Adverse Weather Effects Research Articles

Spatiotemporal analysis of different vegetation indices and relation to meteorological parameters over a tropical urban location and its surroundings

The paper investigates the long-term spatiotemporal characteristics of various satellite-derived vegetation indices (VI), such as the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), as well as Gross Primary Productivity (GPP) and Sun-induced Chlorophyll Fluorescence (SIF) over the Kolkata conurbation and its surrounding areas from 2003 to 2016. Additionally, it analyzes the correlation between these vegetation indices and atmospheric parameters like rainfall, soil moisture (SM), evapotranspiration (ET), and land surface temperature (LST). Monthly variations of these parameters are observed, and inter-annual variability is examined using linear regression techniques. The study also observes the time average spatial correlation between vegetation indices and weather parameters. Moreover, it investigates the time-lag effect (0, 1, 2, and 3 months) using Pearson correlation coefficient analysis between VI and other meteorological parameters. NDVI and EVI exhibit maximum correlation with rainfall, SM, ET, and LST within specific lag periods. NDVI and EVI show a slow response rate to rainfall, and their sensitivity depends on SM and ET. A positive correlation is observed between NDVI and ET, indicating that NDVI increases with vaporized water in the atmosphere. A negative correlation is noted between NDVI and LST in the region studied. The study’s insights are valuable for predicting future vegetation index characteristics based on meteorological parameters in tropical urban areas like Kolkata and its surroundings. This predictive capability can aid in mitigating adverse weather effects on vegetation.

Deep causal inference for understanding the impact of meteorological variations on traffic

Understanding the causal impact of meteorological variations on traffic conditions (e.g., traffic flow and speed) is crucial for effective traffic prediction and management, as well as the mitigation of adverse weather effects on traffic. However, many existing studies focused on establishing associations between meteorological situations and traffic, rather than delving into causal relationships, especially with deep learning techniques. Consequently, the ability to identify specific meteorological conditions that significantly contribute to traffic congestion or delays is still limited. To address this issue, this study proposes the Meteorological-Traffic Causal Inference Variational Auto-Encoder Model (MT-CIVAE) to estimate the causal impact of fine-grained meteorological variations (e.g., rain and temperature) on traffic. Specifically, MT-CIVAE is based on the Variational Auto-Encoder and consists of an encoder to recover the distribution of latent confounders and a decoder to estimate the conditional probabilities of treatments. Transformer encoder layers are incorporated to analyze the spatial and temporal correlations of historical traffic data to further enhance the inference capability. To evaluate the effectiveness of the proposed approach for causal inference, real-world traffic flow and speed datasets collected from California, along with corresponding fine-grained meteorological datasets, are employed. The counterfactual analysis is conducted using artificially generated meteorological conditions as treatments, which allows for the simulation of hypothetical meteorological scenarios and the evaluation of their potential impact on traffic conditions. This study develops deep learning methods for assessing the causal impact of meteorological variations on traffic dynamics, offering explanations and insights that can assist transportation institutions in guiding post-meteorology traffic management strategies.

Precise Adverse Weather Characterization by Deep-Learning-Based Noise Processing in Automotive LiDAR Sensors

With current advances in automated driving, optical sensors like cameras and LiDARs are playing an increasingly important role in modern driver assistance systems. However, these sensors face challenges from adverse weather effects like fog and precipitation, which significantly degrade the sensor performance due to scattering effects in its optical path. Consequently, major efforts are being made to understand, model, and mitigate these effects. In this work, the reverse research question is investigated, demonstrating that these measurement effects can be exploited to predict occurring weather conditions by using state-of-the-art deep learning mechanisms. In order to do so, a variety of models have been developed and trained on a recorded multiseason dataset and benchmarked with respect to performance, model size, and required computational resources, showing that especially modern vision transformers achieve remarkable results in distinguishing up to 15 precipitation classes with an accuracy of 84.41% and predicting the corresponding precipitation rate with a mean absolute error of less than 0.47 mm/h, solely based on measurement noise. Therefore, this research may contribute to a cost-effective solution for characterizing precipitation with a commercial Flash LiDAR sensor, which can be implemented as a lightweight vehicle software feature to issue advanced driver warnings, adapt driving dynamics, or serve as a data quality measure for adaptive data preprocessing and fusion.

