Storm Prediction Research Articles

조선시대(1392-1910) 해일 발생의 시공간적 분포 특성

Analysis and prediction of storm surges are very important because the global warming has raised sea levels and increased the frequency of massive typhoons, accelerating damage of coastal flooding. However, the data for storm surge prediction is lacking due to the short history of observation in South Korea. The purpose of this study is to investigate the spatial and temporal characteristics of the previous surges and tsunamis based on the historical documents published during the Joseon Dynasty. In addition, we tried to evaluate the damage and spatial extent of such disasters, using the expressions about surge records including heights and number of administrative divisions. As a result, a total of 175 records of surges and tsunamis were compiled from 1392 to 1910: 145 events were extracted through the analysis of the ancient documents, and 30 events were from the previous research. Most of the strorm surges occurred along the west coast during summer season. More than half of the total surges were concentrated for 120 years from the mid 1600s to the mid 1700s, which was estimated to be highly relevant to the climate conditions in East Asia during the Little Ice Age. Hazardous areas by storm and tidal surges were also extracted, including Asan, Ganghwa, and Siheung during the Joseon Dyanisity period.

Influence of the Characteristics of Weather Information in a Thunderstorm-Related Power Outage Prediction System

Thunderstorms are one of the most damaging weather phenomena in the United States, but they are also one of the least predictable. This unpredictable nature can make it especially challenging for emergency responders, infrastructure managers, and power utilities to be able to prepare and react to these types of events when they occur. Predictive analytical methods could be used to help power utilities adapt to these types of storms, but there are uncertainties inherent in the predictability of convective storms that pose a challenge to the accurate prediction of storm-related outages. Describing the strength and localized effects of thunderstorms remains a major technical challenge for meteorologists and weather modelers, and any predictive system for storm impacts will be limited by the quality of the data used to create it. We investigate how the quality of thunderstorm simulations affects power outage models by conducting a comparative analysis, using two different numerical weather prediction systems with different levels of data assimilation. We find that limitations in the weather simulations propagate into the outage model in specific and quantifiable ways, which has implications on how convective storms should be represented to these types of data-driven impact models in the future.

An Environmental Study on Tornado Pathlength, Longevity, and Width

AbstractA dataset maintained by the Storm Prediction Center (SPC) of 6300 tornado events from 2009 to 2015, consisting of radar-identified convective modes and near-storm environmental information obtained from Rapid Update Cycle and Rapid Refresh model analysis grids, has been augmented with additional radar information related to the low-level mesocyclones associated with tornado longevity, pathlength, and width. All EF2–EF5 tornadoes [as measured on the enhanced Fujita (EF) scale], in addition to randomly selected EF0–EF1 tornadoes, were extracted from the SPC dataset, which yielded 1268 events for inclusion in the current study. Analysis of those data revealed similar values of the effective-layer significant tornado parameter for the longest-lived (60+ min) tornadic circulations, longest-tracked (≥68 km) tornadoes, and widest tornadoes (≥1.2 km). However, the widest tornadoes occurring west of −94° longitude were associated with larger mean-layer convective available potential energy, storm-top divergence, and low-level rotational velocity. Furthermore, wide tornadoes occurred when low-level winds were out of the southeast, resulting in large low-level hodograph curvature and near-surface horizontal vorticity that was more purely streamwise when compared with long-lived and long-tracked events. On the other hand, tornado pathlength and longevity were maximized with eastward-migrating synoptic-scale cyclones associated with strong southwesterly wind profiles through much of the troposphere, fast storm motions, large values of bulk wind difference and storm-relative helicity, and lower buoyancy.

I tornado negli USA: un'analisi dell'archivio dati (1950-2018)

A statistical analysis of the data contained in the NOAA’s Storm Prediction Center tornado archive (covering the period 1950-2018) has been carried out. The actual average values of the frequencies for the various intensity classes could be effectively provided for the period 1991-2018, because of some inhomogeneities of the archive due to variations in the methods and procedures of tornado reporting. The time series of the frequency of F2, F3, F4 and F5 events showed decreasing trends; this decline seems largely due to a significant reduction of the strongest events. This interpretation is supported by the decreasing trends of normalized economic losses and of number of victims.

