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NASA NTRS2019

Assimilation of Satellite Soil Moisture for Improved Atmospheric Reanalyses

A newly developed, weakly coupled land and atmosphere data assimilation system for NASA’s Global Earth Observing System model is presented, and used to demonstrate the benefit of assimilating satellite soil moisture into an atmospheric reanalysis. Specifically, Advanced Scatterometer and Soil Moisture Ocean Salinity soil moisture retrievals are assimilated into a system that uses the same model, atmospheric assimilation system, and atmospheric observations as the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2). The atmosphere is sensitive to soil moisture only under certain conditions. Hence, while the globally averaged model improvements were small, regionally, the soil moisture assimilation induced some substantial improvements. For example, in a large region spanning from western Europe across southern Russia, the soil moisture assimilation decreased the RMSE against independent station observations of daily maximum 2-m temperature (T(sup 2m, sub max)) by up to 0.4 K, and of 2-m specific humidity (q(exp 2m)) by up to 0.5 g/kg. Over all available stations, the mean T(sup 2m, sub max) RMSE was reduced from 2.82 to 2.79 K, while the mean q(exp 2m) RMSE was reduced from 1.25 to 1.20 g/kg. The soil moisture assimilation also reduced the mean RMSE across 29 flux tower sites from 34.2 to 32.6 Wm(exp −2) for latent heating, and from 37.7 to 36.5 Wm(exp −2) for sensible heating. For all variables evaluated, the soil moisture assimilation improved the model at monthly to seasonal, rather than daily, time scales. Based on the above experiments, it is recommended that satellite soil moisture be assimilated into future reanalyses, including the follow-on to MERRA-2.

Clara Draper↗

NASA NTRS2019

Improvements to an Explicit Algebraic Stress Model for Turbulent Jet Mixing Predictions

Modifications to key coefficients in a k − E based explicit algebraic stress model (EASM) are examined with the objective of improving the prediction of turbulent jet flows. The pressure strain coefficient, C2 and the turbulent diffusion coefficients, σk and σE were investigated. For a series of benchmark subsonic jets at heated and unheated conditions, lowering C2 from the default value of 0.36 to 0.10 resulted in a significant improvement in the jet mixing, when compared to experimental data. Changing σk and σE from default values of 1.00 and 1.4489, respectively, to 0.50 and 0.7244, respectively, improved the initial mixing rate, while reducing the farfield mixing rate and the peak turbulent kinetic energy along the centerline. A high-speed mixing layer was also investigated for performance of baseline and modified EASM coefficients, with similar results as for the jet cases. A flat plate boundary layer was briefly examined to determine the effects of changing the coefficients on the turbulent skin friction coefficient. The change to the pressure strain coefficient, C2 = 0.10 is recommended for future EASM calculation of jets flow; however, it is also recommended that the diffusion coefficients remain at their default values.

Turbulent Mixing↗

NASA NTRS2019

Potential Improvements in Global Carbon Flux Estimates from a Network of Laser Heterodyne Radiometer Measurements of Column Carbon Dioxide

We present observing system simulation experiments (OSSEs) to evaluate the impact of a proposed network of ground-based miniaturized laser heterodyne radiometer (mini-LHR) instruments that measure atmospheric column-averaged carbon dioxide (XCO2) with a 1 ppm precision. A particular strength of this passive measurement approach is its insensitivity to clouds and aerosols due to its direct sun pointing and narrow field of view (0.2°). Developed at the NASA Goddard Space Flight Center (GSFC), these portable, low-cost mini-LHR instruments were designed to operate in tandem with the sun photometers used by the AErosol RObotic NETwork (AERONET). This partnership allows us to leverage the existing framework of AERONET's global ground network of more than 500 sites as well as providing simultaneous measurements of aerosols that are known to be a major source of error in retrievals of XCO2 from passive nadir-viewing satellite observations. We show, using the global 3-D GEOS-Chem chemistry transport model, that a deployment of 50 mini-LHRs at strategic (but not optimized) AERONET sites significantly improves our knowledge of global and regional land-based CO2 fluxes. This improvement varies seasonally and ranges 58%–81% over southern lands, 47%–76% over tropical lands, 71%–92% over northern lands, and 64%–91% globally. We also show significant added value from combining mini-LHR instruments with the existing ground-based NOAA flask network. Collectively, these data result in improved a posteriori CO2 flux estimates on spatial scales of ∼10 km2, especially over North America and Europe, where the ground-based networks are densest. Our studies suggest that the mini-LHR network could also play a substantive role in reducing carbon flux uncertainty in Arctic and tropical systems by filling in geographical gaps in measurements left by ground-based networks and space-based observations. A realized network would also provide necessary data for the quinquennial global stock takes that form part of the Paris Agreement.

