ALBERT

Description: 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 authors demonstrate here that the reanalysis reasonably simulates the diurnal cycle, extremes, and spatial structure of rainfall globally compared to satellite-based precipitation datasets and therefore that it is suitable for examining NLLJ–rainfall linkages. A statistical approach is then introduced to categorize nocturnal precipitation extremes as a function of the NLLJ magnitude, wind direction, and wind frequency for January and July. Statistically significant relationships between NLLJs and nocturnal precipitation extremes exist in at least 10 widely disparate regions around the world, some of which are well known and others that have been undocumented until now. The regions include the U.S. Great Plains, Tibet, northwest China, India, Southeast Asia, southeast China, Argentina, Namibia, Botswana, and Ethiopia. Recent studies have recorded widespread changes in the amplitudes of near-surface diurnal heating cycles that in turn play key roles in driving NLLJs. It will thus be important for future work to address how rainfall extremes may be impacted if trends in diurnal cycles cause the position, magnitude, and frequency of NLLJs to change.

Description: Dynamical downscaling is a computationally expensive method whereby finescale details of the atmosphere may be portrayed by running a limited area numerical weather prediction model (often called a regional climate model) nested within a coarse-resolution global reanalysis or global climate model output. The goal of this study is to assess using sampling techniques to dynamically downscale a small subset of days to approximate the statistical properties of the entire period of interest. Two sampling techniques are explored: one where days are randomly selected and another where representative days are chosen (or targeted) based on a set of selection criteria. The relative merit of using random sampling versus targeted random sampling is demonstrated using daily mean 2-m air temperature (T2M). The first two moments of dynamically downscaled T2M can be approximated within 0.3 K using just 5% of the population of available days during a 20-yr period. Targeted random sampling can reduce the mean absolute error of these estimates by as much as 30% locally. Estimation of the more extreme values of T2M is more uncertain and requires a larger sample size. The potential reduction in computational cost afforded by these sampling techniques could greatly benefit applications requiring high-resolution dynamically downscaled depictions of regional climate, including situations in which an ensemble of regional climate simulations is required to properly characterize uncertainty in the model physics assumptions, scenarios, and so on.

Description: 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 of timing errors for temporal changes of wind. The authors find that the barrier-normal (i.e., zonal) wind component over complex terrain reveals the greatest improvement in skill due to increased horizontal resolution for the cases considered here. In addition, the fine-grid forecasts better replicate the cessation of drainage flow or onset of upslope flow near and within complex terrain. The most surprising result is the marginal benefit of the higher resolution over valley locations not in immediate proximity to the mountain slopes, even though the valley is only about 60 km across (east–west). Overall, the gains in forecast accuracy from finer grid spacing are generally incremental, but increase with greater tolerance for timing errors, culminating in the greatest improvement for forecasts of temporal variance.

Description: Recently, two analog-based postprocessing methods were demonstrated to reduce the systematic and random errors from Weather Research and Forecasting (WRF) Model predictions of 10-m wind speed over the central United States. To test robustness and generality, and to gain a deeper understanding of postprocessing forecasts with analogs, this paper expands upon that work by applying both analog methods to surface stations evenly distributed across the conterminous United States over a 1-yr period. The Global Forecast System (GFS), North American Mesoscale Forecast System (NAM), and Rapid Update Cycle (RUC) forecasts for screen-height wind, temperature, and humidity are postprocessed with the two analog-based methods and with two time series–based methods—a running mean bias correction and an algorithm inspired by the Kalman filter. Forecasts are evaluated according to a range of metrics, including random and systematic error components; correlation; and by conditioning the error distributions on lead time, location, error magnitude, and day-to-day error variability. Results show that the analog methods are generally more effective than time series–based methods at reducing the random error component, leading to an overall reduction in root-mean-square error. Details among the methods differ and are elucidated upon in this study. The relative levels of random and systematic error in the raw forecasts determine, to a large extent, the effectiveness of each postprocessing method in reducing forecast errors. When the errors are dominated by random errors (e.g., where thunderstorms are common), the analog-based methods far outperform the time series–based methods. When the errors are strictly systematic (i.e., a bias), the analog methods lose their advantage over the time series methods. It is shown that slowly evolving systematic errors rarely dominate, so reducing the random error component is most effective at reducing the error magnitude. The results are shown to be valid for all seasons. The analog methods show similar performance to the operational model output statistics (MOS) while showing greater reduction of random errors at certain lead times.

Description: The study describes a method of evaluating numerical weather prediction models by comparing the characteristics of temporal changes in simulated and observed 10-m (AGL) winds. The method is demonstrated on a 1-yr collection of 1-day simulations by the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) over southern New Mexico. Temporal objects, or wind events, are defined at the observation locations and at each grid point in the model domain as vector wind changes over 2 h. Changes above the uppermost quartile of the distributions in the observations and simulations are empirically classified as significant; their attributes are analyzed and interpreted. It is demonstrated that the model can discriminate between large and modest wind changes on a pointwise basis, suggesting that many forecast events have an observational counterpart. Spatial clusters of significant wind events are highly continuous in space and time. Such continuity suggests that displaying maps of surface wind changes with high temporal resolution can alert forecasters to the occurrence of important phenomena. Documented systematic errors in the amplitude, direction, and timing of wind events will allow forecasters to mentally adjust for biases in features forecast by the model.

Description: 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 predicted on the grid. The gridded estimates fit the observed running-mean errors well. Cross validation shows that predictions of systematic error result in a substantial error reduction where observations are not available. The error estimates show a diurnal evolution, and are not strictly functions of terrain elevation. Observation error covariances, localization operators, and covariance functions in the isotropic case must be tuned for a specific forecast system and observing network, but the process is straightforward. Taken together, the results suggest an effective method for systematic error estimation on near-surface mesoscale grids in the absence of a useful ensemble. Correction for those errors may provide benefits to forecast users.

Description: 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 circulation patterns. The purpose of this study is to investigate why the standard verification measures did not better discriminate among the models and to describe alternative measures that might better represent the ability of high-horizontal-resolution models to forecast locally forced mesogamma-scale circulations. The spatial variability of the strength of the diurnal forcing was quantified by spectrally transforming the time series of wind-component data for each observation location. The amount of spectral power in the band with approximately a diurnal period varied greatly from place to place, as did the amount of power in the bands with periods longer (superdiurnal) and shorter (subdiurnal) than the diurnal. It is reasonable that the superdiurnal power is largely in the synoptic-scale motions, and thus can be reasonably predicted by all the models. In contrast, the subdiurnal power is mainly in nondiurnally forced small-scale fluctuations that are generally unpredictable with any horizontal resolution because they are unobserved in three dimensions by the observation network. A strong positive relationship is demonstrated between the strength of the local forcing at each observation location, as measured by the spectral power in the diurnal band of the wind component time series, and forecast skill, as reflected by an alternative verification metric, a measure of anomaly correlation. However, the mean-absolute error showed no relationship to the power in the diurnal band. Two other measures of comparison among the low-level wind forecasts, the direction climatology and the spatial variance, showed a positive correlation between forecast quality and horizontal resolution.