Volcanic ash modeling with the online NMMB-MONARCH-ASH v1.0 model: model description, case simulation, and evaluation

Marti, Alejandro; Folch, Arnau; Jorba, Oriol; Janjić, Zavisă

doi:10.5194/acp-17-4005-2017

Cited by 16 publications

(38 citation statements)

References 77 publications

(91 reference statements)

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“…The version (v1.0) of the NMMB-MONARCH model used here contributes to different model inter-comparisons like the International Cooperative for Aerosol Prediction (ICAP) initiative and the Sand and Dust Storm Warning Advisory and Assessment System (SDS-WAS), a project developed under the umbrella of the World Meteorological Organization (WMO) with a focus on improving the capabilities of sand and dust storm forecasts. For brevity reasons, only the main characteristics of the model are discussed here since a thorough description is provided in Pérez et al (2011, and references therein) as well as in recent publications presenting its developments and applications in gas-phase chemistry (Badia et al, 2017), volcanic ash dispersion (Marti et al, 2017) and data assimilation (Di Tomaso et al, 2017) studies. The spectral variation of the GOCART dust optical properties, utilized as inputs to the radiation transfer scheme, is presented in Sect.…”

Section: Model Descriptionmentioning

confidence: 99%

Direct radiative effects during intense Mediterranean desert dust outbreaks

et al. 2018

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Abstract. The direct radiative effect (DRE) during 20 intense and widespread dust outbreaks, which affected the broader Mediterranean basin over the period March 2000-February 2013, has been calculated with the NMMB-MONARCH model at regional (Sahara and European continent) and shortterm temporal (84 h) scales. According to model simulations, the maximum dust aerosol optical depths (AODs) range from ∼ 2.5 to ∼ 5.5 among the identified cases. At midday, dust outbreaks locally induce a NET (shortwave plus longwave) strong atmospheric warming (DRE ATM values up to 285 W m −2 ; Niger-Chad; dust AODs up to ∼ 5.5) and a strong surface cooling (DRE NETSURF values down to −337 W m −2 ), whereas they strongly reduce the downward radiation at the ground level (DRE SURF values down to −589 W m −2 over the Eastern Mediterranean, for extremely high dust AODs, 4.5-5). During night-time, reverse effects of smaller magnitude are found. At the top of the atmosphere (TOA), positive (planetary warming) DREs up to 85 W m −2 are found over highly reflective surfaces (Niger-Chad; dust AODs up to ∼ 5.5) while negative (planetary cooling) DREs down to −184 W m −2 (Eastern Mediterranean; dust AODs 4.5-5) are computed over dark surfaces at noon. Dust outbreaks significantly affect the mean regional radiation budget, with NET DREs ranging from −8.5 to 0.5 W m −2 , from −31.6 to 2.1 W m −2 , from −22.2 to 2.2 W m −2 and from −1.7 to 20.4 W m −2 for TOA, SURF, NETSURF and ATM, respectively. Although the shortwave DREs are larger than the longwave ones, the latter are comparable or even larger at TOA, particularly over the Sahara at midday. As a response to the strong surface day-time cooling, dust outbreaks cause a reduction in the regional sensible and latent heat fluxes by up to 45 and 4 W m −2 , respectively, averaged over land areas of the simulation domain. Dust outbreaks reduce the temperature at 2 m by up to 4 K during day-time, whereas a reverse tendency of similar magnitude is found during nighttime. Depending on the vertical distribution of dust loads and time, mineral particles heat (cool) the atmosphere by up to 0.9 K (0.8 K) during day-time (night-time) within atmospheric dust layers. Beneath and above the dust clouds, mineral particles cool (warm) the atmosphere by up to 1.3 K (1.2 K) at noon (night-time). On a regional mean basis, negative feedbacks on the total emitted dust (reduced by 19.5 %) and dust AOD (reduced by 6.9 %) are found when dust interacts with the radiation. Through the consideration of dust radiative effects in numerical simulations, the model positive and negative biases for the downward surface SW or LW radiation, respectively, with respect to Baseline Surface Radiation Network (BSRN) measurements, are reduced. In addition, they also reduce the model near-surface (at 2 m) nocturnal cold biases by up to 0.5 K (regional averages), as well as the model warm biases at 950 and 700 hPa, where the dust concentration is maximized, by up to 0.4 K. However, improvements are relatively small and do not happen in all ...

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Section: Model Descriptionmentioning

confidence: 99%

Direct radiative effects during intense Mediterranean desert dust outbreaks

et al. 2018

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“…Using satellite data alone it seems quite difficult to identify volcanic ice from ice formed in meteorological clouds when no ash is present. Detailed models of volcanic plumes [75][76][77] that include thermodynamics are needed in addition to measurements. Incorporation of meteorological data or using transport and weather forecasting models will assist in diagnosing the circumstances favourable to ice formation in volcanic clouds.…”

