Daran L. Rife

Probabilistic weather prediction with an analog ensemble

AbstractThis study explores an analog-based method to generate an ensemble [analog ensemble (AnEn)] in which the probability distribution of the future state of the atmosphere is estimated with a set of past observations that correspond to the best analogs of a deterministic numerical weather prediction (NWP). An analog for a given location and forecast lead time is defined as a past prediction, from the same model, that has similar values for selected features of the current model forecast. The AnEn is evaluated for 0–48-h probabilistic predictions of 10-m wind speed and 2-m temperature over the contiguous United States and against observations provided by 550 surface stations, over the 23 April–31 July 2011 period. The AnEn is generated from the Environment Canada (EC) deterministic Global Environmental Multiscale (GEM) model and a 12–15-month-long training period of forecasts and observations. The skill and value of AnEn predictions are compared with forecasts from a state-of-the-science NWP ensemble s...

Global Distribution and Characteristics of Diurnally Varying Low-Level Jets

Abstract This study documents the global distribution and characteristics of diurnally varying low-level jets (LLJs), including their horizontal, vertical, and temporal structure, with a special emphasis on highlighting the underlying commonalities and unique qualities of the various nocturnal jets. Two tools are developed to accomplish this goal. The first is a 21-yr global reanalysis performed with the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) using a horizontal grid spacing of 40 km. A unique characteristic of the reanalysis is the availability of hourly three-dimensional output, which permits the full diurnal cycle to be analyzed. Furthermore, the horizontal grid spacing of 40 km better resolves many physiographic features that host LLJs than other widely used global reanalyses. This makes possible a detailed examination of the systematic onset and cessation of the jets, including time–height representations of the diurnal cycle. The second tool is an index of nocturnal...

The Operational Mesogamma-Scale Analysis and Forecast System of the U.S. Army Test and Evaluation Command. Part I: Overview of the Modeling System, the Forecast Products, and How the Products Are Used

Given the rapid increase in the use of operational mesoscale models to satisfy different specialized needs, it is important for the community to share ideas and solutions for meeting the many associated challenges that encompass science, technology, education, and training. As a contribution toward this objective, this paper begins a series that reports on the characteristics and performance of an operational mesogammascale weather analysis and forecasting system that has been developed for use by the U.S. Army Test and Evaluation Command. During the more than five years that this four-dimensional weather system has been in use at seven U.S. Army test ranges, valuable experience has been gained about the production and effective use of high-resolution model products for satisfying a variety of needs. This paper serves as a foundation for the rest of the papers in the series by describing the operational requirements for the system, the data assimilation and forecasting system characteristics, and the forecaster training that is required for the finescale products to be used effectively.

Predictability of Low-Level Winds by Mesoscale Meteorological Models

Abstract This study describes the verification of model-based, low-level wind forecasts for the area of the Salt Lake valley and surrounding mountains during the 2002 Salt Lake City, Utah, Winter Olympics. Standard verification statistics (such as bias and mean absolute error) for wind direction and speed were compared for four models: the Eta, Rapid Update Cycle (RUC-2), and Global Forecast System of the National Centers for Environmental Prediction, and the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5). Even though these models had horizontal grid increments that ranged over almost two orders of magnitude, the highest-resolution MM5 with a 1.33-km grid increment exhibited a forecast performance similar to that of the other models in terms of grid-average, conventional verification metrics. This is in spite of the fact that the MM5 is the only model capable of reasonably representing the complex terrain of the Salt Lake City region that exerts a strong influence on the local c...

Verification of Temporal Variations in Mesoscale Numerical Wind Forecasts

Abstract The authors address a particular example of the general question of whether high-resolution forecasts provide additional deterministic skill beyond what can be achieved with a coarser-resolution model. To this end, real-time forecasts using the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) with grid increments of 30 and 3.3 km are compared over a domain centered on the complex terrain region of southern New Mexico during the 1 June 2002 to 1 June 2003 period. The authors use time series of surface data to evaluate the relative ability of the two forecasts to capture significant temporal variations of wind. The authors hypothesize that the additional detail and structure provided by high resolution becomes a “liability” when the forecasts are scored by traditional verification metrics, because such metrics sharply penalize forecasts with small temporal or spatial errors of predicted features. Thus, a set of verification metrics is designed that is increasingly tolerant ...

