scholarly journals Cloud fraction determined by thermal infrared and visible all-sky cameras

2018 ◽  
Author(s):  
Christine Aebi ◽  
Julian Gröbner ◽  
Niklaus Kämpfer
Keyword(s):  
Cloud Amount ◽  
Visible Spectrum ◽  
Thermal Infrared ◽  
Detection Algorithm ◽  
Cloud Fraction ◽  
High Temporal Resolution ◽  
Cloud Detection ◽  
Fractional Cloud ◽  
Significant Difference ◽  
High Level

Abstract. The thermal infrared cloud camera (IRCCAM) is a prototype instrument that determines cloud fraction continuously during day and nighttime with high temporal resolution. It has been developed and tested at Physikalisch-Meteorologisches Observatorium Davos/World Radiation Center (PMOD/WRC) in Davos, Switzerland. The IRCCAM consists of a commercial microbolometer camera sensitive in the 8 μm–14 μm wavelength range. Over a time period of two years, the fractional cloud coverage obtained by the IRCCAM is compared with two other commercial cameras sensitive in the visible spectrum (Mobotix Q24M and Schreder VIS-J1006) as well as with the automated partial cloud amount detection algorithm (APCADA) using pyrgeometer data. In comparison to the visible cloud detection algorithms, the IRCCAM shows median difference values of 0.01 to 0.07 cloud fraction wherein around 90 % of the data are within ±0.25 (±2 oktas) cloud fraction. Thus there is no significant difference in the cloud fraction determination of the IRCCAM in comparison to the other study instruments. Analysis indicates no significant difference in the performance of the IRCCAM during day or nighttime and also not in different seasons. The cloud types where all algorithms are in closest agreement are low-level clouds (with median differences in cloud fraction of −0.01 to 0.02), followed by mid-level (0.00) and high-level clouds (−0.13).

scholarly journals Cloud fraction determined by thermal infrared and visible all-sky cameras

2018 ◽  
Vol 11 (10) ◽  
pp. 5549-5563 ◽  
Author(s):  
Christine Aebi ◽  
Julian Gröbner ◽  
Niklaus Kämpfer
Keyword(s):  
Meteorological Data ◽  
Cloud Amount ◽  
Thermal Infrared ◽  
Detection Algorithm ◽  
Cloud Fraction ◽  
Time Data ◽  
Night Time ◽  
Data Set ◽  
Fractional Cloud ◽  
Sky Radiance

Abstract. The thermal infrared cloud camera (IRCCAM) is a prototype instrument that determines cloud fraction continuously during daytime and night-time using measurements of the absolute thermal sky radiance distributions in the 8–14 µm wavelength range in conjunction with clear-sky radiative transfer modelling. Over a time period of 2 years, the fractional cloud coverage obtained by the IRCCAM is compared with two commercial cameras (Mobotix Q24M and Schreder VIS-J1006) sensitive in the visible spectrum, as well as with the automated partial cloud amount detection algorithm (APCADA) using pyrgeometer data. Over the 2-year period, the cloud fractions determined by the IRCCAM and the visible all-sky cameras are consistent to within 2 oktas (0.25 cloud fraction) for 90 % of the data set during the day, while for day- and night-time data the comparison with the APCADA algorithm yields an agreement of 80 %. These results are independent of cloud types with the exception of thin cirrus clouds, which are not detected as consistently by the current cloud algorithm of the IRCCAM. The measured absolute sky radiance distributions also provide the potential for future applications by being combined with ancillary meteorological data from radiosondes and ceilometers.

scholarly journals The MOPITT Version 9 CO Product: Sampling Enhancements and Validation

2021 ◽  
Author(s):  
Merritt Deeter ◽  
Gene Francis ◽  
John Gille ◽  
Debbie Mao ◽  
Sara Martínez-Alonso ◽  
...  
Keyword(s):  
Carbon Monoxide ◽  
Near Infrared ◽  
Thermal Infrared ◽  
Detection Algorithm ◽  
Temporal Consistency ◽  
Cloud Detection ◽  
Geographical Variability ◽  
Satellite Retrieval ◽  
Source Regions

