The Impact of Aircraft Dropsonde and Satellite Wind Data on Numerical
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62 WEATHER AND FORECASTING VOLUME 23 The Impact of Aircraft Dropsonde and Satellite Wind Data on Numerical Simulations of Two Landfalling Tropical Storms during the Tropical Cloud Systems and Processes Experiment ZHAOXIA PU AND XUANLI LI Department of Meteorology, University of Utah, Salt Lake City, Utah CHRISTOPHER S. VELDEN Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin—Madison, Madison, Wisconsin SIM D. ABERSON NOAA/Hurricane Research Division, Miami, Florida W. TIMOTHY LIU Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California (Manuscript received 17 January 2007, in final form 27 June 2007) ABSTRACT Dropwindsonde, Geostationary Operational Environmental Satellite-11 (GOES-11) rapid-scan atmo- spheric motion vectors, and NASA Quick Scatterometer (QuikSCAT) near-surface wind data collected during NASA’s Tropical Cloud Systems and Processes (TCSP) field experiment in July 2005 were assimi- lated into an advanced research version of the Weather Research and Forecasting (WRF) model using its three-dimensional variational data assimilation (3DVAR) system. The impacts of the mesoscale data as- similation on WRF numerical simulation of Tropical Storms Cindy and Gert (2005) near landfall are examined. Sensitivity of the forecasts to the assimilation of each single data type is investigated. Specifically, different 3DVAR strategies with different analysis update cycles and resolutions are compared in order to identify the better methodology for assimilating the data from research aircraft and satellite for tropical cyclone study. The results presented herein indicate the following. 1) Assimilation of dropwindsonde and satellite wind data into the WRF model improves the forecasts of the two tropical storms up to the landfall time. The QuikSCAT wind information is very important for improving the storm track forecast, whereas the drop- windsonde and GOES-11 wind data are also necessary for improved forecasts of intensity and precipitation. 2) Data assimilation also improves the quantitative precipitation forecasts (QPFs) near landfall of the tropical storms. 3) A 1-h rapid-update analysis cycle at high resolution (9 km) provides more accurate tropical cyclone forecasts than a regular 6-h analysis cycle at coarse (27 km) resolution. The high-resolution rapidly updated 3DVAR analysis cycle might be a practical way to assimilate the data collected from tropical cyclone field experiments. 1. Introduction erosion, and landslides, even far away from the landfall location, resulting in numerous human casualties and As witnessed in recent years, the social, ecological, enormous property damage. Accurate forecasts of and economic impacts of tropical cyclones can be dev- tropical cyclones and their associated precipitation astating. Strong wind, heavy rain, and storm surge as- have been listed as a high-priority research area by the sociated with tropical cyclones may cause flooding, soil U.S. Weather Research Program (e.g., Elsberry and Marks 1999). Tropical cyclones originate over the ocean, where Corresponding author address: Dr. Zhaoxia Pu, Dept. of Me- teorology, University of Utah, 135 S 1460 E, Rm. 819, Salt Lake conventional meteorological observations tend to be City, UT 84112. sparse on a daily routine basis. Due to a lack of data, E-mail: [email protected] deficiencies in model initial conditions can lead to in- DOI: 10.1175/2007WAF2007006.1 © 2008 American Meteorological Society WAF2007006 FEBRUARY 2008 PUETAL. 63 accurate forecasts of tropical cyclones. Our knowledge the impact of remotely sensed and in situ data on me- of the physics processes that control tropical cyclone soscale forecasts of tropical cyclones. evolution is also limited. Thus, the forecasting of tropi- The goal of the study is to assess the potential for cal cyclone track and intensity remains a challenging improving high-resolution numerical forecasts of tropi- problem for those making numerical weather predic- cal cyclones. Specifically, according to Pu and Braun tions. Owing to the improvement of large-scale forecast (2001) and Wu et al. (2006), who used tropical cyclone models and data assimilation techniques, and also to bogus vortices, wind information is important for tropi- the use of satellite and aircraft reconnaissance observa- cal cyclone initialization and prediction. Therefore, the tions, tropical cyclone track forecasts have improved main focus of this study is to incorporate satellite- significantly during the last two decades. (Official error derived wind information with in situ dropwindsonde trends are documented online at http://www.nhc.noaa. data into a mesoscale model with the aim of producing gov/verification.) For