The evaluation of a new model of sun activity with data assimilation technique

2016 ◽  
Author(s):  
N. T. Safiullin ◽  
S. V. Porshnev
Keyword(s):  
Data Assimilation ◽  
New Model ◽  
Sun Activity ◽  
Data Assimilation Technique

scholarly journals Changes in Power Plant NOx Emissions over Northwest Greece Using a Data Assimilation Technique

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 900
Author(s):  
Ioanna Skoulidou ◽  
Maria-Elissavet Koukouli ◽  
Arjo Segers ◽  
Astrid Manders ◽  
Dimitris Balis ◽  
...  
Keyword(s):  
Power Plant ◽  
Data Assimilation ◽  
Energy Production ◽  
Power Plants ◽  
Transport Model ◽  
A Priori ◽  
Filter Method ◽  
Nox Emissions ◽  
A Posteriori ◽  
Data Assimilation Technique

In this work, we investigate the ability of a data assimilation technique and space-borne observations to quantify and monitor changes in nitrogen oxides (NOx) emissions over Northwestern Greece for the summers of 2018 and 2019. In this region, four lignite-burning power plants are located. The data assimilation technique, based on the Ensemble Kalman Filter method, is employed to combine space-borne atmospheric observations from the high spatial resolution Sentinel-5 Precursor (S5P) Tropospheric Monitoring Instrument (TROPOMI) and simulations using the LOTOS-EUROS Chemical Transport model. The Copernicus Atmosphere Monitoring Service-Regional European emissions (CAMS-REG, version 4.2) inventory based on the year 2015 is used as the a priori emissions in the simulations. Surface measurements of nitrogen dioxide (NO2) from air quality stations operating in the region are compared with the model surface NO2 output using either the a priori (base run) or the a posteriori (assimilated run) NOx emissions. Relative to the a priori emissions, the assimilation suggests a strong decrease in concentrations for the station located near the largest power plant, by 80% in 2019 and by 67% in 2018. Concerning the estimated annual a posteriori NOx emissions, it was found that, for the pixels hosting the two largest power plants, the assimilated run results in emissions decreased by ~40–50% for 2018 compared to 2015, whereas a larger decrease, of ~70% for both power plants, was found for 2019, after assimilating the space-born observations. For the same power plants, the European Pollutant Release and Transfer Register (E-PRTR) reports decreased emissions in 2018 and 2019 compared to 2015 (−35% and −38% in 2018, −62% and −72% in 2019), in good agreement with the estimated emissions. We further compare the a posteriori emissions to the reported energy production of the power plants during the summer of 2018 and 2019. Mean decreases of about −35% and−63% in NOx emissions are estimated for the two larger power plants in summer of 2018 and 2019, respectively, which are supported by similar decreases in the reported energy production of the power plants (~−30% and −70%, respectively).

scholarly journals Impact of Four-Dimensional Variational Data Assimilation of Atmospheric Motion Vectors on Tropical Cyclone Track Forecasts

2006 ◽  
Vol 21 (4) ◽  
pp. 663-669 ◽  
Author(s):  
Dongliang Wang ◽  
Xudong Liang ◽  
Yihong Duan ◽  
Johnny C. L. Chan
Keyword(s):  
Tropical Cyclone ◽  
Data Assimilation ◽  
Mesoscale Model ◽  
State University ◽  
Motion Vectors ◽  
Geostationary Meteorological Satellite ◽  
Atmospheric Motion ◽  
Atmospheric Motion Vectors ◽  
Data Assimilation Technique ◽  
The Impact

Abstract The fifth-generation Pennsylvania State University–National Center for Atmospheric Research nonhydrostatic Mesoscale Model is employed to evaluate the impact of the Geostationary Meteorological Satellite-5 water vapor and infrared atmospheric motion vectors (AMVs), incorporated with the four-dimensional variational (4DVAR) data assimilation technique, on tropical cyclone (TC) track predictions. Twenty-two cases from eight different TCs over the western North Pacific in 2002 have been examined. The 4DVAR assimilation of these satellite-derived wind observations leads to appreciable improvements in the track forecasts, with average reductions in track error of ∼5% at 12 h, 12% at 24 h, 10% at 36 h, and 7% at 48 h. Preliminary results suggest that the improvement depends on the quantity of the AMV data available for assimilation.

scholarly journals Numerical Modeling of Marine Circulation with 4D Variational Data Assimilation

2020 ◽  
Vol 8 (7) ◽  
pp. 503
Author(s):  
Vladimir Zalesny ◽  
Valeriy Agoshkov ◽  
Victor Shutyaev ◽  
Eugene Parmuzin ◽  
Natalia Zakharova
Keyword(s):  
Data Assimilation ◽  
Finite Difference Methods ◽  
Variational Data Assimilation ◽  
Adjoint Equations ◽  
Special Choice ◽  
Modeling And Prediction ◽  
Solving Equations ◽  
The Baltic ◽  
Academy Of Sciences ◽  
Data Assimilation Technique