Abstract. Characteristics of the Version 9 (V9) MOPITT ("Measurements of Pollution in the Troposphere") satellite retrieval product for tropospheric carbon monoxide (CO) are described. The new V9 product includes many CO retrievals over land which, in previous MOPITT product versions, would have been discarded by the cloud detection algorithm. Globally, the number of daytime MOPITT retrievals over land has increased by 30–40 % relative to the Version 8 product, although the increase in retrieval coverage exhibits significant geographical variability. Areas benefiting from the improved cloud detection performance include (but are not limited to) source regions often characterized by high aerosol concentrations. The V9 MOPITT product also incorporates a modified calibration strategy for the MOPITT near-infrared (NIR) CO channels, resulting in greater temporal consistency for the NIR-only and thermal infrared-near infrared (TIR-NIR) retrieval variants. Validation results based on in-situ CO profiles acquired from aircraft in a variety of contexts indicate that retrieval biases for V9 are typically within the range of ±5 % and are generally comparable to results for the V8 product.

scholarly journals Calibration of global MODIS cloud amount using CALIOP cloud profiles

2020 ◽  
Author(s):  
Andrzej Z. Kotarba
Keyword(s):  
Cloud Amount ◽  
Satellite Observation ◽  
Cloud Fraction ◽  
Orthogonal Polarization ◽  
Polar Regions ◽  
Cloud Detection ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Cloud Mask

Abstract. The Moderate Resolution Imaging Spectroradiometer (MODIS) cloud detection procedure classifies instantaneous fields of view (IFOV) as either confident cloudy, probably cloudy, probably clear, or confident clear. The cloud amount calculation requires quantitative cloud fractions to be assigned to these classes. The operational procedure used by NASA assumes that confident clear and probably clear IFOV are cloud-free (cloud fraction 0 %), while the remaining categories are completely filled with clouds (cloud fraction 100 %). This study demonstrates that this best guess approach is unreliable, especially on a regional/ local scale. We use data from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument flown on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission, collocated with MODIS/ Aqua IFOV. Based on 33,793,648 paired observations acquired in January and July 2015, we conclude that actual cloud fractions to be associated with MODIS cloud mask categories are 21.5 %, 27.7 %, 66.6 %, and 94.7 %. Spatial variability is significant, even within a single MODIS algorithm path, and the operational approach introduces uncertainties of up to 30 % of cloud amount, notably in the polar regions at night, and in selected locations over the northern hemisphere. Applications of MODIS data at ~10 degrees resolution (or finer) should first assess the extent of the error. Uncertainties were related to the efficiency of the cloud masking algorithm. Until the algorithm can be significantly modified, our method is a robust way to calibrate (correct) MODIS estimates. It can be also used for MODIS/ Terra data, and other missions where the footprint is collocated with CALIPSO.

scholarly journals Adjusting ISCCP cloud detection to increase consistency of cloud amount and reduce artifacts

2020 ◽  
Author(s):  
Kenneth R. Knapp ◽  
Alisa H. Young ◽  
Hilawe Semunegus ◽  
Anand K. Inamdar ◽  
William Hankins
Keyword(s):  
Time Series ◽  
Spatial Data ◽  
Cloud Amount ◽  
Detection Algorithm ◽  
Cloud Detection ◽  
Secular Changes ◽  
View Zenith Angle ◽  
Simple Change ◽  
Radiative Characteristics

AbstractThe International Satellite Cloud Climatology Project (ISCCP) began collecting data in the 1980s to help understand the distribution of clouds. Since then, it has provided important information on clouds in time and space and their radiative characteristics. However, it was apparent from some long term time series of the data that there are some latent artifacts related to the changing satellite coverage over the more than thirty years of the record. Changes in satellite coverage effectively create secular changes in the time series of view zenith angle (VZA) for a given location. There is an inconsistency in the current ISCCP cloud detection algorithm related to VZA: two satellites viewing the same location from different VZAs can produce vastly different estimates of cloud amount. Research is presented which shows that a simple change to the cloud detection algorithm can vastly increase the consistency. This is accomplished by making the cloud/no cloud threshold VZA dependent. The resulting cloud amounts are more consistent between different satellites and the distributions are shown to be more spatially homogenous. Likewise, the more consistent spatial data leads to more consistent temporal statistics.

scholarly journals Calibration of global MODIS cloud amount using CALIOP cloud profiles

