100 Years of Progress in Forecasting and NWP Applications

Abstract

Over the past 100 years, the collaborative effort of the international science community, including government weather services and the media, along with the associated proliferation of environmental observations, improved scientific understanding, and growth of technology, has radically transformed weather forecasting into an effective global and regional environmental prediction capability. This chapter traces the evolution of forecasting, starting in 1919 [when the American Meteorological Society (AMS) was founded], over four eras separated by breakpoints at 1939, 1956, and 1985. The current state of forecasting could not have been achieved without essential collaboration within and among countries in pursuing the common weather and Earth-system prediction challenge. AMS itself has had a strong role in enabling this international collaboration.

Enhancing the operational value of snowpack models with visualization design principles

“…Interactions that compare model data with observation data would be particularly powerful in building trust in the models and addressing issues with their integrity. This process was critical in the adoption and trust in numeric weather predictions models by meteorologists (Benjamin et al, 2019), and just like meteorologists, avalanche forecasters could become active participants in model validation and improvement.…”

“…Numerical environmental and weather prediction models have dramatically transformed the accuracy of weather forecasts and the role of weather forecasters since the 1980s (Benjamin et al, 2019). As model performance improved, forecasting tasks shifted from predicting weather conditions to interpreting and communicating model guidance.…”

“…As model performance improved, forecasting tasks shifted from predicting weather conditions to interpreting and communicating model guidance. A centerpiece in the adoption of prediction models by weather forecasters was the development of visualization tools that allowed them to work directly with gridded modelled data in combination with in situ weather observations and remotesensing data (Benjamin et al, 2019). This setup allowed forecasters to visualize model output along with observations and gradually learn the operational value of the models.…”

Analogue Weather in a Digital World: On the Value of Integrating Both the Physical and the Technological in Day-to-day Experiences of Weather

“…Because of computers, the internet, and the internet of things (IoT) we can digitally observe the weather with a level of precision and accuracy that was impossible even 20 years ago (Benjamin et al, 2018;Stith et al, 2018). Remote-sensing technologies and computer algorithms produce satellite images that allow forecasters to see inside of cloud layers and storms to better understand what is happening in the atmosphere (Ackerman et al, 2018;Mitrescu et al, 2008).…”

Satellites See the World’s Atmosphere

“…Cloud properties from near-real-time retrievals of Minnis et al (2008) are being assimilated in various models in both operational and experimental WRF models. For example, assimilation of hourly cloud-top pressure and temperature in the NOAA operational Rapid Refresh model (Benjamin et al 2016) improved forecasts. Chen et al (2015Chen et al ( , 2016 assimilated cloud water path and found improved WRF analyses and forecasts of temperature and winds, as well as improved threat scores for precipitation.…”