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Abstract
Infrared window (∼11 μm) brightness temperatures from global geostationary meteorological instruments were calibrated using the High Resolution Infrared Radiation Sounder (HIRS) as an independent analysis of the satellite intercalibration performed by the International Satellite Cloud Climatology Project (ISCCP). Criteria for matching geostationary observations with HIRS from previous literature were inadequate to analyze a shift in calibration due to the limited range of resulting temperatures. The result was an inability to determine the impact of a calibration error on observations of cold clouds. To better understand the calibration error, a new set of matchup criteria that collected targets at all temperatures proportionately showed a significant shift in the ISCCP calibration. Using the new criteria, it became apparent that observations of cold temperatures were biased too cold. A correction based on these results removed the bias between the geostationary and HIRS observations.
Abstract
Infrared window (∼11 μm) brightness temperatures from global geostationary meteorological instruments were calibrated using the High Resolution Infrared Radiation Sounder (HIRS) as an independent analysis of the satellite intercalibration performed by the International Satellite Cloud Climatology Project (ISCCP). Criteria for matching geostationary observations with HIRS from previous literature were inadequate to analyze a shift in calibration due to the limited range of resulting temperatures. The result was an inability to determine the impact of a calibration error on observations of cold clouds. To better understand the calibration error, a new set of matchup criteria that collected targets at all temperatures proportionately showed a significant shift in the ISCCP calibration. Using the new criteria, it became apparent that observations of cold temperatures were biased too cold. A correction based on these results removed the bias between the geostationary and HIRS observations.
Abstract
Numerous agencies around the world perform postseason analysis of tropical cyclone position and intensity, a process described as “best tracking.” However, this process is temporally and spatially inhomogeneous because data availability, operational techniques, and knowledge have changed over time and differ among agencies. The net result is that positions and intensities often vary for any given storm for different agencies. In light of these differences, it is imperative to analyze and document the interagency differences in tropical cyclone intensities. To that end, maximum sustained winds from different agencies were compared using data from the International Best Track Archive for Climate Stewardship (IBTrACS) global tropical cyclone dataset. Comparisons were made for a recent 5-yr period to investigate the current differences, where linear systematic differences were evident. Time series of the comparisons also showed temporal changes in the systematic differences, which suggest changes in operational procedures. Initial attempts were made to normalize maximum sustained winds by correcting for known changes in operational procedures. The result was mixed, in that the adjustments removed some but not all of the systematic differences. This suggests that more details on operational procedures are needed and that a complete reanalysis of tropical cyclone intensities should be performed.
Abstract
Numerous agencies around the world perform postseason analysis of tropical cyclone position and intensity, a process described as “best tracking.” However, this process is temporally and spatially inhomogeneous because data availability, operational techniques, and knowledge have changed over time and differ among agencies. The net result is that positions and intensities often vary for any given storm for different agencies. In light of these differences, it is imperative to analyze and document the interagency differences in tropical cyclone intensities. To that end, maximum sustained winds from different agencies were compared using data from the International Best Track Archive for Climate Stewardship (IBTrACS) global tropical cyclone dataset. Comparisons were made for a recent 5-yr period to investigate the current differences, where linear systematic differences were evident. Time series of the comparisons also showed temporal changes in the systematic differences, which suggest changes in operational procedures. Initial attempts were made to normalize maximum sustained winds by correcting for known changes in operational procedures. The result was mixed, in that the adjustments removed some but not all of the systematic differences. This suggests that more details on operational procedures are needed and that a complete reanalysis of tropical cyclone intensities should be performed.
Abstract
The GOES-8/Imager has provided scientifically valuable imagery since its launch in April of 1994. However, without an onboard calibration source most research applications involving its data have been limited to qualitative analysis of the imagery. Presented herein is a review of previous quantitative work, including the prelaunch calibration information, and results of a new calibration effort that compares GOES-8/Imager raw counts of clear ocean scenes to theoretical satellite-detected radiance values from a radiative transfer model. Monthly averages of the calibration coefficient are presented at 6-month intervals from August 1995 through August 1999. Although the technique differs from previous calibration efforts, which compare Geostationary Operational Environmental Satellite observations to some reference instrument, the new calibration results agree well with previous results. The calibration suggests a one-time decrease of 7.6% shortly after launch, and an ongoing annual degradation of 5.6% thereafter.
