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Abstract
Tropical cyclogenesis in the eastern North Pacific (EPAC) basin is related to gap-wind-induced surface relative vorticity, the monsoon trough, and the intertropical convergence zone (ITCZ). There are several gaps in the Central American mountains, on the eastern edge of the EPAC basin, through which wind can be funneled to generate surface wind jets (gap winds). This study focuses on gap winds that occur over the Gulf of Papagayo and Gulf of Tehuantepec. Quick Scatterometer (QuikSCAT) 10-m equivalent neutral winds are used to identify gap wind events that occur during May through November of 2002–08. Dvorak fix locations, Gridded Satellite (GridSat) infrared (IR) data, and National Hurricane Center (NHC) tropical cyclone (TC) reports are used to track the disturbances during the study period. Surface vorticity is tracked using the QuikSCAT winds and the contribution of surface vorticity from the gap winds to the development of each disturbance is categorized as small, medium, or large. Cross-calibrated multiplatform surface wind data are used to verify the tracking of QuikSCAT-computed surface vorticity and to identify when the monsoon trough and the ITCZ are present. It is found that gap winds are present over the Gulf of Papagayo and Gulf of Tehuantepec for about 50% of the QuikSCAT coverage days and that these gap winds appear to contribute to the development of disturbances in the EPAC. Considerably more TCs form when the monsoon trough is present versus the ITCZ and the majority of the contributions from the gap winds also occur when the monsoon trough is present.
Abstract
Tropical cyclogenesis in the eastern North Pacific (EPAC) basin is related to gap-wind-induced surface relative vorticity, the monsoon trough, and the intertropical convergence zone (ITCZ). There are several gaps in the Central American mountains, on the eastern edge of the EPAC basin, through which wind can be funneled to generate surface wind jets (gap winds). This study focuses on gap winds that occur over the Gulf of Papagayo and Gulf of Tehuantepec. Quick Scatterometer (QuikSCAT) 10-m equivalent neutral winds are used to identify gap wind events that occur during May through November of 2002–08. Dvorak fix locations, Gridded Satellite (GridSat) infrared (IR) data, and National Hurricane Center (NHC) tropical cyclone (TC) reports are used to track the disturbances during the study period. Surface vorticity is tracked using the QuikSCAT winds and the contribution of surface vorticity from the gap winds to the development of each disturbance is categorized as small, medium, or large. Cross-calibrated multiplatform surface wind data are used to verify the tracking of QuikSCAT-computed surface vorticity and to identify when the monsoon trough and the ITCZ are present. It is found that gap winds are present over the Gulf of Papagayo and Gulf of Tehuantepec for about 50% of the QuikSCAT coverage days and that these gap winds appear to contribute to the development of disturbances in the EPAC. Considerably more TCs form when the monsoon trough is present versus the ITCZ and the majority of the contributions from the gap winds also occur when the monsoon trough is present.
Abstract
A more versatile and robust technique is developed for determining area-averaged surface vorticity based on vector winds from swaths of remotely sensed wind vectors. This technique could also be applied to determine the curl of stress, and it could be applied to any gridded dataset of winds or stresses. The technique is discussed in detail and compared to two previous studies that focused on early development of tropical systems. Error characteristics of the technique are examined in detail. Specifically, three independent sources of error are explored: random observational error, truncation error, and representation error. Observational errors are due to random errors in the wind observations and determined as a worst-case estimate as a function of averaging spatial scale. The observational uncertainty in the Quick Scatterometer (QuikSCAT)-derived vorticity averaged for a roughly circular shape with a 100-km diameter, expressed as one standard deviation, is approximately 0.5 × 10−5 s−1 for the methodology described herein. Truncation error is associated with the assumption of linear changes between wind vectors. Uncertainty related to truncation has more spatial organization in QuikSCAT data than observational uncertainty. On 25- and 50-km scales, the truncation errors are very large. The third type of error, representation error, is due to the size of the area being averaged compared to values with 25-km length scales. This type of error is analogous to oversmoothing. Tropical and subtropical low pressure systems from three months of QuikSCAT observations are used to examine truncation and representation errors. Representation error results in a bias of approximately −1.5 × 10−5 s−1 for area-averaged vorticity calculated on a 100-km scale compared to vorticity calculated on a 25-km scale. The discussion of these errors will benefit future projects of this nature as well as future satellite missions.
