Regional scale prediction of the onset phase of the Indian southwest monsoon with a high-resolution atmospheric model (original) (raw)
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International Journal of Climatology, 2008
There are several studies showing a skillful empirical prediction of the All India Summer Monsoon Rainfall (AISMR) based on various combination of parameters as the predictors. However, the southwest monsoon rainfall over Kerala, a meteorological subdivision of India, bears a considerably low correlation coefficient with the AISMR. This implies that the existing predictors in the long-range forecast models of the AISMR do not have much influence on the Kerala Summer Monsoon Rainfall (KSMR). This study attempts to examine the relationship of some ocean and atmospheric parameters with the rainfall and to formulate a linear multiple regression model for the long-range forecast over a small area like the Kerala. Parameters having significant correlation (significant at 1% level) with the KSMR were identified for the period 1961-1994. The consistency of the relationship between these parameters and the KSMR was checked by doing a 21-year sliding window correlation (significant at 5% level). Using a stepwise regression method, seven predictors, explaining a significant amount of variance in the KSMR were selected and a linear multiple regression model was developed. The parameters that explain the high inter-annual variability of the KSMR are specific humidity, sensible heat net flux, relative humidity, zonal wind at 70 and 10 hPa, meridional wind and geopotential height. The characteristics of the forecast and its reliability were studied by various statistical techniques such as, Durbin Watson statistics and variance inflation factor. The model has a multiple correlation of 0.943 and coefficient of determination of 88.8%. The root mean square error (RMSE) was 6.60% (15.80%), bias (BIAS) was −0.26% (6.20%), absolute error (ABSE) was 5.33% (13.15%) of mean rainfall for the training (test) period respectively. Climatological predictions were also made and the RMSE was (17.90%), BIAS (−5.40%) and ABSE (15.16%) of mean rainfall. The selected parameters were at least 2 months prior to the monsoon season and hence have predictive value.
Predicting the extremes of Indian summer monsoon rainfall with coupled ocean-atmosphere models
An analysis of the retrospective predictions by seven coupled ocean-atmosphere models from major forecasting centres of Europe and USA, aimed at assessing their ability in predicting the interannual variation of the Indian summer monsoon rainfall (ISMR), particularly the extremes (i.e. droughts and excess rainfall seasons) is presented in this article. On the whole, the skill in prediction of extremes is not bad since most of the models are able to predict the sign of the ISMR anomaly for a majority of the extremes. There is a remarkable coherence between the models in successes and failures of the predictions, with all the models generating loud false alarms for the normal monsoon season of 1997 and the excess monsoon season of 1983. It is well known that the El Niño and Southern Oscillation (ENSO) and the Equatorial Indian Ocean Oscillation (EQUINOO) play an important role in the interannual variation of ISMR and particularly the extremes. The prediction of the phases of these modes and their link with the monsoon has also been assessed. It is found that models are able to simulate ENSO-monsoon link realistically, whereas the EQUINOO-ISMR link is simulated realistically by only one model -the ECMWF model. Furthermore, it is found that in most models this link is opposite to the observed, with the predicted ISMR being negatively (instead of positively) correlated with the rainfall over the western equatorial Indian Ocean and positively (instead of negatively) correlated with the rainfall over the eastern equatorial Indian Ocean. Analysis of the seasons for which the predictions of almost all the models have large errors has suggested the facets of ENSO and EQUINOO and the links with the monsoon that need to be improved for improving monsoon predictions by these models.
Journal of Climate, 2015
This study reports an objective criterion for the real-time extended-range prediction of monsoon onset over Kerala (MOK), using circulation as well as rainfall information from the 16 May initial conditions of the Grand Ensemble Prediction System based on the coupled model CFSv2. Three indices are defined, one from rainfall measured over Kerala and the others based on the strength and depth of the low-level westerly jet over the Arabian Sea. While formulating the criterion, the persistence of both rainfall and low-level wind after the MOK date has been considered to avoid the occurrence of “bogus onsets” that are unrelated to the large-scale monsoon system. It is found that the predicted MOK date matches well with the MOK date declared by the India Meteorological Department, the authorized principal weather forecasting agency under the government of India, for the period 2001–14. The proposed criterion successfully avoids predicting bogus onsets, which is a major challenge in the pr...
Seasonal prediction of the Indian monsoon
CURRENT SCIENCE, 2011
Monsoon' (SPIM), the prediction of Indian summer monsoon rainfall by five atmospheric general circulation models (AGCMs) during 1985-2004 was assessed. The project was a collaborative effort of the coordinators and scientists from the different modelling groups across the country. All the runs were made at the Centre for Development of Advanced Computing (CDAC) at Bangalore on the PARAM Padma supercomputing system. Two sets of simulations were made for this purpose. In the first set, the AGCMs were forced by the observed sea surface temperature (SST) for May-September during 1985-2004. In the second set, runs were made for 1987, 1988, 1994, 1997 and 2002 forced by SST which was obtained by assuming that the April anomalies persist during May-September. The results of the first set of runs show, as expected from earlier studies, that none of the models were able to simulate the correct sign of the anomaly of the Indian summer monsoon rainfall for all the years. However, among the five models, one simulated the correct sign in the largest number of years and the second model showed maximum skill in the simulation of the extremes (i.e. droughts or excess rainfall years).
