Near Real-Time Sub/Seasonal Prediction of Aerosol and Air Quality at the NASA Global Modeling and Assimilation Office

Version 2 of the coupled modeling and analysis system used to produce near real time subseasonal to seasonal forecasts was released almost two years ago by the NASA/Goddard Global Modeling and Assimilation Office. The model runs at approximately 1/2 degree globally in the atmosphere and ocean, contains a realistic description of the cryosphere, and includes an interactive aerosol model. The data assimilation used to produce initial conditions is weakly coupled, in which the atmosphere-only assimilated state is coupled to an ocean data assimilation system using a Local Ensemble Transform Kalman Filter. Results of aerosol-derived air quality (Particulate Matter) from an extensive series of retrospective forecasts will be shown, with particular focus on the continental United States and eastern Asia. In addition, under some circumstances, the interactive aerosol is shown to improve seasonal time scale prediction skill. Plans for a future version of the system with predicted biomass burning from fires will also be discussed

Strengthening reinforced concrete column-beam joints with modular shape memory alloy plate optimized through probabilistic damage prediction

Column-beam joints are one of the most critical zones of concrete structures, especially under unpredicted heavy loads and lateral loads such as seismic. Failure of the joints can even lead to failure of structures in their entirety. The low strength capacity of concrete is a reason of sensitivity of the region. Shape memory alloy (SMA) plates can be employed in order to overcome this weakness and increase the stiffness of joints in existing structures. SMA is a smart material whose functionality, workability and its self-healing feature are under investigation by scientists in the field of structural engineering. In fact, there are two types of alloy: i) superelastic shape memory alloy and ii) shape memory effect that is sensitive to temperature, but it is out of the topic of the research. However, a superelastic form is the most common type of alloy in the field of structural engineering that can be used not only as external reinforcement bolted to the concrete surface but also as internal reinforcement embedded within the concrete elements. The author of this numerical research attempted to implement a plate form of the alloy as external reinforcement to increase stiffness and ductility of the joint. To do so, an experimentally investigated concrete column-beam joint has been modelled in Ansys, and it was loaded under a large number of randomly selected load combinations. The plate initially was designed with a uniform thickness and length in the plastic hinge region of the joint under the critical load combination. Then, probabilistic analysis was carried out to optimize the plate’s thickness. To that end, the stress values of thirty-five predefined nodes on the plate surface were recorded under each load combinations. Results were imported into MATLAB software to run the probabilistic analysis and specifying 0.95 quantile of the stored stresses of the nodes. Design optimization was also carried out based on the probabilistic results in order to design the thickness of the plate at different control nodes. During the course of the research, a set of necessary additional trials have been carried out, as for example with regards to the proper Ansys element type selection for reinforced concrete, determination of limit state functions, and to assess the most suitable parallel processing setup. A fastening technique was also employed to connect the optimized SMA plate to the surface of the concrete joint. Finally, some numerical examples have been run in order to check to what extend the utilized method worked properly. The procedure was applied twice; i) when the load combinations were applied in cyclic form and ii) when the load combinations were exerted in reverse cyclic form. Therefore, two optimized SMA have been designed and examined. The results of the analyses showed that the employed technique enhanced the strength of the joint considerably so that the cracking load of the system reinforced with optimized SMA plate under cyclic loading was 1.4 times greater than the benchmark. The load-carrying capacity of the reinforced system in the elastic regime was higher than the unreinforced structure, and the capability in the plastic regime was even higher. Indicatively, the load-carrying capacity of the reference system at a displacement of 32 mm was approximately 98 kN, whereas the respective resistance value was approximately 66 kN in the system without the plate. Besides, the existence of the plate led to transition of the failure zone from the joint to the beam span, which leads to a lower risk of failure of the entire structure. As a result, the main focus of the research was to describe a novel method that allows for a probability-based prediction of damage in concrete structures that can facilitate the assessment and design of degraded structures under risk of failure

The Use of a Statistical Model of Storm Surge as a Bias Correction for Dynamical Surge Models and its Applicability along the U.S. East Coast

The present study extends the applicability of a statistical model for prediction of storm surge originally developed for The Battery, NY in two ways: I. the statistical model is used as a biascorrection for operationally produced dynamical surge forecasts, and II. the statistical model is applied to the region of the east coast of the U.S. susceptible to winter extratropical storms. The statistical prediction is based on a regression relation between the “storm maximum” storm surge and the storm composite significant wave height predicted ata nearby location. The use of the statistical surge prediction as an alternative bias correction for the National Oceanic and Atmospheric Administration (NOAA) operational storm surge forecasts is shownhere to be statistically equivalent to the existing bias correctiontechnique and potentially applicable for much longer forecast lead times as well as for storm surge climate prediction. Applying the statistical model to locations along the east coast shows that the regression relation can be “trained” with data from tide gauge measurements and near-shore buoys along the coast from North Carolina to Maine, and that it provides accurate estimates of storm surge

