scholarly journals Polar Mesoscale Cyclones in the Northeast Atlantic: Comparing Climatologies from ERA-40 and Satellite Imagery

2006 ◽  
Vol 134 (5) ◽  
pp. 1518-1533 ◽  
Author(s):  
Alan Condron ◽  
Grant R. Bigg ◽  
Ian A. Renfrew
Keyword(s):  
Sea Level ◽  
Satellite Imagery ◽  
Detection Algorithm ◽  
Ocean Modeling ◽  
Small Scale ◽  
Sea Level Pressure ◽  
Mean Sea Level ◽  
Northeast Atlantic ◽  
Level Pressure ◽  
Mesoscale Cyclones

Abstract Polar mesoscale cyclones over the subarctic are thought to be an important component of the coupled atmosphere–ocean climate system. However, the relatively small scale of these features presents some concern as to their representation in the meteorological reanalysis datasets that are commonly used to drive ocean models. Here polar mesocyclones are detected in the 40-Year European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis dataset (ERA-40) in mean sea level pressure and 500-hPa geopotential height, using an automated cyclone detection algorithm. The results are compared to polar mesocyclones detected in satellite imagery over the northeast Atlantic, for the period October 1993–September 1995. Similar trends in monthly cyclone numbers and a similar spatial distribution are found. However, there is a bias in the size of cyclones detected in the reanalysis. Up to 80% of cyclones larger than 500 km are detected in MSL pressure, but this hit rate decreases, approximately linearly, to ∼40% for 250-km-scale cyclones and to ∼20% for 100-km-scale cyclones. Consequently a substantial component of the associated air–sea fluxes may be missing from the reanalysis, presenting a serious shortcoming when using such reanalysis data for ocean modeling simulations. Eight maxima in cyclone density are apparent in the mean sea level pressure, clustered around synoptic observing stations in the northeast Atlantic. They are likely spurious, and a result of unidentified shortcomings in the ERA-40 data assimilation procedure.

scholarly journals CycloneDetector (v1.0) – Algorithm for detecting cyclone and anticyclone centers from mean sea level pressure layer

2021 ◽  
Author(s):  
Martin Prantl ◽  
Michal Žák ◽  
David Prantl
Keyword(s):  
Sea Level ◽  
Pressure Field ◽  
Detection Algorithm ◽  
Probability Of Detection ◽  
Image Processing Algorithm ◽  
Sea Level Pressure ◽  
Mean Sea Level ◽  
Level Pressure ◽  
Automatic Methods

Abstract. Automatic methods for identifying and tracking cyclones were firstly constructed in 1990's and since then there was a big increase in a precision and probability of detection. These methods have been traditionally focused on cyclones (and particularly on tropical cyclones), but the question of anticyclone centers detection remained unsolved since they are usually not a source of turbulent weather, precipitation etc. However, this issue can be important in the era of the climate change. In this paper, an algorithm for an automatic detection of both, cyclones and anticyclones based on mean sea level pressure field, is presented. The algorithm uses two-dimensional raster data as an input and returns a list of detected pressure systems. The main advantages of our solution are easy implementation since it is based on the standard image processing algorithm, sufficient performance of the algorithm, and especially the possibility of high-pressure systems detection. Moreover, the presented solution does not need a direct terrain filtering needed for some algorithms to be done. To validate the quality of detection algorithm results, a comparison against manually prepared data by Met Office was used. It follows from the comparison that the presented algorithm produces results similar to those by Met Office. The most significant differences can be found in the detection of cyclones at the beginning or the end of the lifespan stage. Met Office detects more cyclones in these stages than the presented solution.

scholarly journals Tropical cyclone simulations over Bangladesh at convection permitting 4.4 km & 1.5 km resolution

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Hamish Steptoe ◽  
Nicholas Henry Savage ◽  
Saeed Sadri ◽  
Kate Salmon ◽  
Zubair Maalick ◽  
...  
Keyword(s):  
Wind Speed ◽  
Tropical Cyclone ◽  
Sea Level ◽  
Tropical Cyclones ◽  
Weather Forecasting ◽  
Sea Level Pressure ◽  
Mean Sea Level ◽  
Level Pressure ◽  
Medium Range ◽  
Gust Speed

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.

A reconstruction of Madras (Chennai) mean sea-level pressure using instrumental records from the late 18th and early 19th centuries

2002 ◽  
Vol 22 (9) ◽  
pp. 1119-1142 ◽  
Author(s):  
Rob J. Allan ◽  
Chris J. C. Reason ◽  
Penny Carroll ◽  
Phil D. Jones
Keyword(s):  
Sea Level ◽  
Sea Level Pressure ◽  
Mean Sea Level ◽  
Level Pressure ◽  
Instrumental Records

Solar influence on North Atlantic mean sea level pressure

Nature ◽  
1977 ◽  
Vol 269 (5626) ◽  
pp. 320-322 ◽  
Author(s):  
P. M. KELLY
Keyword(s):  
Sea Level ◽  
North Atlantic ◽  
Sea Level Pressure ◽  
Mean Sea Level ◽  
Level Pressure ◽  
Solar Influence

Glacial-interglacial mean sea level pressure change due to sea level, ice sheet and atmospheric mass changes

1991 ◽  
Vol 3 (4) ◽  
pp. 333-340 ◽  
Author(s):  
Marie-Antoinette Mélières ◽  
Patricia Martinerie ◽  
Dominique Raynaud ◽  
Louis Lliboutry
Keyword(s):  
Sea Level ◽  
Ice Sheet ◽  
Pressure Change ◽  
Sea Level Pressure ◽  
Mean Sea Level ◽  
Level Pressure ◽  
Level Ice ◽  
Atmospheric Mass

scholarly journals The Application of Barnes Filter to Positioning the Center of Landed Tropical Cyclone in Numerical Models

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Haibo Zou ◽  
Shanshan Wu ◽  
Xueting Yi ◽  
Nan Wu
Keyword(s):  
Numerical Model ◽  
Tropical Cyclone ◽  
Sea Level ◽  
Numerical Models ◽  
Small Scale ◽  
Model Output ◽  
Sea Level Pressure ◽  
Level Pressure ◽  
Distance Difference

After a tropical cyclone (TC) making landfall, the numerical model output sea level pressure (SLP) presents many small-scale perturbations which significantly influence the positioning of the TC center. To fix the problem, Barnes filter with weighting parameters C=2500 and G=0.35 is used to remove these perturbations. A case study of TC Fung-Wong which landed China in 2008 shows that Barnes filter not only cleanly removes these perturbations, but also well preserves the TC signals. Meanwhile, the centers (track) obtained from SLP processed with Barnes filter are much closer to the observations than that from SLP without Barnes filter. Based on the distance difference (DD) between the TC center determined by SLP with/without Barnes filter and observation, statistics analysis of 12 TCs which landed China during 2005–2015 shows that in most cases (about 85%) the DDs are small (between −30 km and 30 km), while in a few cases (about 15%) the DDs are large (greater than 30 km even 70 km). This further verifies that the TC centers identified from SLP with Barnes filter are more accurate compared to that directly obtained from model output SLP. Moreover, the TC track identified with Barnes filter is much smoother than that without Barnes filter.

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