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This paper presents a method for reconstructing the historical hourly concentrations of Particulate Matter 2.5 (PM2.5) over the Kathmandu Valley from 1980 to the present. The method uses a machine learning model that is trained using PM2.5 readings from the US Embassy (Phora Durbar) as a ground truth, and the meteorological data from the Modern-Era Retrospective Analysis for Research and Applications v2 (MERRA2) as input. The Extreme Gradient Boosting (XGBoost) model acquires a credible 10-fold cross-validation (CV) score of ~83.4%, an r2-score of ~84%, a Root Mean Square Error (RMSE) of ~15.82 µg/m3, and a Mean Absolute Error (MAE) of ~10.27 µg/m3. Further demonstrating the model's applicability to years other than those for which truth values are unavailable, the multiple cross-tests with an unseen data set offered r2-scores for 2018, 2019, and 2020 ranging from 56% to 67%. The model-predicted data agree with true values and indicate that MERRA2 underestimates PM2.5 over the region. It strongly agrees with ground-based evidence showing substantially higher mass concentrations in the dry pre- and post-monsoon seasons than in the monsoon months. It also shows a strong anti-correlation between PM2.5 concentration and humidity. The results also demonstrate that none of the years fulfilled the annual mean air quality index (AQI) standards set by the World Health Organization (WHO). [ABSTRACT FROM AUTHOR]

The Advanced Topographic Laser Altimeter System (ATLAS) was launched aboard the Ice Cloud and land‐Elevation Satellite‐2 (ICESat‐2) satellite in September 2018. ATLAS is a single wavelength (532 nm) lidar system designed to acquire high resolution measurements of the earth's surface while also obtaining atmospheric backscatter from molecules, clouds, and aerosols. Because ATLAS is optimized for altimetry, the atmospheric data acquired is unique in many respects and requires non‐standard analysis techniques. For example, the high repetition rate laser limits the vertical extent of the profiles to just 14 km and causes atmospheric scattering from above 15 km to be added to the scattering in the lower 0–14 km profile. In addition, the limited vertical range of the acquired profiles renders it difficult to compute the magnitude of the solar background and hinders the application of standard calibration techniques. Despite these limitations, methods have been developed to successfully produce data products that have value to the atmospheric community for cloud and aerosol research and are currently available at the National Snow and Ice Data Center (NSIDC). In this paper we describe the ICESat‐2 atmospheric channel and the methods used to process the ATLAS raw photon count data to obtain calibrated backscatter and higher level products such as layer heights and type, blowing snow, column optical depth and apparent surface reflectance. Plain Language Summary: ICESat‐2 is a polar orbiting satellite equipped with a high repetition rate laser that fires green pulses of light to earth 10,000 times per second. The main objective of ICESat‐2 is the high‐resolution measurement of the height of the earth's surface, with emphasis on the change in elevation of ice sheets that cover most of Greenland and Antarctica. In addition to surface elevation, ICESat‐2 also obtains information on the vertical structure of the atmosphere including the height and thickness of clouds and aerosols. The atmospheric measurements are important for climate studies and because they extend the data record begun by other earth‐orbiting satellite lidars like Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) which has been acquiring atmospheric data since 2006 and is nearing the end of its life. The creation of a long record of cloud and aerosol observations is very important for detecting changes that may be occurring due to humanity's influence on the climate system. Key Points: ICESat‐2 is a satellite lidar that acquires atmospheric backscatter profiles from 0 to 14 km every 280 m along the satellite trackThe high repetition rate laser creates unusual problems for processing the raw data to higher level data productsProducts such as calibrated backscatter, cloud and aerosol layer heights, column optical depth and blowing snow are described and shown [ABSTRACT FROM AUTHOR]

As a common phenomenon over Antarctica, blowing snow (BLSN), especially the large BLSN storms, play an important role in the Antarctic surface mass balance, radiation budget, and planetary boundary layer processes. This study presents the work on BLSN storm identification and analysis with observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. Spectral analysis shows that BLSN identification is feasible with MODIS daytime data. A random forest machine learning model is developed and observations from the Cloud‐Aerosol Lidar with Orthogonal Polarization are used for training. Model performance results show that machine‐learning based classification can achieve over 90% overall accuracy when classifying MODIS pixels into cloud, clear, and BLSN categories. The machine learning model is applied to MODIS observations during the month of October 2009 for BLSN storm analysis. Results show that the size of BLSN storms has a large spectrum and can reach hundreds of thousands km2. The MODIS based BLSN storm frequency map extends the Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observations coverage limit from 82°S to the South Pole. A BLSN storm belt, which extends from the South Pole region to the coastal area between 130°E and 160°E along the Transantarctic Mountains, provides a potential pathway of snow transport. These results are important in improving the understanding of BLSN impact on Antarctic surface mass balance and boundary layer processes. [ABSTRACT FROM AUTHOR]

