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An artificial neural network (ANN) algorithm, employing several Aqua MODerate-resolution Imaging Spectroradiometer (MODIS) channels, the retrieved cloud phase and total cloud visible optical depth, and temperature and humidity vertical profiles is trained to detect multilayer (ML) ice-over-water cloud systems identified by matched 2008 CloudSat and CALIPSO (CC) data. The trained multilayer cloud-detection ANN (MCANN) was applied to 2009 MODIS data resulting in combined ML and single layer detection accuracies of 87 % (89 %) and 86 % (89 %) for snow-free (snow-covered) regions during the day and night, respectively. Overall, it detects 55 % and ∼ 30 % of the CC ML clouds over snow-free and snow-covered surfaces, respectively, and has a relatively low false alarm rate. The net gain in accuracy, which is the difference between the true and false ML fractions, is 7.5 % and ∼ 2.0 % over snow-free and snow/ice-covered surfaces. Overall, the MCANN is more accurate than most currently available methods. When corrected for the viewing-zenith-angle dependence of each parameter, the ML fraction detected is relatively invariant across the swath. Compared to the CC ML variability, the MCANN is robust seasonally and interannually and produces similar distribution patterns over the globe, except in the polar regions. Additional research is needed to conclusively evaluate the viewing zenith angle (VZA) dependence and further improve the MCANN accuracy. This approach should greatly improve the monitoring of cloud vertical structure using operational passive sensors. [ABSTRACT FROM AUTHOR]

An artificial neural network (ANN) algorithm, employing several Aqua MODerate-resolution Imaging Spectroradiometer (MODIS) channels, the retrieved cloud phase and total cloud visible optical depth, and temperature and humidity vertical profiles is trained to detect multilayer (ML) ice-over-water cloud systems identified by matched 2008 CloudSat and CALIPSO (CC) data. The trained MLANN was applied to 2009 MODIS data resulting in combined ML and single layer detection accuracies of 87 % (89 %) and 86 % (89 %) for snow-free (snow-covered) regions during the day and night, respectively. Overall, it detects 55 % and ~30 % of the CC ML clouds over snow-free and snow-covered surfaces, respectively, and has a relatively low false alarm rate. The net gain in accuracy, which is the difference between the true and false ML fractions, is 7.5 % and ~2.0 % over snow-free and snow/ice-covered surfaces. Overall, the MLANN is more accurate than most currently available methods. When corrected for the viewing-zenith-angle dependence of each parameter, the ML fraction detected is relatively invariant across the swath. Compared to the CC ML variability, the MLANN is robust seasonally and interannually, and produces similar distribution patterns over the globe, except in the polar regions. Additional research is needed to conclusively evaluate the VZA dependence and further improve the MLANN accuracy. This approach should greatly improve the monitoring of cloud vertical structure using operational passive sensors. [ABSTRACT FROM AUTHOR]

The decades-long Clouds and Earth's Radiant Energy System (CERES) Project includes both cloud and radiation measurements from instruments on the Aqua, Terra, and Suomi National Polar-orbiting Partnership (SNPP) satellites. To build a reliable long-term climate data record, it is important to determine the accuracies of the parameters retrieved from the sensors on each satellite. Cloud amount, phase, and top height derived from radiances taken by the Visible Infrared Imaging Radiometer Suite (VIIRS) on the SNPP are evaluated relative to the same quantities determined from measurements by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on the Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) spacecraft. The accuracies of the VIIRS cloud fractions are found to be as good as or better than those for the CERES amounts determined from Aqua MODerate-resolution Imaging Spectroradiometer (MODIS) data and for cloud fractions estimated by two other operational algorithms. Sensitivities of cloud fraction bias to CALIOP resolution, matching time window, and viewing zenith angle are examined. VIIRS cloud phase biases are slightly greater than their CERES MODIS counterparts. A majority of cloud phase errors are due to multilayer clouds during the daytime and supercooled liquid water clouds at night. CERES VIIRS cloud-top height biases are similar to those from CERES MODIS, except for ice clouds, which are smaller than those from CERES MODIS. CERES VIIRS cloud phase and top height uncertainties overall are very similar to or better than several operational algorithms, but fail to match the accuracies of experimental machine learning techniques. The greatest errors occur for multilayered clouds and clouds with phase misclassification. Cloud top heights can be improved by relaxing tropopause constraints, improving lapse-rate to model temperature profiles, and accounting for multilayer clouds. Other suggestions for improving the retrievals are also discussed. [ABSTRACT FROM AUTHOR]