Colorful Language: Investigating Public Interpretation of the Storm Prediction Center Convective Outlook

AbstractAlthough severe weather forecast products, such as the Storm Prediction Center (SPC) convective outlook, are much more accurate than climatology at day-to-week time scales, tornadoes and severe thunderstorms claim dozens of lives and cause billions of dollars in damage every year. While the accuracy of this outlook has been well documented, less work has been done to explore the comprehension of the product by non-expert users like the general public. This study seeks to fill this key knowledge gap by collecting data from a representative survey of U.S. adults in the lower 48 states about their use and interpretation of the SPC convective outlook. Participants in this study were asked to rank the words and colors used in the outlook from least to greatest risk, and their answers were compared through visualizations and statistical tests across multiple demographics. Results show that the US public ranks the outlook colors similarly to their ordering in the outlook but switch the positions of several of the outlook words as compared to the operational product. Logistic regression models also reveal that more numerate individuals more correctly rank the SPC outlook words and colors. These findings suggest that the words used in the convective outlook may confuse non-expert users, and that future work should continue to use input from public surveys to test potential improvements in the choice of outlook words. Using more easily understood words may help to increase the outlook’s decision support value and potentially reduce the harm caused by severe weather events.

A method to estimate the probability of strong winds occurrence using weather radar data

AbstractWeather radars are capable of detecting and displaying storm‐related turbulence as well as precipitation rate with fine spatial resolution and reliable quality. Especially, weather radars can enable the detection of atmospheric conditions in the vicinity of cities and thereby help to notify the strong winds. This article investigates the application of weather radar measurements for predicting strong wind and presents a new method for very short‐term storm prediction. In the proposed method, hidden Markov model (HMM) is used to classify atmospheric conditions to “potentially stormy” and “non‐stormy” states using the available radar data, and semi‐Markov theory is used to estimate the probability of storm occurrence with time. In fact, the probability of transition between “potentially stormy,” “non‐stormy,” and “stormy” states is modeled by a semi‐Markov model, to find the unconditional probability of storm occurrence with time. The model is implemented with the use of the data of Tehran C‐band weather radar and anemometer of Tehran international airport. Verification results show that the precision (forecast accuracy) is around 0.19 and the recall is around 0.67 in the presented classification method.

The Impact of the Storm Prediction Center's Convective Outlooks and Watches on Emergency Management Operational Planning

For many emergency managers (EMs) and National Weather Service (NWS) forecasters, Convective Outlooks issued by the Storm Prediction Center (SPC) influence the preparation for near-term severe weather events. However, research into how and when EMs utilize that information, and how it influences their emergency operations plan, is limited. Therefore, to better understand how SPC Convective Outlooks are used for severe weather planning, a survey was conducted of NWS core partners in the emergency management sector. The results show EMs prefer to wait until an Enhanced Risk for severe thunderstorms is issued to prepare for severe weather. In addition, the Day 2 Convective Outlook serves as the threshold for higher, value-based decision making. The survey was also used to analyze how the issuance of different risk levels in SPC Convective Outlooks impact emergency management preparedness compared to preparations conducted when a Convective Watch is issued.

Effect of the Assimilation Frequency of Radar Reflectivity on Rain Storm Prediction by Using WRF-3DVAR

An attempt was made to evaluate the impact of assimilating Doppler Weather Radar (DWR) reflectivity together with Global Telecommunication System (GTS) data in the three-dimensional variational data assimilation (3DVAR) system of the Weather Research Forecast (WRF) model on rain storm prediction in Daqinghe basin of northern China. The aim of this study was to explore the potential effects of data assimilation frequency and to evaluate the outputs from different domain resolutions in improving the meso-scale NWP rainfall products. In this study, four numerical experiments (no assimilation, 1 and 6 h assimilation time interval with DWR and GTS at 1 km horizontal resolution, 6 h assimilation time interval with radar reflectivity, and GTS data at 3 km horizontal resolution) are carried out to evaluate the impact of data assimilation on prediction of convective rain storms. The results show that the assimilation of radar reflectivity and GTS data collectively enhanced the performance of the WRF-3DVAR system over the Beijing-Tianjin-Hebei region of northern China. It is indicated by the experimental results that the rapid update assimilation has a positive impact on the prediction of the location, tendency, and development of rain storms associated with the study area. In order to explore the influence of data assimilation in the outer domain on the output of the inner domain, the rainfall outputs of 3 and 1 km resolution are compared. The results show that the data assimilation in the outer domain has a positive effect on the output of the inner domain. Since the 3DVAR system is able to analyze certain small-scale and convective-scale features through the incorporation of radar observations, hourly assimilation time interval does not always significantly improve precipitation forecasts because of the inaccurate radar reflectivity observations. Therefore, before data assimilation, the validity of assimilation data should be judged as far as possible in advance, which can not only improve the prediction accuracy, but also improve the assimilation efficiency.

