Assimilation of GPM GMI Rainrate Product with Community GSI and Case Study Result
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data assimilation↗
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Results for “GSI”
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Bringing GSI Background Error Covariance Capability to JEDI
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Ricardo Todling↗
Assimilation of GPM Rain Rate Products With GSI Data Assimilation System for Heavy and Light Precipitation Events
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Xuanli Li↗
Seismic Imager Space Telescope
A concept has been developed for a geostationary seismic imager (GSI), a space telescope in geostationary orbit above the Pacific coast of the Americas that would provide movies of many large earthquakes occurring in the area from Southern Chile to Southern Alaska. The GSI movies would cover a field of view as long as 300 km, at a spatial resolution of 3 to 15 m and a temporal resolution of 1 to 2 Hz, which is sufficient for accurate measurement of surface displacements and photometric changes induced by seismic waves. Computer processing of the movie images would exploit these dynamic changes to accurately measure the rapidly evolving surface waves and surface ruptures as they happen. These measurements would provide key information to advance the understanding of the mechanisms governing earthquake ruptures, and the propagation and arrest of damaging seismic waves. GSI operational strategy is to react to earthquakes detected by ground seismometers, slewing the satellite to point at the epicenters of earthquakes above a certain magnitude. Some of these earthquakes will be foreshocks of larger earthquakes; these will be observed, as the spacecraft would have been pointed in the right direction. This strategy was tested against the historical record for the Pacific coast of the Americas, from 1973 until the present. Based on the seismicity recorded during this time period, a GSI mission with a lifetime of 10 years could have been in position to observe at least 13 (22 on average) earthquakes of magnitude larger than 6, and at least one (2 on average) earthquake of magnitude larger than 7. A GSI would provide data unprecedented in its extent and temporal and spatial resolution. It would provide this data for some of the world's most seismically active regions, and do so better and at a lower cost than could be done with ground-based instrumentation. A GSI would revolutionize the understanding of earthquake dynamics, perhaps leading ultimately to effective warning capabilities, to improved management of earthquake risk, and to improved public safety policies. The position of the spacecraft, its high optical quality, large field of view, and large field of regard will make it an ideal platform for other scientific studies. The same data could be simply reused for other studies. If different data, such as multi-spectral data, is required, additional instruments could share the telescope.
Sidick, Erkin↗
A Module for Assimilating Hyperspectral Infrared Retrieved Profiles into the Gridpoint Statistical Interpolation System for Unique Forecasting Applications
Hyperspectral infrared sounder radiance data are assimilated into operational modeling systems however the process is computationally expensive and only approximately 1% of available data are assimilated due to data thinning as well as the fact that radiances are restricted to cloud-free fields of view. In contrast, the number of hyperspectral infrared profiles assimilated is much higher since the retrieved profiles can be assimilated in some partly cloudy scenes due to profile coupling other data, such as microwave or neural networks, as first guesses to the retrieval process. As the operational data assimilation community attempts to assimilate cloud-affected radiances, it is possible that the use of retrieved profiles might offer an alternative methodology that is less complex and more computationally efficient to solve this problem. The NASA Short-term Prediction Research and Transition (SPoRT) Center has assimilated hyperspectral infrared retrieved profiles into Weather Research and Forecasting Model (WRF) simulations using the Gridpoint Statistical Interpolation (GSI) System. Early research at SPoRT demonstrated improved initial conditions when assimilating Atmospheric Infrared Sounder (AIRS) thermodynamic profiles into WRF (using WRF-Var and assigning more appropriate error weighting to the profiles) to improve regional analysis and heavy precipitation forecasts. Successful early work has led to more recent research utilizing WRF and GSI for applications including the assimilation of AIRS profiles to improve WRF forecasts of atmospheric rivers and assimilation of AIRS, Cross-track Infrared and Microwave Sounding Suite (CrIMSS), and Infrared Atmospheric Sounding Interferometer (IASI) profiles to improve model representation of tropopause folds and associated non-convective wind events. Although more hyperspectral infrared retrieved profiles can be assimilated into model forecasts, one disadvantage is the retrieved profiles have traditionally been assigned the same error values as the rawinsonde observations when assimilated with GSI. Typically, satellitederived profile errors are larger and more difficult to quantify than traditional rawinsonde observations (especially in the boundary layer), so it is important to appropriately assign observation errors within GSI to eliminate potential spurious innovations and analysis increments that can sometimes arise when using retrieved profiles. The goal of this study is to describe modifications to the GSI source code to more appropriately assimilate hyperspectral infrared retrieved profiles and outline preliminary results that show the differences between a model simulation that assimilated the profiles as rawinsonde observations and one that assimilated the profiles in a module with the appropriate error values.
