Abstract
Absolute calibration of spaceborne microwave radiometer observations consists of accurate determination of antenna cold space spillover, cross-polarization contamination, and non-linearity coefficients of the receivers. We deem the GMI sensor to be the most accurate calibrated spaceborne microwave radiometer due to its unique calibration design features and its carefully planned orbit maneuvers. We demonstrate how to transfer the GMI calibration to other spaceborne radiometers, whose operations have sufficient time overlap with GMI. Specifically, we show results for WindSat and AMSR2. The sensor intercalibration is based on brightness temperature match-ups between GMI and the other instruments over both open ocean and rainforest scenes. In order to assess the calibration accuracy, we compare the intercalibrated brightness temperatures with radiative transfer model calculations. In addition, we provide in-situ validation results for wind speed and water vapor retrievals from the intercalibrated sensors. The intercalibration methodology allows for the creation of a multi-decadal climate data record from passive microwave satellite observations.
Abstract
Absolute calibration of spaceborne microwave radiometer observations consists of accurate determination of antenna cold space spillover, cross-polarization contamination, and non-linearity coefficients of the receivers. We deem the GMI sensor to be the most accurate calibrated spaceborne microwave radiometer due to its unique calibration design features and its carefully planned orbit maneuvers. We demonstrate how to transfer the GMI calibration to other spaceborne radiometers, whose operations have sufficient time overlap with GMI. Specifically, we show results for WindSat and AMSR2. The sensor intercalibration is based on brightness temperature match-ups between GMI and the other instruments over both open ocean and rainforest scenes. In order to assess the calibration accuracy, we compare the intercalibrated brightness temperatures with radiative transfer model calculations. In addition, we provide in-situ validation results for wind speed and water vapor retrievals from the intercalibrated sensors. The intercalibration methodology allows for the creation of a multi-decadal climate data record from passive microwave satellite observations.
Abstract
The numerical weather prediction model HARMONIE-AROME and a multiple linear regression model (referred to in this article as the TURCLIM model after the local climate observation network) were used to model surface air temperature for 25–31 July 2018 in the City of Turku, Finland, to study their performance in urban areas and surrounding rural areas. The 0200 LT (local standard time) temperatures modeled by the HARMONIE-AROME and TURCLIM models were compared to each other and against the observed temperatures to find the model best suited for modeling the urban heat island effect and other spatial temperature variabilities during heatwaves. Observed temperatures were collected from 74 sites, representing both rural and urban environments. Both models were able to reproduce the spatial nighttime temperature variation. However, HARMONIE-AROME modeled temperatures were systematically warmer than the observed temperatures in stable conditions. Spatial differences between the models were mostly related to the physiographic characteristics: for the urban areas, HARMONIE-AROME modeled on average 1.4°C higher temperatures than the TURCLIM model, while for other land-cover types, the average difference was 0.51°C at maximum. The TURCLIM model performed well when the explanatory variables were able to incorporate enough information on the surrounding physiography. Respectively, systematic cold or warm bias occurred in the areas in which the thermophysically relevant physiography was lacking or was only partly captured by the model.
Significance Statement
As more and more people are living in an urban environment, the demand for accurate urban climate modeling is growing. This study aims to understand the differences between the numerical weather prediction and multiple linear regression modeling and their limitations in modeling surface air temperature in subkilometer scale. The case study shows that models are capable of predicting the spatial variation of 0200 LT nighttime temperature during a heatwave in a high-latitude coastal city. Both models are therefore valuable assets for city planners who need accurate information about the impacts of the physiography on the urban climate. The results indicate that to improve the performance of the models, more accurate physiographic description and higher spatial resolution of the models are needed.
