DeepStar Metocean Studies: 15 years of Discovery

2013 ◽  
Vol 47 (3) ◽  
pp. 19-26 ◽  
Author(s):  
Cortis Cooper ◽  
Tom Mitchell ◽  
George Forristall ◽  
James Stear
Keyword(s):  
Numerical Models ◽  
Academic Research ◽  
Forecast Model ◽  
Large Set ◽  
Loop Current ◽  
Ocean Science ◽  
Historical Databases ◽  
Wind Measurements ◽  
Wind Profiles ◽  
Topographic Rossby Waves

AbstractIn 1998, DeepStar began the first of many successful studies that have resolved important questions concerning meteorological and oceanographic (metocean) processes that can cause large loads or fatigue problems on deepwater facilities. In so doing, these studies have immeasurably enhanced the reliability and safety of deepwater structures and pushed the frontiers of ocean science that have traditionally been the realm of academic research. The efforts have focused on three major phenomena: the Loop Current, Topographic Rossby Waves (TRW), and storm winds. Much of the DeepStar effort has focused on improving numerical models of the respective phenomena because they can provide long historical databases at any site—data that serve as the basis for operating and extreme criteria with reasonable statistical uncertainty. Studies of the Loop include the first measurements of the Loop inflow and turbulence and evaluation of existing numerical models. Most of DeepStar’s efforts on TRWs started in 2008, and in a 5-year period, it has developed a validated numerical model and used it to build a 50-year hindcast database. Efforts are underway to use those results to build a stochastic forecast model. Finally, DeepStar has analyzed a large set of wind measurements taken from the powerful recent hurricanes and found that recommended formulas for wind profiles and spectra have significant bias and will be corrected in future recommended practices.

Conditioning Model Ensembles to Various Observed Data (Field and Regional Level) by Applying Machine-Learning-Augmented Workflows to a Mature Field with 70 Years of Production History

2021 ◽  
pp. 1-18
Author(s):  
Gisela Vanegas ◽  
John Nejedlik ◽  
Pascale Neff ◽  
Torsten Clemens
Keyword(s):  
Machine Learning ◽  
Oil Recovery ◽  
Numerical Models ◽  
Operating Conditions ◽  
Model Parameters ◽  
Large Set ◽  
Model Parameter ◽  
Production History ◽  
Hydrocarbon Fields ◽  
Parameter Distributions

Summary Forecasting production from hydrocarbon fields is challenging because of the large number of uncertain model parameters and the multitude of observed data that are measured. The large number of model parameters leads to uncertainty in the production forecast from hydrocarbon fields. Changing operating conditions [e.g., implementation of improved oil recovery or enhanced oil recovery (EOR)] results in model parameters becoming sensitive in the forecast that were not sensitive during the production history. Hence, simulation approaches need to be able to address uncertainty in model parameters as well as conditioning numerical models to a multitude of different observed data. Sampling from distributions of various geological and dynamic parameters allows for the generation of an ensemble of numerical models that could be falsified using principal-component analysis (PCA) for different observed data. If the numerical models are not falsified, machine-learning (ML) approaches can be used to generate a large set of parameter combinations that can be conditioned to the different observed data. The data conditioning is followed by a final step ensuring that parameter interactions are covered. The methodology was applied to a sandstone oil reservoir with more than 70 years of production history containing dozens of wells. The resulting ensemble of numerical models is conditioned to all observed data. Furthermore, the resulting posterior-model parameter distributions are only modified from the prior-model parameter distributions if the observed data are informative for the model parameters. Hence, changes in operating conditions can be forecast under uncertainty, which is essential if nonsensitive parameters in the history are sensitive in the forecast.

scholarly journals A Review of Progress and Applications of Pulsed Doppler Wind LiDARs