Abstract
The GOES-8/Imager has provided scientifically valuable imagery since its launch in April of 1994. However, without an onboard calibration source most research applications involving its data have been limited to qualitative analysis of the imagery. Presented herein is a review of previous quantitative work, including the prelaunch calibration information, and results of a new calibration effort that compares GOES-8/Imager raw counts of clear ocean scenes to theoretical satellite-detected radiance values from a radiative transfer model. Monthly averages of the calibration coefficient are presented at 6-month intervals from August 1995 through August 1999. Although the technique differs from previous calibration efforts, which compare Geostationary Operational Environmental Satellite observations to some reference instrument, the new calibration results agree well with previous results. The calibration suggests a one-time decrease of 7.6% shortly after launch, and an ongoing annual degradation of 5.6% thereafter.
Abstract
In spite of numerous studies on the remote sensing of aerosols from satellites, the magnitude of aerosol climate forcing remains uncertain. However, data from the Advanced Very High Resolution Radiometer (AVHRR) Pathfinder-Atmosphere (PATMOS) dataset—a statistical reduction of more than 19 yr of AVHRR data (1981–2000)—could provide nearly 20 yr of aerosol history. PATMOS data have a daily 110 × 110 km2 equal-area grid that contains means and standard deviations of AVHRR observations within each grid cell. This research is a first step toward understanding aerosols over land with PATMOS data. Herein, the aerosol optical depth is retrieved over land at numerous Aerosol Robotic Network (AERONET) sites around the globe using PATMOS cloud-free reflectances. First, the surface bidirectional reflectance distribution function (BRDF) is retrieved using a lookup table created with a radiative transfer model and the Rahman BRDF. Aerosol optical depths are then retrieved using the retrieved BRDF parameters and the PATMOS reflectances assuming a globally constant aerosol model. This method is applied to locations with ground truth measurements, where comparisons show that the best retrievals are made by estimating the surface reflectance using observations grouped by month. Random errors (i.e., correlation coefficients and standard error of estimate) in this case are lower than those where the surface BRDF is allowed year-to-year variations. By grouping the comparison results by land cover type, it was found that less noise is expected over forested regions, with a significant potential for retrieval for 80% of all land surfaces. These results and analyses suggest that the PATMOS data can provide valuable information on aerosols over land.
Abstract
In spite of numerous studies on the remote sensing of aerosols from satellites, the magnitude of aerosol climate forcing remains uncertain. However, data from the Advanced Very High Resolution Radiometer (AVHRR) Pathfinder-Atmosphere (PATMOS) dataset—a statistical reduction of more than 19 yr of AVHRR data (1981–2000)—could provide nearly 20 yr of aerosol history. PATMOS data have a daily 110 × 110 km2 equal-area grid that contains means and standard deviations of AVHRR observations within each grid cell. This research is a first step toward understanding aerosols over land with PATMOS data. Herein, the aerosol optical depth is retrieved over land at numerous Aerosol Robotic Network (AERONET) sites around the globe using PATMOS cloud-free reflectances. First, the surface bidirectional reflectance distribution function (BRDF) is retrieved using a lookup table created with a radiative transfer model and the Rahman BRDF. Aerosol optical depths are then retrieved using the retrieved BRDF parameters and the PATMOS reflectances assuming a globally constant aerosol model. This method is applied to locations with ground truth measurements, where comparisons show that the best retrievals are made by estimating the surface reflectance using observations grouped by month. Random errors (i.e., correlation coefficients and standard error of estimate) in this case are lower than those where the surface BRDF is allowed year-to-year variations. By grouping the comparison results by land cover type, it was found that less noise is expected over forested regions, with a significant potential for retrieval for 80% of all land surfaces. These results and analyses suggest that the PATMOS data can provide valuable information on aerosols over land.