Environmental and Radar Characteristics of Gargantuan Hail-Producing Storms

AbstractStorms that produce gargantuan hail (defined here as ≥ 6 inches or 15 cm in maximum dimension), although seemingly rare, can cause extensive damage to property and infrastructure, and cause injury or even death to humans and animals. Currently, we are limited in our ability to accurately predict gargantuan hail and detect gargantuan hail on radar. In this study, we analyze the environments and radar characteristics of gargantuan hail-producing storms to define the parameter space of environments in which gargantuan hail occurs, and compare environmental parameters and radar signatures in these storms to storms producing other sizes of hail. We find that traditionally used environmental parameters used for severe storms prediction, such as most unstable convective available potential energy (MUCAPE) and 0–6 km vertical wind shear, display considerable overlap between gargantuan hail-producing storm environments and those that produce smaller hail. There is a slight tendency for larger MUCAPE values for gargantuan hail cases, however. Additionally, gargantuan hail-producing storms seem to have larger low-level storm-relative winds and larger updraft widths than those storms producing smaller hail, implying updrafts less diluted by entrainment and perhaps maximizing the liquid water content available for hail growth. Moreover, radar reflectivity or products derived from it are not different from cases of smaller hail sizes. However, inferred mesocyclonic rotational velocities within the hail growth region of storms that produce gargantuan hail are significantly stronger than the rotational velocities found for smaller hail categories.

Exploring Spatial Patterns of Virginia Tornadoes Using Kernel Density and Space-Time Cube Analysis (1960–2019)

This study evaluates the spatial-temporal patterns in Virginia tornadoes using the National Weather Service Storm Prediction Center’s Severe Weather GIS (SVRGIS) database. In addition to descriptive statistics, the analysis employs Kernel Density Estimation for spatial pattern analysis and space-time cubes to visualize the spatiotemporal frequency of tornadoes and potential trends. Most of the 726 tornadoes between 1960–2019 occurred in Eastern Virginia, along the Piedmont and Coastal Plain. Consistent with other literature, both the number of tornadoes and the tornado days have increased in Virginia. While 80% of the tornadoes occurred during the warm season, tornadoes did occur during each month including two deadly tornadoes in January and February. Over the 60-year period, a total of 28 people were killed in the Commonwealth. Most tornado activity took place in the afternoon and early evening hours drawing attention to the temporal variability of risk and vulnerability. Spatial analysis results identify significant, non-random clusters of tornado activity and increasing temporal frequency. While this study improves weather-related literacy and addresses a need in the Commonwealth, more research is necessary to further evaluate the synoptic and mesoscale mechanisms of Virginia tornadoes.

Convective-Storm Environments in Subtropical South America from High-Frequency Soundings during RELAMPAGO-CACTI

AbstractDuring the Remote Sensing of Electrification, Lightning, and Mesoscale/Microscale Processes with Adaptive Ground Observations-Cloud, Aerosol, and Complex Terrain Interactions (RELAMPAGO-CACTI) field experiments in 2018–19, an unprecedented number of balloon-borne soundings were collected in Argentina. Radiosondes were launched from both fixed and mobile platforms, yielding 2712 soundings during the period 15 October 2018–30 April 2019. Approximately 20% of these soundings were collected by highly mobile platforms, strategically positioned for each intensive observing period, and launching approximately once per hour. The combination of fixed and mobile soundings capture both the overall conditions characterizing the RELAMPAGO-CACTI campaign, as well as the detailed evolution of environments supporting the initiation and upscale growth of deep convective storms, including some that produced hazardous hail and heavy rainfall. Episodes of frequent convection were characterized by sufficient quantities of moisture and instability for deep convection, along with deep-layer vertical wind shear supportive of organized or rotating storms. A total of 11 soundings showed most unstable convective available potential energy (MUCAPE) exceeding 6000 J kg−1, comparable to the extreme instability observed in other parts of the world with intense deep convection. Parameters used to diagnose severe-storm potential showed that conditions were often favorable for supercells and severe hail, but not for tornadoes, primarily because of insufficient low-level wind shear. High-frequency soundings also revealed the structure and evolution of the boundary layer leading up to convection initiation, convectively generated cold pools, the South American low-level jet (SALLJ), and elevated nocturnal convection. This sounding dataset will enable improved understanding and prediction of convective storms and their surroundings in subtropical South America, as well as comparisons with other heavily studied regions such as the central United States that have not previously been possible.

