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Distributed data stream processing systems (DSPSs) such as Storm, Flink, and Spark Streaming are now routinely used to process continuous data streams in (near) real-time. However, achieving the low latency and high throughput demanded by today's streaming applications can be a daunting task, especially since the performance of DSPSs highly depends on a large number of system parameters that control load balancing, degree of parallelism, buffer sizes, and various other aspects of system execution. This tutorial offers a comprehensive review of the state-of-the-art automatic performance tuning approaches that have been proposed in recent years. The approaches are organized into five main categories based on their methodologies and features: cost modeling, simulation-based, experiment-driven, machine learning, and adaptive tuning. The categories of approaches will be analyzed in depth and compared to each other, exposing their various strengths and weaknesses. Finally, we will identify several open research problems and challenges related to automatic performance tuning for DSPSs.

The data obtained from storm-induced damage to power grids possesses an inherent skewness distribution, which impedes the development of the damage forecasting model. An inaccurate damage forecasting model may fail to accurately forecast the damages and hinder the planning, preventive measures, and restorative actions for a storm event. This study investigates the challenges that must be overcome to yield an accurate model and proposes a machine learning-based damage forecasting method. A robust forecasting model was developed by identifying the key explanatory variables using the G-mean values. The method combines the application of the weighted extreme learning machine (ELM) and long short-term memory model (LSTM) to forecast power grid damage in response to storm events. The weighted ELM is used to classify the grid state for a storm in advance and the LSTM is subsequently used to forecast the number of grid damage cases. The actual storm event data were used to verify the efficacy of the proposed method using the root mean square error. The results demonstrate that the proposed method outperforms the regular forecasting method as it is more robust and accurate.

India is a farming nation, and its financial system is supported by lead harvesting and rainwater. Rainfall Prediction (RP) is a need and essential for every farmer to maintain the investigation of harvest production. RP is the goal of knowledge and proficiency in measuring the status of the environment. Accurate determination of rainfall is crucial for the successful use of water assets, harvesting, and pre-growth of water construction. Rainfall is the key to every action plan in the food production program, water supply system and environment. One of the most difficult responsibilities is to forecast an RP for the weather predictor. RP plays a significant task in farming and business. RP is essential for identifying heavy rains and providing information about normal disaster. Tape occupies a different view of the weather, such as the RP storm path, storm speed, humidity, rainfall prediction, and temperature. RP is the most systematic and technically demanding problem on earth in the previous century. The weather alterations calculate analyzes the behaviour of the weather for a particular stage. RP is a procedure of weather adjustment everywhere, with specific characteristics like moisture and storms being used to calculate rainwater at the exact locality. Various methods evaluate performances such as accuracy, error, accuracy, recall, F-measurement, RMSE, MAE, and RAE. Based on that assessment, this research study can investigate which processes provide the best RP accuracy.

Ice storms have a wide range of impacts on power systems ranging from long outage times to major equipment failures. The diverse spatiotemporal characteristics of ice storms create high uncertainties in system performance and resilience level. Recent extended outages of various interdependent systems such as, power and water systems, due to ice storms have called for further investigation and resilience assessment approaches. In this paper, a planning-based resilience assessment framework is utilized to assess the resilience level of transmission systems. First, a spatiotemporal ice storm model is used to simulate various scenarios of ice storms. Then, a fragility model is implemented to evaluate the probability of failure of system components in terms of weather parameters. Finally, a quantitative resilience assessment method based on combinatorial enumeration is applied to compute a resilience index for the system. The proposed algorithm is carried out on the IEEE 30-bus system mapped on the Eastern region of the United States. Numerous ice storm scenarios are simulated based on real weather-data. The proposed algorithm is able to assess system resilience characteristics considering uncertainties of ice storms.

