GEFSv12 reforecast dataset for supporting subseasonal and hydrometeorological applications

2022 ◽  
Keyword(s):  
Ensemble Kalman Filter ◽  
Initial Conditions ◽  
Phase 1 ◽  
Storm Track ◽  
Phase 2 ◽  
Forecast System ◽  
Data Set ◽  
Weather And Climate ◽  
Environmental Prediction ◽  
Climate Prediction Center

Abstract For the newly implemented Global Ensemble Forecast System version 12 (GEFSv12), a 31-year (1989-2019) ensemble reforecast dataset has been generated at the National Centers for Environmental Prediction (NCEP). The reforecast system is based on NCEP’s Global Forecast System version 15.1 and GEFSv12, which uses the Finite Volume 3 dynamical core. The resolution of the forecast system is ∼25 km with 64 vertical hybrid levels. The Climate Forecast System (CFS) reanalysis and GEFSv12 reanalysis serve as initial conditions for the Phase 1 (1989–1999) and Phase 2 (2000–2019) reforecasts, respectively. The perturbations were produced using breeding vectors and ensemble transforms with a rescaling technique for Phase 1 and ensemble Kalman filter 6-h forecasts for Phase 2. The reforecasts were initialized at 0000 (0300) UTC once per day out to 16 days with 5 ensemble members for Phase 1 (Phase 2), except on Wednesdays when the integrations were extended to 35 days with 11 members. The reforecast data set was produced on NOAA’s Weather and Climate Operational Supercomputing System at NCEP. This study summarizes the configuration and dataset of the GEFSv12 reforecast and presents some preliminary evaluations of 500hPa geopotential height, tropical storm track, precipitation, 2-meter temperature, and MJO forecasts. The results were also compared with GEFSv10 or GEFS Subseasonal Experiment reforecasts. In addition to supporting calibration and validation for the National Water Center, NCEP Climate Prediction Center, and other National Weather Service stakeholders, this high-resolution subseasonal dataset also serves as a useful tool for the broader research community in different applications.

Initial Conditions for Convection-Allowing Ensembles over the Conterminous United States

2020 ◽  
Vol 148 (7) ◽  
pp. 2645-2669
Author(s):  
Craig S. Schwartz ◽  
May Wong ◽  
Glen S. Romine ◽  
Ryan A. Sobash ◽  
Kathryn R. Fossell
Keyword(s):  
United States ◽  
Ensemble Kalman Filter ◽  
Initial Conditions ◽  
Limited Area ◽  
Grid Spacing ◽  
Forecast System ◽  
Skill Scores ◽  
Ensemble Mean ◽  
Horizontal Grid ◽  
Better Than

Abstract Five sets of 48-h, 10-member, convection-allowing ensemble (CAE) forecasts with 3-km horizontal grid spacing were systematically evaluated over the conterminous United States with a focus on precipitation across 31 cases. The various CAEs solely differed by their initial condition perturbations (ICPs) and central initial states. CAEs initially centered about deterministic Global Forecast System (GFS) analyses were unequivocally better than those initially centered about ensemble mean analyses produced by a limited-area single-physics, single-dynamics 15-km continuously cycling ensemble Kalman filter (EnKF), strongly suggesting relative superiority of the GFS analyses. Additionally, CAEs with flow-dependent ICPs derived from either the EnKF or multimodel 3-h forecasts from the Short-Range Ensemble Forecast (SREF) system had higher fractions skill scores than CAEs with randomly generated mesoscale ICPs. Conversely, due to insufficient spread, CAEs with EnKF ICPs had worse reliability, discrimination, and dispersion than those with random and SREF ICPs. However, members in the CAE with SREF ICPs undesirably clustered by dynamic core represented in the ICPs, and CAEs with random ICPs had poor spinup characteristics. Collectively, these results indicate that continuously cycled EnKF mean analyses were suboptimal for CAE initialization purposes and suggest that further work to improve limited-area continuously cycling EnKFs over large regional domains is warranted. Additionally, the deleterious aspects of using both multimodel and random ICPs suggest efforts toward improving spread in CAEs with single-physics, single-dynamics, flow-dependent ICPs should continue.