Abstract
The International Satellite Cloud Climatology Project (ISCCP) B1 data, which were recently rescued at the National Oceanic and Atmospheric Administration’s National Climatic Data Center (NOAA/NCDC), are a resource for the study of the earth’s climate. The ISCCP B1 data represent geostationary satellite imagery for all channels, including the infrared (IR), visible, and IR water vapor sensors. These are global 3-hourly snapshots from satellites around the world, covering the time period from 1979 to present at approximately 10-km spatial resolution. ISCCP B1 data will be used in the reprocessing of the cloud products, resulting in a higher-resolution ISCCP cloud climatology, surface radiation budget (SRB), etc. To realize the promise of a higher-resolution cloud climatology from the B1 data, an independent assessment of the calibration of the visible band was performed. The present study aims to accomplish this by cross-calibrating with the intercalibrated Advanced Very High Resolution Radiometer (AVHRR) reflectance data from the AVHRR Pathfinder Atmospheres–Extended (PATMOS-x) dataset. Since the reflectance calibration approach followed in the PATMOS-x dataset is radiometrically tied to the absolute calibration of the National Aeronautics and Space Administration’s (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) imager instrument, the present intercalibration scheme yields calibration coefficients consistent with MODIS. Results from this study show that the two independent sets (this study and the ISCCP) of results agree to within their mutual uncertainties. An independent approach to calibration based on multiyear observations over spatially and temporally invariant desert sites has also been used for validation. Results reveal that for most of the geostationary satellites, the mean difference with ISCCP calibration is less than 3% with the random errors under 2%. Another result is that this extends the intercalibrated record to beyond what ISCCP provides (prior to 1983 and beyond 2009).
Abstract
The International Satellite Cloud Climatology Project (ISCCP) B1 data, which were recently rescued at the National Oceanic and Atmospheric Administration’s National Climatic Data Center (NOAA/NCDC), are a resource for the study of the earth’s climate. The ISCCP B1 data represent geostationary satellite imagery for all channels, including the infrared (IR), visible, and IR water vapor sensors. These are global 3-hourly snapshots from satellites around the world, covering the time period from 1979 to present at approximately 10-km spatial resolution. ISCCP B1 data will be used in the reprocessing of the cloud products, resulting in a higher-resolution ISCCP cloud climatology, surface radiation budget (SRB), etc. To realize the promise of a higher-resolution cloud climatology from the B1 data, an independent assessment of the calibration of the visible band was performed. The present study aims to accomplish this by cross-calibrating with the intercalibrated Advanced Very High Resolution Radiometer (AVHRR) reflectance data from the AVHRR Pathfinder Atmospheres–Extended (PATMOS-x) dataset. Since the reflectance calibration approach followed in the PATMOS-x dataset is radiometrically tied to the absolute calibration of the National Aeronautics and Space Administration’s (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) imager instrument, the present intercalibration scheme yields calibration coefficients consistent with MODIS. Results from this study show that the two independent sets (this study and the ISCCP) of results agree to within their mutual uncertainties. An independent approach to calibration based on multiyear observations over spatially and temporally invariant desert sites has also been used for validation. Results reveal that for most of the geostationary satellites, the mean difference with ISCCP calibration is less than 3% with the random errors under 2%. Another result is that this extends the intercalibrated record to beyond what ISCCP provides (prior to 1983 and beyond 2009).
Abstract
An objective method for estimating tropical cyclone (TC) intensity using historical hurricane satellite data (HURSAT) is developed and tested. This new method, referred to as feature analogs in satellite imagery (FASI), requires a TC's center location to extract azimuthal brightness temperature (BT) profiles from current imagery as well as BT profiles from imagery 6, 12, and 24 h prior. Instead of using regression techniques, the estimated TC intensity is determined from the 10 closest analogs to this TC based on the BT profiles using a k-nearest-neighbor algorithm. The FASI technique was trained and validated using intensity data from aircraft reconnaissance in the North Atlantic Ocean, where the data were restricted to include storms that are over water and south of 45°N. This subset comprised 2016 observations from 165 storms during 1988–2006. Several tests were implemented to statistically justify the FASI algorithm using n-fold cross validation. The resulting average mean absolute intensity error was 10.9 kt (50% of estimates are within 10 kt, 1 kt = 0.51 m s−1) or 8.4 mb (50% of estimates are within 8 mb); its accuracy is on par with other objective techniques. This approach has the potential to provide global TC intensity estimates that could augment intensity estimates made by other objective techniques.
