scholarly journals Discriminating between clouds and aerosols in the CALIOP version 4.1 data products

2019 ◽  
Vol 12 (1) ◽  
pp. 703-734 ◽  
Author(s):  
Zhaoyan Liu ◽  
Jayanta Kar ◽  
Shan Zeng ◽  
Jason Tackett ◽  
Mark Vaughan ◽  
...  
Keyword(s):  
Training Dataset ◽  
Single Shot ◽  
Volcanic Aerosol ◽  
Data Products ◽  
Aerosol Classification ◽  
Aerosol Fraction ◽  
Level 1 ◽  
532 Nm ◽  
Level 2 ◽  
Calibration Coefficients

Abstract. The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Operations (CALIPSO) mission released version 4.1 (V4) of their lidar level 2 cloud and aerosol data products in November 2016. These new products were derived from the CALIPSO V4 lidar level 1 data, in which the calibration of the measured backscatter data at both 532 and 1064 nm was significantly improved. This paper describes updates to the V4 level 2 cloud–aerosol discrimination (CAD) algorithm that more accurately differentiate between clouds and aerosols throughout the Earth's atmosphere. The level 2 data products are improved with new CAD probability density functions (PDFs) that were developed to accommodate extensive calibration changes in the level 1 data. To enable more reliable identification of aerosol layers lofted into the upper troposphere and lower stratosphere, the CAD training dataset used in the earlier data releases was expanded to include stratospheric layers and representative examples of volcanic aerosol layers. The generic “stratospheric layer” classification reported in previous versions has been eliminated in V4, and cloud–aerosol classification is now performed on all layers detected everywhere from the surface to 30 km. Cloud–aerosol classification has been further extended to layers detected at single-shot resolution, which were previously classified by default as clouds. In this paper, we describe the underlying rationale used in constructing the V4 PDFs and assess the performance of the V4 CAD algorithm in the troposphere and stratosphere. Previous misclassifications of lofted dust and smoke in the troposphere have been largely improved, and volcanic aerosol layers and aerosol layers in the stratosphere are now being properly classified. CAD performance for single-shot layer detections is also evaluated. Most of the single-shot layers classified as aerosol occur within the dust belt, as may be expected. Due to changes in the 532 nm calibration coefficients, the V4 feature finder detects ∼9.0 % more features at night and ∼2.5 % more during the day. These features are typically weakly scattering and classified about equally as clouds and aerosols. For those tropospheric layers detected in both V3 and V4, the CAD classifications of more than 95 % of all cloud and daytime aerosol layers remain unchanged, as do the classifications of ∼89 % of nighttime aerosol layers. Overall, the nighttime net cloud and aerosol fractions remain unchanged from V3 to V4, but the daytime net aerosol fraction is increased by about 2 % and the daytime net cloud fraction is decreased by about 2 %.

scholarly journals Discriminating Between Clouds and Aerosols in the CALIOP Version 4.1 Data Products

2018 ◽  
Author(s):  
Zhaoyan Liu ◽  
Jayanta Kar ◽  
Shan Zeng ◽  
Jason Tackett ◽  
Mark Vaughan ◽  
...  
Keyword(s):  
Training Dataset ◽  
Single Shot ◽  
Volcanic Aerosol ◽  
Data Products ◽  
Aerosol Classification ◽  
Aerosol Fraction ◽  
Level 1 ◽  
532 Nm ◽  
Level 2

