Comparisons of Diurnal Variations of Land Surface Temperatures from Numerical Weather Prediction Analyses, Infrared Satellite Estimates and In Situ Measurements

Wang, Xiaoni; Prigent, Catherine

doi:10.3390/rs12030583

Cited by 15 publications

(7 citation statements)

References 53 publications

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“…In addition, validation of energy balance models of surface temperature has shown that there is a tendency for such models to suffer a cold bias [37,38]. The NWP modelling technique is much more sophisticated in describing the land surface and atmospheric conditions but has been shown to struggle in accuracy with regards to surface temperatures over a variety of landcovers [39,40]. Overall, physical models as they currently exist should be further improved to act as a suitable answer on their own to the filling of LST cloud gaps, especially on large (global) scales.…”

Section: Prior Cloud Gap-filling Researchmentioning

confidence: 99%

An Improved Cloud Gap-Filling Method for Longwave Infrared Land Surface Temperatures through Introducing Passive Microwave Techniques

et al. 2021

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Satellite-derived land surface temperature (LST) data are most commonly observed in the longwave infrared (LWIR) spectral region. However, such data suffer frequent gaps in coverage caused by cloud cover. Filling these ‘cloud gaps’ usually relies on statistical re-constructions using proximal clear sky LST pixels, whilst this is often a poor surrogate for shadowed LSTs insulated under cloud. Another solution is to rely on passive microwave (PM) LST data that are largely unimpeded by cloud cover impacts, the quality of which, however, is limited by the very coarse spatial resolution typical of PM signals. Here, we combine aspects of these two approaches to fill cloud gaps in the LWIR-derived LST record, using Kenya (East Africa) as our study area. The proposed “cloud gap-filling” approach increases the coverage of daily Aqua MODIS LST data over Kenya from <50% to >90%. Evaluations were made against the in situ and SEVIRI-derived LST data respectively, revealing root mean square errors (RMSEs) of 2.6 K and 3.6 K for the proposed method by mid-day, compared with RMSEs of 4.3 K and 6.7 K for the conventional proximal-pixel-based statistical re-construction method. We also find that such accuracy improvements become increasingly apparent when the total cloud cover residence time increases in the morning-to-noon time frame. At mid-night, cloud gap-filling performance is also better for the proposed method, though the RMSE improvement is far smaller (<0.3 K) than in the mid-day period. The results indicate that our proposed two-step cloud gap-filling method can improve upon performances achieved by conventional methods for cloud gap-filling and has the potential to be scaled up to provide data at continental or global scales as it does not rely on locality-specific knowledge or datasets.

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Section: Prior Cloud Gap-filling Researchmentioning

confidence: 99%

An Improved Cloud Gap-Filling Method for Longwave Infrared Land Surface Temperatures through Introducing Passive Microwave Techniques

et al. 2021

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“…Although ECMWF recently formed the global ERA5 dataset since 1950, the one from 1950 to 1978 is not the final version. There are some evaluation and application studies on the specific elements of the datasets at home and abroad which show that the quality of the datasets is significantly improved compared with the previous version [32,41,42]. Therefore, the hourly 2 m temperature data from ERA5 during 1979-2020 are used to form daily and monthly extreme air temperatures, annual frost days (FD), and hot days (HD) in mainland China.…”

Section: Datamentioning

confidence: 99%

Assessment of Seasonal Variability of Extreme Temperature in Mainland China under Climate Change

Yan

Jun

et al. 2021

Sustainability

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Some studies have suggested that variations in the seasonal cycle of temperature and season onset could affect the efficiency in the use of radiation by plants, which would then affect yield. However, the study of the temporal variation in extreme climatic variables is not sufficient in China. Using seasonal trend analysis (STA), this article evaluates the distribution of extreme temperature seasonality trends in mainland China, describes the trends in the seasonal cycle, and detects changes in extreme temperature characterized by the number of hot days (HD) and frost days (FD), the frequency of warm days (TX90p), cold days (TX10p), warm nights (TN90p), and cold nights (TN10p). The results show a statistically significant positive trend in the annual average amplitudes of extreme temperatures. The amplitude and phase of the annual cycle experience less variation than that of the annual average amplitude for extreme temperatures. The phase of the annual cycle in maximum temperature mainly shows a significant negative trend, accounting for approximately 30% of the total area of China, which is distributed across the regions except for northeast and southwest. The amplitude of the annual cycle indicates that the minimum temperature underwent slightly greater variation than the maximum temperature, and its distribution has a spatial characteristic that is almost bounded by the 400 mm isohyet, increasing in the northwest and decreasing in the southeast. In terms of the extreme air temperature indices, HD, TX90p, and TN90p show an increasing trend, FD, TX10p, and TN10p show a decreasing trend. They are statistically significant (p < 0.05). This number of days also suggests that temperature has increased over mainland China in the past 42 years.

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“…show that the quality of the dataset is signi cantly improved compared with the previous version (Zhao et al 2019;Hu et al 2019;Wang et al 2020). Therefore, the hourly 2 m temperature data from EAR5 during 1979-2020 are used to form daily and monthly extreme temperatures, annual frost days (FD) and hot days (HD) in mainland China.…”

Section: Datamentioning

confidence: 99%

Assessment of Seasonal Variability of Extreme Temperature In Mainland China Under Climate Change

Yan

Zhao

et al. 2021

Preprint

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Grain yield may be affected in the future by climate change. Some studies have suggested that variations in the seasonal cycle of temperature and season onset could affect the efficiency in the use of radiation by plants, which would then affect yield. However, the study of the temporal variation in extreme climatic variables is not sufficient in China. Therefore, this article evaluates the distribution of extreme temperature seasonality trends in mainland China, describes the trends in the seasonal cycle, and detects changes in extreme temperature characterized by the number of hot days (HD), and frost days (FD) and the frequency of warm days (Tx90p), cold days (Tx10p), warm nights (Tn90p) and cold nights (Tn10p). All data are from the EAR5 reanalysis for the 1979-2020 periods.The results show a statistically significant positive trend in the annual average amplitudes (A0) of extreme temperatures. The change in Txmax was the smallest, but it also accounted for 84.5% of the total area. The annual amplitude (A1) and phase (F1) experienced less variation than A0 for extreme temperatures in mainland China. A1 of the maximum temperature decreased significantly on the Tibetan Plateau and increased significantly in the Tianshan Mountains and Jungar Basin (mainly Taxman). F1 of the maximum temperature exhibit a negative trend in approximately 30% of mainland China, and the trend appeared in some regions except in the Northeast and Southwest. Although A1 of the minimum temperature was not as large as that of the maximum temperature, its distribution was very characteristic and it was almost bounded by the 400 mm isohyet, increasing in the Northwest and decreasing in the Southeast. In terms of the number of days, there was an increase in HD, Tx90p, and Tn90p, as well as a decrease in FD, Tx10p, and Tn10p. This number of days also indicates that temperature has increased over mainland China in the past 42 years.

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Comparisons of Diurnal Variations of Land Surface Temperatures from Numerical Weather Prediction Analyses, Infrared Satellite Estimates and In Situ Measurements

Cited by 15 publications

References 53 publications

An Improved Cloud Gap-Filling Method for Longwave Infrared Land Surface Temperatures through Introducing Passive Microwave Techniques

An Improved Cloud Gap-Filling Method for Longwave Infrared Land Surface Temperatures through Introducing Passive Microwave Techniques

Assessment of Seasonal Variability of Extreme Temperature in Mainland China under Climate Change

Assessment of Seasonal Variability of Extreme Temperature In Mainland China Under Climate Change

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