LiDAR Point Cloud Augmentation for Adverse Conditions Using Conditional Generative Model

The perception systems of autonomous vehicles face significant challenges under adverse conditions, with issues such as obscured objects and false detections due to environmental noise. Traditional approaches, which typically focus on noise removal, often fall short in such scenarios. Addressing the lack of diverse adverse weather data in existing automotive datasets, we propose a novel data augmentation method that integrates realistically simulated adverse weather effects into clear condition datasets. This method not only addresses the scarcity of data but also effectively bridges domain gaps between different driving environments. Our approach centers on a conditional generative model that uses segmentation maps as a guiding mechanism to ensure the authentic generation of adverse effects, which greatly enhances the robustness of perception and object detection systems in autonomous vehicles, operating under varied and challenging conditions. Besides the capability of accurately and naturally recreating over 90% of the adverse effects, we demonstrate that this model significantly improves the performance and accuracy of deep learning algorithms for autonomous driving, particularly in adverse weather scenarios. In the experiments employing our augmented approach, we achieved a 2.46% raise in the 3D average precision, a marked enhancement in detection accuracy and system reliability, substantiating the model’s efficacy with quantifiable improvements in 3D object detection compared to models without augmentation. This work not only serves as an enhancement of autonomous vehicle perception systems under adverse conditions but also marked an advancement in deep learning models in adverse condition research.

Multi-agent system architecture for winter road maintenance: a real Spanish case study

Road safety remains a critical issue in contemporary society, where the sudden deterioration of road conditions due to weather-related natural phenomena poses significant risks. These abrupt changes can lead to severe safety hazards on the roads, making real-time monitoring and control essential for maintaining road safety. In this context, technological advancements, especially in sensor networks and intelligent systems, play a fundamental role in efficiently managing these challenges. This study introduces an innovative approach that leverages a sophisticated sensor platform coupled with a multi-agent system. This integration facilitates the collection, processing, and analysis of data to preemptively determine the appropriate chemical treatments for roads during severe winter conditions. By employing advanced data analysis and machine learning techniques within a multi-agent framework, the system can predict and respond to adverse weather effects swiftly and with a high degree of accuracy. The proposed system has undergone rigorous testing in a real-world environment, which has verified its operational effectiveness. The results from the deployment of the multi-agent architecture and its predictive capabilities are encouraging, suggesting that this approach could significantly enhance road safety in extreme weather conditions. Furthermore, the proposed architecture allows the system to evolve and scale over time. This paper details the design and implementation of the system, discusses the results of its field tests, and explores potential improvements.

Optimal government subsidy strategy for fresh agricultural products supply chain under adverse weather: a two-stage dynamic game model

ABSTRACT To investigate the effects of various government subsidies on the decision of agricultural products supply chain under adverse weather, this paper depicts the random profit function of adverse weather affecting the output of fresh agricultural products. Then, both the centralised and decentralised decision-making models are set with various government subsidies, i.e. no subsidy, output subsidy, procurement subsidy, one-time subsidy to company and one-time subsidy to farmer. Through comparative analysis and numerical analysis, we explore the effects of adverse weather and different government subsidies on the supply chain decisions including planting effort, procurement price and retail price.

Efficient Deweahter Mixture-of-Experts with Uncertainty-Aware Feature-Wise Linear Modulation

The Mixture-of-Experts (MoE) approach has demonstrated outstanding scalability in multi-task learning including low-level upstream tasks such as concurrent removal of multiple adverse weather effects. However, the conventional MoE architecture with parallel Feed Forward Network (FFN) experts leads to significant parameter and computational overheads that hinder its efficient deployment. In addition, the naive MoE linear router is suboptimal in assigning task-specific features to multiple experts which limits its further scalability. In this work, we propose an efficient MoE architecture with weight sharing across the experts. Inspired by the idea of linear feature modulation (FM), our architecture implicitly instantiates multiple experts via learnable activation modulations on a single shared expert block. The proposed Feature Modulated Expert (FME) serves as a building block for the novel Mixture-of-Feature-Modulation-Experts (MoFME) architecture, which can scale up the number of experts with low overhead. We further propose an Uncertainty-aware Router (UaR) to assign task-specific features to different FM modules with well-calibrated weights. This enables MoFME to effectively learn diverse expert functions for multiple tasks. The conducted experiments on the multi-deweather task show that our MoFME outperforms the state-of-the-art in the image restoration quality by 0.1-0.2 dB while saving more than 74% of parameters and 20% inference time over the conventional MoE counterpart. Experiments on the downstream segmentation and classification tasks further demonstrate the generalizability of MoFME to real open-world applications.

Can infrastructure, built environment, and geographic factor negate weather impact on Strava cyclists?