Abstract. The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Operations (CALIPSO) mission released version 4.1 (V4) of their lidar level 2 cloud and aerosol data products in November 2016. These new products were derived from the CALIPSO V4 lidar level 1 data, in which the calibration of the measured backscatter data at both 532 nm and 1064 nm was significantly improved. This paper describes updates to the V4 level 2 cloud-aerosol-discrimination (CAD) algorithm that more accurately differentiates between clouds and aerosols throughout the Earth's atmosphere. The level 2 data products are improved with new CAD probability density functions (PDFs) that were developed to accommodate the calibration changes in the level 1 data. To enable more reliable identification of aerosol layers lofted into the upper troposphere and lower stratosphere, the CAD training dataset used in the earlier data releases was expanded to include stratospheric layers and representative examples of volcanic aerosol layers. The generic stratospheric layer classification of previous versions has been eliminated in V4, and cloud-aerosol classification is now performed on all layers detected everywhere from the surface to 30 km. Cloud-aerosol classification has been further extended to layers detected at single shot resolution, which were previously classified by default as clouds. In this paper, we describe the underlying rationale used in constructing the V4 PDFs and assess the performance of the V4 CAD algorithm in the troposphere and stratosphere. Previous misclassifications of lofted dust and smoke in the troposphere have been largely improved, and volcanic aerosol layers and aerosol layers in the stratosphere are now being properly classified. CAD performance for single-shot layer detections is also evaluated. Most of the single-shot layers classified as aerosol occur within the dust belt, as may be expected. Due to changes in the 532 nm calibration coefficients, the V4 feature finder detects ~9.0 % more features at night and ~2.5 % more during the day. These features are typically weakly scattering and classified about equally as clouds and aerosols. For those tropospheric layers detected in both V3 and V4, the CAD classifications of more than 95 % of all cloud and daytime aerosol layers remain unchanged, as do the classifications of ~89 % of nighttime aerosol layers. Overall, the nighttime net cloud and aerosol fractions remain unchanged from V3 to V4, but the daytime net aerosol fraction is increased by about 2 % and the daytime net cloud fraction is decreased by about 2 %.

scholarly journals Evaluation Analysis of Landsat Level-1 and Level-2 Data Products Using In Situ Measurements

2020 ◽  
Vol 12 (16) ◽  
pp. 2597
Author(s):  
Cibele Teixeira Pinto ◽  
Xin Jing ◽  
Larry Leigh
Keyword(s):  
In Situ Measurements ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Spectral Bands ◽  
Data Products ◽  
Landsat 7 ◽  
Level 1 ◽  
Level 2 ◽  
Reflectance Data

Landsat Level-1 products are delivered as quantized and calibrated scaled Digital Numbers (DN). The Level-1 DN data can be rescaled to Top-of-Atmosphere (TOA) reflectance applying radiometric rescaling coefficients. Currently, the Level-1 product is the standard data product of the Landsat sensors. The more recent Level-2 data products contain surface reflectance values, i.e., reflectance as it would be measured at ground level in the absence of atmospheric effects; in the near future, these products are anticipated to become the standard products of the Landsat sensors. The purpose of this paper is to present a radiometric performance evaluation of Level-1 and Level-2 data products for the Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) sensors. TOA reflectance and derived surface reflectance values from both data products were evaluated and compared to in situ measurements from eight test sites located in Turkey, Brazil, Chile, the United States, France, and Namibia. The results indicate an agreement between the ETM+ and OLI Level-1 TOA reflectance data and the in situ measurements of 3.9% to 6.5% and 3.9% to 6.0%, respectively, across all spectral bands. Agreement between the in situ measurements and both Level-2 surface reflectance data products were consistently decreased in the shorter wavelength bands, and slightly better in the longer wavelength bands. The mean percent absolute error for Level-2 surface reflectance data ranged from 3.3% to 10% for both Landsat sensors. The significant decay in agreement with the data collected in situ in the short wavelength spectral bands with Level-2 data suggests issues with retrieval of aerosol concentration at some sites. In contrast, the results indicate a reasonably accurate estimate of water vapor in the mid-infrared spectrum. Lastly, despite the less reliable performance of Level-2 data product in the visible spectral region (compared with Level-1 data) in both sensors, the Landsat-8 OLI Level-2 showed an improvement of surface reflectance product over all spectral bands in common with the Landsat-7 ETM+ Level-2 data.

scholarly journals The CALIPSO Automated Aerosol Classification and Lidar Ratio Selection Algorithm

