Abstract

This study presents a new high-resolution satellite-derived ocean surface flux product, XSeaFlux, which is evaluated for its potential use in hurricane studies. The XSeaFlux employs new satellite datasets using improved retrieval methods, and uses a new bulk flux algorithm formulated for high wind conditions. The XSeaFlux latent heat flux (LHF) performs much better than the existing numerical weather prediction reanalysis and satellite-derived flux products in a comparison with measurements from the Coupled Boundary Layer Air–Sea Transfer (CBLAST) field experiment. Also, the XSeaFlux shows well-organized LHF structure and large LHF values in response to hurricane conditions relative to the other flux products. The XSeaFlux dataset is used to interpret details of the ocean surface LHF for selected North Atlantic hurricanes. Analysis of the XSeaFlux dataset suggests that ocean waves, sea spray, and cold wake have substantial impacts on LHF associated with the hurricanes.

1. Introduction

The thermodynamic disequilibrium between the tropical atmosphere and ocean provides an energy source for tropical cyclones (e.g., Kleinschmidt 1951; Emanuel 1986), arising primarily from the undersaturation of near-surface air. The dependence of the air–sea energy transfer rate on wind has been hypothesized to be the principal feedback mechanism that allows hurricanes to develop (e.g., Emanuel 2003). Hence, accurate knowledge of the latent heat flux across the air–sea interface is required for hurricane predictions, whether using a simple diagnostic approach (e.g., Emanuel 1995) or a fully coupled dynamical approach (e.g., Chen et al. 2007). The strong surface winds associated with hurricanes increase surface evaporation so that the latent heat loss by the ocean can exceed 1000 W m−2, which is an order of magnitude larger than the summertime climatological latent heat flux (LHF) values (e.g., Trenberth and Fasullo 2007). To understand the dynamics of individual hurricanes as well as the climate dynamics of hurricanes requires observations of the surface LHF on a scale of tens of kilometers and hours; regardless of the source of water vapor being the LHF from below the storm or an influx of midlevel water vapor.

The surface LHF in hurricanes is difficult to measure and direct observations are rarely available outside of a few dedicated field campaigns [e.g., the Coupled Boundary Layer Air–Sea Transfer (CBLAST; Black et al. 2007)]. In the absence of direct measurements, the LHF can be estimated using a bulk flux algorithm of the following form:

 
formula

where ρa is the density of air, Lυ is the latent heat of vaporization, CE is the turbulent exchange coefficient, U is the near-surface wind speed, Qs is 98% of the saturated specific humidity at the sea surface temperature (SST), and Qa is the near-surface air specific humidity.

Surface analyses from numerical weather prediction (NWP) models provide one source of ocean surface turbulent fluxes, which are calculated from (1) using model-predicted variables. However, there are a number of established problems with NWP fluxes, including variables that are more dependent on physical parameterizations used in the particular model and not strongly constrained by observations. Satellite-derived observations of ocean surface turbulent fluxes (for a summary see Curry et al. 2004) provide an alternative to the NWP fluxes, but uncertainties in the retrievals of near-surface values of air humidity and temperature continue to plague the flux calculations. Current ocean surface flux products derived from satellites and NWP reanalyses typically have resolutions of ~100 km (or coarser) and a temporal resolution of 6 h to 1 day. Particular difficulties in calculating the LHF in hurricanes from bulk flux models in the form of (1) include determination of the turbulent exchange coefficient under conditions of high winds and a highly disturbed sea state and understanding the effects of sea spray. Surface momentum exchange coefficients increase with wind speed and are enhanced by fetch-limited waves or opposing swell, but level off or decrease above an extreme high wind threshold (>45 m s−1; e.g., Powell et al. 2003; Black et al. 2007).

