scholarly journals Prediction of terrestrial and extraterrestrial parameters by modelling and extrapolating their natural regularities

MAUSAM ◽  
2021 ◽  
Vol 52 (1) ◽  
pp. 117-132
Author(s):  
NITYANAND SINGH ◽  
S. K. PATWARDHAN
Keyword(s):  
Time Series ◽  
Harmonic Analysis ◽  
Northern Hemisphere ◽  
Air Temperature ◽  
Zonal Wind ◽  
Surface Air Temperature ◽  
Fundamental Period ◽  
Short Term ◽  
Quasi Biennial Oscillation

Extrapolation of dominant modes of fluctuations after fitting suitable mathematical function to the observed long period time series is one of the approaches to long-term weather or short-term climate prediction. Experiences suggest that reliable predictions can be made from such approaches provided the time series being modeled possesses adequate regularity. Choice of the suitable function is also an important task of the time series modelling-extrapolation-prediction, or TS-MEP, process. Perhaps equally important component of this method is the development of effective filtering module. The filtering mechanism should be such that it effectively suppresses the high frequency, or unpredictable, variations and carves out the low frequency mode, or predictable, variation of the given series. By incorporating a possible solution to these propositions a new TS-MEP method has been developed in this paper. A Variable Harmonic Analysis (VHA) has been developed to decompose the time series into sine and cosine waveforms for any desired wavelength resolution within the data length (or fundamental period). In the Classical Harmonic Analysis (CHA) the wavelength is strictly an integer multiple of the fundamental period. For smoothing the singular spectrum analysis (SSA) has been applied. The SSA provides the mechanism to decompose the series into certain number of principal components (PCs) and then recombine the first few PCs, representing the dominant modes of variation, to get the smoothed version of the actual series.   Twenty-four time series of terrestrial and extraterrestrial parameters, which visibly show strong regularity, are considered in the study. They can be broadly grouped into five categories: (i) inter-annual series of number of storms/depressions over the Indian region, seasonal and annual mean northern hemisphere land-area surface air temperature and the annual mean sunspot number (chosen cases of long term/short term trends or oscillation); (ii) monthly sequence of zonal wind at 50- hPa, 30-hPa levels over Balboa (representative of quasi-biennial oscillation); (iii) monthly sequence of surface air temperature (SAT) over the India region (strongly dominated by seasonality); (iv) monthly sequence of sea surface temperature (SST) of tropical Indian and Pacific Oceans (aperiodic oscillations related to El Nino/La Nina); and (v) sequence of monthly sea level pressure (SLP) of selected places over ENSO region (seasonality and oscillation). Best predictions are obtained for the SLP followed by SAT and SST due to strong domination of seasonality and/or aperiodic oscillations. The predictions are found satisfactory for the lower stratospheric zonal wind over Balboa, which displays quasi-periodic oscillations. Because of a steep declining trend a reliable prediction of number of storms/depressions over India is possible by the method. Prediction of northern hemisphere surface air temperature anomaly is not found satisfactory.

Seasonal to multi-annual speedup and slowdown of Greenland outlet glaciers inferred from time-dependent remote sensing observations

2020 ◽  
Author(s):  
Lizz Ultee ◽  
Bryan Riel ◽  
Brent Minchew
Keyword(s):  
Time Series ◽  
Flow Velocity ◽  
Ice Sheet ◽  
Surface Air Temperature ◽  
Greenland Ice Sheet ◽  
Time Dependent ◽  
Surface Velocity ◽  
Short Term ◽  
Ice Flow

<p>The rate of ice flux from the Greenland Ice Sheet to the ocean depends on the ice flow velocity through outlet glaciers. Ice flow velocity, in turn, evolves in response to multiple geographic and environmental forcings at different timescales. For example, velocity may vary daily in response to ocean tides, seasonally in response to surface air temperature, and multi-annually in response to long-term trends in climate. The satellite observations processed as part of the NASA MEaSUREs Greenland Ice Sheet Velocity Map allow us to analyse variations in ice surface velocity at multiple timescales. Here, we decompose short-term and long-term signals in time-dependent velocity fields for Greenland outlet glaciers based on the methods of Riel et al. (2018). Patterns found in short-term signals can constrain basal sliding relations and ice rheology, while the longer-term signals hint at decadal in/stability of outlet glaciers. We present example velocity time series for outlets including Sermeq Kujalleq (Jakobshavn Isbrae) and Helheim Glacier, and we highlight features indicative of dynamic drawdown or advective restabilization. Finally, we comment on the capabilities of a time series analysis software under development for glaciological applications.</p>

scholarly journals Spatial and Temporal Distribution of Air Temperature in the Northern Hemisphere

