Radiance assimilation in studying Hurricane Katrina

Transcription

1 GEOPHYSICAL RESEARCH LETTERS, VOL. 33, L22811, doi: /2006gl027543, 2006 Radiance assimilation in studying Hurricane Katrina Quanhua Liu 1,2 and Fuzhong Weng 3 Received 11 July 2006; revised 12 September 2006; accepted 18 October 2006; published 25 November [1] The Gridpoint Statistical Interpolation (GSI) scheme released in June 2006 from the National Center for Environmental Prediction (NCEP) and the Weather Research and Forecasting (WRF) model are used in this study for improving hurricane analysis. In addition to incorporating observations from conventional instruments and from operational satellites, Special Sensor Microwave Imager Sounder (SSMIS) measurements are assimilated through GSI. A control-run using current NCEP analysis field and a test-run using additional SSMIS data are compared. The error in forecasting the hurricane center after 30-hour grew up dramatically for the control-run while the error for the test-run remains stable for the entire 48-hour forecasting. The forecasting surface minimum pressure and maximum wind speed from the test-run generally agree with observations better. Results for the test-run also showed that the warm core at 200 hpa is strengthened and extended as Hurricane Katrina was developing toward its mature stage, which is consistent with SSMIS observations. Citation: Liu, Q., and F. Weng (2006), Radiance assimilation in studying Hurricane Katrina, Geophys. Res. Lett., 33, L22811, doi: /2006gl Introduction [2] There are increasing concerns about hurricane activities. The 2005 Atlantic Hurricane season was the busiest one on record with 27 named storms. Hurricanes Dennis, Katrina, Ophelia, Rita and Wilma stroke the United States, and they resulted in tremendous losses of properties and life. Hurricane Katrina alone claimed more than one thousand of lives and the estimated damage is over 100 billion dollars, the costliest natural disaster in the history of the United States. It is very important to predict a hurricane s track and intensity as well as the structure. Threedimensional variation (3D-var) [Xiao et al., 2005] and 4D-var [Li et al., 1994; Zhao et al., 2005] methods and ensemble Kalman filter technique [Anderson, 2001] are commonly used in data assimilation to improve initialization in Hurricane forecasting. The foundation for 4D-var variation method and Kalman filter technique are the same, provided that both apply a linear forecast model and a linear observational operator, as well as Gaussian statistics [Caya et al., 2005]. However, practical considerations in 4D-var and in ensemble Kalman filter, and the effect of nonlinearity can lead to different results [Caya et al., 2005]. The study 1 Joint Center for Satellite Data Assimilation, Camp Springs, Maryland, USA. 2 Also at QSS Group, Inc., Camp Springs, Maryland, USA. 3 Office of Research and Applications, NOAA/NESDIS, Camp Springs, Maryland, USA. Copyright 2006 by the American Geophysical Union /06/2006GL for hurricane Bonnie in 1998 [Zhu et al., 2002] showed that the retrieved temperature profiles from Advanced Microwave Sounder Unit (AMSU) measurements improve the initial analysis from global numerical prediction model into the fifth version of the mesoscale model (MM5). In this study, satellite radiances are assimilated into the mesoscale system through NCEP Gridpoint Statistical Interpolation (GSI), and the analysis fields are then used in the Weather Research and Forecasting (WRF) model. Special Sensor Microwave Imager and Sounder (SSMIS) data is also assimilated in this study since the brightness temperatures of the fourth channel of SSMIS at 54.4 GHz has been qualitatively used for an objective analysis of the warm core of hurricanes [Liu and Weng, 2006]. The purpose of this study is to investigate the effect of cloudy radiance, including SSMIS radiance assimilation in forecasting the surface maximum wind speed, the position track and the surface pressure of the center of Hurricane Katrina. 2. Methodology [3] The Grid Statistical Interpolation (GSI) System developed at NCEP [Wu et al., 2002] and the Weather Research and Forecasting (WRF) model are used in this study. The core of GSI is to minimize the cost function, which utilizes observations and a priori or forecast information through a 3D-var method. The data assimilation system finds optimal analysis fields from forecast fields, conventional observations, some retrieval products as observations, and satellite radiances under dynamic constraints following a set of physical laws. The recently released GSI package includes the newly developed Community Radiative Transfer Model (CRTM) for both clear and cloudy radiance simulations [Han et al., 2006; Weng et al., 2005]. [4] The WRF model is being developed as a collaborative effort among multiple agencies in the United States. The forecasting model describes the evolution of weather mesoscale systems and the model is particularly useful for hurricane studies. The WRF package is mainly composed of standard initialization, WRF