Hurricane Intensity Estimate - Total Operational Weather Readiness - Satellites (TOWR-S)
GOES-R Hurricane Intensity Estimate
Frequently Asked Questions about the GOES-R Hurricane Intensity Estimate Product
1) What is this product?
The GOES-R Hurricane Intensity Estimate product produces an estimate of many hurricane parameters for current storms (as dictated by WMO Tropical Cyclone Forecast Center (TCFC) bulletins). The product can be produced for any storm with at least 25 kts or higher maximum wind speed. The primary products generated by the ADT include the current intensity estimate analysis , in terms of wind speed, mean sea level pressure, and Current Intensity number (CI#), as dictated by the Dvorak Technique. Additional time-averaged and other time-dependent estimate values utilizing specific rules to limit the growth/decay of the intensity estimates over time.
The resolution for this product is 2km horizontal resolution, and it is created both day and night. It's wind-speed estimate is accurate to within 5 m/s (~11 mph). The Dvorak intensity estimate ranges are from Dvorak hurricane intensity scale values of 1.5 – 8, or leading to wind speeds of 12.8 m/s (25 knots) to 87.5 m/s (170 knots) . It is considered quantitative out to 65 degrees local zenith angle and qualitative beyond that.
This product is based on the same algorithm as the Advanced Dvorak Technique (ADT) currently used by the NHC and CPHC (http://www.nhc.noaa.gov/pdf/06velden.pdf)
This product is useful when trying to assess the strength of a tropical cyclone that is too far out in the ocean for direct measurements from a hurricane reconnaissance aircraft. Knowledge of the strength of a tropical cyclone's strength can inform the issuance of hurricane watches and warnings depending on which computer model is best capturing the storm's strength.
2) How often do I receive this data?
The cadence of the Hurricane Intensity Estimate product is 30 minutes, assuming a bulletin is being issued by the National Hurricane Center or the Central Pacific Hurricane Cente (or other international TCFCs) for a storm at the time. If no bulletins are currently active, this product is not produced.
3) How do I display this product in AWIPS-II?
As of this time, due to the unique nature of this product (primarily text information), the Hurricane Intensity Estimate is not viewable in AWIPS. Guidance from the National Hurricane Center is necessary to dictate how the information contained in this product will be extracted and viewed.
4) How do I interpret the color maps associated with this product?
There are no color maps associated with this product due to its text-based nature.
5) What other imagery/products might I use in conjunction with this product?
This product might be used to assess the intensity of a tropical cyclone that is too far out to sea to be directly measured with reconnaissance aircraft. Analyzing the trends in the Dvorak intensity number can provide clues if the overall cyclone is strengthening or weakening. Furthermore, comparing this intensity estimate to various cyclone models can better inform which model is best capturing the current state of the cyclone and the atmosphere.
6) How is this product created?
The GOES-R Hurricane Intensity Estimate product is created only for tropical cyclones or areas of interest for which a WMO Tropical Cyclone Forecast Center (TCFC) bulletin is written by either NHC or CPHC. The product only utilizes the 10.33 um GOES-R IR channel to calculate it's intensity estimate.
It is assumed that the entire storm and TC analysis region (TCFOV) will be within the field of view of the satellite image being examined, centered at the automated storm center position, and will also contain a limited number of bad scan lines and pixels per line. Typical temperature ranges for the IR pixels should be between approximately -100°C and +30°C. The HIE will check for bad pixels and lines in the TCFOV and replace them as necessary with either the adjoining (western) pixel values or adjoining (previous) line values. A bad pixel is defined as a temperature value greater than or equal to 320°K or less than 150°K. If 10 or more pixels on each line within the TCFOV are replaced the line is flagged as bad. If 10 or more bad lines are found in the TCFOV, the TCFOV is deemed to be too corrupted for use and the HIE algorithm will end.1
The imagery can contain data over ocean and land surfaces. The TCFOV in the HIE algorithm for the scene type and intensity derivation routines is a circle of radius 136km, so a box of size 136 X 136 contains the TCFOV (assuming a spatial resolution of 2km in the infrared window channel with GOES-R ABI data). The analysis region for the automated storm centering routines within the HIE algorithm package a bit larger than the scene type/intensity analysis domain at 190km plus 5 extra pixels on each side of the IR data array box. The size of the TCFOV array box containing the IR data for the automated storm center determination processing and scene type/intensity determination schemes are dependent upon the line and element resolution of the data in the IR image.
The ADT automated storm center location algorithm approximates TC position using three methods: polynomial interpolation of TCFC forecasts, linear extrapolation of the history file positions or with an advanced dual methodology examining the TC cloud top spiral features and circular feature (ring) search. As a first guess, the forecast positions from TCFC forecast, with its file name and directory location passed to the HIE algorithm via algorithm inputs (command line, GUI values, or environmental variables, depending on the implantation of the algorithm), are read within the HIE algorithm to obtain the current position using a polynomial interpolation routine. This interpolation routine uses three forecast positions (current, 12 hour, and 24 hour positions) defined in the forecast file to interpolate the current position defined by the time of the image being examined. If the image time falls within the time period defined within the forecast file positions/times, a valid interpolated forecast position will be used within the algorithm, otherwise an extrapolation of the previous history file positions will be used.
The HIE algorithm is based upon the simple premise that various cloud patterns and cloud top temperatures values/fields are related to the current tropical cyclone intensity. Tropical cloud regions that are less organized possess isolated and short-lived convective regions. As these regions become more organized, the convection becomes stronger and more widespread. The released latent heat from the convection supplies further energy to the convective region to increase the duration and vigor of the convection, leading to stronger wind speeds as the pressure within the organizing tropical system decreases related to the surrounding environmental pressure. The process continues to feed upon itself, with stronger convection releasing greater amounts of latent heat and a further reduction in the storm central pressure, which increased the geostrophic and ageostrophic wind speed wind around the storm.
As the convection continues to organize and intensify the satellite-observed cloud top temperature values decrease as the tops of the storms reach higher into the troposphere. Further increase in storm organization enhances the TC secondary circulation, leading to a more symmetrical convective appearance and the development of an eye feature at the center of the storm system. Various environmental factors, such as wind shear, sea surface temperature changes, and land/weather system interactions will limit the maximum intensity of the storm and/or lead to a decrease in the storm intensity through a disruption the convective processes driving the storm development.
Monitoring the changes in convective cloud patterns and cloud-top temperature values from satellite platforms over time can provide vital information about the organization and strength of the storm being observed. Relating these patterns and values to storm intensity is the objective of the HIE. Utilization of infrared imagery from geostationary satellites provides the ideal data to perform this function.
1Olander, Timothy, Chris Veldin, and Jamie Daniels. NOAA NESDIS Center for Satellite Applications and Research GOES-R Advanced Baseline Imager (ABI) Algorithm Theoretical Basis Document for Hurricane Intensity Estimate v.2.0. 15 September 2010. http://www.goes-r.gov/products/ATBDs/baseline/Winds_HIE_v2.0_no_color.pdf