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NASA NTRS2016

CATS Aerosol Typing and Future Directions

The Cloud Aerosol Transport System (CATS), launched in January of 2015, is a lidar remote sensing instrument that will provide range-resolved profile measurements of atmospheric aerosols and clouds from the International Space Station (ISS). CATS is intended to operate on-orbit for at least six months, and up to three years. Status of CATS Level 2 and Plans for the Future:Version. 1. Aerosol Typing (ongoing): Mode 1: L1B data released later this summer; L2 data released shortly after; Identify algorithm biases (ex. striping, FOV (field of view) biases). Mode 2: Processed Released Currently working on correcting algorithm issues. Version 2 Aerosol Typing (Fall, 2016): Implementation of version 1 modifications Integrate GEOS-5 aerosols for typing guidance for non spherical aerosols. Version 3 Aerosol Typing (2017): Implementation of 1-D Var Assimilation into GEOS-5 Dynamic lidar ratio that will evolve in conjunction with simulated aerosol mixtures.

McGill, Matt↗
NASA NTRS2016

Identification of Columnar Aerosol Types Under High Aerosol Optical Depth Conditions for a Single AERONET Site in Korea

Dominant aerosol types were classified using level 2 inversion products for the Anmyon Aerosol Robotic Network (AERONET) site in Korea for the period 19992007. The aerosol types were mineral dust (MD), MD mixed with carbon, and black carbonmixed coarse particles (BCCP) for coarsemode aerosols, black carbon (BC), organic carbon (OC), and secondary inorganic ions (SII) for fine mode aerosols, and mixed particles between. The classification was carried out using a clustering method based on parameters, including single scattering albedo (SSA), absorption Angstrom exponent (AAE), and fine mode volume fraction (FMVF). Among the seven aerosol types, MD was distinct, with the highest AAE and a very low FMVF and SII with the highest SSA and FMVF. BCCP was introduced to designate coarse particles mixed with BC, of which the AAE was lower than 1, despite a low FMVF. In addition to a large difference in AAE between BC andOC, the SSA ofOC was larger than that of BC, indicating the effects of the white smoke produced from the smoldering phase of biomass burning. Monthly variations of the aerosol types were well interpreted by meteorology and emissions and coincided with those in the previous studies. Applying our results to well-characterized global AERONET sites, we confirmed that the aerosol types at Anmyon were valid at other sites. However, the results also showed that the mean properties for aerosol types were influenced by the specific aerosols prevalent at the study sites.

Choi, Yongjoo↗
NASA NTRS2017

Validation of Quasi-Invariant Ice Cloud Radiative Quantities with MODIS Satellite-Based Cloud Property Retrievals

Similarity relations applied to ice cloud radiance calculations are theoretically analyzed and numerically validated. If t(1v) and t(1vg) are conserved where t is optical thickness, v the single-scattering albedo, and g the asymmetry factor, it is possible that substantially different phase functions may give rise to similar radiances in both conservative and non-conservative scattering cases, particularly in the case of large optical thicknesses. In addition to theoretical analysis, this study uses operational ice cloud optical thickness retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) Level 2 Collection5 (C5) and Collection 6 (C6) cloud property products to verify radiative similarity relations. It is found that, if the MODIS C5 and C6 ice cloud optical thickness values are multiplied by their respective (1wg)factors, the resultant products referred to as the effective optical thicknesses become similar with their ratio values around unity. Furthermore, the ratios of the C5 and C6 ice cloud effective optical thicknesses display an angular variation pattern similar to that of the corresponding ice cloud phase function ratios. The MODIS C5 and C6 values of ice cloud similarity parameter, defined as [(1w)(1(exp. 1/2)wg)]12, also tend to be similar.

Radiative transfer↗
NASA NTRS2017

Expanding NASA's Land, Atmosphere Near Real-Time Capability for EOS (LANCE)

NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE) is a virtual system that provides near real-time EOS data and imagery to meet the needs of scientists and application users interested in monitoring a wide variety of natural and man-made phenomena in near real-time. Over the last year: near real-time data and imagery from MOPITT, MISR, OMPS and VIIRS (Land and Atmosphere), the Fire Information for Resource Management System (FIRMS) has been updated and LANCE has begun the process of integrating the Global NRT flood, and Black Marble products. In addition, following the AMSU-A2 instrument anomaly in September 2016, AIRS-only products have replaced the NRT level 2 AIRS+AMSU products. This presentation provides a brief overview of LANCE, describes the new products that are recently available and contains a preview of what to expect in LANCE over the coming year.

