Online Nonlinear Bias Correction in Ensemble Kalman Filter to Assimilate GOES‐R All‐Sky Radiances for the Analysis and Prediction of Rapidly Developing Supercells

Krishnamoorthy, C.; Wang, Xuguang; Johnson, Aaron; Otkin, Jason A.

doi:10.1029/2021ms002711

Cited by 9 publications

(14 citation statements)

References 73 publications

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“…Similar to Chandramouli et al. (2022), the bias is calculated using a binning approach as a function of symmetric brightness temperature, which is the average of the forecast and observed brightness temperature. The estimated bias is therefore non‐linear in that the bias is neither constant nor a linear function of the brightness temperature.…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, the simulated ABI radiances in the present study are also evaluated after applying a non‐linear bias correction following Chandramouli et al. (2022).…”

Section: Introductionmentioning

confidence: 99%

“…Such understanding can also guide ABI radiance data assimilation efforts, including the application of appropriate bias-correction techniques. Therefore, the simulated ABI radiances in the present study are also evaluated after applying a non-linear bias correction following Chandramouli et al (2022).…”

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confidence: 99%

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Verification and Model Configuration Sensitivity of Simulated ABI Radiance Forecasts With the FV3‐LAM Model

Johnson

Wang

Blake

et al. 2023

Earth and Space Science

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This study evaluates simulated radiance forecasts from a series of controlled experiments consisting of FV3‐LAM forecasts with different configurations of model physics and vertical resolution. The forecasts were produced during the 2020 Hazardous Weather Testbed Spring Forecasting Experiments on the same forecast cases. The evaluation includes grid‐point, neighborhood‐based and object‐based verification. The experiments include forecasts that were identical except for the physics (EMC‐LAM vs. EMC‐LAMx), vertical resolution (EMC‐LAMx vs. NSSL‐LAM), or combined initial conditions, physics and vertical resolution (GSL‐LAM). It is found that the EMC‐LAM generally provided better simulated radiance forecasts than the other three configurations at most forecast lead times, due to its unique physics configuration. All configurations generally over‐forecasted high level clouds. EMC‐LAM reduced the over‐forecasting of high clouds, but also under‐forecasted the coverage of mid‐level clouds. In contrast, at early lead times the EMC‐LAM had relatively poor performance relative to the other forecasts. Furthermore, EMC‐LAM was an outlier in terms of the vertical structure of clouds. It is also found that the NSSL‐LAM consistently improved upon the EMC‐LAMx, which had fewer vertical levels than NSSL‐LAM. Compared to EMC‐LAMx, NSSL‐LAM had less cloud over‐forecasting bias, especially with small cloud objects, and less overall error. The differences between EMC‐LAMx and GSL‐LAM were generally much smaller than the differences between EMC‐LAMx and EMC‐LAM/NSSL‐LAM. Finally, it is found that a non‐linear bias correction conditioned on symmetric brightness temperature reduced the overall root‐mean‐square error by about a factor of 2 while improving the unrealistic vertical structure of clouds in the EMC‐LAM.

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Section: Resultsmentioning

confidence: 99%

“…Therefore, the simulated ABI radiances in the present study are also evaluated after applying a non‐linear bias correction following Chandramouli et al. (2022).…”

Section: Introductionmentioning

confidence: 99%

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Verification and Model Configuration Sensitivity of Simulated ABI Radiance Forecasts With the FV3‐LAM Model

Johnson

Wang

Blake

et al. 2023

Earth and Space Science

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“…( 2021 ), and Chan, Chen, and Leung ( 2022 ), Chandramouli et al. ( 2022 )) the effectiveness of current operational EDA methods is likely limited when the ensemble statistics are mixed.…”

