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NASA NTRS2018

WCRP Task Team for the Intercomparison of ReAnalyses (TIRA): Motivation and Progress

Reanalyses have proven to be an important resource for weather and climate related research, as well as societal applications at large. New atmospheric reanalyses in various forms are produced every few years. In addition, land and ocean communities are producing disciplinary uncoupled reanalyses, and regional reanalyses are emerging in addition to global reanalyses. Systems of data information are being developed to facilitate the intercomparison of reanalyses among themselves and with observations and climate change experiments (e.g. CREATE-IP and WRIT). Current research and development in reanalysis is directed at (1) extending the length of reanalyzed period and (2) use of coupled Earth system models for climate reanalysis. While the World Climate Research Programme (WCRP) involvement in the reanalyses communities through its Data Advisory Council (WDAC) has been substantial, for example in organizing international conferences on reanalyses. However, these efforts have previously not included an analysis of the differences among reanalyses and their inherent uncertainties which are important questions for both users and developers of reanalyses. Therefore, the WCRP Task Team for the Intercomparison of ReAnalyses (TIRA) task team has been formed as collaborative effort to address reanalyses role in WCRP. This constitutes a logical progression that fills the needs of the community and contributes to the WCRP mission. The primary charge to TIRA is to develop a reanalysis intercomparison project plan that will attain the following objectives.1)To foster understanding and estimation of uncertainties in reanalysis data by intercomparison and other means)To communicate new developments and best practices among the reanalyses producing centers3)To enhance the understanding of data and assimilation issues and their impact on uncertainties, leading to improved reanalyses for climate assessment4)To communicate the strengths and weaknesses of reanalyses, their fitness for purpose, and best practices in the use of reanalysis datasets by the scientific communityThis contribution outlines the need for a task team on reanalyses, their intercomparison, the objectives of the team and progress thus far. Results for some pilot intercomparisons will also be discussed.

Bosilovich, Michael↗

NASA NTRSDate not supplied

Satellite Data Assimilation in the GEOS Atmospheric Data Assimilation System

The Goddard Earth Observing System (GEOS) is used for weather, climate, and air quality forecasts and producing reanalysis datasets. Its atmospheric data assimilation system (ADAS) is a hybrid–4DEnVar system. Various satellite radiance, atmospheric motion vector (winds), aircraft, and convectional data are assimilated by this system to produce global atmospheric states. The GEOS ADAS is enhanced by considering correlated observational errors among infrared radiance observations and assimilating new microwave radiance data in all-sky conditions. Historical microwave radiance data are tested within the all-sky microwave radiance assimilation framework and show beneficial effect in the preparation of the next GEOS reanalysis product. In this seminar, I will present the procedures of to assimilate microwave radiance data in all-sky conditions in the GEOS-ADAS. I will also discuss our efforts to update GEOS ADAS by assimilating more satellite data such as geostationary infrared observations and new observations made by the Advanced Technology Microwave Sounder (ATMS) aboard NOAA 21 polar orbit satellite.

