scholarly journals Community Global Observing System Simulation Experiment (OSSE) Package (CGOP): Assessment and Validation of the OSSE System Using an OSSE–OSE Intercomparison of Summary Assessment Metrics

2018 ◽  
Vol 35 (10) ◽  
pp. 2061-2078 ◽  
Author(s):  
Sid-Ahmed Boukabara ◽  
Kayo Ide ◽  
Yan Zhou ◽  
Narges Shahroudi ◽  
Ross N. Hoffman ◽  
...  
Keyword(s):  
System Simulation ◽  
Weather Prediction ◽  
Real Data ◽  
New Techniques ◽  
Forecast Performance ◽  
Individual System ◽  
Assessment Metrics ◽  
The Individual ◽  
The Tropics ◽  
Observing System Simulation Experiment

AbstractObserving system simulation experiments (OSSEs) are used to simulate and assess the impacts of new observing systems planned for the future or the impacts of adopting new techniques for exploiting data or for forecasting. This study focuses on the impacts of satellite data on global numerical weather prediction (NWP) systems. Since OSSEs are based on simulations of nature and observations, reliable results require that the OSSE system be validated. This validation involves cycles of assessment and calibration of the individual system components, as well as the complete system, with the end goal of reproducing the behavior of real-data observing system experiments (OSEs). This study investigates the accuracy of the calibration of an OSSE system—here, the Community Global OSSE Package (CGOP) system—before any explicit tuning has been performed by performing an intercomparison of the OSSE summary assessment metrics (SAMs) with those obtained from parallel real-data OSEs. The main conclusion reached in this study is that, based on the SAMs, the CGOP is able to reproduce aspects of the analysis and forecast performance of parallel OSEs despite the simplifications employed in the OSSEs. This conclusion holds even when the SAMs are stratified by various subsets (the tropics only, temperature only, etc.).

scholarly journals An Observing System Simulation Experiment with a Constellation of Radio Occultation Satellites

2018 ◽  
Vol 146 (12) ◽  
pp. 4247-4259 ◽  
Author(s):  
L. Cucurull ◽  
R. Atlas ◽  
R. Li ◽  
M. J. Mueller ◽  
R. N. Hoffman
Keyword(s):  
Radio Occultation ◽  
Simulation Experiment ◽  
System Simulation ◽  
Weather Prediction ◽  
Satellite System ◽  
Prediction Systems ◽  
Global Navigation Satellite ◽  
The Tropics ◽  
Observing System Simulation Experiment ◽  
The Impact

Abstract Experiments with a global observing system simulation experiment (OSSE) system based on the recent 7-km-resolution NASA nature run (G5NR) were conducted to determine the potential value of proposed Global Navigation Satellite System (GNSS) radio occultation (RO) constellations in current operational numerical weather prediction systems. The RO observations were simulated with the geographic sampling expected from the original planned Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2) system, with six equatorial (total of ~6000 soundings per day) and six polar (total of ~6000 soundings per day) receiver satellites. The experiments also accounted for the expected improved vertical coverage provided by the Jet Propulsion Laboratory RO receivers on board COSMIC-2. Except that RO observations were simulated and assimilated as refractivities, the 2015 version of the NCEP’s operational data assimilation system was used to run the OSSEs. The OSSEs quantified the impact of RO observations on global weather analyses and forecasts and the impact of adding explicit errors to the simulation of perfect RO profiles. The inclusion or exclusion of explicit errors had small, statistically insignificant impacts on results. The impact of RO observations was found to increase the length of the useful forecasts. In experiments with explicit errors, these increases were found to be 0.6 h in the Northern Hemisphere extratropics (a 0.4% improvement), 5.9 h in the Southern Hemisphere extratropics (a significant 4.0% improvement), and 12.1 h in the tropics (a very substantial 28.4% improvement).

scholarly journals Assessment of Radio Occultation Observations from the COSMIC-2 Mission with a Simplified Observing System Simulation Experiment Configuration

2017 ◽  
Vol 145 (9) ◽  
pp. 3581-3597 ◽  
Author(s):  
L. Cucurull ◽  
R. Li ◽  
T. R. Peevey
Keyword(s):  
Radio Occultation ◽  
Weather Forecasting ◽  
System Simulation ◽  
Weather Prediction ◽  
Forecast Model ◽  
Forecast Skill ◽  
Weather Forecasts ◽  
Global Coverage ◽  
Prediction Systems ◽  
Observing System Simulation Experiment

