Methods for Improvement of Drift Forecast Models

2010 ◽  
Author(s):  
Christophe Maisondieu ◽  
O̸yvind Breivik ◽  
Jens-Christian Roth ◽  
Arthur A. Allen ◽  
Bertrand Forest ◽  
...  
Keyword(s):  
Field Experiments ◽  
Prediction Models ◽  
Weather Prediction ◽  
The United States ◽  
Search And Rescue ◽  
Environmental Data ◽  
Fishing Vessels ◽  
Forecast Models ◽  
Definition Of

Over the past decades, various operational drift forecast models were developed for trajectory prediction of objects lost at sea for search and rescue operations. Most of these models are now based on a stochastic, Monte Carlo definition of the object’s initial position and its time-evolving search area through computation of an ensemble of equally probable trajectories (Breivik [1]). Uncertainties in environmental forcing, mainly surface currents and wind, as well as the uncertainties inherent in the simplified computation of leeway speed and direction relative to the wind are also accounted for through this ensemble-based approach. Accuracy of the drift forecast obviously depends to a large extent on the quality of the environmental forecast data provided by numerical weather prediction models and ocean models, but it also depends on the level of uncertainty associated with the estimation of the drift properties (leeway) of the objects themselves. The present work mostly focuses on this second aspect of the problem. Drift properties of objects can be described by means of their downwind and crosswind leeway coefficients, according to the definition of leeway as stated by Allen [2, 3]. Assessment of the leeway coefficients is based on a direct method, which requires measurements acquired during field tests. Such field experiments basically entail deploying one or more objects at sea and simultaneously recording the environmental parameters (namely wind speed and motion of the object relative to the ambient water masses, i.e., its leeway) as well as the object’s position while adrift for periods ranging from several hours to several days. Using this method, a large database providing leeway coefficients for more than sixty object classes ranging from medical waste to a person-in-water to small fishing vessels was compiled over the years by the United States Coast Guard (Allen [2]). More recently additional trials were conducted, which allowed evaluation of new objects, including 20-ft shipping containers. We present in this paper the methods and analysis procedures for field determination of leeway coefficients of typical search-and-rescue objects. As an example we present the case study of a 20-ft container and discuss results obtained from a drift forecast model assessing sensitivity of such a model to the quality of environmental data as well as uncertainty levels of some reference parameters.

scholarly journals The Wind Forecast Improvement Project (WFIP): A Public–Private Partnership Addressing Wind Energy Forecast Needs

2015 ◽  
Vol 96 (10) ◽  
pp. 1699-1718 ◽  
Author(s):  
James Wilczak ◽  
Cathy Finley ◽  
Jeff Freedman ◽  
Joel Cline ◽  
Laura Bianco ◽  
...  
Keyword(s):  
Wind Energy ◽  
Wind Power ◽  
Prediction Models ◽  
Weather Prediction ◽  
Substantial Reduction ◽  
Energy Resource ◽  
The United States ◽  
Department Of Energy ◽  
Improvement Project ◽  
Forecast Models

Abstract The Wind Forecast Improvement Project (WFIP) is a public–private research program, the goal of which is to improve the accuracy of short-term (0–6 h) wind power forecasts for the wind energy industry. WFIP was sponsored by the U.S. Department of Energy (DOE), with partners that included the National Oceanic and Atmospheric Administration (NOAA), private forecasting companies (WindLogics and AWS Truepower), DOE national laboratories, grid operators, and universities. WFIP employed two avenues for improving wind power forecasts: first, through the collection of special observations to be assimilated into forecast models and, second, by upgrading NWP forecast models and ensembles. The new observations were collected during concurrent year-long field campaigns in two high wind energy resource areas of the United States (the upper Great Plains and Texas) and included 12 wind profiling radars, 12 sodars, several lidars and surface flux stations, 184 instrumented tall towers, and over 400 nacelle anemometers. Results demonstrate that a substantial reduction (12%–5% for forecast hours 1–12) in power RMSE was achieved from the combination of improved numerical weather prediction models and assimilation of new observations, equivalent to the previous decade’s worth of improvements found for low-level winds in NOAA/National Weather Service (NWS) operational weather forecast models. Data-denial experiments run over select periods of time demonstrate that up to a 6% improvement came from the new observations. Ensemble forecasts developed by the private sector partners also produced significant improvements in power production and ramp prediction. Based on the success of WFIP, DOE is planning follow-on field programs.

