Data Assimilation

Data Assimilation for "Chemical Weather"

assimilation flow diagram

We are developing a framework which combines satellite observations, chemical transport models and data assimilation techniques into a “chemical weather” analysis and prediction system similar to that used for weather forecasting. The data assimilation system uses a variety of satellite products of atmospheric composition with a particuar focus on Terra/MOPITT CO and  Met-Op IASI CO and O3. Two coupled atmospheric chemistry transport models covering a wide range of spatial and temporal scales and both employing the same chemical scheme are used: the global Community Atmosphere Model with Chemistry (CAM-Chem) and the regional Weather Research and Forecasting Model with Chemistry (WRF-Chem). Data assimilation is used to integrate the different observational and modeling capabilities and to provide an optimal estimate of the 4D-distribution of atmospheric pollutants. We use the Data Assimilation Research Testbed (DART) developed at NCAR, which is a community software tool designed for conducting data assimilation studies over a variety of models and observations.

Recent Results 

Demonstration of a Quasi-Real-Time Air Quality Forecast/Data Assimilation System  Using FRAPPÉ and DISCOVER-AQ Field Data


 WRF-Chem DART for U.S.Air quality (AQ) divisions and districts around the country have the important task to protect the public from poor air quality and alert the public when a high pollution day is predicted. Forecasters often rely on models for their forecasts but large uncertainties remain due to uncertainties in emissions, model chemistry, pollutant transport and other model errors. These uncertainties are often responsible for inaccurate AQ forecasts and inaccurate warnings.  Studies have shown that the assimilation of satellite observations of atmospheric trace gases and aerosols can significantly improve AQ forecasts. We are developing and testing a real-time chemical weather forecast system based on the regional chemical Weather Research and Forecast Model with Chemistry (WRF-Chem), and the assimilation of satellite and ground-based chemical observations with the NCAR/DAReS Data Assimilation Research Testbed (DART) ensemble adjustment Kalman filter (EAKF) system. The system is currently evaluated for assimilation of MOPITT CO, other satellite products will be added in the next step. We will use the vast array of observations from the NSF/State of Colorado FRAPPÉ and NASA DISCOVER-AQexperiments that took place in summer 2014 over Colorado to comprehensively test and evaluate WRF-Chem/DART issued AQ predictions. A demonstration of how an advanced forecasting system can improve AQ predictions will provide guidance to the AQ community on future directions in air quality modeling and predictions.   Model simulations are made possible by the 2015 NCAR Strategic Capability (NSC) Program.


Global Reanalysis of CO Observations

Global reanalysis of CO observations

A global reanalysis of CO observations is based on the joint assimilation of conventional meteorological observations and Measurement of Pollution in The Troposphere (MOPITT) multispectral CO retrievals. Under the Data Assimilation Research Testbed (DART) framework, the assimilation is done using an Ensemble Adjustment Kalman Filter technique (EAKF) within the Community Atmosphere Model with Chemistry (CAM-Chem). The first focus has been to assess improvements when the CO distribution is constrained by the assimilation, and investigate the resulting impacts to the chemical system and the non-linear chemistry-climate interactions in the global coupled full chemistry-climate Community Earth System Model (CESM). To do so, results for two twin assimilation experiments has been done for one year: 1) Control Run, where only 6-hourly meteorological observations are assimilated; and 2) MOPITT-Reanalysis, where MOPITT V5J CO profiles are jointly assimilated with meteorological observations. Diagnostics of MOPITT data assimilation demonstrate an efficient assimilation and a well-balanced global error budget. The evaluation has also been completed by four datasets of independent observations. Overall, the best improvements are found when the simulation errors are the greatest (i.e., during spring) and when the MOPITT V5J observations are the most sensitive and accurate (i.e., in the lower/middle troposphere over land). This is especially the case across the northern hemisphere (NH), where we find improvements in the bias and variability of the simulated CO and is illustrated to the right.  [Gaubert et al., Towards a chemical reanalysis in a coupled chemistry-climate model: An evaluation of MOPITT CO assimilation and its impact on tropospheric composition, submitted to J. Geophys. Res.]


Data Assimilation of Aerosol Optical Depth


We are using an emission inversion scheme developed by Saide et al. (2015a) to constrain biomass burning emissions from Central America using satellite aerosol optical depth (AOD). The system is being applied to study aerosol-cloud-radiation interactions that could impact tornado severity in the US (Saide et al., 2015b) for outbreaks in multiple years and at cloud resolving scales capable of resolving tornadic circulations.



We are developing and testing a real-time chemical weather forecast system based on WRF-Chem/DART - the regional chemical Weather Research and Forecast Model with Chemistry (WRF-Chem) and the NCAR/DAReS Data Assimilation Research Testbed (DART) - developed by Mizzi et al. (2016), to assimilate satellite and ground-based chemical observations with the ensemble adjustment Kalman filter (EAKF). WRF-Chem/DART introduced the assimilation of "compact phase space retrievals" (CPSRs) which have been shown to reduce the computational costs of assimilating retrievals between 25% and 50% without loss of forecast skill.  


Saide, P. E., Peterson, D., da Silva, A., Anderson, B., Ziemba, L. D., Diskin, G., Sachse, G., Hair, J., Butler, C., Fenn, M., Jimenez, J. L., Campuzano-Jost, P., Perring, A. E., Schwarz, J. P., Markovic, M. Z., Russell, P., Redemann, J., Shinozuka, Y., Streets, D. G., Yan, F., Dibb, J., Yokelson, R., Toon, O. B., Hyer, E., and Carmichael, G. R.: Revealing important nocturnal and day-to-day variations in fire smoke emissions through a multiplatform inversion, Geophysical research letters, 2015GL063737, 2015a.

Saide, P. E., Spak, S. N., Pierce, R. B., Otkin, J. A., Schaack, T. K., Heidinger, A. K., da Silva, A. M., Kacenelenbogen, M., Redemann, J., and Carmichael, G. R.: Central American biomass burning smoke can increase tornado severity in the U.S, Geophysical research letters, 42, 2014GL062826, 2015b.

Mizzi, A.P., A.F. Arellano, D.P. Edwards, J.L. Anderson, and G.G. Pfister:  Assimilating compact phase space retrievals of atmospheric composition with WRF-Chem/DART: a regional chemical transport/ensemble Kalman filter data assimilation system, Geosci. Model Dev., 9, 965-978, 2016.






ACOM | Atmospheric Chemistry Observations & Modeling