Comparison of different wind data interpolation methods for a region with complex terrain in Central Asia

Reinhardt, Katja; Samimi, Cyrus

doi:10.1007/s00382-018-4101-y

“…This is consistent with previous findings in other studies [29,30,36,58]. The hybrid methods of machine learning with geostatistical methods were developed and have been applied to marine sediment data since 2008 with proven high predictive accuracy [7][8][9]37], but their applications to other data types or terrestrial data are still rare [30,32,33,63,64]. GBM was outperformed by RF in this study, but it performed similarly to RF for the sponge dataset in spm [62].…”

Section: Predictive Accuracy Of Seabed Sand Contentsupporting

confidence: 86%

Developing an Optimal Spatial Predictive Model for Seabed Sand Content Using Machine Learning, Geostatistics, and Their Hybrid Methods

Li

¹

,

Siwabessy

²

,

Huang

³

et al. 2019

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Seabed sediment predictions at regional and national scales in Australia are mainly based on bathymetry-related variables due to the lack of backscatter-derived data. In this study, we applied random forests (RFs), hybrid methods of RF and geostatistics, and generalized boosted regression modelling (GBM), to seabed sand content point data and acoustic multibeam data and their derived variables, to develop an accurate model to predict seabed sand content at a local scale. We also addressed relevant issues with variable selection. It was found that: (1) backscatter-related variables are more important than bathymetry-related variables for sand predictive modelling; (2) the inclusion of highly correlated predictors can improve predictive accuracy; (3) the rank orders of averaged variable importance (AVI) and accuracy contribution change with input predictors for RF and are not necessarily matched; (4) a knowledge-informed AVI method (KIAVI2) is recommended for RF; (5) the hybrid methods and their averaging can significantly improve predictive accuracy and are recommended; (6) relationships between sand and predictors are non-linear; and (7) variable selection methods for GBM need further study. Accuracy-improved predictions of sand content are generated at high resolution, which provide important baseline information for environmental management and conservation.

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“…An integral part in wind farm planning and weather prediction is access to high-quality wind data [1,2]. However, meteorological stations are often heterogeneously distributed with many gaps, and interpolation techniques have to be employed in order to increase spatial resolution [3]. For example, a crucial step in building wind farms is site prospecting, in which national or state-level measurements are used to estimate the 2021 The Authors.…”

Section: Introductionmentioning

confidence: 99%

“…The work [4] lists approximately 200 papers written between 1940 and 2008 that focus on parametric models for time series of wind speed measurements. More modern approaches that directly interpolate wind speed also exist [3].…”

Section: Introductionmentioning

confidence: 99%

“…This effectively results in a reconstruction of the entire wind vector field, hence the name wind field reconstruction. Due to high spatial variability and dependence on both local and upstream terrain features, interpolation of wind data over hourly or minute-wise intervals is considered a particularly hard problem [3]. However, there are reasons why this can be useful, such as predicting the propagation of forest fires and pollutants, modelling the movement of flying animals and insects [5], or formulating initial conditions for atmospheric simulations and numerical weather prediction models [2,6].…”

Section: Introductionmentioning

confidence: 99%

“…There is relatively little research published on wind field reconstruction by the geological community [3]. Recent studies on machine learning interpolation methods like neural networks, support vector machines and random forest in other geological applications such as air temperatures [7], snow depth [8], mineral concentrations [9] and wind speed [4,10,11] have shown promising results.…”

Section: Introductionmentioning

confidence: 99%

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Wind field reconstruction with adaptive random Fourier features

Kiessling

¹

,

Ström

²

,

Tempone

³

2021

Proc. R. Soc. A.

5

0

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We investigate the use of spatial interpolation methods for reconstructing the horizontal near-surface wind field given a sparse set of measurements. In particular, random Fourier features is compared with a set of benchmark methods including kriging and inverse distance weighting. Random Fourier features is a linear model β ( x ) = ∑ k = 1 K β k e i ω k x approximating the velocity field, with randomly sampled frequencies ω k and amplitudes β k trained to minimize a loss function. We include a physically motivated divergence penalty | ∇ ⋅ β ( x ) | 2 , as well as a penalty on the Sobolev norm of β . We derive a bound on the generalization error and a sampling density that minimizes the bound. We then devise an adaptive Metropolis–Hastings algorithm for sampling the frequencies of the optimal distribution. In our experiments, our random Fourier features model outperforms the benchmark models.

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Modeling of atmospheric primary radioclimatic variables in Nigeria for microwave radio propagation applications

Falodun

¹

,

Ojo

²

,

Aboyeji

³

2021

SN Appl. Sci.

3

0

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As modern technology advances, more of the radio communication systems are adopting microwave and millimeter waves’ spectral. However, the effects of meteorological parameters over the radio communication signal operating at microwave and millimeter waves have been the concern of radio engineers. Determination of radio refractivity requires as input meteorological parameters such as temperature, humidity, and atmospheric pressure. To assess the level of degradation due to atmospheric parameters, the ability to easily measure these meteorological parameters is important in planning a good quality radio communication networks. In this study, five (5) years (2013–2017) satellite-based meteorological parameters comprising air temperature, relative humidity, and pressure were obtained from the European Center for Medium-Range Weather Forecast (ECMWF) at four synopsis hours of the day (00:00 LT, 00:06 LT, 12 noon LT, and 18:00 LT) on daily basis to model the required radio climatic variables in Nigeria. The data were obtained for 26 locations based on the four climatic regions in Nigeria. Multiple regression analysis of the data was performed and coefficients (bo, b1, …, b6) were obtained to effectively predict the monthly temperature, pressure, and relative humidity at the surface level. Eight stations were used for validation; two each from the four climatic regions of Nigeria based on the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE). The meteorological parameters were compared based on the multiple regression analysis (MRA) and the modified interpolation techniques (Kriging and Inverse Distance-IDW). The three methods show nearly good accuracy in the prediction, however, the MRA was found to have a better prediction for the meteorological parameter in Nigeria. Hence, in predicting the refractive index parameters using MRA, the allowed RMSEs for temperature, pressure, and relative humidity are 0.559, 4.693, and 0.267, respectively. Likewise, the allowed MAEs for temperature pressure and relative humidity are 0.492, 0.229, and 3.837, respectively.

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Comparison of different wind data interpolation methods for a region with complex terrain in Central Asia

Cited by 12 publications

References 59 publications

Developing an Optimal Spatial Predictive Model for Seabed Sand Content Using Machine Learning, Geostatistics, and Their Hybrid Methods

Developing an Optimal Spatial Predictive Model for Seabed Sand Content Using Machine Learning, Geostatistics, and Their Hybrid Methods

Wind field reconstruction with adaptive random Fourier features

Modeling of atmospheric primary radioclimatic variables in Nigeria for microwave radio propagation applications

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