Total Electron Content (TEC) is an important parameter that considerably affects radio waves propagating through the ionosphere, causing delay errors in GPS satellite signals. Hence, identifying an effective TEC model is important. This research presents an analysis of ionospheric vertical TEC (VTEC) modeling during geomagnetic quiet and disturbed periods. The data were recorded using the Department of Survey and Mapping Malaysia's (DSMM) dual-frequency GPS located in Kedah (geographic coordinates 4.62°N-103.21°E, geomagnetic coordinates 5.64°N-174.98°E), Terengganu (geographic coordinates 6.46°N-100.50°E, geomagnetic coordinates 3.32°S-172.99°E), and Johor (geographic coordinates 1.36°N-104.10°E, geomagnetic coordinates 8.43°S-176.53°E). The VTEC was modeled using the statistical Holt-Winter method and the most recent version of the International Reference Ionospheric model (IRI-2016), using IRI01-corr, IRI-2001, and NeQuick topside electron density options. The daily variation of the measured and modeled VTEC during quiet and disturbed periods was compared. The minimum value of the VTEC was recorded in the early morning time and the maximum was recorded during the post-noon time. The diurnal hourly variation during the quiet periods indicated that IRI01-corr, IRI-2001, and NeQuick overestimated most of the time, while the Holt-Winter method showed better agreement with the GPS-TEC. During the disturbed period, IRI-2016 (IRI01-corr, IRI-2001, and NeQuick) did not show any response to the geomagnetic storm, while the Holt-Winter method underestimated the VTEC trend. However, the Holt-Winter displayed better performance and results compared to the IRI01-corr, IRI-2001, and NeQuick during the quiet and disturbed periods in the equatorial region over Malaysia.

During substorms and storms, the ionosphere was subjected to rather a significant Joule heating, and the power of precipitating energetic particles was also great. If there is a mechanism for the magnetospheric disturbance effect on meteorological processes in the atmosphere, it supposes a more complicated series of many intermediates, and is not associated directly with the energy flux that arrives into the ionosphere during storms. In this study, I have briefly reviewed our present understanding of how these events play a key role in energy transfer from the solar wind into the magnetosphere and ionosphere, which ultimately results in the Earth's ionosphere-atmosphere coupling.

This paper compares the performance of two global optimization algorithms: particle swarm optimization (PSO) and brain storm optimization (BSO). We observe that BSO has clear advantage in the speed of global exploration from a random initialization, while PSO outperformed in the accuracy of local exploitation with a predefined initialization. The possibility of hybridizing PSO and BSO is then investigated with an example of patch antenna circuit model determination. It is shown that the BSO-PSO hybrid algorithm could benefit from the advantages of both PSO and BSO, and therefore outperforms single optimization methods within the same number of iterations.

The paper presents the results of experimental studies of the manifestations of ionospheric storms in the Central European region during the geomagnetic storm on March 21–23, 2017. The time dependences of the critical frequency of the ionospheric F2 layer foF2 and the F2 peak height hmF2, the altitude-time dependences of the electron temperature T e , ion temperature T i ; electron density N e and vertical ionospheric plasma drift velocity V z in the altitude range 190–500 km for disturbed and reference days were obtained. The minor storm was featured by the presence of three negative phases of ionospheric disturbance.

The collected data on magnetic storms and tropical cyclones, which were observed in the North Atlantic, East Pacific, and West Pacific, are analyzed for understanding of the mechanism of magnetospheric disturbances effects on complicated nonlinear system of atmospheric processes. A study of effects of geomagnetospheric storms on tropical cyclogenesis is necessary, because character of some influence has regional peculiarities.

International audience; In this work, we analyze the occurrence of ionospheric irregularities during the 25-26 August 2018 geomagnetic storm. With the minimum SYM-H excursion of-206 nT, this storm is the third largest in the solar cycle 24. It produced strong effects in the ionosphere, especially in the American and Pacific longitudinal sectors. Here we use a combination of ground-based (GNSS) and spaceborne (Swarm and CSES) instruments in order to detect the occurrence of intensive ionospheric irregularities. We show that the most significant impact was done at highlatitudes. In the North American region, the area with irregularities descended to 42-45N. The location of the observed irregularities corresponded, most likely, to the auroral oval region. The use of satellite measurements added more information on the spatial distribution of the irregularities, which is especially important in areas with limited coverage by ground-based GNSS stations.

