scholarly journals A Multispectral Technique for Detecting Low-Level Cloudiness near Sunrise

2007 ◽  
Vol 24 (10) ◽  
pp. 1800-1810 ◽  
Author(s):  
Anthony J. Schreiner ◽  
Steven A. Ackerman ◽  
Bryan A. Baum ◽  
Andrew K. Heidinger
Keyword(s):  
Zenith Angle ◽  
Solar Zenith Angle ◽  
Infrared Range ◽  
Difference Threshold ◽  
Low Clouds ◽  
Time Period ◽  
Dead Vegetation ◽  
The Difference ◽  
Simple Difference ◽  
Visible Reflectance

Abstract A technique using the Geostationary Operational Environmental Satellite (GOES) sounder radiance data has been developed to improve detection of low clouds and fog just after sunrise. The technique is based on a simple difference method using the shortwave (3.7 μm) and longwave (11.0 μm) window bands in the infrared range of the spectrum. The time period just after sunrise is noted for the difficulty in being able to correctly identify low clouds and fog over land. For the GOES sounder cloud product this difficulty is a result of the visible reflectance of the low clouds falling below the “cloud” threshold over land. By requiring the difference between the 3.7- and the 11.0-μm bands to be greater than 5.0 K, successful discrimination of low clouds and fog is found 85% of the time for 21 cases from 14 September 2005 to 6 March 2006 over the GOES-12 sounder domain. For these 21 clear and cloudy cases the solar zenith angle ranged from 87° to 77°; however, the range of solar zenith angles for cloudy cases was from 85° to 77°. The success rate further improved to 95% (20 out of 21 cases) by including a difference threshold of 5.0 K between the 3.7- and 4.0-μm bands, requiring that the 11.0-μm band be greater than 260 K, and limiting the test to fields of view where the surface elevation is below 999 m. These final three limitations were needed to more successfully deal with cases involving snow cover and dead vegetation. To ensure that only the time period immediately after sunrise is included the solar zenith angle threshold for application of these tests is between 89° and 70°.

Observing and Modelling the Surface Radiative Budget and Cloud Radiative Forcing at the Cabauw Experimental Site for Atmospheric Research (CESAR), the Netherlands, 2009–17

2019 ◽  
Vol 32 (21) ◽  
pp. 7209-7225
Author(s):  
Reinout Boers ◽  
Fred Bosveld ◽  
Henk Klein Baltink ◽  
Wouter Knap ◽  
Erik van Meijgaard ◽  
...  
Keyword(s):  
The Netherlands ◽  
Zenith Angle ◽  
Radiative Forcing ◽  
Solar Zenith Angle ◽  
Cold Bias ◽  
Experimental Site ◽  
Radiative Forcings ◽  
Atmospheric Research ◽  
Low Clouds ◽  
The Difference

Abstract A dataset of 9 years in duration (2009–17) of clouds and radiation was obtained at the Cabauw Experimental Site for Atmospheric Research (CESAR) in the Netherlands. Cloud radiative forcings (CRF) were derived from the dataset and related to cloud cover and temperature. Also, the data were compared with RCM output. Results indicate that there is a seasonal cycle (i.e., winter, spring, summer, and autumn) in longwave (CRF-LW: 48.3, 34.4, 30.8, and 38.7 W m−2) and shortwave (CRF-SW: −23.6, −60.9, −67.8, and −32.9 W m−2) forcings at CESAR. Total CRF is positive in winter and negative in summer. The RCM has a cold bias with respect to the observations, but the model CRF-LW corresponds well to the observed CRF-LW as a result of compensating errors in the difference function that makes up the CRF-LW. The absolute value of model CRF-SW is smaller than the observed CRF-SW in summer, mostly because of albedo differences. The majority of clouds from above 2 km are present at the same time as low clouds, so the higher clouds have only a small impact on CRF whereas low clouds dominate their values. CRF-LW is a function of fractional cloudiness. CRF-SW is also a function of fractional cloudiness, if the values are normalized by the cosine of solar zenith angle. Expressions for CRF-LW and CRF-SW were derived as functions of temperature, fractional cloudiness, and solar zenith angle, indicating that CRF is the largest when fractional cloudiness is the highest but is also large for low temperature and high sun angle.

