Daily Series Research Articles

Influence of instrumentation on long temperature time series

In time series of essential climatological variables, many discontinuities are created not by climate factors but changes in the measuring system, including relocations, changes in instrumentation, exposure or even observation practices. Some of these changes occur due to reorganization, cost-efficiency or innovation. In the last few decades, station movements have often been accompanied by the introduction of an automatic weather station (AWS). Our study identifies the biases in daily maximum and minimum temperatures using parallel records of manual and automated observations. They are selected to minimize the differences in surrounding environment, exposition, distance and difference in elevation. Therefore, the type of instrumentation is the most important biasing factor between both measurements. The pairs of weather stations are located in Piedmont, a region of Italy, and in Gaspe Peninsula, a region of Canada. They have 6 years of overlapping period on average, and 5110 daily values. The approach implemented for the comparison is divided in four main parts: a statistical characterization of the daily temperature series; a comparison between the daily series; a comparison between the types of events, heat wave, cold wave and normal events; and a verification of the homogeneity of the difference series. Our results show a higher frequency of warm (+ 10%) and extremely warm (+ 35%) days in the automated system, compared with the parallel manual record. Consequently, the use of a composite record could significantly bias the calculation of extreme events.

Modelling Nigeria Naria Exchange Rate against Some Selected Country’s Currencies Volatility: Application of GARCH Model

This paper examines the exchange rate volatility with GARCH-type model of the daily exchange rate return series from January 2012 – August 2016 for Naira/Chinese Yuan, Naira/India Rupees, Naira/Spain Euro, Naira/UK Pounds and Naira/US Dollar returns. The studies compare estimates of variants of GARCH (1, 1), EGARCH (1, 1), TGARCH (1,1) and GJR-GARCH (1,1) models. The result from all models indict presence of volatility in the five currencies and equally indicate that most of the asymmetric models rejected the existence of a leverage effect except for models with volatility break. For GARCH (1, 1), GJR-GARCH (1, 1,) EGARCH (1,1) and TGARCH (1, 1), it was observed that India have the best exchange rate with the highest log-likelihood (Log L) and the lowest AIC and BIC followed by USA, China, Spain and United Kingdom respectively. The four models was later compared for the exchange rates of the five countries under consideration i.e. China, India, Spain, UK and USA to select the best fitted model for each country and it was discovered that GJR-GARCH (1,1) is the best fitted model for all the countries followed by GARCH (1,1), TGARCH (1,1) and EGARCH (1,1) in that order.

Spatiotemporal variability of daily precipitation concentration and its relationship to teleconnection patterns over the Mediterranean during 1975–2015

AbstractThis study has addressed the spatiotemporal distribution of the daily rainfall concentration and its relation to the teleconnection patterns across the Mediterranean (MR). Daily concentration index (CI) and the ordered n index ( nor) are used at annual time scale to reveal the statistical structure of precipitation across the MR based on 233 daily rainfall series for the period 1975–2015. Eight teleconnection patterns, North Atlantic Oscillation (NAO), Mediterranean Oscillation (MO), Western Mediterranean Oscillation (WeMO), Upper‐Level Mediterranean Oscillation index (ULMO), East Atlantic (EA) pattern, East Atlantic/West Russia (EATL/WRUS) pattern, Scandinavia (SCAND) pattern and Southern Oscillation (SO) at annual time scale are selected. The spatiotemporal patterns in precipitation concentration indices, annual precipitation and their teleconnections with previous large‐scale circulations are investigated. Results show a strong connection between the CI and the nor (r = 0.70, p < .05) which present the same relative areas of high and low concentration. The annual values range from 0.57 to 0.70 for CI and 0.49 to 0.71 for nor index which show a high daily precipitation concentration across the MR. Trend analysis demonstrated mostly significant increasing trends for both indices. This increase is mainly found in south France, northern coastlands of the Iberian Peninsula (IP), Greece and Tunisia. An inverse relationship between the number of rainy days and concentration indices is evident. Both of WeMO and MO can play an important role in modulating rainfall in the northwest Mediterranean. The positive EATL/WRUS phase is mainly connected with positive precipitation mean anomalies in the eastern Mediterranean and vice versa in the west. The high daily precipitation concentration values over south France, northeast Spain, Croatia and Tunisia are linked to the low values of WeMO and high values of EA. These results could pave the way for new possibilities regarding the projection of precipitation concentration and precipitation irregularity in downscaling techniques.

