Data assimilation of FY-4A dust aerosol observations for the CUACE/dust forecasting system

2020 ◽  
Author(s):  
Tao Niu ◽  
Xiaoye Zhang ◽  
Shanling Gong ◽  
Yaqiang Wang ◽  
Hongli Liu ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Atmospheric Chemistry ◽  
Three Dimensional ◽  
Forecast Model ◽  
Short Term ◽  
Operational Forecasts ◽  
Forecasting System ◽  
Dust Loading ◽  
Data Assimilation System ◽  
Sand And Dust Storm

<p>A data assimilation system (DAS) was developed for the Chinese Unified Atmospheric Chemistry Environment– Dust (CUACE/Dust) forecast system and applied in the operational forecasts of sand and dust storm (SDS) in spring in Asia. The system is based on a three dimensional variational method (3D-Var) and uses extensively the measurements of surface visibility (phenomena) and dust loading retrieval from the Chinese geostationary satellite FY-2C. By a number of case studies, the DAS was found to provide corrections to both under- and over-estimates of SDS, presenting a major improvement to the forecasting capability of CUACE/Dust in the short-term variability in the spatial distribution and intensity of dust concentrations in both source regions and downwind areas.  By now The DAS was upgrade to assimilate FY-4A dust aerosol observations. The seasonal mean Threat Score (TS) over the East Asia in spring increased when DAS was used. The forecast results with DAS usually agree with the dust loading retrieved from FY and visibility distribution from surface meteorological stations, which indicates that the 3D-Var method is very powerful by the unification of observation and numerical model to improve the performance of forecast model.</p>

scholarly journals Data assimilation of dust aerosol observations for the CUACE/dust forecasting system

2008 ◽  
Vol 8 (13) ◽  
pp. 3473-3482 ◽  
Author(s):  
T. Niu ◽  
S. L. Gong ◽  
G. F. Zhu ◽  
H. L. Liu ◽  
X. Q. Hu ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Atmospheric Chemistry ◽  
Three Dimensional ◽  
Forecast Model ◽  
System A ◽  
Operational Forecasts ◽  
Forecasting System ◽  
Dust Loading ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. A data assimilation system (DAS) was developed for the Chinese Unified Atmospheric Chemistry Environment – Dust (CUACE/Dust) forecast system and applied in the operational forecasts of sand and dust storm (SDS) in spring 2006. The system is based on a three dimensional variational method (3D-Var) and uses extensively the measurements of surface visibility (phenomena) and dust loading retrieval from the Chinese geostationary satellite FY-2C. By a number of case studies, the DAS was found to provide corrections to both under- and over-estimates of SDS, presenting a major improvement to the forecasting capability of CUACE/Dust in the short-term variability in the spatial distribution and intensity of dust concentrations in both source regions and downwind areas. The seasonal mean Threat Score (TS) over the East Asia in spring 2006 increased from 0.22 to 0.31 by using the data assimilation system, a 41% enhancement. The forecast results with DAS usually agree with the dust loading retrieved from FY-2C and visibility distribution from surface meteorological stations, which indicates that the 3D-Var method is very powerful by the unification of observation and numerical model to improve the performance of forecast model.

scholarly journals Data assimilation of dust aerosol observations for CUACE/Dust forecasting system

2007 ◽  
Vol 7 (3) ◽  
pp. 8309-8332 ◽  
Author(s):  
T. Niu ◽  
S. L. Gong ◽  
G. F. Zhu ◽  
H. L. Liu ◽  
X. Q. Hu ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Atmospheric Chemistry ◽  
Three Dimensional ◽  
System A ◽  
Source Regions ◽  
Operational Forecasts ◽  
Forecasting System ◽  
Dust Loading ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. A data assimilation system (DAS) was developed for the Chinese Unified Atmospheric Chemistry Environment – Dust (CUACE/Dust) forecast system and applied in the operational forecasts of sand and dust storm (SDS) in spring 2006. The system is based on a three dimensional variational method (3D-Var) and uses extensively the measurements of surface visibility and dust loading retrieval from the Chinese geostationary satellite FY-2C. The results show that a major improvement to the capability of CUACE/Dust in forecasting the short-term variability in the spatial distribution and intensity of dust concentrations has been achieved, especially in those areas far from the source regions. The seasonal mean Threat Score (TS) over the East Asia in spring 2006 increased from 0.22 to 0.31 by using the data assimilation system, a 41% enhancement. The assimilation results usually agree with the dust loading retrieved from FY-2C and visibility distribution from surface meteorological stations, which indicates that the 3D-Var method is very powerful for the unification of observation and numerical modeling results.

