Storm Forecasting Research Articles

Testing the Feature Alignment Technique (FAT) in an Ensemble-Based Data Assimilation and Forecast System with Multiple-Storm Scenarios

Abstract Storm displacement errors can arise from a number of potential sources of error within a data assimilation (DA) and forecast system. Conversely, storm displacement errors can cause issues for storm-scale, ensemble-based systems using an ensemble Kalman filter (EnKF), such as NSSL’s Warn-on-Forecast System (WoFS). A previous study developed a fully grid-based feature alignment technique (FAT) to mitigate these phase errors and their impacts. However, that study developed and tested the FAT for single-storm cases. This study advances that work by implementing an object-based merging and matching technique into the FAT and tests the updated FAT in more complex scenarios of multiple storms. Ensemble-based experiments are conducted with and without the FAT for each of the scenarios. The experiments’ analyses and forecasts of storm-related fields are then evaluated using subjective and objective methods. Results from these idealized multiple-storm experiments continue to reveal the potential benefits of correcting storm displacement errors. For example, running the FAT even once can mitigate the “spinup” period experienced by the no-FAT experiments. The new results also show that running the FAT prior to every DA cycling step generally leads to more skillful forecasts at the smaller scales, especially in earlier-initialized forecasts. However, repeatedly running the FAT prior to every DA step can eventually lead to deterioration in analyses and forecasts. Potential solutions to this problem include using longer cycling intervals and running the FAT prior to DA less often. Additional ways to improve the FAT along with other results are presented and discussed. Significance Statement The purpose of this work is to explore the impact of correcting storm displacements on analyses and forecasts of storms using an ensemble-based data assimilation and forecast system in an idealized framework. Storm displacement errors are a common problem in current operational and experimental storm-scale forecast systems, so understanding their impact on these systems and providing a method to help mitigate them is important. Results from this study indicate that correcting storm displacement errors with the feature alignment technique can greatly improve analyses and forecasts in multiple-storm scenarios. Future work will focus on exploring the impact of correcting storm displacement errors in a real-data, storm-scale data assimilation and forecast system.

Development of New Observation Operators for Assimilating GOES-R Geostationary Lightning Mapper Flash Extent Density Data Using GSI EnKF: Tests with Two Convective Events over the United States

Abstract In a prior study, GOES-R Geostationary Lightning Mapper (GLM) flash extent density (FED) data were assimilated using ensemble Kalman filter into a convection-allowing model for a mesoscale convective system (MCS) and a supercell storm. The FED observation operator based on a linear relation with column graupel mass was tuned by multiplying a factor to avoid large FED forecast bias. In this study, new observation operators are developed by fitting a third-order polynomial to GLM FED observations and the corresponding FED forecasts of graupel mass of the MCS and/or supercell cases. The new operators are used to assimilate the FED data for both cases, in three sets of experiments called MCSFit, SupercellFit, and CombinedFit, and their performances are compared with the prior results using the linear operator and with a reference simulation assimilating no FED data. The new nonlinear operators reduce the frequency biases (root-mean-square innovations) in the 0–4-h forecasts of the FED (radar reflectivity) relative to the results using the linear operator for both storm cases. The operator obtained by fitting data from the same case performs slightly better than fitting to data from the other case, while the operator obtained by fitting forecasts of both cases produce intermediate but still very similar results, and the latter is considered more general. In practice, a more general operator can be developed by fitting data from more cases. Significance Statement Prior studies found that assimilation of satellite lightning observation can benefit storm forecasts for up to 4 h. A linear lightning observation operator originally developed for assimilating pseudo-satellite lightning observations was tuned earlier through sensitivity experiments when assimilating real lightning data. However, the linear relation does not fit the model and observational data well and significant bias can exist. This study develops new lightning observation operators by fitting a high-order polynomial to satellite lightning observations and model-predicted quantities that directly relate to lightning. The new operator was found to reduce the frequency biases and root-mean-square innovations for lightning and radar reflectivity forecasts, respectively, up to several hours relative to the linear operator. The methodology can be applied to larger data samples to obtain a more general operator for use in operational data assimilation systems.