MERRA-2 Ocean: The NASA Global Modeling and Assimilation Office's Weakly Coupled Atmosphere-Ocean Reanalysis Using GEOS-S2S Version 3

The NASA Modern Era Reanalysis for Research and Applications (MERRA2) has been a respected and widely used reanalysis that has so far been restricted to the atmosphere. Now a newly released version of the atmosphere/ocean coupled data assimilation system (AODAS) has been developed by the NASA/Goddard Global Modeling and Assimilation Office to perform a retrospective ocean reanalysis from 1982 to present. In addition to assimilating all available in situ data (e.g. Argo, mooring, XBT and CTD data) and altimetry information into the ocean, the new version (GEOS-S2S Version 3) model includes a higher resolution, eddy-permitting ocean model than previous versions, a more realistic implementation of the atmosphere-ocean interface layer, and an improved coupling between glacier and ocean (among other improvements). In addition, this ocean data assimilation was expanded to include the assimilation of satellite sea surface salinity. The MERRA-2 AODAS will be described, and preliminary results will be shown from the assimilation reanalysis and from retrospective forecasts issued using a new ensemble strategy. Following the Global Ocean Data Assimilation Experiment (GODAE) protocols, we will present Class 1 through Class 4 validation results from the ocean reanalysis. Results indicate an improved ocean mixed layer depth, improved salinity near Greenland, an improved diurnal cycle of the sea surface skin temperature, an improved estimate of ocean evaporation, and better representation of western boundary currents (e.g. Gulf Stream) from our new ocean reanalysis. One of the motivations of this project is to provide optimal initial states for ENSO forecasting. Therefore, we will also present some preliminary results of retrospective ENSO forecasts. After thorough testing, it is expected that the GEOS-S2S Version 3 will replace our contributions to North American Multi-Model Ensemble (NMME), WCRP Subseasonal to Seasonal (S2S), and IRI seasonal prediction forecast projects

Impact of Satellite Sea Surface Salinity Observations on ENSO Predictions from the NASA/GMAO Seasonal Forecast System

We assess the impact of satellite sea surface salinity (SSS) observations on dynamical ENSO forecasts. Assimilation of SSS improves the mixed layer depth (MLD) and modulates the Kelvin waves associated with ENSO. In column 2, the initialization differences between experiments that assimilate SSS minus those withholding SSS assimilation are presented. Column 3 shows examples of forecasts generated for the different phases of ENSO assimilating the different satellite SSS. In general, for all phases of ENSO, SSS assimilation improves forecasts. The far right column compares ensemble means for assimilation of individual and combined SMOS, Aquarius, and SMAP SSS forecasts. Finally, the latest forecasts are presented comparing assimilation versus no- assimilation of satellite SSS for single forecasts over the last year

The Use of MERRA-2 Near Surface Meteorology to Understand the Behavior of Planetary Boundary Layer Heights Derived from Wind Profiler Data over the US Great Plains

The atmospheric general circulation model (GCM) that underlies the MERRA-2 reanalysis includes a suite of physical parameterizations that describe the processes that occur in the planetary boundary layer (PBL). The data assimilation system assures that the atmospheric state variables used as input to these parameterizations are constrained to the best fit to all of the available observations. Many studies, however, have shown that the GCM-based estimates of MERRA-2 PBL heights are biased high, and so are not reliable for boundary layer studies.A 20-year record of PBL heights was derived from Wind Profiler (WP) backscatter data measured at a wide network of stations throughout the US Great Plains and has been validated against independent estimates. The behavior of these PBL heights shows geographical and temporal variations that are difficult to attribute to particular physical processes without additional information that are not part of the observational record.In the present study, we use information on physical processes from MERRA-2 to understand the behavior of the WP derived PBL heights. The behavior of the annual cycle of both MERRA-2 and WP PBL heights shows four classes of behavior: (i) canonical, characterized by a monthly progression in PBL height that follows the solar insolation, (ii) double peak, characterized by canonical behavior that is interrupted by a minimum in July, (iii) late peak, characterized by a suppressed heights in May and June, and return to canonical in July and August, and (iv) early peak where the PBL height rises with solar insolation but is suppressed later in the summer. The explanation for these behaviors and the relationship to local precipitation, temperature, sensible and latent heat fluxes, net radiation and aerosol load is articulated using information from MERRA-2

