Adaptive tuning of background error and satellite radiances observation error for operational variational assimilation

The assimilation of microwave and infrared (IR) radiance satellite observations within numerical weather prediction (NWP) models have been an important component in the effort of improving the accuracy of analysis and forecast. Such capabilities were implemented during the development of the high-resolution Copernicus European Regional Reanalysis (CERRA), funded by the Copernicus Climate Change Services (C3S). The CERRA system couples the deterministic system with the ensemble data assimilation to provide periodic updates of the background error covariance matrix. Several key factors for the assimilation of radiances were investigated, including appropriate use of variational bias correction (VARBC), surface-sensitive AMSU-A observations and observation error correlation. Twenty-one-day impact studies during the summer and winter seasons were conducted. Generally, the assimilation of radiances has a small impact on the analysis, while greater impacts are observed on short-range (12 and 24-h) forecasts with an error reduction of 1–2% for the mid and high troposphere. Although, the current configuration provided less accurate forecasts from 09 and 18 UTC analysis times. With the increased thinning distances and the rejection of IASI observation over land, the errors in the analyses and 3 h forecasts on geopotential height were reduced up to 2%.

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Evaluation of Surface Analyses and Forecasts with a Multiscale Ensemble Kalman Filter in Regions of Complex Terrain

Monthly Weather Review ◽

10.1175/2010mwr3612.1 ◽

2011 ◽

Vol 139 (6) ◽

pp. 2008-2024 ◽

Cited By ~ 24

Author(s):

Brian C. Ancell ◽

Clifford F. Mass ◽

Gregory J. Hakim

Keyword(s):

Kalman Filter ◽

High Resolution ◽

Data Assimilation ◽

Ensemble Kalman Filter ◽

Complex Terrain ◽

Error Variance ◽

Small Scale ◽

Observation Error ◽

Grid Spacing ◽

Background Error

Abstract Previous research suggests that an ensemble Kalman filter (EnKF) data assimilation and modeling system can produce accurate atmospheric analyses and forecasts at 30–50-km grid spacing. This study examines the ability of a mesoscale EnKF system using multiscale (36/12 km) Weather Research and Forecasting (WRF) model simulations to produce high-resolution, accurate, regional surface analyses, and 6-h forecasts. This study takes place over the complex terrain of the Pacific Northwest, where the small-scale features of the near-surface flow field make the region particularly attractive for testing an EnKF and its flow-dependent background error covariances. A variety of EnKF experiments are performed over a 5-week period to test the impact of decreasing the grid spacing from 36 to 12 km and to evaluate new approaches for dealing with representativeness error, lack of surface background variance, and low-level bias. All verification in this study is performed with independent, unassimilated observations. Significant surface analysis and 6-h forecast improvements are found when EnKF grid spacing is reduced from 36 to 12 km. Forecast improvements appear to be a consequence of increased resolution during model integration, whereas analysis improvements also benefit from high-resolution ensemble covariances during data assimilation. On the 12-km domain, additional analysis improvements are found by reducing observation error variance in order to address representativeness error. Removing model surface biases prior to assimilation significantly enhances the analysis. Inflating surface wind and temperature background error variance has large impacts on analyses, but only produces small improvements in analysis RMS errors. Both surface and upper-air 6-h forecasts are nearly unchanged in the 12-km experiments. Last, 12-km WRF EnKF surface analyses and 6-h forecasts are shown to generally outperform those of the Global Forecast System (GFS), North American Model (NAM), and the Rapid Update Cycle (RUC) by about 10%–30%, although these improvements do not extend above the surface. Based on these results, future improvements in multiscale EnKF are suggested.

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Improvements in Forecasting Intense Rainfall: Results from the FRANC (Forecasting Rainfall Exploiting New Data Assimilation Techniques and Novel Observations of Convection) Project

Atmosphere ◽

10.3390/atmos10030125 ◽

2019 ◽

Vol 10 (3) ◽

pp. 125 ◽

Cited By ~ 6

Author(s):

Sarah Dance ◽

Susan Ballard ◽

Ross Bannister ◽

Peter Clark ◽

Hannah Cloke ◽

...

