Abstract
The Beobachtung von Ozean und Wolken–Das Trans ITCZ Experiment (BOWTIE) field campaign investigated how convective storm dynamics interact with the ocean surface to shape the structure of the Atlantic intertropical convergence zone (ITCZ). Conducted aboard the German Research Vessel (R/V) Meteor during August and September 2024, the campaign targeted the full meridional extent of the ITCZ while transiting the tropical Atlantic from east to west. The research was driven by evidence suggesting that storm-scale dynamics is pivotal for shaping the broader structure of the ITCZ and its connection to global circulation patterns and energy transport. BOWTIE featured high-resolution atmospheric and oceanic profiling, with a particular focus on the coupled boundary layers. Observations included cloud and humidity profiles, winds, precipitation, sea surface temperature, and upper-ocean physical and biogeochemical properties. A suite of advanced instruments provided vertically resolved cross sections of convective environments and surrounding conditions. BOWTIE was part of the larger international Organized Convection and EarthCARE Studies over the Tropical Atlantic (ORCESTRA) initiative, which coordinated eight campaigns across the Atlantic. During the voyage, the R/V Meteor served as a platform for two additional ORCESTRA campaigns: Soundings and Turbulent eddy measurements in the ITCZ with a Network of Quadcopters (STRINQS), which deployed unmanned aerial vehicles for profiling near-storm environments, and Process Investigation of Clouds and Convective Organization over the Atlantic Ocean (PICCOLO), which brought Colorado State University’s Sea-Pol scanning dual-polarization C-band radar onboard. This article provides an overview of BOWTIE’s scientific goals, campaign design, and observing strategy and presents selected early results from the extensive dataset. The combination of in situ, airborne, and radar measurements offers new insight into how ocean–atmosphere interactions at convective scales shape the ITCZ’s broader structure and behavior.
Significance Statement
The intertropical convergence zone (ITCZ) plays a central role in shaping tropical rainfall and global circulation, yet the processes governing its structure and variability remain incompletely understood. The Beobachtung von Ozean und Wolken–Das Trans ITCZ Experiment (BOWTIE) field campaign provides a coupled observational view of the Atlantic ITCZ by combining ship-based atmospheric and oceanic measurements with scanning and profiling radar, autonomous platforms, and coordinated aircraft and satellite observations. By sampling the full meridional extent of the ITCZ over 40 days and nights, BOWTIE reveals how convective organization, boundary layer dynamics, and upper-ocean variability interact across spatial and temporal scales. These observations advance understanding of the physical processes that regulate tropical rain belts and their day-to-day variability.
Abstract
The Beobachtung von Ozean und Wolken–Das Trans ITCZ Experiment (BOWTIE) field campaign investigated how convective storm dynamics interact with the ocean surface to shape the structure of the Atlantic intertropical convergence zone (ITCZ). Conducted aboard the German Research Vessel (R/V) Meteor during August and September 2024, the campaign targeted the full meridional extent of the ITCZ while transiting the tropical Atlantic from east to west. The research was driven by evidence suggesting that storm-scale dynamics is pivotal for shaping the broader structure of the ITCZ and its connection to global circulation patterns and energy transport. BOWTIE featured high-resolution atmospheric and oceanic profiling, with a particular focus on the coupled boundary layers. Observations included cloud and humidity profiles, winds, precipitation, sea surface temperature, and upper-ocean physical and biogeochemical properties. A suite of advanced instruments provided vertically resolved cross sections of convective environments and surrounding conditions. BOWTIE was part of the larger international Organized Convection and EarthCARE Studies over the Tropical Atlantic (ORCESTRA) initiative, which coordinated eight campaigns across the Atlantic. During the voyage, the R/V Meteor served as a platform for two additional ORCESTRA campaigns: Soundings and Turbulent eddy measurements in the ITCZ with a Network of Quadcopters (STRINQS), which deployed unmanned aerial vehicles for profiling near-storm environments, and Process Investigation of Clouds and Convective Organization over the Atlantic Ocean (PICCOLO), which brought Colorado State University’s Sea-Pol scanning dual-polarization C-band radar onboard. This article provides an overview of BOWTIE’s scientific goals, campaign design, and observing strategy and presents selected early results from the extensive dataset. The combination of in situ, airborne, and radar measurements offers new insight into how ocean–atmosphere interactions at convective scales shape the ITCZ’s broader structure and behavior.
