North American Regional Reanalysis Dataset Research Articles

Reconstructing summer upper-level flow in the northern Rocky Mountains using an alpine larch tree-ring chronology

Mid-latitude mesoscale weather during the climatological summer is strongly influenced by fluctuations in synoptic-scale circulation patterns. Previous research has linked Arctic amplification to alterations in summer synoptic climatology, leading to more extreme weather events in the mid-latitudes. In this study, seasonal (JJA) upper-level (500 hPa) atmospheric flow is reconstructed in the mid-latitudes using an alpine larch Larix lyallii Parl. tree-ring chronology sampled from western Montana. Significant relationships were found between alpine larch radial growth and upper-level flow patterns derived from the North American Regional Reanalysis dataset (1979-2015). Meridional and zonal flows that manifest in ridging are associated with enhanced radial growth of alpine larch (i.e. meridional flow west [r = 0.504, p = 0.001] and zonal flow north [r = 0.642, p < 0.001] of the study site). Meridional and zonal flows associated with troughing result in decreased radial growth (i.e. meridional flow east [r = -0.497, p = 0.001] and zonal flow south [r = -0.584, p < 0.001] of the study site). Using the leave-one-out method, a linear regression model was calibrated and verified between a principal component analysis score derived from measurements of upper-level flow in western North America and alpine larch tree growth. The 444 yr climate reconstruction of summer 500 hPa flow suggests that ridging is becoming more intense over the western United States and Canada since the 1980s.

Spatial patterns of recent US summertime heat trends: Implications for heat sensitivity and health adaptations

Heat is known to cause illness and death not only at extreme temperatures, but also at moderate levels. Although substantial research has shown how summertime temperature distributions have changed over recent decades in the United States, less is known about how the heat index—a potentially more health-applicable metric of heat—has similarly evolved over this period. Moreover, the extent to which these distributional changes have overlapped with indicators of social vulnerability has not been established, despite the applicability of co-varying climatic and sociodemographic characteristics to heat-related health adaptations. Presented here is an analysis of trends in the median, 95th percentile, and ‘warm-tail spread’ (i.e., intra-seasonal range between the upper extreme and median) of warm-season (May-September) maximum heat index between 1979 and 2018 across the conterminous US. Using 40 years of data from the North American Regional Reanalysis dataset, it is shown that most of the US has experienced statistically significant positive trends in summertime heat, and that both the magnitude of trends and the shape of the frequency distributions of these measures vary regionally. Comparisons with data from the Social Vulnerability Index show that the most socially vulnerable counties appear to be warming faster than the least vulnerable, but that opposite patterns hold for trends in warm-tail spread. These findings may be applicable to further studies on climate change, heat adaptations, and environmental justice in the US.

Hydrological modelling using proxies for gauged precipitation and temperature

AbstractPrecipitation and temperature time series suffer from many problems, such as short time, inadequate spatial coverage, missing data, and biases from various causes, which are particularly critical in remote areas such as Northern Canada. The development of alternative datasets for using as proxies for inadequate/missing weather data represents a key research area. In this paper, the performance of 6 alternative datasets is evaluated for hydrological modelling over 12 watersheds located across Canada and the contiguous United States. The datasets can be classified into 3 distinct categories: (a) interpolated gridded data, (b) reanalysis data, and (c) climate model outputs. Hydrological simulations were carried out using a lumped conceptual hydrological model calibrated using standard weather data and compared against results using a calibration specific to each alternative dataset. Prior to the hydrological simulations, the alternative datasets were all evaluated with respect to their ability to reproduce gridded daily precipitation and temperature characteristics over North America. The results show that both the reanalysis data and climate model data adequately represent the spatial pattern of daily precipitation and temperature over North America. The North American Regional Reanalysis (NARR) dataset consistently shows the best performance. With respect to hydrological modelling, the observed discharges are accurately represented by both the gridded and NARR datasets, and more so for the NARR data. The National Centers for Environmental Prediction dataset consistently performs worst as it is unable to even capture the seasonal pattern of observed streamflow for 3 out of the 12 watersheds. These results indicate that the NARR dataset could be used as a proxy for gauged precipitation and temperature for hydrological modelling over watersheds where observational datasets are deficient. The results also illustrate the ability of climate model data to be used for performing hydrological modelling when driven by reanalysis data at their boundaries, and especially so for high‐resolution models.