Keyword(s):

Data Assimilation ◽

Doppler Radar ◽

Weather Prediction ◽

Observation Error ◽

Error Statistics ◽

Convective Rainfall ◽

Background Error ◽

Spatially Correlated ◽

Operational Forecasts ◽

The Impact

The FRANC project (Forecasting Rainfall exploiting new data Assimilation techniques and Novel observations of Convection) has researched improvements in numerical weather prediction of convective rainfall via the reduction of initial condition uncertainty. This article provides an overview of the project’s achievements. We highlight new radar techniques: correcting for attenuation of the radar return; correction for beams that are over 90% blocked by trees or towers close to the radar; and direct assimilation of radar reflectivity and refractivity. We discuss the treatment of uncertainty in data assimilation: new methods for estimation of observation uncertainties with novel applications to Doppler radar winds, Atmospheric Motion Vectors, and satellite radiances; a new algorithm for implementation of spatially-correlated observation error statistics in operational data assimilation; and innovative treatment of moist processes in the background error covariance model. We present results indicating a link between the spatial predictability of convection and convective regimes, with potential to allow improved forecast interpretation. The research was carried out as a partnership between University researchers and the Met Office (UK). We discuss the benefits of this approach and the impact of our research, which has helped to improve operational forecasts for convective rainfall events.

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Assimilation of Stratospheric Temperature and Ozone with an Ensemble Kalman Filter in a Chemistry–Climate Model

Monthly Weather Review ◽

10.1175/2011mwr3540.1 ◽

2011 ◽

Vol 139 (11) ◽

pp. 3389-3404 ◽

Cited By ~ 17

Author(s):

Thomas Milewski ◽

Michel S. Bourqui

Keyword(s):

Kalman Filter ◽

Data Assimilation ◽

Covariance Matrix ◽

Ensemble Kalman Filter ◽

Climate Model ◽

Observation Error ◽

Background Error ◽

Error Covariance Matrix ◽

Error Covariance ◽

Analysis Error

Abstract A new stratospheric chemical–dynamical data assimilation system was developed, based upon an ensemble Kalman filter coupled with a Chemistry–Climate Model [i.e., the intermediate-complexity general circulation model Fast Stratospheric Ozone Chemistry (IGCM-FASTOC)], with the aim to explore the potential of chemical–dynamical coupling in stratospheric data assimilation. The system is introduced here in a context of a perfect-model, Observing System Simulation Experiment. The system is found to be sensitive to localization parameters, and in the case of temperature (ozone), assimilation yields its best performance with horizontal and vertical decorrelation lengths of 14 000 km (5600 km) and 70 km (14 km). With these localization parameters, the observation space background-error covariance matrix is underinflated by only 5.9% (overinflated by 2.1%) and the observation-error covariance matrix by only 1.6% (0.5%), which makes artificial inflation unnecessary. Using optimal localization parameters, the skills of the system in constraining the ensemble-average analysis error with respect to the true state is tested when assimilating synthetic Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) retrievals of temperature alone and ozone alone. It is found that in most cases background-error covariances produced from ensemble statistics are able to usefully propagate information from the observed variable to other ones. Chemical–dynamical covariances, and in particular ozone–wind covariances, are essential in constraining the dynamical fields when assimilating ozone only, as the radiation in the stratosphere is too slow to transfer ozone analysis increments to the temperature field over the 24-h forecast window. Conversely, when assimilating temperature, the chemical–dynamical covariances are also found to help constrain the ozone field, though to a much lower extent. The uncertainty in forecast/analysis, as defined by the variability in the ensemble, is large compared to the analysis error, which likely indicates some amount of noise in the covariance terms, while also reducing the risk of filter divergence.

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Sea Surface Temperature Analysis for Fengyun-3C Data Using Oriented Elliptic Correlation Scales

Sensors ◽

10.3390/s21238067 ◽

2021 ◽

Vol 21 (23) ◽

pp. 8067

Author(s):