Berndt, Emily↗
An engineering assessment of gas/surface interactions in free-molecular aerodynamics
Data available on the gas/surface interaction (GSI) phenomenon, particularly in the rarefied to near-free-molecular flow regime are collected, categorized, and analyzed with a purpose of developing a GSI model that could be used as a guide to spacecraft designers. The study shows that there are not enough useful data for building a high-confidence GSI model. However, sufficient results are obtained to suggest that continued reliance on the diffuse GSI model is inappropriate, particularly in the near-free-molecular regime.
Knox, E. C.↗
The Global Modeling and Assimilation Office (GMAO) 4d-Var and its Adjoint-based Tools
The fifth generation of the Goddard Earth Observing System (GEOS-5) Data Assimilation System (DAS) is a 3d-var system that uses the Grid-point Statistical Interpolation (GSI) system developed in collaboration with NCEP, and a general circulation model developed at Goddard, that includes the finite-volume hydrodynamics of GEOS-4 wrapped in the Earth System Modeling Framework and physical packages tuned to provide a reliable hydrological cycle for the integration of the Modern Era Retrospective-analysis for Research and Applications (MERRA). This MERRA system is essentially complete and the next generation GEOS is under intense development. A prototype next generation system is now complete and has been producing preliminary results. This prototype system replaces the GSI-based Incremental Analysis Update procedure with a GSI-based 4d-var which uses the adjoint of the finite-volume hydrodynamics of GEOS-4 together with a vertical diffusing scheme for simplified physics. As part of this development we have kept the GEOS-5 IAU procedure as an option and have added the capability to experiment with a First Guess at the Appropriate Time (FGAT) procedure, thus allowing for at least three modes of running the data assimilation experiments. The prototype system is a large extension of GEOS-5 as it also includes various adjoint-based tools, namely, a forecast sensitivity tool, a singular vector tool, and an observation impact tool, that combines the model sensitivity tool with a GSI-based adjoint tool. These features bring the global data assimilation effort at Goddard up to date with technologies used in data assimilation systems at major meteorological centers elsewhere. Various aspects of the next generation GEOS will be discussed during the presentation at the Workshop, and preliminary results will illustrate the discussion.
Todling, Ricardo↗
Improving Forecast Skill by Assimilation of AIRS Cloud Cleared Radiances RiCC
ECMWF, NCEP, and GMAO routinely assimilate radiosonde and other in-situ observations along with satellite IR and MW Sounder radiance observations. NCEP and GMAO use the NCEP GSI Data Assimilation System (DAS).GSI DAS assimilates AIRS, CrIS, IASI channel radiances Ri on a channel-by-channel, case-by-case basis, only for those channels i thought to be unaffected by cloud cover. This test excludes Ri for most tropospheric sounding channels under partial cloud cover conditions. AIRS Version-6 RiCC is a derived quantity representative of what AIRS channel i would have seen if the AIRS FOR were cloud free. All values of RiCC have case-by-case error estimates RiCC associated with them. Our experiments present to the GSI QCd values of AIRS RiCC in place of AIRS Ri observations. GSI DAS assimilates only those values of RiCC it thinks are cloud free. This potentially allows for better coverage of assimilated QCd values of RiCC as compared to Ri.
