scholarly journals Impact of Atmospheric and Aerosol Optical Depth Observations on Aerosol Initial Conditions in a strongly-coupled data assimilation system

2019 ◽  
Author(s):  
Milija Zupanski ◽  
Anton Kliewer ◽  
Ting-Chi Wu ◽  
Karina Apodaca ◽  
Qijing Bian ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Initial Conditions ◽  
Positive Impact ◽  
Storm Event ◽  
Strongly Coupled ◽  
Atmospheric Observations ◽  
Coupled Data Assimilation ◽  
The Impact

Abstract. Strongly coupled data assimilation frameworks provide a mechanism for including additional information about aerosols through the coupling between aerosol and atmospheric variables, effectively utilizing atmospheric observations to change the aerosol analysis. Here, we investigate the impact of these observations on aerosol using the Maximum Likelihood Ensemble Filter (MLEF) algorithm with Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) which includes the Godard Chemistry Aerosol Radiation and Transport (GOCART) module. We apply this methodology to a dust storm event over the Arabian Peninsula and examine in detail the error covariance and in particular the impact of atmospheric observations on improving the aerosol initial conditions. The assimilated observations include conventional atmospheric observations and Aerosol Optical Depth (AOD) retrievals. Results indicate a positive impact of using strongly coupled data assimilation and atmospheric observations on the aerosol initial conditions, quantified using Degrees of Freedom for Signal.

scholarly journals Development of a hybrid variational-ensemble data assimilation technique for observed lightning tested in a mesoscale model

2014 ◽  
Vol 21 (5) ◽  
pp. 1027-1041 ◽  
Author(s):  
K. Apodaca ◽  
M. Zupanski ◽  
M. DeMaria ◽  
J. A. Knaff ◽  
L. D. Grasso
Keyword(s):  
Data Assimilation ◽  
Prediction Models ◽  
Mesoscale Model ◽  
Initial Conditions ◽  
Positive Impact ◽  
Weather Prediction ◽  
New Information ◽  
Forecasting System ◽  
Maximum Likelihood Ensemble Filter ◽  
The Impact

Abstract. Lightning measurements from the Geostationary Lightning Mapper (GLM) that will be aboard the Geostationary Operational Environmental Satellite – R Series will bring new information that can have the potential for improving the initialization of numerical weather prediction models by assisting in the detection of clouds and convection through data assimilation. In this study we focus on investigating the utility of lightning observations in mesoscale and regional applications suitable for current operational environments, in which convection cannot be explicitly resolved. Therefore, we examine the impact of lightning observations on storm environment. Preliminary steps in developing a lightning data assimilation capability suitable for mesoscale modeling are presented in this paper. World Wide Lightning Location Network (WWLLN) data was utilized as a proxy for GLM measurements and was assimilated with the Maximum Likelihood Ensemble Filter, interfaced with the Nonhydrostatic Mesoscale Model core of the Weather Research and Forecasting system (WRF-NMM). In order to test this methodology, regional data assimilation experiments were conducted. Results indicate that lightning data assimilation had a positive impact on the following: information content, influencing several dynamical variables in the model (e.g., moisture, temperature, and winds), and improving initial conditions during several data assimilation cycles. However, the 6 h forecast after the assimilation did not show a clear improvement in terms of root mean square (RMS) errors.

scholarly journals Improving air quality forecasting with the assimilation of GOCI aerosol optical depth (AOD) retrievals during the KORUS-AQ period

2020 ◽  
Vol 20 (10) ◽  
pp. 6015-6036
Author(s):  
Soyoung Ha ◽  
Zhiquan Liu ◽  
Wei Sun ◽  
Yonghee Lee ◽  
Limseok Chang
Keyword(s):  
Air Quality ◽  
Data Assimilation ◽  
South Korea ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Three Dimensional ◽  
Upstream Region ◽  
Temporal And Spatial Distribution ◽  
The Impact ◽  
Air Quality Forecasting

