1. Introduction
Landfalling tropical cyclones (TCs) bring storm surge flooding, high winds, heavy precipitation, and tornadoes to coastal regions. After landfall, a TC may dissipate or transition to an extratropical cyclone (i.e., cold-core, low-pressure system) by merging with the midlatitude flow. Alternatively, TCs may maintain their tropical characteristics (i.e., warm-core, low-pressure system) and produce hazardous conditions for inland areas. Andersen and Shepherd (2013) have suggested that anomalously moist soils in the vicinity of the TC enhance the surface latent heat flux (LHF), providing energy to the cyclone that would otherwise be lacking over land. This process is characterized as the “brown ocean” effect. A global analysis of TCs maintaining or increasing in strength inland (TCMIs) over a 30-yr period revealed that precipitation and soil moisture were unusually high preceding most TCMI cases while atmospheric features amenable to extratropical transition were absent (i.e., weak baroclinicity). Sixteen cases were observed in northern Australia, eastern China, India, and the United States (Andersen and Shepherd 2013). Further understanding of the environment conducive to intensification events can improve forecasting and draw attention to this oft-overlooked stage of tropical cyclones.
Tropical cyclones are warm-core, low pressure systems that develop over the tropical oceans. The North Atlantic, north and south Indian, South Pacific, and west Pacific Oceans are ideal development regions when the sea surface temperature (SST) is >26.5°C and the atmosphere is characterized by weak vertical wind shear, conditional instability, enhanced midtroposphere relative humidity, and enhanced lower troposphere relative vorticity (Gray 1968). Latent heat flux from the warm oceans is a primary contributor to TC intensification (Kleinschmidt 1951; Miller 1958; Malkus and Riehl 1960; Liu et al. 2012). Latent and sensible heat transferred from the ocean surface along with potential energy, increase the moist static energy of the boundary layer. Convection converts moist static energy to kinetic energy, thereby intensifying the primary circulation of the TC. Typically, tropical systems weaken or transition to extratropical cyclones after landfall because of the loss of the energy source from the ocean, increased wind shear, and increased friction from the land surface, among other factors (Tuleya 1994). However, in instances of weak baroclinicity, wetter than normal soil conditions may provide adequate energy to sustain TCs in a similar manner to the ocean environment (Fig. 1).
Schematic of the brown ocean effect. Anomalously wet soil moisture conditions enhance evaporation and latent heat release. In a weakly baroclinic environment, the energy can help to sustain or intensify an inland tropical cyclone.
Citation: Journal of Applied Meteorology and Climatology 52, 12; 10.1175/JAMC-D-13-035.1
Strengthened moisture transport, rainfall reinforcement, and an associated latent heat trigger are identified as important factors for inland tracking storm intensity (Dong et al. 2010; Kishtawal et al. 2012). In model simulations, Chang et al. (2009) found that the intensity and longevity of monsoon depressions (MDs) are sensitive to soil moisture. Arndt et al. (2009), Kellner et al. (2012), and Evans et al. (2011) suggest that saturated soils and moisture transport in Oklahoma contributed to the reintensification of Tropical Storm Erin in 2007 by enhancing moist static energy and latent heat release. Emanuel et al. (2008) performed simulations using a simple tropical cyclone model coupled to a one-dimensional soil model to determine the role of large vertical heat fluxes from recently moistened soil on northern Australia cyclone redevelopment. Results indicated that warm, wet soils may help to maintain or energize landfalling TCs through heat transfer. In Asia, freshwater bodies and wetlands are hypothesized to supply energy to typhoons removed from the ocean (Chen 2012). Shen et al. (2002) found that hurricane decay rate is a function of surface water depth. A 2-m layer of water can adequately maintain a hurricane’s intensity over land (e.g., 950 hPa), a 0.5-m layer of water is sufficient to reduce landfall decay (e.g., 965 hPa), and no surface water leads to rapid decay (e.g., 980 hPa) 24 h after landfall.
