Journal articles on the topic 'Dust storms Forecasting Statistical methods'
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1
Liu, Ming, Douglas L. Westphal, Annette L. Walker, Teddy R. Holt, Kim A. Richardson, and Steven D. Miller. "COAMPS Real-Time Dust Storm Forecasting during Operation Iraqi Freedom." Weather and Forecasting 22, no. 1 (February 1, 2007): 192–206. http://dx.doi.org/10.1175/waf971.1.
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Abstract Dust storms are a significant weather phenomenon in the Iraq region in winter and spring. Real-time dust forecasting using the U.S. Navy’s Coupled Ocean–Atmospheric Mesoscale Prediction System (COAMPS) with an in-line dust aerosol model was conducted for Operation Iraqi Freedom (OIF) in March and April 2003. Daily forecasts of dust mass concentration, visibility, and optical depth were produced out to 72 h on nested grids of 9-, 27-, and 81-km resolution in two-way nest interaction. In this paper, the model is described, as are examples of its application during OIF. The model performance is evaluated using ground weather reports, visibility observations, and enhanced satellite retrievals. The comparison of the model forecasts with observations for the severe dust storms of OIF shows that COAMPS predicted the arrival and retreat of the major dust events within 2 h. In most cases, COAMPS predicted the intensity (reduction in visibility) of storms with an error of less than 1 km. The forecasts of the spatial distribution of dust fronts and dust plumes were consistent with those seen in the satellite images and the corresponding cold front observations. A statistical analysis of dust-related visibility for the OIF period reveals that COAMPS generates higher bias, rms, and relative errors at the stations having high frequencies of dust storms and near the source areas. The calculation of forecast accuracy shows that COAMPS achieved a probability of dust detection of 50%–90% and a threat score of 0.3–0.55 at the stations with frequent dust storms. Overall, the model predicted more than 85% of the observed dust and nondust weather events at the stations used in the verification for the OIF period. Comparisons of the forecast rates and statistical errors for the forecasts of different lengths (12–72 h) for both dust and dynamics fields during the strong dust storm of 26 March revealed little dependence of model accuracy on forecast length, implying that the successive COAMPS forecasts were consistent for the severest OIF dust event.
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BYCHKOVA, V. I., D. A. PRIPACHKIN, and K. G. RUBINSHTEIN. "DUST/SAND STORM INITIATION DIAGNOSIS USING EMPIRICAL DATA." Meteorologiya i Gidrologiya, no. 7 (2021): 50–58. http://dx.doi.org/10.52002/0130-2906-2021-7-50-58.
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When forecasting dust/sand storms, it is extremely important to determine meteorological conditions favoring the wind-induced lifting of particles, especially critical wind speed above which the separation of particles from the surface occuru. The results of comparison of dust storm diagnosis skill using five methods for the determination of critical wind speed are presented. The methods are validated using the weather station observation series for 10 years (2010-2019).
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Aghababaeian, Hamidreza, Abbas Ostadtaghizadeh, Ali Ardalan, Ali Asgary, Mehry Akbary, Mir Saeed Yekaninejad, Rahim Sharafkhani, and Carolyn Stephens. "Effect of Dust Storms on Non-Accidental, Cardiovascular, and Respiratory Mortality: A Case of Dezful City in Iran." Environmental Health Insights 15 (January 2021): 117863022110601. http://dx.doi.org/10.1177/11786302211060152.
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Background: Despite the fact that Iran has been exposed to severe dust storms during the past 2 decades, few studies have investigated the health effects of these events in Iran. This study was conducted to assess the association between dust storms and daily non-accidental, cardiovascular, and respiratory mortality in Dezful City (Khuzestan Province, Iran) during 2014 to 2019. Methods: In this study, mortality, meteorological, and climatological data were obtained from the Dezful University of Medical Sciences, Iranian Meteorological Organization, and Department of Environment in Khuzestan Province, respectively. Days of dust storm were identified based on the daily concentration threshold of particulate matter with an aerodynamic diameter of less than 10 μm (PM10) according to Hoffmanns҆ definition, and then an ecological time-series was used to estimate the short-term effects of dust storms on daily mortality. Statistical analysis was performed using a distributed lag linear model (DLM) and a distributed lag non-linear model (DLNM) packages by R software and the study results were reported as excess mortality. Results: During the study period, 15 223 deaths were recorded, and 139 dust storms occurred in Dezful city. In addition, there was statistically significant excess risk of mortality due to dust storms in Dezful City (mortality in the group under 15 years of age, lag4: 34.17% and 15-64 years of age groups, lag5: 32.19%, lag6: 3.28%), also dust storms had statistically significant effects on respiratory mortality (lag6: 5.49%). Conclusion: The findings of the current study indicate that dust storms increase the risk of mortality with some lags. An evidence-based early warning system may be able to aware the people of the health effects of dust storms.
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Mohebbi, Amin, Gabriel T. Green, Simin Akbariyeh, Fan Yu, Brendan J. Russo, and Edward J. Smaglik. "Development of Dust Storm Modeling for Use in Freeway Safety and Operations Management: An Arizona Case Study." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 5 (April 4, 2019): 175–87. http://dx.doi.org/10.1177/0361198119839978.