Forecast Skill↗
Implementing JEDI into NASA GMAO’s Real Time Production Suite
NASA’s Global Modeling and Assimilation Office (GMAO) has prepared their first production system involving the Joint Effort for Data assimilation Integration (JEDI) framework. In this system the central analysis, that drives the deterministic forecast, will be provided using JEDI. This talk outlines the phased approach to implementing JEDI into production that GMAO has designed, and how this approach will allow for a careful analysis of the system against the existing data assimilation framework (GSI). In the first phase of implementation the existing data assimilation system will perform certain actions that are still under development in JEDI. These include thinning the observations and producing satellite bias correction coefficients. JEDI is hooked up to the existing workflow so a single line switch can activate whether the existing or JEDI-based analysis is cycled. Outside of the monumental effort to construct JEDI that is ongoing at the Joint Center for Satellite Data Assimilation (JCSDA), GMAO have undertaken two areas of considerable effort. The talk will describe these efforts and highlight the main challenges that have been encountered. The first area of work is to implement the background error model from the existing data assimilation system into JEDI. The second is to validate the observing system in JEDI against the one in GSI, which has involved several new features being added to the observation operators in JEDI. While the longer-term plans involve trying to improve on the GSI in these two areas, GMAO is keen to have JEDI start from a trusted baseline. This is also key to implementing JEDI quickly so other priorities, such as increasing the number of model levels, can be easily worked on in parallel. GMAO is actively working on a framework to shepherd in the next generation coupled data assimilation system and model. As JEDI is implemented for the first time the plan is to ambitiously cycle through implementations, frequently bringing JEDI features to production. Details of these plans will be given in the talk and we will highlight key implementation and product milestones that we hope to achieve, as well as touch on the development environment that we will use to support frequent refreshing of the production system.
JEDI↗
Development and Implementation of Dynamic Scripts to Execute Cycled WRF/GSI Forecasts
Automating the coupling of data assimilation (DA) and modeling systems is a unique challenge in the numerical weather prediction (NWP) research community. In recent years, the Development Testbed Center (DTC) has released well-documented tools such as the Weather Research and Forecasting (WRF) model and the Gridpoint Statistical Interpolation (GSI) DA system that can be easily downloaded, installed, and run by researchers on their local systems. However, developing a coupled system in which the various preprocessing, DA, model, and postprocessing capabilities are all integrated can be labor-intensive if one has little experience with any of these individual systems. Additionally, operational modeling entities generally have specific coupling methodologies that can take time to understand and develop code to implement properly. To better enable collaborating researchers to perform modeling and DA experiments with GSI, the Short-term Prediction Research and Transition (SPoRT) Center has developed a set of Perl scripts that couple GSI and WRF in a cycling methodology consistent with the use of real-time, regional observation data from the National Centers for Environmental Prediction (NCEP)/Environmental Modeling Center (EMC). Because Perl is open source, the code can be easily downloaded and executed regardless of the user's native shell environment. This paper will provide a description of this open-source code and descriptions of a number of the use cases that have been performed by SPoRT collaborators using the scripts on different computing systems.
Zavodsky, Bradley↗
Development and Implementation of Dynamic Scripts to Execute Cycled GSI/WRF Forecasts
The Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model and Gridpoint Statistical Interpolation (GSI) data assimilation (DA) are the operational systems that make up the North American Mesoscale (NAM) model and the NAM Data Assimilation System (NDAS) analysis used by National Weather Service forecasters. The Developmental Testbed Center (DTC) manages and distributes the code for the WRF and GSI, but it is up to individual researchers to link the systems together and write scripts to run the systems, which can take considerable time for those not familiar with the code. The objective of this project is to develop and disseminate a set of dynamic scripts that mimic the unique cycling configuration of the operational NAM to enable researchers to develop new modeling and data assimilation techniques that can be easily transferred to operations. The current version of the SPoRT GSI/WRF Scripts (v3.0.1) is compatible with WRF v3.3 and GSI v3.0.
Zavodsky, Bradley↗
Development and Implementation of Dynamic Scripts to Execute Cycled GSI/WRF Forecasts
The Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model and Gridpoint Statistical Interpolation (GSI) data assimilation (DA) are the operational systems that make up the North American Mesoscale (NAM) model and the NAM Data Assimilation System (NDAS) analysis used by National Weather Service forecasters. The Developmental Testbed Center (DTC) manages and distributes the code for the WRF and GSI, but it is up to individual researchers to link the systems together and write scripts to run the systems, which can take considerable time for those not familiar with the code. The objective of this project is to develop and disseminate a set of dynamic scripts that mimic the unique cycling configuration of the operational NAM to enable researchers to develop new modeling and data assimilation techniques that can be easily transferred to operations. The current version of the SPoRT GSI/WRF Scripts (v3.0.1) is compatible with WRF v3.3 and GSI v3.0.
