High-resolution Temperature Dataset Research Articles

Global seamless and high-resolution temperature dataset (GSHTD), 2001–2020

Land surface temperature (Ts) and near-surface air temperature (Ta) are key parameters in multiple research fields. In this study, a global seamless (without missing values) and high-resolution (30 arcsecond spatial resolution) temperature (for both Ts and Ta) dataset (GSHTD) from 2001 to 2020 was developed. First, a method called the estimation of the temperature difference (ETD) was proposed to reconstruct both clear- and cloudy-sky Ts. A global seamless 8-day and monthly average all- and clear-sky Ts data were then created using the MODIS Ts data and the ETD method. The seamless monthly average of the mean, maximum and minimum Ta data were further developed using the seamless Ts data, in situ Ta data and Cubist machine learning algorithm. GSHTD has four main advantages. First, GSHTD includes seven types of temperature data: clear-sky daytime and nighttime Ts, all-sky daytime and nighttime Ts, and mean, maximum and minimum Ta. Second, it has global coverage and high spatial resolution. Third, using the ETD method proposed in this study, GSHTD has no missing values. Fourth, the accuracy of GSHTD is high; the average mean absolute errors (MAEs) of ETD in reconstructing the 25 × 25 and 150 × 150 pixel clear-sky daytime (nighttime) Ts data were 0.724 (0.552) and 1.024 (0.895) °C, respectively. The MAEs of ETD were on average 23.2% and 23.7% lower than those of Remotely Sensed DAily land Surface Temperature reconstruction (RSDAST) and interpolation of the mean anomalies (IMAs). The MAEs of the estimated monthly average of the mean, maximum and minimum Ta data were 0.797, 0.994 and 1.056 °C, respectively. The developed GSHTD is freely available at Middle Yangtze River Geoscience Date Center (https://cjgeodata.cug.edu.cn/#/pageDetail?id=97), which will be useful in many studies related to climate change, environmental science and ecology, and epidemiology and human health.

High-Resolution Temperature Datasets in Portugal from a Geostatistical Approach: Variability and Extremes

AbstractClimate research in Portugal is often constrained by the lack of homogeneous, temporally and spatially consistent, and long-term climatic series. To overcome this limitation, the authors developed new high-resolution gridded datasets (~1 km) of daily mean, minimum, and maximum air temperatures over Portugal (1950–2015, 66 yr), based on gridded daily temperatures (E-OBS) at ~25-km spatial resolution. A two-step approach was followed, under the assumption that daily temperature variability in Portugal is mainly controlled by atmospheric large-scale forcing, while local processes are mostly expressed as strong spatial gradients. First, monthly baseline (1971–2000) patterns were estimated at 1-km grid resolution by applying multivariate linear regressions (exploratory variables: elevation, latitude, and distance to coastline). A kriging of residuals from baseline normals of 36 weather stations was applied for bias corrections. Second, bilinearly interpolated daily temperature anomalies were then added to the daily baseline patterns to obtain the final datasets. The method performance was evaluated using fivefold cross-validations. The datasets were also validated using daily temperatures from 23 stations not incorporated in E-OBS. A climatological analysis based on these datasets was carried out, highlighting spatial heterogeneities, seasonality, long-term trends, interannual variability, and extremes. The spatial and temporal variability is generally coherent with previous studies at coarser resolutions. An overall warming trend is apparent for all variables and indices, but showing different strengths and spatial variability. These datasets show important advantages over preexisting data, including more detailed and accurate information on trends and interannual variability of precipitation extremes, and can thus be applied to several areas of research in Portugal, such as hydrology, ecology, agriculture, and forestry.