2009 ◽  
Vol 26 (10) ◽  
pp. 1994-2014 ◽  
Author(s):  
Ali H. Omar ◽  
David M. Winker ◽  
Mark A. Vaughan ◽  
Yongxiang Hu ◽  
Charles R. Trepte ◽  
...  
Keyword(s):  
Identification Algorithm ◽  
Surface Type ◽  
Aerosol Classification ◽  
Lidar Ratio ◽  
One Year ◽  
Aerosol Types ◽  
532 Nm ◽  
Level 2 ◽  
Aerosol Type

Abstract Descriptions are provided of the aerosol classification algorithms and the extinction-to-backscatter ratio (lidar ratio) selection schemes for the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) aerosol products. One year of CALIPSO level 2 version 2 data are analyzed to assess the veracity of the CALIPSO aerosol-type identification algorithm and generate vertically resolved distributions of aerosol types and their respective optical characteristics. To assess the robustness of the algorithm, the interannual variability is analyzed by using a fixed season (June–August) and aerosol type (polluted dust) over two consecutive years (2006 and 2007). The CALIPSO models define six aerosol types: clean continental, clean marine, dust, polluted continental, polluted dust, and smoke, with 532-nm (1064 nm) extinction-to-backscatter ratios Sa of 35 (30), 20 (45), 40 (55), 70 (30), 65 (30), and 70 (40) sr, respectively. This paper presents the global distributions of the CALIPSO aerosol types, the complementary distributions of integrated attenuated backscatter, and the volume depolarization ratio for each type. The aerosol-type distributions are further partitioned according to surface type (land/ocean) and detection resolution (5, 20, and 80 km) for optical and spatial context, because the optically thick layers are found most often at the smallest spatial resolution. Except for clean marine and polluted continental, all the aerosol types are found preferentially at the 80-km resolution. Nearly 80% of the smoke cases and 60% of the polluted dust cases are found over water, whereas dust and polluted continental cases are found over both land and water at comparable frequencies. Because the CALIPSO observables do not sufficiently constrain the determination of the aerosol, the surface type is used to augment the selection criteria. Distributions of the total attenuated color ratios show that the use of surface type in the typing algorithm does not result in abrupt and artificial changes in aerosol type or extinction.

ANALISIS KEMAMPUAN KOMUNIKASI MATEMATIS DALAM MENYELESAIKAN MASALAH STATISTIKA

2018 ◽  
Vol 5 (2) ◽  
pp. 7 ◽  
Author(s):  
Ahmad Aunur Rohman
Keyword(s):  
Level 1 ◽  
Level 2

Penelitian ini dimaksudkan untuk mengetahui bagaimana kemampuan komunikasi matematis mahasiswa terhadap pemahaman statistika. Data dalam penelitian ini berupa hasil pekerjaan tes tertulis tentang kemampuan komunikasi matematis dan wawancara terhadap subjek penelitian. Pengumpulan data diperoleh dengan tes dan wawancara. Uji keabsahan data yang digunakan adalah triangulasi. Data penelitan yang terkumpul dianalisis dengan analisis data non statistik yang terdiri dari tiga alur, yaitu reduksi data, penyajian data, dan penarikan kesimpulan/verifikasi data. Hasil penelitian menunjukkan bahwa 1) Terdapat 5 mahasiswa yang berada pada level 0 (sangat kurang baik); 2) 24 mahasiswa berada pada level 1 (kurang baik); 3) 6 mahasiswa berada pada level 2 (cukup baik); Penelitian ini diharapkan dapat memacu individu lain untuk melakukan penelitian yang lebih baik dan mendalam tentang kemampuan komunikasi matematis.