2020 ◽  
Vol 13 (9) ◽  
pp. 4995-5012
Author(s):  
Andrzej Z. Kotarba
Keyword(s):  
Cloud Amount ◽  
Satellite Observation ◽  
The United States ◽  
Local Scale ◽  
Cloud Fraction ◽  
Orthogonal Polarization ◽  
Polar Regions ◽  
Cloud Detection ◽  
North West ◽  
Modis Data

Abstract. The Moderate Resolution Imaging Spectroradiometer (MODIS) cloud detection procedure classifies instantaneous fields of view (IFOVs) as either “confident clear”, “probably clear”, “probably cloudy”, or “confident cloudy”. The cloud amount calculation requires quantitative cloud fractions to be assigned to these classes. The operational procedure used by the MODIS Science Team assumes that confident clear and probably clear IFOVs are cloud-free (cloud fraction 0 %), while the remaining categories are completely filled with clouds (cloud fraction 100 %). This study demonstrates that this “best-guess” approach is unreliable, especially on a regional/local scale. We use data from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument flown on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission, collocated with Aqua MODIS IFOV. Based on 33 793 648 paired observations acquired in January and July 2015, we conclude that actual cloud fractions to be associated with MODIS cloud mask categories are 21.5 %, 27.7 %, 66.6 %, and 94.7 %. Spatial variability is significant, even within a single MODIS algorithm path, and the operational approach introduces uncertainties of up to 30 % of cloud amount, notably in polar regions at night, and in selected locations over the Northern Hemisphere (e.g. China, the north-west coast of Africa, and eastern parts of the United States). Consequently, applications of MODIS data on a regional/local scale should first assess the extent of the uncertainty. We suggest using CALIPSO-based cloud fractions to improve MODIS cloud amount estimates. This approach can also be used for Terra MODIS data, and other passive cloud imagers, where the footprint is collocated with CALIPSO.

A Quality Control Procedure for FY-3A MWTS Measurements with Emphasis on Cloud Detection Using VIRR Cloud Fraction

2013 ◽  
Vol 30 (8) ◽  
pp. 1704-1715 ◽  
Author(s):  
Juan Li ◽  
Xiaolei Zou
Keyword(s):  
Quality Control ◽  
Detection Algorithm ◽  
Cloud Fraction ◽  
Control Procedure ◽  
Test Results ◽  
Cloud Detection ◽  
Liquid Water Path ◽  
Model Simulations ◽  
Cloud Liquid Water Path ◽  
Environmental Prediction

Abstract A quality control (QC) procedure for satellite radiance assimilation is proposed and applied to radiance observations from the Microwave Temperature Sounder (MWTS) on board the first satellite of the Chinese polar-orbiting Fengyun-3 series (FY-3A). A cloud detection algorithm is incorporated based on the cloud fraction product provided by the Visible and Infrared Radiometer (VIRR) on board FY-3A. Analysis of the test results conducted in July 2011 indicates that most clouds are identifiable by applying an FY-3A VIRR cloud fraction threshold of 37%. This result is verified with the cloud liquid water path data from the Meteorological Operational Satellite A (MetOp-A). On average, 56.1% of the global MWTS data are identified as cloudy by the VIRR-based cloud detection method. Other QC steps include the following: (i) two outmost field of views (FOVs), (ii) use of channel 3 if the terrain altitude is greater than 500 m, (iii) channel 2 over sea ice and land, (iv) coastal FOVs, and (v) outliers with large differences between model simulations and observations. About 82%, 74%, and 29% of the MWTS observations are removed by the proposed QC for channels 2–4, respectively. An approximate 0.5-K scan bias improvement is achieved with QC, with a large impact at edges of the field of regard for channels 2–4. After QC, FY-3A MWTS global data more closely resemble the National Centers for Environmental Prediction (NCEP) forecast data, the global biases and standard deviations are reduced significantly, and the frequency distribution of the differences between observations and model simulations become more Gaussian.

scholarly journals Cloud climatologies from the InfraRed Sounders AIRS and IASI: Strengths, Weaknesses and Applications

2017 ◽  
Author(s):  
Claudia J. Stubenrauch ◽  
Artem G. Feofilov ◽  
Sofia E. Protopapadaki ◽  
Raymond Armante
Keyword(s):  
Cloud Amount ◽  
Co2 Concentration ◽  
Boreal Summer ◽  
Cloud Base ◽  
Cloud Detection ◽  
Ancillary Data ◽  
Total Cloud Amount ◽  
Low Level ◽  
Cloud Properties ◽  
High Level