Significance Statement
The intertropical convergence zone (ITCZ) plays a central role in shaping tropical rainfall and global circulation, yet the processes governing its structure and variability remain incompletely understood. The Beobachtung von Ozean und Wolken–Das Trans ITCZ Experiment (BOWTIE) field campaign provides a coupled observational view of the Atlantic ITCZ by combining ship-based atmospheric and oceanic measurements with scanning and profiling radar, autonomous platforms, and coordinated aircraft and satellite observations. By sampling the full meridional extent of the ITCZ over 40 days and nights, BOWTIE reveals how convective organization, boundary layer dynamics, and upper-ocean variability interact across spatial and temporal scales. These observations advance understanding of the physical processes that regulate tropical rain belts and their day-to-day variability.
Abstract
An analytic formulation of drizzle initiation theory that considers the combined effects of turbulence and aerosols has been lacking. We address this gap by extending previous studies on droplet growth using a Brownian drift-diffusion model for condensation in turbulent clouds alongside the kinetic potential (KP) of nucleation theory. This framework is adapted to describe drizzle initiation and subsequent droplet growth. By incorporating Köhler activation of water-soluble aerosols, we provide an integrated description of how ordinary cloud condensation nuclei (CCN), particles having dry radii < 0.5 µm, and giant CCN (GCCN) impact drizzling and non-drizzling clouds. Water-soluble GCCN in the 0.5 - 3 µm radius range are shown to significantly lower the KP barrier required for drizzle initiation. Near the high end of this range (circa 2 µm radius for marine clouds) the barrier vanishes and drizzle occurs spontaneously. The extended model yields predictions for drizzle formation and seasonal dependence consistent with observations. This framework provides theoretical foundation for understanding how turbulence impacts aerosol-cloud-precipitation interactions and effectiveness of cloud seeding.
Abstract
An analytic formulation of drizzle initiation theory that considers the combined effects of turbulence and aerosols has been lacking. We address this gap by extending previous studies on droplet growth using a Brownian drift-diffusion model for condensation in turbulent clouds alongside the kinetic potential (KP) of nucleation theory. This framework is adapted to describe drizzle initiation and subsequent droplet growth. By incorporating Köhler activation of water-soluble aerosols, we provide an integrated description of how ordinary cloud condensation nuclei (CCN), particles having dry radii < 0.5 µm, and giant CCN (GCCN) impact drizzling and non-drizzling clouds. Water-soluble GCCN in the 0.5 - 3 µm radius range are shown to significantly lower the KP barrier required for drizzle initiation. Near the high end of this range (circa 2 µm radius for marine clouds) the barrier vanishes and drizzle occurs spontaneously. The extended model yields predictions for drizzle formation and seasonal dependence consistent with observations. This framework provides theoretical foundation for understanding how turbulence impacts aerosol-cloud-precipitation interactions and effectiveness of cloud seeding.