Abstract
An objective method for estimating tropical cyclone (TC) intensity using historical hurricane satellite data (HURSAT) is developed and tested. This new method, referred to as feature analogs in satellite imagery (FASI), requires a TC's center location to extract azimuthal brightness temperature (BT) profiles from current imagery as well as BT profiles from imagery 6, 12, and 24 h prior. Instead of using regression techniques, the estimated TC intensity is determined from the 10 closest analogs to this TC based on the BT profiles using a k-nearest-neighbor algorithm. The FASI technique was trained and validated using intensity data from aircraft reconnaissance in the North Atlantic Ocean, where the data were restricted to include storms that are over water and south of 45°N. This subset comprised 2016 observations from 165 storms during 1988–2006. Several tests were implemented to statistically justify the FASI algorithm using n-fold cross validation. The resulting average mean absolute intensity error was 10.9 kt (50% of estimates are within 10 kt, 1 kt = 0.51 m s−1) or 8.4 mb (50% of estimates are within 8 mb); its accuracy is on par with other objective techniques. This approach has the potential to provide global TC intensity estimates that could augment intensity estimates made by other objective techniques.
Abstract
Best track data generally consist of the positions and intensities during the life cycle of a tropical cyclone. Despite the widespread interest in the distribution, frequency, and intensity of tropical cyclones worldwide, no publicly available central repository of global best track data from international agencies has been in existence. While there are numerous international centers that forecast tropical cyclones and archive best track data for their defined regions, most researchers traditionally use best track data from a very small subset of centers to construct global datasets and climatologies. This practice results in tropical cyclones that are either missed and/or misrepresented. While the process of combining positions and intensities from disparate data sources can be arduous, it is worthwhile and necessary in light of their importance. The nature of historical best track data is that they are prone to issues with intensity (maximum surface wind and minimum central pressure), especially in the presatellite era. This study is not a reanalysis effort and makes no attempt to correct any longstanding debates about the accuracy of the historical data. Rather, it simply and objectively combines all of the best track data from each of the regional forecast centers that provided best tracks into one single point for distribution, and the methods used to construct the dataset are the focus of this work. Processes are therefore described herein that detail the combining of tropical cyclone best track data with the techniques used to assess the quality of the minimum central pressure and maximum sustained wind speed of each reported tropical cyclone. The result is a comprehensive global best track compilation dataset that contains information on all documented tropical cyclones: the International Best Track Archive for Climate Stewardship (IBTrACS).
Abstract
Best track data generally consist of the positions and intensities during the life cycle of a tropical cyclone. Despite the widespread interest in the distribution, frequency, and intensity of tropical cyclones worldwide, no publicly available central repository of global best track data from international agencies has been in existence. While there are numerous international centers that forecast tropical cyclones and archive best track data for their defined regions, most researchers traditionally use best track data from a very small subset of centers to construct global datasets and climatologies. This practice results in tropical cyclones that are either missed and/or misrepresented. While the process of combining positions and intensities from disparate data sources can be arduous, it is worthwhile and necessary in light of their importance. The nature of historical best track data is that they are prone to issues with intensity (maximum surface wind and minimum central pressure), especially in the presatellite era. This study is not a reanalysis effort and makes no attempt to correct any longstanding debates about the accuracy of the historical data. Rather, it simply and objectively combines all of the best track data from each of the regional forecast centers that provided best tracks into one single point for distribution, and the methods used to construct the dataset are the focus of this work. Processes are therefore described herein that detail the combining of tropical cyclone best track data with the techniques used to assess the quality of the minimum central pressure and maximum sustained wind speed of each reported tropical cyclone. The result is a comprehensive global best track compilation dataset that contains information on all documented tropical cyclones: the International Best Track Archive for Climate Stewardship (IBTrACS).