Global Precipitation Extremes Associated with Diurnally Varying Low-Level Jets

Abstract Extreme rainfall events have important societal impacts: for example, by causing flooding, replenishing reservoirs, and affecting agricultural yields. Previous literature has documented linkages between rainfall extremes and nocturnal low-level jets (NLLJs) over the Great Plains of North America and the La Plata River basin of South America. In this study, the authors utilize a 21-yr, hourly global 40-km reanalysis based on the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) to examine whether NLLJ–rainfall linkages are common elsewhere on the earth. The reanalysis is uniquely suited for the task because of its comparatively high spatial and temporal resolution and because a companion paper demonstrated that it realistically simulates the vertical, horizontal, and diurnal structure of the winds in well-known NLLJ regions. The companion paper employed the reanalysis to identify and describe numerous NLLJs across the planet, including several previously unknown NLLJs. The ...

Mechanisms for Diurnal Boundary Layer Circulations in the Great Basin Desert

Abstract The purpose of this observation- and model-based study of the Great Basin Desert boundary layer is to illustrate the variety of locally forced circulations that can affect such an area during a diurnal cycle. The area of the Great Basin Desert (or Great Salt Lake Desert) that is studied is located to the southwest of Salt Lake City, Utah. It is characteristic of the arid “basin and range” province of North America in that it contains complex terrain, varied vegetation and substrates, and high water tables associated with salt-encrusted basin flats (playas). The study area is especially well instrumented with surface meteorological stations operated by the U.S. Armys West Desert Test Center and a collection of cooperating mesonets in northeastern Utah. The study period was chosen based on the availability of special radiosonde data in this area. One of the processes that is documented here that is unique to desert environments is the salt breeze that forms around the edge of playas as a result of...

The Operational Mesogamma-Scale Analysis and Forecast System of the U.S. Army Test and Evaluation Command. Part II: Interrange Comparison of the Accuracy of Model Analyses and Forecasts

This study builds upon previous efforts to document the performance of the U.S. Army Test and Evaluation Command’s Four-Dimensional Weather Modeling System using conventional metrics. Winds, temperature, and specific humidity were verified for almost 15 000 forecasts at five U.S. Army test ranges using near-surface mesonet data. The primary objective was to use conventional metrics to characterize the degree to which forecast accuracy varies from range to range, within the diurnal cycle, with elapsed forecast time, and among the seasons. It was found that there are large interrange differences in forecast error, with larger errors typically associated with the ranges located near complex orography. Similarly, significant variations in accuracy were noted for different times in the diurnal cycle, but the diurnal dependency varied greatly among the ranges. Factor of 2 differences in accuracy were also found across the seasons.

Selecting Representative Days for More Efficient Dynamical Climate Downscaling: Application to Wind Energy

AbstractThis paper describes a new computationally efficient and statistically robust sampling method for generating dynamically downscaled climatologies. It is based on a Monte Carlo method coupled with stratified sampling. A small yet representative set of “case days” is selected with guidance from a large-scale reanalysis. When downscaled, the sample closely approximates the long-term meteorological record at a location, in terms of the probability density function. The method is demonstrated for the creation of wind maps to help determine the suitability of potential sites for wind energy farms. Turbine hub-height measurements at five U.S. and European tall tower sites are used as a proxy for regional climate model (RCM) downscaled winds to validate the technique. The tower-measured winds provide an independent test of the technique, since RCM-based downscaled winds exhibit an inherent dependence upon the large-scale reanalysis fields from which the case days are sampled; these same reanalysis fields ...

A Practical Approach to Sequential Estimation of Systematic Error on Near-Surface Mesoscale Grids

Abstract Statistical analysis arguments are used to construct an estimation algorithm for systematic error of near-surface temperatures on a mesoscale grid. The systematic error is defined as the observed running-mean error, and an averaging length of 7 days is shown to be acceptable. Those errors are spread over a numerical weather prediction model grid via the statistical analysis equation. Two covariance models are examined: 1) a stationary, isotropic function tuned with the observed running-mean errors and 2) dynamic estimates derived from a recent history of running-mean forecasts. Prediction of error is possible with a diurnal persistence model, where the error at one time of day can be estimated from data with lags of 24-h multiples. The approach is tested on 6 months of 6-h forecasts with the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) over New Mexico. Results show that for a quantity such as 2-m temperature, the systematic component of error can be effectively predic...