The INvestigation of Convective UpdraftS (INCUS) is a recently selected NASA Earth Ventures Mission. The overarching goal of INCUS is to enhance our understanding of why, when and where tropical convective storms form, and why only some storms produce extreme weather. Life on Earth is bound to convective storms, from the fresh water they supply to the extreme weather they produce. Much of the vertical transport of water and air between Earth’s surface and the upper troposphere is facilitated by convective storms. This vertical transport of water and air, referred to as convective mass flux (CMF), plays a critical role in the weather and climate system through its influence on storm intensity, precipitation rates, upper tropospheric moistening, high cloud feedbacks, and the large-scale circulation. Recent studies have also suggested that CMF may change with changing climates. In spite of the critical role of this vertical transport of water and air within the weather and climate system, much is not understood regarding the way in which various environmental factors govern this mass transport, nor the subsequent impacts of CMF on high clouds and extreme weather. Representation of CMF is also a major source of error in weather and climate models, thereby limiting our ability to predict convective storms and their associated feedbacks on weather through climate timescales. INCUS is a NASA class-D mission. Three RainCube-heritage Ka-band 5-beam scanning radars that are compatible with SmallSat platforms comprise the mission. The satellite platforms will be 30 and 90 seconds apart. Each SmallSat will carry one radar system each, and the middle SmallSat will house a single TEMPEST-D-heritage cross-track-scanning passive microwave radiometer with four channels between 150 and 190 GHz. Through its novel measurements of time-differenced profiles of radar reflectivity, INCUS is the first systematic investigation of the rapidly evolving CMF within tropical convective storms. The primary INCUS objectives are: (1) to determine the predominant environmental properties controlling CMF in tropical convective storms; (2) to determine the relationship between CMF and high anvil clouds; (3) to determine the relationship between CMF and the type and intensity of the extreme weather produced; and (4) to evaluate these relationships between CMF and environmental factors, high anvil clouds, and extreme weather within weather and climate models. The ground breaking observations of convective storms by INCUS are expected to significantly enhance our understanding and prediction of convective processes and extreme weather in current and future climates.

Unlike large, expensive, and high-risk operational satellites, small/cube satellites (SmallSats) are a small, inexpensive, and a low-risk type of satellite. As a NOAA Cooperative Institute with specialties in satellite data processing and data assimilation, CIRA is funded by a Technology Maturation Program (TMP) research project to help NOAA exploit upcoming constellation of SmallSats to be considered for use in operations. In this research, a CSU-led technology demonstration mission entitled “the Temporal Experiment for Storms and Tropical System - Demonstration (TEMPTEST-D)” is used as an example to explore quick and agile methodologies to entrain SmallSats into the NOAA processing stream. Specifically, a workflow that enables TEMPEST-D data for assimilation into the NCEP Global Forecast System (GFS) with Finite-Volume Cube-Sphered (FV3) dycore (FV3GFS) under the Gridpoint Statistical Interpolation (GSI) based hybrid 4DEnVar system is established. One objective of this TMP research project is to assess the impact of SmallSat data on NOAA modeling and assimilation systems used in operations. We begin by asking whether the use of TEMPEST-D data is as good as the use of those obtained from well-established operational satellite sensors. Since the radiometric specification of TEMPEST-D is similar to the Microwave Humidity Sounder (MHS), we address the above question by directly comparing the following three cycled FV3GFS data assimilation and forecasting experiments: 1) the control experiment, which includes all routinely assimilated observations, but only assimilates MHS from the NOAA-19 and MetOp-B platforms, 2) the AddMHS experiment, which is the control plus MHS from the MetOp-A platform, and 3) the AddTEMPEST experiment, which is the control plus TEMPEST-D. By differentiating the AddMHS and AddTEMPEST experiments against the control experiment, we will be able to demonstrate that a cost-effective TEMPEST-D is as beneficial as a well-established operational satellite like MHS, in terms of aiding operational global weather forecasting. In addition, results from this research offers implications of the utility of a constellation of SmallSats microwave radiometers for global weather forecasting.

