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By utilizing progress in millijoule-level pulsed fiber lasers operating in the 1.96 µm spectral range, we introduce a concept utilizing a spaceborne differential absorption barometric lidar designed to operate within the 1.96 µm CO2 absorption band for remote sensing of Martian atmospheric properties. Our focus is on the online wavelength situated in the trough region of two absorption lines, selected due to its insensitivity to laser frequency variations, thus mitigating the necessity for stringent laser frequency stability. Our investigation revolves around a compact lidar configuration, featuring reduced telescope dimensions and lower laser pulse energies. These adjustments are geared towards minimizing costs for potential forthcoming Mars missions. The core measurement objectives encompass the determination of column CO2 absorption optical depth, columnar CO2 abundance, surface atmospheric pressure, and vertical distributions of dust and cloud layers. Through the amalgamation of surface pressure data with atmospheric temperature insights garnered from sounders and utilizing the barometric formula, the prospect of deducing atmospheric pressure profiles becomes feasible. Simulation studies validate the viability of our approach. Notably, the precision of Martian surface pressure measurements is projected to surpass 1 Pa when the aerial dust optical depth is projected to be under 0.7, a typical airborne dust scenario on Mars, considering a horizontal averaging span of 10 km. [ABSTRACT FROM AUTHOR]

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 %. [ABSTRACT FROM AUTHOR]

To investigate the effect of microRNA (miR)-155 on colon cancer chemoresistance to cisplatine and its mechanism. Reverse transcription quantitative polymerase chain reaction was used to measure the levels of miR-155 and forkhead box O3 (FOXO3) in colon cancer specimens and cell lines. Overexpression of miR-155 and miR-155 inhibitor were transfected into colon cancer cell lines to investigate its role of chemoresistance to cisplatin in colon cancer. MTS assays were used to analyse cell viability in vitro. In vivo tumor formation assays were performed in C57BL/6 wild type and miR-155 knockout mice (miR-155-/-). A luciferase reporter assay was used to measure the translation of FOXO3. Additionally, the expression of FOXO3 was detected by western blot analysis. It was identified that miR-155 was markedly upregulated in colon cancer tissue and cell lines. Overexpression of miR-155 enhanced colon cancer cell chemoresistance to cisplatin in vitro and tumorigenesis in vivo. In addition, overexpression of miR-155 was associated with decreased levels of FOXO3, primarily through inhibiting the expression of FOXO3 to increase colon cancer resistanec to cisplatin. The present study demonstrated that miR-155 increased colon cancer drug resistance and decreased FOXO3 expression in vivo and in vitro. This may provide a novel method for the treatment of drug-resistant colon cancer. [ABSTRACT FROM AUTHOR]

Abstract: We advance a novel retrieval technique that combines a Raman and multi-wavelength elastic backscattered signals to retrieve multi-wavelength lidar ratio profiles of aerosol. With profile of backscatter coefficients at 355 nm retrieved from elastic backscatter signal at 355 nm and Raman scattering signal at 387 nm, lidar ratio profiles can be calculated at 532 nm and 1064 nm from the elastic backscatter signals at these wavelengths, taking advantage that the 532 nm/355 nm and 1064 nm/355 nm backscatter ratios are generally approximately equal for two neighboring range bins. This technique has been tested using numerical simulations and applied to lidar measurements at the Hampton University, Hampton, Virginia. [Copyright &y& Elsevier]

Abstract: The CALIPSO satellite mission consistently measures volume (including molecule and particulate) light depolarization ratio of ∼2% for smoke, compared to ∼1% for marine aerosols and ∼15% for dust. The observed ∼2% smoke depolarization ratio comes primarily from the nonspherical habits of particles in the smoke at certain particle sizes. In this study, the depolarization of linearly polarized light by small sphere aggregates and irregular Gaussian-shaped particles is studied, to reveal the physics between the depolarization of linearly polarized light and smoke aerosol shape and size. It is found that the depolarization ratio curves of Gaussian-deformed spheres are very similar to sphere aggregates in terms of scattering-angle dependence and particle size parameters when particle size parameter is smaller than 1.0π. This demonstrates that small randomly oriented nonspherical particles have some common depolarization properties as functions of scattering angle and size parameter. This may be very useful information for characterization and active remote sensing of smoke particles using polarized light. We also show that the depolarization ratio from the CALIPSO measurements could be used to derive smoke aerosol particle size. From the calculation results for light depolarization ratio by Gaussian-shaped smoke particles and the CALIPSO-measured light depolarization ratio of ∼2% for smoke, the mean particle size of South-African smoke is estimated to be about half of the 532nm wavelength of the CALIPSO lidar. [Copyright &y& Elsevier]

Abstract: Five years of CALIPSO lidar layer products are used to study transpacific transport of Asian dust. We focus on possible changes to dust intrinsic optical properties during the course of transport, with specific emphasis on changes to particulate depolarization ratio (PDR). PDR distributions for Asian dust transported across the Pacific are compared to previously reported PDR distributions for African dust transported across the Atlantic. African dust shows a slight decreasing trend in PDR during westward transport across the Atlantic during its most active long-range transport season in summer. Asian dust, on the other hand, shows some spatial variability in PDR over the Pacific during its most active long-range transport season in spring. The dust PDR is generally smaller over the ocean than over the Tarim basin and nearby downwind regions. PDR also shows a decreasing trend with latitude moving northward toward the Arctic, together with an increasing trend in the dust aerosol optical depth (AOD) when passing over polluted Asian regions. Possible explanations include (i) the mixing of dust externally or internally with other types of aerosol over the heavily developed industrial regions in East Asia, and (ii) the downstream mixing of dust plumes from different source regions (i.e., Tarim and Gobi). Dust from different source regions exhibits relatively large differences in PDR, with mean values of 0.34±0.07, 0.28±0.06, and 0.30±0.08, respectively, over the Tarim basin, Gobi Desert and Northwest African source regions. Different transport mechanisms are seen for African dust and Asian dust. Asian dust transport is originated by cold fronts and driven by westerly jets. In contrast, summer African transatlantic dust transport is driven by trade winds and is generally well confined in altitude in the free troposphere throughout the tropics and subtropics. [Copyright &y& Elsevier]

