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- Author or Editor: Christopher S. Velden x
- Journal of Applied Meteorology and Climatology x
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Abstract
Passive microwave observations from the current NOAA series of polar-orbiting satellites of a large sample of North Atlantic tropical cyclones are qualitatively and quantitatively analyzed. Microwave observations can penetrate the cloud cover associated with tropical cyclones and capture the upper-level warm temperature anomaly, which is characteristic of these storms. The data are used to develop a statistical algorithm for estimating surface intensity. Based upon hydrostatic assumptions, linear regression relationships are developed between the satellite-depicted horizontal temperature gradient of the upper-level warm core (ΔT 250), and the surface intensity (ΔP SFC) as measured by reconnaissance reports. A good correlation is found to exist. Results indicate that standard errors of estimate of 8 mb and 13 kts are found for surface pressure and maximum winds, respectively. These errors are reduced when the effects of storm latitude, eye size, and surface-pressure tendency on the relationship are included. Knowledge gained in examining the accuracies and limitations of the current microwave sounders in tropical cyclone applications will be helpful in setting quantitative observational guidelines for future instruments.
Abstract
Passive microwave observations from the current NOAA series of polar-orbiting satellites of a large sample of North Atlantic tropical cyclones are qualitatively and quantitatively analyzed. Microwave observations can penetrate the cloud cover associated with tropical cyclones and capture the upper-level warm temperature anomaly, which is characteristic of these storms. The data are used to develop a statistical algorithm for estimating surface intensity. Based upon hydrostatic assumptions, linear regression relationships are developed between the satellite-depicted horizontal temperature gradient of the upper-level warm core (ΔT 250), and the surface intensity (ΔP SFC) as measured by reconnaissance reports. A good correlation is found to exist. Results indicate that standard errors of estimate of 8 mb and 13 kts are found for surface pressure and maximum winds, respectively. These errors are reduced when the effects of storm latitude, eye size, and surface-pressure tendency on the relationship are included. Knowledge gained in examining the accuracies and limitations of the current microwave sounders in tropical cyclone applications will be helpful in setting quantitative observational guidelines for future instruments.
Abstract
NOAA satellite microwave soundings, which penetrate high clouds, delineate the development and dissipation of the upper tropospheric warm core associated with a tropical cyclone. The storm's “core” may be detected from microwave imagery. Vertical cross sections reveal the intensification of the upper tropospheric warm core as the storm developes, and the downward propagation of the warm core as the storm dissipates. Excellent correlation is found between the horizontal Laplacian of an upper tropospheric temperature field and the intensity of the storm, as categorized by its surface central pressure and maximum sustained wind speed at the eye wall. The microwave monitoring of tropical cyclones is achieved in real time at the University of Wisconsin's Space Science and Engineering Center through high-speed teleconnections to direct readout receiving systems at Wallops Island, Virginia and Redwood City, California.
Abstract
NOAA satellite microwave soundings, which penetrate high clouds, delineate the development and dissipation of the upper tropospheric warm core associated with a tropical cyclone. The storm's “core” may be detected from microwave imagery. Vertical cross sections reveal the intensification of the upper tropospheric warm core as the storm developes, and the downward propagation of the warm core as the storm dissipates. Excellent correlation is found between the horizontal Laplacian of an upper tropospheric temperature field and the intensity of the storm, as categorized by its surface central pressure and maximum sustained wind speed at the eye wall. The microwave monitoring of tropical cyclones is achieved in real time at the University of Wisconsin's Space Science and Engineering Center through high-speed teleconnections to direct readout receiving systems at Wallops Island, Virginia and Redwood City, California.