Hurricane and Typhoon Storm Wind Resolving NOAA NCEI Blended Sea Surface Wind (NBS) Product

Improving forecasts of storms and hurricanes and their potential impacts is highly important to public safety, economic security, commerce, and community infrastructure. One key element of forecast improvement is more accurate and increased spatial–time coverage of observational data for model calibration, quality control and initialization, and/or data assimilation. The National Oceanic and Atmospheric Administration (NOAA) has been producing a global gridded 0.25° and 6-hourly sea surface winds product that has wide applications in marine transportation, marine ecosystem and fisheries, offshore winds, weather and ocean forecasts, and other areas. The NOAA National Centers for Environmental Information (NCEI) Blended Sea winds (NBS) v1.0 product is generated by blending observations from multiple sources (satellites), including scatterometers and microwave radiometers/imagers. However, these sensors do not provide accurate observations of intensive high-speed hurricane winds because their signals saturate in very high winds or degrade in the presence of rain. Recent advancements in satellite wind retrievals revealed that the L-band (1.42 GHz) instrument on the Soil Moisture Active Passive (SMAP) satellite and the AMSR2 All-Weather channel (~6.9 GHz) can provide accurate hurricane winds of up to 65 m/s (145 MPH) without being affected by rain; these data are incorporated in a new version of the Blended Sea Winds, NBS v2.0, using a multi-sensor data fusion technique based on random errors, enabling it to resolve very high winds, especially along the eyewalls of tropical cyclones and hurricanes. NBS v2.0 provides both a long-term record of 30+ years retrospectively since July 1987 and a near-real-time mode with 1-day latency.

Visual-graphic spiral pattern can reduce medical-associated stress during the geomagnetic storm

Introduction. About 18% of all health problems in the working population are related to stress, depression and anxiety. The consequences of industrial stress exacerbated by unfavorable geomagnetic activity, can manifest itself in an increase in the psychophysiological “price” of activity, overstrain and depletion of the regulatory mechanisms of functional systems that ensure the adaptation of the body to vital factors and, ultimately, in a significant deterioration in the health of workers. Medical workers occupy a special place in the structure of professional groups of the working population, since they are at increased risk of neuro-emotional overstrain up to the development of professional burnout syndrome.
 The aim of the study is to develop and test a technique that helps to reduce the level of stress and emotional load affecting the psychophysiological state of a person.
 Materials and methods. The study involved 15 relatively healthy medical students aged 19 to 25 years. The algorithm of the study was as follows: the forecast of geomagnetic storms (G3-5) was tracked on the website of the Laboratory of X-ray Astronomy of the Sun (https://tesis.lebedev.ru /). During the storm a student was given the graphic task, in the form of synchronous outlines of stencils with two hands, before and after completing the experimental task the subject was asked to pass psychological tests: a visual-analog scale of situational emotional state according to the Dembo-Rubinstein method and the Spielberger-Khanin scale of personal and situational anxiety (STAI). The electroencephalogram was recorded using an electroencephalograph recorder "Encephalan-EEGR-19/26".
 Results. The Dembo-Rubinstein technique showed that after passing the experimental task 21% of the subjects significantly improved their emotional state according to a quantitative assessment of the visual-analog scale of situational emotional state. The level of personal anxiety also significantly decreased by 5% and the level of situational anxiety did not change, the assessment was carried out using the Spielberger-Khanin scale of personal and situational anxiety (STAI).
 Limitations. The obtained results require further verification on the larger experimental groups with the involvement of medical workers of different age and specialties; lengthening the period of observation of the recorded indicators after completing of the tactile-graphical task to clarify the timing of changes in indicators of situational emotional state and personal anxiety.
 Conclusions. A simple technique for reducing stress levels in medical students has proved its effectivity in changing the psychophysiological manifestation of stress, since the level of stress activity is directly related to the degree of personal anxiety. This technique can be recommended to medical workers and students as an exercise to improve cognitive activity by reducing stress and anxiety associated with the influence of geomagnetic storms, which increase sensitivity to external stress factors, on the psychophysiological state of a person.