Satellite cloud detection over snow and ice has been difficult for passive remote sensing instruments due to the lack of contrast between clouds and cold/bright surfaces; cloud mask algorithms often heavily rely on shortwave infrared (IR) channels over such surfaces. The Earth Polychromatic Imaging Camera (EPIC) on board the Deep Space Climate Observatory (DSCOVR) does not have infrared channels, which makes cloud detection over snow and ice surfaces even more challenging. This study investigates the methodology of applying EPIC's two oxygen absorption band pair ratios in the A band (764, 780 nm) and B band (688, 680 nm) for cloud detection over the snow and ice surfaces. We develop a novel elevation and zenith-angle-dependent threshold scheme based on radiative transfer model simulations that achieves significant improvements over the existing algorithm. When compared against a composite cloud mask based on geosynchronous Earth orbit (GEO) and low Earth orbit (LEO) sensors, the positive detection rate over snow and ice surfaces increased from around 36 % to 65 % while the false detection rate dropped from 50 % to 10 % for observations of January 2016 and 2017. The improvement in July is less substantial due to relatively better performance in the current algorithm. The new algorithm is applicable for all snow and ice surfaces including Antarctic, sea ice, high-latitude snow, and high-altitude glacier regions. This method is less reliable when clouds are optically thin or below 3 km because the sensitivity is low in oxygen band ratios for these cases. [ABSTRACT FROM AUTHOR]

Recent high‐resolution dropsonde observations from the 2010 Concordiasi field campaign in austral spring season show that surface‐based inversions (SBIs) over Antarctica are frequently eroded, with well‐mixed boundary layers occurring 33% and 18% of the time in West and East Antarctica, respectively. In this study, using the dropsonde observations, we evaluate the performance of the Modern‐Era Retrospective analysis for Research and Applications, version 2 (MERRA2) in representing the Antarctic boundary layer thermodynamic structure. Results show that MERRA2 has a good overall representation of the Antarctic surface stability and correctly predicts 82% of the SBIs. However, an underprediction of less stable boundary layer occurrence, especially over the elevated East Antarctic plateau, is favored during conditions of increased lower tropospheric stability associated with model dynamics, indicating difficulty in parameterizing turbulence in very stable boundary layers. In addition, a lower tropospheric cool bias (first model level and above) is observed in the MERRA2 reanalysis, especially over West Antarctica, which amplifies in the boundary layer during mixed conditions. The near‐surface cold bias is most pronounced when the model fails to predict mixed layers over West Antarctica and is expected to negatively impact the representation of surface energy budget and melt processes. Key Points: MERRA2 has good skill in representing the frequency of less stable and stable Antarctic boundary layersThe model underpredicts turbulence and the frequency of less stable boundary layers especially over elevated regions of East AntarcticaRepercussions of missing boundary layer turbulent processes are more severe for the representation of West Antarctic surface processes [ABSTRACT FROM AUTHOR]

This paper presents the physical basis of the Earth Polychromatic Imaging Camera (EPIC) cloud product algorithms and an initial evaluation of their performance. Since June 2015, EPIC has been providing observations of the sunlit side of the Earth with its 10 spectral channels ranging from the UV to the near-infrared. A suite of algorithms has been developed to generate the standard EPIC Level 2 cloud products that include cloud mask, cloud effective pressure/height, and cloud optical thickness. The EPIC cloud mask adopts the threshold method and utilizes multichannel observations and ratios as tests. Cloud effective pressure/height is derived with observations from the O2 A-band (780 and 764 nm) and B-band (680 and 688 nm) pairs. The EPIC cloud optical thickness retrieval adopts a single-channel approach in which the 780 and 680 nm channels are used for retrievals over ocean and over land, respectively. Comparison with co-located cloud retrievals from geosynchronous earth orbit (GEO) and low earth orbit (LEO) satellites shows that the EPIC cloud product algorithms are performing well and are consistent with theoretical expectations. These products are publicly available at the Atmospheric Science Data Center at the NASA Langley Research Center for climate studies and for generating other geophysical products that require cloud properties as input. [ABSTRACT FROM AUTHOR]

Blowing snow is a frequent and widespread phenomenon over most of Antarctica. The transport and sublimation of blowing snow are important for the mass balance of the Antarctic ice sheet, and the latter is a major contributor to the hydrological cycle in high-latitude regions. Although much is known about blowing snow from surface observations, knowledge of the thermodynamic structure of deep (>50 m) blowing-snow layers is lacking. Here, dropsonde measurements are used to investigate the temperature, moisture, and wind structure of deep blowing-snow layers over Antarctica. The temperature lapse rate within the blowing-snow layer is at times close to dry adiabatic and is on average between dry and moist adiabatic. Initiation of blowing snow causes the surface temperature to increase to a degree proportional to the depth of the blowing-snow layer. The relative humidity with respect to ice is generally largest near the surface (but less than 100%) and decreases with height, reaching a minimum near the top of the layer. These findings are at odds with the generally accepted theory that blowing-snow sublimation will cool and eventually saturate the layer. The observations support the conclusion that high levels of wind-shear-induced turbulence cause mixing and entrainment of warmer air from above the blowing-snow layer, which suppresses humidity and produces the observed well-mixed temperature structure within the layer. The results may have important consequences for understanding the mass balance of the Antarctic ice sheet and the moisture budget of the atmosphere in high latitudes. [ABSTRACT FROM AUTHOR]