Tendency Bias Correction in Coupled and Uncoupled Global Climate Models with a Focus on Impacts over North America

We revisit the bias correction problem in current climate models, taking advantage of state-of-the-art atmospheric reanalysis data and new data assimilation tools that simplify the estimation of short-term (6 hourly) atmospheric tendency errors. The focus is on the extent to which correcting biases in atmospheric tendencies improves the models climatology, variability, and ultimately forecast skill at subseasonal and seasonal time scales. Results are presented for the NASA GMAO GEOS model in both uncoupled (atmosphere only) and coupled (atmosphereocean) modes. For the uncoupled model, the focus is on correcting a stunted North Pacific jet and a dry bias over the central United States during boreal summerlong-standing errors that are indeed common to many current AGCMs. The results show that the tendency bias correction (TBC) eliminates the jet bias and substantially increases the precipitation over the Great Plains. These changes are accompanied by much improved (increased) storm-track activity throughout the northern midlatitudes. For the coupled model, the atmospheric TBCs produce substantial improvements in the simulated mean climate and its variability, including a much reduced SST warm bias, more realistic ENSO-related SST variability and teleconnections, and much improved subtropical jets and related submonthly transient wave activity. Despite these improvements, the improvement in subseasonal and seasonal forecast skill over North America is only modest at best. The reasons for this, which are presumably relevant to any forecast system, involve the competing influences of predictability loss with time and the time it takes for climate drift to first have a significant impact on forecast skill

Comparison of GEOS-5 AGCM Planetary Boundary Layer Depths Computed with Various Definitions

Accurate models of planetary boundary layer (PBL) processes are important for forecasting weather and climate. The present study compares seven methods of calculating PBL depth in the GEOS-5 atmospheric general circulation model (AGCM) over land. These methods depend on the eddy diffusion coefficients, bulk and local Richardson numbers, and the turbulent kinetic energy. The computed PBL depths are aggregated to the Koppen climate classes, and some limited comparisons are made using radiosonde profiles. Most methods produce similar midday PBL depths, although in the warm, moist climate classes, the bulk Richardson number method gives midday results that are lower than those given by the eddy diffusion coefficient methods. Additional analysis revealed that methods sensitive to turbulence driven by radiative cooling produce greater PBL depths, this effect being most significant during the evening transition. Nocturnal PBLs based on Richardson number are generally shallower than eddy diffusion coefficient based estimates. The bulk Richardson number estimate is recommended as the PBL height to inform the choice of the turbulent length scale, based on the similarity to other methods during the day, and the improved nighttime behavior

Seasonal Predictability of Cloud Droplet Number Concentration

Aerosol emissions modify the properties of clouds hence impacting climate. The aerosol indirect effect may have offset part of the global warming caused by anthropogenic greenhouse gas emissions during the industrial era. It however remains unclear whether the same effect is significant over time scales relevant for seasonal and weather climate prediction. Answering such a question has been difficult since most weather prediction systems lack a proper representation of the aerosol evolution and transport and their interaction with clouds. Even in advanced systems it is not clear to what extent cloud microphysical properties are predictable over subseasonal to seasonal time scales. Such an issue is addressed in this study. We use a set of 30 year, four ensemble member, 9 month lead hindcast simulations of the NASA GEOS seasonal prediction system (GEOS-S2S) to study the predictability of cloud droplet number concentration in warm stratocumulus clouds. The latest version GEOS-S2S system implements interactive aerosol as well as a two moment cloud microphysics scheme therefore it is suitable for studying the aerosol indirect effect on climate. Long term retrievals from the MODIS (Moderate Resolution Imaging Spectroradiometer) are used to validate the model predictions and assess its skill in predicting cloud droplet number concentration

Minimally invasive retrofitting of RC joints with externally applied SMA plate - adaptive design optimisation through probabilistic damage simulation

Beam–column joints are the critical section of many reinforced concrete (RC) structure types in which any failure could lead to the collapse of the entire structure. This paper attempts to employ a superelastic shape memory alloy plate as an innovative and adaptive external strengthening element to rehabilitate existing concrete beam–column joints and enhance the structure’s performance. An experimentally investigated beam–column joint is used as the case study, and it is investigated numerically to validate the effects of an innovative strengthening technique based on shape memory alloys. The results show that the proposed technique could increase the joint’s stiffness and reduce the risk of overall failure. A particular innovation in the proposed method is associated with the novel material itself but also with the fact that the increased potential costs of using special alloys are counteracted by its potential to produce these elements in an optimised industrially produced fastened plate. This fits-all construction product further allows a rapid and minimally invasive strengthening technique. Moreover, to achieve this, the plate is adaptively designed against random critical load combinations through probabilistic damage prediction