data assimilation (WRF-var), and two dynamic solvers. The Advanced Research WRF (ARW) solver [Skamarock et al., 2005] is developed primarily at the National Center for Atmosphere Research (NCAR). The Nonhydrostatic Mesoscale Model solver [Janjic et al., 2001] is developed at NCEP [Gopalakrishnan et al., 2002]. Prognostic variables in the WRF are wind field, perturbation potential temperature, perturbation geopotential, and perturbation surface pressure of dry air. There are many cloud microphysical models defining hydrometeors. This study uses cloud water content for ice and liquid clouds and rain water contents. Both GSI and WRF-var can be used for radiance assimilation. We chose GSI because CRTM has been implemented into GSI, allowing us to do both direct cloudy and clear radiance assimilation. L of5

2 [5] In this study, data from Special Sensor Microwave Imager and Sounder (SSMIS) is a new observation assimilated into GSI. The SSMIS combines the capability of the currently existing SSM/I (Special Sensor Microwave/Imager, GHz) and SSM/T-2 (Special Sensor Microwave/ Temperature-2, and GHz), and covers a wide frequency range from 19 to 183 GHz [Simmer, 1994]. The SSMIS utilizes a conical scan with a constant scan angle of 45 degree. The corresponding local zenith angle on the Earth s surface is approximately 53.1 degree. It has been found that the brightness temperature at 54.4 GHz (channel 4 of SSMIS) reveals the warm core structure of hurricanes [Liu and Weng, 2006], since the weighting function of the channel is rarely affected by the surface and its peak locates at 200 hpa or at an altitude of about 12 km. Other SSMIS channels are also useful for improving atmospheric profiles and surface parameters. [6] The majority of satellite data used in GSI are from microwave and infrared (for example, High-Resolution Infrared Sounder (HIRS)/2, HIRS/3, HIRS/4, Advanced Microwave Sounding Unit (AMSU) -A, AMSU-B, and SSMIS) measurements. The spatial resolution of the microwave sensors varies from 15 km to a hundred km. The infrared sensor has much better spatial resolution than that for the microwave sensors. Besides using the same data as in the control-rum, the test-run employs cloudy radiances and SSMIS data. 3. Experimental Result [7] The purposes of the experiment in this study are to demonstrate whether the WRF model forecasts a similar warm core as the SSMIS, and to determine the impact of the new data assimilation scheme on hurricane simulations. The SSMIS observations are newly assimilated. The remarkable achievement of GSI is that we can directly assimilate cloud radiances which are extremely valuable in the data assimilation for severe weather systems. This study s data assimilation differs from the operational data assimilation in many places. In this letter, we only evaluate the total impact (added SSMIS radiance, cloud radiance assimilation, WRF forecasting fields for two-cycle data assimilation in GSI) of the new data assimilation on forecasting severe storms. Our numerical experiment is carried out for Hurricane Katrina in Hurricane Katrina swept over Florida and intensely developed in the Gulf region of Mexico. It reached hurricane category V on 28th August 2005 and headed northstraight to New Orleans. The experiment is composed of a control-run and a test-run. The experiment starts at 00:00 UTC on 27th August 2005 and runs for a 48-hour forecasting. At the start time, Katrina was a category II hurricane and reached category V in 42 hours. Within these 48 hours, the hurricane Katrina moved about 500 km north-west. Our model area in WRF extends about 2600 km by 2200 km in north-south and east-west directions, respectively. The horizontal resolution of the model area in WRF is 6 km. As usual, the control-run uses NCEP 1 by 1 global analysis fields for the evolution of boundaries of the experiment area and for the initial fields at the start time in the experiment area. The test-run applied GSI and WRF models to generate the initial fields. NCEP 1 by 1 global analysis fields for the evolution of boundaries of the experiment area and for the initial fields at 6 UTC on 26th August 2005 are first used in the WRF model to produce a forecasting at 18 UTC for the same day. NCEP GSI is then used to perform data assimilation at 18 UTC using the forecasting fields, satellite radiances, and conventional observations. The new observation SSMIS data is used in the direct cloud data assimilation. The analysis is further used in WRF for the subsequent 6-hour forecast at 00:00 UTC on 27th August NCEP GSI is again used to perform the data assimilation. [8] We first investigated the initial temperature fields from the control-run and test-run. The initial temperature field at 200 hpa (12 km) for the control-run shows a typical warm core (see Figure 1a) at the central area of hurricanes. But the warm core is very smooth and weak due to the coarse spatial resolution of the global analysis and the absence of cloud radiance assimilation. The new initial temperature field at 200 hpa for the test-run is improved