We investigate the response of the ionosphere-thermosphere coupled system to the 22-23 June 2015 geomagnetic storm in the region above the Oukaimeden observatory in Morocco with a Fabry-Perot interferometer and the Swarm satellites. The measured thermospheric neutral winds are strong enough to have two direct consequences. First, they create a strong asymmetry in the equatorial ionisation crests on both sides of the magnetic equator (actually, they make the southern hemisphere crest disappear). This asymmetry is explained by the southerly thermospheric neutral wind that was blowing during the storm and is shown to be favoured by the season. Second, within the northern hemisphere ionisation crest, Swarm A and C detect km-size electron density structures. Those may be ascribed to gradient drift instability.

Geospace storm of 5–6 August, 2019 features are described, and aperiodic and quasi-periodic perturbations in the ionosphere and the geomagnetic field are studied.

We have made measurements of HF radio waves at the Harbin Engineering University, the People's Republic of China, during the geospace storm of August 30-September 2, 2019. Further, we have performed a general analysis of space weather, and afterwards the data and disturbances in the ionosphere have been analysed in order to determine the effect that the ionospheric storm had on the parameters of radio waves. The main results of this analysis could be summarised as follows. The magnetic storm power and energy have been calculated to be 9 × 109W and 1.5 ×1015J, i.e., the storm was moderate. The geospace storm included a strong to moderate negative ionospheric storm. During August 31, 2019 and September 1, 2019, the $F$ region of the ionosphere showed a reduction in the electron density by a factor of 1.4 to 2.4 in comparison with the electron densities observed on the reference day. The geospace storm also caused notable disturbances in the midlatitude $E$ region of the ionosphere, and in the sporadic E s layer. During the ionospheric storm, the heights of reflection could undergo a drastic rise from ~150 km to ~300-310 km. The geospace storm launched atmospheric gravity waves that modulated the electron density in the ionosphere.

Recently, methods are studied to overcome various problems for Named Data Networking(NDN). Among them, a new method which can overcome content storm problem is required to reduce network congestion and deliver content packet to consumer reliably. According to the various studies, the content storm problems could be overcame by scoped interest flooding. However, because these methods do not considers not only network congestion ratio but also the number another different paths, the correspond content packets could be transmitted unnecessary and network congestion could be worse. Therefore, in this paper, we propose a new content forwarding method for NDN to overcome the content storm problem. In the proposed method, if the network is locally congested and another paths are generated, an intermediate node could postpone or withdraw the content packet transmission to reduce congestion.

This presentation reports on progress relating to a storm characterization approach using spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) measurements from the Cyclone Global Navigation Satellite System (CYGNSS) mission. The retrieval concept is based on the use of a forward model for CYGNSS returns, which can produce predicted waveforms for parametric storm models having varying storm features, with particular emphasis placed on the storm maximum wind speed. A “matched filter” approach is then adopted by correlating predicted returns with those observed throughout an entire CYGNSS overpass of a storm; the correlation is performed between predicted and measured DDMs normalized by their root-mean-square (RMS) amplitudes. Storm parameters producing the maximum correlation and minimum RMS error (RMSE) values are then designated the retrieved value from which a complete parametric wind field for storm surge simulation can be generated. It is noted that the utility of this formulation is not limited to tracks passing through the storm eye, making “near-miss” tracks equally usable for attempting to retrieve storm information.

Geomagnetic storm disasters have a significant impact on the safe and stable operation of power systems. Serious geomagnetic storm disasters can lead to cascading accidents in power systems, up to and including voltage collapse accidents. Therefore, it is necessary to analyze and evaluate the operational risks of power systems under magnetic storms. Based on the mechanisms of the cascading failures caused by geomagnetic storms, the technical requirements for the power system safety assessment of geomagnetic storm disasters are analyzed, and an evaluation method is proposed. The key problems and evaluation systems to be solved are also discussed in this paper. A simulation method for the interactions between geomagnetically induced currents (GIC) and power systems is proposed, and a simulation is carried out based on MATLAB and the PowerWorld simulation software. The simulation results show that this method can accurately reflect the impact of faults on power systems and their cascading failures. The present research results provide a reference for further quantifying and preventing the power grid risks caused by magnetic storms.

