Major Weather Events Research Articles

Impacts of Mesonet Observations on Meteorological Surface Analyses

Abstract Given the heterogeneous equipment, maintenance and reporting practices, and siting of surface observing stations, subjective decisions that depend on the application tend to be made to use some observations and to avoid others. This research determines objectively high-impact surface observations of 2-m temperature, 2-m dewpoint, and 10-m wind observations using the adjoint of a two-dimensional variational surface analysis over the contiguous United States. The analyses reflect a weighted blend of 1-h numerical forecasts used as background grids and available observations. High-impact observations are defined as arising from poor observation quality, observation representativeness errors, or accurate observed weather conditions not evident in the background field. The impact of nearly 20 000 surface observations is computed over a sample of 100 analysis hours during 25 major weather events. Observation impacts are determined for each station as well as within broad network categories. For individual analysis hours, high-impact observations are located in regions of significant weather—typically, where the background field fails to define the local weather conditions. Low-impact observations tend to be ones where there are many observations reporting similar departures from the background. When averaged over the entire 100 cases, observations with the highest impact are found within all network categories and depend strongly on their location relative to other observing sites and the amount of variability in the weather; for example, temperature observations have reduced impact in urban areas such as Los Angeles, California, where observations are plentiful and temperature departures from the background grids are small.

Can rodent outbreaks be driven by major climatic events? Evidence from cyclone Nargis in the Ayeyawady Delta, Myanmar

Massive rodent population outbreaks occurred in the Ayeyarwady Delta, Myanmar, in July 2009, 15 months after cyclone Nargis. Satellite imagery with high temporal frequency was used to identify the area and planting time of rice at a landscape scale of > 80 000 ha, and household surveys of farmers were conducted to validate the mapping and to quantify losses. Farmers did not have problems with rodents in 2007-2008; rodents were the principal problem in the 2009 summer and monsoon rice crops. The landscape scale modeling indicated that high rodent densities in 2009 were associated with extended or delayed cropping and harvesting time because of asynchronous planting, and with an increase in the amount of abandoned agricultural land after cyclone Nargis. Asynchronous planting following cyclone Nargis provided abundant high-quality food for an extended period, which in turn led to a lengthened breeding season of rodents. The outbreak of populations 15 months after cyclone Nargis is consistent with the time it would take rodent populations to build from a low base after a major flooding event. To prevent rodent outbreaks effectively, synchronous planting, use of rice varieties with a similar maturation date and good field sanitation are important actions for subsequent rice crops after a major weather event.

The climate gap: environmental health and equity implications of climate change and mitigation policies in California—a review of the literature

Climate change is an issue of great importance for human rights, public health, and socioeconomic equity because of its diverse consequences overall as well as its disproportionate impact on vulnerable and socially marginalized populations. Vulnerability to climate change is determined by a community’s ability to anticipate, cope with, resist, and recover from the impact of major weather events. Climate change will affect industrial and agricultural sectors, as well as transportation, health, and energy infrastructure. These shifts will have significant health and economic consequences for diverse communities throughout California. Without proactive policies to address these equity concerns, climate change will likely reinforce and amplify current as well as future socioeconomic disparities, leaving low-income, minority, and politically marginalized groups with fewer economic opportunities and more environmental and health burdens. This review explores the disproportionate impacts of climate change on vulnerable groups in California and investigates the costs and benefits of the climate change mitigation strategies specified for implementation in the California Global Warming Solutions Act of 2006 (AB 32). Lastly, knowledge gaps, future research priorities, and policy implications are identified.

Teaching and Learning Mathematics through Hurricane Tracking

During hurricane season, maps that track predicted storm paths are commonly seen on television and the Internet. The Weather Channel often receives number-one viewership ratings in regions encountering a major weather event, such as a hurricane or tornado (Kloer 2001). Mathematics teachers can tap into students' curiosity and interest about hurricanes to develop their understanding of mathematical ideas within a real-life context. In this article, we discuss observations and findings after implementing mathematics tasks based on data about hurricanes. Finding patterns and relationships, creating and interpreting graphs, and examining rates of change are just a few of the topics that can be studied. We developed these tasks as part of the Students' Transition Toward Algebra project and have used them with both middle school teachers and students.

Physical effects of weather events on the abundance and diversity of insects in North American forests

We summarize the effects of major weather events such as ice storms, wind storms, and flooding on the abundance and diversity of terrestrial forest insects and their allies. This synthesis indicates that weather events influence both spatial and temporal patterns of forests and insect communities in North American landscapes. The Atlantic and Pacific oceanic sides of the continent are relatively more susceptible to ice and wind storms, respectively. There have been more studies and reports on the responses of forest insects to wind storms, and on economically important subcortical insects than on gall-forming, foliage-feeding, fungal-feeding, litter-dwelling, pollinating, parasitizing, predaceous, root-feeding, and sap-feeding insects. Weather events positively affect populations of subcortical insect species, and impact their colonization patterns and dynamics. Species belonging to genera such as Dendroctonus (Scolytidae) and Monochamus (Cerambycidae) may sometimes cause economic damage by colonizing residual live tree, and dead trees, rendering the wood unsalvageable. Subsequent outbreaks of spruce beetle, Dendroctonus rufipennis (Kirby) and Douglas-fir beetle, Dendroctonus pseudotsugae Hopkins, have been frequently documented in western North American forests following wind events. Wind disturbances have also been observed to accelerate the life-cycles of D. rufipennis and Semanotus litigiosus (Casey) (Cerambycidae), and in rare instances, have caused Monochamus spp. to become primary colonizers. Populations of other important subcortical species have not always increased dramatically following weather events. Foliage- and pollen-feeding insects may experience mortality directly from a weather event or indirectly through habitat alteration. In some cases, forest insects may use storms to migrate to new habitats. Populations of open-habitat and forest specialist litter-dwelling species have increased and decreased, respectively, subsequent to weather events. Forest specialist species generally rebound within a short period of time, suggesting that they are adapted to these periodic weather events. Little is known about the combined effects of post-weather-disturbance management practices such as salvage-logging and prescribed-burning on insects in North American forests.