Abstract
In response to the global low-carbon transition and the growing demand for sustainable development, accurate daily carbon emission forecasting is important for near-real-time emission monitoring, short-term anomaly detection, and operational assessment of sector-specific mitigation measures. However, carbon emission time series exhibit pronounced nonlinearity, non-stationarity, and complex temporal dependencies, posing challenges to the accuracy and robustness of conventional forecasting methods. To address these issues, we propose a novel serial hybrid deep learning framework, termed KET-Serial-ADTimesNet-Mamba, for multi-sector daily carbon emission forecasting. The framework adopts a hierarchical serial architecture that integrates local feature extraction with global temporal modeling: an improved asymmetric-dilated TimesNet (ADTimesNet) is first employed to capture multi-scale periodic patterns and short-term dynamics, and a Mamba module based on a selective state space model is then introduced to efficiently model long-range dependencies. Moreover, the Kangaroo Escape Optimization Technique (KET) is incorporated to adaptively tune key hyperparameters, alleviating the sensitivity of deep models to parameter settings. Experiments on multi-country carbon emission datasets demonstrate that the proposed framework consistently outperforms representative benchmark models in terms of MSE and MAE, and further confirm the effectiveness of the serial design compared with parallel alternatives. This work provides a high-accuracy forecasting framework that can support refined sector-specific emission management and decision-making.
Abstract
In response to the global low-carbon transition and the growing demand for sustainable development, accurate daily carbon emission forecasting is important for near-real-time emission monitoring, short-term anomaly detection, and operational assessment of sector-specific mitigation measures. However, carbon emission time series exhibit pronounced nonlinearity, non-stationarity, and complex temporal dependencies, posing challenges to the accuracy and robustness of conventional forecasting methods. To address these issues, we propose a novel serial hybrid deep learning framework, termed KET-Serial-ADTimesNet-Mamba, for multi-sector daily carbon emission forecasting. The framework adopts a hierarchical serial architecture that integrates local feature extraction with global temporal modeling: an improved asymmetric-dilated TimesNet (ADTimesNet) is first employed to capture multi-scale periodic patterns and short-term dynamics, and a Mamba module based on a selective state space model is then introduced to efficiently model long-range dependencies. Moreover, the Kangaroo Escape Optimization Technique (KET) is incorporated to adaptively tune key hyperparameters, alleviating the sensitivity of deep models to parameter settings. Experiments on multi-country carbon emission datasets demonstrate that the proposed framework consistently outperforms representative benchmark models in terms of MSE and MAE, and further confirm the effectiveness of the serial design compared with parallel alternatives. This work provides a high-accuracy forecasting framework that can support refined sector-specific emission management and decision-making.
Abstract
Land-atmosphere interactions provide a key pathway through which land-surface anomalies shape regional climate variability. Using ERA5 reanalysis data for 1985–2024, we examine the impacts of soil moisture on monthly precipitation during the rainy season in South China (SC) from both local and non-local perspectives. Singular value decomposition (SVD) and correlation-based diagnostics are combined to isolate coupled patterns and to trace their underlying physical processes. Enhanced soil moisture in July increases local evaporation and atmospheric humidity, thereby contributing to greater total rainfall. Concurrently, the augmented latent and sensible heat fluxes lower surface temperatures, reduce boundary layer depth, and boost convective available potential energy. The synergy between these favorable moisture and dynamic conditions underpins the positive soil moisture–precipitation feedback observed at a cross-monthly timescale. Beyond local feedbacks, anomalously wet soils over northern Indochina in July exert a robust positive influence on August rainfall in SC by modifying atmospheric circulation and moisture transport. A comprehensive synthesis indicates a synergistic effect between local soil moisture in SC and that in northern Indochina, collectively promoting increased precipitation in August. These findings shed light on the regional linkage mechanism of soil moisture–precipitation feedback across monthly scales, offering valuable insights for understanding the drivers of rainy-season precipitation in SC and improving short-term climate predictions.