Section: Case Study: Manam Png 31 July 2015mentioning

confidence: 99%

Passive Earth Observations of Volcanic Clouds in the Atmosphere

Prata

Lynch

2019

Atmosphere

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Current Earth Observation (EO) satellites provide excellent spatial, temporal and spectral coverage for passive measurements of atmospheric volcanic emissions. Of particular value for ash detection and quantification are the geostationary satellites that now carry multispectral imagers. These instruments have multiple spectral channels spanning the visible to infrared (IR) wavelengths and provide 1 × 1 km2 to 4 × 4 km2 resolution data every 5–15 min, continuously. For ash detection, two channels situated near 11 and 12 μ m are needed; for ash quantification a third or fourth channel also in the infrared is useful for constraining the height of the ash cloud. This work describes passive EO infrared measurements and techniques to determine volcanic cloud properties and includes examples using current methods with an emphasis on the main difficulties and ways to overcome them. A challenging aspect of using satellite data is to design algorithms that make use of the spectral, temporal (especially for geostationary sensors) and spatial information. The hyperspectral sensor AIRS is used to identify specific molecules from their spectral signatures (e.g., for SO2) and retrievals are demonstrated as global, regional and hemispheric maps of AIRS column SO2. This kind of information is not available on all sensors, but by combining temporal, spatial and broadband multi-spectral information from polar and geo sensors (e.g., MODIS and SEVIRI) useful insights can be made. For example, repeat coverage of a particular area using geostationary data can reveal temporal behaviour of broadband channels indicative of eruptive activity. In many instances, identifying the nature of a pixel (clear, cloud, ash etc.) is the major challenge. Sophisticated cloud detection schemes have been developed that utilise statistical measures, physical models and temporal variation to classify pixels. The state of the art on cloud detection is good, but improvements are always needed. An IR-based multispectral cloud identification scheme is described and some examples shown. The scheme is physically based but has deficiencies that can be improved during the daytime by including information from the visible channels. Physical retrieval schemes applied to ash detected pixels suffer from a lack of knowledge of some basic microphysical and optical parameters needed to run the retrieval models. In particular, there is a lack of accurate spectral refractive index information for ash particles. The size distribution of fine ash (1–63 μ m, diameter) is poorly constrained and more measurements are needed, particularly for ash that is airborne. Height measurements are also lacking and a satellite-based stereoscopic height retrieval is used to illustrate the value of this information for aviation. The importance of water in volcanic clouds is discussed here and the separation of ice-rich and ash-rich portions of volcanic clouds is analysed for the first time. More work is required in trying to identify ice-coated ash particles, and it is suggested that a class of ice-rich volcanic cloud be recognized and termed a ‘volcanic ice’ cloud. Such clouds are frequently observed in tropical eruptions of great vertical extent (e.g., 8 km or higher) and are often not identified correctly by traditional IR methods (e.g., reverse absorption). Finally, the global, hemispheric and regional sampling of EO satellites is demonstrated for a few eruptions where the ash and SO 2 dispersed over large distances (1000s km).

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“…This synthetic application reduces the differences associated with the source term (i.e., different source term quantification because of different wind fields) and allows us to isolate the systematic errors coming from the offline coupling approach. Within this framework, we limited the eruption duration to 12 h, using a constant column height and employing the Mastin et al (2009) relationship (mass eruption rate vs. column height) for the dispersion evaluation of a single bin of ash (one particle class) during the first 48 h of the event. Multiple regional simulations of NMMB-MONARCH-ASH were performed to produce four different offline coupled forecasts, in which meteorological variables are updated at the specified coupling intervals (i.e., 1, 3, 6, and 12 h).…”

Section: Synthetic Case Studymentioning

confidence: 99%

“…In general terms, offline forecasts for the Eyjafjallajökull event tend to overpredict towards the north of the plume and to underpredict towards the south. While results of the 1 h offline fore- Woodhouse -Woodhouse et al, 2013;Mastin -Mastin et al, 2009;Degruyter -Degruyter and Bonadonna, 2012); (c) resulting MER for each coupling strategy (meteorology coupled online or with intervals of time of 1, 3, 6, and 12 h) with the Degruyter option only. Figure 6.…”

Section: Modeling Setupmentioning

confidence: 99%

Volcanic ash modeling with the NMMB-MONARCH-ASH model: quantification of offline modeling errors

Marti

Folch

2018

Atmos. Chem. Phys.

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Abstract. Volcanic ash modeling systems are used to simulate the atmospheric dispersion of volcanic ash and to generate forecasts that quantify the impacts from volcanic eruptions on infrastructures, air quality, aviation, and climate. The efficiency of response and mitigation actions is directly associated with the accuracy of the volcanic ash cloud detection and modeling systems. Operational forecasts build on offline coupled modeling systems in which meteorological variables are updated at the specified coupling intervals. Despite the concerns from other communities regarding the accuracy of this strategy, the quantification of the systematic errors and shortcomings associated with the offline modeling systems has received no attention. This paper employs the NMMB-MONARCH-ASH model to quantify these errors by employing different quantitative and categorical evaluation scores. The skills of the offline coupling strategy are compared against those from an online forecast considered to be the best estimate of the true outcome. Case studies are considered for a synthetic eruption with constant eruption source parameters and for two historical events, which suitably illustrate the severe aviation disruptive effects of European (2010 Eyjafjallajökull) and South American (2011 Cordón Caulle) volcanic eruptions. Evaluation scores indicate that systematic errors due to the offline modeling are of the same order of magnitude as those associated with the source term uncertainties. In particular, traditional offline forecasts employed in operational model setups can result in significant uncertainties, failing to reproduce, in the worst cases, up to 45–70 % of the ash cloud of an online forecast. These inconsistencies are anticipated to be even more relevant in scenarios in which the meteorological conditions change rapidly in time. The outcome of this paper encourages operational groups responsible for real-time advisories for aviation to consider employing computationally efficient online dispersal models.

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Volcanic ash modeling with the online NMMB-MONARCH-ASH v1.0 model: model description, case simulation, and evaluation

Cited by 16 publications

References 77 publications

Direct radiative effects during intense Mediterranean desert dust outbreaks

Direct radiative effects during intense Mediterranean desert dust outbreaks

Passive Earth Observations of Volcanic Clouds in the Atmosphere

Volcanic ash modeling with the NMMB-MONARCH-ASH model: quantification of offline modeling errors

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