Abstract: Lidar ratio (i.e., extinction-to-backscatter ratio) is a key parameter required for retrieving extinction profiles and optical depths from elastic backscatter lidar measurements, and the quality of any extinction retrieval depends critically on the accuracy of the assumed or measured lidar ratio. In this study, we analyze the first two and a half years of the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) data acquired during nighttime. Distributions of the effective lidar ratio (ELR), which is the product of the lidar ratio and an instrument-dependent multiple scattering factor, are derived for opaque dust layers observed by CALIOP over the North Africa. The median and mean ELR values are, respectively, 36.4 and 38.5sr at 532nm and 47.7 and 50.3sr at 1064nm. For these opaque dust layers, the derived ELR decreases as the volume depolarization ratio (VDR) increases, reflecting the impact of multiple scattering within the dense layers. The particulate depolarization ratio is typically ∼0.3 at 532nm for African dust observed by CALIOP. This ratio can increase to ∼0.4 in the presence of significant multiple scattering. Correspondingly, the calculated ELR will decrease to ∼20sr at 532nm and to ∼30sr at 1064nm. The median and mean effective lidar ratio values approach, respectively, to 38 and 40sr at 532nm and 52 and 55sr at 1064nm for smaller VDR values measured in less dense layers where the multiple scattering is relatively insignificant. These values are very close to those derived in previous case studies for moderately dense dust. Case studies are also performed to examine the impacts of multiple scattering. The results obtained are generally consistent with Monte-Carlo simulations. [ABSTRACT FROM AUTHOR]

The Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite was launched in April 2006 to provide global vertically resolved measurements of clouds and aerosols. Correct discrimination between clouds and aerosols observed by the lidar aboard the CALIPSO satellite is critical for accurate retrievals of cloud and aerosol optical properties and the correct interpretation of measurements. This paper reviews the theoretical basis of the CALIPSO lidar cloud and aerosol discrimination (CAD) algorithm, and describes the enhancements made to the version 2 algorithm that is used in the current data release (release 2). The paper also presents a preliminary assessment of the CAD performance based on one full day (12 August 2006) of expert manual classification and on one full month (July 2006) of the CALIOP 5-km cloud and aerosol layer products. Overall, the CAD algorithm works well in most cases. The 1-day manual verification suggests that the success rate is in the neighborhood of 90% or better. Nevertheless, several specific layer types are still misclassified with some frequency. Among these, the most prevalent are dense dust and smoke close to the source regions. The analysis of the July 2006 data showed that the misclassification of dust as cloud occurs for <1% of the total tropospheric cloud and aerosol features found. Smoke layers are misclassified less frequently than are dust layers. Optically thin clouds in the polar regions can be misclassified as aerosols. While the fraction of such misclassifications is small compared with the number of aerosol features found globally, caution should be taken when studies are performed on the aerosol in the polar regions. Modifications will be made to the CAD algorithm in future data releases, and the misclassifications encountered in the current data release are expected to be reduced greatly. [ABSTRACT FROM AUTHOR]

To comparatively study the size of and variation in the 'brain-haematoma' pressure gradient for different surgical methods for hypertensive intracerebral haemorrhage (HICH) and analyse the gradient's influence on surgical procedures and effects of the haemorrhage. Seventy-two patients with HICH treated from 1/2019 to 12/2019 were randomly divided into two groups, namely, the keyhole endoscopy and large trauma craniotomy groups, according to different operative methods. Intraoperative changes in intracranial pressure (ICP) were monitored to calculate intraoperative alterations in the 'brain-haematoma' pressure gradient. Intraoperative characteristics (operative time, bleeding volume, volume of blood transfusion, and haematoma clearance rate) and postoperative characteristics (oedema, postoperative activities of daily living (ADL) scores, mortality rate and rebleeding rate) were compared between the two groups. In the keyhole endoscopy group, ICP decreased slowly; the 'brain-haematoma' pressure gradient was large, averaging 251.1 ± 20.6 mmH2O, and slowly decreased. The mean operative time was 83.6 ± 4.3 min, the mean bleeding volume was 181.2 ± 13.6 ml, no blood transfusions were given, the average postoperative haematoma clearance rate was 95.6%, the rate of severe oedema was 10.9%, and the average postoperative ADL score was 85.2%. In the large trauma craniotomy group, ICP rapidly decreased after craniotomy. When the haematoma was removed, the 'brain-haematoma' pressure gradient was small, averaging 132.3 ± 10.5 mmH2O, and slowly decreased. The mean operative time was 232 ± 26.1 min, the mean bleeding volume was 412.6 ± 35.2 ml, the average volume of blood transfusion was 281.3 ± 13.6 ml, and the average postoperative haematoma clearance rate was 82.3%; moreover, the rate of severe oedema was 72.1%, and the average postoperative ADL score was 39.0%. These differences were statistically significant (P < 0.05). Neither the death rate (P > 0.05, 2.7% VS 2.8%) nor rebleeding rate (P > 0.05, 2.7% VS 2.8%) showed any obvious changes. The magnitude and variation in the 'brain-haematoma' pressure gradient for different surgical methods significantly influence surgical procedures and effects of HICH. During keyhole endoscopy surgery, this gradient was relatively large and slowly decreased; the haematoma was therefore easier to remove. Advantages of this approach include a high haematoma clearance rate, decreased bleeding volume, decreased operative time, reduced trauma, decreased postoperative brain oedema and improved postoperative recovery of neurological function. Chinese Clinical Trial Register: ChiCTR1900020655 registration in 12/01/02,019 registration in 28/02/02,020 Number: NCOMMS-20–08,091. [ABSTRACT FROM AUTHOR]

Cytoprotective mechanisms of heme oxygenases function by derivatizing heme to generate carbon monoxide, ferrous iron, and isomeric biliverdins, followed by rapid NAD(P)H-dependent biliverdin reduction to the antioxidant bilirubin using two non-overlapping biliverdin reductases that display biliverdin isomer-restricted redox activity. Although cytoprotective functions of heme oxygenases are widely recognized, concomitant effects of downstream biliverdin reductases remain incomplete. A computational model predicated on murine hematopoietic single-cell transcriptomic data identified Blvrb as a biological driver linked to the tumor necrosis factor stress pathway as a predominant source of variation defining hematopoietic cell heterogeneity. In vivo studies using Blvrb -deficient mice established the dispensable role of Blvrb in steady-state hematopoiesis, although model validation using aged Blvrb -deficient mice established an important cytoprotective function in stress hematopoiesis with dichotomous megakaryocyte-biased hematopoietic recovery. Defective stress erythropoiesis was evident in Blvrb −/− spleens and in bone marrow erythroid development, occurring in conjunction with defective lipid peroxidation as a marker of oxidant mishandling. Cell autonomous effects on megakaryocyte lineage bias were documented using multipotential progenitor assays. These data provide the first physiological function of murine Blvrb in a non-redundant pathway of stress cytoprotection. Divergent effects on erythroid/megakaryocyte lineage speciation impute a novel redox-regulated mechanism for lineage partitioning. Image 1 • Blvrb is dispensable for normal organ, hematopoietic, and erythrocyte/platelet function in mice. • Blvrb/Hmox pathway is physiologically-relevant for regulating lineage-restricted stress hematopoiesis. • Biliverdin IXβ reductase is required for hematopoetic stress cytoprotection in vivo. [ABSTRACT FROM AUTHOR]