An example of the deficiencies in current NWP and satellite-derived flux products is illustrated in Fig. 1 by the spatial distribution of the LHF for Hurricanes Fabian (1800 UTC 2 September 2003) and Isabel (1800 UTC 13 September 2003). Compared to the National Oceanic and Atmospheric Administration-12 (NOAA-12) Advanced Very High Resolution Radiometer (AVHRR) images of Hurricanes Fabian (2000 UTC 2 September) and Isabel (2100 UTC 13 September), none of the available NWP and satellite-derived flux products reproduce a well-organized and distinct structure of the LHF associated with the hurricanes. Specifically, the National Centers for Environmental Prediction reanalysis II (NCEP2; Kanamitsu et al. 2002) identifies some LHF signals in response to the hurricanes, but is located too far north for Hurricane Fabian and too far northeast for Hurricane Isabel as compared to the observed position of the hurricanes. The Objectively Analyzed air–sea Fluxes (OAFlux; Yu and Weller 2007) does not have discernible LHF signals associated with the hurricanes. The Hamburg Ocean–Atmosphere Parameters and Fluxes version 3 (HOAPS3; Andersson et al. 2007) seems to have the hurricane-related LHF variations at correct locations, but the magnitude of the LHF is weak, and the majority of the data is missing within ~100–200 km of the storm center. The European Centre for Medium-Range Weather Forecasts Interim Reanalysis (ERA-Interim; Simmons et al. 2007) also shows a very weak LHF response to the hurricanes (Manning and Hart 2007).

Fig. 1.

Spatial distribution of LHF (W m−2) for (top) Hurricane Fabian (1800 UTC 2 Sep 2003) and (bottom) Hurricane Isabel (1800 UTC 13 Sep 2003) for the NWP and satellite-derived flux products (NCEP2, OAFlux, HOAPS3, and ERA-Interim). The contours are the NCEP2 surface pressure. NOAA-12 AVHRR image of Hurricane Fabian (2000 UTC 2 Sep 2003) and Isabel (2100 UTC 13 Sep 2003) were obtained online at http://meto.umd.edu/~stevenb/hurr/03/03.html.

Fig. 1.

Spatial distribution of LHF (W m−2) for (top) Hurricane Fabian (1800 UTC 2 Sep 2003) and (bottom) Hurricane Isabel (1800 UTC 13 Sep 2003) for the NWP and satellite-derived flux products (NCEP2, OAFlux, HOAPS3, and ERA-Interim). The contours are the NCEP2 surface pressure. NOAA-12 AVHRR image of Hurricane Fabian (2000 UTC 2 Sep 2003) and Isabel (2100 UTC 13 Sep 2003) were obtained online at http://meto.umd.edu/~stevenb/hurr/03/03.html.

In this paper, we use a new high-resolution satellite-derived ocean surface turbulent flux dataset (called XSeaFlux) that is designed to resolve the extreme surface latent heat flux in hurricanes. Recent new satellite-derived surface and near-surface parameters with improved retrieval methods (see section 2 for details) are applied in this study, in addition to a new bulk flux model that includes careful consideration of the flux transfer at high winds (see section 2 for details). The XSeaFlux LHF is evaluated and interpreted using field observations. Applications of the XSeaFlux data are made to interpret details of ocean surface latent heat fluxes for Hurricanes Fabian and Isabel.

2. Data and methods

The datasets employed in this study include the following:

  • the established ocean surface LHF datasets, including the NCEP2 (NWP reanalysis; Kanamitsu et al. 2002), the OAFlux (a blended product using in situ observations, NWP reanalysis, and satellite product; Yu and Weller 2007), the HOAPS3 (satellite-derived product; Andersson et al. 2007), and the ERA-Interim (NWP reanalysis; Simmons et al. 2007);

  • the new satellite-derived XSeaFlux dataset that provides ocean surface LHF as well as surface and near-surface parameters on a spatial scale of 0.25° and a temporal scale of 6 h;

  • in situ airborne observations of the LHF from the CBLAST field experiment (Drennan et al. 2007) for Hurricanes Fabian and Isabel (2003);

  • North Atlantic hurricane data obtained from the Extended Best Tracks dataset (Demuth et al. 2006).