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 A GSI-Based Coupled EnSRF–En3DVar Hybrid Data Assimilation System for the Operational Rapid Refresh Model: Tests at a Reduced Resolution

2014 ◽  
Vol 142 (10) ◽  
pp. 3756-3780 ◽  
Author(s):  
Yujie Pan ◽  
Kefeng Zhu ◽  
Ming Xue ◽  
Xuguang Wang ◽  
Ming Hu ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Hybrid System ◽  
Control Variable ◽  
Three Dimensional ◽  
Horizontal Resolution ◽  
Precipitation Forecast ◽  
Background Error ◽  
Hybrid Data Assimilation ◽  
Noticeable Improvement ◽  
Forecasting System

Abstract A coupled ensemble square root filter–three-dimensional ensemble-variational hybrid (EnSRF–En3DVar) data assimilation (DA) system is developed for the operational Rapid Refresh (RAP) forecasting system. The En3DVar hybrid system employs the extended control variable method, and is built on the NCEP operational gridpoint statistical interpolation (GSI) three-dimensional variational data assimilation (3DVar) framework. It is coupled with an EnSRF system for RAP, which provides ensemble perturbations. Recursive filters (RF) are used to localize ensemble covariance in both horizontal and vertical within the En3DVar. The coupled En3DVar hybrid system is evaluated with 3-h cycles over a 9-day period with active convection. All conventional observations used by operational RAP are included. The En3DVar hybrid system is run at ⅓ of the operational RAP horizontal resolution or about 40-km grid spacing, and its performance is compared to parallel GSI 3DVar and EnSRF runs using the same datasets and resolution. Short-term forecasts initialized from the 3-hourly analyses are verified against sounding and surface observations. When using equally weighted static and ensemble background error covariances and 40 ensemble members, the En3DVar hybrid system outperforms the corresponding GSI 3DVar and EnSRF. When the recursive filter coefficients are tuned to achieve a similar height-dependent localization as in the EnSRF, the En3DVar results using pure ensemble covariance are close to EnSRF. Two-way coupling between EnSRF and En3DVar did not produce noticeable improvement over one-way coupling. Downscaled precipitation forecast skill on the 13-km RAP grid from the En3DVar hybrid is better than those from GSI 3DVar analyses.

scholarly journals Impact of Lightning Data Assimilation on Forecasts of a Leeward Slope Precipitation Event in the Western Margin of the Junggar Basin

2021 ◽  
Vol 13 (18) ◽  
pp. 3584
Author(s):  
Peng Liu ◽  
Yi Yang ◽  
Yu Xin ◽  
Chenghai Wang
Keyword(s):  
Water Vapor ◽  
Data Assimilation ◽  
Three Dimensional ◽  
Junggar Basin ◽  
Precipitation Event ◽  
Short Term ◽  
Western Margin ◽  
Precipitation Prediction ◽  
Analysis Experiment ◽  
Short Term Forecasting

A moderate precipitation event occurring in northern Xinjiang, a region with a continental climate with little rainfall, and in leeward slope areas influenced by topography is important but rarely studied. In this study, the performance of lightning data assimilation is evaluated in the short-term forecasting of a moderate precipitation event along the western margin of the Junggar Basin and eastern Jayer Mountain. Pseudo-water vapor observations driven by lightning data are assimilated in both single and cycling analysis experiments of the Weather Research and Forecast (WRF) three-dimensional variational (3DVAR) system. Lightning data assimilation yields a larger increment in the relative humidity in the analysis field at the observed lightning locations, and the largest increment is obtained in the cycling analysis experiment. Due to the increase in water vapor content in the analysis field, more suitable thermal and dynamic conditions for moderate precipitation are obtained on the leeward slope, and the ice-phase and raindrop particle contents increase in the forecast field. Lightning data assimilation significantly improves the short-term leeward slope moderate precipitation prediction along the western margin of the Junggar Basin and provides the best forecast skill in cycling analysis experiments.

scholarly journals CUACE/Dust – an integrated system of observation and modeling systems for operational dust forecasting in Asia

2007 ◽  
Vol 7 (4) ◽  
pp. 10323-10342 ◽  
Author(s):  
S. L. Gong ◽  
X. Y. Zhang
Keyword(s):  
East Asia ◽  
Atmospheric Chemistry ◽  
Monitoring Network ◽  
Integrated System ◽  
Dust Aerosol ◽  
China Meteorological Administration ◽  
Source Regions ◽  
Operational Forecasts ◽  
Forecasting System ◽  
Aerosol Module