Aims: Evidence is limited regarding the long-term impact of dietary iron intake on the development of hypertension. We investigated the association between dietary intakes of total, nonheme, and heme iron and hypertension risk in a large prospective cohort of Chinese populations over 26 years. Methods: A total of 16,122 adults (7810 men and 8312 women) who participated in the China Health and Nutrition Survey (1989–2015) were included. Dietary intake was repeatedly assessed by combining three consecutive 24‑h individual dietary recalls with household food inventory weighing at each survey round. Incident hypertension was defined as systolic blood pressure ≥ 140 mmHg and/or diastolic blood pressure ≥ 90 mmHg, diagnosis by physicians, or current use of anti-hypertensive drugs. Results: During a median follow‑up of 11.1 years, 2863 men and 2532 women developed hypertension. After adjustment for non-dietary and dietary factors, a lower risk of hypertension was found in men and women with higher intakes of total, nonheme, or heme iron. The adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for the highest vs. lowest quartiles were 0.76 (0.67, 0.87) in men and 0.85 (0.74, 0.97) in women for total iron intake, 0.77 (0.67, 0.87) in men and 0.85 (0.74, 0.98) in women for nonheme iron intake, and 0.73 (0.62, 0.87) in men and 0.69 (0.58, 0.82) in women for heme iron intake. Dose–response analyses further revealed a U-shaped association of total and nonheme iron intake and an L-shaped association of heme iron intake with hypertension risk in both men and women (all P for non-linearity < 0.001). Conclusions: Our findings emphasize the importance of maintaining moderate iron intake in the prevention of hypertension. Both insufficient and excess intake of iron might increase the risk of hypertension. [ABSTRACT FROM AUTHOR]

Robust optimisation has been applied to the dispatching problem considering fault outage of power systems. The traditional fault outage uncertainty set are too conservative. To solve this problem, a robust contingency constrained unit commitment (CCUC) model considering the outage probability of units and transmission lines is proposed. The proposed model has the following characteristics: (i) the outage probability of units is determined by normal operation time and fault repair time, whereas the outage probability of transmission lines is determined by length and load rate; (ii) based on the probability criterion of unit outage contingencies, the outage probabilities of units and transmission lines are both incorporated into the modelling of uncertainty sets; (iii) the robust CCUC model obtains UC decision of base case and the UC decision under uncertainty scenarios can adjust the output of units by spinning reserve capacity to cope with stochastic fault outage scenarios. The proposed robust CCUC model is solved with Benders‐column and constraint generation (C&CG) method combined with Benders decomposition method and C&CG method. Simulations on the modified IEEE‐RTS‐79 system and IEEE‐RTS‐96 system demonstrate the effectiveness and reliability of the proposed robust CCUC model. [ABSTRACT FROM AUTHOR]

This study applies fuzzy k -means (FKM) cluster analyses to a subset of the parameters reported in the CALIPSO lidar level 2 data products in order to classify the layers detected as either clouds or aerosols. The results obtained are used to assess the reliability of the cloud–aerosol discrimination (CAD) scores reported in the version 4.1 release of the CALIPSO data products. FKM is an unsupervised learning algorithm, whereas the CALIPSO operational CAD algorithm (COCA) takes a highly supervised approach. Despite these substantial computational and architectural differences, our statistical analyses show that the FKM classifications agree with the COCA classifications for more than 94 % of the cases in the troposphere. This high degree of similarity is achieved because the lidar-measured signatures of the majority of the clouds and the aerosols are naturally distinct, and hence objective methods can independently and effectively separate the two classes in most cases. Classification differences most often occur in complex scenes (e.g., evaporating water cloud filaments embedded in dense aerosol) or when observing diffuse features that occur only intermittently (e.g., volcanic ash in the tropical tropopause layer). The two methods examined in this study establish overall classification correctness boundaries due to their differing algorithm uncertainties. In addition to comparing the outputs from the two algorithms, analysis of sampling, data training, performance measurements, fuzzy linear discriminants, defuzzification, error propagation, and key parameters in feature type discrimination with the FKM method are further discussed in order to better understand the utility and limits of the application of clustering algorithms to space lidar measurements. In general, we find that both FKM and COCA classification uncertainties are only minimally affected by noise in the CALIPSO measurements, though both algorithms can be challenged by especially complex scenes containing mixtures of discrete layer types. Our analysis results show that attenuated backscatter and color ratio are the driving factors that separate water clouds from aerosols; backscatter intensity, depolarization, and mid-layer altitude are most useful in discriminating between aerosols and ice clouds; and the joint distribution of backscatter intensity and depolarization ratio is critically important for distinguishing ice clouds from water clouds. [ABSTRACT FROM AUTHOR]

Since the COVID-19 pandemic, telemedicine or non-face-to-face medicine has increased significantly. In practice, various types of medical images are essential to achieve effective telemedicine. Medical image encryption algorithms play an irreplaceable role in the fast and secure transmission and storage of these medical images. However, most of the existing medical image encryption algorithms are full encryption algorithms, which are inefficient and time-consuming, so they are not suitable for emergency medical scenarios. To improve the efficiency of encryption, a small number of works have focused on partial or selective encryption algorithms for medical images, in which different levels of encryption strategies were adopted for different information content regions of medical images. However, these encryption algorithms have inadequate security more or less. In this paper, based on the Logistic map, we designed an improved variable dimension map. Then, an encryption algorithm for medical images was proposed based on it. This algorithm has two modes: (1) full encryption mode and (2) semi-full encryption mode, which can better adapt to different medical scenarios, respectively. In full encryption mode, all pixels of medical images are encrypted by using the confusion-diffusion structure. In semi-full encryption mode, the region of interest of medical images is extracted. The confusion was first adopted to encrypt the region of interest, and then, the diffusion was adopted to encrypt the entire image. In addition, no matter which encryption mode is used, the algorithm provides the function of medical image integrity verification. The proposed algorithm was simulated and analyzed to evaluate its effectiveness. The results show that in semi-full encryption mode, the algorithm has good security performance and lower time consumption; while in full encryption mode, the algorithm has better security performance and is acceptable in time. [ABSTRACT FROM AUTHOR]

