Academic literature on the topic 'Dust storms Forecasting Statistical methods'

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.

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).

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.

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.

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.

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.

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.

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.

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.

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.