Abstract. An integrated sand and dust storm (SDS) forecasting system – CUACE/Dust (the Chinese Unified Atmospheric Chemistry Environment for Dust) has been developed, which consists of a comprehensive dust aerosol module with emission, dry/wet depositions and other atmospheric dynamic processes, and a data assimilation system (DAS) using observational data from the CMA (China Meteorological Administration) ground dust monitoring network and retrieved dust information from a Chinese geostationary satellite – FY-2C. This is the first time that a combination of surface network observations and satellite retrievals of the dust aerosol has been successfully used in the real time operational forecasts in East Asia through a DAS. During its application for the operational SDS forecasts in East Asia for spring 2006, this system captured the major 31 SDS episodes observed by both surface and satellite observations. Analysis shows that the seasonal mean threat score (TS) for 0–24 h forecast over the East Asia in spring 2006 increased from 0.22 to 0.31 by using the DAS, a 41% enhancement. The time series of the forecasted dust concentrations for a number of representative stations for the whole spring 2006 were also evaluated against the surface PM10 monitoring data, showing a very good agreement in terms of the SDS timing and magnitudes near source regions where dust aerosols dominate. This is a summary paper for a special issue of ACP featuring the development and results of the forecasting system.

scholarly journals A THREE-DIMENSIONAL AEROSOL VARIATIONAL DATA ASSIMILATION SYSTEM FOR AIRCRAFT AND SURFACE OBSERVATIONS

2018 ◽  
Vol XLII-3 ◽  
pp. 2215-2218
Author(s):  
Z. Zang ◽  
X. Pan ◽  
W. You ◽  
Y. Liang
Keyword(s):  
Data Assimilation ◽  
Local Time ◽  
Three Dimensional ◽  
Surface Concentration ◽  
Variational Data Assimilation ◽  
Weather Research And Forecasting ◽  
Aircraft Observations ◽  
Data Assimilation System ◽  
Surface Observations ◽  
Assimilation System

A three-dimensional variational data assimilation system is implemented within the Weather Research and Forecasting/Chemistry model, and the control variables consist of eight species of the Model for Simulation Aerosol Interactions and Chemistry scheme. In the experiments, the three-dimensional profiles of aircraft speciated observations and surface concentration observations acquired during the California Research at the Nexus of Air Quality and Climate Change field campaign are assimilated. The data assimilation experiments are performed at 02:00 local time 2 June 2010, assimilating surface observations at 02:00 and aircraft observations from 01:30 to 02:30 local time. The results show that the assimilation of both aircraft and surface observations improves the subsequent forecasts. The improved forecast skill resulting from the assimilation of the aircraft profiles persists a time longer than the assimilation of the surface observations, which suggests the necessity of vertical profile observations for extending aerosol forecasting time.

Short-term wind forecast of a data assimilation/weather forecasting system with wind turbine anemometer measurement assimilation

2017 ◽  
Vol 107 ◽  
pp. 340-351 ◽  
Author(s):  
William Y.Y. Cheng ◽  
Yubao Liu ◽  
Alfred J. Bourgeois ◽  
Yonghui Wu ◽  
Sue Ellen Haupt
Keyword(s):  
Data Assimilation ◽  
Wind Turbine ◽  
Weather Forecasting ◽  
Short Term ◽  
Forecasting System

scholarly journals CUACE/Dust – an integrated system of observation and modeling systems for operational dust forecasting in Asia

2008 ◽  
Vol 8 (9) ◽  
pp. 2333-2340 ◽  
Author(s):  
S. L. Gong ◽  
X. Y. Zhang
Keyword(s):  
East Asia ◽  
Atmospheric Chemistry ◽  
Monitoring Network ◽  
Integrated System ◽  
Dust Aerosol ◽  
China Meteorological Administration ◽  
Source Regions ◽  
Operational Forecasts ◽  
Forecasting System ◽  
Aerosol Module

Abstract. An integrated sand and dust storm (SDS) forecasting system – CUACE/Dust (Chinese Unified Atmospheric Chemistry Environment for Dust) has been developed, which consists of a comprehensive dust aerosol module with emission, dry/wet depositions and other atmospheric dynamic processes, and a data assimilation system (DAS) using observational data from the CMA (China Meteorological Administration) ground dust monitoring network and retrieved dust information from a Chinese geostationary satellite – FY-2C. This is the first time that a combination of surface network observations and satellite retrievals of the dust aerosol has been successfully used in the real time operational forecasts in East Asia through a DAS. During its application for the operational SDS forecasts in East Asia for spring 2006, this system captured the major 31 SDS episodes observed by both surface and satellite observations. Analysis shows that the seasonal mean threat score (TS) for 0–24 h forecast over the East Asia in spring 2006 increased from 0.22 to 0.31 by using the DAS, a 41% enhancement. The time series of the forecasted dust concentrations for a number of representative stations for the whole spring 2006 were also evaluated against the surface PM10 monitoring data, showing a very good agreement in terms of the SDS timing and magnitudes near source regions where dust aerosols dominate. This is a summary paper for a special issue of ACP featuring the development and results of the forecasting system.

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