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Wenjun Tang, Kun Yang, Jun Qin, Jun Li, and Jiangang Ye

Abstract

Surface solar radiation (SSR) over the ocean is essential for studies of ocean–atmosphere interactions and marine ecology, and satellite remote sensing is a major way to obtain the SSR over ocean. A new high-resolution (10 km; 3 h) SSR product has recently been developed, mainly based on the newly released cloud product of the International Satellite Cloud Climatology Project H series (ISCCP-HXG), and is available for the period from July 1983 to December 2018. In this study, we compared this SSR product with in situ observations from 70 buoy sites in the Global Tropical Moored Buoy Array (GTMBA) and also compared it with another well-known satellite-derived SSR product from the Clouds and the Earth’s Radiant Energy System (CERES; edition 4.1), which has a spatial resolution of approximately 100 km. The results show that the ISCCP-HXG SSR product is generally more accurate than the CERES SSR product for both ocean and land surfaces. We also found that the accuracy of both satellite-derived SSR products (ISCCP-HXG and CRERS) was higher over ocean than over land and that the accuracy of ISCCP-HXG SSR improves greatly when the spatial resolution of the product is coarsened to ≥ 30 km.

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Jun Qin, Kun Yang, Shunlin Liang, and Wenjun Tang

Abstract

Photosynthetically active radiation (PAR) is absorbed by plants to carry out photosynthesis. Its estimation is important for many applications such as ecological modeling. In this study, a broadband transmittance scheme for solar radiation at the PAR band is developed to estimate clear-sky PAR values. The influence of clouds is subsequently taken into account through sunshine-duration data. This scheme is examined without local calibration against the observed PAR values under both clear- and cloudy-sky conditions at seven widely distributed Surface Radiation Budget Network (SURFRAD) stations. The results indicate that the scheme can estimate the daily mean PAR at these seven stations under all-sky conditions with root-mean-square error and mean bias error values ranging from 6.03 to 6.83 W m−2 and from −2.86 to 1.03 W m−2, respectively. Further analyses indicate that the scheme can estimate PAR values well with globally available aerosol and ozone datasets. This suggests that the scheme can be applied to regions for which observed aerosol and ozone data are not available.

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Kun Yang, Jun Qin, Long Zhao, Yingying Chen, Wenjun Tang, Menglei Han, Lazhu, Zhuoqi Chen, Ning Lv, Baohong Ding, Hui Wu, and Changgui Lin

Multisphere interactions over the Tibetan Plateau directly impact its surrounding climate and environment at a variety of spatiotemporal scales. Remote sensing and modeling are expected to provide hydrometeorological data needed for these process studies, but in situ observations are required to support their calibration and validation. For this purpose, we have established a dense monitoring network on the central Tibetan Plateau to measure two state variables (soil moisture and temperature) at three spatial scales (1.0°, 0.3°, and 0.1°) and four soil depths (0–5, 10, 20, and 40 cm). The experimental area is characterized by low biomass, high soil moisture dynamic range, and typical freeze–thaw cycle. The network consists of 56 stations with their elevation varying over 4470–4950 m. As auxiliary parameters of this network, soil texture and soil organic carbon content are measured at each station to support further studies. To guarantee continuous and high-quality data, tremendous efforts have been made to protect the data-logger from soil water intrusion, to calibrate soil moisture sensors, and to upscale the point measurements.

As the highest soil moisture network above sea level in the world, our network meets the requirement for evaluating a variety of soil moisture products and for soil moisture scaling analyses. It also directly contributes to the soil–water–ice–air–ecosystem interaction studies on the third pole. The data will be publicized via the International Soil Moisture Network and the recent 2-yr data will become accessible soon.

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Yan Wang, Kun Yang, Zhengyang Pan, Jun Qin, Deliang Chen, Changgui Lin, Yingying Chen, Lazhu, Wenjun Tang, Menglei Han, Ning Lu, and Hui Wu

Abstract

The southern Tibetan Plateau (STP) is the region in which water vapor passes from South Asia into the Tibetan Plateau (TP). The accuracy of precipitable water vapor (PWV) modeling for this region depends strongly on the quality of the available estimates of water vapor advection and the parameterization of land evaporation models. While climate simulation is frequently improved by assimilating relevant satellite and reanalysis products, this requires an understanding of the accuracy of these products. In this study, PWV data from MODIS infrared and near-infrared measurements, AIRS Level-2 and Level-3, MERRA, ERA-Interim, JRA-55, and NCEP final reanalysis (NCEP-Final) are evaluated against ground-based GPS measurements at nine stations over the STP, which covers the summer monsoon season from 2007 to 2013. The MODIS infrared product is shown to underestimate water vapor levels by more than 20% (1.84 mm), while the MODIS near-infrared product overestimates them by over 40% (3.52 mm). The AIRS PWV product appears to be most useful for constructing high-resolution and high-quality PWV datasets over the TP; particularly the AIRS Level-2 product has a relatively low bias (0.48 mm) and RMSE (1.83 mm) and correlates strongly with the GPS measurements (R = 0.90). The four reanalysis datasets exhibit similar performance in terms of their correlation coefficients (R = 0.87–0.90), bias (0.72–1.49 mm), and RMSE (2.19–2.35 mm). The key finding is that all the reanalyses have positive biases along the PWV seasonal cycle, which is linked to the well-known wet bias over the TP of current climate models.

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