Abstract
Land-atmosphere interactions provide a key pathway through which land-surface anomalies shape regional climate variability. Using ERA5 reanalysis data for 1985–2024, we examine the impacts of soil moisture on monthly precipitation during the rainy season in South China (SC) from both local and non-local perspectives. Singular value decomposition (SVD) and correlation-based diagnostics are combined to isolate coupled patterns and to trace their underlying physical processes. Enhanced soil moisture in July increases local evaporation and atmospheric humidity, thereby contributing to greater total rainfall. Concurrently, the augmented latent and sensible heat fluxes lower surface temperatures, reduce boundary layer depth, and boost convective available potential energy. The synergy between these favorable moisture and dynamic conditions underpins the positive soil moisture–precipitation feedback observed at a cross-monthly timescale. Beyond local feedbacks, anomalously wet soils over northern Indochina in July exert a robust positive influence on August rainfall in SC by modifying atmospheric circulation and moisture transport. A comprehensive synthesis indicates a synergistic effect between local soil moisture in SC and that in northern Indochina, collectively promoting increased precipitation in August. These findings shed light on the regional linkage mechanism of soil moisture–precipitation feedback across monthly scales, offering valuable insights for understanding the drivers of rainy-season precipitation in SC and improving short-term climate predictions.
Abstract
Red flag warnings (RFWs) issued by the National Weather Service in the United States are designed to guide people during times of heightened wildfire risk when extra caution is advised. However, the current RFW format is a standardized warning with no reflection of the forecast’s relative severity. We carried out a mixed-mode survey of 240 respondents from 16 California Wildland-Urban Interface (WUI) communities to evaluate public perceptions of four alternate formats that include a level of severity (number, color, adjective, and a combination of color and adjective). For each option, we asked respondents to rate how well they could understand the danger of the forecasted fire weather and how helpful it would be in deciding if and how much to prepare. They then ranked the four alternates and the current format (a generic warning with no severity level) in order of preference. Results showed that the combined option, which included color and adjective, had the highest mean for both ease of understanding and ease of deciding on preparation. This option also had the highest mean ranking (3.42 out of 5), significantly greater than the other options. Interestingly, there were no significant differences between the other four options. These findings suggest that California WUI residents preferred a RFW format that provided more information on the danger of the forecasted fire weather in a way that was easy to understand. However, the system used to indicate severity needs to be carefully selected to ensure it is easily understandable across different socio-demographic groups.
Abstract
Red flag warnings (RFWs) issued by the National Weather Service in the United States are designed to guide people during times of heightened wildfire risk when extra caution is advised. However, the current RFW format is a standardized warning with no reflection of the forecast’s relative severity. We carried out a mixed-mode survey of 240 respondents from 16 California Wildland-Urban Interface (WUI) communities to evaluate public perceptions of four alternate formats that include a level of severity (number, color, adjective, and a combination of color and adjective). For each option, we asked respondents to rate how well they could understand the danger of the forecasted fire weather and how helpful it would be in deciding if and how much to prepare. They then ranked the four alternates and the current format (a generic warning with no severity level) in order of preference. Results showed that the combined option, which included color and adjective, had the highest mean for both ease of understanding and ease of deciding on preparation. This option also had the highest mean ranking (3.42 out of 5), significantly greater than the other options. Interestingly, there were no significant differences between the other four options. These findings suggest that California WUI residents preferred a RFW format that provided more information on the danger of the forecasted fire weather in a way that was easy to understand. However, the system used to indicate severity needs to be carefully selected to ensure it is easily understandable across different socio-demographic groups.
Abstract
This paper characterizes temporal sampling errors from weather radar observations and provides recommendations for sampling requirements of precipitation for atmospheric science and hydrometeorological applications. First, the temporal structure of precipitation is analyzed with high-resolution Phased-Array Radar (PAR) measurements for two typical convective events. The uncertainty in equivalent radar reflectivity is investigated using geostatistics over different temporal domains from 30 seconds to 5 minutes. Then, the uncertainty in precipitation estimates is quantified and studied as a function of the temporal resolution of radar sampling and precipitation rate intensity. Finally, the impact of temporal resolution on estimation accuracy is confirmed with a micro-rain radar (MRR) and a 2-DVD disdrometer. The required time resolution for radar quantitative precipitation estimation (QPE) is estimated as a function of the uncertainty on the radar observations and derived precipitation rates. According to the results, equivalent radar reflectivity requires temporal resolutions below 30-seconds, which common operational radars cannot provide. Quantitative precipitation estimates up to 5 mm/h require temporal resolutions below 1 minute. Quantitative precipitation estimates above 5 mm/h require temporal resolutions below 30-seconds. The results support the sparse collection of recommendations for weather radar revisit frequency for hydrometeorology reported in the literature and indicate that temporal-sampling error can only be reduced by increasing the effective atmospheric sampling rate.