Predictability of Summer Monsoon Rainfall by using High Resolution Regional Model (HRM)
Pakistan Journal of Meteorology, 2012
Numerical Weather Prediction (NWP) models are treated as helping tools for the weather forecasters in this era of science and technology. Generally Evaluation of NWP models is made to gauge the level of reliability in particular region as well as phenomena of specific weather pattern. As Pakistan is an agricultural country and precipitation is basic ingredient for agriculture and in Monsoon season it receives more than 60 % rain of the total annual rain. Therefore precise forecasts are believed to be assurance for successful agriculture with sufficient lead time. A hydrostatic NWP model HRM containing 40 vertical model layers and 7 soil layers, is evaluated at horizontal resolution of 22km and 11km for the prediction of precipitation in South Asian Monsoon season. Four rainfall events of different intensities are simulated at 00UTC by HRM at both selected horizontal resolutions with domain 7˚-45˚N latitudes and 55˚-96˚E longitudes. The model simulated rain has been compared with act...
Empirical Prediction and Predictability of Dry and Wet Spells of the Indian Summer Monsoon
Abstract Prediction of the active (rainy) and break (dry) phases,of the Indian summer monsoon,intraseasonal oscillations (ISO’s) two to three weeks,in advance,is of great importance for food production and water management of the country, but is cur- rently unavailable. Potential predictability inherent in the quasi-periodic nature of the monsoon,ISO’s is estimated,from,daily rainfall and,circulation data for 23 years. The monsoon,breaks are potentially more,predictable than the active condi- tions with perdictability limit of about 20 days for breaks compared,to 10 days for active conditions. The potential for prediction of the monsoon,ISO is explored,by developing,a multiple,linear regression model,that predicts the first four principal components (PCs) of Climate Prediction Center Merged,Analysis of Precipitation (CMAP). The first four PCs of rainfall and the first two PCs of surface pressure are used as pre- dictors. The model,is developed,in a step wise manner,with 10-90 day fi...
Empirical modelling and forecasting of Indian monsoon rainfall
2003
Indian monsoon rainfall data is modelled as a nonli n- ear time series. It is demonstrated that the proposed model accounts for about 50% of the inter-annual variability of the rainfall, as observed in eight sets of data representing All India and regional rainfall va l- ues. The model is capable of statistically forecasting sea - sonal rainfall value one
New Models for Long Range Forecasts of Summer Monsoon Rainfall over North West and Peninsular India
Meteorology and Atmospheric Physics, 2000
New models based on (a) Multivariate Principal Component Regression (PCR) (b) Neural Network (NN) and (c) Linear Discriminant Analysis (LDA) techniques were developed for long-range forecasts of summer monsoon (June±September) rainfall over two homogeneous regions of India, viz., North West India and Peninsular India. The PCR and NN models were developed with two different data sets. One set comprised 42 years (1958±1999) of data with 8 predictors and the other, 49 years (1951±1999) of data with 6 predictors. The predictors were subjected to the Principal Component Analysis (PCA) before model development. Two different neural networks were designed with 2 and 3 hidden neurons. To avoid the nonlinear instability, 20 ensemble runs were made while training the network and the ensemble mean results are discussed. The LDA model was developed with 42 years of data (1958±1999) for classifying three rainfall intervals with equal prior probability of 0.33. Both the PCR and NN models showed useful forecast skill for NW India and Peninsular India. Models with 8 predictors performed better than the models with only 6 predictors. The NN model with 3 hidden neurons performed better than model with 2 hidden neurons. For NW India, the NN model performed better than the PCR model. The RMSE of the NN model and PCR model with 8 predictors for NW India (Peninsular India) during the independent period 1984±99 was 12.57 (12.27) and 12.67 (11.57), respectively. Corresponding ®gures for the models with 6 predictors are 15.07 (13.07) and 13.97 (11.47) respectively. During the independent period, model errors were large in 1991, 1994, 1997 and 1999. However all the models showed deteriorating predictive skill after 1988, both for NW India and Peninsular India. The LDA model correctly classi®ed 627 of grouped cases for NW India and Peninsular India. The LDA model showed better skill in classifying de®cient rainfall (`À 87) over NW India and excess rainfall (b 37) over Peninsular India.
Performance of Regional Climate Model in Simulating Monsoon Onset Over Indian Subcontinent
Pure and Applied Geophysics, 2018
The performance of various Convective Parameterization Schemes (CPSs) of Regional Climate Model version 4.3 (RegCM-4.3) for simulation of onset phase of Indian summer monsoon (ISM) over Kerala was studied for the period of 2001-2010. The onset date and its associated spatial variation were simulated using RegCM-4.3 four core CPS, namely Kuo, Tiedtke, Emanuel and Grell; and with two mixed convection schemes Mix98 (Emanuel over land and Grell over ocean) and Mix99 (Grell over land and Emanuel over ocean) on the basis of criteria given by the India Meteorological Department (IMD) (Pai and Rajeevan in Indian summer monsoon onset: variability and prediction. National Climate Centre, India Meteorological Department, 2007). It has been found that out of six CPS, two schemes, namely Tiedtke and Mix99 simulated the onset date properly. The onset phase is characterized with several transition phases of atmosphere. Therefore, to study the thermal response or the effect of different sea surface temperature (SST), namely ERA interim (ERSST) and weekly optimal interpolation (OI_WK SST) on Indian summer monsoon, the role of two different types of SST has been used to investigate the simulated onset date. In addition, spatial atmospheric circulation pattern during onset phase were analyzed using reanalyze dataset of ERA Interim (EIN15) and National Oceanic and Atmospheric Administration (NOAA), respectively, for wind and outgoing long-wave radiation (OLR) pattern. Among the six convective schemes of RegCM-4.3 model, Tiedtke is in good agreement with actual onset dates and OI_WK SST forcing is better for simulating onset of ISM over Kerala.