Statistical Short-Range Forecast Guidance for Cloud Ceilings Over the Shuttle Landing Facility

WIExecutive SummaryThe forecast cloud ceiling over the Shuttle Landing Facility (SLF) at Kennedy Space Center (KSC) is acritical element in determining whether a GO or NO GO should be issued for a Space Shuttle landing.However, the Spaceflight Meteorology Group (SMG) forecasters indicate that the ceiling at the SLF ischallenging to forecast. The Applied Meteorology Unit (AMU) was tasked to develop a statistical cloudceiling forecast technique to aid forecasters in this critical area. Recent studies in the literature have shownsuccess using statistical methods to improve the Short-term forecast of the Federal Aviation Administration(FAA) ceiling Flight Rules (FR). These studies provided the basis for the AMU task methodology, whichwas to create statistical equations to forecast ceilings based on the Shuttle FR.A 20-year period of record (1978 - 1997) of hourly surface observation data from Daytona Beach(DAB), Orlando (MCO), Patrick Air Force Base (COF), Melbourne (MLB), and the SLF (TTS) was usedfor the equation development. An analysis of the data revealed that the largest number of ceilings below theShuttle FR thresholds at the SLF occurred in the cool season (October -March), with very few occurrencesin the warm season. Therefore, only the cool season data were used to develop the equations. The datawere further stratified into dependent and independent datasets. Equation development was done with thedependent data, and verification done with the independent data. Of the 19 cool seasons in the dataset, 16were chosen for the dependent dataset, leaving 3 cool seasons for the independent dataset.Two types of forecast equations were developed: observations-based (OBS) equations thatincorporated data from the stations listed previously, and persistence climatology (PCL) equations used as abenchrnark against which the OBS equations were tested. Least squares multiple linear regression (MLR)was used as the statistical model for all the equations. Equations were developed for each of three ceilingthresholds at 1-,2-, and 3-hour lead times, and for each hour of the day. The OBS equation developmentbegan with predictor selection using a forward stepwise regression. In most of the OBS equations, thepredictor that explained most of the variance was the observation of the predictand at the initial time. Thisresult was consistent with the findings in the literature. Once the predictors were chosen, the OBS forecastequations were developed. The PCL equations were made up of only two predictors: the observation of thepredictand at the initial time and the climatology of the predictand at the valid time. The climatology termwas a simple mean calculation of the number of ceiling events of each threshold for each hour of every dayof the cool season. The PCL equations were developed after the climatologies were calculated.The OBS and PCL equations were used to make forecasts from all records in the independent data set.The OBS forecast errors were then compared quantitatively to the PCL forecast errors. In order to test theperformance of the OBS equations, the probability of detection (POD) and false alarm rate (FAR) werecalculated. The quantitative comparison indicated that the OBS equations produced an improvement overthe PCL equations. The OBS equation POD values were much larger than the FAR values, which indicatedgood performance overall. A hypothesis test was used to determine whether the OBS equationimprovement was statistically significant. The improvement in skill created by the OBS equations wassignificant above the 99% confidence level. Therefore, the conclusion of the AMU study is that OBSequations produced more accurate forecasts than the PCL equations, and can be used in operations.This success was tempered with other findings during the development. The predictors in the OBSequations only accounted for 55-60% of the variance in the data for the l-hour equations to 35-40% for the3-hour equations. There are several possible reasons for the unexplained variance. Other data, such asra_nsonde, satellite images or soundings, radar, or input from data assimilation software may be needed tofill the gap that the hourly surface observations could not. Another issue is that several meteorologicalphenomena could be responsible for the development of ceilings in east-central Florida during the coolseason, yet the data were only stratified by season and time of day. A phenomenological stratification of thedata would be time-consuming, but may be useful in developing more accurate forecast equations.The OBS equations developed in this study will be useful since they produce improved forecasts overthe PCL equations. They will provide another tool with which to make the ceiling forecasts critical to safeShuttle landings at KSC. Combined with other observational and model data, as well as forecasterexperience, these equations can help to improve the ceiling forecasts at the SLF.