Estimation of daytime downward longwave radiation at the surface from satellite and grid point data

1986 ◽  
Vol 37 (3) ◽  
pp. 136-149 ◽  
Author(s):  
P. Schmetz ◽  
J. Schmetz ◽  
E. Raschke
MAUSAM ◽  
2021 ◽  
Vol 43 (1) ◽  
pp. 21-28
Author(s):  
P. L. KULKARNI ◽  
D. R. TALWALKAR ◽  
SATHY NAIR ◽  
S. G. NARKHEDKAR ◽  
S. RAJAMANI

In the present study, kinematic divergence computed using ECMWF grid point data at 850 hPa  is enhanced by  using the relationship between OLR and divergence. This new enhanced divergence is used to  compute the velocity potential and then, the divergence part of the wind is obtained from velocity potetial. To obtain the rotational part of wind, we computed the vorticity from wind data, and subsequently stream function and obtained and the rotational part of the wind from the stream function. The total wind is the combination of divergent part obtained from modified velocity potential (using OLR data) and rotational part from unmodified stream function. This total wind field is used as initial guess for univariate objective analysis by optimum interpolation scheme so that Initial Guess field contained the more realistic divergent part of the wind. Consequently, the analysed field also will contain the divergent part of the wind.


2017 ◽  
Author(s):  
Chunlüe Zhou ◽  
Yanyi He ◽  
Kaicun Wang

Abstract. Reanalyses have been widely used because they add value to the routine observations by generating physically/dynamically consistent and spatiotemporally complete atmospheric fields. Existing studies have extensively discussed their temporal suitability in global change study. This study moves forward on their suitability for regional climate change study where land–atmosphere interactions play a more important role. Here, surface air temperature (Ta) from 12 current reanalysis products were investigated, focusing on spatial patterns of Ta trends, using homogenized Ta from 1979 to 2010 at ~ 2200 meteorological stations in China. Results show that ~ 80 % of the Ta mean differences between reanalyses and in-situ observations are attributed to station and model-grid elevation differences, denoting good skill in Ta climatology and rebutting the previously reported Ta biases. However, the Ta trend biases in reanalyses display spatial divergence (standard deviation = 0.15–0.30 °C/decade at 1° × 1° grids). The simulated Ta trend biases correlate well with those of precipitation frequency, surface incident solar radiation (Rs), and atmospheric downward longwave radiation (Ld) among the reanalyses (r = −0.83, 0.80 and 0.77, p 


2016 ◽  
Author(s):  
Kwang-Yul Kim ◽  
Benjamin D. Hamlington ◽  
Hanna Na ◽  
Jinju Kim

Abstract. Sea ice melting is proposed as a primary reason for the Artic amplification, although physical mechanism of the Arctic amplification and its connection with sea ice melting is still in debate. In the present study, monthly ERA-interim reanalysis data are analyzed via cyclostationary empirical orthogonal function analysis to understand the seasonal mechanism of sea ice melting in the Arctic Ocean and the Arctic amplification. While sea ice melting is widespread over much of the perimeter of the Arctic Ocean in summer, sea ice remains to be thin in winter only in the Barents-Kara Seas. Excessive turbulent heat flux through the sea surface exposed to air due to sea ice melting warms the atmospheric column. Warmer air increases the downward longwave radiation and subsequently surface air temperature, which facilitates sea surface remains to be ice free. A 1 % reduction in sea ice concentration in winter leads to ~ 0.76 W m−2 increase in upward heat flux, ~ 0.07 K increase in 850 hPa air temperature, ~ 0.97 W m−2 increase in downward longwave radiation, and ~ 0.26 K increase in surface air temperature. This positive feedback mechanism is not clearly observed in the Laptev, East Siberian, Chukchi, and Beaufort Seas, since sea ice refreezes in late fall (November) before excessive turbulent heat flux is available for warming the atmospheric column in winter. A detailed seasonal heat budget is presented in order to understand specific differences between the Barents-Kara Seas and Laptev, East Siberian, Chukchi, and Beaufort Seas.


2021 ◽  
pp. 1-54
Author(s):  
Joseph P. Clark ◽  
Vivek Shenoy ◽  
Steven B. Feldstein ◽  
Sukyoung Lee ◽  
Michael Goss

AbstractThe wintertime (December – February) 1990 - 2016 Arctic surface air temperature (SAT) trend is examined using self-organizing maps (SOMs). The high dimensional SAT dataset is reduced into nine representative SOM patterns, with each pattern exhibiting a decorrelation time scale about 10 days and having about 85% of its variance coming from intraseasonal timescales. The trend in the frequency of occurrence of each SOM pattern is used to estimate the interdecadal Arctic winter warming trend associated with the SOM patterns. It is found that trends in the SOM patterns explain about one-half of the SAT trend in the Barents and Kara Seas, one-third of the SAT trend around Baffin Bay and two-thirds of the SAT trend in the Chukchi Sea. A composite calculation of each term in the thermodynamic energy equation for each SOM pattern shows that the SAT anomalies grow primarily through the advection of the climatological temperature by the anomalous wind. This implies that a substantial fraction of Arctic amplification is due to horizontal temperature advection that is driven by changes in the atmospheric circulation. An analysis of the surface energy budget indicates that the skin temperature anomalies as well as the trend, although very similar to that of the SAT, are produced primarily by downward longwave radiation.


2018 ◽  
Vol 83 ◽  
pp. 122-134 ◽  
Author(s):  
Nsikan I. Obot ◽  
Michael A.C. Chendo ◽  
Elijah O. Oyeyemi

2019 ◽  
Vol 138 (3-4) ◽  
pp. 1375-1394
Author(s):  
A. H. Maghrabi ◽  
M. M. Almutayri ◽  
A. F. Aldosary ◽  
B. I. Allehyani ◽  
A. A. Aldakhil ◽  
...  

2020 ◽  
Author(s):  
Yu Wei ◽  
Xiaotong Zhang ◽  
Li Wenhong ◽  
Ning Hou ◽  
Weiyu Zhang ◽  
...  

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