青高所的太阳能辐射计算模型

In order to obtain hourly cloud parameters, an artificial neural network (ANN) is applied in this study to directly construct a functional relationship between MODIS cloud products and Multifunctional Transport Satellite (MTSAT) geostationary satellite signals. In addition, an efficient parameterization model for SSR retrieval is introduced.

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Initial contribute: 2021-09-07

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tangwj@itpcas.ac.cn
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Classification(s)

Application-focused categoriesNatural-perspectiveAtmospheric regions
Application-focused categoriesIntegrated-perspectiveRegional scale

Detailed Description

English {{currentDetailLanguage}} English

Quoted from:[1] Tang W ,  Qin J ,  Yang K , et al. Retrieving high-resolution surface solar radiation with cloud parameters derived by combining MODIS and MTSAT data[J]. Atmospheric Chemistry & Physics, 2015, 16(4):2543–2557.https://www.sci-hub.ren/10.5194/acp-16-2543-2016

 

Abstract
Cloud parameters (cloud mask, effective particle radius, and liquid/ice water path) are the important inputs in estimating surface solar radiation (SSR). These parameters can be derived from MODIS with high accuracy, but their temporal resolution is too low to obtain high-temporal resolution SSR retrievals. In order to obtain hourly cloud parameters, an artifificial neural network (ANN) is applied in this study to directly construct a functional relationship between MODIS cloud products and Multifunctional Transport Satellite (MTSAT) geostationary satellite signals. In addition, an effificient parameterization model for SSR retrieval is introduced and, when driven with MODIS atmospheric and land products, its root mean square error (RMSE) is about 100 W m -2for 44 Baseline Surface Radiation Network (BSRN) stations. Once the estimated cloud parameters and other information (such as aerosol, precipitable water, ozone) are input to the model, we can derive SSR at high spatiotemporal resolution. The retrieved SSR is fifirst evaluated against hourly radiation data at three experimental stations in the Haihe River basin of China. The mean bias error (MBE) and RMSE in hourly SSR estimate are 12.0 W m -2(or 3.5 %) and 98.5 W m -2(or 28.9 %), respectively. The retrieved SSR is also evaluated against daily radiation data at 90 China Meteorological Administration (CMA) stations. The MBEs are 9.8 W m -2(or 5.4 %); the RMSEs in daily and monthly mean SSR estimates are 34.2 W m -2 (or 19.1 %) and 22.1 W m -2(or 12.3 %), respectively. The accuracy is comparable to oreven higher than two other radiation products (GLASS and ISCCP-FD), and the present method is more computationally effificient and can produce hourly SSR data at a spatial resolution of 5 km.

Introduction

This paper presents a new method to quickly estimate SSR by combining signals of polar-orbit and geostationary satellites. This method includes two steps. The fifirst step is to estimate hourly cloud parameters by combining high-accuracy cloud products of MODIS and high-temporal-resolution top of-the-atmosphere (TOA) radiance data of all MTSAT channels. The second step is to use the cloud information and other auxiliary information in an effificient parameterization model to retrieve SSR at a high spatiotemporal resolution.

Conclusions and remarks

To obtain high-resolution SSR data, this study developed an ANN-based algorithm to estimate cloud parameters (cloud mask, effective particle radius, and liquid/ice water path) from MTSAT imagery. The algorithm was built by the combination of MODIS cloud products and MTSAT data. The estimated cloud parameters and other information (such as aerosol, ozone, PW) were put into a parameterization model to estimate SSR. The estimated SSR was validated against both experimental data and operational station data in China, with an RMSE of 98.5 W m -2 for hourly SSR, 34.2 W m -2 for daily SSR, and 22.1 W m -2 for monthly SSR, as well as an MBE of about 10 W m -2.

Compared with two satellite radiation products (GLASS and ISCCP-FD), the SSR estimate presented in this study has comparable accuracy in terms of RMSE. GLASS underestimates the peak values of SSR, while it overestimates the low values. Our algorithm generally overestimates the SSR, which might be attributed to the underestimation of the cloud water path. The combining of CLOUDSAT and MTSAT in the future may be an alternative method to further improve the accuracy of cloud parameters, because CLOUDSAT has a greater advantage in retrieving cloud parameters than MODIS. 

 

 

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Wenjun Tang, Jun Qin, Kun Yang (2021). 青高所的太阳能辐射计算模型, Model Item, OpenGMS, https://geomodeling.njnu.edu.cn/modelItem/b309ff12-08a4-4728-8405-dd2eccf960f3
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Contributor(s)

Initial contribute : 2021-09-07

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Authorship

:  
tangwj@itpcas.ac.cn
Is authorship not correct? Feed back

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