NASA Lance↗
NASA NTRS2018

An IR Sounding-Based Analysis of the Saharan Air Layer in North Africa

Intense daytime surface heating over barren-to-sparsely vegetated surfaces results in dry convective mixing. In the absence of external forcing such as mountain waves, the dry convection can produce a deep, well-mixed, nearly isentropic boundary layer that becomes a well-mixed residual layer in the evening. These well-mixed layers (WML) retain their unique mid-tropospheric thermal and humidity structure for several days. To detect the SAL and characterize its properties, AIRS Level 2 Ver. 6 temperature and humidity products (2003-Present) are evaluated against rawinsondes and compared to model analysis at each of the 55 rawinsonde stations in northern Africa. To distinguish WML from Saharan air layers (WMLs of Saharan origin), the detection involved a two-step process: 1) algorithm-based detection of WMLs in dry environments (less than 7 g per kilogram mixing ratio) 2) identification of Sahara air layers (SAL) by applying Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) back trajectories to determine the history of each WML. WML occurrence rates from AIRS closely resemble that from rawinsondes, yet rates from model analysis were up to 30% higher than observations in the Sahara due to model errors. Despite the overly frequent occurrence of WMLs from model analysis, HYSPLIT trajectory analysis showed that SAL occurrence rates (given a WML exists) from rawinsondes, AIRS, and model analysis were nearly identical. Although the number of WMLs varied among the data sources, the proportion of WMLs which were classified as SAL was nearly the same. The analysis of SAL bulk properties showed that AIRS and model analysis exhibited a slight warm and moist bias relative to rawinsondes in non-Saharan locations, but model analysis was notably warmer than rawinsondes and AIRS within the Sahara. The latter result is likely associated with the dearth of available data assimilated by model analysis in the Sahara. The variability of SAL thicknesses was reasonably captured by both AIRS and model analysis, but the former favor layers than are thinner than observations. Finally, further analysis of HYSPLIT trajectories revealed that fewer than 10% and 33% of all SAL back trajectories passed through regions with notable precipitation (>100 mm accumulated along the trajectory path) or Aerosol Optical Depth (AOD greater than 0.4, 75th percentile of AOD) on average, respectively. Trajectory analysis indicated that only 57% of Saharan and 24% of non-Saharan WMLs are definitively of Saharan origin (Saharan requirement: Two consecutive days in Sahara and 24 or more of those hours within 72 hours of detection). Non-SAL WMLs either originate from local-to-regionally generated residual layers or from mid-latitude air streams that do not linger over the Sahara for a sufficient time period. Initial analysis shows these non-SAL WMLs tend to be both notably cooler and slightly moister than their SAL counter parts. Continuing analysis will address what role Saharan and non-Saharan air masses characteristics may play on local and regional environmental conditions.

Nicholls, Stephen D.↗
NASA NTRS2017

Global Measurements of Optically Thin Ice Clouds Using CALIOP

Optically thin ice clouds have been shown to have a net warming effect on the globe but, because passive instruments are not sensitive to optically thin clouds, the occurrence frequency of this class of clouds is greatly underestimated in historical passive sensor cloud climatology. One major strength of CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization), onboard the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) spacecraft, is its ability to detect these thin clouds, thus filling an important missing piece in the historical data record. This poster examines the full mission of CALIPSO Level 2 data, focusing on those CALIOP retrievals identified as thin ice clouds according to the definition shown to the right. Using this definition, thin ice clouds are identified and counted globally and vertically for each season. By examining the spatial and seasonal distributions of these thin clouds we hope to gain a better understanding these thin ice clouds and how their global distribution has changed over the mission. This poster showcases when and where CALIOP detects thin ice clouds and examines a case study of the eastern pacific and the effects seen from the El Nino-Southern Oscillation (ENSO).