Could stock hedge Bitcoin risk(s) and vice versa?

This paper is saddled with the task of investigating the Bitcoin market behavior in the presence of a government risk. This is because both the institutional and retail investors’ interests in the Bitcoin market are growing rapidly. Conversely, the seemingly unregulated nature of this market is a serious concern to most economies and results to the placement of ban on Initial Coin Offering (ICO) in some economies by the government. Daily series of return and volume within the window of the ICO ban in China was used for the Bitcoin market and S&P500 stock market to examine the effect of a government risk in the Bitcoin market and possible hedging capabilities. Empirical results show that the ban dampened Bitcoin returns and the returns from each market can predict the other. The Exogenous Dynamic Conditional Correlation (Exo-DCC) model result suggests that, yes! the S&P500 stocks are capable of hedging Bitcoin risk, while Bitcoin can also hedge S&P500 stocks’ risks and vice versa. The Exogenous BEKK (Exo-BEKK) model result shows evidence of bidirectional volatility spill over between the two markets studied. In practice, investors (institutions and retailers) can comfortably form a robust investment portfolio with (at least) these two assets and develop a hedging strategy, such that the impacts of risks on the portfolio’s returns are safely hedged.

Modular Wavelet–Extreme Learning Machine: a New Approach for Forecasting Daily Rainfall

A rainfall forecasting method based on coupling wavelet analysis and a novel artificial neural network technique called extreme learning machine (ELM) is proposed. In this way, the unique characteristics of each technique are combined to capture different patterns in the data. At first, wavelet analysis is used to decompose rainfall time series into wavelet coefficients, and then the wavelet coefficients are used as inputs into the ELM model to forecast rainfall. The accuracy of the model is further improved using a modular learning approach. In the modular learning, an innovative approach to determine the optimum number of clusters entitled threshold cluster number is introduced. The relative performances of the proposed models are compared with the single ELM model for three cases consisting of one daily rainfall series from Iran (Kharjeguil station), one daily rainfall series from India (Ajmer station) and one daily rainfall series from the United States (Barton Pond station). The correlation coefficient (r), root mean square errors (RMSE) and Nash–Sutcliffe efficiency coefficient (NS) statistics are used as the comparing criteria. The comparison results indicate that the proposed modular wavelet–ELM method could significantly increase the forecast accuracy and perform much better than both the wavelet–ELM and single ELM. Moreover, three case study results indicate the importance of determining the optimum number of clusters based on the new concept of threshold cluster number in order to achieve optimum forecast results.

Applying Cascade-Correlation Neural Networks to In-Fill Gaps in Mediterranean Daily Flow Data Series

The analyses of water resources availability and impacts are based on the study over time of meteorological and hydrological data trends. In order to perform those analyses properly, long records of continuous and reliable data are needed, but they are seldom available. Lack of records as in gaps or discontinuities in data series and quality issues are two of the main problems more often found in databases used for climate studies and water resources management. Flow data series from gauging stations are not an exception. Over the last 20 years, forecasting models based on artificial neural networks (ANNs) have been increasingly applied in many fields of natural resources, including hydrology. This paper discusses results obtained on the application of cascade-correlation ANN models to predict daily water flow using Julian day and rainfall data provided by nearby weather stations in the Ebro river watershed (Northeast Spain). Five unaltered gauging stations showing a rainfall-dominated hydrological regime were selected for the study. Daily flow and weather data series covered 30 years to encompass the high variability of Mediterranean environments. Models were then applied to the in-filling of existing gaps under different conditions related to the characteristics of the gaps (6 scenarios). Results showed that when short periods before and after the gap are considered, this is a useful approach, although no general rule applied to all stations and gaps investigated. Models for low-water-flow periods provided better results (r = 0.76–0.8).

How Does the Unique Space‐Time Sampling of the SWOT Mission Influence River Discharge Series Characteristics?