Cloud properties are essential for the Clouds and the Earth's Radiant Energy System (CERES) Project, enabling accurate interpretation of measured broadband radiances, providing a means to understand global cloud-radiation interactions, and constituting an important climate record. Producing consistent cloud retrievals across multiple platforms is critical for generating a multidecadal cloud and radiation record. Techniques used by CERES for retrievals from measurements by the MODerate-Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua platforms are adapted for the application to radiances from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership to continue the CERES record beyond the MODIS era. The algorithm adjustments account for spectral and channel differences, use revised reflectance models, and set new thresholds for detecting thin cirrus clouds at night. Cloud amounts from VIIRS are less than their MODIS counterparts by 0.016 during the day and 0.026 at night, but trend consistently over the 2012–2020 period. The VIIRS mean liquid water cloud fraction differs by ~0.01 from the MODIS amount. The average cloud heights from VIIRS differ from the MODIS heights by less than 0.2 km, except the VIIRS daytime ice cloud heights, which are 0.4 km higher. The mean VIIRS nonpolar optical depths are 17% (1%) larger (smaller) than those from MODIS for liquid (ice) clouds. The VIIRS cloud hydrometeor sizes are generally smaller than their MODIS counterparts. Discrepancies between the MODIS and VIIRS properties stem from spectral and spatial resolution differences, new tests at night, calibration inconsistencies, and new reflectance models. Many of those differences will be addressed in future editions. [ABSTRACT FROM AUTHOR]

Satellite observations from Clouds and the Earth's Radiant Energy System (CERES) radiometers have produced over two decades of world-class data documenting time–space variations in Earth's top-of-atmosphere (TOA) radiation budget. In addition to energy exchanges among Earth and space, climate studies require accurate information on radiant energy exchanges at the surface and within the atmosphere. The CERES Cloud Radiative Swath (CRS) data product extends the standard Single Scanner Footprint (SSF) data product by calculating a suite of radiative fluxes from the surface to TOA at the instantaneous CERES footprint scale using the NASA Langley Fu–Liou radiative transfer model. Here, we describe the CRS flux algorithm and evaluate its performance against a network of ground-based measurements and CERES TOA observations. CRS all-sky downwelling broadband fluxes show significant improvements in surface validation statistics relative to the parameterized fluxes on the SSF product, including a ∼30%–40% (∼20%) reduction in SW↓ (LW↓) root-mean-square error (RMSΔ), improved correlation coefficients, and the lowest SW↓ bias over most surface types. RMSΔ and correlation statistics improve over five different surface types under both overcast and clear-sky conditions. The global mean computed TOA outgoing LW radiation (OLR) remains within <1% (2–3 W m−2) of CERES observations, while the global mean reflected SW radiation (RSW) is excessive by ∼3.5% (∼9 W m−2) owing to cloudy-sky computation errors. As we highlight using data from two remote field campaigns, the CRS data product provides many benefits for studies requiring advanced surface radiative fluxes. [ABSTRACT FROM AUTHOR]

