Modeling very large-fire occurrences over the continental United States from weather and climate forcing

AbstractThe spatial and temporal ordering of precipitation occurrence impacts ecosystems, streamflow, and water availability. For example, both large-scale climate patterns and local landscapes drive weather events, and the typical speeds and directions of these events moving across a basin dictate the timing of flows at its outlet. We address the predictability of precipitation occurrence at a given location, based on the knowledge of past precipitation at surrounding locations. We identify “dominant directions of precipitation influence” across the continental United States based on a gridded daily dataset. Specifically, we apply information theory–based measures that characterize dominant directions and strengths of spatial and temporal precipitation dependencies. On a national average, this dominant direction agrees with the prevalent direction of weather movement from west to east across the country, but regional differences reflect topographic divides, precipitation gradients, and different climatic drivers of precipitation. Trends in these information relationships and their correlations with climate indices over the past 70 years also show seasonal and spatial divides. This study expands upon a framework of information-based predictability to answer questions about spatial connectivity in addition to temporal persistence. The methods presented here are generally useful to understand many aspects of weather and climate variability.

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How Wet and Dry Spells Evolve across the Conterminous United States Based on 555 Years of Paleoclimate Data

Journal of Climate ◽

10.1175/jcli-d-18-0182.1 ◽

2018 ◽

Vol 31 (16) ◽

pp. 6633-6647 ◽

Cited By ~ 3

Author(s):

Michelle Ho ◽

Upmanu Lall ◽

Edward R. Cook

Keyword(s):

United States ◽

Large Scale ◽

Viterbi Algorithm ◽

Severity Index ◽

Climate Forcing ◽

Drought Severity ◽

Spatial Features ◽

Dry Spells ◽

Temporal And Spatial ◽

Wet Spells

Abstract Evolving patterns of droughts and wet spells in the conterminous United States (CONUS) are examined over 555 years using a tree-ring-based paleoclimate reconstruction of the modified Palmer drought severity index (PDSI). A hidden Markov model is used as an unsupervised method of classifying climate states and quantifying the temporal evolution from one state to another. Modeling temporal variability in spatial patterns of drought and wet spells provides the ability to objectively assess and simulate historical persistence and recurrence of similar patterns. The Viterbi algorithm reveals the probable sequence of states through time, enabling an examination of temporal and spatial features and associated large-scale climate forcing. Distinct patterns of sea surface temperature that are known to enhance or inhibit rainfall are associated with some states. Using the current CONUS PDSI field the model can be used to simulate the space–time PDSI pattern over the next few years, or unconditional simulations can be used to derive estimates of spatially concurrent PDSI patterns and their persistence and intensity across the CONUS.

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Unrealized Potential: A Review of Perceptions and Use of Weather and Climate Information in Agricultural Decision Making

Weather Climate and Society ◽

10.1175/wcas-d-12-00062.1 ◽

2014 ◽

Vol 6 (1) ◽

pp. 47-61 ◽

Cited By ~ 57

Author(s):

Amber Saylor Mase ◽

Linda Stalker Prokopy

Keyword(s):

United States ◽

Forecast Accuracy ◽

Developed Countries ◽

The United States ◽

Future Research ◽

Lead Times ◽

Climate Information ◽

Weather Information ◽

Weather And Climate ◽

Weather Risks

Abstract This article reviews research on agricultural decision makers’ use and perceptions of weather and climate information and decision support tools (DSTs) conducted in the United States, Australia, and Canada over the past 30 years. Forty–seven relevant articles, with locations as diverse as Australian rangelands and the southeastern United States, ranging in focus from corn to cattle, were identified. NVivo 9 software was used to code research methods, type of climate information explored, barriers to broader use of weather information, common themes, and conclusions from each article. Themes in this literature include the role of trusted agricultural advisors in the use of weather information, farmers’ management of weather risks, and potential agricultural adaptations that could increase resilience to weather and climate variability. While use of weather and climate information and DSTs for agriculture has increased in developed countries, these resources are still underutilized. Reasons for low use and reduced usefulness highlighted in this literature are perceptions of low forecast accuracy; forecasts presented out of context, reducing farmers’ ability to apply them; short forecast lead times; inflexible management and operations that limit the adaptability of a farm; and greater concern with nonweather risks (such as regulation or market fluctuation). The authors’ main recommendation from reviewing this literature is that interdisciplinary and participatory processes involving farmers and advisors have the potential to improve use of weather and climate DSTs. The authors highlight important gaps revealed by this review, and suggest ways to improve future research on these topics.

