Data Assimilation for Climate Research: Model Parameter Estimation of Large‐Scale Condensation Scheme

Abstract

This study proposes using data assimilation (DA) for climate research as a tool for optimizing model parameters objectively. Mitigating radiation bias is very important for climate change assessments with general circulation models. With the Nonhydrostatic ICosahedral Atmospheric Model (NICAM), this study estimated an autoconversion parameter in a large-scale condensation scheme. We investigated two approaches to reducing radiation bias: examining useful satellite observations for parameter estimation and exploring the advantages of estimating spatially varying parameters. The parameter estimation accelerated autoconversion speed when we used liquid water path, outgoing longwave radiation, or outgoing shortwave radiation (OSR). Accelerated autoconversion reduced clouds and mitigated overestimated OSR bias of the NICAM. An ensemble-based DA with horizontal localization can estimate spatially varying parameters. When liquid water path was used, the local parameter estimation resulted in better cloud representations and improved OSR bias in regions where shallow clouds are dominant.

Weight structure of the Local Ensemble Transform Kalman Filter: A case with an intermediate atmospheric general circulation model

“…The LETKF has been used in operational NWP systems by the Deutscher Wetterdienst (DWD) and the Japan Meteorological Agency (JMA). In addition, recent studies have shown that the LETKF can be used to optimize tuneable parameters of NWP models (Ruiz et al ., 2013; Ruiz and Pulido, 2015; Kotsuki et al ., 2018, 2020).…”

Development of the Real‐Time 30‐s‐Update Big Data Assimilation System for Convective Rainfall Prediction With a Phased Array Weather Radar: Description and Preliminary Evaluation

“…For the SCALE-RM, it would be beneficial to optimize a microphysics parameter such as a coefficient for terminal velocity using the state augmentation method (Aksoy et al, 2006;Kotsuki et al, 2018Kotsuki et al, , 2020 and to use a multi-moment microphysics scheme (e.g., Seiki & Nakajima, 2014) to model clouds and cold pools more reasonably (Dawson et al, 2010). The use of a multi-moment microphysics scheme requires tuning of the observation operator but would enable us to effectively use polarimetric information from the MP-PAWR observations (Putnam et al, 2019;Zhu et al, 2020).…”

Physical characteristics of frozen hydrometeors inferred with parameter estimation

“…Cloud and precipitation parameter estimation has been tried in simpler contexts (e.g. Norris and Da Silva, 2007;Posselt and Vukicevic, 2010;Posselt and Bishop, 2012) and within more complete atmospheric models (Ollinaho et al, 2013;Posselt, 2016;Ruckstuhl and Janjić, 2020;Kotsuki et al, 2020). However it has not yet reached a level of development to have been used in operational weather forecasts.…”

“…The process of learning model parameters using data assimilation is known as parameter estimation, with cloud and precipitation parameters a major target (e.g. Norris and Da Silva, 2007;Ruckstuhl and Janjić, 2020;Kotsuki et al, 2020).…”