Combined use of satellite estimates and rain gauge observations to generate high-quality historical rainfall time series over Ethiopia

Climate data are used in a number of applications including climate risk management and adaptation to climate change. However, the availability of climate data, particularly throughout rural Africa, is very limited. Available weather stations are unevenly distributed and mainly located along main roads in cities and towns. This imposes severe limitations to the availability of climate information and services for the rural community where, arguably, these services are needed most. Weather station data also suffer from gaps in the time series. Satellite proxies, particularly satellite rainfall estimate, have been used as alternatives because of their availability even over remote parts of the world. However, satellite rainfall estimates also suffer from a number of critical shortcomings that include heterogeneous time series, short time period of observation, and poor accuracy particularly at higher temporal and spatial resolutions. An attempt is made here to alleviate these problems by combining station measurements with the complete spatial coverage of satellite rainfall estimates. Rain gauge observations are merged with a locally calibrated version of the TAMSAT satellite rainfall estimates to produce over 30-years (1983-todate) of rainfall estimates over Ethiopia at a spatial resolution of 10 km and a ten-daily time scale. This involves quality control of rain gauge data, generating locally calibrated version of the TAMSAT rainfall estimates, and combining these with rain gauge observations from national station network. The infrared-only satellite rainfall estimates produced using a relatively simple TAMSAT algorithm performed as good as or even better than other satellite rainfall products that use passive microwave inputs and more sophisticated algorithms. There is no substantial difference between the gridded-gauge and combined gauge-satellite products over the test area in Ethiopia having a dense station network; however, the combined product exhibits better quality over parts of the country where stations are sparsely distributed.

The 30-year TAMSAT African rainfall climatology and time-series (TARCAT) dataset

African societies are dependent on rainfall for agricultural and other water-dependent activities, yet rainfall is extremely variable in both space and time and reoccurring water shocks, such as drought, can have considerable social and economic impacts. To help improve our knowledge of the rainfall climate, we have constructed a 30-year (1983–2012), temporally consistent rainfall dataset for Africa known as TARCAT (TAMSAT African Rainfall Climatology And Time-series) using archived Meteosat thermal infra-red (TIR) imagery, calibrated against rain gauge records collated from numerous African agencies. TARCAT has been produced at 10-day (dekad) scale at a spatial resolution of 0.0375°. An intercomparison of TARCAT from 1983 to 2010 with six long-term precipitation datasets indicates that TARCAT replicates the spatial and seasonal rainfall patterns and interannual variability well, with correlation coefficients of 0.85 and 0.70 with the Climate Research Unit (CRU) and Global Precipitation Climatology Centre (GPCC) gridded-gauge analyses respectively in the interannual variability of the Africa-wide mean monthly rainfall. The design of the algorithm for drought monitoring leads to TARCAT underestimating the Africa-wide mean annual rainfall on average by −0.37 mm day−1 (21%) compared to other datasets. As the TARCAT rainfall estimates are historically calibrated across large climatically homogeneous regions, the data can provide users with robust estimates of climate related risk, even in regions where gauge records are inconsistent in time.

Extracting planetary waves from geomagnetic time series using Empirical Mode Decomposition

Empirical Mode Decomposition is presented as an alternative to traditional analysis methods to decompose geomagnetic time series into spectral components. Important comments on the algorithm and its variations will be given. Using this technique, planetary wave modes of 5-, 10-, and 16-day mean periods can be extracted from magnetic field components of three different stations in Germany. In a second step, the amplitude modulation functions of these wave modes can be shown to contain significant contribution from solar cycle variation through correlation with smoothed sunspot numbers. Additionally, the data indicate connections with geomagnetic jerk occurrences, supported by a second set of data providing reconstructed near-Earth magnetic field for 150 years. Usually attributed to internal dynamo processes within the Earth's outer core, the question of who is impacting whom will be briefly discussed here.

Representing the Arctic in global surface temperature time series of recent climate change

The Arctic is an important region in the study of climate change, but monitoring surface temperatures in this region is challenging, particularly in areas covered by sea ice. Here in situ, satellite and reanalysis data were utilised to investigate whether global warming over recent decades could be better estimated by changing the way the Arctic is treated in calculating global mean temperature. The degree of difference arising from using five different techniques, based on existing temperature anomaly dataset techniques, to estimate Arctic SAT anomalies over land and sea ice were investigated using reanalysis data as a testbed. Techniques which interpolated anomalies were found to result in smaller errors than non-interpolating techniques. Kriging techniques provided the smallest errors in anomaly estimates. Similar accuracies were found for anomalies estimated from in situ meteorological station SAT records using a kriging technique. Whether additional data sources, which are not currently utilised in temperature anomaly datasets, would improve estimates of Arctic surface air temperature anomalies was investigated within the reanalysis testbed and using in situ data. For the reanalysis study, the additional input anomalies were reanalysis data sampled at certain supplementary data source locations over Arctic land and sea ice areas. For the in situ data study, the additional input anomalies over sea ice were surface temperature anomalies derived from the Advanced Very High Resolution Radiometer satellite instruments. The use of additional data sources, particularly those located in the Arctic Ocean over sea ice or on islands in sparsely observed regions, can lead to substantial improvements in the accuracy of estimated anomalies. Decreases in Root Mean Square Error can be up to 0.2K for Arctic-average anomalies and more than 1K for spatially resolved anomalies. Further improvements in accuracy may be accomplished through the use of other data sources.