scholarly journals Comparison of quiet-time ionospheric total electron content from the IRI-2016 model and from gridded and station-level GPS observations

2020 ◽  
Vol 38 (3) ◽  
pp. 725-748
Author(s):  
Gizaw Mengistu Tsidu ◽  
Mulugeta Melaku Zegeye
Keyword(s):  
Total Electron Content ◽  
Local Time ◽  
Zenith Angle ◽  
Solar Zenith Angle ◽  
Mean Absolute Error ◽  
Model Performance ◽  
Absolute Error ◽  
Electron Content ◽  
Total Electron ◽  
The Difference

Abstract. Earth's ionosphere is an important medium of radio wave propagation in modern times. However, the effective use of the ionosphere depends on the understanding of its spatiotemporal variability. Towards this end, a number of ground- and space-based monitoring facilities have been set up over the years. The information from these stations has also been complemented by model-based studies. However, assessment of the performance of ionospheric models in capturing observations needs to be conducted. In this work, the performance of the IRI-2016 model in simulating the total electron content (TEC) observed by a network of Global Positioning System (GPS) receivers is evaluated based on the RMSE, the bias, the mean absolute error (MAE) and skill score, the normalized mean bias factor (NMBF), the normalized mean absolute error factor (NMAEF), the correlation, and categorical metrics such as the quantile probability of detection (QPOD), the quantile categorical miss (QCM), and the quantile critical success index (QCSI). The IRI-2016 model simulations are evaluated against gridded International Global Navigation Satellite System (GNSS) Service (IGS) GPS-TEC and TEC observations at a network of GPS receiver stations during the solar minima in 2008 and solar maxima in 2013. The phases of modeled and simulated TEC time series agree strongly over most of the globe, as indicated by a high correlations during all solar activities with the exception of the polar regions. In addition, lower RMSE, MAE, and bias values are observed between the modeled and measured TEC values during the solar minima than during the solar maxima from both sets of observations. The model performance is also found to vary with season, longitude, solar zenith angle, and magnetic local time. These variations in the model skill arise from differences between seasons with respect to solar irradiance, the direction of neutral meridional winds, neutral composition, and the longitudinal dependence of tidally induced wave number four structures. Moreover, the variation in model performance as a function of solar zenith angle and magnetic local time might be linked to the accuracy of the ionospheric parameters used to characterize both the bottom- and topside ionospheres. However, when the NMBF and NMAEF are applied to the data sets from the two distinct solar activity periods, the difference in the skill of the model during the two periods decreases, suggesting that the traditional model evaluation metrics exaggerate the difference in model skill. Moreover, the performance of the model in capturing the highest ends of extreme values over the geomagnetic equator, midlatitudes, and high latitudes is poor, as noted from the decrease in the QPOD and QCSI as well as an increase in the QCM over most of the globe with an increase in the threshold percentile TEC values from 10 % to 90 % during both the solar minimum and the solar maximum periods. The performance of IRI-2016 in simulating observed low (as low as the 10th percentile) and high (higher than the 90th percentile) TEC correctly over equatorial ionization anomaly (EIA) crest regions is reasonably good given that IRI-2016 is a climatological model. However, it is worth noting that the performance of the IRI-2016 model is relatively poor in 2013 compared with 2008 at the highest ends of the TEC distribution. Therefore, this study reveals the strengths and weaknesses of the IRI-2016 model in simulating the observed TEC distribution correctly during all seasons and solar activities for the first time.

scholarly journals Using self organising maps to explore ozone profile validation results – SCIAMACHY limb compared to ground-based lidar observations