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Extreme weather conditions such as strong winds, hail, heavy rainfall, heavy snowfall, and high air temperature impact roads, traffic, and operational decisions. Strong winds in arid regions may pick up fine dust particles and create massive blowing plumes dramatically reducing the visibility. This reduced visibility severely impairs driving ability causing catastrophic crashes. The purpose of this research was to investigate the impacts of dust storms on freeway safety and operations. Interstates 8, 10, 15, 17, 19, and 40 running through Arizona were studied in relation to dust loading and crash risks. To achieve this, nine severe Arizona dust storms (2009–2016) were modeled using Weather Research and Forecasting (WRF) model coupled with a chemistry module (WRF-Chem). WRF is a mesoscale numerical weather prediction system with a software architecture allowing for parallel computation. When coupled with a chemistry module (WRF-Chem), it could be used to model the fate and transport of the particulate matter. Dust hot spots were calculated based on Getis-Ord Gi* statistical method and were correlated to dust storm caused crashes. It was shown that a positive Gi* accompanied by dust loading of at least 50 kgm–2 will result in a crash with a 90% confidence level. The outcome of this research could be used by local and federal transportation agencies to communicate warnings to drivers for improved safety.
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Nkosi, Vusumuzi, Angela Mathee, Suzana Blesic, Thandi Kapwata, Zamantimande Kunene, David Jean du Preez, Rebecca Garland, and Caradee Yael Wright. "Exploring Meteorological Conditions and Human Health Impacts during Two Dust Storm Events in Northern Cape Province, South Africa: Findings and Lessons Learnt." Atmosphere 13, no. 3 (March 5, 2022): 424. http://dx.doi.org/10.3390/atmos13030424.
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Dust storms are meteorological hazards associated with several adverse health impacts including eye irritations, respiratory and cardiovascular disorders, and vehicular road accidents due to poor visibility. This study investigated relations between admissions from a large, public hospital that serves people living in Northern Cape and Free State provinces, South Africa during 2011 to 2017, and meteorological variables (temperature and air quality) during two dust storms, one in October 2014 (spring) and the second in January 2016 (summer), identified from the media as no repository of such events exists for South Africa. Distributed nonlinear lag analysis and wavelet transform analysis were applied to explore the relationships between hospital admissions for respiratory and cardiovascular diseases, eye irritation, and motor vehicle accidents; maximum temperature, and two air quality ‘proxy measures,’ aerosol optical depth and Ångström exponent, were used as ground-based air quality data were unavailable. Eye irritation was the most common dust-related hospital admission after both dust storm events. No statistically significant changes in admissions of interest occurred at the time of the two dust storm events, using either of the statistical methods. Several lessons were learnt. For this type of study, ground-based air quality and local wind data are required; alternative statistical methods of analysis should be considered; and a central dust storm repository would help analyze more than two events. Future studies in South Africa are needed to develop a baseline for comparison of future dust storm events and their impacts on human health.
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MUNGOV, G., and P. DANIEL. "Storm surges in the Western Black Sea. Operational forecasting." Mediterranean Marine Science 1, no. 1 (June 1, 2000): 45. http://dx.doi.org/10.12681/mms.4.
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The frequency of the storm surges in the Black Sea is lower than that in other regions of the World Ocean but they cause significant damages as the magnitude of the sea level set-up is up to 7-8 times greater than that of other sea level variations. New methods and systems for storm surge forecasting and studying their statistical characteristics are absolutely necessary for the purposes of the coastal zone management. The operational forecasting storm surge model of Meteo-France was adopted for the Black Sea in accordance with the bilateral agreement between Meteo-France and NINMH. The model was verified using tide-gauge observations for the strongest storms observed along the Bulgarian coast over the last 10 years.
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Tideman, Sam, Mauricio Santillana, Jonathan Bickel, and Ben Reis. "Internet search query data improve forecasts of daily emergency department volume." Journal of the American Medical Informatics Association 26, no. 12 (September 17, 2019): 1574–83. http://dx.doi.org/10.1093/jamia/ocz154.
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Abstract Objective Emergency departments (EDs) are increasingly overcrowded. Forecasting patient visit volume is challenging. Reliable and accurate forecasting strategies may help improve resource allocation and mitigate the effects of overcrowding. Patterns related to weather, day of the week, season, and holidays have been previously used to forecast ED visits. Internet search activity has proven useful for predicting disease trends and offers a new opportunity to improve ED visit forecasting. This study tests whether Google search data and relevant statistical methods can improve the accuracy of ED volume forecasting compared with traditional data sources. Materials and Methods Seven years of historical daily ED arrivals were collected from Boston Children’s Hospital. We used data from the public school calendar, National Oceanic and Atmospheric Administration, and Google Trends. Multiple linear models using LASSO (least absolute shrinkage and selection operator) for variable selection were created. The models were trained on 5 years of data and out-of-sample accuracy was judged using multiple error metrics on the final 2 years. Results All data sources added complementary predictive power. Our baseline day-of-the-week model recorded average percent errors of 10.99%. Autoregressive terms, calendar and weather data reduced errors to 7.71%. Search volume data reduced errors to 7.58% theoretically preventing 4 improperly staffed days. Discussion The predictive power provided by the search volume data may stem from the ability to capture population-level interaction with events, such as winter storms and infectious diseases, that traditional data sources alone miss. Conclusions This study demonstrates that search volume data can meaningfully improve forecasting of ED visit volume and could help improve quality and reduce cost.
