| Autor: |
Rahul Nigam, Bimal K. Bhattacharya, Dhwanilnath Gharekhan, Parul Patel, Devansh Desai |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
2020 IEEE Bangalore Humanitarian Technology Conference (B-HTC). |
| DOI: |
10.1109/b-htc50970.2020.9297870 |
| Popis: |
Net surface radiation defines the availability of radiative energy on and near the surface to drive many physical, physiological and eco-hydrological processes such as latent heat, sensible heat fluxes and evapotranspiration. Incoming longwave radiation (LWin) is one of the key components of net longwave radiation. One of the prime challenges of modelling radiation budget is estimation of surface incoming longwave radiation. Estimation of incoming longwave radiation in cloudy and foggy conditions has always been a challenge due to the lack of instrumentation and regular measurements at different spatial and temporal scales. In this study, two neural network models (daytime and night-time) were developed for estimation of incoming longwave radiation under foggy sky using half-hourly LWin, and other meteorological parameters such Ta, RH etc. The model provided high correlation of 0.85 (daytime) and 0.86 (night-time) with Root Mean Square Error (RMSE) of 4.9% for both daytime and night-time. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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