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Nathan Yacowar, Louis Garand, and Michel Houde

Sunshine data are received daily on a real time basis at the Quebec Forecast Office and are converted into terms of percent of possible sunshine. On the basis of a survey held in the Quebec Forecast Office, the percent of possible sunshine was associated with the plain language expressions used to describe sky cover. Using this relationship, it was possible to use these observed sunshine reports to objectively verify the worded forecasts of sky cover. Techniques are being developed to forecast the percent of possible sunshine to be used as guidance in our public forecasts.

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B. I. Miller and P. L. Moore

Using a grid system, geostrophic components are computed around tropical cyclones at the 700-, 500-, and 300-mb levels. These components are compared with the subsequent 24-hr motion of the cyclone. The 700- and 500-mb charts appear to be equally good in forecasting hurricane motion. Both are better than the 300-mb level. By combining the 700-mb geostrophic components with the motion of the cyclone during the previous 12 hr, a 24-hr forecast technique is developed. This combination appears to result in slightly better forecasts than from standard techniques now being applied at sea-level and at 500-mb.

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R. J. Barthelmie, P. Crippa, H. Wang, C. M. Smith, R. Krishnamurthy, A. Choukulkar, R. Calhoun, D. Valyou, P. Marzocca, D. Matthiesen, G. Brown, and S. C. Pryor

. Experimental site. The measurement and analysis techniques developed within this project will be deployed for offshore wind characterization, but the initial experiment described herein was undertaken at a wind farm in northern Indiana (7–20 May 2012, inclusive). The instrumentation deployed is described in detail below and summarized in Table 1 . Confidentiality agreements preclude full disclosure of the experimental location but, as shown in the schematic in Fig. 2 , the majority of instruments were

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Lloyd W. Vanderman

During the 1959 season for tropical storms in the Northern Hemisphere, Joint Numerical Weather Prediction (JNWP) Unit computed operationally one or more forecast tracks for 11 hurricanes and tropical storms and 11 typhoons. The 500-mb barotropic forecast flow with the tropical vortex eliminated from the initial 500-mb analysis was employed as the steering current in obtaining these forecasts. A summary of 1959 forecasts and a table of verification of JNWP hurricane forecasts for the years 1956 through 1959 are presented. The improvement and deterioration in forecasts from one year to the next are discussed in terms of sample size, operational changes, and analysis and forecasting techniques specifically designed for forecasting trajectories of tropical cyclones.

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Joshua Soderholm, Hamish McGowan, Harald Richter, Kevin Walsh, Tammy Weckwerth, and Matthew Coleman

thunderstorm frequency on a local scale has immediate applications in minimizing vulnerability and refining disaster preparedness. Within Australia, severe thunderstorms alone account for the greatest insured losses among all natural disasters, where damages from individual events often exceed 1 billion Australian dollars (AUD) ( Australian Emergency Management Institute 2014 ). For a regional forecaster, an understanding of processes that create climatological thunderstorm hotspots is critical for

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Clemens Simmer, Insa Thiele-Eich, Matthieu Masbou, Wulf Amelung, Heye Bogena, Susanne Crewell, Bernd Diekkrüger, Frank Ewert, Harrie-Jan Hendricks Franssen, Johan Alexander Huisman, Andreas Kemna, Norbert Klitzsch, Stefan Kollet, Matthias Langensiepen, Ulrich Löhnert, A. S. M. Mostaquimur Rahman, Uwe Rascher, Karl Schneider, Jan Schween, Yaping Shao, Prabhakar Shrestha, Maik Stiebler, Mauro Sulis, Jan Vanderborght, Harry Vereecken, Jan van der Kruk, Guido Waldhoff, and Tanja Zerenner

state variables highly challenging tasks. Improving our understanding and prediction capabilities of the terrestrial system therefore requires measurement techniques that allow us to characterize and monitor the spatiotemporal evolution of system properties across scales, terrestrial system model platforms that include all relevant processes, and state variable assimilation and parameter estimation methods. The related challenges, which were very thoughtfully summarized and discussed by Lyon et al

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Julian Adem and William L. Donn

A long-range forecasting technique, based on a physical model that emphasizes thermodynamics, is applied to the prediction of anomalies of temperature and precipitation for the Northern Hemisphere. Monthly forecasts are initialized with the sea surface temperature, 700 mb temperature and surface albedo, including variable snow-ice conditions. Application to the hot spell and drought in the summer of 1980 for the contiguous United States shows very encouraging skill when verified for the standard 100-station NOAA grid.

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Merle J. Brown and Kenneth C. Tillotson

Objective forecasting techniques are established for the average value of the ceiling at Denver of duration of three consecutive hourly observations or longer during the 6-hour period beginning 6 hours after the surface map used. Due to marked diurnal effects on ceilings and forecast variables, the study is divided into two parts: one for the forecast period 1130–1730M from the 0530M map, and the other for the forecast period 2330–0530M from the 1730M map. Persistence of ceiling at forecast preparation time (0830M and 2030M) shows such a strong relationship to subsequent ceiling occurrences that rather extreme stratification is necessary to improve upon a simple “persistence” forecast.

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William H. Klein and Harry R. Glahn

Experience over the past decade has shown that objective forecasts of local weather elements can best be obtained by using statistical methods to complement the raw output of numerical prediction models. One of the most successful techniques for accomplishing this is called Model Output Statistics (MOS). The MOS method involves matching observations of local weather with output from numerical models. Forecast equations are then derived by statistical techniques such as screening regression, regression estimation of event probabilities, and the logit model. In this way the bias and inaccuracy of the numerical model, as well as the local climatology, can be built into the forecast system. MOS has been applied by the Techniques Development Laboratory to produce automated forecasts of numerous weather elements including precipitation, temperature, wind, clouds, ceiling, visibility, and thunderstorms. In this paper, the derivation and operational application of MOS forecasts for each of these elements are discussed. Many of the products are transmitted nationwide over facsimile and/or teletypewriter; others are provided for internal use within the National Weather Service. Ultimately, a completely automated, computer-worded, local weather forecast will be produced routinely as part of a program for Automation of Field Operations and Services (AFOS).

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Russ S. Schumacher, Aaron J. Hill, Mark Klein, James A. Nelson, Michael J. Erickson, Sarah M. Trojniak, and Gregory R. Herman


Excessive rainfall is difficult to forecast, and there is a need for tools to aid Weather Prediction Center (WPC) forecasters when generating Excessive Rainfall Outlooks (EROs), which are issued for the contiguous United States at lead times of 1–3 days. To address this need, a probabilistic forecast system for excessive rainfall, known as the Colorado State University-Machine Learning Probabilities (CSU-MLP) system, was developed based on ensemble reforecasts, precipitation observations, and machine learning algorithms, specifically random forests. The CSU-MLP forecasts were designed to emulate the EROs, with the goal being a tool that forecasters can use as a “first guess” in the ERO forecast process. Resulting from close collaboration between CSU and WPC and evaluation at the Flash Flood and Intense Rainfall experiment, iterative improvements were made to the forecast system and it was transitioned into operational use at WPC. Quantitative evaluation shows that the CSU-MLP forecasts are skillful and reliable, and they are now being used as a part of the WPC forecast process. This project represents an example of a successful research-to-operations transition, and highlights the potential for machine learning and other post-processing techniques to improve operational predictions.

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