Seasonal Atmospheric Variations Research Articles

Radiological impact assessment of a dirty bomb using Hotspot Code and AI: Insights from spent nuclear fuel isotopes

This study evaluates the potential radiological effects resulting from the use of a Radiological Dispersal Device (RDD), commonly known as a “dirty bomb,” in Dhamar City, Yemen, focusing on its impact on human health. Seasonal atmospheric variations were analyzed using the HotSpot Health Physics Code to model the Total Effective Dose Equivalent (TEDE) distribution following an explosion. The TEDE’s effect on different human organs was assessed, considering both external and internal radiation exposures. The study demonstrated that winter nights posed the highest radiation risk, with dose distribution areas of 0.018 km2, 0.14 km2, and 3.3 km2 for inner, middle, and outer zones, respectively, surpassing other seasonal and daily values. Conversely, summer recorded the lowest radiation spread. A detailed temporal analysis during winter nights showed an initial TEDE of 9.5 Sv at 0.1 min post-explosion, which decreased exponentially to 1.3 Sv after 2000 min (33.3 h) and further to 0.0038 Sv after 6500 min (108.3 h) with the aid of AI-enhanced “Long Short Term Memory Networks (LSTM)”. The highest radiation doses to human organs within a 100 m2 area during the initial 10 min post-detonation were significantly above permissible limits, with critical organs like the surface bone, liver, and red marrow receiving doses of 68 Sv, 12 Sv, and 5.5 Sv, respectively. These findings underline the need for robust emergency response strategies and long-term health monitoring to mitigate the adverse health impacts of RDDs. The comprehensive assessment presented in this study, enhanced by artificial intelligence, provides essential data for improving public health safety and emergency preparedness in regions susceptible to radiological threats.

Seasonal variations of infrasonic arrivals from long-term ground truth observations in Nevada and implication for event location

AbstractLong-term ground truth observations were collected at two infrasound arrays in Nevada to investigate how seasonal atmospheric variations affect the detection, traveltime and signal characteristics (azimuth, trace velocity, frequency content and amplitudes) of infrasonic arrivals at regional distances. The arrays were located in different azimuthal directions from a munition disposal facility in Nevada. FNIAR, located 154 km north of the source has a high detection rate throughout the year. Over 90 per cent of the detonations have traveltimes indicative of stratospheric arrivals, while tropospheric waveguides are observed from only 27 per cent of the detonations. The second array, DNIAR, located 293 km southeast of the source exhibits strong seasonal variations with high stratospheric detection rates in winter and the virtual absence of stratospheric arrivals in summer. Tropospheric waveguides and thermospheric arrivals are also observed for DNIAR. Modeling through the Naval Research Laboratory Ground to Space atmospheric sound speeds leads to mixed results: FNIAR arrivals are usually not predicted to be present at all (either stratospheric or tropospheric), while DNIAR arrivals are usually correctly predicted, but summer arrivals show a consistent traveltime bias. In the end, we show the possible improvement in location using empirically calibrated traveltime and azimuth observations. Using the Bayesian Infrasound Source Localization we show that we can decrease the area enclosed by the 90 per cent credibility contours by a factor of 2.5.

PERIODICITY ANALYSIS OF GAMMA RADIATION MEASUREMENTS IN THESSALONIKI, NORTHERN GREECE, IN THE PERIOD 1988-2015.