Assessment of solar wind driven ionospheric storm forecasts: The case of the Solar Wind driven autoregression model for Ionospheric Forecast (SWIF)

This paper reports the results of the most recent validation tests on the performance of the Solar Wind driven autoregression model for Ionospheric Forecast (SWIF). SWIF uses the Interplanetary Magnetic Field (IMF) observations at L1 point as a proxy for upcoming ionospheric storm time disturbances, issuing an alert in case certain quantitative criteria are met. These criteria apply to the IMF total magnitude and the IMF-Bz component. The validation tests address the SWIF alert detection efficiency in the period 2014–2020 and were designed to complement previous efforts and allow the comprehensive discussion of SWIF's alert ability over one and a half solar cycle. The model's efficiency is quantified through probability of detection, false alarm rate and the success ratio for SWIF's users. The results are further discussed in terms of the intensity and the interplanetary causes of the storms to reveal high forecasting ability for specific storm scenarios (i.e. intense storms that are usually driven by coronal mass ejections), but also certain limitations in the efficiency of SWIF's alert criteria in capturing storm related interplanetary structures at L1 point: mainly structures that are not related to coronal mass ejections and typically, drive storms of moderate intensity. Additional results obtained by superposed epoch analysis of IMF key components indicate the potential relevance of the IMF-By component in ionospheric forecasting applications. The consideration of the IMF-By in SWIF's formulation can drive future improvements in its forecasting efficiency in all different faces of solar wind forcing. The findings of this work may also help the better understanding of the processes that control the coupling of the solar wind-magnetosphere-ionosphere.

Mega Asian dust event over China on 27–31 March 2021 observed with space-borne instruments and ground-based polarization lidar

Under the background of global climate change, the frequency of extreme weather events is ever-increasing. Climatic anomaly in dust source areas and intensive Mongolian cyclones jointly cause the reoccurrence of super dust storms in spring 2021. A mega dust storm broke out in southeast Mongolia on 27 March and significantly impacted air quality in China until 31 March. However, neither intensity nor affected areas are well depicted by the forecasting model. Here we report the horizontal distribution, transport path, dissipation, and influence on surface air quality of this event with the combination of space-borne and ground-based observations. Prudent analysis indicates a longer duration (5 days) and farther-south affected areas (25°N) than the previous report. Dust plumes were first transported to North and Northeast China, the Bohai Sea, and the Yellow Sea (28 March), and then respectively moved eastward to Korea-Japan regions and southward over mainland China (29 March), with PM10 values > 500 μg m−3 in 64 cities. Dust aerosols over the Yellow Sea retraced to the coastal areas of East China on 29 March, resulting in severe degradation of air quality over mainland China (25–40°N and 105–120°E) during 29–31 March. On 31 March, dust particles in this region were gradually removed, attributed to the wet removal and the enhanced south branch of westerlies. In central China, ground-based polarization lidar observed dust ∼26 h earlier (1600 LT, 27 March) than surface PM10 measurements. The dust plume showed a triple-layer vertical structure. The bottom layer mixed with local anthropogenic aerosols, concentrating most dust particles (>100 μg m−3); the upper two were composed of pure dust particles with lower mass concentration (<50 μg m−3). This study reveals the capacity of comprehensive observations with multiple approaches from both space and ground in real-time monitoring of mega dust storms and validating the sand storm forecasting.