ACCOMPLISHMENTS OF NOAA’S AIRBORNE HURRICANE FIELD PROGRAM AND A BROADER FUTURE APPROACH TO FORECAST IMPROVEMENT

2021 ◽  
pp. 1-79
Author(s):  
Jonathan Zawislak ◽  
Robert F. Rogers ◽  
Sim D. Aberson ◽  
Ghassan J. Alaka ◽  
George Alvey ◽  
...  
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Integrated Approach ◽  
Environmental Modeling ◽  
Research Division ◽  
Forecast Models ◽  
Operational Models ◽  
National Hurricane Center ◽  
Operations Center ◽  
Aircraft Data

AbstractSince 2005, NOAA has conducted the annual Intensity Forecasting Experiment (IFEX), led by scientists from the Hurricane Research Division at NOAA’s Atlantic Oceanographic andMeteorological Laboratory. They partner with NOAA’s Aircraft Operations Center, who maintain and operate the WP-3D and G-IV Hurricane Hunter aircraft, and NCEP’s National Hurricane Center and Environmental Modeling Center, who task airborne missions to gather data used by forecasters for analysis and forecasting and for ingest into operational numerical weather prediction models. The goal of IFEX is to improve tropical cyclone (TC) forecasts using an integrated approach of analyzing observations from aircraft, initializing and evaluating forecast models with those observations, and developing new airborne instrumentation and observing strategies targeted at filling observing gaps and maximizing the data’s impact in model forecasts. This summary article not only highlights recent IFEX contributions towards improved TC understanding and prediction, but also reflects more broadly on the accomplishments of the program during the 16 years of its existence. It describes how IFEX addresses high-priority forecast challenges, summarizes recent collaborations, describes advancements in observing systems monitoring structure and intensity, as well as in assimilation of aircraft data into operational models, and emphasizes key advances in understanding of TC processes, particularly those that lead to rapid intensification. The article concludes by laying the foundation for the “next generation” of IFEX as it broadens its scope to all TC hazards, particularly rainfall, storm-surge inundation, and tornadoes, that have gained notoriety during the last few years after several devastating landfalling TCs.

scholarly journals Low-Atmosphere Drifting Balloons: Platforms for Environment Monitoring and Forecast Improvement

2016 ◽  
Vol 97 (9) ◽  
pp. 1583-1599 ◽  
Author(s):  
A. Doerenbecher ◽  
C. Basdevant ◽  
P. Drobinski ◽  
P. Durand ◽  
C. Fesquet ◽  
...  
Keyword(s):  
Field Experiments ◽  
Prediction Models ◽  
Hydrological Cycle ◽  
Weather Prediction ◽  
Observation System ◽  
Convective Systems ◽  
Leading Role ◽  
Space Agency ◽  
In Situ Investigation ◽  
The Mediterranean

Abstract Balloons are one of the key observing platforms for the atmosphere. Radiosounding is the most commonly used technique and provides over a thousand vertical profiles worldwide every day. These data represent an essential cornerstone of data assimilation for numerical weather prediction systems. Although less common (but equally interesting for the in situ investigation of the atmosphere), drifting boundary layer pressurized balloons (BLPBs) offer rare observational skills. These balloons collect meteorological and/or chemical measurements at isopycnal height as they drift in a quasi-Lagrangian way. The BLPB system presented in this paper was developed by the French Space Agency [Centre National d’Études Spatiales (CNES)] and has been used in field experiments focusing on precipitation in Africa [African Monsoon Multiscale Analysis (AMMA)] and the Mediterranean [Hydrological Cycle in the Mediterranean Experiment (HyMeX)] as well as on air pollution in India [Indian Ocean Experiment (INDOEX)] and the Mediterranean [Transport a Longue Distance et Qualite de l’Air dans le bassin Méditerraneen (TRAQA) and Chemistry–Aerosol Mediterranean Experiment (ChArMeX)]. One important advantage of BLPBs is their capability to explore the lowest layers of the atmosphere above the oceans, areas that remain difficult to access. BLPB had a leading role in a complex adaptive observation system for the forecast of severe precipitation events. These balloons collected data in the marine environment of convective systems, which were assimilated in real time to improve the knowledge of the state of the atmosphere in the numerical prediction models of Météo-France.

scholarly journals Early Season Growth, Yield, and Fruit Quality of Standard and Mini Watermelon Grafted onto Several Commercially Available Cucurbit Rootstocks

2018 ◽  
Vol 28 (4) ◽  
pp. 459-469 ◽  
Author(s):  
Matthew B. Bertucci ◽  
Katherine M. Jennings ◽  
David W. Monks ◽  
Jonathan R. Schultheis ◽  
Penelope Perkins-Veazie ◽  
...  
Keyword(s):  
Leaf Area ◽  
Fruit Quality ◽  
Field Experiments ◽  
Growth Yield ◽  
The United States ◽  
Marketable Yield ◽  
Early Season ◽  
Flesh Firmness ◽  
Lycopene Content

