Applications of Satellite-Derived Ocean Measurements to Tropical Cyclone Intensity Forecasting

G O D A E S P E CIA L ISSU E FEATURE

B Y G U S TAVO GONI , M A R K D E M A RIA , JO H N KNAFF, C H A RLES SAMP SON , ISAA C G I N I S , F R ANCIS B R I NGAS , ALBERTO MAVU ME, C HRI S LAU E R , I . I . L I N , M.M. ALI, PAU L S ANDE RY, S I LVANA RAMOS BUA RQU E , K I RY ONG KANG , AV ICH A L ME HRA , E R I C C H ASS I GNE T, AND GEOR GE HALLIW E LL

176 Oceanography Vol.22, No.3 asgjkfhklgankjhgads ABSTRACT. Sudden tropical cyclone (TC) intensi!cation has been linked with tropical cyclone heat potential (TCHP), high values of upper ocean heat content contained in mesoscale features, particularly has been shown to play a more impor- warm ocean eddies, provided that atmospheric conditions are also favorable. tant role in TC intensity changes (Shay Although understanding of air-sea interaction for TCs is evolving, this manuscript et al., 2000). TCHP shows high spatial summarizes some of the current work being carried out to investigate the role that and temporal variability associated with the upper ocean plays in TC intensi!cation and the use of ocean parameters in oceanic mesoscale features. TC intensi!- forecasting TC intensity. cation has been linked with high values of TCHP contained in these mesoscale INTRODUCTION problem’s complexity and because many features, particularly warm ocean eddies, Tropical cyclones (TCs) occur in of the errors introduced in the track provided that atmospheric conditions are seven ocean basins: Tropical Atlantic, forecast are translated into the intensity also favorable. Because sustained, in situ Northeast Paci!c, Northwest Paci!c, forecast (DeMaria et al., 2005). ocean observations alone cannot resolve Southwest Indian, North Indian, Leipper and Volgenau (1972) !rst global mesoscale features and their Southeast Indian, and South Paci!c recognized the importance of ocean vertical thermal structures, di"erent (Figure 1). TC intensi!cation involves thermal structure in TC intensi!ca- indirect approaches and techniques are several mechanisms, including TC tion. Although sea surface temperature used to estimate TCHP. Most of these dynamics, upper ocean interaction, and (SST) plays a role in TC genesis, the techniques use sea surface height obser- atmosphere circulation. In general, accu- ocean heat content contained between vations derived from satellite altimetry, racy of TC intensity forecasts has lagged the sea surface and the depth of the a parameter that provides information behind TC tracking because of the 26°C isotherm (D26), also referred to as on upper ocean dynamics and vertical

Figure 1. Global map showing the tracks of tropical cyclones (category 1 and above) during the period 2000–2008, with green circles indicating where they formed. "e background color is the satellite-derived mean sea surface temperature during the same years, for June through November in the Northern Hemisphere, and November through April in the Southern Hemisphere.

