scholarly journals Modelling Tropospheric Delays Using the Global Surface Meteorological Parameter Model GPT2

2014 ◽  
Vol 58 (4) ◽  
pp. 301-308 ◽  
Author(s):  
Szabolcs Rózsa
Keyword(s):  
Meteorological Parameter ◽  
Global Surface ◽  
Tropospheric Delays

A Multiyear Global Surface Wind Velocity Dataset Using SSM/I Wind Observations

1996 ◽  
Vol 77 (5) ◽  
pp. 869-882 ◽  
Author(s):  
R. Atlas ◽  
R. N. Hoffman ◽  
S. C. Bloom ◽  
J. C. Jusem ◽  
J. Ardizzone
Keyword(s):  
Wind Velocity ◽  
Surface Wind ◽  
Global Surface

Experimental and Theoretical Studies on Performance of GSHP Combined with Free Cooling System

2013 ◽  
Vol 368-370 ◽  
pp. 1232-1236
Author(s):  
Wei Xue Cao ◽  
Ru Chang ◽  
Can Zhang ◽  
Qiu Li Zhang
Keyword(s):  
Cooling Tower ◽  
Cooling System ◽  
Meteorological Parameter ◽  
Economic Benefits ◽  
Ground Source Heat Pump ◽  
Combined System ◽  
Climate Zones ◽  
Ground Source ◽  
Free Cooling ◽  
Experimental And Theoretical Studies

Ground-Source Heat Pump systems and tower cooling system have been studied in this paper individually by experiment and simulation using TRNSYS, the influencing factors such as meteorological parameter, cooling tower and subunit construction was analyzed. Results show that the combined system has ability to meet the cooling requirements in II building climate zones, the combined system will have energy-saving and obvious economic benefits by working through the year.

Third Revision of the Bottom-up Global Surface Seawater Dimethyl Sulphide Climatology (DMS-Rev3) 

2021 ◽  
Author(s):  
Shrivardhan Hulswar ◽  
George Manville ◽  
Rafel Simo ◽  
Marti Gali ◽  
Thomas G. Bell ◽  
...  
Keyword(s):  
Dimethyl Sulphide ◽  
Surface Seawater ◽  
High Concentration ◽  
Concentration Data ◽  
Bottom Up ◽  
High Productivity ◽  
Geographic Boundaries ◽  
Data Points ◽  
Biogeochemical Provinces ◽  
Global Surface

<p>An updated estimation of the bottom-up global surface seawater dimethyl sulphide (DMS) climatology, DMS-Rev3, is the third of its kind and includes five significant changes from the last climatology, ‘L11’ (Lana et al., 2011) that was released about a decade ago. The first change is the inclusion of new observations that have become available over the last decade, i.e., the total number of observations included in DMS- Rev3 are 865,109 as compared to 47,313 data points used in the last estimation (~1728% increase in raw data). The second was significant improvements in data handling, processing, filtering, to avoid bias due to different observation frequencies. Thirdly, we incorporated the dynamic seasonal changes observed in the ocean biogeochemical provinces and their variable geographic boundaries. Fourth change was refinements in the interpolation algorithm used to fill up the missing data. And finally, an upgraded smoothing algorithm based on observed DMS variability length scales (VLS) which helped reproduce a more realistic distribution of the DMS concentration data. The results show that DMS-Rev3 estimates the global annual mean DMS concentration at 2.34 nM, 4% lower than the current bottom-up ‘L11’ climatology. However, significant regional differences of more than 100% are observed. The largest changes are observed in high concentration regions such as the polar oceans, although oceanic regions which were under-sampled in the past also show large differences. DMS-Rev3 reduces the previously observed patchiness in high productivity regions.</p>

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