A computationally fast approach to maximum‐likelihood deconvolution

Geophysics ◽  
1984 ◽  
Vol 49 (5) ◽  
pp. 550-565 ◽  
Author(s):  
Chong‐Yung Chi ◽  
Jerry M. Mendel ◽  
Dan Hampson
Keyword(s):  
Maximum Likelihood ◽  
Initial Conditions ◽  
Phase 1 ◽  
Real Data ◽  
Phase 2 ◽  
Statistical Parameters ◽  
Two Phase ◽  
Nonminimum Phase ◽  
Fast Approach ◽  
Fine Adjustment

In this paper we derive and implement a maximum‐likelihood deconvolution (MLD) algorithm, based on the same channel and statistical models used by Kormylo and Mendel (1983a), that leads to many fewer computations than their MLD algorithm. Both algorithms can simultaneously estimate a nonminimum phase wavelet and statistical parameters, detect locations of significant reflectors, and deconvolve the data. Our MLD algorithm is implemented by a two‐phase block component method (BCM). The phase‐1 block functions like a coarse adjustment of unknown quantities and provides a set of good initial conditions for the phase‐2 block, which functions like a fine adjustment of unknown quantities. We demonstrate good performance of our algorithm for both synthetic and real data.

Experimental Weekly to Seasonal U.S. Forecasts with the Regional Spectral Model

2004 ◽  
Vol 85 (12) ◽  
pp. 1887-1902 ◽  
Author(s):  
J. Roads
Keyword(s):  
Spectral Model ◽  
Planetary Boundary Layer Height ◽  
Forecast System ◽  
Global Spectral Model ◽  
Layer Height ◽  
Boundary Layer Height ◽  
Regional Spectral Model ◽  
Environmental Prediction ◽  
Climate Prediction Center ◽  
Regional Forecast

Since 27 September 1997, the Scripps Experimental Climate Prediction Center (ECPC) has been making near real-time experimental global and regional dynamical forecasts with the National Centers for Environmental Prediction (NCEP) global spectral model (GSM) and the corresponding regional spectral model (RSM), which is based on the GSM, but which provides higher-resolution simulations and forecasts for limited regions. The global and regional forecast skill of the GSM was previously described in several papers. The purpose of this paper is to describe the RSM-based U.S. regional forecast system, various biases and errors in these regional U.S. forecasts, as well as the significant skill of the of temperature, precipitation, soil moisture, relative humidity, wind speed, and planetary boundary layer height forecasts at weekly to seasonal time scales. The skill of these RSM forecasts is comparable to the skill of the GSM forecasts.

scholarly journals Comparing predictions made by a prediction model, clinical score, and physicians

2013 ◽  
Vol 04 (03) ◽  
pp. 376-391 ◽  
Author(s):  
K.J. Farion ◽  
W. Michalowski ◽  
D. O’Sullivan ◽  
J. Sayyad-Shirabad ◽  
S. Wilk
Keyword(s):  
Prediction Model ◽  
Prediction Models ◽  
Phase 1 ◽  
Characteristic Curve ◽  
Clinical Score ◽  
Machine Learning Techniques ◽  
Phase 2 ◽  
Two Phase ◽  
Data Set ◽  
Predictive Values

SummaryBackground: Asthma exacerbations are one of the most common medical reasons for children to be brought to the hospital emergency department (ED). Various prediction models have been proposed to support diagnosis of exacerbations and evaluation of their severity.Objectives: First, to evaluate prediction models constructed from data using machine learning techniques and to select the best performing model. Second, to compare predictions from the selected model with predictions from the Pediatric Respiratory Assessment Measure (PRAM) score, and predictions made by ED physicians.Design: A two-phase study conducted in the ED of an academic pediatric hospital. In phase 1 data collected prospectively using paper forms was used to construct and evaluate five prediction models, and the best performing model was selected. In phase 2 data collected prospectively using a mobile system was used to compare the predictions of the selected prediction model with those from PRAM and ED physicians.Measurements: Area under the receiver operating characteristic curve and accuracy in phase 1; accuracy, sensitivity, specificity, positive and negative predictive values in phase 2.Results: In phase 1 prediction models were derived from a data set of 240 patients and evaluated using 10-fold cross validation. A naive Bayes (NB) model demonstrated the best performance and it was selected for phase 2. Evaluation in phase 2 was conducted on data from 82 patients. Predictions made by the NB model were less accurate than the PRAM score and physicians (accuracy of 70.7%, 73.2% and 78.0% respectively), however, according to McNemar’s test it is not possible to conclude that the differences between predictions are statistically significant.Conclusion: Both the PRAM score and the NB model were less accurate than physicians. The NB model can handle incomplete patient data and as such may complement the PRAM score. However, it requires further research to improve its accuracy.

