scholarly journals Effects of clouds and precipitation disturbance on the surface radiation budget and energy balance over loess plateau semi-arid grassland in China

2013 ◽  
Vol 62 (20) ◽  
pp. 209201
Author(s):  
Yue Ping ◽  
Zhang Qiang ◽  
Zhao Wen ◽  
Wang Jin-Song ◽  
Wang Run-Yuan ◽  
...  
Keyword(s):  
Energy Balance ◽  
Loess Plateau ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Surface Radiation Budget ◽  
Semi Arid

scholarly journals Retrieving Crop Albedo Based on Radar Sentinel-1 and Random Forest Approach

2021 ◽  
Vol 13 (16) ◽  
pp. 3181
Author(s):  
Abdelhakim Amazirh ◽  
El Houssaine Bouras ◽  
Luis Enrique Olivera-Guerra ◽  
Salah Er-Raki ◽  
Abdelghani Chehbouni
Keyword(s):  
Energy Balance ◽  
Random Forest ◽  
Water Surface ◽  
Surface Albedo ◽  
Surface Energy Balance ◽  
Growing Season ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Surface Radiation Budget ◽  
Semi Arid

Monitoring agricultural crops is of paramount importance for preserving water resources and increasing water efficiency over semi-arid areas. This can be achieved by modelling the water resources all along the growing season through the coupled water–surface energy balance. Surface albedo is a key land surface variable to constrain the surface radiation budget and hence the coupled water–surface energy balance. In order to capture the hydric status changes over the growing season, optical remote sensing becomes impractical due to cloud cover in some periods, especially over irrigated winter crops in semi-arid regions. To fill the gap, this paper aims to generate cloudless surface albedo product from Sentinel-1 data that offers a source of high spatio-temporal resolution images. This can help to better capture the vegetation development along the growth season through the surface radiation budget. Random Forest (RF) algorithm was implemented using Sentinel-1 backscatters as input. The approach was tested over an irrigated semi-arid zone in Morocco, which is known by its heterogeneity in term of soil conditions and crop types. The obtained results are evaluated against Landsat-derived albedo with quasi-concurrent Landsat/Sentinel-1 overpasses (up to one day offset), while a further validation was investigated using in situ field scale albedo data. The best model-hyperparameters selection was dependent on two validation approaches (K-fold cross-validation ‘k = 10’, and holdout). The more robust and accurate model parameters are those that represent the best statistical metrics (root mean square error ‘RMSE’, bias and correlation coefficient ‘R’). Coefficient values ranging from 0.70 to 0.79 and a RMSE value between 0.0002 and 0.00048 were obtained comparing Landsat and predicted albedo by RF method. The relative error ratio equals 4.5, which is acceptable to predict surface albedo.

Key parameters in urban surface radiation budget and energy balance modeling

Urban Climate ◽  
2021 ◽  
Vol 39 ◽  
pp. 100940
Author(s):  
Chunlei Meng ◽  
Chengcheng Huang ◽  
Junxia Dou ◽  
Huoqing Li ◽  
Conglan Cheng
Keyword(s):  
Energy Balance ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Urban Surface ◽  
Surface Radiation Budget

scholarly journals CLARA-A2: the second edition of the CM SAF cloud and radiation data record from 34 years of global AVHRR data

2017 ◽  
Vol 17 (9) ◽  
pp. 5809-5828 ◽  
Author(s):  
Karl-Göran Karlsson ◽  
Kati Anttila ◽  
Jörg Trentmann ◽  
Martin Stengel ◽  
Jan Fokke Meirink ◽  
...  
Keyword(s):  
Surface Albedo ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Climate Data ◽  
Ground Segment ◽  
Surface Radiation Budget ◽  
Avhrr Data ◽  
Data Record ◽  
Development And Validation ◽  
Climate Data Record