Abstract
A more versatile and robust technique is developed for determining area-averaged surface vorticity based on vector winds from swaths of remotely sensed wind vectors. This technique could also be applied to determine the curl of stress, and it could be applied to any gridded dataset of winds or stresses. The technique is discussed in detail and compared to two previous studies that focused on early development of tropical systems. Error characteristics of the technique are examined in detail. Specifically, three independent sources of error are explored: random observational error, truncation error, and representation error. Observational errors are due to random errors in the wind observations and determined as a worst-case estimate as a function of averaging spatial scale. The observational uncertainty in the Quick Scatterometer (QuikSCAT)-derived vorticity averaged for a roughly circular shape with a 100-km diameter, expressed as one standard deviation, is approximately 0.5 × 10−5 s−1 for the methodology described herein. Truncation error is associated with the assumption of linear changes between wind vectors. Uncertainty related to truncation has more spatial organization in QuikSCAT data than observational uncertainty. On 25- and 50-km scales, the truncation errors are very large. The third type of error, representation error, is due to the size of the area being averaged compared to values with 25-km length scales. This type of error is analogous to oversmoothing. Tropical and subtropical low pressure systems from three months of QuikSCAT observations are used to examine truncation and representation errors. Representation error results in a bias of approximately −1.5 × 10−5 s−1 for area-averaged vorticity calculated on a 100-km scale compared to vorticity calculated on a 25-km scale. The discussion of these errors will benefit future projects of this nature as well as future satellite missions.
Abstract
The authors' modeling shows that changes in sea surface temperature (SST) gradients and surface roughness between oil-free water and oil slicks influence the motion of the slick. Physically significant changes occur in surface wind speed, surface wind divergence, wind stress curl, and Ekman transport mostly because of SST gradients and changes in surface roughness between the water and the slick. These remarkable changes might affect the speed and direction of surface oil. For example, the strongest surface wind divergence (convergence) occurring in the transition zones owing to the presence of an oil slick will induce an atmospheric secondary circulation over the oil region, which in turn might affect the surface oil movement. SST-related changes to wind stress curl and Ekman transport in the transition zones appear to increase approximately linearly with the magnitude of SST gradients. Both surface roughness difference and SST gradients give rise to a net convergence of Ekman transport for oil cover. The SST gradient could play a more important role than surface roughness in changes of Ekman transport when SST gradients are large enough (e.g., several degrees per 10 km). The resulting changes in Ekman transport also induce the changes of surface oil movement. Sensitivity experiments show that appropriate selections of modeled parameters and geostrophic winds do not change the conclusions. The results from this idealized study indicate that the feedbacks from the surface oil presence to the oil motion itself are not trivial and should be further investigated for consideration in future oil-tracking modeling systems.
Abstract
The authors' modeling shows that changes in sea surface temperature (SST) gradients and surface roughness between oil-free water and oil slicks influence the motion of the slick. Physically significant changes occur in surface wind speed, surface wind divergence, wind stress curl, and Ekman transport mostly because of SST gradients and changes in surface roughness between the water and the slick. These remarkable changes might affect the speed and direction of surface oil. For example, the strongest surface wind divergence (convergence) occurring in the transition zones owing to the presence of an oil slick will induce an atmospheric secondary circulation over the oil region, which in turn might affect the surface oil movement. SST-related changes to wind stress curl and Ekman transport in the transition zones appear to increase approximately linearly with the magnitude of SST gradients. Both surface roughness difference and SST gradients give rise to a net convergence of Ekman transport for oil cover. The SST gradient could play a more important role than surface roughness in changes of Ekman transport when SST gradients are large enough (e.g., several degrees per 10 km). The resulting changes in Ekman transport also induce the changes of surface oil movement. Sensitivity experiments show that appropriate selections of modeled parameters and geostrophic winds do not change the conclusions. The results from this idealized study indicate that the feedbacks from the surface oil presence to the oil motion itself are not trivial and should be further investigated for consideration in future oil-tracking modeling systems.
