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Overview

The purpose of this web site is to display in a real-time manner tropical cyclone products created and/or developed by NOAA/NESDIS/STAR/RAMMB and associated CIRA scientists over the last 20 years. While there is some overlap with other tropical cyclone web pages an effort has been made to show unique products not displayed elsewhere. To serve these data to the public the web page is also integrated to a database that can accommodate future product development. Because the site is a developing project, current products may be unavailable at earlier times.

Track Forecasts

Track forecasts are provided by three forecast centers via databases contained in the Automated Tropical cyclone Forecast (ATCF) system (Sampson and Schrader 2000). Forecasts for the North Atlantic and eastern North Pacific are provided by the National Hurricane Center (NHC), which is located in Miami, FL. Forecasts for the Central North Pacific (140W to the Dateline) are provided by the Central Pacific Hurricane Center (CPHC ) located in Honolulu, HI. Both NHC and CPHC are part of the NOAA National Weather Service. Forecast information for the western North Pacific, North Indian Ocean, and the Southern Hemisphere are provided by the Joint Typhoon Warning Center (JTWC ) located at Pearl Harbor, HI. The JTWC is part a US Department of Defense and provides tactical tropical cyclone forecasts for the US armed forces. Note that forecasts are 6-hourly in all basins except the Southern Hemisphere (12-hourly) and that forecasts are made through 5 days (120h) except in the North Indian Ocean (through 72h) and the Southern Hemisphere (48h).

Sampson, C. R., and A. J. Schrader, 2000: The Automated Tropical Cyclone Forecasting System (Version 3.2). Bull. Amer. Meteor. Soc., 81, 1131-1240.

Track History

Track history for each storm is created from the operational warnings that are issued every six hours by NHC, CPHC , and JTWC . The positions and intensities are best estimates of those quantities when the warning is issued. THESE ARE NOT BEST TRACKS - having not been reanalyzed in any systematic manner.

Ocean Heat Content & Forecast Track

Daily Oceanic Heat Content or Tropical Cyclone Heat Potential (TCHP) estimates were being provided by Gustavo Goni at the Physical Oceanography Division of the NOAA Atlantic Oceanographic and Meteorological Laboratory located in Miami, FL until July of 2008. Since that time the OHC has been provided by J. Cummings of the Naval Research Lab and is calculated from fields generated by the Naval Coupled Ocean Data Assimilation system (NCODA; Cummings 2005). The spatial grid spacing is 0.2 Latitude x 0.2 Longitude and the units of the estimates are given as kJ/cm^2. A detailed description of how the product is created, product archives and TCHP in other regions can be found at Gustavo's web discussing TCHP . A similar method is employed using the NCODA fields. Tropical cyclone forecasts, as described above, are plotted on values of ocean heat content for reference.

For tropical cyclones in favorable environmental conditions for intensification (i.e., vertical wind shear less than 15 kt, mid-level relative humidity >50 %, and warm SSTs [i.e., >28.5C])and with intensities less than 80kt, values of ocean heat content greater than 50 kJ/cm^2 have been shown to promote greater rates of intensity change.

4km Remapped Color Enhanced Infrared Imagery

Current imagery and loops of 4km remapped and color enhanced infrared (IR) imagery is displayed in an earth fixed coordinate system. IR imagery (~11 um) from five geostationary satellites are remapped to a common 4km resolution Mercator projection in an identical manner as the CIRA Tropical Cyclone Image Archive described in (Mueller et al. (2006) . These images are then centered and displayed using the nearest 5 degree latitude/longitude earth coordinate based on the most recent location and past 12-h movement. The images are also color enhanced with the coldest temperatures/highest clouds displayed as colored shades as shown in this color bar.

Geostationary imagery is available from GOES-East and Meteosat Second Generation (MSG; European Space Agency) in the North Atlantic, GOES-West and MTSAT (Japan) in the East Pacific, MTSAT in the West Pacific, and Meteosat-5 (European Space Agency) in the North Indian Ocean, and MSG, Meteosat-5, MTSAT, GOES-W in the Southern Hemisphere tropical cyclone basins.

AMSU Microwave 89GHz Imagery (4 km Mercator)

IR/WV/Microwave RGB (IR [R], WV [G], MI89 [B])

Passive Microwave Imagery (PMI) from low earth orbiting (LEO) satellites is routinely used in tropical cyclone analyses and forecast because several PMI channels can provide unique information about the location and organization of deep convection, liquid water, rainfall etc. that is often obscured by high clouds and cirrus in conventional Infrared (IR) and water vapor (WV) imagery. Since the late 1980’s PMI in the 85-91 GHz range has been used to determine the location and organization of deep convective elements, even through thick the thick cirrus often associated with developing TCs. IR and WV imagery, which are available from geostationary satellites, have also been routinely used to monitor the organization, location, and intensity of TCs and infer changes in the TCs near environment. In the operational setting these three types of satellite imagery are typically viewed and analyzed separately (i.e., individual PMI images, and loops of WV and IR imagery). To examine the utility of combining the information from these separate imagery products, we have developed a Red Green Blue (RGB) image product that combines IR and WV information from the global fleet of geostationary satellites with the 89-91 GHz channels from several LEO satellites that are available on the NESDIS operational servers. We have plans to display these RGB images on our local RAMMB TC-realtime web page and to potentially make these available to the NHC and Pacific proving ground activities.

1km Remapped Color Enhanced Infrared Imagery

Polar-orbiting satellites, because of their lower altitude orbits, can provide higher resolution imagery, but with limited temporal resolution. Infrared imagery from the NOAA operational satellites and from the NASA Terra and Aqua satellites are utilized in this product. To view imagery from several data sources imagery are remapped to a common Mercator projection with a 1km resolution. The enhancement is identical to that used for the geostationary IR imagery as shown above. Times and satellite are shown in the footer of each image.

1km Remapped Visible Imagery

Using the polar orbiting satellites described above, 1km Mercator remaps of visible imagery are created.

2km Natural Color Imagery

Natural Color imagery approximates the response of normal human vision, providing a depiction of the satellite-observed scene. When satellites have separate channels for red, green and blue portions of the spectrum natural color can be approximated using those channels as input. However, the green channel has few other practical uses other than for providing natural color capabilities and because of this reason is not one of the channels on the future GOES Advanced Baseline Imager. Fortunately, the green component of light can be approximated using near infrared channels and a training set of natural color scenes.

Here we show a comparison between natural color imagery made using an approximation to the green component and natural color created using the observed green component. NASA’s MODIS imagery is used to create these comparisons and the storm-relative tropical cyclone imagery shown here has been remapped to a Mercator projection with 2-km resolution. The purpose of doing so is to provide a visually intuitive depiction that is useful to experts and non-experts alike, improving the interpretation of various features such as vegetation, water bodies, clouds and snow, deserts, etc., based on usage of natural colors to highlight those features. This also shows the sort of natural color capabilities that will be available from the next generation GOES satellites.

Multi-Platform Tropical Cyclone Surface Wind Analysis

Currently, this product combines information from five data sources to create a mid-level (near 700 hPa) wind analysis using a variational approach described in Knaff and DeMaria (2006). The resulting mid-level winds are then adjusted to the surface applying a very simple single column approach. Over the ocean an adjustment factor is applied, which is a function of radius from the center ranging from 0.9 to 0.7, and the winds are turned 20 degrees toward low pressure. Over land, the oceanic winds are reduced by an additional 20% and turned an additional 20 degrees toward low pressure.

The five datasets currently used are the ASCAT scatterometer, which is adjusted upward to 700 hPa in the same manner as the surface winds are adjusted downward, feature track winds in the mid-levels from the operational satellite centers, 2-d flight-level winds estimated from infrared imagery (see Mueller et al 2006 ) and 2-d winds created from Advanced Microwave Sounding Unit (AMSU)- derived height fields and solving the non-linear balance equations as described in Bessho et al (2006). Past analyses also made use of the QuickSCAT scatterometer (i.e., prior to November 2009), but this satellite is no longer producing observations of surface vector winds.

Each of the input data are shown in subpanels following the analysis (i.e., storm-relative). Shown are AMSU winds, Cloud-drift/IR/WV winds, IR-proxy winds and Scatterometer winds; QuikSCAT, when available for past analyses (BLUE) and ASCAT (RED). All input data in these panels has been reduced to a 10-m land or oceanic exposure depending on the location (i.e., non-surface data has been reduced to a 10-m exposure).

How good are the wind estimates? Here is the verification based upon 2007 data . These statistics were based on 1) H*Wind data when available and 2) best track wind radii estimates from NHC. In interpreting the wind radii verification it is important to not that the zero wind radii are included in the verification, which both skews and inflates the MAE verification statistics. Note however detection is improved over climatology provided by Knaff et al. (2007).

