Prediction Of Indian Summer Monsoon Research Articles

Improving Indian Summer Monsoon Prediction

A new study analyzes temperature and moisture variations in different layers of the atmosphere to improve forecasting of Indian summer monsoon precipitation patterns.

Evaluation of the impact of AIRS profiles on prediction of Indian summer monsoon using WRF variational data assimilation system

AbstractThis study investigates the impact of temperature and moisture profiles from Atmospheric Infrared Sounder (AIRS) on the prediction of the Indian summer monsoon, using the variational data assimilation system annexed to the Weather Research and Forecasting model. In this study, three numerical experiments are carried out. The first is the control and includes no assimilation; in the second, named Conv, assimilation of conventional Global Telecommunication System data is performed. The third one, named ConvAIRS, is identical to the Conv except that it also includes assimilation of AIRS profiles. The initial fields of tropospheric temperature and water vapor mixing ratio showed significant improvement over the model domain. Assimilation of AIRS profiles has significant impact on predicting the seasonal mean monsoon characteristics such as tropospheric temperature, low‐level moisture distribution, easterly wind shear, and precipitation. The vertical structure of the root‐mean‐square error is substantially affected by the assimilation of AIRS profiles, with smaller errors in temperature, humidity, and wind magnitude. The consequent improved representation of moisture convergence in the boundary layer (deep convection as well) causes an increase in precipitation forecast skill. The fact that the monsoonal circulation is better captured, thanks to an improved representation of thermal gradients, which in turn leads to more realistic moisture transport, is particularly noteworthy. Several previous data impact studies with AIRS and other sensors have focused on the short or medium range of the forecast. The demonstrated improvement in all the predicted fields associated with the Indian summer monsoon, consequent to the month long assimilation of AIRS profiles, is an innovative finding with large implications to the operational seasonal forecasting capabilities over the Indian subcontinent.

Influence of preonset land atmospheric conditions on the Indian summer monsoon rainfall variability

AbstractA possible link between preonset land atmospheric conditions and the Indian summer monsoon rainfall (ISMR) is explored. It is shown that, the preonset positive (negative) rainfall anomaly over northwest India, Pakistan, Afghanistan, and Iran is associated with decrease (increase) in ISMR, primarily in the months of June and July, which in turn affects the seasonal mean. ISMR in the months of June and July is also strongly linked with the preonset 2 m air temperature over the same regions. The preonset rainfall/2 m air temperature variability is linked with stationary Rossby wave response, which is clearly evident in the wave activity flux diagnostics. As the predictability of Indian summer monsoon relies mainly on the El Niño–Southern Oscillation (ENSO), the found link may further enhance our ability to predict the monsoon, particularly during a non‐ENSO year.

Validation of Seasonal Forecast of Indian Summer Monsoon Rainfall

The experimental seasonal forecast of Indian summer monsoon (ISM) rainfall during June through September using Community Atmosphere Model (CAM) version 3 has been carried out at the Space Applications Centre Ahmedabad since 2009. The forecasts, based on a number of ensemble members (ten minimum) of CAM, are generated in several phases and updated on regular basis. On completion of 5 years of experimental seasonal forecasts in operational mode, it is required that the overall validation or correctness of the forecast system is quantified and that the scope is assessed for further improvements of the forecast over time, if any. The ensemble model climatology generated by a set of 20 identical CAM simulations is considered as the model control simulation. The performance of the forecast has been evaluated by assuming the control simulation as the model reference. The forecast improvement factor shows positive improvements, with higher values for the recent forecasted years as compared to the control experiment over the Indian landmass. The Taylor diagram representation of the Pearson correlation coefficient (PCC), standard deviation and centered root mean square difference has been used to demonstrate the best PCC, in the order of 0.74–0.79, recorded for the seasonal forecast made during 2013. Further, the bias score of different phases of experiment revealed the fact that the ISM rainfall forecast is affected by overestimation in predicting the low rain-rate (less than 7 mm/day), but by underestimation in the medium and high rain-rate (higher than 11 mm/day). Overall, the analysis shows significant improvement of the ISM forecast over the last 5 years, viz. 2009–2013, due to several important modifications that have been implemented in the forecast system. The validation exercise has also pointed out a number of shortcomings in the forecast system; these will be addressed in the upcoming years of experiments to improve the quality of the ISM prediction.