The technology is presented for modeling and prediction of marine hydrophysical fields based on the 4D variational data assimilation technique developed at the Marchuk Institute of Numerical Mathematics, Russian Academy of Sciences (INM RAS). The technology is based on solving equations of marine hydrodynamics using multicomponent splitting, thereby solving an optimality system that includes adjoint equations and covariance matrices of observation errors. The hydrodynamic model is described by primitive equations in the sigma-coordinate system, which is solved by finite-difference methods. The technology includes original algorithms for solving the problems of variational data assimilation using modern iterative processes with a special choice of iterative parameters. The methods and technology are illustrated by the example of solving the problem of circulation of the Baltic Sea with 4D variational data assimilation of sea surface temperature information.

scholarly journals A Comparative Study on Performance of Analysis Nudging and 3DVAR in Simulation of a Heavy Rainfall Event Using WRF Modeling System

2012 ◽  
Vol 2012 ◽  
pp. 1-21 ◽  
Author(s):  
Ashish Routray ◽  
Krishna K. Osuri ◽  
Makarand A. Kulkarni
Keyword(s):  
Data Assimilation ◽  
Heavy Rainfall ◽  
Three Dimensional ◽  
Correlation Coefficients ◽  
Rainfall Event ◽  
Initial Time ◽  
Heavy Rainfall Event ◽  
Synoptic Features ◽  
Wrf Modeling ◽  
Data Assimilation Technique

The present study focuses on the performance-based comparison of simulations carried out using nudging (NUD) technique and three-dimensional variational (3DVAR) data assimilation system (3DV) of a heavy rainfall event occurred during 25–28 June 2005 along the west coast of India. The Indian conventional and nonconventional observations are used in the 3DV experiment. Three numerical experiments are conducted using WRF modeling system, the model is integrated upto 54 hours from the initial time 0000 UTC of 25 June 2005. It is noticed that the meteorological parameters are improved in the resulting high-resolution analyses prepared by NUD and 3DV compared to without data assimilation experiment (i.e., called CNTL experiment). However, after the successful inclusion of observations using the 3DVAR data assimilation technique, the model is able to simulate better structure of the convective organization as well as prominent synoptic features associated with the mid-tropospheric cyclones (MTC) than the NUD experiment and well correlated with the observations. The simulated location and intensity of rainfall is also improved in 3DV simulation as compared with other experiments. Similar results are noticed in the root mean squar errors, correlation coefficients, and Equitable Threat Scores between TRMM and model simulated rainfall for all the three experiments.

scholarly journals Extended and refined multi sensor reanalysis of total ozone for the period 1970–2012

2015 ◽  
Vol 8 (3) ◽  
pp. 3283-3319 ◽  
Author(s):  
R. J. van der A ◽  
M. A. F. Allaart ◽  
H. J. Eskes
Keyword(s):  
Data Assimilation ◽  
Total Ozone ◽  
Transport Model ◽  
Forecast Error ◽  
Satellite Observations ◽  
Weather Forecasts ◽  
Stratospheric Temperature ◽  
The Individual ◽  
Data Assimilation Technique ◽  
Ozone Column

Abstract. The ozone multi-sensor reanalysis (MSR) is a multi-decadal ozone column data record constructed using all available ozone column satellite datasets, surface Brewer and Dobson observations and a data assimilation technique with detailed error modelling. The result is a high-resolution time series of 6 hourly global ozone column fields and forecast error fields that may be used for ozone trend analyses as well as detailed case studies. The ozone MSR is produced in two steps. First, the latest reprocessed versions of all available ozone column satellite datasets are collected, and are corrected for biases as function of solar zenith angle, viewing angle, time (trend), and stratospheric temperature using Brewer/Dobson ground measurements from the World Ozone and Ultraviolet Radiation Data Centre (WOUDC; http://www.woudc.org/). Subsequently the debiased satellite observations are assimilated within the ozone chemistry and data assimilation model TMDAM driven by meteorological analyses of the European Centre for Medium-Range Weather Forecasts (ECMWF). The MSR2 (MSR version 2) reanalysis upgrade described in this paper consists of an ozone record for the 43 year period 1970–2012. The chemistry-transport model and data assimilation system have been adapted to improve the resolution, error modelling and processing speed. BUV satellite observations have been included for the period 1970–1977. The total record is extended with 13 years compared to the first version of the ozone multi sensor reanalysis, the MSR1. The latest total ozone retrievals of 15 satellite instruments are used: BUV-Nimbus4, TOMS-Nimbus7, TOMS-EP, SBUV-7, -9, -11, -14, -16, -17, -18, -19, GOME, SCIAMACHY, OMI and GOME-2. The resolution of the model runs, assimilation and output is increased from 2° x 3° to 1° x 1°. The analysis is driven by three-hourly meteorology from the ERA-interim reanalysis of ECMWF starting from 1979, and ERA-40 before that date. The chemistry parameterization has been updated. The performance of the MSR2 analysis is studied with the help of observation-minus-forecast (OmF) departures from the data assimilation, by comparisons with the individual station observations and with ozone sondes. The OmF statistics show that the mean bias of the MSR2 analyses is less than 1% with respect to debiased satellite observations after 1979.

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