2019 ◽  
Vol 11 (21) ◽  
pp. 2522 ◽  
Author(s):  
Zhengliang Liu ◽  
Janet F. Barlow ◽  
Pak-Wai Chan ◽  
Jimmy Chi Hung Fung ◽  
Yuguo Li ◽  
...  
Keyword(s):  
Wind Energy ◽  
Numerical Models ◽  
Real Life ◽  
Doppler Lidar ◽  
Promising Alternative ◽  
3 Dimensional ◽  
In Situ Techniques ◽  
Energy Assessment ◽  
Wind Shears ◽  
Wind Profiles

Doppler wind LiDAR (Light Detection And Ranging) makes use of the principle of optical Doppler shift between the reference and backscattered radiations to measure radial velocities at distances up to several kilometers above the ground. Such instruments promise some advantages, including its large scan volume, movability and provision of 3-dimensional wind measurements, as well as its relatively higher temporal and spatial resolution comparing with other measurement devices. In recent decades, Doppler LiDARs developed by scientific institutes and commercial companies have been well adopted in several real-life applications. Doppler LiDARs are installed in about a dozen airports to study aircraft-induced vortices and detect wind shears. In the wind energy industry, the Doppler LiDAR technique provides a promising alternative to in-situ techniques in wind energy assessment, turbine wake analysis and turbine control. Doppler LiDARs have also been applied in meteorological studies, such as observing boundary layers and tracking tropical cyclones. These applications demonstrate the capability of Doppler LiDARs for measuring backscatter coefficients and wind profiles. In addition, Doppler LiDAR measurements show considerable potential for validating and improving numerical models. It is expected that future development of the Doppler LiDAR technique and data processing algorithms will provide accurate measurements with high spatial and temporal resolutions under different environmental conditions.

scholarly journals The Loop Current: Observations of Deep Eddies and Topographic Waves

2019 ◽  
Vol 49 (6) ◽  
pp. 1463-1483 ◽  
Author(s):  
Peter Hamilton ◽  
Amy Bower ◽  
Heather Furey ◽  
Robert Leben ◽  
Paula Pérez-Brunius
Keyword(s):  
Large Scale ◽  
Lower Layer ◽  
Baroclinic Instability ◽  
Short Interval ◽  
Loop Current ◽  
Boundary Current ◽  
The West ◽  
Eastern Basin ◽  
Topographic Rossby Waves ◽  
Deep Eddies

AbstractA set of float trajectories, deployed at 1500- and 2500-m depths throughout the deep Gulf of Mexico from 2011 to 2015, are analyzed for mesoscale processes under the Loop Current (LC). In the eastern basin, December 2012–June 2014 had >40 floats per month, which was of sufficient density to allow capturing detailed flow patterns of deep eddies and topographic Rossby waves (TRWs), while two LC eddies formed and separated. A northward advance of the LC front compresses the lower water column and generates an anticyclone. For an extended LC, baroclinic instability eddies (of both signs) develop under the southward-propagating large-scale meanders of the upper-layer jet, resulting in a transfer of eddy kinetic energy (EKE) to the lower layer. The increase in lower-layer EKE occurs only over a few months during meander activity and LC eddy detachment events, a relatively short interval compared with the LC intrusion cycle. Deep EKE of these eddies is dispersed to the west and northwest through radiating TRWs, of which examples were found to the west of the LC. Because of this radiation of EKE, the lower layer of the eastern basin becomes relatively quiescent, particularly in the northeastern basin, when the LC is retracted and a LC eddy has departed. A mean west-to-east, anticyclone–cyclone dipole flow under a mean LC was directly comparable to similar results from a previous moored LC array and also showed connections to an anticlockwise boundary current in the southeastern basin.

scholarly journals Demonstration of an efficient interpolation technique of inverse time and distance for Oceansat-2 wind measurements at 6-hourly intervals

