Tianfeng Chai 1,2, Alice Crawford 1,2, Barbara Stunder 1, Roland Draxler 1, Michael J. Pavolonis 3, Ariel Stein 1 1.NOAA Air Resources Laboratory, College Park, MD 2.Cooperative Institute for Climate and Satellites, University of Maryland, College Park, Maryland 3.NOAA Center for Satellite Applications and Research, Madison, WI Improve volcanic ash simulation with Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) dispersion model by assimilating satellite observations Motivation Currently NOAA National Weather Service (NWS) runs the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) dispersion model with unit mass release rate to predict the transport and dispersion of volcanic ash. The model predictions provide information for the Volcanic Ash Advisory Centers (VAAC) to issue advisories to meteorological watch offices, area control centers, and flight information centers. Quantitative forecasts can be generated by estimating the volcanic ash source terms based on the satellite retrievals of volcanic ash mass loadings. Approaches 1.An emission inversion system based on the HYSPLIT dispersion model and a three-dimensional variational data assimilation (3D- Var) approach was able to recover the 2011 Fukushima nuclear accident radionuclide releases using global air concentration measurements [1]. This emission inversion system is further extended here to assimilate satellite observations of volcanic ash. 2.MODIS (MOderate Resolution Imaging Spectroradiometer) volcanic ash mass loadings are used to estimate the volcanic eruption source terms distributed at various time and heights. 3.The impact of such satellite-observation-constrained source terms on the forecasts will be assessed using the subsequent observations which are not assimilated.. HYSPLIT mass loading operator and TCM A Fortran code that generates a Transfer Coefficient Matrix (TCM), "sat2array.f", reads in multiple HYSPLIT dispersion runs where volcanic ashes are released from different heights and at different time. The TCM records the sensitivities of the observed ash mass loadings with respect to all independent HYSPLIT dispersion runs. Using the 2008 Kasatochi eruption as an example, Figure 2 shows the average TCM for the HYSPLIT predictions of the two volcanic ash mass loading retrievals shown in Figure 1. Summary, discussion, and future work 1.An inverse system based on HYSPLIT has been built to solve the effective release rates by assimilating satellite observations; 2.A Fortran code, "sat2array.f", has been completed to generate HYSPLIT TCM by matching model concentrations to the observed mass loadings and ash cloud heights with several “-z” options; 3.Different meteorological fields, including NARR, GDAS, and ECMWF, have been tested. Both the HYSPLIT results and the inversion source terms are significantly affected by the choices; 4.In the current MODIS mass loading data, there is no differentiation between "no ash" region or the region blocked by clouds. Deciding on the “no ash” region and utilizing such information in the inverse modeling will be further investigated; 5.We currently assume four different particle sizes (0.6µm, 0.8%; 2µm, 6.8%; 6µm, 25.4%; 20µm, 67%) at all releases time/location. It might not be realistic and may require adjustment. We will explore methods to utilize the MODIS effective radius in the future; 6.The effect of the “optimal” release rates on the future volcanic ash plume predictions will be evaluated. In addition, tests will be extended to more volcanic eruptions. Emission Inversion with HYSPLIT The inverse problem is formulated under a variational data assimilation framework. The source terms are found by minimizing a cost functional defined in Eq.(1), which integrates the differences between model predictions and observations, source deviations from the a priori, as well as a smoothness penalty term. Here q kt is a discrete 2-D source term that varies with height (k th layer) and time (t). L m are the HYSPLIT mass loadings corresponding to the mth satellite retrieval point L m o. σ kt 2 and ε m 2 represent the uncertainties of the a priori source term q kt and the satellite observation L m o. The smoothness penalty term P smooth can be used to adjust the final solution and make the modified minimization problem better conditioned. Figure 1. MODIS volcanic ash mass loadings (left) and ash plume top heights (right) of the 2008 Kasatochi eruption in Aleutian Islands (shown with “+”). Top: 13:00-14:00 UTC on Aug 8, 2008; bottom: 00:00-01:00 UTC on Aug 9, Acknowledgement This research is in response to requirements and funding by the Federal Aviation Administration (FAA). The views expressed are those of the authors and do not necessarily represent the official policy or position of the FAA.. For questions and comments, please Figure 3. MODIS volcanic ash mass loadings and their HYSPLIT counterparts form two test cases. MODIS observations are from 13:00-14:00Z on Aug 8, 2008 and 00:00-01:00Z on Aug. 9, 2008 shown in Figure 1. HYSPLIT counterparts are vertically integrated concentrations using the “optimal” release rates obtained from the current inverse modeling system. The “sat2array” code with “z-2” option is used to map the 3-D HYSPLIT concentrations to the satellite mass loadings. Case 1 (left) has original release rates and mass loadings as control and metric variables in the inverse modeling setup. Case 2 (right) has logarithmic operations for both control and metric variables when minimizing the cost function in the inverse model to get the optimal release rates. Figure 2. Contour plot of the average sensitivities for the HYSPLIT predictions of the mass loadings in Figure 1 with respect to the source terms at different height and time. The sensitivity unit is hr/m 2. GDAS meteorological fields are used. The HYSPLIT mass loadings are integrated over the entire domain height, i.e. with “-z-2” option for "sat2array". Four different particle sizes, 0.6 µm, 2.0 µm, 6.0 µm, and 20.0µm, contributing 0.8%, 6.8%, 25.4%, and 67% to the volcanic ash mass, respectively, at all source locations and release intervals. References: 1. Source term estimation using air concentration measurements and Lagrangian dispersion model – Experiments with pseudo and real cesium-137 observations from the Fukushima nuclear accident, Chai, T., R. R. Draxler, and A. Stein, Atmospheric Environment, 106, pp , doi: /j.atmosenv , 2015 Satellite observations of volcanic ash The MODIS satellite retrievals of 2008 Kasatochi volcanic ash clouds are used here as an example. Fig. 1 shows both the mass loadings and ash cloud top heights from the first two MODIS retrievals. In addition, MODIS retrievals include effective particle radius information. Emission inversion results Using the TCM obtained, e.g., the one shown in Figure 2, the cost function defined in Eq.1 can be minimized to obtain the optimal source terms. Such objectively quantified source terms are expected to generate better volcanic ash predictions at later times. Figure 3 shows the comparisons of the observed and predicted mass loadings using the optimal source terms from two test cases with different minimization schemes.