Homogenization of Tianjin monthly near-surface wind speed using RHtestsV4 for 1951–2014

Abstract

Historical Chinese surface meteorological records provided by the special fund for basic meteorological data from the National Meteorological Information Center (NMIC) were processed to produce accurate wind speed data. Monthly 2-min near-surface wind speeds from 13 observation stations in Tianjin covering 1951-2014 were homogenized using RHtestV4 combined with their metadata. Results indicate that 10 stations had significant breakpoints-77% of the Tianjin stations-suggesting that inhomogeneity was common in the Tianjin wind speed series. Instrument change accounted for most changes, based on the metadata, including changes in type and height, especially for the instrument type. Average positive quantile matching (QM) adjustments were more than negative adjustments at 10 stations; positive biases with a probability density of 0.2 or more were mainly concentrates in the range 0.2 m s −1 to 1.2 m s −1 , while the corresponding negative biases were mainly in the range −0.1 to −1.2 m s −1 . Here, changes in variances and trends in the monthly mean surface wind speed series at 10 stations before and after adjustment were compared. Climate characteristics of wind speed in Tianjin were more reasonably reflected by the adjusted data; inhomogeneity in wind speed series was largely corrected. Moreover, error analysis reveals that there was a high consistency between the two datasets here and that from the NMIC, with the latter as the reference. The adjusted monthly near-surface wind speed series shows a certain reliability for the period 1951-2014 in Tianjin.

An approach to homogenize daily peak wind gusts: An application to the Australian series

“…() used the RHtestV2 data homogenization package (Wang and Feng, ) for Canadian monthly mean wind speed data, and Si et al . () applied the RHtestV4 for Tianjin (China) monthly mean wind speed data; (b) Petrović et al . (), Li et al .…”

“…Due to the variety of factors affecting the measurement of wind speed, that is, its high natural short-term variance (Balling and Cerveny, 2005;Jakob, 2010), long-term trends (Vautard et al, 2010;McVicar et al, 2012), and the relatively high spatial variability (Azorin-Molina et al, 2014), as well as the high sensitivity of wind to local site conditions (WMO, 2017), implementing quality control and homogenization procedures on wind series has been challenging. To summarize, only a few approaches have been developed for mean wind speed so far in recent years: (a) Wang (2008) and Wan et al (2010) used the RHtestV2 data homogenization package (Wang and Feng, 2007) for Canadian monthly mean wind speed data, and Si et al (2018) Szentimrey, 1999Szentimrey, , 2008 to homogenize daily wind speed series for Ireland, for the greater Beijing area (China) and Hungary, respectively; (c) Štěpánek et al (2013), Azorin-Molina et al (2014), and Minola et al (2016) used the AnClim package (Štěpánek, 2004) to detect sudden break points in monthly wind speed series for the Czech Republic, Spain and Portugal, and Sweden, respectively; (d) Guijarro (2015) and Azorin-Molina et al (2018b) applied the Climatol package to detect artificial change points and adjust inhomogeneities in monthly wind speed time series in Spain and Portugal and Saudi Arabia, respectively; and (e) Laapas and Venäläinen (2017) Alexandersson, 1986) and the Maronna-Yohai test to monthly, seasonal, and annual aggregates. None of these methods stands out as being best, so different approaches have been applied thereby justifying the need of benchmarking the performance of homogenization of wind speed data, as Venema et al (2012) conducted in HOME for air temperature and precipitation.…”

Construction of homogenized daily surface air temperature for the city of Tianjin during 1887–2019

“…However, in the current study, there may still be some systematic biases (possibly some potential breakpoints missed) in the adjusted time series, since metadata of Tianjin Station are not consistently available in the climatological archives over the whole century as well as not documented during the period before 1921. Climate data homogenization does not always follow a consistent pattern (Si et al, 2018(Si et al, , 2019. It is necessary to constantly improve the existing methodology and explore new techniques in order to obtain reliable homogenized data products.…”

Reply on RC2

“…However, in the current study, there may be some systematic biases (possibly some potential breakpoints missed) still in the adjusted time series, since metadata of Tianjin station are not consistently available in the climatological archives over the whole century as well as not being documented during the period before 1921. Climate data homogenization does not always follow a consistent pattern (Si et al, 2018;. It is necessary to constantly improve the existing methodology and explore new techniques in order to obtain reliable homogenized data products.…”