Gridded data as a source of missing data replacement in station records

Meher, Jitendra Kumar; Das, Lalu

doi:10.1007/s12040-019-1079-8

Cited by 19 publications

(7 citation statements)

References 31 publications

(27 reference statements)

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“…The bilinear interpolation method uses the distance‐weighted average of the four nearest grid values to give an estimate at a point of interest. This grid‐to‐point methodology has been applied satisfactorily in previous studies (e.g., Bromwich and Fogt, 2004; Bao and Zhang, 2013; Ebrahimi et al ., 2017; Mayor et al ., 2017; Meher and Das, 2019). The bilinear interpolation has been chosen due to its more realistic local interpretation instead of using the coarse grid value.…”

Section: Methodsmentioning

confidence: 99%

Comparison between observations and gridded data sets over complex terrain in the Chilean Andes: Precipitation and temperature

Schumacher

Justino

Fernández

et al. 2020

Intl Journal of Climatology

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This study describes the performance of five gridded data sets in reproducing precipitation and/or temperature over the complex terrain in the high Chilean Andes. The relationship of instrumental observations and the gridded data sets with climate modes of variability and the trends of indices of climate extremes are also explored between the period 1980-2015. The mismatches between gridded data sets are larger in northern and southern regions in relation to precipitation, while for temperature, disagreement is higher in central region. However, better results are delivered by the Climatic Research Unit and Global Precipitation Climatology Centre followed by Re-Analysis Interim Project. The El Niño Southern Oscillation and Pacific Decadal Oscillation indices are well correlated with precipitation in North and South Chile. Additional, trend analyses reveal a significant downward (upward) tendency for precipitation (temperature), especially in central region, delivered by observed and the majority of gridded data sets. Furthermore, the consecutive number of dry days is increasing in all regions at the annual scale. This study allows a better understanding of the capacity of global data sets and thus contributes to further climate research within this Andean region.

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Section: Methodsmentioning

confidence: 99%

Comparison between observations and gridded data sets over complex terrain in the Chilean Andes: Precipitation and temperature

Schumacher

Justino

Fernández

et al. 2020

Intl Journal of Climatology

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“…Comparisons between BayNNE and commonly used ensembling and interpolation methods are shown in Table 1. Interpolation in non-polar regions, including predominantly large gaps in the tropics from incomplete satellite coverage, is compared against bilinear interpolation [1,25], and spatiotemporal kriging [38,41] using a stochastic variational Gaussian process [8] on 3 year sections of observational data. Spatial and temporal extrapolation skill (root mean squared error) is compared to a uniformly globally weighted multi-model mean [9,22] and 2 weighted means where weights per model are found from the ability of a model to replicate observations in the training set [20,36].…”

Section: Resultsmentioning

confidence: 99%

Ensembling geophysical models with Bayesian Neural Networks

Sengupta,

Amos,

Hosking

et al. 2020

Preprint

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Ensembles of geophysical models improve prediction accuracy and express uncertainties. We develop a novel data-driven ensembling strategy for combining geophysical models using Bayesian Neural Networks, which infers spatiotemporally varying model weights and bias, while accounting for heteroscedastic uncertainties in the observations. This produces more accurate and uncertaintyaware predictions without sacrificing interpretability. Applied to the prediction of total column ozone from an ensemble of 15 chemistry-climate models, we find that the Bayesian neural network ensemble (BayNNE) outperforms existing methods for ensembling physical models, achieving a 49.4% reduction in RMSE for temporal extrapolation, and a 67.4% reduction in RMSE for polar data voids, compared to a weighted mean. Uncertainty is also well-characterized, with 91.9% of the data points in our extrapolation validation dataset lying within 2 standard deviations and 98.9% within 3 standard deviations.

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“…The spatial extent of precipitation is maximum with high intensity over J&K, followed by HP and UK, respectively. This is due to the fact that vigor of WDs decreases as they move from J&K along WH towards central Himalayas [60]. Daily extreme precipitation amounts are found to be reaching beyond 50 mm day À1 in IMDAA.…”

Section: Winter Seasonmentioning

confidence: 96%

Hydrological Extremes in Western Himalayas-Trends and Their Physical Factors

Nischal¹,

Rohtash²,

Sreehari³

et al. 2023

Natural Hazards - New Insights

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Recent exacerbation of extreme precipitation events (EPEs) and related massive disasters in western Himalayas (WH) underpins the influence of climate change. Such events introduce significant losses to life, infrastructure, agriculture, in turn the country’s economy. This chapter provides an assessment of long-term (1979–2020) as well as recent changes (2000–2020) in precipitation extremes over WH for summer (JJAS) and winter (DJF) seasons. Different high-resolution multi-source climate datasets have been utilized to compute the spatiotemporal trends in intensity and frequency of EPEs. The hotspots of rising extremes over the region have been quantified using the percentile approach where daily precipitation exceeds the 95th percentile threshold at a given grid. The findings reveal geographically heterogeneous trends among different datasets; however, precipitation intensity and frequency show enhancement both spatially and temporally (though insignificant). For both seasons, dynamic and thermodynamic parameters highlight the role of increased air temperatures and, as a result, available moisture in the atmosphere, signifying the consequences of global warming. Rising precipitation extremes in summer are sustained by enhanced moisture supply combined with increased instability and updraft, due to orography, in the atmosphere whereas winter atmosphere is observing an increase in baroclinicity, available kinetic energy, vertical shear and instability, contributing to a rise in precipitation extremes.

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Gridded data as a source of missing data replacement in station records

Cited by 19 publications

References 31 publications

Comparison between observations and gridded data sets over complex terrain in the Chilean Andes: Precipitation and temperature

Comparison between observations and gridded data sets over complex terrain in the Chilean Andes: Precipitation and temperature

Ensembling geophysical models with Bayesian Neural Networks

Hydrological Extremes in Western Himalayas-Trends and Their Physical Factors

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