Abstract. The Korean Geostationary Ocean Color Imager (GOCI) satellite has monitored the East Asian region in high temporal (e.g., hourly) and spatial resolution (e.g., 6 km) every day for the last decade, providing unprecedented information on air pollutants over the upstream region of the Korean Peninsula. In this study, the GOCI aerosol optical depth (AOD), retrieved at the 550 nm wavelength, is assimilated to enhance the quality of the aerosol analysis, thereby making systematic improvements to air quality forecasting over South Korea. For successful data assimilation, GOCI retrievals are carefully investigated and processed based on data characteristics such as temporal and spatial distribution. The preprocessed data are then assimilated in the three-dimensional variational data assimilation (3D-Var) technique for the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). For the Korea–United States Air Quality (KORUS-AQ) period (May 2016), the impact of GOCI AOD on the accuracy of surface PM2.5 prediction is examined by comparing with effects of other observations including Moderate Resolution Imaging Spectroradiometer (MODIS) sensors and surface PM2.5 observations. Consistent with previous studies, the assimilation of surface PM2.5 measurements alone still underestimates surface PM2.5 concentrations in the following forecasts, and the forecast improvements last only for about 6 h. When GOCI AOD retrievals are assimilated with surface PM2.5 observations, however, the negative bias is diminished and forecast skills are improved up to 24 h, with the most significant contributions to the prediction of heavy pollution events over South Korea.

scholarly journals Hybrid variational-ensemble assimilation of lightning observations in a mesoscale model

2014 ◽  
Vol 1 (1) ◽  
pp. 917-952
Author(s):  
K. Apodaca ◽  
M. Zupanski ◽  
M. DeMaria ◽  
J. A. Knaff ◽  
L. D. Grasso
Keyword(s):  
Data Assimilation ◽  
Prediction Models ◽  
Mesoscale Model ◽  
Initial Conditions ◽  
Positive Impact ◽  
Weather Prediction ◽  
New Information ◽  
Forecasting System ◽  
Maximum Likelihood Ensemble Filter ◽  
The Impact

Abstract. Lightning measurements from the Geostationary Lightning Mapper (GLM) that will be aboard the Goestationary Operational Environmental Satellite – R Series will bring new information that can have the potential for improving the initialization of numerical weather prediction models by assisting in the detection of clouds and convection through data assimilation. In this study we focus on investigating the utility of lightning observations in mesoscale and regional applications suitable for current operational environments, in which convection cannot be explicitly resolved. Therefore, we examine the impact of lightning observations on storm environment. Preliminary steps in developing a lightning data assimilation capability suitable for mesoscale modeling are presented in this paper. World Wide Lightning Location Network (WWLLN) data was utilized as a proxy for GLM measurements and was assimilated with the Maximum Likelihood Ensemble Filter, interfaced with the Nonhydrostatic Mesoscale Model core of the Weather Research and Forecasting system (WRF-NMM). In order to test this methodology, regional data assimilation experiments were conducted. Results indicate that lightning data assimilation had a positive impact on the following: information content, influencing several dynamical variables in the model (e.g., moisture, temperature, and winds), improving initial conditions, and partially improving WRF-NMM forecasts during several data assimilation cycles.

Examining the Impact of SMAP Soil Moisture Retrievals on Short-Range Weather Prediction under Weakly and Strongly Coupled Data Assimilation with WRF-Noah

2019 ◽  
Vol 147 (12) ◽  
pp. 4345-4366 ◽  
Author(s):  
Liao-Fan Lin ◽  
Zhaoxia Pu
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Weather Prediction ◽  
Surface Model ◽  
Strongly Coupled ◽  
Soil Moisture Data ◽  
Analysis Scheme ◽  
Coupled Data Assimilation ◽  
The Impact

Abstract Remotely sensed soil moisture data are typically incorporated into numerical weather models under a framework of weakly coupled data assimilation (WCDA), with a land surface analysis scheme independent from the atmospheric analysis component. In contrast, strongly coupled data assimilation (SCDA) allows simultaneous correction of atmospheric and land surface states but has not been sufficiently explored with land surface soil moisture data assimilation. This study implemented a variational approach to assimilate the Soil Moisture Active Passive (SMAP) 9-km enhanced retrievals into the Noah land surface model coupled with the Weather Research and Forecasting (WRF) Model under a framework of both WCDA and SCDA. The goal of the study is to quantify the relative impact of assimilating SMAP data under different coupling frameworks on the atmospheric forecasts in the summer. The results of the numerical experiments during July 2016 show that SCDA can provide additional benefits on the forecasts of air temperature and humidity compared to WCDA. Over the U.S. Great Plains, assimilation of SMAP data under WCDA reduces a warm bias in temperature and a dry bias in humidity by 7.3% and 19.3%, respectively, while the SCDA case contributes an additional bias reduction of 2.2% (temperature) and 3.3% (humidity). While WCDA leads to a reduction of RMSE in temperature forecasts by 4.1%, SCDA results in additional reduction of RMSE by 0.8%. For the humidity, the reduction of RMSE is around 1% for both WCDA and SCDA.