Zavodsky, Bradley↗
High-Resolution Mesoscale Model Setup for the Eastern Range and Wallops Flight Facility
Mesoscale weather conditions can have an adverse effect on space launch, landing, ground processing, and weather advisories, watches, and warnings at the Eastern Range (ER) in Florida and Wallops Flight Facility (WFF) in Virginia. During summer, land-sea interactions across Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) lead to sea breeze front formation, which can spawn deep convection that can hinder operations and endanger personnel and resources. Many other weak locally-driven low-level boundaries and their interactions with the sea breeze front and each other can also initiate deep convection in the KSC/CCAFS area. These convective processes often last 60 minutes or less and pose a significant challenge to the local forecasters. Surface winds during the transition seasons (spring and fall) pose the most difficulties for the forecasters at WFF. They also encounter problems forecasting convective activity and temperature during those seasons. Therefore, accurate mesoscale model forecasts are needed to better forecast a variety of unique weather phenomena. Global and national scale models cannot properly resolve important local-scale weather features at each location due to their horizontal resolutions being much too coarse. Therefore, a properly tuned local data assimilation (DA) and forecast model at a high resolution is needed to provide improved capability. To accomplish this, a number of sensitivity tests were performed using the Weather Research and Forecasting (WRF) model in order to determine the best DA/model configuration for operational use at each of the space launch ranges to best predict winds, precipitation, and temperature. A set of Perl scripts to run the Gridpoint Statistical Interpolation (GSI)/WRF in real-time were provided by NASA's Short-term Prediction Research and Transition Center (SPoRT). The GSI can analyze many types of observational data including satellite, radar, and conventional data. The GSI/WRF scripts use a cycled GSI system similar to the operational North American Mesoscale (NAM) model. The scripts run a 12-hour pre-cycle in which data are assimilated from 12 hours prior up to the model initialization time. A number of different model configurations were tested for both the ER and WFF by varying the horizontal resolution on which the data assimilation was done. Three different grid configurations were run for the ER and two configurations were run for WFF for archive cases from 27 Aug 2013 through 10 Nov 2013. To quantify model performance, standard model output will be compared to the Meteorological Assimilation Data Ingest System (MADIS) data. The MADIS observation data will be compared to the WRF forecasts using the Model Evaluation Tools (MET) verification package. In addition, the National Centers for Environmental Prediction's Stage IV precipitation data will be used to validate the WRF precipitation forecasts. The author will summarize the relative skill of the various WRF configurations and how each configuration behaves relative to the others, as well as determine the best model configuration for each space launch range.
Watson, Leela R.↗
First Proton-Induced Cross Sections on a Stored Rare Ion Beam: Measurement of Te 118 ( p , γ ) for Explosive Nucleosynthesis
We present the first nuclear cross-section measurements of (p,$\gamma$) and (p,n) reactions on 118 Te at energies relevant for the $\gamma$-process nucleosynthesis. Absolute cross-section values for center-of-mass energies of 6, 7 and 10 MeV are provided, together with a theoretical extrapolation to the Gamow window. This experiment marks the first time that direct proton-induced reactions have been measured on a radioactive ion beam at the Experimental Storage Ring (ESR) at GSI, Darmstadt. This paves the way for a large variety of measurements, delivering new constraints for explosive nucleosynthesis and for physics beyond nuclear stability.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Half-life determination of heavy ions in a storage ring considering feeding and depleting background processes
Heavy-ion storage rings have relatively large momentum acceptance which allows for multiple ion species to circulate at the same time. This needs to be considered in radioactive decay measurements of highly charged ions, where atomic charge exchange reactions can significantly alter the intensities of parent and daughter ions. In this study, we investigate this effect using the decay curves of ion numbers in the recent 205 Tl 81+ bound-state beta decay experiment conducted using the Experimental Storage Ring at GSI Darmstadt. To understand the intricate dynamics of ion numbers, we present a set of differential equations that account for various atomic and nuclear reaction processes—bound state beta decay, atomic electron recombination and capture, and electron ionization. By incorporating appropriate boundary conditions, we develop a set of differential equations that accurately simulate the decay curves of various simultaneously stored ions in the storage ring: 205 Tl 81+ , 205 Pb 81+ , 205 Pb 82+ , 200 Hg 79+ , and 200 Hg 80+ . Through a quantitative comparison between simulations and experimental data, we provide insights into the detailed reaction mechanisms governing stored heavy ions within the storage ring. Our approach effectively models charge-changing processes, reduces the complexity of the experimental setup, and provides a simpler method for measuring the decay half-lives of highly charged ions in storage rings.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
