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\"A book for young readers about hurricanes\"-- Provided by publisher.

Extreme meteorological conditions favor the development of severe storms and tornadoes that may have largely impacts on the population despite its relatively short life. Tornadic severe storms have been documented around the World. In Mexico (MEX), the study of the occurrence of tornadoes and severe storms is relatively new. In this research, we have selected an event of severe tornadic storm in Ciudad Acuña, Mexico. The storm was driven by a frontal system moving southward from USA converging with a warmer moist air flux from the Gulf of Mexico. The tornado strikes on the Northeast of Mexico, in Coahuila State, on May 25, 2015. Imagery of infrared channel from GOES 13 satellite and the presence of a hook echo in radar data of May 25, 2015, indicate a supercell structure. The maximum values of radial velocity were about −20 and 15 m s −1 . In this study, the WRF model was used in order to simulate the mesoscale meteorological conditions of the tornado. Model simulations capture atmospheric features observed in Doppler radar. The simulated storm-relative helicity values were between 400 and 500 m 2  s −2 . The simulated convective available potential energy values were of 3000 J kg −1 . These values were higher than values for convective storms, located over the region of Ciudad Acuña in Mexico and Del Rio in USA. The supercell was a result of high humidity and temperature gradients, conditioned by frontal activity and moisture flux intensifications from the Gulf of Mexico.

Learn about tornadoes.

Today, the scientists and researchers from almost every corner of the world are well aware of the adverse effects of geomagnetic storms on our highly expensive electrical and electromagnetic systems such as power grid transmission, satellite communication, RF communication systems, etc. The severe damaging effects of these storms on human health and on other inhabitants of the biosphere are also not unknown. A survey of literature on space science reveals that location or space dependency of these geomagnetic storms has already been established. Now the question may arise—Are geomagnetic storms time-dependent? To answer this question, a laborious exercise has been performed. K-index data obtained from the US Department of Commerce, NOAA, Space Weather Prediction Center, for the year, started from 1994 to 2014 (21 years) over three different locations namely Fredericksburg, College, and Estimated Planetary, are analyzed statistically for the eight different time periods namely 00:00–03:00, 03:00–06:00, 06:00–09:00, 09:00–12:00, 12:00–15:00, 15:00–18:00, 18:00–21:00, 21:00–24:00 hours respectively. Results thus obtained have concluded that apart from space or location dependency the geomagnetic storms are time-dependent also.

\"Blizzards can be particularly dangerous weather events, especially for the unprepared. People out in the storm are at risk for hypothermia and frostbite, and those inside have to worry about power and phone outages. Cars may get stuck on the highway, and pilots may feel unsure about landing a plane. Readers learn the severity of blizzards of the past, including 2010s Snowmageddon, as well as how these blizzards form. Dramatic, full-color photographs show readers blizzards aftermath, and safety tips help readers prepare for a blizzard should a blizzard watch be issued in their area.\"--Provided by publisher.

This article provides an overview of the experimental design, execution, education and public outreach, data collection, and initial scientific results from the Remote Sensing of Electrification, Lightning, and Mesoscale/Microscale Processes with Adaptive Ground Observations (RELAMPAGO) field campaign. RELAMPAGO was a major field campaign conducted in the Córdoba and Mendoza provinces in Argentina and western Rio Grande do Sul State in Brazil in 2018–19 that involved more than 200 scientists and students from the United States, Argentina, and Brazil. This campaign was motivated by the physical processes and societal impacts of deep convection that frequently initiates in this region, often along the complex terrain of the Sierras de Córdoba and Andes, and often grows rapidly upscale into dangerous storms that impact society. Observed storms during the experiment produced copious hail, intense flash flooding, extreme lightning flash rates, and other unusual lightning phenomena, but few tornadoes. The five distinct scientific foci of RELAMPAGO—convection initiation, severe weather, upscale growth, hydrometeorology, and lightning and electrification—are described, as are the deployment strategies to observe physical processes relevant to these foci. The campaign’s international cooperation, forecasting efforts, and mission planning strategies enabled a successful data collection effort. In addition, the legacy of RELAMPAGO in South America, including extensive multinational education, public outreach, and social media data gathering associated with the campaign, is summarized.

\"Vibrant, full-color photographs and accessible text help introduce young readers to hurricanes. Readers will learn how warm water and wind combine to form these powerful storms, which parts of the world are most affected and when, and what steps to take to prepare for these severe weather events\"-- Provided by publisher.

