Historical and near-real time SST retrievals from MetOp AVHRR FRAC with the advanced clear-sky processor for ocean

2021 ◽  
Author(s):  
Victor Pryamitsyn ◽  
Boris Petrenko ◽  
Alexander Ignatov ◽  
Olafur Jonasson ◽  
Yury Kihai
Keyword(s):  
Real Time ◽  
Clear Sky

scholarly journals Prediction of Solar Power Using Near-Real Time Satellite Data

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5865
Author(s):  
Abhnil Amtesh Prasad ◽  
Merlinde Kay
Keyword(s):  
Real Time ◽  
Satellite Data ◽  
Solar Power ◽  
Short Term ◽  
Motion Vectors ◽  
Clear Sky ◽  
Cloud Motion ◽  
Visible Images ◽  
Grid Operators ◽  
Short Term Prediction

Solar energy production is affected by the attenuation of incoming irradiance from underlying clouds. Often, improvements in the short-term predictability of irradiance using satellite irradiance models can assist grid operators in managing intermittent solar-generated electricity. In this paper, we develop and test a satellite irradiance model with short-term prediction capabilities using cloud motion vectors. Near-real time visible images from Himawari-8 satellite are used to derive cloud motion vectors using optical flow estimation techniques. The cloud motion vectors are used for the advection of pixels at future time horizons for predictions of irradiance at the surface. Firstly, the pixels are converted to cloud index using the historical satellite data accounting for clear, cloudy and cloud shadow pixels. Secondly, the cloud index is mapped to the clear sky index using a historical fitting function from the respective sites. Thirdly, the predicated all-sky irradiance is derived by scaling the clear sky irradiance with a clear sky index. Finally, a power conversion model trained at each site converts irradiance to power. The prediction of solar power tested at four sites in Australia using a one-month benchmark period with 5 min ahead prediction showed that errors were less than 10% at almost 34–60% of predicted times, decreasing to 18–26% of times under live predictions, but it outperformed persistence by >50% of the days with errors <10% for all sites. Results show that increased latency in satellite images and errors resulting from the conversion of cloud index to irradiance and power can significantly affect the forecasts.

A new approach to the real-time assessment and intraday forecasting of clear-sky direct normal irradiance

Solar Energy ◽  
2018 ◽  
Vol 167 ◽  
pp. 35-51 ◽  
Author(s):  
Rémi Chauvin ◽  
Julien Nou ◽  
Julien Eynard ◽  
Stéphane Thil ◽  
Stéphane Grieu
Keyword(s):  
Real Time ◽  
New Approach ◽  
The Real ◽  
Clear Sky ◽  
Direct Normal Irradiance ◽  
Time Assessment

scholarly journals Clear-sky Irradiance Model for Real-time Sky Imager Application

2015 ◽  
Vol 69 ◽  
pp. 1999-2008 ◽  
Author(s):  
J. Nou ◽  
R. Chauvin ◽  
S. Thil ◽  
J. Eynard ◽  
S. Grieu
Keyword(s):  
Real Time ◽  
Clear Sky ◽  
Sky Imager

scholarly journals Global clear-sky surface skin temperature from multiple satellites using a single-channel algorithm with viewing zenith angle correction

2016 ◽  
Author(s):  
Benjamin R. Scarino ◽  
Patrick Minnis ◽  
Thad Chee ◽  
Kristopher M. Bedka ◽  
Christopher R. Yost ◽  
...  
Keyword(s):  
Skin Temperature ◽  
Real Time ◽  
Single Channel ◽  
Weather Prediction ◽  
Surface Radiation ◽  
Earth Orbit ◽  
Viewing Angle ◽  
Clear Sky ◽  
Geo Satellite ◽  
Surface Skin Temperature