Abstract
Fields of atmospheric motion vectors (AMVs) are routinely derived by tracking features in sequential geostationary satellite infrared, water vapor, and visible-channel imagery. While AMVs produced operationally by global data centers are routinely evaluated against rawinsondes, there is a relative dearth of validation opportunities over the tropical oceans—in particular, in the vicinity of tropical disturbances when anomalous flow fields and strongly sheared environments commonly exist. A field experiment in 2010 called Pre-Depression Investigation of Cloud-Systems in the Tropics (PREDICT) was conducted in the tropical west Atlantic Ocean and provides an opportunity to evaluate the quality of tropical AMVs and analyses derived from them. The importance of such a verification is threefold: 1) AMVs often provide the only input data for numerical weather prediction (NWP) over cloudy areas of the tropical oceans, 2) NWP data assimilation methods are increasingly reliant on accurate flow-dependent observation-error characteristics, and 3) global tropical analysis and forecast centers often rely on analyses and diagnostic products derived from the AMV fields. In this paper, the authors utilize dropsonde information from high-flying PREDICT aircraft to identify AMV characteristics and to better understand their errors in tropical-disturbance situations. It is found that, in general, the AMV observation errors are close to those identified in global validation studies. However, some distinct characteristics are uncovered in certain regimes associated with tropical disturbances. High-resolution analyses derived from the AMV fields are also examined and are found to be more reflective of anomalous flow fields than the respective Global Forecast System global model analyses.
Abstract
Fields of atmospheric motion vectors (AMVs) are routinely derived by tracking features in sequential geostationary satellite infrared, water vapor, and visible-channel imagery. While AMVs produced operationally by global data centers are routinely evaluated against rawinsondes, there is a relative dearth of validation opportunities over the tropical oceans—in particular, in the vicinity of tropical disturbances when anomalous flow fields and strongly sheared environments commonly exist. A field experiment in 2010 called Pre-Depression Investigation of Cloud-Systems in the Tropics (PREDICT) was conducted in the tropical west Atlantic Ocean and provides an opportunity to evaluate the quality of tropical AMVs and analyses derived from them. The importance of such a verification is threefold: 1) AMVs often provide the only input data for numerical weather prediction (NWP) over cloudy areas of the tropical oceans, 2) NWP data assimilation methods are increasingly reliant on accurate flow-dependent observation-error characteristics, and 3) global tropical analysis and forecast centers often rely on analyses and diagnostic products derived from the AMV fields. In this paper, the authors utilize dropsonde information from high-flying PREDICT aircraft to identify AMV characteristics and to better understand their errors in tropical-disturbance situations. It is found that, in general, the AMV observation errors are close to those identified in global validation studies. However, some distinct characteristics are uncovered in certain regimes associated with tropical disturbances. High-resolution analyses derived from the AMV fields are also examined and are found to be more reflective of anomalous flow fields than the respective Global Forecast System global model analyses.
Abstract
This study investigates the assignment of pressure heights to satellite-derived atmospheric motion vectors (AMVs), commonly known as cloud-drift and water vapor–motion winds. Large volumes of multispectral AMV datasets are compared with collocated rawinsonde wind profiles collected by the U.S. Department of Energy Atmospheric Radiation Measurement Program at three geographically disparate sites: the southern Great Plains, the North Slope of Alaska, and the tropical western Pacific Ocean. From a careful analysis of these comparisons, the authors estimate that mean AMV observation errors are ∼5–5.5 m s−1 and that vector height assignment is the dominant factor in AMV uncertainty, contributing up to 70% of the error. These comparisons also reveal that in most cases the RMS differences between matched AMVs and rawinsonde wind values are minimized if the rawinsonde values are averaged over specified layers. In other words, on average, the AMV values better correlate to a motion over a mean tropospheric layer rather than to a traditionally assigned discrete level. The height assignment behavioral characteristics are specifically identified according to AMV height (high cloud vs low cloud), type (spectral bands; clear vs cloudy), geolocation, height assignment method, and amount of environmental vertical wind shear present. The findings have potentially important implications for data assimilation of AMVs, and these are discussed.
