Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Forecasting techniques”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Short-term solar radiation forecast using total sky imager via transfer learning

Ground-based sky cameras, which capture hemispherical images, have been extensively used for localized monitoring of clouds. This paper proposes a short-term forecasting approach based on transfer learning using Total Sky-Imager (TSI) images of the Southern Great Plains (SGP) site obtained from the Atmospheric Radiation Measurement (ARM) dataset. An accurate estimation of solar irradiance using TSI is key for short-term solar energy generation forecasting and optimal energy consumption planning. We make use of deep neural network architectures such as AlexNet and ResNet-101 to extract the underlying deep convolution features from TSI images and then train using an ensemble learning approach to model and forecast solar radiation. We demonstrate the performance of the proposed approach by showcasing the best and worst cases. Thus, the transfer learning approach significantly reduces the time and resources required for modeling solar radiation. We outperform with reference to another state-of-art technique for solar modeling using TSI images at different forecast lead times.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Intra-hour Solar Irradiance Forecast in Multiple Locations using Deep Transfer Learning

In recent years, solar power system installation imposes several challenges on the operations of local and regional power grids due to the inherent variability of ground-level solar irradiance. This work proposes a novel real-time solar forecast methodology for intra-hour solar irradiance based on deep transfer learning from ground-based sky imager for time horizons ranging from 5-15 min. There are three unique aspects of the proposed methodology: (1) a Deep Learning based algorithm development which is modeled as a classification approach rather than a traditional regression approach; (2) the use of the Transfer Learning technique to show generalization capability, robustness, and portability of baseline model in the newly deployed location where availability of enough data for training is typically scarce, and (3) redefinition of point-based irradiation forecast error estimation technique with a window-based one that is more intuitive and user-friendly. The system is developed using multiple years of irradiance and sky image recording in New Jersey and one-year data from Colorado, USA. The method is validated against ground telemetry from these two locations of diverse geographic and climatic conditions. Results show that the forecasting method proposed in this work is robust and highly accurate (8% MAPE error) for multiple locations deployment.

Deep Learning, Convolution Neural Networks, transf↗
NASA NTRS1989

Short-term forecasting of thunderstorms at Kennedy Space Center, based on the surface wind field

Techniques incorporating wind convergence that can be used for the short-term prediction of thunderstorm development are described. With these techniques, the convergence signal is sensed by the wind network array 15 to 90 min before actual storm development. Particular attention is given to the convergence cell technique (which has been applied at the Kennedy Space Center) where each convective region is analyzed independently. It is noted that, while the monitoring of areal and cellular convergence can be used to help locate the seeds of developing thunderstorms and pinpoint the lightning threat areas, this forecasting aid cannot be used in isolation.

Watson, Andrew I.↗
NASA NTRS1993

Next generation initiation techniques

Four-dimensional data assimilation strategies can generally be classified as either current or next generation, depending upon whether they are used operationally or not. Current-generation data-assimilation techniques are those that are presently used routinely in operational-forecasting or research applications. They can be classified into the following categories: intermittent assimilation, Newtonian relaxation, and physical initialization. It should be noted that these techniques are the subject of continued research, and their improvement will parallel the development of next generation techniques described by the other speakers. Next generation assimilation techniques are those that are under development but are not yet used operationally. Most of these procedures are derived from control theory or variational methods and primarily represent continuous assimilation approaches, in which the data and model dynamics are 'fitted' to each other in an optimal way. Another 'next generation' category is the initialization of convective-scale models. Intermittent assimilation systems use an objective analysis to combine all observations within a time window that is centered on the analysis time. Continuous first-generation assimilation systems are usually based on the Newtonian-relaxation or 'nudging' techniques. Physical initialization procedures generally involve the use of standard or nonstandard data to force some physical process in the model during an assimilation period. Under the topic of next-generation assimilation techniques, variational approaches are currently being actively developed. Variational approaches seek to minimize a cost or penalty function which measures a model's fit to observations, background fields and other imposed constraints. Alternatively, the Kalman filter technique, which is also under investigation as a data assimilation procedure for numerical weather prediction, can yield acceptable initial conditions for mesoscale models. The third kind of next-generation technique involves strategies to initialize convective scale (non-hydrostatic) models.

