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Swati Basu, G. R. Iyengar, and A. K. Mitra

Abstract

The impact of two parameterization schemes for the atmospheric boundary layer in predicting monsoon circulation over the Indian region has been studied using a Global Spectral Model. The performance of the nonlocal closure scheme for the boundary layer has been tested in the operational global model of the National Centre for Medium Range Weather Forecasting (NCMRWF) for its possible implementation and operational use. Keeping the parameterization schemes for all other physical processes the same, the performance of the nonlocal closure scheme is studied and compared with the performance of the operational local closure scheme of the boundary layer processes. Incorporation of the nonlocal closure scheme shows marginal impact in the prediction of the flow pattern. However, systematic improvement in the precipitation distribution over the Indian region is seen with the incorporation of a nonlocal closure scheme during the month of August 1999. Location of the precipitation maximum along the west coast of the southern peninsula is better predicted. The skill score for predicting areas of higher precipitation is increased with the nonlocal closure scheme. The systematic errors of the model are also reduced with the inclusion of the nonlocal closure scheme. An efficient transport mechanism in the vertical associated with the nonlocal closure scheme could be one of the main reasons for the better performance.

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Someshwar Das, A. K. Mitra, G. R. Iyengar, and J. Singh

Abstract

The operational global spectral model of the National Center for Medium Range Weather Forecasting (NCMRWF) at T80 resolution and 18 vertical levels has been used to study the skill of medium-range forecasts using three different parameterizations of deep convection namely, a Kuo–Anthes-type cumulus parameterization scheme referred to as “KUO” scheme, the relaxed Arakawa–Schubert (RAS) scheme, and the simplified Arakawa–Schubert (SAS) scheme, during an active phase of the Indian summer monsoon. Several medium-range forecasts (up to 5 days) have been made using the initial conditions of July and August of 1999, when the monsoon was active over the Indian region. Skill scores of predicted rainfall, rmse of wind and temperature, systematic errors, and genesis and tracks of the monsoon depressions predicted by the three schemes have been studied. Results indicate that, in general, the areas of light (heavy) rainfall are overestimated (underestimated) by KUO, which also fails to predict the rain-shadow effect observed over southern peninsular India. RAS and SAS produce fairly good forecasts of the observed rainfall; however, the best forecast is produced by SAS in most of the rainfall categories over the Indian region. The rmse of wind and temperature do not show significant differences among the three schemes over the global domain; however, they indicate considerable differences over the Indian region. The rmse of wind is slightly higher in RAS and SAS because of overestimation of the strength of the low-level westerly jet and upper-level tropical easterly jet. Errors in temperature forecasts are considerably reduced by RAS and SAS on all days. Systematic errors of the forecasts indicate that KUO tries to weaken the observed southwesterly flow and the low-level jet during the monsoon. RAS and SAS try to intensify the easterlies over the north Indian plains and to strengthen the monsoon trough. They shift the core of the tropical easterly jet stream to the south of its normal position. SAS reduces the cold bias almost everywhere over the Indian region. The improved simulation of temperature by SAS results in the reduction of rmse. The reduction of cold bias and improved simulated temperature by SAS indicate a proper redistribution of heat by deep convective clouds over the region by this scheme. Study of the lows and monsoon depressions indicated that the best forecast of the location of the genesis was produced by RAS. All three schemes were able to predict the tracks of the depression fairly well in the 24 h, but SAS produced relatively fewer errors when compared with the other two schemes. In most of the cases, SAS was also able to maintain the system up to 72 h, whereas the other two schemes weakened the systems.

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A. K. Mitra, M. Das Gupta, S. V. Singh, and T. N. Krishnamurti