Abstract
This study investigates the assignment of pressure heights to satellite-derived atmospheric motion vectors (AMVs), commonly known as cloud-drift and water vapor–motion winds. Large volumes of multispectral AMV datasets are compared with collocated rawinsonde wind profiles collected by the U.S. Department of Energy Atmospheric Radiation Measurement Program at three geographically disparate sites: the southern Great Plains, the North Slope of Alaska, and the tropical western Pacific Ocean. From a careful analysis of these comparisons, the authors estimate that mean AMV observation errors are ∼5–5.5 m s−1 and that vector height assignment is the dominant factor in AMV uncertainty, contributing up to 70% of the error. These comparisons also reveal that in most cases the RMS differences between matched AMVs and rawinsonde wind values are minimized if the rawinsonde values are averaged over specified layers. In other words, on average, the AMV values better correlate to a motion over a mean tropospheric layer rather than to a traditionally assigned discrete level. The height assignment behavioral characteristics are specifically identified according to AMV height (high cloud vs low cloud), type (spectral bands; clear vs cloudy), geolocation, height assignment method, and amount of environmental vertical wind shear present. The findings have potentially important implications for data assimilation of AMVs, and these are discussed.
Abstract
An improved version of the Automated Rotational Center Hurricane Eye Retrieval (ARCHER) tropical cyclone (TC) center-fixing algorithm, introduced here as “ARCHER-2,” is presented with a characterization of its accuracy and precision and a comparison with alternative methods. The algorithm is calibrated for 37- and 85–92-GHz microwave imagers; geostationary imagery at visible, near-infrared, and longwave infrared window channels; and scatterometer ambiguities. In addition to a center fix, ARCHER-2 produces a quantitative estimate of expected error that can be used automatically or manually to evaluate the suitability of a result. The median center-fix error ranges from 24 (using scatterometer) to 49 (using infrared window) km relative to the National Hurricane Center best track. Multisatellite, multisensor results can also be used together to produce a TC-track estimate that selects from the best of all of the available imagery in the ancillary “ARCHER-Track” product. The median error of ARCHER-Track varies between 17 and 38 km, depending on TC intensity and data latency. The bias of the product’s expected error varies between 0% and 12%, which translates to an average of only 4 km. When compared with operational, subjective center-fix estimates, the ARCHER-Track approach improves on 29%–43% of these cases at the tropical-depression and tropical-storm stages, at which further assistance is typically sought. This result demonstrates that ARCHER-2 and ARCHER-Track can complement and accelerate operational forecasting where needed and can furnish other automated TC-analysis methods with well-characterized center-fix information.
Abstract
An improved version of the Automated Rotational Center Hurricane Eye Retrieval (ARCHER) tropical cyclone (TC) center-fixing algorithm, introduced here as “ARCHER-2,” is presented with a characterization of its accuracy and precision and a comparison with alternative methods. The algorithm is calibrated for 37- and 85–92-GHz microwave imagers; geostationary imagery at visible, near-infrared, and longwave infrared window channels; and scatterometer ambiguities. In addition to a center fix, ARCHER-2 produces a quantitative estimate of expected error that can be used automatically or manually to evaluate the suitability of a result. The median center-fix error ranges from 24 (using scatterometer) to 49 (using infrared window) km relative to the National Hurricane Center best track. Multisatellite, multisensor results can also be used together to produce a TC-track estimate that selects from the best of all of the available imagery in the ancillary “ARCHER-Track” product. The median error of ARCHER-Track varies between 17 and 38 km, depending on TC intensity and data latency. The bias of the product’s expected error varies between 0% and 12%, which translates to an average of only 4 km. When compared with operational, subjective center-fix estimates, the ARCHER-Track approach improves on 29%–43% of these cases at the tropical-depression and tropical-storm stages, at which further assistance is typically sought. This result demonstrates that ARCHER-2 and ARCHER-Track can complement and accelerate operational forecasting where needed and can furnish other automated TC-analysis methods with well-characterized center-fix information.