Synoptic and Mesoscale Environment of Convection during the North American Monsoon across Central and Southern Arizona

Abstract This paper comprehensively analyzes the synoptic and mesoscale environment associated with North American monsoon–related thunderstorms affecting central and southern Arizona. Analyses of thunderstorm environments are presented using reanalysis data, severe thunderstorm reports, and cloud-to-ground lightning information from 2003 to 2013, which serves as a springboard for lightning-prediction models provided in a companion paper. Spatial and temporal analyses of lightning strikes indicate thunderstorm frequencies maximize between 2100 and 0000 UTC, when the greatest frequencies are concentrated over higher terrain. Severe thunderstorm reports typically occur later in the day (between 2300 and 0100 UTC), while reports are maximized in the Tucson and Phoenix metropolitan areas. Composite analyses of the synoptic-scale patterns associated with severe thunderstorm days and nonthunderstorm days during the summer using the North American Regional Reanalysis dataset are presented. Severe thunderstorm cases tend to be associated with a stronger midlevel anticyclone and deep-layer moisture over portions of the southwestern United States. By September, severe weather patterns tend to associate with a midlevel trough along the Pacific coast. Specific parameters associated with severe thunderstorms are analyzed across the Tucson and Phoenix areas, where severe weather reporting is more consistent. Greater convective available potential energy, low-level lapse rates, and downdraft convective available potential energy are associated with severe thunderstorm (especially severe wind) environments compared to those with nonsevere thunderstorms, while stronger effective bulk wind differences (at least 15–20 kt, where 1 kt = 0.51 m s−1) can be used to distinguish severe hail environments.

Trend analysis of fire season length and extreme fire weather in North America between 1979 and 2015

We have constructed a fire weather climatology over North America from 1979 to 2015 using the North American Regional Reanalysis dataset and the Canadian Fire Weather Index (FWI) System. We tested for the presence of trends in potential fire season length, based on a meteorological definition, and extreme fire weather using the non-parametric Theil–Sen slope estimator and Mann–Kendall test. Applying field significance testing (i.e. joint significance of multiple tests) allowed the identification of the locations of significant trends, taking into account spatial correlations. Fire season length was found to be increasing over large areas of North America, especially in eastern Canada and the south-western US, which is consistent with a later fire season end and an earlier fire season start. Both positive and negative trends in potential fire spread days and the 99th percentile of FWI occurred in Canada and the contiguous United States, although the trends of largest magnitude and statistical significance were mostly positive. In contrast, the proportion of trends with significant decreases in these variables were much lower, indicating an overall increase in extreme fire weather. The smaller proportion of significant positive trends found over Canada reflects the truncation of the time series, necessary because assimilation of precipitation observations over Canada ceased in the reanalysis post-2002.

Characteristics of major ice storms in the central United States

A 30-yr (1979–2009) climatology of major ice storms with 19.05 mm (0.75 in) ice accumulation or greater across the central United States is presented. An examination of the United States Army Cold Regions Research and Engineering Laboratory Ice Storm Database, in conjunction with National Climatic Data Center Storm Data, revealed 20 (out of 51) ice storms during the period which satisfied the selection criteria herein. Using the General Meteorological Package with the North American Regional Reanalysis dataset, system-relative composites using both traditional (i.e., mean) and probabilistic (i.e., frequency analysis of median, quartiles, and threshold exceedance) composite-analysis measures for the major ice storms were created. The composite analysis was computed at start, maximum coverage, and end times to depict the evolution of features used to diagnose the synoptic and mesoscale environment potentially favorable for major ice storms within the central United States. At the maximum-coverage time, the composite analysis indicates the presence of sub-freezing surface temperatures to the north of a southwest-to-northeast oriented quasi-stationary front below a strong 850-hPa jet streak. The low-level jet streak bisects the quasi-stationary front and is enhanced by the direct thermal circulation associated with an upper-level jet streak. These features are responsible for supplying warm and moist air into the elevated warm layer, which provides an environment to completely melt frozen precipitation. An upper-level long-wave trough is anchored over the southwestern United States and there is some evidence that it leads to short-wave troughs ejecting into the plains. These troughs may be responsible for multiple rounds of enhanced vertical motion, precipitation, and freezing rain. Finally, an analysis of probabilistic measures of the 300- and 850-hPa jet streaks and elevated warm-layer characteristics indicates the importance of these fields in major ice storms in this region.