Coupled data assimilation in climate research: A brief review of applications in ocean and land

2018 ◽  
Vol 3 (2) ◽  
Author(s):  
Kazuyoshi Suzuki 1 ◽  
Milija ZUPANSKI 2
Keyword(s):  
Data Assimilation ◽  
Observational Data ◽  
Initial Conditions ◽  
Monitoring Networks ◽  
Coupled Models ◽  
Climate Change Research ◽  
Empirical Parameter ◽  
Strongly Coupled ◽  
Weakly Coupled ◽  
Coupled Data Assimilation

Regions of the cryosphere, including the poles, that are currently unmonitored are expanding, therefore increasing the importance of satellite observations for such regions. With the increasing availability of satellite data in recent years, data assimilation research that combines forecasting models with observational data has begun to flourish. Coupled land/ice-atmosphere/ocean models generally improve the forecasting ability of models. Data assimilation plays an important role in such coupled models, by providing initial conditions and/or empirical parameter estimation. Coupled data assimilation can generally be divided into three types: uncoupled, weakly coupled, or strongly coupled. This review provides an overview of coupled data assimilation, introduces examples of its use in research on sea ice-ocean interactions and the land, and discusses its future outlook. Assimilation of coupled data constitutes an effective method for monitoring cold regions for which observational data are scarce and should prove useful for climate change research and the design of efficient monitoring networks in the future.

scholarly journals The impact of data assimilation on the prediction of Asian desert dust using an operational 4D-Var system

2018 ◽  
Author(s):  
Angela Benedetti ◽  
Francesca Di Giuseppe ◽  
Luke Jones ◽  
Vincent-Henri Peuch ◽  
Samuel Rémy ◽  
...  
Keyword(s):  
Air Quality ◽  
Data Assimilation ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Asian Dust ◽  
Added Value ◽  
Desert Dust ◽  
Deep Blue ◽  
Dust Events ◽  
The Impact

Abstract. Asian Dust is a seasonal meteorological phenomenon which affects East Asia, and has severe consequences on the air quality of China, North and South Korea and Japan. Despite the continental extent, the prediction of severe episodes and the anticipation of their consequences is challenging. Three one-year experiments were run to assess the skill of the model of the European Centre for Medium-Range Weather Forecasts (ECMWF) in monitoring Asian dust and understand its relative contribution to air quality over China. Data used were the MODIS Dark Target and the Deep Blue Aerosol Optical Depth. In particular the experiments aimed at understanding the added value of data assimilation runs over a model run without any aerosol data. The year 2013 was chosen as representative for the availability of independent Aerosol Optical Depth (AOD) data from two established ground-based networks (AERONET and CARSNET), which could be used to evaluate experiments. Particulate Matter (PM) data from the China Environmental Protection Agency (CEPA) were also used in the evaluation. Results show that the assimilation of satellite AOD data is beneficial to predict the extent and magnitude of desert-dust events and to improve the forecast of such events. The availability of observations from the MODIS Deep Blue algorithm over bright surfaces is an asset, allowing for a better localization of the sources and definition of the dust events. In general both experiments constrained by data assimilation perform better that the unconstrained experiment, generally showing smaller mean normalized bias and fractional gross error with respect to the independent verification datasets. The impact of the assimilated satellite observations is larger at analysis time, but lasts well into the forecast. While assimilation is not a substitute for model development and characterization of the emission sources, results indicate that it can play a big role in delivering improved forecasts of Asian Dust.

scholarly journals Impact of radar data assimilation using WRF three-dimensional variational system, for the simulation of a heavy rainfall case in Central Italy

2013 ◽  
Vol 6 (4) ◽  
pp. 7315-7353
Author(s):  
I. Maiello ◽  
R. Ferretti ◽  
S. Gentile ◽  
M. Montopoli ◽  
E. Picciotti ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Radial Velocity ◽  
Heavy Rainfall ◽  
Initial Conditions ◽  
Positive Impact ◽  
Central Italy ◽  
Three Dimensional ◽  
Warm Start ◽  
The Impact ◽  
Dwr Data

