Search NASA⌕ Search

SEARCH · Search NASA

Results for “cloud predictor”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Untangling causality in midlatitude aerosol–cloud adjustments

Aerosol–cloud interactions represent the leading uncertainty in our ability to infer climate sensitivity from the observational record. The forcing from changes in cloud albedo driven by increases in cloud droplet number (Nd) (the first indirect effect) is confidently negative and has narrowed its probable range in the last decade, but the sign and strength of forcing associated with changes in cloud macrophysics in response to aerosol (aerosol–cloud adjustments) remain uncertain. This uncertainty reflects our inability to accurately quantify variability not associated with a causal link flowing from the cloud microphysical state to the cloud macrophysical state. Once variability associated with meteorology has been removed, covariance between the liquid water path (LWP) averaged across cloudy and clear regions (here characterizing the macrophysical state) and Nd (characterizing the microphysical) is the sum of two causal pathways linking Nd to LWP: Nd altering LWP (adjustments) and precipitation scavenging aerosol and thus depleting Nd. Only the former term is relevant to constraining adjustments, but disentangling these terms in observations is challenging. We hypothesize that the diversity of constraints on aerosol–cloud adjustments in the literature may be partly due to not explicitly characterizing covariance flowing from cloud to aerosol and aerosol to cloud. Here, we restrict our analysis to the regime of extratropical clouds outside of low-pressure centers associated with cyclonic activity. Observations from MAC-LWP (Multisensor Advanced Climatology of Liquid Water Path) and MODIS are compared to simulations in the Met Office Unified Model (UM) GA7.1 (the atmosphere model of HadGEM3-GC3.1 and UKESM1). The meteorological predictors of LWP are found to be similar between the model and observations. There is also agreement with previous literature on cloud-controlling factors finding that increasing stability, moisture, and sensible heat flux enhance LWP, while increasing subsidence and sea surface temperature decrease it. A simulation where cloud microphysics are insensitive to changes in Nd is used to characterize covariance between Nd and LWP that is induced by factors other than aerosol–cloud adjustments. By removing variability associated with meteorology and scavenging, we infer the sensitivity of LWP to changes in Nd. Application of this technique to UM GA7.1 simulations reproduces the true model adjustment strength. Observational constraints developed using simulated covariability not induced by adjustments and observed covariability between Nd and LWP predict a 25 %–30 % overestimate by the UM GA7.1 in LWP change and a 30 %–35 % overestimate in associated radiative forcing.

Daniel T. McCoy↗
NASA NTRS2022

Automatic variable selection in ecological niche modeling: A case study using Cassin’s Sparrow (Peucaea cassinii)

MERRA/Max provides a feature selection approach to dimensionality reduction that enables direct use of global climate model outputs in ecological niche modeling. The system accomplishes this reduction through a Monte Carlo optimization in which many independent MaxEnt runs, operating on a species occurrence file and a small set of randomly selected variables in a large collection of variables, converge on an estimate of the top contributing predictors in the larger collection. These top predictors can be viewed as potential candidates in the variable selection step of the ecological niche modeling process. MERRA/Max’s Monte Carlo algorithm operates on files stored in the underlying filesystem, making it scalable to large data sets. Its software components can run as parallel processes in a high-performance cloud computing environment to yield near real-time performance. In tests using Cassin’s Sparrow (Peucaea cassinii) as the target species, MERRA/Max selected a set of predictors from Worldclim’s Bioclim collection of 19 environmental variables that have been shown to be important determinants of the species’ bioclimatic niche. It also selected biologically and ecologically plausible predictors from a more diverse set of 86 environmental variables derived from NASA’s Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) reanalysis, an output product of the Goddard Earth Observing System Version 5 (GEOS-5) modeling system. We believe these results point to a technological approach that could expand the use global climate model outputs in ecological niche modeling, foster exploratory experimentation with otherwise difficult-to-use climate data sets, streamline the modeling process, and, eventually, enable automated bioclimatic modeling as a practical, readily accessible, low-cost, commercial cloud service.