Abstract
Precise center-fixing of tropical cyclones (TCs) is critical for operational forecasting, intensity estimation, and visualization. Current procedures are usually performed with manual input from a human analyst, using multispectral satellite imagery as the primary tools. While adequate in many cases, subjective interpretation can often lead to variance in the estimated center positions. In this paper an objective, robust algorithm is presented for resolving the rotational center of TCs: the Automated Rotational Center Hurricane Eye Retrieval (ARCHER). The algorithm finds the center of rotation using spirally oriented brightness temperature gradients in the TC banding patterns in combination with gradients along the ring-shaped edge of a possible eye. It is calibrated and validated using 85–92-GHz passive microwave imagery because of this frequency’s relative ubiquity in TC applications; however, similar versions of ARCHER are also shown to work effectively with other satellite imagery of TCs. In TC cases with estimated low to moderate vertical wind shear, the accuracy (RMSE) of the ARCHER estimated center positions is 17 km (9 km for category 1–5 hurricanes). In cases with estimated high vertical shear, the accuracy of the ARCHER estimated center positions is 31 km (21 km for category 2–5 hurricanes).
Abstract
Precise center-fixing of tropical cyclones (TCs) is critical for operational forecasting, intensity estimation, and visualization. Current procedures are usually performed with manual input from a human analyst, using multispectral satellite imagery as the primary tools. While adequate in many cases, subjective interpretation can often lead to variance in the estimated center positions. In this paper an objective, robust algorithm is presented for resolving the rotational center of TCs: the Automated Rotational Center Hurricane Eye Retrieval (ARCHER). The algorithm finds the center of rotation using spirally oriented brightness temperature gradients in the TC banding patterns in combination with gradients along the ring-shaped edge of a possible eye. It is calibrated and validated using 85–92-GHz passive microwave imagery because of this frequency’s relative ubiquity in TC applications; however, similar versions of ARCHER are also shown to work effectively with other satellite imagery of TCs. In TC cases with estimated low to moderate vertical wind shear, the accuracy (RMSE) of the ARCHER estimated center positions is 17 km (9 km for category 1–5 hurricanes). In cases with estimated high vertical shear, the accuracy of the ARCHER estimated center positions is 31 km (21 km for category 2–5 hurricanes).
Abstract
Conventional methods of viewing and combining retrieved geophysical fields from polar-orbiting satellites often complicate the work of end users because of the erratic time differences between overpasses, the significant time gaps between elements of a composite image, or simply the different requirements for interpretation between contributing instruments. However, it is possible to mitigate these issues for any number of retrieved quantities in which the tracer lifetime exceeds the sampling time. This paper presents a method that uses “advective blending” to create high-fidelity composites of data from polar-orbiting satellites at high temporal resolution, including a characterization of error as a function of time gap between satellite overpasses. The method is especially effective for tracers with lifetimes of longer than 7 h. Examples are presented using microwave-based retrievals of total precipitable water (TPW) over the ocean, from the Cooperative Institute for Meteorological Satellite Studies (CIMSS) Morphed Integrated Microwave Imagery at CIMSS TPW product (MIMIC-TPW). The mean average error of a global 0.25° × 0.25° product at 1-h resolution is 0.5–2 mm, which is very reasonable for most applications.
Abstract
Conventional methods of viewing and combining retrieved geophysical fields from polar-orbiting satellites often complicate the work of end users because of the erratic time differences between overpasses, the significant time gaps between elements of a composite image, or simply the different requirements for interpretation between contributing instruments. However, it is possible to mitigate these issues for any number of retrieved quantities in which the tracer lifetime exceeds the sampling time. This paper presents a method that uses “advective blending” to create high-fidelity composites of data from polar-orbiting satellites at high temporal resolution, including a characterization of error as a function of time gap between satellite overpasses. The method is especially effective for tracers with lifetimes of longer than 7 h. Examples are presented using microwave-based retrievals of total precipitable water (TPW) over the ocean, from the Cooperative Institute for Meteorological Satellite Studies (CIMSS) Morphed Integrated Microwave Imagery at CIMSS TPW product (MIMIC-TPW). The mean average error of a global 0.25° × 0.25° product at 1-h resolution is 0.5–2 mm, which is very reasonable for most applications.
