Analysis of Geostationary Satellite Imagery Using a Temporal-Differencing Technique

d’Entremont, Robert P.; Gustafson, Gary B.

doi:10.1175/1087-3562(2003)007<0001:aogsiu>2.0.co;2

Cited by 11 publications

(3 citation statements)

References 10 publications

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“…Cloud detection for each image was performed using the coupled Cloud Depiction and Forecast System model, which implements a hybrid, three‐step procedure comprising temporal differencing, dynamic thresholding and spectral discrimination [ d'Entremont and Gustafson , 2003]. Each step uses a different temporal, spatial, or spectral cloud signature, and therefore, the model can detect clouds on each image.…”

Section: Datamentioning

confidence: 99%

An algorithm for estimating downward shortwave radiation from GMS 5 visible imagery and its evaluation over China

Liu

Liang

2010

J. Geophys. Res.

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[1] This paper presents an operational scheme to estimate downward shortwave radiation (DSR) over China from the visible-band top-of-atmosphere reflectance of the Geostationary Meteorological Satellite (GMS) 5 imagery. The proposed algorithm retrieves surface reflectance and atmospheric parameters directly from GMS 5 images by searching lookup tables, which are created by the radiative transfer model SBDART and consider the effects of water vapor absorption and surface altitude variations. Experiments show that the DSR retrieval is more sensitive to the selection of aerosol type and less to that of the cloud type. Uncertainty in the reflectance of a bright surface leads to a considerable DSR retrieval error (±(6-9%)). The instantaneous retrieved DSR is evaluated by field measurements on the Tibetan Plateau, and the daily retrieved DSR is compared with one year's ground-based measurements at 96 stations in China. The results show that the estimated DSR is in good agreement with ground measurements with a correlation coefficient of ∼0.9 and a bias of 1.5%. Root-mean square differences in the daily DSR are 17.7% for all-sky and 13.1% for clear-sky conditions. These results suggest that the proposed method applied to the GMS 5 satellite data can accurately estimate temporally and spatially continuous instantaneous and daily DSR. These DSR data sets will be useful for a wide range of applications.Citation: Lu, N., R. Liu, J. Liu, and S. Liang (2010), An algorithm for estimating downward shortwave radiation from GMS 5 visible imagery and its evaluation over China,

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Section: Datamentioning

confidence: 99%

An algorithm for estimating downward shortwave radiation from GMS 5 visible imagery and its evaluation over China

Liu

Liang

2010

J. Geophys. Res.

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“…The AGRMET system computed longwave and shortwave energy estimates based on hourly, global fractional cloud cover analyses provided by the USAF Cloud Depiction and Forecast System Version-II WWMCA (Gustafson and d'Entremont 2000;d'Entremont and Gustafson 2003;HQ AFWA 2005;d'Entremont et al 2016;Fig. 1).…”

Section: A Surface Longwave and Shortwave Radiationmentioning

confidence: 99%

History and Development of the USAF Agriculture Meteorology Modeling System and Resulting USAF–NASA Strategic Partnership

Eylander

Kumar

Peters‐Lidard

et al. 2022

Weather and Forecasting

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The USAF Weather (AFW) supports a number of military and US Government agencies by providing authoritative weather analysis and forecast products for any location globally, including soil moisture analyses. The long history of supporting soil moisture products and partnering with other U.S. government agencies led to the partnering between the United States Air Force (USAF) and NASA Goddard Space Flight Center, resulting in a merger of those organizations modeling systems, collaborative development of the Land Information System (LIS), and operational fielding of the system within the USAF 557th Weather Wing (557WW; formerly, Headquarters Air Force Weather Agency or HQ AFWA). In 2009, the USAF implemented the NASA LIS and later made it the primary software system to generate global soil hydrology and energy budget products. The implementation of LIS delivered a significant upgrade over the existing Land Data Assimilation System (LDAS) the USAF operated, the Agriculture Meteorology (AGRMET) system. Implementation enabled the rapid integration of new LDAS technology into USAF operations, and led to a long-term NASA-USAF partnership resulting in continued development, integration, and implementation of new LIS capabilities. This paper documents both the history of the USAF Weather organization capabilities enabling the generation of soil moisture and other land surface analysis products, and describes the USAF-NASA partnership leading to the development of the merged LIS-AGRMET system. The article also presents a successful example of a mutually beneficial partnership that has enabled cutting-edge land analysis capabilities at the USAF, while transitioning NASA software and satellite data into USAF operations.