The operation of transmission systems with large share of wind power is specially challenging under storm conditions. Under the stormy wind speed conditions, wind turbine protection system is designed to shut down the turbine to avoid excessive mechanical load. The sudden loss of wind power from large offshore plants is difficult to forecast accurately, which results in a large amount of power imbalance. The severity of such a wind power imbalance towards frequency stability needs to be studied for the future power systems. In addition, the overhead transmission lines can also be affected during storms, thereby increasing their probability of failure in the operation of power system under the islanded conditions. This paper investigates how the stormy weather can threaten the frequency stability of future Danish power system with large share of wind power and how to avoid the frequency instability through proper control and defence strategies such as high-voltage direct current (HVDC) control and load shedding. Sensitivity studies are performed for ramp rates of HVDC control, loadshedding strategies, inertia of the system with different volumes of disturbances to understand their impact on frequency stability.

We investigate an equatorial plasma depletion (EPD) event in Japan near the early morning sector (04:30–7:30 LT) during the recovery phase of a geomagnetic storm occurred on Memorial weekend (May 28) 2017 by using multi-instrumental measurements, including ground-based Global Navigation Satellite Systems (GNSS) network, ionosonde stations, and space-based Swarm and Defense Meteorological Satellite Program (DMSP) missions. The detailed 2-D evolution of the early morning EPD is well observed using GNSS ROTI maps with high temporal-spatial resolution. Moreover, ROTI data from BeiDou Geostationary Earth Orbit satellites are used to detect the EPD signature for the first time. The dawn EPD signature was also observed at the topside ionosphere by in situ electron density (Ne) measurements and ROTI results by space-borne Swarm Alpha (A), Charlie (C), and DMSP F17 satellites, indicating that the equatorial depletions arose to higher altitudes. We suggest that both the eastward disturbance dynamo electric field and overshielding electric field near the early morning sector created a favorable condition for the growth of the Rayleigh–Taylor (R-T) instability, and thus result in the generation of the dawn EPD.

In this paper, ground based Global Positioning System (GPS) Total Electron Content (TEC) observations collected from the GPS Aided GEO Augmented Navigation (GAGAN) network over low latitude Indian subcontinent are assimilated into the Thermosphere Ionosphere Electrodynamics General Circulation Model (TIEGCM). Gauss-Markov Kalman filter methodology is utilized and the background model error covariance (BMEC) matrix is framed using solar, geomagnetic, cross tail potential and hemispheric power indices using multivariate principal component analysis (MPCA) method. The model variations through different high latitude electric field models (Weimer and Heelis) are utilized in the data assimilation analysis. The proper attempt is modeled for providing TEC forecasts during geomagnetic storm conditions from 15th to 20th March 2015 days over Indian region. The more concentration is given in understanding the equatorial ionization anomaly (EIA) characteristics during the initial, main and recovery phase of St. Patrick's Day March 2015 geomagnetic storm. The proposed model forecast outperforms the persistence forecast with maximum forecast score (1.6) and forecast skill (2.2) during recovery phase of storm. TEC forecasts are validated with current time step GPS observations and 93 % correlation is observed on quiet day (10th March 2015) and 82 % correlation is noticed on main phase of storm (17th March 2015). Also proposed TEC model analysis and forecast results are compared with International Global Navigation Satellite Service (IGS) TEC stations datasets. A clear negative storm effect (EIA inhibition) has been forecasted well with the proposed Kalman filter and MPCA methodology aspects.

Convective storm nowcasting has long been an important issue and has attracted substantial interest. 3D radar images and 3D re-analysis data contain spatiotemporal information of the convective processes. This paper proposes a multi-channel 3D convolutional recurrent neural network (3D-CRN) for convective storm nowcasting, which aims to learn spatiotemporal information directly from these 3D radar and reanalysis data. 3D-CRN is composed of two sub-networks: three multi-channel 3D convolutional networks are used as the front-end spatial sub-networks, and the convLSTM encoder-decoder is constructed as a back-end temporal sub-network. By combing two subnets into one unified network, 3D-CRN can be jointly trained effectively. In order to give forecasts at different lead time simultaneously, we construct a many-to-many encoder-decoder structure to avoid tedious need to train several models respectively. Experimental results show the effectiveness of the proposed method.