Palmer, Paul I.↗

NASA NTRS2019

Investigating the Utility of Hyperspectral Sounders in the 9.6 μm Band to Improve Ozone Analyses

Currently, hyperspectral sounder brightness temperatures assimilated in the Goddard Earth Observing System - Atmospheric Data Assimilation System (GEOS-ADAS) are limited to assimilating temperature and moisture. The ozone sensitive 9.6 micron region is sensed by several hyperspectral sounders including AIRS (Atmospheric InfraRed Sounder), IASI (Infrared Atmospheric Sounding Interferometer), and CrIS (Cross-track Infrared Sounder). Direct assimilation of brightness temperatures in the 9.6 micron region have been used previously to improve ozone analyses. This has recently been achieved by ECMWF (European Centre for Medium-Range Weather Forecast) (Dragani and McNally, 2013; Eresmaa et al., 2017), and while every system presents its challenges, it should be possible to take advantage of this spectral region using the GEOS-ADAS. For this study, channels were selected from available operational subsets evaluating information content, and minimizing inter-channel correlation. Additionally, information such as channel selections made by other studies, and vertical sensitivities of ozone and temperature were considered in developing the study. The analyses produced show improvements verified against ozonesondes taken from SHADOZ (Southern Hemisphere Additional Ozonesondes), and WOUDC (World Ozone and Ultraviolet Data Center). The addition of ozone channels does degrade forecast skill in the Tropics, on the border of statistical significance. Overall, the addition of these channels in some form could improve ozone analyses in the GEOS-ADAS.

Karpowicz, Bryan M.↗

NASA NTRS2017

Improvement of Reynolds-Stress and Triple-Product Lag Models

The Reynolds-stress and triple product Lag models were created with a normal stress distribution which was defined by the accepted 4:3:2 distribution of streamwise, spanwise and wall normal stresses, and a ratio of τ(sub w) = 0.3k in the log layer region of high Reynolds number flat plate flow, which implies R11(+) = 4/(9/2)∗.3 ≈ 2.96. More recent measurements show a more complex picture of the log layer region at high Reynolds numbers. The first cut at improving these models along with the direction for future refinements is described. Comparison with recent high Reynolds number data shows areas where further work is needed, but also shows inclusion of the modeled turbulent transport terms improve the prediction where they influence the solution. Additional work is needed to develop a model that better matches experiments, but there is significant improvement in many of the details of the log layer behavior.

Turbulence↗

NASA NTRS2018

Observations and Recommendations for the Calibration of Landsat 8 OLI and Sentinel 2 MSI for Improved Data Interoperability

Combining data from multiple sensors into a single seamless time series, also known as data interoperability, has the potential for unlocking new understanding of how the Earth functions as a system. However, our ability to produce these advanced data sets is hampered by the differences in design and function of the various optical remote-sensing satellite systems. A key factor is the impact that calibration of these instruments has on data interoperability. To address this issue, a workshop with a panel of experts was convened in conjunction with the Pecora 20 conference to focus on data interoperability between Landsat and the Sentinel 2 sensors. Four major areas of recommendation were the outcome of the workshop. The first was to improve communications between satellite agencies and the remote-sensing community. The second was to adopt a collections-based approach to processing the data. As expected, a third recommendation was to improve calibration methodologies in several specific areas. Lastly, and the most ambitious of the four, was to develop a comprehensive process for validating surface reflectance products produced from the data sets. Collectively, these recommendations have significant potential for improving satellite sensor calibration in a focused manner that can directly catalyze efforts to develop data that are closer to being seamlessly interoperable.