Jianjun Jin↗

NASA NTRS2015

High Resolution Nature Runs and the Big Data Challenge

NASA's Global Modeling and Assimilation Office at Goddard Space Flight Center is undertaking a series of very computationally intensive Nature Runs and a downscaled reanalysis. The nature runs use the GEOS-5 as an Atmospheric General Circulation Model (AGCM) while the reanalysis uses the GEOS-5 in Data Assimilation mode. This paper will present computational challenges from three runs, two of which are AGCM and one is downscaled reanalysis using the full DAS. The nature runs will be completed at two surface grid resolutions, 7 and 3 kilometers and 72 vertical levels. The 7 km run spanned 2 years (2005-2006) and produced 4 PB of data while the 3 km run will span one year and generate 4 BP of data. The downscaled reanalysis (MERRA-II Modern-Era Reanalysis for Research and Applications) will cover 15 years and generate 1 PB of data. Our efforts to address the big data challenges of climate science, we are moving toward a notion of Climate Analytics-as-a-Service (CAaaS), a specialization of the concept of business process-as-a-service that is an evolving extension of IaaS, PaaS, and SaaS enabled by cloud computing. In this presentation, we will describe two projects that demonstrate this shift. MERRA Analytic Services (MERRA/AS) is an example of cloud-enabled CAaaS. MERRA/AS enables MapReduce analytics over MERRA reanalysis data collection by bringing together the high-performance computing, scalable data management, and a domain-specific climate data services API. NASA's High-Performance Science Cloud (HPSC) is an example of the type of compute-storage fabric required to support CAaaS. The HPSC comprises a high speed Infinib and network, high performance file systems and object storage, and a virtual system environments specific for data intensive, science applications. These technologies are providing a new tier in the data and analytic services stack that helps connect earthbound, enterprise-level data and computational resources to new customers and new mobility-driven applications and modes of work. In our experience, CAaaS lowers the barriers and risk to organizational change, fosters innovation and experimentation, and provides the agility required to meet our customers' increasing and changing needs

big data analysis↗

NASA NTRSDate not supplied

Developing A Continuous Ozone Record Through the SAGE and Aura Missions With NASA Reanalysis Products

During the last quarter of the 20th century, the Stratospheric Aerosol and Gas Experiment (SAGE) missions were crucial in monitoring the loss and the subsequent recovery of the stratospheric ozone layer. Due to the employed solar occultation and self-calibration method, the SAGE monitors have produced stable data throughout the lifetime of each instrument. However, over ten years passed between the end of the SAGE II and SAGE III/M3M missions in 2005 and the launch of SAGE III/ISS instrument in 2017, leaving a gap in the data that much be bridged in order to assess the trends in the ozone record. Reanalysis products, such as the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2), are attractive candidates for trend analysis due to the statistically optimized combination of multiple observing systems and the regular temporal and spatial coverage. In this study, we explore using the SAGE records to develop a stable reanalysis data product, suitable for trend analysis, from the start of the SAGE II record in 1984 through the present. Changes in the assimilated observation systems can introduce discontinuities within the MERRA-2 ozone record, such as in 2004 when the MERRA-2 system shifted from assimilating ozone retrievals collected by SBUV instruments to those collected by instruments onboard the Aura satellite. We follow the radiative transfer procedure outlined by Wargan et al. (2018) to address discontinuities in the MERRA-2 ozone dataset at the 2004 transition and during the Aura record. SAGE II ozone profiles are used to address discontinuities in upper stratospheric ozone associated with changes in the MERRA-2 meteorological observing system in 1998 and 1995. Lastly, we will use the resulting bias-corrected MERRA-2 ozone fields to assess the relative performance of the data from different SAGE sensors.

SAGE↗

NASA NTRS2012

On the Reprocessing and Reanalysis of Observations for Climate

The long observational record is critical to our understanding of the Earth s climate, but most observing systems were not developed with a climate objective in mind. As a result, tremendous efforts have gone into assessing and reprocessing the data records to improve their usefulness in climate studies. Many challenges remain, such as tracking the improvement of processing algorithms and limited spatial coverage. Reanalyses have fostered significant research, yet reliable global trends in many physical fields are not yet attainable, despite significant advances in data assimilation and numerical modeling. Communication of the strengths, limitations and uncertainties of reprocessed observations and reanalysis data, not only among the community of developers, but also with the extended research community, including the new generations of researchers and the decision makers is crucial for further advancement of the observational data records. WCRP provides the means to bridge the different motivating objectives on which national efforts focus.