This paper presents a machine learning model to estimate surface rainfall from TEMPEST-D observations. An artificial neural network (ANN) was chosen to build the rainfall estimation model from TEMPEST-D measurements. TEMPEST-D brightness temperature (TB) observations performed at five frequencies (i.e. 87, 164, 174, 178 and 181 GHz) were used as inputs, and the Multi-Radar/Multi-Sensor System (MRMS) radar-only rain rate product at the surface was used as ground truth and target to train the ANN model. A spatial alignment algorithm was developed to align the TEMPEST-D observed storm with the storm measurement from ground radar. The training data set was generated from 14 storm events observed simultaneously by the ground radar network and TEMPEST-D over the continental U.S. Two storm events were used for independent testing. The testing showed that estimated rainfall matched well with the MRMS surface rainfall product in terms of rainfall intensity, area, and precipitation system pattern. The structural similarity index measure scores for the two independent test cases are 0.72 and 0.81.

A new method to estimate the vertical profile of drop size distribution (DSD) parameters is developed for the Dual-frequency Precipitation Radar (DPR) on the core satellite of the Global Precipitation Measurement (GPM) mission. In the new method, the adjustment parameter e for the relation between precipitation rate $R$ and mass-weighted mean diameter D m can change vertically. Statistical analysis for one-month test products shows that e tends to be larger (smaller) in solid (liquid) precipitation layer and the dependence of e on the storm top becomes weak in the new method compared with the standard product version 06A.

CYMS is an ESA-funded project aiming at scaling up an operational service for Tropical Cyclone monitoring, in view of its potential integration as part of a Copernicus Service. In 2020, the demonstration of such a service has been operated and user's requirements refined. More than 90 scenes of TC have been acquired worldwide with Sentinel-1 A, Sentinel-1 B and Radarsat-2 thanks to the late programming acquisitions. These images have been processed into ocean surface wind field and disseminated to the user community. The user feedback on the data confirms that the capabilities of SAR to probe the ocean surface at high resolution is unique and offer potential for science applications related to the analysis of the TC inner core structure, possibly bringing new insight on the processes within the eye. Those observations are also crucial over regions lacking any aircraft observation or ground-based meteorological Radars for monitoring and validating the cyclone forecasts. One of the main requirements is the need for Near Real-Time distributions of CYMS observations to allow their operational use. This is currently stated as a project but one of the objectives is to ensure that one of the Copernicus services hosts a service dedicated to Cyclone Observation to allow acquisitions on Cyclones in a Copernicus framework.

On 2nd and 3rd October 2020, Storm Alex hit northern Italy and southern France regions with 500 mm of rainfall in about 24 hours. This triggered devastating flash floods and landslides, causing severe damages and 15 fatalities. This study presents a landslide inventory map obtained by using a generalized deep-learning model, avoiding human interaction in the workflow by skipping the time-consuming training step. A total of 1,249 landslides have been mapped with this approach in minutes after a suitable post-event satellite image was available for processing. Our results show how deep-learning strategies applied to remote sensing data can help in the aftermath of catastrophic events for the rapid detection and mapping of landslide phenomena.

In this article, we propose a novel deep metric learning approach to multi-hazard damage detection in remote-sensing (RS) images. The model is realized through a triplet network, which addresses the challenge of detecting disaster-induced damage with high accuracy for built-objects using pre- and post-event RS data. Previous efforts using a machine learning methodology do not sufficiently address the significant inter- and intra-class variations amid the disaster-scene complexities in built-environment RS images. Through this model, numerical evaluation is conducted based on wind storm disasters, and high performance is observed.

This paper presents some of the first nearly simultaneous observations between TEMPEST-D and RainCube, two CubeSat missions supported by NASA for on-orbit validation of technology for studying the Earth's atmosphere. This paper presents simultaneous observations by a Ka-band radar and multi-frequency millimeter-wave radiometers over precipitation systems, in three widely dispersed locations over the globe. The first storm was near Mexico's Pacific coast, whereas the second storm was over the South Pacific Ocean near the Solomon Islands, and the third storm was near Houston, Texas, USA. The comparisons showed good physical consistency between the TEMPEST-D and RainCube observations.

The Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) mission was selected by NASA as part of the Earth Venture–Instrument (EVI-3) program. The TROPICS Engineering Qualification Unit has been refurbished for flight, and a launch is planned for June 2021 on a SpaceX Falcon 9 to a 550-km sun synchronous orbit. This Pathfinder mission will provide risk reduction for the subsequent TROPICS constellation mission, which comprises six CubeSats in three low-Earth low-inclination orbital planes, with launches planned beginning in early 2022. Each of these identical CubeSats will host a high performance radiometer to provide temperature profiles using seven channels near the 118.75 GHz oxygen absorption line, water vapor profiles using three channels near the 183 GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel at 205 GHz that is more sensitive to precipitation-sized ice particles. The TROPICS mission highlights a number of aspirations of future earth observing sysems, including high revisit rate, system resilience, rapid technology infusion, and low cost. This paper presents these elements with an eye toward future operational architectures for weather and climate monitoring.

The Stepped Frequency Microwave Radiometer (SFMR) is a key instrument in tropical cyclones and high-latitude winter storms research. Through the observed brightness temperature (Tb) over a range of C-band frequencies, the SFMR derives wind-speed and rain rate. However, the instrument requires 5 to 10 seconds of averaging to cycle through all the frequencies, so regions of strong wind gradients and/or narrow rain features may be overlooked. The University of Massachusetts Amherst Microwave Remote Sensing Laboratory (MIRSL) developed a specialized version of the SFMR, the UMass Simultaneous Frequency Microwave Radiometer (USFMR) that operates six frequency channels simultaneously, eliminating the averaging time. In collaboration with NOAA/NESDIS/STAR we plan to use this instrument in studies of high latitude winter storms. We describe the instrument hardware, recent comparisons with operational SFMR measurements during hurricane flight in 2019, and current and planned investigation of retrieval inconsistencies in non-tropical cyclone environments. In collaboration with NOAA/NESDIS/STAR we plan to use this instrument in studies of high latitude winter storms. We describe the instrument hardware, recent comparisons with operational SFMR measurements during hurricane flight in 2019, and current and planned investigation of retrieval inconsistencies in non-tropical cyclone environments.

Nowcasting plays an important role in issuing warnings about hazardous weather. Traditional nowcasting methods are implemented by solving flow equation. Nowcasting using Collaborative Adaptive Sensing of the Atmosphere (CASA) radar network has provided useful nowcast. In recent years, machine learning approach has been studied to supplement the traditional nowcasting methods. This paper examines nowcasting using machine learning method in high plain area (Denver) and southern plain area (Dallas Forth Worth). We compare the performance and evaluate the performance of machine learning method in different climatic regions. The machine learning model structures are pure convolution layer and combination of convolution and recurrent layers.

Sandy beaches play an important role for economic and recreational activities as well as for coastal protection. Storm impact and recovery involve complex dynamic processes from short to longer-term. These result in a broad range of morphological change which can be assessed from remote sensing such as airborne LiDAR (Light Detecting and Ranging) system. This study monitors the response and recovery of a sandy multi-barred beach after a severe storm. A new algorithm for automated bar extraction developed in Python is applied to the digital surface models of pre-, immediately after storm and 2 months after the event. The severe storm from 8-12/02/2020 caused erosion of the backshore and dunefoot as well as a significant re-arrangement of the intertidal bar-through system. The findings are of critical importance in improving our knowledge as well as forecasting coastal evolution and future hazards.

This study utilizes airborne lidar and unmanned aircraft system (UAS) structure-from-motion (SfM) derived elevation models to examine the impact of Hurricane Michael on Little St. George Island's subaerial beach and foredune system and its respective short-term recovery. Volumetric change detection, horizontal shoreline movement, and feature extraction analyses were conducted for the purposes of monitoring its geomorphic evolution succeeding the storm event. Results of the study indicate Hurricane Michael had considerable impacts on the island, causing a net loss of 198,786 m3 of sediment and an average reduction of dune crest elevations by 0.62 m. Derived results of the recovery period suggest that, despite an average increase of dune crests elevations by 0.04 m, Little St. George Island has sustained a net loss of approximately 14,340 m3.

