Search Results
You are looking at 21 - 30 of 40 items for
- Author or Editor: Jin Huang x
- Refine by Access: All Content x
Optimum Estimation of Coastal Currents Using Moving VehiclesAbstract
Ocean acoustic tomography (OAT) deploys most moored stations on the periphery of the tomographic region to sense the solenoidal current field. Moving vehicle tomography (MVT), an advancement of OAT, not only samples the region from various angles for improving the resolution of mapped currents but also acquires information about the irrotational flow due to the sampling points inside the region. To reconstruct a complete two-dimensional current field, the spatial modes derived from the open-boundary modal analysis (OMA) are preferable to the conventional truncated Fourier series since the OMA technique describes the solenoidal and irrotational flows efficiently in which all modes satisfy the coastline and open boundary conditions. Comparisons of the reconstructions are presented using three different representations of currents. The first two representations explain only the solenoidal flow: the truncated Fourier series and the OMA Dirichlet modes. The third representation, accounting for the solenoidal and irrotational flows, uses all the OMA modes. For reconstructing the solenoidal flow, the OMA representation with the Dirichlet modes performs better than the Fourier series. A large difference appears near the bay mouth, where the OMA-Dirichlet reconstruction shows a better fit to the uniform currents. However, considerable uncertainty exists outside the bay mouth where the irrotational currents dominate. This can be improved by the third representation with the inclusion of the Neumann and boundary modes. The reconstruction results using field data were validated against the acoustic Doppler current profiler (ADCP) measurements. Additionally, incorporating constraints from ADCP measurements enhances the accuracy of the reconstruction.
Significance Statement
This study contributes toward improving our understanding of accurately measuring oceanic circulation patterns over large areas without relying solely upon stationary sensors or satellite imagery. The study combines multiple sources, such as shipboard ADCP and tomographic techniques, to obtain a complete picture of what is happening beneath surface waters across entire regions under investigation. It has important implications for fields such as climate science, marine biology, and fisheries management, where accurate knowledge of the movement and distribution of water masses is crucial for predicting future trends and making informed decisions.
Abstract
Ocean acoustic tomography (OAT) deploys most moored stations on the periphery of the tomographic region to sense the solenoidal current field. Moving vehicle tomography (MVT), an advancement of OAT, not only samples the region from various angles for improving the resolution of mapped currents but also acquires information about the irrotational flow due to the sampling points inside the region. To reconstruct a complete two-dimensional current field, the spatial modes derived from the open-boundary modal analysis (OMA) are preferable to the conventional truncated Fourier series since the OMA technique describes the solenoidal and irrotational flows efficiently in which all modes satisfy the coastline and open boundary conditions. Comparisons of the reconstructions are presented using three different representations of currents. The first two representations explain only the solenoidal flow: the truncated Fourier series and the OMA Dirichlet modes. The third representation, accounting for the solenoidal and irrotational flows, uses all the OMA modes. For reconstructing the solenoidal flow, the OMA representation with the Dirichlet modes performs better than the Fourier series. A large difference appears near the bay mouth, where the OMA-Dirichlet reconstruction shows a better fit to the uniform currents. However, considerable uncertainty exists outside the bay mouth where the irrotational currents dominate. This can be improved by the third representation with the inclusion of the Neumann and boundary modes. The reconstruction results using field data were validated against the acoustic Doppler current profiler (ADCP) measurements. Additionally, incorporating constraints from ADCP measurements enhances the accuracy of the reconstruction.
Significance Statement
This study contributes toward improving our understanding of accurately measuring oceanic circulation patterns over large areas without relying solely upon stationary sensors or satellite imagery. The study combines multiple sources, such as shipboard ADCP and tomographic techniques, to obtain a complete picture of what is happening beneath surface waters across entire regions under investigation. It has important implications for fields such as climate science, marine biology, and fisheries management, where accurate knowledge of the movement and distribution of water masses is crucial for predicting future trends and making informed decisions.
