WEATHER AND FORECASTING

Scope & Guideline

Connecting Research and Practice in Meteorology

Introduction

Immerse yourself in the scholarly insights of WEATHER AND FORECASTING with our comprehensive guidelines detailing its aims and scope. This page is your resource for understanding the journal's thematic priorities. Stay abreast of trending topics currently drawing significant attention and explore declining topics for a full picture of evolving interests. Our selection of highly cited topics and recent high-impact papers is curated within these guidelines to enhance your research impact.
LanguageEnglish
ISSN0882-8156
PublisherAMER METEOROLOGICAL SOC
Support Open AccessNo
CountryUnited States
TypeJournal
Convergefrom 1992 to 2024
AbbreviationWEATHER FORECAST / Weather Forecast.
Frequency6 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
Address45 BEACON ST, BOSTON, MA 02108-3693, UNITED STATES

Aims and Scopes

The journal "Weather and Forecasting" focuses on advancing the field of meteorology and atmospheric sciences through rigorous research on weather prediction and forecasting methodologies. It aims to disseminate high-quality research that enhances our understanding of weather phenomena and improves forecasting techniques.
  1. Numerical Weather Prediction (NWP):
    The journal emphasizes the development and evaluation of numerical models for weather forecasting, including advancements in model physics, data assimilation techniques, and resolution enhancement.
  2. Severe Weather Forecasting:
    Research on the prediction of severe weather events such as hurricanes, tornadoes, and heavy precipitation is a core focus, with studies investigating methodologies to improve accuracy and reliability.
  3. Impact-Based Decision Support Systems (IDSS):
    The journal explores the creation and validation of decision support tools that integrate weather forecasts with risk assessments to aid public safety and emergency management.
  4. Machine Learning and Artificial Intelligence in Meteorology:
    The incorporation of machine learning techniques for data analysis and forecasting is increasingly prevalent, focusing on innovative applications that enhance predictive capabilities.
  5. Climate Variability and Predictability:
    Research addressing the impacts of climate phenomena such as El Niño and their relationship with weather patterns is an important aspect, contributing to longer-term forecasting.
Recent publications in "Weather and Forecasting" indicate several emerging themes that highlight the evolving landscape of meteorological research and forecasting practices.
  1. Integration of Remote Sensing Data:
    Research increasingly focuses on utilizing remote sensing technologies to enhance weather prediction accuracy, reflecting advancements in satellite and radar technology.
  2. Real-Time Data Assimilation Techniques:
    There is a growing interest in real-time data assimilation methods that improve the responsiveness and accuracy of weather forecasts, particularly during severe weather events.
  3. Probabilistic Forecasting and Uncertainty Quantification:
    The trend towards probabilistic forecasting is rising, with an emphasis on quantifying uncertainty in predictions to better inform decision-making processes.
  4. Climate Change Impact Studies:
    Emerging research is increasingly focused on understanding how climate change affects weather patterns and forecasting, with implications for long-term prediction and adaptation strategies.
  5. Public Engagement and Communication of Weather Information:
    Studies exploring effective communication strategies for conveying weather information to the public are gaining prominence, particularly in the context of severe weather warnings.

Declining or Waning

While the journal continues to publish a wide range of topics, some areas of focus have shown signs of declining prominence in recent years, reflecting shifts in research interests and technological advancements.
  1. Traditional Statistical Forecasting Methods:
    There is a noticeable decrease in research focusing on conventional statistical methods for weather prediction, as newer machine learning approaches gain traction.
  2. Local Climate Studies:
    Research specifically centered on localized climate phenomena has become less common, possibly overshadowed by broader studies addressing global or regional climate impacts.
  3. Historical Weather Data Analysis:
    The journal has seen fewer contributions dedicated to the analysis of historical weather data, as the emphasis shifts towards real-time forecasting and predictive modeling.
  4. Empirical Model Development:
    The development of empirical models based on observational data appears to be waning, with a growing preference for sophisticated numerical and hybrid modeling approaches.
  5. Basic Meteorological Education and Outreach:
    Papers focused on educational aspects and outreach initiatives in meteorology have declined, possibly due to the journal's shift towards more technical and research-driven content.

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