Cycling participation is context-sensitive and weather condition is reportedly a significant factor. How weather affects cyclists with different demographics, trip purposes, and in the context of cycling infrastructure, built environment and geographic factors is less well understood by existing literature. This paper applies autoregressive models to explain difference in Strava cycling volume from the same hour of the previous day as a function of change in weather conditions, and day of the week; the contextual effect of cycling infrastructure, built environment and geographic factors is accounted for using interaction terms. We use Strava crowdsourced cycling data in Sydney, Australia, as a case study; commute and leisure cyclists, male and female, young and older cyclists are modeled separately. We find weather conditions have a statistically significant effect on cycling participation; rain, rainfall in the last 2 hours and wind are general deterrents to cycling. Physically separated cycling lanes reduce the adverse effect of precipitation on leisure cyclists and male cyclists but have little effect in retaining commute cyclists and female cyclists. The adverse effect of precipitation and wind on commute cycling is amplified in areas with good access to jobs, possibly due to the availability of better alternative modes of transport. Inland locations generally attenuate effects of windy conditions, except for young adults. This paper sheds light on factors attenuating adverse weather effects on cycling participation and provides useful guidance for future cycling infrastructure.

Exploring the Application of Large-Scale Pre-Trained Models on Adverse Weather Removal.

Image restoration under adverse weather conditions (e.g., rain, snow, and haze) is a fundamental computer vision problem that has important implications for various downstream applications. Distinct from early methods that are specially designed for specific types of weather, recent works tend to simultaneously remove various adverse weather effects based on either spatial feature representation learning or semantic information embedding. Inspired by various successful applications incorporating large-scale pre-trained models (e.g., CLIP), in this paper, we explore their potential benefits for leveraging large-scale pre-trained models in this task based on both spatial feature representation learning and semantic information embedding aspects: 1) spatial feature representation learning, we design a Spatially Adaptive Residual (SAR) encoder to adaptively extract degraded areas. To facilitate training of this model, we propose a Soft Residual Distillation (CLIP-SRD) strategy to transfer spatial knowledge from CLIP between clean and adverse weather images; 2) semantic information embedding, we propose a CLIP Weather Prior (CWP) embedding module to enable the network to adaptively respond to different weather conditions. This module integrates the sample-specific weather priors extracted by the CLIP image encoder with the distribution-specific information (as learned by a set of parameters) and embeds these elements using a cross-attention mechanism. Extensive experiments demonstrate that our proposed method can achieve state-of-the-art performance under various and severe adverse weather conditions. The code will be made available.

Effect of Localised Pressure Depression and Rain on Aerodynamic Characteristics of MALE UAV

This paper presents the effect of the local low-pressure region in the atmosphere and rain on aerodynamiccharacteristics of medium altitude long endurance unmanned aerial vehicle (MALE-UAV) configuration duringcruise/loiter. Computations are performed using CFD++, a commercial CFD software suite. A large low-pressuredepression past the MALE UAV (symmetrically and asymmetrically) with pressure 10 – 15 % lower than the freestream pressure and a widespread rainfall type with a rainfall rate of 1195 mm/hr., are considered for CFD simulation.A large low pressure that spans the whole MALE-UAV results in a decrement in both lift and drag, but does not affect the yawing and rolling moments significantly. However, a low-pressure region that engulfs only one-half of MALE UAV causes sudden/abrupt changes in rolling and yawing moments. The effect of rain causes a significant decrease in a lift at higher alpha, accompanied by a decrease in stall angle of 2 degrees, and a significant increase in drag. From the study, a Standard Operating Procedure (SOP) was adopted to fly UAVs in adverse weather effects,such that the aircraft can be operated with a velocity higher than 1.3Vstall and at a power setting not less than 75% of max power capacity.

Energy-Based Detection of Adverse Weather Effects in LiDAR Data

Autonomous vehicles rely on LiDAR sensors to perceive the environment. Adverse weather conditions like rain, snow, and fog negatively affect these sensors, reducing their reliability by introducing unwanted noise in the measurements. In this work, we tackle this problem by proposing a novel approach for detecting adverse weather effects in LiDAR data. We reformulate this problem as an outlier detection task and use an energy-based framework to detect outliers in point clouds. More specifically, our method learns to associate low energy scores with inlier points and high energy scores with outliers allowing for robust detection of adverse weather effects. In extensive experiments, we show that our method performs better in adverse weather detection and has higher robustness to unseen weather effects than previous state-of-the-art methods. Furthermore, we show how our method can be used to perform simultaneous outlier detection and semantic segmentation. Finally, to help expand the research field of LiDAR perception in adverse weather, we release the SemanticSpray dataset, which contains labeled vehicle spray data in highway-like scenarios. The dataset is available at http://dx.doi.org/10.18725/OPARU-48815 .

