A daily 5-km all-sky sea-surface longwave radiation product based on statistically modified deep neural network and spatiotemporal analysis for 1981–2018

Xu, Jianglei; Liang, Shunlin; Ma, Han; He, Tao; Zhang, Yufang; Zhang, Guodong

doi:10.1016/j.rse.2023.113550

Cited by 4 publications

(1 citation statement)

References 85 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Given knowledge of the vertical characteristics of the atmosphere and clouds, SDLR can be accurately calculated by atmospheric radiative transfer models. Due to the extensive input parameters and complicated calculation processes required by radiative transfer models, SDLR estimation at high-resolution scales often relies on empirical models [3,[6][7][8], parameterized models [4,[9][10][11][12][13], and machine learning methods [14][15][16][17][18][19][20]. Although machine learning models offer the highest accuracy, empirical and parameterized models have clear analytical forms and interpretability, making them irreplaceable in satellite estimation.…”

Section: Introductionmentioning

confidence: 99%

A Cloud Water Path-Based Model for Cloudy-Sky Downward Longwave Radiation Estimation from FY-4A Data

Yu,

Xin,

Zhang

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

Clouds are a critical factor in regulating the climate system, and estimating cloudy-sky Surface Downward Longwave Radiation (SDLR) from satellite data is significant for global climate change research. The models based on cloud water path (CWP) are less affected by cloud parameter uncertainties and have superior accuracy in SDLR satellite estimation when compared to those empirical and parameterized models relying mainly on cloud fraction or cloud-base temperature. However, existing CWP-based models tend to overestimate the low SDLR values and underestimate the larger SDLR. This study found that this phenomenon was caused by the fact that the models do not account for the varying relationships between cloud radiative effects and key parameters under different Liquid Water Path (LWP) and Precipitable Water Vapor (PWV) ranges. Based upon this observation, this study utilized Fengyun-4A (FY-4A) cloud parameters and ERA5 data as data sources to develop a new CWP-based model where the model coefficients depend on the cloud phase and cloud water path range. The accuracy of the new model’s estimated SDLR is 20.8 W/m2 for cloudy pixels, with accuracies of 19.4 W/m2 and 23.5 W/m2 for overcast and partly cloudy conditions, respectively. In contrast, the accuracy of the old CWP-based model was 22.4, 21.2, and 24.8 W/m2, respectively. The underestimation and overestimation present in the old CWP-based model are effectively corrected by the new model. The new model exhibited higher accuracy under various station locations, cloud cover scenarios, and cloud phase conditions compared to the old one. Comparatively, the new model showcased its most remarkable improvements in situations involving overcast conditions, water clouds with low PWV and low LWP values, ice clouds with large PWV, and conditions with PWV ≥ 5 cm. Over a temporal scale, the new model effectively captured the seasonal variations in SDLR.

show abstract

Section: Introductionmentioning

confidence: 99%

A Cloud Water Path-Based Model for Cloudy-Sky Downward Longwave Radiation Estimation from FY-4A Data

Yu,

Xin,

Zhang

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

Robust remote sensing retrieval of key eutrophication indicators in coastal waters based on explainable machine learning

Zhu,

Cui,

Runa

et al. 2024

ISPRS Journal of Photogrammetry and Remote Sensing

View full text Add to dashboard Cite

Simulation Study on the Effects of Relative Humidity (RH) on Long-Wave Radiative Heat Gain in Residential Buildings

Chen,

Xue,

Jin

et al. 2024

Buildings

View full text Add to dashboard Cite

Long-wave radiation cooling plays a pivotal role in building thermal design. Utilizing a steady-state method to determine surface heat transfer coefficients across diverse climates can result in discrepancies between the designed and actual cooling performance of a building’s envelope. To evaluate the influence of climate and surface emissivity on building heat transfer during summer, the numerical simulation method was employed to calculate the summer long-wave radiation for nine typical residential buildings across various climate regions. This study assesses the applicable meteorological conditions and distribution range of sky radiation technology. The findings indicate that buildings can effectively dissipate heat through sky radiation when the outdoor relative humidity is below 60% and the summer temperature difference exceeds 12 °C. Analysis of meteorological characteristics across different zones reveals a positive correlation between temperature deviations and sky radiative cooling potential, thereby identifying suitable climate zones for the implementation of sky radiative cooling technology.

show abstract

A daily 5-km all-sky sea-surface longwave radiation product based on statistically modified deep neural network and spatiotemporal analysis for 1981–2018

Cited by 4 publications

References 85 publications

A Cloud Water Path-Based Model for Cloudy-Sky Downward Longwave Radiation Estimation from FY-4A Data

A Cloud Water Path-Based Model for Cloudy-Sky Downward Longwave Radiation Estimation from FY-4A Data

Robust remote sensing retrieval of key eutrophication indicators in coastal waters based on explainable machine learning

Simulation Study on the Effects of Relative Humidity (RH) on Long-Wave Radiative Heat Gain in Residential Buildings

Contact Info

Product

Resources

About