Grafting watermelon (Citrullus lanatus) is a common practice in many parts of the world and has recently received increased interest in the United States. The present study was designed to evaluate early season growth, yield, and fruit quality of watermelon in response to grafting and in the absence of known disease pressure in a fumigated system. Field experiments were conducted using standard and mini watermelons (cv. Exclamation and Extazy, respectively) grafted onto 20 commercially available cucurbit rootstocks representing four species: giant pumpkin (Cucurbita maxima), summer squash (Cucurbita pepo), bottle gourd (Lagenaria siceraria), and interspecific hybrid squash [ISH (C. maxima × Cucurbita moschata)]. Nongrafted ‘Exclamation’ and ‘Extazy’ were included as controls. To determine early season growth, leaf area was measured at 1, 2, and 3 weeks after transplant (WAT). At 1 WAT, nongrafted ‘Exclamation’ produced the smallest leaf area; however, at 3 WAT, nongrafted ‘Exclamation’ produced the largest leaf area in 2015, and no differences were observed in 2016. Leaf area was very similar among rootstocks in the ‘Extazy’ study, with minimal differences observed. Marketable yield included fruit weighing ≥9 and ≥3 lb for ‘Exclamation’ and ‘Extazy’, respectively. In the ‘Exclamation’ study, highest marketable yields were observed in nongrafted ‘Exclamation’, and ‘Exclamation’ grafted to ‘Pelops’, ‘TZ148’, and ‘Coloso’, and lowest marketable yields were observed when using ‘Marvel’ and ‘Kazako’ rootstocks, which produced 47% and 32% of nongrafted ‘Exclamation’ yield, respectively. In the ‘Extazy’ study, the highest marketable yield was observed in nongrafted ‘Extazy’, and ‘Kazako’ produced the lowest yields (48% of nongrafted ‘Extazy’). Fruit quality was determined by measuring fruit acidity (pH), soluble solids concentration (SSC), lycopene content, and flesh firmness from a sample of two fruit from each plot from the initial two harvests of each year. Across both studies, rootstock had no effect on SSC or lycopene content. As reported in previous studies, flesh firmness was increased as a result of grafting, and nongrafted ‘Exclamation’ and ‘Extazy’ had the lowest flesh firmness among standard and mini watermelons, respectively. The present study evaluated two scions with a selection of 20 cucurbit rootstocks and observed no benefits in early season growth, yield, or phytonutrient content. Only three of 20 rootstocks in each study produced marketable yields similar to the nongrafted treatments, and no grafted treatment produced higher yields than nongrafted ‘Exclamation’ or ‘Extazy’. Because grafted seedlings have an associated increase in cost and do not produce increased yields, grafting in these optimized farming systems and using fumigated soils does not offer an advantage in the absence of soilborne pathogens or other stressors that interfere with watermelon production.

Corporatization and Deprivatization of Health Services in Canada

1987 ◽  
Vol 17 (4) ◽  
pp. 567-584 ◽  
Author(s):  
Bruce J. Fried ◽  
Raisa B. Deber ◽  
Peggy Leatt
Keyword(s):  
Health Services ◽  
Structural Changes ◽  
Current System ◽  
The United States ◽  
Universal Health Insurance ◽  
Socioeconomic Groups ◽  
Government Involvement ◽  
Definition Of ◽  
Universal Health

Canada's system of health services has been shaped by the forces and values in the Canadian political, cultural, social, and economic environment; these forces continue to place constraints on future changes. We distinguish between “corporatization” and “privatization,” and the implications of each for improved efficiency of the system. Although the organization of health services is, in certain provinces, undergoing significant structural changes, there is evidence that rather than privatizing, the system may actually be continuing to experience what we have termed deprivatization, as the scope of government involvement expands to include a more comprehensive definition of health care. Trends in Canada differ considerably from those in the United States; universal health insurance has curbed the ability and desire of institutions to exclude members of some socioeconomic groups from receiving care. U.S.-based models, if applied to Canada, could lead to both higher costs and lower quality of care. Considerable efficiencies can be realized within Canada's current system.

scholarly journals Driftsondes: Providing In Situ Long-Duration Dropsonde Observations over Remote Regions

