US Weather Research Articles

Increased drought and extreme events over continental United States under high emissions scenario

The frequency, severity, and extent of climate extremes in future will have an impact on human well-being, ecosystems, and the effectiveness of emissions mitigation and carbon sequestration strategies. The specific objectives of this study were to downscale climate data for US weather stations and analyze future trends in meteorological drought and temperature extremes over continental United States (CONUS). We used data from 4161 weather stations across the CONUS to downscale future precipitation projections from three Earth System Models (ESMs) participating in the Coupled Model Intercomparison Project Phase Six (CMIP6), specifically for the high emission scenario SSP5 8.5. Comparing historic observations with climate model projections revealed a significant bias in total annual precipitation days and total precipitation amounts. The average number of annual precipitation days across CONUS was projected to be 205 ± 26, 184 ± 33, and 181 ± 25 days in the BCC, CanESM, and UKESM models, respectively, compared to 91 ± 24 days in the observed data. Analyzing the duration of drought periods in different ecoregions of CONUS showed an increase in the number of drought months in the future (2023–2052) compared to the historical period (1989–2018). The analysis of precipitation and temperature changes in various ecoregions of CONUS revealed an increased frequency of droughts in the future, along with longer durations of warm spells. Eastern temperate forests and the Great Plains, which encompass the majority of CONUS agricultural lands, are projected to experience higher drought counts in the future. Drought projections show an increasing trend in future drought occurrences due to rising temperatures and changes in precipitation patterns. Our high-resolution climate projections can inform policy makers about the hotspots and their anticipated future trajectories.

Prospects for Machine Learning Activity within the United States National Weather Service

Abstract The National Weather Service (NWS) Office of Science and Technology Integration commissioned a report to assess the status of artificial intelligence (AI) and machine learning (ML) activity within the agency with a view toward identifying existing obstacles and recommending future directions. The purpose of this essay is to communicate the steps that the NWS plans to take to realize the potential benefits of AI in operations. AI activities are growing rapidly within atmospheric sciences, and the NWS is part of this growth. However, the activity is fragmented and lacks the needed infrastructure for improved coordination of effort. Current obstacles to progress include insufficient workforce training in AI/ML, a lack of curated datasets and software that can be used for development and evaluation of these approaches, the absence of a centralized clearing house available to NWS personnel for technical expertise and consultation, limited operational compute resources, and a lack of a clear end-to-end project pathway that encompasses exploration, development, testbed/proving ground, and operational implementation. These limitations are addressable. Training materials specific to NWS interests can be developed through collaboration with existing NOAA centers. Establishing a reference library staffed with AI/ML consultants tasked with collaborating with operational units would reduce siloed efforts and enhance productivity. Establishing funding vehicles for theme-based projects with a sustainable pathway through operational implementation would help bridge the research-to-operations “valley of death.” Given the growth of AI/ML across the U.S. Weather Enterprise and the already substantial involvement of academic and private sector entities, these developments within the NWS will be of interest to the atmospheric science field.

Time-varying causal impacts of the continental US weather risks on food price

Time-varying causal impacts of the continental US weather risks on food price

Tropospheric parameters from multi-GNSS and numerical weather models: case study of severe precipitation and flooding in Germany in July 2021

Storms and floods are the most frequent natural disaster in western and central Europe. Due to climate change, intensive storms with prolonged rain episodes will continue to cause even more destructive flooding. The good understanding and forecasting of such events are thus of uttermost importance. One of the ways to improve weather forecasts is the assimilation of external data, such as the global navigation satellite systems (GNSS). In this study, a preparation of the multi-GNSS tropospheric products—zenith total delays, tropospheric gradients and slant total delays—for future operational assimilation is shown. For a severe precipitation event in July 2021 in Germany, the GNSS parameters from three systems—GPS, GLONASS and Galileo—are compared to three Numerical Weather Models (NWMs)—ERA5 reanalysis of ECMWF, ICON run by the German Weather Service and GFS run by the US Weather Service. The flood that followed the rainfall was the deadliest natural disaster in Germany since 1962. The results show that all considered GNSS solutions have a similar level of agreement with the NWMs. However, for the flood region in western Germany, the biases from the multi-GNSS solutions are smaller compared to the GPS-only solution. From the models, ICON has the highest agreement with the GNSS data for all considered tropospheric parameters. The best agreement with the ICON is probably due to its high horizontal resolution and, thus, low representative errors and the fine tuning of DWD’s regional model for the specific region (Germany).