Sensitivity of the Simulated Kingston Basin—Lake Ontario Summer Temperature Profile using FVCOM

Using an unstructured grid finite volume model (Finite Volume Coastal Ocean Model (FVCOM)), numerical model simulations were generated and compared for the 2006 summer hydrography of the Lake Ontario–Kingston Basin system. Simulations are forced with surface forcing from two different sources: observations collected by Environment Canada and output generated by the North American Regional Reanalysis (NARR). Simulated temperatures are compared with observations collected during the same time period to assess the viability of the NARR dataset as a source of long-term surface forcing fluxes. Root mean square errors for vertical temperature profiles, comparisons to lake surface temperatures, and Kingston Basin current observations, indicate that using NARR data to force an FVCOM model simulates the hydrography as well as simulations forced with in situ observations. RÉSUMÉ [Traduit par la rédaction] À l'aide d'un modèle aux volumes finis à grille non structurée (Finite Volume Coastal Ocean Model (FVCOM)), nous avons généré et comparé des simulations par modèle numérique pour l'hydrographie de l’été 2006 du système lac Ontario – bassin Kingston. Les simulations sont forcées par un forçage en surface provenant de deux sources différentes : les observations recueillies par Environnement Canada et la sortie produite par le NARR (North American Regional Reanalysis). Nous comparons les températures simulées avec les observations recueillies durant la même période pour évaluer la viabilité de l'ensemble de données NARR en tant que source de flux de forçage en surface à long terme. Les erreurs-types pour les profils verticaux de température, les comparaisons avec les températures de surface du lac et les observations du courant dans le bassin Kingston indiquent que l'utilisation des données NARR pour forcer un modèle FVCOM simule l'hydrographie aussi bien que les simulation forcées par des observations in situ.

Climatic Variability of Near-Surface Turbulent Kinetic Energy over the United States: Implications for Fire-Weather Predictions

AbstractRecent research suggests that high levels of ambient near-surface atmospheric turbulence are often associated with rapid and sometimes erratic wildland fire spread that may eventually lead to large burn areas. Previous research has also examined the feasibility of using near-surface atmospheric turbulent kinetic energy (TKEs) alone or in combination with the Haines index (HI) as an additional indicator of anomalous atmospheric conditions conducive to erratic or extreme fire behavior. However, the application of TKEs-based indices for operational fire-weather predictions in the United States on a regional or national basis first requires a climatic assessment of the spatial and temporal patterns of the indices that can then be used for testing their operational effectiveness. This study provides an initial examination of some of the spatial and temporal variability patterns across the United States of TKEs and the product of HI and TKEs (HITKEs) using data from the North American Regional Reanalysis dataset covering the 1979–2008 period. The analyses suggest that there are regional differences in the behavior of these indices and that regionally dependent threshold values for TKEs and HITKEs may be needed for their potential use as operational indicators of anomalous atmospheric turbulence conditions conducive to erratic fire behavior. The analyses also indicate that broad areas within the northeastern, southeastern, and southwestern regions of the United States have experienced statistically significant positive trends in TKEs and HITKEs values over the 1979–2008 period, with the most substantial increases in values occurring over the 1994–2008 period.