Warner, Tom↗
NASA NTRS2018

Introduction to Data Assimilation

Atmospheric data assimilation is a class of techniques used for producing descriptions of fields of air temperature, pressure, humidity, wind, etc. on a spatial grid or in terms of a finite functional representation. These are then used to initialize numerical weather forecasts or to analyze the atmosphere for other purposes. The techniques combine past, present, and even future observations in an approximate statistically optimal way. Various types of statistical or physically-based models and their corresponding adjoints are employed to relate diverse fields in both time and space and to relate what is observed to what is being analyzed. Computationally, the problem is very demanding and onstraining on the techniques that can be employed on a routine basis.

Atmospheric data assimilation↗
NASA NTRS2022

Space Weather Modeling and Prediction for Intermediate Time-scales

Much of solar activity within a sunspot cycle occurs as bursts, or 'seasons' of strong activity over several months, separated by periods of much less activity. The most important space weather effects occur during these bursts. Previous modeling and forecasting efforts have focused on time-scales of hours-to-days and decades-to-centuries. The recent discovery of Rossby waves in the Sun, together with recently developed global models of solar MHD Rossby waves and their interactions with differential rotation and spot-producing magnetic fields, reveal the opportunity to simulate and predict the occurrence, strength and location of enhanced activity bursts a few weeks up to several months in advance. We now have a golden opportunity to fill in this gap in time-scales of forecasting space weather. This requires a) continuous observations of solar Rossby waves by various techniques; b) development of coupled nonlinear MHD models that simulate both global Rossby waves and the much smaller spatial scale emergence of new active regions; c) application of advanced data assimilation techniques to couple surface observations to update the model-system to integrate forward in time for creating forecasts months ahead. Then it will be possible to build operational prediction models to meet the needs of customers and stakeholders, including support of future NASA missions, regarding what kind and level of space weather to expect a few weeks to several months ahead.

SMD↗
NASA NTRS2019

GMAO Seasonal Forecast Ensemble Exploration

GMAO Sub/Seasonal prediction system (S2S) has recently been upgraded. A complete set (1981-2016) of 9-months hindcasts for the previous and current versions (S2S-1.0 and S2S-2.1 respectively) allows for the evaluation of the forecast skill and a study of various characteristics of the ensemble forecasts in particular. We compared the intra-seasonal, interannual and intra-ensemble SST variability of the two systems against the observed. Focusing on the ENSO SST indices, we analyzed the consistency of the forecasts ensembles by studying rank histograms and comparing the ensemble spread with the standard error of the estimate.The S2S-2.1 ensemble appears to be more consistent with observations in Niño1+2 region compared to S2S-1.0, while in the central equatorial Pacific ocean this measure is comparably good for both systems. The S2S-1.0 system tends to be under dispersive, while the new system is under dispersive only at very short lead times, but tends to be over dispersive at long leads and for forecasts verifying in spring in Niño 3.4 region.Overall, the new system has greater skill in predicting ENSO. The evaluation techniques tested here will be applied for testing of the next generation sub/seasonal forecast system under development.

Borovikov, Anna↗
DOE OSTI2022

Unstructured clinical notes within the 24 hours since admission predict short, mid & long-term mortality in adult ICU patients

Mortality prediction for intensive care unit (ICU) patients is crucial for improving outcomes and efficient utilization of resources. Accessibility of electronic health records (EHR) has enabled data-driven predictive modeling using machine learning. However, very few studies rely solely on unstructured clinical notes from the EHR for mortality prediction. In this work, we propose a framework to predict short, mid, and long-term mortality in adult ICU patients using unstructured clinical notes from the MIMIC III database, natural language processing (NLP), and machine learning (ML) models. Depending on the statistical description of the patients’ length of stay, we define the short-term as 48-hour and 4-day period, the mid-term as 7-day and 10-day period, and the long-term as 15-day and 30-day period after admission. We found that by only using clinical notes within the 24 hours of admission, our framework can achieve a high area under the receiver operating characteristics (AU-ROC) score for short, mid and long-term mortality prediction tasks. The test AU-ROC scores are 0.87, 0.83, 0.83, 0.82, 0.82, and 0.82 for 48-hour, 4-day, 7-day, 10-day, 15-day, and 30-day period mortality prediction, respectively. We also provide a comparative study among three types of feature extraction techniques from NLP: frequency-based technique, fixed embedding-based technique, and dynamic embedding-based technique. Lastly, we provide an interpretation of the NLP-based predictive models using feature-importance scores.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1979

KAZIA: A computer system for solar-terrestrial data processing and objective prediction

A computer system for solar terrestrial data processing is described. The data base consists mainly of information contained in Ursigrams exchanged between the IUWDS Regional Warning Centers. The system structure and its functions are presented. Solar flare forecasting is described, as one of the prediction techniques incorporated in the system.