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Zhang, Shixuan, Zhaoxia Pu, Derek J. Posselt, and Robert Atlas. "Impact of CYGNSS Ocean Surface Wind Speeds on Numerical Simulations of a Hurricane in Observing System Simulation Experiments." Journal of Atmospheric and Oceanic Technology 34, no. 2 (February 2017): 375–83. http://dx.doi.org/10.1175/jtech-d-16-0144.1.
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AbstractThe NASA Cyclone Global Navigation Satellite System (CYGNSS) was launched in late 2016. It will make available frequent ocean surface wind speed observations throughout the life cycle of tropical storms and hurricanes. In this study, the impact of CYGNSS ocean surface winds on numerical simulations of a hurricane case is assessed with a research version of the Hurricane Weather Research and Forecasting Model and a Gridpoint Statistical Interpolation analysis system in a regional observing system simulation experiment framework. Two different methods for reducing the CYGNSS data volume were tested: one in which the winds were thinned and one in which the winds were superobbed.The results suggest that assimilation of the CYGNSS winds has great potential to improve hurricane track and intensity simulations through improved representations of the surface wind fields, hurricane inner-core structures, and surface fluxes. The assimilation of the superobbed CYGNSS data seems to be more effective in improving hurricane track forecasts than thinning the data.
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Pereyra-Castro, Karla, and Ernesto Caetano. "Wind-Ramp Predictability." Atmosphere 13, no. 3 (March 11, 2022): 453. http://dx.doi.org/10.3390/atmos13030453.
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The intermittent nature of wind resources is challenging for their integration into the electrical system. The identification of weather systems and the accurate forecast of wind ramps can improve wind-energy management. In this study, extreme wind ramps were characterized at four different geographical sites in terms of duration, persistence, and weather system. Mid-latitude systems are the main cause of wind ramps in Mexico during winter. The associated ramps last around 3 h, but intense winds are sustained for up to 40 h. Storms cause extreme wind ramps in summer due to the downdraft contribution to the wind gust. Those events last about 1 to 3 h. Dynamic downscaling is computationally costly, and statistical techniques can improve wind forecasting. Evaluation of the North American Mesoscale Forecast System (NAM) operational model to simulate wind ramps and two bias-correction methods (simple bias and quantile mapping) was done for two selected sites. The statistical adjustment reduces the excess of no-ramps (≤|0.5| m/s) predicted by NAM compared to observed wind ramps. According to the contingency table-derived indices, the wind-ramp distribution correction with simple bias method or quantile mapping method improves the prediction of positive and negative ramps.
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Rudniev, Yevhen, Vyacheslav Galchenko, Elvira Filatieva, and Mykola Antoshchenko. "ON THE DEVELOPMENT OF A GENERAL METHOD FOR FORECASTING THE DANGEROUS PROPERTIES OF COAL SEAMS." JOURNAL of Donetsk Mining Institute, no. 2 (2021): 135–48. http://dx.doi.org/10.31474/1999-981x-2021-2-135-148.
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Purpose: to establish a quantitative effect on the dust-generating ability of mine layers of the degree of metamorphic transformations of fossil coals, mining-geological and mining conditions of mining operations and to provide proposals for the development of a general methodology for predicting other hazardous properties of mine layers. Methodology: research is based on the experience of using regulatory documents and analysis of the results of experimental and calculated data on dust formation of fine fractions for various observation conditions. Results: based on the analysis of statistical models of shallow and steeply dipping mine layers by groups of their dustiness, the effect of specific dust release, dust content in broken coal, moisture and thickness of the seams was established. Of all the possible factors that determine the specific dust release, the main ones are the design features of mining machines. The yield of small grades when tested by the falling weight method can serve as an indicator of the strength of coal. The use of such methods eliminates the errors caused by determining the degree of coal change during the transition from its actual destruction by a coal mining machine to the reference mode. The use of carbon content as one of the main indicators of the degree of metamorphism makes it possible to analyze the level of influence of both the sum of the remaining main components of the organic mass (hydrogen, nitrogen, sulfur, oxygen) and their separate influence. In order to bring the state of coal as close as possible to production conditions, it is necessary to additionally take into account moisture, mineral inclusions and their composition. The petrographic composition and some physical and mechanical properties are reliably characterized by the vitrinite reflectance index. The extreme limits of influence on dust formation of the design features of mass-produced combines differ by 43.4 times. The influence of metamorphic transformations at the extreme limits of the yield of the 1-0 mm class when testing coals in a pile driver is estimated by a difference of 5.6 times. The release of volatile substances taken as the main indicator of the degree of metamorphism in the normative base for the safe conduct of mining operations does not unambiguously characterize the gradation of mine layers according to their dustiness groups. The thickness of the mines being developed and the angles of occurrence do not determine their tendency to dust formation, but are the main criteria for choosing mining equipment for crushing coal in the massif. The general methodology for predicting the hazardous properties of coal mine layers, using the example of their dust-forming ability, should take into account the influence of factors of three blocks – metamorphic transformations, mining and geological and mining technical conditions. Scientific novelty: for the first time, a quantitative assessment of the effect on the dust-generating capacity of mine layers of a combination of factors of metamorphic transformation of fossil coals, mining-geological and mining-technical conditions of mining operations has been established. Practical value: the results obtained make it possible to substantiate and develop a general methodology for predicting the hazardous properties of mine layers, which will contribute to improving the regulatory framework for the safe conduct of mining operations.
