Proactive Quality Control: Observing System Experiments Using the NCEP Global Forecast System

Abstract

Proactive Quality Control (PQC) is a fully-flow dependent QC based on Ensemble Forecast Sensitivity to Observations (EFSO). Past studies showed in several independent cases that GFS forecasts can be improved by rejecting observations identified as detrimental by EFSO. However, the impact of cycling PQC in sequential data assimilation has, so far, only been examined using the simple Lorenz ’96 model. Using a low-resolution spectral GFS model that assimilates PrepBUFR (no radiance) observations with the Local Ensemble Transform Kalman Filter (LETKF), this study aims to become a bridge between a simple model and the implementation into complex operational systems. We demonstrate the major benefit of cycling PQC in a sequential data assimilation framework through the accumulation of improvements from previous PQC-updates. Such accumulated PQC improvement is much larger than the “current” PQC improvement that would be obtained at each analysis cycle using “future” observations. As a result, it is unnecessary to use “future” information, and hence this allows the operational implementation of cycling PQC. The results show that the analysis and forecast are improved the most by rejecting all the observations estimated to be detrimental by EFSO, but that major improvements also come from rejecting just the most detrimental 10% observations. The forecast improvements brought by PQC are observed throughout the 10 days of integration and provide more than 12-h forecast leadtime gain. An important finding is that PQC not only reduces substantially the root-mean-squared forecast errors but also the forecast biases.

Estimating Ocean Observation Impacts on Coupled Atmosphere‐Ocean Models Using Ensemble Forecast Sensitivity to Observation (EFSO)

“…We want to emphasize that EFSO estimation is not necessarily equivalent to the intrinsic quality of observation, as it depends on both the backgrounds and observations (Chen & Kalnay, 2020; Hotta et al., 2017; Lien et al., 2018). Thus, the relatively lower beneficial impact observed for moored buoys should not be interpreted as flaws in the instrument.…”

“…Lien et al (2018) employed EFSO to aid data selection and guide observation use for new measurements in DA. A proactive quality control (PQC) system that removes instantaneous detrimental observations based on EFSO has been shown to be effective in enhancing the analyses and forecast quality (Chen & Kalnay, 2020;Hotta et al, 2017).…”

Assimilating atmospheric infrasound data to constrain atmospheric winds in a two‐dimensional grid

“…One requires increments verification to see if these increments are right. Nonetheless, cases like this stress the need for quality control of observations, which is routinely done for conventional observations in operational DA (see e.g., Chen and Kalnay, 2020).…”

Machine Learning Enables Real‐Time Proactive Quality Control: A Proof‐Of‐Concept Study

“…In ML‐based PQC, this study uses ML only for obtaining a reference state in PQC and employs the physics‐based imperfect Lorenz96 model for ensemble and extended forecasts as in model‐based PQC. Similar to Chen and Kalnay (2019); Chen and Kalnay (2020), the two PQC experiments use the forecast improved by PQC as the first guess when assimilating next observations. We performed these two PQC experiments from the analyzed ensemble from No‐PQC at t = 200, 000 for 2,000 cycles using the imperfect Lorenz96 model.…”

“…(2017) proposed proactive quality control (PQC) that denies detrimental observations diagnosed by EFSO. Previous studies demonstrated that PQC successfully improves the forecast accuracy in a low‐dimensional dynamical system and a low‐resolution operational NWP system (Chen & Kalnay, 2019, 2020; Hotta, Chen, et al., 2017). However, in general it is impossible to perform EFSO and PQC in real‐time because they require future observations.…”