Ryan, R.↗
NASA NTRS2018

Improving the SMAP Level-4 Soil Moisture Product

The NASA Soil Moisture Active Passive (SMAP) mission generates, among other data sets, the Level 4 Soil Moisture (L4 SM) product. The L4 SM algorithm is based on the assimilation of SMAP radiometer brightness temperature observations into the NASA Catchment land surface model using a spatially distributed ensemble Kalman filter. The L4 SM data are published with a mean latency of approx. 2.5 days from the time of observation and provide global, three-hourly, 9 km resolution estimates of surface and root-zone soil moisture and related land surface states and fluxes. In 2018, the product was upgraded from Version 3 to Version 4. Underlying the new version is a revised modeling system that includes improved input parameter datasets for land cover, topography, and vegetation height that are based on recent, high quality space-borne remote sensing observations. Land cover inputs were updated to the GlobCover2009 product, which is based on satellite observations from the Medium Resolution Imaging Spectrometer. Topographic statistics now rely on observations from the Shuttle Radar Topography Mission. Finally, vegetation height inputs are derived from space-borne Lidar measurements. Additionally, SMAP Level-2 soil moisture retrievals and in situ soil moisture measurements were used to calibrate a particular Catchment model parameter that governs the recharge of soil moisture from the models root-zone excess reservoir into the surface excess reservoir. Specifically, the replenishment of soil moisture near the surface from below under non-equilibrium conditions was substantially reduced, which brings the models surface soil moisture more in line with the SMAP Level 2 and in situ soil moisture. Finally, the calibration of the assimilated SMAP brightness temperatures changed substantially from Version 3 to Version 4. Considerable effort went into the version upgrade, creating an expectation that the new version is improved over the old version. Indeed, some aspects of the new version are clearly better. However, other aspects are not, and on balance, the overall improvement is modest at best. In this presentation we summarize the skill of the new and old versions vs. independent in situ measurements and in terms of data assimilation diagnostics, including, for example, the statistics of the (soil moisture) analysis increments and the observation minus forecast (brightness temperatures) residuals. We share our experience with trying to improve to the L4 SM product and the lessons learned from the effort.

Reichle, Rolf↗
NASA NTRS2015

The Quick Fire Emissions Dataset (QFED): Documentation of Versions 2.1, 2.2 and 2.4: Technical Report Series on Global Modeling and Data Assimilation - Volume 38

Biomass burning is an important source of particulates and trace gases and a major element of the terrestrial carbon cycle. Well constrained emissions from vegetation fires are needed to model direct and indirect effects of biomass burning aerosols, to model homogeneous and heterogeneous chemistry in the atmosphere, and to perform credible Earth system analysis, and climate and air pollution studies. To improve the performance of NASA Goddard Earth Observing System Model (GEOS) in the areas of atmospheric constituent modeling with a focus on biomass burning we developed the Quick Fire Emissions Dataset (QFED). The QFED emissions are based on the fire radiative power (top-down) approach and draw on the cloud correction method developed in the Global Fire Assimilation System (GFAS). Location and fire radiative power of fires are obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) Level 2 fire products (MOD14 and MYD14) and the MODIS Geolocation products (MOD03 and MYD03). QFED strengths are high spatial and temporal resolutions and near-real time availability. Daily mean emissions are available at 0.3125 times 0.25 degrees and in recent versions also at 0.1 times 0.1 degrees. QFED provides emissions of black carbon, organic carbon, sulfur dioxide, carbon monoxide, carbon dioxide, PM2.5, ammonia, nitrogen oxides, methyl ethyl ketone, propylene, ethane, propane, n- and i-butane, acetaldehyde, formaldehyde, acetone and methane. Two QFED product systems are maintained by the NASA Global Modeling and Assimilation Office (GMAO): one that produces near real-time daily emissions used operationally in the GEOS-5 Data Assimilation System, and one that produces an extended historical dataset with daily emissions from March 2000 to the present. The historical dataset also provides monthly mean and monthly climatological emissions.

Koster, Randal D.↗
NASA NTRS2018

The Salinity Retrieval Algorithms for the NASA Aquarius Version 5 and SMAP Version 3 Releases

The Aquarius end-of-mission (Version 5) salinity data set was released in December 2017. This article gives a comprehensive overview of the main steps of the Level 2 salinity retrieval algorithm. In particular, we will discuss the corrections for wind induced surface roughness, atmospheric oxygen absorption, reflected galactic radiation and side-lobe intrusion from land surfaces. Most of these corrections have undergone major updates from previous versions, which has helped mitigating temporal and zonal biases. Our article also discusses the ocean target calibration for Aquarius Version 5. We show how formal error estimates for the Aquarius retrievals can be obtained by perturbing the input to the algorithm. The performance of the Aquarius Version 5 salinity retrievals is evaluated against salinity measurements from the ARGO network and the HYCOM model. When stratified as function of sea surface temperature or sea surface wind speed, the difference between Aquarius Version 5 and ARGO is within +/-0.1 psu. The estimated global RMS uncertainty for monthly 100 km averages is 0.128 psu for the Aquarius Version 5 retrievals. Finally, we show how the Aquarius Version 5 salinity retrieval algorithm is adapted to retrieve salinity from the Soil-Moisture Active Passion (SMAP) mission.

retrieval algorithm↗
NASA NTRS2018

The Salinity Retrieval Algorithms for the NASA Aquarius Version 5 and SMAP Version 3 Releases