Oceanography September 2009 177 thermal structure. #is article highlights the 20°C isotherm in tropical regions accurate than, those from much more the importance of collecting a variety (Goni et al., 1996). Climatological rela- general, dynamical models. For recent of data, particularly satellite-derived tionships are used to determine D26 category 5 hurricanes, TCHP input observations, for tropical cyclone inten- from the depth of the 20°C isotherm. improved SHIPS forecasts by about 5%, si!cation studies. NHC forecasters use these TCHP !elds with larger improvements for indi- qualitatively for their subjective TC vidual storms (Mainelli et al., 2008). A NORTH ATLANTIC OCEAN intensity forecasts and quantitatively validation performed on 685 Atlantic An operational satellite-altimetry-based in the Statistical Hurricane Intensity SHIPS forecasts from 2004–2007 TCHP analysis was implemented at Prediction Scheme (SHIPS; DeMaria shows that the average improve- the National Oceanic and Atmospheric and Kaplan, 1994). SHIPS is an empirical ment of SHIPS due to the inclusion of Administration (NOAA) National model that uses a multiple regression TCHP and Geostationary Operational Hurricane Center (NHC) in 2004 method to forecast intensity changes Environmental Satellite (GOES) SST data (Mainelli et al., 2008). #is approach out to 120 h. #e 2008 version of is as much as 3% for the 96-h forecast uses sea surface height anomaly !elds SHIPS includes 21 predictors, mostly (Figure 2, le$). Nearly all improvements derived from altimetry and historical related to atmospheric conditions. #e at the longer forecast intervals are due hydrographic observations in a statistical ocean predictors are SST and TCHP. to TCHP because that input is averaged regression analysis to determine the Despite its simplicity, SHIPS forecasts along the storm track. Although not as depth of the main thermocline, usually are currently comparable to, or more large as the sample of just the category 5 hurricanes, this result indicates that Gustavo Goni ([email protected]) is Oceanographer, National Oceanic and TCHP input improved the operational Atmospheric Administration (NOAA) Atlantic Oceanographic and Meteorological SHIPS forecasts, especially at the longer Laboratory (AOML), Miami, FL, USA. Mark DeMaria is Chief, NOAA National forecast intervals. Environmental Satellite, Data, and Information Service (NESDIS), Regional and Mesoscale Altimetry observations are also used Meteorology Branch, Fort Collins, CO, USA. John Knaﬀ is Meteorologist, NOAA NESDIS to initialize the ocean component of a Regional and Mesoscale Meteorology Branch, Fort Collins, CO, USA. Charles Sampson is coupled hurricane prediction model with Meteorologist, Marine Meteorology Division, Naval Research Laboratory, Monterey, CA, !elds extracted from data-assimilative USA. Isaac Ginis is Professor of Oceanography, University of Rhode Island, Graduate School ocean hindcasts generated as part of of Oceanography, RI, USA. Francis Bringas is Research Associate, Cooperative Institute for the Global Ocean Data Assimilation Marine and Atmospheric Sciences, University of Miami, Miami, FL, USA. Alberto Mavume Experiment (GODAE). #ese hindcasts is Chair, Marine Science and Oceanography Group, Eduardo Mondlane University, Maputo, rely heavily on altimetry to properly Mozambique. Chris Lauer provides computer programming and technical support to the locate mesoscale features, such as ocean NOAA National Hurricane Center, Tropical Prediction Center, Miami, FL, USA. I.-I. Lin is currents and eddies. Halliwell et al. Associate Professor, Department of Atmospheric Sciences, National Taiwan University, (2008) examined this initialization Taipei, Taiwan. M.M. Ali is Scientist and Head, Oceanography Division, National Remote approach in ocean model simulations of Sensing Centre, Hyderabad, India. Paul Sandery is a member of the ocean forecast- the response to hurricane Ivan (2004) ing team, Center for Australian Weather and Climate Research, Melbourne, Australia. in the Northwest Caribbean and Gulf Silvana Ramos-Buarque is Meteorologist, Météo-France/Mercator Ocean, Ramonville St. of Mexico. #is simulation was driven Agne, France. KiRyong Kang is an oceanographer at the National Typhoon Center, Korea by quasi-realistic forcing generated by Meteorological Administration, Jeju, South Korea. Avichal Mehra is Physical Scientist, NOAA blending !elds extracted from the Navy National Centers for Environmental Prediction, Environmental Modeling Center, Camp Coupled Ocean/Atmospheric Mesoscale Springs, MD, USA. Eric Chassignet is Professor and Director, Center for Ocean-Atmospheric Prediction System atmospheric model Prediction Studies, Florida State University, Tallahassee, FL, USA. George Halliwell is with higher-resolution !elds obtained Research Scientist, NOAA AOML, Miami, FL, USA, and Professor, Rosenstiel School of Marine from the NOAA/Atlantic Oceanographic and Atmospheric Science, University of Miami, Miami, FL, USA. and Meteorological Laboratory-

178 Oceanography Vol.22, No.3 135 3 SHIPS 3 STIPS t t

2 2 393 234 544

738 1 1 640 Percent Inprovemen Percent Inprovemen 890 822 0 0

01224364860728496 108 120 01224364860728496 108 120 Forecast Interval [h] Forecast Interval [h] Figure 2. (left) Percent improvement of the 2004–2007 operational Statistical Hurricane Intensity Prediction Scheme (SHIPS) forecasts for the Atlantic sample of over-water cases west of 50°W due to the inclusion of input from altimetry-derived tropical cyclone heat potential (TCHP) and GOES- derived sea surface temperature (SST) ﬁelds. (right) Percent improvement resulting from the use of TCHP information in the Statistical Typhoon Intensity Prediction Scheme (STIPS). "is homogeneous comparison between STIPS with TCHP and STIPS without TCHP is based on forecasts of 63 western North Paciﬁc tropical cyclones. "e number of cases used at each forecast time is given at the top of each bar.