EVALUASI PELATIHAN DI PT. PUPUK KUJANG CIKAMPEK

2018 ◽  
Vol 3 (2) ◽  
Author(s):  
Lania Muharsih ◽  
Ratih Saraswati
Keyword(s):  
Training Evaluation ◽  
Work Productivity ◽  
Learning Behavior ◽  
Level 3 ◽  
Level 1 ◽  
Four Levels ◽  
A Company ◽  
Level 2 ◽  
Evaluation Sheet ◽  
Assessment Factor

This study aims to determine the training evaluation at PT. Kujang Fertilizer. PT. Pupuk Kujang is a company engaged in the field of petrochemicals. Evaluation sheet of PT. Fertilizer Kujang is made based on Kirkpatrick's theory which consists of four levels of evaluation, namely reaction, learning, behavior, and results. At level 1, namely reaction, in the evaluation sheet is in accordance with the theory of Kirkpatrick, at level 2 that is learning should be held pretest and posttest but only made scale. At level 3, behavior, according to theory, but on assessment factor number 3, quantity and work productivity should not need to be included because they are included in level 4. At level 4, that is the result, here is still lacking to get a picture of the results of the training that has been carried out because only based on answers from superiors without evidence of any documents.   Keywords: Training Evaluation, Kirkpatrick Theory.    Penelitian ini bertujuan mengetahui evaluasi training di PT. Pupuk Kujang. PT. Pupuk Kujang merupakan perusahaan yang bergerak di bidang petrokimia. Lembar evaluasi PT. Pupuk Kujang dibuat berdasarkan teori Kirkpatrick yang terdiri dari empat level evaluasi, yaitu reaksi, learning, behavior, dan hasil. Pada level 1 yaitu reaksi, di lembar evaluasi tersebut sudah sesuai dengan teori dari Kirkpatrick, pada level 2 yaitu learning seharusnya diadakan pretest dan posttest namun hanya dibuatkan skala. Pada level 3 yaitu behavior, sudah sesuai teori namun pada faktor penilaian nomor 3 kuantitas dan produktivitas kerja semestinya tidak perlu dimasukkan karena sudah termasuk ke dalam level 4. Pada level 4 yaitu hasil, disini masih sangat kurang untuk mendapatkan gambaran hasil dari pelatihan yang sudah dilaksanakan karena hanya berdasarkan dari jawaban atasan tanpa bukti dokumen apapun.   Kata kunci: Evaluasi Pelatihan, Teori Kirkpatrick.

scholarly journals Celebrate Diversity?

2021 ◽  
Author(s):  
Christian Huber ◽  
Philipp Nicolay ◽  
Simone Weber
Keyword(s):  
Eine Hohe ◽  
Soziale Integration ◽  
Level 1 ◽  
Soziale Ausgrenzung ◽  
Level 2

ZusammenfassungSchulleistungen und soziale Integration sind im deutschen Schulsystem eng miteinander verbunden. Unklar ist, welche Rolle die Leistungs- und Verhaltensheterogenität für diesen Zusammenhang spielt. Vertreter*innen eines klassischen sozialpsychologischen Zugangs sagen für eine hohe Leistungs- und Verhaltensheterogenität eine Erhöhung des Risikos auf soziale Ausgrenzung (Risikohypothese) voraus, Vertreter*innen eines inklusionspädagogischen Zugangs eine Schutzfunktion (Schutzhypothese). Die vorliegende Studie untersucht, inwieweit die Leistungs- und Verhaltensheterogenität den Zusammenhang zwischen einem Förderbedarf in den Bereichen Lernen/Verhalten und sozialer Integration moderiert und ob die Richtung dieses Modertoreffekt im Sinne der Schutz- oder der Risikohypothese gedeutet werden kann. Hierzu wurden N = 1352 Grundschüler*innen in drei unterschiedlich heterogenen Schulsystemen (jahrgangsgetrennt, 1/2/3/4; teilweise jahrgangsübergreifend, 1–2/3–4; jahrgangsübergreifend, 1–4) in einer Querschnittstudie untersucht. Erhoben wurden, neben der sozialen Integration, Informationen über einen besonderen Förderbedarf in den Bereichen Lernen und Verhalten und Schulleistung. Das Schulsystem ging als unabhängige Variable auf Level 2, der Förderbedarf als unabhängige Variable auf Level 1 und die soziale Integration als abhängige Variable in die mehrebenenanalytische Auswertung ein. Analog zu bisher vorliegenden Befunden wurde ein signifikanter Haupteffekt beider Förderbedarfe auf die soziale Integration gefunden. Ferner zeigten vier von sechs Cross-Level-Effekten für den Förderbedarf Lernen Hinweise auf eine Gültigkeit der Schutzhypothese, kein Cross-Level-Effekt war im Sinne der Risikohypothese interpretierbar. Dieser Befund war nicht auf den Förderbedarf Verhalten übertragbar. Die Ergebnisse legen nahe, dass sich der Zusammenhang zwischen sozialer Integration und Förderbedarf Lernen mit zunehmender Leistungs- und Verhaltensheterogenität abschwächt. Gleichzeitig zeigte sich in den stark heterogenen Lerngruppen ein insgesamt erhöhtes Ausgrenzungsrisiko aller Schüler*innen unabhängig ihres Förderbedarfs. Die Ergebnisse lassen den Schluss zu, dass sich für Schüler*innen mit erhöhtem Förderbedarf Lernen im jahrgangsübergreifenden Lernen eine höhere Chance für eine gute soziale Integration einstellen könnte.