Abstract
This study investigated monthly and annual reference evapotranspiration 
Abstract
This study investigated monthly and annual reference evapotranspiration
Abstract
Extreme weather events such as cold-air outbreaks (CAOs) pose great threats to human life and the socioeconomic well-being of modern society. In the past, our capability to predict their occurrences has been constrained by the 2-week predictability limit for weather. We demonstrate here for the first time that a rapid increase of air mass transported into the polar stratosphere, referred to as the pulse of the stratosphere (PULSE), can often be predicted with a useful degree of skill 4–6 weeks in advance by operational forecast models. We further show that the probability of the occurrence of continental-scale CAOs in midlatitudes increases substantially above normal conditions within a short time period from 1 week before to 1–2 weeks after the peak day of a PULSE event. In particular, we reveal that the three massive CAOs over North America in January and February of 2014 were preceded by three episodes of extreme mass transport into the polar stratosphere with peak intensities reaching a trillion tons per day, twice that on an average winter day. Therefore, our capability to predict the PULSEs with operational forecast models, in conjunction with its linkage to continental-scale CAOs, opens up a new opportunity for 30-day forecasts of continental-scale CAOs, such as those occurring over North America during the 2013/14 winter. A real-time forecast experiment inaugurated in the winter of 2014/15 has given support to the idea that it is feasible to forecast CAOs 1 month in advance.
Abstract
Extreme weather events such as cold-air outbreaks (CAOs) pose great threats to human life and the socioeconomic well-being of modern society. In the past, our capability to predict their occurrences has been constrained by the 2-week predictability limit for weather. We demonstrate here for the first time that a rapid increase of air mass transported into the polar stratosphere, referred to as the pulse of the stratosphere (PULSE), can often be predicted with a useful degree of skill 4–6 weeks in advance by operational forecast models. We further show that the probability of the occurrence of continental-scale CAOs in midlatitudes increases substantially above normal conditions within a short time period from 1 week before to 1–2 weeks after the peak day of a PULSE event. In particular, we reveal that the three massive CAOs over North America in January and February of 2014 were preceded by three episodes of extreme mass transport into the polar stratosphere with peak intensities reaching a trillion tons per day, twice that on an average winter day. Therefore, our capability to predict the PULSEs with operational forecast models, in conjunction with its linkage to continental-scale CAOs, opens up a new opportunity for 30-day forecasts of continental-scale CAOs, such as those occurring over North America during the 2013/14 winter. A real-time forecast experiment inaugurated in the winter of 2014/15 has given support to the idea that it is feasible to forecast CAOs 1 month in advance.
Abstract
Regional variations in seasonal mean Indian summer monsoon rainfall and circulation for the period 1979–2009 are investigated using multiple data products. The focus is on four separate regions: the Western Ghats (WG), the Ganges basin (GB), the Bay of Bengal (BB), and Bangladesh–northeastern India (BD). Data reliability varies strongly by region, with particularly low correlations between different products for the BB and BD regions. Correlations between regions are generally not statistically significant, indicating rainfall varies independently in these four regions. The diagnosed associations between rainfall, circulation, and sea surface temperatures can be sensitive to the choice of rainfall product, and multiple precipitation products may need to be analyzed in this region to ensure that the results are robust.
Enhanced precipitation in the BD region is associated with anomalous anticyclonic circulation at 850 mb and westerly anomalies along the foothills of the Tibetan Plateau, while precipitation in the other regions is associated with cyclonic flow and easterlies. These associations provide a dynamical explanation for previously reported weak, negative correlations between BD and the other regions.
In addition to observed products, atmosphere-only simulations made using the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS) during Project Athena are analyzed. While the simulations do not reproduce the observed interannual variations in rainfall, the fidelity of the simulated precipitation and circulation structure is comparable to or even outperforms the different state-of-the-art reanalysis products considered. Accuracy in representing interannual variability and regional structure thus appears to be independent.
Abstract
Regional variations in seasonal mean Indian summer monsoon rainfall and circulation for the period 1979–2009 are investigated using multiple data products. The focus is on four separate regions: the Western Ghats (WG), the Ganges basin (GB), the Bay of Bengal (BB), and Bangladesh–northeastern India (BD). Data reliability varies strongly by region, with particularly low correlations between different products for the BB and BD regions. Correlations between regions are generally not statistically significant, indicating rainfall varies independently in these four regions. The diagnosed associations between rainfall, circulation, and sea surface temperatures can be sensitive to the choice of rainfall product, and multiple precipitation products may need to be analyzed in this region to ensure that the results are robust.