Abstract

A system for objectively producing daily large-scale analysis of rainfall for the Indian region has been developed and tested by using only available real-time rain gauge data and quantitative precipitation estimates from INSAT-1D IR data. The system uses a successive correction method to produce the analysis on a regular latitude–longitude grid. Quantitative precipitation estimates from the Indian National Satellite System (INSAT) operational geostationary satellite, INSAT-1D, IR data are used as the initial guess in the objective analysis method. Accumulated 24-h (daily) rainfall analyses are prepared each day by merging satellite and rain gauge data. The characteristics of the output from this analysis system have been examined by comparing the accumulated monthly observed rainfall with other available independent widely used datasets from the Global Precipitation Climatology Project (GPCP) and Climate Prediction Center Merged Analysis of Precipitation (CMAP) analyses. The monthly data prepared from the daily analyses are also compared with the subjectively analyzed India Meteorological Department (IMD) monthly rainfall maps. This comparison suggests that even with only the available real-time data from INSAT and rain gauge, it is possible to construct a usable large-scale rainfall map on regular latitude–longitude grids. This analysis, which uses a higher resolution and more local rain gauge data, is able to produce realistic details of the Indian summer monsoon rainfall patterns. The magnitude and distribution of orographic rainfall near the west coast of India is very different from and more realistic compared to both the GPCP and CMAP patterns. Due to the higher spatial resolution of the analysis system, the regions of heavy and light rain are demarcated clearly over the Indian landmass. Over the oceanic regions of the Arabian Sea, Bay of Bengal, and the equatorial Indian Ocean, the agreement of the analyzed rainfall at the monthly timescale is quite good compared to the other two datasets. For NWP and other model verification of large-scale rainfall, this dataset will be useful. In the field of rainfall monitoring within weather and climate research, this technique will have real-time applications with data from current (METSAT) and future (INSAT-3A and INSAT-3D) Indian geostationary satellites.

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T. N. Krishnamurti, J. Sanjay, A. K. Mitra, and T. S. V. Vijaya Kumar

Abstract

This paper addresses a procedure to extract error estimates for the physical and dynamical components of a forecast model. This is a two-step process in which contributions to the forecast tendencies from individual terms of the model equations are first determined using an elaborate bookkeeping of the forecast. The second step regresses these estimates of tendencies from individual terms of the model equations against the observed total tendencies. This process is executed separately for the entire horizontal and vertical transform grid points of a global model. The summary of results based on the corrections to the physics and dynamics provided by the regression coefficients highlights the component errors of the model arising from its formulation. This study provides information on geographical and vertical distribution of forecast errors contributed by features such as nonlinear advective dynamics, the rest of the dynamics, deep cumulus convection, large-scale condensation physics, radiative processes, and the rest of physics. Several future possibilities from this work are also discussed in this paper.

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Suryachandra A. Rao, B. N. Goswami, A. K. Sahai, E. N. Rajagopal, P. Mukhopadhyay, M. Rajeevan, S. Nayak, L. S. Rathore, S. S. C. Shenoi, K. J. Ramesh, R. S. Nanjundiah, M. Ravichandran, A. K. Mitra, D. S. Pai, S. K. R. Bhowmik, A. Hazra, S. Mahapatra, S. K. Saha, H. S. Chaudhari, S. Joseph, P. Sreenivas, S. Pokhrel, P. A. Pillai, R. Chattopadhyay, M. Deshpande, R. P. M. Krishna, Renu S. Das, V. S. Prasad, S. Abhilash, S. Panickal, R. Krishnan, S. Kumar, D. A. Ramu, S. S. Reddy, A. Arora, T. Goswami, A. Rai, A. Srivastava, M. Pradhan, S. Tirkey, M. Ganai, R. Mandal, A. Dey, S. Sarkar, S. Malviya, A. Dhakate, K. Salunke, and Parvinder Maini

Abstract

In spite of the summer monsoon’s importance in determining the life and economy of an agriculture-dependent country like India, committed efforts toward improving its prediction and simulation have been limited. Hence, a focused mission mode program Monsoon Mission (MM) was founded in 2012 to spur progress in this direction. This article explains the efforts made by the Earth System Science Organization (ESSO), Ministry of Earth Sciences (MoES), Government of India, in implementing MM to develop a dynamical prediction framework to improve monsoon prediction. Climate Forecast System, version 2 (CFSv2), and the Met Office Unified Model (UM) were chosen as the base models. The efforts in this program have resulted in 1) unparalleled skill of 0.63 for seasonal prediction of the Indian monsoon (for the period 1981–2010) in a high-resolution (∼38 km) seasonal prediction system, relative to present-generation seasonal prediction models; 2) extended-range predictions by a CFS-based grand multimodel ensemble (MME) prediction system; and 3) a gain of 2-day lead time from very high-resolution (12.5 km) Global Forecast System (GFS)-based short-range predictions up to 10 days. These prediction skills are on par with other global leading weather and climate centers, and are better in some areas. Several developmental activities like coupled data assimilation, changes in convective parameterization, cloud microphysics schemes, and parameterization of land surface processes (including snow and sea ice) led to the improvements such as reducing the strong model biases in the Indian summer monsoon simulation and elsewhere in the tropics.

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