Adaptive Observation Error Inflation for Assimilating All-Sky Satellite Radiance

Abstract

An empirical flow-dependent adaptive observation error inflation (AOEI) method is proposed for assimilating all-sky satellite brightness temperatures through observing system simulation experiments with an ensemble Kalman filter. The AOEI method adaptively inflates the observation error when the absolute difference (innovation) between the observed and simulated brightness temperatures is greater than the square root of the combined variance of the uninflated observational error variance and ensemble-estimated background error variance. This adaptive method is designed to limit erroneous analysis increments where there are large representativeness errors, as is often the case for cloudy-affected radiances, even if the forecast model and the observation operator (the radiative transfer model) are perfect. The promising performance of this newly proposed AOEI method is demonstrated through observing system simulation experiments assimilating all-sky brightness temperatures from GOES-R (now GOES-16) in comparison with experiments using an alternative empirical observation error inflation method proposed by Geer and Bauer. It is found that both inflation methods perform similarly in the accuracy of the analysis and in the containment of potential representativeness errors; both outperform experiments using a constant observation error without inflation. Besides being easier to implement, the empirical AOEI method proposed here also shows some advantage over the Geer–Bauer method in better updating variables at large scales. Large representative errors are likely to be compounded by unavoidable uncertainties in the forecast system and/or nonlinear observation operator (as for the radiative transfer model), in particular in the areas of moist processes, as will be the case for real-data cloudy radiances, which will be further investigated in future studies.

Added Value of Assimilating Himawari‐8 AHI Water Vapor Radiances on Analyses and Forecasts for “7.19” Severe Storm Over North China

“…Assimilating surface channel radiances over land will be more challenging and requires careful treatment of surface emissivity. In addition, the AHI all-sky radiance assimilation is expected to produce better analyses over cloudy and precipitating areas (Minamide & Zhang, 2017;Okamoto, 2017;Yang et al, 2016;Zhang et al, 2016). These will be explored in future work.…”

Assimilating Every‐10‐minute Himawari‐8 Infrared Radiances to Improve Convective Predictability

“…Third, there are several methods to adaptively inflate observation errors in order to effectively assimilate allsky satellite radiances (Geer & Bauer, 2011;Minamide & Zhang, 2017;Okamoto, 2017;Okamoto et al, 2014) although we used fixed observation errors in this study.…”

Representation of Model Error in Convective‐Scale Data Assimilation: Additive Noise Based on Model Truncation Error

“…There are some new approaches as the Adaptive Background Error Inflation (Minamide & Zhang, 2019), which attempts to treat model error and non-Gaussian sampling error adaptively, as well as a new approach that allows for using more ensemble members in the treatment of model error than forecasted with additive noise (Sommer & Janjić, 2018). These could be explored for convective scale data assimilation in the future together with the Adaptive Observation Error Inflation (Minamide & Zhang, 2017). We plan to supplement or compare the additive noise with other approaches that account for subgrid-scale model error, such as physically based stochastic perturbation scheme for turbulence (Kober & Craig, 2016;Rasp et al, 2018), which is flow dependent, and an advanced warm bubble technique which can automatically detect and trigger missing convective cells.…”