Bosilovich, Michael G.↗

NASA NTRS2003

A Comparison of Latent Heat Fluxes over Global Oceans for Four Flux Products

To improve our understanding of global energy and water cycle variability, and to improve model simulations of climate variations, it is vital to have accurate latent heat fluxes (LHF) over global oceans. Monthly LHF, 10-m wind speed (U10m), 10-m specific humidity (Q10h), and sea-air humidity difference (Qs-Q10m) of GSSTF2 (version 2 Goddard Satellite-based Surface Turbulent Fluxes) over global Oceans during 1992-93 are compared with those of HOAPS (Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data), NCEP (NCEP/NCAR reanalysis). The mean differences, standard deviations of differences, and temporal correlation of these monthly variables over global Oceans during 1992-93 between GSSTF2 and each of the three datasets are analyzed. The large-scale patterns of the 2yr-mean fields for these variables are similar among these four datasets, but significant quantitative differences are found. The temporal correlation is higher in the northern extratropics than in the south for all variables, with the contrast being especially large for da Silva as a result of more missing ship data in the south. The da Silva has extremely low temporal correlation and large differences with GSSTF2 for all variables in the southern extratropics, indicating that da Silva hardly produces a realistic variability in these variables. The NCEP has extremely low temporal correlation (0.27) and large spatial variations of differences with GSSTF2 for Qs-Q10m in the tropics, which causes the low correlation for LHF. Over the tropics, the HOAPS LHF is significantly smaller than GSSTF2 by approx. 31% (37 W/sq m), whereas the other two datasets are comparable to GSSTF2. This is because the HOAPS has systematically smaller LHF than GSSTF2 in space, while the other two datasets have very large spatial variations of large positive and negative LHF differences with GSSTF2 to cancel and to produce smaller regional-mean differences. Our analyses suggest that the GSSTF2 latent heat flux, surface air humidity, and winds are likely to be more realistic than the other three flux datasets examined, although those of GSSTF2 are still subject to regional biases.

Chou, Shu-Hsien↗

NASA NTRS1998

Prediction of Particle Number Density and Particle Properties in the Flow Field Observed by the Nephelometer Experiment on the Galileo Probe

This report summarizes the work performed to assist in the analysis of data returned from the Galileo Probe's Nephelometer instrument. A computation of the flow field around the Galileo Probe during its descent through the Jovian atmosphere was simulated. The behavior of cloud particles that passed around the Galileo probe was then computed and the number density in the vicinity of the Nephelometer instrument was predicted. The results of our analysis support the finding that the number density of cloud particles was not the same in each of the four sampling volumes of the Nephelometer instrument. The number densities calculated in this study are currently being used to assist in the reanalysis of the data returned from the Galileo Probe.

Naughton, Jonathan W.↗

NASA NTRS1998

Prediction of Particle Number Density and Particle Properties in the Flow Field Observed by the Nephelometer Experiment on the Galileo Probe

Naughton, Jonathan W.↗

NASA NTRSDate not supplied

Senteniel-6 Radio Occultation Product Released by NASA GES DISC to Supplement Satellite Remote Sensing Datasets for PBL Study

The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) curates hyperspectral atmospheric sounder remote sensing and numerical model reanalysis datasets which have been utilized in the Planetary Boundary Layer (PBL) research and applications. The hyperspectral sounder remote-sensing datasets include the Atmospheric Infrared Sounder (AIRS) on the Aqua satellite to the Cross-track Infrared Sounder (CrIS) on Suomi--National Polar- orbiting Partnership (NPP) and National Oceanic and Atmospheric Administration -20 (N NOAA-20)/ Joint Polar-orbiting Satellite System -1 (JPSS-1). The Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) global reanalysis product provides a data record commencing in 1980. The sounder remote sensing and reanalysis datasets include temperature, water vapor, and trace gas profile down to the PBL, and also have a derived PBL height as well. A nearly 10-year (June 2006 to December 2015) seasonal and annual PBL height climatology dataset from COSMIC Global Navigation Satellite System (GNSS) radio occultation (RO) measurement is also available from the GES DISC. In collaboration with Sentinel-6 Project, the GES DISC is implementing curation activities for GNSS RO products from the Sentinel-6A/Sentinel-6 Michael Freilich satellite launched on November 21, 2020. Sentinel-6A RO products provide refractivity, temperature, and humidity profile with finer vertical resolution, leveraging PBL research and application as a supplement to the hyperspectral sounder remote sensing and reanalysis products. The public release of Sentinel- 6A RO products is scheduled for mid-October of 2021. In this presentation, we will introduce all Senitnel-6A products and services, and demonstrate use cases studying the PBL by combining these products with other GES DISC archived data products.