Aircraft-based Tropical Cyclone Surface Wind Analysis

This product, high resolution (top) and lower-resolution (middle), seeks to create a real-time and fully automated surface wind analysis system by combining the existing satellite-based six-hourly multi-platform tropical cyclone surface wind analysis (MTCSWA operational version) and aircraft reconnaissance data. This product applies an automated quality control procedure and variational analysis techniques developed for use in the MTCSWA and previous studies that make use of flight-level wind observations to produce analyses. The operational version of MTCSWA is used as a first guess field for aircraft reconnaissance wind data (flight-level and SFMR) that are be composited over a maximum of a 9-hour period of time and analyzed using data weights and smoothness constraints that are found to create an optimal analysis using those inputs. The center location will be determined using a combination of operational best track and aircraft-based center positions and detailed positions are determined by a tensioned cubic spline. Flight-level to surface wind reductions follow the operational rules developed by NHC following Franklin et al (2003) and as interpreted in Table 1 of Knaff et al. (2015) and the inflow angles are calculated using the parameterization developed in (Zhang and Uhlhorn 2012). Also following the findings of Franklin et al. (2003), asymmetries to the reduction factors are applied. The asymmetries consist of a four percent variation of the eyewall region reduction factors and a 17 percent variation of the outer and far field reduction factors with the maximum being on the left of TC motion. The resulting two-dimensional wind analysis would produce 1-minute sustained winds valid for 10-meter marine exposure in the region where aircraft reconnaissance is typically available (0-200km) with sufficient detail to resolve the radii of maximum winds and the wind radii. The maximum winds are still difficult to estimate as operational 30Hz data is used and there is a fundamental mismatch between surface SFMR winds and flight-level winds reduced to the surface, and at this time no attempt is made to determine the slant angle between flight-level winds and surface observations. This inner core is almost completely determined by aircraft input (lower figure provides aircraft coverage information) and the outer regions of the storm are determined by blending aircraft observations with the multi-platform tropical cyclone surface wind analysis (MTCSWA).

References:

Franklin, J. L., M. L. Black, and K. Valde, 2003: GPS dropwindsonde wind profiles in hurricanes and their operational implications. Wea. Forecasting, 18, 3244.

Knaff, J.A., S.P. Longmore, R.T. DeMaria, and D.A. Molenar, 2015: Improved tropical-cyclone flight-level wind estimates using routine infrared satellite reconnaissance. J. Appl. Meteor. Climat., 54:2, 463-478. doi: http://dx.doi.org/10.1175/JAMC-D-14-0112.1

Zhang, J.A., and E.W. Uhlhorn, 2012: Hurricane sea surface inflow angle and an observation-based parametric model. Mon. Wea. Rev., 140, 3587-3605. doi:10.1175/MWR-D-11-00339.1

Digital Dvorak Intensity Estimates

Using the infrared (IR) images collected as part of the CIRA tropical cyclone IR image archive, which are displayed in an earth relative format as a product on this web page. Center positions are extrapolated using the current position and the past 12-h mean motion vector. Tropical cyclone intensity estimates can be made using two temperatures derived from the IR imagery. The first is the warmest pixel in the eye, and second is the warmest pixel on the coldest circle between 24 and 111 km from the cyclone center. Using these values a Raw T-number can be created by using the locally developed Table That expands upon the table published in Dvorak (1984). Each T-number has an intensity, in terms of maximum 1-minute sustained winds, associated with it and can be converted to an intensity.

While Raw T-numbers give an estimate of how strong a given storm is, the quantity is noisy, and because it is an instantaneous measure does not properly account for the relatively slow decay process of tropical cyclone winds. To remove the noisy nature of the Raw T-numbers time averaging is employed to produce a 6-h running mean of the raw T-numbers. This 6-h running mean is considered the T-number associated with the current intensity if the 6-h running mean is not decreasing at more than 1.5 T-numbers per day. If the 6-h running mean is decreasing very rapidly, a maximum of 1.5 T-number per day decay rate is prescribed. This final value of the 6-h running mean with a decay rule applied is considered the current intensity number or CI. The CI, as with any T-number estimate, can be converted into a intensity. However, it is important to note that THIS TECHNIQUE IS ONLY VALID FOR STORMS OF HURRICANE INTENSITY (65 kt) OR GREATER.

The image shown on this web page shows the time series of warning intensity and the Digital Dvorak estimate of intensity at the top and the time series of raw T-number estimates, the 6-h running mean, and the CI at the bottom.

References:

Dvorak, V., 1984: Tropical cyclone intensity analysis using satellite data. NOAA Technical Report NESDIS 11, 47 pp. [Available from NOAA/NESDIS, 5200 Auth Rd. Washington DC, 20233].

Advanced Microwave Sounding Unit (AMSU) - Based Intensity Estimates

The Advanced Microwave Sounding Unit or AMSU, which is an instrument on the NOAA operational polar-orbiting satellites, has the capability to make temperature soundings. Using a combination of AMSU-based soundings, the hydrostatic relationship, and statistics, a tropical cyclone intensity estimate can be made. The methodology used here is discussed in Demuth et al (2004) and updated in Demuth et al. (2006). Estimates are created by the National Centers for Environmental Prediction and the estimates as well as other products are available for the last two days at NCEP Central Operations. While overall statistics are comparable with the Dvorak technique, this method is most useful and accurate for tropical cyclones with intensities less than ~90 kt. Shown in this product are time series of the operational warning intensities versus the AMSU-based intensity estimates.

AMSU-Based Azimuthal Mean Radial/Height Cross Sections

Using the AMSU-derived azimuthally averaged temperature and height files radial/height cross sections of temperature and tangential wind are created (see Demuth et al (2004) ). The tangential wind field is derived using the 2-d gradient wind equations. Note that the resolution horizontal of the AMSU instrument results in a smooth temperature field and an unrealistically low tangential wind speeds. These images are useful in determining the thermal structure of the tropical cyclone.

Storm Relative 16km Microwave-Based Total Precipitable Water Imagery

The relative lack of environmental moisture around a tropical cyclone can adversely affect the deep convection and negatively impact the storm and result in weakening. Luckily there is several low earth orbiting satellites that provide estimates of the amount of water vapor in the atmospheric column, commonly referred to a total precipitable water (TPW). TPW estimates from a single satellite platform, however, often suffer from inadequate temporal coverage and poor refresh rates. To partially rectify this issue, the information from three Advanced Microwave Sounding Units (AMSU) on NOAA satellites and five Special Sensor Microwave Imagers (SSMI) on DOD satellites are combined via a blending algorithm described in Kidder and Jones (2007). Such a product has a refresh rate of approximately 6 hours and a spatial resolution of approximately 16km. This product shows the TPW around the tropical cyclone and to further enhance its utility the images are centered on the current storm location and when looped show TPW features moving to and from the storm center.

Total Precipitable Water

The relative lack of environmental moisture around a tropical cyclone can adversely affect the deep convection and negatively impact the storm and result in weakening. Luckily there is several low earth orbiting satellites that provide estimates of the amount of water vapor in the atmospheric column, commonly referred to a total precipitable water (TPW). TPW estimates from a single satellite platform, however, often suffer from inadequate temporal coverage and poor refresh rates. To partially rectify this issue, Kidder and Jones (2007) blend low-Earth microwave sensor observations. Such a product has a refresh rate of approximately 1 hours. This product shows the TPW around the tropical cyclone and to further enhance its utility the images are centered on the current storm location and when looped show TPW features moving to and from the storm center.

Advected Layer Precipitable Water

The CIRA Advected Layer Precipitable Water product is created from low-Earth orbiting satellites and offers a four-dimensional structure of water vapor in and around tropical cyclones. Retrieval is done in clear and cloudy (not precipitating) regions. Data swaths are advected up to 10 hours before a common time via GFS wind forecasts and averaged. The four layers are surface to 850 hPa, 850 to 700 hPa, 700 to 500 hPa, and 500 to 300 hPa. For the tropical cyclone centric plots, the 0 to 800 km radial average layer precipitable water is provided.

References:
Forsythe, J. M., S. Q. Kidder, K. K. Fuell, A. LeRoy, G. J. Jedlovec, and A. S. Jones, 2015: A multisensor, blended, layered water vapor product for weather analysis and forecasting. J. Operational Meteor., 3, 41–58. http://dx.doi.org/10.15191/nwajom.2015.0305
RAMMB, 2018: JPSS Advected Layer Precipitable Water Product Quick Guide. https://rammb.cira.colostate.edu/training/visit/training_sessions/advected_layer_precipitable_water_product/QuickGuide_LPW_Advected_20180216.pdf

Storm Relative 16km Geostationary Water Vapor Imagery

To compliment the 16km storm relative TPW product listed above, water vapor imagery, with a spectral weight near 6.7 um is displayed with the same resolution, projection, and storm relative geometry. Water Vapor imagery is helpful in determining the location of deep convection, indicated by the coldest pixels, relative upper-level moisture content in areas devoid of deep convection, and upper-level atmospheric motions via animation of these images. The imagery can be used to infer favorable and unfavorable regions of environmental forcing (e.g., areas of increased vertical wind shear or atmospheric subsidence).

