Barbara G. Brown

Extreme events in a changing climate: Variability is more important than averages

Extreme events act as a catalyst for concern about whether the climate is changing. Statistical theory for extremes is used to demonstrate that the frequency of such events is relatively more dependent on any changes in the variability (more generally, the scale parameter) than in the mean (more generally, the location parameter) of climate. Moreover, this sensitivity is relatively greater the more extreme the event. These results provide additional support for the conclusions that experiments using climate models need to be designed to detect changes in climate variability, and that policy analysis should not rely on scenarios of future climate involving only changes in means.

Time Series Models to Simulate and Forecast Wind Speed and Wind Power

Abstract A general approach for modeling wind speed and wind power is described. Because wind power is a function of wind speed, the methodology is based on the development of a model of wind speed. Values of wind power are estimated by applying the appropriate transformations to values of wind speed. The wind speed modeling approach takes into account several basic features of wind speed data, including autocorrelation, non-Gaussian distribution, and diurnal nonstationarity. The positive correlation between consecutive wind speed observations is taken into account by fitting an autoregressive process to wind speed data transformed to make their distribution approximately Gaussian and standardized to remove diurnal nonstationarity. As an example, the modeling approach is applied to a small set of hourly wind speed data from the Pacific Northwest. Use of the methodology for simulating and forecasting wind speed and wind power is discussed and an illustration of each of these types of applications is presen...

Object-Based Verification of Precipitation Forecasts. Part I: Methodology and Application to Mesoscale Rain Areas

Abstract A recently developed method of defining rain areas for the purpose of verifying precipitation produced by numerical weather prediction models is described. Precipitation objects are defined in both forecasts and observations based on a convolution (smoothing) and thresholding procedure. In an application of the new verification approach, the forecasts produced by the Weather Research and Forecasting (WRF) model are evaluated on a 22-km grid covering the continental United States during July–August 2001. Observed rainfall is derived from the stage-IV product from NCEP on a 4-km grid (averaged to a 22-km grid). It is found that the WRF produces too many large rain areas, and the spatial and temporal distribution of the rain areas reveals regional underestimates of the diurnal cycle in rain-area occurrence frequency. Objects in the two datasets are then matched according to the separation distance of their centroids. Overall, WRF rain errors exhibit no large biases in location, but do suffer from a ...

The THORPEX Interactive Grand Global Ensemble

Ensemble forecasting is increasingly accepted as a powerful tool to improve early warnings for high-impact weather. Recently, ensembles combining forecasts from different systems have attracted a considerable level of interest. The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Globa l Ensemble (TIGGE) project, a prominent contribution to THORPEX, has been initiated to enable advanced research and demonstration of the multimodel ensemble concept and to pave the way toward operational implementation of such a system at the international level. The objectives of TIGGE are 1) to facilitate closer cooperation between the academic and operational meteorological communities by expanding the availability of operational products for research, and 2) to facilitate exploring the concept and benefits of multimodel probabilistic weather forecasts, with a particular focus on high-impact weather prediction. Ten operational weather forecasting centers producing daily global ensemble ...

Intercomparison of Spatial Forecast Verification Methods

Abstract Advancements in weather forecast models and their enhanced resolution have led to substantially improved and more realistic-appearing forecasts for some variables. However, traditional verification scores often indicate poor performance because of the increased small-scale variability so that the true quality of the forecasts is not always characterized well. As a result, numerous new methods for verifying these forecasts have been proposed. These new methods can mostly be classified into two overall categories: filtering methods and displacement methods. The filtering methods can be further delineated into neighborhood and scale separation, and the displacement methods can be divided into features based and field deformation. Each method gives considerably more information than the traditional scores, but it is not clear which method(s) should be used for which purpose. A verification methods intercomparison project has been established in order to glean a better understanding of the proposed me...