Understanding Simulated Extreme Precipitation Events in Madison, Wisconsin, and the Role of Moisture Flux Convergence during the Late Twentieth and Twenty-First Centuries*

AbstractUnderstanding extreme precipitation events in the current and future climate system is an important aspect of climate change for adaptation and mitigation purposes. The current study investigates extreme precipitation events over Madison, Wisconsin, during the late twentieth and late twenty-first centuries using 18 coupled ocean–atmosphere general circulation models that participated in the Coupled Model Intercomparison Project (CMIP3). An increase of ~10% is found in the multimodel average of annual precipitation received in Madison by the end of the twenty-first century, with the largest increases projected to occur during winter [December–February (DJF)] and spring [March–May (MAM)]. It is also found that the observed seasonal cycle of precipitation in Madison is not accurately captured by the models. The multimodel average shows a strong seasonal peak in May, whereas observations peak during midsummer. Model simulations also do not accurately capture the annual cycle of extreme precipitation events in Madison, which also peak in summer. Instead, the timing of model-simulated extreme events exhibits a bimodal distribution that peaks during spring and fall. However, spatial composites of average daily precipitation simulated by GCMs during Madison’s wettest 1% of precipitation events during the twentieth century strongly resemble the spatial pattern produced in observations. The role of specific humidity and vertically integrated moisture flux convergence (MFC) during extreme precipitation events in Madison is investigated in twentieth- and twenty-first-century simulations. Spatial composites of MFC during the wettest 1% of days during the twentieth-century simulations agree well with results from the North American Regional Reanalysis dataset (NARR), suggesting that synoptic-scale dynamics are vital to extreme precipitation events.

Hydroclimate and variability in the Great Lakes region as derived from the North American Regional Reanalysis

We investigated the seasonal and interannual variability of the moisture budget in the Great Lakes region of the United States using the North American Regional Reanalysis (NARR) data set from 1979 through 2007. The much higher spatial and temporal resolution and improved precipitation and land surface data assimilation of the NARR data set compared with its global counterparts enable more accurate depictions of the moisture budget and hydrological cycle in the Great Lakes region. The analyses reveal that in the past three decades except for two drought years, the evaporation over the region is insufficient to account for the total precipitation. Transport mechanisms supply additional moisture, with a net gain in moisture associated with meridional transport by southerly winds overcoming a net loss in moisture due to zonal transport by westerly winds. The interannual variability of the moisture deficit (the difference between evaporation and precipitation) is associated mainly with the interannual variability in the moisture flux convergence. These results highlight the critical importance of remote moisture sources and large‐scale moisture transport to the hydrological cycle in the Great Lakes region. The trend analyses show an upward trend for evaporation that is consistent with warming over the region in all seasons during the NARR data period. Precipitation exhibits an increasing trend in spring and winter, with the largest increase in winter, but no clear trends in summer and autumn.

The Synergistic Relationship between Soil Moisture and the Low-Level Jet and Its Role on the Prestorm Environment in the Southern Great Plains

Abstract Changes in low-level moisture alter the convective parameters [e.g., convective available potential energy (CAPE), lifted index (LI), and convective inhibition (CIN)] as a result of alterations in the latent and sensible heat energy exchange. Two sources for low-level moisture exist in the southern Great Plains: 1) moisture advection by the low-level jet (LLJ) from the Gulf of Mexico and 2) evaporation and transpiration from the soils and vegetation in the region. The primary focus of this study is to examine the spatial distribution of soil moisture on a daily basis and to determine the effect it has on the convective parameters. The secondary objective is to investigate how the relationship between soil moisture and convective parameters is altered by the presence of an LLJ. The soil moisture data were obtained through newly developed procedures and advances in technology aboard the Tropical Rainfall Measuring Mission Microwave Imager. The convective parameter data were obtained through the North American Regional Reanalysis dataset. The study examined seven warm seasons (April–September) from 1998 to 2004 and found that the convective environment is more unstable (CAPE > 900 J kg−1, LI < −2°C) but more strongly capped (CIN > 70 J kg−1) on days with an LLJ present. Spearman’s rank correlation analysis showed a less stable atmosphere with increased soil moisture, after soil moisture reached 5%, on most days. Additional analysis determined that on all synoptic-type days the probability of reaching various thresholds of convective intensity increased as soil moisture values increased. The probabilities were even greater on days with an LLJ present than on the days without an LLJ present. An examination of four days representing each synoptic-type day indicates that on the daily scale the intensity of the convective environment is closely related to the high soil moisture and the presence of an LLJ.

