Trends In Series Research Articles

Evaluation of meteorological time series trends in Southeastern Anatolia, Turkey

In this study, trend analyses of six climatic variables (mean, minimum, and maximum temperature, relative humidity, wind speed, and precipitation) for 1966-2020 are conducted for the Southeastern Anatolia Region, which is the main focus of the integrated development project in Turkey (Turkish acronym GAP). The trends for seasonal and annual periods are determined using the Mann-Kendall (MK) test, and Sen's slope method and regression analyses are used to find the trends' slopes. Moreover, Innovative Trend Analysis (ITA) is also used to find the time series changes for low, medium, and high values. As a result of the analyses, the mean, minimum, and maximum temperatures in the GAP region show increasing trends according to both methods. Significant trends are obtained at a limited number of stations for the precipitation, relative humidity, and wind speed with the MK test, while consistent decreasing trends are found at most stations with the ITA method.

Turbidity dynamics in Indian peninsular river mouths derived from Kd490 reveals key anthropogenic drivers

Large rivers, which act as natural integrators of surface processes, contribute massive volume of terrestrial materials to the coastal oceans. However, the accelerated climate warming and increasing anthropogenic activities recorded in recent years have been severely affecting the hydrologic and physical regimes of river systems. These changes have a direct impact on river discharge and runoff, some of which are occurred rapidly in the past two decades. Here, we present a quantitative analysis on the effects of changes in surface turbidity at coastal river mouths using diffuse attenuation coefficient at 490 nm (Kd490) as a proxy of turbidity for six major Indian peninsular rivers. The time series (2000−2022) trends of Kd490 obtained from Moderate Resolution Imaging Spectrometer (MODIS) images shows a significant decreasing trend in Kd values (p < 0.001) at the mouths of the Narmada, Tapti, Cauvery, Krishna, Godavari, and Mahanadi rivers. This is despite an increased rainfall trend observed for the six studied river basins which can likely intensifies the surface runoff and deliver more sediments, suggesting that other factors such as land use changes and increased number of dam constructions are primarily responsible for the decreased sediment load from rivers to coastal mouths.

Epidemiological characteristics and spatiotemporal patterns of hand, foot, and mouth disease in Hubei, China from 2009 to 2019.

Hand, foot, and mouth disease (HFMD) is a public health issue in Hubei and studies of- spatiotemporal clustering at a fine scale are limited. The purpose of this research was to analyze the epidemiological characteristics, temporal variation characteristics, and spatiotemporal clustering of HFMD cases at the town level from 2009 to 2019 to improve public health outcomes. Mathematical statistics, a seasonal index, wavelet analysis, and spatiotemporal scans were used to analyze epidemiological characteristics, time series trends, and spatiotemporal clusters of HFMD in Hubei. EV-A71 (Enterovirus A71) and CVA16 (Coxsackievirus A16) constitute the two primary pathogens of the HFMD epidemic in Hubei, among which EV-A71 is the dominant pathogen, especially in 2016. In terms of age distribution, a major peak occurred at 0-5 years and a very small increase appeared at 25-35 years, with the former having a higher incidence among males than females and the latter having the opposite difference between males and females. The number/rate of HFMD cases exhibited a considerable increase followed by a moderate decline from 2009 to 2019, with the first large peak in April-July and a smaller peak in November-December. HFMD in Hubei exhibited the characteristics of a 270-day cycle with multiscale nesting, which was similar to the periodicity of HFMD cases caused by EV-A71 (9 months). Cities with a higher incidence of HFMD formed a part of an "A-shaped urban skeleton". Subdistricts had the highest incidence of HFMD, followed by towns and villages. The spatiotemporal scan results showed one most likely cluster and 22 secondary clusters, which was consistent with the geographic location of railways and rivers in Hubei. These findings may be helpful in the prevention and control of HFMD transmission and in implementing effective measures in Hubei Province.

