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Abstract. The advanced microwave sounding unit (AMSU) was finally launched in May 1998 aboard the NOAA 15 satellite. Algorithms are provided for retrieving the total precipitable water (TPW) and cloud liquid water (CLW) over oceans using the AMSU measurements at 23.8 and 31.4 GHz. Extensive comparisons are made between the AMSU retrievals of CLW and TPW and those obtained using other satellite instruments (Special Sensor Microwave Imager (SSM/I) and Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI)) and ground-based radiometers. The AMSU TPW is also compared against radiosonde data, where all of the results are in good agreement with rms differences less than 3 mm and biases less than 1 mm over the range between 5 and 60 mm. The CLW comparisons show greater variability, although the time series of the AMSU and ground-based sensors follow each other and cover the same dynamic range of 0-0.5 mm. The AMSU CLW also compares well with the other satellite measurements, although a bias exists between AMSU and TMI when the CLW exceeds 0.5 mm.

Studies of perceptual learning have revealed a great deal of plasticity in adult humans. In this study, we systematically investigated the effects and mechanisms of several forms (trial-by-trial, block, and session rewards) and levels (no, low, high, subliminal) of monetary reward on the rate, magnitude, and generalizability of perceptual learning. We found that high monetary reward can greatly promote the rate and boost the magnitude of learning and enhance performance in untrained spatial frequencies and eye without changing interocular, interlocation, and interdirection transfer indices. High reward per se made unique contributions to the enhanced learning through improved internal noise reduction. Furthermore, the effects of high reward on perceptual learning occurred in a range of perceptual tasks. The results may have major implications for the understanding of the nature of the learning rule in perceptual learning and for the use of reward to enhance perceptual learning in practical applications.

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