Satellite Radiance Data Assimilation Using the WRF-3DVAR System for Tropical Storm Dianmu (2021) Forecasts

This study investigated the impact of the assimilation of satellite radiance observations in a three-dimensional variational data assimilation system (3DVAR) that could improve the tracking and intensity forecasts of the Tropical Storm Dianmu in 2021, which occurred over parts of southeast mainland Asia. The weather research and forecasting (WRF) model was used to conduct the assimilation experiments of the storm. Four sets of numerical experiments were performed using the WRF. In the first, the control experiment, only conventional data in Binary Universal Form for the Representation of Meteorological Data (PREPBUFR) observations from the National Centers for Environmental Prediction (NCEP) were assimilated. The second experiment (RDA1) was performed with PREPBUFR observations and satellite radiance data from the Advanced Microwave Unit-A (AMSU-A), and the Advanced Technology Microwave Sounder (ATMS). PREPBUFR observations and the High-resolution Infrared Radiation Sounder (HIRS-4) were used in the third experiment (RDA2). The fourth experiment (ALL-OBS) used the assimilation of PREPBUFR observations and all satellite radiance data (AMSU-A, ATMS, and HIRS-4). The community radiative transfer model was used on the forward operator for the satellite radiance assimilation, along with quality control and bias correction procedures, before assimilating the radiance data. To evaluate the impact of the assimilation experiments, a forecast starting on 00 UTC 23 September 2021, was produced for 72 h. The results showed that the ALL-OBS experiment improved the short-term forecast up to ~24 h lead time, as compared to the assimilation considering only PREPBUFR observations. When all observations were assimilated into the model, the storm’s landfall position, intensity, and structure were accurately predicted. In the deterministic forecast, the tracking errors of the ALL-OBS experiment was consistently less than 40 km within 24 h. The case study of Tropical Storm Dianmu exhibited the significant positive impact of all observations in the numerical model, which could improve updates for initial conditions and storm forecasting.

Diagnosing Supercell Environments: A Machine Learning Approach

Abstract The importance of discriminating between environments supportive of supercell thunderstorms and those that are not supportive is widely recognized due to significant hazards associated with supercell storms. Previous research has led to forecast indices such as the energy helicity index and the supercell composite parameter to aid supercell forecasts. In this study three machine learning models are developed to identify environments supportive of supercells: a support vector machine, an artificial neural network, and an ensemble of gradient boosted trees. These models are trained and tested using a sample of over 1000 Rapid Update Cycle version 2 (RUC-2) model soundings from near-storm environments of both supercell and nonsupercell storms. Results show that all three machine learning models outperform classifications using either the energy helicity index or supercell composite parameter by a statistically significant margin. Using several model interpretability methods, it is concluded that generally speaking the relationships learned by the machine learning models are physically reasonable. These findings further illustrate the potential utility of machine learning–based forecast tools for severe storm forecasting. Significance Statement Supercell thunderstorms are a type of thunderstorm that are important to forecast because they produce more tornadoes, hail, and wind gusts compared to other types of thunderstorms. This study uses machine learning to create models that predict if a supercell thunderstorm or nonsupercell thunderstorm is favored for a given environment. These models outperform current methods of assessing if a storm that forms will be a supercell. Using these models as guidance forecasters can better understand and predict if atmospheric conditions are favorable for the development of supercell thunderstorms. Improving forecasts of supercell thunderstorms using machine learning methods like those used in this study has the potential to limit the economic and societal impacts of these storms.

Formation of Coronal Mass Ejection and Posteruption Flow of Solar Wind on 2010 August 18 Event