AbstractThe Surface Water and Ocean Topography (SWOT) satellite mission will, for the first time, provide simultaneous, high‐resolution measurements of water surface elevation and extent. Here we explore the applicability of SWOT's unique sampling to capture discharge frequency behavior throughout the Mississippi River Basin. Our findings suggest the mission may capture key variability in river discharge series. SWOT orbit specifications, US Geological Survey (USGS) discharge measurements, and potential uncertainty estimates are used to generate SWOT‐like river discharges. Frequency distributions and specific quantiles derived from synthetic SWOT discharge series are compared to those derived from daily USGS discharge series. Based on the Kolmogorov‐Smirnov test, SWOT temporal sampling has essentially no impact on derived frequency distributions. When including potential uncertainty, 78% of derived distributions are statistically identical. The combined effects of temporal sampling and discharge uncertainty mitigates the negative bias used for SWOT discharge uncertainty at larger discharge quantiles (i.e., ≥75% quantiles).

A stochastic weather model for generating daily precipitation series at ungauged locations in the Catskill Mountain region of New York state

AbstractInformation on the variability of precipitation in time and space is critical for many water resource projects. However, precipitation records at the location of interest are often either limited or unavailable due to an inadequate network of rainfall measurements. To address this need, regionalization methods have been employed to characterize spatial patterns of precipitation and to transfer precipitation information from one location to another where records are scarce. Hence, the overall objective of the present paper is to propose a stochastic weather model for generating daily precipitation at ungauged locations. The proposed approach consists of two components: (a) a regionalization approach for identifying homogeneous groups of observed daily precipitation series, and (b) a stochastic model for constructing daily precipitation events at ungauged locations within homogeneous groups. This statistical approach identifies groups of precipitation stations with similar statistical characteristics based on the combination of two multivariate statistical techniques: principal component analysis (PCA) and ordinal factor analysis (OFA). While the application of PCA in climatological regionalization studies based on precipitation amount is common, the application of OFA to include precipitation occurrence in the identification of regions is unusual. The feasibility of the approach is assessed using daily precipitation data from a network of precipitation stations in the Catskill Mountain region of New York State, United States.

A partial correlation vine based approach for modeling and forecasting multivariate volatility time-series

A novel approach for dynamic modeling and forecasting of realized covariance matrices is proposed. Realized variances and realized correlation matrices are jointly estimated. The one-to-one relationship between a positive definite correlation matrix and its associated set of partial correlations corresponding to any vine specification is used for data transformation. The model components therefore are realized variances as well as realized standard and partial correlations corresponding to a daily log-return series. As such, they have a clear practical interpretation. A method to select a regular vine structure, which allows for parsimonious time-series and dependence modeling of the model components, is introduced. Being algebraically independent the latter do not underlie any algebraic constraint. The proposed model approach is outlined in detail and motivated along with a real data example on six highly liquid stocks. The forecasting performance is evaluated both with respect to statistical precision and in the context of portfolio optimization. Comparisons with Cholesky decomposition based benchmark models support the excellent prediction ability of the proposed model approach.

On the day-of-the-week effects of Bitcoin markets: international evidence

Purpose The purpose of this paper is to examine the day-of-the-week effects of Bitcoin (BTC) markets on the exchange level from January 2014 to September 2018. Design/methodology/approach The in-depth study on the day-of-the-week effects is conducted by using data consisting of Bitcoin prices denominated in 20 fiat currencies from 23 Bitcoin trading exchanges through the method of rolling sample for calendar effect proposed by Zhang et al. (2017). Findings It is shown by the empirical results that different patterns of the day-of-the-week effects are observed on Bitcoin denominated in various fiat currencies by referring to the price data collected from exchanges. Furthermore, the patterns of the day-of-the-week effects are also available after adjusting Bitcoin prices denominated in domestic currencies into USD. Research limitations/implications Because of the discontinuity of data for some daily return series, estimation with dynamic variance is not applicable. It is assumed that the error item follows normal distribution with constant variance. Originality/value The day-of-the-week effects are wide-spread in Bitcoin markets, and they are not mainly caused by movements of foreign exchange rates. Actually, empirical findings in this study provide evidence for inefficiency of Bitcoin markets.

