Urban centers are major sources of anthropogenic carbon dioxide (CO2) and methane (CH4) emissions yet attributing their sources at the city scale is challenging due to the coexistence of multiple, variable emission sources. Stable isotopes of CH4 and CO2 provide a means to constraint these sources by distinguishing between emission signatures. Here, we investigate CH4 and CO2 mole fractions and their isotopic compositions over Montreal, Canada, using coordinated aircraft, UAV, and ground-based measurements. The highest CH4 and CO2 mole fractions were observed during the morning flight when the aircraft descended into the shallow stable boundary layer, where overnight accumulation of surface emissions produced strong vertical gradients. Enhanced afternoon boundary-layer mixing reduced these gradients and lowered observed mole fractions. Isotopic analyses identified traffic-related CO2 enhancements near the Montreal-Trudeau International Airport and suggested natural gas combustion as the dominant downtown CO2 source. In contrast, CH4 isotopic signatures did not allow unambiguous source attribution, highlighting the complexity of urban methane emissions. The results demonstrate that coordinated airborne and ground-based observations, particularly low altitude UAV profiling under stable boundary-layer conditions improve the interpretation of urban greenhouse gas sources.

At 2020 UTC 16 March 2024, the Sundhnuksg & imath;gar crater row located north of the town of Grindav & imath;k on the Reykjanes Peninsula, Iceland, erupted. Forty minutes after the start of the eruption, a significant rain shower started 20 km downwind of the eruption site to the west of the Keflav & imath;k International Airport. Since there was no precipitation over the peninsula prior to the eruption and the precipitation event started shortly after the eruption and downwind from the volcano, we were led to believe that it was linked to the eruption. In the style of a mystery novel, we sought to elucidate the mechanism linking the eruption and the rain shower. Two different mechanisms were initially investigated: 1) the volcano heat and moisture led to convection and 2) the volcanic ash created condensation nuclei. Using radar data, ground-based measurements, and model soundings, it was found that the eruption could not be directly responsible for the precipitation event through either of these mechanisms. As such, another process had to be involved, namely, that the eruption acted as an obstacle to the atmospheric flow causing precipitation. SIGNIFICANCE STATEMENT: Shortly after a volcano in southwest Iceland erupted, an unforecasted heavy rain shower started west of the Keflav & imath;k International Airport, the main airport deserving Reykjavik, Iceland. Such a heavy shower could have disrupted the airport activities and might have been a hazard to landing and departing aircraft should it have occurred over the airport. We investigated how the volcano caused the rainstorm as the rain started shortly after and downwind of the eruption. The uncovered process proved to be unexpected, justifying this publication: It appears that the eruption acted as an obstacle that wind had to leap over, and this sudden ascending motion quickly produced significant precipitation downwind.

The uncertainty of the analysis and the error growth of the forecast from two of the best prediction systems based on radar data assimilation are studied to determine how close we are from accurately forecasting thunderstorms and what additional information would be required to achieve this goal. Even if these two state-of-the-art systems frequently assimilate all available measurements for one, or assimilate radar data every 30 s for the other, considerable uncertainty remains in unobserved atmospheric properties such as temperature or humidity. Worse, limited improvement can be expected as limited direct measurements exist, and essentially all the covariance-based information is being exploited. Unless a massive investment in new measurement technologies occurs, or radically new ideas are proposed, forecast accuracy will remain insufficient to properly simulate the complete lifetime of a thunderstorm, limiting the accuracy and value of threat forecasting.

In a future world where most of the energy must come from intermittent renewable energy sources such as wind or solar energy, it would be more efficient if, for each demand area, we could determine the locations for which the output of an energy source would naturally match the demand fluctuations from that area. In parallel, meteorological weather systems such as midlatitude cyclones are often organized in a way that naturally shapes where areas of greater energy need (e.g., regions with more cold air) are with respect to windier or sunnier areas, and these are generally not collocated. As a result, the best places to generate renewable energy may not be near consumption sites; these may be determined, however, by common meteorological patterns. Using data from a reanalysis of six decades of past weather, we determined the complementarity between different sources of energy as well as the relationships between renewable supply and demand at daily averaged time scales for several North American cities. In general, demand and solar power tend to be slightly positively correlated at nearby locations away from the Rocky Mountains; however, wind power often must be obtained from greater distances and at altitude for energy production to be better timed with consumption. Significance Statement Weather patterns such as high and low pressure systems shape where and when energy is needed for warming or cooling; they also shape how much renewable energy from winds and the sun can be produced. Hence, they determine the regions where more energy is likely to be available in periods of unusually high need for each demand location. Finding where those areas are may result in more timely renewable energy production in the future to help reduce fossil fuel use for energy production.