Adverse Weather Impacts on Winter Wheat, Maize and Potato Yield Gaps in northern Belgium

Adverse weather conditions greatly reduce crop yields, leading to economic losses and lower food availability. The characterization of adverse weather and the quantification of their potential impact on arable farming is necessary to advise farmers on feasible and effective adaptation strategies and to support decision making in the agriculture sector. This research aims to analyze the impact of adverse weather on the yield of winter wheat, grain maize and late potato using a yield gap approach. A time-series analysis was performed to identify the relationship between (agro-)meteorological indicators and crop yields and yield gaps in Flanders (northern Belgium) based on 10 years of field trial and weather data. Indicators were calculated for different crop growth stages and multiple soils. Indicators related to high temperature, water deficit and water excess were analyzed, as the occurrence frequency and intensity of these weather events will most likely increase by 2030–2050. The concept of “yield gap” was used to analyze the effects of adverse weather in relation to other yield-reducing factors such as suboptimal management practices. Winter wheat preferred higher temperatures during grain filling and was negatively affected by wet conditions throughout the growing season. Maize was especially vulnerable to drought throughout the growing season. Potato was more affected by heat and drought stress during tuber bulking and by waterlogging during the early growth stages. The impact of adverse weather on crop yield was influenced by soil type, and optimal management practices mitigated the impact of adverse weather.

New method for detecting and removing random-valued impulse noise from images

The paper proposes a method for detecting and removing impulse noise in images, which determines the similarity between pixels by distance and the difference in brightness values in the local detector window. An impulse noise model is considered, where distorted pixels take random values and also randomly appear in the image. Pixels identified as pulses are recovered with an adaptive median filter. The impulses are determined in the detector window, whose size is calculated in the Euclidean metric and increases with increasing noise intensity in the image. In the experimental part, we discuss visual differences between familiar methods and the one proposed herein on three images for three different impulse noise intensities. In the approximation of image fragments, it is seen that the proposed method copes with the task in the best way, which is also confirmed by numerical estimates of the quality of image reconstruction from impulse noise based on the peak signal-to-noise ratio and the structural similarity index. The proposed method can be used when solving problems of cleaning images under conditions of distorting impulses and for eliminating distortions caused by adverse weather effects, such as raindrops and snow.

Capacity Performance Analysis for Terrestrial THz Channels

The outdoor terrestrial terahertz (THz) communication links have recently attracted great research and commercial interest in response to the emerging bandwidth-hungry demands for extremely high-speed wireless data transmissions. However, their development is hindered by the random behavior of the atmospheric channel due to the molecular attenuation, adverse weather effects, and atmospheric turbulence (along with free space path loss (FSPL) and pointing errors) due to the stochastic misalignments between the transmitter and the receiver. Thus, in this work, we investigate the joint influence of these detrimental effects on both capacities, i.e., average (ergodic) and outage, of such a typical line of sight (LOS) THz communication link. Specifically, atmospheric turbulence-induced intensity fluctuations can be modeled by using either the suitable gamma or the well-known gamma–gamma distribution for weak and moderate to strong turbulence conditions, respectively. Additionally, weak to strong stochastic misalignment-induced intensity fluctuations, due to generalized pointing errors with non-zero boresight (NZB), are emulated by the appropriate Beckman distribution. Taking into additional consideration the unavoidable presence of FSPL and the different but realistic water vapor concentration values along with the influence of weather conditions, an outage performance analysis has been conducted. Considering the abovementioned significant effects, novel analytical mathematical expressions have been extracted for both average (ergodic) and outage capacity, which are critical metrics that first incorporate the total influence of all of the above significant effects on the THz links’ performance. Through the derived expressions, proper analytical results verified by simulations are presented and demonstrate the validity of our analysis. It is notable that the derived expressions can accommodate realistic parameter values involved in all the above-mentioned major effects and link characteristics. In this context, they provide encouraging quantitative results and outcomes for both capacity metrics under investigation. The latter enables the design and the establishment of modern and future high-speed THz links, which are expected to cover longer propagation distances and thus become even more vulnerable to atmospheric turbulence effect. This is modeled and incorporated in our analysis and expressions contrary to most of the previous works in the open technical literature.