calibration; geometric; radiometric; Landsat; Sent↗

NASA NTRS2019

Assimilation of Satellite Soil Moisture for Improved Atmospheric Reanalyses

Satellite soil moisture observations are assimilated into NASA's Global Earth Observing System using a weakly coupled land/atmosphere data assimilation system. Specifically, Advanced Scatterometer and Soil Moisture Ocean Salinity soil moisture retrievals are assimilated with an Ensemble Kalman Filter into a system that uses the same model, atmospheric assimilation system (3DVar), and atmospheric observations as the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). The first set of experiments investigate the impact of the soil moisture assimilation on the model states and fluxes within the cycling DA system. Comparison to independent ground-based observations from global networks of soil moisture, latent and sensible heating, and 2m temperature and specific humidity showed small, but positive, improvements in the global mean statistics. Additionally, in some regions, the soil moisture assimilation induced more substantial improvements. For all variables evaluated, the soil moisture assimilation improved the model at monthly to seasonal, rather than daily, time scales. Based on the above experiments, it is recommended that satellite soil moisture be assimilated into future reanalyses, including the follow-on to MERRA-2.

Draper, Clara↗

NASA NTRS2019

Improvements to Ozone Analyses using Hyperspectral Sounders in the 9.6 μm Band

Previously, hyperspectral sounder brightness temperatures assimilated in the Goddard Earth Observing System Atmospheric Data Assimilation System (GEOS-ADAS) were limited to assimilating temperature and moisture. The ozone sensitive 9.6 m region is sensed by several hyperspectral sounders including AIRS (Atmospheric InfraRed Sounder), IASI (Infrared Atmospheric Sounding Interferometer), and CrIS (Cross-track Infrared Sounder). Direct assimilation of brightness temperatures in the 9.6 m region have been operational at ECMWF for several years (Dragani and McNally, 2013; Eresmaa et al., 2017). With this study, similar improvements using the GEOS-ADAS are presented. Channels were selected from available operational subsets evaluating information content and minimizing inter-channel correlation. Additionally, information such as channel selections made by other studies, and vertical sensitivities of ozone and temperature were considered in developing the study. The analyses produced show improvements verified against ozonesondes taken from SHADOZ (Southern Hemisphere Additional Ozonesondes). It is anticipated that inclusion of these ozone sensitive channels will be used to improve NASA GMAO products in the near future.

GEOS-ADAS↗

NASA NTRS2019

Testing and Improving a UAV-Based System Designed for Wetland Methane Source Measurements

Wetlands are the single highest emitting methane source category, but the magnitude of wetland fluxes remains difficult to fully characterize due to their large spatial extent and heterogeneity. Fluxes can vary with land surface conditions, vegetation type, and seasonal changes in environmental conditions. Unmanned aerial vehicles (UAVs) are an emerging platform to better characterize spatial variability in these natural ecosystems. While presenting some advantages over traditional techniques like towers and flux chambers, in that they are mobile vertically and horizontally, their use is still challenging, requiring continued improvement in sensor technology and field measurement approaches. In this work, we employ a small, fast response laser spectrometer on a Matrice 600 hexacopter. The system was previously deployed successfully for 40 flights conducted in a four-day period in 2018 near Fairbanks, Alaska. These flights revealed several potential areas for improvement, including: vertical positioning accuracy, the need for sensor health indicators, and approaches to deal with low wind speeds. An additional set of flights was conducted this year near Antioch in California. Flights were conducted several meters above ground up to 15-25 m in a curtain pattern. These curtains were flown both upwind and downwind of a tower site, allowing us to calculate a mass balance methane flux estimate that can be compared to eddy covariance fluxes from the tower. Testing will better characterize the extent to which altitude drifts in-flight and how GPS values compare with measurements from the onboard LIDAR, as well as the agreement between two-dimensional wind speed and direction on the ground versus measured onboard the UAV. Hardware improvements to the sensor and GPS are being considered to help reduce these sources of uncertainty. Results of this testing and how system performance relates to needs for quantifying wetland fluxes, will be presented.

