolution of monsoon onset characteristics over the Arabian Sea both in the analyses and forecasts is found to be in good agreement with the earlier studies. However, the magnitudes of net tropospheric moisture build-up and tropospheric temperature increase differ with respect to analyses and corresponding forecast fields. In addition, all important characteristics of the advancement and withdrawal of monsoon over the Indian sub-continent viz. stagnation, revival etc., are brought out reasonably well by the analysis and forecast system.With 10 Figures     

The influence of systematic errors on the asian summer monsoon circulation

1. IntroductionThe Asian summer monsoon circulation is a thermally driven circulation, which arisesprimarily from the temperature differences between the warmer continental areas of theNorthern Hemisphere and the oceans of the Southern Hemisphere. The complex feedback between the flow field and the heating, especially through the interaction between thelarge--scale flow and moist convection, is yet to be well understood. Nevertheless, this facetensures the prominence of the summer monsoon ci…     

Weakening of Indian summer monsoon in recent decades

The analysis of 43 years of NCEP-NCAR reanalysis data and station observations reveals the connections between tropospheric temperature variations and the weakening of the Indian summer monsoon circulation. The Indian summer monsoon variation is strongly linked to tropospheric temperature over East Asia, showing significant positive correlations of mean tropospheric temperature with all-Indian summer rainfall and the monsoon circulation intensity. The result shows that Indian summer monsoon circulation underwent two weakening processes in recent decades. The first occurred in circa the mid-1960s, and the other occurred in circa the late 1970s. The finding indicates that the mean tropospheric temperature may play a crucial role in the weakening of the Indian summer monsoon intensity via changing land-sea thermal contrast. The role of the tropospheric temperature contrast between East Asia and the tropical area from the eastern Indian Ocean to the tropical western Pacific is to weaken the Indian summer monsoon circulation.     

印度夏季风的减弱及其与对流层温度的关系

对43aNCEP/NCAR再分析资料和台站实际观测资料的分析,揭示了对流层温度变化和印度夏季风环流减弱之间的联系。印度夏季风的变化与东亚上空对流层温度具有密切的关系,主要表现为对流层平均温度与整个印度夏季降雨和季风环流强度之间存在显著的正相关。结果表明:印度夏季风环流在近几十年经历了两次减弱过程,第一次减弱约发生在20世纪60年代中期,第二次减弱则发生在20世纪70年代后期;通过改变海陆热力对比,对流层平均温度在印度夏季风减弱过程中可能起着重要作用,东亚地区与东印度洋至西太平洋热带地区之间的对流层温度差异导致了印度夏季风环流的减弱。     

Radiative effects of black carbon aerosols on Indian monsoon: a study using WRF-Chem model

The Weather Research and Forecasting model with Chemistry (WRF-Chem) is utilized to examine the radiative effects of black carbon (BC) aerosols on the Indian monsoon, for the year 2010. Five ensemble simulations with different initial conditions (1st to 5th December, 2009) were performed and simulation results between 1st January, 2010 to 31st December, 2010 were used for analysis. Most of the BC which stays near the surface during the pre-monsoon season gets transported to higher altitudes with the northward migration of the Inter Tropical Convergence Zone (ITCZ) during the monsoon season. In both the seasons, strong negative SW anomalies are present at the surface along with positive anomalies in the atmosphere, which results in the surface cooling and lower tropospheric heating, respectively. During the pre-monsoon season, lower troposphere heating causes increased convection and enhanced meridional wind circulation, bringing moist air from Indian Ocean and Bay of Bengal to the North-East India, leading to increased rainfall there. However, during the monsoon season, along with cooling over the land regions, a warming over the Bay of Bengal is simulated. This differential heating results in an increased westerly moisture flux anomaly over central India, leading to increased rainfall over northern parts of India but decreased rainfall over southern parts. Decreased rainfall over southern India is also substantiated by the presence of increased evaporation over Bay of Bengal and decrease over land regions.     