Effect of Adverse Weather on Air Transport: Port Harcourt International Airport in Focus

This work focuses on the effect of adverse weather on air transport in Port Harcourt International Airport, Omagwa. Among the weather elements considered in this article are, rainfall, cloudage, relative humidity, sunshine duration and intensity, wind speed, temperature and other weather-related variables like thunderstorm, clear air turbulent etc. These conditions are diurnal and temporally checked in the airport. In an attempt to understand the effect of adverse weather on air transport, a questionnaire that covers all stakeholders was designed and administered to the relevant stakeholders. The study among other things observed that adverse weather is a challenge to aviation industry. It causes low visibility, can increase the rate of plane crash and can even lead to damages of plane parts. The study shows that the effect of adverse weather in the study area is seasonal. It was also observed that adverse weather leads to flight delay, diversions and cancellations, which cause passengers to spend more money. Finally, the study discovered that announcement of sudden adverse weather condition triggers health issues among passengers. This work used tables’ graphs and simple % for analysis. This work suggests the installation of latest weather monitoring devices in the airport, construction of paved runway and reliable weather forecast report should be made available to passengers’ days and hours before travelling to minimize flight delay and cancellations.

The role of meteorological factors in suicide mortality in Wuhu, a humid city along the Yangtze River in Eastern China.

As the climate continues to change, suicide is becoming more frequent. In this study, absolute humidity (AH) was included for the first time and Wuhu, a typical subtropical city along the Yangtze River, was taken as the research object to explore the impact of suicide death risk on meteorological factors. The daily meteorological factors and suicide mortality data of Wuhu city from 2014 to 2020 were collected. Guided by structural equation model (SEM), a time series analysis method combining distributed lag nonlinear model (DLNM) and generalized additive model (GAM) was adopted. To investigate the correlation among different populations, we stratified age and gender at different meteorological levels. A total of 1259 suicide deaths were collected in Wuhu. The results indicated that exceedingly low and low levels of AH short-term exposure increased suicide mortality, with the maximum effect occurring at lag 14 for both levels of exposure, when the relative risk (RR) was 1.131 (95% CI: 1.030, 1.242) and 1.065 (95% CI: 1.006, 1.127), respectively. Exposure to exceedingly high and exceedingly low levels of temperature mean (T mean) also increased suicide mortality, with maximum RR values of 1.132 (lag 14, 95% CI: 1.015, 1.263) and 1.203 (lag 0, 95% CI: 1.079, 1.340), sequentially. As for diurnal temperature range (DTR), low-level exposure decreased the risk of suicide, while high-level exposure increased this risk, with RR values of 0.955 (lag 0, 95% CI: 0.920, 0.991, minimum) and 1.060 (lag 0, 95% CI: 1.018, 1.104, maximum), sequentially. Stratified analysis showed that AH and DTR increased the suicide death risk in male and elderly people, while the risk effect of T mean have no effect on young people only. In summary, male and elderly people appear to be more vulnerable to adverse weather effects.

Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges.

With recent developments, the performance of automotive radar has improved significantly. The next generation of 4D radar can achieve imaging capability in the form of high-resolution point clouds. In this context, we believe that the era of deep learning for radar perception has arrived. However, studies on radar deep learning are spread across different tasks, and a holistic overview is lacking. This review paper attempts to provide a big picture of the deep radar perception stack, including signal processing, datasets, labelling, data augmentation, and downstream tasks such as depth and velocity estimation, object detection, and sensor fusion. For these tasks, we focus on explaining how the network structure is adapted to radar domain knowledge. In particular, we summarise three overlooked challenges in deep radar perception, including multi-path effects, uncertainty problems, and adverse weather effects, and present some attempts to solve them.

On the safety assessment of an automatic train operation system

The paper examines the automatic train operation system as part of the locomotive control and protection system, the remote supervision centre’s means for control of onboard and trackside machine vision facilities. The focus is on the dependence of the system’s safety and dependability on the dependability characteristics of its components and adverse weather effects. The criteria of a system’s wrong-side and right-side failures were defined, the graph models were constructed of the safety and dependability states of an automatic train operation system. The Markovian graph method of calculating the safety and dependability of complex systems was substantiated. That allowed defining such key safety indicators of an automatic train operation system as the mean time to wrong-side failure, probability of wrong-side failure, wrong-side failure rate. The study established that the safety of an automatic train operation system primarily depends on the dependability of machine vision facilities. The growth of the system’s wrong-side failure rate is limited to half the failure rate of machine vision facilities. It was also established that the dependability of an automatic train operation system is defined by the failure rate of a locomotive control and protection system and the failure rate of machine vision facilities. The conducted analysis allows concluding that in order to achieve an acceptable level of safety of an automatic train operation system, efforts should focus on machine vision redundancy, ensuring the SIL4 functional safety of on-board and trackside machine vision facilities, as well as regular comparison of the outputs of on-board and trackside machine vision facilities, redundant output comparison, integration of the outputs in motion. Additionally, adverse weather effects are to be countered by improving the efficiency of machine learning of the machine vision software.