Golston, Levi↗

NASA NTRS2019

Improved Ozone Analyses Using Direct Assimilation of 9.6μm Radiances from Hyperspectral Sounders

Previously, hyperspectral sounder brightness temperatures assimilated in the Goddard Earth Observing System Atmospheric Data Assimilation System (GEOS-ADAS) were limited to assimilating temperature and moisture. The ozone sensitive 9.6 m region is sensed by several hyperspectral sounders including AIRS (Atmospheric InfraRed Sounder), IASI (Infrared Atmospheric Sounding Interferometer), and CrIS (Cross-track Infrared Sounder). Direct assimilation of brightness temperatures in the 9.6 m region have been operational at ECMWF for several years (Dragani and McNally, 2013; Eresmaa et al., 2017). With this study, similar improvements using the GEOS-ADAS are presented. Channels were selected from available operational subsets evaluating information content and minimizing inter-channel correlation. Additionally, information such as channel selections made by other studies, and vertical sensitivities of ozone and temperature were considered. The analyses produced show improvements verified against ozonesondes taken from SHADOZ (Southern Hemisphere Additional Ozonesondes), and WOUDC (World Ozone and Ultraviolet Data Center). While care was taken to minimize inter-channel correlation through channel selection, a key feature available in the GEOS-ADAS is the ability to account for correlated error. The importance of inter-channel correlated error is highlighted by performing assimilation experiments with and without inter-channel correlation in the GEOS-ADAS. It is anticipated that inclusion of these ozone sensitive channels will be used to improve NASA GMAO products in the near future.

Karpowicz, Bryan M.↗

NASA NTRS2020

Multi-Objective Flight Control for Ride Quality Improvement for Flexible Aircraft

This paper describes a multi-objective flight control system design for ride quality improvement for flexible aircraft using multi-functional distributed flight control surfaces. A multi-objective optimal control design is developed to provide an acceleration suppression capability in conjunction with a gust load alleviation in order to provide ride quality improvement. A gust estimation is developed to estimate the gust load using a recursive least-squares algorithm. A ride quality assessment study is conducted using a flexible wing generic transport model. Six different flight control designs are implemented. The study shows that ride quality can be significantly improved with the acceleration suppression control.

Nguyen, Nhan↗

NASA NTRS2019

Management by Trajectory: Improving Predictability for Airspace Operations

In the present-day National Airspace System, the air traffic management system attempts to predict the trajectory for each flight based on the flight plan and scheduled or controlled departure time. However, gaps in trajectory data and models, coupled with tactical control actions that are not communicated to automation systems or other stakeholders, lead to trajectory predictions that are less accurate than they could be. This affects traffic flow management performance. Management by Trajectory (MBT) is a NASA concept for air traffic management in which every flight operates in accordance with a 4D trajectory that is negotiated between the airspace user and the FAA to account for the airspace user’s goals while complying with NAS constraints. The primary benefit of MBT is an improvement in system performance due to increased trajectory predictability and stability, which result from managing traffic in all four dimensions (2D route, vertical, and time), ensuring that changes to the flight’s trajectory are incorporated into the assigned trajectory, and utilizing improved time or arrival control standards. Importantly, the performance improvements support increasing efficiency without increasing collision risk. This paper provides an overview of MBT and describes fast-time simulation results evaluating the safety, performance, and efficiency effects of MBT.

