SEARCH · Engineering Papers
Results for “GSI”
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.
Comparing symmetry energy predictions and recent constraints from the ASY-EOS experiment at GSI
Not Available
Preliminary Results Cycling GEOS-JEDI with GSI-based Background Errors
The first phase of transitioning the NASA GMAO GEOS atmospheric data assimilation capabilities to JEDI involves the replacement of the Grid-point Statistical Interpolation (GSI) with a corresponding JEDI analysis. This includes taking JEDI's Unified Observation Operator (UFO), its underlying dependencies, and the JEDI solver that enables a hybrid 4DEnVar strategy similar to what is used in the current GEOS-GSI system. Variational analysis involves at least two main components associated with the observation and background cost function terms. The first is directly related to the UFO, which is being carefully validated in a joint collaboration between GMAO and NCEP to demonstrate consistency with corresponding observations usage in GSI. The second component is the background term, which in a hybrid system involves the ability to set up both a climatologically-based term and an ensemble-based term. JEDI provides the means to implement both terms through its BUMP component. Use of BUMP would require a complete re-tune of both climatological and ensemble, which is a non-trivial exercise we would prefer to avoid. As an alternative, the work here studies the results of interfacing the GSI-background error capability (GSIBEC) into JEDI through SABER. With this, the exact same background error covariance formulation used in GSI can be employed in JEDI without need for re-tuning. This brief summary covers the work done to interface GSIBEC into JEDI and shows preliminary results where the background error covariances of the control (GEOS-GSI) and experiment (GEOS-JEDI) are identical in corresponding cycling experiments. The cycling exercise is obviously preliminary and so much can be expected from GEOS-JEDI when compared to GEOS-GSI. There is still a number of features that need closer attention and although in some cases in principle ready to cycle have been intentionally either turned off or not fully exercised (e.g., VarBC is applied but not cycled). Other features are still pending implementation, one such example is the implementation of the Tangent Linear Normal Mode Constraint. Still, results are quite encouraging as hopefully the discussion here illustrates.
A Preliminary Comparison of GEOS-GSI with GEOS-JEDI Using the Complete GEOS Observing System
Transitioning the NASA GMAO GEOS data assimilation capabilities to JEDI involves replacing the GEOS Gridpoint Statistical Interpolation (GSI) with a JEDI-based analysis. Performing fair comparisons between the corresponding GEOS-JEDI system and the present GEOS-GSI has involved downgrading the latter's capabilities to compensate for missing capabilities in the former. As the capabilities in JEDI mature and start matching those of GSI’s, experimentation has evolved from simple 3DVAR using only radiosondes, to adding satellite observations without VarBC, to now having GEOS-JEDI use hybrid 4DEnVar and full observing system, VarBC and most of the knobs typically used in the present operational (and experimental) version of GEOS-GSI. This presentation provides a discussion of the process and pathway taken to replace GSI with JEDI in GEOS at GMAO. The presentation also shows latest results of testing in the still relatively simply context of 3D-FGAT, but now having JEDI use the whole of the typical observing system used in GEOS. Results are encouraging but preliminary, with various caveats having been identified during the attempt to cycle JEDI analyses. Solutions to most of the identified problems have been worked out, but a few pending issues are still being worked out.
Monitoring Stock-Specific Abundance, Run Timing, and Straying of Chinook Salmon in the Columbia River Using Genetic Stock Identification (GSI)
Abstract Genetic stock identification (GSI) provides a method of characterizing stock-specific abundance, run timing, and straying as they pertain to the population demographics and behavior of both hatchery and wild fish and thus assists with fisheries management. In this study, tissues were collected and genotyped at 13 microsatellite loci from adult Chinook Salmon Oncorhynchus tshawytscha that passed Bonneville Dam during four consecutive migration years (2004–2007). GSI methods were used to estimate the proportions of stocks in a broad genetic baseline, with high-density coverage of Chinook Salmon across the eastern Pacific Rim. The estimates of abundance for interior Columbia River stocks showed widespread decline consistent with a shift to poor ocean conditions during the years when the fish in this study were at sea, but divergent stock- and age-specific patterns were also observed. For example, the upper Columbia River spring-run stock experienced a unique net gain in abundance during this period, and the interior Columbia River summer–fall-run stock experienced a delayed decline of 4-year-olds. Stocks with early run timing shifted to later migrations. However, run timing distributions did not shift unidirectionally for all stocks across years, and stock membership in three major run timing categories was maintained. Of 9,215 total adults sampled, 27 (0.3%) were out-of-basin strays and a quarter of the strays were putatively of wild origin. The general concordance of GSI results with those based on more traditional methods supports the effectiveness of GSI as a tool for fisheries management. Received May 5, 2013; accepted October 23, 2013 Published online February 10, 2014
