Estimating the Satellite Equatorial Crossing Time Biases in the Daily, Global Outgoing Longwave Radiation Dataset

Abstract

Due to its long record length (approximately 25 years), the outgoing longwave radiation (OLR) dataset has been used in a multitude of climatological studies including studies on tropical circulation and convection, the El Niño?Southern Oscillation (ENSO) phenomenon, and the earth's radiation budget. Although many of the climatological studies using OLR have proven invaluable, proper interpretation of the low-frequency components of the data could be limited by the presence of biases introduced by changes in the satellite equatorial crossing time (ECT). Since long-term global changes could be masked or contaminated by this instrumental bias, it is necessary to take steps to ensure that the daily, global OLR dataset is free from such biases and is as accurate as possible. The goal of this study is to derive a method for estimating the ECT biases in the daily, global OLR dataset. Our analysis utilizes a Procrustes targeted empirical orthogonal function rotation (REOF) on an interpolated OLR dataset to try to isolate and remove the two major ECT biases?afternoon satellite orbital drift and the abrupt transitions from a morning satellite to an afternoon satellite?from the dataset. Two targeted REOF analyses are performed to separate and distinguish between these two artificial satellite bias modes. A ?common ECT? of approximately 0245 LST is established for the dataset by removing an estimate of these two ECT biases. Results from the analysis indicate that changes in ECTs can cause large regional biases over both ocean and tropical landmasses. The afternoon satellite ECT drift-bias accounts for 0.4% of the pentad anomaly variance. During a single satellite series (e.g., NOAA-11), the afternoon drift-bias can introduce a difference as large as 10.5 W m?2 in the OLR values collected over most tropical landmasses. The morning to afternoon satellite transition bias accounts for 0.9% of the pentad anomaly variance, and is shown to cause a bias of 12 W m?2 in the OLR values over most tropical landmasses during the NOAA-SR satellite series. The data are corrected by removing a statistically derived synthetic eigenvector that is associated with each of the ECT bias modes. This synthetic eigenvector is used instead of the exact values of the satellite bias eigenvector to ensure that only the artificial variability is removed from the dataset. The two REOF modes produced in this study are nearly orthogonal to each other having a correlation of only 0.17. This near orthogonality suggests that the use of the two-mode method presented in this study can more adequately describe the individual nature of each of the two ECT biases than a single REOF mode examined in previous studies. However, due to the presence of other forms of variability, it is likely that this study's estimate of the ECT bias includes ECT-related bias as well as some aspects of variability that may be associated with sensor changes, intersatellite calibration and/or natural climate variability. The strengths and limitations of the above technique are discussed, as are suggestions for future efforts.