The satellite-derived XSeaFlux LHF has been developed to take advantage of new developments of the SEAFLUX project (e.g., Curry et al. 2004). The high-resolution XSeaFlux dataset (0.25° and 6-hourly) used here incorporates the following satellite-derived input variables:

  • SST: A new high-resolution SST analysis (version 2) has been developed based upon the operational SST product produced by NOAA (Reynolds et al. 2007) using optimum interpolation (OI), which has a spatial resolution of 0.25° and temporal resolution of 1 day. The XSeaFlux product includes a parameterization and additional satellite products to allow for determination of the diurnal cycle of SST (following Clayson and Weitlich 2007) to produce a 6-hourly SST dataset.

  • U: A blended ocean surface wind speed (at 10 m above sea level) analysis has been produced by the National Climatic Data Center (Zhang et al. 2006), which has a spatial spacing of 0.25° and temporal spacing of 6 h. A spatial-temporally weighted interpolation is used to integrate wind speed derived from multiple satellites, including the Special Sensor Microwave Imager (SSM/I) on board the Defense Meteorological Satellite Program, the Tropical Rainfall Measuring Mission Microwave Imager (TMI), the Quick Scatterometer (QuikSCAT), and the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). Note: wind speed for multiple satellites is obtained from the Remote Sensing Systems for the uniformity of the retrieval algorithms. The blended multiple-satellite observations provide improved space and time coverage, and reduce the subsampling aliases and random errors.

  • Qa and Ta: A new method for retrievals of Qa and Ta over the oceans has been developed by using the SSM/I data with a first-guess SST and a neural network technique, in addition to improvements gained by properly accounting for the effects of high cloud liquid water contents (Roberts et al. 2010). Comparisons of the Qa and Ta retrievals from this methodology to a database of over 200 000 ship and buoy observations demonstrated that the root-mean-square (rms) error of this algorithm for Qa is found to be about 1.3 g kg−1 accompanied with a small positive bias of 0.16 g kg−1, while for Ta the rms error is 1.3°C with a bias of less than 0.1°C. The data have a spatial resolution of 0.25° and temporal resolution of 6 h. For missing data points, an optimal interpolation scheme is used in both space and time (e.g., Zhang et al. 2006).

The XSeaFlux LHF is calculated from the satellite-derived input values of SST, U, Qa, and Ta using a bulk flux model in (1). The bulk flux model employed here is based upon the surface renewal algorithm of Clayson et al. (1996), which has been shown by Brunke et al. (2003) to perform very well against eddy correlation observations of surface latent heat fluxes in the tropical oceans. Brunke et al. (2003) found that bulk flux models begin to diverge rapidly from the verification data at wind speeds exceeding 15 m s−1. To account for the impact of high winds and a highly disturbed sea state on the turbulent exchange coefficients, we employ the adjustment described by Bourassa (2006) to account for the influence of ocean waves and Andreas et al. (2008) to account for the influence of sea spray.

To account for the effects of ocean waves, following Bourassa (2006), we replace U in the bulk flux model by the magnitude of the vector difference |UUw|, where Uw is substituted by 80% of the orbital velocity at the crest of the dominant waves. This substitution is justified in Bourassa (2006) for determining surface stress. The magnitude of the orbital velocity (Uorb) is determined based on the dominant waves, and is in the mean direction of the propagating waves. This approach to modeling wave influences works by modification of the vertical wind shear rather than modification of Charnock’s parameter. One advantage of this approach is that the same value of Charnock’s parameter is a good fit to wind waves and swell. The orbital velocity is approximated as , where Hs is significant wave height and Tp is the dominant wave period. Thus, the dependence of CE on wind speed is atypical in that we replace the traditional earth-relative wind speed with |UUw|. The Uw increases as wave slope increases, resulting in a reduced value of the latent heat flux over steep waves, relative to the latent heat flux for the same 10-m wind speed and a nonmoving surface with the same roughness. This result is similar to using the 10-m wind without considering Uw, and reducing CE as waves become steeper. The wave information is obtained from the outputs of the new generation wave model (WAVEWATCH III) developed at NOAA/NCEP, which has a spatial resolution of 1° × 1.25° and a temporal resolution of 6 h.

