A surface wind speed map for Mexico based on NARR and observational data

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.

In remote Canada, sparse meteorological stations hinder reliable snow data collection, especially for assessing historical records. Yet, these data are crucial to evaluate the effect of climate change on ecosystems and species. Changes in snow cover influence wildlife ecology and can impact body condition, survival, or reproduction. This study validated the SNOWPACK model, forced with North American Regional Reanalysis (NARR) data, to estimate snow cover data for Ram Mountain, Alberta. We used snow depth data from five stations covering the 2022–2023 and 2023–2024 snow seasons for validation. We adjusted NARR radiation and precipitation data and SNOWPACK phase change temperature to improve model fit with snow depth and end date observations. The simulation, after bias calibration, showed an average error of less than 6 cm for snow depth and plus or minus sixteen days for the snow cover end date over the two winters. This parameterized model was then used to reconstruct snow cover data from 1979 to 2024. Though no clear trend in snow cover duration or start/end dates emerged, snow depth increased by an average of 5 mm/year. Our findings suggest that SNOWPACK can estimate snow cover characteristics in remote areas, making them valuable for reconstructing long-term snow cover trends in alpine environments.

Snow cover plays a key role in the climate system by influencing the transfer of energy and mass between the soil and the atmosphere. In particular, snow water equivalent (SWE) is of primary importance for climatological and hydrological processes and is a good indicator of climate variability and change. Efforts to quantify SWE over land from spaceborne passive microwave measurements have been conducted since the 1980s, but a more suitable method has yet to be developed for hemispheric-scale studies. Tools such as snow thermodynamic models allow for a better understanding of the snow cover and can potentially significantly improve existing snow products at the regional scale. In this study, the use of three snow models [SNOWPACK, CROCUS, and Snow Thermal Model (SNTHERM)] driven by local and reanalysis meteorological data for the simulation of SWE is investigated temporally through three winter seasons and spatially over intensively sampled sites across northern Québec. Results show that the SWE simulations are in agreement with ground measurements through three complete winter seasons (2004/05, 2005/06, and 2007/08) in southern Québec, with higher error for 2007/08. The correlation coefficients between measured and predicted SWE values ranged between 0.72 and 0.99 for the three models and three seasons evaluated in southern Québec. In subarctic regions, predicted SWE driven with the North American Regional Reanalysis (NARR) data fall within the range of measured regional variability. NARR data allow snow models to be used regionally, and this paper represents a first step for the regionalization of thermodynamic multilayered snow models driven by reanalysis data for improved global SWE evolution retrievals.

Spatial and temporal near-surface air temperature variabilities and trends were analyzed for 30 locations in Illinois based on annual data derived from station records and the North American Regional Reanalysis (NARR) dataset from 1979 to 2006. A high correlation was found between the two datasets for interannual variability at most locations. Temperatures were generally higher at urban stations than non-urban stations, while non-urban NARR data points showed higher temperatures than urban data points. Differences in medians were not statistically significant in either dataset. Significant positive temperature trends were found in the majority of the weather stations and in all NARR data points, with generally stronger trends in the NARR data. Observed trends from the station records were generally stronger in metropolitan areas and weaker for non-urban areas, but the reanalysis data did not show a remarkable difference between urban and non-urban trends.

ABSTRACTUpper-level winds are primarily measured by tracking the position of radiosonde balloons as they rise through the atmosphere. Radiosondes are released from a global network at 0000 utc and 1200 utc each day, and data are quickly available to the world's weather services, as well as to the public. The network is relatively coarse spatially, and there are normally only two soundings per day. In an endeavour to provide better spatial and temporal upper-level regional wind measurements, the O-QNet (a demonstration very high frequency wind profiler network for Ontario and Quebec) was installed and has been operated by York, Western, and McGill Universities with support from Mardoc Inc., The Weather Network, and Environment and Climate Change Canada. Initial funding was provided by the Canada Foundation for Innovation, the Ontario Innovation Trust, and Mardoc Inc. The present paper reports primarily on comparisons between winds measured by the O-QNet profilers and the winds reported in the North American Regional Reanalysis (NARR). There is good agreement between the two, which provides support for the use of NARR data to investigate longer term variations in upper-level winds associated with climate change for this region.