Snow grain size is an important parameter for cryosphere studies. As a proof of concept, this paper presents an approach to retrieve this parameter over Greenland, East and West Antarctica ice sheets from surface reflectances observed with the Geoscience Laser Altimeter System (GLAS) onboard the Ice, Cloud, and land Elevation Satellite (ICESat) at 1064 nm. Spaceborne lidar observations overcome many of the disadvantages in passive remote sensing, including difficulties in cloud screening and low sun angle limitations and hence tend to provide more accurate and stable retrievals. Results from the GLAS L2A campaign, which began on 25 September and lasted until 19 November, 2003, show that the mode of the grain size distribution over Greenland is the largest (~300 µm) among the three, West Antarctica is the second (~220 µm) and East Antarctica is the smallest (~190 µm). Snow grain sizes are larger over the coastal regions compared to inland the ice sheets. These results are consistent with previous studies. Applying the broadband snow surface albedo parameterization scheme developed by Garder and Sharp (2010) to the retrieved snow grain size, ice sheet surface albedo is also derived. In the future, more accurate retrievals can be achieved with multiple wavelengths lidar observations. [ABSTRACT FROM AUTHOR]

Abstract: The Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR) was designed to measure the atmosphere and surface properties over the whole sunlit half of the Earth from the L1 Lagrangian point. It has 10 spectral channels ranging from the UV to the near-IR, including two pairs of oxygen (O2) A-band (779.5 and 764nm) and B-band (680 and 687.75nm) reference and absorption channels selected for the cloud height measurements. This paper presents the radiative transfer analysis pertinent to retrieving cloud top height and cloud geometrical thickness with EPIC A- and B-band observations. Due to photon cloud penetration, retrievals from either O2 A- or B-band channels alone gives the corresponding cloud centroid height, which is lower than the cloud top. However, we show both the sum and the difference between the retrieved cloud centroid heights in the A and B bands are functions of cloud top height and cloud geometrical thickness. Based on this fact, the paper develops a new method to retrieve cloud top height and cloud geometrical thickness simultaneously for fully cloudy scenes over ocean surface. First, cloud centroid heights are calculated for both A and B bands using the ratios between the reflectances of the absorbing and reference channels; then the cloud top height and the cloud geometrical thickness are retrieved from the two dimensional look up tables that relate the sum and the difference between the retrieved centroid heights for A and B bands to the cloud top height and the cloud geometrical thickness. This method is applicable for clouds thicker than an optical depth of 5. [Copyright &y& Elsevier]

Cloud detection/screening is a fundamental step in satellite data analysis. For the Ice, Cloud, and land Elevation Satellite (ICESat) and its successor ICESat-2, clouds can significantly affect the accuracy of the surface elevation retrievals. This paper proposes a new method for cloud screening in support of the ICESat-2 mission with focus on the polar ice sheet regions. The method utilizes the apparent surface reflectance (ASR) at the backscattering direction as the cloud screening test. The basis of this method is that clouds produce a strong signal by significantly decreasing the ASR. We show that depending on the height and microphysics of the cloud, the ASR decreases 8%–17% for clouds with an optical depth of 0.1 and 57%–85% for clouds with an optical depth 1.0. Data from ICESat's 1064-nm channel is used to demonstrate the feasibility of the method. It is shown that cloud detectability is a function of surface reflectance variability. Generally, the smaller the surface reflectance variability, the more accurate is cloud detection. Unlike ICESat, which used a 1064-nm laser, ICESat-2 adopts a 532-nm photon counting system for its laser altimeter. With both modeling studies and results from the Moderate Resolution Imaging Spectroradiometer (MODIS), we demonstrate that the ASR variability is much smaller for the 532-nm channel than that for the 1064-nm channel. Hence, the 532-nm channel is better suited for cloud screening than the 1064-nm channel. [ABSTRACT FROM PUBLISHER]

In support of the Ice, Cloud, and land Elevation Satellite (ICESat)-II mission, this paper studies the bias in surface- elevation measurements caused by undetected thin clouds. The ICESat-II satellite may only have a 1064-nm single-channel lidar onboard. Less sensitive to clouds than the 532-nm channel, the 1064-nm channel tends to miss thin clouds. Previous studies have demonstrated that scattering by cloud particles increases photon-path length, thus resulting in biases in ice-sheet-elevation measurements from spaceborne lidars. This effect is referred to atmospheric path delay. This paper complements previous studies in! the following ways: First, atmospheric path delay is estimated over the ice sheets based on cloud statistics from the Geoscience Laser Altimeter System onboard ICESat and the Moderate Resolution Imaging Spectroradlometer (MODIS) onboard Terra Aqua. Second, the effect of cloud particle size and shape is studied with the state-of-the-art phase functions developed for MODIS cirrus- cloud microphysical model. Third, the contribution of various orders of scattering events to the path delay is studied, and analytical model of the first-order scattering contribution is developed. This paper focuses on the path delay as a function of telescope field of view (FOV). The results show that reducing telescope FOV can significantly reduce the expected path delay. As an I , ample, the average path delays for FOV : 167 µad (a 100-m-diameter circle on the surface) caused by thin undetected clouds by the 1064-nm channel over Greenland and East Antarctica illustrated. [ABSTRACT FROM AUTHOR]