Abstract
This paper characterizes temporal sampling errors from weather radar observations and provides recommendations for sampling requirements of precipitation for atmospheric science and hydrometeorological applications. First, the temporal structure of precipitation is analyzed with high-resolution Phased-Array Radar (PAR) measurements for two typical convective events. The uncertainty in equivalent radar reflectivity is investigated using geostatistics over different temporal domains from 30 seconds to 5 minutes. Then, the uncertainty in precipitation estimates is quantified and studied as a function of the temporal resolution of radar sampling and precipitation rate intensity. Finally, the impact of temporal resolution on estimation accuracy is confirmed with a micro-rain radar (MRR) and a 2-DVD disdrometer. The required time resolution for radar quantitative precipitation estimation (QPE) is estimated as a function of the uncertainty on the radar observations and derived precipitation rates. According to the results, equivalent radar reflectivity requires temporal resolutions below 30-seconds, which common operational radars cannot provide. Quantitative precipitation estimates up to 5 mm/h require temporal resolutions below 1 minute. Quantitative precipitation estimates above 5 mm/h require temporal resolutions below 30-seconds. The results support the sparse collection of recommendations for weather radar revisit frequency for hydrometeorology reported in the literature and indicate that temporal-sampling error can only be reduced by increasing the effective atmospheric sampling rate.
Abstract
Climate change research must ensure international data comparability over long periods, and deep-ocean temperature measurements require accurate evaluation of thermometers because temperature changes there are small. We calibrated three reference thermometers (SBE 35) at the triple point of water and the gallium melting point, as defined by the International Temperature Scale of 1990, and confirmed that they were extremely stable over a 20-year period; the temporal drift for two of the three was within ±0.2 mK decade−1. We evaluated the pressure sensitivities of two conductivity-temperature-depth (CTD) thermometers in a laboratory up to 68 MPa; the results agreed with values estimated using SBE 35s in the deep ocean. We evaluated temperature hysteresis of the three SBE 35s in the laboratory; one showed no hysteresis, and the other two exhibited hysteresis of 0.3–0.5 mK. Pressure hysteresis was examined in the deep ocean. Of 22 CTD thermometers, more than half showed estimated pressure hysteresis of 0.5–1 mK. The overall expanded uncertainty of the deep ocean temperature measurement (depths greater than 20 MPa) by the CTD thermometer with small hysteresis calibrated in reference to the SBE 35 is estimated to be 0.8 mK To eliminate the influence of systematic errors due to hysteresis, we strongly recommend aligning the temperature data 0.3 seconds ahead of the pressure data to account for the temperature data delay due to the sensor’s response time, and applying the in situ calibration coefficients obtained from up-cast data to the continuous up-cast profile, thereby modifying both continuous profile data and data from discrete water sampling depths from the up-cast.