Dynamical Seasonal Prediction of Indian Summer Monsoon using AGCM: Weighted Ensemble Mean Approach

This study examines the fidelity of portable unified model's Atmospheric General Circulation Model in ensemble seasonal prediction of Indian Summer Monsoons of 1999-2004, driven by May SST anomaly persistent boundary conditions. Simple Ensemble-mean (EM) is inappropriate due to the presence of large deviation among the ensemble members in simulation of Indian Summer Monsoon Rainfall (ISMR). Thus 'Weighted Ensemble Mean' (WEM) method is used in the present study. In WEM method, weights are determined for all ensemble members at each model grid point using daily precipitation anomaly to distinguish the most reliable members and outliers among the ensemble members. Ensemble-mean then obtained by weighted combinations of all ensemble members is referred as 'Weighted Ensemble Mean'. The WEM prediction of ISMR better matches with observations than EM in majority of the monsoons. Further, WEM estimated using monthly and seasonal mean weights are assessed with respect to WEM from daily mean weights. WEM with monthly/seasonal weighting is found to be similar to EM in most of the monsoons and hence daily weighting is more suited approach than monthly/seasonal weighting.

Seasonal prediction of Indian summer monsoon: Sensitivity to persistent SST

In the present study, the assessment of the Community Atmosphere Model (CAM) developed at National Centre for Atmospheric Research (NCAR) for seasonal forecasting of Indian Summer Monsoon (ISM) with different persistent SST is reported. Towards achieving the objective, 30-year model climatology has been generated using observed SST. Upon successful simulation of climatological features of ISM, the model is tested for the simulation of ISM 2011 in forecast mode. Experiments have been conducted in three different time-phases, viz., April, May and June; using different sets of initial conditions (ICs) and the persistent SSTs of the previous months of the time-phases. The spatial as well as temporal distribution of model simulated rainfall suggest a below normal monsoon condition throughout the season in all the experiments. However, the rainfall anomaly shows some positive signature over north-east part of India in the month of June and August whereas the central Indian landmass had positive anomaly during August and September. The monthly accumulated AllIndia rainfall (AIR) over land for June to September 2011 are predicted to be 101% (17.6 cm), 86% (24.3 cm), 83% (21.0 cm) and 95% (15.5 cm) of normal AIR, respectively. This makes the seasonal accumulated AIR 78.4 cm which is 11% below the normal rainfall of 87.6 cm. The model prediction for the months of June and July is comparable with the observation; however, the simulation would not be able to capture the high rainfall during August and September. The intention behind this work is to assess the shortcomings in the CAM model prediction, which can later be improved for future monsoon forecast experiments. The word ‘Monsoon’ is a seasonal long-range weather phenomenon and repeats every year along the entire tropical belt of earth; however, Indian summer monsoon (ISM) has a very special context with respect to India’s socio-economic conditions. The socio-economic condition in India is very much dependent on the performance of ISM in terms of excess, deficit or normal rainfall during the month of June to September every year. The rainfall during the summer accounts for almost all the annual rainfall in 75% of the geographical area and 78% of

Prediction of Indian summer monsoon in short to medium range time scale with high resolution global forecast system (GFS) T574 and T382