The prediction of tropical cyclone rapid intensification is one of the most pressing unsolved problems in hurricane forecasting. The signatures of gravity waves launched by strong convective updrafts are often clearly seen in airglow and carbon dioxide thermal emission spectra under favorable atmospheric conditions. By continuously monitoring the Atlantic hurricane belt from the main development region to the vulnerable sections of the continental United States at high cadence, it will be possible to investigate the utility of storm-induced gravity wave observations for the diagnosis of impending storm intensification. Such a capability would also enable significant improvements in our ability to characterize the 3D transient behavior of upper-atmospheric gravity waves and point the way to future observing strategies that could mitigate the risk to human life caused by severe storms. This paper describes a new mission concept involving a midinfrared imager hosted aboard a geostationary satellite positioned at approximately 80°W longitude. The sensor’s 3-km pixel size ensures that the gravity wave horizontal structure is adequately resolved, while a 30-s refresh rate enables improved definition of the dynamic intensification process. In this way the transient development of gravity wave perturbations caused by both convective and cyclonic storms may be discerned in near–real time.

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Knowledge of cloud-base height (CBH) is important to describe cloud radiative feedbacks in numerical models and is of practical relevance to the aviation community. Whereas satellite remote sensing with passive radiometers traditionally has provided a ready means for estimating cloud-top height (CTH) and cloud water path (CWP), assignment of CBH requires heavy assumptions on the distribution of CWP within the cloud profile. An attempt to retrieve CBH has been included as part of the VIIRS environmental data records, produced operationally as part of the Suomi-National Polar-Orbiting Partnership (SNPP) and the forthcoming Joint Polar Satellite System. Through formal validation studies tied to the program, it was found that the operational CBH algorithmfailed tomeet performance specifications inmany cases. This paper presents a new methodology for retrieving CBH of the uppermost cloud layer, developed through statistical analyses relating cloud geometric thickness (CGT) toCTHandCWP. The semiempirical approach, which relates these parameters via piecewise fitting, enlists A-Train satellite data [CloudSat cloud profiling radar (CPR), CALIPSO/CALIOP, and Aqua MODIS]. CBH is provided as the residual difference between CTH and CGT. By eliminating cloud type-dependent assumptions on CWP distribution, artifacts common to the operational algorithm ( which contribute to high errors) are reduced. Special accommodations are made for handling optically thin cirrus and deep convection. An application to SNPP VIIRS is demonstrated, and the results are compared against global CloudSat observations. From the VIIRS-CloudSat daytime matchups (September-October 2013 and January-May 2015), the new algorithm outperforms the operational SNPP VIIRS algorithm, particularly when the retrieved CTH is accurate. Best performance is expected for single-layer liquid-phase clouds.

Gravity waves (disturbances to the density structure of the atmosphere whose restoring forces are gravity and buoyancy) comprise the principal form of energy exchange between the lower and upper atmosphere. Wave breaking drives the mean upper atmospheric circulation, determining boundary conditions to stratospheric processes, which in turn influence tropospheric weather and climate patterns on various spatial and temporal scales. Despite their recognized importance, very little is known about upper-level gravity wave characteristics. The knowledge gap is mainly due to lack of global, high-resolution observations from currently available satellite observing systems. Consequently, representations of wave-related processes in global models are crude, highly parameterized, and poorly constrained, limiting the description of various processes influenced by them. Here we highlight, through a series of examples, the unanticipated ability of the Day/Night Band (DNB) on the NOAA/NASA Suomi National Polar-orbiting Partnership environmental satellite to resolve gravity structures near the mesopause via nightglow emissions at unprecedented subkilometric detail. On moonless nights, the Day/Night Band observations provide all-weather viewing of waves as they modulate the nightglow layer located near the mesopause (∼ 90 km above mean sea level). These waves are launched by a variety of physical mechanisms, ranging from orography to convection, intensifying fronts, and even seismic and volcanic events. Cross-referencing the Day/Night Band imagery with conventional thermal infrared imagery also available helps to discern nightglow structures and in some cases to attribute their sources. The capability stands to advance our basic understanding of a critical yet poorly constrained driver of the atmospheric circulation.