Abstract. The cloud retrieval scheme developed at the Laboratoire de Météorologie Dynamique (LMD) can now be easily adapted to any Infrared (IR) sounder: the CIRS (Clouds from IR Sounders) retrieval applies improved radiative transfer, as well as an original method accounting for atmospheric spectral transmissivity changes associated with CO2 concentration. The latter is essential when considering long-term time series of cloud properties. For the 13-year and 8-year global climatologies of cloud properties from observations of the Atmospheric IR Sounder (AIRS) and of the IR Atmospheric Interferometer (IASI), respectively, we used the latest ancillary data (atmospheric profiles, surface emissivities and atmospheric spectral transmissivities). The A-Train active instruments, lidar and radar of the CALIPSO and CloudSat missions, provide a unique opportunity to evaluate the retrieved AIRS cloud properties such as cloud amount and height as well as to explore the vertical structure of different cloud types. CIRS cloud detection agreement with CALIPSO-CloudSat is about 84%–85% over ocean, 79%–82% over land and 70%–73% over ice / snow, depending on atmospheric ancillary data. Global cloud amount has been estimated to 67%–70%. CIRS cloud height coincides with the middle between the cloud top and the apparent cloud base (real base for optically thin clouds or height at which the cloud reaches opacity) independent of cloud emissivity, which is about 1 km below cloud top for low-level clouds and about 1.5 km to 2.5 km below cloud top for high-level clouds, slightly increasing because the apparent vertical cloud extent is slightly larger for large cloud emissivity. IR sounders are in particular advantageous for the retrieval of upper tropospheric cloud properties, with a reliable cirrus identification down to an IR optical depth of 0.1, day and night. Total cloud amount consists of about 40% high-level clouds and about 40% low-level clouds and 20% mid-level clouds, the latter two only detected when not hidden by upper clouds. Upper tropospheric clouds are most abundant in the tropics, where high opaque clouds make out 7.5%, thick cirrus 27.5% and thin cirrus about 21.5% of all clouds. The asymmetry in upper tropospheric cloud amount between Northern and Southern hemisphere with annual mean of 5% has a pronounced seasonal cycle with a maximum of 25% in boreal summer, which can be linked to the shift of the ITCZ peak latitude. Comparing tropical geographical change patterns of high opaque clouds with that of thin cirrus as a function of changing tropical mean surface temperature indicates that their response to climate change may be quite different, with potential consequences on the atmospheric circulation.

scholarly journals Near-Ultraviolet to Near-Infrared Band Thresholds Cloud Detection Algorithm for TANSAT-CAPI

2021 ◽  
Vol 13 (10) ◽  
pp. 1906
Author(s):  
Ning Ding ◽  
Jianbing Shao ◽  
Changxiang Yan ◽  
Junqiang Zhang ◽  
Yanfeng Qiao ◽  
...  
Keyword(s):  
Near Infrared ◽  
Lightweight Design ◽  
Thermal Infrared ◽  
Detection Algorithm ◽  
Main Function ◽  
Cloud Detection ◽  
Infrared Band ◽  
Band Ratio ◽  
Near Ultraviolet ◽  
Imaging Detector

Cloud and aerosol polarization imaging detector (CAPI) is one of the important payloads on the China Carbon Dioxide Observation Satellite (TANSAT), which can realize multispectral polarization detection and accurate on-orbit calibration. The main function of the instrument is to identify the interference of clouds and aerosols in the atmospheric detection path and to improve the retrieval accuracy of greenhouse gases. Therefore, it is of great significance to accurately identify the clouds in remote sensing images. However, in order to meet the requirement of lightweight design, CAPI is only equipped with channels in the near-ultraviolet to near-infrared bands. It is difficult to achieve effective cloud recognition using traditional visible light to thermal infrared band spectral threshold cloud detection algorithms. In order to solve the above problem, this paper innovatively proposes a cloud detection method based on different threshold tests from near ultraviolet to near infrared (NNDT). This algorithm first introduces the 0.38 μm band and the ratio of 0.38 μm band to 1.64 μm band, to realize the separation of cloud pixels and clear sky pixels, which can take advantage of the obvious difference in radiation characteristics between clouds and ground objects in the near-ultraviolet band and the advantages of the band ratio in identifying clouds on the snow surface. The experimental results show that the cloud recognition hit rate (PODcloud) reaches 0.94 (ocean), 0.98 (vegetation), 0.99 (desert), and 0.86 (polar), which therefore achieve the application standard of CAPI data cloud detection The research shows that the NNDT algorithm replaces the demand for thermal infrared bands for cloud detection, gets rid of the dependence on the minimum surface reflectance database that is embodied in traditional cloud recognition algorithms, and lays the foundation for aerosol and CO2 parameter inversion.