scholarly journals An Atmospheric Visibility Grading Method Based on Ensemble Learning and Stochastic Weight Average

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 869
Author(s):  
Xiuguo Zou ◽  
Jiahong Wu ◽  
Zhibin Cao ◽  
Yan Qian ◽  
Shixiu Zhang ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Traffic Accidents ◽  
Feature Sets ◽  
Atmospheric Visibility ◽  
Average Accuracy ◽  
Level 3 ◽  
Visual Characteristics ◽  
Level 1 ◽  
Grading Model ◽  
Level 2

In order to adequately characterize the visual characteristics of atmospheric visibility and overcome the disadvantages of the traditional atmospheric visibility measurement method with significant dependence on preset reference objects, high cost, and complicated steps, this paper proposed an ensemble learning method for atmospheric visibility grading based on deep neural network and stochastic weight averaging. An experiment was conducted using the scene of an expressway, and three visibility levels were set, i.e., Level 1, Level 2, and Level 3. Firstly, the EfficientNet was transferred to extract the abstract features of the images. Then, training and grading were performed on the feature sets through the SoftMax regression model. Subsequently, the feature sets were ensembled using the method of stochastic weight averaging to obtain the atmospheric visibility grading model. The obtained datasets were input into the grading model and tested. The grading model classified the results into three categories, with the grading accuracy being 95.00%, 89.45%, and 90.91%, respectively, and the average accuracy of 91.79%. The results obtained by the proposed method were compared with those obtained by the existing methods, and the proposed method showed better performance than those of other methods. This method can be used to classify the atmospheric visibility of traffic and reduce the incidence of traffic accidents caused by atmospheric visibility.

scholarly journals Identifying trauma patients with benefit from direct transportation to Level-1 trauma centers

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Charlie A. Sewalt ◽  
Benjamin Y. Gravesteijn ◽  
Daan Nieboer ◽  
Ewout W. Steyerberg ◽  
Dennis Den Hartog ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Trauma Center ◽  
Trauma Patients ◽  
Trauma Centers ◽  
Absolute Benefit ◽  
Predicted Probability ◽  
Prehospital Triage ◽  
Level 1 ◽  
Level 1 Trauma Center ◽  
Level 2