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Malyshevska, O. S. "Forecasting of Non-Carcinogenic Risk for Population Health from Manufacturing of Mechanical Processing of Secondary Polymers." Ukraïnsʹkij žurnal medicini, bìologìï ta sportu 6, no. 3 (June 26, 2021): 212–19. http://dx.doi.org/10.26693/jmbs06.03.212.
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The most hygienically safe process of recycling polymers is mechanical recycling, which does not cause the destruction of polymers, products of which dangerously affect all components of the environment and man. The purpose of the study is to predict the non-carcinogenic risk to public health from the production of mechanical processing of secondary polymers, depending on the presence or absence of the stage of mechanical activation in the processing process. Materials and methods: sanitary-epidemiological examination; risk assessment of dangerous factors for public health; instrumental assessment methods; statistical processing of results – determination of arithmetic mean values, standard error, quadratic deviation. Results and discussion. It is established that the total indicators of air pollution by polymer dust, nitrogen dioxide and carbon monoxide do not exceed the permissible level of pollution, and the degree of danger of this category of production is assessed as safe. Analysis of the obtained values of hazard coefficients of chemicals for the production of mechanical processing of secondary polymer raw materials without the stage of mechanical activation showed that the risk level for the maximum concentration of polymer dust and nitrogen dioxin at a distance of 25 m is assessed as alarming. The hazard factor for the maximum concentration of carbon monoxide at all distances studied did not exceed the permissible limits. For the production of mechanical processing of secondary polymer raw materials with the stage of mechanical activation, the level of risk for any of the test substances and at all distances studied did not exceed the allowable. Conclusion. Indices of danger of impact on individual organs and systems of the body (respiratory system, central nervous system, cardiovascular system, blood, eyes) of the investigated substances (polymer dust, nitrogen dioxide, carbon monoxide) at a distance of 50 m from the processing of secondary polymer raw materials without stages of mechanical activation are admissible, and for production with a stage of mechanical activation – minimum. It is established that according to the indicators of non-carcinogenic risk, sanitary-protection zone of production facilities for mechanical processing of secondary polymer raw materials without the stage of mechanical activation cannot be less than 50 m, and with the stage of mechanical activation can be reduced to 25 m
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Kozyrev, N. A., A. A. Usol’tsev, A. N. Prudnikov, R. E. Kryukov, and A. R. Mikhno. "Study of properties of cored wire based on ferrochrome gas-cleaning dust." Ferrous Metallurgy. Bulletin of Scientific , Technical and Economic Information 75, no. 3 (June 27, 2019): 365–72. http://dx.doi.org/10.32339/0135-5910-2019-3-365-372.
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Applying of wear-resistant alloyed coatings by build-up welding is one of methods to provide high operation properties of technological, metallurgical in particular equipment. Technologies of strengthening by direct alloying or reducing of alloying materials from oxide phases by reducing agent directly in the arc during building-up are most efficient ones. For build-up, cored wires are used frequently nowadays. A possibility to manufacture cored wires based on ferrochrome production gas-cleaning dust and powders of silicon, aluminum, and aluminum production gas-cleaning dust as reducing agents considered. Chemical composition and relation between cored wires components quoted. Manufacturing of 5 mm diameter wire accomplished at laboratory facility by running through draw plate. Duringestimation of efficiency of the manufactured cored wires application a regime of build-up was selected, study of chemical composition of built-up metal carried-out, wearing tests made, measuring of built-up samples hardness carried-out. Coefficients of chrome recovery coefficient at different relation between filling materials were calculated. Statistical processing of the study results accomplished statistical dependences of components content influence onthe built-up layer properties plotted. Study of the built-up showed, that chrome recovery in the built-up layer depends completely on the cored wire filling coefficient. At that under other conditions being equal, the hardness always correlates with the wear, and increase of chrome concentration results in reduction of the surface wear. Dependents of mass share elements comprising the cored wire content on built-up layer hardness and its wear resistance determined. The dependences obtained can be used for forecasting of builtup layer hardness and its wear resistance while built-up metal chemical composition varying.
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Syed Zafar, S. N. A., Roslan Umar, N. H. Sabri, M. H. Jusoh, A. Yoshikawa, S. Abe, and T. Uozumi. "A statistical analysis of the relationship between Pc4 and Pc5 ULF waves, solar winds and geomagnetic storms for predicting earthquake precursor signatures in low latitude regions." IOP Conference Series: Earth and Environmental Science 880, no. 1 (October 1, 2021): 012010. http://dx.doi.org/10.1088/1755-1315/880/1/012010.