The Radiometric Camera-by-Camera Cloud Mask (RCCM) is archived at the NASA Langley Distributed Active Archive Center as one of the standard products from the Multi-angle Imaging SpectroRadiometer (MISR) mission. The RCCM algorithm applied over land surfaces uses an Automated Threshold Selection Algorithm (ATSA) to derive thresholds that are applied to a cloud masking test to determine whether a given image pixel is clear or contains cloud. In this article, we established a framework for the selection of ATSA and the cloud masking tests, which is not only suitable for the RCCM over land, but cloud detection for other satellite missions. Using this framework, we have undertaken the largest comparison of existing histogram-based ATSAs (16 in total) and applied them to four cloud masking tests that can be constructed from the MISR radiances, namely the red channel bidirectional reflectance function (BRF), the standard deviation (STDV) of the red channel BRF, the normalized difference vegetation index (NDVI), and a parameter D that is constructed by optimizing the information from NDVI and red channel BRF for cloud detection. The cloud masking tests and ATSAs are applied to 35 MISR scenes from six snow-free land surface types. To evaluate their performance, reference cloud masks are constructed for the 35 scenes using interactive, supervised learning, visualization software. Independent of the ATSA and as a single cloud masking test, D performed the best in terms of having the lowest misclassification rate using the best possible threshold, the highest bimodal rate in the shape of the histograms derived from the 35 scenes, and the least sensitivity to errors in the choice of threshold. Of the 16 ATSAs, the methods of Li and Lee [Li, C. H. , and Lee, C. K. , (1993). Minimum cross-entropy thresholding. Pattern Recognition, 26(4), 617-625. ] and Pal and Bhandari [Pal, N. R. , and Bhandari, D. , (1993). Image thresholding: some new techniques. Signal Processing, 33, 139–158. ] performed the best when applied to D, with essentially unbiased performance and a root mean square of 15% when compared to cloud masks using the best possible thresholds. It is recommended that increased performance of the RCCM-land algorithm can be had through an increase in the space–time sampling used to generate histograms of D and the addition of a STDV cloud masking test to improve the detection of small cumulus clouds. [Copyright &y& Elsevier]

Remote sensing from Earth‐observing satellites is now providing valuable information about ocean phytoplankton distributions. This paper presents the new ocean subsurface optical properties obtained from two space‐based lidars: the Cloud‐Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard the Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observations satellite and the Advanced Topographic Laser Altimeter System (ATLAS) aboard the Ice, Cloud, and land Elevation Satellite‐2 satellite. Obtaining reliable estimates of subsurface biomass necessitates removing instrument artifacts peculiar to each sensor, that is, polarization crosstalk artifacts in the CALIOP signals and after pulsing effects arising from the ATLAS photodetectors. We validate the optical properties derived from the corrected lidar backscatter signals using MODerate‐resolution Imaging Spectroradiometer ocean color measurements and autonomous biogeochemical Argo float profiles. Our results support the continued use of present and future spaceborne lidars to study the global plankton system and characterize its vertical structures in the upper ocean. Key Points: Global ocean subsurface properties are retrieved from Cloud‐Aerosol Lidar with Orthogonal Polarization (CALIOP)The CALIOP crosstalk artifact and Advanced Topographic Laser Altimeter System (ATLAS) after pulsing effects are removedHigh vertical resolution of ocean subsurface profiles can be obtained from ATLAS/Ice, Cloud, and land Elevation Satellite‐2 [ABSTRACT FROM AUTHOR]