Herein, a numerical water and heat flow model (“HYDRUS-1D”) is utilized to simulate soil characteristics and surface energy fluxes over observed regions of TC intensification. Four TCMI regions are simulated to output a time series of energy flux values two weeks leading up to the event. Fully coupled land surface–atmospheric models have been successfully employed in similar studies (Emanuel et al. 2008; Evans et al. 2011; Kellner et al. 2012). However, the accuracy of noncoupled versus coupled models for this particular application is beyond the scope of this research. It has been demonstrated that HYDRUS-1D effectively adapts meteorological data to simulate the surface energy balance and provides many options for soil parameters (e.g., soil type, soil texture, temperature and moisture profiles, plant rooting depth, and water table). HYDRUS-1D is valuable as a meteorological application because it solves coupled equations governing liquid water, water vapor, and heat transport within the soil. The model is tested public domain code that requires a minimal amount of computational resources, it has a straightforward user interface, and it is well documented (Saito et al. 2006). While recent reanalysis datasets provide surface energy flux data globally, using a soil model such as HYDRUS-1D allows flexibility and control over the environmental conditions. Specifically, the model is used to simulate heat and moisture fluxes over bare soil conditions. Vegetation moderates the LHF of an area while energy fluxes over bare soil are more variable. Previous work has suggested that TCMIs are unique to Australia because of the ability of sandy soil to suddenly release huge amounts of energy when moistened (Emanuel et al. 2008). By simulating the energy balance of each intensification region without vegetation, the fluxes can be directly linked to soil texture.
The objectives of this research are to 1) simulate the soil profiles of TC intensification regions in the 2 weeks leading up to TCMI events, 2) determine the trends and magnitudes of surface energy fluxes prior to a TC tracking over and intensifying, and 3) compare the simulated energy flux values with typical values over the tropical ocean. Although the purpose of this study coincides with Emanuel et al. (2008) (i.e., quantification of surface energy fluxes in relation to TCs), the focus is on the feasibility of moist soil acting as a substitute for the ocean environment, identifying an LHF threshold for inland intensification using multiple cases, and comparing flux trends across four major landfall regions. The remainder of the paper is divided into three sections. Section 2 describes the inland TC case studies, data, and model setup. The results are presented in section 3, followed by a discussion and conclusions in section 4.
2. Cases and model setup
a. Case studies
Andersen and Shepherd (2013) identified 16 inland tropical cyclone maintenance and intensification events globally between 1979 and 2008. These events displayed a drop in minimum central pressure and/or an increase in maximum sustained wind speed at least 350 km inland from the coast while maintaining warm-core tropical structures (determined by thermal wind calculations). From this dataset, the strongest intensification case (i.e., greatest pressure drop) from each major hurricane basin that is minimally influenced by strong baroclinicity is chosen for analysis (Fig. 2). The TC best track, minimum central pressure, and maximum sustained wind speed are obtained from the International Best Track Archive for Climate Stewardship (IBTrACS; http://www.ncdc.noaa.gov/oa/ibtracs/index.php). IBTrACS merges TC information from Regional Specialized Meteorological Centers, international centers, and individuals to create the most complete global best track dataset available (Knapp et al. 2010).
TCMI case studies for HYDRUS-1D model runs. Each is the strongest intensification event (i.e., greatest pressure drop inland) originating from its respective hurricane basin between 1979 and 2008 based on work by Andersen and Shepherd (2013). The arrow denotes the point of intensification.
Citation: Journal of Applied Meteorology and Climatology 52, 12; 10.1175/JAMC-D-13-035.1
Tropical Storm (TS) Erin developed on 15 August 2007 in the Gulf of Mexico. It achieved tropical storm status with 35-kt (1 kt = 0.51 m s−1) winds and subsequently made landfall near Corpus Christi, Texas. Reintensification occurred over Oklahoma, with the strength surpassing that of its ocean counterpart. The wind was recorded as 50 kt and pressure at 995 hPa. Studies have highlighted the anomalously wet soil in Oklahoma at this time and possible influence on TS Erin (Arndt et al. 2009; Evans et al. 2011; Kellner et al. 2012; Andersen and Shepherd 2013).