Section: Introductionmentioning

confidence: 99%

“…More evidence of mixed statistics can be found in Supporting Information S1. Though current EDA methods have been remarkably successful at assimilating cloud-affected satellite radiance observations (e.g., F. Zhang et al (2016), Geer et al (2018), Chan, Zhang, et al (2020), Jones et al (2020), Li et al (2021), Mallick and Jones (2022), M. Zhang et al (2022), , Hartman et al (2021), and Chan, Chen, and Leung (2022), Chandramouli et al (2022)) the effectiveness of current operational EDA methods is likely limited when the ensemble statistics are mixed.…”

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confidence: 99%

The Potential Benefits of Handling Mixture Statistics via a Bi‐Gaussian EnKF: Tests With All‐Sky Satellite Infrared Radiances

Chan

Chen

Anderson

2023

J Adv Model Earth Syst

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The meteorological characteristics of cloudy atmospheric columns can be very different from their clear counterparts. Thus, when a forecast ensemble is uncertain about the presence/absence of clouds at a specific atmospheric column (i.e., some members are clear while others are cloudy), that column's ensemble statistics will contain a mixture of clear and cloudy statistics. Such mixtures are inconsistent with the ensemble data assimilation algorithms currently used in numerical weather prediction. Hence, ensemble data assimilation algorithms that can handle such mixtures can potentially outperform currently used algorithms. In this study, we demonstrate the potential benefits of addressing such mixtures through a bi-Gaussian extension of the ensemble Kalman filter (BGEnKF). The BGEnKF is compared against the commonly used ensemble Kalman filter (EnKF) using perfect model observing system simulated experiments (OSSEs) with a realistic weather model (the Weather Research and Forecast model). Synthetic all-sky infrared radiance observations are assimilated in this study. In these OSSEs, the BGEnKF outperforms the EnKF in terms of the horizontal wind components, temperature, specific humidity, and simulated upper tropospheric water vapor channel infrared brightness temperatures. This study is one of the first to demonstrate the potential of a Gaussian mixture model EnKF with a realistic weather model. Our results thus motivate future research toward improving numerical Earth system predictions though explicitly handling mixture statistics. Plain Language SummaryThe accuracy of a computer weather forecast often depends on the accuracy of the information inputted into the computer forecast system. The accuracy of the input in turn depends on the accuracy of the input-constructing algorithm. Such algorithms often use probabilistic forecasts from an earlier point in time and current atmospheric measurements to construct the inputs. A common assumption in such algorithms is that the probabilistic forecasts follow a multivariate normal distribution (henceforth called the normality assumption). However, in the frequent situation where the probabilistic forecast is uncertain about the presence/absence of clouds, the normality assumption is violated. This is because clear atmospheric columns and cloudy atmospheric columns have distinctly different thermodynamic and dynamic characteristics. These two types of columns thus have different statistics. As such, when a probabilistic forecast is uncertain about the presence/absence of clouds, it has mixed statistics (henceforth termed mixed probabilistic forecast). Addressing such mixtures can potentially improve forecasts. In this study, we propose a new input-constructing algorithm that can explicitly handle mixed probabilistic forecasts. This new algorithm is nearly as fast as currently available algorithms. More importantly, our experiments demonstrate that our new algorithm can produce more accurate forecast inputs than an existing popular algorithm. Our work thus suggests that...

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All-sky infrared radiance data assimilation of FY-4A AGRI with different physical parameterizations for the prediction of an extremely heavy rainfall event

Zhang

Liu

et al. 2023

Atmospheric Research

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Online Nonlinear Bias Correction in Ensemble Kalman Filter to Assimilate GOES‐R All‐Sky Radiances for the Analysis and Prediction of Rapidly Developing Supercells

Cited by 9 publications

References 73 publications

Verification and Model Configuration Sensitivity of Simulated ABI Radiance Forecasts With the FV3‐LAM Model

Verification and Model Configuration Sensitivity of Simulated ABI Radiance Forecasts With the FV3‐LAM Model

The Potential Benefits of Handling Mixture Statistics via a Bi‐Gaussian EnKF: Tests With All‐Sky Satellite Infrared Radiances

All-sky infrared radiance data assimilation of FY-4A AGRI with different physical parameterizations for the prediction of an extremely heavy rainfall event

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