Two decades of mass-balance observations on Aldegondabreen, Spitsbergen: interannual variability and sensitivity to climate change

Abstract Aldegondabreen is a relatively small (5.3 km2) land-terminating glacier located in Nordeskiöld Land of Svalbard, ~10 km southwest of Barentsburg. Cumulative mass balance during 2002–20 equalled −21.79 m w.e., which corresponds to 37% of the total mass loss. The annual mass balance (Ba) varied from −0.24 to −2.19 m w.e., while the winter mass balance (Bw) ranged between 0.36 and 0.85 m w.e. Ba and Bw were strongly correlated with the positive degree-day sums and solid precipitation amounts, respectively, measured at the Barentsburg weather station. There was also a strong correlation (r = 0.76) between Ba and Bw, which shows that winter accumulation amplifies the consecutive summer ice melt by changing the surface albedo. The trend in both observational series is not detectable because the period from 2005 to 2013 was characterized by relatively high Bw and not very negative Ba values. This was also observed on the other Svalbard glaciers, and was related to prevailing north-westerly flows over Svalbard during the summer. Therefore, the decadal periodicity of the Aldegondabreen mass balance follows general archipelago patterns that are determined by regional-scale factors. Thus, the surface mass-balance time series, which is now the longest one in the central part of the Spitsbergen Island, is representative for the archipelago.

Research on the Effect of Knowledge Stock on Technological Advance and Economic Growth in Republic of Korea

The Republic of Korea’s R&amp;D investment has been rapidly increasing over the long term. This R&amp;D investment is accumulated as knowledge stock, which means the amount of knowledge held by individual countries. However, is the rapid increase in R&amp;D investment contributing to the advancement of domestic science and technology and economic growth? In general, technological advance will lead to a country’s sustainable economic growth. If so, how much has it contributed? This is a very important question for a neoclassical economist. This study reviewed previous studies on “knowledge stock”, “TFP estimation”, and “R&amp;D investment, TFP and economic growth relationship”, and sequentially analyzed the relationship among knowledge stock, TFP, and economic growth through a three-step process. The analysis procedure first performs data processing and time series trend analysis, then time series characteristics analysis, and finally Orderful Least Squares (OLS). The analysis results showed that all three models had a positive (+) relationship and most of them were derived to be statistically significant. On this basis, three policy implications are presented. First, the knowledge stock is estimated to have had a positive (+) effect on economic growth, and the continuous increase in domestic R&amp;D investment is believed to have acted as a growth engine. In particular, financial of the government by financial resources and the mid-to-high tech industry groups by industrial characteristics are statistically positively significant. Next, domestic knowledge stocks have had a positive effect on TFP but the efficiency is judged to be decreasing. It is implied that high-tech industries need to form knowledge stocks above the threshold through continuous R&amp;D investment. Finally, TFP was analyzed to have had a high impact on economic growth, and this confirmed that innovation-led growth was important. It is still relevant that innovation-led sustainable growth is more important than factor led in the Republic of Korea.

Forecasting of local tax realization with least square method in Tanggamus Regency

Purpose: This study aims to identify the contribution of local taxes to regional revenues in Tanggamus Regency and predict the pattern of realization of regional tax revenues in the coming period. Research methodology: This research is a quantitative study with a time series trend analysis according to the type of regional tax and an overview of the target and its realization. The sample used in this study was data from seven types of regional taxes obtained from the Regional Financial Management Agency of Tanggamus Regency. The data analysis process uses the least squares method, often used to forecast, and the application uses Minitab software with fitted line plots and descriptive statistics features. Results: This study's results showed that the seven tax subjects seemed to go through changes and tended to grow in the future. Limitations: This research is limited to the specific scope of taxes selected and does not cover all other types of taxes and levies. Contributions: This study's ability to predict the future helps make plans for regional tax revenue, especially when it comes to planning for policies that might change in the near future. Novelty: This research is new because it has a new update on how to use predictive methods with Minitab and an analysis of how local taxes have changed over time.