2013 ◽  
Vol 94 (11) ◽  
pp. 1661-1674 ◽  
Author(s):  
Stephen A. Cohn ◽  
Terry Hock ◽  
Philippe Cocquerez ◽  
Junhong Wang ◽  
Florence Rabier ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Field Experiments ◽  
Prediction Models ◽  
Weather Prediction ◽  
Lower Boundary ◽  
Remote Sensors ◽  
Long Duration ◽  
Level Data ◽  
Research Aircraft

Constellations of driftsonde systems— gondolas floating in the stratosphere and able to release dropsondes upon command— have so far been used in three major field experiments from 2006 through 2010. With them, high-quality, high-resolution, in situ atmospheric profiles were made over extended periods in regions that are otherwise very difficult to observe. The measurements have unique value for verifying and evaluating numerical weather prediction models and global data assimilation systems; they can be a valuable resource to validate data from remote sensing instruments, especially on satellites, but also airborne or ground-based remote sensors. These applications for models and remote sensors result in a powerful combination for improving data assimilation systems. Driftsondes also can support process studies in otherwise difficult locations—for example, to study factors that control the development or decay of a tropical disturbance, or to investigate the lower boundary layer over the interior Antarctic continent. The driftsonde system is now a mature and robust observing system that can be combined with flight-level data to conduct multidisciplinary research at heights well above that reached by current research aircraft. In this article we describe the development and capabilities of the driftsonde system, the exemplary science resulting from its use to date, and some future applications.

scholarly journals A Dynamical-Statistical-Analog Ensemble Forecast Model: Theory and an Application to Heavy Rainfall Forecasts of Landfalling Tropical Cyclones

2020 ◽  
Vol 148 (4) ◽  
pp. 1503-1517 ◽  
Author(s):  
Fumin Ren ◽  
Chenchen Ding ◽  
Da-Lin Zhang ◽  
Deliang Chen ◽  
Hong-li Ren ◽  
...  
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Forecast Model ◽  
The United States ◽  
Ensemble Forecast ◽  
Initial Value ◽  
Forecast Performance ◽  
Typhoon Track ◽  
Perfect Model ◽  
Accumulated Rainfall

Abstract Combining dynamical models with statistical algorithms is an important way to improve weather and climate prediction. In this study, a concept of a perfect model, whose solutions are from observations, is introduced, and a dynamical-statistical-analog ensemble forecast (DSAEF) model is developed as an initial-value problem of the perfect model. This new analog-based forecast model consists of the following three steps: (i) construct generalized initial value (GIV), (ii) identify analogs from historical observations, and (iii) produce an ensemble of predictands. The first step includes all appropriate variables, not only at an instant state but also during their temporal evolution, that play an important role in determining the accuracy of each predictand. An application of the DSAEF model is illustrated through the prediction of accumulated rainfall associated with 21 landfalling typhoons occurring over South China during the years of 2012–16. Assuming a reliable forecast of landfalling typhoon track, two different experiments are conducted, in which the GIV is constructed by including (i) typhoon track only; and (ii) both typhoon track and landfall season. Results show overall better performance of the second experiment than the first one in predicting heavy accumulated rainfall in the training sample tests. In addition, the forecast performance of both experiments is comparable to the operational numerical weather prediction models currently used in China, the United States, and Europe. Some limitations and future improvements as well as comparisons with some existing analog ensemble models are also discussed.

scholarly journals A New Visibility Parameterization for Warm-Fog Applications in Numerical Weather Prediction Models

2006 ◽  
Vol 45 (11) ◽  
pp. 1469-1480 ◽  
Author(s):  
I. Gultepe ◽  
M. D. Müller ◽  
Z. Boybeyi
Keyword(s):  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Mesoscale Model ◽  
Weather Prediction ◽  
Liquid Water Content ◽  
Parameterization Scheme ◽  
Numerical Weather ◽  
Time Period ◽  
Simulation Domain ◽  
Forecast Models

Abstract The objective of this work is to suggest a new warm-fog visibility parameterization scheme for numerical weather prediction (NWP) models. In situ observations collected during the Radiation and Aerosol Cloud Experiment, representing boundary layer low-level clouds, were used to develop a parameterization scheme between visibility and a combined parameter as a function of both droplet number concentration Nd and liquid water content (LWC). The current NWP models usually use relationships between extinction coefficient and LWC. A newly developed parameterization scheme for visibility, Vis = f (LWC, Nd), is applied to the NOAA Nonhydrostatic Mesoscale Model. In this model, the microphysics of fog was adapted from the 1D Parameterized Fog (PAFOG) model and then was used in the lower 1.5 km of the atmosphere. Simulations for testing the new parameterization scheme are performed in a 50-km innermost-nested simulation domain using a horizontal grid spacing of 1 km centered on Zurich Unique Airport in Switzerland. The simulations over a 10-h time period showed that visibility differences between old and new parameterization schemes can be more than 50%. It is concluded that accurate visibility estimates require skillful LWC as well as Nd estimates from forecasts. Therefore, the current models can significantly over-/underestimate Vis (with more than 50% uncertainty) depending on environmental conditions. Inclusion of Nd as a prognostic (or parameterized) variable in parameterizations would significantly improve the operational forecast models.