Gamma radiation measurements were performed during the last 27 y, starting from 1988, with a NaI(Tl)-based Xetex 501A radiation monitor located outside the Nuclear Technology Laboratory of the Aristotle University of Thessaloniki in Northern Greece, and a time series was created. Measurements were also performed in the same place during 1995-98 and 2013-15 with portable high purity germanium (HPGe) detector. The total absorbed dose rate in air decreases exponentially with time. The total absorbed dose rate in air is the sum of the gamma dose rates due to (1) uranium series, (2) thorium series, (3) 40K and (4) 137Cs (due to the Chernobyl accident). In addition, a small contribution due to cosmic radiation is measured by the radiation monitor. From the time-dependence measurements with the HPGe detector, it was found that the time dependence of the absorbed gamma dose rate in air due to (1) uranium series, (2) thorium series and (3) 40K is quite constant. On the contrary, gamma dose rate due to 137Cs decreases exponentially with an effective half-life (t½) of ∼13.5 y, stronger than expected due to the natural decay of 137Cs. Time series analysis of the mean monthly total absorbed dose rate in air was performed. Fourier analysis reveals several periodicities, and applying Zhao-Atlas-Marks transform unravels the time distribution of those periodicities. There are three main discernible periodicities: 12 ± 0.2, 42.3 ± 2.9 and 53.2 ± 3.2 months. One of them is of a seasonal character (annual cycle) and can be linked to seasonal atmospheric variations and is strongly visible from 1988 to 2002 and 2008 to 2014. The other two (42.3 ± 2.9 and 53.2 ± 3.2 months) were found to be also related to meteorological parameters (air temperature), and they were very intense during the years 2002-4 when the annual periodicity was weak. Apart from the three main periodicities, there are also four others (14.7, 18.6, 21.3 and 27.3 months) with lower magnitudes; of which, three agree well with literature data periodicities in solar activity. Different possible mechanisms that can influence the gamma radiation measurements, due to solar activity, were discussed.

Extreme Learning Machine for the Predictions of Length of Day

Abstract This work presents short- and medium-term predictions of length of day (LOD) up to 500 days by means of extreme learning machine (ELM). The EOP C04 time-series with daily values from the International Earth Rotation and Reference Systems Service (IERS) serve as the data basis. The influences of the solid Earth and ocean tides and seasonal atmospheric variations are removed from the C04 series. The residuals are used for training of the ELM. The results of the prediction are compared with those from other prediction methods. The accuracy of the prediction is equal to or even better than that by other approaches. The most striking advantages of employing ELM instead of other algorithms are its noticeably reduced complexity and high computational efficiency.

Prediction of length-of-day using extreme learning machine

Traditional artificial neural networks (ANN) such as back-propagation neural networks (BPNN) provide good predictions of length-of-day (LOD). However, the determination of network topology is difficult and time consuming. Therefore, we propose a new type of neural network, extreme learning machine (ELM), to improve the efficiency of LOD predictions. Earth orientation parameters (EOP) C04 time-series provides daily values from International Earth Rotation and Reference Systems Service (IERS), which serves as our database. First, the known predictable effects that can be described by functional models—such as the effects of solid earth, ocean tides, or seasonal atmospheric variations—are removed a priori from the C04 time-series. Only the residuals after the subtraction of a priori model from the observed LOD data (i.e., the irregular and quasi-periodic variations) are employed for training and predictions. The predicted LOD is the sum of a prior extrapolation model and the ELM predictions of the residuals. Different input patterns are discussed and compared to optimize the network solution. The prediction results are analyzed and compared with those obtained by other machine learning-based prediction methods, including BPNN, generalization regression neural networks (GRNN), and adaptive network-based fuzzy inference systems (ANFIS). It is shown that while achieving similar prediction accuracy, the developed method uses much less training time than other methods. Furthermore, to conduct a direct comparison with the existing prediction techniques, the mean-absolute-error (MAE) from the proposed method is compared with that from the EOP prediction comparison campaign (EOP PCC). The results indicate that the accuracy of the proposed method is comparable with that of the former techniques. The implementation of the proposed method is simple.