¿Está indicada la irradiación del tumor primario en el estadio IV?

De-novo metastatic breast cancer is a rare entity, occurring in 3–10% of cases. The most common treatment for these patients is systemic therapy (chemotherapy, hormonal therapy, targeted therapies, and combinations of these). The subgroup of patients with oligometastatic disease and low systemic disease burden may benefit more from adding a locoregional treatment. This literature review aims to analyze the benefits and disadvantages of locoregional treatment in patients with de-novo oligometastatic breast cancer. Retrospective series report that this subgroup of patients may benefit from locoregional treatment, despite presenting with metastatic disease, by improving overall survival and local control. Additionally, several retrospective series have shown that locoregional treatment with surgery, surgery and adjuvant radiotherapy, or exclusive radiotherapy present similar results in local control. Therefore, exclusive radiotherapy may be a viable alternative in these patients. In recent years, attempts have been made to demonstrate the trend of retrospective series in prospective trials. These studies have shown great heterogeneity. Some with positive results in improving overall survival and others with negative results, but not statistically significant. The main limitation of retrospective series is the selection bias, which could explain the differences in the results obtained. For this reason, locoregional treatment in these patients should be approached by multidisciplinary teams on an individualized basis, with greater consideration for oligometastatic patients.

Probabilistic and Statistical Analysis of Aviation Accidents

The aim of this paper is to study the role that probabilistic and statistical analysis tools, such as ARIMA models, can have in the improvement of aviation safety levels. This is to be achieved by using available accident data, organizing it probabilistically, based on the different variables involved giving a better understanding of them as well as trough time series analyses forecast future values and trends. The results achieved provide valuable information usable by different aviation entities to be used on the accident prevention field through a proactive approach with regards to identified key areas.

Strategies for Producing Reliable Trends Forecasting of COVID-19 Pandemic in Malaysia using Dynamic Mode Decomposition

Dynamic Mode Decomposition (DMD) with time delay embedding is used to predict dynamic patterns in univariate time series. An important pattern that can be extracted using DMD is the trend or global change in a time series which is useful for producing reliable forecast. DMD utilizes the computationally effi cient singular value decomposition (SVD) to produce a low rank approximation of the linear operator that brings about the dynamic patterns in the time series. Trend in the time series is translated as dynamic modes of the operator with low frequencies. The time evolution of this low frequency pattern produces forecast of the time series. In this paper, we outline the strategies for extracting trend component from COVID-19 time series of Malaysia. It is discovered that, other than identifying modes with slow varying frequencies, we need to also resolve the time stamp delay, so that mean-square error of the reconstructed time series is minimal. Information of the magnitude and phase of DMD modes are useful to identify persistent patterns and remove nonstationary ones. We compare the performance of DMD with another SVD-based method which is the singular spectrum analysis (SSA) and our results highlight certain fundamental difference between these two methods. The forecasts from SSA tend to lean towards the direction of maximum variance, producing low reconstruction error but slow to detect sudden changes in the time series. On the other hand, forecasts from DMD captures the phases of dominant modes that dictates the overall global pattern, hence providing a better prediction of future dynamics of the time series.

Iterative memory-driven load forecast network model for accuracy improvement

High penetration of renewable energy generation and intensive application of new-type load control measures in power system greatly increase the difficulty of load forecast (LF). Long-term dependencies in load series limit LF accuracy improvement of back propagation neural network (BPNN) and its combination variants. Inspired by coexisting features of long-term trend and non-trend fluctuation in load series, a novel iterative memory-driven load forecast network (IMDLFN) model is proposed to forecast system-level load in the scenario. It creatively adopts memory-driven units to convey long-term trend information learned from the previous time steps and to autonomously balance long-term memory and short-term memory, which effectively deals with long-term dependencies and perfectly circumvents weight selection problem of BPNN combination variants. LF for municipal power system in China demonstrates that the proposed model can remarkably improve LF accuracy especially for trend/non-trend coexisting load series.