Object-Based Verification of Precipitation Forecasts. Part II: Application to Convective Rain Systems

The authors develop and apply an algorithm to define coherent areas of precipitation, emphasizing mesoscale convection, and compare properties of these areas with observations obtained from NCEP stage-IV precipitation analyses (gauge and radar combined). In Part II, fully explicit 12–36-h forecasts of rainfall from the Weather Research and Forecasting model (WRF) are evaluated. These forecasts are integrated on a 4-km mesh without a cumulus parameterization. Rain areas are defined similarly to Part I, but emphasize more intense, smaller areas. Furthermore, a time-matching algorithm is devised to group spatially and temporally coherent areas into rain systems that approximate mesoscale convective systems. In general, the WRF model produces too many rain areas with length scales of 80 km or greater. Rain systems typically last too long, and are forecast to occur 1–2 h later than observed. The intensity distribution among rain systems in the 4-km forecasts is generally too broad, especially in the late afternoon, in sharp contrast to the intensity distribution obtained on a coarser grid with parameterized convection in Part I. The model exhibits the largest positive size and intensity bias associated with systems over the Midwest and Mississippi Valley regions, but little size bias over the High Plains, Ohio Valley, and the southeast United States. For rain systems lastin g6ho rmore, the critical success index for matching forecast and observed rain systems agrees closely with that obtained in a related study using manually determined rain systems.

IMPROVING QPE AND VERY SHORT TERM QPF An Initiative for a Community-Wide Integrated Approach

Accurate quantitative precipitation estimates (QPE) and very short term quantitative precipitation forecasts (VSTQPF) are critical to accurate monitoring and prediction of water-related hazards and water resources. While tremendous progress has been made in the last quarter-century in many areas of QPE and VSTQPF, significant gaps continue to exist in both knowledge and capabilities that are necessary to produce accurate high-resolution precipitation estimates at the national scale for a wide spectrum of users. Toward this goal, a national next-generation QPE and VSTQPF (Q2) workshop was held in Norman, Oklahoma, on 28–30 June 2005. Scientists, operational forecasters, water managers, and stakeholders from public and private sectors, including academia, presented and discussed a broad range of precipitation and forecasting topics and issues, and developed a list of science focus areas. To meet the nations needs for the precipitation information effectively, the authors herein propose a community-wide int...

The Method for Object-Based Diagnostic Evaluation (MODE) Applied to Numerical Forecasts from the 2005 NSSL/SPC Spring Program

Abstract The authors use a procedure called the method for object-based diagnostic evaluation, commonly referred to as MODE, to compare forecasts made from two models representing separate cores of the Weather Research and Forecasting (WRF) model during the 2005 National Severe Storms Laboratory and Storm Prediction Center Spring Program. Both models, the Advanced Research WRF (ARW) and the Nonhydrostatic Mesoscale Model (NMM), were run without a traditional cumulus parameterization scheme on horizontal grid lengths of 4 km (ARW) and 4.5 km (NMM). MODE was used to evaluate 1-h rainfall accumulation from 24-h forecasts valid at 0000 UTC on 32 days between 24 April and 4 June 2005. The primary variable used for evaluation was a “total interest” derived from a fuzzy-logic algorithm that compared several attributes of forecast and observed rain features such as separation distance and spatial orientation. The maximum value of the total interest obtained by comparing an object in one field with all objects in ...

The Great Colorado Flood of September 2013

AbstractDuring the second week of September 2013, a seasonally uncharacteristic weather pattern stalled over the Rocky Mountain Front Range region of northern Colorado bringing with it copious amounts of moisture from the Gulf of Mexico, Caribbean Sea, and the tropical eastern Pacific Ocean. This feed of moisture was funneled toward the east-facing mountain slopes through a series of mesoscale circulation features, resulting in several days of unusually widespread heavy rainfall over steep mountainous terrain. Catastrophic flooding ensued within several Front Range river systems that washed away highways, destroyed towns, isolated communities, necessitated days of airborne evacuations, and resulted in eight fatalities. The impacts from heavy rainfall and flooding were felt over a broad region of northern Colorado leading to 18 counties being designated as federal disaster areas and resulting in damages exceeding

Current Icing Potential: Algorithm Description and Comparison with Aircraft Observations

2 billion (U.S. dollars). This study explores the meteorological and hydrological ingredients...