Abstract
A geostationary satellite–derived cloud product that is based on a tropical-overshooting-top (TOT) detection algorithm is described for applications over tropical oceans. TOTs are identified using a modified version of a midlatitude overshooting-top detection algorithm developed for severe-weather applications. The algorithm is applied to identify TOT activity associated with Atlantic Ocean tropical cyclones (TCs). The detected TOTs can serve as a proxy for “hot towers,” which represent intense convection with possible links to TC rapid intensification (RI). The purpose of this study is to describe the adaptation of the midlatitude overshooting-top detection algorithm to the tropics and to provide an initial exploration of possible correlations between TOT trends in developing TCs and subsequent RI. This is followed by a cursory examination of the TOT parameter’s potential as a predictor of RI both on its own and in multiparameter RI forecast schemes. RI forecast skill potential is investigated by examining empirical thresholds of TOT activity and trends within prescribed radii of a large sample of developing North Atlantic TC centers. An independent test on Atlantic TCs in 2006–07 reveals that an empirically based TOT scheme has potential as a predictor for RI occurring in the subsequent 24 h, especially for RI maximum wind thresholds of 25 and 30 kt (24 h)−1 (1 kt ≈ 0.5 m s−1). As expected, the stand-alone TOT-based RI scheme is comparatively less accurate than existing objective multiparameter RI prediction methods. A preliminary experiment that adds TOT-based predictors to an objective logistic regression-based scheme is shown to improve slightly the forecast skill of RI, however.
Abstract
A geostationary satellite–derived cloud product that is based on a tropical-overshooting-top (TOT) detection algorithm is described for applications over tropical oceans. TOTs are identified using a modified version of a midlatitude overshooting-top detection algorithm developed for severe-weather applications. The algorithm is applied to identify TOT activity associated with Atlantic Ocean tropical cyclones (TCs). The detected TOTs can serve as a proxy for “hot towers,” which represent intense convection with possible links to TC rapid intensification (RI). The purpose of this study is to describe the adaptation of the midlatitude overshooting-top detection algorithm to the tropics and to provide an initial exploration of possible correlations between TOT trends in developing TCs and subsequent RI. This is followed by a cursory examination of the TOT parameter’s potential as a predictor of RI both on its own and in multiparameter RI forecast schemes. RI forecast skill potential is investigated by examining empirical thresholds of TOT activity and trends within prescribed radii of a large sample of developing North Atlantic TC centers. An independent test on Atlantic TCs in 2006–07 reveals that an empirically based TOT scheme has potential as a predictor for RI occurring in the subsequent 24 h, especially for RI maximum wind thresholds of 25 and 30 kt (24 h)−1 (1 kt ≈ 0.5 m s−1). As expected, the stand-alone TOT-based RI scheme is comparatively less accurate than existing objective multiparameter RI prediction methods. A preliminary experiment that adds TOT-based predictors to an objective logistic regression-based scheme is shown to improve slightly the forecast skill of RI, however.
Abstract
Errors in the height assignment of some satellite-derived winds exist because the satellites sense radiation emitted from a finite layer of the atmosphere rather than a specific level. Problems in data assimilation may arise because the motion of a measured layer is often represented by a single-level value. In this research, Geostationary Operational Environmental Satellite (GOES)–derived cloud and water-vapor motion winds are compared with collocated rawinsonde observations (raobs). The satellite winds are compared with the entire profile of the collocated raob data to determine the vertical error characteristics of the satellite winds. These results are then tested in numerical weather prediction. Comparisons with the entire profile of the collocated raobs indicate that clear-air water-vapor winds represent deeper layers than do either infrared or water-vapor cloud-tracked winds. In addition, it is found that if the vertical gradient of moisture is smooth and uniform from near the height assignment upward, the clear-air water-vapor wind tends to represent a deeper layer than if the moisture gradient contains a sharp peak. The information from the comparisons is then used in numerical model simulations of two separate events to test the results. In the first case, the use of the satellite data results in improved storm tracks during the initial ∼24-h forecast period. Mean statistics indicate that the use of satellite winds generally improves the simulation with time. The simulation results suggest that it is beneficial to spread the satellite wind information over multiple levels, particularly when the moisture profile is used to define the vertical influence.