Abstract
The numerical weather prediction model HARMONIE-AROME and a multiple linear regression model (referred to in this article as the TURCLIM model after the local climate observation network) were used to model surface air temperature for 25–31 July 2018 in the City of Turku, Finland, to study their performance in urban areas and surrounding rural areas. The 0200 LT (local standard time) temperatures modeled by the HARMONIE-AROME and TURCLIM models were compared to each other and against the observed temperatures to find the model best suited for modeling the urban heat island effect and other spatial temperature variabilities during heatwaves. Observed temperatures were collected from 74 sites, representing both rural and urban environments. Both models were able to reproduce the spatial nighttime temperature variation. However, HARMONIE-AROME modeled temperatures were systematically warmer than the observed temperatures in stable conditions. Spatial differences between the models were mostly related to the physiographic characteristics: for the urban areas, HARMONIE-AROME modeled on average 1.4°C higher temperatures than the TURCLIM model, while for other land-cover types, the average difference was 0.51°C at maximum. The TURCLIM model performed well when the explanatory variables were able to incorporate enough information on the surrounding physiography. Respectively, systematic cold or warm bias occurred in the areas in which the thermophysically relevant physiography was lacking or was only partly captured by the model.
Significance Statement
As more and more people are living in an urban environment, the demand for accurate urban climate modeling is growing. This study aims to understand the differences between the numerical weather prediction and multiple linear regression modeling and their limitations in modeling surface air temperature in subkilometer scale. The case study shows that models are capable of predicting the spatial variation of 0200 LT nighttime temperature during a heatwave in a high-latitude coastal city. Both models are therefore valuable assets for city planners who need accurate information about the impacts of the physiography on the urban climate. The results indicate that to improve the performance of the models, more accurate physiographic description and higher spatial resolution of the models are needed.
Abstract
This study examined future changes in the microphysical properties of surface solid precipitation over Hokkaido, Japan. A process-tracking model that predicts the mass of the hydrometeors generated by each cloud microphysical process was implemented in a meteorological model. This implementation aimed to analyze the mass fraction of hydrometeors resulting from depositional growth and the riming process to the total mass of surface solid precipitation. Results from pseudo–global warming experiments suggest two potential future changes in the characteristics of surface solid precipitation over Hokkaido. First, the rimed particles are expected to increase and be dominant over the west and northwest coast of Hokkaido, where heavy snowfall occurs primarily due to the lake effect. Second, the mass fraction from depositional growth under relatively higher temperatures is expected to increase. This increase is anticipated to be dominant over the eastern part and mountainous area of Hokkaido. Additionally, the fraction of liquid precipitation to total precipitation is expected to increase in the future. These results suggest that the microphysical properties of solid precipitation in Hokkaido are expected to be similar to those observed in the current climate over Hokuriku, the central part of Japan even in warmer climate conditions.
Significance Statement
This study examines potential future changes in the growth processes contributing to surface precipitation particles in Hokkaido, Japan. The surface solid precipitation particles in the western and eastern regions of Hokkaido are mainly generated through depositional growth that occurs within the temperature ranges −36° to −20°C and −20° to −10°C, respectively. A future shift is anticipated, with riming becoming the primary process. This shift suggests that snowfall particles will be heavier than those in the current climate, which would increase the snow-removal workload. The change in precipitation characteristics could influence adaptation and mitigation strategies for climate change in cold regions.
Abstract
This study examined future changes in the microphysical properties of surface solid precipitation over Hokkaido, Japan. A process-tracking model that predicts the mass of the hydrometeors generated by each cloud microphysical process was implemented in a meteorological model. This implementation aimed to analyze the mass fraction of hydrometeors resulting from depositional growth and the riming process to the total mass of surface solid precipitation. Results from pseudo–global warming experiments suggest two potential future changes in the characteristics of surface solid precipitation over Hokkaido. First, the rimed particles are expected to increase and be dominant over the west and northwest coast of Hokkaido, where heavy snowfall occurs primarily due to the lake effect. Second, the mass fraction from depositional growth under relatively higher temperatures is expected to increase. This increase is anticipated to be dominant over the eastern part and mountainous area of Hokkaido. Additionally, the fraction of liquid precipitation to total precipitation is expected to increase in the future. These results suggest that the microphysical properties of solid precipitation in Hokkaido are expected to be similar to those observed in the current climate over Hokuriku, the central part of Japan even in warmer climate conditions.
Significance Statement
This study examines potential future changes in the growth processes contributing to surface precipitation particles in Hokkaido, Japan. The surface solid precipitation particles in the western and eastern regions of Hokkaido are mainly generated through depositional growth that occurs within the temperature ranges −36° to −20°C and −20° to −10°C, respectively. A future shift is anticipated, with riming becoming the primary process. This shift suggests that snowfall particles will be heavier than those in the current climate, which would increase the snow-removal workload. The change in precipitation characteristics could influence adaptation and mitigation strategies for climate change in cold regions.