The Advanced Topographic Laser Altimeter System (ATLAS) aboard the Ice, Cloud, and land Elevation Satellite‐2 (ICESat‐2) has been making very high resolution measurements of the Earth's surface elevation since October 2018. ATLAS uses photomultiplier tubes (PMTs) as detectors in photon counting mode, so that a single photon reflected back to the receiver triggers a detection within the ICESat‐2 data acquisition system. However, one characteristic of ICESat‐2 detected photons is the possible presence of afterpulses, defined as small amplitude pulses occurring after the primary signal pulse due to photon arrival. The disadvantage of these afterpulses is that they often confound the accurate measurements of low level signals following a large amplitude of signal and can degrade energy resolution and cause errors in pulse counting applications. This paper discusses and summarizes the after‐pulsing effects exhibited by the ATLAS PMTs based on on‐orbit measurements over different seasons and geographic regions. The potential impacts of these after‐pulsing effects on altimetry and ocean subsurface retrievals are discussed. Plain Language Summary: After‐pulsing effects occurring in the ICESat‐2 Advanced Topographic Laser Altimeter System (ATLAS) are characterized from the on‐orbit measurements acquired over different surface types. Multiple echoes due to after‐pulsing effects in the ATLAS photomultiplier tubes (PMTs) are clearly seen below the Earth's surface where the signal should be totally attenuated. The afterpulses captured from on‐orbit measurements are caused by three different reasons: (1) the effects of the dead‐time circuit (∼3 ns) due to PMT saturation; (2) the effects of optical reflections within the ATLAS receiver optical components; (3) PMT afterpulses. The echoes separated by ∼0.45 m are attributed to the effect of the dead‐time circuit (∼3 ns) due to PMT saturation. The echoes at ∼2.3 and ∼4.2 m below the primary surface returns are caused by the optical reflections within the ATLAS receiver optical components, while the echoes from ∼10 to ∼45 m away from the primary surface signal are due to the PMT afterpulses with a longer time delay. The ICESat‐2 ATLAS instrument response is derived from both a measurement of the transmitted laser pulse shape and measured photon events arising from land surfaces with different surface albedos. The ICESat‐2 on‐orbit measurements demonstrate that the ATLAS impulse response during different months and over different surface types is essentially identical. Key Points: The effects of after‐pulsing by the Advanced Topographic Laser Altimeter System (ATLAS) aboard the ICESat‐2 satellite are describedThe transient response of the ATLAS receiver is characterized over different measurement regimesThe potential impacts of these detector artifacts on ICESat‐2 science studies are discussed [ABSTRACT FROM AUTHOR]

• A radiative transfer simulator for EPIC onboard DSCOVR is developed. • The simulator is used to perform sensitivity study of cloud remote sensing. • The ultraviolet channel 388 nm can be used to assist cloud top height retrieval. • The oxygen absorption bands can be used to increase the cloud masking confidence. The Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR) views nearly the whole sunlit face of the Earth with 10 spectral bands ranging from the UV to the near-infrared, including two oxygen absorbing bands centered at 764 nm (A-band) and 687.75 nm (B-band). Clouds are among the primary remote sensing targets using EPIC images because of their important impacts on the Earth's radiation budget. In order to facilitate the EPIC cloud data product development, we have built a radiative transfer simulator featuring flexible cloud microphysical parameters, gas absorptions, and the instrument line shape functions for each EPIC band. The radiative transfer simulator is used to explore the sensitivity of EPIC observations on liquid-phase cloud microphysical parameters, including optical depth, geometric thickness, and cloud top height. We found that the ratios of the reflectances in the oxygen A and B bands to their respective continuum measurements can be used to increase the confidence level of cloud masking over scenes with sun-glint. In addition, the 388 nm band can be used to differentiate low and high clouds with the uncertainty of roughly 2–3 km. Combining this information with the oxygen absorption bands, the cloud geometric thickness can be obtained with the rough uncertainty of 3–4 km. [ABSTRACT FROM AUTHOR]

• The EPIC instrument lacks onboard calibration and the indirect moon-view calibration for its oxygen bands is subject to large uncertainties. • Large discrepancies between the model simulations and EPIC measurements over South Pole indicate possible low bias in EPIC absolute calibration. • Multi-year trends in oxygen band ratios can be accounted by orbit shifting which means the instrument is stable over the past 7 years. The Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR) satellite orbiting the Sun at the Lagrange-1 point was launched without onboard calibration systems. Vicarious calibration is conducted for 8 of the 10 UV/VIS/NIR channels using other low earth orbiting satellite instruments, while its two O 2 bands (688 nm and 764 nm) rely on indirect moon-view calibrations because the same narrow-band O 2 bands are not readily available from other in-flight instruments. This study compares EPIC measurements from the four O 2 bands aiming at examining sensor stability over a uniquely suited location, i.e., the permanently snow-covered South Pole. The study utilizes radiative transfer model simulations with in-situ atmospheric soundings taken at South Pole during months of December and January from 2015 to 2022. The absolute discrepancy between the model simulations and observations is less than 1.0% for the two reference bands, but 5.75% and 15.63% for the 688 nm, and 764 nm absorption bands, respectively. The simulated A-band and B-band ratios are 16.09% and 4.74% higher than that from the observations. Various sensitivities are conducted to estimate possible contributions to the discrepancies from input atmospheric profiles, spectral surface albedos and surface BRDF. While none of the input uncertainties is likely to account for the large discrepancies in the oxygen absorption bands, a small shift in the instrument response function could be the main reason for these biases. On the other hand, the model simulations are able to capture systematic variations with observed angular measurements and explain the multi-year trends found in observed O 2 band ratios due to satellite orbit shifting. When model simulated contributions from the angle variations are deducted from the observed O 2 band ratios, the residual O 2 band ratios are found to be stable since 2015. [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]

