scholarly journals Revisions to the Australian tropical cyclone best track database

2021 ◽  
Vol 71 (2) ◽  
pp. 203
Author(s):  
Joseph B. Courtney ◽  
Gary R. Foley ◽  
Johannes L. van Burgel ◽  
Blair Trewin ◽  
Andrew D. Burton ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
Satellite Imagery ◽  
Geostationary Satellite ◽  
Wind Pressure ◽  
Current Intensity ◽  
Climate Research ◽  
Satellite Technology ◽  
Intensity Information ◽  
Weather And Climate ◽  
Infra Red

The Australian tropical cyclone (TC) best track database (BT) maintained by the Bureau of Meteorology has records since 1909 of varying quality and completeness. Since 2005 a series of efforts to improve the database have included: removing internal inconsistencies, adding fixes, and identifying errors using comparisons with other datasets; upgrading intensity information since 1973 including adding maximum winds (Vm) prior to 1984–85, rederiving Dvorak Current Intensity numbers from archived material and accounting for different wind–pressure relationships used; a partial reanalysis of satellite imagery including microwave imagery using the HURSAT dataset since 1987; and considering an objective intensity dataset. The BT homogeneity is reviewed in the context of improvements in satellite technology, observational coverage, scientific developments, BT procedures and the subjective variation between analysts across time and offices. The scale of these variances is greatest in the early stages prior to 1981 in the absence of geostationary satellite imagery until 1978, satellite calibration issues from 1978–80 and prior to the introduction of the enhanced infra-red Dvorak technique in 1981. The current era since 2003 is considered to be the most accurate, comprehensive and homogeneous corresponding to the expansion of the TC database to include the current suite of fields; the application of microwave and scatterometry imagery; greater standardisation of BT practices and slight changes in the application of the Dvorak technique. These improvements have generated a more consistent dataset that could be used for weather and climate research and other TC-related work.

Uncertainty of Tropical Cyclone Best-Track Information

2012 ◽  
Vol 27 (3) ◽  
pp. 715-729 ◽  
Author(s):  
Ryan D. Torn ◽  
Chris Snyder
Keyword(s):  
Wind Speed ◽  
Tropical Cyclone ◽  
Maximum Wind Speed ◽  
Sea Level Pressure ◽  
Position Uncertainty ◽  
Maximum Wind ◽  
Intensity Information ◽  
Weather And Climate ◽  
The Mean ◽  
National Hurricane Center

Abstract With the growing use of tropical cyclone (TC) best-track information for weather and climate applications, it is important to understand the uncertainties that are contained in the TC position and intensity information. Here, an attempt is made to quantify the position uncertainty using National Hurricane Center (NHC) advisory information, as well as intensity uncertainty during times without aircraft data, by verifying Dvorak minimum sea level pressure (SLP) and maximum wind speed estimates during times with aircraft reconnaissance information during 2000–09. In a climatological sense, TC position uncertainty decreases for more intense TCs, while the uncertainty of intensity, measured by minimum SLP or maximum wind speed, increases with intensity. The standard deviation of satellite-based TC intensity estimates can be used as a predictor of the consensus intensity error when that consensus includes both Dvorak and microwave-based estimates, but not when it contains only Dvorak-based values. Whereas there has been a steady decrease in seasonal TC position uncertainty over the past 10 yr, which is likely due to additional data available to NHC forecasters, the seasonal TC minimum SLP and maximum wind speed values are fairly constant, with year-to-year variability due to the mean intensity of all TCs during that season and the frequency of aircraft reconnaissance.

scholarly journals An intercomparison of tropical cyclone best-track products for the southwest Pacific

2016 ◽  
Vol 16 (6) ◽  
pp. 1431-1447 ◽  
Author(s):  
Andrew D. Magee ◽  
Danielle C. Verdon-Kidd ◽  
Anthony S. Kiem
Keyword(s):  
Tropical Cyclone ◽  
Vertical Wind Shear ◽  
Geostationary Satellite ◽  
Vertical Shear ◽  
Tropical Cyclogenesis ◽  
Southwest Pacific ◽  
Zonal Winds ◽  
Spatio Temporal ◽  
Joint Typhoon Warning Center

Abstract. Recent efforts to understand tropical cyclone (TC) activity in the southwest Pacific (SWP) have led to the development of numerous TC databases. The methods used to compile each database vary and are based on data from different meteorological centres, standalone TC databases and archived synoptic charts. Therefore the aims of this study are to (i) provide a spatio-temporal comparison of three TC best-track (BT) databases and explore any differences between them (and any associated implications) and (ii) investigate whether there are any spatial, temporal or statistical differences between pre-satellite (1945–1969), post-satellite (1970–2011) and post-geostationary satellite (1982–2011) era TC data given the changing observational technologies with time. To achieve this, we compare three best-track TC databases for the SWP region (0–35° S, 135° E–120° W) from 1945 to 2011: the Joint Typhoon Warning Center (JTWC), the International Best Track Archive for Climate Stewardship (IBTrACS) and the Southwest Pacific Enhanced Archive of Tropical Cyclones (SPEArTC). The results of this study suggest that SPEArTC is the most complete repository of TCs for the SWP region. In particular, we show that the SPEArTC database includes a number of additional TCs, not included in either the JTWC or IBTrACS database. These SPEArTC events do occur under environmental conditions conducive to tropical cyclogenesis (TC genesis), including anomalously negative 700 hPa vorticity (VORT), anomalously negative vertical shear of zonal winds (VSZW), anomalously negative 700 hPa geopotential height (GPH), cyclonic (absolute) 700 hPa winds and low values of absolute vertical wind shear (EVWS). Further, while changes in observational technologies from 1945 have undoubtedly improved our ability to detect and monitor TCs, we show that the number of TCs detected prior to the satellite era (1945–1969) are not statistically different to those in the post-satellite era (post-1970). Although data from pre-satellite and pre-geostationary satellite periods are currently inadequate for investigating TC intensity, this study suggests that SPEArTC data (from 1945) may be used to investigate long-term variability of TC counts and TC genesis locations.