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Managing your risk: Weather and climate impacts on crop insurance. Fact sheet using national-scale data.

10.32747/2018.7201761.ch ◽

2018 ◽

Author(s):

Julian Reyes ◽

Emile Elias ◽

Andrew Eischens ◽

Mark Shilts

Keyword(s):

United States ◽

Risk Management ◽

Crop Insurance ◽

The United States ◽

Climate Impacts ◽

National Scale ◽

Fact Sheet ◽

Weather And Climate ◽

Insurance Data ◽

Scale Data

A fact sheet produced by the USDA Southwest Climate Hub using publicly available crop insurance data from the USDA Risk Management Agency for the United States.

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Status and Climate Applications of the 19th Century Forts and Volunteer Observer Database

Journal of Applied and Service Climatology ◽

10.46275/joasc.2021.09.001 ◽

2021 ◽

Vol 2021 (002) ◽

pp. 1-14

Author(s):

Nancy Westcott ◽

◽

Jason Cooper ◽

Karen Andsager ◽

Leslie Stoecker ◽

...

Keyword(s):

United States ◽

19Th Century ◽

The United States ◽

Weather Data ◽

Climate Data ◽

Historical Climate ◽

Daily Data ◽

Weather And Climate ◽

The 19Th Century ◽

Global Datasets

The Climate Data Modernization Program Forts and Volunteer Observer Database (CDMP-Forts) currently consists of 450 keyed and 355 quality-controlled stations for the period 1788–1892, reaching across the United States. In conjunction with the Global Historical Climate Network (GHCN) daily data, this resource is invaluable for examining 19th century weather and climate in the United States. CDMP-Forts is incomplete, however, with a considerable amount of data remaining to be digitally transcribed and quality controlled. It is the intent of this paper to provide an overview of the processes involved in rescuing these data and to show important ways these data can be used and the considerations that may have to be taken to create meaningful analyses. Finally, the dataset is placed in the context of other global datasets and efforts to rescue historical weather data.

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Impacts of Future Weather and Climate Extremes on United States Infrastructure

10.1061/9780784415863 ◽

2021 ◽

Author(s):

◽

Mari R. Tye ◽

Jason P. Giovannettone ◽

Amir AghaKouchak ◽

R. Edward Beighley ◽

...

Keyword(s):

United States ◽

Climate Extremes ◽

Weather And Climate

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Statistical Model for Forecasting Monthly Large Wildfire Events in Western United States

Journal of Applied Meteorology and Climatology ◽

10.1175/jam2513.1 ◽

2007 ◽

Vol 46 (7) ◽

pp. 1020-1030 ◽

Cited By ~ 64

Author(s):

Haiganoush K. Preisler ◽

Anthony L. Westerling

Keyword(s):

United States ◽

Statistical Model ◽

Spatial Scales ◽

Severity Index ◽

Fire Danger ◽

Western United States ◽

Drought Severity ◽

Large Fire ◽

Confidence Bounds ◽

Regression Techniques

Abstract The ability to forecast the number and location of large wildfire events (with specified confidence bounds) is important to fire managers attempting to allocate and distribute suppression efforts during severe fire seasons. This paper describes the development of a statistical model for assessing the forecasting skills of fire-danger predictors and producing 1-month-ahead wildfire-danger probabilities in the western United States. The method is based on logistic regression techniques with spline functions to accommodate nonlinear relationships between fire-danger predictors and probability of large fire events. Estimates were based on 25 yr of historic fire occurrence data (1980–2004). The model using the predictors monthly average temperature, and lagged Palmer drought severity index demonstrated significant improvement in forecasting skill over historic frequencies (persistence forecasts) of large fire events. The statistical models were particularly amenable to model evaluation and production of probability-based fire-danger maps with prespecified precisions. For example, during the 25 yr of the study for the month of July, an area greater than 400 ha burned in 3% of locations where the model forecast was low; 11% of locations where the forecast was moderate; and 76% of locations where the forecast was extreme. The statistical techniques may be used to assess the skill of forecast fire-danger indices developed at other temporal or spatial scales.

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