This paper reviews the development of the ensemble Kalman filter (EnKF) for atmospheric data assimilation. Particular attention is devoted to recent advances and current challenges. The distinguishing properties of three well-established variations of the EnKF algorithm are first discussed. Given the limited size of the ensemble and the unavoidable existence of errors whose origin is unknown (i.e., system error), various approaches to localizing the impact of observations and to accounting for these errors have been proposed. However, challenges remain; for example, with regard to localization of multiscale phenomena (both in time and space). For the EnKF in general, but higher-resolution applications in particular, it is desirable to use a short assimilation window. This motivates a focus on approaches for maintaining balance during the EnKF update. Also discussed are limited-area EnKF systems, in particular with regard to the assimilation of radar data and applications to tracking severe storms and tropical cyclones. It seems that relatively less attention has been paid to optimizing EnKF assimilation of satellite radiance observations, the growing volume of which has been instrumental in improving global weather predictions. There is also a tendency at various centers to investigate and implement hybrid systems that take advantage of both the ensemble and the variational data assimilation approaches; this poses additional challenges and it is not clear how it will evolve. It is concluded that, despite more than 10 years of operational experience, there are still many unresolved issues that could benefit from further research. Contents Introduction ...4490 Popular flavors of the EnKF algorithm ...4491 General description...4491 Stochastic and deterministic filters...4492 The stochastic filter...4492 The deterministic filter...4492 Sequential or local filters...4493 Sequential ensemble Kalman filters...4493 The local ensemble transform Kalman filter...4494 Extended state vector...4494 Issues for the development of algorithms...4495 Use of small ensembles ...4495 Monte Carlo methods...4495 Validation of reliability...4497 Use of group filters with no inbreeding...4498 Sampling error due to limited ensemble size: The rank problem...4498 Covariance localization...4499 Localization in the sequential filter...4499 Localization in the LETKF...4499 Issues with localization...4500 Summary...4501 Methods to increase ensemble spread ...4501 Covariance inflation...4501 Additive inflation...4501 Multiplicative inflation...4502 Relaxation to prior ensemble information...4502 Issues with inflation...4503 Diffusion and truncation...4503 Error in physical parameterizations...4504 Physical tendency perturbations...4504 Multimodel, multiphysics, and multiparameter approaches...4505 Future directions...4505 Realism of error sources...4506 Balance and length of the assimilation window ...4506 The need for balancing methods...4506 Time-filtering methods...4506 Toward shorter assimilation windows...4507 Reduction of sources of imbalance...4507 Regional data assimilation ...4508 Boundary conditions and consistency across multiple domains...4509 Initialization of the starting ensemble...4510 Preprocessing steps for radar observations...4510 Use of radar observations for convective-scale analyses...4511 Use of radar observations for tropical cyclone analyses...4511 Other issues with respect to LAM data assimilation...4511 The assimilation of satellite observations ...4512 Covariance localization...4512 Data density...4513 Bias-correction procedures...4513 Impact of covariance cycling...4514 Assumptions regarding observational error...4514 Recommendations regarding satellite observations...4515 Computational aspects ...4515 Parameters with an impact on quality...4515 Overview of current parallel algorithms...4516 Evolution of computer architecture...4516 Practical issues...4517 Approaching the gray zone...4518 Summary...4518 Hybrids with variational and EnKF components ...4519 Hybrid background error covariances...4519 E4DVar with the α control variable...4519 Not using linearized models with 4DEnVar...4520 The hybrid gain algorithm...4521 Open issues and recommendations...4521 Summary and discussion ...4521 Stochastic or deterministic filters...4522 The nature of system error...4522 Going beyond the synoptic scales...4522 Satellite observations...4523 Hybrid systems...4523 Future of the EnKF...4523 APPENDIX A ...4524 Types of Filter Divergence ...4524 Classical filter divergence...4524 Catastrophic filter divergence...4524 APPENDIX B ...4524 Systems Available for Download ...4524 References ...4525

\"Discusses the science behind tornadoes and their effects\"--Provided by publisher.

In this study we compared 3.7 million rawinsonde observations from 232 stations over Europe and North America with proximal vertical profiles from ERA5 and MERRA-2 to examine how well reanalysis depicts observed convective parameters. Larger differences between soundings and reanalysis are found for thermodynamic theoretical parcel parameters, low-level lapse rates, and low-level wind shear. In contrast, reanalysis best represents temperature and moisture variables, mid-tropospheric lapse rates, and mean wind. Both reanalyses underestimate CAPE, low-level moisture, and wind shear, particularly when considering extreme values. Overestimation is observed for low-level lapse rates, midtropospheric moisture, and the level of free convection. Mixed-layer parcels have overall better accuracy when compared to most-unstable parcels, especially considering convective inhibition and lifted condensation level. Mean absolute error for both reanalyses has been steadily decreasing over the last 39 years for almost every analyzed variable. Compared to MERRA-2, ERA5 has higher correlations and lower mean absolute errors. MERRA-2 is typically drier and less unstable over central Europe and the Balkans, with the opposite pattern over western Russia. Both reanalyses underestimate CAPE and CIN over the Great Plains. Reanalyses are more reliable for lower elevation stations and struggle along boundaries such as coastal zones and mountains. Based on the results from this and prior studies we suggest that ERA5 is likely one of the most reliable available reanalyses for exploration of convective environments, mainly due to its improved resolution. For future studies we also recommend that computation of convective variables should use model levels that provide more accurate sampling of the boundary layer conditions compared to less numerous pressure levels.