The profile classification module in GPM DPR level-2 algorithm has developed various products using rich information from the dual-frequency observations. These products perform functions in rain type classification, melting layer detection, identifying surface snowfall as well as graupel hail (GH). Extensive evaluation and validation activities have been performed on these products and illustrate excellent performance. Extreme precipitation such as hail has raised interest due to its huge impact on society. In the coming new version of GPM DPR algorithm, we will introduce a new hail product to identify hail along a vertical profile. Different from the existing “flagGraupelHail” product, which combines graupel and hail together, the new product will separate them. It should be noted here that presence of hail in the storm does not mean hail on the ground.

Dust storms have long been considered one of the most visible signs of soil erosion, and popularity in the trend has grown in recent years. PM2.5, a critical element of environmental pollution, has been attributed to negative health impacts in humans, including premature death after extended exposure. However, analyses of air quality and its effect on numerous societally important problems are hampered by restricted surface assessment and a lack of surveillance with sufficient spatial resolution. The use of satellite imagery for dust storm detection is vital for the identification of emission approximations and bio-conservatives studies. In this paper, data of the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) has been employed to look into the dust storm patterns in the UAE for the last 10 years (2010–2020). This is further supported with the images retrieved from the database and the annual developments are compared.

This paper proposes optimal production scheduling using a practical production simulator and modified brain storm optimization (MBSO). So far, in a production scheduling research area, for the purpose of practical applications, various practical performance measures and constraints have been proposed. Recently, production scheduling researches in consideration of energy consumptions and CO 2 emissions have been conducted. However, in the various production scheduling researches, each research only treats practical performance measures, practical constraints, energy consumptions, and CO 2 emissions partially. Hence, no researches have considered the various practical performance measures and constraints, energy consumptions, and CO 2 emissions totally. In this paper, a production schedule optimization framework is proposed using a production simulator considering constraints that cannot be mathematically formulated. It is verified that the proposed MBSO based method can find higher quality solutions than the conventional PSO and DEEPSO based methods. It is also verified that there is a significant difference among the conventional PSO and DEEPSO based methods, and the proposed MBSO based method with 0.05 significant level using the Friedman test.

Satellite communication in the future is shifting to higher frequency Ka and V-bands. The fundamental issue in designing viable earth-to-satellite communication links at these bands is rain fade. In tropical areas, the problem is increased severely by the fact that high-intensity rainfall occurs for the majority of times in the year. One of the potential solutions presented by researchers to combat attenuation due to rain is the time diversity method. To explore the improvement, measured real-time rain attenuation data are necessary for time diversity analysis. However, the problem is that data in higher frequency bands such as the Ka and V bands are not available in most of the places. As a result, the Synthetic Storm Technique (SST) can be used to convert real-time rain rate data into real-time rain attenuation data at any required frequency. This research analyses data collected at IIUM's Kuala Lumpur campus over one-year period using one-minute integration time. Synthetic Storm Technique (SST) is used to estimate rain attenuation for Ku, Ka, and V-bands based on one-year recorded rain rate data. Time diversity gains are assessed and compared to available measured gain at Ku-band and that predicted by Matricciani's model using anticipated rain attenuation time series.

Conventionally, the short term prediction of precipitation (or nowcast) observed by a network of weather radars is conducted by means of advection schemes. Such schemes often lead to underestimated areas of predicted precipitation cells and they do not include the vertical structure of the cells, which can carry important information on the evolution of precipitation. To mitigate such limitations on nowcasts, storm cells have been treated as 3D targets, allowing the exploitation of their vertical and dynamic structure. In this work, storm cell targets are identified and tracked from 2D reflectivity nationwide fields collected by the German meteorological service DWD. Then, the vertical structure of the cells are analyzed in terms of the polarimetric-based differential reflectivity Z DR -column, which is a predictor of potential changes in precipitation. Knowing the location and tendency of storm cells and their associated Z DR columns, the nowcast skills can be further improved, mainly in localized regions.