Assimilation of Dual-Polarimetric Radar Observations with WRF GSI
Dual-polarimetric (dual-pol) radar typically transmits both horizontally and vertically polarized radio wave pulses. From the two different reflected power returns, more accurate estimate of liquid and solid cloud and precipitation can be provided. The upgrade of the traditional NWS WSR-88D radar to include dual-pol capabilities will soon be completed for the entire NEXRAD network. Therefore, the use of dual-pol radar network will have a broad impact in both research and operational communities. The assimilation of dual-pol radar data is especially challenging as few guidelines have been provided by previous research. It is our goal to examine how to best use dual-pol radar data to improve forecast of severe storm and forecast initialization. In recent years, the Development Testbed Center (DTC) has released the community Gridpoint Statistical Interpolation (GSI) DA system for the Weather Research and Forecasting (WRF) model. The community GSI system runs in independently environment, yet works functionally equivalent to operational centers. With collaboration with the NASA Short-term Prediction Research and Transition (SPoRT) Center, this study explores regional assimilation of the dual-pol radar variables from the WSR-88D radars for real case storms. Our presentation will highlight our recent effort on incorporating the horizontal reflectivity (ZH), differential reflectivity (ZDR), specific differential phase (KDP), and radial velocity (VR) data for initializing convective storms, with a significant focus being on an improved representation of hydrometeor fields. In addition, discussion will be provided on the development of enhanced assimilation procedures in the GSI system with respect to dual-pol variables. Beyond the dual-pol variable assimilation procedure developing within a GSI framework, highresolution (≤1 km) WRF model simulations and storm scale data assimilation experiments will be examined, emphasizing both model initialization and short-term forecast of precipitation fields and processes. Further details of the methodology of data assimilation, the impact of different dual-pol variables, the influence on precipitation forecast will be presented at the conference.
Assimilation of Dual-Polarimetric Radar Observations with WRF GSI
Dual-polarimetric (dual-pol) radar typically transmits both horizontally and vertically polarized radio wave pulses. From the two different reflected power returns, more accurate estimate of liquid and solid cloud and precipitation can be provided. The upgrade of the traditional NWS WSR-88D radar to include dual-pol capabilities will soon be completed for the entire NEXRAD network. Therefore, the use of dual-pol radar network will have a broad impact in both research and operational communities. The assimilation of dual-pol radar data is especially challenging as few guidelines have been provided by previous research. It is our goal to examine how to best use dual-pol radar data to improve forecast of severe storm and forecast initialization. In recent years, the Development Testbed Center (DTC) has released the community Gridpoint Statistical Interpolation (GSI) DA system for the Weather Research and Forecasting (WRF) model. The community GSI system runs in independently environment, yet works functionally equivalent to operational centers. With collaboration with the NASA Short-term Prediction Research and Transition (SPoRT) Center, this study explores regional assimilation of the dual-pol radar variables from the WSR-88D radars for real case storms. Our presentation will highlight our recent effort on incorporating the horizontal reflectivity (ZH), differential reflectivity (ZDR), specific differential phase (KDP), and radial velocity (VR) data for initializing convective storms, with a significant focus being on an improved representation of hydrometeor fields. In addition, discussion will be provided on the development of enhanced assimilation procedures in the GSI system with respect to dual-pol variables. Beyond the dual-pol variable assimilation procedure developing within a GSI framework, highresolution (≤1 km) WRF model simulations and storm scale data assimilation experiments will be examined, emphasizing both model initialization and short-term forecast of precipitation fields and processes. Further details of the methodology of data assimilation, the impact of different dual-pol variables, the influence on precipitation forecast will be presented at the conference.
Introducing Object-Oriented Concepts into GSI
Enhancements are now being made to the Gridpoint Statistical Interpolation (GSI) data assimilation system to expand its capabilities. This effort opens the way for broadening the scope of GSI's applications by using some standard object-oriented features in Fortran, and represents a starting point for the so-called GSI refactoring, as a part of the Joint Effort for Data-assimilationI ntegration (JEDI) project of JCSDA.
