Abstract/Summary

The microwave humidity sounders Special Sensor Microwave Water Vapor Profiler (SSMT-2), Advanced Microwave Sounding Unit-B (AMSU-B) and Microwave Humidity Sounder (MHS) to date have been providing data records for 25 years. So far, the data records lack uncertainty information essential for constructing consistent long time data series. In this study, we assess the quality of the recorded data with respect to the uncertainty caused by noise. We calculate the noise on the raw calibration counts from the deep space views (DSVs) of the instrument and the noise equivalent differential temperature (NEΔT) as a measure for the radiometer sensitivity. For this purpose, we use the Allan deviation that is not biased from an underlying varying mean of the data and that has been suggested only recently for application in atmospheric remote sensing. Moreover, we use the bias function related to the Allan deviation to infer the underlying spectrum of the noise. As examples, we investigate the noise spectrum in flight for some instruments. For the assessment of the noise evolution in time, we provide a descriptive and graphical overview of the calculated NEΔT over the life span of each instrument and channel. This overview can serve as an easily accessible information for users interested in the noise performance of a specific instrument, channel and time. Within the time evolution of the noise, we identify periods of instrumental degradation, which manifest themselves in an increasing NEΔT, and periods of erratic behaviour, which show sudden increases of NEΔT interrupting the overall smooth evolution of the noise. From this assessment and subsequent exclusion of the aforementioned periods, we present a chart showing available data records with NEΔT  <  1 K. Due to overlapping life spans of the instruments, these reduced data records still cover without gaps the time since 1994 and may therefore serve as a first step for constructing long time series. Our method for count noise estimation, that has been used in this study, will be used in the data processing to provide input values for the uncertainty propagation in the generation of a new set of Fundamental Climate Data Records (FCDRs) that are currently produced in the project Fidelity and Uncertainty in Climate data records from Earth Observation (FIDUCEO).