Storms and Swarms: The Role of the US Army Signal Corps' Weather Observers during the Rocky Mountain Locust Plague of the 1870s

Locust outbreaks have occurred around the world throughout history. But they did not pose a serious problem in the United States until the explosion of westward settlement in the aftermath of the Civil War. An insect known as the Rocky Mountain locust, which became migratory when under environmental stress, was the culprit. Earlier outbreaks had created problems for farmers in the Great Plains, but the massive infestation in the mid-1870s caused extensive damage and threatened to halt the nation's expansion to the Pacific. An unlikely ally in the fight against the locusts came in the form of the network of weather observers belonging to the US Army Signal Corps. In conjunction with its mission for providing military communications, the Signal Corps became responsible for establishing and operating the US weather bureau from 1870 to 1891. Its national network of weather stations was well suited for providing reports on the locust outbreaks. Working with the commission of professional scientists formed to study the problem and find solutions, the Signal Corps' observers contributed vital information to support that effort. The partnership between the commission and the army represented an early attempt by the federal government to use applied science to tackle a national problem. Ultimately, the farmers themselves brought about the demise of the Rocky Mountain locust. By cultivating the locust's breeding grounds in the river valleys and using the land for grazing, the farmers drove the Rocky Mountain locust to extinction.

Quantifying long-term phenological patterns of aerial insectivores roosting in the Great Lakes region using weather surveillance radar.

Organisms have been shifting their timing of life history events (phenology) in response to changes in the emergence of resources induced by climate change. Yet understanding these patterns at large scales and across long time series is often challenging. Here we used the US weather surveillance radar network to collect data on the timing of communal swallow and martin roosts and evaluate the scale of phenological shifts and its potential association with temperature. The discrete morning departures of these aggregated aerial insectivores from ground-based roosting locations are detected by radars around sunrise. For the first time, we applied a machine learning algorithm to automatically detect and track these large-scale behaviors. We used 21 years of data from 12 weather surveillance radar stations in the Great Lakes region to quantify the phenology in roosting behavior of aerial insectivores at three spatial levels: local roost cluster, radar station, and across the Great Lakes region. We show that their peak roosting activity timing has advanced by 2.26 days per decade at the regional scale. Similar signals of advancement were found at the station scale, but not at the local roost cluster scale. Air temperature trends in the Great Lakes region during the active roosting period were predictive of later stages of roosting phenology trends (75% and 90% passage dates). Our study represents one of the longest-term broad-scale phenology examinations of avian aerial insectivore species responding to environmental change and provides a stepping stone for examining potential phenological mismatches across trophic levels at broad spatial scales.

Trends of extreme US weather events in the changing climate

Trends in extreme 100-y events of temperature and rainfall amounts in the continental United States are estimated, to see effects of climate change. This is a nontrivial statistical problem because climate change effects have to be extracted from "noisy" weather data within a limited time range. We use nonparametric Bayesian methods to estimate the trends of extreme events that have occurred between 1979 and 2019, based on data for temperature and rainfall. We focus on 100-y events for each month in [Formula: see text] geographical areas looking at hourly temperature and 5-d cumulative rainfall. Distribution tail models are constructed using extreme value theory (EVT) and data on 33-y events. This work shows it is possible to aggregate data from spatial points in diverse climate zones for a given month and fit an EVT model with the same parameters. This surprising result means there are enough extreme event data to see the trends in the 41-y record for each calendar month. The yearly trends of the risk of a 100-y high-temperature event show an average 2.1-fold increase over the last 41 y of data across all months, with a 2.6-fold increase for the months of July through October. The risk of high rainfall extremes increases in December and January 1.4-fold, but declines by 22% for the spring and summer months.