Abstract
Composite analysis from NCEP–NCAR reanalysis datasets over the period 1948–2007 indicates that stronger East Asian winter monsoons (EAWM) and stronger Australian summer monsoons (ASM) generally coexist in boreal winters preceding the onset of El Niño, although the EAWM tend to be weak after 1990, probably because of the decadal shift of EAWM and the change in El Niño events from cold-tongue type to warm-pool type. The anomalous EAWM and ASM enhance surface westerlies over the western tropical Pacific Ocean (WTP). It is proposed that the enhanced surface westerlies over the WTP prior to El Niño onset are generally associated with the concurrent anomalous EAWM and ASM. A simple analytical atmospheric model is constructed to test the hypothesis that the emergence of enhanced surface westerlies over the WTP can be linked to concurrent EAWM and ASM anomalies. Model results indicate that, when anomalous northerlies from the EAWM converge with anomalous southerlies from the ASM, westerly anomalies over the WTP are enhanced. This result provides a possible explanation of the co-impact of the EAWM and the ASM on the onset of El Niño through enhancing the surface westerly over the WTP.
Abstract
Composite analysis from NCEP–NCAR reanalysis datasets over the period 1948–2007 indicates that stronger East Asian winter monsoons (EAWM) and stronger Australian summer monsoons (ASM) generally coexist in boreal winters preceding the onset of El Niño, although the EAWM tend to be weak after 1990, probably because of the decadal shift of EAWM and the change in El Niño events from cold-tongue type to warm-pool type. The anomalous EAWM and ASM enhance surface westerlies over the western tropical Pacific Ocean (WTP). It is proposed that the enhanced surface westerlies over the WTP prior to El Niño onset are generally associated with the concurrent anomalous EAWM and ASM. A simple analytical atmospheric model is constructed to test the hypothesis that the emergence of enhanced surface westerlies over the WTP can be linked to concurrent EAWM and ASM anomalies. Model results indicate that, when anomalous northerlies from the EAWM converge with anomalous southerlies from the ASM, westerly anomalies over the WTP are enhanced. This result provides a possible explanation of the co-impact of the EAWM and the ASM on the onset of El Niño through enhancing the surface westerly over the WTP.
Abstract
The Hurricane Research Division (HRD) Real-time Hurricane Wind Analysis System (H*Wind) is a software application used by NOAA’s HRD to create a gridded tropical cyclone wind analysis based on a wide range of observations. These analyses are used in both forecasting and research applications. Although mean bias and RMS errors are listed, H*Wind lacks robust uncertainty information that considers the contributions of random observation errors, relative biases between observation types, temporal drift resulting from combining nonsimultaneous measurements into a single analysis, and smoothing and interpolation errors introduced by the H*Wind analysis. This investigation seeks to estimate the total contributions of these sources, and thereby provide an overall uncertainty estimate for the H*Wind product.
A series of statistical analyses show that in general, the total uncertainty in the H*Wind product in hurricanes is approximately 6% near the storm center, increasing to nearly 13% near the tropical storm force wind radius. The H*Wind analysis algorithm is found to introduce a positive bias to the wind speeds near the storm center, where the analyzed wind speeds are enhanced to match the highest observations. In addition, spectral analyses are performed to ensure that the filter wavelength of the final analysis product matches user specifications. With increased knowledge of bias and uncertainty sources and their effects, researchers will have a better understanding of the uncertainty in the H*Wind product and can then judge the suitability of H*Wind for various research applications.
