A Comparison Of SSM/I-Derived Global Marine Surface Specific Humidity Datasets

Observations of near-surface humidity over the oceans are needed for climate and air-sea interaction applications. Observations from ships have been used to generate fields of near surface specific humidity (q_a) in datasets based on observations archived in the International Comprehensive Ocean-Atmosphere Data Set (ICOADS). Such in situ-based humidity datasets have been produced by the University of Wisconsin at Milwaukee, by the UK National Oceanography Centre (NOC), and by the Centre for Ocean-Atmosphere Prediction Studies at the Florida State University. These datasets provide long time series estimates of q_a, but are compromised by poor sampling in some regions, especially in the Southern Hemisphere. Using satellite observations to provide wider coverage for q_a would therefore be extremely valuable.

Satellite-based microwave sensors, such as the Special Sensor Microwave Imager (SSM/I), have provided a means to retrieve q_a since the 1980s. Seven publically available satellite measurement-derived monthly mean humidity datasets are compared with one another and with in situ q_a from the NOC Surface Flux and Meteorological dataset. The datasets are shown to be markedly different in terms of their large-scale means, and their smaller-scale spatial and temporal structure Differences show a range of yearly, global mean q_a of ~ 1 g kg^-1. Comparison of the datasets derived using the same satellite measurements of brightness temperature reveals differences in q_a that depend on the source of satellite data; the processing and quality control applied to the data; and the algorithm used to derive q_a from the satellite measurements of brightness temperature. Regional differences between satellite-derived q_a due to the choice of input data, quality control, and retrieval algorithm can all exceed the accuracy requirements for surface flux calculation of ~ 0.3 g kg^-1 and can be several g kg^-1 in monthly means for some periods and regions.