This paper describes the development and validation of a method to accurately identify snow/ice cover, surface melting, land surface and open water in polar regions using polar-orbiting Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) lidar measurements from the Cloud and Aerosol Lidar and Infrared Pathfinder Observation (CALIPSO) mission. The technique is based on the relationship between integrated attenuated backscatter color ratio and integrated depolarization ratio, and is proven to efficiently separate snow/ice cover and surface melting from open water and land surfaces. The method has been applied to 10 years (2006–2016) of CALIOP data to study the seasonal and inter-annual variability of Arctic sea ice cover and its declining trend. Results show that the area fraction of snow cover over land at latitudes > 60°N varied between 0.9 during winter and 0.1 in summer. The CALIOP observations of Arctic sea ice cover exhibit a strong seasonal cycle and significant inter-annual variability, which are consistent with the passive microwave-based sea ice results. The > 10 years of CALIOP continuous observations of the snow/ice cover will benefit the communities modeling snow/ice melting and climate change. [ABSTRACT FROM AUTHOR]

Rapid disaster assessment is critical for public security and rescue. As a secondary disaster of large-scale meteorological disasters, power outages cause severe outcomes and thus need to be monitored efficiently and without being costly. Power outage detection from space-borne remote sensing imagery offers a broader coverage and is more temporally sensitive than ground-based surveys are. However, it is challenging to determine the affected area accurately and quantitatively evaluate its severity. Therefore, a new method is proposed to solve the above problems by building a power outage detection model (PODM) and drawing a power outage spatial distribution map (POSDM). This paper takes the winter storm Uri, of 2021, as the meteorological disaster background and Harris County, Texas, which was seriously affected, as the research object. The proposed method utilises the cloud-free VIIRS DNB nadir and close nadir images (<60 degrees) collected during the 3 months before and 15 days after Uri. The core idea beneath the proposed method is to compare the radiance difference in the affected area before and after the disaster, and a large difference in radiance indicates the happening of power outages. The raw radiance of night light measurement is first corrected to remove lunar and atmospheric effects to improve accuracy. Then, the maximum and minimum pixels in the target area of the image are considered outliers and iteratively eliminated until the standard deviation change before and after elimination is less than 1% to finalize the outlier removals. The case study results in Harris show that the PODM detects 28% of outages (including traffic area) compared to 17% of outages (living area only) reported by ground truth data, indicating general agreement with the proposed method. [ABSTRACT FROM AUTHOR]

Nighttime PM2.5 detection by remote sensing can expand understanding of PM2.5 spatiotemporal patterns due to wider coverage compared to ground monitors and by supplementing traditional daytime detection. However, using remote sensing data to invert PM2.5 at night is still challenging. Compared with daytime detection, which operates on sunlight, nighttime detection operates on much weaker moonlight and artificial light sources, complicating signal extraction. Moreover, as the attempts to sense PM2.5 remotely using satellite data are relatively recent, the existing nighttime models are still not mature, overlooking many important factors such as stray light, seasonality in meteorological effects, and observation angle. This paper attempts to improve the accuracy of nighttime PM2.5 detection by proposing an inversion model that takes these factors into consideration. The Visible Infrared Imaging Radiometer Suite/Day/Night Band (VIIRS/DNB) on board the polar-orbiting Suomi National Polar-orbiting Partnership (Suomi NPP) and National Oceanic Atmospheric Administration-20 (NOAA-20) was used to establish a nighttime PM2.5 inversion model in the Beijing area from 1 March 2018 to 28 February 2019. The model was designed by first studying the effects of these factors through a stepwise regression, then building a multivariate regression model to compensate for these effects. The results showed that the impact of satellite viewing zenith angle (VZA) was strongest, followed by seasonality and moonlight. Total accuracy was measured using correlation coefficient (R) compared to ground measurements, achieving 0.87 over the urban area and 0.74 over the suburbs. Specifically, the proposed method works efficiently at subsatellite points, which in this case correspond to VZA from 0 and 5°. In spring, summer, autumn, and winter, the R reached 0.95, 0.93, 0.94, and 0.97 at subsatellite points in the urban area, while it was 0.88, 0.82, 0.85, and 0.77 in the suburbs. [ABSTRACT FROM AUTHOR]

The features detected in monolayer atmospheric columns sounded by the Cloud-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) aboard 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 reclassified more appropriately as confident cloud layers through the use of 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 of 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. [ABSTRACT FROM AUTHOR]

Abstract: The performance of a space-based 2.1-μm coherent Doppler wind lidar (CDWL) measurement at a single laser shot in clear-air conditions is computer simulated, based on the coherent Doppler lidar theory developed in the recent decades, and using the global aerosol distribution derived from one year (March 2007–February 2008) of the CALIPSO lidar measurements. The accuracy of radial wind velocity good estimates and the fraction of good estimates, depending on backscattered signals from aerosols, generally decrease with altitude. A critical altitude is defined as the altitude below which the good estimate fraction of velocity estimates is larger than 90.0%. With a laser pulse energy of 250mJ at an off-nadir pointing angle of 45°, a telescope of 1m in diameter and a vertical range resolution of ∼800m, this critical altitude can reach an altitude of 4.0–5.0km between 20°S and 40°N where dust and biomass burning aerosols are ubiquitous. The critical altitude gradually decreases as approaching the two poles and drops to 0.5–1.5km in the polar regions. When the laser pulse energy is reduced to 100mJ, the critical altitude is generally decreased by ∼0.5km and can still reach an altitude of 3.5–4.5km in the dust and smoke aerosol enriched tropical and subtropical regions. A laser pulse energy of only a few millijoules can still achieve velocity measurements with an RMS error smaller than 1ms−1 and a good estimate fraction better than 90% in the lowest kilometers of the troposphere. [Copyright &y& Elsevier]