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Abstract Short-term earthquake forecasting is impossible due to the seismometer’s limited sensitivity in detecting the generation of micro-fractures prior to an earthquake. Therefore, there is a strong desire for a non-seismological approach, and one of the most established methods is geomagnetic disturbance observation. Previous research shows that disturbances in the ground geomagnetic field serves as a potential precursor for earthquake studies. It was discovered that electromagnetic waves (EM) in the Ultra-Low Frequency (ULF) range are a promising tool for studying the seismomagnetic effect of earthquake precursors. This study used a multiple regression approach to analyse the preliminary study on the relationship between Pc4 (6.7-22 mHz) and Pc5 (1.7-6.7 mHz) ULF magnetic pulsations, solar wind parameters and geomagnetic indices for predicting earthquake precursor signatures in low latitude regions. The ground geomagnetic field was collected from Davao station (7.00° N, 125.40° E), in the Philippines, which experiences nearby earthquake events (Magnitude <5.0, depth <100 km and epicentre distance from magnetometer station <100 km). The Pc5 ULF waves show the highest variance with four solar wind parameters, namely SWS, SWP, IMF-Bz, SIE and geomagnetic indices (SYM/H) prior to an earthquake event based on the regression model value of R2 = 0.1510. Furthermore, the IMF-Bz, SWS, SWP, SWE, and SYM/H were found to be significantly correlated with Pc5 ULF geomagnetic pulsation. This Pc5 ULF magnetic pulsation behaviour in solar winds and geomagnetic storms establishes the possibility of using Pc5 to predict earthquakes.
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Dahl, Nathan, and Ming Xue. "Prediction of the 14 June 2010 Oklahoma City Extreme Precipitation and Flooding Event in a Multiphysics Multi-Initial-Conditions Storm-Scale Ensemble Forecasting System." Weather and Forecasting 31, no. 4 (July 12, 2016): 1215–46. http://dx.doi.org/10.1175/waf-d-15-0116.1.
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Abstract Prolonged heavy rainfall produced widespread flooding in the Oklahoma City area early on 14 June 2010. This event was poorly predicted by operational models; however, it was skillfully predicted by the Storm-Scale Ensemble Forecast produced by the Center for Analysis and Prediction of Storms as part of the Hazardous Weather Testbed 2010 Spring Experiment. In this study, the quantitative precipitation forecast skill of ensemble members is assessed and ranked using a neighborhood-based threat score calculated against the stage IV precipitation data, and Oklahoma Mesonet observations are used to evaluate the forecast skill for surface conditions. Statistical correlations between skill metrics and qualitative comparisons of relevant features for higher- and lower-ranked members are used to identify important processes. The results demonstrate that the development of a cold pool from previous convection and the movement and orientation of the associated outflow boundary played dominant roles in the event. Without assimilated radar data from this earlier convection, the modeled cold pool was too weak and too slow to develop. Furthermore, forecast skill was sensitive to the choice of microphysics parameterization; members that used the Thompson scheme produced initial cold pools that propagated too slowly, substantially increasing errors in the timing and placement of later precipitation. The results also suggest important roles played by finescale, transient features in the period of outflow boundary stalling and reorientation associated with the heaviest rainfall. The unlikelihood of a deterministic forecast reliably predicting these features highlights the benefit of using convection-allowing/convection-resolving ensemble forecast methods for events of this kind.
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Li, Huiqi, Chengsi Liu, Ming Xue, Jun Park, Lianglyu Chen, Youngsun Jung, Rong Kong, and Chong-Chi Tong. "Use of Power Transform Total Number Concentration as Control Variable for Direct Assimilation of Radar Reflectivity in GSI En3DVar and Tests with Six Convective Storms Cases." Monthly Weather Review 150, no. 4 (April 2022): 821–42. http://dx.doi.org/10.1175/mwr-d-21-0041.1.
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Abstract When using a double-moment microphysics scheme, both hydrometeor mixing ratios and number concentrations are part of the state variables that are needed to initialize convective-scale forecasting. In the Thompson microphysics scheme, both mixing ratio and total number concentration of rainwater (Ntr) are predicted and they are also involved in the reflectivity observation operator. In such a case, when directly assimilating reflectivity using Ntr as the control variable (denoted as CVnr) within a variational framework, the large dynamic range of Ntr and the nonlinear relationship between reflectivity and Ntr prevent efficient minimization convergence. Using logarithmic Ntr as the control variable (CVlognr) alleviates the problem to some extent but can produce spurious analysis increments in Ntr. In this study, a general power transform of Ntr is proposed as the new control variable for Ntr (CVpnr) where the nonlinearity of transform can be adjusted by tuning the exponent parameter. This formulation is implemented within the Gridpoint Statistical Interpolation ensemble-3DVar system. The performance of CVpnr with an optimal exponent parameter value of 0.4 is compared with those of CVnr and CVlognr for the analysis and prediction of a supercell case of 16 May 2017 in more detail. CVpnr with optimal exponent yields faster convergence of cost function minimization than CVnr. Subjective and objective evaluations of analyzed and predicted reflectivity and hourly precipitation indicate that the optimized CVpnr outperforms the other two methods. When applied to five additional cases from May 2017, overall statistics show that CVpnr produces generally superior forecasts and is therefore the preferred choice.