Hurricane Research Division H*WIND climatological ocean temperature !eld more intense (Figure 3, right panel). product (Powell et al., 1998) to resolve prior to the passage of a hurricane. For #is assimilation improved the actual the inner-core structure of the storm. the 2008 Atlantic hurricane season, storm’s intensity forecast with respect to Halliwell et al. (2008) concluded that for the full version of this procedure was that obtained without assimilating the the ocean component of the Hurricane implemented in the NOAA Geophysical altimetry !elds. Weather Research and Forecast System Fluid Dynamics Laboratory (GFDL) Investigation of global ocean vari- (HWRF; Surgi et al., 2006) to correctly and HWRF models, which can also ability and, in particular, of sea height forecast intensity, it must correctly assimilate real-time in situ data, such and SST has become increasingly forecast the rate of SST cooling in the as airborne-dropped expendable bathy- important. Regional variability of these coupled forecast runs. #is capability thermograph pro!les. GFDL coupled parameters indicates, for example, that can only be realized if ocean features are hurricane-ocean model sensitivity TCHP time series in the Gulf of Mexico correctly initialized in the ocean model. experiments for selected hurricanes were exhibit an increase of (0.20 ± 0.05) Yablonsky and Ginis (2008) created run with and without altimeter data kJ cm-2 per year since 1993 (Goni, 2008). a new feature-based ocean initializa- assimilation to evaluate the impact of #is increase in TCHP values (Figure 4) tion procedure to account for spatial assimilating mesoscale oceanic features could be related to a more pronounced and temporal variability of mesoscale on both the SST cooling under the intrusion of the LC into the Gulf of oceanic features in the Gulf of Mexico, storm and the subsequent change in Mexico and to the generation of a larger including the Loop Current (LC) and storm intensity. For hurricane Katrina number of rings. It is known that the eddies. Using this methodology, near- (2005), the presence of the LC and of a warm rings in the Gulf contribute to TC real-time maps of sea surface height warm ring, as given by the assimilated intensi!cation; hence, further investiga- and/or D26 derived from altimetry, are altimeter data (Figure 3, le$ panel), tion is required to determine whether used to adjust the position of the LC and reduced SST cooling along the hurricane this trend also contributes to additional insert these eddies into the background track and allowed the storm to become intensi!cation occurrences.

Oceanography September 2009 179 OTHER OCEAN BASINS climatological warm layer is relatively In the Paci!c and Indian basins, a #irty Northwest Paci!c category 5 shallow. Here, D26 is typically 60 m and statistical-dynamical model similar to typhoons that occurred during the the TCHP approximately 50 kJ cm-2. SHIPS, called the Statistical Typhoon typhoon seasons of 1993–2005 were #erefore, ocean features become critical Intensity Prediction Scheme (STIPS; examined using observations corre- for typhoon intensi!cation to category 5 Kna" et al., 2005) is used. STIPS is run sponding to 13 years of satellite altim- because they can e"ectively deepen the at the Naval Research Laboratory in etry, in situ, and climatological upper warm layer (D26 reaching 100 m and Monterey, California, and is provided to ocean thermal structure data; best track the TCHP ~ 110 kJ cm-2) to restrain JTWC to make TC intensity forecasts in typhoon data from the US Joint Typhoon typhoons’ self-induced ocean cooling. In the western North Paci!c, South Paci!c, Warning Center (JTWC); and an ocean the past 13 years, eight out of the 30 cate- and Indian oceans. #e version of the mixed layer model (Lin et al., 2008). gory 5 typhoons (i.e., 27%) corresponded STIPS model used in the Northwest Results show that the climatological to this type. #e second condition occurs Paci!c and North Indian oceans uses upper ocean thermal structure is an in the central region of the subtropical the TCHP !elds (http://www.aoml.noaa. important factor in determining how gyre (121°E–170°E, 10°N–21°N), where gov/phod/cyclone) calculated along the warm mesoscale ocean features a"ect the background climatological warm forecast track as a predictor. #is updated intensi!cation of category 5 TCs. Two layer is deep (typically D26 ~ 105–120 m 13-predictor version of the STIPS model di"erent conditions were found. #e and the TCHP ~ 80–120 kJ cm-2). In was run in parallel for the last three years !rst is in the western North Paci!c south this region, typhoons can intensify with its predecessor, which does not use eddy zone (127°E–170°E, 21°N–26°N) to category 5 when traveling above the TCHP information. An indepen- and the Kuroshio (127°E–170°E, 21°N– waters with cyclonic or anticyclonic dent and homogeneous sample of these 30°N) region, where the background mesoscale features. parallel forecasts for 63 Northwest Paci!c