How much influence does landscape-scale physiography have on air temperature in a mountain environment?

Spatio-temporal patterns of temperature in mountain environments are complex due to both regional synoptic-scale and landscape-scale physiographic controls in these systems. Understanding the nature and magnitude of these physiographic effects has practical and theoretical implications for the development of temperature datasets used in ecosystem assessment and climate change impact studies in regions of complex terrain. This study attempts to quantify the absolute and relative influence of landscape-scale physiographic factors in mediating regional temperatures and assess how these influences vary in time. Our approach was to decompose the variance in in situ temperature measurements into components associated with regional free-air temperature estimates and local physiographic effects. Near-surface air temperature data, collected between 1995 and 2006 from 16 meteorological stations in the Lake Tahoe region of California, USA were regressed against free-air temperature (North American Regional Reanalysis dataset) for the same period. Residuals from this fit represent spatial deviations from the regional mean and were modeled as a function of physiographic position on the landscape using variables derived from terrain analysis techniques. Linear models relating temperature residuals to physiographic variables explained roughly 10–90% of the variance in temperature residuals and had root mean squared error of 1.2–2.0 °C, depending upon the type of measurement and time of year. Results demonstrate that: (1) regional temperature patterns were the principle driver of surface temperatures explaining roughly 70–80% of the variance in in situ measurements; (2) the remaining variance was largely explained by spatial variability in landscape-scale physiographic variables; (3) the influence of physiographic drivers varied seasonally and was influenced by regional conditions. Periods of well-mixed atmospheric conditions lend themselves to the use of simple elevation-based lapse rate models for temperature estimation whereas other physiographic effects become more prominent during periods of enhanced atmospheric stability; and lastly (4) small differences in temperature due to landscape position, when integrated over time, can have a prominent effect on water balance and thus hydrologic and ecologic processes.

Movement ecology of migration in turkey vultures

We develop individual-based movement ecology models (MEM) to explore turkey vulture (Cathartes aura) migration decisions at both hourly and daily scales. Vulture movements in 10 migration events were recorded with satellite-reporting GPS sensors, and flight behavior was observed visually, aided by on-the-ground VHF radio-tracking. We used the North American Regional Reanalysis dataset to obtain values for wind speed, turbulent kinetic energy (TKE), and cloud height and used a digital elevation model for a measure of terrain ruggedness. A turkey vulture fitted with a heart-rate logger during 124 h of flight during 38 contiguous days showed only a small increase in mean heart rate as distance traveled per day increased, which suggests that, unlike flapping, soaring flight does not lead to greatly increased metabolic costs. Data from 10 migrations for 724 hourly segments and 152 daily segments showed that vultures depended heavily upon high levels of TKE in the atmospheric boundary layer to increase flight distances and maintain preferred bearings at both hourly and daily scales. We suggest how the MEM can be extended to other spatial and temporal scales of avian migration. Our success in relating model-derived atmospheric variables to migration indicates the potential of using regional reanalysis data, as here, and potentially other regional, higher-resolution, atmospheric models in predicting changing movement patterns of soaring birds under various scenarios of climate and land use change.