scholarly journals Intercomparison of In Situ Sensors for Ground-Based Land Surface Temperature Measurements

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5268
Author(s):  
Praveena Krishnan ◽  
Tilden P. Meyers ◽  
Simon J. Hook ◽  
Mark Heuer ◽  
David Senn ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Field Experiments ◽  
Prediction Models ◽  
Weather Prediction ◽  
Surface Air Temperature ◽  
Near Surface ◽  
Oak Ridge

Land surface temperature (LST) is a key variable in the determination of land surface energy exchange processes from local to global scales. Accurate ground measurements of LST are necessary for a number of applications including validation of satellite LST products or improvement of both climate and numerical weather prediction models. With the objective of assessing the quality of in situ measurements of LST and to evaluate the quantitative uncertainties in the ground-based LST measurements, intensive field experiments were conducted at NOAA’s Air Resources Laboratory (ARL)’s Atmospheric Turbulence and Diffusion Division (ATDD) in Oak Ridge, Tennessee, USA, from October 2015 to January 2016. The results of the comparison of LSTs retrieved by three narrow angle broadband infrared temperature sensors (IRT), hemispherical longwave radiation (LWR) measurements by pyrgeometers, forward looking infrared camera with direct LSTs by multiple thermocouples (TC), and near surface air temperature (AT) are presented here. The brightness temperature (BT) measurements by the IRTs agreed well with a bias of <0.23 °C, and root mean square error (RMSE) of <0.36 °C. The daytime LST(TC) and LST(IRT) showed better agreement (bias = 0.26 °C and RMSE = 0.67 °C) than with LST(LWR) (bias > 1.1 and RMSE > 1.46 °C). In contrast, the difference between nighttime LSTs by IRTs, TCs, and LWR were <0.47 °C, whereas nighttime AT explained >81% of the variance in LST(IRT) with a bias of 2.64 °C and RMSE of 3.6 °C. To evaluate the annual and seasonal differences in LST(IRT), LST(LWR) and AT, the analysis was extended to four grassland sites in the USA. For the annual dataset of LST, the bias between LST (IRT) and LST (LWR) was <0.7 °C, except at the semiarid grassland (1.5 °C), whereas the absolute bias between AT and LST at the four sites were <2 °C. The monthly difference between LST (IRT) and LST (LWR) (or AT) reached up to 2 °C (5 °C), whereas half-hourly differences between LSTs and AT were several degrees in magnitude depending on the site characteristics, time of the day and the season.

scholarly journals Atmospheric Scale Interaction on Wintertime Intermountain West Low-Level Inversions

2010 ◽  
Vol 25 (4) ◽  
pp. 1196-1210 ◽  
Author(s):  
Robert R. Gillies ◽  
Shih-Yu Wang ◽  
Marty R. Booth
Keyword(s):  
Pacific Northwest ◽  
Prediction Models ◽  
Salt Lake ◽  
Weather Prediction ◽  
Salt Lake City ◽  
The United States ◽  
Intermountain West ◽  
The Pacific ◽  
Medium Range ◽  
Lake City

Abstract Persistent winter inversions result in poor air quality in the Intermountain West of the United States. Although the onset of an inversion is relatively easy to predict, the duration and the subsequent breakup of a persistent inversion event remains a forecasting challenge. For this reason and for this region, historic soundings were analyzed for Salt Lake City, Utah, with reanalysis and station data to investigate how persistent inversion events are modulated by synoptic and intraseasonal variabilities. The results point to a close linkage between persistent inversions and the dominant intraseasonal (30 day) mode that characterizes the winter circulation regime over the Pacific Northwest. Meteorological variables and pollution (e.g., particulate matter of ≤2.5-μm diameter, PM2.5) revealed coherent variations with this intraseasonal mode. The intraseasonal mode also modulates the characteristics of the synoptic (6 day) variability and further influences the duration of persistent inversions in the Intermountain West. The interaction between modes suggests that a complete forecast of persistent inversions is more involved and technically beyond numerical weather prediction models intended for the medium range (∼10 day). Therefore, to predict persistent inversions, the results point to the adoption of standard medium-range forecasts with a longer-term climate diagnostic approach.

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