Fernandes, Alicia B.↗

NASA NTRSDate not supplied

Improving Structural Test and Analysis Correlation Using Digital Image Correlation Boundary Measurements

Simplified idealizations, such as clamped or simply supported, are commonly used as support boundary conditions in modeling and analysis of tested aerospace structures. However, these simplifications are not always appropriate and characterization of the response of non-rigid boundaries is often required to improve test and analysis correlation. While e analysis models are typically modified to better represent the test, this modification is often not practical due to the complexities of the boundary response. A study is conducted to examine the use of digital image correlation (DIC) to measure the boundary support structure response and then to adjust the test data to remove the effects of the boundary response in order to improve test and analysis correlation. A high-aspect ratio composite wing test is the subject of the study. Three sources of boundary flexibility contributing to rigid body rotation of the test wing are identified and quantified using both DIC and conventional test data. The wing displacement test data are adjusted using DIC data for these sources. Wing displacement test data is also adjusted using direct measurement of wing rotation from a DIC system that monitored the top surface, root region of the wing, and using available conventional instrumentation. The adjusted data exhibits improved test and analysis correlation and demonstrated the benefit of using DIC along with conventional instrumentation to collect and interpret test data.

Andrew E Lovejoy↗

NASA NTRSDate not supplied

Recent Progress in OVERFLOW Convergence Improvements

Improvements have been made to the implicit symmetric successive overrelaxation algorithm in the OVERFLOW 2.3 structured, overset grid, computational fluid dynamics flow solver. These improvements, consisting of implicit boundary conditions, improved flux Jacobian linearizations, and CFL number ramping, are a series of evolutionary changes to the linear solver that have resulted in increased nonlinear convergence rates and faster time to solution. A series of test cases are presented that demonstrate the effect of the changes through comparison with the original SSOR path and other linear solver implementations within OVERFLOW.

Joseph M Derlaga↗

NASA NTRS2020

Improved Groundwater Table and L-Band Brightness Temperature Estimates for Northern Hemisphere Peatlands Using New Model Physics and SMOS Observations in a Global Data Assimilation Framework

There is an urgent need to include northern peatland hydrology in global Earth system models to better understand land-atmosphere interactions and sensitivities of peatland functions to climate change, and, ultimately, to improve climate change predictions. In this study, we introduced for the first time peatland-specific model physics into an assimilation scheme for L-band brightness temperature (Tb) data from the Soil Moisture Ocean Salinity (SMOS) mission to improve groundwater table estimates. We conducted two sets of model-only and data assimilation experiments using the Catchment Land Surface Model (CLSM), applying (over peatlands only) in one of them a peatland-specific adaptation (PEATCLSM). The evaluation against in-situ measurements of peatland groundwater table depth indicates the superiority of PEATCLSM model physics and additionally improved performance after assimilating SMOS Tb observations. The better performance of PEATCLSM over nearly all Northern Hemisphere peatlands is further supported by the better agreement between SMOS Tb observations and Tb estimates from the model-only and data assimilation runs. Within the data assimilation scheme, PEATCLSM reduces Tb observation-minus-forecast residuals and leads to reduced data assimilation updates of water storage components and, thus, reduced water budget imbalances in the assimilation system.

SMOS↗

NASA NTRS2021

Improved Convective Ice Microphysics Parameterization in the NCAR CAM Model

Partitioning deep convective cloud condensates into components that sediment and detrain, known to be a challenge for global climate models, is important for cloud vertical distribution and anvil cloud formation. In this study, we address this issue by improving the convective microphysics scheme in the National Center for Atmospheric Research Community Atmosphere Model version 5.3 (CAM5.3). The improvements include: (1) considering sedimentation for cloud ice crystals that do not fall in the original scheme, (2) applying a new terminal velocity parameterization that depends on the environmental conditions for convective snow, (3) adding a new hydrometeor category, “rimed ice,” to the original four-class (cloud liquid, cloud ice, rain, and snow) scheme, and (4) allowing convective clouds to detrain snow particles into stratiform clouds. Results from the default and modified CAM5.3 models were evaluated against observations from the U.S. Department of Energy Tropical Warm Pool-International Cloud Experiment (TWP-ICE) field campaign. The default model overestimates ice amount, which is largely attributed to the underestimation of convective ice particle sedimentation. By considering cloud ice sedimentation and rimed ice particles and applying a new convective snow terminal velocity parameterization, the vertical distribution of ice amount is much improved in the midtroposphere and upper troposphere when compared to observations. The vertical distribution of ice condensate also agrees well with observational best estimates upon considering snow detrainment. Comparison with observed convective updrafts reveals that current bulk model fails to reproduce the observed updraft magnitude and occurrence frequency, suggesting spectral distributions be required to simulate the subgrid updraft heterogeneity.