Prediction of Cytokine Storm and Mortality in Patients with COVID-19 Admitted to ICU: Do Markers Tell the Story?

Introduction: COVID-19 has caused approximately one million deaths worldwide as of November 24, 2020. Markers of disease activity like ferritin, C-reactive protein (CRP), and D-dimers are frequently monitored to detect the best opportunity for intensive treatment. Methods: All patients of >18 years of age were included. The primary variables of interest, ferritin, CRP, and D-dimers, for each patient throughout hospitalization were recorded. Primary clinical outcomes of length of stay in ICU and survival were recorded. Demographics: age, gender, BMI, and nationality. Ferritin, CRP, and D-dimers were recorded daily if available for the whole ICU stay, and all other variables were recorded on admission day to ICU. Results: The sample includes 235 records. More than 95% of patients have all markers on the day of admission to ICU were ferritin (median 1,278; IQR 1,424), D-dimer 1.21 (3.4), and CRP 129.5 (121). Daily average levels of markers were different from their admission day level: ferritin 1,395 (1,331), D-dimer 3.11 (5.52), and CRP 107 (75.8). Multiple logistic regression analysis determined that average CRP during the stay was the only predictor of survival. Discussion: Data on markers utilization to detect the acute phase of inflammation help clinicians focus on the opportunity window for intensive treatment. Conclusion: Average CRP during the stay in ICU is higher than CRP on admission. Average CRP is the only factor that predicts survival.

Attenuating the Effects of Novel COVID-19 (SARS-CoV-2) Infection-Induced Cytokine Storm and the Implications.

The COVID-19 pandemic constitutes an arduous global health challenge, and the increasing number of fatalities calls for the speedy pursuit of a remedy. This review emphasizes the changing aspects of the COVID-19 disease, featuring the cytokine storm’s pathological processes. Furthermore, we briefly reviewed potential therapeutic agents that may modulate and alleviate cytokine storms. The literature exploration was made using PubMed, Embase, MEDLINE, Google scholar, and China National Knowledge Infrastructure databases to retrieve the most recent literature on the etiology, diagnostic markers, and the possible prophylactic and therapeutic options for the management of cytokine storm in patients hospitalized with COVID-19 disease. The causative agent, severe acute respiratory coronavirus-2 (SARS-CoV-2), continually threatens the efficiency of the immune system of the infected individuals. As the first responder, the innate immune system provides primary protection against COVID-19, affecting the disease’s progression, clinical outcome, and prognosis. Evidence suggests that the fatalities associated with COVID-19 are primarily due to hyper-inflammation and an aberrant immune function. Accordingly, the magnitude of the release of pro-inflammatory cytokines such as interleukin (IL)-1, (IL-6), and tumor necrosis alpha (TNF-α) significantly differentiate between mild and severe cases of COVID-19. The early prediction of a cytokine storm is made possible by several serum chemistry and hematological markers. The prompt use of these markers for diagnosis and the aggressive prevention and management of a cytokine release syndrome is critical in determining the level of morbidity and fatality associated with COVID-19. The prophylaxis and the rapid treatment of cytokine storm by clinicians will significantly enhance the fight against the dreaded COVID-19 disease.

An Hourly Climatology of Operational MRMS MESH-Diagnosed Severe and Significant Hail with Comparisons to Storm Data Hail Reports

AbstractThe Multi-Radar Multi-Sensor (MRMS) system generates an operational suite of derived products in the National Weather Service useful for real-time monitoring of severe convective weather. One such product generated by MRMS is the maximum estimated size of hail (MESH) that estimates hail size based on the radar reflectivity properties of a storm above the environmental 0°C level. The MRMS MESH product is commonly used across the National Weather Service (NWS), including the Storm Prediction Center (SPC), to diagnose the expected hail size in thunderstorms. Previous work has explored the relationship between the MRMS MESH product and severe hail (≥25.4 mm or 1 in.) reported at the ground. This work provides an hourly climatology of severe MRMS MESH across the contiguous United States from 2012 to 2019, including an analysis of how the MESH climatology differs from the severe hail reports climatology. Results suggest that the MESH can provide beneficial hail risk information in areas where population density is low. Evidence also shows that the MESH can provide potentially beneficial information about severe hail occurrence during the night in locations that are climatologically favored for upscale convective growth and elevated convection. These findings have important implications for the use of MESH as a verification dataset for SPC probabilistic hail forecasts as well as severe weather watch decisions in areas of higher hail risk but low population density.