Enhanced precipitation in the BD region is associated with anomalous anticyclonic circulation at 850 mb and westerly anomalies along the foothills of the Tibetan Plateau, while precipitation in the other regions is associated with cyclonic flow and easterlies. These associations provide a dynamical explanation for previously reported weak, negative correlations between BD and the other regions.
In addition to observed products, atmosphere-only simulations made using the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS) during Project Athena are analyzed. While the simulations do not reproduce the observed interannual variations in rainfall, the fidelity of the simulated precipitation and circulation structure is comparable to or even outperforms the different state-of-the-art reanalysis products considered. Accuracy in representing interannual variability and regional structure thus appears to be independent.
Abstract
Recent studies have highlighted the role of subsurface ocean dynamics in modulating eastern Pacific (EPac) hurricane activity on interannual time scales. In particular, the well-known El Niño–Southern Oscillation (ENSO) recharge–discharge mechanism has been suggested to provide a good understanding of the year-to-year variability of hurricane activity in this region. This paper investigates the influence of equatorial subsurface subannual and intraseasonal oceanic variability on tropical cyclone (TC) activity in the EPac. That is to say, it examines previously unexplored time scales, shorter than interannual, in an attempt to explain the variability not related to ENSO. Using ocean reanalysis products and TC best-track archive, the role of subannual and intraseasonal equatorial Kelvin waves (EKW) in modulating hurricane intensity in the EPac is examined. It is shown first that these planetary waves have a clear control on the subannual and intraseasonal variability of thermocline depth in the EPac cyclone-active region. This is found to affect ocean subsurface temperature, which in turn fuels hurricane intensification with a marked seasonal-phase locking. This mechanism of TC fueling, which explains up to 30% of the variability of TC activity unrelated to ENSO (around 15%–20% of the total variability), is embedded in the large-scale equatorial dynamics and therefore offers some predictability with lead time up to 3–4 months at seasonal and subseasonal time scales.
Abstract
Recent studies have highlighted the role of subsurface ocean dynamics in modulating eastern Pacific (EPac) hurricane activity on interannual time scales. In particular, the well-known El Niño–Southern Oscillation (ENSO) recharge–discharge mechanism has been suggested to provide a good understanding of the year-to-year variability of hurricane activity in this region. This paper investigates the influence of equatorial subsurface subannual and intraseasonal oceanic variability on tropical cyclone (TC) activity in the EPac. That is to say, it examines previously unexplored time scales, shorter than interannual, in an attempt to explain the variability not related to ENSO. Using ocean reanalysis products and TC best-track archive, the role of subannual and intraseasonal equatorial Kelvin waves (EKW) in modulating hurricane intensity in the EPac is examined. It is shown first that these planetary waves have a clear control on the subannual and intraseasonal variability of thermocline depth in the EPac cyclone-active region. This is found to affect ocean subsurface temperature, which in turn fuels hurricane intensification with a marked seasonal-phase locking. This mechanism of TC fueling, which explains up to 30% of the variability of TC activity unrelated to ENSO (around 15%–20% of the total variability), is embedded in the large-scale equatorial dynamics and therefore offers some predictability with lead time up to 3–4 months at seasonal and subseasonal time scales.
Abstract
The effect of tropical cyclone (TC) size on TC-induced sea surface temperature (SST) cooling and subsequent TC intensification is an intriguing issue without much exploration. Via compositing satellite-observed SST over the western North Pacific during 2004–19, this study systematically examined the effect of storm size on the magnitude, spatial extension, and temporal evolution of TC-induced SST anomalies (SSTA). Consequential influence on TC intensification is also explored. Among the various TC wind radii, SSTA are found to be most sensitive to the 34-kt wind radius (R34) (1 kt ≈ 0.51 m s−1). Generally, large TCs generate stronger and more widespread SSTA than small TCs (for category 1–2 TCs, R34: ∼270 vs 160 km; SSTA: −1.7° vs −0.9°C). Despite the same effect on prolonging residence time of TC winds, the effect of doubling R34 on SSTA is more profound than halving translation speed, due to more wind energy input into the upper ocean. Also differing from translation speed, storm size has a rather modest effect on the rightward shift and timing of maximum cooling. This study further demonstrates that storm size regulates TC intensification through an oceanic pathway: large TCs tend to induce stronger SST cooling and are exposed to the cooling for a longer time, both of which reduce the ocean’s enthalpy supply and thereby diminish TC intensification. For larger TCs experiencing stronger SST cooling, the probability of rapid intensification is half of smaller TCs. The presented results suggest that accurately specifying storm size should lead to improved cooling effect estimation and TC intensity prediction.