Abstract
Intense tropical cyclones (TCs) generally produce a cloud-free center with calm winds, called the eye. The Automated Rotational Center Hurricane Eye Retrieval (ARCHER) algorithm is used to analyze Hurricane Satellite (HURSAT) B1 infrared satellite imagery data for storms occurring globally from 1982 to 2015. HURSAT B1 data provide 3-hourly observations of TCs. The result is a 34-yr climatology of eye location and size. During that time period, eyes are identified in about 13% of all infrared images and slightly more than half of all storms produced an eye. Those that produce an eye have (on average) 30 h of eye scenes. Hurricane Ioke (1992) had the most eye images (98, which is 12 complete days with an eye). The median wind speed of a system with an eye is 97 kt (50 m s−1) [cf. 35 kt (18 m s−1) for those without an eye]. Eyes are much more frequent in the Northern Hemisphere (particularly in the western Pacific) but eyes are larger in the Southern Hemisphere. The regions where eyes occur are expanding poleward, thus expanding the area at risk of TC-related damage. Also, eye scene occurrence can provide an objective measure of TC activity in place of those based on maximum wind speeds, which can be affected by available observations and forecast agency practices.
Abstract
Intense tropical cyclones (TCs) generally produce a cloud-free center with calm winds, called the eye. The Automated Rotational Center Hurricane Eye Retrieval (ARCHER) algorithm is used to analyze Hurricane Satellite (HURSAT) B1 infrared satellite imagery data for storms occurring globally from 1982 to 2015. HURSAT B1 data provide 3-hourly observations of TCs. The result is a 34-yr climatology of eye location and size. During that time period, eyes are identified in about 13% of all infrared images and slightly more than half of all storms produced an eye. Those that produce an eye have (on average) 30 h of eye scenes. Hurricane Ioke (1992) had the most eye images (98, which is 12 complete days with an eye). The median wind speed of a system with an eye is 97 kt (50 m s−1) [cf. 35 kt (18 m s−1) for those without an eye]. Eyes are much more frequent in the Northern Hemisphere (particularly in the western Pacific) but eyes are larger in the Southern Hemisphere. The regions where eyes occur are expanding poleward, thus expanding the area at risk of TC-related damage. Also, eye scene occurrence can provide an objective measure of TC activity in place of those based on maximum wind speeds, which can be affected by available observations and forecast agency practices.
Abstract
Using the International Best Track Archive for Climate Stewardship (IBTrACS), the climatology of tropical cyclones is compared between two global best track datasets: 1) the World Meteorological Organization (WMO) subset of IBTrACS (IBTrACS-WMO) and 2) a combination of data from the National Hurricane Center and the Joint Typhoon Warning Center (NHC+JTWC). Comparing the climatologies between IBTrACS-WMO and NHC+JTWC highlights some of the heterogeneities inherent in these datasets for the period of global satellite coverage 1981–2010. The results demonstrate the sensitivity of these climatologies to the choice of best track dataset. Previous studies have examined best track heterogeneities in individual regions, usually the North Atlantic and west Pacific. This study puts those regional issues into their global context. The differences between NHC+JTWC and IBTrACS-WMO are greatest in the west Pacific, where the strongest storms are substantially weaker in IBTrACS-WMO. These disparities strongly affect the global measures of tropical cyclone activity because 30% of the world’s tropical cyclones form in the west Pacific. Because JTWC employs similar procedures throughout most of the globe, the comparisons in this study highlight differences between WMO agencies. For example, NHC+JTWC has more 96-kt (~49 m s−1) storms than IBTrACS-WMO in the west Pacific but fewer in the Australian region. This discrepancy probably points to differing operational procedures between the WMO agencies in the two regions. Without better documentation of historical analysis procedures, the only way to remedy these heterogeneities will be through systematic reanalysis.