Assimilation of GPS-Derived Atmospheric Propagation Delay in DInSAR Data Processing

Microwave radiation is almost insensitive in terms of power attenuation to the presence of atmosphere; the atmosphere is however an error source in repeat pass interferometry due to propagation delay variations. This effect represents a main limitation in the detection and monitoring of weak deformation patterns in differential interferometric Synthetic Aperture Radar (DInSAR), especially in emergency conditions. Due to the wavelength reduction current, X-Band sensors are even more sensitive to such error sources: procedures adopted in classical advanced DInSAR for atmospheric filtering may fail in the presence of higher revisiting rates. In this work, we show such effect on data acquired by the COSMO-SkyMed constellation. The dataset has been acquired with very high revisiting rates during the emergency phase. This feature allows clearly showing the inability of standard filtering adopted in common processing chains in handling seasonal atmospheric delay variations over temporal intervals spanning periods shorter than 1 year. We discuss a procedure for the mitigation of atmospheric propagation delay (APD) that is based on the integration of data of GPS systems which carries out measurements with large observation angles diversity practically in continuous time. The proposed algorithm allows a robust assimilation of the GPS atmospheric delay measurements in the multipass DInSAR processing and found on a linear approximation with the height of the atmospheric delay corresponding to a stratified atmosphere. Achieved results show a significant mitigation of the seasonal atmospheric variations.

Prediction of Length-of-day Using Gaussian Process Regression

The predictions of Length-Of-Day (LOD) are studied by means of Gaussian Process Regression (GPR). The EOP C04 time-series with daily values from the International Earth Rotation and Reference Systems Service (IERS) serve as the data basis. Firstly, well known effects that can be described by functional models, for example effects of the solid Earth and ocean tides or seasonal atmospheric variations, are removed a priori from the C04 time-series. Only the differences between the modelled and actual LOD, i.e. the irregular and quasi-periodic variations, are employed for training and prediction. Different input patterns are discussed and compared so as to optimise the GPR model. The optimal patterns have been found in terms of the prediction accuracy and efficiency, which conduct the multi-step ahead predictions utilising the formerly predicted values as inputs. Finally, the results of the predictions are analysed and compared with those obtained by other prediction methods. It is shown that the accuracy of the predictions are comparable with that of other prediction methods. The developed method is easy to use.

Prediction of Earth orientation parameters by artificial neural networks

Earth orientation parameters (EOPs) [polar motion and length of day (LOD), or UT1–UTC] were predicted by artificial neural networks. EOP series from various sources, e.g. the C04 series from the International Earth Rotation Service and the re-analysis optical astrometry series based on the HIPPARCOS frame, served for training the neural network for both short-term and long-term predictions. At first, all effects which can be described by functional models, e.g. effects of the solid Earth tides and the ocean tides or seasonal atmospheric variations of the EOPs, were removed. Only the differences between the modeled and the observed EOPs, i.e. the quasi-periodic and irregular variations, were used for training and prediction. The Stuttgart neural network simulator, which is a very powerful software tool developed at the University of Stuttgart, was applied to construct and to validate different types of neural networks in order to find the optimal topology of the net, the most economical learning algorithm and the best procedure to feed the net with data patterns. The results of the prediction were analyzed and compared with those obtained by other methods. The accuracy of the prediction is equal to or even better than that by other prediction methods.

The 11-year solar cycle, the 27-day Sun's rotation and the area of the stratospheric Aleutian high

The effect of the 11-year solar cycle on the 30-hPa geopotential height and temperature fields in the area of the Aleutian high caused by solar activity oscillations resulting from the Sun's rotation (27.2 d) is investigated, applying methods of statistical cross-spectral analysis to daily data for the period from 1965 to 1998. The area of the stratospheric Aleutian high is considered as an indicator of the solar influence on the winter stratosphere proceeding from the results by LABITZKE and VAN LOON (1988), and VAN LOON and LABITZKE (1990). An effect of the 11-year solar cycle on the response of the summer middle stratosphere to solar activity oscillations on the time scale of the Sun's rotation is not found. In contrast to summer, the atmospheric responses in winter demonstrate clear differences between maximum and minimum of the 11-year solar cycle for the 27.2 d solar rotation periodicity and for the two other oscillations of 29.4 d and 25.3 d, resulting from the modulation of the 27.2 d solar-induced periodicity by the annual atmospheric variation. The atmospheric response for the fourth periodicity studied, the 17 d oscillation, which is supposed to be a normal mode of the atmosphere, close to the known 16-day wave (MADDEN, 1978), also shows a clear dependence on the 11-year solar cycle. For all the periodicities studied the coherence between the 10.7 cm solar radio flux and the 30-hPa height/temperature fields in the Aleutian high area in winter is on the average stronger at maxima than at minima of the 11-year solar cycle. The corresponding amplitudes of the solar-induced geopotential height and temperature perturbations are also larger at high than at low solar activity, with the largest differences revealed at the moderate and polar latitudes. Thus, we conclude that the response of the winter 30-hPa height/temperature fields in the area of the Aleutian high to solar oscillations on the time scale of the Sun's rotation is on the average stronger at high than at low solar activity. We suppose that the influence of the 11-year solar cycle on the stratospheric Aleutian high area includes the modulation of the intensity of interaction between the solar induced 27 d oscillation and seasonal atmospheric variations.