The objective of the National Aeronautics and Space Administration's (NASA) Orbiting Carbon Observatory-2 (OCO-2) mission is to retrieve the column-averaged carbon dioxide (CO2) dry air mole fraction (XCO2) from satellite measurements of reflected sunlight in the near-infrared. These estimates can be biased by clouds and aerosols, i.e., contamination, within the instrument's field of view. Screening of the most contaminated soundings minimizes unnecessary calls to the computationally expensive Level 2 (L2) XCO2 retrieval algorithm. Hence, robust cloud screening methods have been an important focus of the OCO-2 algorithm development team. Two distinct, computationally inexpensive cloud screening algorithms have been developed for this application. The A-Band Preprocessor (ABP) retrieves the surface pressure using measurements in the 0.76 µm O2 A band, neglecting scattering by clouds and aerosols, which introduce photon path-length differences that can cause large deviations between the expected and retrieved surface pressure. The Iterative Maximum A Posteriori (IMAP) Differential Optical Absorption Spectroscopy (DOAS) Preprocessor (IDP) retrieves independent estimates of the CO2 and H2O column abundances using observations taken at 1.61 µm (weak CO2 band) and 2.06 µm (strong CO2 band), while neglecting atmospheric scattering. The CO2 and H2O column abundances retrieved in these two spectral regions differ significantly in the presence of cloud and scattering aerosols. The combination of these two algorithms, which are sensitive to different features in the spectra, provides the basis for cloud screening of the OCO-2 data set.To validate the OCO-2 cloud screening approach, collocated measurements from NASA's Moderate Resolution Imaging Spectrometer (MODIS), aboard the Aqua platform, were compared to results from the two OCO-2 cloud screening algorithms. With tuning of algorithmic threshold parameters that allows for processing of ≃ 20–25 % of all OCO-2 soundings, agreement between the OCO-2 and MODIS cloud screening methods is found to be ≃ 85 % over four 16-day orbit repeat cycles in both the winter (December) and spring (April–May) for OCO-2 nadir-land, glint-land and glint-water observations.No major, systematic, spatial or temporal dependencies were found, although slight differences in the seasonal data sets do exist and validation is more problematic with increasing solar zenith angle and when surfaces are covered in snow and ice and have complex topography. To further analyze the performance of the cloud screening algorithms, an initial comparison of OCO-2 observations was made to collocated measurements from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). These comparisons highlight the strength of the OCO-2 cloud screening algorithms in identifying high, thin clouds but suggest some difficulty in identifying some clouds near the surface, even when the optical thicknesses are greater than 1.

The launch of the NASA CloudSat in April 2006 enabled the first satellite-based global observation of vertically resolved cloud information. However, CloudSat's nonscanning W-band (94 GHz) Cloud Profiling Radar (CPR) provides only a nadir cross section, or "curtain,'' of the atmosphere along the satellite ground track, precluding a full three-dimensional (3D) characterization and thus limiting its utility for certain model verification and cloud-process studies. This paper details an algorithm for extending a limited set of vertically resolved cloud observations to form regional 3D cloud structure. Predicated on the assumption that clouds of the same type (e. g., cirrus, cumulus, and stratocumulus) often share geometric and microphysical properties as well, the algorithm identifies cloud-type-dependent correlations and uses them to estimate cloud-base height and liquid/ice water content vertical structure. These estimates, when combined with conventional retrievals of cloud-top height, result in a 3D structure for the topmost cloud layer. The technique was developed on multiyear CloudSat data and applied to Moderate Resolution Imaging Spectroradiometer (MODIS) swath data from the NASA Aqua satellite. Data-exclusion experiments along the CloudSat ground track show improved predictive skill over both climatology and type-independent nearest-neighbor estimates. More important, the statistical methods, which employ a dynamic range-dependent weighting scheme, were also found to outperform type-dependent near-neighbor estimates. Application to the 3D cloud rendering of a tropical cyclone is demonstrated.

