Accelerating assimilation development for new observing systems using EFSO

Lien, Guo-Yuan; Hotta, Daisuke; Kalnay, Eugenia; Miyoshi, Takemasa; Chen, Tse Chun

doi:10.5194/npg-25-129-2018

Cited by 13 publications

(18 citation statements)

References 29 publications

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“…In this paper, we aim to explore the impact of cycling PQC using an intermediate complexity system to act as a bridge between the promising results obtained in CK19 using the simple Lorenz (1996) model and a full operational system. For this purpose, we use the GFS-LETKF system developed by Lien et al (2016), which reduces the complexity of the DA system and uses a lower resolution of the spectral GFS model (compared to the operational systems) in order to expedite the execution of the experiments while still using a realistic configuration of a forecasting model, data assimilation system, and observations. The underlying philosophy behind the design of the GFS-LETKF system is to have a simple configuration of a DA system coupled with the realistic spectral GFS model to allow fast experiments exploring innovative data assimilation techniques.…”

Section: Experimental Design a Da System Configurationmentioning

confidence: 99%

“…Other studies have explored the applications of FSO impacts. It was shown in Lien et al (2018) that the longterm-averaged EFSO impact provides detailed information for optimizing data selection and the design of quality control procedures. Kotsuki et al (2017) found that using EFSO impact as an ordering method in a serial ensemble square root filter for the Lorenz (1996) model significantly improved the analysis accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, we use a low-resolution spectral GFS model coupled with the local ensemble transform Kalman filter (LETKF; Hunt et al 2007) and assimilate real observations from the NCEP PrepBUFR dataset, as in Lien et al (2016Lien et al ( , 2018. This study is a bridge between the idealized Lorenz (1996) model used by CK19 and the implementation of EFSO/PQC in a complex operational system.…”

Section: Introductionmentioning

confidence: 99%

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

Chen

Kalnay

2020

Monthly Weather Review

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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.

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Section: Experimental Design a Da System Configurationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Chen

Kalnay

2020

Monthly Weather Review

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“…Hotta et al (, hereafter H17) proposed a proactive quality control method (PQC) that detects detrimental observations with the EFSO. Lien et al () proposed an efficient approach to assimilating a new observing system using the EFSO. Recently, Buehner et al () proposed a new hybrid FSO, in which DA uses the adjoint model and FSO employs ensemble forecasts, for a hybrid ensemble‐variational data assimilation system.…”

Section: Introductionmentioning

confidence: 99%

“…Subsequent analysis fields of DA systems (hereafter, subsequent analyses) are usually used as the reference. It is also possible to use analysis fields from other DA systems as the reference (Lien et al , ). Recently, Todling () proposed using observation departures (i.e.…”

Section: Introductionmentioning

confidence: 99%

On the properties of ensemble forecast sensitivity to observations

Kotsuki

Kurosawa

Miyoshi

2019

Quart J Royal Meteoro Soc

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Evaluating impacts of observations on the skill of numerical weather prediction (NWP) is important. The Ensemble Forecast Sensitivity to Observation (EFSO) provides an efficient approach to diagnosing observation impacts, quantifying how much each observation improves or degrades a subsequent forecast with a given verification reference. This study investigates the sensitivity of EFSO impact estimates to the choice of the verification reference, using a global NWP system consisting of the Non‐hydrostatic Icosahedral Atmospheric Model (NICAM) and the Local Ensemble Transform Kalman Filter (LETKF). The EFSO evaluates observation impacts with the moist total energy norm and with recently proposed observation‐based verification metrics. The results show that each type of observation mainly contributes to the improvement of forecast departures of the observed variable maybe due to the limitation of localization in the EFSO. The EFSO overestimates the fraction of beneficial observations when verified with subsequent analyses, especially for shorter lead times such as 6 h. We may avoid this overestimation to some extent by verifying with observations, analyses from other data assimilation (DA) systems, or analyses of an independent run with the same DA system. In addition, this study demonstrates two important issues possibly leading to overestimating observation impacts. First, observation impacts would be overestimated if we apply relaxation‐to‐prior methods to the initial conditions of the ensemble forecasts in the EFSO; therefore, the ensemble forecasts in the EFSO should be independent of the ensemble forecasts in the DA cycle. Second, deterministic baseline forecasts of the EFSO, which represent the forecast without DA, should be initialized by the ensemble mean of the first guess at the analysis time, not by the previous analysis.

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The importance of appropriate verification metrics for the assessment of observation impact in a convection‐permitting modelling system

Necker¹,

Weißmann²,

Sommer³

2018

Quart J Royal Meteoro Soc

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Over the past 15 years, adjoint-based, ensemble-based and hybrid methods have been developed for estimating observation impact based on the forecast sensitivity to observation impact (FSOI). These methods are now commonly used in global modelling systems. However, little attention has been given to assessing observation impact in regional convection-permitting modelling systems. This study presents the first evaluation of ensemble-based estimates of observation impact over an extended period of six weeks in such a convection-permitting modelling system, namely the regional ensemble system of Deutscher Wetterdienst. Another aspect that has received little attention is the choice of the forecast-error verification metric. Nearly all previous studies used the difference between the forecast and a subsequent analysis of the same modelling system expressed in terms of energy (total energy norm). While such a self-verification generally needs to be treated with caution, it appears unsuitable for convection-permitting regional forecasts. Firstly, total energy does not really reflect parameters that forecast users are interested in, and important forecast quantities such as surface wind gusts and precipitation are not even part of the analysis. Secondly, systematic analysis and forecast errors are non-negligible in the presence of convection, especially for important variables that are related to convection. To overcome this issue, we introduce the use of independent radar observations for the verification of observation impact and compare results for a variety of different observation-based metrics for a six-week high-impact weather period in summer 2016. This revealed a particular sensitivity of the estimated impact to model as well as observation biases and sensitivity studies indicated that even small biases can have an influence on the estimated impact. Additionally, we demonstrate that FSOI can be used to identify biases through comparison of results for different metrics.

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Accelerating assimilation development for new observing systems using EFSO

Cited by 13 publications

References 29 publications

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

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

On the properties of ensemble forecast sensitivity to observations

The importance of appropriate verification metrics for the assessment of observation impact in a convection‐permitting modelling system

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