The 11-year solar cycle, the sun’s rotation, and the middle stratosphere in winter: Part I: Response of zonal means

The effect of the 11-year solar cycle on the response of the stratospheric geopotential height and temperature fields at 10 and 30 hPa in winter to solar activity oscillations with periods related to the period of the Sun’s rotation (27.2 days) is discussed, applying methods of statistical spectral analysis to daily data for the period from 1965 to 1996. Atmospheric responses for three periodicities — 27.2 days (period of the Sun’s rotation), 25.3 days (periodicity caused by the modulation of the 27.2 days oscillation by annual atmospheric variation), and 54.4 days (doubled period of the solar rotation) — are studied. A significant effect of the 11-year solar cycle on the atmospheric response to the 27.2 days solar periodicity has not been found. We explain it by a frequency shift of the response from the 27.2 days to the 25.3 days periodicity via amplitude modulation. For the 25.3 days oscillation, prominent differences between the maximum and minimum of the 11-year solar cycle have been found in the coherence between the 10.7 cm solar radio flux and the height/temperature fields: the relationships are stronger at solar maximum than at the minimum of the 11-year cycle. The same differences, but to a greater extent, are revealed for the oscillation with a period of 54.4 days. Coherence and amplitude estimates for this doubled solar rotation periodicity exhibit strong differences between extrema of the 11-year solar cycle. Phase estimates also demonstrate a clear difference between high and low solar activity: on the average, the delay of the atmospheric response after the solar signal is smaller at solar maximum than at solar minimum. Thus, we conclude that the mechanism of the influence of the 11-year solar cycle on the winter middle stratosphere can include both a direct effect of the frequency corresponding to the doubled solar rotation periodicity and an indirect effect of modulation of the intensity of the interaction between the solar 27.2 days oscillation and seasonal atmospheric variations.

Temperature changes at depths to 150 metres near the active crater of Aso Volcano: preliminary analysis of seasonal and volcanic effects

Two holes were drilled to depths of 150 m and 70 m from the surface about 200 m from the active crater of Aso Volcano and quartz thermometers were installed in the holes at depth intervals of 30 m and 35 m, respectively. This series of observations is one of the first measurements of temperature at depth so close to an active crater. The ground temperature at a depth of 2 m had an annual variation with a range of about 10°C, as expected, clearly corresponding to the atmospheric temperature variation, but delayed by about 1 month. Temperatures measured at depths from 30 m to 70 m had very small annual temperature variations. The range of temperature at 30 m depth was about 0.04°C. The temperature at a depth of 60 m, however, was particularly stable, probably because at this depth the hole is in the middle of a massive lava flow. At depths of 70 m or more, small (less than 0.2°C) annual temperature variations were again observed. These variations are probably due to the effects of surface water descending to these levels through cracks and fissures. At 120 m depth, the average temperature is about 17.5°C, over 5°C above the surface average temperature, and the annual temperature variation has a range of about 2°C, out of phase with the atmospheric changes. This is probably due to the interaction of rainfall descending from the surface with convecting hotter fluids from below. The temperature gradient below 100 m depth is very high, with the average temperature at a depth of 150 m being about 31°C. The temperature variations at this depth are dominated by long-period variations, with a steady decline after a peak in November–December 1989, overlain by a rather irregular seasonal variation, with a range of about 0.5°C. October 1989 was the time of most active volcanic activity, with Strombolian eruptions depositing ash to a distance of 50 km from the crater, accompanied by very high amplitude volcanic tremor. So the temperature changes at 150 m seem to be mainly the result of volcanic activity. The maximum temperature at this depth occurred 1 or 2 months after the peak of observed volcanic activity. So it is likely that the temperature variations show a delayed influence of the level of volcanic activity. These results show that in the unconsolidated materials often found by active volcanic craters, the effects of seasonal atmospheric variations are carried to substantial depths by groundwater flows, so that at Aso Volcano, only at the maximum depth of 150 m are the temperature variations clearly dominated by the level of volcanic activity.