Abstract
The Hurricane Research Division (HRD) Real-time Hurricane Wind Analysis System (H*Wind) is a software application used by NOAA’s HRD to create a gridded tropical cyclone wind analysis based on a wide range of observations. These analyses are used in both forecasting and research applications. Although mean bias and RMS errors are listed, H*Wind lacks robust uncertainty information that considers the contributions of random observation errors, relative biases between observation types, temporal drift resulting from combining nonsimultaneous measurements into a single analysis, and smoothing and interpolation errors introduced by the H*Wind analysis. This investigation seeks to estimate the total contributions of these sources, and thereby provide an overall uncertainty estimate for the H*Wind product.
A series of statistical analyses show that in general, the total uncertainty in the H*Wind product in hurricanes is approximately 6% near the storm center, increasing to nearly 13% near the tropical storm force wind radius. The H*Wind analysis algorithm is found to introduce a positive bias to the wind speeds near the storm center, where the analyzed wind speeds are enhanced to match the highest observations. In addition, spectral analyses are performed to ensure that the filter wavelength of the final analysis product matches user specifications. With increased knowledge of bias and uncertainty sources and their effects, researchers will have a better understanding of the uncertainty in the H*Wind product and can then judge the suitability of H*Wind for various research applications.
Abstract
In this first part of a two-part study, the three-dimensional structure of the inner-core boundary layer (BL) is investigated in a full-physics simulation of Hurricane Irma (2017). The BL structure is highlighted during periods of intensity change, with focus on features and mechanisms associated with storm decay. The azimuthal structure of the BL is shown to be linked to the vertical wind shear and storm motion. The BL inflow becomes more asymmetric under increased shear. As BL inflow asymmetry amplifies, asymmetries in the low-level primary circulation and thermodynamic structure develop. A mechanism is identified to explain the onset of pronounced structural asymmetries in coincidence with external forcing (e.g., through shear) that would amplify BL inflow along limited azimuth. The mechanism assumes enhanced advection of absolute angular momentum along the path of the amplified inflow (e.g., amplified downshear), which results in local spinup of the vortex and development of strong supergradient flow downwind and along the BL top. The associated agradient force results in the outward acceleration of air immediately above the BL inflow, affecting fields including divergence, vertical motion, entropy advection, and inertial stability. In this simulation, descending inflow in coincidence with amplified shear is identified as the conduit through which low-entropy air enters the inner-core BL, thereby hampering convection downwind and resulting in storm decay.
Abstract
In this first part of a two-part study, the three-dimensional structure of the inner-core boundary layer (BL) is investigated in a full-physics simulation of Hurricane Irma (2017). The BL structure is highlighted during periods of intensity change, with focus on features and mechanisms associated with storm decay. The azimuthal structure of the BL is shown to be linked to the vertical wind shear and storm motion. The BL inflow becomes more asymmetric under increased shear. As BL inflow asymmetry amplifies, asymmetries in the low-level primary circulation and thermodynamic structure develop. A mechanism is identified to explain the onset of pronounced structural asymmetries in coincidence with external forcing (e.g., through shear) that would amplify BL inflow along limited azimuth. The mechanism assumes enhanced advection of absolute angular momentum along the path of the amplified inflow (e.g., amplified downshear), which results in local spinup of the vortex and development of strong supergradient flow downwind and along the BL top. The associated agradient force results in the outward acceleration of air immediately above the BL inflow, affecting fields including divergence, vertical motion, entropy advection, and inertial stability. In this simulation, descending inflow in coincidence with amplified shear is identified as the conduit through which low-entropy air enters the inner-core BL, thereby hampering convection downwind and resulting in storm decay.