GRAPES-GFS模式暴雨预报天气学检验特征

本文采用天气学检验方法,对2016年度国家气象中心GRAPES全球数值预报系统(GRAPES-GFS)业务预报暴雨过程及2013-2015年部分回算个例进行了检验,并结合对比欧洲中期天气预报中心确定性预报模式(EC模式)和国家气象中心全球谱模式T639L60(T639模式)降水预报,梳理总结业务GRAPES-GFS模式预报性能优势和系统性偏差特征。被检验暴雨过程共38次,其中南方暴雨过程20次,北方暴雨过程6次,热带扰动或台风降水过程12次。依靠预报员主观天气学检验分析,从降水预报效果检验出发,结合主要影响天气系统和示踪物理量检验,梳理总结模式预报系统性偏差,以期全面发掘该业务预报模式性能。结果表明对短期时效内的降水预报,GRAPES-GFS模式预报稳定性较好,整体明显优于T639模式。但还存在诸如对对流性降水预报较实况偏北或对主雨带南侧暖区降水预报不足的偏差特征;另对弱高空波动背景下的对流性降水预报偏弱;而在降水预报强度大致正确的情况下,对降水系统南侧偏南气流控制区域预报湿度偏大,对副热带地区的低涡系统预报偏强。     

Application of multimodel ensemble techniques for real time district level rainfall forecasts in short range time scale over Indian region

During the summer monsoon (1 June to 30 September) 2007, real-time district level rainfall forecasts in short-range time scale were generated for Indian region applying multimodel ensemble technique. The pre-assigned grid point weights on the basis of correlation coefficients (CC) between the observed values and forecast values are determined for each constituent model at the resolution of 0.5° × 0.5° utilizing two seasons datasets (1 June to 30 September, 2005 and 2006), and the multimodel ensemble forecasts (day 1 and day 2 forecasts) are generated at the same resolution on a real-time basis. The ensemble forecast fields are then used to prepare forecasts for each district taking the average value of all grid points falling in a particular district. In this paper we examined the performance skill of the multimodel ensemble-based real-time district level short-range forecast of rainfall. It has clearly emerged from the results that the multimodel ensemble technique reported in this study is superior to each ensemble member. District wise performance of the ensemble rainfall forecast reveals that the technique, in general, is capable of providing reasonably good forecast skill over most districts of the country, particularly over the districts where the monsoon systems are dominant. Though the procedure shows appreciable skill to predict occurrence or non-occurrence of rainfall at the district level, it always underestimates rainfall amount, particularly in heavy rainfall events. Possible reasons of this failure may be due to model bias and poor data assimilation procedure.     

Effect of 3DVAR assimilation of MODIS temperature and humidity profiles on the dynamic and thermodynamic features of three monsoon depressions over the Bay of Bengal

The three-dimensional variational data assimilation (3DVAR) technique in the advanced weather research and forecast model is used to study the impact of assimilating Moderate Resolution Spectroradiometer (MODIS) retrieved temperature and humidity profiles on the dynamic and thermodynamic features for three monsoon depressions over the Bay of Bengal, India. For better understanding of the role of various physical processes in the evolution of monsoon depression, a detailed diagnostic study is performed on all the three depression cases. Numerical experiments were conducted in a system of two-way nested domains with a horizontal resolution of 36 and 12 km, respectively. The assimilation of MODIS data did improve the mean sea level pressure patterns and spatial distribution of rainfall patterns in all the three monsoon depression cases studied. Higher values of equitable threat score and lower bias values are seen consistently for the entire rainfall threshold range and for all the three depression cases with 3DVAR assimilation of MODIS temperature and humidity profiles. The current operational regional models in India do not ingest the MODIS temperature and humidity profiles and hence the present study is particularly relevant to the operational forecasting community in India in their ongoing efforts to improve weather forecasting over India.     