The state of the space environment plays a significant role in the forecasting of geomagnetic storms produced by disturbances of the solar wind (SW). Coronal mass ejections (CMEs) passing through the heliosphere often have a prolonged (up to several days) trail with declining speed, which affects propagation of the subsequent SW streams. We studied the CME and posteruption plasma flows behind the CME rear in the event on 2010 August 18 that was observed in quadrature by several space-based instruments. Observations of the eruption in the corona with EUV telescopes and coronagraphs revealed several discrete outflows followed by a continuous structureless posteruption stream. The interplanetary coronal mass ejection (ICME) associated with this CME was registered by the Plasma and Suprathermal Ion Composition instrument aboard the Solar Terrestrial Relations Observatory between August 20, 16:14 UT and August 21, 13:14 UT, after which the SW disturbance was present over 3 days. Kinematic consideration with the use of the gravitational and drag-based models has shown that the discrete plasma flows can be associated with the ICME, whereas the posteruption outflow arrived in the declining part of the SW transient. We simulated the Fe ion charge distributions of the ICME and post-CME parts of the SW using the plasma temperature and density in the ejection region derived from the differential emission measure analysis. The results demonstrate that in the studied event, the post-ICME trailing region was associated with the posteruption flow from the corona rather than with the ambient SW entrained by the CME.

Simulated multispectral temperature and atmospheric composition retrievals for the JPL GEO-IR Sounder

Abstract. Satellite measurements enable quantification of atmospheric temperature, humidity, wind fields, and trace gas vertical profiles. The majority of current instruments operate on polar orbiting satellites and either in the thermal and mid-wave or in the shortwave infrared spectral regions. We present a new multispectral instrument concept for improved measurements from geostationary orbit (GEO) with sensitivity to the boundary layer. The JPL GEO-IR Sounder, which is an imaging Fourier transform spectrometer, uses a wide spectral range (1–15.4 µm) encompassing both reflected solar and thermal emission bands to improve sensitivity to the lower troposphere and boundary layer. We perform retrieval simulations for both clean and polluted scenarios that also encompass different temperature and humidity profiles. The results illustrate the benefits of combining shortwave and thermal infrared measurements. In particular, the former adds information in the boundary layer, while the latter helps to separate near-surface and mid-tropospheric variability. The performance of the JPL GEO-IR Sounder is similar to or better than currently operational instruments. The proposed concept is expected to improve weather forecasting as well as severe storm tracking and forecasting and also benefit local and global air quality and climate research.

Situational uncertainty interacts with anxiety sensitivity and distress intolerance to predict anticipated worry and preparation for a hurricane

ABSTRACT Background There is evidence for the impact of emotional intolerance on reactivity to stressors, but it is unknown whether the level of situational uncertainty may moderate this relationship. We examined whether situational uncertainty moderated the relationship between emotional intolerance and anticipated anxious responding to hurricane forecasts, considering three aspects of emotional tolerance: anxiety sensitivity, distress intolerance, and hurricane-specific distress intolerance. Methods Participants (N = 358) were Florida residents who experienced Hurricane Irma. Participants were presented with two hypothetical storm forecasts that varied in level of uncertainty: 5-day forecast (high uncertainty) and 3-day forecast (low uncertainty). Participants rated their anticipated worry and preparation for each forecast. Results Significant interactions between forecast uncertainty and both anxiety sensitivity and hurricane-specific distress intolerance emerged on anticipated worry, such that there was a stronger relationship in the high uncertainty condition. Forecast uncertainty also moderated the relationship between anxiety sensitivity and anticipated preparation in the same direction. There were no significant interactions between forecast uncertainty and distress intolerance on either anticipated worry or preparation. Conclusions Specific aspects of emotional intolerance appear to have a stronger influence on anticipated worry and preparatory behavior in high uncertainty situations. These findings suggest that distinct emotional tolerance factors may influence these responses.

Improving cyclone wind fields using deep convolutional neural networks and their application in extreme events

Precise forecasting of tropical cyclone-induced storm surges is necessary to avoid any significant damages to coastal communities. The numerical models need wind and pressure fields as a surface forcing to predict these events. An increase in the quality of the available wind field data greatly benefits the forecast. There remain inherent limitations in the quality of real-time wind forecasts for near-field and far-field regions surrounding the storm eye. Wind fields from global models usually underestimate the inner core winds. In contrast, parametric winds well represent the cyclone winds in the inner region. Prior studies used techniques to superpose inner and outer core winds from parametric and global models, respectively. However, for real-time deployment, such methods are computationally expensive and mathematically complex. Machine Learning (ML) techniques can be employed for real-time use once the mapping is trained to machines. In the present paper, a blending strategy that generates enhanced wind fields is trained using deep convolutional neural network architecture; it can be easily deployed once the architecture learns the mapping. Numerical simulations using ML, conventional blended winds are performed and validated against available observations. The study reveals that simulations based on ML blended winds performed as expected and are suitable for real-time operational forecasts.