Comparison of five empirical potential models for aluminosilicate systems: Albite and anorthite as test cases

In this work we have carried out a systematic investigation of the performance of different force fields in correctly reproducing the structure, densities as a function of temperature and the properties (Young's and Bulk moduli and the coefficient of thermal expansion) of both albite and anorthite crystals and glasses. After this first step of comparison, we selected the potential which gave the better overall performances (the Bertani-Menziani-Pedone potential) and investigated the effect of the cooling rate on structure and properties of albite and anorthite glasses and verified the predicting power of MD simulations for the simulation of the coefficient of thermal expansion (CTE) for a series of several aluminosilicate glasses whose compositions go from the albite-like one to the anorthite-like one.The tested interatomic potentials provide reliable short-range order around the constituting cations, but some differences are detected in the medium-range order described by the T-O-T angles (T= network formers cations, Si and Al), the Al/Si distributions and Qn distributions. In general, all the potentials overestimate the CTE because of the high quenching rate used but we have shown that extrapolated properties at room temperature provide a more reliable comparison with experiments. The CTE trend of the albite-anorthite plagioclase glass series is well reproduced and associated with the substitution of Na with Ca ions rather than the Al/Si one. The error associated with the computed CTE values decreases with box dimension and we found that simulation boxes containing 50k atoms are necessary to reliably estimate this property.

Generation of climate change scenarios for rainfall and temperature using SDSM in a Mediterranean environment: a case study of Boufakrane river watershed, Morocco

Climate change is the global phenomenon of climate transformation characterized by changes in the planet’s habitual climate. The main objective of this work is to assess and predict climate change for the 2015–2100 period, in the Boufakrane river watershed, Morocco. In this study, the Statistical DownScaling Model (SDSM) method has been used to generate the climate scenarios for rainfall and temperature related to Representative Concentration Pathway (RCP) scenarios such as RCP 2.6, RCP 4.5, and RCP 8.5. For this purpose, the region’s annual, monthly, and daily rainfall records were analyzed. A rainfall index was calculated to differentiate and distinguish between dry and humid years of the climate series, and a non-parametric Pettitt’s test has been applied to determine the trend of precipitation series for the projection period. The application of Pettitt’s test with a confidence interval of 99% and a significance level of 0.05 shows a break in the 1979/80 rainfall series, with an average of 675.78 mm and 511.94 mm respectively before and after the break. The combined application of the two approaches of SDSM, and the non-parametric Pettitt test for the period 2015–2100, showed a downward trend of annual rainfall with 17.29% for RCP 2.6. While no trend was recorded for the two scenarios RCP 4.5, and RCP 8.5. Therefore, this study highlighted the importance and urgent need for developing strategies and decision-making plans for climate change adaptation for sustainable water resources management.Graphical

Factors associated with receipt of mRNA-1273 vaccine at a United States national retail pharmacy during the COVID-19 pandemic

IntroductionThe emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) prompted accelerated vaccine development of novel messenger RNA (mRNA)-based vaccines by Moderna and Pfizer, which received FDA Emergency Use Authorization in December 2020. The purpose of this study was to examine trends in primary series administration and multi-dose completion rates with Moderna’s mRNA-1273 vaccine administered at a United States retail pharmacy. MethodsWalgreens pharmacy data were joined to publicly available data sets to examine trends in mRNA-1273 primary series and multi-dose completion across patient race/ethnicity, age, gender, distance to first vaccination, and community characteristics. Eligible patients received their first dose of mRNA-1273 administered by Walgreens between December 18, 2020 and February 28, 2022. Variables significantly associated with on-time second dose (all patients) and third dose (immunocompromised patients) in univariate analyses were included in linear regression models. A subset of patients in selected states were studied to identify differences in early and late vaccine adoption. ResultsPatients (N = 4,870,915) who received ≥ 1 dose of mRNA-1273 were 57.0% White, 52.6% female, and averaged 49.4 years old. Approximately 85% of patients received a second dose during the study period. Factors associated with on-time second dose administration included older age, race/ethnicity, traveling ≤ 10 miles for the first dose, higher community-level health insurance, and residing in areas with low social vulnerability. Only 51.0% of immunocompromised patients received the third dose as recommended. Factors associated with third dose administration included older age, race/ethnicity, and small-town residence. Early adopters accounted for 60.6% of patients. Factors associated with early adoption included older age, race/ethnicity, and metropolitan residence. ConclusionOver 80% of patients received their on-time second dose of mRNA-1273 vaccine per CDC recommendations. Patient demographics and community characteristics were associated with vaccine receipt and series completion. Novel approaches to facilitate series completion during a pandemic should be further studied.