Disentangling climatic and nest predator impact on reproductive output reveals adverse high‐temperature effects regardless of helper number in an arid‐region cooperative bird

Climate exerts a major influence on reproductive processes, and an understanding of the mechanisms involved and which factors might mitigate adverse weather is fundamental under the ongoing climate change. Here, we study how weather and nest predation influence reproductive output in a social species, and examine whether larger group sizes can mitigate the adverse effects of these factors. We used a 7‐year nest predator‐exclusion experiment on an arid‐region cooperatively breeding bird, the sociable weaver. We found that dry and, especially, hot weather were major drivers of nestling mortality through their influence on nest predation. However, when we experimentally excluded nest predators, these conditions were still strongly associated with nestling mortality. Group size was unimportant against nest predation and, although positively associated with reproductive success, it did not mitigate the effects of adverse weather. Hence, cooperative breeding might have a limited capacity to mitigate extreme weather effects.

Contrary to Expectations: Seasonal Variation in Sickle Cell Crisis — a Nation Wide Data Analysis

Background: Sickle cell crisis is a painful complication of sickle cell disease in adults and children. Acute episodes of severe pain (crises) are the primary reason for hospitalizations. Although infection, dehydration, and acidosis (all of which favor sickling) can act as triggers, in most instances, no predisposing cause is identified. Hospitalizations for sickle cell crisis have been steadily rising over the past decade. Though previous studies have described seasonal variations for in African and Caribbean populations, but there are no large data showing similar trends with patients in the United States. We reviewed a large national hospitalization database to determine whether rates of sickle cell crisis have seasonal variations over the past decade. Methods: We examined the Nationwide Inpatient Sample (NIS), a nationally representative survey of hospitalizations conducted by the Healthcare Cost and Utilization Project in collaboration with participating states. It is the largest all-payer inpatient dataset in the United States and includes a 20% sample of United States community hospitals that approximates 20% of all United States community hospitals. The Nationwide Inpatient Sample (NIS) database was used to estimate annual number of hospitalizations from 2000 - 2012. Identification of sickle cell disease related hospitalizations was based on the designation of the prior validated International Classification of Diseases (9th Edition) Clinical Modification (ICD-9-CM) diagnosis code 282.62 (Sickle cell crisis) as the principal discharge diagnosis. The frequency of hospitalization per month cumulative over 13 years was calculated and divided by number of days in that month to determine the mean hospitalizations per day for each month. All calculations were carried out using the weighted estimates approximating nationwide population estimates. Results: An estimated 744,072 hospitalizations with primary diagnosis of sickle cell crisis occurred in the US from 2000 to 2012. More specifically, the mean number of hospitalizations per day in each month is shown in Fig. 1. The mean number of hospitalizations each day was highest in January (2134 per day) and thereafter the hospitalization rate dropped to a nadir in June (1988 per day). There was a significant rising trend of admissions from June towards winter months with couple of peaks in September and January as depicted in the Fig. 1. In general, the number of hospitalization was maximum in the winter months and minimum in summer months as demonstrated in Fig .2. Conclusion: Contrary to expectations, we identified for the first time in United States an impressive pattern of seasonal variation in hospitalizations for sickle cell crisis with a notable increase in winter and fall and a significant drop during summer months. The seasonal pattern may reflect viral or other infections responsible for precipitation of sickle cell crisis. Otherwise, exposure to low temperatures causes increased viscosity of blood and peripheral vasoconstriction triggering sickle cell crisis. This statistical analysis calls for a definitive need to educate patients and providers on how to mitigate the adverse effects of weather on sickle cell crisis. Further efforts need to be done to identify triggers and methods to determine the relationship of these variations and reduce this predictive burden on the overall health care system. Download : Download high-res image (49KB) Download : Download full-size image Figure 1 . Disclosures No relevant conflicts of interest to declare.