There are several sea spray models in the literature (e.g., Fairall et al. 1994; Andreas and Emanuel 2001), that rely on assumptions on the size distribution of spray droplets and how the droplets evaporate. Here, we use a spray flux model based on Andreas et al. (2008), whereby the LHF is a combination of the interfacial latent heat flux (LHFi), which is computed with the bulk interfacial flux algorithm of the aforementioned flux model, and the “nominal” spray flux that is computed with the fast microphysical spray parameterization (LHFs). Specifically, Andreas et al. (2008) hypothesize that 50-μm droplets are good indicators of the total spray latent heat flux and model it as , where is the approximate residence time for droplets with an initial radius of 50-μm, is the radius these droplets have when they fall back into the sea, and the 50 μm reiterates the hypothesis that 50-μm droplets lead the spray latent heat flux. Here is the wind function, which is expressed as based on the analyses of both the Humidity Exchange over the Sea (HEXOS) experiment and the Fronts and Atlantic Storm Track Experiment (FASTEX).

3. Evaluation of latent heat fluxes for Hurricanes Fabian and Isabel

Hurricanes Fabian and Isabel (September 2003) are selected for intensive analysis and evaluation because of the availability of field measurements from the CBLAST Hurricane Program (Black et al. 2007; Drennan et al. 2007; Uhlhorn et al. 2007). As part of CBLAST, a NOAA WP-3D research aircraft was instrumented to conduct direct turbulent flux measurements in the high wind boundary layer of Hurricanes Fabian and Isabel. Data used in this study include flux flights made during Hurricane Fabian on 2–4 September 2003, and during Hurricane Isabel on 12–14 September 2003. The CBLAST observations were collected between 1800 and 2200 UTC along the flight track.

To obtain the parameters (LHF, SST, U, and Qa) from the aforementioned NWP and satellite-derived products at the CBLAST positions, we extract the parameters that have the smallest distance and time difference relative to the CBLAST observations (e.g., falling within half grid spacing and temporal scale of different products). Figure 2 compares time series of the NWP and satellite-derived LHF with the CBLAST LHF. None of the current flux products captures the extremely large LHF values observed on 3 September associated with Hurricane Fabian. Furthermore, the NCEP2 LHF values are biased low for much of the time relative to the CBLAST. The OAFlux LHF values are substantially smaller than the CBLAST, and do not capture the observed variations. The HOAPS3 has large negative biases on 14 September. The ERA-Interim substantially underestimates the LHF associated with Hurricane Isabel. Thus, current NWP and satellite-derived flux products do not reproduce the LHF variations associated with Hurricanes Isabel and Fabian. Also, there are large discrepancies among these flux products arising from their determination of LHF through the use of different sources of the input meteorological state variables (see Table 1) and the use of different bulk flux algorithms (e.g., Liu and Curry 2006).

Fig. 2.

Time series of the LHF (W m−2) for Hurricanes Fabian (2–4 Sep 2003) and Isabel (12–14 Sep 2003) from the CBLAST, and NWP and satellite-derived flux products (NCEP2, OAFlux, HOAPS3, and ERA-Interim).

Fig. 2.

Time series of the LHF (W m−2) for Hurricanes Fabian (2–4 Sep 2003) and Isabel (12–14 Sep 2003) from the CBLAST, and NWP and satellite-derived flux products (NCEP2, OAFlux, HOAPS3, and ERA-Interim).

Table 1.

Summary of input meteorological state variables. Values in parentheses are the weighting assigned to different sources of input variables.