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Laviola, Sante, Agata Moscatello, Mario Marcello Miglietta, Elsa Cattani, and Vincenzo Levizzani. "Satellite and Numerical Model Investigation of Two Heavy Rain Events over the Central Mediterranean." Journal of Hydrometeorology 12, no. 4 (August 1, 2011): 634–49. http://dx.doi.org/10.1175/2011jhm1257.1.
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Abstract Two heavy rain events over the Central Mediterranean basin, which are markedly different by genesis, dimensions, duration, and intensity, are analyzed. Given the relative low frequency of this type of severe storms in the area, a synoptic analysis describing their development is included. A multispectral analysis based on geostationary multifrequency satellite images is applied to identify cloud type, hydrometeor phase, and cloud vertical extension. Precipitation intensity is retrieved from (i) surface rain gauges, (ii) satellite data, and (iii) numerical model simulations. The satellite precipitation retrieval algorithm 183-Water vapor Strong Lines (183-WSL) is used to retrieve rain rates and cloud hydrometeor type, classify stratiform and convective rainfall, and identify liquid water clouds and snow cover from the Advanced Microwave Sounding Unit-B (AMSU-B) sensor data. Rainfall intensity is also simulated with the Weather Research and Forecasting (WRF) numerical model over two nested domains with horizontal resolutions of 16 km (comparable to that of the satellite sensor AMSU-B) and 4 km. The statistical analysis of the comparison between satellite retrievals and model simulations demonstrates the skills of both methods for the identification of the main characteristics of the cloud systems with a suggested overall bias of the model toward very low rain intensities. WRF (in the version used for the experiment) seems to classify as low rain intensity regions those areas where the 183-WSL retrieves no precipitation while sensing a mixture of freshly nucleated cloud droplets and a large amount of water vapor; in these areas, especially adjacent to the rain clouds, large amounts of cloud liquid water are detected. The satellite method performs reasonably well in reproducing the wide range of gauge-detected precipitation intensities. A comparison of the 183-WSL retrievals with gauge measurements demonstrates the skills of the algorithm in discriminating between convective and stratiform precipitation using the scattering and absorption of radiation by the hydrometeors.
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Ukhorskiy, A. Y., M. I. Sitnov, A. S. Sharma, and K. Papadopoulos. "Combining global and multi-scale features in a description of the solar wind-magnetosphere coupling." Annales Geophysicae 21, no. 9 (September 30, 2003): 1913–29. http://dx.doi.org/10.5194/angeo-21-1913-2003.
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Abstract. The solar wind-magnetosphere coupling during substorms exhibits dynamical features in a wide range of spatial and temporal scales. The goal of our work is to combine the global and multi-scale description of magnetospheric dynamics in a unified data-derived model. For this purpose we use deterministic methods of nonlinear dynamics, together with a probabilistic approach of statistical physics. In this paper we discuss the mathematical aspects of such a combined analysis. In particular we introduce a new method of embedding analysis based on the notion of a mean-field dimension. For a given level of averaging in the system the mean-filed dimension determines the minimum dimension of the embedding space in which the averaged dynamical system approximates the actual dynamics with the given accuracy. This new technique is first tested on a number of well-known autonomous and open dynamical systems with and without noise contamination. Then, the dimension analysis is carried out for the correlated solar wind-magnetosphere database using vBS time series as the input and AL index as the output of the system. It is found that the minimum embedding dimension of vBS - AL time series is a function of the level of ensemble averaging and the specified accuracy of the method. To extract the global component from the observed time series the ensemble averaging is carried out over the range of scales populated by a high dimensional multi-scale constituent. The wider the range of scales which are smoothed away, the smaller the mean-field dimension of the system. The method also yields a probability density function in the reconstructed phase space which provides the basis for the probabilistic modeling of the multi-scale dynamical features, and is also used to visualize the global portion of the solar wind-magnetosphere coupling. The structure of its input-output phase portrait reveals the existence of two energy levels in the system with non-equilibrium dynamical features such as hysteresis which are typical for non-equilibrium phase transitions. Further improvements in space weather forecasting tools may be achieved by a combination of the dynamical description for the global component and a statistical approach for the multi-scale component.Key words. Magnetospheric physics (solar wind– magnetosphere interactions; storms and substorms) – Space plasma physics (nonlinear phenomena)
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Karami, Sara, Nasim Hossein Hamzeh, Faezeh Noori, and Abbas Ranjbar. "Investigation of dust storms in Ilam and the performance analysis of simulation of 6 numerical prediction models at a severe dust storm in west of Iran." Journal of Air Pollution and Health, July 6, 2019. http://dx.doi.org/10.18502/japh.v4i2.1237.
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Introduction: Many countries encounter dust storms phenomenon that is one of the meteorological problems leading to many disturbances. Materials and methods: Although the dust storm is historically recorded as an old event in some provinces of Iran, but it becomes a new event in some parts such as Ilam province. Results: After statistical investigation of dust storms in Ilam province, the dust storm from 3td to 6th July 2016 are studied. The source of this dust storm was the eastern areas of Syria and central Iraq base on the satellite images, the outputs of HYSPLIT and WRF-Chem models. Conclusion: Model outputs in intensity of surface dust concentration of MACC-ECMWF, NASA-GEOS, NCEP-NGAC, NMMB-BSC, and BSCDREAM8b models are compared to the observation data in Ilam city and results show that NASA-GEOS model has better performance. In display of dust dispersion on Iran, the middle of all models is more compatible with reality.