Performance of national centers for environmental prediction based global forecast system (GFS) T574/L64 and GFS T382/L64 over Indian region has been evaluated for the summer monsoon season of 2011. The real-time model outputs are generated daily at India Meteorological Department, New Delhi for the forecasts up to 7 days. Verification of rainfall forecasts has been carried out against observed rainfall analysis. Performance of the model is also examined in terms of lower tropospheric wind circulation, vertical structure of specific humidity and precipitable water content. Case study of a monsoon depression is also illustrated. Results obtained show that, in general, both the GFS T382 and T574 forecasts are skillful to capture climatologically heavy rainfall regions. However, the accuracy in prediction of location and magnitude of rainfall fluctuates considerably. The verification results, at the spatial scale of 50 km resolution, in a regional spatial scale and country as a whole, in terms of continuous skill score, time series and categorical statistics, have demonstrated superiority of GFS T574 against T382 over Indian region. Both the model shows bias of lower tropospheric drying and upper tropospheric moistening. A bias of anti-cyclonic circulation in the lower tropospheric level lay over the central India, where rainfall as well as precipitable water content shows negative bias. Considerable differences between GFS T574 and T382 are noticed in the structure of model bias in terms of lower tropospheric wind circulation, vertical structure of specific humidity and precipitable water contents. The magnitude of error for these parameters increases with forecast lead time in both GFS T574 and T382. The results documented are expected to be useful to the forecasters, monsoon researchers and modeling community.

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 Nino 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 Nino 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.

Predictability of Indian Monsoon Circulation with High Resolution ECMWF Model in the Perspective of Tropical Forecast During the Tropical Convection Year 2008

To address some of the issues of project Year of Tropical Convection (YOTC) and the project ATHENA as ongoing international activities, an endeavor has been made for the first time to study the predictability of Indian summer monsoon in the backdrop of tropical predictability using 850 hPa atmospheric circulations with the high resolution (T1279) ECMWF model during the boreal summer of 2008 as one of the focus years of YOTC. The major findings obtained from the statistical forecast have been substantiated by the dynamical prediction in terms of the systematic error energy, its growth rate and the attribution of the dominant nonlinear dynamical processes to error growth. The systematic error energy of T1279 (16 km resolution) ECMWF model are generated in African landmass, India and its adjoining oceanic region, in near equatorial west Pacific and around the Madagascar region where the root mean square errors are observed and the zonal wind anomaly shows poor forecast skill. As far as the inadequate predictability of Indian summer monsoon by T1279 ECMWF model (revealed from the results of project ATHENA) is concerned, the systematic error energy and the error growth over Arabian Sea, in the eastern and western India due to the nonlinear convergence and divergence of error flux along with the erroneous Mascarene high may possibly be the determining factors for not showing any discernable improvement in Indian monsoon during the medium range forecast up to 240 h. This work suggests that the higher resolution of ECMWF model may not necessarily lead to the better forecast of Indian monsoon circulations during 2008 unless a methodology can be devised to isolate the errors due to the nonlinear processes that are inherent within the system.

ENSO, IOD and Indian Summer Monsoon in NCEP climate forecast system

El Nino-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) and Indian Summer Monsoon rainfall features are explored statistically and dynamically using National Centers for Environment Prediction (NCEP) Climate Forecast System (CFSv1) freerun in relation to observations. The 100 years of freerun provides a sufficiently long homogeneous data set to find out the mean state, periodicity, coherence among these climatic events and also the influence of ENSO and IOD on the Indian monsoon. Differences in the occurrence of seasonal precipitation between the observations and CFS freerun are examined as a coupled ocean–atmosphere system. CFS simulated ENSO and IOD patterns and their associated tropical Walker and regional Hadley circulation in pure ENSO (PEN), pure IOD (PIO) and coexisting ENSO-IOD (PEI) events have some similarity to the observations. PEN composites are much closer to the observation as compared to PIO and PEI composites, which suggest a better ENSO prediction and its associated teleconnections as compared to IOD and combined phenomenon. Similar to the observation, the model simulation also show that the decrease in the Indian summer monsoon rainfall during ENSO phases is associated with a descending motion of anomalous Walker circulation and the increase in the Indian summer monsoon rainfall during IOD phase is associated with the ascending branch of anomalous regional Hadley circulation. During co-existing ENSO and IOD years, however, the fate of Indian summer monsoon is dictated by the combined influence of both of them. The shift in the anomalous descending and ascending branches of the Walker and Hadley circulation may be somewhat attributed to the cold (warm) bias over eastern (western) equatorial Indian Ocean basin, respectively in the model. This study will be useful for identifying some of the limitations of the CFS model and consequently it will be helpful in improving the model to unravel the realistic coupled ocean–atmosphere interactions for the better prediction of Indian Summer Monsoon.