The objective of this study was to investigate the glucose and insulin response of pigs with intrauterine growth retardation (IUGR) to a high-fat (HF) feeding regimen and to observe the underlying toll-like receptor-4 (TLR4) signalling pathway. Weaned piglets with IUGR or normal birthweight (NBW) (n = 20 each) received during the whole fattening period control diets (0% lard) or HF diets (HF, 10% lard). At about 110 kg body weight, pigs were euthanised to collect hepatic samples. Compared with NBW pigs, IUGR pigs had lower daily gain and feed intake. Growth rate of pigs was increased by HF feeding. Pigs fed HF diets had lower peak concentrations of glucose and insulin, which decreased more slowly than in pigs that received the control diets. The mRNA expression abundances of peroxisome proliferator-activated receptor coactivator-1α(PGC-1α), TLR4, myeloid differentiation factor 88 (MyD88), tumour necrosis factor-α(TNF-α), interleukin 6 (IL-6) and IL-1 receptor antagonist (IL-1Rn) were significantly affected by HF feeding, and IL-6 and IL-1Rn mRNA expressions were up-regulated in IUGR pigs compared with NBW pigs. Western blot analysis indicated that HF feeding elevated the protein expressions of TLR4 signalling pathway, as TLR4, MyD88, IκB kinaseβand nuclear factor-κB, and insulin signalling-related proteins, as phosphorylated insulin receptor substrate-1 and phosphorylated protein kinase B. In summary, hepatic TLR4 signalling pathway and inflammatory response induced by HF feeding played an important role in the aggravated development of insulin resistance in pigs. [ABSTRACT FROM PUBLISHER]

Gene expression profiles of blood can reflect the physiopathologic status of the immune system. The dynamic microRNA (miRNA) expression profiles of peripheral blood from pigs at different developmental stages, and how differential expression of miRNAs might relate to immune system development, are unknown. In this study, peripheral blood samples taken at five developmental stages were used to construct 15 miRNA libraries (three biological replicates/stage): 0 days (newborn), 30 days (weaning), 60 days (weaned), and 180 and 360 days (puberty). We identified 295 known mature miRNAs. Hierarchical clustering of the miRNA expression profile showed significant differences between individuals at the neonatal and postnatal stages. Functional enrichment analysis revealed that miRNAs differentially expressed between pairwise comparisons of the developmental stages were over-represented in immune-related pathways such as toll-like receptor signaling. The time-course of expression of the over-representated miRNAs exhibited a pattern of steady decline over time, for both the complete miRNA compendium and immune-related miRNAs. We identified six marker miRNAs that were highly negatively correlated with chronologic age and enriched for genes involved in immune-related pathways. This study of a peripheral blood miRNA transcriptome offers insight into immune system development in swine and provides a resource for pig genome annotation. [ABSTRACT FROM AUTHOR]

Polar ocean ecosystems are experiencing rapid environmental change, but measuring the associated phytoplankton responses is challenging using traditional satellite passive ocean color measurements due to signal contamination from clouds and sea ices and to low solar elevation angles. Active satellite lidar measurements allow retrieval of ocean phytoplankton properties under conditions prohibitive to passive ocean color sensors. The ICESat-2 satellite lidar measurements provide two-dimensional distributions of upper ocean phytoplankton properties. The spring phytoplankton blooms extending about 230 km horizontally from dense packs ice near Antarctic marginal ice zones and 15 m vertically below ocean surface are observed from space for the first time. Our findings highlight the advantages of satellite lidar technology for understanding high latitude plankton ecology and biogeochemistry. • The vertical structures of the ice-edge blooms are first uncovered from space. • The ice-edge blooms extend as far as 230 km horizontally and 15 m vertically. • ICESat-2 lidar measurements can be used for ocean biology studies. • High latitude plankton in both day and night can be observed from ICESat-2. [ABSTRACT FROM AUTHOR]

An analytic transfer inverse model for Earth Polychromatic Imaging Camera (EPIC) observations is proposed to retrieve the cloud-top pressure (CTP) with the consideration of in-cloud photon penetration. In this model, an analytic equation was developed to represent the reflection at the top of the atmosphere from above cloud, in cloud, and below cloud. The coefficients of this analytic equation can be derived from a series of EPIC simulations under different atmospheric conditions using a nonlinear regression algorithm. With estimated cloud pressure thickness, the CTP can be retrieved from EPIC observation data by solving the analytic equation. To simulate the EPIC measurements, a program package using the double- k approach was developed. Compared to line-by-line calculation, this approach can calculate high-accuracy results with a 100-fold computation time reduction. During the retrieval processes, two kinds of retrieval results, i.e., baseline CTP and retrieved CTP, are provided. The baseline CTP is derived without considering in-cloud photon penetration, and the retrieved CTP is derived by solving the analytic equation, taking into consideration in-cloud and below-cloud interactions. The retrieved CTPs for the oxygen A and B bands are smaller than their related baseline CTP. At the same time, both baseline CTP and retrieved CTP at the oxygen B band are larger than those at the oxygen A band. Compared to the difference in baseline CTP between the B band and A band, the difference in retrieved CTP between these two bands is generally reduced. Out of around 10 000 cases, in retrieved CTP between the A and B bands we found an average bias of 93 mb with a standard deviation of 81 mb. The cloud layer top pressure from Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) measurements is used for validation. Under single-layer cloud situations, the retrieved CTPs for the oxygen A band agree well with the CTPs from CALIPSO, the mean difference of which within 5 mb in the case study. Under multiple-layer cloud situations, the CTPs derived from EPIC measurements may be larger than the CTPs of high-level thin clouds due to the effect of photon penetration. [ABSTRACT FROM AUTHOR]