Hazardous Weather Communication En Español: Challenges, Current Resources, and Future Practices

AbstractAccording to recent Census data, the Hispanic or Latino population represents nearly 1 in 5 Americans today, where 71.1% of these individuals speak Spanish at home. Despite increased efforts among the weather enterprise, establishing effective risk communication strategies for Spanish-speaking populations has been an uphill battle. No frameworks exist for translating weather information into the Spanish language, nor are there collective solutions that address this problem within the weather world. The objective of this article is threefold. First, the current translation issue in Spanish is highlighted. Through research conducted at the NOAA/NWS Storm Prediction Center, situations are revealed where regional varieties of Spanish contributed to inconsistent risk messaging across the bilingual weather community. Second, existing resources are featured so that interested readers are aware of ongoing efforts to translate weather information into Spanish. Organizations within the weather service, like the NWS Multimedia Assistance in Spanish Team and the NWS Spanish Outreach Team, are highlighted for their pioneer work on Spanish weather communication. Last, a framework for translation standardization in the atmospheric sciences is introduced, along with future initiatives that are being sought by NWS and AMS to enhance Spanish hazardous weather communication.

Assimilating All-sky Infrared Radiances from Himawari-8 Using the 3DVar Method for the Prediction of a Severe Storm over North China

Although radar observations capture storm structures with high spatiotemporal resolutions, they are limited within the storm region after the precipitation formed. Geostationary satellites data cover the gaps in the radar network prior to the formation of the precipitation for the storms and their environment. The study explores the effects of assimilating the water vapor channel radiances from Himawari-8 data with Weather Research and Forecasting model data assimilation system (WRFDA) for a severe storm case over north China. A fast cloud detection scheme for Advanced Himawari imager (AHI) radiance is enhanced in the framework of the WRFDA system initially in this study. The bias corrections, the cloud detection for the clear-sky AHI radiance, and the observation error modeling for cloudy radiance are conducted before the data assimilation. All AHI radiance observations are fully applied without any quality control for all-sky AHI radiance data assimilation. Results show that the simulated all-sky AHI radiance fits the observations better by using the cloud dependent observation error model, further improving the cloud heights. The all-sky AHI radiance assimilation adjusts all types of hydrometeor variables, especially cloud water and precipitation snow. It is proven that assimilating all-sky AHI data improves hydrometeor specifications when verified against the radar reflectivity. Consequently, the assimilation of AHI observations under the all-sky condition has an overall improved impact on both the precipitation locations and intensity compared to the experiment with only conventional and AHI clear-sky radiance data.

Toward More Realistic Simulation and Prediction of Dust Storms on Mars

Written by: Claire E. Newman (Aeolis Res.) Tanguy Bertrand (NASA Ames) Joseph Battalio (Yale Univ.) Mackenzie Day (UCLA) Manuel de la Torre Juarez (JPL) Meredith K. Elrod (NASA GSFC) Francesca Esposito (INAF-OAC) Lori Fenton (SETI Inst.) Claus Gebhardt (UAEU) Steven J. Greybush (Penn. State) Scott D. Guzewich (NASA GSFC) Henrik Kahanpaa (FMI) Melinda Kahre (NASA Ames) Ozgur Karatekin (Royal Obs. Belg.) Brian Jackson (Boise State Univ.) Mathieu Lapotre (Stanford Univ.) Christopher Lee (Aeolis Res.) Stephen R. Lewis (Open Univ.) Ralph D. Lorenz (APL) German Martinez Martinez (LPI) Javier Martin-Torres (Aberdeen U.) Michael A. Mischna (JPL) Luca Montabone (SSI) Lynn Neakrase (New Mexico State) Alexey Pankine (SSI) Jorge Pla-Garcia (CAB/SwRI/SSI) Peter L. Read (Univ. of Oxford) Isaac B. Smith (PSI/York Univ.) Michael D. Smith (NASA GSFC) Alejandro Soto (SwRI) Aymeric Spiga (Sorbonne Univ.) Christy Swann (NRL SSC) Leslie Tamppari (JPL) Orkun Temel (Royal Obs. Belgium) Daniel Viudez Moreiras (CAB) Danika Wellington (Ariz. State) Paulina Wolkenberg (INAF) Gerhard Wurm (Duisburg-Essen) Maria-Paz Zorzano (CAB)