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“…Cloud detection was performed pixel-by-pixel using the coupled Cloud Depiction and Forecast System model using the reflectance of visible bands and the brightness temperature of infrared bands [46]. This procedure incorporated temporal differencing, dynamic thresholding, and spectral discrimination to detect clouds with the appropriate optical thickness.…”

Section: Pre-processing the Imagesmentioning

confidence: 99%

A Lookup-Table-Based Approach to Estimating Surface Solar Irradiance from Geostationary and Polar-Orbiting Satellite Data

Zhang

Huang

et al. 2018

Remote Sensing

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Incoming surface solar irradiance (SSI) is essential for calculating Earth's surface radiation budget and is a key parameter for terrestrial ecological modeling and climate change research. Remote sensing images from geostationary and polar-orbiting satellites provide an opportunity for SSI estimation through directly retrieving atmospheric and land-surface parameters. This paper presents a new scheme for estimating SSI from the visible and infrared channels of geostationary meteorological and polar-orbiting satellite data. Aerosol optical thickness and cloud microphysical parameters were retrieved from Geostationary Operational Environmental Satellite (GOES) system images by interpolating lookup tables of clear and cloudy skies, respectively. SSI was estimated using pre-calculated offline lookup tables with different atmospheric input data of clear and cloudy skies. The lookup tables were created via the comprehensive radiative transfer model, Santa Barbara Discrete Ordinate Radiative Transfer (SBDART), to balance computational efficiency and accuracy. The atmospheric attenuation effects considered in our approach were water vapor absorption and aerosol extinction for clear skies, while cloud parameters were the only atmospheric input for cloudy-sky SSI estimation. The approach was validated using one-year pyranometer measurements from seven stations in the SURFRAD (SURFace RADiation budget network). The results of the comparison for 2012 showed that the estimated SSI agreed with ground measurements with correlation coefficients of 0.94, 0.69, and 0.89 with a bias of 26.4 W/m 2 , −5.9 W/m 2 , and 14.9 W/m 2 for clear-sky, cloudy-sky, and all-sky conditions, respectively. The overall root mean square error (RMSE) of instantaneous SSI was 80.0 W/m 2 (16.8%), 127.6 W/m 2 (55.1%), and 99.5 W/m 2 (25.5%) for clear-sky, cloudy-sky (overcast sky and partly cloudy sky), and all-sky (clear-sky and cloudy-sky) conditions, respectively. A comparison with other state-of-the-art studies suggests that our proposed method can successfully estimate SSI with a maximum improvement of an RMSE of 24 W/m 2. The clear-sky SSI retrieval was sensitive to aerosol optical thickness, which was largely dependent on the diurnal surface reflectance accuracy. Uncertainty in the pre-defined horizontal visibility for 'clearest sky' will eventually lead to considerable SSI retrieval error. Compared to cloud effective radius, the retrieval error of cloud optical thickness was a primary factor that determined the SSI estimation accuracy for cloudy skies. Our proposed method can be used to estimate SSI for clear and one-layer cloud sky, but is not suitable for multi-layer clouds overlap conditions as a lower-level cloud cannot be detected by the optical sensor when a higher-level cloud has a higher optical thickness.

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Analysis of Geostationary Satellite Imagery Using a Temporal-Differencing Technique

Cited by 11 publications

References 10 publications

An algorithm for estimating downward shortwave radiation from GMS 5 visible imagery and its evaluation over China

An algorithm for estimating downward shortwave radiation from GMS 5 visible imagery and its evaluation over China

History and Development of the USAF Agriculture Meteorology Modeling System and Resulting USAF–NASA Strategic Partnership

A Lookup-Table-Based Approach to Estimating Surface Solar Irradiance from Geostationary and Polar-Orbiting Satellite Data

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