scholarly journals Assessing the benefits of Imaging Infrared Radiometer observations to the CALIOP version 4 cloud and aerosol discrimination algorithm

2021 ◽  
Author(s):  
Thibault Vaillant de Guélis ◽  
Gérard Ancellet ◽  
Anne Garnier ◽  
Laurent C.-Labonnote ◽  
Jacques Pelon ◽  
...  
Keyword(s):  
Good Accuracy ◽  
Distribution Functions ◽  
Orthogonal Polarization ◽  
Marine Aerosols ◽  
High Confidence ◽  
Probability Distribution Functions ◽  
Brightness Temperatures ◽  
The Tropics ◽  
Aerosol Types ◽  
Infrared Radiometer

Abstract. The features detected in monolayer atmospheric columns sounded by the Cloud and Aerosol Lidar with Orthogonal Polarization (CALIOP) and classified as cloud or aerosol layers by the CALIOP version 4 (V4) cloud and aerosol discrimination (CAD) algorithm are reassessed using perfectly collocated brightness temperatures measured by the Imaging Infrared Radiometer (IIR) onboard the same satellite. Using the IIR’s three wavelength measurements of layers that are confidently classified by the CALIOP CAD algorithm, we calculate two-dimensional (2-D) probability distribution functions (PDFs) of IIR brightness temperature differences (BTDs) for different cloud and aerosol types. We then compare these PDFs with 1-D radiative transfer simulations for ice and water clouds and dust and marine aerosols. Using these IIR 2-D BTD signature PDFs, we develop and deploy a new IIR-based CAD algorithm and compare the classifications obtained to the results reported by the CALIOP-only V4 CAD algorithm. IIR observations are shown to be able to identify clouds with a good accuracy. The IIR cloud identifications agree very well with layers classified as confident clouds by the V4 CAD algorithm (88 %). More importantly, simultaneous use of IIR information reduces the ambiguity in a notable fraction of "not confident" V4 cloud classifications. 28 % and 14 % of the ambiguous V4 cloud classifications are confirmed thanks to the IIR observations in the tropics and in the midlatitudes respectively. IIR observations are of relatively little help in deriving high confidence classifications for most aerosols, as the low altitudes and small optical depths of aerosol layers yield IIR signatures that are similar to those from clear skies. However, misclassifications of aerosol layers, such as dense dust or elevated smoke layers, by the V4 CAD algorithm can be corrected to cloud layer classification by including IIR information. 10 %, 16 %, and 6 % of the ambiguous V4 dust, polluted dust, and tropospheric elevated smoke respectively are found to be misclassified cloud layers by the IIR measurements.

scholarly journals Long term assessment of the CALIPSO Imaging Infrared Radiometer (IIR) calibration and stability through comparisons with MODIS/Aqua and SEVIRI/Meteosat

2016 ◽  
Author(s):  
Anne Garnier ◽  
Noëlle A. Scott ◽  
Jacques Pelon ◽  
Raymond Armante ◽  
Laurent Crépeau ◽  
...  
Keyword(s):  
Radiative Transfer Model ◽  
Transfer Model ◽  
Clear Sky ◽  
Brightness Temperatures ◽  
Medium Resolution ◽  
Wide Range ◽  
Air Mass Types ◽  
June July ◽  
The Tropics ◽  
Infrared Radiometer