Transitioning GlideinWMS, a multi domain distributed workload manager, from GSI proxies to tokens and other granular credentials
GlideinWMS is a distributed workload manager that has been used in production for many years to provision resources for experiments like CERN’s CMS, many Neutrino experiments, and the OSG. Its security model was based mainly on GSI (Grid Security Infrastructure), using X.509 certificate proxies and VOMS (Virtual Organization Membership Service) extensions. Even when other credentials, like SSH keys, were used to authenticate with resources, proxies were also added all the time, to establish the identity of the requestor and the associated memberships or privileges. This single credential was used for everything and was, often implicitly, forwarded wherever needed. The addition of identity and access tokens and the phase-out of GSI forced us to reconsider the security model of GlideinWMS, to handle multiple credentials which can differ in type, technology, and functionality. Both identity tokens and access tokens are supported. GSI proxies even if no more mandatory, are still used, together with various JWT (JSON Web Token) based tokens and other certificates. The functionality of the credentials, defined by issuer, audience, and scope, also differ: a credential can allow access to a computing resource, or can protect the GlideinWMS framework from tampering, or can grant read or write access to storage, can provide an identity for accounting or auditing, or can provide a combination of any the formers. Furthermore, the tools in use do not include automatic forwarding and renewal of the new credentials so credential lifetime and renewal requirements became part of the discussion as well. In this paper, we will present how GlideinWMS was able to change its design and code to respond to all these changes.
Improving Incremental Balance in the GSI 3DVAR Analysis System
The Gridpoint Statistical Interpolation (GSI) analysis system is a unified global/regional 3DVAR analysis code that has been under development for several years at the National Centers for Environmental Prediction (NCEP)/Environmental Modeling Center. It has recently been implemented into operations at NCEP in both the global and North American data assimilation systems (GDAS and NDAS). An important aspect of this development has been improving the balance of the analysis produced by GSI. The improved balance between variables has been achieved through the inclusion of a Tangent Linear Normal Mode Constraint (TLNMC). The TLNMC method has proven to be very robust and effective. The TLNMC as part of the global GSI system has resulted in substantial improvement in data assimilation both at NCEP and at the NASA Global Modeling and Assimilation Office (GMAO).
Use of MODIS Cloud Top Pressure to Improve Assimilation Yields of AIRS Radiances in GSI
Radiances from hyperspectral sounders such as the Atmospheric Infrared Sounder (AIRS) are routinely assimilated both globally and regionally in operational numerical weather prediction (NWP) systems using the Gridpoint Statistical Interpolation (GSI) data assimilation system. However, only thinned, cloud-free radiances from a 281-channel subset are used, so the overall percentage of these observations that are assimilated is somewhere on the order of 5%. Cloud checks are performed within GSI to determine which channels peak above cloud top; inaccuracies may lead to less assimilated radiances or introduction of biases from cloud-contaminated radiances.Relatively large footprint from AIRS may not optimally represent small-scale cloud features that might be better resolved by higher-resolution imagers like the Moderate Resolution Imaging Spectroradiometer (MODIS). Objective of this project is to "swap" the MODIS-derived cloud top pressure (CTP) for that designated by the AIRS-only quality control within GSI to test the hypothesis that better representation of cloud features will result in higher assimilated radiance yields and improved forecasts.
NASA SPoRT Modeling and Data Assimilation Research and Transition Activities Using WRF, LIS and GSI
weather research and forecasting ===== The NASA Short‐term Prediction Research and Transition (SPoRT) program has numerous modeling and data assimilation (DA) activities in which the WRF model is a key component. SPoRT generates realtime, research satellite products from the MODIS and VIIRS instruments, making the data available to NOAA/NWS partners running the WRF/EMS, including: (1) 2‐km northwestern‐hemispheric SST composite, (2) daily, MODIS green vegetation fraction (GVF) over CONUS, and (3) NASA Land Information System (LIS) runs of the Noah LSM over the southeastern CONUS. Each of these datasets have been utilized by specific SPoRT partners in local EMS model runs, with select offices evaluating the impacts using a set of automated scripts developed by SPoRT that manage data acquisition and run the NCAR Model Evaluation Tools verification package. SPoRT is engaged in DA research with the Gridpoint Statistical Interpolation (GSI) and Ensemble Kalman Filter in LIS for soil moisture DA. Ongoing DA projects using GSI include comparing the impacts of assimilating Atmospheric Infrared Sounder (AIRS) radiances versus retrieved profiles, and an analysis of extra‐tropical cyclones with intense non‐convective winds. As part of its Early Adopter activities for the NASA Soil Moisture Active Passive (SMAP) mission, SPoRT is conducting bias correction and soil moisture DA within LIS to improve simulations using the NASA Unified‐WRF (NU‐WRF) for both the European Space Agency's Soil Moisture Ocean Salinity and upcoming SMAP mission data. SPoRT has also incorporated real‐time global GVF data into LIS and WRF from the VIIRS product being developed by NOAA/NESDIS. This poster will highlight the research and transition activities SPoRT conducts using WRF, NU‐WRF, EMS, LIS, and GSI.
