Cosmic ray-induced ionization in the atmosphere: spatial and temporal changes

Usoskin, Ilya; Gladysheva, O. G.; Kovaltsov, G. A.

doi:10.1016/j.jastp.2004.07.037

Cited by 99 publications

(82 citation statements)

References 26 publications

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“…The mean of the ionization values is found to be 640 cm −3 , which checks with that of Usoskin et al (2004). The spatial and temporal data coverage include the region covering Europe, the Mediterranean Sea, and north Africa that is between (22.5 • N; 57.5 • N) and (7.5 • W; 47.5 • E) during the period between July 1983 and August 2000.…”

Section: Datasupporting

confidence: 68%

“…Tulunay et al, 2004a). Therefore, there is a clear need for observational studies to assess the evidence for the operation of the mechanisms proposed at climate space and time scales (Todd and Kniveton, 2001;Usoskin et al, 2004). While the observations can be interpreted, the observed data are used as input to the data driven models.…”

mentioning

confidence: 99%

“…They did not consider the cosmic factors. Visa and Iivarinen (1997) presented NN based cloud classifier and showed its evolution capability. In their work, they used Advanced Very High Resolution Radiometer (AVHRR) images.…”

mentioning

confidence: 99%

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A fuzzy neural network model to forecast the percent cloud coverage and cloud top temperature maps

Tulunay¹,

Şenalp²,

Öz³

et al. 2008

Ann. Geophys.

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Abstract. Atmospheric processes are highly nonlinear. A small group at the METU in Ankara has been working on a fuzzy data driven generic model of nonlinear processes. The model developed is called the Middle East Technical University Fuzzy Neural Network Model (METU-FNN-M). The METU-FNN-M consists of a Fuzzy Inference System (METU-FIS), a data driven Neural Network module (METU-FNN) of one hidden layer and several neurons, and a mapping module, which employs the Bezier Surface Mapping technique. In this paper, the percent cloud coverage (%CC) and cloud top temperatures (CTT) are forecast one month ahead of time at 96 grid locations. The probable influence of cosmic rays and sunspot numbers on cloudiness is considered by using the METU-FNN-M.

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Section: Datasupporting

confidence: 68%

mentioning

confidence: 99%

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A fuzzy neural network model to forecast the percent cloud coverage and cloud top temperature maps

Tulunay¹,

Şenalp²,

Öz³

et al. 2008

Ann. Geophys.

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show abstract

“…To create the corresponding relative change in ionization dQ(t) it is assumed that a major Forbush decrease is on the same scale as variations in ionization over the solar cycle, i.e. ≈10% (Usoskin et al, 2004) and F (t) is scaled such that the base level is at zero and the minimum is at 10%:…”

Section: Sensitivity Studymentioning

confidence: 99%

Model of optical response of marine aerosols to Forbush decreases

2010

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Abstract. In order to elucidate the effect of galactic cosmic rays on cloud formation, we investigate the optical response of marine aerosols to Forbush decreases -abrupt decreases in galactic cosmic rays -by means of modeling. We vary the nucleation rate of new aerosols, in a sectional coagulation and condensation model, according to changes in ionization by the Forbush decrease. From the resulting size distribution we then calculate the aerosol optical thickness and Angstrom exponent, for the wavelength pairs 350, 450 nm and 550, 900 nm. In the cases where the output parameters from the model seem to compare best with atmospheric observations we observe, for the shorter wavelength pair, a change in Angstrom exponent, following the Forbush Decrease, of −6 to +3%. In some cases we also observe a delay in the change of Angstrom exponent, compared to the maximum of the Forbush decrease, which is caused by different sensitivities of the probing wavelengths to changes in aerosol number concentration and size. For the long wavelengths these changes are generally smaller. The types and magnitude of change is investigated for a suite of nucleation rates, condensable gas production rates, and aerosol loss rates. Furthermore we compare the model output with observations of 5 of the largest Forbush decreases after year 2000. For the 350, 450 nm pair we use AERONET data and find a comparable change in signal while the Angstrom Exponent is lower in the model than in the data, due to AERONET being mainly sampled over land. For 550, 900 nm we compare with both AERONET and MODIS and find little to no response in both model and observations. In summary our study shows that the optical properties of aerosols show a distinct response to Forbush Decreases, assuming that the nucleation of fresh aerosols is driven by ions. Shorter wavelengths seem more favorable for observing these effects and great care should be taken when analyzing observations, in order to avoid the signal being drowned out by noise.

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“…the use of approximate transport equations to describe particle propagation within the atmosphere; MC techniques simulating a vast number of individual events). For the Earth's case there is a vast body of literature on atmospheric cascade models: very often the FLUKA (Battistoni et al, 2007) or the Geant4 (Agostinelli et al, 2003;Allison et al, 2006) simulation toolkits are used, but the CRII model calculating the cosmicray-induced ionization in the atmosphere is very important as well (Usoskin et al, 2004(Usoskin et al, , 2010. CORSIKA (Heck et al, 1998) is a MC program used for the detailed study of extended atmospheric shower evolution and of the showers' properties.…”

Section: Introductionmentioning

confidence: 99%

Solar energetic particle interactions with the Venusian atmosphere

et al. 2016

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Abstract. In the context of planetary space weather, we estimate the ion production rates in the Venusian atmosphere due to the interactions of solar energetic particles (SEPs) with gas. The assumed concept for our estimations is based on two cases of SEP events, previously observed in near-Earth space: the event in October 1989 and the event in May 2012. For both cases, we assume that the directional properties of the flux and the interplanetary magnetic field configuration would have allowed the SEPs' arrival at Venus and their penetration to the planet's atmosphere. For the event in May 2012, we consider the solar particle properties (integrated flux and rigidity spectrum) obtained by the Neutron Monitor Based Anisotropic GLE Pure Power Law (NMBANGLE PPOLA) model (Plainaki et al., 2010) applied previously for the Earth case and scaled to the distance of Venus from the Sun. For the simulation of the actual cascade in the Venusian atmosphere initiated by the incoming particle fluxes, we apply the DYASTIMA code, a Monte Carlo (MC) application based on the Geant4 software (Paschalis et al., 2014). Our predictions are afterwards compared to other estimations derived from previous studies and discussed. Finally, we discuss the differences between the nominal ionization profile due to galactic cosmic-ray-atmosphere interactions and the profile during periods of intense solar activity, and we show the importance of understanding space weather conditions on Venus in the context of future mission preparation and data interpretation.

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Cosmic ray-induced ionization in the atmosphere: spatial and temporal changes

Cited by 99 publications

References 26 publications

A fuzzy neural network model to forecast the percent cloud coverage and cloud top temperature maps

A fuzzy neural network model to forecast the percent cloud coverage and cloud top temperature maps

Model of optical response of marine aerosols to Forbush decreases

Solar energetic particle interactions with the Venusian atmosphere

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