Predictability of Seasonal Atmospheric Variations

Abstract Results from a set of 120-day ensemble integrations of a T63L19 version of the European Centre for Medium-Range Weather Forecasts (ECMWF) model are described. The integrations, started from observed initial conditions, used observed global sea surface temperature (SST) as a lower boundary condition. Each ensemble comprised three members initiated from consecutive analyses one day apart. The ensembles were analyzed over the last 90 days of the integration period, corresponding to conventional calendar seasons. Interannual variations in the atmosphere for the period 1986 to 1990 were studied in this way. The sign and magnitude of tropical Pacific SST anomalies were chosen to define an El Nino-Southern Oscillation (ENSO) index. Difference fields were formed from seasons in which this index was 1) large and of opposite sign and 2) small (and of opposite sign). The skill and spread of the ensemble simulations were determined over nine areas covering the globe. In general, the skill of the ensemble dif...

The interrelation between the two hemispheres and long-range forecasting

This investigation represents an attempt to illustrate some important aspects of the general circulation and the interrelation of seasonal atmospheric variations between northern and southern hemispheres. It presents evidence that the winter circulation of the northern hemisphere has a significant influence on subsequent seasons in the southern hemisphere. The idea is backed up by specific examples, showing that winter rainfall at Malta and Cairo is strongly correlated with the subsequent seasonal rainfall in Zimbabwe. These findings, which potentially allow to forecast Zimbabwe rainfalls several months in advance of the rainy season, are interpreted physically in terms of the variations of major features of the general circulation, such as the strength of the Winter Siberian Anticyclone (WSA) and the El-Nino-Southern Oscillation (ENSO). These two features appear to be related, from the point of view that the forecasting techniques of the study also allow the possibility of predicting the outstanding ENSO events several months before January.

Thermo-Physical Characteristics of Glaciers — Toward a Rational Classification

Thermo-Physical Characteristics of Glaciers — Toward a Rational Classification

Thermo-Physical Characteristics of Glaciers — Toward a Rational Classification

Thermo-Physical Characteristics of Glaciers — Toward a Rational Classification

Models of F1-region ion composition variations

Rocket observations of F1-region ion composition are analyzed in terms of solar zenith angle and seasonal variations to derive a model for use in incoherent scatter radar studies and to deduce corresponding variations in the neutral atmosphere. The p = [O +] [e] profile is shown to be characterized adequately by a parameter z 50 defining the altitude of p=0.5 and a parameter H ∞ defining the thickness of the transition from p = 0 to p=1. Solar zenith angle variations are predominantly characterized by the sunrise-sunset transition. Large annual variations are found for both z 50 and H ∞; these variations appear to be caused by seasonal atmospheric variations associated with global circulation. A large semiannual variation is found for z 50; this variation is in phase with that deduced from satellite drag data but its amplitude is much larger than would be indicated by either the drag observations or recent theoretical estimates of atmospheric seasonal variations. No semiannual variation of H ∞ is found. The data show little variation with solar activity.