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Current human-induced warming has led to approximately a 30-fold increase in the occurrence probability of 2021 northwestern Pacific concurrent marine and terrestrial summer heat.
Current human-induced warming has led to approximately a 30-fold increase in the occurrence probability of 2021 northwestern Pacific concurrent marine and terrestrial summer heat.
Abstract
The Met Office in the United Kingdom has developed a completely new probabilistic postprocessing system called IMPROVER to operate on outputs from its operational numerical weather prediction (NWP) forecasts and precipitation nowcasts. The aim is to improve weather forecast information to the public and other stakeholders while better exploiting the current and future generations of underpinning kilometer-scale NWP ensembles. We wish to provide seamless forecasts from nowcasting to medium range, provide consistency between gridded and site-specific forecasts, and be able to verify every stage of the processing. The software is written in a modern modular framework that is easy to maintain, develop, and share. IMPROVER allows forecast information to be provided with greater spatial and temporal detail and a faster update frequency than previous postprocessing. Independent probabilistic processing chains are constructed for each meteorological variable consisting of a series of processing stages that operate on predefined grids and blend outputs from several NWP inputs to give a frequently updated, probabilistic forecast solution. Probabilistic information is produced as standard, with the option of extracting a most likely or yes–no outcome if required. Verification can be performed at all stages, although it is only currently switched on for the most significant stages when run in real time. IMPROVER has been producing real-time output since March 2021 and became operational in spring 2022.
Abstract
The Met Office in the United Kingdom has developed a completely new probabilistic postprocessing system called IMPROVER to operate on outputs from its operational numerical weather prediction (NWP) forecasts and precipitation nowcasts. The aim is to improve weather forecast information to the public and other stakeholders while better exploiting the current and future generations of underpinning kilometer-scale NWP ensembles. We wish to provide seamless forecasts from nowcasting to medium range, provide consistency between gridded and site-specific forecasts, and be able to verify every stage of the processing. The software is written in a modern modular framework that is easy to maintain, develop, and share. IMPROVER allows forecast information to be provided with greater spatial and temporal detail and a faster update frequency than previous postprocessing. Independent probabilistic processing chains are constructed for each meteorological variable consisting of a series of processing stages that operate on predefined grids and blend outputs from several NWP inputs to give a frequently updated, probabilistic forecast solution. Probabilistic information is produced as standard, with the option of extracting a most likely or yes–no outcome if required. Verification can be performed at all stages, although it is only currently switched on for the most significant stages when run in real time. IMPROVER has been producing real-time output since March 2021 and became operational in spring 2022.
Abstract
U.S. numerical weather prediction (NWP) is critical for both economic reasons and the protection of life and property. Unfortunately, the nation’s global model prediction skill substantially lags the performance of leading international NWP centers, with problems extending to regional prediction. This paper reviews the history of U.S. activities in NWP, describes why U.S. weather prediction is not fulfilling its potential, and proposes actions that could restore U.S NWP to world leadership. Key suggestions include the creation of a U.S. Numerical Weather Prediction Center, better coordination of the efforts of the research and operational NWP communities, and increasing NOAA computational resources by at least ten fold.
Abstract
U.S. numerical weather prediction (NWP) is critical for both economic reasons and the protection of life and property. Unfortunately, the nation’s global model prediction skill substantially lags the performance of leading international NWP centers, with problems extending to regional prediction. This paper reviews the history of U.S. activities in NWP, describes why U.S. weather prediction is not fulfilling its potential, and proposes actions that could restore U.S NWP to world leadership. Key suggestions include the creation of a U.S. Numerical Weather Prediction Center, better coordination of the efforts of the research and operational NWP communities, and increasing NOAA computational resources by at least ten fold.
Abstract
The presence of persistent heavy fog in northern India during winter creates hazardous situations for transportation systems and disrupts the lives of about 400 million people. The meteorological factors responsible for its genesis and predictability are not yet completely understood in this region. Given its high potential for socio-economic impact, there is a pressing need for extensive research that understands the inherently complex nature of the phenomena through field observations and modeling exercises. WiFEX is a first-of-its-kind multi-institutional initiative dealing with intensive ground-based measurement campaigns for developing a suitable fog forecasting capability under the aegis of the smart cities mission of India. Measuring campaigns were conducted between the 2015–2020 winters at the Indira Gandhi International Airport, New Delhi, covering more than 90 dense fog events. The field experiments involved extensive suites of in-situ instruments and gathered simultaneous observations of micro-meteorological conditions, radiative fluxes, turbulence, droplet/aerosols microphysics, aerosol optical properties, fog water-chemistry, and vertical thermodynamical structure to describe the environmental stability in which fog develops. An operational modeling framework, the WRF model, was set up to provide fog predictions during the measurement campaign. These field observations helped to interpret the strengths and deficiencies in the numerical modeling framework. Four scientific objectives were pursued: (a) the life cycle of optically thin and thick fog, (b) microphysical properties in the polluted boundary layer, (c) fog water chemistry, gas/aerosol partitioning during the fog lifecycle, and (d) numerical prediction of fog. This paper presents an overview of WiFEX and a synthesis of selected observational and modeling analyses/findings related to the above-mentioned scientific topics.