The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission was launched in April 2006 and has continuously acquired collocated multisensor observations of the spatial and optical properties of clouds and aerosols in the earth’s atmosphere. The primary payload aboard CALIPSO is the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), which makes range-resolved measurements of elastic backscatter at 532 and 1064 nm and linear depolarization ratios at 532 nm. CALIOP measurements are important in reducing uncertainties that currently limit understanding of the global climate system, and it is essential that these measurements be accurately calibrated. This work describes the procedures used to calibrate the 532-nm measurements acquired during the nighttime portions of the CALIPSO orbits. Accurate nighttime calibration of the 532-nm parallel-channel data is fundamental to the success of the CALIOP measurement scheme, because the nighttime calibration is used to infer calibration across the day side of the orbits and all other channels are calibrated relative to the 532-nm parallel channel. The theoretical basis of the molecular normalization technique as applied to space-based lidar measurements is reviewed, and a comprehensive overview of the calibration algorithm implementation is provided. Also included is a description of a data filtering procedure that detects and removes spurious high-energy events that would otherwise introduce large errors into the calibration. Error estimates are derived and comparisons are made to validation data acquired by the NASA airborne high–spectral resolution lidar. Similar analyses are also presented for the 532-nm perpendicular-channel calibration technique. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, Message Passing Interface (MPI) based parallel computation and particle swarm optimization (PSO) algorithm are combined to form the parallel particle swarm optimization (PPSO) method for solving the dynamic optimal reactive power dispatch (DORPD) problem in power systems. In the proposed algorithm, the DORPD problem is divided into smaller ones, which can be carried out concurrently by multi-processors. This method is evaluated on a group of IEEE power systems test cases with time-varying loads in which the control of the generator terminal voltages, tap position of transformers and reactive power sources are involved to minimize the transmission power loss and the costs of adjusting the control devices. The simulation results demonstrate the accuracy of the PPSO algorithm and its capability of greatly reducing the runtimes of the DORPD programs. [Copyright &y& Elsevier]

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. [ABSTRACT FROM AUTHOR]

Lidars are uniquely capable of collecting high-precision and high spatiotemporal resolution observations that have been used for atmospheric process studies from the ground, aircraft, and space for many years. The Aeolus mission, the first space-borne Doppler wind lidar, was developed by the European Space Agency (ESA) and launched in August 2018. Its novel Atmospheric LAser Doppler INstrument (ALADIN) observes profiles of the component of the wind vector and aerosol/cloud optical properties along the instrument's line-of-sight (LOS) direction on a global scale. A total of two airborne lidar systems have been developed at NASA Langley Research Center in recent years that collect measurements in support of several NASA Earth Science Division focus areas. The coherent Doppler Aerosol WiNd (DAWN) lidar measures vertical profiles of LOS velocity along selected azimuth angles that are combined to derive profiles of horizontal wind speed and direction. The High Altitude Lidar Observatory (HALO) measures high resolution profiles of atmospheric water vapor (WV) and aerosol and cloud optical properties. Because there are limitations in terms of spatial and vertical detail and measurement precision that can be accomplished from space, airborne remote sensing observations like those from DAWN and HALO are required to fill these observational gaps and to calibrate and validate space-borne measurements. Over a 2-week period in April 2019, during their Aeolus Cal/Val Test Flight campaign, NASA conducted five research flights over the eastern Pacific Ocean with the DC-8 aircraft. The purpose was to demonstrate the following: (1) DAWN and HALO measurement capabilities across a range of atmospheric conditions, (2) Aeolus Cal/Val flight strategies and comparisons of DAWN and HALO measurements with Aeolus, to gain an initial perspective of Aeolus performance, and (3) ways in which atmospheric dynamic processes can be resolved and better understood through simultaneous observations of wind, WV, and aerosol profile observations, coupled with numerical model and other remote sensing observations. This paper provides a brief description of the DAWN and HALO instruments, discusses the synergistic observations collected across a wide range of atmospheric conditions sampled during the DC-8 flights, and gives a brief summary of the validation of DAWN, HALO, and Aeolus observations and comparisons. [ABSTRACT FROM AUTHOR]

In this paper, we describe a new two-dimensional and multi-channel feature detection algorithm (2D-McDA) and demonstrate its application to lidar backscatter measurements from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission. Unlike previous layer detection schemes, this context-sensitive feature finder algorithm is applied to a 2-D lidar "scene", i.e., to the image formed by many successive lidar profiles. Features are identified when an extended and contiguous 2-D region of enhanced backscatter signal rises significantly above the expected "clear air" value. Using an iterated 2-D feature detection algorithm dramatically improves the fine details of feature shapes and can accurately identify previously undetected layers (e.g., subvisible cirrus) that are very thin vertically but horizontally persistent. Because the algorithm looks for contiguous 2-D patterns using successively lower detection thresholds, it reports strongly scattering features separately from weakly scattering features, thus potentially offering improved discrimination of juxtaposed cloud and aerosol layers. Moreover, the 2-D detection algorithm uses the backscatter signals from all available channels: 532 nm parallel, 532 nm perpendicular and 1064 nm total. Since the backscatter from some aerosol or cloud particle types can be more pronounced in one channel than another, simultaneously assessing the signals from all channels greatly improves the layer detection. For example, ice particles in subvisible cirrus strongly depolarize the lidar signal and, consequently, are easier to detect in the 532 nm perpendicular channel. Use of the 1064 nm channel greatly improves the detection of dense smoke layers, because smoke extinction at 532 nm is much larger than at 1064 nm, and hence the range-dependent reduction in lidar signals due to attenuation occurs much faster at 532 nm than at 1064 nm. Moreover, the photomultiplier tubes used at 532 nm are known to generate artifacts in an extended area below highly reflective liquid clouds, introducing false detections that artificially lower the apparent cloud base altitude, i.e., the cloud base when the cloud is transparent or the level of complete attenuation of the lidar signal when it is opaque. By adding the information available in the 1064 nm channel, this new algorithm can better identify the true apparent cloud base altitudes of such clouds. [ABSTRACT FROM AUTHOR]

In this paper we describe a new two-dimensional and multi-channel feature detection algorithm (2D-McDA) and demonstrate its application to lidar backscatter measurements from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission. Unlike previous layer detection schemes, this context sensitive feature finder algorithm is applied to a 2D lidar scene i.e., to the image formed by many successive lidar profiles. Features are identified when an extended and contiguous 2D region of enhanced backscatter signal rises significantly above the expected clear air value. Using an iterated 2D feature detection algorithm dramatically improves the fine details of feature shapes and can accurately identify previously undetected layers (e.g., subvisible cirrus) that are very thin vertically but horizontally persistent. Because the algorithm looks for consistent 2D patterns, it potentially offers improved discrimination of juxtaposed cloud and aerosol layers. Moreover, the 2D detection algorithm uses the backscatter signals from all available channels: 532 nm parallel, 532 nm perpendicular, and 1064 nm total. Since the backscatter from some aerosol or cloud particle types can be more pronounced in one channel than another, simultaneously assessing the signals from all channels greatly improves the layer detection. For example, ice particles in subvisible cirrus strongly depolarize the lidar signal and, consequently, are easier to detect in the 532 nm perpendicular channel. Use of the 1064 nm channel greatly improves the detection of dense smoke layers, because smoke extinction at 532 nm is much larger than at 1064 nm, and hence the range-dependent reduction in lidar signals due to attenuation occurs much faster at 532 nm than at 1064 nm. Moreover, the photomultiplier tubes used at 532 nm are known to generate artifacts in an extended area below highly reflective liquid clouds, introducing false detections that artificially lower the apparent cloud base altitude, i.e. the cloud base when the cloud is transparent or the level of complete attenuation of the lidar signal when it is opaque. By adding the information available in the 1064 nm channel, this new algorithm can better identify the true apparent cloud base altitudes of such clouds. [ABSTRACT FROM AUTHOR]