Summary of input meteorological state variables. Values in parentheses are the weighting assigned to different sources of input variables.
Summary of input meteorological state variables. Values in parentheses are the weighting assigned to different sources of input variables.

Figure 3 compares time series of the XSeaFlux LHF with the CBLAST LHF (Note: the XSeaFlux LHF is not available for much of 3 September as a result of the missing input wind speed values owing to heavy precipitation and sampling problems.) The XSeaFlux shows a much better agreement with the CBLAST. For the times when the XSeaFlux LHF values are available, the XSeaFlux LHF values have a bias of 26.9 W m−2, which is a factor of 3–6 smaller than the biases of the NCEP2, OAFlux, HOAPS3, and ERA-Interim (Table 2). Moreover, the XSeaFlux LHF values are more strongly correlated with the CBLAST as compared to the NCEP2, OAFlux, HOAPS3, and ERA-Interim (Table 2).

Fig. 3.

As in Fig. 2, but for the CBLAST and XSeaFlux.

Fig. 3.

As in Fig. 2, but for the CBLAST and XSeaFlux.

Table 2.

Comparison of LHF between the NWP and satellite-derived datasets and CBLAST.

Comparison of LHF between the NWP and satellite-derived datasets and CBLAST.
Comparison of LHF between the NWP and satellite-derived datasets and CBLAST.

Figure 4 compares time series of input variables (SST, U, and Qa) for the CBLAST, XSeaFlux, and ERA-Interim, which is the highest-resolution NWP analysis that is available. The CBLAST-observed SST varies from ~26° to ~30°C for Hurricanes Fabian and Isabel. Comparisons suggest that the XSeaFlux SST capture the observed variations during the hurricanes, although sometimes they show cold biases on the magnitude of ~0.5°–1°C (Fig. 4a). The ERA-Interim SST does not capture the observed variations, since it uses weekly specified SST. Also, the XSeaFlux SST shows somewhat localized warming in the domain of hurricanes, which is absent in the ERA-Interim SST (Fig. 5). The observed U varies from 16 to 28 m s−1 for Hurricanes Fabian and Isabel. Comparisons show that the XSeaFlux U tracks the CBLAST observations very closely. By contrast, the ERA-Interim U does not capture the observed variations (Fig. 4b), and greatly underestimates wind speed in the domain of the hurricanes as compared to the XSeaFlux U (Fig. 5). The observed Qa varies between 15 and 18.5 g kg−1 for Hurricane Fabian and fluctuates around 19 g kg−1 for Hurricane Isabel. Comparisons indicate that Qa from both the XSeaFlux and ERA-interim overestimates CBLAST Qa observations for Hurricane Fabian, but show good agreement with the CBLAST observations for Hurricane Isabel (Fig. 4c). However, the ERA-Interim Qa has a more distinct spatial structure and larger values of Qa in the domain of the hurricanes as compared to the XSeaFlux Qa (Fig. 5).

Fig. 4.

Time series of (a) SST (°C), (b) U (m s−1), and (c) Qa (g kg−1) from the CBLAST, XSeaFlux, and ERA-Interim.

Fig. 4.

Time series of (a) SST (°C), (b) U (m s−1), and (c) Qa (g kg−1) from the CBLAST, XSeaFlux, and ERA-Interim.

Fig. 5.

Spatial distribution of (a),(b),(g),(h) SST (°C); (c),(d),(i),(j) U (m s−1); and (e),(f),(k),(l) Qa (g kg−1) for (top) Hurricane Fabian (1800 UTC 2 Sep 2003) and (bottom) Hurricane Isabel (1800 UTC 13 Sep 2003).

Fig. 5.

Spatial distribution of (a),(b),(g),(h) SST (°C); (c),(d),(i),(j) U (m s−1); and (e),(f),(k),(l) Qa (g kg−1) for (top) Hurricane Fabian (1800 UTC 2 Sep 2003) and (bottom) Hurricane Isabel (1800 UTC 13 Sep 2003).