Abstract
Indian summer monsoon rainfall (ISMR; June–September) has both temporal and spatial variability causing floods and droughts in different seasons and locations, leading to a strong or weak monsoon. Here, the authors present the contribution of all-India monthly, seasonal, and regional rainfall to the ISMR, with an emphasis on the strong and weak monsoons. Here, regional rainfall is restricted to the seasonal rainfall over four regions defined by the India Meteorological Department (IMD) primarily for the purpose of forecasting regional rainfall: northwest India (NWI), northeast India (NEI), central India (CI), and south peninsula India (SPIN). In this study, two rainfall datasets provided by IMD are used: 1) all-India monthly and seasonal (June–September) rainfall series for the entire Indian subcontinent as well as seasonal rainfall series for the four homogeneous regions for the period 1901–2013 and 2) the latest daily gridded rainfall data for the period 1951–2014, which is used for assessment at the extent to which the four regions are appropriate for the intended purpose. Rainfall during July–August contributes the most to the total seasonal rainfall, regardless of whether it is a strong or weak monsoon. Although NEI has the maximum area-weighted rainfall, its contribution is the least toward determining a strong or weak monsoon. It is the rainfall in the remaining three regions (NWI, CI, and SPIN) that controls whether an ISMR is strong or weak. Compared to monthly rainfall, regional rainfall dominates the strong or weak rainfall periods.
Abstract
Indian summer monsoon rainfall (ISMR; June–September) has both temporal and spatial variability causing floods and droughts in different seasons and locations, leading to a strong or weak monsoon. Here, the authors present the contribution of all-India monthly, seasonal, and regional rainfall to the ISMR, with an emphasis on the strong and weak monsoons. Here, regional rainfall is restricted to the seasonal rainfall over four regions defined by the India Meteorological Department (IMD) primarily for the purpose of forecasting regional rainfall: northwest India (NWI), northeast India (NEI), central India (CI), and south peninsula India (SPIN). In this study, two rainfall datasets provided by IMD are used: 1) all-India monthly and seasonal (June–September) rainfall series for the entire Indian subcontinent as well as seasonal rainfall series for the four homogeneous regions for the period 1901–2013 and 2) the latest daily gridded rainfall data for the period 1951–2014, which is used for assessment at the extent to which the four regions are appropriate for the intended purpose. Rainfall during July–August contributes the most to the total seasonal rainfall, regardless of whether it is a strong or weak monsoon. Although NEI has the maximum area-weighted rainfall, its contribution is the least toward determining a strong or weak monsoon. It is the rainfall in the remaining three regions (NWI, CI, and SPIN) that controls whether an ISMR is strong or weak. Compared to monthly rainfall, regional rainfall dominates the strong or weak rainfall periods.
Abstract
Techniques are presented for the computation and quality control of true winds from vessels at sea. Correct computation of true winds and quality-control methods are demonstrated for complete data. Additional methods are presented for estimating true winds from incomplete data. Recommendations are made for both existing data and future applications.
Quality control of automated weather station (AWS) data at the World Ocean Circulation Experiment Surface Meteorological Data Center reveals that only 20% of studied vessels report all parameters necessary to compute a true wind. Required parameters include the ship’s heading, course over the ground (COG), speed over the ground, wind vane zero reference, and wind speed and direction relative to the vessel. If any parameter is omitted or incorrect averaging is applied, AWS true wind data display systematic errors. Quantitative examples of several problems are shown in comparisons between collocated winds from research vessels and the NASA scatterometer (NSCAT). Procedures are developed to identify observational shortcomings and to quantify the impact of these shortcomings in the determination of true wind observations.
Methods for estimating true winds are presented for situations where heading or COG is missing. Empirical analysis of two vessels with high-quality AWS data showed these estimates to be more accurate when the vessel heading is available. Large differences between the heading and COG angles at low ship speeds make winds estimated using the course unreliable (direction errors exceeding 60°) for ship speeds less than 2.0 m s−1. The threshold where the direction difference between a course estimated and true wind reaches an acceptable level (±10°) depends upon the ship, winds, and currents in the vessel’s region of operation.
Abstract
Techniques are presented for the computation and quality control of true winds from vessels at sea. Correct computation of true winds and quality-control methods are demonstrated for complete data. Additional methods are presented for estimating true winds from incomplete data. Recommendations are made for both existing data and future applications.