Storm Relative 1km Geostationary Visible Imagery

The current suite of geostationary satellites provides visible imagery during daylight hours at higher resolution than many of the infrared channels. Such imagery is useful, especially when animated, for position estimation and monitoring the degree of convective organization. The native visible imagery has been remapped to a one-km Mercator projection and the digital data has been stretched over its full range - allowing a more esthetically pleasing appearance. The center location is based on the last operational position estimate and the previous 12-hr motion.

Multi platform Tropical Cyclone Kinetic Energy and Intensity

From the Multi platform satellite wind analysis discussed above a flight level (~ 700 hPa) Kinetic Energy is calculated within 200km of the cyclone center. The calculated KE is then categorized (0-5) so that their probability distribution is identical to the Saffir- Simpson Hurricane Intensity Scale (0-5). The KE is then plotted versus the maximum surface wind from these same wind analysis and provided every six hours. Tropical cyclones tend to grow as they weaken, but this is not always the case and large storms typically have larger values of KE and thus are more destructive when they affect land. This product allows the real-time monitoring of the potential destructive potential of a given storm and allows inter comparison with past events either produced on this web page or from actual flight level wind data. The methods for calculating and categorizing the KE as well as analyses of several past events are described in Maclay et al. (2008).

2km Storm Relative IR Imagery with BD Enhancement Curve

The same infrared imagery shown in the earth relative framework is displayed in a storm relative framework, with a 2km resolution and enhanced with the "BD Curve" which is useful for directly inferring intensity via the Dvorak Enhanced IR (EIR) technique. Scaling is provided by two lightly hatched circles around the center. The two circles have radii of 1 and 2 degrees latitude, respectively.

Geostationary Derived Motion Winds

The geostationary derived motion winds product is a level two product for geostationary satellites that is calculated by using a sequence of visible or infrared spectral bands to track the motion of cloud features and water vapor gradients. During the night, only the infrared are used. During the day, visible is also used to calculate. derived motion winds. Red wind barbs are for winds between 0 and 450 hPa. Yellow is for 450 to 600 hPa. Blue is for 600 hPa to the surface.

Geostationary Lightning Mapper

Geostationary Lightning Mapper product shows "flashes" from total lightning (i.e., in-cloud and cloud-to-ground). Flashes are comprised of events from the imager that have been connected into groups before being tracked in time to be identified as a flash. The flashes are shown for the last six hours.

Multi platform Tropical Cyclone MSLP and Maximum Winds

Minimum Sea Level Pressure is calculated directly from the azimuthally averaged gradient level tangential winds produced by the multi platform tropical cyclone wind analysis. The circular domain for the numerical integration has a 600km radius. The pressure deficit resulting from the integration is then added to an environmental pressure. The environmental pressure (Penv) is interpolated from NCEP analyses in a circle 600 km from the cyclone center. The maximum surface winds produced by the analysis are also shown.

AMSU Area-Averaged Wind Shears and Layer Means

These products use the balanced 3-D wind field derived from the AMSU temperature retrievals to estimate the area averaged vertical wind shear and mass weighted deep-layer mean wind in two layers (200 to 850hPa and 500 to 850Hpa). For these calculations the area averaging is calculated in the area contained within 0 to 600km from the center of the cyclone. These are displayed for each AMSU retrieval time available. These may be useful for detecting rapid changes in the synoptic wind field. The reliability of the vertical wind shear estimates is documented in Zehr et al. (2008).

IR-based TC size

Tropical cyclone size, the radius of where the TC wind field is indistinguishable from the background flow in a climatological environment, is empirically estimated from IR imagery and storm latitude. Principle components of the storm centered, azimuthally averaged IR brightness temperatures and the sine of the latitude have been regressed on the azimuthal mean tangential winds around TCs at 500k radius (V500) using a 1995-2011 Atlantic and East Pacific data set. Using the same dataset the climatological TC size (as defined above),radial decay of tangential winds beyond 500 km radius and V500 has also been estimated. Combining the V500 estimate along with the climatological TC information allows us to estimate TC size. This TC size metric is reported in units of degrees latitude. More information on how to calculate this metric can be found in Knaff et al. (2013), which is being reviewed for publication in the Journal of Climate.

Time Series of the Simplified Holland B parameter calculated from the TC Vitals

The simplified Holland B parameter [SHB, Knaff et al. (2010)] is a powerful TC structure diagnostic that is easily calculated from routinely available data. The SHB is related to the shape of the tangential wind profile beyond the radius of maximum wind (RMW), and is insensitive to variations of radius maximum winds. Large values of SHB (order 2.25) imply compact tangential wind profiles while, small values (<1.0) are related to broad tangential wind profiles. SHB also increase with both the intensity and radial extent of the wind field. It is noteworthy that TCs that are weak have a generally large range of SHB, between 0.5 and 2.25, while very intense TCs have SHB values in a narrow range between 1.75 and 2.3. More details of the sensitivity of SHB to TC structure are provided in Knaff et al. (2010). Here we have calculated SHB from the initial tropical cyclone conditions provided by NHC and JTWC (sometimes referred to as TC vitals or the TC bogus) as a function of time, indicated by the red line. The time series provides information on the structural evolution of the TC. The empirically derived lower and upper bounds of the SHB as a function of intensity are also provided by the thin black lines in the figure.

Day/Night Visible Imagery VIIRS

This product displays storm relative imagery from Joint Polar Satellite System (JPSS) spacecraft, the Suomi NPP (National Polar-orbiting Partnership), launched on October 28, 2011. Polar-orbiting satellites, because of their lower altitude orbits, can provide higher resolution imagery, but with limited temporal resolution. Imagery from Visible Infrared Imaging Radiometer Suite (VIIRS) is used for this product. VIIRS data are collected for each SNPP pass over tropical cyclone center. This product creates a loop combining two band from VIIRS. For the day-time images it uses the high resolution visible band (I01, 375 m resolution at nadir). For the night image the unique VIIRS Day Night Band (DNB) is used (742 m resolution across the swath). Both images are remapped to Mercator projection at their native resolution, and 6 by 6 degrees latitude/longitude box is displayed around the storm center interpolated from latest available ATCF data. Additional information about VIIRS is available at http://rammb.cira.colostate.edu/projects/npp/.

Enhanced Infrared (IR) VIIRS

This product displays storm relative imagery from Joint Polar Satellite System (JPSS) spacecraft, the Suomi NPP (National Polar-orbiting Partnership), launched on October 28, 2011. Polar-orbiting satellites, because of their lower altitude orbits, can provide higher resolution imagery, but with limited temporal resolution. Imagery from Visible Infrared Imaging Radiometer Suite (VIIRS) is used for this product. VIIRS data are collected for each SNPP pass over tropical cyclone center. The image from M15 longwave IR band is remapped to Mercator projection at it's native 750 m resolution, and 6 by 6 degrees latitude/longitude box is displayed around the storm center interpolated from latest available ATCF data. The image is color enhanced to emphasize the coldest temperature/highest clouds. Additional information about VIIRS is available at http://rammb.cira.colostate.edu/projects/npp/.

Advanced Technology Microwave Sounder (ATMS)-based Sounding and MPI

The Advanced Technology Microwave Sounder (ATMS) is an instrument onboard the JPSS S-NPP operational polar-orbiting satellite, launched in October, 2011 (http://www.star.nesdis.noaa.gov/jpss/instruments.php). ATMS operates in conjunction with CrIS to profile atmospheric temperature and moisture, providing higher spatial resolution compared to its predecessor, the Advanced Microwave Sounding Unit (AMSU). ATMS data are processed using the Microwave Integrated Retrieval System (MIRS) algorithm. The ATMS-MIRS temperatures and moisture profiles are used together with weekly Reynolds sea surface temperatures (SST) for the current product. ATMS Maximum Potential Intensity (AMPI) estimates are obtained using Bister and Emanuel’s (1998) algorithm, using as input the temperature profile, mixing ratio profile, and sea level pressure (SLP) azimuthally averaged between 200 and 800 km from the storm center, and SST at the center of the storm. For comparison we also calculate RMPI, the SST-based MPI (DeMaria and Kaplan, 1994). In addition to the sounding shown at the plot are: SST at the storm center (SST, degC), environmental SLP (SLP, mb), AMPI (kt), RMPI (kt), environmental CAPE (CAPEenv, J/kg), CAPE at the radius of maximum winds (CAPErmw, J/kg), and saturated CAPE at the radius of maximum winds (CAPErmws, J/kg) This is an experimental product that is currently in the testing phase.