Assimilation of GPM GMI Rainfall Product with WRF GSI
The Global Precipitation Measurement (GPM) is an international mission to provide next-generation observations of rain and snow worldwide. The GPM built on Tropical Rainfall Measuring Mission (TRMM) legacy, while the core observatory will extend the observations to higher latitudes. The GPM observations can help advance our understanding of precipitation microphysics and storm structures. Launched on February 27th, 2014, the GPM core observatory is carrying advanced instruments that can be used to quantify when, where, and how much it rains or snows around the world. Therefore, the use of GPM data in numerical modeling work is a new area and will have a broad impact in both research and operational communities. The goal of this research is to examine the methodology of assimilation of the GPM retrieved products. The data assimilation system used in this study is the community Gridpoint Statistical Interpolation (GSI) system for the Weather Research and Forecasting (WRF) model developed by the Development Testbed Center (DTC). The community GSI system runs in independently environment, yet works functionally equivalent to operational centers. With collaboration with the NASA Short-term Prediction Research and Transition (SPoRT) Center, this research explores regional assimilation of the GPM products with case studies. Our presentation will highlight our recent effort on the assimilation of the GPM product 2AGPROFGMI, the retrieved Microwave Imager (GMI) rainfall rate data for initializing a real convective storm. WRF model simulations and storm scale data assimilation experiments will be examined, emphasizing both model initialization and short-term forecast of precipitation fields and processes. In addition, discussion will be provided on the development of enhanced assimilation procedures in the GSI system with respect to other GPM products. Further details of the methodology of data assimilation, preliminary result and test on the impact of GPM data and the influence on precipitation forecast will be presented at the conference.
Breit–Wigner distribution, quantum beats and GSI Anomaly
The relationship between Breit–Wigner distribution as an underlying basis for decaying unstable quantum systems and GSI experiment(anomaly) has not been addressed properly in the literatures. We show that quantum beats can be obtained using a superposition of two Breit–Wigner distributions. This modified distribution can explain the GSI time anomaly with quantum beats resulting from the existence of two energy levels of the decaying ion.
Mechanism of 'GSI oscillations' in electron capture by highly charged hydrogen-like atomic ions
We suggest a qualitative explanation of oscillations in electron capture decays of hydrogen-like {sup 140}Pr and {sup 142}Pm ions observed recently in an ion experimental storage ring (ESR) of Gesellschaft fuer Schwerionenforschung (GSI) mbH, Darmstadt, Germany. This explanation is based on the electron multiphoton Rabi oscillations between two Zeeman states of the hyperfine ground level with the total angular momentum F = 1/2. The Zeeman splitting is produced by a constant magnetic field in the ESR. Transitions between these states are produced by the second, sufficiently strong alternating magnetic field that approximates realistic fields in the GSI ESR. The Zeeman splitting amounts to only about 10{sup -5} eV. This allows explaining the observed quantum beats with the period 7 s.
Towards the Handing of Cloud-Affected Infrared Radiances in the GSI
In the gridpoint statistical interpolation (GSI) data assimilation algorithm, only thermal infrared measurements determined to be uncontaminated by clouds are assimilated. Using this approach, typically only 19-29% of footprints are deemed to have no cloud affects through the measured spectra. This study will discuss the efforts underway at the Global Modeling and Assimilation Office (GMAO) at NASA Goddard Space Flight Center, in conjunction with the Joint Center for Satellite Data Assimilation (JCSDA), to actively assimilate these morecomplicated observations by using a graybody assumption. In the GSI, cloud top pressure and effective cloud amount are retrieved concurrently using a minimum residual method. This study will address the limitations and advantages of the technique and the modifications underway to the assimilation system to incorporate those two parameters into the radiative transfer forward operators and TL/AD calculations. Furthermore, it will explain the efforts underway to incorporate these parameters into the control vector so that they can be altered variationally as part of the minimization.
Use of MODIS Cloud Top Pressure to Improve Assimilation Yields of AIRS Radiances in GSI
Improvements to global and regional numerical weather prediction have been demonstrated through assimilation of data from NASA's Atmospheric Infrared Sounder (AIRS). Current operational data assimilation systems use AIRS radiances, but impact on regional forecasts has been much smaller than for global forecasts. Previously, it has been shown that cloud top designation associated with quality control procedures within the Gridpoint Statistical Interpolation (GSI) system used operationally by a number of Joint Center for Satellite Data Assimilation (JCSDA) partners may not provide the best representation of cloud top pressure (CTP). Because this designated CTP determines which channels are cloud-free and, thus, available for assimilation, ensuring the most accurate representation of this value is imperative to obtaining the greatest impact from satellite radiances. This paper examines the assimilation of hyperspectral sounder data used in operational numerical weather prediction by comparing analysis increments and numerical forecasts generated using operational techniques with a research technique that swaps CTP from the Moderate-resolution Imaging Spectroradiometer (MODIS) for the value of CTP calculated from the radiances within GSI.