Improving volcanic ash predictions with the HYSPLIT dispersion model by assimilating MODIS satellite retrievals

Abstract

Abstract. Currently, the National Oceanic and Atmospheric Administration (NOAA) National Weather Service (NWS) runs the HYSPLIT dispersion model with a 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, flight information centers, and others. This research aims to provide quantitative forecasts of ash distributions generated by objectively and optimally estimating the volcanic ash source strengths, vertical distribution, and temporal variations using an observation-modeling inversion technique. In this topdown approach, a cost functional is defined to quantify the differences between the model predictions and the satellite measurements of column-integrated ash concentrations weighted by the model and observation uncertainties. Minimizing this cost functional by adjusting the sources provides the volcanic ash emission estimates. As an example, MODIS (Moderate Resolution Imaging Spectroradiometer) satellite retrievals of the 2008 Kasatochi volcanic ash clouds are used to test the HYSPLIT volcanic ash inverse system. Because the satellite retrievals include the ash cloud top height but not the bottom height, there are different model diagnostic choices for comparing the model results with the observed mass loadings. Three options are presented and tested. Although the emission estimates vary significantly with different options, the subsequent model predictions with the different release estimates all show decent skill when evaluated against the unassimilated satellite observations at later times.Among the three options, integrating over three model layers yields slightly better results than integrating from the surface up to the observed volcanic ash cloud top or using a single model layer. Inverse tests also show that including the ash-free region to constrain the model is not beneficial for the current case. In addition, extra constraints on the source terms can be given by explicitly enforcing "no-ash" for the atmosphere columns above or below the observed ash cloud top height. However, in this case such extra constraints are not helpful for the inverse modeling. It is also found that simultaneously assimilating observations at different times produces better hindcasts than only assimilating the most recent observations.

Inverse modeling of fire emissions constrained by smoke plume transport using HYSPLIT dispersion model and geostationary satellite observations

“…Daiichi Nuclear Power plant accident in 2011 (Chai et al, 2015). It was then modified to estimate the volcanic ash source strengths, vertical distribution, and temporal variations by assimilating MODIS satellite retrievals of volcanic ash clouds while using ash cloud top height information as well (Chai et al (2017). It was found that simultaneously assimilating observations at different times produces better hindcasts than only assimilating the most recent observations.…”

Atmospheric Dispersion Modelling at the London VAAC: A Review of Developments since the 2010 Eyjafjallajökull Volcano Ash Cloud

“…To generate the VAAs and VAGs, the VAACs use both observations and modelling systems. Atmospheric dispersion models, initialized with a set of source terms that describe the release of ash into the atmosphere, are used to forecast the onwards transport and deposition of the ash cloud (e.g., [5][6][7][8][9][10][11]). Numerical Weather Prediction (NWP) models are used to simulate the atmospheric conditions and provide the meteorological fields needed to drive the dispersion model.…”

Weak-constraint inverse modeling using HYSPLIT Lagrangian dispersion model and Cross Appalachian Tracer Experiment (CAPTEX) observations – Effect of including model uncertainties on source term estimation

“…Another active field for STE applications is the estimation of the volcanic ash emissions. Many attempts have been made for several major volcano eruptions (Wen and Rose, 1994;Prata and Grant, 2001;Wilkins et al, 2014Wilkins et al, , 2016Chai et al, 2017).…”

“…Reanalysis (NARR) at 32-km (Mesinger et al, 2006 To avoid running the HYSPLIT modeling repeatedly, a TCM is generated similar to the previous HYSPLIT inverse modeling 5 studies (Chai et al, 2015(Chai et al, , 2017. As described in Draxler and Rolph (2012), independent simulations are performed with a unit emission rate from each source location and a pre-defined time segment.…”