We present a comparison of two methods to estimate the soiling losses from photovoltaic (PV) system operational data. We evaluate 21 months of data from an outdoor soiling test in a hot, desert climate with a reference system which was cleaned once a week. First, we present an analyst-based result that uses accepted industry practices to estimate trends based on data fit and records of cleaning and storm events, representative of an industry-standard, labor-intensive approach. Second, we present a completely automated analysis, in which the cleaning events are selected through an unsupervised statistical learning algorithm. The algorithm generates estimates of linear soiling rates that are in close agreement with the rates determined by the human analyst, at orders of magnitude faster speed. The speed of processing is particularly advantageous in analysis of multiple setups and long duration data (years of minute(s) data collection).

As the intensity and frequency of storm events are projected to increase due to climate change, local agencies urgently need a timely and reliable framework for flood forecasting, downscale from watershed to street level in urban areas. Integrated with property data with various hydrometeorological data, the flood prediction model can also provide further insight into environmental justice, which will aid households and government agencies’ decision-making. This study uses deep learning (DL) methods and radar-based rainfall data to predict the inundated areas and analyze the property quickly and demographic data concerning stream proximity to provide a way to quantify socioeconomic impacts. We expect that our DL-based models will improve the accuracy of forecasting floods and provide a better picture of which communities bear the worst burdens of flooding, and encourage city officials to address the underlying causes of flood risk.

A new model for the application of Up-Link Power Control in satellite communication systems is presented. The model consists in the combination of an ARIMA model to forecast the rain rate (5 minutes in the future) and of the Enhanced Synthetic Storm Technique (E-SST) to generate time series of the rain attenuation at two frequencies, from which the frequency scaling ratio can be calculated for the real time application of Up-Link Power Control. The performance of the model, tested against the Alphasat Ka- and Q-band propagation data collected in Milan, indicate that the proposed frequency scaling approach is accurate enough to allow the real-time effective application of ULPC in EHF satellite communication systems.

Publicly available fire operation data opens new ways to assist fire stations in planning and resource distribution tasks. Previous work has predicted attributes of certain operations, as well as of thunderstorms, but fire department storm operations themselves have not been predicted yet. We present an approach to predict if storm operations will occur for individual fire stations on specific days, based on time, location, and weather data. As days with storm operations are rare, we artificially balance samples with SMOTE, then compare the prediction performance of different machine learning models with an outlier detection on unbalanced data and uninformed prediction models as baseline. To evaluate our approach, we aggregate datasets for 10 fire stations in Upper Austria, Austria, and predict their storm operations. Our approach thereby achieves a median AUC of 0.91 across fire stations, which is an improvement of 0.44 over baseline models.

Extreme rainfall events have become more frequent and more intense in recent times, leading to increased severe flooding. Potential impacts, especially to transportation infrastructure such as roads and bridges, are magnified due to their age. Also, transportation infrastructure experiences strain to keep up with increasing land development, which causes more flow into streams. To mitigate the resulting impacts of bridge overtopping and roadway washouts on the public, a software tool is proposed that contributes to decision making in disaster response for extreme rainfall events. To create the software tool, an accurate, high resolution surface terrain model (including channels, roadways, and infrastructure for bridges and culverts) is produced of the selected study area using QL2 LiDAR data sets. Additionally, stormwater flow models are generated for flow at stream and bridge intersections, and through culverts at various storm return periods. The resulting software tool will integrate bridge overtopping and roadway washout location identification to show the severity of impact for selected watersheds in near real time. This tool will be in the form of open source software (e.g., Python) and an interactive map to display the locations and levels of severity based on forecasted rain data input for the study area. The paper first presents the proposed method, details the data collection and processing techniques, and then discusses a case study. The results validate the first steps for data collection and processing to support further modeling and analysis.

Dust storms affect the performance of microwave links. Several dust storm attenuation prediction models were proposed by researchers based on theoretical assumptions of either single - particle scattering theorems or numerical methods. The climate conditions and signal variations on microwave link operated at 21 GHz were monitored concurrently in Khartoum, Sudan for one year period. It was observed that dust storms are accompanied with increase of relative humidity and decrease in the received signal. The attenuation predicted by theoretical models and the measured attenuation at 21GHz were compared for a dry and humid case. The predicted attenuations in humid condition are varied significantly and increased 5 times more than dry dust condition. From these results it’s obvious that all the theoretical methods are unable to determine the attenuation of microwave propagation in dust storm at 21GHz and it’s essential to improve available models by considering the effect of humidity.