The mainstay of the global radio occultation (RO) system, the COSMIC constellation of six satellites launched in April 2006, is already past the end of its nominal lifetime and the number of soundings is rapidly declining because the constellation is degrading. For about the last decade, COSMIC profiles have been collected and their retrievals assimilated in numerical weather prediction systems to improve operational weather forecasts. The success of RO in increasing forecast skill and COSMIC’s aging constellation have motivated planning for the COSMIC-2 mission, a 12-satellite constellation to be deployed in two launches. The first six satellites (COSMIC-2A) are expected to be deployed in December 2017 in a low-inclination orbit for dense equatorial coverage, while the second six (COSMIC-2B) are expected to be launched later in a high-inclination orbit for global coverage. To evaluate the potential benefits from COSMIC-2, an earlier version of the NCEP’s operational forecast model and data assimilation system is used to conduct a series of observing system simulation experiments with simulated soundings from the COSMIC-2 mission. In agreement with earlier studies using real RO observations, the benefits from assimilating COSMIC-2 observations are found to be most significant in the Southern Hemisphere. No or very little gain in forecast skill is found by adding COSMIC-2A to COSMIC-2B, making the launch of COSMIC-2B more important for terrestrial global weather forecasting than that of COSMIC-2A. Furthermore, results suggest that further improvement in forecast skill might better be obtained with the addition of more RO observations with global coverage and other types of observations.

scholarly journals A Severe Weather Quick Observing System Simulation Experiment (QuickOSSE) of Global Navigation Satellite System (GNSS) Radio Occultation (RO) Superconstellations

2017 ◽  
Vol 145 (2) ◽  
pp. 637-651 ◽  
Author(s):  
S. Mark Leidner ◽  
Thomas Nehrkorn ◽  
John Henderson ◽  
Marikate Mountain ◽  
Tom Yunck ◽  
...  
Keyword(s):  
Global Navigation Satellite System ◽  
Simulation Experiment ◽  
System Simulation ◽  
Weather Prediction ◽  
Satellite System ◽  
Navigation Satellite ◽  
Global Navigation ◽  
Global Navigation Satellite ◽  
Observing System Simulation Experiment ◽  
The Impact

Global Navigation Satellite System (GNSS) radio occultations (RO) over the last 10 years have proved to be a valuable and essentially unbiased data source for operational global numerical weather prediction. However, the existing sampling coverage is too sparse in both space and time to support forecasting of severe mesoscale weather. In this study, the case study or quick observing system simulation experiment (QuickOSSE) framework is used to quantify the impact of vastly increased numbers of GNSS RO profiles on mesoscale weather analysis and forecasting. The current study focuses on a severe convective weather event that produced both a tornado and flash flooding in Oklahoma on 31 May 2013. The WRF Model is used to compute a realistic and faithful depiction of reality. This 2-km “nature run” (NR) serves as the “truth” in this study. The NR is sampled by two proposed constellations of GNSS RO receivers that would produce 250 thousand and 2.5 million profiles per day globally. These data are then assimilated using WRF and a 24-member, 18-km-resolution, physics-based ensemble Kalman filter. The data assimilation is cycled hourly and makes use of a nonlocal, excess phase observation operator for RO data. The assimilation of greatly increased numbers of RO profiles produces improved analyses, particularly of the lower-tropospheric moisture fields. The forecast results suggest positive impacts on convective initiation. Additional experiments should be conducted for different weather scenarios and with improved OSSE systems.

scholarly journals Community Global Observing System Simulation Experiment (OSSE) Package (CGOP): Description and Usage

2016 ◽  
Vol 33 (8) ◽  
pp. 1759-1777 ◽  
Author(s):  
Sid-Ahmed Boukabara ◽  
Isaac Moradi ◽  
Robert Atlas ◽  
Sean P. F. Casey ◽  
Lidia Cucurull ◽  
...  
Keyword(s):  
General Circulation ◽  
System Simulation ◽  
Weather Prediction ◽  
Circulation Model ◽  
Earth Observing System ◽  
Operational Systems ◽  
Cubed Sphere ◽  
New Sensors ◽  
Observing System Simulation Experiment ◽  
Observing Systems

AbstractA modular extensible framework for conducting observing system simulation experiments (OSSEs) has been developed with the goals of 1) supporting decision-makers with quantitative assessments of proposed observing systems investments, 2) supporting readiness for new sensors, 3) enhancing collaboration across the community by making the most up-to-date OSSE components accessible, and 4) advancing the theory and practical application of OSSEs. This first implementation, the Community Global OSSE Package (CGOP), is for short- to medium-range global numerical weather prediction applications. The CGOP is based on a new mesoscale global nature run produced by NASA using the 7-km cubed sphere version of the Goddard Earth Observing System, version 5 (GEOS-5), atmospheric general circulation model and the January 2015 operational version of the NOAA global data assimilation (DA) system. CGOP includes procedures to simulate the full suite of observing systems used operationally in the global DA system, including conventional in situ, satellite-based radiance, and radio occultation observations. The methodology of adding a new proposed observation type is documented and illustrated with examples of current interest. The CGOP is designed to evolve, both to improve its realism and to keep pace with the advance of operational systems.