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Karami, Sara, Zahra Ghassabi, and Parviz Rezazadeh. "Investigating the mechanism of dust transferring from Iraq to the north of Alborz mountains in Iran." Journal of Air Pollution and Health, December 18, 2022. http://dx.doi.org/10.18502/japh.v7i4.11385.
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Introduction: Recently, due to climate change, the number and intensity of dust sources are increasing, which leads to the occurrence of dust storms. Atmospheric patterns governing the region, topography and surface features are effective on transportation and dispersion of dust particles.
Materials and methods: In this study, a severe dust phenomenon on the 3th and 4th March, 2022, was investigated. The dust was emitted from the east of Iraq and passed through the Zagros and Alborz mountain ranges toward the Caspian sea. Meteorological data of the country, satellite data, ERA5 reanalysis data and HYSPLIT and output from the Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem) were used.
Results: Aerosol Optical Depth (AOD) anomaly compared to the long-term indicated that the intense transport of dust particles from eastern Iraq and reaching the Caspian Sea is not a common phenomenon. Synoptic analysis showed that the dust particles in the source area ascended to the mid-levels of the atmosphere and a large part of the particles passed through the Zagros Mountains. Then, the dust entered the Caspian Sea by passing through the Manjil valley. The vertical profile of WRF-Chem model output showed the ascent of dust particles up to 600 hPa near the dust source and its passage over mountainous areas.
Conclusion: The main factors in the formation of this unusual dust phenomenon are: severe two-year drought in the Middle East, reduction of vegetation and cold front of the dynamic low-pressure located in the east of the Black Sea, east of Turkey and northern Iraq moving eastward to the Caspian sea in the following hours.
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Gholami, Hamid, and Aliakbar Mohammadifar. "Novel deep learning hybrid models (CNN-GRU and DLDL-RF) for the susceptibility classification of dust sources in the Middle East: a global source." Scientific Reports 12, no. 1 (November 11, 2022). http://dx.doi.org/10.1038/s41598-022-24036-5.
Full textAbstract:
AbstractDust storms have many negative consequences, and affect all kinds of ecosystems, as well as climate and weather conditions. Therefore, classification of dust storm sources into different susceptibility categories can help us mitigate its negative effects. This study aimed to classify the susceptibility of dust sources in the Middle East (ME) by developing two novel deep learning (DL) hybrid models based on the convolutional neural network–gated recurrent unit (CNN-GRU) model, and the dense layer deep learning–random forest (DLDL-RF) model. The Dragonfly algorithm (DA) was used to identify the critical features controlling dust sources. Game theory was used for the interpretability of the DL model’s output. Predictive DL models were constructed by dividing datasets randomly into train (70%) and test (30%) groups, six statistical indicators being then applied to assess the DL hybrid model performance for both datasets (train and test). Among 13 potential features (or variables) controlling dust sources, seven variables were selected as important and six as non-important by DA, respectively. Based on the DLDL-RF hybrid model – a model with higher accuracy in comparison with CNN-GRU–23.1, 22.8, and 22.2% of the study area were classified as being of very low, low and moderate susceptibility, whereas 20.2 and 11.7% of the area were classified as representing high and very high susceptibility classes, respectively. Among seven important features selected by DA, clay content, silt content, and precipitation were identified as the three most important by game theory through permutation values. Overall, DL hybrid models were found to be efficient methods for prediction purposes on large spatial scales with no or incomplete datasets from ground-based measurements.
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Kluth, G., J. F. Ripoll, S. Has, A. Fischer, M. Mougeot, and E. Camporeale. "Machine Learning Methods Applied to the Global Modeling of Event-Driven Pitch Angle Diffusion Coefficients During High Speed Streams." Frontiers in Physics 10 (May 5, 2022). http://dx.doi.org/10.3389/fphy.2022.786639.
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Whistler-mode waves in the inner magnetosphere cause electron precipitation in the atmosphere through the physical process of pitch-angle diffusion. The computation of pitch-angle diffusion relies on quasi-linear theory and becomes time-consuming as soon as it is performed at high temporal resolution from satellite measurements of ambient wave and plasma properties. Such an effort is nevertheless required to capture accurately the variability and complexity of atmospheric electron precipitation, which are involved in various Earth’s ionosphere-magnetosphere coupled problems. In this work, we build a global machine-learning model of event-driven pitch-angle diffusion coefficients for storm conditions based on the data of a variety of storms observed by the NASA Van Allen Probes. We first proceed step-by-step by testing 8 nonparametric machine learning methods. With them, we derive machine learning based models of event-driven diffusion coefficients for the storm of March 2013 associated with high-speed streams. We define 3 diagnostics that allow highlighting of the properties of the selected model and selection of the best method. Three methods are retained for their accuracy/efficiency: spline interpolation, the radial basis method, and neural networks (DNN), the latter being selected for the second step of the study. We then use event-driven diffusion coefficients computed from 32 high-speed stream storms in order to build for the first time a statistical event-driven diffusion coefficient that is embedded within the retained DNN model. We achieve a global mean event-driven model in which we introduce a two-parameter dependence, with both the Kp-index and time kept as in an epoch analysis following the storm evolution. The DNN model does not entail any issue to reproduce quite perfectly its target, i.e., averaged diffusion coefficients, with rare exception in the Landau resonance region. The DNN mean model is then used to analyze how mean diffusion coefficients behave compared with individual ones. We find a poor performance of any mean models compared with individual events, with mean diffusion coefficients computing the general trend at best, due to their large variability. The DNN-based model allows simple and fast data exploration of pitch-angle diffusion among its multiple variables. We finally discuss how to conduct uncertainty quantification of Fokker-Planck simulations of storm conditions for space weather nowcasting and forecasting.
