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.

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.

scholarly journals Analysis of Atmospheric and Ionospheric Variations Due to Impacts of Super Typhoon Mangkhut (1822) in the Northwest Pacific Ocean

2021 ◽  
Vol 13 (4) ◽  
pp. 661
Author(s):  
Mohamed Freeshah ◽  
Xiaohong Zhang ◽  
Erman Şentürk ◽  
Muhammad Arqim Adil ◽  
B. G. Mousa ◽  
...  
Keyword(s):  
Wind Speed ◽  
Pacific Ocean ◽  
Tropical Cyclone ◽  
Sea Level ◽  
Northwest Pacific ◽  
Northwest Pacific Ocean ◽  
Sea Level Pressure ◽  
Level Pressure ◽  
Super Typhoon ◽  
The Northwest Pacific Ocean

The Northwest Pacific Ocean (NWP) is one of the most vulnerable regions that has been hit by typhoons. In September 2018, Mangkhut was the 22nd Tropical Cyclone (TC) over the NWP regions (so, the event was numbered as 1822). In this paper, we investigated the highest amplitude ionospheric variations, along with the atmospheric anomalies, such as the sea-level pressure, Mangkhut’s cloud system, and the meridional and zonal wind during the typhoon. Regional Ionosphere Maps (RIMs) were created through the Hong Kong Continuously Operating Reference Stations (HKCORS) and International GNSS Service (IGS) data around the area of Mangkhut typhoon. RIMs were utilized to analyze the ionospheric Total Electron Content (TEC) response over the maximum wind speed points (maximum spots) under the meticulous observations of the solar-terrestrial environment and geomagnetic storm indices. Ionospheric vertical TEC (VTEC) time sequences over the maximum spots are detected by three methods: interquartile range method (IQR), enhanced average difference (EAD), and range of ten days (RTD) during the super typhoon Mangkhut. The research findings indicated significant ionospheric variations over the maximum spots during this powerful tropical cyclone within a few hours before the extreme wind speed. Moreover, the ionosphere showed a positive response where the maximum VTEC amplitude variations coincided with the cyclone rainbands or typhoon edges rather than the center of the storm. The sea-level pressure tends to decrease around the typhoon periphery, and the highest ionospheric VTEC amplitude was observed when the low-pressure cell covers the largest area. The possible mechanism of the ionospheric response is based on strong convective cells that create the gravity waves over tropical cyclones. Moreover, the critical change state in the meridional wind happened on the same day of maximum ionospheric variations on the 256th day of the year (DOY 256). This comprehensive analysis suggests that the meridional winds and their resulting waves may contribute in one way or another to upper atmosphere-ionosphere coupling.

scholarly journals Improvements to a Tropical Cyclone Initialization Scheme and Impacts on Forecasts

2014 ◽  
Vol 142 (11) ◽  
pp. 4340-4356 ◽  
Author(s):  
Hiep Van Nguyen ◽  
Yi-Leng Chen
Keyword(s):  
Tropical Cyclone ◽  
Sea Level ◽  
Southern China ◽  
Computational Time ◽  
Sea Level Pressure ◽  
Level Pressure ◽  
Warm Core ◽  
Vortex Size ◽  
Time Required ◽  
Vortex Initialization

Abstract This study makes improvements to the tropical cyclone (TC) initialization method introduced by Nguyen and Chen (i.e., the NC2011 scheme). The authors found that prescribing sea level pressure associated with the initial vortex using a modified Fujita formula has very little impact on the vortex structure and intensity during a series of 1-h model integration and relocation. On the other hand, inserting an artificial warm core makes the vortex spin up much faster. When a warm core is inserted during the initial spinup process, the computational time required for model initialization is reduced by ½–⅓. Because prescribed sea level pressure is not required to spin up the vortex, information on vortex size, such as radius of maximum wind, is no longer needed. The performance of the improved NC2011 scheme with an initial prescribed warm core during the initial spinup process is tested for typhoons that made landfall over southern China or Vietnam in 2006. Before landfall, these storms were over the open ocean where conventional data were sparse, without special observations. Two sets of model runs, with (NC2011-CTRL) and without (CTRL) vortex initialization, are performed for comparison. The initial and time-dependent boundary conditions are from the NCEP Final Analyses (FNL). There are twelve 48-h simulations in each run set. Results show that the vortex initialization improves TC track and intensity simulations.