Typhoon Nat developed on 14 September 1991 in the Philippine Sea. It reached category 3 strength on the Saffir–Simpson hurricane wind scale before tracking over Taiwan and making landfall near Chaozhou, Guangdong (China). Nat weakened over land but maintained a relatively constant pressure near the end of its life cycle with a slight pressure drop (minimum central pressure of 1008 hPa). TC best track datasets in the west Pacific suffer from discrepancies due to varying algorithms used by the Joint Typhoon Warning Center, Japan Meteorological Agency, and the China Meteorological Administration (Barcikowska et al. 2012). Therefore, the postlandfall records for Typhoon Nat are not as reliable as more recent cases because of uncertain or missing data near the end of its life cycle, but it proved to be the only case from the west Pacific that maintained a warm core over land without significant meridional flow to complicate the analysis. Considering the overall lack of TCMI cases in eastern Asia (Andersen and Shepherd 2013), it is more than likely that extratropical transition or dissipation dominates in this region (perhaps because of the extreme terrain). Nevertheless, it is still useful to include a typhoon in this study in order to gauge possible surface energy flux differences between the four primary landfall regions.
Tropical system 2007172N15088 developed 20 June 2007 in the Bay of Bengal. It strengthened to a tropical depression and made landfall on the east coast of India near Ethamukkala, Andhra Pradesh. After initially weakening, it regained strength in central India while moving northwest on 23 June. The wind was recorded at 20 kt and the pressure at 1007 hPa.
Cyclone Wylva developed 14 February 2001 in the Gulf of Carpentaria. It reached tropical storm strength at 41-kt winds before passing near Robinson River Airport in the Northern Territory of Australia. The pressure was steady for about 2 days after landfall and then began dropping as Wylva moved west (minimum central pressure of 988 hPa). Hurricane Satellite (HURSAT; http://www.ncdc.noaa.gov/oa/rsad/hursat/) visible satellite images for each TCMI at the time of intensification are shown in Fig. 3.
HURSAT visible satellite images for (a) Typhoon Nat (1991), (b) TS Erin (2007), (c) Cyclone Wylva (2001), and (d) 2007172N15088.
Citation: Journal of Applied Meteorology and Climatology 52, 12; 10.1175/JAMC-D-13-035.1
b. Model setup
HYDRUS-1D (http://www.pc-progress.com/en/Default.aspx?h1d-description) is a finite-element model for simulating the one-dimensional movement of water, heat, and solutes in soil. It numerically solves the Richards equation for water flow and advection dispersion equations for heat and solutes. The heat transport equation includes heat flow by conduction, convection, and vapor phase and takes into account soil water content (Saito et al. 2006).
Each model run simulates the water and energy balance of a TCMI region in the 2 weeks antecedent to the intensification event to produce LHF, SHF, and soil temperature time series. Note that HYDRUS produces time series for one specified location. Therefore, the heat flow and meteorological boundary conditions are taken from assimilated reanalysis data area-averaged means (a 6° × 6° geographical area centered on the storm during intensification as indicated by the best track data). Research has shown that the cyclonic, convergent inflow is within a 4°–6° radius of the TC center and the potential vorticity signature is well within a 500-km radius (Frank 1977; Evans and Hart 2003).