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Adetola, O. A., O. J. Olukunle, A. P. Olalusi, and O. O. Olubanjo. "Models Establishment for Predicting Interaction between Tillage Practices and Some Selected Engineering Properties of Cassava (Manihot esculenta) Tubers." Asian Journal of Advances in Agricultural Research, November 23, 2020, 34–48. http://dx.doi.org/10.9734/ajaar/2020/v14i330135.
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Enough technical data on the physical and mechanical properties of tubers are needed for machine design and crop parameters in modeling and designing cassava processing. The main objective of this study was to establish models for forecasting interaction between the tillage practices and some physical and mechanical properties of cassava tubers. Eight tillage practices have been used and are coded in alphabets such as, ploughing + harrowing (A); ploughing + harrowing + ridging (B); manual ridging (C); flat manual clearing (D); ploughing + harrowing + manual digging to a depth of 30 cm + 10 cm of saw-dust placed at the base (E); ploughing + harrowing + ridging +10 cm of saw-dust placed at the base (F); manual ridging + 10 cm of saw-dust placed at the base (G) and manual digging to a depth of 30 cm + 10 cm of saw-dust placed at the base (H). The experiment was conducted in TME (Tropical Manihot esculenta) 419 with two soil conditions using recommended nutrient dosage. The experimental design was split-split plot design with three replications. The physical and mechanical properties of cassava were determined 10 months after planting using standard equations and methods. Statistical Package for Social Science window 21 versions was used for data analysis. Models were developed using Linear Regression. Manual Ridging + 10 cm Sawdust placed at the base + TME 419 + Rainfed + 933.75 kg/ha of NPK 15:15:15 fertilizer tillage practice gave the highest yield of 15.02 tons/ha with uniform physical and mechanical properties. The model equations revealed that tillage had positive impact on the selected physical and mechanical properties of cassava tubers such as length, width, thickness, size, aspect ratio, surface area, sphericity, roundness, unit mass, unit volume, true density, bulk mass, bulk volume, bulk density, porosity, angle of repose, coefficient of static friction, compressive extension at break, compressive strain at break, compressive load at break, compressive stress at break, energy at break, modulus automatic, compressive load at yield, compressive extension at yield, and compressive strain at yield and compressive stress Models developed could be used by engineers in designing improved equipment and machineries for harvesting and processing of cassava tubers into useful products.
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"Ecological assessment of surface water quality in a rainless period under the conditions of urban water collection." Visnyk of V.N. Karazin Kharkiv National University, series Geology. Geography. Ecology, no. 54 (2021). http://dx.doi.org/10.26565/2410-7360-2021-54-22.
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Problem formulation. The global climate warming influences the balance of water reservoirs by lower precipitation, higher evaporation, erosion of soils and other changes. On the territory of Ukraine, the periods without rains, low snow level, warm winters and dry summers, strong winds and dust storms became frequent during the last decades. This led to a change in many hydrological parameters in comparison with the hydrological regimes usual in temperate climates. Therefore, the determination of the ecological assessment of the state of water reservoirs in the conditions of the urban drainage basin in the rainless period is an insufficiently studied and urgent issue. The aim of the article is estimation of quality of the river water in the system of rivers Lopan-Kharkov-Udy on the urban landscale of Kgarkov region. Materials and methods. Field sampling of water and bottom sediments to provide an environmental assessment of the water reservoir; comparison of the results obtained with similar ones obtained for rainy periods and periods of snow melting in different hydrological regimes, and statistical analysis of the measured data. Results. Analysis of the open source data revealed that Kharkov region is among the top three in terms of drinking and technical water needs for various purposes. The region has a maximum possible drought risk index (0.8-1) and high values of water stress indices (2-3) and coastal flood risk (2-3). Water samples taken from different sections of the Lopan and Udy rivers in different seasons showed that some of the parameters exceed the corresponding control values, especially in rainless periods, but in general water quality indices correspond to 1-2 quality classes (depending on the season) for the river Lopan and the 1st class of quality - for the river Udy. Numerical calculations on a mathematical model using the measurement data showed that global warming at 0.5-1° C by 2030 will lead to a significant deterioration in water quality and its availability as the required amount of drinking and industrial water for the region. Scientific novelty. Novel mathematical model is proposed for simple estimations of the water pollution over time at different groth rate of the average temperature accounting for known number of pollutants. Practical significance. The obtained results are useful for further development of the system of water management on urgan territories, testing different hypothesis and scenarios.