2014 ◽  
Vol 7 (4) ◽  
pp. 4373-4406
Author(s):  
J. A. E. van Gijsel ◽  
R. Zurita-Milla ◽  
P. Stammes ◽  
S. Godin-Beekmann ◽  
T. Leblanc ◽  
...  
Keyword(s):  
Zenith Angle ◽  
Solar Zenith Angle ◽  
A Priori ◽  
Relative Difference ◽  
Full Time ◽  
Self Organizing Map ◽  
Ozone Profile ◽  
The Difference ◽  
Ground Based Lidar ◽  
Lidar Observations

Abstract. Traditional validation of atmospheric profiles is based on the intercomparison of two or more datasets in predefined ranges or classes of a given observational characteristic such as latitude or solar zenith angle. In this study we train a self organizing map (SOM) with a full time series of relative difference profiles of SCIAMACHY limb v5.02 and lidar ozone profiles from seven observation sites. Each individual observation characteristic is then mapped to the obtained SOM to investigate to which degree variation in this characteristic is explanatory for the variation seen in the SOM map. For the studied datasets, altitude-dependent relations for the global dataset were found between the difference profiles and studied variables. From the lowest altitude studied (18 km) ascending, the most influencing factors were found to be longitude, followed by solar zenith angle and latitude, sensor age and again solar zenith angle together with the day of the year at the highest altitudes studied here (up to 45 km). Clustering into three classes showed that there are also some local dependencies, with for instance one cluster having a much stronger correlation with the sensor age (days since launch) between 36 and 42 km. It was shown that the proposed approach provides a powerful tool for the exploring of differences between datasets without being limited to a-priori defined data subsets.

scholarly journals Using self-organising maps to explore ozone profile validation results – SCIAMACHY limb compared to ground-based lidar observations

2015 ◽  
Vol 8 (5) ◽  
pp. 1951-1963
Author(s):  
J. A. E. van Gijsel ◽  
R. Zurita-Milla ◽  
P. Stammes ◽  
S. Godin-Beekmann ◽  
T. Leblanc ◽  
...  
Keyword(s):  
Zenith Angle ◽  
Solar Zenith Angle ◽  
A Priori ◽  
Relative Difference ◽  
Data Sets ◽  
Full Time ◽  
Data Set ◽  
Partial Correlations ◽  
The Difference ◽  
Ground Based Lidar

Abstract. Traditional validation of atmospheric profiles is based on the intercomparison of two or more data sets in predefined ranges or classes of a given observational characteristic such as latitude or solar zenith angle. In this study we trained a self-organising map (SOM) with a full time series of relative difference profiles of SCIAMACHY limb v5.02 and lidar ozone profiles from seven observation sites. Each individual observation characteristic was then mapped to the obtained SOM to investigate to which degree variation in this characteristic is explanatory for the variation seen in the SOM map. For the studied data sets, altitude-dependent relations for the global data set were found between the difference profiles and studied variables. From the lowest altitude studied (18 km) ascending, the most influencing factors were found to be longitude, followed by solar zenith angle and latitude, sensor age and again solar zenith angle together with the day of the year at the highest altitudes studied here (up to 45 km). After accounting for both latitude and longitude, residual partial correlations with a reduced magnitude are seen for various factors. However, (partial) correlations cannot point out which (combination) of the factors drives the observed differences between the ground-based and satellite ozone profiles as most of the factors are inter-related. Clustering into three classes showed that there are also some local dependencies, with for instance one cluster having a much stronger correlation with the sensor age (days since launch) between 36 and 42 km. The proposed SOM-based approach provides a powerful tool for the exploration of differences between data sets without being limited to a priori defined data subsets.