2013 ◽  
Vol 6 (3) ◽  
pp. 177-182
Keyword(s):  
Time Series ◽  
20Th Century ◽  
Northern Hemisphere ◽  
Air Temperature ◽  
Temporal Distribution ◽  
Surface Air Temperature ◽  
Temperature Time Series ◽  
Mean Values ◽  
The Mean ◽  
Temperature Time

In the present study, the spatial and temporal surface air temperature variability for the Northern Hemisphere has been examined, for the period 1900-1996. Factor Analysis has been applied to 5o Latitude x 10o Longitude grid box data covering the area from almost the equator to 70o N. These data are anomalies of the mean annual air temperature from the respective mean values of the period 1961- 1990. The analysis showed that, mainly 20 regions were determined in the Northern Hemisphere with significantly covariant air temperature time series. The comparison of the trends of the mean annual surface air temperature time series of these regions, revealed such common characteristics as the minimum of the first decade of the 20th century and the recent years warming. The results of this study are also compared to the respective results of a former study in which data for the last half of the century (1948-1996) have been analyzed. The findings extracted indicate the stability of climate distribution in Northern Hemisphere during the 20th century.

scholarly journals Construction of a surface air temperature series for Qingdao in China for the period 1899 to 2014

2018 ◽  
Vol 10 (1) ◽  
pp. 643-652
Author(s):  
Yan Li ◽  
Birger Tinz ◽  
Hans von Storch ◽  
Qingyuan Wang ◽  
Qingliang Zhou ◽  
...  
Keyword(s):  
Time Series ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Warming Trend ◽  
Observation Data ◽  
China Meteorological Administration ◽  
Ascii Format ◽  
Long Term Trends ◽  
The City

Abstract. We present a homogenized surface air temperature (SAT) time series at 2 m height for the city of Qingdao in China from 1899 to 2014. This series is derived from three data sources: newly digitized and homogenized observations of the German National Meteorological Service from 1899 to 1913, homogenized observation data of the China Meteorological Administration (CMA) from 1961 to 2014 and a gridded dataset of Willmott and Matsuura (2012) in Delaware to fill the gap from 1914 to 1960. Based on this new series, long-term trends are described. The SAT in Qingdao has a significant warming trend of 0.11 ± 0.03 ∘C decade−1 during 1899–2014. The coldest period occurred during 1909–1918 and the warmest period occurred during 1999–2008. For the seasonal mean SAT, the most significant warming can be found in spring, followed by winter. The homogenized time series of Qingdao is provided and archived by the Deutscher Wetterdienst (DWD) web page under overseas stations of the Deutsche Seewarte (http://www.dwd.de/EN/ourservices/overseas_stations/ueberseedoku/doi_qingdao.html) in ASCII format. Users can also freely obtain a short description of the data at https://doi.org/https://dx.doi.org/10.5676/DWD/Qing_v1. And the data can be downloaded at http://dwd.de/EN/ourservices/overseas_stations/ueberseedoku/data_qingdao.txt.

Long-term variations in surface air pressure and surface air temperature in the Northern Hemisphere mid-latitudes

2017 ◽  
Vol 42 (7) ◽  
pp. 461-470 ◽  
Author(s):  
Yu. P. Perevedentsev ◽  
A. A. Vasil’ev ◽  
K. M. Shantalinskii ◽  
V. V. Gur’yanov
Keyword(s):  
Northern Hemisphere ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Air Pressure ◽  
Long Term Variations

scholarly journals Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1151
Author(s):  
Carolina Gijón ◽  
Matías Toril ◽  
Salvador Luna-Ramírez ◽  
María Luisa Marí-Altozano ◽  
José María Ruiz-Avilés
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Analysis ◽  
Data Traffic ◽  
Medium Term ◽  
Short Term ◽  
Traffic Forecasting ◽  
Series Analysis ◽  
Radio Planning

Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average and Additive Holt–Winters. Results show that SL models outperform time series approaches, while reducing data storage capacity requirements. More importantly, unlike in short-term and medium-term traffic forecasting, non-deep SL approaches are competitive with deep learning while being more computationally efficient.

scholarly journals An Efficient Prediction Model for Water Discharge in Schoharie Creek, NY

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Katerina G. Tsakiri ◽  
Antonios E. Marsellos ◽  
Igor G. Zurbenko
Keyword(s):  
New York ◽  
Time Series ◽  
Water Discharge ◽  
Winter Period ◽  
Statistical Methodology ◽  
Summer Period ◽  
Short Term ◽  
Efficient Prediction ◽  
Climatic Time Series

Flooding normally occurs during periods of excessive precipitation or thawing in the winter period (ice jam). Flooding is typically accompanied by an increase in river discharge. This paper presents a statistical model for the prediction and explanation of the water discharge time series using an example from the Schoharie Creek, New York (one of the principal tributaries of the Mohawk River). It is developed with a view to wider application in similar water basins. In this study a statistical methodology for the decomposition of the time series is used. The Kolmogorov-Zurbenko filter is used for the decomposition of the hydrological and climatic time series into the seasonal and the long and the short term component. We analyze the time series of the water discharge by using a summer and a winter model. The explanation of the water discharge has been improved up to 81%. The results show that as water discharge increases in the long term then the water table replenishes, and in the seasonal term it depletes. In the short term, the groundwater drops during the winter period, and it rises during the summer period. This methodology can be applied for the prediction of the water discharge at multiple sites.