Abstract. The quality of the calibrated radiances of the medium-resolution Imaging Infrared Radiometer (IIR) on-board the CALIPSO satellite is quantitatively controlled since the beginning of the mission in June 2006. Two complementary “relative” and “stand-alone” approaches are used, which are related to comparisons of measured brightness temperatures, and to model-to-observations comparisons, respectively. In both cases, IIR channels 1 (8.65 μm), 2 (10.6 μm), and 3 (12.05 μm) are paired with MODIS/Aqua “companion” channels 29, 31, and 32, respectively, as well as with SEVIRI/Meteosat companion channels IR8.7, IR10.8 and IR12, respectively. These pairs were selected before launch to meet radiometric, geometric and space-time constraints. The pre-launch studies were based on simulations and sensitivity studies using the 4A/OP radiative transfer model fed with the more than 2300 atmospheres of the climatological TIGR dataset further sorted out in five air mass types. Over the 9.5 years of operation since launch, in a semi-operational process, collocated measurements of IIR and of its companion channels have been compared at all latitudes over ocean, during day and night, and for all types of scenes in a wide range of brightness temperatures when dealing with the relative approach. The relative approach shows an excellent stability of IIR2-MODIS31 and IIR3-MODIS32 brightness temperature differences (BTD) since launch A slight trend of the IIR1-MODIS29 BTD, equal to −0.02 K/year on average over 9.5 years, is detected by the relative approach at all latitudes and all scene temperatures. For the stand-alone approach, clear sky measurements only are considered, which are directly compared with simulations using 4A/OP and collocated ERA-Interim reanalyses. The clear sky mask is derived from collocated observations from IIR and the CALIPSO lidar. Simulations for clear sky pixels in the tropics reproduce the differences between IIR1 and MODIS29 within 0.02 K, and between IIR2 and MODIS31 within 0.04 K, whereas IIR3-MODIS32 is larger than simulated by 0.26 K. The stand-alone approach indicates that the trend identified from the relative approach originates from MODIS29, whereas no trend (less than ±0.004 K/year) is evidenced for any of the IIR channels. Finally, a year-by-year seasonal bias between nighttime and daytime IIR-MODIS BTDs was found at mid-latitude in the northern hemisphere by the relative approach. It is due to a nighttime IIR bias as determined by the stand-alone approach, which originates from a calibration drift during day-to-night transitions. The largest bias is in June/July with IIR2 and IIR3 too warm by 0.4 K on average, and IIR1 too warm by 0.2 K.

scholarly journals Long-term assessment of the CALIPSO Imaging Infrared Radiometer (IIR) calibration and stability through simulated and observed comparisons with MODIS/Aqua and SEVIRI/Meteosat

2017 ◽  
Vol 10 (4) ◽  
pp. 1403-1424 ◽  
Author(s):  
Anne Garnier ◽  
Noëlle A. Scott ◽  
Jacques Pelon ◽  
Raymond Armante ◽  
Laurent Crépeau ◽  
...  
Keyword(s):  
Satellite Observation ◽  
Initial Guess ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Clear Sky ◽  
Brightness Temperatures ◽  
Wide Range ◽  
Resolution Imaging ◽  
The Tropics ◽  
Infrared Radiometer

Abstract. The quality of the calibrated radiances of the medium-resolution Imaging Infrared Radiometer (IIR) on-board the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) satellite is quantitatively evaluated from the beginning of the mission in June 2006. Two complementary relative and stand-alone approaches are used, which are related to comparisons of measured brightness temperatures and to model-to-observations comparisons, respectively. In both cases, IIR channels 1 (8.65 µm), 2 (10.6 µm), and 3 (12.05 µm) are paired with the Moderate Resolution Imaging Spectroradiometer (MODIS)/Aqua Collection 5 companion channels 29, 31, and 32, respectively, as well as with the Spinning Enhanced Visible and Infrared Imager (SEVIRI)/Meteosat companion channels IR8.7, IR10.8, and IR12, respectively. These pairs were selected before launch to meet radiometric, geometric, and space-time constraints. The prelaunch studies were based on simulations and sensitivity studies using the 4A/OP radiative transfer model and the more than 2300 atmospheres of the climatological Thermodynamic Initial Guess Retrieval (TIGR) input dataset further sorted into five air mass types. Using data from over 9.5 years of on-orbit operation, and following the relative approach technique, collocated measurements of IIR and of its companion channels have been compared at all latitudes over ocean, during day and night, and for all types of scenes in a wide range of brightness temperatures. The relative approach shows an excellent stability of IIR2–MODIS31 and IIR3–MODIS32 brightness temperature differences (BTDs) since launch. A slight trend within the IIR1–MODIS29 BTD, that equals −0.02 K yr−1 on average over 9.5 years, is detected when using the relative approach at all latitudes and all scene temperatures. For very cold scene temperatures (190–200 K) in the tropics, each IIR channel is warmer than its MODIS companion channel by 1.6 K on average. For the stand-alone approach, clear sky measurements only are considered, which are directly compared with simulations using 4A/OP and collocated ERA-Interim (ERA-I) reanalyses. The clear sky mask is derived from collocated observations from IIR and the CALIPSO lidar. Simulations for clear sky pixels in the tropics reproduce the differences between IIR1 and MODIS29 within 0.02 K and between IIR2 and MODIS31 within 0.04 K, whereas IIR3–MODIS32 is larger than simulated by 0.26 K. The stand-alone approach indicates that the trend identified from the relative approach originates from MODIS29, whereas no trend (less than ±0.004 K yr−1) is identified for any of the IIR channels. Finally, using the relative approach, a year-by-year seasonal bias between nighttime and daytime IIR–MODIS BTD was found at mid-latitude in the Northern Hemisphere. It is due to a nighttime IIR bias as determined by the stand-alone approach, which originates from a calibration drift during day-to-night transitions. The largest bias is in June and July when IIR2 and IIR3 are warmer by 0.4 K on average, and IIR1 is warmer by 0.2 K.