Advanced Technology Microwave Sounder (ATMS) and Dropsonde Collocation

This product displays the vertical temperature and moisture profiles of collocated dropsondes and Advanced Technology Microwave Sounder (ATMS) retrievals. ATMS is an instrument onboard the JPSS S-NPP operational polar-orbiting satellite, launched in October, 2011 (http://www.star.nesdis.noaa.gov/jpss/instruments.php). ATMS operates in conjunction with CrIS to profile atmospheric temperature and moisture, providing higher spatial resolution compared to its predecessor, the Advanced Microwave Sounding Unit (AMSU). ATMS data are processed using the Microwave Integrated Retrieval System (MIRS) algorithm. The red and blue lines are T and Td profiles from ATMS, respectively, and the purple and teal lines are T and Td from the dropsondes. Printed at the bottom of the plot are the ATCF storm ID, the ATMS carrying satellite id, the distance from the TC center to the dropsonde, the heading angle, and the horizontal distance between the dropsonde release location and ATMS sounding. For the collocation the ATMS sounding closest to the dropsonde was selected within one hour and 100 km from the dropsonde release time and location. A separate plot is created for each TC within 2000 km of a dropsonde, thus it is possible that the same dropsonde could be used in more than one plot if multiple systems are present.

Advanced Microwave Sounding Unit (AMSU) and Dropsonde Collocation

This product displays the vertical temperature and moisture profiles of collocated dropsondes and Advanced Microwave Sounding Unit (AMSU) retrievals. The product is displayed for AMSUs onboard NOAA-18, NOAA-19, MetOp-A, and MetOp-B polar-orbiting satellites. AMSU data are processed using the Microwave Integrated Retrieval System (MIRS) algorithm, which provides simultaneous retrievals of atmospheric temperature and moisture. In addition, data from AMSU onboard of Metop-B are processed using a high-resolution algorithm, where each AMSU footprint is matched to 9 Microwave Humidity Sounder footprints. The red and blue lines are the T and Td profiles from AMSU, respectively, and the purple and teal lines are the T and Td from the dropsondes. Printed at the bottom of the plot are the ATCF storm ID, the satellite ID, the distance from the TC center to the dropsonde, the heading angle, and the horizontal distance between the dropsonde release location and the AMSU sounding. For the collocation, the AMSU sounding closest to the dropsonde is selected within one hour and 100 km from the dropsonde release time and location. The timestamp for each plot is created based on the dropsonde release time. It is possible that retrievals from more than one satellite can be collocated to the same dropsonde. In such cases, it is necessary to adjust the timestamp due to file naming restrictions. For the NOAA-18 retrievals, the timestamp is the actual dropsonde release time; for NOAA-19 the timestamp is the dropsonde release time plus 1 minute; for MetOp-A the timestamp is the dropsonde release time plus 2 minutes; for MetOp-B the timestamp is the dropsonde release time plus 3 minutes. A separate plot is created for each TC within 2000 km of a dropsonde, therefore a particular dropsonde could be used in collocations associated with more than one system.

Model Diagnostic Plots

The suite of model diagnostic plots provides a large-scale, storm-centric environmental context for tropical cyclones. This environmental information is calculated using a similar approach to the SHIPS large-scale diagnostics. However, these diagnostic quantities are calculated on the model storm center and not the official forecast storm position. With this context, the diagnostics provide insight into how deviations to the forecasted track and errors in environment quantities impact storm intensity forecasts.

The panels and panel information are as follows: 1) storm intensity/maximum sustained winds from the model tracker (i.e., raw value that has not been processed by the interpolator) either provided by the modeling center (e.g., NOAA NWS NCEP, ECMWF, FNMOC) or Automated Tropical Cyclone Forecast (ATCF) system database aid-deck files, 2) storm track/position out to 5-days assuming the model vortex is not lost, 3) deep-layer vertical wind shear calculated using azimuthally averaged 0 to 500 km in radius meridional and zonal winds from 200- and 850-hPa, 4) sea surface temperature, and 5) middle-tropospheric relative humidity calculated using azimuthally averaged 200 to 800 km in radius relative humidity from 500- and 700-hPa. The intensity, shear, SST, and mid-RH panels show a 10-day window centered at the analysis time. To the left of each vertical line is the history either derived from the model analysis or ATCF best-track and to the right is the forecast from the model fields or ATCF-provided official forecast.

There are three flavors of the model diagnostic product: 1) multi-model diagnostic plot with multiple deterministic models and includes intensity forecasts from NHC, CPHC, JTWC, SHIPS, and LGEM, 2) multi-run model diagnostic plot with the last 6 runs from a specific model, and 3) Global Ensemble Forecast System that shows diagnostics calculated from the current ensemble run.

References:
Slocum, C. J., M. N. Razin, J. A. Knaff, S. P. Stow, 2022: Does ERA5 mark a new era for resolving the tropical cyclone environment? J. Climate., Accepted. McNoldy, B. D., M. DeMaria, V. Tallapragada, and T. Marchok, 2010: HWRF performance diagnostics from the 2009 Atlantic hurricane season. Preprints, 29th Conf. on Hurricanes and Tropical Meteorology, Tucson, AZ, Amer. Meteor. Soc., 5 pp., [Available online at https://ams.confex.com/ams/pdfpapers/167993.pdf].

Ensemble Track Ellipses

Track, Intensity and Radius (34 kt) ensemble diagnostic plots. Ellipses include 1 σ of the ensemble spread each 24 hrs. Intensity and R34 plots display distribution of adeck metrics per modeling center.

GFS Simulated Brightness Temperature

The simulated brightness temperature product shows output generated using the Community Radiative Transfer Model from the forecast temperature and moisture fields. The large-scale plot shows middle-tropospheric water vapor absorption channel with a spectral response function centered near 7 µm and the zoomed-in subset plot shows the longwave infrared window channel with a spectral response function centered near 11 µm.

HWRF Simulated Brightness Temperature

The simulated brightness temperature product shows output generated using the Community Radiative Transfer Model from the forecast temperature and moisture fields. The large-scale plot shows middle-tropospheric water vapor absorption channel with a spectral response function centered near 7 µm and the zoomed-in subset plot shows the longwave infrared window channel with a spectral response function centered near 11 µm.

Predicted Intensity Model Errors (PRIME)

This product is an experimental algorithm to estimate the confidence of the intensity forecasts from NHC’s primary intensity models (DSHP, LGEM, HWFI, and GHMI) and their consensus. The technique builds on the results of Bhatia and Nolan (2013) who demonstrated that the errors and biases of DSHP, LGEM, and GFDL have significant systematic variability as a function of a number of storm environmental variables that are available in real time, including the magnitude of the vertical shear, the direction of the shear, the initial intensity, and the maximum potential intensity. The intensity model error is estimated from a linear combination of these predictors and other variables. Plots of each model intensity forecast and its bias-corrected forecast, a histogram of predicted mean absolute errors for all four models, and a corrected consensus are displayed. PRIME is currently available for Atlantic TCs only.

Experimental Wind Speed Probabilities

These products are experimental versions of the Monte Carlo wind speed probability model (MC model). The MC model estimates the probability that any given location will experience 34, 50, or 64 kt winds over a given forecast period. These wind speed probabilities are based on official track, intensity, and wind structure forecasts and climatological error statistics (DeMaria et al. 2009; 2013). Experience with the MC model has motivated several improvements that were developed under the support of the Joint Hurricane Testbed (JHT) and are currently being tested at CIRA. Improvements are displayed as either a wind speed probability difference plot (CONTROL - EXPERIMENTAL) or wind speed probabilities.

Dvorak Fix-Based Wind Radii (Text; ATCF ANAL fixes)

Routine subjective Dvorak (1984) intensity and center fixes are used along with matching infrared images to estimate TC wind radii. The key inputs from the Dvorak fixes are the current intensity, current latitude. Short-term storm motion is also determined via the most recently occurring previous Dvorak intensity + center fix. Currently only Dvorak information from TAFB/NHC, SAB/KNES, and JTWC(PGTW) are used for these estimate. The output from this procedure is text files that have been formatted to be ingested into the ATCF as analysis type fixes. The standard Analysis fix format is available at http://www.nrlmry.navy.mil/atcf_web/docs/database/new/newfdeck.txt.

The method uses routine information (storm intensity, storm motion, storm latitude, and patterns in a single IR image) are used to create wind radii estimates. The matching IR image is used to create an estimate of TC size (or R5) following Knaff et al. (2014). Both R5 and current intensity are used to estimate azimuthally averaged wind radii based on regression relationships with historical azimuthally averaged wind radii. Given those estimates and a climatological estimate of wind radii, a modified Rankine vortex is created for each wind speed threshold (34-, 50-, 64-knots) wherein the vortex asymmetries are estimated using relationships developed for the wind-radii climatology and persistence model in the Atlantic Basin, which are a function of TC speed and motion (Knaff et al. 2007, Table 1). For more detailed information on the methodology please see Knaff et al. (2016).

This methodology will perform most poorly for TCs that have lower intensities, have lost their deep convective signal, are moving fast, are very large, and are occurring at higher latitudes - conditions associated with highly asymmetric TCs and/or those undergoing extra-tropical transition.

References:

Dvorak, V.F., 1984: Tropical cyclone intensity analysis using satellite data. NOAA Tech. Rep. 11, 45 pp. [Available from NOAA/NESDIS, NOAA Center for Weather and Climate Prediction, 5830 University Research Court College Park, MD 20740]

Knaff, J.A., C. R. Sampson, M. DeMaria, T. P. Marchok, J. M. Gross, and C. J. McAdie, 2007: Statistical tropical cyclone wind radii prediction using climatology and persistence. Wea. Forecasting, 22:4, 781791.