Three-hourly near-surface air temperature (°C), near-surface wind speed (km day−1), solar radiation flux (MJ m−2 day−1), 0–10-cm soil temperature (°C), and 40–100-cm soil temperature (°C) are obtained from Global Land Data Assimilation System, version 2 (GLDAS-2), Noah model experiment (http://ldas.gsfc.nasa.gov/gldas/). Near-surface relative humidity (%) and precipitation rate or influx (mm day−1) are obtained from Modern-Era Retrospective Analysis for Research and Applications (MERRA; https://gmao.gsfc.nasa.gov/merra/). The Natural Resources Conservation Service (http://soils.usda.gov) provides information on regional soil types. A description of the case studies is shown in Table 1. TCMI occurrence is most common in Australia and least common in the United States (Andersen and Shepherd 2013).
Description of the four case studies for HYDRUS runs. The last day of each simulation is the time of intensification. Pressure refers to minimum central pressure, and wind speed refers to maximum sustained wind speed.
3. Results
a. HYDRUS
The soil temperature and sensible and latent heat fluxes from the model runs are used to assess the surface conditions prior to the tropical cyclone. The soil temperature profiles for bare soil show that the surface is most susceptible to diurnal temperature variations (Fig. 4). In southeastern China, northern Australia, and central India, the temperatures slightly decrease with time, while soil temperatures in the south-central United States increase. The United States and Australia have the greatest diurnal fluctuations and highest maximum temperatures. The reason for this observation may be related to high solar radiation during the day (summertime TCMI occurrence) and radiational cooling at night (clear skies). In Australia and India, deeper soil remains warmer on average compared to the other cases owing to the tropical climate. In all regions, the daily maximum surface temperature equals or surpasses the 26.5°C SST threshold: 26.5°C in China, 37.7°C in the United States, 39.9°C in Australia, and 33.7°C in India.
HYDRUS-generated soil temperature (°C) at the surface and at 10- and 40-cm depths for (a) Typhoon Nat (China), (b) TS Erin (United States), (c) Cyclone Wylva (Australia), and (d) tropical system 2007172N15088 (India) over bare soil.
Citation: Journal of Applied Meteorology and Climatology 52, 12; 10.1175/JAMC-D-13-035.1
SHF time series for each region are shown in Fig. 5. Note that the model output is in incremental time steps; therefore, the time series plots undergo some smoothing to generate 3-hourly data. SHF magnitudes over China and India are very low, less than 30 W m−2 for the duration of the simulations. For much of the time, SHF is negative, implying that the ground temperature is relatively cooler than the air. This is not surprising as Typhoon Nat occurred in early autumn and the Indian tropical system occurred during the wet monsoon season. TS Erin occurred during the hottest month of the year; therefore, the United States features high daytime SHF (up to 220 W m−2). While Australia exhibited the highest soil temperatures, the SHF trails that of the United States with daily values reaching 50–150 W m−2.
HYDRUS-generated surface sensible heat flux (W m−2) over 2 weeks prior to (a) Typhoon Nat, (b) TS Erin, (c) Cyclone Wylva, and (d) tropical system 2007172N15088 for bare soil (solid line) simulations. MERRA precipitation flux (mm day−1) is plotted for reference (in gray).
Citation: Journal of Applied Meteorology and Climatology 52, 12; 10.1175/JAMC-D-13-035.1
The HYDRUS LHF time series and MERRA total precipitation flux (mm day−1) are shown in Fig. 6. In the regions where intensification is to occur, LHF is highest in the afternoons/evenings and lowest in the mornings. As solar radiation warms the boundary layer throughout the day, evaporation increases along with LHF. For these case studies, the LHF values are consistently higher in China and India. In the U.S. case, LHF is more variable over time and spikes after precipitation events. In the United States and Australia, the trend of LHF follows most closely to the trend of precipitation because of drier ambient conditions. The daily maximums for all regions often reach 200 W m−2 or greater. The soil texture does not appear to have a significant effect on the LHF magnitudes across regions; however, the diurnal changes are sharper over Australia. Taken together with SHF, it is apparent that the U.S. and Australia study regions have both moderately high heat and moisture fluxes.
As in Fig. 5, but for latent heat flux (W m−2).