instance, National Oceanic and the best possible analyses and forecasts. In particular, Atmospheric Administration (NOAA) synoptic sur- the impact of dropwindsonde data, GOES-11 rapid- veillance missions are very useful in improving indi- scan atmospheric motion vectors (AMVs), and Quik- vidual tropical cyclone track forecasts (Franklin and SCAT near-surface wind observations gathered during DeMaria 1992; Franklin et al. 1993; Burpee et al. 1996; TCSP on mesoscale model forecasts of tropical cy- Aberson 2002; Aberson and Sampson 2003; Aberson clones is demonstrated. Two tropical cyclones that oc- and Etherton 2006). Satellite-derived observations curred during TCSP, Cindy and Gert, near their respec- have also improved tropical cyclone analyses and fore- tive landfalls, are chosen for the study. The impacts of casts (e.g., Velden et al. 1992; Velden et al. 1998; Pu et the data on forecasts of track, intensity, and quantita- tive precipitation forecasts (QPFs) through improved al. 2002; Pu and Tao 2004; Zhu et al. 2002; Hou et al. strategies for mesoscale data assimilation are explored. 2004, Chen 2007). However, relatively little progress The Advanced Research Weather Research and has been made in hurricane intensity forecasts (Houze Forecasting (ARW, hereafter WRF) model and its et al. 2006). In addition, only minor attention (Rogers three-dimensional variational data assimilation et al. 2003) has been given to improving tropical cy- (3DVAR) system are used. A brief description of the clone quantitative precipitation forecasts (QPFs). two tropical cyclones is given in section 2. The data To better understand tropical cyclone structure and sources for the experiments are summarized in section intensity change, field experiments, such as the annual 3. The WRF and its 3DVAR system, and the design of NOAA Hurricane Field Program, the National Aero- the data assimilation experiments, are introduced in nautics and Space Administration (NASA) Convective section 4. The sensitivity to the assimilation of each and Moisture Experiment (CAMEX; Kakar et al. data type and all data to the forecast of Tropical Storm 2006), and the Hurricane Rainband and Intensity Gert is examined in section 5. The impact of the data Change Experiment in 2005 (RAINEX; Houze et al. assimilation on the forecast of Tropical Storm Cindy is 2006), have been conducted to collect data during in- discussed in section 6. Summary and conclusions are tensive observing periods. During July 2005, NASA, in presented in section 7. collaboration with NOAA, executed the Tropical Cloud Systems and Process (TCSP) field experiment (Halverson et al. 2007) based in Costa Rica. The goal 2. Overview of Tropical Storms Cindy and Gert was to improve the understanding of tropical cyclogen- (2005) esis and intensity change. During TCSP, in situ drop- The 2005 Atlantic hurricane season (Beven et al. windsondes from the NOAA P-3 aircraft and many 2008) is notable for having the most named storms in a types of special remotely sensed datasets were collected single season (28). Five storms, including three hurri- along with regular satellite and aircraft reconnaissance canes (Cindy, Dennis, and Emily), were active in July and surveillance data [such as dropwindsonde data during the TCSP field campaign. Cindy and Gert were from the NOAA Gulfstream-IV (G-IV), P-3s, and the chosen for study because TCSP aircraft data were col- U.S. Air Force C-130 aircraft]. With routinely available lected during their approach toward landfall in the satellite data, such as those from the NOAA Geosta- United States and Mexico, respectively. tionary Operational Environmental Satellite (GOES), According to the reports from the Tropical Predic- NASA Quick Scatterometer (QuikSCAT), and the tion Center (information online at http://www.nhc. Tropical Rainfall Measuring Mission (TRMM) satel- noaa.gov), Cindy formed from a vigorous tropical wave lite, TCSP not only offered an opportunity to study and developed into a tropical depression in the north- tropical cyclone development in detail, but also to study western Caribbean Sea on 3 July. It reached tropical 64 WEATHER AND FORECASTING VOLUME 23 FIG. 1. QuikSCAT surface vector wind data at 1200 UTC 5 Jul 2005. storm strength after exiting the Yucatán Peninsula on 5 3. Special observations collected during TCSP July. The storm then headed to the north across the Gulf of Mexico and made landfall as a hurricane near TCSP was an earth science research program spon- Grand Isle, Louisiana, late on 6 July and again as a sored by the NASA Science Mission Directorate. The tropical storm near Ansley, Mississippi, a few hours field experiment was based out of the Juan Santamaría later. The storm weakened over Mississippi and Ala- International Airport in San José, Costa Rica, and car- bama before evolving