Abstract. The second edition of the satellite-derived climate data record CLARA (The CM SAF Cloud, Albedo And Surface Radiation dataset from AVHRR data – second edition denoted as CLARA-A2) is described. The data record covers the 34-year period from 1982 until 2015 and consists of cloud, surface albedo and surface radiation budget products derived from the AVHRR (Advanced Very High Resolution Radiometer) sensor carried by polar-orbiting, operational meteorological satellites. The data record is produced by the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF) project as part of the operational ground segment. Its upgraded content and methodology improvements since edition 1 are described in detail, as are some major validation results. Some of the main improvements to the data record come from a major effort in cleaning and homogenizing the basic AVHRR level-1 radiance record and a systematic use of CALIPSO-CALIOP cloud information for development and validation purposes. Examples of applications studying decadal changes in Arctic summer surface albedo and cloud conditions are provided.

scholarly journals Clouds damp the radiative impacts of polar sea ice loss

2020 ◽  
Vol 14 (8) ◽  
pp. 2673-2686 ◽  
Author(s):  
Ramdane Alkama ◽  
Patrick C. Taylor ◽  
Lorea Garcia-San Martin ◽  
Herve Douville ◽  
Gregory Duveiller ◽  
...  
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Surface Radiation ◽  
Radiation Budget ◽  
The Arctic ◽  
Cloud Properties ◽  
Surface Radiation Budget ◽  
Cloud Radiative Effect ◽  
Radiative Impact ◽  
The Impact

Abstract. Clouds play an important role in the climate system: (1) cooling Earth by reflecting incoming sunlight to space and (2) warming Earth by reducing thermal energy loss to space. Cloud radiative effects are especially important in polar regions and have the potential to significantly alter the impact of sea ice decline on the surface radiation budget. Using CERES (Clouds and the Earth's Radiant Energy System) data and 32 CMIP5 (Coupled Model Intercomparison Project) climate models, we quantify the influence of polar clouds on the radiative impact of polar sea ice variability. Our results show that the cloud short-wave cooling effect strongly influences the impact of sea ice variability on the surface radiation budget and does so in a counter-intuitive manner over the polar seas: years with less sea ice and a larger net surface radiative flux show a more negative cloud radiative effect. Our results indicate that 66±2% of this change in the net cloud radiative effect is due to the reduction in surface albedo and that the remaining 34±1 % is due to an increase in cloud cover and optical thickness. The overall cloud radiative damping effect is 56±2 % over the Antarctic and 47±3 % over the Arctic. Thus, present-day cloud properties significantly reduce the net radiative impact of sea ice loss on the Arctic and Antarctic surface radiation budgets. As a result, climate models must accurately represent present-day polar cloud properties in order to capture the surface radiation budget impact of polar sea ice loss and thus the surface albedo feedback.

scholarly journals Daily Radiation Budget of the Baltic Sea Surface from Satellite Data

2015 ◽  
Vol 22 (3) ◽  
pp. 50-56 ◽  
Author(s):  
Tomasz Zapadka ◽  
Adam Krężel ◽  
Marcin Paszkuta ◽  
Mirosław Darecki
Keyword(s):  
Baltic Sea ◽  
Empirical Data ◽  
Statistical Error ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Satellite Monitoring ◽  
The Baltic Sea ◽  
Surface Radiation Budget ◽  
The Baltic ◽  
Oil Rig

Abstract Recently developed system for assessment of radiation budget for the Baltic Sea has been presented and verified. The system utilizes data from various sources: satellite, model and in situ measurements. It has been developed within the SatBałtyk project (Satellite Monitoring of the Baltic Sea Environment - www.satbaltyk.eu) where the energy radiation budget is one of the key element. The SatBałtyk system generates daily maps of the all components of radiation budget on every day basis. We show the scheme of making daily maps, applied algorithms and empirical data collection within the system. An empirical verification of the system has been carried out based on empirical data collected on the oil rig placed on the Baltic Sea. This verification concerned all the components of the surface radiation budget. The average daily NET products are estimated with statistical error ca. 13 Wm-2. The biggest absolute statistical error is for LWd component and equals 14 Wm-2. The relative error in relation to the average annual values for whole Baltic is the biggest for SWu and reaches 25%. All estimated components have correlation coefficient above 0.91.

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