Abstract
Climate change research must ensure international data comparability over long periods, and deep-ocean temperature measurements require accurate evaluation of thermometers because temperature changes there are small. We calibrated three reference thermometers (SBE 35) at the triple point of water and the gallium melting point, as defined by the International Temperature Scale of 1990, and confirmed that they were extremely stable over a 20-year period; the temporal drift for two of the three was within ±0.2 mK decade−1. We evaluated the pressure sensitivities of two conductivity-temperature-depth (CTD) thermometers in a laboratory up to 68 MPa; the results agreed with values estimated using SBE 35s in the deep ocean. We evaluated temperature hysteresis of the three SBE 35s in the laboratory; one showed no hysteresis, and the other two exhibited hysteresis of 0.3–0.5 mK. Pressure hysteresis was examined in the deep ocean. Of 22 CTD thermometers, more than half showed estimated pressure hysteresis of 0.5–1 mK. The overall expanded uncertainty of the deep ocean temperature measurement (depths greater than 20 MPa) by the CTD thermometer with small hysteresis calibrated in reference to the SBE 35 is estimated to be 0.8 mK To eliminate the influence of systematic errors due to hysteresis, we strongly recommend aligning the temperature data 0.3 seconds ahead of the pressure data to account for the temperature data delay due to the sensor’s response time, and applying the in situ calibration coefficients obtained from up-cast data to the continuous up-cast profile, thereby modifying both continuous profile data and data from discrete water sampling depths from the up-cast.
Abstract
The U.S. Department of Agriculture National Agricultural Statistics Service (NASS) collects crop soil (top-soil and subsoil) moisture conditions in U.S. agricultural regions. NASS dynamically assesses crop soil moisture conditions in four categories—very short, short, adequate, and surplus, and publishes weekly statistics for states. Previous studies mapped NASS crop soil moisture (SM) condition categories to NASA’s Soil Moisture Active Passive (SMAP) L4 weekly surface and rootzone soil moisture by using NASS survey-reported category percentages to determine corresponding threshold values from the cumulative distributions of SMAP soil moisture. However, a notable challenge arose in croplands with coarse-textured soils, where frequent irrigation is necessary due to rapid drainage. Yet, the SMAP product often inaccurately indicates dry conditions in these soils, failing to capture the effects of irrigation due to coarse resolution and mixed pixel. To address this issue, this study utilizes soil moisture anomalies to better reflect crop soil moisture conditions in irrigated lands. Soil moisture anomalies were categorized into the same four classes, with classification thresholds determined by mapping cumulative distributions of SMAP surface and rootzone anomalies to NASS categories. We compared SMAP SM-based with SMAP anomaly-based estimates for the soil moisture conditions in two case study regions: the Columbia Basin in Washington (which includes irrigated and non-irrigated croplands) and Iowa (primarily non-irrigated), followed by an uncertainty analysis of the anomaly-based results. The key finding is that anomaly-based mapping yields a different and more distinct representation of soil moisture conditions for irrigated versus non-irrigated croplands in Washington’s Columbia Basin during a drought year.
Abstract
The U.S. Department of Agriculture National Agricultural Statistics Service (NASS) collects crop soil (top-soil and subsoil) moisture conditions in U.S. agricultural regions. NASS dynamically assesses crop soil moisture conditions in four categories—very short, short, adequate, and surplus, and publishes weekly statistics for states. Previous studies mapped NASS crop soil moisture (SM) condition categories to NASA’s Soil Moisture Active Passive (SMAP) L4 weekly surface and rootzone soil moisture by using NASS survey-reported category percentages to determine corresponding threshold values from the cumulative distributions of SMAP soil moisture. However, a notable challenge arose in croplands with coarse-textured soils, where frequent irrigation is necessary due to rapid drainage. Yet, the SMAP product often inaccurately indicates dry conditions in these soils, failing to capture the effects of irrigation due to coarse resolution and mixed pixel. To address this issue, this study utilizes soil moisture anomalies to better reflect crop soil moisture conditions in irrigated lands. Soil moisture anomalies were categorized into the same four classes, with classification thresholds determined by mapping cumulative distributions of SMAP surface and rootzone anomalies to NASS categories. We compared SMAP SM-based with SMAP anomaly-based estimates for the soil moisture conditions in two case study regions: the Columbia Basin in Washington (which includes irrigated and non-irrigated croplands) and Iowa (primarily non-irrigated), followed by an uncertainty analysis of the anomaly-based results. The key finding is that anomaly-based mapping yields a different and more distinct representation of soil moisture conditions for irrigated versus non-irrigated croplands in Washington’s Columbia Basin during a drought year.