Orbit Simulator for Satellite and Near Space Platforms Supporting Observing System Simulation Experiments

2021 ◽  
Author(s):  
Likun Wang ◽  
Narges Shahroudi ◽  
Eric Maddy ◽  
Kevin Garrett ◽  
Sid Boukabara ◽  
...  
Keyword(s):  
System Simulation ◽  
Weather Prediction ◽  
Satellite System ◽  
Satellite Constellations ◽  
Near Space ◽  
Satellite Applications ◽  
Joint Center ◽  
Global Navigation Satellite ◽  
Cross Track ◽  
Observing System Simulation Experiment

AbstractDeveloped at the National Oceanic and Atmospheric Administration (NOAA) and the Joint Center for Satellite Data Assimilation (JCSDA), the Community Global Observing System Simulation Experiment (OSSE) Package (CGOP) provides a vehicle to quantitatively evaluate the impacts of emerging environmental observing systems or emerging in-situ or remote sensing instruments on NOAA numerical weather prediction (NWP) forecast skill. The typical first step for the OSSE is to simulate observations from the so-called “nature run”. Therefore, the observation spatial, temporal, and view geometry are needed to extract the atmospheric and surface variables from the nature run, which are then input to the observation forward operator (e.g., radiative transfer models) to simulate the new observations. This is a challenge for newly proposed systems for which instruments are not yet built or platforms are not yet deployed. To address this need, this study introduces an orbit simulator to compute these parameters based on the specific hosting platform and onboard instrument characteristics, which has been recently developed by the NOAA Center for Satellite Applications and Research (STAR) and added to the GCOP framework. In addition to simulating existing polar-orbiting and geostationary orbits, it is also applicable to emerging near space platforms (e.g., stratospheric balloons), cube satellite constellations, and Tundra orbits. The observation geometry simulator includes not only passive microwave and infrared sounders but also Global Navigation Satellite System/Radio Occultation (GNSS/RO) instruments. For passive atmospheric sounders, it calculates the geometric parameters of proposed instruments on different platforms, such as time varying location (latitude and longitude), scan geometry (satellite zenith and azimuth angles), and Ground Instantaneous Field of View (GIFOV) parameters for either cross-track or conical scanning mechanisms. For RO observations, it determines the geometry of the transmitters and receivers either on satellites or stratospheric balloons and computes their slant paths. The simulator has been successfully applied for recent OSSE studies (e.g., evaluating the impacts of future geostationary hyperspectral infrared sounders and RO observations from stratospheric balloons).

scholarly journals Supervised dimensionality reduction for big data

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Joshua T. Vogelstein ◽  
Eric W. Bridgeford ◽  
Minh Tang ◽  
Da Zheng ◽  
Christopher Douville ◽  
...  
Keyword(s):  
Dimensionality Reduction ◽  
Data Science ◽  
Real Data ◽  
Low Rank ◽  
Conditional Moment ◽  
Desktop Computer ◽  
Reduction Techniques ◽  
Reduction Methods ◽  
The Individual ◽  
Low Dimensional

AbstractTo solve key biomedical problems, experimentalists now routinely measure millions or billions of features (dimensions) per sample, with the hope that data science techniques will be able to build accurate data-driven inferences. Because sample sizes are typically orders of magnitude smaller than the dimensionality of these data, valid inferences require finding a low-dimensional representation that preserves the discriminating information (e.g., whether the individual suffers from a particular disease). There is a lack of interpretable supervised dimensionality reduction methods that scale to millions of dimensions with strong statistical theoretical guarantees. We introduce an approach to extending principal components analysis by incorporating class-conditional moment estimates into the low-dimensional projection. The simplest version, Linear Optimal Low-rank projection, incorporates the class-conditional means. We prove, and substantiate with both synthetic and real data benchmarks, that Linear Optimal Low-Rank Projection and its generalizations lead to improved data representations for subsequent classification, while maintaining computational efficiency and scalability. Using multiple brain imaging datasets consisting of more than 150 million features, and several genomics datasets with more than 500,000 features, Linear Optimal Low-Rank Projection outperforms other scalable linear dimensionality reduction techniques in terms of accuracy, while only requiring a few minutes on a standard desktop computer.

scholarly journals Controlling response dependence in the measurement of change using the Rasch model

2017 ◽  
Vol 27 (12) ◽  
pp. 3709-3725 ◽  
Author(s):  
David Andrich
Keyword(s):  
Rasch Model ◽  
Real Data ◽  
Repeated Measurements ◽  
Measurement Of Change ◽  
Location Parameters ◽  
Response Dependence ◽  
The Individual ◽  
First Time ◽  
The Rasch Model ◽  
Research And Policy