Antibiotic prescribing trends in primary care 2014–2022

Antimicrobial resistance (AMR) is a global healthcare challenge that governments and health systems are tackling primarily through antimicrobial stewardship (AMS). This should, improve antibiotic use, avoid inappropriate prescribing, reduce prescription numbers, aligning with national/international AMS targets. In primary care in the United Kingdom (UK) antibiotics are mainly prescribed for patients with urinary and respiratory symptoms (22.7% and 46% of all antibiotic prescriptions respectively).This study aimed to capture the time-series trends (2014–2022) for commonly prescribed antibiotics for respiratory and urinary tract infections in primary care in England. Trends for Amoxicillin, Amoxicillin sodium, Trimethoprim, Clarithromycin, Erythromycin, Erythromycin ethylsuccinate, Erythromycin stearate, Doxycycline hyclate, Doxycycline monohydrate and Phenoxymethylpenicillin (Penicillin V) were determined. In doing so providing evidence regarding meeting UK antibiotic prescribing rate objectives (a 15% reduction in human antibiotic use 2019–2024).Time series trend analysis of 62,949,272 antibiotic prescriptions from 6,370 General Practices in England extracted from the National Health Service (NHS) Business Services Authority web portal were explored. With additional investigation of prescribing rate trends by quintiles of the Index of Multiple Deprivation (IMD).Overall, there is a downwards trend in antibiotic prescribing for those explored. There is an association between IMD, geographical location, and higher antibiotic prescribing levels (prescribing hot spots). England has a well-documented North-South divide of health inequalities, this is reflected in antibiotic prescribing. The corona virus pandemic (COVID-19) impacted on AMS, with a rise in doxycycline and trimethoprim prescriptions notable in higher IMD areas. Since then, prescribing appears to have returned to pre-pandemic levels in all IMDs and continued to decline.AMS efforts are being adhered to in primary care in England. This study provides further evidence of the link between locality and poorer health outcomes (reflected in higher antibiotic prescribing). Further work is required to address antibiotic use in hot spot areas.

Global ocean colour trends in biogeochemical provinces

Satellite-derived ocean colour data provide continuous, daily measurements of global waters and are an essential tool for monitoring these waters in a changing climate. Merging observations from different satellite sensors is necessary for long-term and continuous climate research because the lifetime of these sensors is limited. A key issue in deriving long-term trends from merged ocean colour data is the inconsistency between the spatiotemporal coverage of the different sensor datasets that can lead to spurious multi-year fluctuations or trends in the time series. This study used the merged ocean colour satellite dataset produced by the Ocean Colour Climate Change Initiative (OC-CCI version 6.0) to infer global and local trends in optically active constituents. We applied a novel correction method to the OC-CCI dataset that results in a spatiotemporally consistent dataset, allowing the examination of long-term trends of optically active constituents with greater accuracy. We included sea surface temperature, salinity, and several climate oscillations in our analysis to gain insight into the underlying processes of derived trends. Our results indicate a significant increase in chlorophyll-a concentration in the polar waters, a decrease in chlorophyll-a concentration in some equatorial waters, and point to ocean darkening, predominantly in the polar waters, due to an increase in non-phytoplankton absorption. This study contributes to broader knowledge of global trends of optically active constituents and their relation to a changing environment.