Citation: Journal of Applied Meteorology and Climatology 52, 12; 10.1175/JAMC-D-13-035.1
The total 2-week mean LHF indicates there is a ~70 W m−2 area-averaged threshold for potential TCMI occurrence consistent with previous findings (Andersen and Shepherd 2013) (Fig. 7). The total 2-week maximum LHF values indicate daytime fluxes reach 300 W m−2 or higher over the inland intensification regions. With the exception of Nat, the TCMIs intensified during the nighttime hours and likely encountered the residual effects of daytime surface fluxes. Specifically, the high daytime fluxes increase the low-level atmospheric moisture, which is subsequently swept into the TC when it moves over.
The 2-week mean and maximum LHF (W m−2) antecedent to the TCMI for HYDRUS bare soil simulations.
Citation: Journal of Applied Meteorology and Climatology 52, 12; 10.1175/JAMC-D-13-035.1
b. Land versus ocean
HYDRUS estimates of 3-hourly land surface LHF antecedent to TCMI events respond to the diurnal pattern of the boundary layer with maximum values occurring in the daytime. The magnitude of LHF is better understood with a comparison of values over land versus tropical ocean. LHF within TCs are not precisely known because of the lack of accurate observations. However, remote sensing and algorithms have been used to calculate reasonable estimates (Guimond et al. 2011). Note that in this study, LHF magnitudes over an area prior to the storm are of greater interest than those within the storm since the goal is to determine how favorable the environment is leading up to the TC.
According to Zhang and Rossow (1997), the mean annual LHF peaks around 115–125 W m−2 near 15°N–S. Trenberth and Fasullo (2007) estimate that the background LHF over the tropical ocean is approximately 120 W m−2. High-resolution satellite-derived LHF estimates of the pre-TC ocean environment have been found to be 100–200 W m−2 (Liu et al. 2011). In the western North Pacific, TC LHF maxima have been estimated between 150 and 190 W m−2 (Gao and Chiu 2010). According to the HYDRUS results, daytime values of LHF over the study regions of southeastern China, south-central United States, northern Australia, and central India reach well above these estimates.
To facilitate a meaningful comparison, MERRA LHF over ocean prestorm (6° × 6° region adjacent to the coast where the TC is to form or strengthen) and over land (same 6° × 6° region used for HYDRUS runs) are analyzed. “Prestorm” is considered to cover the 3 days prior to the TC making an appearance in the region. HYDRUS has a slightly lower 3-day mean LHF in three of four intensification regions when compared to the same land region from the MERRA dataset (Fig. 8, left). This is likely due to the fact that HYDRUS is initialized and run with area-averaged conditions, while MERRA means are calculated from x–y gridded LHF values. The two datasets match within approximately 45 W m−2 for all regions. When comparing HYDRUS land versus MERRA ocean fluxes, the prestorm means are similar in magnitude with the exception of TCMI Wylva. Prior to Wylva, the ocean area exhibited about a 60% greater flux than the land area. Australia’s proximity to the equator and the timing of the TCMI (i.e., hottest month of the year) might suggest that the offshore SSTs are relatively higher than the other three regions and lead to enhanced LHF.
Three-day (left) mean and (right) maximum LHF over ocean (MERRA) and land (MERRA and HYDRUS) along TCMI tracks.
Citation: Journal of Applied Meteorology and Climatology 52, 12; 10.1175/JAMC-D-13-035.1
The prestorm maximum values reveal the same underestimation of LHF by HYDRUS in the inland regions (up to 140 W m−2 in Australia). Despite this bias, the HYDRUS inland fluxes are remarkably higher than the ocean fluxes (>200%) in China and India. In the United States and Australia, the land and ocean fluxes are nearly equal. Near-zero nighttime values over land lower the mean, while the daytime fluxes are much higher and are quantified by the prestorm maximum values. The diurnal trend is subdued over the ocean where evaporation is relatively continuous.