The advantages of using person location estimates from the Rasch model over raw scores for the measurement of change using a common test include the linearization of scores and the automatic handling of statistical properties of repeated measurements. However, the application of the model requires that the responses to the items are statistically independent in the sense that the specific responses to the items on the first time of testing do not affect the responses at a second time. This requirement implies that the responses to the items at both times of assessment are governed only by the invariant location parameters of the items at the two times of testing and the location parameters of each person each time. A specific form of dependence that is pertinent when the same items are used is when the observed response to an item at the second time of testing is affected by the response to the same item at the first time, a form of dependence which has been referred to as response dependence. This paper presents the logic of applying the Rasch model to quantify, control and remove the effect of response dependence in the measurement of change when the same items are used on two occasions. The logic is illustrated with four sets of simulation studies with dichotomous items and with a small example of real data. It is shown that the presence of response dependence can reduce the evidence of change, a reduction which may impact interpretations at the individual, research, and policy levels.

scholarly journals The genetics ofUstilago maydis

1961 ◽  
Vol 2 (2) ◽  
pp. 204-230 ◽  
Author(s):  
Robin Holliday
Keyword(s):  
Wild Type Strain ◽  
Ultra Violet ◽  
Smut Fungi ◽  
Linkage Groups ◽  
New Techniques ◽  
Microbial Genetics ◽  
Type Locus ◽  
Haploid Chromosome Number ◽  
Growth Requirements ◽  
The Individual

1. Many of the Ustilaginales, or smut fungi, appear to have the qualities necessary for the application of modern techniques of microbial genetics.Ustilago maydisis considered the most suitable species.2. Investigations of the mating system confirm reports that the production of diploid brandspores in the host is controlled by alleles at two loci.3. Genetic markers were obtained by inducing mutations in a wild-type strain with ultra-violet light. Of 100 biochemical mutants which were isolated, the growth requirements of 94 were identified. Thirty of these were used in genetic tests.4. The compact growth of colonies on artificial media allowed new techniques to be developed by means of which large samples of progeny could be isolated and identified easily. The analysis of brandspore colonies consisting of the products of single meiotic divisions is the quickest method for detecting linkage, but its accurate measurement appears to be achieved by examining the individual members of tetrads.5. Linkage was detected relatively rarely, but eight markers, including theamating-type locus, were assigned to one or other of two linkage groups. Although recombination values were not always determined accurately owing to irregular basidiospore germination, the auxotrophic markers in each group could be mapped in a linear order. Since no indication of other linkage groups was obtained, the genetic evidence is so far consistent with cytological reports that the basic haploid chromosome number is two in the smut fungi.6. Three linked markers were used to investigate chromatid interference by tetrad analysis. None was detected in a total of eighteen double exchanges.

Practical and Intrinsic Predictability of Severe and Convective Weather at the Mesoscales

2012 ◽  
Vol 69 (11) ◽  
pp. 3350-3371 ◽  
Author(s):  
Christopher Melhauser ◽  
Fuqing Zhang
Keyword(s):  
Weather Prediction ◽  
Squall Line ◽  
Cold Pool ◽  
Initial Condition ◽  
Large Variability ◽  
Forecast Performance ◽  
Sensitivity Experiments ◽  
Bow Echo ◽  
Upper Level ◽  
Convective Weather

Abstract This study explores both the practical and intrinsic predictability of severe convective weather at the mesoscales using convection-permitting ensemble simulations of a squall line and bow echo event during the Bow Echo and Mesoscale Convective Vortex (MCV) Experiment (BAMEX) on 9–10 June 2003. Although most ensemble members—initialized with realistic initial condition uncertainties smaller than the NCEP Global Forecast System Final Analysis (GFS FNL) using an ensemble Kalman filter—forecast broad areas of severe convection, there is a large variability of forecast performance among different members, highlighting the limit of practical predictability. In general, the best-performing members tend to have a stronger upper-level trough and associated surface low, producing a more conducive environment for strong long-lived squall lines and bow echoes, once triggered. The divergence in development is a combination of a dislocation of the upper-level trough, surface low with corresponding marginal environmental differences between developing and nondeveloping members, and cold pool evolution by deep convection prior to squall line formation. To further explore the intrinsic predictability of the storm, a sequence of sensitivity experiments was performed with the initial condition differences decreased to nearly an order of magnitude smaller than typical analysis and observation errors. The ensemble forecast and additional sensitivity experiments demonstrate that this storm has a limited practical predictability, which may be further improved with more accurate initial conditions. However, it is possible that the true storm could be near the point of bifurcation, where predictability is intrinsically limited. The limits of both practical and intrinsic predictability highlight the need for probabilistic and ensemble forecasts for severe weather prediction.

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