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12A.3 High-Resolution Mesoscale Model Setup for the Eastern Range and Wallops Flight Facility

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12A.3 High-Resolution Mesoscale Model Setup for the Eastern Range and Wallops Flight Facility 12A.3 High-Resolution Mesoscale Model Setup for the Eastern Range and Wallops Flight Facility Leela R. Watson* ENSCO, Inc/Applied Meteorology Unit Bradley T. Zavodsky NASA Short-term Prediction Research and Transition Center 1. INTRODUCTION determine the best model configuration for operational use at each of the ranges to best predict winds, Mesoscale weather conditions can have an adverse precipitation, and temperature (Watson 2013). This effect on space launch, landing, and ground processing report details the continuation of that work and provides at the Eastern Range (ER) in Florida and Wallops Flight a recommended local data assimilation (DA) and Facility (WFF) in Virginia. During summer, land-sea numerical forecast model design optimized for the ER interactions across Kennedy Space Center (KSC) and and WFF to support space launch activities. Cape Canaveral Air Force Station (CCAFS) lead to sea breeze front formation, which can spawn deep convection that can hinder operations and endanger 2. DATA AND MODEL CONFIGURATION personnel and resources. Many other weak locally The important aspects of this study were choice of driven low-level boundaries and their interactions with DA software, the observational data ingest, period of the sea breeze front and each other can also initiate record (POR), the DA/model configurations, and the deep convection in the KSC/CCAFS area. These subtle DA/modeling system Perl scripts supplied by SPoRT. weak boundary interactions often make forecasting of operationally important weather very difficult at 2.1 Data Assimilation Software KSC/CCAFS during the convective season (May-Oct). These convective processes often build quickly, last a DA is an important component in producing quality short time (60 minutes or less), and occur over small numerical weather prediction forecasts. DA methods are distances, all of which also poses a significant challenge used to create a best estimate of the state of the to the local forecasters who are responsible for issuing atmosphere at the forecast initial time using information weather advisories, watches, and warnings. Surface from observations and a background model forecast. winds during the transition seasons of spring and fall This is accomplished through the minimization of an pose the most difficulties for the forecasters at WFF. objective function that measures the weighted distance They also encounter problems forecasting convective of the analysis from the observations and the activity and temperature during those seasons. background model. The weights assigned to each term Therefore, accurate mesoscale model forecasts are are based on the error characteristics of the needed to aid in their decision making. observations and the background model (Kleist et al. 2009). This creates an initial state that more closely Both the ER and WFF benefit greatly from high- resembles the state of the atmosphere at the model resolution mesoscale model output to better forecast a initialization time. variety of unique weather phenomena. Global and national scale models cannot properly resolve important The DA software chosen for this task was the local-scale weather features at each location due to Gridpoint Statistical Interpolation (GSI) system their horizontal resolutions being much too coarse. developed by the National Centers for Environmental Therefore, a properly tuned model at a high resolution is Prediction (NCEP). The GSI system is a three- needed to provide improved capability. This task is part dimensional variational DA system that is a freely of a multi-year effort in which the Applied Meteorology available, community system designed to be flexible, Unit (AMU) is tuning the Weather Research and state-of-the-art, and can run efficiently on various Forecasting (WRF) model individually for each range. parallel computing platforms. It can be applied to both The goal of the first year was to tune the WRF model regional and global applications. based on the best model resolution and run time while using reasonable computing capabilities. *The AMU 2.2 Observational Data Ingest performed a number of sensitivity tests in order to GSI can ingest large quantities of atmospheric observations and has developed capabilities for data * Corresponding author address: Leela R. Watson, ENSCO, thinning, quality controlling, and satellite radiance bias Inc., 1980 North Atlantic Ave, Suite 830, Cocoa Beach, FL correction (Wang 2010). Table 1 lists some of the 32931; e-mail: [email protected] conventional and satellite radiance/brightness temperature observations that GSI can assimilate. Table 1. List of conventional and satellite observations Most of these observations are complex and many that can