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Duane E. Waliser and Wufeng Zhou

Abstract

The objective of this study is to examine the impacts from satellite equatorial crossing time (ECT) changes on the outgoing longwave radiation (OLR) and highly reflective cloud (HRC) datasets and to design appropriate and robust methods to remove these satellite-dependent biases. The OLR record covers the period from June 1974 to July 1996 and is on a 2.5° grid extending from 30°S to 30°N over the global Tropics. The HRC record covers the period from January 1971 to December 1987 and is on a 2° grid extending from 25°S to 25°N over the global Tropics. Rotated empirical orthogonal function analysis (REOF) is performed on both the monthly OLR and HRC anomalies to help distinguish between artificial modes of variability and those associated with real variability.

Results from the analysis show that significant errors are introduced by changes in the satellite ECT, and they appear differently in the two datasets. The primary satellite-related bias in the OLR appears as the fourth REOF mode, which accounts for 4.4% of the OLR anomaly variance. Its spatial pattern exhibits a strong surface signature over land, with the opposite sign over many of the deep convective regions of the ocean. During some periods, these biases result in widespread errors of over 10 W m−2, which are sustained for several months to over a year. In other cases, the transition between satellites induces abrupt, artificial changes in the OLR as high as 16 W m−2. In the HRC, the satellite-related bias appears as the leading two REOF modes, which account for 13.1% of the HRC anomaly variance. The spatial patterns of the HRC biases are indicative of an overall change in the mean climatological convection pattern. The above results can be understood by considering the sampling and radiometric characteristics of the OLR and HRC datasets.

To remove the satellite ECT biases, the REOF time series of the satellite-related modes are modified by using the detailed knowledge of the satellite ECTs so that only artificial variability related to the satellite changes is captured and the natural variability is excluded. These modified time series are used in conjunction with their associated spatial patterns to compute the satellite-related artificial variability, which is then removed from the two datasets. These datasets provide an improved resource to study intraseasonal and longer timescale regional climate variations, large-scale interannual variability, and global-scale climate trends. Analyses of the long-term trends in both datasets show that the satellite biases induce artificial trends in the data and that these artificial trends are reduced in the corrected datasets. Further, each of the corrected datasets exhibits a trend in the tropical western-central Pacific that appears spatially independent of the satellite biases and agrees with results of previous studies that indicate an increase in precipitation has occurred in this region over the period encompassed by these datasets.

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