Interpolation of climate variables and temperature modeling

Samanta, Sailesh; Pal, Dilip Kumar; Lohar, D.; Pal, Babita

doi:10.1007/s00704-011-0455-3

Cited by 98 publications

(51 citation statements)

References 11 publications

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“…Second, the spatio-temporal patterns of the TLR varied greatly across the different geographical regions of Taiwan. Consequently, the variations of the TLR among the different regions imply that the use of a constant, or socalled standard TLR, such as -6.0 or -6.5°C km -1 (e.g., Willmott and Matsuura 2001; Asaoka and Kominami 2012;Samanta et al 2012) is unjustifiable when predicting full-scale temperatures, especially in complex terrains and under monsoon conditions, as in Taiwan. For example, air temperature of Mt.…”

Section: Regression Formula T = α + β × Ementioning

confidence: 99%

“…Some numerical models use a specified TLR that remains constant in space and time (e.g., the MT-CLIM model, Running et al 1987), but this approach will likely lead to large errors (Huang et al 2008). Many papers have offered no rationale for their use of the traditional constant value of -6.5°C km -1 (e.g., Stutter et al 2006; Asaoka and Kominami 2012; Samanta et al 2012). However, this traditional TLR value is merely a spatially global and temporally climatic average (Blandford et al 2008).…”

Section: Introductionmentioning

confidence: 99%

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Spatio-Temporal Variation and Monsoon Effect on the Temperature Lapse Rate of a Subtropical Island

Chiu¹,

Lin²,

Tsai³

2014

Terr. Atmos. Ocean. Sci.

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Temperature lapse rate (TLR) has been widely used in the prediction of mountain climate and vegetation and in many ecological models. The aims of this paper are to explore the spatio-temporal variations and monsoon effects on the TLR in the subtropical island of Taiwan with its steep Central Mountain Region (CMR). A TLR analysis using the 32-year monthly mean air temperatures and elevations from 219 weather stations (sea level to 3852 m a.s.l.) was performed based on different geographical regions and monsoon exposures. The results revealed that the average TLR for all of Taiwan is -5.17°C km -1 , with a general tendency to be steeper in summer and shallower in winter. The results are also shallower than the typical or global average TLR of -6.5°C km -1 . During the prevailing northeast monsoon season (winter), the TLR exhibits a contrast between the windward side (steeper, -5.97°C km -1 ) and the leeward side (shallower, -4.51°C km -1 ). From the diagnosis on spatial characteristics of monthly cloud amount and vertical atmospheric profiles, this contrasting phenomenon may be explained by the warming effect of onshore stratus clouds (500 -2500 m depth) on cold and dry Siberian monsoon air on the windward side of the CMR. On the southwestern leeward side of the CMR, the low-level (1500 m), the weak ventilation atmosphere and temperature inversion make the TLR shallower than on the windward side.Key words: Temperature lapse rate, Prevailing monsoon, Spatio-temporal variation, Taiwan Citation: Chiu, C. A., P. H. Lin, and C. Y. Tsai, 2014: Spatio-temporal variation and monsoon effect on the temperature lapse rate of a subtropical island.

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Section: Regression Formula T = α + β × Ementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Variation and Monsoon Effect on the Temperature Lapse Rate of a Subtropical Island

Chiu¹,

Lin²,

Tsai³

2014

Terr. Atmos. Ocean. Sci.

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“…2c) were used in the field to locate sample points and record the coordinates of all sample points for effective mapping. Noise levels for both weekdays (WD) and weekends (WE) were mapped using inverse distance weighting (IDW) interpolation technique as stated by Burrough and Rachael 1998; Ayanlade 2009; Samanta et al 2012; Pokhrel et al 2013. This was performing in Quantum GIS environment.…”

Section: Methodsmentioning

confidence: 99%

Influences of wind speed and direction on atmospheric particle concentrations and industrially induced noise

Ayanlade

Oyegbade

2016

SpringerPlus

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PurposeIn this study, the spatial and temporal relationship of wind speed, atmospheric particles concentration, and the industrial-induced noise levels during different times of the day were examined, using sawmill industrial location around Ile-Ife in Osun of Nigeria as a case study.MethodsMobile devices were used to measure noise level and basic meteorological parameters were examined and their influences on the noise levels distribution were assessed. The maximum and minimum sound levels; Lmax and Lmin, the PM10 and PM1 particle concentrations, wind speeds and directions were measured in the morning (7–9 a.m.), afternoon (12–2) and evening (4–6 p.m.) over 14 consecutive days.ResultsThe results revealed that the noise level varies spatiotemporally, much more consistent spatial distribution along the vicinity of sawmill industries. A higher level of noise occurred during the weekday (WD), Leq > 70 dB(A), compared to weekends (WE). Extreme average noise levels are associated with the immediate neighbourhood of sawmill industrial areas during WD compared to streets and road annexes of the study area. The results also show a very weak relationship between noise and PM10 and PMcoarse for both WD and WE with r < 0.35 for PM1 and r < 0.20 for PMcoarse. There appears to be a moderate significant correlation between noise level and PM1 during some meteorological conditions with r > 0.51.ConclusionThe slight relationship between noise and PM1 is perhaps a result of wind movement that carries particles from the source region since booth noise and particles mostly originate from the sawmill. The study concludes that wind speeds and directions have a significant influence on both noise level and particle concentration within the study sites.

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“…A wide variety of approaches has been used, including climate model simulations (Daly et al, 2002;Choi et al, 2003;Mitchell and Jones, 2005;Appelhans et al, 2014;Tew et al, 2014) and satellite observations (Samanta et al, 2012;Du et al, 2014;Shi et al, 2016). However, there is no optimal method for all cases.…”

Section: Introductionmentioning

confidence: 99%

Mapping temperature using a Bayesian statistical method and a high accuracy surface modelling method in the Beijing-Tianjin-Hebei region, China

Zhao

Chen

et al. 2017

Met. Apps

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A more accurate Bayesian statistical technique together with a high accuracy surface modelling method (HASM) was used to improve the accuracy of temperature fields in the Beijing–Tianjin–Hebei (BTH) region, China. Bayesian statistical inference theory was first applied to fill missing daily values for meteorological stations on an annual scale. A mixed interpolator was then employed to simulate the annual mean temperature and was compared with other methods. The annual mean temperature was produced and the inter‐decade change was investigated. The results show that the values filled by Bayesian statistical inference theory agree well with actual observations. A comparison with other interpolators shows that combining the Bayesian estimation method with the HASM gives better results than those of the other methods tested in this study. The construction of the annual mean temperature for 10 years shows the change interval and the spatial distribution of temperature in the BTH region. The annual temperature over 10 years changed from less than −1 °C in the northwest to more than 14 °C in the southeast. From 1981–1990 (T1) to 1991–2000 (T2) and T2 to 2001–2010 (T3) the temperature change was spatially stationary and varied mainly from −0.05 °C to 0.05 °C, except for some areas such as the southwest of Tangshan city and the south of Tianjin city where land cover and land use has exhibited large decadal variation during the past 30 years.

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Interpolation of climate variables and temperature modeling

Cited by 98 publications

References 11 publications

Spatio-Temporal Variation and Monsoon Effect on the Temperature Lapse Rate of a Subtropical Island

Spatio-Temporal Variation and Monsoon Effect on the Temperature Lapse Rate of a Subtropical Island

Influences of wind speed and direction on atmospheric particle concentrations and industrially induced noise

Mapping temperature using a Bayesian statistical method and a high accuracy surface modelling method in the Beijing-Tianjin-Hebei region, China

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