terminal velocity↗

NASA NTRSDate not supplied

Light Improves Alertness and Mood during the Sleep Inertial Period following Slow Wave Sleep

Introduction: Waking from sleep, especially slow wave sleep (SWS), is associated with reduced alertness known as sleep inertia. Light improves alertness during sleep deprivation and circadian misalignment. In this study, we assessed the efficacy of light to improve alertness and mood immediately after waking from SWS. Methods: Twelve participants kept a sleep schedule of 8.5 h for 5 nights and 5 h for one night prior to the overnight laboratory visit (confirmed by actigraphy). Participants went to bed at their scheduled habitual bedtime in the laboratory and were monitored by standard polysomnography. After at least 5 min of SWS, participants were awoken and exposed to either red ambient light (control) or blue-enriched bright light (light) for 1 h. During this time, participants completed a subjective scale of alertness (Karolinska Sleepiness Scale, KSS) and visual analogue scales (VAS) of mood at 2 min, 17 min, 32 min, and 47 min after waking. Following this sleep inertia measurement period, all lights were turned off and participants were allowed to return to sleep. They were then awoken again from their subsequent SWS period and exposed to the opposite condition (control or light). A linear mixed-effects model with fixed effects of condition, time, and condition*time and a random effect of participant was used to determine the impact of light across the testing period. An average of baseline responses (pre-sleep) was included as a covariate. Results: Compared to the control condition, participants exposed to bright blue-enriched light reported feeling more alert (KSS: F1,77=4.955, p=.029; VASalert: F1,77=8.226, p=.005), more cheerful (VAScheerful: F1,77=8.615, p=.004), less depressed (VASdepressed: F1,77=4.649, p=.034), and less lethargic (VASlethargic: F1,77=5.652, p=.020). Discussion: Exposure to blue-enriched bright light immediately after waking from SWS may help to improve subjective alertness and mood. Future analyses will explore whether these findings extend to effects on cognitive performance.

alertness↗

NASA NTRS2020

An Observation‐Driven Approach to Improve Vegetation Phenology in a Global Land Surface Model

An empirical model calibration approach is presented that aims to approximate missing biosphere processes in a global land surface model without the need for substantial model structural changes. The strategy is implemented here using the NASA Catchment‐CN land surface model and Moderate Resolution Imaging Spectroradiometer (MODIS) observations of the fraction of absorbed photosynthetically active radiation (FPAR). Existing plant functional types (PFTs) of the Catchment‐CN model are divided into three subtypes, based on the bias between the model‐simulated and MODIS‐observed FPAR. Separate sets of vegetation parameters for each subtype are then calibrated at a small number of grid cells with homogeneous, single‐PFT land cover, using MODIS FPAR reference observations from 2003 to 2009. The effectiveness of the empirical approach at improving the realism of modeled vegetation dynamics is investigated with two global model simulations for the period 2010–2016, one using the newly calibrated parameter values and the other using the original values. Globally, the calibrated parameters reduce the root mean square error (RMSE) of the modeled FPAR with respect to MODIS by 0.029 (∼10%) on average. In some regions, substantially larger RMSE reductions are achieved. RMSE reductions are primarily driven by model bias reductions, with neutral effects on the temporal correlation skill. While the empirical approach is suitable for achieving consistent model improvements, it is shown to be sensitive to the characteristics of the model error, specifically a dominance of the bias component in the case of Catchment‐CN. Ultimately, more fundamental model structural changes may be required to achieve better improvements.

J. Kolassa↗