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

Abstract

This 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.

Climatic features of the Red Sea from a regional assimilative model

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Sensitivity of initial‐condition and cloud microphysics to the forecasting of monsoon rainfall in South Asia

“…Data assimilation is another approach (Kalnay, 2003) that has been used frequently to improve the ISMR forecasts (e.g. Rakesh et al, 2009b;Routray et al, 2010;Sowjanya et al, 2013;Raju et al, 2015).…”

Assimilation of the Rain Gauge Measurements Using Particle Filter

“…Accurate rainfall prediction from the numerical weather prediction (NWP) model is one of the most challenging concerns for weather modeling society in general and during Indian summer monsoon in particular. Attada et al (2015Attada et al ( , 2018 demonstrated the importance of the temperature and moisture profiles assimilation on the Indian summer monsoon rainfall prediction. The rainfall assimilation has large impact on weather forecast mainly over the tropics, in which moist convection plays a prominent role, and links directly or indirectly to humidity, cloud cover, latent heating, and the divergent component of the large-scale circulation (Hou et al, 2004;Marecal & Mahfouf, 2003).…”