Abstract
Humid heat and associated heat stress have increased in frequency, intensity, and duration across the globe, particularly at lower latitudes. One of the more robust metrics for heat stress impacts on the human body is wet-bulb globe temperature (WBGT), because it incorporates temperature, humidity, wind speed, and solar radiation. WBGT can typically only be measured using nonstandard instrumentation (e.g., black globe thermometers). However, estimation formulas have been developed to calculate WBGT using standard surface meteorological variables. This study evaluates several WBGT estimation formulas for the southeastern United States using North Carolina Environment and Climate Observing Network (ECONet) and U.S. Military measurement campaign data as verification. The estimation algorithm with the smallest mean absolute error was subsequently chosen to evaluate summer WBGT trends and extremes at 39 ASOS stations with long continuous (1950–2023) data records. Trend results showed that summer WBGT has increased throughout much of the southeastern United States, with larger increases at night than during the day. Although there were some surprisingly large WBGT trends at higher elevation locations far from coastlines, the greatest increases were predominantly located in the Florida Peninsula and Louisiana. Increases in the intensity and frequency of extreme (90th percentile) WBGTs were particularly stark in large coastal urban centers (e.g., New Orleans, Tampa, and Miami). Some locations like New Orleans and Tampa have experienced more than two additional extreme heat stress days and nights per decade since 1950, with an exponential escalation in the number of extreme summer nights during the most recent decade.
Significance Statement
Humid heat and associated heat stress pose threats to health in the moist subtropical climate of the southeastern United States. Wet-bulb globe temperature (WBGT) is a robust metric for heat stress but must be estimated using complex algorithms. We first evaluated the accuracy of three WBGT algorithms in the southeastern United States, using measured verification data. Subsequently, we used the most accurate algorithm to investigate WBGT trends and extremes since 1950 in 39 cities. Results showed that summer heat stress has increased throughout the region, especially at night. Increases in the intensity and frequency of extreme heat stress were most prevalent at urban coastal locations in Florida and Louisiana, emphasizing the impacts of increased urbanization and evaporation on heat stress.
Abstract
Humid heat and associated heat stress have increased in frequency, intensity, and duration across the globe, particularly at lower latitudes. One of the more robust metrics for heat stress impacts on the human body is wet-bulb globe temperature (WBGT), because it incorporates temperature, humidity, wind speed, and solar radiation. WBGT can typically only be measured using nonstandard instrumentation (e.g., black globe thermometers). However, estimation formulas have been developed to calculate WBGT using standard surface meteorological variables. This study evaluates several WBGT estimation formulas for the southeastern United States using North Carolina Environment and Climate Observing Network (ECONet) and U.S. Military measurement campaign data as verification. The estimation algorithm with the smallest mean absolute error was subsequently chosen to evaluate summer WBGT trends and extremes at 39 ASOS stations with long continuous (1950–2023) data records. Trend results showed that summer WBGT has increased throughout much of the southeastern United States, with larger increases at night than during the day. Although there were some surprisingly large WBGT trends at higher elevation locations far from coastlines, the greatest increases were predominantly located in the Florida Peninsula and Louisiana. Increases in the intensity and frequency of extreme (90th percentile) WBGTs were particularly stark in large coastal urban centers (e.g., New Orleans, Tampa, and Miami). Some locations like New Orleans and Tampa have experienced more than two additional extreme heat stress days and nights per decade since 1950, with an exponential escalation in the number of extreme summer nights during the most recent decade.
Significance Statement
Humid heat and associated heat stress pose threats to health in the moist subtropical climate of the southeastern United States. Wet-bulb globe temperature (WBGT) is a robust metric for heat stress but must be estimated using complex algorithms. We first evaluated the accuracy of three WBGT algorithms in the southeastern United States, using measured verification data. Subsequently, we used the most accurate algorithm to investigate WBGT trends and extremes since 1950 in 39 cities. Results showed that summer heat stress has increased throughout the region, especially at night. Increases in the intensity and frequency of extreme heat stress were most prevalent at urban coastal locations in Florida and Louisiana, emphasizing the impacts of increased urbanization and evaporation on heat stress.