2017 ◽  
Vol 8 (3) ◽  
pp. 101-112 ◽  
Author(s):  
J Swain ◽  
P A Umesh ◽  
A S N Murty
Keyword(s):  
Wind Speed ◽  
State Of The Art ◽  
Numerical Models ◽  
Ocean Surface ◽  
The State ◽  
Wind Fields ◽  
Interpolation Technique ◽  
Ocean Surface Winds ◽  
Wind Measurements ◽  
Global Oceans

Indian Space Research Organization had launched Oceansat-2 on 23 September 2009, and the scatterometer onboard was a space-borne sensor capable of providing ocean surface winds (both speed and direction) over the globe for a mission life of 5 years. The observations of ocean surface winds from such a space-borne sensor are the potential source of data covering the global oceans and useful for driving the state-of-the-art numerical models for simulating ocean state if assimilated/blended with weather prediction model products. In this study, an efficient interpolation technique of inverse distance and time is demonstrated using the Oceansat-2 wind measurements alone for a selected month of June 2010 to generate gridded outputs. As the data are available only along the satellite tracks and there are obvious data gaps due to various other reasons, Oceansat-2 winds were subjected to spatio-temporal interpolation, and 6-hour global wind fields for the global oceans were generated over 1 × 1 degree grid resolution. Such interpolated wind fields can be used to drive the state-of-the-art numerical models to predict/hindcast ocean-state so as to experiment and test the utility/performance of satellite measurements alone in the absence of blended fields. The technique can be tested for other satellites, which provide wind speed as well as direction data. However, the accuracy of input winds is obviously expected to have a perceptible influence on the predicted ocean-state parameters. Here, some attempts are also made to compare the interpolated Oceansat-2 winds with available buoy measurements and it was found that they are reasonably in good agreement with a correlation coefficient of R > 0.8 and mean deviation 1.04 m/s and 25° for wind speed and direction, respectively.

scholarly journals A Comparison of Model Error Representations in Mesoscale Ensemble Data Assimilation

2015 ◽  
Vol 143 (10) ◽  
pp. 3893-3911 ◽  
Author(s):  
Soyoung Ha ◽  
Judith Berner ◽  
Chris Snyder
Keyword(s):  
Data Assimilation ◽  
Numerical Models ◽  
Wrf Model ◽  
Forecast Model ◽  
Model Error ◽  
Ensemble Forecasts ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
Independent Observations ◽  
Almost All

Abstract Mesoscale forecasts are strongly influenced by physical processes that are either poorly resolved or must be parameterized in numerical models. In part because of errors in these parameterizations, mesoscale ensemble data assimilation systems generally suffer from underdispersiveness, which can limit the quality of analyses. Two explicit representations of model error for mesoscale ensemble data assimilation are explored: a multiphysics ensemble in which each member’s forecast is based on a distinct suite of physical parameterization, and stochastic kinetic energy backscatter in which small noise terms are included in the forecast model equations. These two model error techniques are compared with a baseline experiment that includes spatially and temporally adaptive covariance inflation, in a domain over the continental United States using the Weather Research and Forecasting (WRF) Model for mesoscale ensemble forecasts and the Data Assimilation Research Testbed (DART) for the ensemble Kalman filter. Verification against independent observations and Rapid Update Cycle (RUC) 13-km analyses for the month of June 2008 showed that including the model error representation improved not only the analysis ensemble, but also short-range forecasts initialized from these analyses. Explicitly accounting for model uncertainty led to a better-tuned ensemble spread, a more skillful ensemble mean, and higher probabilistic scores, as well as significantly reducing the need for inflation. In particular, the stochastic backscatter scheme consistently outperformed both the multiphysics approach and the control run with adaptive inflation over almost all levels of the atmosphere both deterministically and probabilistically.

Intercomparison of laser Doppler wind measurements with other methods and a forecast model

1998 ◽  
Vol 7 (6) ◽  
pp. 1473-1487 ◽  
Author(s):  
Christian Werner ◽  
Stephan Rahm ◽  
Susanne Lehner ◽  
Michael Buchhold ◽  
Victor Banakh ◽  
...  
Keyword(s):  
Forecast Model ◽  
Laser Doppler ◽  
Wind Measurements

scholarly journals Simulated or measured soil moisture: Which one is adding more value to regional landslide early warning?

