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Abstract
The evolution of upper-tropospheric thermal patterns associated with extratropical cyclone events is often not well represented by the conventional observational network, especially in marine situations. In this paper, a potential tool for qualitatively analyzing tropopause-level thermal structure and variations based on remotely sensed passive microwave data from satellites is examined. Specifically, warm anomalies associated with tropopause undulations in upper-tropospheric waves are captured in imagery from the 54.96-GHz channel of the Microwave Sounding Unit (MSU) onboard the current series of NOAA polar-orbiting satellites. Examples of this imagery during selected western North Atlantic cyclone events are presented, and the potential usefulness of these observations in analysis and forecasting is discussed.
Abstract
The evolution of upper-tropospheric thermal patterns associated with extratropical cyclone events is often not well represented by the conventional observational network, especially in marine situations. In this paper, a potential tool for qualitatively analyzing tropopause-level thermal structure and variations based on remotely sensed passive microwave data from satellites is examined. Specifically, warm anomalies associated with tropopause undulations in upper-tropospheric waves are captured in imagery from the 54.96-GHz channel of the Microwave Sounding Unit (MSU) onboard the current series of NOAA polar-orbiting satellites. Examples of this imagery during selected western North Atlantic cyclone events are presented, and the potential usefulness of these observations in analysis and forecasting is discussed.
Atmospheric and oceanographic field experiments are an important part of research programs aimed at enhancing observational analyses of meteorological and oceanic phenomena, validating new datasets, and/or supporting hypotheses. The Bulletin of the American Meteorological Society (BAMS) has chronicled many field programs, with a primary focus on the enhanced observational assets that were assembled to enable the projects' investigations. However, these field program summaries often overlook the multiple roles that satellite digital data, multispectral imagery, and derived products can play in premission planning, real-time forecasting and mission guidance, and extensive post–field phase analysis. In turn, these intensive observing periods often serve as crucial validation datasets for remotely sensed products and derived fields.
Atmospheric and oceanographic field experiments are an important part of research programs aimed at enhancing observational analyses of meteorological and oceanic phenomena, validating new datasets, and/or supporting hypotheses. The Bulletin of the American Meteorological Society (BAMS) has chronicled many field programs, with a primary focus on the enhanced observational assets that were assembled to enable the projects' investigations. However, these field program summaries often overlook the multiple roles that satellite digital data, multispectral imagery, and derived products can play in premission planning, real-time forecasting and mission guidance, and extensive post–field phase analysis. In turn, these intensive observing periods often serve as crucial validation datasets for remotely sensed products and derived fields.
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.
Satellite imagery from the VISSR (Visible Infrared Spin Scan Radiometer) Atmospheric Sounder (VAS) 6.7-μm water-vapor absorption band is normally available to the National Hurricane Center (NHC) in real time (half-hourly intervals, 16 hours a day) through a remote Man-computer Interactive Data Access System (McIDAS) workstation located in the forecast center. Synoptic features that are not readily apparent in “visible” imagery or “11-μm-infrared” imagery are often well defined in the VAS “water-vapor” imagery with the help of special enhancement software that exists on McIDAS. A good example is Hurricane Elena (1985). Its erratic path in the Gulf of Mexico was responsible for the evacuation of nearly a million people in low-lying coastal areas during a three-day period. Imagery from the VAS 6.7-μm water-vapor channel clearly shows the interaction of a midlatitude trough with the hurricane, and supports other evidence that suggests this was responsible for altering Elena's course.
Satellite imagery from the VISSR (Visible Infrared Spin Scan Radiometer) Atmospheric Sounder (VAS) 6.7-μm water-vapor absorption band is normally available to the National Hurricane Center (NHC) in real time (half-hourly intervals, 16 hours a day) through a remote Man-computer Interactive Data Access System (McIDAS) workstation located in the forecast center. Synoptic features that are not readily apparent in “visible” imagery or “11-μm-infrared” imagery are often well defined in the VAS “water-vapor” imagery with the help of special enhancement software that exists on McIDAS. A good example is Hurricane Elena (1985). Its erratic path in the Gulf of Mexico was responsible for the evacuation of nearly a million people in low-lying coastal areas during a three-day period. Imagery from the VAS 6.7-μm water-vapor channel clearly shows the interaction of a midlatitude trough with the hurricane, and supports other evidence that suggests this was responsible for altering Elena's course.
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
Vertical wind shear is well known in the tropical cyclone (TC) forecasting community as an important environmental influence on storm structure and intensity change. The traditional way to define deep-tropospheric vertical wind shear in most prior research studies, and in operational forecast applications, is to simply use the vector difference of the 200- and 850-hPa wind fields based on global model analyses. However, is this rather basic approach to approximate vertical wind shear adequate for most TC applications? In this study, the traditional approach is compared to a different methodology for generating fields of vertical wind shear as produced by the University of Wisconsin Cooperative Institute for Meteorological Satellite Studies (CIMSS). The CIMSS fields are derived with heavy analysis weight given to available high-density satellite-derived winds. The resultant isobaric analyses are then used to create two mass-weighted layer-mean wind fields, one upper and one lower tropospheric, which are then differenced to produce the deep-tropospheric vertical wind shear field. The principal novelty of this approach is that it does not rely simply on the analyzed winds at two discrete levels, but instead attempts to account for some of the variable vertical wind structure in the calculation. It will be shown how the resultant vertical wind shear fields derived by the two approaches can diverge significantly in certain situations; the results also suggest that in many cases it is superior in depicting the wind structure's impact on TCs than the simple two-level differential that serves as the common contemporary vertical wind shear approximation.