Significance Statement
Storm size has long been speculated to play a crucial role in modulating the TC self-induced sea surface temperature (SST) cooling and thus potentially influence TC intensification through ocean negative feedback. Nevertheless, systematic analysis is lacking. Here we show that larger TCs tend to generate stronger SST cooling and have longer exposure to the cooling effect, both of which enhance the strength of the negative feedback. Consequently, larger TCs undergo weaker intensification and are less likely to experience rapid intensification than smaller TCs. These results demonstrate that storm size can influence TC intensification not only from the atmospheric pathway, but also via the oceanic pathway. Accurate characterization of this oceanic pathway in coupled models is important to accurately forecast TC intensity.
Abstract
The effect of tropical cyclone (TC) size on TC-induced sea surface temperature (SST) cooling and subsequent TC intensification is an intriguing issue without much exploration. Via compositing satellite-observed SST over the western North Pacific during 2004–19, this study systematically examined the effect of storm size on the magnitude, spatial extension, and temporal evolution of TC-induced SST anomalies (SSTA). Consequential influence on TC intensification is also explored. Among the various TC wind radii, SSTA are found to be most sensitive to the 34-kt wind radius (R34) (1 kt ≈ 0.51 m s−1). Generally, large TCs generate stronger and more widespread SSTA than small TCs (for category 1–2 TCs, R34: ∼270 vs 160 km; SSTA: −1.7° vs −0.9°C). Despite the same effect on prolonging residence time of TC winds, the effect of doubling R34 on SSTA is more profound than halving translation speed, due to more wind energy input into the upper ocean. Also differing from translation speed, storm size has a rather modest effect on the rightward shift and timing of maximum cooling. This study further demonstrates that storm size regulates TC intensification through an oceanic pathway: large TCs tend to induce stronger SST cooling and are exposed to the cooling for a longer time, both of which reduce the ocean’s enthalpy supply and thereby diminish TC intensification. For larger TCs experiencing stronger SST cooling, the probability of rapid intensification is half of smaller TCs. The presented results suggest that accurately specifying storm size should lead to improved cooling effect estimation and TC intensity prediction.
Significance Statement
Storm size has long been speculated to play a crucial role in modulating the TC self-induced sea surface temperature (SST) cooling and thus potentially influence TC intensification through ocean negative feedback. Nevertheless, systematic analysis is lacking. Here we show that larger TCs tend to generate stronger SST cooling and have longer exposure to the cooling effect, both of which enhance the strength of the negative feedback. Consequently, larger TCs undergo weaker intensification and are less likely to experience rapid intensification than smaller TCs. These results demonstrate that storm size can influence TC intensification not only from the atmospheric pathway, but also via the oceanic pathway. Accurate characterization of this oceanic pathway in coupled models is important to accurately forecast TC intensity.
Abstract
The Weather Research and Forecasting (WRF) model–based variational data assimilation system (WRF-Var) has been extended from three- to four-dimensional variational data assimilation (WRF 4D-Var) to meet the increasing demand for improving initial model states in multiscale numerical simulations and forecasts. The initial goals of this development include operational applications and support to the research community. The formulation of WRF 4D-Var is described in this paper. WRF 4D-Var uses the WRF model as a constraint to impose a dynamic balance on the assimilation. It is shown to implicitly evolve the background error covariance and to produce the flow-dependent nature of the analysis increments. Preliminary results from real-data 4D-Var experiments in a quasi-operational setting are presented and the potential of WRF 4D-Var in research and operational applications are demonstrated. A wider distribution of the system to the research community will further develop its capabilities and to encourage testing under different weather conditions and model configurations.