Quality control of automated weather station (AWS) data at the World Ocean Circulation Experiment Surface Meteorological Data Center reveals that only 20% of studied vessels report all parameters necessary to compute a true wind. Required parameters include the ship’s heading, course over the ground (COG), speed over the ground, wind vane zero reference, and wind speed and direction relative to the vessel. If any parameter is omitted or incorrect averaging is applied, AWS true wind data display systematic errors. Quantitative examples of several problems are shown in comparisons between collocated winds from research vessels and the NASA scatterometer (NSCAT). Procedures are developed to identify observational shortcomings and to quantify the impact of these shortcomings in the determination of true wind observations.
Methods for estimating true winds are presented for situations where heading or COG is missing. Empirical analysis of two vessels with high-quality AWS data showed these estimates to be more accurate when the vessel heading is available. Large differences between the heading and COG angles at low ship speeds make winds estimated using the course unreliable (direction errors exceeding 60°) for ship speeds less than 2.0 m s−1. The threshold where the direction difference between a course estimated and true wind reaches an acceptable level (±10°) depends upon the ship, winds, and currents in the vessel’s region of operation.
Abstract
An air–sea interaction model that includes turbulent transport due to capillary waves (surface ripples) is developed. The model differs from others in that the physical premises are applicable to low wind speeds (10-m wind speed, U 10 < 5 m s−1) as well as higher wind speeds. Another new feature of the model is an anisotropic roughness length, which allows a crosswind component of the stress to be modeled. The influence of the angle between the mean wind direction and the mean direction of wave propagation is included in the anisotropic roughness length.
Most models are not accurate at low wind speeds and tend to underestimate fluxes in low wind speed regions such as the tropical oceans. Improvements over previous models are incorporated in the momentum roughness length parameterization. In addition, the dimensionless constant in the relationship between the capillary wave component of momentum roughness length and friction velocity is reevaluated using both wave tank data and field data. The new value is found to be 0.06, a factor of 3 smaller than the original estimate of 0.18. Modeling the influence of capillary waves is shown to improve the accuracy of modeled surface fluxes and drag coefficients. Several sets of tropical observations are used to examine mean increases in modeled fluxes due to capillary waves. The changes in latent heat fluxes are compared to proposed increases due to convective overturning (sometimes called gustiness) and are found to be larger by a factor of 4. For U 10 < 7 m s−1, the mean estimates for tropical fluxes of momentum and latent heat are found to increase by 0.004 N m−2 and 6 W m−2.
Abstract
An air–sea interaction model that includes turbulent transport due to capillary waves (surface ripples) is developed. The model differs from others in that the physical premises are applicable to low wind speeds (10-m wind speed, U 10 < 5 m s−1) as well as higher wind speeds. Another new feature of the model is an anisotropic roughness length, which allows a crosswind component of the stress to be modeled. The influence of the angle between the mean wind direction and the mean direction of wave propagation is included in the anisotropic roughness length.
Most models are not accurate at low wind speeds and tend to underestimate fluxes in low wind speed regions such as the tropical oceans. Improvements over previous models are incorporated in the momentum roughness length parameterization. In addition, the dimensionless constant in the relationship between the capillary wave component of momentum roughness length and friction velocity is reevaluated using both wave tank data and field data. The new value is found to be 0.06, a factor of 3 smaller than the original estimate of 0.18. Modeling the influence of capillary waves is shown to improve the accuracy of modeled surface fluxes and drag coefficients. Several sets of tropical observations are used to examine mean increases in modeled fluxes due to capillary waves. The changes in latent heat fluxes are compared to proposed increases due to convective overturning (sometimes called gustiness) and are found to be larger by a factor of 4. For U 10 < 7 m s−1, the mean estimates for tropical fluxes of momentum and latent heat are found to increase by 0.004 N m−2 and 6 W m−2.