Abstract
Vertical wind shear is well known in the tropical cyclone (TC) forecasting community as an important environmental influence on storm structure and intensity change. The traditional way to define deep-tropospheric vertical wind shear in most prior research studies, and in operational forecast applications, is to simply use the vector difference of the 200- and 850-hPa wind fields based on global model analyses. However, is this rather basic approach to approximate vertical wind shear adequate for most TC applications? In this study, the traditional approach is compared to a different methodology for generating fields of vertical wind shear as produced by the University of Wisconsin Cooperative Institute for Meteorological Satellite Studies (CIMSS). The CIMSS fields are derived with heavy analysis weight given to available high-density satellite-derived winds. The resultant isobaric analyses are then used to create two mass-weighted layer-mean wind fields, one upper and one lower tropospheric, which are then differenced to produce the deep-tropospheric vertical wind shear field. The principal novelty of this approach is that it does not rely simply on the analyzed winds at two discrete levels, but instead attempts to account for some of the variable vertical wind structure in the calculation. It will be shown how the resultant vertical wind shear fields derived by the two approaches can diverge significantly in certain situations; the results also suggest that in many cases it is superior in depicting the wind structure's impact on TCs than the simple two-level differential that serves as the common contemporary vertical wind shear approximation.
ABSTRACT
A consensus-based algorithm for estimating the current intensity of global tropical cyclones (TCs) from meteorological satellites is described. The method objectively combines intensity estimates from infrared and microwave-based techniques to produce a consensus TC intensity estimate, which is more skillful than the individual members. The method, called Satellite Consensus (SATCON), can be run in near–real time and employs information sharing between member algorithms and a weighting strategy that relies on the situational precision of each member. An evaluation of the consensus algorithm’s performance in comparison with its individual members and other available operational estimates of TC intensity is presented. It is shown that SATCON can provide valuable objective intensity estimates for poststorm assessments, especially in the absence of other data such as provided by reconnaissance aircraft. It can also serve as a near-real-time estimator of TC intensity for forecasters, with the ability to quickly reconcile differences in objective intensity methods and thus decrease the uncertainty and amount of time spent on the intensity analysis. Near-real-time SATCON estimates are being provided to global operational TC forecast centers.
ABSTRACT
A consensus-based algorithm for estimating the current intensity of global tropical cyclones (TCs) from meteorological satellites is described. The method objectively combines intensity estimates from infrared and microwave-based techniques to produce a consensus TC intensity estimate, which is more skillful than the individual members. The method, called Satellite Consensus (SATCON), can be run in near–real time and employs information sharing between member algorithms and a weighting strategy that relies on the situational precision of each member. An evaluation of the consensus algorithm’s performance in comparison with its individual members and other available operational estimates of TC intensity is presented. It is shown that SATCON can provide valuable objective intensity estimates for poststorm assessments, especially in the absence of other data such as provided by reconnaissance aircraft. It can also serve as a near-real-time estimator of TC intensity for forecasters, with the ability to quickly reconcile differences in objective intensity methods and thus decrease the uncertainty and amount of time spent on the intensity analysis. Near-real-time SATCON estimates are being provided to global operational TC forecast centers.
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
A deep learning convolutional neural network model is used to explore the possibilities of estimating tropical cyclone (TC) intensity from satellite images in the 37- and 85–92-GHz bands. The model, called “DeepMicroNet,” has unique properties such as a probabilistic output, the ability to operate from partial scans, and resiliency to imprecise TC center fixes. The 85–92-GHz band is the more influential data source in the model, with 37 GHz adding a marginal benefit. Training the model on global best track intensities produces model estimates precise enough to replicate known best track intensity biases when compared to aircraft reconnaissance observations. Model root-mean-square error (RMSE) is 14.3 kt (1 kt ≈ 0.5144 m s−1) compared to two years of independent best track records, but this improves to an RMSE of 10.6 kt when compared to the higher-standard aircraft reconnaissance-aided best track dataset, and to 9.6 kt compared to the reconnaissance-aided best track when using the higher-resolution TRMM TMI and Aqua AMSR-E microwave observations only. A shortage of training and independent testing data for category 5 TCs leaves the results at this intensity range inconclusive. Based on this initial study, the application of deep learning to TC intensity analysis holds tremendous promise for further development with more advanced methodologies and expanded training datasets.
Abstract
A deep learning convolutional neural network model is used to explore the possibilities of estimating tropical cyclone (TC) intensity from satellite images in the 37- and 85–92-GHz bands. The model, called “DeepMicroNet,” has unique properties such as a probabilistic output, the ability to operate from partial scans, and resiliency to imprecise TC center fixes. The 85–92-GHz band is the more influential data source in the model, with 37 GHz adding a marginal benefit. Training the model on global best track intensities produces model estimates precise enough to replicate known best track intensity biases when compared to aircraft reconnaissance observations. Model root-mean-square error (RMSE) is 14.3 kt (1 kt ≈ 0.5144 m s−1) compared to two years of independent best track records, but this improves to an RMSE of 10.6 kt when compared to the higher-standard aircraft reconnaissance-aided best track dataset, and to 9.6 kt compared to the reconnaissance-aided best track when using the higher-resolution TRMM TMI and Aqua AMSR-E microwave observations only. A shortage of training and independent testing data for category 5 TCs leaves the results at this intensity range inconclusive. Based on this initial study, the application of deep learning to TC intensity analysis holds tremendous promise for further development with more advanced methodologies and expanded training datasets.