Knaff, J.A., S. P. Longmore, and D. A. Molenar, 2014: An objective satellite-based tropical cyclone size climatology. J. Climate, 27, 455-476.

Knaff, J.A., C.J. Slocum, K.D. Musgrave, C.R. Sampson, and B. Strahl: 2015: Using routinely available information to estimate tropical cyclone wind structure. In press, Mon. Wea. Rev. doi: http://dx.doi.org/10.1175/MWR-D-15-0267.1

Radius of Maximum Wind (RMW)

One of the key parameters to any vortex is the radius of maximum wind (RMW). In tropical cyclones this quantity is difficult to estimate from satellite or ancillary data alone during most of the formative and much of the decaying stages. When there is an eye the RMW is known to be located near the edge of the eye feature. To help determine the radial location of the RMW, we have developed several methods to make such estimates. These include (Mueller et al. 2006) which is based on estimates of intensity, latitude and information contained in IR images, IR_2R where intensity, latitude, and patterns are used to estimate RMW, but in two regimes. These regimes are 1) when there is a well-defined convective core and 2) when there is not a well-defined convective core. Both IR methods were trained on aircraft analyses at flight-level, though with different analyses. To estimate the surface RMW an inward slope is applied from the 3km flight level following Stern et al. (2014). To compliment these two IR methods, an eye-based method is also applied. These estimates are only available when an eye feature is detected, based on the criteria discussed in Chen and Wu (2022) with slight modifications to the cold ring width (20 km). In such cases, the minimum enclosing circle is computed for the –45°C brightness temperature contour. Its radius is then used to both classify the scene (clear-eye vs. unclear-eye), and to estimate the RMW based on the methods described in Tsukada and Horinouchi (2023). To accompany these satellite based methods, a RMW estimates, based on the behavior of the angular momentum surfaces and a function of the current intensity, the Coriolis parameter and a measure of the TC size are also used (Chavas_TCVitals). This method is based on the theoretical methods developed in Chavas et al. (2014) and is the result of a statistical fit to the RMW values in the extended best track where the dependent variables are 1/2fR34 and Vmax. TC size in this case is estimated from the radius of gales. Finally, the RMW estimates from the experimental version of the multi-satellite-platform tropical cyclone surface wind analysis (MTCSWA), and when aircraft reconnaissance is available, surface RMW values from the vortex fixes are plotted. All of these estimates are also compared to values in the operational best track.

References:

Mueller, K. J., M. DeMaria, J. A. Knaff, J. P. Kossin, and T. H. Vonder Haar: 2006: Objective Estimation of Tropical Cyclone Wind Structure from Infrared Satellite Data. Wea Forecasting, 21(6), 9901005.

Stern, D.P., J.R. Brisbois, and D.S. Nolan, 2014: An Expanded Dataset of Hurricane Eyewall Sizes and Slopes. J. Atmos. Sci., 71, 27472762, https://doi.org/10.1175/JAS-D-13-0302.1.

Chen, Y.-L., and C.-C. Wu, 2022: On the Two Types of Tropical Cyclone Eye Formation: Clearing Formation and Banding Formation. Monthly Weather Review, https://doi.org/10.1175/MWR-D-21-0239.1.

Tsukada, T., and T. Horinouchi, 2023: Strong Relationship between Eye Radius and Radius of Maximum Wind of Tropical Cyclones. Monthly Weather Review, 151, 569–588, https://doi.org/10.1175/MWR-D-22-0106.1.

Satellite-Based 34, 50, and 64-kt Wind Radii Estimates

Several satellite-based wind radii estimates in geographic quadrants are compiled in real-time from various sources including real-time ATCF databases, NESDIS operational products, and CIRA experimental products. These include estimates based on infrared instruments (DVRK- JTWC/PGTW, DVRK-TAFB, DVRK-SAB/KNES) see Knaff et al. (2016), Microwave sensors based on statistical retrievals (NOAA15, NOAA18, NOAA19, METOPA) (see Demuth et al. 2006) and those based on the Microwave Integrated Retrieval System (Boukabara et al. 2011; MIRS-NOAA18, MIRS-NOAA19, MIRS-META, MIRS-METB, MIRS-ATMS), and methods that combine information from geostationary (IR flight-level proxy winds, cloud/feature tracked winds) and low-earth orbiting satellites (Scatterometry, 2-D microwave sounder-based wind fields) such as MTCSWA-OPS from NESDIS and MTCSWA-EXP from CIRA/RAMMB, see Mueller et al. (2006) Knaff et al. (2011, 2015). Suggested uses include using such information in a subjective manner to provide initial estimates TC wind structure, and wind radii extremes. The scatter of these estimates may also be used to provide some assessment of uncertainty.

References:

Boukabara, S.-A, K. Garrett, W. Chen, F. Iturbide-Sanchez, C. Grassotti, C. Kongoli, R. Chen, Q. Liu, B. Yan, F. Weng, R. Ferraro, T. Kleespies, and H. Meng, 2011: MiRS: An All-Weather 1DVAR Satellite Data Assimilation & Retrieval System. IEEE Trans. Geosci. Remote Sens., vol. 49, no. 9, pp. 3249-3272, Sep. 2011, Digital Object Identifier: 10.1109/TGRS.2011.2158438

Demuth, J., M. DeMaria, and J.A. Knaff, 2006: Improvement of Advanced Microwave Sounding Unit Tropical Cyclone Intensity and Size Estimation Algorithms, J. Appl. Meteor. Clim., 45:11, 15731581.

Knaff, J.A., S.P. Longmore, R.T DeMaria, D.A. Molenar, 2015: Improved tropical cyclone flight-level wind estimates using routine infrared satellite reconnaissance. J. App. Meteor. Climate. 54, 463478.

Knaff, J. A., M. DeMaria, D. A. Molenar, C. R. Sampson and M. G. Seybold, 2011: An automated, objective, multi-satellite platform tropical cyclone surface wind analysis. J. Appl. Meteorol. Climatol. 50:10, 2149-2166. doi: 10.1175/2011JAMC2673.1

Mueller, K.J., M. DeMaria, J.A. Knaff, J.P. Kossin, T.H. Vonder Haar: 2006: Objective Estimation of Tropical Cyclone Wind Structure from Infrared Satellite Data. Wea. Forecasting, 21:6, 9901005.

Eye Probability Forecast

This product was trained on output of an infrared eye detection algorithm developed in DeMaria (2016), which has probability of detection of nearly 95% with relatively low false alarm rates. The forecasts are based on information contained in the GFS-based large scale environment, current and past storm conditions, and information derived from the latest available IR image. The table below shows factors related to eye probability forecasts. These forecasts are demonstrably skillful versus climatology (~ 11%) for all time periods and skillful versus persistence at leads times of 12h and longer, based on independent forecasts. Brier skill scores for versus persistence are -14%, 35%, 47%, 47%, and 50% at 6, 12, 18, 24 and 36h. Note two schemes were developed for 18h, and those forecast are averaged for the product and the verification statistics.

	Eye P(t=0)	12-h intensity trend	Coincident vertical wind shear	Current Intensity	Coincident Tangential wind at 850hPa (r=500km)	Coincident Oceanic Heat Content	IR PC 2	IR PC 3	IR PC 4	Pixel Count T<-50C (r=50 -300 km)	Standard deviation of IR (r = 100-300 km)	Fractional TC Size (IR)
6h	x	x	x					x		x
12h	x	x	x					x		x	x
18h(1)	x	x	x							x	x
18h(2)		x		x	x	x		x	x	x	x	x
24h		x	x		x	x				x	x	x
36h		x	x		x	x				x	x	x

References:

DeMaria, Robert. Automated Tropical Cyclone Eye Detection Using Discriminant Analysis. 2015. Colorado State University Digital Repository. 9 Nov 2016. <http://hdl.handle.net/10217/170410>.

RIPA Products

Details on RIPA products coming soon.

Dvorak Cloud Pattern

The Dvorak tropical cyclone cloud pattern product includes patterns from satellite analysts, the CIMSS Automated Dvorak Technique, and RAMMB Tropical Cyclone Cloud Pattern algorithm. Tropical cyclone convective organization is often subjectively determined by satellite analysts as part of the subjective Dvorak technique for tropical cyclone intensity estimation. As a result of making these routine intensity estimate, cloud patterns are a byproduct of the process from groups like the NESDIS Satellite Analysis Branch Tropical Cyclone Team. Common cloud patterns include curved band, shear, eye, and covered.

References:

COMET, 2014: Tropical cyclone intensity analysis. Accessed 2 March 2023. <https://www.meted.ucar.edu/bom/tropical_intensity/index.htm>.

DeMaria, M., J. A. Knaff, and R. Zehr, 2013: Assessing hurricane intensity using satellites. Satellite-based Applications on Climate Change. Springer. 151-163. <https://rammb.cira.colostate.edu/resources/docs/DeMaria_Knaff_StarBook2013.pdf>.