Effectiveness of Brushing Associated with Oral Irrigation in Maintenance of Peri-Implant Tissues and Overdentures: Clinical Parameters and Patient Satisfaction.

2020 ◽  
Author(s):  
Marcela Moreira Salles ◽  
Viviane de Cássia Oliveira ◽  
Ana Paula Macedo ◽  
Claudia Helena Silva-Lovato ◽  
Helena de Freitas Oliveira Paranhos
Keyword(s):  
Patient Satisfaction ◽  
Analogue Scale ◽  
Ring System ◽  
Complete Dentures ◽  
Gingival Index ◽  
Prosthetic Rehabilitation ◽  
Mean Values ◽  
Probing Depth ◽  
Significant Difference ◽  
High Level

Implant-supported prostheses hygiene and peri-implant tissues health are considered to be predictive factors for successful prosthetic rehabilitation. Therefore, the purpose of this study was to evaluate the effectiveness of brushing associated with oral irrigation measured as biofilm-removing capacity, maintenance of healthy oral tissues, and patient satisfaction. A randomized, crossover clinical trial was conducted with 38 patients who wore conventional maxillary complete dentures and mandibular overdentures retained by the O-ring system. The patients were instructed to use the following hygiene methods for 14 days: mechanical brushing [MB (brush and dentifrice - Control)]; and MB with oral irrigation [WP (Waterpik - Experimental)]. Biofilm-removing capacity and maintenance of healthy oral tissues were evaluated by the Modified Plaque Index (PI), Gingival Index (GI), Probing Depth (PD), and Bleeding on Probing Index (BP) recorded at baseline and after each method. In parallel, patients answered a specific questionnaire using a Visual Analogue Scale after each hygiene method. Data were analyzed by Friedman and Wilcoxon tests (α=0.05). The results showed significantly lower PI, GI, PD, and BP indices after application of the hygiene methods (P<0.001) than those observed at baseline. However, no significant difference was found between MB and WP. The satisfaction questionnaire responses to both methods showed high mean values for all questions, with no statistically significant difference found between the answers given after the use of MB and WP (P>0.05). The findings suggest that WP was effective in reducing PI, GI, PD, and BP indices and provided a high level of patient satisfaction.

REALIZATION OF PARENTAL INDICES OF COW PRODUCTIVITY IN DIFFERENT MILKING BREEDS AT BREEDING FARM "AGROFIRMA "PAKHMA"

2020 ◽  
Keyword(s):  
Milk Yield ◽  
Cattle Breed ◽  
Holstein Cattle ◽  
Milk Productivity ◽  
Cattle Breeds ◽  
Significant Difference ◽  
Production Indices ◽  
High Level ◽  
Beta Lactoglobulin ◽  
Percentage Protein

Combination of genotypes of main types’ genetic markers causes milk productivity in cattle. The article studies correlation between kappa casein genotypes (C3N3), beta-lactoglobulin (LGB), their complexes and milk production indices in cows bred under equal conditions at a breeding farm. The calculation of parental milk yield and fat indices between the three breeds showed a significant difference (P>0,999) of PCI of Ayrshire and Holstein cattle breed milk yield - 3395 kg, PCI of fat - 0,22%; between Holstein and Yaroslavl cattle breeds - 3200 kg, PCI offat - 0,16%. A significant difference of realization indices of protein between all three breeds was not stated. The highest realization ofparental milk yield index was stated - between Ayrshire and Yaroslavl cattle breeds- 113%, Holstein cattle breed - 88%. As for the fat and protein indices, all three cattle breeds showed a high realization of PCI from 98% to 109%. However, Holstein cattle breed showed the highest percentage: protein - 101%, fat - 109%. A significant difference of PCI realization of milk productivity in CSN3 and LGB genotypes was not stated. It was also stated that there is a tendency of high level parental indices realization when there is a higher number of B-allele variants in genotypes of cows.

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