Abstract
Quasi-linear convective systems (QLCSs) are responsible for approximately a quarter of all tornado events in the United States, but no field campaigns have focused specifically on collecting data to understand QLCS tornadogenesis. The Propagation, Evolution, and Rotation in Linear Storms (PERiLS) project was the first observational study of tornadoes associated with QLCSs ever undertaken. Participants were drawn from more than 10 universities, laboratories, and institutes, with over 100 students participating in field activities. The PERiLS field phases spanned 2 years, late winters and early springs of 2022 and 2023, to increase the probability of intercepting significant tornadic QLCS events in a range of large-scale and local environments. The field phases of PERiLS collected data in nine tornadic and nontornadic QLCSs with unprecedented detail and diversity of measurements. The design and execution of the PERiLS field phase and preliminary data and ongoing analyses are shown.
Abstract
Quasi-linear convective systems (QLCSs) are responsible for approximately a quarter of all tornado events in the United States, but no field campaigns have focused specifically on collecting data to understand QLCS tornadogenesis. The Propagation, Evolution, and Rotation in Linear Storms (PERiLS) project was the first observational study of tornadoes associated with QLCSs ever undertaken. Participants were drawn from more than 10 universities, laboratories, and institutes, with over 100 students participating in field activities. The PERiLS field phases spanned 2 years, late winters and early springs of 2022 and 2023, to increase the probability of intercepting significant tornadic QLCS events in a range of large-scale and local environments. The field phases of PERiLS collected data in nine tornadic and nontornadic QLCSs with unprecedented detail and diversity of measurements. The design and execution of the PERiLS field phase and preliminary data and ongoing analyses are shown.
Abstract
This study explores the performance of a dense optical flow method in comparison to pattern-matching techniques for retrieving atmospheric motion vectors (AMVs) from water vapor images. Using high-resolution simulated datasets that represent various weather phenomena, we assess the performance of these methods across different weather regimes, time intervals, and pressure levels and quantify the uncertainties associated with retrieved winds. The optical flow algorithm consistently outperforms the feature matching approach. Notably, it produces wind speeds and AMVs that closely resemble the wind fields from the simulations, and unlike the feature matching algorithm, the optical flow algorithm exhibits consistent performance across different time intervals. In contrast, the feature matching approach yields vector fields that exhibit oversmoothing in certain areas and erratic behavior in others, while also producing less detailed, regionally constant speed maps. Furthermore, unlike feature matching, the optical flow method effectively calculates AMV near regions with missing data, generating a dense AMV field for every pixel in a pair of images. This superior performance and flexibility significantly influence the planning for future satellite missions aimed at retrieving atmospheric winds. As such, our work plays a critical role in determining the mission architecture and projected instrument performance for future atmospheric wind retrieval satellite missions. The study underscores the potential of the optical flow algorithm as a robust and efficient approach for atmospheric motion retrieval, thus contributing to advances in climate research and weather prediction.
Significance Statement
This research investigates the efficacy of two methods, optical flow and feature matching, for detecting atmospheric winds, referred to as atmospheric motion vectors, from satellite images of water vapor. Employing detailed simulated datasets that replicate real-world weather patterns, we found that optical flow consistently outperforms feature matching in various aspects. Notably, the optical flow method is not only more precise but also maintains its accuracy across different scenarios. These insights are critical for the design of future satellite missions focused on advancing our understanding of the atmosphere and enhancing weather predictions. This study contributes to advancements in climate research and supports improved weather forecasting, benefiting both scientific and societal needs.
Abstract
This study explores the performance of a dense optical flow method in comparison to pattern-matching techniques for retrieving atmospheric motion vectors (AMVs) from water vapor images. Using high-resolution simulated datasets that represent various weather phenomena, we assess the performance of these methods across different weather regimes, time intervals, and pressure levels and quantify the uncertainties associated with retrieved winds. The optical flow algorithm consistently outperforms the feature matching approach. Notably, it produces wind speeds and AMVs that closely resemble the wind fields from the simulations, and unlike the feature matching algorithm, the optical flow algorithm exhibits consistent performance across different time intervals. In contrast, the feature matching approach yields vector fields that exhibit oversmoothing in certain areas and erratic behavior in others, while also producing less detailed, regionally constant speed maps. Furthermore, unlike feature matching, the optical flow method effectively calculates AMV near regions with missing data, generating a dense AMV field for every pixel in a pair of images. This superior performance and flexibility significantly influence the planning for future satellite missions aimed at retrieving atmospheric winds. As such, our work plays a critical role in determining the mission architecture and projected instrument performance for future atmospheric wind retrieval satellite missions. The study underscores the potential of the optical flow algorithm as a robust and efficient approach for atmospheric motion retrieval, thus contributing to advances in climate research and weather prediction.