A Time-Series Analysis of the H alpha Emission Line in V3885 Sagitarii

Flickering is a phenomenon related to mass accretion observed among many classes of astrophysical objects. In this paper we present a study of flickering emission lines and the continuum of the cataclysmic variable V3885 Sgr. The flickering behavior was first analyzed through statistical analysis and the power spectra of lightcurves. Autocorrelation techniques were then employed to estimate the flickering timescale of flares. A cross-correlation study between the line and its underlying continuum variability is presented. The cross-correlation between the photometric and spectroscopic data is also discussed. Periodograms, calculated using emission-line data, show a behavior that is similar to those obtained from photometric datasets found in the literature, with a plateau at lower frequencies and a power-law at higher frequencies. The power-law index is consistent with stochastic events. The cross-correlation study indicates the presence of a correlation between the variability on Ha and its underlying continuum. Flickering timescales derived from the photometric data were estimated to be 25 min for two lightcurves and 10 min for one of them. The average timescales of the line flickering is 40 min, while for its underlying continuum it drops to 20 min.

Stabilizing Nonstationary Electrochemical Time Series

Electrochemical systems are ideal working-horses for studying oscillatory dynamics. Experimentally obtained time series, however, are usually associated with a spontaneous drift in some uncontrollable parameter that triggers transitions among different oscillatory patterns, despite the fact that all controllable parameters are kept constant. Herein we present an empirical method to stabilize experimental potential time series. The method consists of applying a negative galvanodynamic sweep to compensate the spontaneous drift and was tested for the oscillatory electro-oxidation of methanol on platinum. For a wide range of applied currents, the base system presents spontaneous transitions from quasi-harmonic to mixed mode oscillations. Temporal patterns were stabilized by galvanodynamic sweeps at different rates. The procedure resulted in a considerable increase in the number of oscillatory cycles from 5 to 20 times, depending on the specific temporal pattern. The spontaneous drift has been associated with uncompensated oscillations, in which the coverage of some adsorbed species are not reestablished after one cycle; i.e., there is a net accumulation and/or depletion of adsorbed species during oscillations. We interpreted the rate of the galvanodynamic sweep in terms of the time scales of the poisoning processes that underlies the uncompensated oscillations and thus the spontaneous slow drift.

Financial time series analysis : Chaos and neurodynamics approach

This work aims at combining the Chaos theory postulates and Artificial Neural Networks classification and predictive capability, in the field of financial time series prediction. Chaos theory, provides valuable qualitative and quantitative tools to decide on the predictability of a chaotic system. Quantitative measurements based on Chaos theory, are used, to decide a-priori whether a time series, or a portion of a time series is predictable, while Chaos theory based qualitative tools are used to provide further observations and analysis on the predictability, in cases where measurements provide negative answers. Phase space reconstruction is achieved by time delay embedding resulting in multiple embedded vectors. The cognitive approach suggested, is inspired by the capability of some chartists to predict the direction of an index by looking at the price time series. Thus, in this work, the calculation of the embedding dimension and the separation, in Takens‘ embedding theorem for phase space reconstruction, is not limited to False Nearest Neighbor, Differential Entropy or other specific method, rather, this work is interested in all embedding dimensions and separations that are regarded as different ways of looking at a time series by different chartists, based on their expectations. Prior to the prediction, the embedded vectors of the phase space are classified with Fuzzy-ART, then, for each class a back propagation Neural Network is trained to predict the last element of each vector, whereas all previous elements of a vector are used as features.

Do crude oil price changes affect economic growth of India, Pakistan and Bangladesh? : A multivariate time series analysis

This paper analyzes empirically the effect of crude oil price change on the economic growth of Indian-Subcontinent (India, Pakistan and Bangladesh). We use a multivariate Vector Autoregressive analysis followed by Wald Granger causality test and Impulse Response Function (IRF). Wald Granger causality test results show that only India’s economic growth is significantly affected when crude oil price decreases. Impact of crude oil price increase is insignificantly negative for all three countries during first year. In second year, impact is negative but smaller than first year for India, negative but larger for Bangladesh and positive for Pakistan.

