On q-Sumudu Transforms of Certain q-Polynomials
Summary (1 min read)
Introduction
- This report was prepared as an account of work sponsored by the U.S. Government.
- Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the U.S. Government or any agency thereof.
- 14 Figures 1. Locations of ARM SMOS stations and OKM stations addressed in this study.
1. Objective and Overview
- Given the wealth of observations available from these networks, this study provided the unique opportunity to determine, within a quantifiable statistical limit, an optimal distance between stations deployed for observation of the climatological values of temperature and relative humidity.
- Before the observations were compared, quality control (QC) beyond the standard ARM range test was added through implementation of tighter range tests specified by data quality objectives (DQOs).
- The Pearson correlation coefficient (ρ) and root-mean-square difference (RMSD) were the statistics used to quantify the relationship between station pairs.
- The calculated slope and intercept values were comparable across most domains, and spatial differences in temperature were smaller than those for relative humidity.
2. Analysis Methodology and Results
- The data analyzed included 30-min-averaged temperature and relative humidity observations from both the ARM SMOS and OKM data archives.
- The original DQO lower limit (-2%) had been selected on the basis of the sensor’s error and measurable range.
- The five ARM SMOS station pairs in each dense OK domain, when viewed independently from the OKM stations, were used as the secondary sparse domains for comparison with the primary sparse KS domain (Table 1).
- Finally, all ρ and RMSD values from each domain were plotted together against the distances between station pairs to allow overall spatial patterns to be identified.
3. Preliminary Conclusions
- The information gained by plotting ρ and RMSD versus distance (Figures 7-10) provided insight into the spatial variability of temperature and relative humidity across KS and OK.
- The sample sizes for the distance analyses of the sparse domains were small enough so that subtle changes in values of ρ or RMSD could greatly impact the calculated slopes and intercepts.
- At a given distance, ρ values were larger for temperature than for relative humidity, while RMSD values were smaller for temperature.
- Temperature is less spatially heterogeneous, and therefore sufficient spatial variability may be captured even if stations are placed at a greater distance than the network average of ~ 30 km between an OKM station and the closest neighboring station.
- Extending this study to time periods when other climatic conditions prevailed could have affected the results.
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Cites background from "On q-Sumudu Transforms of Certain q..."
...In [8] (see also [9]), some q-analogues of the natural exponential functions, sine functions, cosine functions, hyperbolic sine functions, and hyperbolic cosine functions are, respectively, given in terms of Fox′s H-function as follows: eq(–x) = G(q)H1,0 0,2 ( x(1 – q); q ∣ ∣∣ ∣∣ (0, 1)(1, 1) ) , (12)...
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References
3,622 citations
"On q-Sumudu Transforms of Certain q..." refers background in this paper
...For further detail and properties about q-hypergeometric functions see [5, 12, 13] and many others....
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"On q-Sumudu Transforms of Certain q..." refers background in this paper
...For further detail and properties about q-hypergeometric functions see [5, 12, 13] and many others....
[...]
440 citations
"On q-Sumudu Transforms of Certain q..." refers methods in this paper
...[17] G. K. Watugala, Sumudu transform: a new integral transform to solve differential equations and control engineering problems....
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...In 1993, the Sumudu transform was proposed originally by Watugala [17] and he applied it to the solution of ordinary differential equations in control engineering problems....
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...Its main advantage is the fact that it may be used to solve problems without resorting to a new frequency domain, because it preserves scale and unit properties [4]....
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