Assessment of Indian summer monsoon simulation by Community Atmosphere Model (CAM3)

Seasonal prediction of Indian Summer Monsoon (ISM) has been attempted for the current year 2011 using Community Atmosphere Model (CAM) developed at the National Centre for Atmospheric Research (NCAR). First, 30 years of model climatology starting from 1981 to 2010 has been generated to capture the variability of ISM over the Indian region using 30 seasonal simulations. The simulated model climatology has been validated with different sets of observed climatology, and it was observed that the simulated climatological rainfall is affected by model bias. Subsequently, a bias correction procedure using the Tropical Rainfall Measuring Mission (TRMM) 3B43 rainfall has been proposed. The bias-corrected rainfall climatology shows both spatial and temporal variability of ISM satisfactorily. Further, four sets of 10-member ensemble simulations of ISM 2009 and 2010 have been performed in hindcast mode using observed sea surface temperature (SST) and persistence of April SST anomaly, and it has been found that the bias-corrected model rainfall captures the seasonal variability of ISM reasonably well with some discrepancies in these two contrasting monsoon years. With this positive background, the seasonal prediction of ISM 2011 has been carried out in forecast mode with the assumption of persistence of May SST anomaly from June through September 2011. The model assessment shows an 11% deficiency in All-India Rainfall (AIR) of ISM 2011. In particular, the monthly accumulated rains are predicted to be 101% (17.6 cm), 86% (24.3 cm), 83% (21.0 cm) and 95% (15.5 cm) of normal AIR for the months of June, July, August and September, respectively.

Indian summer monsoon simulation studies with different orographic representations in a spectral GCM

AbstractThe current study focuses on two aspects of the representation of orography in a spectral general circulation model (GCM). The most important issue is the reduction of the ripple effect, especially the oceanic surface deformations, which can create tendency for unrealistic oceanic rainfall patches in the model climate. In this study, seven types of digital filters were tried at horizontal resolution equivalent to spectral truncation at T80 to identify the most effective filter. Digital filtering results in much greater reduction in orographic barrier, and hence, the blocking effect compared to mean orography. The second point is retention of the barrier effect. In the past, several attempts have been made to address this. Techniques have been developed in the past to construct an optimal orography, to enable more region‐specific enhancement of mountains with less impact over oceans and plains. These techniques include site‐specific kernels and the cost‐function‐minimization. An attempt has been made in the current study to develop a digitally filtered, locally enhanced, optimal orography called the Filtered‐Modified Orography (FMO), for its application to Indian summer monsoon simulation. A number of sensitivity experiments have been conducted to determine the possible impacts of FMO on the seasonal simulation and the medium‐range prediction of Indian summer monsoon. Results of this study show that two‐dimensional (2‐D) Lanczos filter is the most effective in reducing the ripples and the associated errors in the orographic representation. FMO is able to enhance the peaks of the Himalayan range and Western Ghats, and in consequence, it produces a better simulation of Indian summer monsoon circulation features and the associated rainfall in the seasonal and medium time scale. This procedure for local enhancement is found to be more beneficial than global enhancement of orography in a spectral GCM so far as the regional weather prediction is concerned. Copyright © 2008 Royal Meteorological Society

A case study on the prediction of Indian summer monsoon of 1987 with the help of a global spectral model and sensitivity of the model to vertical resolution

A Global spectral model has been integrated for 240 hours for each day of an active epoch during the monsoon season of 1987. The mean error, root mean square error ,the correlation coefficient between analysis and forecast changes and root mean square vector wind error have been computed for each of the seven initial conditions for 24 hours to 240 hours forecast segments. The rainfall forecasts have also been evaluated subjectively against the realised rainfall values. An experiment on sensitivity has also been carried out with the same model for two vertical resolutions. It is seen that forecasts produced by the lower resolution limited physics model is closer to the analysis produced by using the 6-hr forecasts of a higher resolution, better physics model as first guess. This implies that higher vertical resolution produces changes which are in opposite direction.to that produced by adding more physics.