Stasiewicz, K.↗
NASA NTRS1982

Weather radar research at the USA's storm laboratory

Radar research that is directed toward improving storm forecasts and hazard warnings and studying lightning is discussed. The two moderately sensitive Doppler weather radars in central Oklahoma, with their wide dynamic range, have demonstrated the feasibility of mapping wind fields in all weather conditions from the clear skies of quiescent air and disturbed prestorm air near the earth's surface to the optically opaque interior of severe and sometimes tornadic thunderstorms. Observations and analyses of Doppler weather radar data demonstrate that improved warning of severe storm phenomena and improved short-term forecast of storms may be available when Doppler techniques are well integrated into the national network of weather radars. When used in combination with other sensors, it provides an opportunity to learn more about the complex interrelations between the wind, water, and electricity in storms.

Doviak, R. J.↗
NASA NTRS1987

Nitric acid forecast experiments

Results of two three-dimensional forecasts of the time evolution of the distribution of HNO3 in the stratosphere are reported. The first is for the February 1979 stratospheric warming, and the second is for a period in March, 1979 when the relative importance of photochemistry and dynamics is thought to be rapidly changing. The zonal mean results of the model calculations are in general qualitative agreement with the LIMS HNO3 observations. However, the calculated three-dimensional fields show significant differences from the observations. The results provide insight into what must be done to form a successful constituent forecast model and provide information on the modeling technique and the self-consistency of the observed dynamical and constituent fields.

Rood, Richard B.↗
NASA NTRS1999

Neuroprotective "agents" in surgery. Secret "agent" man, or common "agent" machine?

The search for clinically-effective neuroprotective agents has received enormous support in recent years--an estimated $200 million by pharmaceutical companies on clinical trials for traumatic brain injury alone. At the same time, the pathophysiology of brain injury has proved increasingly complex, rendering the likelihood of a single agent "magic bullet" even more remote. On the other hand, great progress continues with technology that makes surgery less invasive and less risky. One example is the application of endovascular techniques to treat coronary artery stenosis, where both the invasiveness of sternotomy and the significant neurological complication rate (due to microemboli showering the cerebral vasculature) can be eliminated. In this paper we review aspects of intraoperative neuroprotection both present and future. Explanations for the slow progress on pharmacologic neuroprotection during surgery are presented. Examples of technical advances that have had great impact on neuroprotection during surgery are given both from coronary artery stenosis surgery and from surgery for Parkinson's disease. To date, the progress in neuroprotection resulting from such technical advances is an order of magnitude greater than that resulting from pharmacologic agents used during surgery. The progress over the last 20 years in guidance during surgery (CT and MRI image-guidance) and in surgical access (endoscopic and endovascular techniques) will soon be complemented by advances in our ability to evaluate biological tissue intraoperatively in real-time. As an example of such technology, the NASA Smart Probe project is considered. In the long run (i.e., in 10 years or more), pharmacologic "agents" aimed at the complex pathophysiology of nervous system injury in man will be the key to true intraoperative neuroprotection. In the near term, however, it is more likely that mundane "agents" based on computers, microsensors, and microeffectors will be the major impetus to improved intraoperative neuroprotection.

Review Literature↗
NASA NTRS2021

Bias-Correcting Carbon Fluxes Derived From Land-Surface Satellite Data for Retrospective and Near Real-Time Assimilation Systems