Feng Ding↗

NASA NTRS1989

The tropospheric response pattern to solar activity forcing

It is tempting to speculate on the possibility that solar flares sometimes are the initial cause of and atmospheric disturbance, which cumulative effect may give rise to a correlation at the 11 year timescale. Reasons to reconsider the possible relevance of solar flare response studies are stated. The discovery of the apparently decisive role of the Quasi-Biennial Oscillations (QBO) in establishing the atmospheric response pattern to solar forcing may throw new light on some of the earlier published relations. Reanalysis of old data in some cases may be advisable. Data on solar flares and their effects on the earth's atmosphere might be a promising candidate for reexamination.

Schuurmans, C. J. E.↗

NASA NTRS1992

A reanalysis of the orbital inclination of Cygnus XR-1 determined polarimetrically

The orbital inclination of the Cyg XR-1/HDE 226868 system was determined by Dolan and Tapia (1989) to be 62 deg (+5 deg, -37 deg) from polarimetric observations. Together with the spectroscopic mass function of the binary, this corresponds to a mass of 6.3 solar masses for the X-ray source. Numerical simulations show that there is no straightforward way to removing tidal deformation polarization (TDP) from polarimetric observations of close binaries. A reanalysis of the data shows that the existence of TDP in the Stokes parameter light curves observed from Cyg XR-1 does not significantly bias the mass derived to larger values. The mass of Cyg XR-1 must be significantly larger than 5 solar masses. This fact alone does not require Cyg XR-1 to be a black hole, however. Recent theoretical investigations of possible equations of state of matter at nuclear density show that the maximum mass of a compact object which is not a black hole may be much greater than 5 solar masses.

Dolan, Joseph F.↗

NASA NTRS2001

A Program To Detect and Characterize Extra-Solar Giant Planets

This grant report highlights activity in the following areas: (1) Improvement in Precise Radial Velocity (PRV) analysis code; (2) Reanalysis of previous data; (3) Improvements to the AFOE (Advanced Fiber Optic Echelle) spectrograph; (4) Development of PRV capabilities for the Hectochelle; (5) Extra-solar planet studies; (6) Longer-term plans for the AFOE; (7) Completion and publication of the analysis of the transiting gas-giant planet HD 209458b.

Noyes, Robert W.↗

NASA NTRS2016

Investigating Ozone Sources in California Using AJAX Airborne Measurements and Models: Implications for Stratospheric Intrusion and Long Range Transport

High ozone concentrations at low altitudes near the surface were detected from airborne Alpha Jet Atmospheric eXperiment (AJAX) measurements on May 30, 2012. We investigate the causes of the elevated ozone concentrations using the airborne measurements and various models. GEOSchem and WRF-STILT model simulations show that the contribution from local sources is small. From MERRA reanalysis, it is found that high potential vorticity (PV) is observed at low altitudes. This high PV appears to be only partially coming through the stratospheric intrusions because the air inside the high PV region is moist, which shows that mixing appears to be enhanced in the low altitudes. Considering that diabatic heating can also produce high PV in the lower troposphere, high ozone is partially coming through stratospheric intrusion, but this cannot explain the whole ozone concentration in the target areas of the western U.S. A back-trajectory model is utilized to see where the air masses originated. The air masses of the target areas came from the lower stratosphere (LS), upper (UT), mid- (MT), and lower troposphere (LT). The relative number of trajectories coming from LS and UT is low (7.7% and 7.6%, respectively) compared to that from LT (64.1%), but the relative ozone concentration coming from LS and UT is high (38.4% and 20.95%, respectively) compared to that from LT (17.7%). The air mass coming from LT appears to be mostly coming from Asia. Q diagnostics show that there is sufficient mixing along the trajectory to indicate that ozone from the different origins is mixed and transported to the western U.S. This study shows that high ozone concentrations can be detected by airborne measurements, which can be analyzed by integrated platforms such as models, reanalysis, and satellite data.