John L. Schnase↗
NASA NTRS2005

Forecasting Lightning at Kennedy Space Center/Cape Canaveral Air Force Station, Florida

The Applied Meteorology Unit (AMU) developed a set of statistical forecast equations that provide a probability of lightning occurrence on Kennedy Space Center (KSC) I Cape Canaveral Air Force Station (CCAFS) for the day during the warm season (May September). The 45th Weather Squadron (45 WS) forecasters at CCAFS in Florida include a probability of lightning occurrence in their daily 24-hour and weekly planning forecasts, which are briefed at 1100 UTC (0700 EDT). This information is used for general scheduling of operations at CCAFS and KSC. Forecasters at the Spaceflight Meteorology Group also make thunderstorm forecasts for the KSC/CCAFS area during Shuttle flight operations. Much of the current lightning probability forecast at both groups is based on a subjective analysis of model and observational data. The objective tool currently available is the Neumann-Pfeffer Thunderstorm Index (NPTI, Neumann 1971), developed specifically for the KSCICCAFS area over 30 years ago. However, recent studies have shown that 1-day persistence provides a better forecast than the NPTI, indicating that the NPTI needed to be upgraded or replaced. Because they require a tool that provides a reliable estimate of the daily thunderstorm probability forecast, the 45 WS forecasters requested that the AMU develop a new lightning probability forecast tool using recent data and more sophisticated techniques now possible through more computing power than that available over 30 years ago. The equation development incorporated results from two research projects that investigated causes of lightning occurrence near KSCICCAFS and over the Florida peninsula. One proved that logistic regression outperformed the linear regression method used in NPTI, even when the same predictors were used. The other study found relationships between large scale flow regimes and spatial lightning distributions over Florida. Lightning, probabilities based on these flow regimes were used as candidate predictors in the equation development. Fifteen years (1 989-2003) of warm season data were used to develop the forecast equations. The data sources included a local network of cloud-to-ground lightning sensors called the Cloud-to-Ground Lightning Surveillance System (CGLSS), 1200 UTC Florida synoptic soundings, and the 1000 UTC CCAFS sounding. Data from CGLSS were used to determine lightning occurrence for each day. The 1200 UTC soundings were used to calculate the synoptic-scale flow regimes and the 1000 UTC soundings were used to calculate local stability parameters, which were used as candidate predictors of lightning occurrence. Five logistic regression forecast equations were created through careful selection and elimination of the candidate predictors. The resulting equations contain five to six predictors each. Results from four performance tests indicated that the equations showed an increase in skill over several standard forecasting methods, good reliability, an ability to distinguish between non-lightning and lightning days, and good accuracy measures and skill scores. Given the overall good performance the 45 WS requested that the equations be transitioned to operations and added to the current set of tools used to determine the daily lightning probability of occurrence.