The results indicate that the land surface is capable of producing a nearly equal or greater magnitude LHF as the ocean during the daytime hours closely preceding a TC. Because of the area-averaging method used with HYDRUS, the moisture fluxes produced are likely on the conservative side. Given that LHF is a primary contributor to TC formation and intensification, the land surface may play an important role in aiding the maintenance and intensification of TCs when soils are abundantly moist and LHF is high.
4. Discussion and conclusions
Tropical cyclones often weaken or transition to extratropical cyclones once reaching land. However, there are instances in which a TC has remained a warm-core structure and intensified inland away from the primary oceanic energy source (Arndt et al. 2009; Dong et al. 2010; Evans et al. 2011; Kellner et al. 2012; Andersen and Shepherd 2013). Building upon the work of Andersen and Shepherd (2013), four tropical cyclone maintenance or intensification events were chosen as case studies to run HYDRUS-1D, a water and heat flow model. The surface energy balance was simulated for each intensification region leading up to the cyclone to quantify the available heat and moisture fluxes and determine if patterns are related to soil texture.
Time series were generated two weeks prior to each TCMI event for a bare soil configuration over a region centered on the TCMI location at the time of inland intensification. Soil temperature and LHF were generally high in each region, with China and India exhibiting the highest LHF and lowest SHF. The soil texture (i.e., hydraulic conductivity) does not appear to have a significant effect on the LHF magnitudes across regions. While TCMIs are most common over Australia, the LHF over the study period is not noticeably greater over sandy soil than over clay (China and India) or loam (United States). However, the diurnal changes are sharper over Australia and the high LHF is accompanied by high SHF. This agrees with the notion of “sudden energy release” when the sandy soil is wetted. For saturated soils, hydraulic conductivity is significantly greater for coarse-textured soils than clays or loams (Radcliffe and Šimůnek 2010). The U.S. region exhibits a similar trend, with relatively high LHF and SHF, yet TCMIs are much less common (Andersen and Shepherd 2013). TCMI frequency in Australia is likely a function of latitude, total landfalls, terrain characteristics, and atmospheric conditions.
For all HYDRUS runs, the total 2-week maximum LHF values are comparable to or exceed typical ocean LHF magnitudes. The total 2-week mean LHF indicates there is a ~70 W m−2 area-averaged threshold for TCMI occurrence. MERRA reanalysis 3-day (prestorm) LHFs were utilized for a land versus ocean comparison. The prestorm mean LHFs prior to the TC reveal that the oceanic LHF was higher than the land LHF in three of four regions. Considering that LHF is diminished at nighttime over land, the daytime flux values are remarkably high. The 3-day maximum LHF values inland are well above those over the ocean in the China and India intensification regions, while LHF magnitudes ocean versus land were nearly equal over the U.S. and Australia regions. This suggests that LHF maxima over land have a similar magnitude to those found over the ocean prior to the appearance of a TCMI and provide plausibility of the brown ocean effect. While HYDRUS provides a unique avenue to assessing the surface energy balance by including liquid water and vapor transport in soils, the 1D design is a limitation to exploring the spatial distribution of LHF. It may be concluded, however, that the daytime flux magnitudes output by HYDRUS are underestimations due to the area-averaging method.
Further analysis is needed to determine the relative importance of short-term soil moisture anomalies to other contributing factors of TC intensification. Although Evans et al. (2011) examined the seasonal soil moisture signal influence on TS Erin, precipitation recycling and soil moisture memory warrant investigation in other landfall regions where the land surface–atmospheric feedback may be stronger or weaker. Additionally, it was suggested that anomalously wet soils encouraged moisture transport from the Gulf (Evans et al. 2011). Analysis of synoptic-scale features for additional TCMIs could determine if this is a common trait. A thorough investigation of soil moisture patterns, timing of rainfall events, and structure and speed of TCs in relation to intensification potential would be beneficial.