gas composition↗

NASA NTRS2015

Investigation of Ozone Sources in California Using AJAX Airborne Measurements and Models: Implications for Stratospheric Intrusion and Long Range Transport

High ozone concentrations at low altitudes near the surface were detected from airborne Alpha Jet Atmospheric eXperiment (AJAX) measurements on May 30, 2012. We investigate the causes of the elevated ozone concentrations using the airborne measurements and various models. GEOS-chem and WRF-STILT model simulations show that the contribution from local sources is small. From MERRA reanalysis, it is found that high potential vorticity (PV) is observed at low altitudes. This high PV appears to be only partially coming through the stratospheric intrusions because the air inside the high PV region is moist, which shows that mixing appears to be enhanced in the low altitudes. Considering that diabatic heating can also produce high PV in the lower troposphere, high ozone is partially coming through stratospheric intrusion, but this cannot explain the whole ozone concentration in the target areas of the western U.S. A back-trajectory model is utilized to see where the air masses originated. The air masses of the target areas came from the lower stratosphere (LS), upper (UT), mid- (MT), and lower troposphere (LT). The relative number of trajectories coming from LS and UT is low (7.7 and 7.6, respectively) compared to that from LT (64.1), but the relative ozone concentration coming from LS and UT is high (38.4 and 20.95, respectively) compared to that from LT (17.7). The air mass coming from LT appears to be mostly coming from Asia. Q diagnostics show that there is sufficient mixing along the trajectory to indicate that ozone from the different origins is mixed and transported to the western U.S. This study shows that high ozone concentrations can be detected by airborne measurements, which can be analyzed by integrated platforms such as models, reanalysis, and satellite data.

low altitude↗

NASA NTRS2020

Computationally Efficient Modeling and Data Assimilation of Near-Surface Variability

Near-surface (< 20m) ocean exhibits high variability due to coupled interactions, for e.g., with the atmosphere, sea ice, land, etc. Here we focus on atmospheric heat and momentum (wind) forcing, which are known to cause diurnal variability within the mixed layer. Only recently with a combination of sufficiently high vertical/horizontal resolution (75L, 1/4deg) and sub-daily atmospheric forcing fields, ocean models are starting to resolve this diurnal variability. However, the computation expense of such a high vertical resolution is burdensome in the context of coupled modeling and data assimilation. An alternative approach is to parameterize this diurnal variability with a prognostic model, that is embedded into the ocean model.In the first part of this presentation, we will demonstrate results with the above two approaches, by comparing them to profiles of near-surface temperature and salinity. In the context of data assimilation and reanalysis, this modeling capability opens the door to re-examine and perhaps improve specification of background (or, ensemble) error characteristics. The second half of this talk will focus on illustrating diurnally varying errors within an ensemble DA, and possible approaches to improve localization (horizontal/vertical) to extract maximum possible observational information content from in-situ and satellite observations of sea surface temperature.

Akella, Santha↗

NASA NTRS2020

Reconstruction of Historical Surface Mass Balance, 1984–2017 from GreenTrACS Multi-offset Ground-penetrating Radar

We present continuous estimates of snow and firn density, layer depth and accumulation from a multi-channel, multi-offset, ground-penetrating radar traverse. Our method uses the electromagnetic velocity, estimated from waveform travel-times measured at common-midpoints between sources and receivers. Previously, common-midpoint radar experiments on ice sheets have been limited to point observations. We completed radar velocity analysis in the upper ~2 m to estimate the surface and average snow density of the Greenland Ice Sheet. We parameterized the Herron and Langway (1980) firn density and age model using the radar-derived snow density, radar-derived surface mass balance (2015–2017) and reanalysis-derived temperature data. We applied structure-oriented filtering to the radar image along constant age horizons and increased the depth at which horizons could be reliably interpreted. We reconstructed the historical instantaneous surface mass balance, which we averaged into annual and multidecadal products along a 78 km traverse for the period 1984–2017. We found good agreement between our physically constrained parameterization and a firn core collected from the dry snow accumulation zone, and gained insights into the spatial correlation of surface snow density.