Lambert, Winfred↗
NASA NTRS2022

Predicting the Penetration of a Shielded TPS Tile

All spacecraft are subject to the possibility of high-speed particle impacts during their mission life. In low earth orbit, those impacts could be the result of collisions with pieces of orbital debris or with meteorites. Beyond LEO, and especially beyond GEO, those impacts will likely be caused by meteorites. Such high-speed impacts on spacecraft surfaces create debris clouds that travel towards and eventually impact other downstream spacecraft components. In addition to the impulsive load that such debris clouds would impart to the spacecraft elements with which they subsequently collide, the largest fragment in these debris clouds poses a significant threat on its own to those spacecraft elements. In order to be able to assess the severity of the threat posed by such a fragment, it is important to be able to predict the extent of damage sustained by the impacted spacecraft element. In this paper, we present a new process for determining the penetration depth in a shielded TPS tile system. The requirements for the process were that it be applicable across a full spectrum of impact low velocities (i.e. from ~ 7 km/s to above 50 km/s), that it be applicable over a wide range of projectile materials densities (i.e. ranging from water to steel, for example), and that it be applicable over a wide range of trajectory obliquities (i.e. not just normal impacts). Furthermore, the process developed should be sufficiently flexible so that it can be used for shield design parameter and / or system configuration trade studies (i.e. be written in terms of as many shield and TPS tile system parameters as possible). Following the initial impact of the projectile on the outer wall of the dual-wall shield, a debris cloud (i.e. the primary debris cloud) is created that travels towards and impacts the inner wall of the shield. Within this debris cloud is a combination of solid, liquid, and vaporized material, depending on the impact velocity and the impedance mismatch of the projectile and outer wall materials. The impact of this debris cloud on the inner wall creates another debris cloud (i.e. the secondary debris cloud) that then travels towards and impacts the TPS tile. Within the particulate distribution of the material in each of these debris clouds is a so-called largest fragment. The process used to calculate the penetration depth in a shielded TPS tile system developed herein consists of the following three steps: 1. Does the initial projectile perforate the outer wall of the shield? If NO, the TPS tile remains undamaged. If YES, calculate the size and speed of the largest fragment exiting the outer wall of the shield and traveling towards the shield’s inner wall. 2. Does the largest fragment in the debris cloud exiting the outer wall of the shield perforate the inner wall of the shield? If NO, the TPS tile again remains undamaged. If YES, calculate the size and speed of the largest fragment exiting the rear of the shield inner wall. 3. Calculate the penetration depth of the largest fragment in the debris cloud exiting the inner wall into the TPS. Perforation of the inner and outer shield walls is predicted using the Cour-Palais penetration depth equation and an associated failure criterion [1]. The largest debris cloud fragment diameters and associated velocities are calculated using regression equations developed from a mix of hydrocode and experimental diameter and velocity information [2]. Finally, TPS penetration depths are calculated using an empirical penetration depth predictor equation based on high-speed impact test data and are calibrated using a limited number of hydrocode penetration depth predictions for water, nylon, aluminum, Dunite, and steel projectiles The predictions of the penetration depth calculation process are compared against the predictions of more than 60 SPHC hydrocode runs. These comparisons show that the process used to calculate TPS tile penetration depths usually yields values that are within 1 mm of the predictions of hydrocode simulations. If a maximum allowable TPS penetration depth is known, the process developed herein can now be used to develop a failure limit equation that would predict whether or not an impacting particle would penetrate deeply enough into the TPS to result in a “failed” or “not failed” end state. References [1] E.L. Christiansen, “Design and performance equations for advanced meteoroid and debris shields”, International Journal of Impact Engineering, Vol. 14, 1993, pp. 145-156. [2] W.P. Schonberg, “Predicting the size of the largest particle fragment in a debris cloud created by an orbital debris impact and its associated velocity”, 2021 Applied Space Environments Conference, NASA Jet Propulsion Laboratory, Pasadena, California, November, 2021.

Meteroid↗
NASA NTRS1998

Total Lightning and Radar Storm Characteristics Associated with Severe Storms in Central Florida

A number of prior studies have examined the association of lightning activity with the occurrence of severe weather and tornadoes, in particular. High flash rates are often observed in tornadic storms (Taylor, 1973; Johnson, 1980; Goodman and Knupp, 1993) but not always. Taylor found that 23% of nontornadic storms and 1% of non-severe storms had sferics rates comparable to the tornadic storms. MacGorman (1993) found that storms with mesocyclones produced more frequent intracloud (IC) lightning than cloud-to-ground (CG) lightning. MacGorman (1993) and others suggest that the lightning activity accompanying tomadic storms will be dominated by intracloud lightning-with an increase in intracloud and total flash rates as the updraft increases in depth, size, and velocity. In a recent study, Perez et al. (1998) found that CG flash rates alone are too variable to be a useful predictor of (F4, F5) tornado formation. Studies of non-tomadic storms have also shown that total lightning flash rates track the updraft, with rates increasing as the updraft intensities and decreasing rapidly with cessation of vertical growth or downburst onset (Goodman et al., 1988; Williams et al., 1989). Such relationships result from the development of mixed phase precipitation and increased hydrometer collisions that lead to the efficient separation of charge. Correlations between updraft strength and other variables such as cloud-top height, cloud water mass, and hail size have also been observed.