Radiometric calibration of space-based elastic backscatter lidars is accomplished by comparing the measured backscatter signals to theoretically expected signals computed for some well-characterized calibration target. For any given system and wavelength, the choice of calibration target is dictated by several considerations, including signal-to-noise ratio (SNR) and target availability. This paper describes the newly implemented procedures used to calibrate the 1064 nm measurements acquired by CALIOP (i.e., the Cloud-Aerosol Lidar with Orthogonal Polarization), the two-wavelength (532 and 1064 nm) elastic backscatter lidar currently flying on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission. CALIOP's 532 nm channel is accurately calibrated by normalizing the molecular backscatter from the uppermost aerosol-free altitudes of the CALIOP measurement region to molecular model data obtained from NASA's Global Modeling and Assimilation Office. However, because CALIOP's SNR for molecular backscatter measurements is prohibitively lower at 1064 nm than at 532 nm, the direct high-altitude molecular normalization method is not a viable option at 1064 nm. Instead, CALIOP's 1064 nm channel is calibrated relative to the 532 nm channel using the backscatter from a carefully selected subset of cirrus cloud measurements. In this paper we deliver a full account of the revised 1064 nm calibration algorithms implemented for the version 4.1 (V4) release of the CALIPSO lidar data products, with particular emphases on the physical basis for the selection of "calibration quality" cirrus clouds and on the new averaging scheme required to characterize intra-orbit calibration variability. The V4 procedures introduce latitudinally varying changes in the 1064 nm calibration coefficients of 25 % or more, relative to previous data releases, and are shown to substantially improve the accuracy of the V4 1064 nm attenuated backscatter coefficients. By evaluating calibration coefficients derived using both water clouds and ocean surfaces as alternate calibration targets, and through comparisons to independent, collocated measurements made by airborne high spectral resolution lidar, we conclude that the CALIOP V4 1064 nm calibration coefficients are accurate to within 3 %. [ABSTRACT FROM AUTHOR]

The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) version 4.10 (V4) level 2 aerosol data products, released in November 2016, include substantial improvements to the aerosol subtyping and lidar ratio selection algorithms. These improvements are described along with resulting changes in aerosol optical depth (AOD). The most fundamental change in the V4 level 2 aerosol products is a new algorithm to identify aerosol subtypes in the stratosphere. Four aerosol subtypes are introduced for stratospheric aerosols: polar stratospheric aerosol (PSA), volcanic ash, sulfate/other, and smoke. The tropospheric aerosol subtyping algorithm was also improved by adding the following enhancements: (1) all aerosol subtypes are now allowed over polar regions, whereas the version 3 (V3) algorithm allowed only clean continental and polluted continental aerosols; (2) a new "dusty marine" aerosol subtype is introduced, representing mixtures of dust and marine aerosols near the ocean surface; and (3) the "polluted continental" and "smoke" subtypes have been renamed "polluted continental/smoke" and "elevated smoke", respectively. V4 also revises the lidar ratios for clean marine, dust, clean continental, and elevated smoke subtypes. As a consequence of the V4 updates, the mean 532 nm AOD retrieved by CALIOP has increased by 0.044 (0.036) or 52 % (40 %) for nighttime (daytime). Lidar ratio revisions are the most influential factor for AOD changes from V3 to V4, especially for cloud-free skies. Preliminary validation studies show that the AOD discrepancies between CALIOP and AERONET–MODIS (ocean) are reduced in V4 compared to V3. [ABSTRACT FROM AUTHOR]

This paper presents an innovative retrieval method that translates the CALIOP land surface laser pulse returns into the surface bidirectional reflectance. To better analyze the surface returns, the CALIOP receiver impulse response and the downlinked samples' distribution at 30m vertical resolution are discussed. The saturated laser pulse magnitudes from snow and ice surfaces are recovered based on information extracted from the tail end of the surface signal. The retrieved snow surface bidirectional reflectance is compared with reflectance from both CALIOP cloud-covered regions and MODIS BRDF--albedo model parameters. In addition to the surface bidirectional reflectance, the column top-of-atmosphere bidirectional reflectances are calculated from the CALIOP lidar background data and compared with the bidirectional reflectances derived from WFC radiance measurements. The retrieved CALIOP surface bidirectional reflectance and column top-of-atmosphere bidirectional reflectance results provide unique information to complement existing MODIS standard data products and are expected to have valuable applications for modelers. [ABSTRACT FROM AUTHOR]

Data products from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on board Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) were recently updated following the implementation of new (version 4) calibration algorithms for all of the Level 1 attenuated backscatter measurements. In this work we present the motivation for and the implementation of the version 4 nighttime 532 nm parallel channel calibration. The nighttime 532 nm calibration is the most fundamental calibration of CALIOP data, since all of CALIOP's other radiometric calibration procedures - i.e., the 532 nm daytime calibration and the 1064 nm calibrations during both nighttime and daytime - depend either directly or indirectly on the 532 nm nighttime calibration. The accuracy of the 532 nm nighttime calibration has been significantly improved by raising the molecular normalization altitude from 30-34 km to the upper possible signal acquisition range of 36-39 km to substantially reduce stratospheric aerosol contamination. Due to the greatly reduced molecular number density and consequently reduced signal-to-noise ratio (SNR) at these higher altitudes, the signal is now averaged over a larger number of samples using data from multiple adjacent granules. Additionally, an enhanced strategy for filtering the radiation-induced noise from high-energy particles was adopted. Further, the meteorological model used in the earlier versions has been replaced by the improved Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), model. An aerosol scattering ratio of 1:01±0:01 is now explicitly used for the calibration altitude. These modifications lead to globally revised calibration coefficients which are, on average, 2-3% lower than in previous data releases. Further, the new calibration procedure is shown to eliminate biases at high altitudes that were present in earlier versions and consequently leads to an improved representation of stratospheric aerosols. Validation results using airborne lidar measurements are also presented. Biases relative to collocated measurements acquired by the Langley Research Center (LaRC) airborne High Spectral Resolution Lidar (HSRL) are reduced from 3:6%±2:2%in the version 3 data set to 1:6%±2:4% in the version 4 release. [ABSTRACT FROM AUTHOR]