Clouds pose many operational hazards to the aviation community in terms of ceilings and visibility, turbulence, and aircraft icing. Realistic descriptions of the three-dimensional (3D) distribution and temporal evolution of clouds in numerical weather prediction models used for flight planning and routing are therefore of central importance. The introduction of satellite-based cloud radar (CloudSat) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) sensors to the National Aeronautics and Space Administration A-Train is timely in light of these needs but requires a new paradigm of model-evaluation tools that are capable of exploiting the vertical-profile information. Early results from the National Center for Atmospheric Research Model Evaluation Toolkit (MET), augmented to work with the emergent satellite-based active sensor observations, are presented here. Existing horizontal-plane statistical evaluation techniques have been adapted to operate on observations in the vertical plane and have been extended to 3D object evaluations, leveraging blended datasets from the active and passive A-Train sensors. Case studies of organized synoptic-scale and mesoscale distributed cloud systems are presented to illustrate the multiscale utility of the MET tools. Definition of objects on the basis of radar-reflectivity thresholds was found to be strongly dependent on the model's ability to resolve details of the cloud's internal hydrometeor distribution. Contoured-frequency-by-altitude diagrams provide a useful mechanism for evaluating the simulated and observed 3D distributions for regional domains. The expanded MET provides a new dimension to model evaluation and positions the community to better exploit active-sensor satellite observing systems that are slated for launch in the near future.

Large meteors (or superbolides [Ceplecha Z, et al. (1999) Meteoroids 1998:37-54]), although rare in recorded history, give sobering testimony to civilization's inherent vulnerability. A not-so-subtle reminder came on the morning of February 15, 2013, when a large meteoroid hurtled into the Earth's atmosphere, forming a superbolide near the city of Chelyabinsnk, Russia, ∼1,500 km east of Moscow, Russia [Ivanova MA, et al. (2013) Abstracts of the 76th Annual Meeting of the Meteoritical Society, 5366]. The object exploded in the stratosphere, and the ensuing shock wave blasted the city of Chelyabinsk, damaging structures and injuring hundreds. Details of trajectory are important for determining its specific source, the likelihood of future events, and potential mitigation measures. Earth-viewing environmental satellites can assist in these assessments. Here we examine satellite observations of the Chelyabinsk superbolide debris trail, collected within minutes of its entry. Estimates of trajectory are derived from differential views of the significantly parallax-displaced [e.g., Hasler AF (1981) Bull Am Meteor Soc 52:194-212] debris trail. The 282.7 ± 2.3° azimuth of trajectory, 18.5 ± 3.8° slope to the horizontal, and 17.7 ± 0.5 km/s velocity derived from these satellites agree well with parameters inferred from the wealth of surface-based photographs and amateur videos. More importantly, the results demonstrate the general ability of Earth-viewing satellites to provide valuable insight on trajectory reconstruction in the more likely scenario of sparse or nonexistent surface observations.

The NOAA operational total precipitable water (TPW) anomaly product is available to forecasters to display percentage of normal TPW in real time for applications like heavy precipitation forecasts. In this work, the TPW anomaly is compared to multilayer cloud frequency and vertical structure. The hypothesis is tested that the TPW anomaly is reflective of changes in cloud vertical distribution, and that anomalously moist atmospheres have more and deeper clouds, while dry atmospheres have fewer and thinner clouds. Cloud vertical occurrence profiles from the CloudSat 94-GHz radar and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) are composited according to TPW anomaly for summer and winter from 2007 to 2010. Three geographic regions are examined: the North Pacific (NPAC), the tropical east Pacific (Nino), and the Mississippi Valley (MSVL), which is a land-only region. Cloud likelihood increases as TPW anomaly values increase beyond 100% over MSVL and Nino. Over NPAC, shallow boundary layer cloud occurrence is not a function of TPW anomaly, while high clouds and deep clouds throughout the troposphere are more likely at higher TPW anomalies. In the Nino region, boundary layer clouds grow vertically as the TPW anomaly increases, and the anomaly range is smaller than in the midlatitudes. In summer, the MSVL region resembles Nino, but boundary layer clouds are observed less frequently than expected. The wintertime MSVL results do not show any compelling relationship, perhaps because of the difficulties in computing TPW anomaly in a very dry atmosphere.