The Regime Dependence of Optimally Weighted Ensemble Model Consensus Forecasts of Surface Temperature

Abstract Previous methods for creating consensus forecasts weight individual ensemble members based upon their relative performance over the previous N days, implicitly making a short-term persistence assumption about the underlying flow regime. A postprocessing scheme in which model performance is linked to underlying weather regimes could improve the skill of deterministic ensemble model consensus forecasts. Here, principal component analysis of several synoptic- and mesoscale fields from the North American Regional Reanalysis dataset provides an objective means for characterizing atmospheric regimes. Clustering techniques, including K-means and a genetic algorithm, are developed that use the resulting principal components to distinguish among the weather regimes. This pilot study creates a weighted consensus from 48-h surface temperature predictions produced by the University of Washington Mesoscale Ensemble, a varied-model (differing physics and parameterization schemes) multianalysis ensemble with eight members. Different optimal weights are generated for each weather regime. A second regime-dependent consensus technique uses linear regression to predict the relative performance of the ensemble members based upon the principal components. Consensus forecasts obtained by the regime-dependent schemes are compared using cross validation with traditional N-day ensemble consensus forecasts for four locations in the Pacific Northwest, and show improvement over methods that rely on the short-term persistence assumption.

Observed relation between evapotranspiration and soil moisture in the North American monsoon region

Soil moisture control on evapotranspiration is poorly understood in ecosystems experiencing seasonal greening. In this study, we utilize a set of multi‐year observations at four eddy covariance sites along a latitudinal gradient in vegetation greening to infer the ET‐θ relation during the North American monsoon. Results reveal significant seasonal, interannual and ecosystem variations in the observed ET‐θ relation directly linked to vegetation greening. In particular, monsoon‐dominated ecosystems adjust their ET‐θ relation, through changes in unstressed ET and plant stress threshold, to cope with differences in water availability. Comparisons of the observed relations to the North American Regional Reanalysis dataset reveal large biases that increase where vegetation greening is more significant. The analysis presented here can be used to guide improvements in land surface model parameterization in water‐limited ecosystems.

A Nonstationary Extreme Value Analysis for the Assessment of Changes in Extreme Annual Wind Speed over the Gulf of St. Lawrence, Canada

Abstract Changes in the extreme annual wind speed in and around the Gulf of St. Lawrence (Canada) were investigated through a nonstationary extreme value analysis of the annual maximum 10-m wind speed obtained from the North American Regional Reanalysis (NARR) dataset as well as observed data from selected stations of Environment Canada. A generalized extreme value distribution with time-dependent location and scale parameters was used to estimate quantiles of interest as functions of time at locations where significant trend was detected. A Bayesian method, the generalized maximum likelihood approach, is implemented to estimate the parameters. The analysis yielded shape parameters very close to 0, suggesting that the distribution can be modeled using the Gumbel distribution. A similar analysis using a nonstationary Gumbel model yielded similar quantiles with narrower credibility intervals. Overall, little change was detected over the period 1979–2004. Only 7% of the investigated grids exhibited trends at the 5% significant level, and the analysis performed on the reanalysis data at locations of significant trend indicated a rise in the median extreme annual wind speed by up to 2 m s−1 per decade in the southern coastal areas with a corresponding increase in the 90% and 99% quantiles of the extreme annual wind speeds by up to 5 m s−1 per decade. Also in the northern part of the gulf and some offshore areas in the south, the 50%, 90%, and 99% quantile values of the extreme annual wind speeds are noted to drop by up to 1.5, 3, and 5 m s−1, respectively. While the directions of the changes in the annual extremes at the selected stations are similar to those of the reanalysis data at nearby grid cells, the magnitudes and significance levels of the changes are generally inconsistent. Change at the same significance level over the same period of the NARR dataset was noted only at 2 stations out of 13.