The eco-friendly synthesis and application of Fe nanoparticles (Fe NPs) with higher activity and stability has aroused wide attention in the field of pollutant remediation. In this work, 15 plants extracts were selected for the plant-mediated synthesis of Fe NPs. The as-synthesized particles’ morphology and structure were characterized by transmission electron microscopy, X-ray diffraction and UV–Vis spectroscopy. The contents of four main active biomolecules in the 15 extracts were determined, and comparative studies were further carried out to clarify the key component closely related to the reducing capacity. The results demonstrate that polyphenol is the leading ingredient involved in the biosynthesis of Fe NPs. Then Fe products synthesized by three extracts with distinct content of polyphenol were employed to remove Cr (VI) in the aqueous solution, indicating that the activity of the Fe NPs for Cr (VI) removal is consistent with the reducing capacity of the extracts. Furthermore, the Fe NPs synthesized by S. jambos （ L. ） Alston extract (SJA-Fe NPs) showed significant removal capacity of Cr(VI) with 698.6 mg Cr(VI) per g of iron. [ABSTRACT FROM AUTHOR]

The adhesion of lunar material is strong and harmful to the functional surfaces of spacecraft systems. Using lunar regolith simulant instead of rare real one to understand the adhesive property of lunar regolith is an economical approach. In this work, we firstly introduced a theoretical model of the adhesive behavior between lunar regolith simulant micro-particle and different aerospace materials. In this model, total adhesion force is determined by Van der Waals force, electrostatic force and capillary force. Surface energy, surface potential, particle size and inertia force are the key factors affecting adhesion. Then we designed an experimental method using centrifugal technique and image processing to quantify and visualize the adhesion effect. By using a newly developed lunar regolith simulant BHLD1000 and four types of aerospace functional surfaces, we found the size of adhesive micro-particles are a few micrometers and the adhesion forces on them are a few nN on average. This experimental value is three orders of magnitude smaller than the theoretical one (several μN) due to particle irregularity and surface roughness. The adhesion model, the experimental approach and measurement results developed here are useful in understanding the real adhesion of lunar regolith on the moon as well as designing dust-proof tools for future lunar explorations. • An adhesion model between lunar regolith simulant and typical aerospace materials is proposed. • Lunar regolith simulant BHLD1000 with a wide particle size distribution is developed for experimental study. • High Speed centrifuge technology with specially-made accessories is used to help quantify the adhesion indirectly. • Optical microscopy with picture processing is discussed to embody the particles detaching manner statistically. • Adhesion force between BHLD1000 and typical aerospace materials is evaluated in laboratory environment. [ABSTRACT FROM AUTHOR]

A gravimetric method with in situ density measurement is used to determine the adsorption isotherms and kinetic characteristics of CO on Chinese dry coal plug at 293.29, 311.11, 332.79 and 352.57 K and pressures up to 19 MPa. The adsorption and desorption process is reversible, which shows that it is a process of physical adsorption for CO on coal. The excess adsorption increases with the increasing pressure at low pressures until the CO phase transitions pressure is reached. Above this pressure, the excess adsorption decreases with the increasing pressure. The adsorption behaviour is described using the CO density instead of pressure in four thermodynamic models such as modified Langmuir, Langmuir + k, DR and DR + k. It is found that the modified Langmuir + k and DR + k models are more suitable for liquid and supercritical CO adsorption, respectively. The adsorption kinetics data are also obtained during the measurement of adsorption isotherms. The experimental data are fluctuant at the initial time range due to the temperature variation in the adsorption cell after high-pressure CO is injected. The diffusivity is estimated using a modified unipore model. It is observed that the kinetic parameter C, accounting for the effect of gas diffusivity, increases with the increasing pressure at low pressures and has no obvious relations with pressure at high pressures. In this study, C value has no dependencies with temperature for CO, and the order of magnitude of the effective diffusivity is approximately 10 to 10 s. [ABSTRACT FROM AUTHOR]

Abstract: Insufficient suppression of the elastic-scattering signal in the rotational Raman (RR) detection channels can result in a retrieval error particularly when the temperature of a thick cloud is measured using an RR lidar. To solve this problem, a technique is presented to obtain relative transmission factors for the two RR channels' thereby correcting for the influence of residual elastic-signal on the temperature retrieval. The feasibility of this technique is demonstrated by applying the algorithm to the Hampton University (HU) lidar measurements. Intercomparisons of these temperature retrievals from both water-phase and cirrus clouds show good agreement with radiosonde measurements. [Copyright &y& Elsevier]

Abstract: Land surface hydrology is important to regional climate, ecosystem, agriculture, and even human activities. Changes in soil moisture can produce considerable impacts on socioeconomics. Analysis of assimilation model results, especially those from the Community Land Model, shows that soil moisture over Oklahoma region is continuously reduced from 1980 to 2009. The potential drying trend in the Oklahoma region is evaluated by observations taken during last three decades in this study. Satellite data from Global Precipitation Climatology Project exhibit a clear precipitation decrease in the Oklahoma region during the last decade or so compared with those of two or three decades ago. Accompanying with the precipitation variation, land surface net radiation and temperature over the region are found increases by satellite and/or in-situ measurements. These changes in regional climate conditions also likely result in reduction of regional evaporation and enhancement of sensible heat transport from land surface into the atmosphere as indicated in assimilated data. These observed and modeled evidences of the changes in regional water and energy cycles lead us to conclude that the soil moisture over the Oklahoma region was reduced during the last decade. This soil moisture drop could increase a risk in water shortage for agriculture in the Oklahoma state if the dry period continues. Further investigations on the drying in the Oklahoma State or even entire Southern Great Plains are needed to mitigate potential droughts, reductions in vegetation products, and other socioeconomic impacts. [Copyright &y& Elsevier]

Abstract: CALIPSO aerosol backscatter enhancement in the transition zone between clouds and clear sky areas is revisited with particular attention to effects of data selection based on the confidence level of cloud–aerosol discrimination (CAD). The results show that backscatter behavior in the transition zone strongly depends on the CAD confidence level. Higher confidence level data has a flatter backscatter far away from clouds and a much sharper increase near clouds (within 4km), thus a smaller transition zone. For high confidence level data it is shown that the overall backscatter enhancement is more pronounced for small clear-air segments and horizontally larger clouds. The results suggest that data selection based on CAD reduces the possible effects of cloud contamination when studying aerosol properties in the vicinity of clouds. [Copyright &y& Elsevier]