Significance Statement
This research investigates the efficacy of two methods, optical flow and feature matching, for detecting atmospheric winds, referred to as atmospheric motion vectors, from satellite images of water vapor. Employing detailed simulated datasets that replicate real-world weather patterns, we found that optical flow consistently outperforms feature matching in various aspects. Notably, the optical flow method is not only more precise but also maintains its accuracy across different scenarios. These insights are critical for the design of future satellite missions focused on advancing our understanding of the atmosphere and enhancing weather predictions. This study contributes to advancements in climate research and supports improved weather forecasting, benefiting both scientific and societal needs.
Abstract
Precipitation regionalization, serving as a foundation for extrapolating information from gauged to ungauged sites, contributes to a comprehensive understanding of the spatial distribution of precipitation. However, existing studies have focused mainly on precipitation, neglecting the influence of climate background and meteorological circulation. Moreover, there is a lack of specialized analysis of regionalization results. This study proposes a novel approach to precipitation regionalization that considers covariates in the circulation field and periodic features. The method utilizes Barnett–Preisendorfer canonical correlation analysis coupled with principal component analysis (BPCCA) to select covariates and multivariate self-organizing map (SOM) clustering for preliminary regionalization. Wavelet decomposition is further used for regional feature analysis. The methodology is empirically applied to analyze summer precipitation in Shanxi Province, identifying homogeneous regions characterized by diverse spatiotemporal distributions. The results successfully identified 12 distinct regions of precipitation, effectively capturing the influence of topographic and atmospheric factors. According to the periodic and trend characteristics of each region at different time scales, we merged the division results on the three frequencies and six regions were ultimately differentiated. Specifically, a decreasing trend was observed in the southern and southeastern parts of Shanxi, as well as in the western part of the Lüliang Mountains. They had a significant 4-yr period mode, and the decreasing trend was more significant in the southern region. In contrast, northern Shanxi showed no trend and a significant 8-yr period mode. This proposed method presents an effective strategy for enhancing precipitation regionalization and extracting valuable information from the circulation field and multiple frequencies.
Abstract
Precipitation regionalization, serving as a foundation for extrapolating information from gauged to ungauged sites, contributes to a comprehensive understanding of the spatial distribution of precipitation. However, existing studies have focused mainly on precipitation, neglecting the influence of climate background and meteorological circulation. Moreover, there is a lack of specialized analysis of regionalization results. This study proposes a novel approach to precipitation regionalization that considers covariates in the circulation field and periodic features. The method utilizes Barnett–Preisendorfer canonical correlation analysis coupled with principal component analysis (BPCCA) to select covariates and multivariate self-organizing map (SOM) clustering for preliminary regionalization. Wavelet decomposition is further used for regional feature analysis. The methodology is empirically applied to analyze summer precipitation in Shanxi Province, identifying homogeneous regions characterized by diverse spatiotemporal distributions. The results successfully identified 12 distinct regions of precipitation, effectively capturing the influence of topographic and atmospheric factors. According to the periodic and trend characteristics of each region at different time scales, we merged the division results on the three frequencies and six regions were ultimately differentiated. Specifically, a decreasing trend was observed in the southern and southeastern parts of Shanxi, as well as in the western part of the Lüliang Mountains. They had a significant 4-yr period mode, and the decreasing trend was more significant in the southern region. In contrast, northern Shanxi showed no trend and a significant 8-yr period mode. This proposed method presents an effective strategy for enhancing precipitation regionalization and extracting valuable information from the circulation field and multiple frequencies.