Abstract. Surface skin temperature (Ts) is an important parameter for characterizing the energy exchange at the ground/water-atmosphere interface. The Satellite ClOud and Radiation Property retrieval System (SatCORPS) employs a single-channel thermal-infrared- (TIR-) method to retrieve Ts over clear-sky land and ocean surfaces from data taken by geostationary-Earth orbit (GEO) satellite and low-Earth orbit (LEO) satellite imagers. GEO satellites can provide somewhat continuous estimates of Ts over the diurnal cycle in non-polar regions, while polar Ts retrievals from LEO imagers, such as the Advanced Very High Resolution Radiometer (AVHRR) can complement the GEO measurements. The combined global coverage of remotely sensed Ts, along with accompanying cloud and surface radiation parameters, produced in near-real time and from historical satellite data, should be beneficial for both weather and climate applications. For example, near-real-time hourly Ts observations can be assimilated in high-temporal resolution numerical weather prediction models and historical observations can be used for validation or assimilation of climate models. Key drawbacks to the utility of TIR-derived Ts, data include the limitation to clear-sky conditions, the reliance on a particular set of analyses/reanalyses necessary for atmospheric corrections, and the dependence on viewing angle. Therefore, Ts validation with established references is essential, as is proper evaluation of Ts sensitivity to atmospheric correction source. This article presents improvements on the NASA Langley GEO satellite and AVHRR TIR-based Ts product, derived using a single-channel technique. The resulting clear-sky skin temperature values are validated with surface references and independent satellite products. Furthermore, an empirical means of correcting for the viewing-angle dependency of satellite land surface temperature (LST) is explained and validated. Application of a daytime nadir-normalization model yields improved accuracy and precision of GOES-13 LST relative to independent Moderate-resolution Imaging Spectroradiometer (MYD11_L2) LST and Atmospheric Radiation Measurement Program/NOAA ESRL Surface Radiation network ground stations. These corrections serve as a basis for a means to improve satellite-based LST accuracy, thereby leading to better monitoring and utilization of the data. The immediate availability and broad coverage of these skin temperature observations should prove valuable to modelers and climate researchers looking for improved forecasts and better understanding of the global climate model.

scholarly journals ANALYSIS OF POSITIONING RESULTS OBTAINED BY A SINGLE BASE RTK WITH AUTONOMOUS BASE START AND TILT OPTION

2018 ◽  
Vol 13 (1) ◽  
Author(s):  
Danijel Šugar
Keyword(s):  
Real Time ◽  
Reliable Method ◽  
Base Station ◽  
Communication Link ◽  
Post Processing ◽  
Single Base ◽  
Gnss Receiver ◽  
Clear Sky ◽  
Differential Corrections ◽  
Set Up

HPPS service of the CROPOS system is today a fast, reliable, precise and commonly used tool for coordinates determination in Croatia. The advantages of a networked RTK method are well known, but in some situations, a single-base RTK method could be a reliable method for coordinates determination, even without a base station having known coordinates. Single-base RTK method with Autonomous base start can be set up on any (unknown) station with a clear sky and GNSS satellites visibility enabled. Differential corrections are usually broadcast to the rover GNSS receiver via a communication link, enabling the coordinates determination with cm-level precision in real time. Simultaneously, the base GNSS receiver collects static observations for base station determination in post-processing and subsequent rover coordinates shift. In this paper, the above mentioned method was tested on the ground, together with TILT option integrated into newest Topcon GNSS receiver.

scholarly journals Real-time Clear-sky Model and Cloud Cover for Direct Normal Irradiance Prediction

2018 ◽  
Vol 1072 ◽  
pp. 012003 ◽  
Author(s):  
Xin Zhao ◽  
Haikun Wei ◽  
Yu Shen ◽  
Kanjian Zhang
Keyword(s):  
Real Time ◽  
Cloud Cover ◽  
Clear Sky ◽  
Direct Normal Irradiance

scholarly journals A new approach to the real-time assessment of the clear-sky direct normal irradiance

2016 ◽  
Vol 40 (15-16) ◽  
pp. 7245-7264 ◽  
Author(s):  
Julien Nou ◽  
Rémi Chauvin ◽  
Stéphane Thil ◽  
Stéphane Grieu
Keyword(s):  
Real Time ◽  
New Approach ◽  
The Real ◽  
Clear Sky ◽  
Direct Normal Irradiance ◽  
Time Assessment