Abstract: A new boundary integral equation formulation for solving plane elasticity problems involving orthotropic media is presented in this paper. Based on the real variable fundamental solutions of the considered problems, a limit theorem for the transformation from domain integral equations into boundary integral equations (BIEs) and a novel decomposition technique to the fundamental solutions, the regularized BIEs with indirect unknowns, which do not involve the direct calculation of CPV and HFP integrals, are established. The limiting process is done in global coordinates and no separate numerical treatment for strong and weak singular integrals was necessary. The current method does not need to transform the considered problems into isotropic ones as is normally done in the existing literature, so no inverse transform is required. The numerical implementation is carried out using both discontinuous quadratic elements and exact elements, which is developed to model its boundary with negligible error. The validity of the proposed scheme is demonstrated by three numerical examples. Excellent agreement between the numerical results and exact solutions was obtained even with using small amounts of element. [Copyright &y& Elsevier]

Abstract: Quantifying above-cloud aerosol can help improve the assessment of aerosol intercontinental transport and climate impacts. In this study we conduct an integrated analysis of aerosols above clouds by using multi-sensor A-Train measurements, including above-cloud aerosol optical depth at 532nm (AOD532) from CALIPSO lidar, the UV aerosol index (AI) from OMI, and cloud fraction and cloud optical depth (COD) from MODIS. The analysis of Saharan dust outflow and Southwest African smoke outflow regions shows that the above-cloud AOD correlates positively with AI in an approximately linear manner, and that the AOD532/AI ratio decreases with increasing COD. The dependence of AOD532/AI ratio on COD doesn''t depend on aerosol type when potential biases in the CALIOP AOD measurements are empirically accounted for. Our results may suggest the potential of combining OMI AI and MODIS cloud measurements to empirically derive above-cloud AOD with a spatial coverage much more extensive than CALIPSO measurements, which needs to be further explored in the future. [Copyright &y& Elsevier]

Abstract: Ice water content (IWC) is a standard product of cloud radar measurements. In this work, cloud radar cross-sections of various ice clouds are modeled to examine the relationship between the radar signal and the IWC. We report that using backscatter signal at cloud radar wavelength to retrieve IWC results in large uncertainties. Particle size distribution is the primary cause for the uncertainty in the retrieved IWC at radar wavelengths, though particle shape and orientation also play significant roles. Particularly in this study, we demonstrate that using both transmitted waves through the clouds (extinction) and backscattered waves from the clouds to retrieve the mean particle size and then using the mean particle size for IWC retrieval reduces the uncertainty. IWC retrieval can be improved with size distribution derived from dual wavelength cloud radar. [ABSTRACT FROM AUTHOR]

The current cloud thermodynamic phase discrimination by Cloud-Aerosol Lidar Pathfinder Satellite Observations (CALIPSO) is based on the depolarization of backscattered light measured by its lidar [Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP)]. It assumes that backscattered light from ice crystals is depolarizing, whereas water clouds, being spherical, result in minimal depolarization. However, because of the relationship between the CALIOP field of view (FOV) and the large distance between the satellite and clouds and because of the frequent presence of oriented ice crystals, there is often a weak correlation between measured depolarization and phase, which thereby creates significant uncertainties in the current CALIOP phase retrieval. For water clouds, the CALIOP-measured depolarization can be large because of multiple scattering, whereas horizontally oriented ice particles depolarize only weakly and behave similarly to water clouds. Because of the nonunique depolarization–cloud phase relationship, more constraints are necessary to uniquely determine cloud phase. Based on theoretical and modeling studies, an improved cloud phase determination algorithm has been developed. Instead of depending primarily on layer-integrated depolarization ratios, this algorithm differentiates cloud phases by using the spatial correlation of layer-integrated attenuated backscatter and layer-integrated particulate depolarization ratio. This approach includes a two-step process: 1) use of a simple two-dimensional threshold method to provide a preliminary identification of ice clouds containing randomly oriented particles, ice clouds with horizontally oriented particles, and possible water clouds and 2) application of a spatial coherence analysis technique to separate water clouds from ice clouds containing horizontally oriented ice particles. Other information, such as temperature, color ratio, and vertical variation of depolarization ratio, is also considered. The algorithm works well for both the 0.3° and 3° off-nadir lidar pointing geometry. When the lidar is pointed at 0.3° off nadir, half of the opaque ice clouds and about one-third of all ice clouds have a significant lidar backscatter contribution from specular reflections from horizontally oriented particles. At 3° off nadir, the lidar backscatter signals for roughly 30% of opaque ice clouds and 20% of all observed ice clouds are contaminated by horizontally oriented crystals. [ABSTRACT FROM AUTHOR]

The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) is a two-wavelength polarization lidar that performs global profiling of aerosols and clouds in the troposphere and lower stratosphere. CALIOP is the primary instrument on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite, which has flown in formation with the NASA A-train constellation of satellites since May 2006. The global, multiyear dataset obtained from CALIOP provides a new view of the earth’s atmosphere and will lead to an improved understanding of the role of aerosols and clouds in the climate system. A suite of algorithms has been developed to identify aerosol and cloud layers and to retrieve a variety of optical and microphysical properties. CALIOP represents a significant advance over previous space lidars, and the algorithms that have been developed have many innovative aspects to take advantage of its capabilities. This paper provides a brief overview of the CALIPSO mission, the CALIOP instrument and data products, and an overview of the algorithms used to produce these data products. [ABSTRACT FROM AUTHOR]

Accurate knowledge of the vertical and horizontal extent of clouds and aerosols in the earth’s atmosphere is critical in assessing the planet’s radiation budget and for advancing human understanding of climate change issues. To retrieve this fundamental information from the elastic backscatter lidar data acquired during the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission, a selective, iterated boundary location (SIBYL) algorithm has been developed and deployed. SIBYL accomplishes its goals by integrating an adaptive context-sensitive profile scanner into an iterated multiresolution spatial averaging scheme. This paper provides an in-depth overview of the architecture and performance of the SIBYL algorithm. It begins with a brief review of the theory of target detection in noise-contaminated signals, and an enumeration of the practical constraints levied on the retrieval scheme by the design of the lidar hardware, the geometry of a space-based remote sensing platform, and the spatial variability of the measurement targets. Detailed descriptions are then provided for both the adaptive threshold algorithm used to detect features of interest within individual lidar profiles and the fully automated multiresolution averaging engine within which this profile scanner functions. The resulting fusion of profile scanner and averaging engine is specifically designed to optimize the trade-offs between the widely varying signal-to-noise ratio of the measurements and the disparate spatial resolutions of the detection targets. Throughout the paper, specific algorithm performance details are illustrated using examples drawn from the existing CALIPSO dataset. Overall performance is established by comparisons to existing layer height distributions obtained by other airborne and space-based lidars. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]