The Aquarius end-of-mission (Version 5) salinity data set was released in December 2017. This article gives a comprehensive overview of the main steps of the Level 2 salinity retrieval algorithm. In particular, we will discuss the corrections for wind induced surface roughness, atmospheric oxygen absorption, reflected galactic radiation and side-lobe intrusion from land surfaces. Most of these corrections have undergone major updates from previous versions, which has helped mitigating temporal and zonal biases. Our article also discusses the ocean target calibration for Aquarius Version 5. We show how formal error estimates for the Aquarius retrievals can be obtained by perturbing the input to the algorithm. The performance of the Aquarius Version 5 salinity retrievals is evaluated against salinity measurements from the ARGO network and the HYCOM model. When stratified as function of sea surface temperature or sea surface wind speed, the difference between Aquarius Version 5 and ARGO is within +-0.1 psu. The estimated global RMS uncertainty for monthly 100 km averages is 0.128 psu for the Aquarius Version 5 retrievals. Finally, we show how the Aquarius Version 5 salinity retrieval algorithm is adapted to retrieve salinity from the Soil-Moisture Active Passion (SMAP) mission.

calibration↗
NASA NTRS2018

Spatial Correlation Structures in SMAP Near-Surface Soil Moisture (How Spatially Correlated are the Temporal Variations of Soil Moisture at Different Locations? and Why Is This of Interest?)

Spatial correlation structures can describe the degree to which soil moisture at a specified location co-varies in time with that at other, remote locations. Using four years of warm season SMAP Level 2 near-surface soil moisture data, we compute these spatial correlation structures for points across North America. The character of these structures is seen to differ geographically; the structures found for the west-central US, for example, are significantly more spatially extensive. We then demonstrate how these structures can potentially be used to reconstruct soil moisture fields during the pre-SMAP era. In this exercise, we consider as "truth" the soil moistures produced in a long-term offline land surface model simulation (1980-2014) that utilizes precipitation forcing based on a high density of precipitation gauges. Then, for a given location within the continent, we construct an"estimated" soil moisture time series based solely on historical soil moisture information simulated at least 300 km distant from the location, using the SMAP-based spatial correlation structures to determine how to make best use of the remote information. The reconstructed soil moistures are found to have significant skill relative to the assumed truth, suggesting that the same approach, when applied in areas of low rain gauge density (i.e., in areas for which historically simulated soil moistures are necessarily inaccurate), could provide useful historical soil moisture estimates through the SMAP-guided extraction of relevant information from neighboring gauged regions.

Koster, Randal↗
NASA NTRS2018

Swelling of Transported Smoke from Savanna fires over the Southeast Atlantic Ocean

We use the recently released Version 4 (V4) lidar data products from CALIPSO to study the smoke plumes transported from Southern African biomass burning areas. The significant improvements in CALIPSO V4 Level 1 calibration and the V4 Level 2 aerosol subtyping algorithms, the latter being particularly relevant to biomass burning smoke over this area, lead to a better representation of their optical properties. For the first time, we show evidence of smoke particles increasing in size, evidenced in their particulate color ratios, as they are transported over the South Atlantic Ocean from the source regions over Southern Africa. This is likely due to hygroscopic swelling of the smoke particles and is reflected in the higher relative humidity in the middle troposphere for profiles with smoke. This finding may have implications for radiative forcing estimates over this area and is relevant to the ORACLES field mission that is currently underway.

Kar, J.↗
NASA NTRS2018

Swelling of Transported Smoke from Savanna Fires over the Southeast Atlantic Ocean

We use the recently released Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Version 4.1 (V4) lidar data to study the smoke plumes transported from Southern African biomass burning areas. Significant improvements in the CALIPSO V4 Level 1 calibration and V4 Level 2 algorithms lead to a better representation of their optical properties, with the aerosol subtype improvements being particularly relevant to smoke over this area. For the first time, we show evidence of smoke particles increasing in size, evidenced in their particulate color ratios, as they are transported over the South Atlantic Ocean from the source regions over Southern Africa. We hypothesize that this is due to hygroscopic swelling of the smoke particles and is reflected in the higher relative humidity in the middle troposphere for profiles with smoke. This finding may have implications for radiative forcing estimates over this area and is also relevant to the ORACLES field mission.