Polarization performance simulation for the GeoXO atmospheric composition instrument: NO2 retrieval impacts

Abstract. NOAA's Geostationary Extended Observations (GeoXO) constellation will continue and expand on the capabilities of the current generation of geostationary satellite systems to support US weather, ocean, atmosphere, and climate operations. It is planned to consist of a dedicated atmospheric composition instrument (ACX) to support air quality forecasting and monitoring by providing capabilities similar to missions such as TEMPO (Tropospheric Emission: Monitoring Pollution), currently planned to launch in 2023, as well as OMI (Ozone Monitoring Instrument), TROPOMI (TROPOspheric Monitoring Instrument), and GEMS (Geostationary Environment Monitoring Spectrometer) currently in operation. As the early phases of ACX development are progressing, design trade-offs are being considered to understand the relationship between instrument design choices and trace gas retrieval impacts. Some of these choices will affect the instrument polarization sensitivity (PS), which can have radiometric impacts on environmental satellite observations. We conducted a study to investigate how such radiometric impacts can affect NO2 retrievals by exploring their sensitivities to time of day, location, and scene type with an ACX instrument model that incorporates PS. The study addresses the basic steps of operational NO2 retrievals: the spectral fitting step and the conversion of slant column to vertical column via the air mass factor (AMF). The spectral fitting step was performed by generating at-sensor radiance from a clear-sky scene with a known NO2 amount, the application of an instrument model including both instrument PS and noise, and a physical retrieval. The spectral fitting step was found to mitigate the impacts of instrument PS. The AMF-related step was considered for clear-sky and partially cloudy scenes, for which instrument PS can lead to errors in interpreting the cloud content, propagating to AMF errors and finally to NO2 retrieval errors. For this step, the NO2 retrieval impacts were small but non-negligible for high NO2 amounts; we estimated that a typical high NO2 amount can cause a maximum retrieval error of 0.25×1015 molec. cm−2 for a PS of 5 %. These simulation capabilities were designed to aid in the development of a GeoXO atmospheric composition instrument that will improve our ability to monitor and understand the Earth's atmosphere.

A multi-objective paleo-informed reconstruction of western US weather regimes over the past 600 years

A multi-objective paleo-informed reconstruction of western US weather regimes over the past 600 years

A deep learning framework for predicting burglaries based on multiple contextual factors

One of the most common types of crime, burglary often results in serious psychological trauma and has financial consequences. Predicting burglaries is a challenging task due to their high degree of randomness. In this study, we propose predicting burglaries based on various contextual factors and incorporating these factors in a unique deep learning framework – DeePrison (DEEp learning framework for Predicting buRglarIes baSed On multiple coNtextual factors). DeePrison is able to capture the dependencies between burglaries and their different contextual factors with respect to time and space and thus provide accurate burglary prediction. Our framework incorporates multiple contextual factors derived from different sources, such as socio-demographic attributes from the US Census Bureau and weather conditions generated from the US National Weather Service. We highlight the contribution of unique contextual factors, such as historical records of criminals’ behavioral patterns. We demonstrate our method’s performance, using two large real-world datasets of burglary records from different areas in the world: Israel and New York City. DeePrison significantly outperforms the state-of-the-art solutions for different types of regions, with different granularity levels, obtaining an F1 score and AUC above 0.64 and 0.73 respectively.

Exploratory Data Analysis of Accuracy of US Weather Forecastes

How reliable are the weather forecasts? Based on data collected from onehundred and thirteen cities in the United States over 38 months on threevariables, maximum temperature, minimum temperature and precipitation,accuracy of the weather forecasts was examined. The same day forecast hasbeen extremely accurate, especially for the maximum temperature, whilethe forecast errors and variability increase as forecasts go further out indays. Some cities have larger or smaller forecast errors than the others.For long-term weather forecasts, the maximum and minimum temperatureforecast errors has decreasing correlations overtime, respectively; However,the correlation between maximum and minimum temperature forecast errorsis positive and increasing overtime. The 7-days forecast errors of precipitationare pretty accurate.