Goodman, Steven J.↗
NASA NTRSDate not supplied

Using AI/ML to Address Satellite Cloud Remote Sensing Challenges

Various AI/ML tools, employed within the Clouds and the Earth's Radiant Energy System (CERES) Satellite Cloud and Radiation Property retrieval System (SatCORPS) project, are being used to mitigate satellite radiance artifacts and thereby yield more accurate cloud and radiation data products. Neural network and K-nearest neighbor approaches have been developed that enable us to better address common passive satellite remote sensing challenges, such as corrupted imagery, day/night cloud property discontinuities, solar terminator artifacts, inadequate knowledge of the land surface emission temperature (i.e., skin temperature), and poor assumptions about vertical cloud structure, that have otherwise proven difficult to solve using more conventional methods. Fixing these problems promotes a more consistent Earth radiation budget record. These efforts demonstrate effective use of AI/ML architecture to exploit complex, multivariate predictor relationships and produce usable output at satellite spatial and temporal resolutions that would otherwise be ignored or have large biases.

Benjamin Scarino↗
NASA NTRS2018

Modeling Key Predictors of Airport Runway Configurations Using Learning Algorithms

Advanced traffic flow management automation will need accurate predictions of airport runway configurations. Terminal area weather and traffic demand are generally considered to be the most significant factors in predicting runway configuration. Weather information is forecasted across multiple features, including wind direction, wind speed, gusts, cloud ceilings, visibility, temperature, and precipitation, among many others. We use machine learning techniques on historical weather and runway data to determine weather features that correlate well with runway configurations. We analyze the predictive capability of weather features using different learning models trained on data from four major U.S. airports: Atlanta (ATL), Washington – Dulles (IAD), New York – Kennedy (JFK), and San Francisco (SFO). Wind direction alone is strongly correlated with runway configurations above all other examined factors, as expected. This correlation is the most significant component of the ~80% prediction accuracy in selecting between the two most frequently used runway configurations. However, individual airports show variations on how well the runway configuration decisions correlate with wind direction. While wind direction was identified as the most significant indicator of configuration decisions in ATL, IAD, and JFK, it did not emerge as such at SFO. Traffic demand was not found to be a strong factor in predicting runway configurations at any of the airports analyzed. In rare instances, when high demand cannot be accommodated within the current configuration, temporary changes are likely to be attributable to demand. However, these occurrences are so limited in number that their overall effect is not sufficient to consider traffic demand as a major indicator of runway configuration at the airports analyzed.

Bilimoria, Karl D.↗
NASA NTRS1994

A high-resolution survey of interstellar Na I D1 lines

We present high-resolution (0.5 km/s) spectra, obtained with the McDonald Observatory 2.7 m coude echelle spectrograph, of interstellar Na I D1 absorption toward 38 bright stars. Numerous narrow, closely blended absorption components, showing resolved Na I hyperfine structure, are evident in these spectra; such narrow components appear in both low halo and quite local gas, as well as in gas toward more distant disk stars. We have used the method of profile fitting in an attempt to determine column densities, line widths, and velocities for the individual interstellar clouds contributing to the observed absorption lines. The resulting sample of 276 clouds is significantly larger, and likely more complete, than several previous samples of 'individual' interstellar clouds, and allows more precise investigation of various statistical properties. We find that the cloud column density (N) and line width parameter (b) are not correlated, for 0.3 km/s approximately less than b approximately less than 1.5 km/s and 10.0/sq cm approximately less than log (N(Na I)) approximately less than 11.6/sq cm. The median b is about 0.73 km/s, the median log N is about 11.09/sq cm, and the median separation between adjacent components is about 2.0 km/s. All these are overestimates of the true median values, however, due to our inability to completely resolve all the component structure present in some cases; even at a resolution of 0.5 km/s, we may have discerned only 60% of the full number of individual components actually present. The one-dimensional dispersion of component velocities, in the local standard of rest, is approximately 8.6 km/s; the distribution of velocities is broader and displaced to more negative velocities for the weaker components. If 80 K is a representative temperature for the interstellar clouds seen in Na I absorption, then at least 38% (and probably the majority) of the clouds have subsonic internal turbulent motions. The range in N(H I) observed at a given N(Na I) increases as N(Na I) decreases below about 10(exp 11)/sq cm, so that N(Na I) becomes a less reliable predictor of N(H I) at low column densities. These spectra will be quite useful in future detailed studies of these lines of sight with the Hubble Space Telescope (HST) Goddard High Resolution Spectrograph (GHRS) echelle -- to determine accurate velocities and to reveal the detailed interstellar component structures that cannot be discerned at the 3.5 km/s resolution available with the GHRS.