Future studies may extend this research by using a larger sample size to compare TCMIs within and across regions or add null cases to better understand differences between intensifying and weakening TCs. This study and previous works are primarily feasibility studies for the soil moisture–cyclone feedback hypothesis. High spatial and temporal resolution modeling would provide more refined evidence of the heat and moisture transport to the TC. Additionally, point observations from meteorological stations are ideal input where available and could lead to more precise results for multiple TCMI events. A hurricane simulation model coupled with a sophisticated soil model can simulate a TCMI event and lead to better understanding of the brown ocean effect.
Acknowledgments
This research is funded by a NASA Earth and Space Science Fellowship under Grant NNX11AL86H.
REFERENCES
Andersen, T. K., and J. M. Shepherd, 2013: A spatio-temporal analysis of tropical cyclone maintenance or intensification inland. Int. J. Climatol., doi:10.1002/joc.3693, in press.
Arndt, D. S., J. B. Basara, R. A. McPherson, B. G. Illston, G. D. McManus, and D. B. Demko, 2009: Observations of the overland reintensification of Tropical Storm Erin (2007). Bull. Amer. Meteor. Soc., 90, 1079–1093.
Barcikowska, M., F. Feser, and H. von Storch, 2012: Usability of best track data in climate statistics in the western North Pacific. Mon. Wea. Rev., 140, 2818–2830.
Bosilovich, M. G., and W.-Y. Sun, 1999: Numerical simulation of the 1993 Midwestern flood: Land atmosphere interactions. J. Climate, 12, 1490–1505.
Carsel, R. F., and R. S. Parrish, 1988: Developing joint probability distributions of soil water retention characteristics. Water Resour. Res., 24, 755–769, doi:10.1029/WR024i005p00755.
Chang, H., D. Niyogi, A. Kumar, C. M. Kishtawal, J. Dudhia, F. Chen, U. C. Mohanty, and M. Shepherd, 2009: Possible relation between land surface feedback and the post-landfall structure of monsoon depressions. Geophys. Res. Lett., 36, L15826, doi:10.1029/2009GL037781.
Chen, L.-S., 2012: Research progress on the structure and intensity change for the landfalling tropical cyclones. J. Trop. Meteor., 18, 113–118.
Clark, C. A., and R. W. Arritt, 1995: Numerical simulations of the effect of soil moisture and vegetation cover on the development of deep convection. J. Appl. Meteor., 34, 2029–2045.
Dong, M., L. Chen, Y. Li, and C. Lu, 2010: Rainfall reinforcement associated with landfalling tropical cyclones. J. Atmos. Sci., 67, 3541–3558.
Emanuel, K., J. Callaghan, and P. Otto, 2008: A hypothesis for the redevelopment of warm-core cyclones over northern Australia. Mon. Wea. Rev., 136, 3863–3872.
Evans, C., R. S. Schumacher, and T. J. Galarneau, 2011: Sensitivity in the overland reintensification of Tropical Cyclone Erin (2007) to near-surface soil moisture characteristics. Mon. Wea. Rev., 139, 3848–3870.
Evans, J. L., and R. E. Hart, 2003: Objective indicators of the life cycle evolution of extratropical transition for Atlantic tropical cyclones. Mon. Wea. Rev., 131, 909–925.
Frank, W. M., 1977: The structure and energetics of the tropical cyclone. Mon. Wea. Rev., 105, 1119–1135.
Gao, S., and L. S. Chiu, 2010: Surface latent heat flux and rainfall associated with rapidly intensifying tropical cyclones over the western North Pacific. Int. J. Remote Sens., 31, 4699–4710, doi:10.1080/01431161.2010.485149.
Gray, W. M., 1968: Global view of the origin of tropical disturbances and storms. Mon. Wea. Rev., 96, 669–700.
Guimond, S. R., M. A. Bourassa, and P. D. Reasor, 2011: A latent heat retrieval and its effects on the intensity and structure change of Hurricane Guillermo (1997). Part I: The algorithm and observations. J. Atmos. Sci., 68, 1549–1567.