Abstract
In this article, we examine the time-series properties of the temperatures in Latin America. We look at the presence of time trends in the context of potential long-memory processes, looking at the average, maximum, and minimum values from 1901 to 2021. Our results indicate that when looking at the average data, there is a tendency to return to the mean value in all cases. However, it is noted that in the cases of Guatemala, Mexico, and Brazil, which are the countries with the highest degree of integration, the process of reversion could take longer than in the remaining countries. We also point out that the time trend coefficient is significantly positive in practically all cases, especially in temperatures in the Caribbean islands such as Antigua and Barbuda, Aruba, and the British Virgin Islands. When analyzing the maximum and minimum temperatures, the highest degrees of integration are observed in the minimum values, and the highest values are obtained again in Brazil, Guatemala, and Mexico. The time trend coefficients are significantly positive in almost all cases, with the only two exceptions being Bolivia and Paraguay. Looking at the range (i.e., the difference between maximum and minimum temperatures), evidence of orders of integration above 0.5 is found in nine countries (Aruba, Brazil, Colombia, Cuba, Ecuador, Haiti, Panama, the Turks and Caicos Islands, and Venezuela), implying that shocks in the range will take longer to disappear than in the rest of the countries.
Abstract
In this article, we examine the time-series properties of the temperatures in Latin America. We look at the presence of time trends in the context of potential long-memory processes, looking at the average, maximum, and minimum values from 1901 to 2021. Our results indicate that when looking at the average data, there is a tendency to return to the mean value in all cases. However, it is noted that in the cases of Guatemala, Mexico, and Brazil, which are the countries with the highest degree of integration, the process of reversion could take longer than in the remaining countries. We also point out that the time trend coefficient is significantly positive in practically all cases, especially in temperatures in the Caribbean islands such as Antigua and Barbuda, Aruba, and the British Virgin Islands. When analyzing the maximum and minimum temperatures, the highest degrees of integration are observed in the minimum values, and the highest values are obtained again in Brazil, Guatemala, and Mexico. The time trend coefficients are significantly positive in almost all cases, with the only two exceptions being Bolivia and Paraguay. Looking at the range (i.e., the difference between maximum and minimum temperatures), evidence of orders of integration above 0.5 is found in nine countries (Aruba, Brazil, Colombia, Cuba, Ecuador, Haiti, Panama, the Turks and Caicos Islands, and Venezuela), implying that shocks in the range will take longer to disappear than in the rest of the countries.
Abstract
The World Meteorological Organization (WMO) is a specialized agency of the United Nations (UN) system, with an intergovernmental mandate for coordinating the generation and exchange of weather, climate and water information across its Members. WMO has played a vital role in coordinating production and dissemination of weather forecasts from short to medium range whereby global weather forecasts from large operational centers are made available to all WMO Members to serve needs of stakeholders at the local level. In recent decades, there has also been an increasing demand for similar forecasts on longer lead times that include prediction on sub-seasonal, seasonal, and annual to decadal leads. To address the increasing requirements for forecast services by Members, WMO has been actively accrediting and coordinating the essential forecast infrastructure that includes provision of forecasts from WMO designated global producing centers and collection of forecasts by lead centers to facilitate the dissemination of information and products to WMO Members and relevant non-governmental organizations. Although the basic ingredients of the infrastructure are now in place, the uptake of the forecast information has been sub-optimal. To engage the community in developing solutions to enhance the utilization of available information, this paper summarizes the WMO infrastructure for long-range forecasts, particularly for seasonal timescale, and follows with a discussion of current issues that are hindering their uptake. Finally, a set of proposals to advance the utilization of the available information from the WMO long-lead forecast infrastructure are discussed.
Abstract
The World Meteorological Organization (WMO) is a specialized agency of the United Nations (UN) system, with an intergovernmental mandate for coordinating the generation and exchange of weather, climate and water information across its Members. WMO has played a vital role in coordinating production and dissemination of weather forecasts from short to medium range whereby global weather forecasts from large operational centers are made available to all WMO Members to serve needs of stakeholders at the local level. In recent decades, there has also been an increasing demand for similar forecasts on longer lead times that include prediction on sub-seasonal, seasonal, and annual to decadal leads. To address the increasing requirements for forecast services by Members, WMO has been actively accrediting and coordinating the essential forecast infrastructure that includes provision of forecasts from WMO designated global producing centers and collection of forecasts by lead centers to facilitate the dissemination of information and products to WMO Members and relevant non-governmental organizations. Although the basic ingredients of the infrastructure are now in place, the uptake of the forecast information has been sub-optimal. To engage the community in developing solutions to enhance the utilization of available information, this paper summarizes the WMO infrastructure for long-range forecasts, particularly for seasonal timescale, and follows with a discussion of current issues that are hindering their uptake. Finally, a set of proposals to advance the utilization of the available information from the WMO long-lead forecast infrastructure are discussed.