The impact of the error of each input variable (SST, U, and Qa) on the resultant error of the XSeaFlux LHF for hurricane conditions is estimated. Specifically, the averaged absolute percentage error (APE) of each input variable relative to the CBLAST observations is calculated. Then the APE of each variable is added to the time series of that variable as inputs for the new bulk flux model to estimate the resultant APE of LHF. As shown in Table 3, the APE of Qa contributes the largest error to the LHF, whereas the APE of U has the least impact. The temporal variability is clearly dominated by the changes in wind speed.

Table 3.

Absolute percentage error (APE) of SST, U, and Qa between XSeaFlux and CBLAST, and induced APE in LHF.

Absolute percentage error (APE) of SST, U, and Qa between XSeaFlux and CBLAST, and induced APE in LHF.
Absolute percentage error (APE) of SST, U, and Qa between XSeaFlux and CBLAST, and induced APE in LHF.

To further interpret how the XSeaFlux dataset represents LHF spatial variations in the domain of the hurricanes, Fig. 6 shows the spatial distribution of the XSeaFlux LHF at 1800 UTC 2 September for Hurricane Fabian and at 1800 UTC 13 September for Hurricane Isabel. In contrast to Fig. 1, the high-resolution XSeaFlux shows a well-organized and distinct structure of the LHF associated with the hurricanes, and the largest LHF exceeds 1000 W m−2 near the storm center, and decreases outwards. This result illustrates the importance of spatial resolution in the estimation of heat fluxes, and suggests that further improvements could be made with better resolution in both space and time.

Fig. 6.

As in Fig. 1, but for the XSeaFlux.

Fig. 6.

As in Fig. 1, but for the XSeaFlux.

4. Analysis of hurricane energetics using XSeaFlux data

The high-resolution LHF field available from the XSeaFlux dataset enables a broad range of scientific questions to be addressed regarding hurricane energetics.

Possible impacts of ocean waves on near-surface wind speed and drag coefficient have been discussed in theoretical studies and for various regions (e.g., Bourassa 2006; Kara et al. 2007), but there is little quantitative understanding of the impact of spatial and temporal variability of ocean waves on the LHF associated with hurricanes. Figures 7a,b shows the spatial distribution of the LHF in Hurricane Fabian in response to ocean waves. Ocean waves produce an asymmetric LHF response (e.g., enhancing the LHF in the forward half of the hurricane relative to the storm motion), of the magnitude of ~10–30 (~10–40) W m−2 for Hurricane Fabian (Isabel), and reducing the LHF mainly in the right back quadrant of the storm, of the magnitude of ~10–30 (~10) W m−2 for Hurricane Fabian (Isabel). Note that ocean waves also enhance the LHF in the direct backward of Hurricane Isabel. This asymmetry depends on the translational speed of the hurricane.

Fig. 7.

Differences in the LHF (W m−2) with and without parameterizations of (a),(b) ocean waves and (c),(d) sea sprays for (left) Hurricane Fabian (1800 UTC 2 Sep 2003) and (right) Hurricane Isabel (1800 UTC 13 Sep 2003).

Fig. 7.

Differences in the LHF (W m−2) with and without parameterizations of (a),(b) ocean waves and (c),(d) sea sprays for (left) Hurricane Fabian (1800 UTC 2 Sep 2003) and (right) Hurricane Isabel (1800 UTC 13 Sep 2003).

