No abstract

Tropical Rainfall Measuring Mission (TRMM) data during June-August 1998 are used to investigate diurnal variations of rain and cloud systems over the tropics and midlatitudes. The peak time of the coldest minimum brightness temperature derived from the Visible and Infrared Scanner (VIRS) and the maximum rain rate derived from the Precipitation Radar (PR) and the TRMM Microwave Imager (TMI) are compared. Time distributions are generally consistent with previous studies. However, it is found that systematic shifts in peak time relative to each sensor appeared over land, notably over western North America, the Tibetan Plateau, and oceanic regions such as the Gulf of Mexico. The peak time shift among PR, TMI, and VIRS is a few hours.The relationships among the amplitude of diurnal variation, convective frequency, storm height, and rain amount are further investigated and compared to the systematic peak time shifts. The regions where the systematic shift appears correspond to large amplitude of diurnal variation, high convective frequency, and high storm height. Over land and over ocean near the coast, the relationships are rather clear, but not over open ocean.The sensors likely detect different stages in the evolution of convective precipitation, which would explain the time shift. The PR directly detects near-surface rain. The TMI observes deep convection and solid hydrometeors, sensing heavy rain during the mature stage. VIRS detects deep convective clouds in mature and decaying stages. The shift in peak time particularly between PR (TMI) and VIRS varies by region.

The purpose of this paper is to quantify the sensitivity

Biases in climatological and extreme precipitation estimates are assessed for 11 global observational datasets constructed with merged satellite measurements and/or rain gauge networks. Specifically, the biases in extreme precipitation are contrasted with mean-state biases. Extreme precipitation is defined by a 99th percentile threshold (R99p) on a daily, 1°×1°grid for 50°S-50°N. The spatial pattern of extreme precipitation lacks distinct features such as the ITCZ that is evident in the global climatological map, and the climatology and extremes share little in common in terms of the spatial characteristics of inter-product biases. The time series also exhibit a larger spread in the extremes than in the climatology. Further, when analysed from 2001 to 2013, they show relatively consistent decadal stability in the climatology over ocean while the dispersion is larger for the extremes over ocean. This contrast is not observed over land. Overall, the results suggest that the inter-product biases apparent in the climatology are a poor predictor of the extreme-precipitation biases even in a qualitative sense.

Abstract-Uncertainties in the retrievals of microwave land surface emissivities were quantified over two types of land surfaces: desert and tropical rainforest. Retrievals from satellite-based microwave imagers, including SSM/I, TMI and AMSR-E, were studied. Our results show that there are considerable differences between the retrievals from different sensors and from different groups over these two land surface types. In addition, the mean emissivity values show different spectral behavior across the frequencies. With the true emissivity assumed largely constant over both of the two sites throughout the study period, the differences are largely attributed to the systematic and random errors in the retrievals. Generally these retrievals tend to agree better at lower frequencies than at higher ones, with systematic differences ranging 1~4% (3~12 K) over desert and 1~7% (3~20 K) over rainforest. The random errors within each retrieval dataset are in the range of 0.5~2% (2~6 K). In particular, at 85.0/89.0 GHz, there are very large differences between the different retrieval datasets, and within each retrieval dataset itself. Further investigation reveals that these differences are mostly likely caused by rain/cloud contamination, which can lead to random errors up to 10~17 K under the most severe conditions. Index Terms-microwave radiometry, remote sensing, land surface emissivity, measurement uncertainty, systematic errors, random errors, brightness temperature.