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  • Author or Editor: Yang Gao x
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Yang Hong, Kuo-Lin Hsu, Soroosh Sorooshian, and Xiaogang Gao

Abstract

A satellite-based rainfall estimation algorithm, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Cloud Classification System (CCS), is described. This algorithm extracts local and regional cloud features from infrared (10.7 μm) geostationary satellite imagery in estimating finescale (0.04° × 0.04° every 30 min) rainfall distribution. This algorithm processes satellite cloud images into pixel rain rates by 1) separating cloud images into distinctive cloud patches; 2) extracting cloud features, including coldness, geometry, and texture; 3) clustering cloud patches into well-organized subgroups; and 4) calibrating cloud-top temperature and rainfall (T bR) relationships for the classified cloud groups using gauge-corrected radar hourly rainfall data. Several cloud-patch categories with unique cloud-patch features and T bR curves were identified and explained. Radar and gauge rainfall measurements were both used to evaluate the PERSIANN CCS rainfall estimates at a range of temporal (hourly and daily) and spatial (0.04°, 0.12°, and 0.25°) scales. Hourly evaluation shows that the correlation coefficient (CC) is 0.45 (0.59) at a 0.04° (0.25°) grid scale. The averaged CC of daily rainfall is 0.57 (0.63) for the winter (summer) season.

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Li Yaodong, Wang Yun, Song Yang, Hu Liang, Gao Shouting, and Rong Fu

Abstract

Summer convective systems (CSs) initiated over the Tibetan Plateau identified by the International Satellite Cloud Climatology Project (ISCCP) deep convection database and associated Tropical Rainfall Measuring Mission (TRMM) precipitation for 1998–2001 have been analyzed for their basic characteristics in terms of initiation, distribution, trajectory, development, life cycle, convective intensity, and precipitation. Summer convective systems have a dominant center over the Hengduan Mountain and a secondary center over the Yaluzangbu River Valley. Precipitation associated with these CSs contributes more than 60% of total precipitation over the central-eastern area of the Tibetan Plateau and 30%–40% over the adjacent region to its southeast. The average CS life cycle is about 36 h; 85% of CSs disappear within 60 h of their initiation. About 50% of CSs do not move out of the Tibetan region, with the remainder split into eastward- and southward-moving components. These CSs moving out the Tibetan Plateau are generally larger, have longer life spans, and produce more rainfall than those staying inside the region. Convective system occurrences and associated rainfall present robust diurnal variations. The midafternoon maximum of CS initiation and associated rainfall over the plateau is mainly induced by solar heating linked to the unique Tibetan geography. The delayed afternoon–late night peak of rainfall from CSs propagating out of this region is a combined outcome of multiple mechanisms working together. Results suggest that interactions of summer Tibetan CSs with the orientation of the unique Tibetan geography and the surrounding atmospheric circulations are important for the development, intensification, propagation, and life span of these CSs.

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Liang Chang, Shiqiang Wen, Guoping Gao, Zhen Han, Guiping Feng, and Yang Zhang

Abstract

Characteristics of temperature inversions (TIs) and specific humidity inversions (SHIs) and their relationships in three of the latest global reanalyses—the European Centre for Medium-Range Weather Forecasts Interim Reanalysis (ERA-I), the Japanese 55-year Reanalysis (JRA-55), and the ERA5—are assessed against in situ radiosonde (RS) measurements from two expeditions over the Arctic Ocean. All reanalyses tend to detect many fewer TI and SHI occurrences, together with much less common multiple TIs and SHIs per profile than are seen in the RS data in summer 2008, winter 2015, and spring 2015. The reanalyses generally depict well the relationships among TI characteristics seen in RS data, except for the TIs below 400 m in summer, as well as above 1000 m in summer and winter. The depth is simulated worst by the reanalyses among the SHI characteristics, which may result from its sensitivity to the uncertainties in specific humidity in the reanalyses. The strongest TI per profile in RS data exhibits more robust dependency on surface conditions than the strongest SHI per profile, and the former is better presented by the reanalyses than the latter. Furthermore, all reanalyses have difficulties simulating the relationships between TIs and SHIs, together with the correlations between the simultaneous inversions. The accuracy and vertical resolution in the reanalyses are both important to properly capture occurrence and characteristics of the Arctic inversions. In general, ERA5 performs better than ERA-I and JRA-55 in depicting the relationships among the TIs. However, the representation of SHIs is more challenging than TIs in all reanalyses over the Arctic Ocean.

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Gang Hong, Ping Yang, Bo-Cai Gao, Bryan A. Baum, Yong X. Hu, Michael D. King, and Steven Platnick

Abstract

This study surveys the optical and microphysical properties of high (ice) clouds over the Tropics (30°S–30°N) over a 3-yr period from September 2002 through August 2005. The analyses are based on the gridded level-3 cloud products derived from the measurements acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard both the NASA Earth Observing System Terra and Aqua platforms. The present analysis is based on the MODIS collection-4 data products. The cloud products provide daily, weekly, and monthly mean cloud fraction, cloud optical thickness, cloud effective radius, cloud-top temperature, cloud-top pressure, and cloud effective emissivity, which is defined as the product of cloud emittance and cloud fraction. This study is focused on high-level ice clouds. The MODIS-derived high clouds are classified as cirriform and deep convective clouds using the International Satellite Cloud Climatology Project (ISCCP) classification scheme. Cirriform clouds make up more than 80% of the total high clouds, whereas deep convective clouds account for less than 20% of the total high clouds. High clouds are prevalent over the intertropical convergence zone (ITCZ), the South Pacific convergence zone (SPCZ), tropical Africa, the Indian Ocean, tropical America, and South America. Moreover, land–ocean, morning–afternoon, and summer–winter variations of high cloud properties are also observed.

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