For wind speeds less than roughly 20 m s−1, the turbulent heat transfer occurs almost exclusively at the air–sea interface. But with increasing wind speed, sea spray production increases, and heat and moisture transfer also occurs at the surface of the spray droplets. When spray droplets leave the sea surface no heat is removed in contrast to evaporation. The cooling is due to the droplets’ heat loss in the air by evaporation and sensible heat loss. The droplets eventually fall down, producing surface cooling. There has been considerable debate on the effects of sea spray on heat transfer. Fairall et al. (1994) suggested a doubling of CE due to spray effects for U at 20 m s−1, whereas Makin (1998) predicts a 20% increase of CE for U at 30 m s−1. The results of Andreas and Emanuel (2001) lie in between. As shown in Figs. 7c,d, including a spray flux parameterization (Andreas et al. 2008) results in a substantial increase of the LHF for high wind regime (greater than ~18 m s−1), of the magnitude of ~60–150 W m−2. The impact of sea spray is a factor of 4–5 larger than that of ocean waves for high winds. Thus, small error in sea spray parameterization would offset the impact of ocean waves for high wind conditions. Farther away from the high winds, the impact of sea spray is comparable to or smaller than that of the ocean waves. For Hurricane Isabel (Fig. 8, dotted gray line), the spray-enhanced LHF averaged within the radius of 34-kt wind (~17.5 m s−1) fluctuates mainly between 10 and 100 W m−2 on 8–14 September, then increases to ~280 W m−2 as Hurricane Isabel changed its path to north-northwestward on 15–16 September, and decreases to ~100–200 W m−2 during landfall on 18 September.

Fig. 8.

Time series of the surface energy loss as the LHF (×1012 W m−2, dotted black line), and difference in the LHF (W m−2) with and without parameterization of sea spray (dotted gray line) within radius of 34-kt wind (~17.5 m s−1) of the storm center, and the category for Hurricane Isabel (bar).

Fig. 8.

Time series of the surface energy loss as the LHF (×1012 W m−2, dotted black line), and difference in the LHF (W m−2) with and without parameterization of sea spray (dotted gray line) within radius of 34-kt wind (~17.5 m s−1) of the storm center, and the category for Hurricane Isabel (bar).

To explore the potential role of hurricanes in the climate system, Trenberth and Fasullo (2007) estimated the enthalpy loss by the tropical ocean due to hurricanes within 400 km of the storm center based on a simple relationship between surface enthalpy fluxes and wind speed. Here we provide direct estimations from the XSeaFlux data of the surface energy loss by the ocean as LHF for Hurricane Isabel since its LHF has the least missing values. Specifically, the LHF values are calculated based on the average of the LHF within the radius of 34-kt wind (~17.5 m s−1), which is obtained from the tropical cyclone extended best-track dataset (Demuth et al. 2006).

As shown in Fig. 8 (dotted black line), Hurricane Isabel pumps a considerable amount of heat out of the ocean, and that amount is generally increasing with time in the evolution of the hurricane, which is partly due to the increase of the hurricane size. The latent heat loss by the ocean from the time that Hurricane Isabel changed its path to north-northwestward (on 15–16 September) to when it made landfall (on 18 September) is about a factor of 2–3 larger than the heat loss when Isabel was major hurricanes (categories 4 and 5, see bars in Fig. 8), suggesting that there is no simple relationship between surface energy loss and hurricane intensity (category). The time-integrated surface energy loss contributed by Hurricane Isabel is of the magnitude of 1.1 × 1020 J, which is mixed deeper into ocean and that then must be compensated for by ocean heat transports.

The high-resolution XSeaFlux dataset enables resolution of the cold wake behind the hurricane. Figure 9 shows spatial distribution of the XSeaFlux SST and LHF at 1800 UTC 13 and 17 September for Hurricane Isabel. In the wake of the hurricane, the SST is cooled by up to ~2°–3°C with the largest cooling occurring in a narrow band in the back quadrant of the storm and trailing effects of the cold wake can be seen along the previous track of the hurricane. The cold wake has substantial impacts on air–sea fluxes. Prestorm (undisturbed) LHF has a range of ~100–200 W m−2, which is reduced to ~20–80 W m−2 after the passage of the storm. Using measurements from an array of air-deployed floats and surface drifters, D’Asaro et al. (2007) also suggested a ~77% (61%) reduction of heat flux due to the effect of SST cooling when averaged over a 100-km (300 km) radius for Hurricane Frances in 2004. Thus, large reduction in heat flux could result in reduced intensity of the storm, if the cold wake is more toward the storm center, which might occur for a slowly moving storm.