During near-0 & DEG;C surface conditions, diverse precipitation types (p-types) are possible, including rain, drizzle, freezing rain, freezing drizzle, ice pellets, wet snow, snow, and snow pellets. Near-0 & DEG;C precipitation affects wide swaths of the United States and Canada, impacting aviation, road transportation, power generation and distribution, winter recreation, ecology, and hydrology. Fundamental challenges remain in observing, diagnosing, simulating, and forecasting near-0 & DEG;C p-types, particularly during transitions and within complex terrain. Motivated by these challenges, the field phase of the Winter Precipitation Type Research Multiscale Experiment (WINTRE-MIX) was conducted from 1 February to 15 March 2022 to better understand how multiscale processes influence the variability and predictability of p-type and amount under near-0 & DEG;C surface conditions. WINTRE-MIX took place near the U.S.-Canadian border, in northern New York and southern Quebec, a region with plentiful near-0 & DEG;C precipitation influenced by terrain. During WINTRE-MIX, existing advanced mesonets in New York and Quebec were complemented by deployment of 1) surface instruments, 2) the National Research Council Convair-580 research aircraft with W-and X-band Doppler radars and in situ cloud and aerosol instrumentation, 3) two X-band dual-polarization Doppler radars and a C-band dual-polarization Doppler radar from the University of Illinois, and 4) teams collecting manual hydrometeor observations and radiosonde measurements. Eleven intensive observing periods (IOPs) were coordinated. Analysis of these WINTRE-MIX IOPs is illuminating how synoptic dynamics, mesoscale dynamics, and microscale processes combine to determine p-type and its predictability under near-0 & DEG;C conditions. WINTRE-MIX research will contribute to improving nowcasts and forecasts of near-0 & DEG;C precipitation through evaluation and refinement of observational diagnostics and numerical forecast models.

In the ensemble Kalman filter (EnKF), the covariance localization radius is usually small when assimilating radar observations because of high density of the radar observations. This makes the region away from precipitation difficult to correct using only radar data stating “no echo” if no other observations are available, as there is no reason to correct the background. To correct errors away from innovating radar observations, a multiscale localization (MLoc) method adapted to dense observations like those from radar is proposed. In this method, different scales are corrected successively by using the same reflectivity observations, but with a different degree of smoothing and localization radius at each step. In the context of observing system simulation experiments, single and multiple assimilation experiments are conducted with the MLoc method. Results show that the MLoc assimilation updates areas that are away from the innovative observations and improves on average the analysis and forecast quality in single cycle and cycling assimilation experiments. The forecast gains are maintained until the end of the forecast period, illustrating the benefits of correcting different scales.

Tropospheric and lower‐stratospheric motions at mesoscales and larger are a mixture of waves and two‐dimensional (2‐D) turbulence. Determining their relative importance is necessary, since waves are capable of coordinated systematic momentum transport accompanying the wave propagation, and associated wind forcing, in ways that 2‐D turbulence is not. This can impact weather forecasting. Using a network of ten windprofiler radars in eastern Ontario and western Quebec in Canada, plus an additional one in the Arctic, the relative roles of internal gravity (buoyancy) waves and two‐dimensional turbulence are examined at temporal scales from about 3–4 hrs to several tens of hours (horizontal spatial scales of typically one or two hundred kilometres to a few thousand kilometres), with the purpose of investigating the respective roles of these two distinct characteristic fluid motions as functions of location, season and year. The emphasis is on studies of spectral slope variability, rather than absolute spectral magnitudes, giving a perspective not previously substantially presented. In particular, we have found a frequency band in which gravity‐wave Doppler shifting produces distinctly different spectral slopes than those predicted for 2‐D turbulence, and these differences are employed to distinguish the flow fields. The network used (excluding the Arctic site) covers an area of ∼106 km2 and includes a variety of different terrains. Radial velocities have been recorded on time scales of minutes for data lengths covering durations of up to 12 years. Altitude coverage is from 1 km to typically 14 km, at 500 m resolution. Results suggest a region from ∼2 to ∼5 km altitude (deeper for some radars) where waves are weaker and 2‐D turbulence appears to be generally more significant, but where occasional bursts of gravity‐wave activity can occur, while above typically 6–8 km, gravity waves increase in significance. There are distinct site‐to‐site variations.

