Hindcast skill improvement in Climate Forecast System (CFSv2) using modified cloud scheme

Abstract

Two sets of CFSv2 retrospective forecast experiments are performed to check the model's fidelity for operational forecast usage for the prediction of Indian summer monsoon rainfall (ISMR). The first experiment (Exp1) is identical to the present operational mode of the model. The second experiment (Exp2) includes major changes in terms of the different cumulus parameterization scheme, modified cloud microphysics scheme and the variable critical relative humidity. These changes have already shown enhancement in the seasonal viability of the model in the free-run mode. This study has carried out exclusive hindcast experiments by combining the above mentioned major changes. There is a marked improvement in the spatial distribution of the precipitation and the amplitude of the annual cycle of ISMR. The underestimation of the peak of the annual cycle of ISMR in Exp1 is enhanced by 23% in Exp2. Because of better simulations of clouds and tropospheric temperature gradient, the point of maximum precipitation has migrated northwards from equator (Exp1) to 20 N (Exp2). These improvements also impress upon all the other aspect of the ocean-atmosphere coupled interaction, namely planetary-scale Hadley circulation, air-sea interactions and most of the facets of monsoon teleconnections. The skill of extended Indian monsoon rainfall region (65 -95 E, 5 -35 N) has increased from 0.50 in Exp1 to 0.67 in Exp2 and the same holds true for other regions as well. The skill of Niño3.4 index enhances from 0.58 in Exp1 to 0.67 in Exp2. The dynamical wind shear based monsoon performance indices also show the surge in the skill score. The significant improvement of seasonal skill scores across all the variables clearly shows the dynamical consistency and at the same time establishes the superiority of the Exp2 for seasonal forecast. This work will add new dimension to develop a new genre of monsoon forecasting model.

Monsoon Mission: A Targeted Activity to Improve Monsoon Prediction across Scales

“…This was undertaken to address the inadequacy of the model in simulating the mixed-phase hydrometeors and the ratio of convective to stratiform rainfall, which has a bearing on the simulation of MISOs. The modified microphysics of Hazra et al (2017), revised SAS of Han and Pan (2011), and observation-based modified critical relative humidity (De et al 2016) was tested in retrospective forecast mode, and improvements in the seasonal forecast skill were reported (Pokhrel et al 2018). Another major monsoon-affecting bias was the excessive snow simulation over the Eurasian region by CFSv2, which reduced the north-south temperature gradient and resulted in the simulation of a weak monsoon (Saha et al 2013).…”

“…Modified cloud microphysics (ice and cloud microphysics, Phani et al 2016, Abhik et al 2017, Saha et al 2018, Pokhrel et al 2018 Super parameterization in CFS (SP-CFS, Goswami et al 2015) Multilayer snow scheme in the land surface model (Saha et al 2017) Stochastic parameterization (Goswami et al 2017a,b,c) Weakly coupled data assimilation system (Sluka et al 2016) New high-resolution ocean model EnKF-based coupled data assimilation system SUMMARY AND CONCLUSIONS. Motivated by India's need for global models to represent its monsoon better, in order to improve forecast and gain skill that was known to be possible to achieve, the MM program was launched in 2012.…”

Development of a Random Forest Climate Model Correction Algorithm

“…Reanalyses are a type of data assimilation that combine local climate observations with short-range weather forecasts to create an authoritative climate dataset, giving the best possible picture of the entire climate at a given time (Dueben & Bauer, 2018;Hersbach et al, 2020). Many studies have used reanalyses to improve the performance of speci c models by tweaking their parameterisations to produce more accurate outcomes (Chawla et al, 2013;Pokhrel et al, 2018;Yang & Kim, 2019). Moreover, the ERA5 reanalysis we used in this paper has previously been used as a ground truth value to test climate data in multiple research works (Chang & Guillas, 2019;Grönquist et al, 2019;Dou et al, 2020;Oses et al, 2020;Grönquist et al, 2021).…”

Prediction Skill of GEFSv12 for Southwest Summer Monsoon Rainfall and Associated Extreme Rainfall Events on Extended Range Scale over India

“…The Monsoon Mission project of the Ministry of Earth Sciences, Govt of India, has adopted NCEP CFSv2 (Saha et al 2014) as the primary modeling system for advancing monsoon prediction research (Rao et al 2019). They focused on improving the ISM predictions on different time scales through improving physical parameterization of the CFSv2 model and subsequently to the improvement of model fidelity in capturing the mean and intraseasonal variability of ISM in different time scales (Abhilash et al 2013a(Abhilash et al ,b, 2014Ganai et al 2015Ganai et al , 2016Ganai et al , 2019Ramu et al 2016;Hazra et al 2017;Abhik et al 2017;Pokhrel et al 2018;Mukhopadhyay et al 2019;Saha et al 2019;Krishna et al 2019). The assimilation of conventional and nonconventional observation datasets as initial conditions to numerical weather forecast models conspicuously improves the prediction skill of extreme events over India (Kar et al 2006;Routray et al 2010;Deb et al 2010;Ahasan et al 2013;Dube et al 2014;Satyanarayana and Kar 2016).…”