Accurate cloud and precipitation forecasts are a fundamental component of short-range data assimilation/model prediction systems such as the NOAA 3-km High-Resolution Rapid Refresh (HRRR) or the 13-km Rapid Refresh (RAP). To reduce cloud and precipitation spinup problems, a nonvariational assimilation technique for stratiform clouds was developed within the Gridpoint Statistical Interpolation (GSI) data assimilation system. One goal of this technique is retention of observed stratiform cloudy and clear 3D volumes into the subsequent model forecast. The cloud observations used include cloud-top data from satellite brightness temperatures, surface-based ceilometer data, and surface visibility. Quality control, expansion into spatial information content, and forward operators are described for each observation type. The projection of data from these observation types into an observation-based cloud-information 3D gridded field is accomplished via identification of cloudy, clear, and cloud-unknown 3D volumes. Updating of forecast background fields is accomplished through clearing and building of cloud water and cloud ice with associated modifications to water vapor and temperature. Impact of the cloud assimilation on short-range forecasts is assessed with a set of retrospective experiments in warm and cold seasons using the RAPv5 model. Short-range (1–9 h) forecast skill is improved in both seasons for cloud ceiling and visibility and for 2-m temperature in daytime and with mixed results for other measures. Two modifications were introduced and tested with success: use of prognostic subgrid-scale cloud fraction to condition cloud building (in response to a high bias) and removal of a WRF-based rebalancing. Significance Statement: Short-range weather prediction models are particularly dependent on accurate initial representation of the current state of the atmosphere, including clouds. We describe a method for using satellite and surface-based observations of stratiform clouds to update the model cloud state at the initial time. The method is tested with the NOAA RAP model, and leads to improvement for forecast accuracy for several hours including for rare low-cloud events that are important for aviation safety. This stratiform-cloud data assimilation is also applied in the convective-scale HRRR model, where it complements radar/lightning assimilation for convective clouds described elsewhere. Assimilation to provide accurate initial cloud fields is critical to improve forecasts for aviation, energy, and severe weather applications. [ABSTRACT FROM AUTHOR]

Satellite retrievals of cloud droplet effective radius (푟 푒) and optical depth (t) from the Thirteenth Geostationary Operational Environmental Satellite (GOES-13), and the MOderate resolution Imaging Spectroradiometer (MODIS) onboard Aqua and Terra are evaluated with airborne data collected over the midlatitude boundary layer during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). The airborne dataset comprises in-situ 푟푒 from the Cloud Droplet Probe (CDP) and remotely sensed 푟푒 and t from the airborne Research Scanning Polarimeter (RSP). GOES-13 and MODIS (Aqua and Terra) 푟푒 values are systematically greater than those from the CDP and RSP by at least 4.8 µm (GOES-13) and 1.7 µm (MODIS) despite relatively high linear correlations coefficients (푟 = 0.52 - 0.68). In contrast, the satellite t underestimates its RSP counterpart by -3.0, with 푟 = 0.76 - 077. Overall, MODIS yields better agreement with airborne data than GOES-13, with biases consistent with those reported for subtropical stratocumulus clouds. While the negative bias in satellite t is mostly due to the retrievals having been collected in highly heterogeneous cloud scenes, the causes for the positive bias in satellite 푟푒, especially for GOES-13, are more complex. Although the high viewing zenith angle (~65°) and coarser pixel resolution for GOES- 13 could explain a 푟푒 bias of at least 0.7 µm, the higher GOES-13 푟푒 bias relative to that from MODIS is likely rooted in other factors. In this regard, a near monotonic increase was also observed in GOES-13 푟푒 up to 1.0 µm with satellite scattering angle (푒) over the angular range 116°-165°, that is, 푟푒 increases toward the backscattering direction. Understanding the variations of 푟푒 with 푟푒 will require the combined use of theoretical computations along with intercomparisons of satellite retrievals derived from sensors with dissimilar viewing geometry. [ABSTRACT FROM AUTHOR]

A variety of satellite and ground-based observations are used to study how diurnal variations of cloud radiative heating affect the life cycle of anvil clouds over the tropical western Pacific Ocean. High clouds thicker than 2 km experience longwave heating at cloud base, longwave cooling at cloud top, and shortwave heating at cloud top. The shortwave and longwave effects have similar magnitudes during midday, but only the longwave effect is present at night, so high clouds experience a substantial diurnal cycle of radiative heating. Furthermore, anvil clouds are more persistent or laterally expansive during daytime. This cannot be explained by variations of convective intensity or geographic patterns of convection, suggesting that shortwave heating causes anvil clouds to persist longer or spread over a larger area. It is then investigated if shortwave heating modifies anvil development by altering turbulence in the cloud. According to one theory, radiative heating drives turbulent overturning within anvil clouds that can be sufficiently vigorous to cause ice nucleation in the updrafts, thereby extending the cloud lifetime. High-frequency air motion and ice-crystal number concentration are shown to be inversely related near cloud top, however. This suggests that turbulence depletes or disperses ice crystals at a faster rate than it nucleates them, so another mechanism must cause the diurnal variation of anvil clouds. It is hypothesized that radiative heating affects anvil development primarily by inducing a mesoscale circulation that offsets gravitational settling of cloud particles. [ABSTRACT FROM AUTHOR]