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 Calibrated Probabilistic Forecasting Using Ensemble Model Output Statistics and Minimum CRPS Estimation

2005 ◽  
Vol 133 (5) ◽  
pp. 1098-1118 ◽  
Author(s):  
Tilmann Gneiting ◽  
Adrian E. Raftery ◽  
Anton H. Westveld ◽  
Tom Goldman
Keyword(s):  
Sea Level ◽  
Ensemble Prediction ◽  
Ensemble Model ◽  
Model Output ◽  
Sea Level Pressure ◽  
Forecast Bias ◽  
Level Pressure ◽  
Probabilistic Forecasts ◽  
Ensemble Model Output Statistics ◽  
Model Output Statistics

Abstract Ensemble prediction systems typically show positive spread-error correlation, but they are subject to forecast bias and dispersion errors, and are therefore uncalibrated. This work proposes the use of ensemble model output statistics (EMOS), an easy-to-implement postprocessing technique that addresses both forecast bias and underdispersion and takes into account the spread-skill relationship. The technique is based on multiple linear regression and is akin to the superensemble approach that has traditionally been used for deterministic-style forecasts. The EMOS technique yields probabilistic forecasts that take the form of Gaussian predictive probability density functions (PDFs) for continuous weather variables and can be applied to gridded model output. The EMOS predictive mean is a bias-corrected weighted average of the ensemble member forecasts, with coefficients that can be interpreted in terms of the relative contributions of the member models to the ensemble, and provides a highly competitive deterministic-style forecast. The EMOS predictive variance is a linear function of the ensemble variance. For fitting the EMOS coefficients, the method of minimum continuous ranked probability score (CRPS) estimation is introduced. This technique finds the coefficient values that optimize the CRPS for the training data. The EMOS technique was applied to 48-h forecasts of sea level pressure and surface temperature over the North American Pacific Northwest in spring 2000, using the University of Washington mesoscale ensemble. When compared to the bias-corrected ensemble, deterministic-style EMOS forecasts of sea level pressure had root-mean-square error 9% less and mean absolute error 7% less. The EMOS predictive PDFs were sharp, and much better calibrated than the raw ensemble or the bias-corrected ensemble.

scholarly journals Boosting performance in machine learning of geophysical flows via scale separation

2020 ◽  
Author(s):  
Davide Faranda ◽  
Mathieu Vrac ◽  
Pascal Yiou ◽  
Flavio Maria Emanuele Pons ◽  
Adnane Hamid ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Sea Level ◽  
Recurrent Neural Networks ◽  
Multiple Scales ◽  
Geophysical Flows ◽  
Small Scale ◽  
Pressure Data ◽  
Sea Level Pressure ◽  
Level Pressure

Abstract. Recent advances in statistical and machine learning have opened the possibility to forecast the behavior of chaotic systems using recurrent neural networks. In this article we investigate the applicability of such a framework to geophysical flows, known to involve multiple scales in length, time and energy and to feature intermittency. We show that both multiscale dynamics and intermittency introduce severe limitations on the applicability of recurrent neural networks, both for short-term forecasts, as well as for the reconstruction of the underlying attractor. We suggest that possible strategies to overcome such limitations should be based on separating the smooth large-scale dynamics from the intermittent/small-scale features. We test these ideas on global sea-level pressure data for the past 40 years, a proxy of the atmospheric circulation dynamics. Better short and long term forecasts of sea-level pressure data can be obtained with an optimal choice of spatial coarse grain and time filtering.

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