Mesoscale FDDA Experiments with WTM and ACARS Data Chia-Bo Chang*,1 and Robert Dumais2
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The Open Atmospheric Science Journal, 2008, 2, 117-130 117 Open Access Mesoscale FDDA Experiments with WTM and ACARS Data Chia-Bo Chang*,1 and Robert Dumais2 1Department of Geosciences, Texas Tech University, Lubbock, Texas 79409, USA 2Army Research Laboratory, White Sands Missile Range, NM 88002, USA Abstract: This is a real-data Four Dimensional Data Assimilation (FDDA) study using MM5 in conjunction with West Texas Mesonet surface observations and ACARS (Aircraft Communications Addressing and Reporting System) profile data collected by commercial aircraft during both en route and ascent/descent phases of their flights. The high-frequency mesonet data and ACARS wind and temperature profiles are ideal for testing the effects of FDDA on short-term mesos- cale numerical weather prediction. The mesonet experiments involved 35 sites with an average horizontal spacing of about 30 km, while in the ACARS case ninety five profiles were used. Results indicated that nudging the MM5 model with the surface-based data over the relatively small area of the mesonet domain had limited impact on the model’s performance. In the ACARS runs, FDDA had long-lasting impact throughout the entire model atmosphere. FDDA appeared to improve the quantitative precipitation forecasting skill of MM5 and reduce slightly the model’s warm bias at the surface. The study suggests that ACARS has potential to significantly enhance our expertise in short-term mesoscale modeling and to sup- port the need to rapidly and accurately adjust high-resolution meteorological model forecasts to real-time observations. INTRODUCTION (Newtonian relaxation) [6] was employed in the MM5 FDDA. The focus of this paper is on mesoscale modeling and FDDA. Because of its small spatial size mesoscale weather There are other more sophisticated data assimilation is strongly influenced by fast-changing local conditions such methods involving the Kalman filter or three-dimensional as cloud, friction, and surface heating. To catch these fast- variational (3DVAR) procedures [7], but these advanced changing local events so as to maximize the model perform- treatments are much more computationally intensive and ance, state-of-the-art mesoscale numerical weather predic- require background information generally not available in a tion (NWP) often makes use of FDDA of high-frequency fast-response scenario, e.g., prediction of atmospheric observations to update the model state during the time inte- boundary layer dispersion for emergency response. We be- gration [1]. The West Texas Mesonet (WTM) surface obser- lieve that the Newtonian relaxation technique represents a vations reported at five minute frequency, along with the good balance between complexity, timeliness, and accuracy, NOAA ACARS observations taken by commercial aircraft at which is an important guiding principle for short-term pre- about every ten minutes while crisscrossing the nation [2], diction and nowcasting. offer valuable data for mesoscale FDDA. Other researchers Relevant scientific questions examined include: such as those at NCAR’s Research Applications Laboratory [3, 4] have provided some previous examination of the im- a) can data assimilation using high frequency surface pact and quality of ACARS in mesoscale FDDA. A better observations alone significantly influence the model understanding of how to take advantage of these high resolu- forecasts? tion data sets can significantly advance our expertise in b) how sensitive are the model forecasts to changes in mesoscale NWP. (1) the nudging parameters and (2) the input ACARS In this study, real-data assimilation experiments based on data density? the WTM surface tower data and ACARS profile data were c) how does the model’s quantitative precipitation fore- conducted using the MM5 prediction system [5] to reveal the casting (QPF) respond to FDDA? impact of high-frequency FDDA on mesoscale NWP. We were particularly interested in the data’s impact on boundary The rest of this paper is organized as follows: Sections 2 layer (BL) and precipitation forecasting. and 3 give, respectively, a brief overview of WTM and ACARS including the quality of the data sets. Section 4 de- As shown later, the WTM observations were confined to scribes the data assimilation procedures, and synthesizes the a relatively small domain over the Southern Plains, while the results of three real-data case studies. Major findings and ACARS observations covered a much more widespread area. conclusions are summarized in Section 5. The empirical method known as observational nudging WTM A detailed technical overview of the WTM surface