Satellite quantification of aerosol effects on clouds relies on aerosol optical depth (AOD) as a proxy for aerosol concentration or cloud condensation nuclei (CCN). However, the lack of error characterization of satellite-based results hampers their use for the evaluation and improvement of global climate models. We show that the use of AOD for assessing aerosol-cloud interactions (ACI) is inadequate over vast oceanic areas in the subtropics. Instead, we postulate that a more physical approach that consists of matching vertically resolved aerosol data from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite at the cloud-layer height with Aqua Moderate-resolution Imaging Spectroradiometer (MODIS) cloud retrievals reduces uncertainties in satellite-based ACI estimates. Combined aerosol extinction coefficients (σ) below cloud-top (σBC) from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and cloud droplet number concentrations (Nd) from Aqua-MODIS yield high correlations across a broad range of σBC values, with σBC quartile correlations > 0.78. In contrast, CALIOP-based AOD yields correlations with MODIS Nd of less than 0.62 for the two lower AOD quartiles. Moreover, σBC explains 41 % of the spatial variance in MODIS Nd, whereas AOD only explains 17 %, primarily caused by the lack of spatial covariability in the eastern Pacific. Compared with σBC, near-surface σ weakly correlates in space with MODIS Nd, accounting for a 16 % variance. It is concluded that the linear regression calculated from ln(Nd)-ln(σBC) (the standard method for quantifying ACI) is more physically meaningful than that derived from the Nd-AOD pair. [ABSTRACT FROM AUTHOR]

Numerical weather prediction models tend to underestimate cloud presence and therefore often overestimate global horizontal irradiance (GHI). The assimilation of cloud water path (CWP) retrievals from geostationary satellites using an ensemble Kalman filter (EnKF) led to improved short-term GHI forecasts of the Weather Research and Forecasting (WRF) model in mid-latitudes in case studies. An evaluation of the method under tropical conditions and a quantification of this improvement for study periods of more than a few days is still missing. This paper focuses on the assimilation of CWP retrievals in three phases (ice, supercooled, and liquid) in a 6-hourly cycling procedure, and on the impact of this method on short-term forecasts of GHI for Reunion Island, a tropical island in the South-West Indian Ocean. The multi-layer gridded cloud properties of NASA Langley's Satellite ClOud and Radiation Property retrieval System (SatCORPS) are assimilated using the EnKF of the Data Assimilation Research Testbed (DART) manhattan release (revision 12002) and the advanced research WRF (ARW) v3.9.1.1. The ability of the method to improve cloud analyses and GHI forecasts is demonstrated and a comparison using independent radiosoundings shows a reduction of specific humidity bias in the WRF analyses, especially in the low and mid troposphere. Ground-based GHI observations at 12 sites on Reunion Island are used to quantify the impact of CWP DA. Over a total of 44 days during austral summer time, when averaged over all sites, CWP data assimilation has a positive impact on GHI forecasts for all lead times between 5 and 14 hours. Root Mean Squared Error and Mean Absolute Error are reduced by 4% and 3% respectively. [ABSTRACT FROM AUTHOR]