Effect of solar zenith angle on satellite cloud retrievals based on O2–O2 absorption band

2021 ◽  
Vol 42 (11) ◽  
pp. 4224-4240
Author(s):  
Gyuyeon Kim ◽  
Yong-Sang Choi ◽  
Sang Seo Park ◽  
Jhoon Kim
Keyword(s):  
Absorption Band ◽  
Zenith Angle ◽  
Solar Zenith Angle

Sun-induced production of vitamin D3 throughout 1 year in tropical and subtropical regions: relationship with latitude, cloudiness, UV-B exposure and solar zenith angle

2021 ◽  
Vol 20 (2) ◽  
pp. 265-274
Author(s):  
Angela C. G. B. Leal ◽  
Marcelo P. Corrêa ◽  
Michael F. Holick ◽  
Enaldo V. Melo ◽  
Marise Lazaretti-Castro
Keyword(s):  
Zenith Angle ◽  
Vitamin D3 ◽  
Solar Zenith Angle

scholarly journals Large numbers of vertebrates began rapid population decline in the late 19th century

2016 ◽  
Vol 113 (49) ◽  
pp. 14079-14084 ◽  
Author(s):  
Haipeng Li ◽  
Jinggong Xiang-Yu ◽  
Guangyi Dai ◽  
Zhili Gu ◽  
Chen Ming ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Population Size ◽  
Threatened Species ◽  
Driving Forces ◽  
Population Decline ◽  
Cumulative Effect ◽  
Extinction Time ◽  
Time Period ◽  
Large Numbers ◽  
The Difference

Accelerated losses of biodiversity are a hallmark of the current era. Large declines of population size have been widely observed and currently 22,176 species are threatened by extinction. The time at which a threatened species began rapid population decline (RPD) and the rate of RPD provide important clues about the driving forces of population decline and anticipated extinction time. However, these parameters remain unknown for the vast majority of threatened species. Here we analyzed the genetic diversity data of nuclear and mitochondrial loci of 2,764 vertebrate species and found that the mean genetic diversity is lower in threatened species than in related nonthreatened species. Our coalescence-based modeling suggests that in many threatened species the RPD began ∼123 y ago (a 95% confidence interval of 20–260 y). This estimated date coincides with widespread industrialization and a profound change in global living ecosystems over the past two centuries. On average the population size declined by ∼25% every 10 y in a threatened species, and the population size was reduced to ∼5% of its ancestral size. Moreover, the ancestral size of threatened species was, on average, ∼22% smaller than that of nonthreatened species. Because the time period of RPD is short, the cumulative effect of RPD on genetic diversity is still not strong, so that the smaller ancestral size of threatened species may be the major cause of their reduced genetic diversity; RPD explains 24.1–37.5% of the difference in genetic diversity between threatened and nonthreatened species.

scholarly journals Effects of Diurnal Variations on Tropical Equilibrium States: A Two-Dimensional Cloud-Resolving Modeling Study

2007 ◽  
Vol 64 (2) ◽  
pp. 656-664 ◽  
Author(s):  
Shouting Gao ◽  
Yushu Zhou ◽  
Xiaofan Li
Keyword(s):  
Equilibrium State ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Zenith Angle ◽  
Solar Zenith Angle ◽  
Diurnal Variations ◽  
Equilibrium States ◽  
Sea Surface ◽  
Two Dimensional ◽  
Time Invariant

Abstract Effects of diurnal variations on tropical heat and water vapor equilibrium states are investigated based on hourly data from two-dimensional cloud-resolving simulations. The model is integrated for 40 days and the simulations reach equilibrium states in all experiments. The simulation with a time-invariant solar zenith angle produces a colder and drier equilibrium state than does the simulation with a diurnally varied solar zenith angle. The simulation with a diurnally varied sea surface temperature generates a colder equilibrium state than does the simulation with a time-invariant sea surface temperature. Mass-weighted mean temperature and precipitable water budgets are analyzed to explain the thermodynamic differences. The simulation with the time-invariant solar zenith angle produces less solar heating, more condensation, and consumes more moisture than the simulation with the diurnally varied solar zenith angle. The simulation with the diurnally varied sea surface temperature produces a colder temperature through less latent heating and more IR cooling than the simulation with the time-invariant sea surface temperature.

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