Abstract
This study attempted to forecast the morning fog expansion (MFE), commonly referred to as the “sea of clouds,” utilizing an artificial intelligence (AI) algorithm. The radiation fog phenomenon that contributes to the sea of clouds is caused by various weather conditions. Hence, the MFE was predicted using datasets from public meteorological observations and a mesoscale numerical model (MSM). In this study, a machine learning technique, the gradient-boosting method, was adopted as the AI algorithm. The Miyoshi basin in Japan, renowned for its MFE, was selected as the experimental region. Training models were developed using datasets from October to December 2018–21. Subsequently, these models were applied to forecast MFE in 2022. The model employing the upper-atmospheric prediction data from the MSM demonstrated the highest robustness and accuracy among the proposed models. For untrained data in the fog season during 2022, the model was confirmed to be sufficiently reliable for forecasting MFE, with a high hit rate of 0.935, a low Brier score of 0.119, and a high area under the curve (AUC) of 0.944. Furthermore, the analysis of the importance of the features elucidated that the meteorological factors, such as synoptic-scale weak wind, temperatures close to the dewpoint temperature, and the absence of middle-level cloud cover at midnight, strongly contribute to the MFE. Therefore, the incorporation of upper-level meteorological elements improves the forecast accuracy for MFE.
Significance Statement
An AI-driven forecasting model for predicting morning fog expansion (MFE), sea of clouds, which often affects local livelihoods, was constructed. Fog forecasting machine learning techniques were utilized in the Japanese region famous for the morning fog. This study revealed that more accurate forecasting models incorporate numerically predicted weather elements sourced from the public routine system rather than real-time observed weather elements. Notably, the upper-level wind speed reflecting synoptic-scale dynamics, surface dewpoint depression, and middle-level cloud cover play significant roles in governing MFE. Therefore, incorporating upper-level meteorological elements into the features to machine learning is crucial for improving the forecasting accuracy of MFE.
Abstract
This study attempted to forecast the morning fog expansion (MFE), commonly referred to as the “sea of clouds,” utilizing an artificial intelligence (AI) algorithm. The radiation fog phenomenon that contributes to the sea of clouds is caused by various weather conditions. Hence, the MFE was predicted using datasets from public meteorological observations and a mesoscale numerical model (MSM). In this study, a machine learning technique, the gradient-boosting method, was adopted as the AI algorithm. The Miyoshi basin in Japan, renowned for its MFE, was selected as the experimental region. Training models were developed using datasets from October to December 2018–21. Subsequently, these models were applied to forecast MFE in 2022. The model employing the upper-atmospheric prediction data from the MSM demonstrated the highest robustness and accuracy among the proposed models. For untrained data in the fog season during 2022, the model was confirmed to be sufficiently reliable for forecasting MFE, with a high hit rate of 0.935, a low Brier score of 0.119, and a high area under the curve (AUC) of 0.944. Furthermore, the analysis of the importance of the features elucidated that the meteorological factors, such as synoptic-scale weak wind, temperatures close to the dewpoint temperature, and the absence of middle-level cloud cover at midnight, strongly contribute to the MFE. Therefore, the incorporation of upper-level meteorological elements improves the forecast accuracy for MFE.
Significance Statement
An AI-driven forecasting model for predicting morning fog expansion (MFE), sea of clouds, which often affects local livelihoods, was constructed. Fog forecasting machine learning techniques were utilized in the Japanese region famous for the morning fog. This study revealed that more accurate forecasting models incorporate numerically predicted weather elements sourced from the public routine system rather than real-time observed weather elements. Notably, the upper-level wind speed reflecting synoptic-scale dynamics, surface dewpoint depression, and middle-level cloud cover play significant roles in governing MFE. Therefore, incorporating upper-level meteorological elements into the features to machine learning is crucial for improving the forecasting accuracy of MFE.