Estimates of gridded relative changes in 24-h extreme rainfall intensities based on pooled frequency analysis

The potential effect of climate change needs to be considered in urban infrastructure design and risk assessment to improve reliability. The present study proposes a methodology for obtaining grid-scale relative changes for updating 24-h extreme rainfall intensity, through the estimation of rainfall intensity quantiles from baseline and future simulations using a pooled frequency analysis approach. Coherence of relative changes over return periods and time horizons is analysed, and adjustments are proposed to facilitate the application of relative changes in practice. The approach is applied to Canada, using gridded daily precipitation series from model combinations belonging to the North American Coordinated Regional Climate Downscaling Experiment. Multi-model 10th, 50th and 90th percentile relative changes are provided for six return periods, considering two future scenarios (RCP 4.5 and RCP 8.5), and two horizons (2050 and 2080). Overall, estimated relative changes varied smoothly and formed a number of clusters of similar values across the country. Relative changes for RCP 8.5 are recommended for 2050, whereas either those for RCP 4.5 or RCP 8.5 could be used for 2080. As an example, median multi-model 50th percentile relative change over Canada is found to be 14%, 16% and 27% for RCP 4.5 – 2080, RCP 8.5 – 2050, and RCP 8.5 – 2080, respectively.

Could network analysis of horizontal visibility graphs be faithfully used to infer long-term memory properties in real-world time series?

Mapping time series to complex networks has great potential to investigate their temporal structures. Some previous studies have been carried on time series generated from idealized stochastic models and found that there are inherent associations between long-term memory (LTM) of pure long-range correlated time series and topological parameters (TPs) of their networks, which is thought as a new prospective to extract information from time series. However, output time series from natural systems seldom take so idealized structures as those from idealized stochastic models, and there is usually some unconsidered information inherited in them, which makes these derived associations questionable. To check and generalize these conclusions acquired from idealized stochastic models, horizontal visibility graph (HVG) algorithm is employed to map time series to their horizontal visibility networks. Firstly synthetic time series with known mixed correlations (apart from LTM) have been analyzed and results indicate that topological parameters (TPs) of HVG networks are not solely dominated by the strength of LTM, other factors such as white noise, short-term correlation (STC) and nonlinear correlations are also playing crucial roles. Taking this fact into account, after some preprocessing treatments have been carried out, the LTM of daily mean air temperature series can indeed be inferred by means of the inherent associations between LTM of pure long-range correlated time series and TPs of their networks.

Analysis of a new spatial interpolation weighting method to estimate missing data applied to rainfall records

In the present work, two new generalized weighted methods of imputation of missing data are developed and tested using a daily rainfall series. The proposed methodology allows to fully rebuild the time series while preserving its statistical properties. Rainfall records in the state of Tabasco, Mexico, during the period 1980-2012 were used to test and evaluate the proposed methodology. The imputation of missing data in a given weather station is performed by using daily data from neighboring stations with a similar rainfall behavior. The choice of optimal parameters for the proposed formulae is based on minimizing the mean absolute error (MAE) via an evolutionary strategy (CMA-ES). The K -means method was used with the Euclidean distance in order to select the adequate neighboring weather stations. Five different methods were applied to estimate the optimal number of clusters: the elbow method, gap statistics, TraceW, Hartigan and Krzanowski-Lai indices. In addition, the structural stability of the chosen clusters was evaluated in order to demonstrate that these represent the correct data structure and are not the result of an artificial internal procedure of the grouping algorithm. Results from two different statistical tests, Friedman and Nemenyi post hoc, showed that our two new methods produce significantly and statistically better estimation when compared to existing methods in the literature.