Welty, Daniel E.↗
NASA NTRSDate not supplied

A Neural Network Parametrization of Volumetric Cloud Fraction Profiles Using Satellite Observations and MERRA-2 Reanalysis Meteorological Data

Clouds play a crucial role in regulating the hydrologic cycle and Earth's radiative energy budget, yet they are often poorly represented in global climate models (GCMs). This study applies deep machine learning techniques to develop a physical parameterization of volumetric cloud fraction (VCF), the fraction of a 3-D grid volume occupied by clouds using satellite lidar-radar measurements. The neural network (NN) captures the complicated relationships between observed VCF profiles and collocated meteorological variables from MERRA-2 reanalysis data. Our results show that the NN model, particularly a sequence-to-sequence long short-term memory (LSTM) network with a sixfactor loss function, effectively learns the underlying cloud physical processes. The NN model outperforms MERRA-2 reanalysis in representing low-level clouds in tropical and subtropical regions and low- and middle-level clouds over midlatitude storm-track regions, and also improves VCF histograms. These improvements are reflected in the vertical distributions of zonally, meridionally, and globally averaged VCFs, geographic distributions of low-, middle-, and high-level clouds, and seasonal variations in monthly-mean VCF. Furthermore, the NN predictions effectively capture the El Niño-Southern Oscillation (ENSO) effects and other interannual variations. The NN parameterization is further evaluated through a sensitivity analysis, in which a single predictor is perturbed at a time. This reveals that relative humidity (RH) is the dominant factor influencing variations in globally averaged VCF at low and middle altitudes, followed by temperature. At higher altitudes, temperature becomes the primary driver of VCF through its effect on RH. Changes in wind components had minimal impact on globally averaged VCF.

Shan Zeng↗
NASA NTRS1993

Relationships between optical extinction, backscatter and aerosol surface and volume in the stratosphere following the eruption of Mt. Pinatubo

The eruption of the Mt. Pinatubo volcano in the Philippines in June 1991 has resulted in increases in the surface and mass concentrations of aerosol particles in the lower stratosphere. Airborne measurements made at midlatitudes between 15 and 21 km from August 1991 to March 1992 show that, prior to December 1991, the Pinatubo aerosol cloud varied widely in microphysical properties such as size distribution, number, surface and volume concentrations and was also spatially variable. Aerosol surface area concentration was found to be highly correlated to extinction at visible and near-infrared wavelenghts throughout the measurement period. Similarly, backscatter at common lidar wavelengths was a good predictor of aerosol volume concentrations. These results support the use of satellite extinction measurements to estimate aerosol volume or mass if temporal changes in the relationships between the variables are considered.

Brock, Charles A.↗
NASA NTRS1993

Relationships between Optical Extinction, Backscatter and Aerosol Surface and Volume in the Stratosphere following the Eruption of Mt. Pinatubo

The eruption of the Mt. Pinatubo volcano in the Philippines in June 1991 has resulted in increases in the surface and mass concentrations of aerosol particles in the lower stratosphere. Airborne measurements made at midlatitudes between 15 and 21 km from August 1991 to March 1992 show that, prior to December 1991, the Pinatubo aerosol cloud varied widely in microphysical properties such as size distribution, number, surface and volume concentrations and was also spatially variable. Aerosol surface area concentration was found to be highly correlated to extinction at visible and near-infrared wavelengths throughout the measurement period. Similarly, backscatter at common lidar wavelengths was a good predictor of aerosol volume concentrations. These results support the use of satellite extinction measurements to estimate aerosol surface and of lidar measurements to estimate aerosol volume or mass if temporal changes in the relationships between the variables are considered.

Brock, Charles A.↗
NASA NTRSDate not supplied

Improving the Use of Surface-Sensitive Radiances in the GMAO Hybrid-4DEnVar System

The planetary boundary layer (PBL) was designated as an incubation-class targeted observable (TO) in the 2018 Decadal Survey. As no single instrument source will provide enough information to constrain the global PBL, the assimilation of a wide range of observations in data assimilation (DA) systems will play a critical role, and GMAO has put in efforts to enhance surface-sensitive radiance assimilation. Although a vast number of microwave radiance observations are used in the GEOS global DA system, very few surface-sensitive radiances are currently used over land due to large uncertainty of land surface emissivity in the CRTM as well as cloud detection issue. Dynamically varying emissivity is retrieved from observations of window channels in the GEOS system for non-scattering FOVs and applied to sounding channel assimilation. Moreover, the original radiance bias correction is found not to work well over land, and shows drifting bias when the original emissivity sensitivity bias predictor with a dynamically varying emissivity retrieval is used. Hence, the radiance bias correction has been modified, and the quality control procedure has been adapted accordingly. Cycled experiment results show improvement in the temperature forecast at lower model levels; further refinement of this system continues.