Dvorak, V. F., 1984: Tropical cyclone intensity analysis using satellite data. Tech. Rep. 11, NOAA, Washington, D.C., 45 pp.

89 GHz Passive Microwave Imagery

Passive microwave observations in the ice-scattering frequency range (85 – 92 GHz) provide insight into the convective structure of precipitating systems. In this frequency range, large precipitation-sized ice particles in deep convection (i.e., graupel) scatter terrestrial radiation, producing low brightness temperatures. Stronger convection produces larger ice particles, which scatter more terrestrial radiation and result in lower brightness temperatures. Varying surface emissivities may also produce low brightness temperatures in single-polarization observations in this frequency range, but their signatures are usually not as distinct as the signatures from deep convection. The signature of deep convection in this frequency range is not affected by the presence of smaller ice particles associated with the cirrus outflow of a tropical cyclone, thus allowing for a more direct view of convection underneath the cloud tops. The displayed 85 – 92 GHz brightness temperatures come from fortuitous overpasses of passive microwave sensors aboard low-Earth orbiting satellites from the NASA GPM Constellation (insert link: https://gpm.nasa.gov/missions/GPM/constellation) and are inter-calibrated such that the differences in the observed brightness temperatures between the different GPM Constellation sensors are attributed mainly to the different characteristics of the sensors (Berg et al. 2016). We prioritize observations from the horizontal or quasi-horizontal polarizations, and only use the vertical or quasi-vertical polarizations when a sensor does not make observations in the horizontal or quasi-horizontal polarization. The resolution of the observation is sensor-dependent, with conical-scanning sensors having constant resolution throughout its swath and cross-track-scanning sensors having a higher resolution at nadir that decreases to a lower resolution at the swath edge. The displayed imagery come from fortuitous overpasses of passive microwave sensors aboard low-Earth orbiting satellites from the NASA GPM Constellation (insert link: https://gpm.nasa.gov/missions/GPM/constellation), and show the vertically or quasi-vertically polarized brightness temperatures in the 85 – 92 GHz frequency range, inter-calibrated such that the differences in the observed brightness temperatures between the different GPM Constellation sensors are attributed mainly to the different characteristics of the sensors (Berg et al. 2016). The resolution of the observation is sensor-dependent, with conical-scanning sensors having constant resolution throughout its swath and cross-track-scanning sensors having a higher resolution at nadir that decreases to a lower resolution at the swath edge.

References:

Berg, W., and Coauthors, 2016: Intercalibration of the GPM microwave radiometer constellation. J. Atmos. Oceanic Technol., 33, 2639-2654, <https://doi.org/10.1175/JTECH-D-16-0100.1.>.

37 GHz Passive Microwave Imagery

Passive microwave observations near the 37 GHz frequency provide insight into the precipitation structure of precipitating systems. At this frequency, the ocean has low emissivity and therefore possesses low brightness temperatures. However, liquid precipitation absorbs and more efficiently re-emits terrestrial radiation, producing higher brightness temperatures relative to the lower brightness temperatures of the ocean surface. Therefore, liquid precipitation over the ocean manifests itself as areas of higher brightness temperatures imposed on a larger area of lower brightness temperatures. Similar to observations in the 89 - 92 GHz frequency range, observations at 37 GHz are generally insensitive to the presence of upper-level clouds, thus allowing for a more direct view of liquid precipitation underneath the cloud tops. However, the presence of large ice particles in deep convection can scatter radiation in this frequency, producing localized areas of lower brightness temperatures. Simultaneously, land surfaces appear as higher brightness temperatures, which often mask the signature from liquid precipitation. The displayed imagery come from fortuitous overpasses of passive microwave sensors aboard low-Earth orbiting satellites from the NASA GPM Constellation (insert link: https://gpm.nasa.gov/missions/GPM/constellation), and show the vertically polarized brightness temperatures near 37 GHz frequency, inter-calibrated such that the differences in the observed brightness temperatures between the different GPM Constellation sensors are attributed mainly to the different characteristics of the sensors (Berg et al. 2016). Observations near 37 GHz are available only from conical-scanning sensors and therefore, may not be as frequently available as observations in the 85 - 92 GHz frequency range.

References:

Berg, W., and Coauthors, 2016: Intercalibration of the GPM microwave radiometer constellation. J. Atmos. Oceanic Technol., 33, 2639-2654, <https://doi.org/10.1175/JTECH-D-16-0100.1.>.

Multi-Product Passive Microwave Imagery

Observations from different passive microwave frequencies provide information about different aspects of the atmosphere. For example, observations in the 85 - 92 GHz frequency range provide information on the location and strength of deep convection, while observations near the 37 GHz frequency provide information on the location of low-level liquid precipitation. When combined, observations from the different passive microwave frequencies allow for the estimation of the precipitation rate at the surface through passive-microwave-based precipitation retrieval algorithms such as the NASA Goddard PROFiling Algorithm (GPROF; Kummerow et al. 2015). The displayed imagery come from fortuitous overpasses of passive microwave sensors aboard low-Earth orbiting satellites from the NASA GPM Constellation (insert link: https://gpm.nasa.gov/missions/GPM/constellation), and show observations from the 37 GHz frequency and the 85 - 92 GHz frequency range, alongside the surface precipitation rate output from GPROF. Note that since observations near 37 GHz are available only from conical-scanning sensors, they may not be as frequently available as observations from the 85 - 92 GHz frequency range or the GPROF precipitation output. The resolution of the observation is sensor-dependent, with conical-scanning sensors having constant resolution throughout its swath and cross-track-scanning sensors having a higher resolution at nadir that decreases to a lower resolution at the swath edge.

References:

Kummerow, C. D., D. L. Randel, M. Kulie, N.-Y. Wang, R. Ferraro, S. J. Munchak, and V. Petkovic, 2015: The evolution of the Goddard profiling algorithm to a fully parametric scheme. J. Atmos. Oceanic Technol., 32, 165-176, <https://doi.org/10.1175/JTECH-D-15-0039.1.>.

SHIPS-based Rapid Intensification Random Forest Model

Unavailable

Synthetic Passive Microwave 89 GHz Imagery (Consensus, 4 km Mercator)

Passive microwave imagery centered at 89 GHz provides information regarding tropical cyclone location, deep convection, ice, and liquid water structure; however, this imagery is only available from polar-orbiting satellites, limiting the revisit time and resulting in coverage gaps and increasing latency. To overcome this limitation, we have developed synthetic 89 GHz imagery from geostationary satellite data using machine learning. This product displays 89 GHz imagery at 4 km on a Mercator grid from a combination of various machine learning algorithms, including Random Forest, fully-connected neural networks and convolutional neural networks.

Synthetic Passive Microwave 89 GHz Imagery (ANN, 4 km Mercator)

Passive microwave imagery centered at 89 GHz provides information regarding tropical cyclone location, deep convection, ice, and liquid water structure; however, this imagery is only available from polar-orbiting satellites, limiting the revisit time and resulting in coverage gaps and increasing latency. To overcome this limitation, we have developed synthetic 89 GHz imagery from geostationary satellite data using machine learning. This product displays 89 GHz imagery at 4 km on a Mercator grid from a fully-connected neural network.

Synthetic Passive Microwave 89 GHz Imagery (CNN, 4 km Mercator)

Passive microwave imagery centered at 89 GHz provides information regarding tropical cyclone location, deep convection, ice, and liquid water structure; however, this imagery is only available from polar-orbiting satellites, limiting the revisit time and resulting in coverage gaps and increasing latency. To overcome this limitation, we have developed synthetic 89 GHz imagery from geostationary satellite data using machine learning. This product displays 89 GHz imagery at 4 km on a Mercator grid from a convolutional neural network.

Observed & Simulated Infrared (IR) Imagery with Storm Structure

A multi-panel, looping display of observed satellite infrared (IR) imagery and simulated satellite infrared IR imagery to compare the observed structure of a given tropical cyclone to the simulated structure from multiple models. The simulated IR imagery is shown for HFSA, HFSB, HWRF, and HMON. The observed and simulated IR imagery are overlaid with the wind radii quadrants retrieved from the respective IR fields. Underneath each panel, the wind radii information are tabulated in text for each quadrant and wind radii threshold (i.e., radius of 34-, 50-, and 64-knot winds).

Multi-model Track Forecast Verification

A multi-panel display comprising a map of the OFCL, HFSA, HFSB, HWRF, and HMON track forecasts and panels for the track forecast mean absolute error, skill relative to a baseline model, along-track biases, and cross-track biases.

Multi-model Intensity Forecast Verification

A multi-panel display comprising a time series of the OFCL, HFSA, HFSB, HWRF, and HMON intensity forecasts and panels for the intensity forecast mean absolute error, skill relative to a baseline model, and biases.