Although radar is our most useful tool for monitoring severe weather, the benefits of assimilating its data are often short lived. To understand why, we documented the assimilation requirements, the data characteristics, and the common practices that could hinder optimum data assimilation by traditional approaches. Within storms, radars provide dense measurements of a few highly variable storm outcomes (precipitation and wind) in atmospherically unstable conditions. However, statistical relationships between errors of observed and unobserved quantities often become nonlinear because the errors in these areas tend to become large rapidly. Beyond precipitating areas lie large regions for which radars provide limited new information, yet whose properties will soon shape the outcome of future storms. For those areas, any innovation must consequently be projected from sometimes distant precipitating areas. Thus, radar data assimilation must contend with a double need at odds with many traditional assimilation implementations: correcting in-storm properties with complex errors while projecting information at unusually far distances outside precipitating areas. To further complicate the issue, other data properties and practices, such as assimilating reflectivity in logarithmic units, are not optimal to correct all state variables. Therefore, many characteristics of radar measurements and common practices of their assimilation are incompatible with necessary conditions for successful data assimilation. Facing these dataset-specific challenges may force us to consider new approaches that use the available information differently.

The statistical properties of the radar echoes from biological, precipitation, and ground targets observed with the McGill S-band dual-polarization radar have been used to devise a polarimetric and a nonpolarimetric fuzzy logic algorithm for pixel-by-pixel target identification. Radar observations of migrating birds show distinctly different polarimetric features during their relative approach and departure from the radar site illustrating the dependency of radar parameters on the canting angle and scattering cross section. The devised algorithms have been tested with two independent events, each consisting of 2 h of radar observations with a 5-min temporal resolution. One event consisted of precipitation without birds while the other contained only birds. The misclassifications were 10.12% and 9.6%, respectively, for the two cases for the nonpolarimetric algorithm, and 1.99% and 0.92% for the polarimetric algorithm. The results indicate that even though nonpolarimetric radar membership functions may be considered adequate for separating radar echo returns from birds, precipitation, and ground targets, they are not sufficiently skilled if a greater accuracy is required. Target identification without polarimetric variables especially fails in the region of zero isodop and in precipitation with an echo top below 4 km.

Abstract Historical data provides observational information crucial to our understanding of the evolution of geophysical processes. However, there is a gap between predigital age observations, which are typically handwritten, and data that is discoverable and analysable. The data rescue protocols here address this gap, covering the information lifecycle from handwritten register pages to transcription‐ready content, describing the historical data, the database design for the data rescue, and the development of an application design to transcribe the meteorological information directly from an image file to the database. The preparatory steps necessary to organize, curate, image, and structure the meteorological information, prior to transcribing the historical data, are outlined here in an integrated methodology. The initial organization, the development of an image file nomenclature to link the rescued data to the original source, and the description of a metadata schema to optimize the transcription application are all vital to the process of ensuring traceability and transparency in the data rescue process. Taken together, these steps describe best practices guidelines for similar projects. Although we designed the methodology and application to be used in any data rescue context, our particular concern was to accommodate the needs of citizen scientists. We thus focused on making our application easily maintained, flexible, direct to database, clear, and simple to use. Open Practices This article has earned an Open Data badge for making publicly available the digitally‐shareable data necessary to reproduce the reported results. The data is available at https://citsci.geog.mcgill.ca. Learn more about the Open Practices badges from the Center for Open Science: https://osf.io/tvyxz/wiki.

