Atmospheric Data Research Articles

Fatal interactions of albatrosses with weather radiosondes/balloons on the Southern and Southeastern coasts of Brazil

Weather radiosondes play a crucial role in gathering atmospheric data for weather modeling and forecasting. However, their impact on marine wildlife, particularly seabirds, has raised concerns regarding the potential threats posed by these instruments. This study aims to assess the adverse effects of weather balloons on albatrosses, with a focus on the Southwest Atlantic region. The research reveals seven cases of entanglement of radiosonde equipment, leading to severe injuries and mortality along the Southern and Southeastern coasts of Brazil. Recommendations for mitigating the environmental impact of weather balloons include the adoption of biodegradable materials in their design and the implementation of improved retrieval protocols. Furthermore, the study stresses the importance of continued monitoring and research to address the interaction of weather radiosondes with marine animals. This approach is vital for ensuring the sustainable collection of scientific data while minimizing harm to marine life and ecosystems.

Assessment of model projections of climate‐change induced extreme storms on the south‐east coast of Australia

AbstractGeneral circulation models (GCMs) and their downscaled regional‐scale equivalents have been important tools for climate‐change studies. However, there has been limited assessment of the performance of GCMs and downscaled models in simulating extreme storms in temperate coastal environments. This study assesses the model characterization of extreme storms on the heavily populated coast of south‐east Australia. Twenty‐year average recurrence interval (ARI) storm intensities derived from generalized extreme value (GEV) distributions based on observed and large‐scale atmospheric model data are compared. Changes in extreme storms from past climate to a high‐emission future scenario are also investigated. Simulations of storm minimum surface pressures compared favourably with measured data. Both the GCMs and downscaled models reproduced the observed decrease with increasing latitude along the coast in the 20 year ARI of minimum surface pressure. Both indicated that the minimum storm surface pressure should change negligibly in a high‐emission future. Although the models underestimated the maximum daily precipitation significantly, models are improving significantly with CMIP epoch and downscaling. In the high‐emission future scenario, the GCMs and NARCliM projected the 20 year ARI maximum daily precipitation would increase in the order of 25%. GCMs and the corresponding downscaled products presently do not represent the extreme value distributions of historical wind speed data well, overestimating at smaller values of ARI and significantly underestimating in larger values of ARI. Significant changes in the magnitude of the 20 year ARI maximum daily‐average onshore wind speed are not projected for the high‐emission future.

Convection‐Permitting ICON‐LAM Simulations for Renewable Energy Potential Estimates Over Southern Africa

AbstractRenewable energy is recognized in Africa as a means for climate change mitigation, but also to provide access to electricity in sub‐Saharan Africa, where three‐quarters of the global population without electricity resides. Reliable and highly resolved renewable energy potential (REP) information is indispensable to support power plants expansion. Existing atmospheric data sets over Africa that are used for REP estimates are often characterized by data gaps, or coarse resolution. With the aim to overcome these challenges, the ICOsahedral Nonhydrostatic (ICON) Numerical Weather Prediction (ICON‐NWP) model in its Limited Area Mode (ICON‐LAM) is implemented and run over southern Africa in a hindcast dynamical downscaling setup at a convection‐permitting 3.3 km horizontal resolution. The simulation time span covers contrasting solar and wind weather years from 2017 to 2019. To assess the suitability of the novel simulations for REP estimates, the simulated hourly 10 m wind speed (sfcWind) and hourly surface solar irradiance (rsds) are extensively evaluated against a large compilation of in situ observations, satellite, and composite data products. ICON‐LAM reproduces the spatial patterns, temporal evolution, the variability, and absolute values of sfcWind sufficiently well, albeit with a slight overestimation and a mean bias (mean error (ME)) of 1.12 m s−1 over land. Likewise the simulated rsds with an ME of 50 W m−2 well resembles the observations. This new ICON simulation data product will be the basis for ensuing REP estimates that will be compared with existing lower resolution data sets.

Hybrid multilayer perceptron and convolutional neural network model to predict extreme regional precipitation dominated by the large-scale atmospheric circulation

Recent advances in deep learning have provided tools that enable meteorologists to predict extreme precipitation using massive atmospheric data. However, individual models are constrained by imbalanced samples and prone to false alarms owing to the rarity of extreme precipitation events. In this study, a novel ensemble learning model, that is, hybrid multilayer perceptron and convolutional neural network (MLP-CNN) model is proposed for the binary prediction of extreme precipitation in Central-Eastern China (CEC), with a daily time horizon. The MLP-CNN model achieves an overall accuracy of 86% in predicting extreme and non-extreme precipitation days using the anomalous fields of two large-scale atmospheric predictors, i.e., geopotential height at 500 hPa and vertically integrated water vapor transport. Subsequently, we employ the MLP-CNN to predict extreme precipitation with a 1–15 day leadtime. The performance of MLP-CNN tends to decrease with increasing leading time of circulation anomalies. However, 1–2 days of advance forecasting can be considered a reference for predicting the occurrence probabilities of extreme precipitation. Finally, based on various evaluation metrics, MLP-CNN outperforms the independent predictions from MLP, CNN, and two other machine learning models (i.e., random forest and support vector machine). Overall, in scenarios where samples are limited, the utilization of hybrid models presents an opportunity for optimizing predictions for extreme precipitation.