OPP-Miner: Order-Preserving Sequential Pattern Mining for Time Series.

Traditional sequential pattern mining methods were designed for symbolic sequence. As a collection of measurements in chronological order, a time series needs to be discretized into symbolic sequences, and then users can apply sequential pattern mining methods to discover interesting patterns in time series. The discretization will not only cause the loss of some important information, which partially destroys the continuity of time series, but also ignore the order relations between time-series values. Inspired by order-preserving matching, this article explores a new method called order-preserving sequential pattern (OPP) mining, which does not need to discretize time series into symbolic sequences and represents patterns based on the order relations of time series. An inherent advantage of such representation is that the trend of a time series can be represented by the relative order of the values underneath time series. We propose an OPP-Miner algorithm to mine frequent patterns in time series with the same relative order. OPP-Miner employs the filtration and verification strategies to calculate the support and uses the pattern fusion strategy to generate candidate patterns. To compress the result set, we also study to find the maximal OPPs. Experimental results validate that OPP-Miner is not only efficient but can also discover similar subsequences in time series. In addition, case studies show that our algorithms have high utility in analyzing the COVID-19 epidemic by identifying critical trends and improve the clustering performance. The algorithms and data can be downloaded from https://github.com/wuc567/Pattern-Mining/tree/master/OPP-Miner.

Drought Assessment across Erbil Using Satellite Products

In this article, meteorological and agricultural droughts across the Erbil province, Iraq, were assessed using remote sensing data and satellite products. To this end, the long-term (2000–2022) Standardized Precipitation Evapotranspiration index (SPEI) at 1- and 3-month accumulation periods (SPEI-1 and SPEI-3) as well as the Normalized Difference Vegetation Index (NDVI) across Erbil were utilized. While the former was retrieved from the global SPEI data repository, the latter was derived from Moderate Resolution Imaging Spectroradiometer (MODIS) products. The spatiotemporal variations in the SPEI indices indicated that two to nine extreme drought events occurred in the province with an increasing northward pattern. An increasing trend in the long-term NDVI series was also detected, having more diversity in vegetation coverage in the northern part of the province. The relationship between the SPEI and MODIS-NDVI was found to be positive but insignificant. Thus, we concluded that short-term meteorological droughts were not the only reason for the agricultural droughts in Erbil. Furthermore, the climate characteristics related to the cumulative water balance over a previous season is not an important trigger for the spatial variation in vegetation coverage across the province.

Case study of rainfall and temperature assessment through trend and homogeneity analyses in Vadodara and Chhotaudepur district of Gujarat State, India

This study aims to assess the climate change impact on the rainfall and temperature data of the Vadodara-Chhotaudepur district of India and to focus on the environmental challenges related to the rainfall and temperature in the present state of development, land use, industrialization, and urbanization. The study utilized nine trend analysis methods, namely linear regression (LR), Sen's robust slope estimator (SS), Mann-Kendall test (MK), Spearman's rank correlation (SRC), the trend-free pre-whitening (TFPW), variance correction approach by Hamed and Rao (1998) with MK test (MK-CF1), variance correction approach by Yue and Wang (2004) with MK test (MK-CF2), block bootstrap with MK test (BBS-MK), and graphical method as innovative trend analysis (ITA), applied on monthly, annual, and seasonal scales. Additionally, the study also employed four homogeneity analysis methods, including Pettitt's test, standard normal homogeneity test (SNHT), Buishand's test, and Von Neumann Ratio test (VNRTs). The IMD (Indian meteorological department) gridded long-term rainfall data from 1901 to 2019 and temperature data from 1951 to 2019 are used in the present study to assess the homogeneity and trends of the data series. Results showed a warming trend of maximum temperature (MaxT) and minimum temperature (MinT) at the monthly, annual, and seasonal time scale and significant (at 5%) warming trend in annual MinT in the entire study area. Annual rainfall showed negative trend in the study area with significant (at 5%) negative trend in eastern deforested area western industrial and area adjacent to thermal power station. The change point is detected in annual rainfall time series in eastern forest area in 1959 and western area in 1983, i.e., after development of the industries and commissioning of thermal power station in the western study area. The trend rate of MaxT and MinT has been recognized as 0.004°C/year and 0.019°C/year, respectively, for the data period of 1951 to 2019. The annual rainfall trend rate has been observed as -0.743mm/year for the data range 1901 to 2019. All trend analysis methods revealed consistent results except MK-CF2 method, which portraits greater number of significant trends in trend analysis methods.