Zhihong Liao ◽

Bin Xu ◽

Junxia Gu ◽

Chunxiang Shi

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Kalman Filtering ◽

Sea Surface ◽

Quality Analysis ◽

Observation Error ◽

Change Analysis ◽

Background Error ◽

Filtering Method ◽

Correlation Scale

Sea surface temperature (SST) is critical for global climate change analysis and research. In this study, we used visible and infrared scanning radiometer (VIRR) sea surface temperature (SST) data from the Fengyun-3C (FY-3C) satellite for SST analysis, and applied the Kalman filtering methods with oriented elliptic correlation scales to construct SST fields. Firstly, the model for the oriented elliptic correlation scale was established for SST analysis. Secondly, observation errors from each type of SST data source were estimated using the optimal matched datasets, and background field errors were calculated using the model of oriented elliptic correlation scale. Finally, the blended SST analysis product was obtained using the Kalman filtering method, then the SST fields using the optimum interpolation (OI) method were chosen for comparison to validate results. The quality analysis for 2016 revealed that the Kalman analysis with a root-mean-square error (RMSE) of 0.3243 °C had better performance than did the OI analysis with a RMSE of 0.3911 °C, which was closer to the OISST product RMSE of 0.2897 °C. The results demonstrated that the Kalman filtering method with dynamic observation error and background error estimation was significantly superior to the OI method in SST analysis for FY-3C SST data.

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C-GLORSv5: an improved multi-purpose global ocean eddy-permitting physical reanalysis

10.5194/essd-2016-38 ◽

2016 ◽

Author(s):

Andrea Storto ◽

Simona Masina

Keyword(s):

Bias Correction ◽

General Circulation ◽

Thermohaline Circulation ◽

Heat Content ◽

Global Ocean ◽

Observation Error ◽

Background Error ◽

Steric Sea Level ◽

Previous Version ◽

Correction Scheme

Abstract. Global ocean reanalyses combine in-situ and satellite ocean observations with a general circulation ocean model to estimate the time-evolving state of the ocean, and they represent a valuable tool for a variety of applications, ranging from climate monitoring and process studies to downstream applications, initialization of long-range forecasts and regional studies. The purpose of this paper is to document the recent upgrade of C-GLORS (version 5), a state-of-the-art ocean reanalysis produced at the Centro Euro-Mediterraneo per i Cambiamenti Climatici that covers the meteorological satellite era (1980–present) and it is being updated in delayed time mode. The reanalysis is run at eddy-permitting resolution (1/4 degree horizontal resolution and 50 vertical levels) and consists of a three-dimensional variational data assimilation system, a surface nudging and a bias correction scheme. With respect to the previous version (v4), C-GLORSv5 contains a number of improvements. In particular, background- and observation- error covariances have been retuned, allowing a flow-dependent inflation in the globally averaged background-error variance. An additional constraint on the sea-ice thickness was introduced, leading to a realistic ice volume evolution. Finally, the bias correction scheme and the initialization strategy were retuned. Results document that the new reanalysis outperforms the previous version, especially in representing the variability of global heat content and associated steric sea level, the upper ocean temperature and the thermohaline circulation. The dataset is available in NetCDF format at doi:10.1594/PANGAEA.857995.

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Towards a Data-Derived Observation Error Covariance Matrix for Satellite Measurements

Remote Sensing ◽

10.3390/rs11151770 ◽

2019 ◽

Vol 11 (15) ◽

pp. 1770

Author(s):

Yan-An Liu ◽

Zhenglong Li ◽

Melin Huang

Keyword(s):

Real Time ◽

Weather Forecast ◽

Incident Radiation ◽

Observation Error ◽

Background Error ◽

R Matrix ◽

Error Covariance ◽

Instrument Noise ◽

Numerical Weather Forecast ◽

True Values

The observation error covariance (R) matrix is a key component in the data assimilation (DA) process for retrieval of atmospheric state parameters (ASPs), also impacting the subsequent numerical weather forecast. However, one commonly used type of R matrix depends on instrument noise, which contravenes reality because the retrieved ASPs would depend on the instrument used. Other types of R matrix rely on the observation operator (H), analyzed state ( x a ), background error covariance (B) matrix or the background state ( x b ), and the selected forecast ensemble. All these dependences reduce the representativeness of the R matrix, since the correctness of H needs verification and no true values exist for x a or x b . As such, a better method to correctly specify the R matrix is needed. Through the physical mechanism occurring between incident radiation and particles in the atmosphere, which complies with the phenomena of energy absorption and emission, correlations among bands or channels in a detected atmospheric radiance spectrum occur. This paper thus proposes a data-derived R matrix based on a large number (N) of detected atmospheric radiance spectra constructed from N real-time measurements, where N real-time measurements can be acquired by staring at some observation location of interest during a short amount of time. This data-derived R matrix for satellite radiance observations does not rely on any assumed quantities and is unambiguous. Technically, recording N real-time measurements is achievable by modifying the trigger configuration of data recording from ground.