Distributed Beams: Concept of Operations for Polarimetric Rotating Phased Array Radar

Important requirements for a future generation of weather surveillance radars include improvements in data quality and more rapid update of volumetric data. Phased array radar (PAR) is a candidate technology capable of providing the required functionality. The rotating PAR (RPAR) is a potential architecture that could improve the capabilities of the current parabolic-reflector-based US Weather Surveillance Radar—1988 Doppler (WSR-88D) operational network and is more affordable than other candidate PAR architectures. However, RPAR concept of operations that support observational needs has to be developed. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Distributed Beams</i> (DB) technique introduced in this article provides a way to either reduce the scan times or to reduce the variance of radar-variable estimates by azimuthally spoiling the transmit beam while receiving multiple digital beams as the radar rotates in azimuth. Specifically, the rotation speed of the pedestal is derived from the duration of the coherent processing interval (CPI) to produce the desired spatial sampling. This results in beams from subsequent CPIs in approximately the same directions, which increases the number of available data samples for processing. The increased number of available samples can be coherently processed to reduce the variance of estimates. Alternatively, by reducing the number of samples per CPI and increasing the RPAR’s rotation rate, the scan time can be reduced without increasing the variance of estimates. Results presented demonstrate both applications of the DB technique for dual-polarization observations. Given that this technique makes use of spoiled transmit beams, its benefits come at the expense of degraded angular resolution (beamwidth and sidelobe levels), and reduced sensitivity compared with the use of pencil beams. The technique could be implemented as part of an RPAR concept of operations to meet requirements for the future weather surveillance network if certain tradeoffs are accounted for in the radar design process.

New weather indices for China: based on DCC-GARCH and GRU models

Because China is a major production and operations hub, it is at the centre of the world's supply chain. However, as weather can significantly affect supply chain operations, to better hedge risk, weather derivatives need to be introduced to Chinese financial markets to secure multinational supply chains. Using historical records over a decade, three models are proposed and new weather indices are created and improved using DCC-GARCH and GRU models. A simulation test from 2008 to 2017 data proved the indices to be feasible and stable, and matched 92% of the risk across two dimensions: 1) the changing Chinese weather data; 2) a connection with US weather index.

New weather indices for China: based on DCC-GARCH and GRU models

Because China is a major production and operations hub, it is at the centre of the world's supply chain. However, as weather can significantly affect supply chain operations, to better hedge risk, weather derivatives need to be introduced to Chinese financial markets to secure multinational supply chains. Using historical records over a decade, three models are proposed and new weather indices are created and improved using DCC-GARCH and GRU models. A simulation test from 2008 to 2017 data proved the indices to be feasible and stable, and matched 92% of the risk across two dimensions: 1) the changing Chinese weather data; 2) a connection with US weather index.

Detecting and Tracking Communal Bird Roosts in Weather Radar Data

The US weather radar archive holds detailed information about biological phenomena in the atmosphere over the last 20 years. Communally roosting birds congregate in large numbers at nighttime roosting locations, and their morning exodus from the roost is often visible as a distinctive pattern in radar images. This paper describes a machine learning system to detect and track roost signatures in weather radar data. A significant challenge is that labels were collected opportunistically from previous research studies and there are systematic differences in labeling style. We contribute a latent-variable model and EM algorithm to learn a detection model together with models of labeling styles for individual annotators. By properly accounting for these variations we learn a significantly more accurate detector. The resulting system detects previously unknown roosting locations and provides comprehensive spatio-temporal data about roosts across the US. This data will provide biologists important information about the poorly understood phenomena of broad-scale habitat use and movements of communally roosting birds during the non-breeding season.

Ladder Fuel Treatments Effect Burn Area of Forest Fires in Semi-Arid High Elevation Climates

Forest fires destroy millions of acres, threaten populations, and are becoming increasingly severe. Efforts to reduce the starting of wildfires has led to increasing quantities of ladder fuels. Ladder fuels are any fuels that create a vertical continuity in a forest. When a fire does occur, these ladder fuels promote the spread of fires to the forest crown. We hypothesized that if the density of ladder fuel is reduced, the burn area in a semi-arid high-altitude forest will decrease. To test the hypothesis, a series of computer simulations were run testing 50 random ignition sites in a representative semi-arid high elevation forest with different levels of ladder fuel treatment using the FlameMap wildfire simulator with US Forest Service landscape and weather data. There was a large variation in the extent of the fires based on the ignition location, ranging from 13 to 22,506 acres. The study showed a statistically significant reduction in average total burn area after 10 days when 5% or more of the total acreage had been treated (p &amp;lt; 0.01). There were further reductions in burn area with increasing ladder fuel treatment from a 9% reduction with 5% treatment to a 51% reduction with 20% treatment (p &amp;lt; 0.001). This study supports the effectiveness of ladder fuel treatments in reducing burn area.