Validation of EMI-2 Radiometric Performance with TROPOMI over Dome C Site in Antarctica

(1) The Environmental Trace Gases Monitoring Instrument-2(EMI-2) is a high-quality spaceborne imaging spectrometer that launched in September 2021. To evaluate its radiometric calibration performance in-flight, the UV2 and VIS1 bands of EMI-2 were cross-calibrated by the corresponding bands (band3 and band4) of TROPOMI over the pseudo-invariant calibration site Dome C. (2) After angle limitation and cloud filtering of the Earth radiance data measured by EMI-2 and TROPOMI over Dome C, the top of atmosphere (TOA) reflectance time series were calculated. The spectral adjustment factors (SAF) were derived from the solar spectrum measured by the sensor to minimize the uncertainties caused by the different spectral response functions (SRF) of sensors. In addition, a correction method based on the radiative transfer model (RTM) SCIATRAN was used to suppress unaccounted angular dependence of atmospheric scattering. The radiation performance of EMI-2 is evaluated using the TOA reflectance ratio of EMI-2 and TROPOMI, combining the SAF correction and RTM-based correction methods. (3) It was shown that the time series trending of the TOA reflectance ratio between EMI-2 measurements and TROPOMI demonstrate flat characteristics and strong correlation. The mean reflectance ratios range from 0.998 to 1.09. The standard deviation of the reflection ratio is less than 3%. For 328 nm, 335 nm, 340 nm, 460 nm, and 490 nm, the mean values are close to one, and the relative radiometric bias estimated through EMI-2 and TROPOMI intercalibration is less than 3%, and for other wavelengths, the biases are less than 6%, except for 416 nm, which behaves higher than 7%. The cross-calibration results show that the radiometric calibration of EMI-2 is within the relative accuracy requirement.

Time Series Analysis of Sentiment: A Comparison of the US and UK Coronavirus Subreddits

In this paper, we investigate the dynamics of the social media response on Reddit to the COVID-19 pandemic during its first year (February 2020–2021). The emergence of region-specific subreddits allows us to compare the reactions of individuals posting their opinions on social media about the global pandemic from two perspectives — the UK and the US. In particular, we look at the volume of posts and comments on these two subreddits, and at the sentiment expressed in these posts and comments over time as a measure of the public level of engagement and response. Whilst an analysis of volume allows us to quantify how interested people are about the pandemic as it unfolds, sentiment analysis goes beyond this and informs us about how people respond towards the pandemic based on the textual content in the posts and comments. The research looks to develop a framework for analyzing the social response on Reddit to a large-scale event in terms of the level of engagement measured through post and comment volumes, and opinion measured through an analysis of sentiment applied to the post content. In order to compare the subreddits, we show the trend in the time series through the application of moving average methods. We also show how to identify the lag between time series and align them using cross-correlation. Moreover, once aligned, we apply moving correlations to the time series to measure their degree of correspondence to see if there is a similar response to the pandemic across the two groups (UK and US). The results indicate that both subreddits were posting in high volumes at specific points during the pandemic, and that, despite the generally negative sentiment in the posts and comments, a gradual decrease in negativity leading up to the start of 2021 is observed as measures are put in place by governments and organizations to contain the virus and provide necessary support the affected populations.