To properly use radar refractivity data quantitatively, good knowledge of its errors is required. The data quality of refractivity critically depends on the phase measurements of ground targets that are used for the refractivity estimation. In this study, the observational error structure of refractivity is first estimated based on quantifying the uncertainties of phase measurements, data processing, and the refractivity estimation method. New correlations between the time series of phase measurements at different elevation angles and between polarizations are developed to assess the bulk phase variability of individual targets. Then, the observational error of refractivity is obtained by simulating the uncertainties of phase measurements through the original refractivity estimation method. Resulting errors in refractivity are found to be smaller than 1 N-unit in areas densely populated with reliable point-like stationary ground targets but grow as the target density becomes sparse.

This study investigates the how riming in stratiform precipitation impacts polarimetric signatures. Using a vertically pointing Doppler X-band radar, cases can be separated into one of three groups: unrimed to lightly rimed, riming with no bimodal spectra and fall speeds greater than 2.0 m s(-1), and riming with bimodal velocity spectra. By averaging polarimetric variables over a 20 degrees by 10-km box near the X-band radar, different signatures were documented for each of the three groups. These polarimetric signatures were then compared with a simplified T-matrix scattering model. Differential reflectivity Z(DR) was the one polarimetric variable to consistently vary across all three groups. Unrimed to lightly rimed cases had profiles of polarimetric signatures similar to numerous previous studies. Riming cases without detectable bimodal spectra had Z(DR) values on the order of 0.2 dB lower than unrimed to lightly rimed cases, while cases with bimodal spectra had Z(DR) values about 0.2-0.4 dB higher than unrimed to lightly rimed cases. Both signatures were reproduced using populations of aggregates, dendrites, and needles in the T-matrix scattering model. While these signatures show the potential to identify riming, they are not enough larger than measurement biases and case-to-case variability to be confidently used without confirmation from other data sources, such as a vertically pointing radar.

To satisfy the needs of the meteorological and aeroecological communities wanting a simple but effective way of flagging each other’s unwanted echo for a variety of different operational radar systems, we evaluated the ability of an estimate of depolarization ratio (DR) based on differential reflectivity (ZDR) and copolar correlation coefficient (ρHV) measurements to separate both types of echoes. The method was tested with data collected by S- and C-band radars used in the United States and Canada. The DR-based method that does not require training achieved 96% separation between weather and biological echoes. Since the misclassifications are typically caused by isolated pixels in the melting layer or at the edge of echo patterns, the addition of a despeckling algorithm considerably reduces further these false alarms, resulting in an increase in correct identification approaching 99% on test cases.

The likely spread of the current spruce budworm (SBW; Choristoneura fumiferana [Clem.]) outbreak from high to low density areas brings to the forefront a pressing need to understand its dispersal dynamics and to document mass exodus flights in relation to weather patterns. In this study, we used the weather surveillance radar of Val d'Irene in eastern Canada in combination with weather information from the Rapid Update Cycle (RUC) model output to track and document a SBW mass exodus flight that occurred on July 15-16th 2013. Analyses confirmed the potential of using weather radar and RUC data to help assess SBW mass exodus dynamics. Weather surveillance radar data suggested that the mass exodus flight originated from both the northern and southern sides of the St-Lawrence River estuary with most individuals originating from severely defoliated areas on the north shore. During the exodus flight, SBW moths may have covered a distance of over 200 km. Detailed large-scale assessment of this mass exodus flight using radar data allowed for the identification of convergence zones and a liftoff from a lightly defoliated area which has never been documented before. Based on radar and lower tropospheric weather data, SBW dispersed downwind in a rather shallow layer, probably between 400 and 800 m. These results imply that moths were generally dispersing in the vicinity of the top of the temperature inversion zone where both temperature and wind were highest throughout the exodus flight period. We advocate that the use of weather radar technology coupled with data on lower tropospheric weather conditions might benefit other monitoring tools already being used and may also help calibrate SBW atmospheric transport models. (C) 2016 Published by Elsevier B.V. All rights reserved.