Kar, J.↗
NASA NTRS2018

Swelling of Transported Smoke from Savanna Fires over the Southeast Atlantic Ocean

We use the recently released Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Version 4.1 (V4) lidar data to study the smoke plumes transported from Southern African biomass burning areas. Significant improvements in the CALIPSO V4 Level 1 calibration and V4 Level 2 algorithms lead to a better representation of their optical properties, with the aerosol subtype improvements being particularly relevant to smoke over this area. For the first time, we show evidence of smoke particles increasing in size, as demonstrated by their particulate color ratios, as they are transported over the South Atlantic Ocean from the source regions over Southern Africa. We hypothesize that this is due to hygroscopic swelling of the smoke particles and is reflected in the higher relative humidity in the middle troposphere for profiles with smoke. This finding may have implications for radiative forcing estimates over this area and is also relevant to the ORACLES field mission.

J. Kar↗
NASA NTRS2019

Tropopause Laminar Cirrus and Its Role in the Lower Stratosphere Total Water Budget

Laminar cirrus are thin, extensive, isolated layers of ice clouds frequently observed in the tropical tropopause layer. Widespread laminar cirrus significantly affects tropical tropopause layer total water and thermal budget. In this study, we extract laminar cirrus from the Cloud‐Aerosol Lidar with Orthogonal Polarization Level 1 attenuated total backscatter images for January 2009, in order to characterize statistical properties of laminar cirrus cloud length, base, thickness, optical depth, and layer partial ice water path. These characteristics are used to develop an algorithm identifying laminar cirrus automatically from the Cloud‐Aerosol Lidar with Orthogonal Polarization Level 2 layer product for 2008–2017. The nearly 10‐year records reveal that tropopause laminar cirrus occurrence (30–40% of total cirrus) is strongly anticorrelated with the tropopause temperatures in that colder tropopause in frequent (super)saturation during boreal winter favors in situ formation of clouds. Interannually, anomalously warmer troposphere temperature (ΔT), easterly shear of the quasi‐biennial oscillation, and stronger upwelling branch of the Brewer‐Dobson circulation enhance laminar cirrus formation via cooling of the tropopause. The tropopause laminar cirrus carries ~0.05 mg/m(exp 3) (~0.5 ppmv) of ice water content during boreal winter and <0.01 mg/m(exp 3) during summer, which is anticorrelated with the seasonal variations of water vapor (H2O) observed by the Microwave Lime Sounder, indicating a temperature‐regulated partition between vapor and ice. Interannually, in cirrus‐rich region 1 ppmv decrease in H2O corresponds to 0.2–0.3 ppmv increase in ice water content. Frequently situated in (super)saturated air, tropopause laminar cirrus are likely to survive multiple lifecycles of the sublimation‐deposition processes, and may contribute up to 10% to the total water budget in the lower stratosphere. Satellites constantly observe thin, isolated, extensive layer of cirrus around the tropopause. The so‐called “laminar” cirrus occurrence and their ice amount are strongly regulated by temperature, such that colder temperatures favor more frequent (super)saturation, which results in more frequent laminar cirrus with more ice amount and therefore less water vapor. In this study we analyze laminar cirrus and water vapor from the Cloud‐Aerosol Lidar with Orthogonal Polarization and Microwave Lime Sounder observations, and hypothesize that laminar cirrus could act as an important transient water storage and contribute to the total water budget in the lower stratosphere.

Tao Wang↗
NASA NTRS2019

External Geolocation for Swath Data

Swath products (called Level 1 and Level 2 by NASA) are in the original instrument geometry (not gridded). Geolocation information is needed for remapping but also understanding the product. Geolocation data can be as large or larger than some L2 products. Storing this information in each L1/L2 product increases archive volume and for users who work with multiple products, the information would be duplicated. In this talk, MODIS is used as an example –other satellite and aircraft instruments have similar issues.

Geolocation↗
NASA NTRS2019

Application of High-Dimensional Fuzzy K-Means Cluster Analysis to CALIOP/CALIPSO Version 4.1 Cloud-Aerosol Discrimination

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

Zeng, Shan↗
NASA NTRS2019

Impact of Microwave Sounder Calibration on Precipitation for the Global Precipitation Measurement Mission

Cross-track microwave sounders make up a significant percentage of the radiometers included in the Global Precipitation Measurement (GPM) constellation. Therefore, it is important to properly assess the calibration of each sounder instrument and to understand the impact of the calibration on the derived precipitation rates. This ensures an accurate precipitation product is produced for the entire constellation. This paper will use data from past and current microwave sounders to show how offsets in the calibration can impact the precipitation using the GPM Level 2 GPROF algorithm. Potential improvements to the instrument calibration will be assessed by analyzing how they would positively impact the precipitation trends and agreement among the constellation sensors.

Microwave radiometry↗