2021 ◽  
Author(s):  
Adrian Wicki ◽  
Per-Erik Jansson ◽  
Peter Lehmann ◽  
Christian Hauck ◽  
Manfred Stähli
Keyword(s):  
Soil Moisture ◽  
Early Warning ◽  
Prediction Models ◽  
Numerical Models ◽  
Forecast Model ◽  
Early Warning Systems ◽  
Transfer Model ◽  
Statistical Framework ◽  
Landslide Early Warning

Abstract. The inclusion of soil wetness information in empirical landslide prediction models was shown to improve the forecast goodness of regional landslide early warning systems (LEWS). However, it is still unclear which source of information – numerical models or in-situ measurements – are of higher value for this purpose. In this study, soil moisture dynamics at 133 grassland sites in Switzerland were simulated for the period of 1981 to 2019 using a physically-based 1D soil moisture transfer model (CoupModel). A common parametrization set was defined for all sites except for site-specific soil hydrological properties, and the model performance was assessed at a subset of 14 sites where in-situ soil moisture measurements were available on the same plot. A previously developed statistical framework was applied to fit an empirical landslide forecast model, and ROC analysis was used to assess the forecast goodness. To assess the sensitivity of the landslide forecasts, the statistical framework was applied to different CoupModel parametrizations, to various distances between simulation sites and landslides, and to measured soil moisture from a subset of 35 sites for comparison with a measurement-based forecast model. We found that (i) simulated soil moisture is a skilful predictor for regional landslide activity, (ii) that it is sensitive to the formulation of the upper and lower boundary conditions, and (iii) that the information content is strongly distance-dependent. Compared to a measurement-based landslide forecast model, the model-based forecast performs better as the homogenization of hydrological processes and the site representation can lead to a better representation of triggering event conditions. However, it is limited in reproducing critical antecedent saturation conditions due to an inadequate representation of the long-term water storage.

scholarly journals Doppler Aerosol WiNd (DAWN) Lidar during CPEX 2017: Instrument Performance and Data Utility

2020 ◽  
Vol 12 (18) ◽  
pp. 2951 ◽  
Author(s):  
Steven Greco ◽  
George D. Emmitt ◽  
Michael Garstang ◽  
Michael Kavaya
Keyword(s):  
Horizontal Wind ◽  
Tropical Atmosphere ◽  
Velocity Measurements ◽  
Data Set ◽  
Data Utility ◽  
Convective Processes ◽  
Wind Measurements ◽  
Data Coverage ◽  
Wind Profiles ◽  
Active Convection

During 25 May–24 June 2017, NASA’s Doppler Aerosol WiNd (DAWN) lidar was flown on board a NASA DC-8 aircraft as part of the Convective Processes EXperiment (CPEX) airborne campaign based out of Ft. Lauderdale, FL. Central to DAWN’s deployment was the goal of obtaining high time and spatial resolution wind velocity measurements, particularly with respect to the convective life cycle. We describe the processes involved in deriving wind profiles from DAWN observations and evaluate the performance of DAWN in terms of data coverage, resolution and frequency. Comparisons with dropsonde wind measurements show an overall low bias of <0.20 m/s with a RMSD of ~1.6 and R2 > 0.92 for both u and v components for the data set as a whole (over 160 comparisons). From this CPEX experience, we find that the DAWN wind profiles are of high precision, ~30 m vertical resolution and with horizontal spacing as fine as 3–7 km, and rival dropsondes for horizontal wind coverage (aerosols and clouds permitting). Case studies illustrate the benefit of using the DAWN to investigate and characterize the dynamics of the tropical atmosphere over open ocean waters in conditions ranging from undisturbed to active convection.