glacier geophysics↗

NASA NTRSDate not supplied

Community Radiative Transfer Model: Implementing A New Cloud Scattering Database and Developing the Forward Radar Module

The Mie theory is used by many fast RT models to estimate the optical properties of single particles. The Mie theory assumes spherical shapes for ice or snow particles with mixture of air and ice. However, hydrometeors scattering radiation at microwave frequencies have different shapes, sizes, and orientations. Therefore, using Mie theory to determine their optical properties leads to large uncertainties in all-sky radiative transfer calculations. The discrete dipole approximation (DDA) which approximates the optical properties of large objects in terms of discrete dipoles has shown promises in calculating the scattering properties of particles with different shapes in the microwave frequencies. The goal of the research was to enhance the CRTM scattering calculations for frozen hydrometeors in the microwave frequencies using the DDA technique. Given that such optical properties cannot be practically calculated on the fly, pre-computed look-up tables need to be implemented into the fast RT models to calculate the scattering properties of these particles using the DDA technique and the inputs provided by the users. Therefore, we implemented such pre-computed DDA databases into CRTM. In addition to using stand-alone CRTM calculations using collocated ATMS and reanalysis profiles, the data assimilation experiments conducted using the NOAA FV3GFS forecast system will be used to evaluate the scattering improvements. Additionally, we used the backscattering information from the DDA database to implement a radar simulator into CRTM. The radar operators takes advantage of CRTM different modules to calculate clouds absorption and scattering properties. In addition to the forward model both adjoint and tangent linear of the radar simulator are implemented and evaluated as well. The radar simulator is currently being tested within the JEDI/GEOS data assimilation framework to facilitate the assimilation of radar measurements such as CloudSat CPR and GPM DPR into the NASA GEOS model.

Isaac Moradi↗

NASA NTRSDate not supplied

Recent Developments in the Assimilation of Microwave and Radar Observations Into NWP Models

Microwave observations play a very important role in improving the weather forecasts. Although these observations are routinely assimilated into NWP models in clear-sky conditions, assimilation of all-sky microwave observations is very limited. Two main factors contributing to this limitation are inaccuracy in the input cloud and hydrometeor profiles used as input to the radiative transfer model and also error in scattering calculations performed by the radiative transfer model itself. The Mie theory is used by many fast RT models to estimate the optical properties of single particles. The Mie theory assumes spherical shapes for ice or snow particles with mixture of air and ice. However, hydrometeors scattering radiation at microwave frequencies have different shapes, sizes, and orientations. Therefore, using Mie theory to determine their optical properties leads to large uncertainties in all-sky radiative transfer calculations. The discrete dipole approximation (DDA) which approximates the optical properties of large objects in terms of discrete dipoles has shown promise in calculating the scattering properties of particles with different shapes in the microwave frequencies. This presentation focuses on recent advancements in the CRTM scattering calculations for frozen hydrometeors in the microwave frequencies using the DDA technique. In addition to using stand-alone CRTM calculations using collocated ATMS and reanalysis profiles, the data assimilation experiments conducted using the NOAA FV3GFS forecast system are used to evaluate the scattering improvements. Additionally, the backscattering information from the DDA database was used to implement a radar simulator into CRTM. The radar operator takes advantage of CRTM different modules to calculate clouds absorption and scattering properties. In addition to the forward model both adjoint and tangent linear of the radar simulator are implemented and evaluated as well. The radar simulator is currently being tested within the JEDI/GEOS data assimilation framework to facilitate the assimilation of radar measurements such as CloudSat CPR and GPM DPR into the NASA GEOS model.

Isaac Moradi↗