in both structure and strength of the warm core (see Figure 1b). The new initial field displays the vortex structure and has the warmest temperature at the hurricane center. The initial temperature field at 850 hpa ( at an altitude of about 1.5 km) for the control-run is very smooth (see Figure 1c). The new initial temperature field at 850 hpa for the test-run shows the warm core in the central area of the hurricane, as identified in Figure 1b, and shows the cold temperature associated with the spiral rain band (see Figure 1d). Overall, the temperature field directly from global analysis is weak and lacks of detailed features associated with cumulus clouds. The data assimilation for the test-run preserves the fine structure associated with clouds in the hurricane system. Figure 2 compares the 48-hour forecasting of the surface minimum pressure (see Figure 2a) and surface maximum wind speed (see Figure 2b) of Hurricane Katrina from the control-run (dashed line) and test-run (dotted line) with observations (solid line). The observations are the final best track data from the U.S. National Hurricane Center. The initial state for both control-run and test-run are weak in comparison to the observation. The surface minimum pressure and surface maximum wind speed at the start time have been improved by using the new data analysis for the testrun in comparison to the control-run. [9] More important are probably the detailed atmospheric structures, which contain rich hurricane information in the data assimilation. The forecasting surface minimum pressure and surface maximum wind speed for the test-run in general agree with the observations better. The forecasting errors in Figure 2 could be caused by insufficient spatial resolution we chose in the WRF model, lack of feedback from ocean due to the absence of an ocean dynamic model, or many other factors. Forecasting the track of the hurricane center is important to the hurricane s development and the area to be affected. In this study, the location of the hurricane center for the control-run at the start time is better than that for the test-run, which indicates that for this case the global data assimilation model does a pretty good job on the location of the hurricane center. The relatively large error in the initial field for the test-run partially results from the WRF forecasting. The reason for using WRF forecast rather than NCEP analysis is to avoid repeat use of the satellite data in the radiance assimilation for the test run, since the radiance has been utilized in NCEP analysis. Benefiting from the accurate (30 km) storm center at the 2of5

3 Figure 1. The initial temperature field at 200 hpa for the (a) control-run, (b) test-run, and at 850 hpa for the (c) controlrun, (d) test-run. start time, forecasting the position of the surface minimum pressure from the control-run at the first 24 hours is better than that from the test-run. After that time, the forecasting error of the central position of the hurricane from the control-run dramatically grew up. The error in the position is about 180 km at the end of the 48-hour forecasting. The forecasting accuracy of the storm center from the test-run is very stable for the entire 48-hour forecasting and the error in forecasting the center is less than 60 km. This accurate forecasting for the test-run may be partly caused by the better initial temperature and other fields in the new data assimilation. This may imply that the fields of temperature, wind, and clouds play more important roles in the later time part of forecasting. [10] It is known that the warm and moist air near the ocean surface provides energy to hurricanes. The latent heat Figure 2. Comparison of forecasting surface (a) minimum pressure and (b) maximum wind speed for Hurricane Katrina between 00:00 27th 00:00 29th August 2005 from the control-run (dashed line) and test-run (dotted line) with the observations (solid line). 3of5

4 Figure 3. The 24-hour forecasting at 00 UTC on 28th August 2005 of the surface pressure, surface wind vector, upward latent heat flux, and 6-hour accumulated precipitation for cumulus cloud. release in towering cumulonimbus clouds warms the middle and upper troposphere, and the radial pressure gradient pushes low-level inflow of warm and moist air toward the low-pressure center and upward [Palmen and Newton, 1969]. Figure 3 shows the surface field at 24 hour forecasting (00 UTC 28th August 2005) of the pressure, wind speed, upward heat flux, and the 6-hour accumulated cumulus precipitation. The surface pressure has a very large gradient near the center of the hurricane. The large gradient of the surface pressure suggests that higher spatial resolution, associated with finer time resolution, may be necessary. But, this demand often exceeds the computation capacity for a small research group. The surface field in Figure 3 shows the maximum wind in the eye wall around the hurricane eye. Large upward heat flux is found in the eye wall. The upward heat flux achieves about 450 Watt per square meter. The 6-hour accumulated cumulus precipitation partly reflects the activity during the hurricane development. The precipitation exhibits typical spiral rain bands. [11] We also study the time series of the warm core at 200 hpa from WRF outputs. The warm core strengthened as Katrina was developing. The results consist with SSMIS observations [Liu and Weng, 2006]. 