be assimilated into GSI. of them need to be reformatted into Binary Universal Form for the Representation of meteorological data Conventional observations (BUFR) format or quality controlled before being used by GSI. NCEP produces quality-controlled data in BUFR Radiosondes Pibal winds Synthetic format, called PrepBUFR files that can be used directly Conventional ASDAR tropical in GSI. aircraft reports aircraft cyclone winds MODIS IR and reports MDCARS 2.3 Model Configuration and Test Cases water vapor GMS, JMA, aircraft reports The AMU determined the main model configuration winds and EUMETSAT from the first phase of the Range Modeling task. The Surface land METEOSAT and GOES ARW core was used with the Lin microphysical scheme observations cloud drift IR water vapor and the YSU PBL scheme for both the ER and WFF. Doppler radial and visible cloud top Although the AMU previous work found the optimal velocities winds winds horizontal grid spacing for the ER was a 2-km outer and SBUV ozone SSM/I wind QuikScat, 0.67-km inner domain and a 4-km outer and 1.33-km profiles, MLS speeds ASCAT and inner domain for WFF, the AMU conducted additional (including NRT) VAD OSCAT wind testing on the optimal horizontal grid spacing once the ozone, and (NEXRAD) speed and DA was added. Specifically, different configurations OMI total ozone winds direction were run to determine the impact on the forecasts of Wind profilers: SST GPS running the DA over differing grid resolutions. For the US, JMA Doppler wind precipitable ER, the AMU ran three configurations: Dropsondes Lidar data water Single domain: 1-km domain in which the DA GEOS hourly Tail Dopppler estimates was run (referred to as ‘1 dom’, Figure 1), IR and coud top Radar radial Tropical storm Nested domain: 2-km outer and 0.67-km inner wind velocity and VITAL domain in which the DA was run on the outer GPS Radio super- Radar radial 2-km domain (referred to as ‘2 doms’, Figure occultation observation wind and 2), refractivity and METAR cloud reflectivity Triple nested domain: 9-km outer, 3-km middle, bending angle observations Mosaic and 1-km inner domain on which the DA was profiles SSM/I and PM2.5 run on the 9-km outer domain (referred to as ‘5 TRMM TMI Surface ship doms’, Figure 3). precipitation and buoy estimates observation For WFF, the AMU ran two configurations: Nested domain: 4-km outer and 1.33-km inner Satellite radiance/brightness temperature domain in which the DA was run on the outer observations 4-km domain (referred to as ‘2 doms’, Figure 4), SBUV: n17, HIRS: metop- GOES_IMG: Triple nested domain: 9-km outer, 3-km middle, n18, n19 a, metop-b, g11, g12 and 1-km inner domain on which the DA was AIRS:aqua n17, n19 AMSU-A: run on the 9-km outer domain (referred to as ‘5 MHS: metop-a, SSMI: f14, metop-a, doms’, Figure 3). metop-b, n18, f15 metop-b, n15, n19 SNDR: g11, n18, n19, aqua All other parameters were the same for each model run. AMSRE: aqua g12, g13 AMSU-B: GOME: metop- OMI: aura metop-b, n17 a, metop-b, SEVIRI: m08, SSMIS: f16 ATMS: NPP m09, m10 IASI: metop-a, metop-b Figure 1. Map of the ER showing the single 1-km (D01) model domain boundary (1 dom). Figure 2. Map of the ER showing the nested 2-km outer (D01) and 0.67-km inner (D02) model domain boundaries (2 doms). Figure 3. Map of the triple nest configuration showing the 9-km outer (D01), 3-km middle (D02 and D04), and 1-km inner (D03 and D05) model domain boundaries over the ER and WFF (5 doms). Figure 4. Map of WFF showing the nested 4-km outer (D01) and 1.33-km inner (D02) model domain boundaries (2 doms). The AMU ran each DA/model simulation at the very little influence on model output from the satellite specified horizontal resolution with 35 irregularly observations, which have been shown to have the spaced, vertical sigma levels up to 50 mb. Each run was largest positive impact on most forecast systems. Once initiated four times per day at 0000, 0600, 1200, and the pre-cycling is complete, a 12-hour forecast is run. 1800 UTC and integrated 12 hours using the 13-km Rapid Refresh (RAP) model for boundary conditions A schematic of the GSI/WRF pre-cycling system is and as the background model first-guess field, Land shown in Figure 5. Model initialization times are shown Information System (LIS) data from SPoRT for land in green, pre-cycle times are shown in blue, and surface data, and sea surface temperature (SST) data forecasts are shown in red. As stated above, the model from both NCEP’s Real-time Global SSTs and the is run four times per day. For a model initialization time SPoRT 2-km SST composites. Initial conditions were of 0000 UTC (t00Z in Figure 5), the GSI/WRF scripts created using a cycled GSI/WRF approach that is are started between 0000 and 0100 UTC. The scripts described in Section 0. The POR for the test cases was begin their pre-cycling 12 hours prior to initialization from 1200 UTC 27 August 2013 to 0600 UTC 10 time, in this case at 1200 UTC of the previous day.
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