A 50-year analysis of hydrological trends and processes in a Mediterranean catchment

Abstract. The Réal Collobrier hydrological observatory in south-eastern France, managed by Irstea since 1966, constitutes a benchmark site for regional hydro-climatology. Because of the dense network of stream gauges and rain gauges available, this site provides a unique opportunity to evaluate long-term hydro-meteorological Mediterranean trends. The main catchment (70 km2) and its sub-catchments are located in the Massif des Maures of south-eastern France, close to the Mediterranean coast. The vegetation is composed of forest mainly calcified on crystalline soils (maquis of heath, cork-oak, maritime pine and chestnut). Direct human influence has been negligible over the past 50 years. The land use and land cover has remained almost unchanged, with the notable exception of a wildfire in 1990 that impacted a small sub-catchment. Therefore changes in the hydrological response of the catchments are caused by changes in climate and/or physical conditions. This study investigates changes in observational data using up to 50-year daily series of precipitation and streamflow. The analysis used several climate indices describing distinct modes of variability, at inter-annual and seasonal timescales. Trends were assessed by the Mann–Kendall method. The analysis also used hydrological indices describing drought events based on daily data for a description of low flows, in particular in terms of timing and severity. The analysis shows that there is a marked tendency towards a decrease in the water resources of the Réal Collobrier catchment in response to climate trends, with a consistent increase in drought severity and duration. But the changes are variable among the sub-catchments.

Bitcoin Price Anomalies: P2P Trading on LocalBitcoins

The purpose of this paper is to understand the high Australian dollar Bitcoin prices on LocalBitcoins (localbitcoins.com), which appear to be out of line with market prices. The findings indicate that the price driver is not a reaction to market conditions, but a consequence of the high risk payment methods deemed acceptable by LocalBitcoins. With sellers being allowed to offer Bitcoins in exchange for gift card codes, Bitcoin is traded at four to five times the market price. These trades are typically small in value and the construction of a value-weighted daily Bitcoin price series reveals more accurately LocalBitcoins’ daily price movements. Benchmarking against CoinDesk’s Bitcoin Price Index (BPI) shows that LocalBitcoins trades can fall above or below this benchmark. When viewed in relation to the LocalBitcoins two market system, a value-weighted price can be calculated to determine a daily premium and discount. Value-weighted prices can also be used to compare trades on LocalBitcoins with the more traditional Bitcoin exchange BTC Markets. These comparisons reveal that prices on LocalBitcoins are not more volatile than prices on BTC Markets or the BPI. It is recommended that any analysis of price behavior on LocalBitcoins take into account the dollar value of the trades to address the adverse impact that high price-low value trades have on volatility. Otherwise, a conclusion of high volatility will continue to persist.

Investigating extreme rainfall non-stationarity of upper Tapi river basin, India

ABSTRACT The present study takes into consideration the daily rainfall series and annual maximum rainfall series of 28 rain gauge stations of the Upper Tapi basin for investigation of extreme rainfall stationarity. The Mann-Kendall (MK) trend test and Autocorrelation coefficient function (ACF) plots are applied to the daily rainfall series. The Mann-Kendall rank statistic method and the CUSUM test are applied to the annual maximum rainfall series. The shortfall in the trend of daily rainfall time series has been assessed through Mann-Kendall trend analysis. Auto Correlation Function plots show non-stationarity. Mann-Kendal rank statistics and CUSUM tests indicate non-homogeneity in the time series of rainfall. The analysis has been carried out by using the high-resolution daily gridded (1° latitude x 1° longitude) datasets. The shortfall in the trend of daily rainfall-gridded datasets is observed. Nonstationarity is observed by ACF plots. Change point analysis shows nonhomogeneity. It is also observed from the analysis between gridded data stations and their adjacent rain gauge stations that there is variability in findings of stationarity tests. Rain gauge station data offer accurate results.

A quality control approach to better characterize the spatial distribution of snow depth over New Brunswick, Canada