Abstract
Errors in the height assignment of some satellite-derived winds exist because the satellites sense radiation emitted from a finite layer of the atmosphere rather than a specific level. Problems in data assimilation may arise because the motion of a measured layer is often represented by a single-level value. In this research, Geostationary Operational Environmental Satellite (GOES)–derived cloud and water-vapor motion winds are compared with collocated rawinsonde observations (raobs). The satellite winds are compared with the entire profile of the collocated raob data to determine the vertical error characteristics of the satellite winds. These results are then tested in numerical weather prediction. Comparisons with the entire profile of the collocated raobs indicate that clear-air water-vapor winds represent deeper layers than do either infrared or water-vapor cloud-tracked winds. In addition, it is found that if the vertical gradient of moisture is smooth and uniform from near the height assignment upward, the clear-air water-vapor wind tends to represent a deeper layer than if the moisture gradient contains a sharp peak. The information from the comparisons is then used in numerical model simulations of two separate events to test the results. In the first case, the use of the satellite data results in improved storm tracks during the initial ∼24-h forecast period. Mean statistics indicate that the use of satellite winds generally improves the simulation with time. The simulation results suggest that it is beneficial to spread the satellite wind information over multiple levels, particularly when the moisture profile is used to define the vertical influence.
Abstract
Assigning accurate heights to convective cloud tops that penetrate into the upper troposphere–lower stratosphere (UTLS) region using infrared (IR) satellite imagery has been an unresolved issue for the satellite research community. The height assignment for the tops of optically thick clouds is typically accomplished by matching the observed IR brightness temperature (BT) with a collocated rawinsonde or numerical weather prediction (NWP) profile. However, “overshooting tops” (OTs) are typically colder (in BT) than any vertical level in the associated profile, leaving the height of these tops undetermined using this standard approach. A new method is described here for calculating the heights of convectively driven OTs using the characteristic temperature lapse rate of the cloud top as it ascends into the UTLS region. Using 108 MODIS-identified OT events that are directly observed by the CloudSat Cloud Profiling Radar (CPR), the MODIS-derived brightness temperature difference (BTD) between the OT and anvil regions can be defined. This BTD is combined with the CPR- and NWP-derived height difference between these two regions to determine the mean lapse rate, −7.34 K km−1, for the 108 events. The anvil height is typically well known, and an automated OT detection algorithm is used to derive BTD, so the lapse rate allows a height to be calculated for any detected OT. An empirical fit between MODIS and geostationary imager IR BT for OTs and anvil regions was performed to enable application of this method to coarser-spatial-resolution geostationary data. Validation indicates that ~75% (65%) of MODIS (geostationary) OT heights are within ±500 m of the coincident CPR-estimated heights.
Abstract
Assigning accurate heights to convective cloud tops that penetrate into the upper troposphere–lower stratosphere (UTLS) region using infrared (IR) satellite imagery has been an unresolved issue for the satellite research community. The height assignment for the tops of optically thick clouds is typically accomplished by matching the observed IR brightness temperature (BT) with a collocated rawinsonde or numerical weather prediction (NWP) profile. However, “overshooting tops” (OTs) are typically colder (in BT) than any vertical level in the associated profile, leaving the height of these tops undetermined using this standard approach. A new method is described here for calculating the heights of convectively driven OTs using the characteristic temperature lapse rate of the cloud top as it ascends into the UTLS region. Using 108 MODIS-identified OT events that are directly observed by the CloudSat Cloud Profiling Radar (CPR), the MODIS-derived brightness temperature difference (BTD) between the OT and anvil regions can be defined. This BTD is combined with the CPR- and NWP-derived height difference between these two regions to determine the mean lapse rate, −7.34 K km−1, for the 108 events. The anvil height is typically well known, and an automated OT detection algorithm is used to derive BTD, so the lapse rate allows a height to be calculated for any detected OT. An empirical fit between MODIS and geostationary imager IR BT for OTs and anvil regions was performed to enable application of this method to coarser-spatial-resolution geostationary data. Validation indicates that ~75% (65%) of MODIS (geostationary) OT heights are within ±500 m of the coincident CPR-estimated heights.