Kellner, O., D. Niyogi, M. Lei, and A. Kumar, 2012: The role of anomalous soil moisture on the inland reintensification of Tropical Storm Erin (2007). Nat. Hazards, 139, 1573–1600.
Kishtawal, C. M., D. Niyogi, A. Kumar, M. L. Bozeman, and O. Kellner, 2012: Sensitivity of inland decay of North Atlantic tropical cyclones to soil parameters. Nat. Hazards, 63, 1527–1542, doi:10.1007/s11069-011-0015-2.
Kleinschmidt, E., Jr., 1951: Gundlagen einer Theorie des tropischen Zyklonen. Arch. Meteor., Geophys. Bioklimatol., Ser. A, 4, 53–72.
Knapp, K. R., M. C. Kruk, D. H. Levinson, H. J. Diamond, and C. J. Neumann, 2010: The International Best Track Archive for Climate Stewardship (IBTrACS): Unifying tropical cyclone best track data. Bull. Amer. Meteor. Soc., 91, 363–376.
Liu, C.-X., Q.-L. Wan, F. Liao, and Z.-K. Zhao, 2012: Surface observations in the tropical cyclone environment over the South China Sea. J. Trop. Meteor., 18, 263–274.
Liu, J., J. A. Curry, C. A. Clayson, and M. A. Bourassa, 2011: High-resolution satellite surface latent heat fluxes in North Atlantic hurricanes. Mon. Wea. Rev., 139, 2735–2747.
Lynn, B. H., W.-K. Tao, and P. J. Wetzel, 1998: A study of landscape–generated deep moist convection. Mon. Wea. Rev., 126, 928–942.
Malkus, J. S., and H. Riehl, 1960: On the dynamics and energy transformations in steady-state hurricanes. Tellus, 12, 1–20.
Miller, B. I., 1958: On the maximum intensity of hurricanes. J. Meteor., 15, 184–195.
Radcliffe, D. E., and J. Šimůnek, 2010: Soil Physics with HYDRUS: Modeling and Applications. CRC Press, 373 pp.
Saito, H., J. Šimůnek, and B. P. Mohanty, 2006: Numerical analysis of coupled water, vapor, and heat transport in the vadose zone. Vadose Zone J., 5, 784–800, doi:10.2136/vzj2006.0007.
Scanlon, B., K. Keese, R. C. Reedy, J. Šimůnek, and B. Andraski, 2003: Variations in flow and transport in thick desert vadose zones in response to paleoclimatic forcing (0–90 kyr): Monitoring, modeling, and uncertainties. Water Resour. Res., 39, 1179, doi:10.1029/2002WR001604.
Shen, W., I. Ginis, and R. E. Tuleya, 2002: A numerical investigation of land surface water on landfalling hurricanes. J. Atmos. Sci., 59, 789–802.
Šimůnek, J., M. Šejna, H. Saito, M. Sakai, and M. T. van Genuchten, 2009: The HYDRUS-1D software package for simulating the one-dimensional movement of water, heat, and multiple solutes in variably-saturated media, version 4.08. University of California, Riverside, Dept. of Environmental Sciences HYDRUS Software Series 3, 330 pp. [Available online at http://www.pc-progress.com/Downloads/Pgm_hydrus1D/HYDRUS1D-4.08.pdf.]
Trenberth, K. E., and J. Fasullo, 2007: Water and energy budgets and hurricanes and implications for climate change. J. Geophys. Res., 112, D23107, doi:10.1029/2006JD008304.
Tuleya, R. E., 1994: Tropical storm development and decay: Sensitivity to surface boundary conditions. Mon. Wea. Rev., 122, 291–304.
Zhang, Y.-C., and W. B. Rossow, 1997: Estimating meridional energy transports by the atmospheric and oceanic general circulations using boundary fluxes. J. Climate, 10, 2358–2373.