The ability to monitor and understand natural and anthropogenic variability in atmospheric carbon dioxide(CO2) is a growing need of many stakeholders across the world. Systems that assimilate satellite observations, given their short latency and dense spatial coverage, into high resolution global models are valuable, if not essential, tools for addressing this need. A notable drawback of modern assimilation systems is the long latency of many vital in-put datasets, e.g., inventories, in situ measurements, and re-processed remote-sensing data can trail the current date by months to years. This paper describes techniques for bias-correcting surface fluxes derived from satellite observations of the Earth’s surface to be consistent with constraints from inventories and in situ CO2 datasets. The techniques are applicable in both short-term forecasts and retrospective simulations, thus taking advantage of the coverage and short latency of satellite data while reproducing the major features of long-term inventory and in situ records. Our approach begins with a standard collection of diagnostic fluxes which incorporate a variety of remote-sensing driver data, viz. vegetation indices, fire radiative power, and nighttime lights. We then apply an empirical sink so that global budgets of the diagnostic fluxes match given atmospheric and oceanic growth rates for each year. This step removes coherent, systematic flux errors that produce biases inCO2which mask the signals an assimilation system hopes to capture. Depending on the simulation mode, the empirical sink uses different choices of atmospheric growth rates: estimates based on observations in retrospective mode and projections based on seasonal fore-casts of sea surface temperature in forecasting mode. The retrospective fluxes, when used in simulations with NASA’s Goddard Earth Observing System (GEOS), reproduce marine boundary layer measurements with comparable skill to those using fluxes from a modern inversion system. The forecasted fluxes show promising accuracy in their application to the analysis of changes in the carbon cycle as they occur.

CO2↗
NASA NTRS2009

Recent Weather Technologies Delivered to America's Space Program by the Applied Meteorology Unit

The Applied Meteorology Unit (AMU) is a unique joint venture of NASA, the Air Force and the National Weather Service (NWS) and has been supporting the Space Program for nearly two decades. The AMU acts as a bridge between the meteorological research community and operational forecasters by developing, evaluating and transitioning new technology and techniques to improve weather support to spaceport operations at the Eastern Range (ER) and Kennedy Space Center. Its primary customers are the 45th Weather Squadron at Cape Canaveral Air Force Station (CCAFS), the Spaceflight Meteorology Group at Johnson Space Center and the National Weather Service Office in Melbourne, FL. Its products are used to support NASA's Shuttle and ELV programs as well as Department of Defense and commercial launches from the ER. Shuttle support includes landing sites beyond the ER. The AMU is co-located with the Air Force operational forecasters at CCAFS to facilitate continuous two-way interaction between the AMU and its operational customers. It is operated under a NASA, Air Force, and NWS Memorandum of Understanding (MOU) by a competitively-selected contractor. The contract, which is funded and managed by NASA, provides five full time professionals with degrees in meteorology or related fields, some of whom also have operational experience. NASA provides a Ph.D.- level NASA civil service scientist as Chief of the AMU. The AMU is tasked by its customers through a unique, nationally recognized process. The tasks are limited to development, evaluation and operational transition of technology to improve weather support to spaceport operations and providing expert advice to the customers. The MOU expressly forbids using the AMU resources to conduct operations or do basic research. The presentation will provide a brief overview of the AMU and how it is tasked by its customers to provide high priority products and services. The balance of the presentation will cover a sampling of products delivered over the last 18 years that are currently in operational use. Each example will describe the problem to be solved, the solution provided, and the operational benefits of implementing that solution.

Bauman, WIlliam, H., III↗
DOE OSTI2022

Development of Short-Term Forecasting Models Using Plant Asset Data and Feature Selection

Nuclear power plants collect and store large volumes of heterogeneous data from various components and systems. With recent advances in machine learning (ML) techniques, these data can be leveraged to develop diagnostic and short-term forecasting models to better predict future equipment condition. Maintenance operations can then be planned in advance whenever degraded performance is predicted, thus resulting in fewer unplanned outages and the optimization of maintenance activities. This enables lower maintenance costs and improves the overall economics of nuclear power. This paper focuses on developing a short-term forecasting process that leverages a feature selection process to distill large volumes of heterogeneous data and predict specific equipment parameters. A variety of feature selection methods, including Shapley Additive Explanations (SHAP) and variance inflation factor (VIF), were used to select the optimal features as inputs for three ML methods: long short-term memory (LSTM) networks, support vector regression (SVR), and random forest (RF). Each combination of model and input features was used to predict a pump bearing temperature both 1 and 24 hours in advance, based on actual plant system data. The optimal inputs for the LSTM and SVR were selected using the SHAP values, while the optimal input for the RF consisted solely of the response variable itself. Each model produced similar 1-hour-ahead predictions, with root mean square errors (RMSEs) of roughly 0.006. For the 24-hour-ahead predictions, differences could be seen between LSTM, SVR, and RF, as reflected by model performances of 0.036 +- 0.014, 0.0026 +- 0, and 0.063 +- 0.004 RMSE, respectively. As big data and continuous online monitoring become more widely available, the proposed feature selection process can be used for many applications beyond the prediction of process parameters within nuclear infrastructure.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Efficient Probabilistic Prediction and Uncertainty Quantification of Tropical Cyclone–Driven Storm Tides and Inundation