The NASA's Earth Polychromatic Imaging Camera (EPIC) on board the Deep Space Climate Observatory (DSCOVR) satellite has been making multiple observations of the entire sunlit Earth in a given day from the Sun-Earth Largangian L1 point since the summer of 2015. EPIC contains 10 narrow channels in the 317–780 nm solar spectral range. The data acquired with EPIC have already been used in a variety of scientific investigations, including the study of the global ozone levels, aerosol index and aerosol optical depth, UV reflectivity of clouds over land and ocean, cloud height over land and ocean, and vegetation indices. In this article, we report that EPIC data, particularly for the data measured with narrow channels centered near 443, 551, and 680 nm, can also have important applications in remote sensing of ocean color in different geographical regions. We have modified a version of a multi-channel atmospheric correction algorithm for Moderate Resolution Imaging SpectroRadiometer (MODIS) ocean color applications and adapted the algorithm for processing EPIC data. We present three case studies on water leaving reflectance retrievals from EPIC data acquired over a large turbid river, inland lakes, and oceans. We conclude that a future ocean color instrument on board a satellite at the L1 point, which provides continuous view of the full sunlit disk of the Earth, will complement and extend ocean color observations with the low Earth observing polar orbital and geostationary satellite instruments in both the spatial and time domains. [ABSTRACT FROM AUTHOR]

The analysis of climate variability and change is generally carried out using momentum statistics such as trends, means, variance, etc. Those techniques are the cornerstone of any statistical analysis but they do not take into account the complexity of the system. Chaos theory opened paths to analyze those changes by expanding the ideas of information theory to reconstruct the dynamics of strange attractors, and the algorithms developed in that field can be applied to the Earth's system to study the complexity of climate and its evolution. Earth's emission of radiation can be described as a nonlinear system mixing deterministic processes with stochastic variations driven by solar radiation. In our analysis, we use the Approximate Entropy algorithm along with MERRA-2 data (Modern-Era Retrospective analysis for Research and Applications, Version 2) to evaluate the changes in the complexity of climate since 1980. Our analysis is performed with Earth's shortwave and longwave radiation data to quantify the changes in the nonlinearity of the system in the near past based on satellite observations. [ABSTRACT FROM AUTHOR]

Highlights • Analytical 1D radiative transfer model developed for EPIC/DSCOVR's in-band/continuum radiance ratios in O 2 A- and B-bands. • We quantify the impact of random measurement error on the retrieved cloud top height (CTH). • We quantify the systematic bias in retrieved CTH when in-cloud light penetration is ignored in the forward model. • Confirm with a different approach a previous assessment of EPIC's cloud profiling capability: only one piece of information. • A remarkable invariance of mean pathlength in arbitrarily-shaped non-absorbing media is verified for plane-parallel slabs. Abstract In a companion paper [Davis et al., JQSRT 216, 6–16 (2018)], we used a numerical 1D radiative transfer (RT) model and the statistical formalism of optimal estimation to quantify cloud information content in the O 2 A- and B-band channels of the Earth Polychromatic Imaging Camera (EPIC) on the Deep Space Climate ObserVatoRy (DSCOVR) platform that images the Earth's sunlit hemisphere from the vantage of the Lagrange-1 point. These two pairs of "in-band" and nearby "reference" radiances are combined into differential optical absorption spectroscopic (DOAS) ratios for both A- and B-bands, from which one can derive, in principle, both cloud top height (CTH) and cloud geometric thickness (CGT). However, Davis et al. show that under most circumstances, there is much redundancy between the two DOAS ratios and, in practice, only CTH can be reliably and accurately retrieved. Here, we derive a simplified analytical 1D RT model for the DOAS ratios to gain physical insights as well as quantify both the CTH retrieval bias from neglecting in-cloud absorption and the impact of measurement error on CTH and CGT retrievals. Using this alternative approach, we again show that only CTH can be inferred reliably when unavoidable measurement error is factored in. Finally, our new theoretical developments are related to a recently uncovered invariance property of the mean path cumulated by light in arbitrarily-shaped optical media of arbitrary opacity with arbitrary scattering properties, as long as it is conservative. [ABSTRACT FROM AUTHOR]