File Specification for GEOS-5 FP (Forward Processing)
The GEOS-5 FP Atmospheric Data Assimilation System (GEOS-5 ADAS) uses an analysis developed jointly with NOAA's National Centers for Environmental Prediction (NCEP), which allows the Global Modeling and Assimilation Office (GMAO) to take advantage of the developments at NCEP and the Joint Center for Satellite Data Assimilation (JCSDA). The GEOS-5 AGCM uses the finite-volume dynamics (Lin, 2004) integrated with various physics packages (e.g, Bacmeister et al., 2006), under the Earth System Modeling Framework (ESMF) including the Catchment Land Surface Model (CLSM) (e.g., Koster et al., 2000). The GSI analysis is a three-dimensional variational (3DVar) analysis applied in grid-point space to facilitate the implementation of anisotropic, inhomogeneous covariances (e.g., Wu et al., 2002; Derber et al., 2003). The GSI implementation for GEOS-5 FP incorporates a set of recursive filters that produce approximately Gaussian smoothing kernels and isotropic correlation functions. The GEOS-5 ADAS is documented in Rienecker et al. (2008). More recent updates to the model are presented in Molod et al. (2011). The GEOS-5 system actively assimilates roughly 2 × 10(exp 6) observations for each analysis, including about 7.5 × 10(exp 5) AIRS radiance data. The input stream is roughly twice this volume, but because of the large volume, the data are thinned commensurate with the analysis grid to reduce the computational burden. Data are also rejected from the analysis through quality control procedures designed to detect, for example, the presence of cloud. To minimize the spurious periodic perturbations of the analysis, GEOS-5 FP uses the Incremental Analysis Update (IAU) technique developed by Bloom et al. (1996). More details of this procedure are given in Appendix A. The assimilation is performed at a horizontal resolution of 0.3125-degree longitude by 0.25- degree latitude and at 72 levels, extending to 0.01 hPa. All products are generated at the native resolution of the horizontal grid. The majority of data products are time-averaged, but four instantaneous products are also available. Hourly data intervals are used for two-dimensional products, while 3-hourly intervals are used for three-dimensional products. These may be on the model's native 72-layer vertical grid or at 42 pressure surfaces extending to 0.1 hPa. This document describes the gridded output files produced by the GMAO near real-time operational FP, using the most recent version of the GEOS-5 assimilation system. Additional details about variables listed in this file specification can be found in a separate document, the GEOS-5 File Specification Variable Definition Glossary. Documentation about the current access methods for products described in this document can be found on the GMAO products page: http://gmao.gsfc.nasa.gov/products/.
GSI↗
All-Sky Microwave Imager Data Assimilation at NASA GMAO
Efforts in all-sky satellite data assimilation at the Global Modeling and Assimilation Office (GMAO) at NASA Goddard Space Flight Center have been focused on the development of GSI configurations to assimilate all-sky data from microwave imagers such as the GPM Microwave Imager (GMI) and Global Change Observation Mission-Water (GCOM-W) Advanced Microwave Scanning Radiometer 2 (AMSR-2). Electromagnetic characteristics associated with their wavelengths allow microwave imager data to be relatively transparent to atmospheric gases and thin ice clouds, and highly sensitive to precipitation. Therefore, GMAOs all-sky data assimilation efforts are primarily focused on utilizing these data in precipitating regions. The all-sky framework being tested at GMAO employs the GSI in a hybrid 4D-EnVar configuration of the Goddard Earth Observing System (GEOS) data assimilation system, which will be included in the next formal update of GEOS. This article provides an overview of the development of all-sky radiance assimilation in GEOS, including some performance metrics. In addition, various projects underway at GMAO designed to enhance the all-sky implementation will be introduced.
NWP↗
Investigation of the Potential Saturation of Information from Global Navigation Satellite System Radio Occultation Observations with an Observing System Simulation Experiment
The potential impact of large numbers of Global Navigation Satellite System radio occultation (GNSS-RO) observations on numerical weather prediction is investigated using a global observing system simulation experiment (OSSE). The hybrid four-dimensional ensemble variational Gridpoint Statistical Interpolation (GSI) data assimilation system and Global Earth Observing System (GEOS) model are used to ingest up to 100,000 GNSS-RO soundings per day in addition to the current suite of conventional and radiance data. Analysis quality, forecast skill, and forecast sensitivity to observation impact are examined with differing quantities of additional GNSS-RO profiles. It is found that saturation of information from additional RO soundings has not been reached with 100,000 soundings per day. There are some indications of suboptimal performance of the GSI in handling GNSS-RO observations particularly in the middle and lower tropospheric extratropics.
GNSS-RO↗