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
Keyword(s):  
Data Assimilation ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Initial Conditions ◽  
Strongly Coupled ◽  
Coupled Data Assimilation ◽  
Data Assimilation System ◽  
Assimilation System

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 Implementation of a 3-D-Var system for atmospheric profiling data assimilation into the RAMS model: initial results

2013 ◽  
Vol 6 (2) ◽  
pp. 3581-3610
Author(s):  
S. Federico
Keyword(s):  
Relative Humidity ◽  
Data Assimilation ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Wind Profiler ◽  
Short Term ◽  
Recursive Filters ◽  
Data Assimilation System ◽  
Rams Model ◽  
Assimilation System

Abstract. This paper presents the current status of development of a three-dimensional variational data assimilation system. The system can be used with different numerical weather prediction models, but it is mainly designed to be coupled with the Regional Atmospheric Modelling System (RAMS). Analyses are given for the following parameters: zonal and meridional wind components, temperature, relative humidity, and geopotential height. Important features of the data assimilation system are the use of incremental formulation of the cost-function, and the use of an analysis space represented by recursive filters and eigenmodes of the vertical background error matrix. This matrix and the length-scale of the recursive filters are estimated by the National Meteorological Center (NMC) method. The data assimilation and forecasting system is applied to the real context of atmospheric profiling data assimilation, and in particular to the short-term wind prediction. The analyses are produced at 20 km horizontal resolution over central Europe and extend over the whole troposphere. Assimilated data are vertical soundings of wind, temperature, and relative humidity from radiosondes, and wind measurements of the European wind profiler network. Results show the validity of the analysis solutions because they are closer to the observations (lower RMSE) compared to the background (higher RMSE), and the differences of the RMSEs are consistent with the data assimilation settings. To quantify the impact of improved initial conditions on the short-term forecast, the analyses are used as initial conditions of a three-hours forecast of the RAMS model. In particular two sets of forecasts are produced: (a) the first uses the ECMWF analysis/forecast cycle as initial and boundary conditions; (b) the second uses the analyses produced by the 3-D-Var scheme as initial conditions, then is driven by the ECMWF forecast. The improvement is quantified by considering the horizontal components of the wind, which are measured at a-synoptic times by the European wind profiler network. The results show that the RMSE is effectively reduced at the short range (1–2 h). The results are in agreement with the set-up of the numerical experiment.

scholarly journals Simulation of Monsoon Depressions Using WRF-VAR: Impact of Different Background Error Statistics and Lateral Boundary Conditions

2014 ◽  
Vol 142 (10) ◽  
pp. 3586-3613 ◽  
Author(s):  
A. Routray ◽  
S. C. Kar ◽  
P. Mali ◽  
K. Sowjanya
Keyword(s):  
Data Assimilation ◽  
Boundary Conditions ◽  
Initial Conditions ◽  
Variational Data Assimilation ◽  
Error Statistics ◽  
Background Error ◽  
Single Observation ◽  
Monsoon Depressions ◽  
Data Assimilation System ◽  
Assimilation System

Abstract In a variational data assimilation system, background error statistics (BES) spread the influence of the observations in space and filter analysis increments through dynamic balance or statistical relationships. In a data-sparse region such as the Bay of Bengal, BES play an important role in defining the location and structure of monsoon depressions (MDs). In this study, the Indian-region-specific BES have been computed for the Weather Research and Forecasting (WRF) three-dimensional variational data assimilation system. A comparative study using single observation tests is carried out using the computed BES and global BES within the WRF system. Both sets of BES are used in the assimilation cycles and forecast runs for simulating the meteorological features associated with the MDs. Numerical experiments have been conducted to assess the relative impact of various BES in the analysis and simulations of the MDs. The results show that use of regional BES in the assimilation cycle has a positive impact on the prediction of the location, propagation, and development of rainbands associated with the MDs. The track errors of MDs are smaller when domain-specific BES are used in the assimilation cycle. Additional experiments have been conducted using data from the Interim European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-Interim) as initial and boundary conditions (IBCs) in the assimilation cycle. The results indicate that the use of domain-dependent BES and high-resolution ERA-I data as IBCs further improved the initial conditions for the model leading to better forecasts of the MDs.

scholarly journals A Hybrid Global Ocean Data Assimilation System at NCEP

2015 ◽  
Vol 143 (11) ◽  
pp. 4660-4677 ◽  
Author(s):  
Stephen G. Penny ◽  
David W. Behringer ◽  
James A. Carton ◽  
Eugenia Kalnay
Keyword(s):  
Data Assimilation ◽  
Initial Conditions ◽  
Global Ocean ◽  
Climate Forecast ◽  
Forecast System ◽  
Climate Forecast System ◽  
Ocean Data Assimilation ◽  
Data Assimilation System ◽  
Ncep Climate Forecast System ◽  
Assimilation System