Abstract This study proposes and assesses a methodology to obtain high-quality probabilistic predictions and uncertainty information of near-landfall tropical cyclone–driven (TC-driven) storm tide and inundation with limited time and resources. Forecasts of TC track, intensity, and size are perturbed according to quasi-random Korobov sequences of historical forecast errors with assumed Gaussian and uniform statistical distributions. These perturbations are run in an ensemble of hydrodynamic storm tide model simulations. The resulting set of maximum water surface elevations are dimensionality reduced using Karhunen–Loève expansions and then used as a training set to develop a polynomial chaos (PC) surrogate model from which global sensitivities and probabilistic predictions can be extracted. The maximum water surface elevation is extrapolated over dry points incorporating energy head loss with distance to properly train the surrogate for predicting inundation. We find that the surrogate constructed with third-order PCs using elastic net penalized regression with leave-one-out cross validation provides the most robust fit across training and test sets. Probabilistic predictions of maximum water surface elevation and inundation area by the surrogate model at 48-h lead time for three past U.S. landfalling hurricanes (Irma in 2017, Florence in 2018, and Laura in 2020) are found to be reliable when compared to best track hindcast simulation results, even when trained with as few as 19 samples. The maximum water surface elevation is most sensitive to perpendicular track-offset errors for all three storms. Laura is also highly sensitive to storm size and has the least reliable prediction. Significance Statement The purpose of this study is to develop and evaluate a methodology that can be used to provide high-quality probabilistic predictions of hurricane-induced storm tide and inundation with limited time and resources. This is important for emergency management purposes during or after the landfall of hurricanes. Our results show that sampling forecast errors using quasi-random sequences combined with machine learning techniques that fit polynomial functions to the data are well suited to this task. The polynomial functions also have the benefit of producing exact sensitivity indices of storm tide and inundation to the forecasted hurricane properties such as path, intensity, and size, which can be used for uncertainty estimation. The code implementing the presented methodology is publicly available on GitHub.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2009

Transitioning NPOESS Data to Weather Offices: The SPoRT Paradigm with EOS Data

Real-time satellite information provides one of many data sources used by NWS weather forecast offices (WFOs) to diagnose current weather conditions and to assist in short-term forecast preparation. While GOES satellite data provides relatively coarse spatial resolution coverage of the continental U.S. on a 10-15 minute repeat cycle, polar orbiting imagery has the potential to provide snapshots of weather conditions at high-resolution in many spectral channels. Additionally, polar orbiting sounding data can provide additional information on the thermodynamic structure of the atmosphere in data sparse regions of at asynoptic observation times. The NASA Short-term Prediction Research and Transition (SPoRT) project has demonstrated the utility of polar orbiting MODIS and AIRS data on the Terra and Aqua satellites to improve weather diagnostics and short-term forecasting on the regional and local scales. SPoRT scientists work directly forecasters at selected WFOS in the Southern Region (SR) to help them ingest these unique data streams into their AWIPS system, understand how to use the data (through on-site and distance learn techniques), and demonstrate the utility of these products to address significant forecast problems. This process also prepares forecasters for the use of similar observational capabilities from NPOESS operational sensors. NPOESS environmental data records (EDRs) from the Visible 1 Infrared Imager I Radiometer Suite (VIIRS), the Cross-track Infrared Sounder (CrlS) and Advanced Technology Microwave Sounder (ATMS) instruments and additional value-added products produced by NESDIS will be available in near real-time and made available to WFOs to extend their use of NASA EOS data into the NPOESS era. These new data streams will be integrated into the NWs's new AWIPS II decision support tools. The AWIPS I1 system to be unveiled in WFOs in 2009 will be a JAVA-based decision support system which preserves the functionality of the existing systems and offers unique development opportunities for new data sources and applications in the Service Orientated Architecture ISOA) environment. This paper will highlight some of the SPoRT activities leading to the integration of VllRS and CrIS/ATMS data into the display capabilities of these new systems to support short-term forecasting problems at WFOs.