A Fingerprinting Technique for Major Weather Events

Abstract Advances in numerical weather prediction have occurred on numerous fronts, from sophisticated physics packages in the latest mesoscale models to multimodel ensembles of medium-range predictions. Thus, the skill of numerical weather forecasts continues to increase. Statistical techniques have further increased the utility of these predictions. The availability of large atmospheric datasets and faster computers has made pattern recognition of major weather events a feasible means of statistically enhancing the value of numerical forecasts. This paper examines the utility of pattern recognition in assisting the prediction of severe and major weather in the Middle Atlantic region. An important innovation in this work is that the analog technique is applied to NWP forecast maps as a pattern-recognition tool rather than to analysis maps as a forecast tool. A technique is described that employs a new clustering algorithm to objectively identify the anomaly patterns or “fingerprints” associated with past events. The potential refinement and applicability of this method as an operational forecasting tool employed by comparing numerical weather prediction forecasts with fingerprints already identified for major weather events are also discussed.

Improvements in the weather prediction over the Indian region using regional spectral model

Florida State University Regional Spectral Model (FSURSM) has been installed at India Meteorological Department, New Delhi and test runs have been made. A suitable change in the model code were made to use analysis and forecast fields as initial and boundary conditions for the model from the global T-80 model run at the National Centre for Medium Range Weather Forecasting (NCMRWF). The resolution of the regional spectral model is 0.75° transform grid, which is equivalent to the resolution of a T-170 global model. The model has been integrated for 3 days and studies undertaken to test the performance of the regional model in respect of some of the major weather events that occurred during December 2002 and February 2003. Using initial analyses from ECMWF and NCMRWF, the impact of initial fields on model forecast is also investigated. The case studies show that the forecasts from the regional spectral model are reasonably close to the observed features. The intensity and movement of model simulated systems are in good agreement with the observations.

Zoobenthic variability associated with a flood and drought in the Hawkesbury estuary, New South Wales: Some consequences for environmental monitoring

Changes in the Hawkesbury zoobenthos associated with a major flood and drought are described and comparisons made in the patterns of change among estuarine reaches and among sites within reaches. The consequences for environmental monitoring and management are discussed. Replicate grab samples were taken from four sites in each of the lower, middle and upper reaches. Reductions in the mean number of species per grab (S) following the flood were significant only in the lower reaches and at one site in the middle reaches. Increases in S accompanied the drought in all reaches but intra-reach variation in temporal patterns occurred in both S and in the most abundant species found. Thus, major weather events are associated with temporal changes whose patterns differ on both small and large spatial scales. Consequently, the results from fixed-factor sampling designs, which are widely used, may be unrepresentative of other areas. Unfortunately, the alternative approach of stratified random sampling will probably be both prohibitively expensive and difficult to implement in the complex estuarine benthic habitat. Further, short-term studies will probably be grossly unrepresentative of natural temporal variation. Attempts to reduce expenses by using only one or two abundant species as characterising communities or as indicators of physicochemical conditions may be unreliable because of variation in both time and space in dominant species and the lack of pollution-response knowledge for local species.

The Yield of Avalanche Snow at Rogers Pass, British Columbia, Canada

Abstract The annual mass of snow moved by avalanches was observed at 45 avalanche paths over a period of 19 years by measuring the volume and density of each individual avalanche. Several other methods to estimate the total annual avalanche mass were applied but they were found less efficient owing to inaccuracy and difficulties in timing. For the data set, the yield ratio, i.e. the percentage of snow removed annually by avalanches, had a mean value of 11.2% and a 30 year maximum of 30.9%. The yield ratio varied strongly among avalanche paths and yearly, and could not be explained satisfactorily. The exposure to wind, inclination of the avalanche track, control by artillery, and the winter weather can all have an influence on the value of the parameter. The temporal variability of avalanche yield is erratic but weather patterns frequently associated with high or low yield ratios could be identified. High values are usually associated with large avalanches triggered by major weather events in the late part of the winter.

The Yield of Avalanche Snow at Rogers Pass, British Columbia, Canada

AbstractThe annual mass of snow moved by avalanches was observed at 45 avalanche paths over a period of 19 years by measuring the volume and density of each individual avalanche. Several other methods to estimate the total annual avalanche mass were applied but they were found less efficient owing to inaccuracy and difficulties in timing.For the data set, the yield ratio, i.e. the percentage of snow removed annually by avalanches, had a mean value of 11.2% and a 30 year maximum of 30.9%. The yield ratio varied strongly among avalanche paths and yearly, and could not be explained satisfactorily. The exposure to wind, inclination of the avalanche track, control by artillery, and the winter weather can all have an influence on the value of the parameter.The temporal variability of avalanche yield is erratic but weather patterns frequently associated with high or low yield ratios could be identified. High values are usually associated with large avalanches triggered by major weather events in the late part of the winter.