Abstract Seasonal forecasting with a coupled model requires accurate initial conditions for the ocean. A hybrid data assimilation has been implemented within the National Centers for Environmental Prediction (NCEP) Global Ocean Data Assimilation System (GODAS) as a future replacement of the operational three-dimensional variational data assimilation (3DVar) method. This Hybrid-GODAS provides improved representation of model uncertainties by using a combination of dynamic and static background error covariances, and by using an ensemble forced by different realizations of atmospheric surface conditions. An observing system simulation experiment (OSSE) is presented spanning January 1991 to January 1999, with a bias imposed on the surface forcing conditions to emulate an imperfect model. The OSSE compares the 3DVar used by the NCEP Climate Forecast System (CFSv2) with the new hybrid, using simulated in situ ocean observations corresponding to those used for the NCEP Climate Forecast System Reanalysis (CFSR). The Hybrid-GODAS reduces errors for all prognostic model variables over the majority of the experiment duration, both globally and regionally. Compared to an ensemble Kalman filter (EnKF) used alone, the hybrid further reduces errors in the tropical Pacific. The hybrid eliminates growth in biases of temperature and salinity present in the EnKF and 3DVar, respectively. A preliminary reanalysis using real data shows that reductions in errors and biases are qualitatively similar to the results from the OSSE. The Hybrid-GODAS is currently being implemented as the ocean component in a prototype next-generation CFSv3, and will be used in studies by the Climate Prediction Center to evaluate impacts on ENSO prediction.

scholarly journals CIRA/CSU Four-Dimensional Variational Data Assimilation System

2005 ◽  
Vol 133 (4) ◽  
pp. 829-843 ◽  
Author(s):  
Milija Zupanski ◽  
Dusanka Zupanski ◽  
Tomislava Vukicevic ◽  
Kenneth Eis ◽  
Thomas Vonder Haar
Keyword(s):  
Data Assimilation ◽  
Initial Conditions ◽  
Control Variable ◽  
Three Dimensional ◽  
Atmospheric Modeling ◽  
Variational Data Assimilation ◽  
Regional Atmospheric Modeling ◽  
The Impact ◽  
Data Assimilation System ◽  
Assimilation System

A new four-dimensional variational data assimilation (4DVAR) system is developed at the Cooperative Institute for Research in the Atmosphere (CIRA)/Colorado State University (CSU). The system is also called the Regional Atmospheric Modeling Data Assimilation System (RAMDAS). In its present form, the 4DVAR system is employing the CSU/Regional Atmospheric Modeling System (RAMS) nonhydrostatic primitive equation model. The Weather Research and Forecasting (WRF) observation operator is used to access the observations, adopted from the WRF three-dimensional variational data assimilation (3DVAR) algorithm. In addition to the initial conditions adjustment, the RAMDAS includes the adjustment of model error (bias) and lateral boundary conditions through an augmented control variable definition. Also, the control variable is defined in terms of the velocity potential and streamfunction instead of the horizontal winds. The RAMDAS is developed after the National Centers for Environmental Prediction (NCEP) Eta 4DVAR system, however with added improvements addressing its use in a research environment. Preliminary results with RAMDAS are presented, focusing on the minimization performance and the impact of vertical correlations in error covariance modeling. A three-dimensional formulation of the background error correlation is introduced and evaluated. The Hessian preconditioning is revisited, and an alternate algebraic formulation is presented. The results indicate a robust minimization performance.

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.

From Monitoring to Forecasting the Land Surface Condition Using a Land Data Assimilation System: Application over the Contiguous United States

2020 ◽  
Author(s):  
Anthony Mucia ◽  
Clément Albergel ◽  
Bertrand Bonan ◽  
Yongjun Zheng ◽  
Jean-Christophe Calvet
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Initial Conditions ◽  
Global Land ◽  
Land Data Assimilation ◽  
Land Data Assimilation System ◽  
Data Assimilation System ◽  
Assimilation System

<p>LDAS-Monde is a global Land Data Assimilation System developed in the research department of Météo-France (CNRM) to monitor Land Surface Variables (LSVs) at various scales, from regional to global. With LDAS-Monde, it is possible to assimilate satellite derived observations of Surface Soil Moisture (SSM) and Leaf Area Index (LAI) e.g. from the Copernicus Global Land Service (CGLS). It is an offline system normally driven by atmospheric reanalyses such as ECMWF ERA5.</p><p>In this study we investigate LDAS-Monde ability to use atmospheric forecasts to predict LSV states up to weeks in advance. In addition to the accuracy of the forecast predictions, the impact of the initialization on the LSVs forecast is addressed. To perform this study, LDAS-Monde is forced by a fifteen-day forecast from ECMWF for the 2017-2018 period over the Contiguous United States (CONUS) at 0.2<sup>o</sup> x 0.2<sup>o</sup> spatial resolution. These LSVs forecasts are initialized either by the model alone (LDAS-Monde open-loop, no assimilation, Fc_ol) or by the analysis (assimilation of SSM and LAI, Fc_an). These two sets of forecast are then assessed using satellite derived observations of SSM and LAI, evapotranspiration estimates, as well as in situ measurements of soil moisture from the U.S. Climate Reference Network (USCRN). Results indicate that for the three evaluation variables (SSM, LAI, and evapotranspiration), LDAS-Monde provides reasonably accurate predictions two weeks in advance. Additionally, the initial conditions are shown to make a positive impact with respect to LAI, evapotranspiration, and deeper layers of soil moisture when using Fc_an. Moreover, this impact persists in time, particularly for vegetation related variables. Other model variables (such as runoff and drainage) are also affected by the initial conditions. Future work will focus on the transfer of this predictive information from a research to stakeholder tool.</p>

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