On the use of Mobile-DOAS measurements for air quality satellite validation

2021 ◽  
Author(s):  
Alexis Merlaud ◽  
Frederik Tack ◽  
Michel Van Roozendael ◽  
Henk Eskes ◽  
John Douros
Keyword(s):  
Air Quality ◽  
Real Time ◽  
Spatial Resolution ◽  
Atmospheric Composition ◽  
Negative Bias ◽  
Clear Sky ◽  
Satellite Validation ◽  
Resolution Model ◽  
Local Measurements ◽  
Ozone Monitoring

&lt;p&gt;The TROPOMI/S5p instrument was launched in October 2017, aiming to measure from space the atmospheric composition for air quality and ozone monitoring. Since 30 April 2018, TROPOMI/S5p routinely delivers NO2 tropospheric VCDs in quasi-real-time. The first comparisons between this operational TROPOMI product and measurements from the ground and aircraft generally show good correlations but also a negative bias over polluted areas. Such a bias is expected from the low spatial resolution of the CTM used in the operational TROPOMI retrieval and several studies reported a better agreement with local measurements of NO2 VCDs when using a higher resolution model for the satellite AMFs, in practice, changing the original TM5-MP for the CAMS Ensemble. We compare mobile-DOAS measurements with the two aforementioned versions of the TROPOMI retrievals (TM5-MP and CAMS). Our Mobile-DOAS measurements were performed with the BIRA-IASB Mobile-DOAS during 19 clear sky days. We sampled polluted and clean areas during TROPOMI overpasses in Belgium and Germany between June 2018 and September 2020. Beside studying the effect of the CTM model on the comparisons, we investigate the general added-values of such mobile-DOAS measurements for the validation of TROPOMI/S5p and forthcoming missions.&lt;/p&gt;

scholarly journals An Improved Cloud Detection Method for GF-4 Imagery

2020 ◽  
Vol 12 (9) ◽  
pp. 1525
Author(s):  
Ming Lu ◽  
Feng Li ◽  
Bangcheng Zhan ◽  
He Li ◽  
Xue Yang ◽  
...  
Keyword(s):  
Real Time ◽  
Detection Methods ◽  
Support Vector ◽  
Optical Remote Sensing ◽  
Cloud Detection ◽  
Dynamic Features ◽  
Improve Image Quality ◽  
Clear Sky ◽  
Important Means ◽  
The Difference

Clouds are significant barriers to the application of optical remote sensing images. Accurate cloud detection can help to remove contaminated pixels and improve image quality. Many cloud detection methods have been developed. However, traditional methods either rely heavily on thermal infrared bands or clear-sky images. When traditional cloud detection methods are used with Gaofen 4 (GF-4) imagery, it is very difficult to separate objects with similar spectra, such as ice, snow, and bright sand, from clouds. In this paper, we propose a new method, named Real-Time-Difference (RTD), to detect clouds using a pair of images obtained by the GF-4 satellite. The RTD method has four main steps: (1) data preprocessing, including transforming digital value (DN) to Top of Atmosphere (TOA) reflectance, and orthographic and geometric correction; (2) the computation of a series of cloud indexes for a single image to highlight clouds; (3) the calculation of the difference between a pair of real-time images in order to obtain moved clouds; and (4) confirming the clouds and background by analyzing their physical and dynamic features. The RTD method was validated in three sites located in the Hainan, Liaoning, and Xinjiang areas of China. The results were compared with those of a popular classifier, Support Vector Machine (SVM). The results showed that RTD outperformed SVM; for the Hainan, Liaoning, and Xinjiang areas, respectively, the overall accuracy of RTD reached 95.9%, 94.1%, and 93.9%, and its Kappa coefficient reached 0.92, 0.88, and 0.88. In the future, we expect RTD to be developed into an important means for the rapid detection of clouds that can be used on images from geostationary orbit satellites.

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