scholarly journals Loop Current and Deep Eddies

2008 ◽  
Vol 38 (7) ◽  
pp. 1426-1449 ◽  
Author(s):  
L-Y. Oey
Keyword(s):  
Baroclinic Instability ◽  
Subsequent Development ◽  
Current Mode ◽  
Dominant Mode ◽  
Loop Current ◽  
The Waves ◽  
Topographic Rossby Waves ◽  
Vertical Scales ◽  
Mode A ◽  
Deep Eddies

Abstract In contrast to the Loop Current and rings, much less is known about deep eddies (deeper than 1000 m) of the Gulf of Mexico. In this paper, results from a high-resolution numerical model of the Gulf are analyzed to explain their origin and how they excite topographic Rossby waves (TRWs) that disperse energy to the northern slopes of the Gulf. It is shown that north of Campeche Bank is a fertile ground for the growth of deep cyclones by baroclinic instability of the Loop Current. The cyclones have horizontal (vertical) scales of about 100 km (1000∼2000 m) and swirl speeds ∼0.3 m s−1. The subsequent development of these cyclones consists of two modes, A and B. Mode-A cyclones evolve into the relatively well-known frontal eddies that propagate around the Loop Current. Mode-A cyclone can amplify off the west Florida slope and cause the Loop Current to develop a “neck” that sometimes leads to shedding of a ring; this process is shown to be the Loop Current’s dominant mode of upper-to-deep variability. Mode-B cyclones are “shed” and propagate west-northwestward at speeds of about 2–6 km day−1, often in concert with an expanding loop or a migrating ring. TRWs are produced through wave–eddy coupling originating primarily from the cyclone birthplace as well as from the mode-B cyclones, and second, but for longer periods of 20∼30 days only, also from the mode-A frontal eddies. The waves are “channeled” onto the northern slope by a deep ridge located over the lower slope. For very short periods (≲10 days), the forcing is a short distance to the south, which suggests that the TRWs are locally forced by features that have intruded upslope and that most likely have accompanied the Loop Current or a ring.

scholarly journals Simulations of Polarimetric, X-Band Radar Signatures in Supercells. Part I: Description of Experiment and Simulated ρhv Rings

2017 ◽  
Vol 56 (7) ◽  
pp. 1977-1999 ◽  
Author(s):  
Jeffrey C. Snyder ◽  
Howard B. Bluestein ◽  
Daniel T. Dawson II ◽  
Youngsun Jung
Keyword(s):  
Cross Correlation ◽  
Numerical Models ◽  
Time Lag ◽  
Polarimetric Radar ◽  
Microphysics Scheme ◽  
Convective Storms ◽  
X Band ◽  
Model Configuration ◽  
Wind Profiles ◽  
Forward Operator

AbstractWith the development of multimoment bulk microphysical schemes and polarimetric radar forward operators, one can better examine convective storms simulated in high-resolution numerical models from a simulated polarimetric radar perspective. Subsequently, relationships between observable and unobservable quantities can be examined that may provide useful information about storm intensity and organization that otherwise would be difficult to obtain. This paper, Part I of a two-part sequence, describes the bulk microphysics scheme, polarimetric radar forward operator, and numerical model configuration used to simulate supercells in eight idealized, horizontally homogenous environments with different wind profiles. The microphysical structure and evolution of copolar cross-correlation coefficient (ρhv) rings associated with simulated supercells are examined in Part I, whereas Part II examines ZDR columns, ZDR rings, and KDP columns. In both papers, some systematic differences between the signature seen at X and S bands are discussed. The presence of hail is found to affect ρhv much more at X band than at S band (and is found to affect ZDR more at S band than at X band), which corroborates observations. The ρhv half ring is found to be associated with the presence of large, sometimes wet, hail aloft, with an ~20-min time lag between increases in the size of the ρhv ring aloft and the occurrence of a large amount of hail near the ground in some simulations.

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