[1] When dQ/dt-Q plots of stream recession are constructed for individual recession events, the slopes of the dQ/dt-Q curves are near constant in log space, but the intercepts vary seasonally. Because the intercepts increase during the summer (indicating an increase in the recession rate at a given discharge), it has often been assumed that increased evapotranspiration (ET) leads to increased recession rates. To test this assumption, we related the intercepts of dQ/dt-Q curves from 72 recession events to the concurrent ET and watershed moisture storage as determined from the National Center for Environmental Prediction (NCEP) North American Regional Reanalysis (NARR) data set. The analysis suggests that at least for the nine watersheds from Illinois and New York we studied, shifts in recession rate during prolonged rain-free periods had little linkage to concurrent ET. Instead, we observe that the shifting has a moderately strong linkage to watershed moisture storage during the recession event. While this seeming lack of dependence on ET during these prolonged rain-free periods is not necessarily reflective of more normal conditions, we suggest it provides some insight into underlying subsurface controls at the watershed scale. In particular, we hypothesize that the shift in intercept in dQ/dt-Q curves results from spatial heterogeneities in watershed surficial geology; under dryer conditions near-stream subsurface zones with high hydraulic conductivities contribute most streamflow but under wetter conditions subsurface zones in upland areas with lower hydraulic conductivities also contribute.

Winds at 80 m elevation have been estimated with a stability-based height adjustment scheme to study the feasibility of utility-scale wind energy offshore of North Carolina. Data sources are the daily ASCAT 10 m wind field as measured by the METOP-A satellite, the North American Regional Reanalysis (NARR) estimates of near-surface atmospheric temperature, pressure and humidity and the National Climate Data Center's optimally-interpolated Advanced Very High Resolution Radiometer (AVHRR-OI) sea surface temperature (SST). The study focuses on the 2008–2012 time period. The COARE V3.0 algorithm is used to provide the stability-based height adjustment scheme. Data from six buoys have been used to establish validity of the remotely-sensed and modeled fields. A variety of results were produced, including long-term average wind speeds, wind power density and capacity factor at hub height, and a monthly climatology of these quantities. In this note the focus is on seasonal variations utilizing the monthly climatologies. Using the climatologies the largest differences between the neutral and stability-based schemes occur in winter and spring when and where stable atmospheric conditions are most common. There is a significant difference in the annual variation of atmospheric stability north and south of Cape Hatteras. Only the inner shelf region is prone to stable conditions during winter and spring south of Cape Hatteras, whereas the full width of the shelf is prone to stable conditions from fall into early summer north of Cape Hatteras. Where stable conditions occur the stability-based 80 m wind speeds are 2–3 m/s greater than 80 m neutral wind speeds, and where unstable conditions occur the stability-based 80 m wind speeds are 0.5–1 m/s less than the 80 m neutral wind speeds. The result is a pattern in the 80 m stability-based winds with notably greater speeds near the coastline than expected from a neutral height adjustment.

The California Pacific coast is characterized by considerable wind resource and areas of dense population, propelling interest in offshore wind energy as the United States moves toward a sustainable and decarbonized energy future. Reanalysis models continue to serve the wind energy community in a multitude of ways and the need for validation in locations where observations have been historically limited, such as offshore environments, is strong. The U.S. Department of Energy (DOE) owns two lidar buoys that collect wind speed observations across the wind turbine rotor layer along with meteorological and oceanographic data near the surface to characterize the wind resource. Lidar buoy data collected from recent deployments off the northern California coast near Humboldt County and the central California coast near Morro Bay allow for validation of commonly used reanalysis products. In this article, wind speeds from the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2), the Climate Forecast System version 2 (CFSv2), the North American Regional Reanalysis (NARR), the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5), and the analysis system of the Rapid Refresh (RAP) are validated at heights within the wind turbine rotor layer ranging from 50 m to 100 m. The validation results offer guidance on the performance and uncertainty associated with utilizing reanalyses for offshore wind resource characterization, providing the offshore wind energy community with information on the conditions that lead to reanalysis error. At both California coast locations, the reanalyses tend to underestimate the observed rotor-level wind resource. Occasions of large reanalysis error occur in conjunction with wind ramps, stable atmospheric conditions, wind speeds associated with peak turbine power production (> 10 m s-1), and mischaracterization of the diurnal wind speed cycle in summer months.