Abstract
The Weather Research and Forecasting (WRF) model–based variational data assimilation system (WRF-Var) has been extended from three- to four-dimensional variational data assimilation (WRF 4D-Var) to meet the increasing demand for improving initial model states in multiscale numerical simulations and forecasts. The initial goals of this development include operational applications and support to the research community. The formulation of WRF 4D-Var is described in this paper. WRF 4D-Var uses the WRF model as a constraint to impose a dynamic balance on the assimilation. It is shown to implicitly evolve the background error covariance and to produce the flow-dependent nature of the analysis increments. Preliminary results from real-data 4D-Var experiments in a quasi-operational setting are presented and the potential of WRF 4D-Var in research and operational applications are demonstrated. A wider distribution of the system to the research community will further develop its capabilities and to encourage testing under different weather conditions and model configurations.
Abstract
Devastating Japan in October 2019, Supertyphoon (STY) Hagibis was an important typhoon in the history of the Pacific. A striking feature of Hagibis was its explosive rapid intensification (RI). In 24 h, Hagibis intensified by 100 knots (kt; 1 kt ≈ 0.51 m s−1), making it one of the fastest-intensifying typhoons ever observed. After RI, Hagibis’s intensification stalled. Using the current typhoon intensity record holder, i.e., STY Haiyan (2013), as a benchmark, this work explores the intensity evolution differences of these two high-impact STYs. We found that the extremely high prestorm sea surface temperature reaching 30.5°C, deep/warm prestorm ocean heat content reaching 160 kJ cm−2, fast forward storm motion of ∼8 m s−1, small during-storm ocean cooling effect of ∼0.5°C, significant thunderstorm activity at its center, and rapid eyewall contraction were all important contributors to Hagibis’s impressive intensification. There was 36% more air–sea flux for Hagibis’s RI than for Haiyan’s. After its spectacular RI, Hagibis’s intensification stopped, despite favorable environments. Haiyan, by contrast, continued to intensify, reaching its record-breaking intensity of 170 kt. A key finding here is the multiple pathways that storm size affected the intensity evolution for both typhoons. After RI, Hagibis experienced a major size expansion, becoming the largest typhoon on record in the Pacific. This size enlargement, combined with a reduction in storm translational speed, induced stronger ocean cooling that reduced ocean flux and hindered intensification. The large storm size also contributed to slower eyewall replacement cycles (ERCs), which prolonged the negative impact of the ERC on intensification.
Abstract
Devastating Japan in October 2019, Supertyphoon (STY) Hagibis was an important typhoon in the history of the Pacific. A striking feature of Hagibis was its explosive rapid intensification (RI). In 24 h, Hagibis intensified by 100 knots (kt; 1 kt ≈ 0.51 m s−1), making it one of the fastest-intensifying typhoons ever observed. After RI, Hagibis’s intensification stalled. Using the current typhoon intensity record holder, i.e., STY Haiyan (2013), as a benchmark, this work explores the intensity evolution differences of these two high-impact STYs. We found that the extremely high prestorm sea surface temperature reaching 30.5°C, deep/warm prestorm ocean heat content reaching 160 kJ cm−2, fast forward storm motion of ∼8 m s−1, small during-storm ocean cooling effect of ∼0.5°C, significant thunderstorm activity at its center, and rapid eyewall contraction were all important contributors to Hagibis’s impressive intensification. There was 36% more air–sea flux for Hagibis’s RI than for Haiyan’s. After its spectacular RI, Hagibis’s intensification stopped, despite favorable environments. Haiyan, by contrast, continued to intensify, reaching its record-breaking intensity of 170 kt. A key finding here is the multiple pathways that storm size affected the intensity evolution for both typhoons. After RI, Hagibis experienced a major size expansion, becoming the largest typhoon on record in the Pacific. This size enlargement, combined with a reduction in storm translational speed, induced stronger ocean cooling that reduced ocean flux and hindered intensification. The large storm size also contributed to slower eyewall replacement cycles (ERCs), which prolonged the negative impact of the ERC on intensification.