Multi-model Track & Intensity Forecast Verification

A multi-panel display comprising homogeneous verification measures that are cumulatively aggregated throughout the life cycle of a tropical cyclone. The display includes the official National Hurricane Center forecast (OFCL; gray) and early (interpolated) guidance from four mesoscale model forecasts: the Hurricane Analysis and Forecast System Version 1.0 models A and B (HFAI/HFBI), the Hurricane Weather Research and Forecasting model (HWFI), and the Hurricanes in a Multiscale Ocean-coupled Nonhydrostatic model (HMNI). The homogeneous verification measures include the mean of the absolute track error, mean of the absolute intensity error, along- and cross-track biases, and intensity biases. A homogeneous verification requires that a forecast is present for a given cycle and verifying time from each operational forecast and model included in the verification. The samples are cumulatively updated as newer forecasts become available. The date-time group listed in the animation denotes that all forecast cycles preceding and including the specified date-time group are included in the homogeneous verification so long as the system remains a tropical cyclone.

AI Model Track Forecasts

Track Forecast Data from Data-Driven AI Models. Experimental Models included are: NGRP = Graphcast (GFS initial conditions) EGRP = Graphcast (IFS initial conditions) NPNG = PanguWeather (GFS initial conditions) EPNG = PanguWeather (IFS initial conditions) NFOR = FourCastNet V2 (GFS initial conditions) EFOR = FourCastNet V2 (IFS initial conditions) NAUR = Aurora (GFS initial conditions) EAUR = Aurora (IFS initial conditions) AIFS = ECMWFs AIFS FNV3 = GoogleDeepMind "Experimental" AI Model

AI Weather Model Forecast Verification

A multi-panel display comprising homogeneous track and intensity forecast verification measures that are cumulatively aggregated throughout the life cycle of tropical cyclones in the North Atlantic and eastern North Pacific basins. Note that all the models included in the display are considered “late” forecast guidance; that is, guidance that is not available to forecasters until after the issuance of their forecast advisories. The display includes verification measures for a set of four artificial intelligence weather prediction models (AIWP) running in-house at CIRA with two sources of initial conditions (t = 0 h forecasts): the National Centers for Environmental Prediction (NCEP) Global Forecast System (prefix “N”) and the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (prefix “E”). These four AIWP models include: NAUR/EAUR: Microsoft Aurora NPNG/EPNG: Huawei Cloud Pangu-Weather NFOR/EFOR: NVIDIA FourCastNet NGRP/EGRP: Google DeepMind GraphCast AICN: AIWP Consensus (A simple consensus with an unweighted average of the above eight forecast models) Additional models extracted from the NHC public ATCF aid files (a-decks) include: GDMN: Google DeepMind FGN (latest version of the FGN model forecast made available to NHC by Google DeepMind) GENC: Google DeepMind GenCast GRPH: Google DeepMind GraphCast (version that is run/made available by Google DeepMind) EAIO: ECMWF AIFS

DMW TC diagnostics

Tropical cyclone outflow layer from GOES Atmospheric Motion Vectors for intensity forecasting.

FRIA Forecast

Unavailable

FRIA Probability

Unavailable

FRIA Ensemble

Unavailable

Hourly TC-Centric Analysis of Upper-layer (350 to 100 hPa) Derived Motion Winds

Derived Motion Winds (DMWs) are routinely estimated from geostationary satellites. Here we use DMWs produced from Himawari and GOES satellites to provide an hourly analysis of upper -layer (350 to 100 hPa) following active tropical cyclones. The wind analysis method is described in Knaff and Slocum (2024) and applied to DMV observations using azimuthal and radial filter weights that are appropriate for the non-balanced upper-level outflow of tropical cyclones. Note: DMVs from Meteosat satellites are not used here, but may be in the near future. Knaff, J. A., and C. J. Slocum, 2024: An automated method to analyze tropical cyclone surface winds from real-time aircraft reconnaissance observations. Wea. Forecasting, 39, 333–349, https://doi.org/10.1175/WAF-D-23-0077.1

DMV-based TC Environmental Metrics (Shear, Divergence, Gradient Balance)

Using an analysis of Derived Motion Winds a proxy for vertical wind shear, divergence, and a percentage of gradient balance of the flow is estimated. Vertical wind shear is calculated as the average DMV winds within 500 km of the cyclone center minus the storm motion, and divergence is calculated within 1000 km of the cyclone center. Gradient winds are estimated from the observed vorticity field and the difference between the gradient winds and the observed winds are used to provide a percentage of gradient balance.

85 – 92 GHz Polarization-Corrected Brightness Temperature

Passive microwave observations in the ice-scattering frequency range (85 – 92 GHz) provide insight into the convective structure of precipitating systems. In this frequency range, large precipitation-sized ice particles in deep convection (i.e., graupel) scatter terrestrial radiation, producing low brightness temperatures. Stronger convection produces larger ice particles, which scatter more terrestrial radiation and result in lower brightness temperatures. Varying surface emissivities may also produce low brightness temperatures in single-polarization observations in this frequency range. Applying polarization correction removes the low brightness temperatures produced by the varying surface emissivities, allowing the forecaster to focus solely on the convective ice-scattering signatures. The signature of deep convection in this frequency range is not affected by the presence of smaller ice particles associated with the cirrus outflow of a tropical cyclone, thus allowing for a more direct view of convection underneath the cloud tops. The displayed 85 – 92 GHz polarization-corrected brightness temperatures come from fortuitous overpasses of passive microwave sensors aboard low-Earth orbiting satellites from the NASA GPM Constellation (insert link: https://gpm.nasa.gov/missions/GPM/constellation) and are inter-calibrated such that the differences in the observed brightness temperatures between the different GPM Constellation sensors are attributed mainly to the different characteristics of the sensors (Berg et al. 2016). However, polarization-corrected brightness temperatures are available only from conical-scanning sensors of the GPM Constellation, since conical-scanning sensors possess both the horizontal and vertical polarization observations required to apply the polarization correction. We apply the polarization correction equation developed in Cecil and Chronis (2018). Berg, W., and Coauthors, 2016: Intercalibration of the GPM microwave radiometer constellation. J. Atmos. Oceanic Technol., 33, 2639–2654, https://doi.org/10.1175/JTECH-D-16-0100.1. Cecil, D. J. and T. Chronis, 2018: Polarization-corrected temperatures for 10-, 19-, 37-, and 89-GHz passive microwave frequencies. J. Appl. Meteor. Climatol., 57, 2249–2265, https://doi.org/10.1175/JAMC-D-18-0022.1

183.31 ± 3.0 GHz Brightness Temperature

This is an experimental exploitation of the 183.31 GHz brightness temperature observations. In the absence of ice in the viewing column, observations at or near this frequency provide information on the distribution of atmospheric water vapor. At 183.31 GHz, atmospheric water vapor absorbs terrestrial radiation. Therefore, the brightness temperatures observed by the sensor corresponds to the radiation emitted near the top of the atmospheric water vapor layer, with more column water vapor producing lower brightness temperature. Observing frequencies near 183.31 GHz—from 183.31 ± 1.0 GHz to 183.31 ± 7.0 GHz—correspond to observations of atmospheric water vapor at increasingly lower levels in the atmosphere. The product shown here, 183.31 ± 3.0 GHz, shows the distribution of atmospheric water vapor at around the 800 – 400 mb layer. However, the presence of ice particles in the viewing column, such as those produced in deep convection, can scatter more radiation and produce even lower brightness temperatures. The displayed 183.31 ± 3.0 GHz brightness temperatures come from fortuitous overpasses of passive microwave sensors aboard low-Earth orbiting satellites from the NASA GPM Constellation (insert link: https://gpm.nasa.gov/missions/GPM/constellation) and are inter-calibrated such that the differences in the observed brightness temperatures between the different GPM Constellation sensors are attributed mainly to the different characteristics of the sensors (Berg et al. 2016). The resolution of the observation is sensor-dependent, with conical-scanning sensors having constant resolution throughout its swath and cross-track-scanning sensors having a higher resolution at nadir that decreases to a lower resolution at the swath edge. Berg, W., and Coauthors, 2016: Intercalibration of the GPM microwave radiometer constellation. J. Atmos. Oceanic Technol., 33, 2639–2654, https://doi.org/10.1175/JTECH-D-16-0100.1.

37 GHz Polarization-Corrected Brightness Temperature

Single-polarization brightness temperature observations at frequencies near 37 GHz are sensitive to 1) the presence of liquid water, which show up as higher brightness temperatures relative to the lower brightness temperature of the ocean surface, and 2) the presence of ice particles associated with deep convection, which show up as lower brightness temperatures that can be similar to the lower brightness temperature of the ocean surface. Simultaneously, land surfaces manifest as higher brightness temperatures relative to the ocean. Applying polarization correction to the 37 GHz brightness temperature observations serves to reduce the effects of different background temperatures due to the different emissivities of land and ocean surfaces and to focus the observations on the effects of ice scattering signals associated with deep convection. The displayed 37 GHz polarization-corrected brightness temperatures come from fortuitous overpasses of passive microwave sensors aboard low-Earth orbiting satellites from the NASA GPM Constellation (insert link: https://gpm.nasa.gov/missions/GPM/constellation), inter-calibrated such that the differences in the observed brightness temperatures between the different GPM Constellation sensors are attributed mainly to the different characteristics of the sensors (Berg et al. 2016). However, observations near 37 GHz are available only from conical-scanning sensors of the GPM Constellation, since conical-scanning sensors possess both the horizontal and vertical polarization observations required to apply the polarization correction, and the high spatial resolution needed for useful operational applications. Berg, W., and Coauthors, 2016: Intercalibration of the GPM microwave radiometer constellation. J. Atmos. Oceanic Technol., 33, 2639–2654, https://doi.org/10.1175/JTECH-D-16-0100.1.

