# Presentation: 352903

## Assimilation of Aerosol Data from Multiple Data Sets into a Numerical Aerosol Model

**Assimilation of Aerosol Data from Multiple Data Sets into a Numerical Aerosol Model**
- by
- Robert W. Bergstrom1, Howard Houben1, Philip B. Russell2, Robert Chatfield2, Hong Guan1
- 1 Bay Area Environmental Research Institute
- 2NASA Ames Research Center

**Overview**
- Changes in atmospheric gases and aerosols by human activity can result in anthropogenic climate change.  Prediction of the changes by numerical climate models is an important area of current research.  A promising new technique is to assimilate satellite data into the numerical model to obtain a better scientific understanding of aerosol and gas sources, evolution, and transport.  There are number of ways to assimilate this satellite data.  One way is to use a derived satellite product (such as the aerosol optical thickness or gas amount) and nudge the model trajectory towards the observations; a second way is to use the observed radiance.  Experience with numerical weather prediction based on satellite soundings suggests that the second way will ultimately be more successful.  In particular, this approach allows the consistent use of data from multiple platforms.  In addition, it will allow the observational determination of forecast error statistics (based on the subgrid-scale covariances of the innovation vector) that are required to optimize the assimilation process.
- We are utilizing an existing aerosol and gas climate model (CAM 3) along with a newly improved radiative transfer model to address this problem. The adjoint of this radiative transfer model performs efficient retrievals of the information to the model grid.  At present, we are using these capabilities in a sequential assimilation fashion, to improve the modeling of aerosol transport.  Our study concentrates on the INTEX A/B field experiments and will ultimately incorporate data from geostationary satellites, polar-orbiting satellites, and suborbital platforms.  Using all the data, we can utilize an adjointed transport model that will allow these data to contribute to a variational assimilation that will better determine aerosol sources.
- In this poster we show the difference between a purely forward radiative transfer model estimate of the aerosol properties and a data assimilation approach that uses the adjoint radiative transfer model to retrieve the aerosol information.

**Data**

**Example:** Vertical level structure of CAM 3.0

**Numerical Model**

**Example:**  ICARTT / INTEX A

** ****Forward Model Retrieval**
- This is the usual method of analysis of the data. The steps are to compute the measured variable for a range of parameters.  Then using a table look-up type approach (with some kind of best fit) determine the parameters.  Often there are more unknowns than measurements so many parameters must be assumed - such as a typical aerosol model and the irradiance correlated with the optical depth (satellite retrieval).  As the number of parameters to be retrieved grows, the table becomes multi-dimensional and it is difficult to find the optimal solution by guessing.  Furthermore, this approach does not take model errors into account

**Example: Determination of Aerosol Single Scattering Albedo:  ICART/INTEX  July 21st**
- Using spectral radiative flux measurements and aerosol optical depth measurements from the J31 aircraft, the aerosol spectral single scattering albedo  and absorption optical depth can be estimated by means of a radiative transfer model.

**2.  Data Assimilation Technique**
- In this case, two forward models are needed.  One an aerosol Chemistry and Transport Model and two a radiative transfer model. Both models are linearized (effectively incorporating the very high dimension look-up table into the computer code).  In addition, adjoint versions of the models are prepared.  These essentially calculate the gradients in the look-up table and thereby allow minimization algorithms to navigate to an optimal solution.  Data from all sources can be incorporated in the solution.  Since the problem is then over-determined, the method is essentially a chi-square minimization.  However, especially when the transport model is included, it is important to allow for model forecast errors.  In this case the chi-square statistic includes not only innovations (residuals between model predictions and observations), but also increments (differences between the analyses and model forecasts). We show some results of a pilot study in which 13 channels of data (from the NASA Ames Airborne Tracking Sunphotometer) are used to determine column aerosol amounts for 1900 cloud-free spectral measurements during an INTEX A flight.
**Determination of the aerosol components**
- Here we assume that we know the spectral absorption and scattering properties of the aerosol components and use the data to determine the amount of black carbon, sulfate, dust and sea salt.
- Typical Aerosol Spectral Radiative Property Dependence
- 		     Scattering Factor 	        Absorption	                   Asymmetry
- Sulfate			 
- Black Carbon		 
- Organic Carbon	 	 
- Mineral Dust		 
- Sea Salt			 

- Example of resolution possible with CAM 3.0

**Figure 1** shows sample aerosol optical depth spectra (red curves) with the spectral fits (blue symbols) from the data assimilation procedure.

**Results**

**Figure 2** shows the aerosol type solutions for the 1900 measured spectra.  The vertical grey lines mark the times of the spectra shown in Figure 1.  Based on the spectral shapes, there is no clear evidence of absorbing aerosol on this flight.  The time periods when large sea salt column amounts are retrieved are in the vicinity of cloud-contaminated observations.  It should be noted that this data set does not distinguish between seal salt and cloud particles (which are both spectrally flat).

**Figure 3** plots a measure of goodness of fit (the chi-square statistic) as a function of total aerosol optical depth.  (The ability to perform statistical analyses of the solutions is an important benefit of the data assimilation approach.)  There is a clear deterioration of the quality of our fits with higher optical depths.  At this time, we attribute this to improper cloud screening (and we are in the process of re-evaluating the data in this light).