GIS-based Tests for Quality Control of Meteorological Data and Spatial Interpolation of Climate Data

Chiu, Ching-An; Lin, Po‐Hsiung; Lu, King-Cherng

doi:10.1659/mrd.00030

Cited by 36 publications

(33 citation statements)

References 35 publications

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“…1b) and the spatial and temporal patterns of precipitation (Chiu et al 2009), Taiwan can be divided into four geographical climatic regions: the northwest region (NWr), with 104 weather stations (n = 104), the northeast region (NEr; n = 43), the southwest region (SWr; n = 60), and the southeast region (SEr; n = 12). These stations within the four climatic regions can be regrouped into a north region (Nr = NWr + NEr; n = 147), a south region (Sr = SWr + SEr; n = 72), a west region (Wr = NWr + SWr; n = 164), and an east region (Er = NEr + SEr; n = 55).…”

Section: Geographical Climatic Regionsmentioning

confidence: 99%

“…However, continuous and complete temperature records are often unavailable, especially in mountainous areas, where meteorological stations are usually sparse (Chiu et al 2009;Burt and Holden 2010). Because air temperature gradually decreases with elevation in the troposphere, the change rate, called the temperature lapse rate (TLR), can facilitate the spatial interpolation of temperature at a given place and time to generate a climate grid (e.g., Bolstad et al 1998;Stahl et al 2006;Cannon et al 2012).…”

Section: Introductionmentioning

confidence: 99%

“…For January -March, the TLR was a second-order function, between -3.22 and -3.61°C km -1 , but it was steeper by -0.42 to -0.50°C km -1 for each kilometer of increase in elevation. Chiu et al (2009) used linear regression to interpolate temperature using the elevations and coordinates of stations as predictors. The island-wide monthly TLR values were between -4.36 and -5.68°C km -1 , and monthly temperatures were slightly modified by the geographical coordinates of the stations during JanuaryJune and September -December.…”

Section: Introductionmentioning

confidence: 99%

“…Meanwhile, the altitudinal distribution patterns of plants in Taiwan are noticeably affected by the northeast monsoon (Chiou et al 2010). The spatial distribution of precipitation between the summer half-year and winter half-year affected by alternate monsoons is highly uneven (Chiu et al 2009). Thus, it is necessary to further explore how the spatio-temporal variation and monsoon affect the TLR.…”

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: Geographical Climatic Regionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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.

Self Cite

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“…Thus, except for computing ET o at measuring weather stations, temperatures have to be interpolated for each 5 × 5 km 2 grid from observations at nearby weather stations. Among numerous spatial interpolation methods, Residual Kriging was considered to be the best method for temperature estimation because of the complex relations between temperature and orography as well as the climate characteristics of Taiwan (Chiu et al 2009). …”

Section: Spatial Interpolation Of Daily Maximum and Minimum Temperaturesmentioning

confidence: 99%

Mapping reference evapotranspiration from meteorological satellite data and applications

Yao

Li²,

Juan

et al. 2017

Terr. Atmos. Ocean. Sci.

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Reference evapotranspiration (ET o ) is an agrometeorological variable widely used in hydrology and agriculture. The FAO-56 Penman-Monteith combination method (PM method) is a standard for computing ET o for water management. However, this scheme is limited to areas where climatic data with good quality are available. Maps of 10-day averaged ET o at 5 km × 5 km grid spacing for the Taiwan region were produced by multiplying pan evaporation (E pan ), derived from ground solar radiation (GSR) retrieved from satellite images using the Heliosat-3 method, by a fixed pan coefficient (K p ). Validation results indicated that the overall mean absolute percentage error (MAPE) and normalized root-mean-square deviation (NRMSD) were 6.2 and 7.7%, respectively, when compared with ET o computed by the PM method using spatially interpolated 10-day averaged daily maximum and minimum temperature datasets and GSR derived from satellite inputs. Land coefficient (K L ) values based on the derived ET o estimates and long term latent heat flux measurements, were determined for the following landscapes: Paddy rice (Oryza sativa), subtropical cypress forest (Chamaecyparis obtusa var. formosana and Chamaecyparis formosensis), warm-to-temperate mixed rainforest (Cryptocarya chinensis, Engelhardtia roxburghiana, Tutcheria shinkoensis, and Helicia formosana), and grass marsh (Brachiaria mutica and Phragmites australis). The determined land coefficients are indispensable to scale ET o in estimating regional evapotranspiration.

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Spatio-temporal variability of vertical gradients of major meteorological observations around the Tibetan Plateau

2015

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ABSTRACT:The near-surface air temperature lapse rate (TLR), wind speed gradient (WSG), and precipitation gradient (PG) provide crucial parameters used in models of mountain climate and hydrology. The complex mountain terrain and vast area of the Tibetan Plateau (TP) make such factors particularly important. With daily data from 161 meteorological stations over the past 43 years , we analyse the spatio-temporal variations of TLRs, WSGs, and PGs over and around TP, derived using linear regression methods and dividing the study area into zones based on spatial variations. Results of this study include: (1) The observed TLR varies from −0.46 to −0.73 ∘ C (100 m) −1 , with averaged TLRs of −0.60, −0.62, and −0.59 ∘ C (100 m) −1 for T max , T min , and T mean , respectively. The averaged TLR is slightly less than the global mean of −0.65 ∘ C (100 m) −1 . The spatial variability of TLR relates to climate conditions, wherein the TLR increases in dry conditions and in cold months (October-April), while it lessens in humid regions and during warm months (May-September). (2) The estimated annual WSG ranges from 0.07 to 0.17 m s −1 (100 m) −1 . Monthly WSGs show a marked seasonal shift, in which higher WSGs can be explained by the high intensity of prevailing wind. (3) Positive summer PGs vary from 12.08 in the central TP to 26.14 mm (100 m) −1 in northeastern Qinghai and the southern TP, but a reverse gradient prevails in Yunnan and parts of Sichuan Province. (4) The regional warming over TP is more evident in winter, and T min demonstrated the most prominent warming compared with T max and T mean . Environments at high elevations experience more rapid changes in temperatures (T max , T min , and T mean ) than those at low elevations, which is especially true in winter and for T min . Furthermore, inter-annual variation of TLRs is linked to elevation-dependent warming.

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GIS-based Tests for Quality Control of Meteorological Data and Spatial Interpolation of Climate Data

Cited by 36 publications

References 35 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

Mapping reference evapotranspiration from meteorological satellite data and applications

Spatio-temporal variability of vertical gradients of major meteorological observations around the Tibetan Plateau

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