Lake-effect snow (LES) storms pose numerous hazards, including extreme snowfall and blizzard conditions, and insight into the large-scale precursor conditions associated with LES can aid local forecasters and potentially allow risks to be mitigated. In this study, a synoptic climatology of severe LES events over Lakes Erie and Ontario was created using an updated methodology based on previous studies with similar research objectives. Principal component analysis (PCA) coupled with cluster analysis (CA) was performed on a case set of LES events from a study domain encompassing both lakes, grouping LES events with similar spatial characteristics into the primary composite structures for LES. Synoptic scale composites were constructed for each cluster using the North American Regional Reanalysis (NARR). Additionally, one case from each cluster was simulated using the Weather Research and Forecast (WRF) model to analyze mesoscale conditions associated with each of the clusters. Three synoptic setups were identified that consisted of discrepancies, mostly in the surface fields, from a common pattern previously identified as being conducive to LES, which features a dipole and upper-level low pressure anomaly located near the Hudson Bay. Mesoscale conditions associated with each composite support differing LES impacts constrained to individual lakes or a combination of both.

Alaska is experiencing effects of global climate change that are due, in large part, to the positive feedback mechanisms associated with polar amplification. The major risk factors include loss of sea ice and glaciers, thawing permafrost, increased wildfires, and ocean acidification. Reanalyses, integral to understanding mechanisms of Alaska’s past climate and to helping to calibrate modeling efforts, are based on the output of weather forecast models that assimilate observations. This study evaluates temperature and precipitation from five reanalyses at monthly and daily time scales for the period 1979–2009. Monthly data are evaluated spatially at grid points and for six climate zones in Alaska. In addition, daily maximum temperature, minimum temperature, and precipitation from reanalyses are compared with meteorological-station data at six locations. The reanalyses evaluated in this study include the NCEP–NCAR reanalysis (R1), North American Regional Reanalysis (NARR), Climate Forecast System Reanalysis (CFSR), ERA-Interim, and the Modern-Era Retrospective Analysis for Research and Applications (MERRA). Maps of seasonal bias and standard deviation, constructed from monthly data, show how the reanalyses agree with observations spatially. Cross correlations between the monthly gridded and daily station time series are computed to provide a measure of confidence that data users can assume when selecting reanalysis data in a region without many surface observations. A review of natural hazards in Alaska indicates that MERRA is the top reanalysis for wildfire and interior-flooding applications. CFSR is the recommended reanalysis for North Slope coastal erosion issues and, along with ERA-Interim, for heavy precipitation in southeastern Alaska.

Potential (PET) and reference (RET) evapotranspiration are computed at 2-kilometer, daily resolution throughout Florida as part of a long-term multiagency partnership between the U.S. Geological Survey and all 5 Water Managements Districts in Florida. In 2015, a pilot study was initiated to historically extend (1985 to 1995) this database and potentially improve calculations with (1) albedos measured by the Moderate Resolution Imaging Spectroradiometer (MODIS) and (2) forecasts of air temperature, relative humidity and wind speed from climate models such as North American Regional Reanalysis (NARR) simulations. During the pilot study, experiments revealed that radial-basis functions created superior regional distributions of air temperature, relative humidity and wind speed data over Florida, in contrast with inverse-distance interpolation that was plagued with "bulls-eye" effects. Due to sparse historical weather station data, NARR simulation results were used with radial-basis interpolation to historically extend the database from 1985 to 1995, for better characterization of the long-term variability in PET and RET. In summary, the pilot study improved some data sources, while revealing previously unknown variability in PET and RET created by the "covered karst" terrain of central Florida, urbanization, and flooding events in regional wetlands such as the subtropical south Florida Everglades.