Jedlovec, Gary↗
NASA NTRS2010

Tool for Forecasting Cool-Season Peak Winds Across Kennedy Space Center and Cape Canaveral Air Force Station

The expected peak wind speed for the day is an important element in the daily morning forecast for ground and space launch operations at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). The 45th Weather Squadron (45 WS) must issue forecast advisories for KSC/CCAFS when they expect peak gusts for >= 25, >= 35, and >= 50 kt thresholds at any level from the surface to 300 ft. In Phase I of this task, the 45 WS tasked the Applied Meteorology Unit (AMU) to develop a cool-season (October - April) tool to help forecast the non-convective peak wind from the surface to 300 ft at KSC/CCAFS. During the warm season, these wind speeds are rarely exceeded except during convective winds or under the influence of tropical cyclones, for which other techniques are already in use. The tool used single and multiple linear regression equations to predict the peak wind from the morning sounding. The forecaster manually entered several observed sounding parameters into a Microsoft Excel graphical user interface (GUI), and then the tool displayed the forecast peak wind speed, average wind speed at the time of the peak wind, the timing of the peak wind and the probability the peak wind will meet or exceed 35, 50 and 60 kt. The 45 WS customers later dropped the requirement for >= 60 kt wind warnings. During Phase II of this task, the AMU expanded the period of record (POR) by six years to increase the number of observations used to create the forecast equations. A large number of possible predictors were evaluated from archived soundings, including inversion depth and strength, low-level wind shear, mixing height, temperature lapse rate and winds from the surface to 3000 ft. Each day in the POR was stratified in a number of ways, such as by low-level wind direction, synoptic weather pattern, precipitation and Bulk Richardson number. The most accurate Phase II equations were then selected for an independent verification. The Phase I and II forecast methods were compared using an independent verification data set. The two methods were compared to climatology, wind warnings and advisories issued by the 45 WS, and North American Mesoscale (NAM) model (MesoNAM) forecast winds. The performance of the Phase I and II methods were similar with respect to mean absolute error. Since the Phase I data were not stratified by precipitation, this method's peak wind forecasts had a large negative bias on days with precipitation and a small positive bias on days with no precipitation. Overall, the climatology methods performed the worst while the MesoNAM performed the best. Since the MesoNAM winds were the most accurate in the comparison, the final version of the tool was based on the MesoNAM winds. The probability the peak wind will meet or exceed the warning thresholds were based on the one standard deviation error bars from the linear regression. For example, the linear regression might forecast the most likely peak speed to be 35 kt and the error bars used to calculate that the probability of >= 25 kt = 76%, the probability of >= 35 kt = 50%, and the probability of >= 50 kt = 19%. The authors have not seen this application of linear regression error bars in any other meteorological applications. Although probability forecast tools should usually be developed with logistic regression, this technique could be easily generalized to any linear regression forecast tool to estimate the probability of exceeding any desired threshold . This could be useful for previously developed linear regression forecast tools or new forecast applications where statistical analysis software to perform logistic regression is not available. The tool was delivered in two formats - a Microsoft Excel GUI and a Tool Command Language/Tool Kit (Tcl/Tk) GUI in the Meteorological Interactive Data Display System (MIDDS). The Microsoft Excel GUI reads a MesoNAM text file containing hourly forecasts from 0 to 84 hours, from one model run (00 or 12 UTC). The GUI then displays e peak wind speed, average wind speed, and the probability the peak wind will meet or exceed the 25-, 35- and 50-kt thresholds. The user can display the Day-1 through Day-3 peak wind forecasts, and separate forecasts are made for precipitation and non-precipitation days. The MIDDS GUI uses data from the NAM and Global Forecast System (GFS), instead of the MesoNAM. It can display Day-1 and Day-2 forecasts using NAM data, and Day-1 through Day-5 forecasts using GFS data. The timing of the peak wind is not displayed, since the independent verification showed that none of the forecast methods performed significantly better than climatology. The forecaster should use the climatological timing of the peak wind (2248 UTC) as a first guess and then adjust it based on the movement of weather features.

Barrett, Joe H., III↗