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All-Sky Microwave Radiance Assimilation in NCEP’s GSI Analysis System

Monthly Weather Review ◽

10.1175/mwr-d-15-0445.1 ◽

2016 ◽

Vol 144 (12) ◽

pp. 4709-4735 ◽

Cited By ~ 42

Author(s):

Yanqiu Zhu ◽

Emily Liu ◽

Rahul Mahajan ◽

Catherine Thomas ◽

David Groff ◽

...

Keyword(s):

Quality Control ◽

Bias Correction ◽

Microwave Radiometer ◽

Cloud Water ◽

Development Effort ◽

Observation Error ◽

Background Error ◽

Radiance Assimilation ◽

Analysis System ◽

Sky Conditions

Abstract The capability of all-sky microwave radiance assimilation in the Gridpoint Statistical Interpolation (GSI) analysis system has been developed at the National Centers for Environmental Prediction (NCEP). This development effort required the adaptation of quality control, observation error assignment, bias correction, and background error covariance to all-sky conditions within the ensemble–variational (EnVar) framework. The assimilation of cloudy radiances from the Advanced Microwave Sounding Unit-A (AMSU-A) microwave radiometer for ocean fields of view (FOVs) is the primary emphasis of this study. In the original operational hybrid 3D EnVar Global Forecast System (GFS), the clear-sky approach for radiance data assimilation is applied. Changes to data thinning and quality control have allowed all-sky satellite radiances to be assimilated in the GSI. Along with the symmetric observation error assignment, additional situation-dependent observation error inflation is employed for all-sky conditions. Moreover, in addition to the current radiance bias correction, a new bias correction strategy has been applied to all-sky radiances. In this work, the static background error variance and the ensemble spread of cloud water are examined, and the levels of cloud variability from the ensemble forecast in single- and dual-resolution configurations are discussed. Overall, the all-sky approach provides more realistic simulated brightness temperatures and cloud water analysis increments, and improves analysis off the west coasts of the continents by reducing a known bias in stratus. An approximate 10% increase in the use of AMSU-A channels 1–5 and a 12% increase for channel 15 are also observed. The all-sky AMSU-A radiance assimilation became operational in the 4D EnVar GFS system upgrade of 12 May 2016.

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An Ensemble-Based Four-Dimensional Variational Data Assimilation Scheme. Part I: Technical Formulation and Preliminary Test

Monthly Weather Review ◽

10.1175/2008mwr2312.1 ◽

2008 ◽

Vol 136 (9) ◽

pp. 3363-3373 ◽

Cited By ~ 146

Author(s):

Chengsi Liu ◽

Qingnong Xiao ◽

Bin Wang

Keyword(s):

Data Assimilation ◽

Preliminary Test ◽

Background Field ◽

Variational Data Assimilation ◽

Water Model ◽

Observation Error ◽

Ensemble Forecasts ◽

Background Error ◽

Background Error Covariance ◽

Error Covariance

Abstract Applying a flow-dependent background error covariance (𝗕 matrix) in variational data assimilation has been a topic of interest among researchers in recent years. In this paper, an ensemble-based four-dimensional variational (En4DVAR) algorithm, designed by the authors, is presented that uses a flow-dependent background error covariance matrix constructed by ensemble forecasts and performs 4DVAR optimization to produce a balanced analysis. A great advantage of this En4DVAR design over standard 4DVAR methods is that the tangent linear and adjoint models can be avoided in its formulation and implementation. In addition, it can be easily incorporated into variational data assimilation systems that are already in use at operational centers and among the research community. A one-dimensional shallow water model was used for preliminary tests of the En4DVAR scheme. Compared with standard 4DVAR, the En4DVAR converges well and can produce results that are as good as those with 4DVAR but with far less computation cost in its minimization. In addition, a comparison of the results from En4DVAR with those from other data assimilation schemes [e.g., 3DVAR and ensemble Kalman filter (EnKF)] is made. The results show that the En4DVAR yields an analysis that is comparable to the widely used variational or ensemble data assimilation schemes and can be a promising approach for real-time applications. In addition, experiments were carried out to test the sensitivities of EnKF and En4DVAR, whose background error covariance is estimated from the same ensemble forecasts. The experiments indicated that En4DVAR obtained reasonably sound analysis even with larger observation error, higher observation frequency, and more unbalanced background field.

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