Abstract. This work is a first assessment of the role of Doppler Weather radar (DWR) data in a mesoscale model for the prediction of a heavy rainfall. The study analyzes the event occurred during 19–22 May 2008 in the urban area of Rome. The impact of the radar reflectivity and radial velocity acquired from Monte Midia Doppler radar, on the assimilation into the Weather Research Forecasting (WRF) model version 3.2, is discussed. The goal is to improve the WRF high resolution initial condition by assimilating DWR data and using ECMWF analyses as First Guess thus improving the forecast of surface rainfall. Several experiments are performed using different set of Initial Conditions (ECMWF analyses and warm start or cycling) and a different assimilation strategy (3 h-data assimilation cycle). In addition, 3DVAR (three-dimensional variational) sensitivity tests to outer loops are performed for each of the previous experiment to include the non-linearity in the observation operators. In order to identify the best ICs, statistical indicators such as forecast accuracy, frequency bias, false alarm rate and equitable threat score for the accumulated precipitation are used. The results show that the assimilation of DWR data has a positive impact on the prediction of the heavy rainfall of this event, both assimilating reflectivity and radial velocity, together with conventional observations. Finally, warm start results in more accurate experiments as well as the outer loops strategy.

scholarly journals Multivariate localization functions for strongly coupled data assimilation in the bivariate Lorenz ’96 system

2021 ◽  
Author(s):  
Zofia Stanley ◽  
Ian Grooms ◽  
William Kleiber
Keyword(s):  
Data Assimilation ◽  
Spatial Scales ◽  
Background Error ◽  
Strongly Coupled ◽  
Localization Function ◽  
Background Error Covariance ◽  
Error Covariance ◽  
Coupled Data Assimilation ◽  
Lorenz 96 ◽  
The Impact

Abstract. Localization is widely used in data assimilation schemes to mitigate the impact of sampling errors on ensemble-derived background error covariance matrices. Strongly coupled data assimilation allows observations in one component of a coupled model to directly impact another component through inclusion of cross-domain terms in the background error covariance matrix. When different components have disparate dominant spatial scales, localization between model domains must properly account for the multiple length scales at play. In this work we develop two new multivariate localization functions, one of which is a multivariate extension of the fifth-order piecewise rational Gaspari-Cohn localization function; the within-component localization functions are standard Gaspari-Cohn with different localization radii while the cross-localization function is newly constructed. The functions produce non-negative definite localization matrices, which are suitable for use in variational data assimilation schemes. We compare the performance of our two new multivariate localization functions to two other multivariate localization functions and to the univariate analogs of all four functions in a simple experiment with the bivariate Lorenz '96 system. In our experiment the multivariate Gaspari-Cohn function leads to better performance than any of the other localization functions.

scholarly journals Strongly Coupled Data Assimilation Experiments with Linearized Ocean–Atmosphere Balance Relationships

2018 ◽  
Vol 146 (4) ◽  
pp. 1233-1257 ◽  
Author(s):  
Andrea Storto ◽  
Matthew J. Martin ◽  
Bruno Deremble ◽  
Simona Masina
Keyword(s):  
Data Assimilation ◽  
General Circulation ◽  
Circulation Model ◽  
Variational Data Assimilation ◽  
Global Ocean ◽  
Background Error ◽  
Near Surface ◽  
Strongly Coupled ◽  
Coupled Data Assimilation ◽  
The Impact

Coupled data assimilation is emerging as a target approach for Earth system prediction and reanalysis systems. Coupled data assimilation may be indeed able to minimize unbalanced air–sea initialization and maximize the intermedium propagation of observations. Here, we use a simplified framework where a global ocean general circulation model (NEMO) is coupled to an atmospheric boundary layer model [Cheap Atmospheric Mixed Layer (CheapAML)], which includes prognostic prediction of near-surface air temperature and moisture and allows for thermodynamic but not dynamic air–sea coupling. The control vector of an ocean variational data assimilation system is augmented to include 2-m atmospheric parameters. Cross-medium balances are formulated either through statistical cross covariances from monthly anomalies or through the application of linearized air–sea flux relationships derived from the tangent linear approximation of bulk formulas, which represents a novel solution to the coupled assimilation problem. As a proof of concept, the methodology is first applied to study the impact of in situ ocean observing networks on the near-surface atmospheric analyses and later to the complementary study of the impact of 2-m air observations on sea surface parameters, to assess benefits of strongly versus weakly coupled data assimilation. Several forecast experiments have been conducted for the period from June to December 2011. We find that especially after day 2 of the forecasts, strongly coupled data assimilation provides a beneficial impact, particularly in the tropical oceans. In most areas, the use of linearized air–sea balances outperforms the statistical relationships used, providing a motivation for implementing coupled tangent linear trajectories in four-dimensional variational data assimilation systems. Further impacts of strongly coupled data assimilation might be found by retuning the background error covariances.

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