scholarly journals Population Pharmacokinetic Analyses for BC-3781 Using Phase 2 Data from Patients with Acute Bacterial Skin and Skin Structure Infections

2014 ◽  
Vol 59 (1) ◽  
pp. 282-288 ◽  
Author(s):  
C. M. Rubino ◽  
B. Xue ◽  
S. M. Bhavnani ◽  
W. T. Prince ◽  
Z. Ivezic-Schoenfeld ◽  
...  
Keyword(s):  
Renal Function ◽  
Body Size ◽  
Phase 1 ◽  
Compartment Model ◽  
Volume Of Distribution ◽  
Population Pharmacokinetic ◽  
Phase 2 ◽  
Data Set ◽  
Skin Structure ◽  
Population Pk

ABSTRACTBC-3781, a pleuromutilin antimicrobial agent, is being developed for the treatment of patients with acute bacterial skin and skin structure infections (ABSSSI) and community-acquired bacterial pneumonia. Data from a phase 2 study of patients with ABSSSI were used to refine a previous population pharmacokinetic (PK) model and explore potential predictors of PK variability. The previously derived population PK model based on data from three phase 1 studies was applied to sparse sampling data from a phase 2 ABSSSI study and modified as necessary. Covariate analyses were conducted to identify descriptors (e.g., body size, renal function, age) associated with interindividual variability in PK. All population PK analyses were conducted by using Monte Carlo parametric expectation maximization implemented in S-ADAPT 1.5.6. The population PK data set contained 1,167 concentrations from 129 patients; 95% of the patients had 5 or more PK samples (median, 11). The previous population PK model (three-compartment model with first-order elimination and nonlinear protein binding) provided an acceptable and unbiased fit to the data from the 129 patients. Population PK parameters were estimated with acceptable precision; individual clearance values were particularly well estimated (median individual precision of 9.15%). Graphical covariate evaluations showed no relationships between PK and age or renal function but modest relationships between body size and clearance and volume of distribution, which were not statistically significant when included in the population PK model. This population PK model will be useful for subsequent PK-pharmacodynamic analyses and simulations conducted to support phase 3 dose selection. (This study has been registered at ClinicalTrials.gov under registration no. NCT01119105.)

scholarly journals A Comparison of Simple Analytical Methods for Determination of Fluoride in Microlitre-Volume Plasma Samples

2018 ◽  
Vol 53 (3) ◽  
pp. 275-283
Author(s):  
F. Vida Zohoori ◽  
Anne Maguire ◽  
E. Angeles Martinez-Mier ◽  
Marília Afonso Rabelo Buzalaf ◽  
Roy Sanderson ◽  
...  
Keyword(s):  
Phase 1 ◽  
Fluoride Concentration ◽  
Diffusion Method ◽  
Plasma Samples ◽  
Phase 2 ◽  
Phase 3 ◽  
Preliminary Measurement ◽  
Data Set ◽  
Suitable Alternative ◽  
Significant Difference

The aim was to compare potential methods for fluoride analysis in microlitre-volume plasma samples containing nano-gram amounts of fluoride. Methods: A group of 4 laboratories analysed a set of standardised biological samples as well as plasma to determine fluoride concentration using 3 methods. In Phase-1, fluoride analysis was carried out using the established hexamethyldisiloxane (HMDS)-diffusion method (1 mL-aliquot/analysis) to obtain preliminary measurement of agreement between the laboratories. In Phase-2, the laboratories analysed the same samples using a micro-diffusion method and known-addition technique with 200 µL-aliquot/analysis. Coefficients of Variation (CVs) and intra-class correlation coefficients (ICCs) were estimated using analysis of variance to evaluate the amount of variation within- and between-laboratories. Based on the results of the Phase-2 analysis, 20 human plasma samples were analysed and compared using the HMDS-diffusion method and known-addition technique in Phase-3. Results: Comparison of Phase-1 results showed no statistically significant difference among the laboratories for the overall data set. The mean between- and within-laboratory CVs and ICCs were < 0.13 and ≥0.99, respectively, indicating very low variability and excellent reliability. In Phase-2, the overall results for between-laboratory variability showed a poor CV (1.16) and ICC (0.44) for the micro-diffusion method, whereas with the known-addition technique the corresponding values were 0.49 and 0.83. Phase-3 results showed no statistically significant difference in fluoride concentrations of the plasma samples measured with HMDS-diffusion method and known- addition technique, with a mean (SE) difference of 0.002 (0.003) µg/mL. In conclusion, the known-addition technique could be a suitable alternative for the measurement of fluoride in plasma with microlitre-volume samples.