POSSIBILITIES OF GLOBAL AND REGIONAL MODELS APPLICATION AT PREDICTION OF RAINFALL GENERATED MUDFLOWS

The article considers the compilation of a short-term forecast of mudflow forming sediments using numerical forecasting methods, since this factor is an important condition for the preparation of rainfall mudflow forecasts, it is necessary to determine the degree of accuracy and efficiency of the methodology by which the forecast was made. Accurate and reliable forecasts are the guarantor of increasing the effectiveness of protecting the population. To forecast rainfall, the US Weather Bureau model was used and the results of forecasting rainfall and debris flows of storm origin for Ile-Alatau weather stations are shown. The results of a short-term forecast of precipitation with a lead time of 1 day and with a justification of 83% were also illustrated. For forecasting mudflows using well-known techniques, the accuracy of forecasts for the adopted period was unsatisfactory. This attempt should continue, since the series (daily data for the mudflow hazard period) that were used in this work cover only 2 years

Probabilistic assessment and projections of US weather and climate risks and economic damages

Weather and climate extremes cause significant economic damages and fatalities. Over the last few decades, the frequency of these disasters and their economic damages have significantly increased in the USA. The prediction of the future evolution of these damages and their relation to global warming and US economic growth is essential for deciding on cost-efficient mitigation pathways. Here we show using a probabilistic extreme value statistics framework that both the increase in US Gross Domestic Product per capita and global warming are significant covariates in probabilistically modeling the increase in economic damages. We also provide evidence that the Pacific Decadal Oscillation affects the number of fatalities. Using the Intergovernmental Panel on Climate Change scenarios, we estimate the potential future economic risks. We find that by 2060, the extreme risks (as measured by 200-year effective return level) will have increased by 3–5.4 times. The damage costs due to extreme risks are projected to be between 0.1 and 0.7% of US Gross Domestic Product by 2060 and could reach 5–16% by 2100.

Re-evaluation of area reduction factors and their impact on floods in South Africa

The hydraulic design of structures depends on rainfall and runoff records to model and predict flood frequency for design purposes. The amount of runoff (and consequently flooding) from a catchment area has a direct relationship to the amount of rainfall in that catchment area, but as the point rainfall in any given area is likely to be more than the representative average rainfall over the entire area, a factor needs to be derived to transform point rainfall to representative areal rainfall. This factor is known as the Area Reduction Factor (ARF) and has been calculated and researched in many previous independent studies. The traditional methods for estimating ARFs are empirical in nature and include methods such as the UK Flood Studies Report (NERC 1975), the US Weather Bureau (USWB 1957), and Alexander (1990). The research presented in this paper focuses on re-evaluating ARF’s estimates using the UK Flood Studies Report (NERC 1975) and Alexander’s method (Alexander 1990) by following an analytical approach based on Bell’s method (Bell 1976). This research uses statistical methods to evaluate the validity of previously published ARFs. The results presented in this paper indicate that the empirical methods currently in use (specifically Bell’s nomogram) to derive ARFs may result in conservative estimates of flood peaks. There is ample evidence to suggest that modern analytical techniques can be applied to longer rainfall records in order to make a more accurate estimate of the ARFs.

Teresa Mannion and the Emergence of Extreme Weather Culture in Ireland

In this article I consider how registers of weather media carry/convey cultural information, specifically how texts about extreme weather articulate with investment in a supposed post-recession restored normality marked by the Irish government's commitment to deregulated transnational capitalism. I maintain that, in a process of cross-cultural remediation, sensationalist codes of US weather media that discursively manage awareness of systemic climate problems are just starting to infiltrate the Irish broadcasting environment. In early December 2015 RTÉ’s Teresa Mannion covered a strong gale, Storm Desmond, amidst inclement conditions in Salthill, Co Galway. Modelling the kind of ‘body at risk’ coverage consummately performed by US Weather Channel personnel, Mannion could barely speak over the lashing rain and strong winds in a dramatic broadcast that quickly became a viral video. This article analyses the fascination with Mannion's piece and its memetic, and attends to the nature of the pleasure taken in her on-camera discomfiture and the breach of gendered territory committed by Mannion at a time when national popular culture in Ireland is under increased obligation to identify and explain climate change-related extreme weather.