The National Oceanic and Atmospheric Administration (NOAA) Deep Space Climate Observatory (DSCOVR) spacecraft was launched on 11 February 2015 and in June 2015 achieved its orbit at the first Lagrange point (L1), 1.5 million km from Earth toward the sun. There are two National Aeronautics and Space Administration (NASA) Earth-observing instruments on board: the Earth Polychromatic Imaging Camera (EPIC) and the National Institute of Standards and Technology Advanced Radiometer (NISTAR). The purpose of this paper is to describe various capabilities of the DSCOVR EPIC instrument. EPIC views the entire sunlit Earth from sunrise to sunset at the backscattering direction (scattering angles between 168.5° and 175.5°) with 10 narrowband filters: 317, 325, 340, 388, 443, 552, 680, 688, 764, and 779 nm. We discuss a number of preprocessing steps necessary for EPIC calibration including the geolocation algorithm and the radiometric calibration for each wavelength channel in terms of EPIC counts per second for conversion to reflectance units. The principal EPIC products are total ozone (O3) amount, scene reflectivity, erythemal irradiance, ultraviolet (UV) aerosol properties, sulfur dioxide (SO2) for volcanic eruptions, surface spectral reflectance, vegetation properties, and cloud products including cloud height. Finally, we describe the observation of horizontally oriented ice crystals in clouds and the unexpected use of the O2 B-band absorption for vegetation properties. [ABSTRACT FROM AUTHOR]

We use computational 1D radiative transfer modeling and the formalism of optimal estimation to quantify the cloud information content in the oxygen A- and B-band channels of the Earth Polychromatic Imaging Camera (EPIC) on the Deep Space Climate ObserVatoRy (DSCOVR) platform. EPIC/DSCOVR images the sunlit hemisphere of our planet from  ≈ 1,500,000 km away with  ≈ 8 km pixels at the center of the disc. EPIC pixel-scale spectral data is used to estimate in-band/continuum radiance ratios for O 2 ’s A- and B-bands, from which one can derive, in principle, both cloud top height and cloud geometrical thickness. We use the general framework of optimal estimation theory to show that in practice, once measurement error is factored in, only cloud top height can be reliably inferred. With that limitation in mind, we discuss the ramifications for retrieval algorithm development. [ABSTRACT FROM AUTHOR]

Growth performance and meat quality are important traits for the pig industry and consumers. Adipose tissue is the main site at which fat storage and fatty acid synthesis occur. Therefore, we combined high-throughput transcriptomic sequencing in adipose and muscle tissues with the quantification of corresponding phenotypic features using seven Chinese indigenous pig breeds and one Western commercial breed (Yorkshire). We obtained data on 101 phenotypic traits, from which principal component analysis distinguished two groups: one associated with the Chinese breeds and one with Yorkshire. The numbers of differentially expressed genes between all Chinese breeds and Yorkshire were shown to be 673 and 1056 in adipose and muscle tissues, respectively. Functional enrichment analysis revealed that these genes are associated with biological functions and canonical pathways related to oxidoreductase activity, immune response, and metabolic process. Weighted gene coexpression network analysis found more coexpression modules significantly correlated with the measured phenotypic traits in adipose than in muscle, indicating that adipose regulates meat and carcass quality. Using the combination of differential expression, QTL information, gene significance, and module hub genes, we identified a large number of candidate genes potentially related to economically important traits in pig, which should help us improve meat production and quality. [ABSTRACT FROM AUTHOR]

The trans-Atlantic dust transport has important implications for human and ecosystem health, the terrestrial and oceanic biogeochemical cycle, weather systems, and climate. This study provides an observation-based multiyear estimate of trans-Atlantic dust transport using a 7-year (2007–2013) record of the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) measurements of the three dimensional distribution of aerosol backscatter, extinction and depolarization ratio in both cloud-free and above-cloud conditions. We estimate that on a basis of the 7-year average and integration over 10°S–30°N, 182 Tg a − 1 dust leaves the coast of North Africa at 15°W, of which 132 Tg a − 1 and 43 Tg a − 1 reaches 35°W and 75°W, respectively. These flux estimates have an overall known uncertainty of ±(45–70)%. Because of lack of reliable observations, uncertainties associated with the diurnal variation of dust and the missing below-cloud dust cannot be quantified. Significant seasonal variations are observed in both the magnitude of total dust mass flux and its meridional and vertical distributions. The interannual variability of annual dust mass flux is highly anti-correlated with the prior-year Sahel Precipitation Index. Using only cloud-free aerosol observations to calculate dust mass flux could introduce a high bias when compared with all-sky conditions that include both cloud-free and above-cloud aerosol observations. The bias is about 20% at 35°W and 75°W in boreal winter and spring based on the 7-year average, as long as dust within and below low-level clouds is negligible. [ABSTRACT FROM AUTHOR]

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]