A verification of spatio-temporal monsoon rainfall variability across Indian region using NWP model output

Evaluation of weather forecasting systems and assessment of existing verification procedures are essential to achieve desirable seamless rainfall prediction. Prediction of wet and dry spells is quite useful in agriculture and hydrology but very few attempts have been made so far to resolve the issue using numerical model output. Performance of five state-of-the-art global atmospheric general circulation models and their ensemble mean has been examined in predicting the parameters of wet and dry spells (WSs/DSs) during monsoon period of 2008–2011 over seven subzones of the Indian region. The number of WSs across the region is found to be underestimated, while total duration and rainfall amount of WSs (DSs) overestimated (underestimated). Start of the first WS is late and ends of the last WS early in the model forecast. More uncertainty is noticed in the prediction of DS rainfall and its duration than that of the WS. The percentage area of India under wet conditions (rainfall amount over each grid is more than its daily mean monsoon rainfall) and rainwater over the wet area is overestimated by about 59 and 32 %, respectively, in all models.     

Monthly prediction of rainfall over India and its homogeneous zones during monsoon season: a supervised principal component regression approach on general circulation model products

A supervised principal component regression (SPCR) technique has been employed on general circulation model (GCM) products for developing a monthly scale deterministic forecast of summer monsoon rainfall (June–July–August–September) for different homogeneous zones and India as a whole. The time series of the monthly observed rainfall as the predictand variable has been used from India Meteorological Department gridded (1°?×?1°) rainfall data. Lead 0 (forecast initialized in the same month) monthly products from GCMs are used as predictors. The sources of these GCMs are International Research Institute for Climate and Society, Columbia University, National Center for Environmental Prediction, and Japan Agency for Marine Earth Science and Technology. The performance of SPCR technique is judged against simple ensemble mean of GCMs (EM) and it is found that over almost all the zones the SPCR model gives better skill than EM in June, August, and September months of monsoon. The SPCR technique is able to capture the year to year observed rainfall variability in terms of sign as well as the magnitude. The independent forecasts of 2007 and 2008 are also analyzed for different monsoon months (Jun–Sep) in homogeneous zones and country. Here, 1982–2006 have been considered as development year or training period. Results of the study suggest that the SPCR model is able to catch the observational rainfall over India as a whole in June, August, and September in 2007 and June, July, and August in 2008.     

Indian summer monsoon rainfall predictability and variability associated with Northwest Pacific circulation in a suit of coupled model hindcasts

The Northwest Pacific (NWP) circulation (subtropical high) is an important component of the East Asian summer monsoon system. During summer (June–August), anomalous lower tropospheric anticyclonic (cyclonic) circulation appears over NWP in some years, which is an indicative of stronger (weaker) than normal subtropical high. The anomalous NWP cyclonic (anticyclonic) circulation years are associated with negative (positive) precipitation anomalies over most of Indian summer monsoon rainfall (ISMR) region. This indicates concurrent relationship between NWP circulation and convection over the ISMR region. Dry wind advection from subtropical land regions and moisture divergence over the southern peninsular India during the NWP cyclonic circulation years are mainly responsible for the negative rainfall anomalies over the ISMR region. In contrast, during anticyclonic years, warm north Indian Ocean and moisture divergence over the head Bay of Bengal-Gangetic Plain region support moisture instability and convergence in the southern flank of ridge region, which favors positive rainfall over most of the ISMR region. The interaction between NWP circulation (anticyclonic or cyclonic) and ISMR and their predictability during these anomalous years are examined in the present study. Seven coupled ocean–atmosphere general circulation models from the Asia-Pacific Economic Cooperation Climate Center and their multimodel ensemble mean skills in predicting the seasonal rainfall and circulation anomalies over the ISMR region and NWP for the period 1982–2004 are assessed. Analysis reveals that three (two) out of seven models are unable to predict negative (positive) precipitation anomalies over the Indian subcontinent during the NWP cyclonic (anticyclonic) circulation years at 1-month lead (model is initialized on 1 May). The limited westward extension of the NWP circulation and misrepresentation of SST anomalies over the north Indian Ocean are found to be the main reasons for the poor skill (of some models) in rainfall prediction over the Indian subcontinent. This study demonstrates the importance of the NWP circulation variability in predicting summer monsoon precipitation over South Asia. Considering the predictability of the NWP circulation, the current study provides an insight into the predictability of ISMR. Long lead prediction of the ISMR associated with anomalous NWP circulation is also discussed.     