Abstract
Using the International Best Track Archive for Climate Stewardship (IBTrACS), the climatology of tropical cyclones is compared between two global best track datasets: 1) the World Meteorological Organization (WMO) subset of IBTrACS (IBTrACS-WMO) and 2) a combination of data from the National Hurricane Center and the Joint Typhoon Warning Center (NHC+JTWC). Comparing the climatologies between IBTrACS-WMO and NHC+JTWC highlights some of the heterogeneities inherent in these datasets for the period of global satellite coverage 1981–2010. The results demonstrate the sensitivity of these climatologies to the choice of best track dataset. Previous studies have examined best track heterogeneities in individual regions, usually the North Atlantic and west Pacific. This study puts those regional issues into their global context. The differences between NHC+JTWC and IBTrACS-WMO are greatest in the west Pacific, where the strongest storms are substantially weaker in IBTrACS-WMO. These disparities strongly affect the global measures of tropical cyclone activity because 30% of the world’s tropical cyclones form in the west Pacific. Because JTWC employs similar procedures throughout most of the globe, the comparisons in this study highlight differences between WMO agencies. For example, NHC+JTWC has more 96-kt (~49 m s−1) storms than IBTrACS-WMO in the west Pacific but fewer in the Australian region. This discrepancy probably points to differing operational procedures between the WMO agencies in the two regions. Without better documentation of historical analysis procedures, the only way to remedy these heterogeneities will be through systematic reanalysis.
Abstract
The historical global “best track” records of tropical cyclones extend back to the mid-nineteenth century in some regions, but formal analysis of these records is encumbered by temporal heterogeneities in the data. This is particularly problematic when attempting to detect trends in tropical cyclone metrics that may be attributable to climate change. Here the authors apply a state-of-the-art automated algorithm to a globally homogenized satellite data record to create a more temporally consistent record of tropical cyclone intensity within the period 1982–2009, and utilize this record to investigate the robustness of trends found in the best-track data. In particular, the lifetime maximum intensity (LMI) achieved by each reported storm is calculated and the frequency distribution of LMI is tested for changes over this period.
To address the unique issues in regions around the Indian Ocean, which result from a discontinuity introduced into the satellite data in 1998, a direct homogenization procedure is applied in which post-1998 data are degraded to pre-1998 standards. This additional homogenization step is found to measurably reduce LMI trends, but the global trends in the LMI of the strongest storms remain positive, with amplitudes of around +1 m s−1 decade−1 and p value = 0.1. Regional trends, in m s−1 decade−1, vary from −2 (p = 0.03) in the western North Pacific, +1.7 (p = 0.06) in the south Indian Ocean, +2.5 (p = 0.09) in the South Pacific, to +8 (p < 0.001) in the North Atlantic.
Abstract
The historical global “best track” records of tropical cyclones extend back to the mid-nineteenth century in some regions, but formal analysis of these records is encumbered by temporal heterogeneities in the data. This is particularly problematic when attempting to detect trends in tropical cyclone metrics that may be attributable to climate change. Here the authors apply a state-of-the-art automated algorithm to a globally homogenized satellite data record to create a more temporally consistent record of tropical cyclone intensity within the period 1982–2009, and utilize this record to investigate the robustness of trends found in the best-track data. In particular, the lifetime maximum intensity (LMI) achieved by each reported storm is calculated and the frequency distribution of LMI is tested for changes over this period.
To address the unique issues in regions around the Indian Ocean, which result from a discontinuity introduced into the satellite data in 1998, a direct homogenization procedure is applied in which post-1998 data are degraded to pre-1998 standards. This additional homogenization step is found to measurably reduce LMI trends, but the global trends in the LMI of the strongest storms remain positive, with amplitudes of around +1 m s−1 decade−1 and p value = 0.1. Regional trends, in m s−1 decade−1, vary from −2 (p = 0.03) in the western North Pacific, +1.7 (p = 0.06) in the south Indian Ocean, +2.5 (p = 0.09) in the South Pacific, to +8 (p < 0.001) in the North Atlantic.
