A New Approach for Estimating the Observation Impact in Ensemble–Variational Data Assimilation

Buehner, Mark; Du, Ping; Bédard, Joël

doi:10.1175/mwr-d-17-0252.1

Cited by 26 publications

(19 citation statements)

References 25 publications

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“…Compared to operational systems (e.g. O13; LM14; H17; C18; Buehner et al , ), AIRCFT and AMSU‐A radiances generally show smaller contributions in the NICAM‐LETKF maybe because fewer observations are assimilated (Figure d).…”

Section: Resultsmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

“…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%

On the properties of ensemble forecast sensitivity to observations

Kotsuki

Kurosawa

Miyoshi

2019

Quart J Royal Meteoro Soc

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“…Fourth, the Hybrid EnVar DA system is used in many operational centers, and the observations assimilated in the EnKF subsystem within are usually not identical to those assimilated in the main variational system. For this reason and the fact that it is more consistent with EnVar, the hybrid formulation of FSO (Buehner et al 2018) may be preferable in such DA systems. It is thus worth comparing the PQC performance based on both HFSO and EFSO in an EnVar system.…”

Section: Summary and Discussionmentioning

confidence: 99%

“…This ensemble FSO (EFSO) approach directly maps, using the readily available ensemble forecasts from the DA system, the forecast error changes between two consecutive forecasts by DA to each individual observation. Recently, a hybrid approach (HFSO; Buehner et al 2018) was developed that projects the forecast sensitivity to analysis using the ensemble forecasts as in EFSO, but Denotes content that is immediately available upon publication as open access.…”

Section: Introductionmentioning

confidence: 99%

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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“…Kunii et al ; Ota et al ). Recently, Buehner et al () combined the two approaches to estimate observation impact in a hybrid DA system.…”

Section: Introductionmentioning

confidence: 99%

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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A New Approach for Estimating the Observation Impact in Ensemble–Variational Data Assimilation

Cited by 26 publications

References 25 publications

On the properties of ensemble forecast sensitivity to observations

On the properties of ensemble forecast sensitivity to observations

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

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

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