Browsing by Author "Mohanty M.R."

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Coupling of Community Land Model with RegCM4 for Indian Summer Monsoon Simulation
(2017) Maurya R.K.S.; Sinha P.; Mohanty M.R.; Mohanty U.C.
Three land surface schemes available in the regional climate model RegCM4 have been examined to understand the coupling between land and atmosphere for simulation of the Indian summer monsoon rainfall. The RegCM4 is coupled with biosphere�atmosphere transfer scheme (BATS) and the National Center for Atmospheric Research (NCAR) Community Land Model versions 3.5, and 4.5 (CLM3.5 and CLM4.5, respectively) and model performance is evaluated for recent drought (2009) and normal (2011) monsoon years. The CLM4.5 has a more distinct category of surface and it is capable of representing better the land surface characteristics. National Centers for Environmental Prediction (NCEP) and Department of Energy (DOE) reanalysis version 2 (NNRP2) datasets are considered as driving force to conduct the experiments for the Indian monsoon region (30�E�120�E; 30�S�50�N). The NNRP2 and India Meteorological Department (IMD) gridded precipitation data are used for verification analysis. The results indicate that RegCM4 simulations with CLM4.5 (RegCM4�CLM4.5) and CLM3.5 (RegCM4�CLM3.5) surface temperature (at 2�ms) have very low warm biases (~1��C), while with BATS (RegCM4�BATS) has a cold bias of about 1�3��C in peninsular India and some parts of central India. Warm bias in the RegCM4�BATS is observed over the Indo-Gangetic plain and northwest India and the bias is more for the deficit year as compared to the normal year. However, the warm (cold) bias is less in RegCM4�CLM4.5 than other schemes for both the deficit and normal years. The model-simulated maximum (minimum) surface temperature and sensible heat flux at the surface are positively (negatively) biased in all the schemes; however, the bias is higher in RegCM4�BATS and lower in RegCM4�CLM4.5 over India. All the land surface schemes overestimated the precipitation in peninsular India and underestimated in central parts of India for both the years; however, the biases are less in RegCM4�CLM4.5 and more in RegCM4�CLM3.5 and RegCM4�BATS. During both the years, BATS scheme in RegCM4 failed to represent low precipitation over the leeward than windward side of the Western Ghats, while CLM schemes (both versions) in the RegCM4 are able to depict this feature. In the topographic regions, such as the Western Ghats, northeast India and state of Jammu and Kashmir, RegCM4�BATS overestimates the rainfall amount with higher bias. Statistical analysis using anomaly correlation coefficient, root mean square error, equitable threat score, and critical success index confirms that RegCM4�CLM performs better than RegCM4�BATS in the simulation of the Indian summer monsoon. � 2017, Springer International Publishing AG.
Inter-comparison and evaluation of mixed-convection schemes in RegCM4 for Indian summer monsoon simulation
(2019) Sinha P.; Maurya R.K.S.; Mohanty M.R.; Mohanty U.C.
The present study evaluates the performances of mixed-convection schemes (MCSs) in which different cumulus schemes are activated over the land and ocean separately to simulating the Indian Summer Monsoon (ISM). The regional climate model RegCM4.4 is used and the initial and boundary conditions are derived from the National Centers for Environmental Prediction (NCEP) and Department of Energy (DOE) reanalysis version 2 (NCEP-R2). The NCEP-R2 and the Indian Meteorological Department (IMD) precipitation analysis at 0.25� � 0.25� are used to evaluate the model simulated results. The four cumulus schemes Grell, MIT-Emanuel, Tiedtke, and Kain and Fritsch have been used to setting ten different combinations of MCSs. The MCSs having Tiedtke/Emanuel scheme over the land and the Grell scheme over the ocean (L:Ti_O:Gr; L:Em_O:Gr) are found qualitatively and quantitatively better to simulating the semi-permanent characteristics of the ISM. These MCSs are effectual in predicting seasonal mean precipitation intensity and distribution during the ISM and closer to IMD precipitation analysis than any other MCSs. It is found that the MCS having Grell scheme over the land and ocean, spatial gradient of precipitation is reasonably well when compared to IMD precipitation analysis. The representation of precipitation over the central India is poor in all the MCSs; this is probably due to increased cloud cover in RegCM4 which in turn, leads to reducing surface air temperature over land and thus reduce convective instability in the model simulations. The present study proposes a statistically based screening method to identify the useful MCSs that have reasonable skills. The best performing MCSs examined for normal and deficit monsoon years are used to simulate 15 consecutive monsoon seasons (1982�1996). Simulations of several monsoon years confirm that the two MCSs namely L:Ti_O:Gr and L:Em_O:Gr are suitable to simulate the ISM. � 2018 Elsevier B.V.
Moisture flux adjustments in RegCM4 for improved simulation of Indian summer monsoon precipitation
(2019) Mohanty M.R.; Sinha P.; Maurya R.K.S.; Mohanty U.C.