COMPARATIVE ANALYSIS OF SPECTRA OF THE 2000-YEAR NORTHERN HEMISPHERE AVERAGE SURFACE AIR TEMPERATURE RECONSTRUCTIONS

2021 ◽  
pp. 5-13
Author(s):  
N. M. DATSENKO ◽  
◽  
D. M. SONECHKIN ◽  
B. YANG ◽  
J.-J. LIU ◽  
...  
Keyword(s):  
High Resolution ◽  
Northern Hemisphere ◽  
Air Temperature ◽  
Wavelet Transforms ◽  
Spectral Composition ◽  
Low Frequency ◽  
Surface Air Temperature ◽  
Temporal Variations ◽  
Continuous Wavelet Transforms ◽  
Stable Estimation

The spectral composition of temporal variations in the Northern Hemisphere mean surface air temperature is estimated and compared in 2000-year paleoclimatic reconstructions. Continuous wavelet transforms of these reconstructions are used for the stable estimation of energy spectra. It is found that low-frequency parts of the spectra (the periods of temperature variations of more than 100 years) based on such high-resolution paleoclimatic indicators as tree rings, corals, etc., are similar to the spectrum of white noise, that is never observed in nature. This seems unrealistic. The famous reconstruction called “Hockey Stick” is among such unrealistic reconstructions. Reconstructions based not only on high-resolution but also on low-resolution indicators seem to be more realistic, since the low-frequency parts of their spectra have the pattern of red noise. They include the “Boomerang” reconstruction showing that some warm periods close to the present-day one were observed in the past.

scholarly journals Assessing the effects of air temperature and rainfall on malaria incidence: an epidemiological study across Rwanda and Uganda

2016 ◽  
Vol 11 (1s) ◽  
Author(s):  
Felipe J. Colón-González ◽  
Adrian M. Tompkins ◽  
Riccardo Biondi ◽  
Jean Pierre Bizimana ◽  
Didacus Bambaiha Namanya
Keyword(s):  
Time Series ◽  
Air Temperature ◽  
Malaria Incidence ◽  
Temporal Correlation ◽  
Early Warning Systems ◽  
Socioeconomic Indicators ◽  
Malaria Surveillance ◽  
Short Term ◽  
Climatic Information ◽  
Short Term Effects

We investigate the short-term effects of air temperature, rainfall, and socioeconomic indicators on malaria incidence across Rwanda and Uganda from 2002 to 2011. Delayed and nonlinear effects of temperature and rainfall data are estimated using generalised additive mixed models with a distributed lag nonlinear specification. A time series cross-validation algorithm is implemented to select the best subset of socioeconomic predictors and to define the degree of smoothing of the weather variables. Our findings show that trends in malaria incidence agree well with variations in both temperature and rainfall in both countries, although factors other than climate seem to play an important role too. The estimated short-term effects of air temperature and precipitation are nonlinear, in agreement with previous research and the ecology of the disease. These effects are robust to the effects of temporal correlation. The effects of socioeconomic data are difficult to ascertain and require further evaluation with longer time series. Climate-informed models had lower error estimates compared to models with no climatic information in 77 and 60% of the districts in Rwanda and Uganda, respectively. Our results highlight the importance of using climatic information in the analysis of malaria surveillance data, and show potential for the development of climateinformed malaria early warning systems.

scholarly journals Track Irregularity Time Series Analysis and Trend Forecasting

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Jia Chaolong ◽  
Xu Weixiang ◽  
Wang Futian ◽  
Wang Hanning
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Short Term ◽  
Measuring Point ◽  
Trend Forecasting ◽  
Changing Trend ◽  
Track Irregularity ◽  
Linear Differential

The combination of linear and nonlinear methods is widely used in the prediction of time series data. This paper analyzes track irregularity time series data by using gray incidence degree models and methods of data transformation, trying to find the connotative relationship between the time series data. In this paper, GM(1,1)is based on first-order, single variable linear differential equations; after an adaptive improvement and error correction, it is used to predict the long-term changing trend of track irregularity at a fixed measuring point; the stochastic linear AR, Kalman filtering model, and artificial neural network model are applied to predict the short-term changing trend of track irregularity at unit section. Both long-term and short-term changes prove that the model is effective and can achieve the expected accuracy.

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