Precipitation Recycling Variability and Ecoclimatological Stability—A Study Using NARR Data. Part I: Central U.S. Plains Ecoregion

Abstract Precipitation recycling is one of the key mechanisms linking the land surface and atmospheric dynamics. This work explores the physical mechanisms that modulate precipitation recycling variability at the daily-to-intraseasonal time scales in the central U.S. plains ecoregion using a set of land–atmosphere variables derived from the North American Regional Reanalysis dataset. Recycling estimates are performed using the Dynamic Recycling Model, which allows for analysis at shorter time scales than the previous bulk recycling models. In the central U.S. plains ecoregion local evapotranspiration only becomes an important contributor to precipitation when moisture of advective origin, the largest contributor to precipitation, diminishes. Consequently, the recycling ratio is negatively correlated to precipitation. The dominant mechanism is a negative feedback, which ensures that, even when precipitation is low, evapotranspiration continues to feed moisture into the overlying atmosphere and contribute to rainfall. Consequently, in the central U.S. plains, precipitation recycling acts as a mechanism for ecoclimatological stability through local negative feedbacks. Additionally, the zonal and meridional winds and moisture fluxes were also found to be important drivers of recycling variability. As winds decrease, the air has more time to traverse the region and capture moisture of evaporative origin. Evapotranspiration variability is not an important driver for recycling ratio variability in the central U.S. plains. Only during the extremely dry 1988 summer drought, when soil moisture storage was depleted, did the recycling ratio variability closely follow evapotranspiration.

Climatology of High Wind Events in the Owens Valley, California

AbstractThe climatology of high wind events in the Owens Valley, California, a deep valley located just east of the southern Sierra Nevada, is described using data from six automated weather stations distributed along the valley axis in combination with the North American Regional Reanalysis dataset. Potential mechanisms for the development of strong winds in the valley are examined.Contrary to the common belief that strong winds in the Owens Valley are westerly downslope windstorms that develop on the eastern slope of the Sierra Nevada, strong westerly winds are rare in the valley. Instead, strong winds are highly bidirectional, blowing either up (northward) or down (southward) the valley axis. High wind events are most frequent in spring and early fall and they occur more often during daytime than during nighttime, with a peak frequency in the afternoon. Unlike thermally driven valley winds that blow up valley during daytime and down valley during nighttime, strong winds may blow in either direction regardless of the time of the day. The southerly up-valley winds appear most often in the afternoon, a time when there is a weak minimum of northerly down-valley winds, indicating that strong wind events are modulated by local along-valley thermal forcing.Several mechanisms, including downward momentum transfer, forced channeling, and pressure-driven channeling all play a role in the development of southerly high wind events. These events are typically accompanied by strong south-southwesterly synoptic winds ahead of an upper-level trough off the California coast. The northerly high wind events, which typically occur when winds aloft are from the northwest ahead of an approaching upper-level ridge, are predominantly caused by the passage of a cold front when fast-moving cold air behind the surface front undercuts and displaces the warmer air in the valley. Forced channeling by the sidewalls of the relatively narrow valley aligns the wind direction with the valley axis and enhances the wind speeds.

GOES Climatology and Analysis of Thunderstorms with Enhanced 3.9-μm Reflectivity

Abstract By combining observations from the Geostationary Operational Environmental Satellite (GOES) 3.9- and 10.7-μm channels, the reflected component of the 3.9-μm radiance can be isolated. In this paper, these 3.9-μm reflectivity measurements of thunderstorm tops are studied in terms of their climatological values and their utility in diagnosing cloud-top microphysical structure. These measurements provide information about internal thunderstorm processes, including updraft strength, and may be useful for severe weather nowcasting. Three years of summertime thunderstorm-top 3.9-μm reflectivity values are analyzed to produce maps of climatological means across the United States. Maxima occur in the high plains and Rocky Mountain regions, while lower values are observed over much of the eastern United States. A simple model is used to establish a relationship between 3.9-μm reflectivity and ice crystal size at cloud top. As the mean diameter of a cloud-top ice crystal distribution decreases, more solar radiation near 3.9 μm is reflected. Using the North American Regional Reanalysis dataset, the thermodynamic environment that favors thunderstorms with large 3.9-μm reflectivity values is identified. In the high plains and mountains, environments with relatively dry boundary layers, steep lapse rates, and large vertical shear values favor thunderstorms with enhanced 3.9-μm reflectivity. Thunderstorm processes that lead to small ice crystals at cloud top are discussed, and a possible relationship between updraft strength and 3.9-μm reflectivity is presented.