The complexity of the Indian summer monsoon precipitation makes it�s prediction a challenging task as it is not only influenced by the large-scale flows but also by the micro-scale features. In a dynamical model, precipitation is resulted from the formation of clouds. The cloud formation and its processes occur at a micro scale. Current state-of-the-art dynamical models lack proper representation of the cloud processes, particularly at high resolutions for which the cloud processes are parameterized, thereby poorly resolving the precipitation. This study aims at examining the impact of the cloud parameters on the simulation of Indian summer monsoon precipitation in Regional Climate Model version 4 (RegCM4). The autoconversion coefficient which determines the conversion of cloud water into precipitation in the Explicit Moisture Convergence scheme is adjusted in the RegCM4. The impact of autoconversion is experimented with ten different values and it is found that it has a significant effect on the simulation of precipitation during summer monsoon season. The experiments are conducted by changing autoconversion from 1.5 � 10?4 to 7.5 � 10?4/s along with the default value of 2.5 � 10?4/s. On changing the autoconversion values from 60 to 300% of the default value, the precipitation pattern improves over most parts of India. The model simulates the rainfall better when the autoconversion coefficient is changed to 7.5 � 10?4/s. With the best outcome with the adjusted autoconversion and control configuration, the model is simulated for seventeen monsoon seasons and the analyses of RegCM4 simulated vertically integrated moisture transport, convective available potential energy and atmospheric moisture budget suggest that the model efficacy is enhanced in higher autoconversion value than the control one. Statistical evaluations using bias, correlation coefficients, comprehensive rating matrices and skill score confirm the suitability of higher autoconversion rate for summer monsoon simulations. The model with adjusted autoconversion coefficient (at 7.5 � 10?4/s) has improved the representation of seasonal precipitation distribution and its year-to-year variation including other derived features. The rainfall pattern is improved over North West India and North East India especially, the monsoon core regions. The mean seasonal rainfall is in phase 94% of the time with the modeler-adjusted moisture as compared to 82% in the control in the long term simulation. � 2018, Springer-Verlag GmbH Germany, part of Springer Nature.
RegCM4 model sensitivity to horizontal resolution and domain size in simulating the Indian summer monsoon
(2018) Maurya R.K.S.; Sinha P.; Mohanty M.R.; Mohanty U.C.
The sensitivity to model resolution and domain size of the regional climate model RegCM4 is examined in simulating a deficit (1987), excess (1988), and normal (1989) Indian summer monsoon (ISM) rainfall. The initial and boundary conditions are prescribed by the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim reanalysis at 1.5� � 1.5� (EIN15). The model simulated precipitation is compared with India Meteorological Department (IMD) gridded precipitation data (0.25� � 0.25�) and other parameters are compared with EIN15. In the present study, eight different horizontal resolutions such as 60, 55, 50, 45, 40, 36, 27 and 18 km are used whereas four different domain sizes (D01, D02, D03, and D04; largest to smallest) centered at the Indian landmass are considered to investigate the model performance. Results illustrate that the RegCM4 has the capability to depict the important semi-permanent features of the ISM, however, skills vary with the resolutions and domain sizes. Experiments with different resolutions reveal that the lower (850 hPa) and upper (200 hPa) tropospheric circulations are stronger over the Arabian Sea and Bay of Bengal in the 36 and 40 km grid-spacing simulations and closer to EIN15, however, it is weaker in the high-resolution simulations at 27 and 18 km grid-spacing. The biases in vertically integrated moisture transport (VIMT) are positive in the model simulations with resolutions from 36 to 60 km, but, it is negative with 27 and 18 km grid resolution. The simulated precipitation intensity increases as one moves from coarse to higher resolution till grid spacing 36 km. The model efficiency reduces to simulate higher precipitation intensity over the maximum precipitation zones of India during summer monsoon season when grid spacing 27 km and 18 km are used. The overall performance is better with the use of 36�40 km grid spacing than other resolutions in simulating ISM and associated rainfall. The sensitivity of domain sizes is examined by confining the model resolution at 40 km. The model simulated meteorological parameters, as well as derived parameters (such as pattern and intensity of precipitation, geopotential height, relative humidity, air temperature, wind, and CAPE), are represented better in D02 than other domains. The present study proposes a Comprehensive Rating Matrices (CRM) to evaluate the model performance considering all experiments (horizontal resolution and domain size) and different homogeneous regions, including errors obtained using different statistical methods. The CRM and statistical based skill score (SS) suggest that the performance of RegCM4 is the best at 40 km horizontal resolution followed by 36 km and 45 km grid-spacing and the D02 domain is suitable for ISM simulation. � 2018
A review on the monthly and seasonal forecast of the Indian summer monsoon
(2019) Mohanty U.C.; Sinha P.; Mohanty M.R.; Maurya R.K.S.; Nageswara Rao N.M.M.; Pattanaik D.R.
The importance seasonal rainfall associated with the Indian summer monsoon is very significant for an agricultural-dependent economy like India. An imbalance in the seasonal rainfall can create havocs in the form of droughts or flood. Other than agricultural sector, various other sectors are also widely associated with the monsoon rainfall and can have a direct impact on the economy of India. With large sectors at stake due to monsoon rainfall, the demand for a skillful prediction of Indian summer monsoon rainfall has been ever increasing. This review article focuses on the recent developments and success of the statistical and dynamical methods for the prediction of Indian summer monsoon rainfall. Statistical methods were widely used in the late 20th century, when the availability of computational power was limited. But, with advancements in computational technologies dynamical methods were developed and used with reasonable success. This review has provided a glimpse of the long history of India Meteorological Department (IMD) operational forecast system, including the recent efforts and the success by the Indian scientific community using advanced global climate models and multi-statistical approaches. Recent scientific studies have also been discussed for the creation of a hybrid-dynamical-statistical model where the results of dynamically downscaled products are statistically corrected using various statistical methods, thereby creating a robust method for a skillful prediction system. � 2019, India Meteorological Department. All rights reserved.