sta- *Address correspondence to this author at the Department of Geosciences, Texas Tech University, Lubbock, Texas 79409, USA; tions and completed site locations can be found in [8]. Fig. E-mail: [email protected] (1) shows a sample plot of WTM surface tower data over an 1874-2823/08 2008 Bentham Open 118 The Open Atmospheric Science Journal, 2008, Volume 2 Chang and Dumais area of about 300 km (west to east) by 350 km (south to data sets can be found on the website http://acweb.fsl.noaa. north). Each square box of approximately 50 km by 50 km gov. ACARS wind and temperature data are collected by represents a county. There are 45 observation sites with an many commercial aircraft during both en route and as- average spacing close to 30 km. The surface data are now cent/descent modes of their flights at a very high frequency. being distributed in real time via the Internet to users access- At flight altitudes of about 23,000 ft, data are generally taken ing the WTM homepage (www.mesonet.ttu.edu). The every 5-6 minutes. Nearer to airports the data spacing is de- mesonet online data are updated in real time every five min- creased by some airlines. Below 18,000 ft, a vertical resolu- utes. The National Weather Service (NWS) has been incor- tion of 1000 to 2000 ft. is quite common. More than 150 porating these observations into their hourly weather aircrafts provide data with a vertical resolution of 300 ft. roundup product since 2001 and is available to the public during the first minute after take-off. The temporal and spa- and news media. The redundancy of data sources will ensure tial distribution of the data is such that ACARS can provide that data used for any projects has a backup in case of a valuable up-to-date weather data for short-range forecasts of server failure at Reese Center, which is located in the box of atmospheric boundary layer winds. 4th row from the bottom and 3rd column from the Texas-New The quality of ACARS data had been examined by many Mexico state line. researchers [2, 9, 10]. Estimated wind vector accuracy was 84 1014 68 1017 68 7 6 + 1 5 65 65 67 about 1.8 m/s and estimated temperature accuracy was about 68 o 7472 0.5 C. When ACARS was compared to radiosondes, root 66 72 76 6668 85 mean square (RMS) deviations were 7.4 degrees in direction 77 69 67 78 73 79 76 77 8371 1015 and 5.3 m/s in speed. In comparing just the ACARS as- 77 1017 66 69 80 4 3 +852 68 67 69 6967 65 80 69 88 cent/descent winds and temperatures with radiosondes, it 82 71 o 66 82 72 80 68 82 was found that temperature differences were less than 2 C on 66 83 82 87 o 66 67 70 94 percent of all occasions, and less than 1 C greater than 68 74 77 68 69 80 101478 68 8280 4 85 percent of the time. Wind speed RMS deviations were 4.1 8269 78 67 67 82 816967 78 73 67 69 70 m/s, while direction RMS differences were 35 degrees 70 66 68 81 67 84 82 79 81 79 (mostly due to light and variable wind situations). 63 64 79 78 71 78 65 69 71 78 80 68 77 71 MM5/FDDA EXPERIMENTS 64 66 67 78 79 69 77 79 78 72 66 69 MM5 provides various options for parameterizing pre- 64 66 69 68 72 7110161 017 + 1 0 cipitation and BL physics. The following BL-related physi- 77 64 +10 72 70 79 66 cal parameterization options were selected for the simulation 66 77 101477 1013 experiments. 070802 1800 Panhandle Sfc P1ot • Grell moist-convection [11]. Fig. (1). A sample plot of WTM surface tower observations. As described by Schroeder et al. [8], robust quality con- • Atmospheric radiation with the effects of clouds [12] trol (QC) procedures for the WTM data have been devel- • MRF [13] planetary boundary layer oped. In developing a QC algorithm, care is taken to remove • Surface heat and moisture fluxes from the ground the erroneous data while maintaining the integrity of the valid data as much as possible. The first step in the QC pro- • Surface energy budget to calculate the ground tem- cedure is to replace any missing data with interpolation from perature the nearest two data points. Small sub-sets of the time series • Multi-layer soil thermal diffusion are examined to identify spikes in the data. This is done by moving a 7-point data window through the data, computing We experimented with various nesting configurations in mean and standard deviation data for that window, and look- MM5 including one-, two-, and three-grid nest runs in con- ing for data points that are significantly out of line with the junction with horizontal grid spacings ranging from 3 km to statistics. The filtering pass is done three times to remove the 20 km. The model appeared to be quite reliable and robust in anomalies. These procedures work very well at removing bad all nesting configurations. A close examination of the model data while not altering the valid data points.