Extended two-dimensional belief function based on divergence measurement

2020 ◽  
pp. 1-8
Author(s):  
Jianping Fan ◽  
Jing Wang ◽  
Meiqin Wu
Keyword(s):  
Belief Function ◽  
Distribution Functions ◽  
Divergence Measure ◽  
Uncertain Information ◽  
Belief Functions ◽  
Two Dimensional ◽  
Probability Distribution Functions ◽  
Basic Probability ◽  
The Difference ◽  
Supporting Degree

The two-dimensional belief function (TDBF = (mA, mB)) uses a pair of ordered basic probability distribution functions to describe and process uncertain information. Among them, mB includes support degree, non-support degree and reliability unmeasured degree of mA. So it is more abundant and reasonable than the traditional discount coefficient and expresses the evaluation value of experts. However, only considering that the expert’s assessment is single and one-sided, we also need to consider the influence between the belief function itself. The difference in belief function can measure the difference between two belief functions, based on which the supporting degree, non-supporting degree and unmeasured degree of reliability of the evidence are calculated. Based on the divergence measure of belief function, this paper proposes an extended two-dimensional belief function, which can solve some evidence conflict problems and is more objective and better solve a class of problems that TDBF cannot handle. Finally, numerical examples illustrate its effectiveness and rationality.

scholarly journals Development and Evaluation of a Fluctuating Plume Model for Odor Impact Assessment

2021 ◽  
Vol 11 (8) ◽  
pp. 3310
Author(s):  
Marzio Invernizzi ◽  
Federica Capra ◽  
Roberto Sozzi ◽  
Laura Capelli ◽  
Selena Sironi
Keyword(s):  
Experimental Data ◽  
Distribution Functions ◽  
Plume Model ◽  
Probability Distribution Functions ◽  
Weibull Pdf ◽  
Fluctuating Plume ◽  
Concentration Statistics ◽  
Modified Weibull ◽  
Instantaneous Concentration ◽  
Mean Concentration

For environmental odor nuisance, it is extremely important to identify the instantaneous concentration statistics. In this work, a Fluctuating Plume Model for different statistical moments is proposed. It provides data in terms of mean concentrations, variance, and intensity of concentration. The 90th percentile peak-to-mean factor, R90, was tested here by comparing it with the experimental results (Uttenweiler field experiment), considering different Probability Distribution Functions (PDFs): Gamma and the Modified Weibull. Seventy-two percent of the simulated mean concentration values fell within a factor 2 compared to the experimental ones: the model was judged acceptable. Both the modelled results for standard deviation, σC, and concentration intensity, Ic, overestimate the experimental data. This evidence can be due to the non-ideality of the measurement system. The propagation of those errors to the estimation of R90 is complex, but the ranges covered are quite repeatable: the obtained values are 1–3 for the Gamma, 1.5–4 for Modified Weibull PDF, and experimental ones from 1.4 to 3.6.