The effects of hydro-meteorological and surface variables on the frequency of Asian dust events (FAE) were investigated using ground station and satellite-based data. Present weather codes 7, 8, and 9 derived from surface synoptic observations (SYNOP) were used for counting FAE. Surface wind speed (SWS), air temperature (Ta), relative humidity (RH), and precipitation were analyzed as hydro-meteorological variables for FAE. The Normalized Difference Vegetation Index (NDVI), land surface temperature (LST), and snow cover fraction (SCF) were used to consider the effects of surface variables on FAE. The relationships between FAE and hydro-meteorological variables were analyzed using Z-score and empirical orthogonal function (EOF) analysis. Although all variables expressed the change of FAE, the degrees of expression were different. SWS, LST, and Ta (indices applicable when Z-score was 0. EOF analysis for the FAE showed the seasonal cycle, change pattern, and surface influences related to dryness condition for the FAE. The intensity of SWS was the main cause for change of FAE, but surface variables such as LST, SCF, and NDVI also were expressed because wet surface conditions suppress FAE. These results demonstrate that not only SWS and precipitation, but also surface variables, are important and useful precursors for monitoring Asian dust events.

Motivated by the intensity and severity of winds and temperature falls that frequently accompany rapidly developing cold fronts in northern Utah, this paper presents a 25-yr climatology of strong cold frontal passages over the Intermountain West and adjoining western United States. Using conventional surface observations and the North American Regional Reanalysis, strong cold frontal passages are identified based on a temperature fall of 7°C or greater in a 2–3-h period, a concurrent pressure rise of 3 hPa or greater, and the presence of a large-scale 700-hPa temperature gradient of at least 6°C (500 km)−1. The number of strong cold frontal passages exhibits a strong continental signature with very few events (<10) along the Pacific coast and more than 200 events east of the Continental Divide. The number of events increases dramatically from the Cascade Mountains and Sierra Nevada to northern Utah, indicating that the Intermountain West is a frequent cold front breeding ground. A composite of the 25 strongest events at Salt Lake City (based on the magnitude of the temperature fall) reveals that confluent deformation acting on a broad baroclinic zone over central Nevada commonly initiates Intermountain frontogenesis. The confluent deformation develops in southwesterly large-scale flow and appears to be enhanced by flow deflection around the Sierra Nevada. Quasi-stationary development and intensification of the southwest–northeast-oriented cold front then occurs as a mobile upper-level trough approaches from the west. The front becomes mobile as cold advection and ascent associated with the upper-level trough overtake the low-level front. Cloud and precipitation observations suggest that differential diabatic heating contributes to the rapid frontal intensification in many events.

Extreme fire behavior in the San Francisco Bay Area (SFBA) has historically been associated with strong offshore wind events referred to locally as Diablo winds. A 17 year surface-based climatological analysis was performed to establish a definition of Diablo winds and to identify their frequency and spatial distribution. Synoptic composites of events were constructed using North American Regional Reanalysis, and high resolution Weather Research and Forecasting (WRF) model simulations were used to investigate the mesoscale dynamics of three significant Diablo wind events. Diablo winds were defined as dry northeasterly, downslope winds that occur in the SFBA with minimum sustained wind speeds of 6 m s-1. Climatological analysis results illustrate that Diablo winds most frequently impact the Coast Ranges nearest the Sacramento Valley and occur, on average, 2.5 times annually. The highest monthly frequency occurs in October when live fuel moisture is at its driest, creating the most severe fire danger for the SFBA during that time. Numerical simulations of significant Diablo events revealed that Diablo winds have complex character with contributions from small-scale downslope winds, as well as large-scale mountain waves interacting with terrain.

Large Lakes-A Vital Resource and International Heritage