scholarly journals Five priorities for weather and climate research

Nature ◽  
2017 ◽  
Vol 552 (7684) ◽  
pp. 168-170 ◽  
Author(s):  
Øystein Hov ◽  
Deon Terblanche ◽  
Gregory Carmichael ◽  
Sarah Jones ◽  
Paolo M. Ruti ◽  
...  
Keyword(s):  
Climate Research ◽  
Weather And Climate

scholarly journals Short-Term Tropical Cyclone Intensity Forecasting from Satellite Imagery Based on the Deviation Angle Variance Technique

2020 ◽  
Vol 35 (1) ◽  
pp. 285-298 ◽  
Author(s):  
Liang Hu ◽  
Elizabeth A. Ritchie ◽  
J. Scott Tyo
Keyword(s):  
Tropical Cyclone ◽  
Satellite Imagery ◽  
Forecast Model ◽  
Intensity Change ◽  
Deviation Angle ◽  
Short Term ◽  
Cyclone Intensity ◽  
The North ◽  
Intensity Forecast ◽  
The Relationship

Abstract The deviation angle variance (DAV) is a parameter that characterizes the level of organization of a cloud cluster compared with a perfectly axisymmetric tropical cyclone (TC) using satellite infrared (IR) imagery, and can be used to estimate the intensity of the TC. In this study, the DAV technique is further used to analyze the relationship between satellite imagery and TC future intensity over the North Atlantic basin. The results show that the DAV of the TC changes ahead of the TC intensity change, and this can be used to predict short-term TC intensity. The DAV-IR 24-h forecast is close to the National Hurricane Center (NHC) 24-h forecast, and the bias is lower than NHC and other methods during weakening periods. Furthermore, an improved TC intensity forecast is obtained by incorporating all four satellite bands. Using SST and TC latitude as the other two predictors in a linear regression model, the RMSE and MAE of the DAV 24-h forecast are 13.7 and 10.9 kt (1 kt ≈ 0.51 m s−1), respectively, and the skill space of the DAV is about 5.5% relative to the Statistical Hurricane Intensity Forecast model with inland decay (Decay-SHIFOR) during TC weakening periods. Considering the DAV is an independent intensity technique, it could potentially add value as a member of the suite of operational intensity forecast techniques, especially during TC weakening periods.

scholarly journals Tropical Cyclone Intensity Estimation Using Multi-Dimensional Convolutional Neural Networks from Geostationary Satellite Data

2019 ◽  
Vol 12 (1) ◽  
pp. 108 ◽  
Author(s):  
Juhyun Lee ◽  
Jungho Im ◽  
Dong-Hyun Cha ◽  
Haemi Park ◽  
Seongmun Sim
Keyword(s):  
Neural Networks ◽  
Tropical Cyclone ◽  
Convolutional Neural Networks ◽  
Mean Squared Error ◽  
Single Channel ◽  
Three Dimensional ◽  
Geostationary Satellite ◽  
Lower Atmosphere ◽  
Long Time ◽  
3D Cnn

For a long time, researchers have tried to find a way to analyze tropical cyclone (TC) intensity in real-time. Since there is no standardized method for estimating TC intensity and the most widely used method is a manual algorithm using satellite-based cloud images, there is a bias that varies depending on the TC center and shape. In this study, we adopted convolutional neural networks (CNNs) which are part of a state-of-art approach that analyzes image patterns to estimate TC intensity by mimicking human cloud pattern recognition. Both two dimensional-CNN (2D-CNN) and three-dimensional-CNN (3D-CNN) were used to analyze the relationship between multi-spectral geostationary satellite images and TC intensity. Our best-optimized model produced a root mean squared error (RMSE) of 8.32 kts, resulting in better performance (~35%) than the existing model using the CNN-based approach with a single channel image. Moreover, we analyzed the characteristics of multi-spectral satellite-based TC images according to intensity using a heat map, which is one of the visualization means of CNNs. It shows that the stronger the intensity of the TC, the greater the influence of the TC center in the lower atmosphere. This is consistent with the results from the existing TC initialization method with numerical simulations based on dynamical TC models. Our study suggests the possibility that a deep learning approach can be used to interpret the behavior characteristics of TCs.

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