A Novel Convective Storm Location Prediction Model Based on Machine Learning Methods

A weather radar is a frequently used device in remote sensing to identify meteorological phenomena using electromagnetic waves. It can observe atmospheric conditions in a wide area with a remarkably high spatiotemporal resolution, and its observation results are useful to meteorological research and services. Recent research on data analysis using radar data has concentrated on applying machine learning techniques to solve complicated problems, including quality control, quantitative precipitation estimation, and convective storm prediction. Convective storms, which consist of heavy rains and winds, are closely related to real-life and cause significant loss of life and property. This paper proposes a novel approach utilizing the given convective storms’ temporal properties based on machine learning models to predict future locations. The experimental results showed that the machine learning-based prediction models are capable of nowcasting future locations of convective storms with a slight difference.

Assessment of convection‐permitting versions of the Unified Model over the Lake Victoria basin region

AbstractThe Met Office runs an operational high‐resolution convection‐permitting model over Tropical Africa to support National Meteorological Services across the continent. This article evaluates the prediction of convective storms in two convection‐permitting versions of the Met Office Unified Model over the Lake Victoria basin region in East Africa. Two model configurations are compared for a case of severe convection: the first was tuned for tropical regions and the second was based on the operational UK model configuration. The tropical configuration compares better with satellite‐derived rainfall observations in terms of domain‐average rain‐rate and the distribution of rain‐rates is better, particularly for the more intense rain‐rates. However, both configurations generally produce too much rain, too many small storms, and a lack of light rain, which is compensated for by heavy rain in the cores of the storms. The tropical configuration was then assessed over two rainy seasons. In both seasons, the onset time of night‐time precipitation over Lake Victoria is delayed and the amount of precipitation is underestimated, which suggests the model may miss night‐time storms that occur over the lake. During the first 12–24 hr of the simulations, the precipitation field is still spinning up from the lower‐resolution global model fields used to initialise the convection‐permitting model. This leads to large overestimates in the domain‐average precipitation during this period and highlights the need to run convection‐permitting models for at least two days to reduce the impact of cold‐starting from a non‐convective‐permitting analysis. Despite the spin‐up issues, fractions skill score analysis shows that forecast skill decreases with increasing lead time. This implies that there is inherent skill in the early stages of the model forecast and underlines that short‐term forecast (0–48 hr) improvements in the model are likely to be achieved when using an improved analysis for initialisation.

A Peculiar ICME Event in August 2018 Observed With the Global Muon Detector Network

AbstractWe demonstrate that global observations of high‐energy cosmic rays contribute to understanding unique characteristics of a large‐scale magnetic flux rope causing a magnetic storm in August 2018. Following a weak interplanetary shock on August 25, 2018, a magnetic flux rope caused an unexpectedly large geomagnetic storm. It is likely that this event became geoeffective because the flux rope was accompanied by a corotating interaction region and compressed by high‐speed solar wind following the flux rope. In fact, a Forbush decrease was observed in cosmic‐ray data inside the flux rope as expected, and a significant cosmic‐ray density increase exceeding the unmodulated level before the shock was also observed near the trailing edge of the flux rope. The cosmic‐ray density increase can be interpreted in terms of the adiabatic heating of cosmic rays near the trailing edge of the flux rope, as the corotating interaction region prevents free expansion of the flux rope and results in the compression near the trailing edge. A northeast‐directed spatial gradient in the cosmic‐ray density was also derived during the cosmic‐ray density increase, suggesting that the center of the heating near the trailing edge is located northeast of Earth. This is one of the best examples demonstrating that the observation of high‐energy cosmic rays provides us with information that can only be derived from the cosmic ray measurements to observationally constrain the three‐dimensional macroscopic picture of the interaction between coronal mass ejections and the ambient solar wind, which is essential for prediction of large magnetic storms.