4. Discussion [12] The use of the SSMIS data and direct clear and cloudy data assimilation improve the initial model states for hurricane studies. Preliminary results for the Hurricane Katrina study show that new data assimilation improves the surface minimum pressure by about 5 hpa. In comparison to the initial field for the control-run, the initial temperature field for the test-run displays additional features associated with clouds. The forecasting accuracy in the surface minimum pressure and surface maximum wind speed for the test-run is in general better than that for the control-run. The forecasting accuracy of the hurricane center position during the first 30 hours for both controlrun and test-run is better than 60 km. The forecasting accuracy of the position for test-run is stable and better than 60 km for the entire 48-hour forecasting. The model evolution of the warm core from WRF forecasting outputs showed that the strength and the area of the warm core grows up as the hurricane increased in strength, which is consistent with SSMIS measurements. [13] There exist fundamental issues in hurricane forecasting that need to be discussed and addressed. Long-term and community efforts are required to solve some of these issues. The ocean dynamic model needs to be coupled with WRF model to take account for the feedback of ocean response on the upward heat flux and the precipitation of cumulus clouds. The representation of cloud microphysics in WRF needs to be improved to include the particle size of various clouds for better cloudy radiance assimilation. 4of5

5 [14] Acknowledgments. Authors would like to thank Ninghai Sun for providing SSMIS data. This study is funded by the Joint Center for Satellite Data Assimilation. The views expressed in this publication are those of the authors and do not necessarily represent those of NOAA. References Anderson, J. L. (2001), An ensemble adjustment Kalman filter for data assimilation, Mon. Weather Rev., 129, Caya, A., J. Sun, and C. Snyder (2005), A comparison between the 4DVAR and the ensemble Kalman filter techniques for radar data assimilation, Mon. Weather Rev., 133, Gopalakrishnan, S. G., et al. (2002), An operational multiscale hurricane forecasting system, Mon. Weather Rev., 130, Han, Y., P. van Delst, Q. Liu, F. Weng, B. Yan, R. Treadon, and J. Derber (2006), Community Radiative Transfer Model (CRTM) Version 1, NOAA NESDIS Tech. Rep. 122, 22 pp., Natl. Oceanic and Atmos. Admin, Silver Spring, Md. Janjic, Z. I., J. P. Gerrity Jr., and S. Nickovic (2001), An alternative approach to nonhydrostatic modeling, Mon. Weather Rev., 129, Li, Y., I. M. Navon, W. Yang, X. Zou, J. R. Bates, S. Moorthi, and R. W. Higgins (1994), Four-dimensional variational data assimilation experiments with a multilevel semi-lagrangian semi-implicit general circulation model, Mon. Weather Rev., 122, Liu, Q., and F. Weng (2006), Detecting the warm core of a hurricane from the Special Sensor Microwave Imager Sounder, Geophys. Res. Lett., 33, L06817, doi: /2005gl Palmen, E., and C. W. Newton (1969), Atmospheric Circulation Systems, 603 pp., Elsevier, New York. Simmer, C. (1994), Satellitenfernerkundung hydrologischer Parameter der Atmosphaere mit Mikrowellen, 313 pp., Verlag Dr. Kovac, Hamburg, Germany. Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, W. Wang, and J. G. Powers (2005), A description of the Advanced Research WRF Version 2, NCAR Tech Notes-468+STR, 88 pp., Natl. Cent. for Atmos. Res., Boulder, Colo. Weng, F., Y. Han, P. van Delst, Q. Liu, and B. Yan (2005), JCSDA Community radiative transfer model (CRTM), paper presented at Fourteenth International ATOVS Study Conference, Bur. of Meteorol. Res., Beijing. Wu, W.-S., R. J. Purser, and D. F. Parrish (2002), Three-dimensional variational analysis with spatially inhomogeneous covariances, Mon. Weather Rev., 130, Xiao, Q., Y. Kuo, J. Sun, W. Lee, E. Lim, Y. Guo, and D. M. Barker (2005), Assimilation of Doppler radar observations with a regional 3DVAR system: Impact of Doppler velocities on forecasts of a heavy rainfall case, J. Appl. Meteorol., 44(6), Zhao, Y., B. Wang, Z. Ji, X. Liang, G. Deng, and X. Zhang (2005), Improved track forecasting of a typhoon reaching landfall from fourdimensional variational data assimilation of AMSU-A retrieved data, J. Geophys. Res., 110, D14101, doi: /2004jd Zhu, T., D. Zhang, and F. Weng (2002), Impact of the advanced microwave sounding unit measurements on hurricane prediction, Mon. Weather Rev., 130, Q. Liu, Joint Center for Satellite Data Assimilation, 5200 Auth Road, Room 7042, Camp Springs, MD 20746, USA. (quanhua.liu@noaa.gov) F. Weng, Office of Research and Applications, NOAA/NESDIS, 5200 Auth Road, Room 601, Camp Springs, MD 20746, USA. 5of5