This paper reports on the early mission performance of the radar and other major aspects of the CloudSat mission. The Cloudsat cloud profiling radar (CPR) has been operating since 2 June 2006 and has proven to be remarkably stable since turn‐on. A number of products have been developed using these space‐borne radar data as principal inputs. Combined with other A‐Train sensor data, these new observations offer unique, global views of the vertical structure of clouds and precipitation jointly. Approximately 11% of clouds detected over the global oceans produce precipitation that, in all likelihood, reaches the surface. Warm precipitating clouds are both wetter and composed of larger particles than nonprecipitating clouds. The frequency of precipitation increases significantly with increasing cloud depth, and the increased depth and water path of precipitating clouds leads to increased optical depths and substantially more sunlight reflected from precipitating clouds compared to than nonprecipitating warm clouds. The CloudSat observations also provide an authoritative estimate of global ice water paths. The observed ice water paths are larger than those predicted from most climate models. CloudSat observations also indicate that clouds radiatively heat the global mean atmospheric column (relative to clear skies) by about 10 Wm−2. Although this heating appears to be contributed almost equally by solar and infrared absorption, the latter contribution is shown to vary significantly with latitude being influenced by the predominant cloud structures of the different region in questions.

Within 2 months of its launch in April 2006 as part of the Earth Observing System A-Train satellite constellation, the National Aeronautics and Space Administration Earth System Science Pathfinder (ESSP) CloudSat mission began making significant contributions toward broadening the understanding of detailed cloud vertical structures around the earth. Realizing the potential benefit of CloudSat to both the research objectives and operational requirements of the U. S. Navy, the Naval Research Laboratory coordinated early on with the CloudSat Data Processing Center to receive and process first-look 94-GHz Cloud Profiling Radar datasets in near-real time (4-8 h latency), thereby making the observations more relevant to the operational community. Applications leveraging these unique data, described herein, include 1) analysis/validation of cloud structure and properties derived from conventional passive radiometers, 2) tropical cyclone vertical structure analysis, 3) support of research field programs, 4) validation of numerical weather prediction model cloud fields, and 5) quantitative precipitation estimation in light rainfall regimes.

The Cloud Profiling Radar, the sole science instrument of the CloudSat Mission, is a 94-GHz nadir-looking radar that measures the power backscattered by hydrometeors (clouds and precipitation) as a function of distance from the radar. This instrument has been acquiring global time series of vertical cloud structures since June 2, 2006. In this paper, an overview of the radar performance and status, to date, is provided together with a description of the basic data products and the surface clutter rejection algorithm introduced for the Release 04 data product release.

This paper documents the development of the first integrated data set of global vertical profiles of clouds, aerosols, and radiation using the combined NASA A-Train data from the Aqua Clouds and Earth's Radiant Energy System (CERES) and Moderate Resolution Imaging Spectroradiometer (MODIS), Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), and CloudSat. As part of this effort, cloud data from the CALIPSO lidar and the CloudSat radar are merged with the integrated column cloud properties from the CERES-MODIS analyses. The active and passive datasets are compared to determine commonalities and differences in order to facilitate the development of a 3-dimensional cloud and aerosol dataset that will then be integrated into the CERES broadband radiance footprint. Preliminary results from the comparisons for April 2007 reveal that the CERES-MODIS global cloud amounts are, on average, 0.14 less and 0.15 greater than those from CALIPSO and CloudSat, respectively. These new data will provide unprecedented ability to test and improve global cloud and aerosol models, to investigate aerosol direct and indirect radiative forcing, and to validate the accuracy of global aerosol, cloud, and radiation data sets especially in polar regions and for multi-layered cloud conditions.