Fig. 9.

Spatial distribution of (a),(b) LHF (W m−2) and (c),(d) SST (°C) for Hurricane Isabel (left) 1800 UTC 13 Sep 2003 and (right) 1800 UTC 17 Sep 2003.

Fig. 9.

Spatial distribution of (a),(b) LHF (W m−2) and (c),(d) SST (°C) for Hurricane Isabel (left) 1800 UTC 13 Sep 2003 and (right) 1800 UTC 17 Sep 2003.

5. Conclusions

In this paper, we have investigated the ocean surface latent heat flux associated with two North Atlantic hurricanes. A new satellite-derived ocean surface flux dataset is described, XSeaFlux, which combines new satellite-derived input variables using improved retrieval methods and a new bulk flux algorithm that includes parameterizations for ocean waves and sea spray. The XSeaFlux and existing NWP reanalysis and satellite-derived flux products are evaluated against the CBLAST field measurements during Hurricanes Fabian and Isabel in 2003. Comparison results suggest that the LHF of the newly developed high-resolution XSeaFlux (0.25° and 6 hourly) is closest to the CBLAST observations. For hurricane domain comparisons, the XSeaFlux shows well-organized LHF structures and large LHF values in response to the hurricanes. By contrast, the other flux products (NCEP2, OAFlux, HOAPS3, and ERA-Interim) produce no discernible or weak LHF signals, and no distinct structure in response to the hurricanes.

Applications of the XSeaFlux data have been made to interpret details of the ocean surface latent heat flux for Hurricanes Fabian and Isabel in 2003. Preliminary analysis suggests that ocean waves, sea spray, and cold wake have substantial impacts on latent heat flux associated with the hurricanes. This new satellite-derived hurricane latent heat flux dataset has the potential to provide important new insights into hurricane thermodynamics and dynamics, particularly when coupled with observations of the structure of latent heat release within a hurricane (Guimond et al. 2010). Figure 10 shows the LHF averaged over January–December 2003 for the NCEP2, OAFlux, HOASP3, ERA-Interim, and XSeaFlux. The general pattern of the XSeaFlux LHF resembles those of the existing NWP reanalysis and satellite-derived flux products (e.g., the largest LHF is found in the trade wind belts of both hemispheres and in the western boundary current regions: the Kuroshio and Gulf Stream) due to strong winds coupling with large sea–air humidity difference in these regions, and the smallest LHF is found in the eastern equatorial Pacific and Atlantic due to upwelling induced cold SST associated with weak winds, and in the high latitudes due to the poleward decrease of SST. Thus, this new satellite-derived ocean latent heat flux dataset is not only better for hurricane-related studies, but also good for other applications. However, we note that these products differ considerably in magnitude, with the ERA-Interim being the strongest and the OAFlux being the weakest.

Fig. 10.

Spatial distribution of the annual mean LHF (W m−2) in 2003 for (a) NCEP2, (b) OAFlux, (c) HOAPS3, (d) ERA-Interim, and (e) XSeaFlux.

Fig. 10.

Spatial distribution of the annual mean LHF (W m−2) in 2003 for (a) NCEP2, (b) OAFlux, (c) HOAPS3, (d) ERA-Interim, and (e) XSeaFlux.

Further efforts to improve the retrievals and accuracy of wind and specific humidity for hurricane conditions are needed. Efforts to make in situ flux measurements in hurricanes are needed to support continuing bulk flux algorithm improvements, and assess and improve the accuracy of the satellite-derived latent heat fluxes in hurricanes.

Acknowledgments

We thank for Jun Zhang providing the CBLAST data. This research was supported by NASA NEWS and NSF Polar Programs (0838920).

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