The DYAMOND Winter data collection

2021 ◽  
Author(s):  
Julia Duras ◽  
Florian Ziemen ◽  
Daniel Klocke
Keyword(s):  
Data Collection ◽  
General Circulation ◽  
Climate Models ◽  
Initial Conditions ◽  
Climate Modelling ◽  
Data Sets ◽  
Data Set ◽  
Weather And Climate ◽  
Data Collections ◽  
User Friendly

&lt;p&gt;The DYAMOND project (&lt;strong&gt;DY&lt;/strong&gt;namics of the &lt;strong&gt;A&lt;/strong&gt;tmospheric general circulation &lt;strong&gt;M&lt;/strong&gt;odeled &lt;strong&gt;O&lt;/strong&gt;n &lt;strong&gt;N&lt;/strong&gt;on-hydrostatic &lt;strong&gt;D&lt;/strong&gt;omains) is the first initiative for a model intercomparison of global storm resolving (km-scale) climate simulations. The analysis of these simulations advances the understanding of the climate system and improves the next-generation of weather and climate models. In a first phase, a period of 40 days from 1st of August 2016 was simulated, with all models starting from the same initial conditions. The resulting data set is referred to as &amp;#8221;DYAMOND Summer&amp;#8221; data. In its second, currently ongoing phase &amp;#8221;DYAMOND Winter&amp;#8221;, participating models simulate 40 days starting on the 20th of January 2020, also covering the period of the EUREC4A field experiment. While the DYAMOND Summer only included atmosphere models, the DYAMOND Winter data set also includes coupled atmosphere-ocean models resolving ocean-eddies, atmospheric storms and their interactions. &lt;br&gt;The analysis of these simulations allows to identify robust features common to this class of new models, and provides insights into implementation-dependence of the results and a hint of the future of climate modelling (e.g. &lt;em&gt;Arnold et al., 2020 &lt;/em&gt;; &lt;em&gt;Dueben et al., 2020 &lt;/em&gt;; &lt;em&gt;Stevens et al., 2020 &lt;/em&gt;; &lt;em&gt;Wedi et al., 2020 &lt;/em&gt;).&amp;#160;&lt;br&gt;The Centre of Excellence in Simulation of Weather and Climate in Europe (ESiWACE) and the German Climate computing centre (DKRZ) are making this data available to the research community. For this purpose, a user-friendly central point of access, the so-called &amp;#8220;DYAMOND data library&amp;#8221; has been developed. It provides access to the Summer and Winter data collections. A growing community with a lively exchange (e.g. during regular Hackathons) further simplifies the usage of these data sets.&amp;#160;&lt;/p&gt;&lt;p&gt;The presentation will introduce the DYAMOND project with a focus on the new DYAMOND Winter data collection. It will present the corresponding experiment protocol and the participating models. To invite scientists to use these data sets, different ways of using the data on the supercomputer of DKRZ will be described in detail.&lt;/p&gt;

scholarly journals Performance of the New NCEP Global Ensemble Forecast System in a Parallel Experiment

2017 ◽  
Vol 32 (5) ◽  
pp. 1989-2004 ◽  
Author(s):  
Xiaqiong Zhou ◽  
Yuejian Zhu ◽  
Dingchen Hou ◽  
Yan Luo ◽  
Jiayi Peng ◽  
...  
Keyword(s):  
Ensemble Kalman Filter ◽  
Initial Conditions ◽  
Forecast Model ◽  
Horizontal Resolution ◽  
Ensemble Forecast ◽  
Wind Fields ◽  
Forecast System ◽  
Medium Range Forecast ◽  
Verification Period ◽  
Medium Range

