Sensor intercomparison of distributed surface radiation measurement system

2015 ◽  
Author(s):  
Baocheng Dou ◽  
Jianguang Wen ◽  
Xiuhong Li ◽  
Qiang Liu ◽  
Qing Xiao ◽  
...  
2009 ◽  
Author(s):  
Thomas Ruhtz ◽  
René Preusker ◽  
Andre Hollstein ◽  
Jonas v. Bismarck ◽  
Marco Starace ◽  
...  

2011 ◽  
Vol 2 (2) ◽  
pp. 164-170 ◽  
Author(s):  
Z. Petrušić ◽  
U. Jovanović ◽  
I. Jovanović ◽  
D. Mančić

2018 ◽  
Vol 31 (8) ◽  
pp. 3301-3325 ◽  
Author(s):  
Hailong Wang ◽  
Casey D. Burleyson ◽  
Po-Lun Ma ◽  
Jerome D. Fast ◽  
Philip J. Rasch

AbstractLong-term Atmospheric Radiation Measurement (ARM) datasets collected at the three tropical western Pacific (TWP) sites are used to evaluate the ability of the Community Atmosphere Model (CAM5) to simulate the various types of clouds, their seasonal and diurnal variations, and their impact on surface radiation. A number of CAM5 simulations are conducted at various horizontal grid spacing (around 2°, 1°, 0.5°, and 0.25°) with meteorological constraints from analysis or reanalysis. Model biases in the seasonal cycle of cloudiness are found to be weakly dependent on model resolution. Positive biases (up to 20%) in the annual mean total cloud fraction appear mostly in stratiform ice clouds. Higher-resolution simulations do reduce the positive bias in ice clouds, but they inadvertently increase the negative biases in convective clouds and low-level liquid clouds, leading to a positive bias in annual mean shortwave fluxes at the sites, as high as 65 W m−2 in the 0.25° simulation. Such resolution-dependent biases in clouds can adversely lead to biases in ambient thermodynamic properties and, in turn, produce feedback onto clouds. Both the model and observations show distinct diurnal cycles in total, stratiform, and convective cloud fractions; however, they are out of phase by 12 h and the biases vary by site. The results suggest that biases in deep convection affect the vertical distribution and diurnal cycle of stratiform clouds through the transport of vapor and/or the detrainment of liquid and ice. The approach used here can be easily adapted for the evaluation of new parameterizations being developed for CAM5 or other global or regional models.


2017 ◽  
Author(s):  
Tim Carlsen ◽  
Gerit Birnbaum ◽  
André Ehrlich ◽  
Johannes Freitag ◽  
Georg Heygster ◽  
...  

Abstract. The effective size of snow grains (reff) affects the reflectivity of snow surfaces and thus the local surface energy budget in particular in polar regions. Therefore, the specific surface area (SSA) was monitored for a two-month period in central Antarctica (Kohnen research station) during austral summer 2013/14. The data were retrieved on the basis of spectral surface albedo measurements collected by the COmpact RAdiation measurement System (CORAS, ground-based) and the Spectral Modular Airborne Radiation measurement sysTem (SMART, airborne). The Snow Grain Size and Pollution amount (SGSP) algorithm, originally developed to analyze spaceborne reflectance measurements by the MODerate Resolution Imaging Spectroradiometer (MODIS), was modified and applied to the ground-based and airborne observations collected in this study. Furthermore, spectral ratios of surface albedo at 1280 nm and 1100 nm wavelength were used to reduce the retrieval uncertainty. Additionally, the algorithm originally developed for cloudless conditions was adapted to handle overcast conditions. Optical in situ observations of SSA utilizing an IceCube device were used to validate the retrieval results. The SSA retrieved from CORAS observations varied between 27 m2 kg-1 and 86 m2 kg-1. Snowfall events caused distinct SSA maxima which were often followed by a gradual decrease in SSA due to snow metamorphism and wind-induced transport of fresh fallen ice crystals (vice versa for reff). SSA retrieved by data from CORAS and MODIS agree with the in situ observations within the ranges given by the measurement uncertainties. However, SSA retrieved by the airborne SMART observations underestimated the ground-based observations by a factor of 2.1 (overestimation of reff).


2006 ◽  
Vol 53 (4) ◽  
pp. 2276-2280 ◽  
Author(s):  
T. Kawaguchi ◽  
K. Futagami ◽  
M. Matoba ◽  
G. Wakabayashi ◽  
N. Ikeda ◽  
...  

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