GPROF Surface Precipitation Rate

The NASA Goddard Profiling Algorithm (GPROF) is a passive-microwave-based precipitation retrieval algorithm. To estimate the precipitation in the passive microwave scene, GPROF uses a Bayesian estimation approach by matching the combination of observed brightness temperatures from the various frequencies available for each sensor and a sensor-specific a-priori database of brightness temperatures and precipitation profiles (Kummerow et al. 2015). The a-priori database comes from one year of observations. Since observations of tropical cyclones represent a very small fraction of that one year, GPROF tends to underestimate the high precipitation rates typically found in tropical cyclones. The displayed GPROF surface precipitation rates come from fortuitous overpasses of passive microwave sensors aboard low-Earth orbiting satellites from the NASA GPM Constellation (insert link: https://gpm.nasa.gov/missions/GPM/constellation). The resolution of the observation is sensor-dependent, with conical-scanning sensors having constant resolution throughout its swath and cross-track-scanning sensors having a higher resolution at nadir that decreases to a lower resolution at the swath edge. Kummerow, C. D., D. L. Randel, M. Kulie, N.-Y. Wang, R. Ferraro, S. J. Munchak, and V. Petkovic, 2015: The evolution of the Goddard Profiling Algorithm to a full parametric scheme. J. Atmos. Oceanic Techol., 32, 2265–2280, https://doi.org/10.1175/JTECH-D-15-0039.1

GPM DPR 2 km Radar Reflectivity

The radar reflectivity observations come from the GPM Core Observatory satellite’s Dual-frequency Precipitation Radar (DPR; Kojima et al. 2012). Radar observations work by sending out a pulse of electromagnetic signals and “listening” for the signals reflected back by precipitation particles. Larger and/or more numerous precipitation particles produce a larger reflected signal (or “echo”). The GPM DPR makes radar observations at two electromagnetic frequencies, 13.6 GHz (Ku-band) and 35.5 GHz (Ka-band). The displayed radar reflectivity field comes from the Ku-band reflectivity at approximately 2 km altitude above the surface of the earth. Kojima, M., T. Miura, K. Furukawa, Y. Hyakusoku, T. Ishikiri, H. Kai, T. Iguchi, H. Hanado, and K. Nakagawa, 2012: Dual-frequency Precipitation Radar (DPR) development on the Global Precipitation Measurement (GPM) core observatory. SPIE Proceedings, Vol. 8528: Earth Observing Missions and Sensors: Development, Implementation, and Characterization II, SPIE, 234–243. https://doi.org/10.1117/12.976823.

VIIRS Day/Night Band Near-Constant Contrast

The Near-Constant Contrast (NCC) is a derived product of the 0.7um Day/Night Band (DNB) that provides unique visible imagery at night. DNB detects a broad range of light intensities (8-orders of magnitude in radiance space) and is very sensitive to low levels of light, including reflected and emitted sources.

VIIRS EDR Infrared (IR) 750m 10.763μm

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VIIRS EDR Visible 750m 0.672μm

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ECMWF AIFS Model Data

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Synthetic Passive Microwave 37 GHz Imagery (Consensus, 4 km Mercator)

Passive microwave imagery centered at 37 GHz provides complimentary information to images of 89 GHz for detecting tropical cyclone structure. In general, 37 GHz images are more sensitive to lower-level atmospheric layers, allowing them to be have the capacity to detect low-level circulation centers and rain-bands. Since 37 GHz generally senses the atmosphere nearer the surface than 89 GHz, parallax error is less, which allows for more accurate determination of tropical cyclone eyes and eye-walls. Here we show 37 GHz color composites, where the polarization corrected image is placed into the red gun (scaled from 260 to 280 K reverse tonality), the vertical polarization is placed into the green gun (scaled from 160 to 300 K), and the horizontal polarization image is placed into the blue gun (scaled from 180 to 310 K). In the resulting color composite product, the sea surface is green, deep convection appears pink, and low-level water clouds and rain appear cyan. One limitation of passive microwave imagery at 37 GHz is the limited revisit time of polar-orbiting satellites over tropical cyclones. To overcome this limitation, we developed synthetic imagery from geostationary satellite data using machine learning. This product displays synthetic tropical cyclone-centered 37 GHz imagery at 4 km on a Mercator grid from a combination of deep neural networks, including fully-connected and convolutional architectures.

Synthetic Passive Microwave 37 GHz Imagery (ANN, 4 km Mercator)

Passive microwave imagery centered at 37 GHz provides complimentary information to images of 89 GHz for detecting tropical cyclone structure. In general, 37 GHz images are more sensitive to lower-level atmospheric layers, allowing them to be have the capacity to detect low-level circulation centers and rain-bands. Since 37 GHz generally senses the atmosphere nearer the surface than 89 GHz, parallax error is less, which allows for more accurate determination of tropical cyclone eyes and eye-walls. Here we show 37 GHz color composites, where the polarization corrected image is placed into the red gun (scaled from 260 to 280 K reverse tonality), the vertical polarization is placed into the green gun (scaled from 160 to 300 K), and the horizontal polarization image is placed into the blue gun (scaled from 180 to 310 K). In the resulting color composite product, the sea surface is green, deep convection appears pink, and low-level water clouds and rain appear cyan. One limitation of passive microwave imagery at 37 GHz is the limited revisit time of polar-orbiting satellites over tropical cyclones. To overcome this limitation, we developed synthetic imagery from geostationary satellite data using machine learning. This product displays synthetic tropical cyclone-centered 37 GHz imagery at 4 km on a Mercator grid from a fully-connected neural network.

Synthetic Passive Microwave 37 GHz Imagery (CNN, 4 km Mercator)

Passive microwave imagery centered at 37 GHz provides complimentary information to images of 89 GHz for detecting tropical cyclone structure. In general, 37 GHz images are more sensitive to lower-level atmospheric layers, allowing them to be have the capacity to detect low-level circulation centers and rain-bands. Since 37 GHz generally senses the atmosphere nearer the surface than 89 GHz, parallax error is less, which allows for more accurate determination of tropical cyclone eyes and eye-walls. Here we show 37 GHz color composites, where the polarization corrected image is placed into the red gun (scaled from 260 to 280 K reverse tonality), the vertical polarization is placed into the green gun (scaled from 160 to 300 K), and the horizontal polarization image is placed into the blue gun (scaled from 180 to 310 K). In the resulting color composite product, the sea surface is green, deep convection appears pink, and low-level water clouds and rain appear cyan. One limitation of passive microwave imagery at 37 GHz is the limited revisit time of polar-orbiting satellites over tropical cyclones. To overcome this limitation, we developed synthetic imagery from geostationary satellite data using machine learning. This product displays synthetic tropical cyclone-centered 37 GHz imagery at 4 km on a Mercator grid from a convolutional neural network.

Synthetic Passive Microwave 89 GHz Imagery (Diffusion)

Passive microwave imagery centered at 89 GHz provides information regarding tropical cyclone location, deep convection, ice, and liquid water structure; however, this imagery is only available from polar-orbiting satellites, limiting the revisit time and resulting in coverage gaps and increasing latency. To overcome this limitation, we have developed synthetic 89 GHz imagery from geostationary satellite data using machine learning. This product displays 89 GHz imagery at 2 km on a Mercator projection predicted by a score-based diffusion model.

Synthetic Passive Microwave 37 GHz Imagery (Diffusion)

Passive microwave observations near the 37 GHz frequency provide insight into the precipitation structure of systems. This frequency is sensitive to both ice scattering, indicating deep convection, and liquid water emissions, indicating rain; however, a limitation of passive microwave imagery is their limited revisit time. To provide continuous, high-resolution imagery, we developed synthetic 37 GHz imagery from geostationary satellite data using a score-based diffusion model. This product displays the 37 GHz color composite (Mercator projection, 2 km grid spacing), which highlights the emissions and scattering signals in a single image: the sea surface is green, deep convection appears pink, and low-level water clouds and rain appear cyan.

GISMO 89, 37, 183 GHz Brightness Temperature

Geostationary Imagery for Synthesized Microwave Observations (GISMO) - GISMO uses available channels from the global constellation of geostationary satellites to generate synthetic passive microwave imagery to mimic the imagery available from low-Earth orbiting. The GISMO machine learning algorithm is trained using passive microwave observations from GMI and AMSR2 available from the Tropical Cyclone PRecipitation, Infrared, Microwave, and Environmental Dataset (TC PRIMED).