Yanqiu Zhu↗
NASA NTRS2012

The Invasive Species Forecasting System (ISFS): An iRODS-Based, Cloud-Enabled Decision Support System for Invasive Species Habitat Suitability Modeling

The Invasive Species Forecasting System (ISFS) is an online decision support system that allows users to load point occurrence field sample data for a plant species of interest and quickly generate habitat suitability maps for geographic regions of interest, such as a national park, monument, forest, or refuge. Target customers for ISFS are natural resource managers and decision makers who have a need for scientifically valid, model- based predictions of the habitat suitability of plant species of management concern. In a joint project involving NASA and the Maryland Department of Natural Resources, ISFS has been used to model the potential distribution of Wavyleaf Basketgrass in Maryland's Chesapeake Bay Watershed. Maximum entropy techniques are used to generate predictive maps using predictor datasets derived from remotely sensed data and climate simulation outputs. The workflow to run a model is implemented in an iRODS microservice using a custom ISFS file driver that clips and re-projects data to geographic regions of interest, then shells out to perform MaxEnt processing on the input data. When the model completes, all output files and maps from the model run are registered in iRODS and made accessible to the user. The ISFS user interface is a web browser that uses the iRODS PHP client to interact with the ISFS/iRODS- server. ISFS is designed to reside in a VMware virtual machine running SLES 11 and iRODS 3.0. The ISFS virtual machine is hosted in a VMware vSphere private cloud infrastructure to deliver the online service.

Gill, Roger↗
NASA NTRS1994

Effects of a polar stratosphere cloud parameterization on ozone depletion due to stratospheric aircraft in a two-dimensional model

A parameterization of Type 1 and 2 polar stratospheric cloud (PSC) formation is presented which is appropriate for use in two-dimensional (2-D) photochemical models of the stratosphere. The calculations of PSC frequency of occurrence and surface area density uses climatological temperature probability distributions obtained from National Meteorological Center data to avoid using zonal mean temperatures, which are not good predictors of PSC behavior. The parameterization does not attempt to model the microphysics of PSCs. The parameterization predicts changes in PSC formation and heterogeneous processing due to perturbations of stratospheric trace constituents. It is therefore useful in assessing the potential effects of a fleet of stratospheric aircraft (high speed civil transports, or HSCTs) on stratospheric composition. the model calculated frequency of PSC occurrence agrees well with a climatology based on stratospheric aerosol measurement (SAM) 2 observations. PSCs are predicted to occur in the tropics. Their vertical range is narrow, however, and their impact on model O3 fields is small. When PSC and sulfate aerosol heterogeneous processes are included in the model calculations, the O3 change for 1980 - 1990 is in substantially better agreement with the total ozone mapping spectrometer (TOMS)-derived O3 trend than otherwise. The overall changes in model O3 response to standard HSCT perturbation scenarios produced by the parameterization are small and tend to decrease the model sensitivity to the HSCT perturbation. However, in the southern hemisphere spring a significant increase in O3 sensitivity to HSCT perturbations is found. At this location and time, increased PSC formation leads to increased levels of active chlorine, which produce the O3 decreases.

