Automated retrievals of volcanic ash and dust cloud properties from upwelling infrared measurements

Abstract

A fully automated, globally applicable algorithm to retrieve ash and dust cloud properties from infrared satellite measurements is presented. The algorithm, which will serve as the official operational algorithm of the next generation Geostationary Operational Environmental Satellite (GOES-R), utilizes an optimal estimation framework that allows uncertainties in the measurements and forward model to be taken into account and uncertainty estimates for each of the retrieved parameters to be determined. The retrieval approach is globally applicable because background atmospheric water vapor, surface temperature, and surface emissivity are explicitly accounted for on a pixel-by-pixel basis. The retrieval is demonstrated using the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on-board the Second Generation Meteosat. Ash clouds from the 2010 eruption of Eyjafjallajökull in Iceland and the 2010 eruption of Soufriere Hills in the eastern Caribbean and a Saharan dust cloud were analyzed, and the accuracy of the retrieval was evaluated using spaceborne lidar measurements. The validation analysis shows that the retrieved ash/ dust cloud height, cloud emissivity, and effective particle radius generally agrees well with lidar measurements, especially when volcanic ash clouds are assumed to be composed of andesite and dust clouds composed of kaolinite.

A probabilistic inverse method for volcanic ash dispersion modelling

“…Both deterministic and probabilistic forecasts clearly predict ash at the location of the calipso track. Additionally, regions identified as containing ash by the mtsat-2 satellite using the algorithm of Pavolonis [11] are shown in Figure 4. These are also in good agreement with the forecasts, although clearly there are regions predicted to have ash by the dispersion model but which are not predicted by the …”

“…In such situations ash is detected by other methods such the brightness difference temperature method [10] and the algorithm of Pavolonis [11]. Ash was only detected by these methods from about 1930 utc.…”

Stereoscopic Estimation of Volcanic Ash Cloud-Top Height from Two Geostationary Satellites

“…Backward trajectory modeling [27,28] + estimate possible even for clouds drifted away from the source -requires wind field data for a large area and a reliable trajectory model (e.g., turbulence not easy to handle); homogenous wind field results with high uncertainty of the source height Brightness temperature [3,17,27] + easy to apply, possible with instruments with a short revisit time -requires atmospheric profile and emissivity of the cloud; assumption of thermal equilibrium; problems around tropopause O 2 A-band absorption [29] + high accuracy -requires high spectral resolution data (not available on many satellites, long revisit time); good performance only over dark surfaces; requires radiative transfer modeling; daytime only CO 2 absorption [21,30,31] + good performance also with semi-transparent clouds -accurate only in the high levels of the troposphere; problems around tropopause Shadow length [3,32] + easy to apply; requires no additional data -possible only during daytime; retrieves the height of the cloud horizontal edge and not its top Stereoscopy [17,23,[33][34][35][36][37] + high accuracy; requires no additional data; based on geometryÑno problems in the case of ash reaching the stratosphere -requires simultaneous data from two different viewpoints Optimal estimation [38][39][40] + includes error estimaté requires atmospheric profiles, ash optical properties, and radiative transfer…”

Earth Observations from DSCOVR EPIC Instrument

“…7) has been made (Herman et al 2018) with the assimilated ozone product from the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), based on Microwave Limb Sounder (MLS) and total column ozone from the OMI. All of the structures in the EPIC ozone retrieval are present in the MERRA-2 (Bluth et al 1993(Bluth et al , 1997Carn et al 2003Carn et al , 2015Carn et al , 2016Carn and Krotkov 2016;Guo et al 2004;Krotkov et al 1999a,b;Krueger 1983;Krueger et al 1995Krueger et al , 2000Li et al 2017;Pavolonis et al 2013;Prata 1989;Prata and Kerkmann 2007;Prata et al 2003Realmuto 2000;Wen and Rose 1994 (Prata 1989;Realmuto 2000;Ackerman et al 2008;Pavolonis et al 2013). DSCOVR EPIC provides the first opportunity to observe transient volcanic clouds globally from L1.…”