Abstract
The presence of persistent heavy fog in northern India during winter creates hazardous situations for transportation systems and disrupts the lives of about 400 million people. The meteorological factors responsible for its genesis and predictability are not yet completely understood in this region. Given its high potential for socio-economic impact, there is a pressing need for extensive research that understands the inherently complex nature of the phenomena through field observations and modeling exercises. WiFEX is a first-of-its-kind multi-institutional initiative dealing with intensive ground-based measurement campaigns for developing a suitable fog forecasting capability under the aegis of the smart cities mission of India. Measuring campaigns were conducted between the 2015–2020 winters at the Indira Gandhi International Airport, New Delhi, covering more than 90 dense fog events. The field experiments involved extensive suites of in-situ instruments and gathered simultaneous observations of micro-meteorological conditions, radiative fluxes, turbulence, droplet/aerosols microphysics, aerosol optical properties, fog water-chemistry, and vertical thermodynamical structure to describe the environmental stability in which fog develops. An operational modeling framework, the WRF model, was set up to provide fog predictions during the measurement campaign. These field observations helped to interpret the strengths and deficiencies in the numerical modeling framework. Four scientific objectives were pursued: (a) the life cycle of optically thin and thick fog, (b) microphysical properties in the polluted boundary layer, (c) fog water chemistry, gas/aerosol partitioning during the fog lifecycle, and (d) numerical prediction of fog. This paper presents an overview of WiFEX and a synthesis of selected observational and modeling analyses/findings related to the above-mentioned scientific topics.
Abstract
Further long-term investments in high-quality, research-driven, fit-for-purpose observations of atmospheric composition are needed globally to meet urgent societal needs related to weather, climate, air quality, and other environmental issues. Challenges include maintaining current observing systems in the face of eroding budgets for long-term monitoring and filling the geographical gaps for key constituents needed for sound services and policies. The observing systems can be bolstered through science-for-services applications, by embracing interoperable observation systems and standardized metadata, and ensuring that the data are findable, accessible, interoperable, and reusable. There is an urgent need to move from opportunities-driven one-component observations to more systematic, planned multifunctional infrastructure, where the observational data flow is coupled with Earth system models to serve both operational and research purposes. This approach fosters a community where user experience feeds back into the research components and where mature research results are translated into operational applications. This will lead to faster exploration and exploitation of atmospheric composition information and more impactful applications for science and society. We discuss here the urgent need to (i) achieve global coverage, (ii) harmonize infrastructure operations, (iii) establish focused policies, and (iv) strengthen coordination of atmospheric composition infrastructure.
Abstract
Further long-term investments in high-quality, research-driven, fit-for-purpose observations of atmospheric composition are needed globally to meet urgent societal needs related to weather, climate, air quality, and other environmental issues. Challenges include maintaining current observing systems in the face of eroding budgets for long-term monitoring and filling the geographical gaps for key constituents needed for sound services and policies. The observing systems can be bolstered through science-for-services applications, by embracing interoperable observation systems and standardized metadata, and ensuring that the data are findable, accessible, interoperable, and reusable. There is an urgent need to move from opportunities-driven one-component observations to more systematic, planned multifunctional infrastructure, where the observational data flow is coupled with Earth system models to serve both operational and research purposes. This approach fosters a community where user experience feeds back into the research components and where mature research results are translated into operational applications. This will lead to faster exploration and exploitation of atmospheric composition information and more impactful applications for science and society. We discuss here the urgent need to (i) achieve global coverage, (ii) harmonize infrastructure operations, (iii) establish focused policies, and (iv) strengthen coordination of atmospheric composition infrastructure.
Abstract
While numerous collaborations exist between the atmospheric sciences research community and the U.S. National Weather Service (NWS), collaborative research field studies between undergraduate (UG) students at universities and the NWS are less common. The Summertime Canyon Observations and Research to Characterize Heat Extreme Regimes (SCORCHER) study was an UG student-driven research field campaign conducted in Palo Duro Canyon State Park, Texas, United States, during the summer of 2021. The SCORCHER campaign was mainly aimed at improving our basic scientific understanding of extreme heat, public safety, and forecasting applications, and creating an empowering UG educational field research experience. This “In Box” article highlights the collaborative study design, execution, and lessons learned.
Abstract
While numerous collaborations exist between the atmospheric sciences research community and the U.S. National Weather Service (NWS), collaborative research field studies between undergraduate (UG) students at universities and the NWS are less common. The Summertime Canyon Observations and Research to Characterize Heat Extreme Regimes (SCORCHER) study was an UG student-driven research field campaign conducted in Palo Duro Canyon State Park, Texas, United States, during the summer of 2021. The SCORCHER campaign was mainly aimed at improving our basic scientific understanding of extreme heat, public safety, and forecasting applications, and creating an empowering UG educational field research experience. This “In Box” article highlights the collaborative study design, execution, and lessons learned.
