scholarly journals Some climatological characteristics of mean sea level pressure in Bangladesh

MAUSAM ◽  
2021 ◽  
Vol 42 (2) ◽  
pp. 195-200
Author(s):  
A. MOBASSHER ◽  
M. H. RASHID

On the basis of climatological data of 30 years (1951-1980) for 16 stations a climatological study of mean sea level pressure in Bangladesh has been accomplished. Spatial distribution and actual variation of mean sea level pressure have been studied. Attempt has been made to explain the cause of annual variation of mean sea, level pressure in Bangladesh from the point of view of synoptic meteorology. "Stability" of the meteorological stations of Bangladesh with respect to mean sea level pressure has been quired. The spatial variations of correlation of coefficients with regard to mean sea level pressure have been analysed. Finally, some characteristics of probabilities of mean sea level pressure at different materials for selected stations have been obtained.

MAUSAM ◽  
2021 ◽  
Vol 47 (2) ◽  
pp. 149-156
Author(s):  
A. MOBASSHER ◽  
M. A. SAFIULLAH ◽  
D. P. PAUL ◽  
M. M. HOQUE

Using climatological data for 30 years (1951-1980) for twelve stations. some characteristics of relative humidity (RH) in Bangladesh have been studied. In doing so, annual variation, spatial distribution, diurnal and annual amplitudes of RH have been investigated. The correlation characteristics of RH between Dhaka and some other stations have been analysed. Finally, the date of beginning and ending and the duration of RH in some defined limits (above 75%, 80% and 85%) have been discussed: An attempt has also been made to explain the cause of temporal and spatial variations from synoptic point of view.


2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Hamish Steptoe ◽  
Nicholas Henry Savage ◽  
Saeed Sadri ◽  
Kate Salmon ◽  
Zubair Maalick ◽  
...  

AbstractHigh resolution simulations at 4.4 km and 1.5 km resolution have been performed for 12 historical tropical cyclones impacting Bangladesh. We use the European Centre for Medium-Range Weather Forecasting 5th generation Re-Analysis (ERA5) to provide a 9-member ensemble of initial and boundary conditions for the regional configuration of the Met Office Unified Model. The simulations are compared to the original ERA5 data and the International Best Track Archive for Climate Stewardship (IBTrACS) tropical cyclone database for wind speed, gust speed and mean sea-level pressure. The 4.4 km simulations show a typical increase in peak gust speed of 41 to 118 knots relative to ERA5, and a deepening of minimum mean sea-level pressure of up to −27 hPa, relative to ERA5 and IBTrACS data. The downscaled simulations compare more favourably with IBTrACS data than the ERA5 data suggesting tropical cyclone hazards in the ERA5 deterministic output may be underestimated. The dataset is freely available from 10.5281/zenodo.3600201.


1991 ◽  
Vol 3 (4) ◽  
pp. 333-340 ◽  
Author(s):  
Marie-Antoinette Mélières ◽  
Patricia Martinerie ◽  
Dominique Raynaud ◽  
Louis Lliboutry

2013 ◽  
Vol 28 (3) ◽  
pp. 704-710 ◽  
Author(s):  
John T. Abatzoglou ◽  
Renaud Barbero ◽  
Nicholas J. Nauslar

Abstract Santa Ana winds (SAW) are among the most notorious fire-weather conditions in the United States and are implicated in wildfire and wind hazards in Southern California. This study employs large-scale reanalysis data to diagnose SAW through synoptic-scale dynamic and thermodynamic factors using mean sea level pressure gradient and lower-tropospheric temperature advection, respectively. A two-parameter threshold model of these factors exhibits skill in identifying surface-based characteristics of SAW featuring strong offshore winds and extreme fire weather as viewed through the Fosberg fire weather index across Remote Automated Weather Stations in southwestern California. These results suggest that a strong northeastward gradient in mean sea level pressure aligned with strong cold-air advection in the lower troposphere provide a simple, yet effective, means of diagnosing SAW from synoptic-scale reanalysis. This objective method may be useful for medium- to extended-range forecasting when mesoscale model output may not be available, as well as being readily applied retrospectively to better understand connections between SAW and wildfires in Southern California.


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