Linear contrail coverage, optical property, and radiative forcing data over the Northern Hemisphere (NH) are derived from a year (2012) of Terra and Aqua Moderate-resolution Imaging Spectroradiometer (MODIS) imagery, and are compared with previously published 2006 results (Duda et al., 2013; Bedka et al., 2013; Spangenberg et al., 2013) using a consistent retrieval methodology. Differences in the observed Terra-minus-Aqua screened contrail coverage and patterns in the 2012 annual-mean air traffic estimated with respect to satellite overpass time suggest that most contrails detected by the contrail detection algorithm (CDA) form approximately 2 h before overpass time. The 2012 screened NH contrail coverage (Mask B) shows a relative 3 % increase (from 0.136 % to 0.140 %) compared to 2006 data for Terra and increased by almost 7 % (0.134 % to 0.143 %) for Aqua. A new post-processing algorithm added to the contrail mask processing estimated that the total contrail cirrus coverage visible in the MODIS imagery may be three to four times larger than the linear contrail coverage detected by the CDA. This estimate is similar in magnitude to the spreading factor estimated by Minnis et al. (2013). Contrail property retrievals of the 2012 data indicate that both contrail optical depth and contrail effective diameter decreased approximately 10 % between 2006 and 2012. The decreases may be attributed to better background cloudiness characterization, changes in the waypoint screening, or changes in contrail temperature. The total mean contrail radiative forcing (TCRF) for all 2012 Terra observations were -6.3, 14.3, and 8.0 mW m-2 for the shortwave (SWCRF), longwave (LWCRF), and net forcings, respectively. These values are approximately 20 % less than the corresponding 2006 Terra estimates. The decline in TCRF results from the decrease in normalized CRF, partially offset by the 3 % increase in overall contrail coverage in 2012. The TCRFs for 2012 Aqua are similar, -6.4, 15.5, and 9.0 mW m-2 for shortwave, longwave, and net radiative forcing. The strong correlation between the relative changes in both total SWCRF and LWCRF between 2006 and 2012 and the corresponding relative changes in screened contrail coverage over each air traffic region suggests that regional changes in TCRF from year to year are dominated by interannual changes in contrail coverage over each area. [ABSTRACT FROM AUTHOR]

Abstract: Retrievals of convective cloud microphysical properties based on passive satellite imagery are difficult. To help quantify their uncertainties, ice water paths (IWPs) retrieved from the NASA Clouds and the Earth's Radiant Energy System project using Geostationary Operational Environmental Satellite (GOES) and Terra/Aqua MODerate‐resolution Imaging Spectroradiometer observations are compared with IWPs retrieved from Next‐Generation Radar (NEXRAD) observations over a large domain (32°N to 40°N and 105°W to 91°W) during the 2011 Midlatitude Continental Convective Clouds Experiment field campaign. Based on comparisons of pixel‐level (4 km × 4 km) daytime IWP retrievals from NEXRAD and GOES, it is found that NEXRAD‐ and GOES‐retrieved mean IWPs are 2.03 and 1.83 kg m−2, respectively, for ice‐phase cloud in thick anvil area. Their mean difference of 0.20 kg m−2 (with 95% confidence interval: 0.14–0.26 kg m−2) is within the uncertainty of NEXRAD retrievals. However, the low correlation between pixel‐to‐pixel comparisons indicates a large variation in GOES‐retrieved IWP. For mixed‐phase clouds in thick anvil areas, in addition to IWPs, total water paths (TWPs, sum of ice and liquid water path) are estimated with aid of aircraft measurements for NEXRAD retrievals and corrected using a TWP parameterization for GOES retrievals. The mean values of estimated TWPs from NEXRAD (corrected using aircraft in situ measurements) and GOES are similar. GOES and Clouds and the Earth's Radiant Energy System‐MODerate‐resolution Imaging Spectroradiometer‐retrieved IWPs/TWPs generally do not exceed 5 kg m−2. Large differences and low correlations exist between satellite and NEXRAD retrievals in stratiform rain areas. Possible reasons for the differences between retrievals are discussed. [ABSTRACT FROM AUTHOR]