Exploration of a Future NOAA Infrared Sounder in Geostationary Earth Orbit

A geostationary (GEO) hyperspectral infrared sounder (HyIRS) is capable of providing high spectral (0.625 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup> ), temporal (every 30 min) and spatial (4 km) resolution observations over the continental U.S. (CONUS). Frequent observations from a GEO-HyIRS at high spatial resolution are expected to contribute to the generation of three-dimensional structures of atmospheric temperature and humidity, and wind. These new observations will provide valuable information for timely forecasts of severe storms over the CONUS and the overall Western Hemisphere. Infrared (IR) sounder observations from a geostationary orbit open a new set of possibilities, including the capability of monitoring the diurnal cycle of atmospheric patterns, which is difficult from Low Earth Orbit IR sounders and the capability of timely and accurate retrievals of several trace gases. In this article, the feasibility of adding a HyIRS into the next generation of U.S. geostationary environmental satellites is studied. The configuration of a notional U.S. GEO-HyIRS sensor and its ground data processing system are discussed. A hyperspectral IR data simulator is developed and reported as part of this engineering study, where proxy data is used to model the end-to-end ground processing system. Various considerations for the configuration and the calibration and validation of the instrument are addressed.

Research on Sand-Dust Storm Forecasting Based on Deep Neural Network With Stacking Ensemble Learning

Weather forecasting is an important factor affecting production and life. With the development of technology, weather forecasting methods such as weather map forecasting, numerical weather forecasting, and quantitative forecasting methods have emerged. However, these traditional data analysis methods have shortcomings such as incomplete analysis, insufficient objectivity, inability to quantitatively predict the weather, and low prediction accuracy. The import of neural networks into the field of weather forecasting helps to alleviate the above shortcomings and improve the accuracy of weather forecasting. In this paper, the LSTM network and the CNN network are used to predict the sand-dust storm weather. In order to improve the prediction performance, we use the Stacking integration algorithm to fuse the LSTM and CNN models. To improve the experimental scientific and comprehensive, using fully connected network and support vector machine as meta-classifiers, two LSTM-CNN integrated sand-dust storm prediction models are established. At last, the above integrated model is used in the prediction of sand-dust storms in Inner Mongolia. The experimental results show that compared with a single LSTM or CNN model, the Stacking ensemble model has different degrees of improvement in model evaluation indicators such as accuracy, precision, recall, and f1-score. The Stacking ensemble model uses the fully connected network model as the meta-classifier is even better. These prove that the Stacking ensemble algorithm improves the sand-dust storm classification effect and generalization ability of a single neural network to a certain extent.

Highwater Mark Collection after Post Tropical Storm Dorian and Implications for Prince Edward Island, Canada

Prince Edward Island (PEI), Canada has been experiencing the consequences of a rising sea level and intense storms on its coasts in recent years. The most recent severe event, Post Tropical Storm Dorian (Dorian), began impacting Prince Edward Island on 7 September 2019 and lasted for over 20 h until the morning of 8 September 2019. The measurement of highwater marks (HWM) from the storm was conducted between 25 September and 25 October 2019 using a high precision, survey grade methodology. The HWM measured included vegetation lines, wrack lines, beach, cliff, and dune morphological features, and tide gauge data at 53 locations in the Province along coastal areas that are exposed to high tides, storm surge, high winds, and wave runup. Photos were taken to provide evidence on the nature of the HWM data locations. The data reveal that Dorian caused extensive coastal floods in many areas along the North and South Coast of Prince, Queens and Western Kings Counties of Prince Edward Island. The floods reached elevations in excess of 3.4 m at some locations, posing threats to local infrastructure and causing damage to natural features such as sand dunes in these areas. The HWM data can provide useful information for community and emergency response organizations as plans are developed to cope with the rising sea level and increased frequency of highwater events as predicted by researchers. As Dorian has caused significant damage in several coastal areas in PEI, better planning using an enhanced storm forecasting and coastal flood warning system, in conjunction with flood stage values, could possibly have reduced the impacts of the storm in the impacted areas. This could help enhance public understanding of the potential impacts in local areas and how they can prepare and adapt for these events in the future.