AbstractThe aim of this work is to develop, from a high‐resolution climate analysis, a quality control standard methodology applied to manual snow cover (HS) series managed by the Snow Survey Database in New Brunswick (Canada). The database collected snow depth data biweekly starting at the end of January until the end of April. A 30‐year (1981–2010) analysis of 60 weather stations belonging to two independent meteorological networks was performed. A quality control of the climatic series was performed to evaluate the homogeneity. Three snow depth climatic areas were defined by means of two geostatistical methods (Kriging and Cluster analyses) applied on monthly snow depth, precipitation and temperature data series. Then, for each cluster, the climatological thresholds that characterize a snow fall event during the cold months were detected. Subsequently, a quality control on the daily snow depth series recorded during the January to April period was performed. For each daily series, outlier values were identified by checking both the sudden day‐to‐day changes and extreme thresholds (95th percentile). The quality control was then carried out to the manual series and the observed doubtful events were compared with the snow depth values recorded in the nearby stations. The results show that for the daily snow depth series, the highest number of suspect events was recorded during the months of March and April, and the analysis also shows that there are rain‐on‐snow events. As for the manual records, questionable snow depth errors randomly distributed in the series were highlighted. Finally, in order to improve the spatial distribution of stations located in the Canadian territory, the results give evidence that, thanks to the high‐resolution climatic analysis, the proposed approach provides all the benchmarks required to conduct a quality control of snow depth series in absence of other auxiliary variables.

Trend assessment for daily snow depths with changepoint considerations

AbstractThis paper develops methods to estimate a long‐term trend in a daily snow depth record. The methods use a storage equation model for the daily snow depths that allows for seasonality, support set features (snow depths cannot be negative), correlation, and mean level shift changepoint features. Changepoints can occur in snow processes whenever observing stations move or station instrumentation is changed; they are critical features to consider when estimating a long‐term trend. A likelihood objective function is developed for the storage model and is used to estimate model parameters. Genetic algorithms are used to optimize a minimum descriptive length model selection criterion that estimates the changepoint numbers and locations. The methods are applied in the analysis of a daily series recorded near Warm Lake, Idaho, from 1948 to 2009.

The Onset and Growth of the 2018 Martian Global Dust Storm

AbstractWe analyze the onset and initial expansion of the 2018 Martian Global Dust Storm (GDS 2018) using ground‐based images in the visual range. This is the first case of a confirmed GDS initiating in the Northern Hemisphere. A dusty area extending about 1.4×105 km2 and centered at latitude +31.7°±1.8° and west longitude 18°±5°W in Acidalia Planitia was captured on 30 and 31 May 2018 (Ls = 184.9°). From 1 to 8 June, daily image series showed the storm expanding southward along the Acidalia corridor with velocities of 5 m/s and simultaneously progressing eastward and westward with horizontal velocities ranging from 5 to 40 m/s. By 8 June the dust reached latitude ‐55° and later penetrated in the South polar region, whereas in the North the dust progression stopped at latitude approximately +46°. We compare the onset and expansion stage of this GDS with the previous confirmed storms.

KARAKTERISTIK HIDROLOGI PADA DUA DAS KECIL DI WILAYAH UPT PSDA DI SURABAYA : ANALISIS MENGGUNAKAN INDICATORS OF HYDROLOGIC ALTERATION (IHA)

This research proposes to evaluate whether there has been a change in hydrological regimes of the watershed. The research was conducted at the the administrative area of UPT PSDA Surabaya. Furthermore, Surabaya-Perning and Lamong-Simoanggrok watersheds were used as a sample for this study. The analysis was conducted using the Indicator of Hydrologic Alteration (IHA). The main input is the daily discharge data series from the two watersheds. The discharge data range from 1996 to 2015. Then, the time series data is divided into two periods, the pre-impact period (1996-2005) and the post-impact period (2006-2015). A total of 33 IHA parameters classified into 5 categories were used to evaluate the change. The results of the assessment are then used for RVA (Range of Variable approach) calculations. RVA calculations were used to discover changes occurring from the pre-impact period to the post-impact period. The data series from the two watersheds also analyzed using Environmental Flow Components (EFC) and Flow Duration Curves (FDC). The result of the research shows the difference in parameter value that indicating the hydrological change between the pre-impact period and the post-impact period. RVA analysis results show an increased flow in the Surabaya-Perning watershed from January to September and December. RVA analysis results also show an increased flow in the Lamong-Simoanggrok watershed from January to July and December. The results of the EFC analysis can provide information about flows that have the potential to cause floods and droughts. The Surabaya-Perning watershed has the potential to encounter a small flood with a value 254,7 m3/s which last for 9 days with a chance of occurring 0,5 times every 2 years.