Abstract A new version of the Global Ensemble Forecast System (GEFS, v11) is tested and compared with the operational version (v10) in a 2-yr parallel run. The breeding-based scheme with ensemble transformation and rescaling (ETR) used in the operational GEFS is replaced by the ensemble Kalman filter (EnKF) to generate initial ensemble perturbations. The global medium-range forecast model and the Global Forecast System (GFS) analysis used as the initial conditions are upgraded to the GFS 2015 implementation version. The horizontal resolution of GEFS increases from Eulerian T254 (~52 km) for the first 8 days of the forecast and T190 (~70 km) for the second 8 days to semi-Lagrangian T574 (~34 km) and T382 (~52 km), respectively. The sigma pressure hybrid vertical layers increase from 42 to 64 levels. The verification of geopotential height, temperature, and wind fields at selected levels shows that the new GEFS significantly outperforms the operational GEFS up to days 8–10 except for an increased warm bias over land in the extratropics. It is also found that the parallel system has better reliability in the short-range probability forecasts of precipitation during warm seasons, but no clear improvement in cold seasons. There is a significant degradation of TC track forecasts at days 6–7 during the 2012–14 TC seasons over the Atlantic and eastern Pacific. This degradation is most likely a sampling issue from a low number of TCs during these three TC seasons. The results for an extended verification period (2011–14) and the recent two hurricane seasons (2015 and 2016) are generally positive. The new GEFS became operational at NCEP on 2 December 2015.

scholarly journals Acquisition of angular momentum in the proto-galactic phases

1988 ◽  
Vol 130 ◽  
pp. 544-544
Author(s):  
R. Caimmi ◽  
E. Andriani ◽  
L. Secco
Keyword(s):  
Angular Momentum ◽  
Mass Spectrum ◽  
Intermediate Phase ◽  
Initial Conditions ◽  
Phase 1 ◽  
Dynamical Evolution ◽  
Second Phase ◽  
Phase 2 ◽  
Maximum Expansion ◽  
M 10

Following Peebles (Ap.J. 155, 393; Astron.Astrophys. 11, 377)and Efstathiou and Silk (Fund.Cosm.Phys. 9, 1) we assume that a developing proto-galaxy of mass M=1011M⊙ gains angular momentum during a first Phase 1 exten ding from recombination to the maximum expansion and then a second Phase 2 from the maximum expansion to a relaxed virialized structure passing through an intermediate Phase I.A very simple model is proposed in which a first mechanism connected with increasing density perturbatio ons leading to a torque ∼ t2/3, is applied to a spherical homogeneus structure. The initial conditions for its dynamical evolution are taken from a cosmological flat model (Ω = 1) with an isothermal perturbation mass spectrum. Phase 2 begins when the proto-galaxy is completely separated from the background and until this Phase it has been recognized that the initial spherical symmetry turns out to be well preserved, provided the angular momentum gained does not exceed 3.6 1073 g cm2 sec−1.

Comparing Methodologies in a Series of Speech Outcome Studies

2017 ◽  
Vol 55 (1) ◽  
pp. 35-44 ◽  
Author(s):  
Rebecka Ahl ◽  
Anne Harding-Bell
Keyword(s):  
Phase 1 ◽  
Intraclass Correlation ◽  
Large Data ◽  
Data Sets ◽  
Phase 2 ◽  
Phase 3 ◽  
Data Set ◽  
Speech Characteristics ◽  
Cleft Palate Repair ◽  
Listening Process

Background: Development of the speech audit tool Cleft Audit Protocol for Speech Augmented (CAPS-A) facilitated intercenter comparison of speech outcomes following cleft palate repair. The CAPS-A protocol recommends consensus listening by 3 speech and language therapists, 2 of whom must be CAPS-A trained. Allowing 15 minutes per sample, 15 to 20 samples can be assessed each day. Centers typically have resources to audit 15 to 75 samples per year but not to report speech outcomes of larger data sets for research. This 3-phased outcome study examines how the implementation of the CAPS-A protocol might be modified without compromising reliability. Methodology: In phase 1, 2 external listeners independently rated 42 speech samples; in phase 2, 2 external listeners consensus listened 25% of 140 samples before 1 listener independently rated the remainder; phase 3 compared 124 Great Ormond Street Speech Assessment (GOS.SP.ASS’98) records from live assessments with CAPS-A-rated video samples. Results: Hypernasality, nasal airflow, and passive cleft speech characteristics were rated to identify signs of velopharyngeal dysfunction across all phases. Phase 1 demonstrated suboptimal correlation, intraclass correlation coefficient (ICC) ranging between 0.39 and 0.72. However, the “modified” CAPS-A consensus listening process in phase 2 achieved a mean ICC of 0.91. Phase 3 revealed only moderate agreement between GOS.SP.ASS’98 and CAPS-A. Conclusion: A large data set of speech samples was successfully managed by establishing good interrater reliability on 25% of the data, which calibrated the listeners and validated a decision for only 1 of 2 listeners to rate the remaining speech samples. The recommended implementation of the CAPS-A protocol can therefore be modified for more efficient speech outcome reporting.

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