Considine, David B.↗
NASA NTRS2015

Constraints on Cumulus Parameterization from Simulations of Observed MJO Events

Two recent activities offer an opportunity to test general circulation model (GCM) convection and its interaction with large-scale dynamics for observed Madden-Julian oscillation (MJO) events. This study evaluates the sensitivity of the Goddard Institute for Space Studies (GISS) GCM to entrainment, rain evaporation, downdrafts, and cold pools. Single Column Model versions that restrict weakly entraining convection produce the most realistic dependence of convection depth on column water vapor (CWV) during the Atmospheric Radiation Measurement MJO Investigation Experiment at Gan Island. Differences among models are primarily at intermediate CWV where the transition from shallow to deeper convection occurs. GCM 20-day hindcasts during the Year of Tropical Convection that best capture the shallow–deep transition also produce strong MJOs, with significant predictability compared to Tropical Rainfall Measuring Mission data. The dry anomaly east of the disturbance on hindcast day 1 is a good predictor of MJO onset and evolution. Initial CWV there is near the shallow–deep transition point, implicating premature onset of deep convection as a predictor of a poor MJO simulation. Convection weakly moistens the dry region in good MJO simulations in the first week; weakening of large-scale subsidence over this time may also affect MJO onset. Longwave radiation anomalies are weakest in the worst model version, consistent with previous analyses of cloud/moisture greenhouse enhancement as the primary MJO energy source. The authors’ results suggest that both cloud-/moisture-radiative interactions and convection–moisture sensitivity are required to produce a successful MJO simulation.

Madden-Julian Oscillation↗
NASA NTRS1990

The relationship of extratropical outgoing longwave radiation to monthly geopotential teleconnection patterns

The relationship between low frequency variations in extratropical fields of outgoing longwave radiation (OLR) and geopotential teleconnection patterns as determined by rotated principal components analysis of the NMC 500-mb heights is investigated in the Northern Hemisphere. The monthly broadband OLR is obtained from the Nimbus-6 and Nimbus-7 Wide-Field-Of-View radiometer record. Each of the main 500-mb teleconnection patterns has a characteristic signal in the OLR field for the month in which the 500-mb pattern occurs. The OLR signals mark cloud and diabatic heating events that are associated with the teleconnection patterns. A demonstration is given of correlation between extratropical monthly OLR and geopotential height. Coupled with the expected tropospheric response to radiation on monthly time scale. This demonstration stresses the importance of the radiation simulation in model studies of the low frequency variability of atmospheric circulation. The extratropical OLR does not appear to be a useful predictor for the 500-mb teleconnection patterns on a monthly time scale.

Charlock, Thomas P.↗
NASA NTRS2015

Earth Science Data Fusion with Event Building Approach

Objectives of the NASA Information And Data System (NAIADS) project are to develop a prototype of a conceptually new middleware framework to modernize and significantly improve efficiency of the Earth Science data fusion, big data processing and analytics. The key components of the NAIADS include: Service Oriented Architecture (SOA) multi-lingual framework, multi-sensor coincident data Predictor, fast into-memory data Staging, multi-sensor data-Event Builder, complete data-Event streaming (a work flow with minimized IO), on-line data processing control and analytics services. The NAIADS project is leveraging CLARA framework, developed in Jefferson Lab, and integrated with the ZeroMQ messaging library. The science services are prototyped and incorporated into the system. Merging the SCIAMACHY Level-1 observations and MODIS/Terra Level-2 (Clouds and Aerosols) data products, and ECMWF re- analysis will be used for NAIADS demonstration and performance tests in compute Cloud and Cluster environments.

Lukashin, C.↗
NASA NTRSDate not supplied

Retrieve Methane from IR sounder measurements Using Machine Learning-Enhanced Physical Inversion

The sensitivity of IR sounder measurements to atmospheric CH 4 is often limited due to interferences from signals of other trace gases, insufficient thermal contrast, and cloud blockage. In order to resolve the geographical and vertical distribution of atmospheric CH 4 profiles, accurate scene-dependent a priori information is critically needed to support an optimal estimation method-based physical inversion scheme. Following the principles of indexing, representation, and retrieval, a spectral fingerprinting methodology is developed to address the needs for both accuracy and computational efficiency in sounder-based CH 4 retrieval. Within this framework, a clustering method based on machine learning is first employed to stratify and identify the a priori state within the pre-constructed database, using optimized spectral radiances as predictors. The corresponding radiative kernel is then used to establish the physical inversion scheme for finding the solution. High-quality data from CH 4 data assimilation systems like the Carbon-Tracker and the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis, as well as the state-of-art sounder products are used to build the training database, including radiative kernels. We will demonstrate the results retrieved from CrIS observations and the associated validation work.

Wan Wu↗