A study on drought features of the Indian summer monsoon 2002

In this paper, a diagnostic study is carried out with global analysis data sets to determine how the large scale atmospheric circulation affecting the anomalous drought of the Indian summer monsoon 2002. The daily analysis obtained from National Centre for Environmental Prediction/National Centre for Atmospheric Research (NCEP/NCAR) for the month of July is used to investigate the mean circulation characteristics and the large scale energetics over the Indian monsoon domain. Examination of rainfall revealed that the summer monsoon (JJAS) rainfall of 2002 over India is 22% below normal in which the large deficit of 56% below normal rainfall in July. The recent past drought during summer season of 2004 and 2009 are 12 and 23%, respectively, below normal rainfall. The large deficit of rainfall in 2009 is from the June month with 48% below normal rainfall, where as 2004 drought contributed from July (19%) and August (24%). Another significant facet of the rainfall in July 2002 is lowest ever recorded in the past 138 years (1871–2008). The circulation features illustrated weak low level westerly wind at 850 hPa (Somali Jet) in July during large deficit rainfall years of 1987 and 2002 with a reduction of about 30% when compared with the excess and normal rainfall years of 1988 and 2003. Also, tropical easterly jet at 150 hPa reduced by 15% during the deficit rainfall year of 2002 against the excess rainfall year of 1988. Both the jet streams are responsible for low level convergence and upper level divergence leading to build up moisture and convective activity to sustain the strength of the monsoon circulation. These changes are well reflected in reduction of tropospheric moisture profile considerably. It is found that the maximum number of west pacific cyclonic system during July 2002 is also influenced for large deficit rainfall over India. The dynamic, thermodynamic and energetic clearly show the monsoon break type situation over India in the month of July 2002 resulting less convective activity and the reduction of moisture. The large diabatic heating, flux convergence of heat and moisture over south east equatorial Indian Ocean are also responsible for drought situation in July 2002 over the Indian region.     

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.     

Intercomparison of the characteristic features of the southwest monsoon over india as reproduced in the mean monthly analyses and forecasts of some operational NWP centres during 1995

Summary An intercomparison of the characteristic features of the Indian summer monsoon has been carried out for the monsoon months (June to September) of 1995 using the mean monthly analyses/forecasts from the operational centres of ECMWF, JMA, UKMO and NCMRWF. This exercise was undertaken to determine how well the large scale monsoon features over India were reproduced in the operational output in 1995 and also to assess the performance of the NCMRWF assimilation/forecast system. For this purpose, precipitation, mean sea level pressure, circulation features in the lower (850 hPa) and upper (200 hPa) troposphere, mid-tropospheric (500 hPa) temperature, and latent heat flux were examined.It is found that all the dominant features of the Indian summer monsoon are fairly well represented in the analysis and medium range forecasts of the ECMWF, JMA and UKMO. The NCMRWF output agrees well with those from other centres except for a sharp gradient in precipitation across the west coast which was not captured well in the forecasts due to the relatively coarse horizontal resolution of the model compared to that used at other operational centres. Other important features of the southwest monsoon, like the heat low over the northwestern part of the country, the lower level westerly jet and upper level easterly jet etc. are found to be reasonably well represented in the output of all operational centres. The JMA analyses and forecasts possessed greater levels of moisture compared to the NCMRWF output possibly due to the synthetic moisture information used at JMA. The evolution characteristics of the summer monsoon onset over the southern tip of India are found to be comparable in the output of JMA and NCMRWF.With 13 Figures