Strength and Challenges of global model MPAS with regional mesh refinement for mid-latitude storm forecasting: a case study

Abstract. With the rising share of renewable energy sources like wind energy in the energy mix, high-impact weather events like mid-latitude storms increasingly affect energy production, grid stability and safety and reliable forecasting becomes very relevant for e.g. transmission system operators to allow for actions to reduce imbalances. Traditionally, meteorological forecasts are provided by limited-area weather prediction models (LAMs), which can use high enough model resolution to represent the range of atmospheric scales of motions associated with such storm structures. While generally satisfactory, deterioration and insufficient deepening of large-scale storm structures are observed when they are introduced near the lateral boundaries of the LAM due to inadequate spatial and temporal interpolation. Global models with regional mesh refinement capabilities like the Model for Prediction Across Scales (MPAS) have the potential to provide an alternative, while avoiding sharp resolution jumps and lateral boundaries. In this study, MPAS' capabilities of simulating key evaluation metrics like storm intensity, storm location and storm duration are investigated based on a case study and assessed in comparison with buoy measurements, forecast products from the Climate Forecast System (CFSv2) and simulations with the Weather Research and Forecasting (WRF) LAM. Quasi-uniform and variable-resolution MPAS mesh configurations with different model physics settings are designed to analyze the impact of the mesh refinement and model physics on the model performance. MPAS shows good performance in predicting storm intensity based on the local minimum sea level pressure, while time of local minimum sea level pressure (storm duration) was generally estimated too late (too long) in comparison with the buoy measurements in part due to an early west-wards shift of the storm center in MPAS. The variable-resolution configurations showed a combination of an additional south-westwards shift and deviations in the sea level pressure field south-west of the storm center that introduced additional bias to the time of local minimum sea level pressure at some locations. The study highlights the need for a more detailed analysis of applied mesh refinements for particular applications and emphasizes the importance of methods like data assimilation techniques to prevent model drifts.

Performance Evaluation of Linear and Nonlinear Models for Short-Term Forecasting of Tropical-Storm Winds

Wind-sensitive structures usually suffer from violent vibrations or severe damages under the action of tropical storms. It is of great significance to forecast tropical-storm winds in advance for the sake of reducing or avoiding consequent losses. The model used for forecasting becomes a primary concern in engineering applications. This paper presents a performance evaluation of linear and nonlinear models for the short-term forecasting of tropical storms. Five extensively employed models are adopted to forecast wind speeds using measured samples from the tropical storm Rumbia, which facilitates a comparison of the predicting performances of different models. The analytical results indicate that the autoregressive integrated moving average (ARIMA) model outperforms the other models in the one-step ahead prediction and presents the least forecasting errors in both the mean and maximum wind speeds. However, the support vector regression (SVR) model has the worst performance on the selected dataset. When it comes to the multi-step ahead forecasting, the prediction error of each model increases as the number of steps expands. Although each model shows an insufficient ability to capture the variation of future wind speed, the ARIMA model still appears to have the least forecasting errors. Hence, the ARIMA model can offer effective short-term forecasting of tropical-storm winds in both one-step and multi-step scenarios.