Gulf of Mexico − Tropical cyclone heat potential (TCHP) 08/28/2005

30˚N

−200

25˚N −200

−2 00 Wind Speed (mph) −200

>155 (cat.5) −200 131 − 155 (cat.4) −200 111 − 130 (cat.3) 20˚N 96 − 110 (cat.2) 74 − 95 (cat.1) −200 39 − 73 (TS) 0 − 38 (TD) −200

100˚W95˚W 90˚W 85˚W 80˚W 75˚W

0 25 50 75 100 125 TCHP (kJ cm−2)

Figure 3. (left) Track of Hurricane Katrina in the Gulf of Mexico during August 2005 superimposed on the tropical cyclone heat potential (TCHP) ﬁeld derived from altimetry. "e color of the circles indicates storm category. (right) Minimum atmospheric pressure at sea level during the passage of Hurricane Rita in the Gulf of Mexico in August 2005, showing the actual observations (black) and the reduction of error in the Geophysical Fluid Dynamics Laboratory model output with (red) and without (green) initializing the model with the TCHP produced at the NOAA National Hurricane Center.

180 Oceanography Vol.22, No.3 TCs showed modest improvements research on the impact of coupling on simulation due to feedback of cool SSTs. in intensity prediction when TCHP TC intensity forecasting skill in the In the simulation, the rapid rise in LCP information was used (Figure 1, right). region. #e coupled system comprises a$er 50 h occurred when the storm Improvements for this sample with use the BoM Tropical Cyclone Limited Area made landfall over Cape York Peninsula, of TCHP information were statistically Prediction System (TCLAPS) forecasting Australia. Further work is being done signi!cant in the 24-h to 120-h forecasts. model (Davidson and Weber, 2000), with the CLAM system to couple a wave #e BLUElink> operational Ocean the Ocean-Atmosphere-Sea-Ice-Soil model and to improve ocean initializa- Model, Analysis, and Prediction (OASIS) coupler (Valcke et al., 2003), tion and model physics at the air-sea System (OceanMAPS) (Brassington and a regional version of the BLUElink> interface and in the oceanic mixed layer. et al., 2007) of the Australian Bureau ocean forecasting system. Preliminary #e link between TC intensi!cation of Meteorology (BoM) is performing results show that TC intensity is sensitive and TCHP has also been identi!ed in the routine monitoring, analyses, and to ocean heat content, and that %uctua- North Indian Ocean, showing that TCs forecasts of various measures of ocean tions in the lowest central pressure (LCP) intensify (dissipate) a$er traveling over heat content and their respective clima- between 10–20 hPa is related to vari- anticyclonic (cyclonic) eddies. Results tological anomalies (http://godae.bom. ability in mesoscale upper ocean thermal obtained for tropical cyclone 01A in gov.au/oceanmaps_analysis/ocean_hc/ structure and feedback into the storm via the Arabian Sea in 2002 show a correla- ocean_hc.shtml), including ocean heat air-sea heat %uxes. Use of more accurate tion of 0.92 between the intensity and content in the upper 50 and 200 m, SSTs from the reanalysis is also important the sea height anomaly (SHA) values TCHP !elds, and D26. A Coupled in improving TC intensity. #e coupled under the track of the tropical cyclone. Limited Area Modeling (CLAM) system simulation produced a less-intense and However, the correlation value is only was recently developed to carry out faster-moving storm than the uncoupled 0.07 between the intensity and SST

Figure 4. Time series showing the monthly residuals (anomalies with the seasonal cycle removed) of tropical cyclone heat potential values in the Gulf of Mexico from 1993–2008. "ese values exhibit an increase that may be partly related to a more western intrusion of the Loop Current into the Gulf of Mexico as revealed by contours of the jet of this current and associated rings obtained from altimetry observations for 1996 and 2004 (maps in the upper panels).

