Abstract
AbstractDefault values for many parameters in Numerical Weather Prediction models are typically adopted based on theoretical or experimental investigations by scheme designers. Short‐range forecasts are substantially affected by the specification of parameters in the Weather Research and Forecasting (WRF) model. The presence of a multitude of parameters and several output variables in the WRF model renders appropriate parameter value identification quite challenging. This study aims to identify the parameters that most strongly influence the model output variables using a Global Sensitivity Analysis (GSA) method. Morris One‐At‐a‐Time (MOAT), a GSA method, is used to identify the sensitivities of 23 chosen tunable parameters corresponding to seven physical parameterization schemes of the WRF model. The sensitivity measures (MOAT mean and standard deviation) are evaluated for 11 output variables simulated by the WRF model, corresponding to different parameters. Twelve high‐intensity 4‐day precipitation events during the Indian summer monsoon during 2015, 2016, and 2017 over India's monsoon core region are considered for the study. Though the parameter sensitivities vary depending on the model output variable, overall results suggest a general trend. The consistency of sensitivity analysis results with different initial and lateral boundary conditions is also assessed.
Highlights
Indian summer monsoon (ISM) or the southwest monsoon is one of the oldest global monsoon phenomena
Several meteorological variables such as daily accumulated precipitation (PR), relative humidity (RH), surface air temperature (SAT), wind speed (WS), surface air pressure (SAP), downward short-wave radiative flux (DSWRF) and downward long-wave radiative flux (DLWRF) and atmospheric variables such as total precipitable water (TPW), planetary boundary layer height (PBLH), cloud fraction (CF) and outgoing long-wave radiation at the top of the atmosphere (OLR) from the model simulations are used to evaluate the sensitivity of the selected parameters
A higher value of the Morris one-step-at-a-time (MOAT)-mean implies that the parameter is more sensitive, whereas a higher MOAT standard deviation value indicates that the parameter is more dependent on other parameters to affect the output
Summary
Indian summer monsoon (ISM) or the southwest monsoon is one of the oldest global monsoon phenomena. Bursting refers to the quirk increase in mean daily rainfall and is a characteristic of ISM. The ISM rainfall (ISMR) is more often than not referred to as June-September rainfall. The monsoon core region (MCR; 69◦E to 88◦E and 18◦N to 28◦N) is a critical zone where ISMR plays a crucial role [3] corresponding to mean monsoon and intraseasonal variability. Besides being a conspicuous contributor to the food security and water resources of South Asian region, the ISM rainfall, representing an abundant heat source, has a significant impact on the global climate and general circulation [4, 5]. Accurate predictions of the ISMR over the monsoon core region are critical for the water resource management in India and for a superior seasonal forecast across the globe
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