Probability Distribution Functions for the Random Forced Burgers Equation

1997 ◽  
Vol 78 (10) ◽  
pp. 1904-1907 ◽  
Author(s):  
Weinan E ◽  
Konstantin Khanin ◽  
Alexandre Mazel ◽  
Yakov Sinai
Keyword(s):  
Probability Distribution ◽  
Burgers Equation ◽  
Distribution Functions ◽  
Probability Distribution Functions

Exploring probability distribution functions best-fitting the kinetic energy of coarse particles at above threshold flow conditions

2021 ◽  
Author(s):  
Hamed Farhadi ◽  
Manousos Valyrakis
Keyword(s):  
Probability Distribution ◽  
Kinetic Energy ◽  
Test Section ◽  
Particle Transport ◽  
River Flow ◽  
Distribution Functions ◽  
Transport Processes ◽  
Flow Conditions ◽  
Probability Distribution Functions ◽  
Transport Regime

<p>Applying an instrumented particle [1-3], the probability density functions of kinetic energy of a coarse particle (at different solid densities) mobilised over a range of above threshold flow conditions conditions corresponding to the intermittent transport regime, were explored. The experiments were conducted in the Water Engineering Lab at the University of Glasgow on a tilting recirculating flume with 800 (length) × 90 (width) cm dimension. Twelve different flow conditions corresponding to intermittent transport regime for the range of particle densities examined herein, have been implemented in this research. Ensuring fully developed flow conditions, the start of the test section was located at 3.2 meters upstream of the flume outlet. The bed surface of the flume is flat and made up of well-packed glass beads of 16.2 mm diameter, offering a uniform roughness over which the instrumented particle is transported. MEMS sensors are embedded within the instrumented particle with 3-axis gyroscope and 3-axis accelerometer. At the beginning of each experimental run, instrumented particle is placed at the upstream of the test section, fully exposed to the free stream flow. Its motion is recorded with top and side cameras to enable a deeper understanding of particle transport processes. Using results from sets of instrumented particle transport experiments with varying flow rates and particle densities, the probability distribution functions (PDFs) of the instrumented particles kinetic energy, were generated. The best-fitted PDFs were selected by applying the Kolmogorov-Smirnov test and the results were discussed considering the light of the recent literature of the particle velocity distributions.</p><p>[1] Valyrakis, M.; Alexakis, A. Development of a “smart-pebble” for tracking sediment transport. In Proceedings of the International Conference on Fluvial Hydraulics (River Flow 2016), St. Louis, MO, USA, 12–15 July 2016.</p><p>[2] Al-Obaidi, K., Xu, Y. & Valyrakis, M. 2020, The Design and Calibration of Instrumented Particles for Assessing Water Infrastructure Hazards, Journal of Sensors and Actuator Networks, vol. 9, no. 3, 36.</p><p>[3] Al-Obaidi, K. & Valyrakis, M. 2020, Asensory instrumented particle for environmental monitoring applications: development and calibration, IEEE sensors journal (accepted).</p>

Design for Project Change Management: Part 2 — Attribute Design

2004 ◽  
Author(s):  
D. Xue ◽  
S. Y. Cheing ◽  
P. Gu
Keyword(s):  
Optimal Design ◽  
Probability Distribution ◽  
Taguchi Method ◽  
Systematic Approach ◽  
Distribution Functions ◽  
Probability Distribution Functions ◽  
Construction Tasks ◽  
Design Attribute ◽  
Value Changes ◽  
Design Aspect

This research introduces a new systematic approach to identify the optimal design configuration and attributes to minimize the potential construction project changes. The second part of this paper focuses on the attribute design aspect. In this research, the potential changes of design attribute values are modeled by probability distribution functions. Attribute values of the design whose construction tasks are least sensitive to the changes of these attribute values are identified based upon Taguchi Method. In addition, estimation of the potential project change cost due to the potential design attribute value changes is also discussed. Case studies in pipeline engineering design and construction have been conducted to show the effectiveness of the introduced approach.

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