Forecasting with Vector Nonlinear Time Series Models

This work concerns forecasting with vector nonlinear time series models when errorsare correlated. Point forecasts are numerically obtained using bootstrap methods andillustrated by two examples. Evaluation concentrates on studying forecast equality andencompassing. Nonlinear impulse responses are further considered and graphically sum-marized by highest density region. Finally, two macroeconomic data sets are used toillustrate our work. The forecasts from linear or nonlinear model could contribute usefulinformation absent in the forecasts form the other model.

An analysis of linkage between foreign direct investment and GDP per Capita in Pakistan : A time series analysis

This study aims to investigate the relation between foreign direct investment (FDI) and per capita gross domestic product (GDP) in Pakistan. The study is based on a basic Cobb-Douglas production function. Population over age 15 to 64 is used as a proxy for labor in the investigation. The other variables used are gross capital formation, technological gap and a dummy variable measuring among other things political stability. We find positive correlation between GDP per capita in Pakistan and two variables, FDI and population over age 15 to 64. The GDP gap (gap between GDP of USA and GDP of Pakistan) is negatively correlated with GDP per capita as expected. Political instability, economic crisis, wars and polarization in the society have no significant impact on GDP per capita in the long run.

Growth, increasing returns, and public infrastructure : time series evidence

Initial endogenous growth models emphasized the importance of external effects and increasing retums in explaining growth. Empirically, this hypothesis can be confumed if the coefficient of physical capital per hour is unity in the aggregate production function. Previous estimates using time series data rejected this hypothesis, although cross-country estimates did nol The problem lies with the techniques employed, which are unable to capture low-frequency movements of high-frequency data. Using cointegration, new time series evidence confum the theory and conform to cross-country evidence. The implied Solow residual, which takes into account externaI effects to aggregate capital, has its behavior analyzed. The hypothesis that it is explained by government expenditures on infrasttucture is confIrmed. This suggests a supply-side role for government affecting productivity.

Do shocks permanently change output? : Local persistency in economic time series

While it is recognized that output fuctuations are highly persistent over certain range, less persistent results are also found around very long horizons (Conchrane, 1988), indicating the existence of local or temporary persistency. In this paper, we study time series with local persistency. A test for stationarity against locally persistent alternative is proposed. Asymptotic distributions of the test statistic are provided under both the null and the alternative hypothesis of local persistency. Monte Carlo experiment is conducted to study the power and size of the test. An empirical application reveals that many US real economic variables may exhibit local persistency.

A note on Chambers's "long memory and aggregation in macroeconomic time series"

Chambers (1998) explores the interaction between long memory and aggregation. For continuous-time processes, he takes the aliasing effect into account when studying temporal aggregation. For discrete-time processes, however, he seems to fail to do so. This note gives the spectral density function of temporally aggregated long memory discrete-time processes in light of the aliasing effect. The results are different from those in Chambers (1998) and are supported by a small simulation exercise. As a result, the order of aggregation may not be invariant to temporal aggregation, specifically if d is negative and the aggregation is of the stock type.

The impact of OFDI on economic growth countries. An econometric approach using panel data and time-series evidence

The thesis at hand adds to the existing literature by investigating the relationship between economic growth and outward foreign direct investments (OFDI) on a set of 16 emerging countries. Two different econometric techniques are employed: a panel data regression analysis and a time-series causality analysis. Results from the regression analysis indicate a positive and significant correlation between OFDI and economic growth. Additionally, the coefficient for the OFDI variable is robust in the sense specified by the Extreme Bound Analysis (EBA). On the other hand, the findings of the causality analysis are particularly heterogeneous. The vector autoregression (VAR) and the vector error correction model (VECM) approaches identify unidirectional Granger causality running either from OFDI to GDP or from GDP to OFDI in six countries. In four economies causality among the two variables is bidirectional, whereas in five countries no causality relationship between OFDI and GDP seems to be present.

Forecasting multivariate time series under present-value-model short- and long-run co-movement restrictions

It is well known that cointegration between the level of two variables (e.g. prices and dividends) is a necessary condition to assess the empirical validity of a present-value model (PVM) linking them. The work on cointegration,namelyon long-run co-movements, has been so prevalent that it is often over-looked that another necessary condition for the PVM to hold is that the forecast error entailed by the model is orthogonal to the past. This amounts to investigate whether short-run co-movememts steming from common cyclical feature restrictions are also present in such a system. In this paper we test for the presence of such co-movement on long- and short-term interest rates and on price and dividend for the U.S. economy. We focuss on the potential improvement in forecasting accuracies when imposing those two types of restrictions coming from economic theory.