Recent studies have found that flight through deep convective storms and ingestion of high mass concentrations of ice crystals, also known as high ice water content (HIWC), into aircraft engines can adversely impact aircraft engine performance. These aircraft engine icing events caused by HIWC have been documented during flight in weak reflectivity regions near convective updraft regions that do not appear threatening in onboard weather radar data. Three airborne field campaigns were conducted in 2014 and 2015 to better understand how HIWC is distributed in deep convection, both as a function of altitude and proximity to convective updraft regions, and to facilitate development of new methods for detecting HIWC conditions, in addition to many other research and regulatory goals. This paper describes a prototype method for detecting HIWC conditions using geostationary (GEO) satellite imager data coupled with in-situ total water content (TWC) observations collected during the flight campaigns. Three satellite-derived parameters were determined to be most useful for determining HIWC probability: 1) the horizontal proximity of the aircraft to the nearest overshooting convective updraft or textured anvil cloud, 2) tropopause-relative infrared brightness temperature, and 3) daytime-only cloud optical depth. Statistical fits between collocated TWC and GEO satellite parameters were used to determine the membership functions for the fuzzy logic derivation of HIWC probability. The products were demonstrated using data from several campaign flights and validated using a subset of the satellite-aircraft collocation database. The daytime HIWC probability was found to agree quite well with TWC time trends and identified extreme TWC events with high probability. Discrimination of HIWC was more challenging at night with IR-only information. The products show the greatest capability for discriminating TWC ≥ 0.5 g m-3. Product validation remains challenging due to vertical TWC uncertainties and the typically coarse spatio-temporal resolution of the GEO data. [ABSTRACT FROM AUTHOR]

The study discusses the Arctic Radiation-IceBridge Sea and Ice Experiment (ARISE) an aircraft project on the cloud properties and radiative effects that support remote sensing and climate modeling approaches to the study of Arctic ice decline. It reports that the project helped NASA obtain data during the period between sea ice minimum in late summer and autumn and the start of refreezing. Topics discusses include the experiment design and execution, initial results and their implications.

Retrievals of ice cloud properties using infrared measurements at 3.7, 6.7, 7.3, 8.5, 10.8, and 12.0 μm can provide consistent results regardless of solar illumination, but are limited to cloud optical thicknesses τ < ∼6. This paper investigates the variations in radiances at these wavelengths over a deep convective cloud system for their potential to extend retrievals of τ and ice particle size De to optically thick clouds. Measurements from an imager, an interferometer, the Cloud Physics Lidar (CPL), and the Cloud Radar System (CRS) aboard the NASA ER-2 aircraft during the NASA TC4 (Tropical Composition, Cloud and Climate Coupling) experiment flight during 5 August 2007, are used to examine the retrieval potential of infrared radiances over optically thick ice clouds. Simulations based on coincident in situ measurements and combined cloud τ from CRS and CPL measurements are comparable to the observations. They reveal that brightness temperatures at these bands and their differences (BTD) are sensitive to τ up to ∼20 and that for ice clouds having τ > 20, the 3.7-10.8 μm and 3.7-6.7 μm BTDs are the most sensitive to De. Satellite imagery appears to be consistent with these results suggesting that τ and De could be retrieved for greater optical thicknesses than previously assumed. But, because of sensitivity of the BTDs to uncertainties in the atmospheric profiles of temperature, humidity, and ice water content, and sensor noise, exploiting the small BTD signals in retrieval algorithms will be very challenging. [ABSTRACT FROM AUTHOR]

When radiometers on satellites point toward Earth with the goal of sensing an important variable quantitatively, rather than just creating a pleasing image, the task at hand is often not simple. The electromagnetic energy detected by the radiometers is a puzzle of various signals; it must be solved to quantify the specific physical variable. This task, called the retrieval or remote-sensing process, is important to most satellite-based observation programs. It would be ideal to test the algorithms for retrieval processes in a sealed laboratory, where all the relevant parameters could be easily measured. The size and complexity of the Earth make this impractical. NASA's Clouds and the Earth's Radiant Energy System (CERES) project has done the next-best thing by developing a long-term radiation observation site over the ocean. The relatively low and homogeneous surface albedo of the ocean make this type of site a simpler environment for observing and validating radiation parameters from satellite-based instruments. To characterize components of the planet's energy budget, CERES uses a variety of retrievals associated with several satellite-based instruments onboard NASA's Earth Observing System (EOS). A new surface observation project called the CERES Ocean Validation Experiment (COVE), operating on a rigid ocean platform, is supplying data to validate some of these instruments and retrieval products. This article describes the ocean platform and the types of observations being performed there, and highlights of some scientific problems being addressed. [ABSTRACT FROM AUTHOR]