Oceanography September 2009 181 values (Ali et al., 2007). Additionally, A !rst evaluation of the Mercator Ocean FUTURE WORK inclusion of SHA into the !$h-genera- global ocean forecast system’s ability to #e current open ocean observing tion National Center for Atmospheric simulate realistic variability of ocean system was mainly designed for climate Research Mesoscale Model (MM5) heat content !elds during TC events was and not for TC intensi!cation studies. reduces the intensity and track errors made by processing the point-to-point Although there are e"orts underway to for the same tropical cyclone. #e track correlations between atmospheric pres- improve this system to investigate TC error is reduced from 733 km using sure (Pa) and Mercator-derived TCHP genesis regions, current sustained in situ National Centers for Environmental values (Ramos-Buarque and Landes, ocean observations (e.g., XBTs, Argo

Prediction (NCEP) SST !elds to 419 km 2008). Pa is predicted from satellite %oats, moorings, surface dri$ers) do not using SHA !elds (Ali et al., 2007). observations in a TC’s center. Twenty fully support TC intensi!cation studies. Recent analyses of cyclone track data TCs, mostly positioned in the North #erefore, indirect methodologies in the Mozambique Channel for 1994– Atlantic and Northwest Paci!c, were that employ satellite observations and 2007 by author Mavume and colleagues considered. #e correlation reaches numerical modeling are being used to allowed identi!cation of 15 intense 14% for 119 days (points). #e delayed monitor the upper ocean for TC inten- cyclones with landfall in Mozambique or correlation between Pa for the day J and si!cation research. Studies performed in Madagascar. #ere is no doubt that high TCHP for any day J-1 over 62 days is all ocean basins indicate an ocean role in TCHP values in the region are impor- 11%. #e di"erence between correla- TC intensi!cation that still needs to be tant. However, an assessment of these tions for J:J and J:J-1 is not signi!cant adequately investigated and quanti!ed. 15 TCs did not show a clear tendency because TCHP is associated with the Future work will include detailed anal- for intensi!cation over warm eddies, but low frequencies of ocean processes. ysis of other upper ocean parameters, there was intensi!cation over cyclonic Also, a parameter proportional to the such as heat content, and mean tempera- eddies, similar to what was found in the temperature di"erence above 26°C ture in the mixed layer to di"erent northwest Paci!c Ocean. It was hypoth- integrated over the Mercator Oceanic depths or isotherms, including isotherms esized that improved knowledge of the Mixed Layer (OML), called Interacting below 26°C. Models based on statis- vertical density pro!le, and not just of Tropical Cyclone Heat Content (ITCHC; tical methodologies show a correlation temperature, is necessary to further Vanroyen at al., 2008), was evaluated. between upper ocean thermal structure understand the ocean’s role in TC inten- While TCHP quanti!es the energy and TC intensi!cation, where mesoscale si!cation because of the e"ect of salinity contained between the sea surface and ocean features with minimum TCHP on density and mixed-layer depth. D26, ITCHC quanti!es the energy avail- values of ~ 50 kJ cm-2 may contribute #e ocean’s role in TC intensi!ca- able in OML. Correlations were carried to intensi!cation of strong storms. It is tion can be investigated globally using out between Pa and the averaged ITCHC clear that improved estimates of TCHP high-horizontal-resolution global over TC’s inner circle related to upper in ocean and ocean-atmospheric coupled GODAE analyses and forecasts in ocean heat loss primarily due to wind models are critical for improvement in near-real time (i.e., Mercator Ocean, stress (radius of 110 km). #ese correla- TC intensity forecasting. Results from Hybrid Coordinate Ocean Model tions for J:J and J:J-1 are 22% and 42%, some of the current e"orts presented [HYCOM]). #ese systems are forced respectively. If the atmospheric surface here highlight the importance of contin- with atmospheric conditions supplied forcing is realistic, the Mercator aver- uous support for altimetric missions able by the European Centre for Medium- aged ITCHC can be used as a powerful to resolve mesoscale features. range Weather Forecasts (ECMWF), predictor for TC intensi!cation. Several observational research e"orts NCEP, or the Navy Operational Global Otherwise, when the surface forcing are also underway to better understand Atmospheric Prediction System is not realistic, a very useful TCHP TCs’ boundary layers and air-sea inter- (NOGAPS), and they assimilate the preserves an acceptable level of predict- action. For example, one of the goals altimeter-derived SHA !elds (Chassignet ability related to the low frequency of the Intensity Forecast Experiment is et al., 2007, 2009; Drévillon et al., 2008). of ocean processes. to develop and re!ne technologies to

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