Abstract Background Prehospital triage protocols typically try to select patients with Injury Severity Score (ISS) above 15 for direct transportation to a Level-1 trauma center. However, ISS does not necessarily discriminate between patients who benefit from immediate care at Level-1 trauma centers. The aim of this study was to assess which patients benefit from direct transportation to Level-1 trauma centers. Methods We used the American National Trauma Data Bank (NTDB), a retrospective observational cohort. All adult patients (ISS > 3) between 2015 and 2016 were included. Patients who were self-presenting or had isolated limb injury were excluded. We used logistic regression to assess the association of direct transportation to Level-1 trauma centers with in-hospital mortality adjusted for clinically relevant confounders. We used this model to define benefit as predicted probability of mortality associated with transportation to a non-Level-1 trauma center minus predicted probability associated with transportation to a Level-1 trauma center. We used a threshold of 1% as absolute benefit. Potential interaction terms with transportation to Level-1 trauma centers were included in a penalized logistic regression model to study which patients benefit. Results We included 388,845 trauma patients from 232 Level-1 centers and 429 Level-2/3 centers. A small beneficial effect was found for direct transportation to Level-1 trauma centers (adjusted Odds Ratio: 0.96, 95% Confidence Interval: 0.92–0.99) which disappeared when comparing Level-1 and 2 versus Level-3 trauma centers. In the risk approach, predicted benefit ranged between 0 and 1%. When allowing for interactions, 7% of the patients (n = 27,753) had more than 1% absolute benefit from direct transportation to Level-1 trauma centers. These patients had higher AIS Head and Thorax scores, lower GCS and lower SBP. A quarter of the patients with ISS > 15 were predicted to benefit from transportation to Level-1 centers (n = 26,522, 22%). Conclusions Benefit of transportation to a Level-1 trauma centers is quite heterogeneous across patients and the difference between Level-1 and Level-2 trauma centers is small. In particular, patients with head injury and signs of shock may benefit from care in a Level-1 trauma center. Future prehospital triage models should incorporate more complete risk profiles.

Trauma Center Outcomes After Transition From Level 2 to Level 1: A National Trauma Data Bank Analysis

2021 ◽  
Vol 264 ◽  
pp. 499-509
Author(s):  
Sung Huang Laurent Tsai ◽  
Greg Michael Osgood ◽  
Joseph K. Canner ◽  
Amber Mehmood ◽  
Oluwafemi Owodunni ◽  
...  
Keyword(s):  
Trauma Center ◽  
Data Bank ◽  
National Trauma ◽  
National Trauma Data Bank ◽  
Level 1 ◽  
Level 2

Penerapan Capability Maturity Model Integration Untuk Mengukur Tingkat Kematangan Organisasi Dalam Proses Pengembangan Perangkat Lunak (Studi Kasus: Direktorat Innovation Center Universitas Amikom Yogyakarta)

Respati ◽  
2020 ◽  
Vol 15 (1) ◽  
pp. 43
Author(s):  
Ita Permatahati ◽  
Wing Wahyu Winarno ◽  
Mei P Kurniawan
Keyword(s):  
Software Development ◽  
Development Process ◽  
Model Integration ◽  
Maturity Model ◽  
Software Development Process ◽  
Maturity Level ◽  
Capability Maturity ◽  
Level 1 ◽  
A Company ◽  
Level 2

INTISARIMenerapkan standarisasi pada suatu perusahaan yang bergerak dibidang pengembangan perangkat lunak merupakan salah satu cara untuk meningkatkan kualitasnya. CMMI merupakan salah satu standarisasi yang penulis pilih untuk mengetahui tingkat kematangan dari divisi Innovation CenterAmikom. CMMI yang digunakan ialah CMMI for Development versi 1.3 yang fokus terhadap tingkat kematangan di level 2 dengan 6 proses area. Penelitian ini mengambil 1 sample proyek di Innovation Center(IC) yaitu apliksai presensi berbasis mobile. Berdasarkan hasil dari pengukuran yang telah dilakukan, proses pengembangan lunak di IC berada di tingkat 1 (Initial) yang diketahui bahwa belum semua praktik yang ada di masing-masing 6 proses area diterapkan. Kata kunci— proses pengembangan perangkat lunak, CMMI, CMMI-DEV, Representasi Bertingkat, Tingkat Kematangan. ABSTRACTApplying standards to a company engaged in the development of devices is one way to improve its quality. CMMI is one of the standards chosen by the author to find out the level of maturity of the Innovation Center at Amikom. CMMI is used for CMMI for Development version 1.3 which focuses on the level of maturity at level 2 with 6 process areas. This study took 1 sample project at the Innovation Center (IC), a mobile-based presence application. Based on the results of the measurements that have been made, the development process at the IC is at level 1 (Initial) related to all the practices that exist in each of the 6 process areas that are applied.Kata kunci—  software development process, CMMI, CMMI-DEV, Leveled Representation, Maturity Level.

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