Forecasting wind waves in the US Atlantic Coast using an artificial neural network model: Towards an AI-based storm forecast system

This study presents an artificial intelligence (AI) model to forecast time-series wind waves in the US Atlantic Coast. The fundamental technique in the proposed model is the Long Short-term Memory model, which is able to learn patterns from data sequence. The model is trained using historical wind data, wave data, temperature data, and atmospheric pressure data. Then the model is used to forecast significant wave height, average wave period, and mean wave direction. A 2-year meteorological data measured at four NOAA buoy stations in the US Atlantic Coast are used to train and evaluate the model’s forecast skills. The results show the artificial neural network model converges fast and does not have over-fitting nor under-fitting issues. Furthermore, a short-term forecast (e.g., 1 to 6 h) achieves higher accuracy than a long-term forecast (e.g., 24 to 48 h). This study shows that sufficient forecast accuracy can be obtained by using the input hours equal to the forecast lead time. Comparison using rose plots confirms that the artificial neural network model reproduces wave height and wave period statistics very well. The artificial neural network model is used to forecast storm waves induced by Hurricanes Isaias and Eta in the US South Atlantic region, and two winter storms in the US North Atlantic region. The model forecast is compared with NOAA measurement. The results show that the artificial neural network model is able to accurately forecast both significant wave height and average wave period associated with storm events when the forecast lead time is relatively short (e.g., 1 to 6 h). The proposed artificial neural network model can serve as an alternative tool to traditional coastal models for wave prediction and storm forecast.

Enhanced Simulation of an Asian Dust Storm by Assimilating GCOM-C Observations

Dust aerosols have great effects on global and regional climate systems. The Global Change Observation Mission-Climate (GCOM-C), also known as SHIKISAI, which was launched on 23 December 2017 by the Japan Aerospace Exploration Agency (JAXA), is a next-generation Earth observation satellite that is used for climate studies. The Second-Generation Global Imager (SGLI) aboard GCOM-C enables the retrieval of more precious global aerosols. Here, the first assimilation study of the aerosol optical thicknesses (AOTs) at 500 nm observed by this new satellite is performed to investigate a severe dust storm in spring over East Asia during 28–31 March 2018. The aerosol observation assimilation system is an integration of the four-dimensional local ensemble transform Kalman filter (4D-LETKF) and the Spectral Radiation Transport Model for Aerosol Species (SPRINTARS) coupled with the Non-Hydrostatic Icosahedral Atmospheric Model (NICAM). Through verification with the independent observations from the Aerosol Robotic Network (AERONET) and the Asian Dust and Aerosol Lidar Observation Network (AD-Net), the results demonstrate that the assimilation of the GCOM-C aerosol observations can significantly enhance Asian dust storm simulations. The dust characteristics over the regions without GCOM-C observations are better revealed from assimilating the adjacent observations within the localization length, suggesting the importance of the technical advances in observation and assimilation, which are helpful in clarifying the temporal–spatial structure of Asian dust and which could also improve the forecasting of dust storms, climate prediction models, and aerosol reanalysis.

An initialisation scheme for tropical cyclones in the South China Sea

AbstractWe propose an initialisation scheme for weak tropical cyclones (TCs) in the South China Sea (SCS) by combining composite and analysis vortices to improve forecasts of weak TCs in the region. The composite vortex is obtained from good samples of analyses of TCs in the SCS, and the analysis vortex is obtained from either European Centre for Medium‐Range Weather Forecasts (ECMWF) analysis data or an operational assimilation system. The new scheme has been applied to 57 cases of weak TCs, and the effect on the forecasting of four tropical storms has been studied in detail. It can be concluded that the proposed scheme can reduce track errors and intensity errors of weak TCs, compared with the control experiment. The possible reasons for these results are investigated in a case study for Linfa. Results of batch experiments show that the new method significantly improves TC track forecasting of tropical storms, severe tropical storms and typhoons, and demonstrates significant advantages in forecasting intensity of tropical storms, compared with the control experiment. It also performs better in predicting intensity of TCs in the SCS compared with ECMWF Atmospheric Model high resolution.