Short-term prediction of UT1-UTC and LOD via Dynamic Mode Decomposition and combination of least-squares and vector autoregressive model

Abstract This study presents a short-term forecast of UT1-UTC and LOD using two methods, i.e. Dynamic Mode Decomposition (DMD) and combination of Least-Squares and Vector Autoregression (LS+VAR). The prediction experiments were performed separately for yearly time spans, 2018-2022. The prediction procedure started on January 1 and ended on December 31, with 7-day shifts between subsequent 30-day forecasts. Atmospheric Angular Momentum data (AAM) were used as an auxiliary time series to potentially improve the prediction accuracy of UT1-UTC and LOD in LS+VAR procedure. An experiment was also conducted with and without elimination of effect of zonal tides from UT1-UTC and LOD time series. Two approaches to using the best steering parameters for the methods were applied:. First, an adaptive approach, which observes the rule that before every single forecast, a preliminary one must be performed on the pre-selected sets of parameters, and the one with the smallest prediction error is then used for the final prediction; and second, an averaged approach, whereby several forecasts are made with different sets of parameters (the same parameters as in adaptive approach) and the final values are calculated as the averages of these predictions. Depending on the method and data combination mean absolute prediction errors (MAPE) for UT1-UTC vary from 0.63 ms to 1.43ms for the 10th day and from 3.07 ms to 8.05ms for the 30th day of the forecast. Corresponding values for LOD vary from 0.110 ms to 0.245 ms for the 10th day and from 0.148 ms to 0.325 ms for the 30th day.

A hybrid model based on STL with simple exponential smoothing and ARMA for wind forecast in a Brazilian nuclear power plant site

In the event of a nuclear accident with release of radioactive effluents into the environment, the Environmental Control System (ECS) at the Central Nuclear Almirante Álvaro Alberto (CNAAA) nuclear complex estimates the movement of the radioactive plume produced and provides forecast for one and two hours ahead. The prediction is based on the current accident and atmospheric data collected from meteorological stations spread across the site. In this way, the ECS estimates the spatial dose and dose rates distribution at ground level. The one- and two-hour forecast made by the ECS use the naïve method, which repeats the last known value. Its results are satisfactory if the next value to be collected does not differ substantially from the last known value. However, observing real data, acquired in loco, this fact does not occur in practice, which can make the forecast considerably erroneous.The purpose of this work is to investigate new approaches to improve ECS forecasting method, making it more accurate in real situations. To this end, the Seasonal and Trend decomposition using Loess (STL) was used together with the Simple Exponential Smoothing (SES) method and the Autoregressive Moving Average (ARMA) model to forecast weather parameters at the Angra site. The STL method was chosen since it has a robust mathematical decomposition method, capable of satisfactorily dealing with time series that have a high frequency of acquisition, the so-called intraday series. This model uses the Loess method, which is a smoothing technique based on locally adjusted regressions. The SES method and ARMA model are the techniques that most adequately fit the trend and remainder components generated by the STL decomposition, respectively. In the scope of this work, the method is applied to forecast wind speed measurements made at one of the meteorological towers used by the ECS. Using the hybrid model developed, an increase the of 101.39% in the Mean Absolute Percentage Error (MAPE), was achieved when compared to the ECS forecast method.

Developing a General Daily Lake Evaporation Model and Demonstrating Its Application in the State of Texas

AbstractOpen water evaporation, which often consumes a large fraction of annual storage (especially in arid and semi‐arid regions), is a controlling variable for modern water resource management. Developing a daily reservoir evaporation data set is necessary for reservoir operations to consider the influence of evaporation in a timely manner. However, over the past few decades, the quantification of reservoir evaporation has primarily relied on monthly Class A pan evaporation observations, which might lead to largely biased estimations because they do not incorporate the effects of heat storage and fetch. In this study, we developed a general Daily Lake Evaporation Model (DLEM) based on Penman's equation combined with daily atmospheric reanalysis data sets, to improve the validity and frequency of reservoir evaporation monitoring. Compared with daily evaporation estimates from eddy covariance systems, the DLEM showed good quality and reliability, with R2 values ranging from 0.50 to 0.73 and RMSE values ranging from 1.13 to 1.97 mm day−1 in four different sites. By applying DLEM to all 188 major reservoirs in Texas, we generated a long‐term data set of (1 January 1981–31 December 2021) daily evaporation rates. The results reveal a clear geographic distribution and strong seasonality of evaporation throughout Texas, where the mean evaporation rate is highest during July, with 6.85 mm day−1. Trend analysis indicates that the annual average evaporation rate has significantly increased since 1981 at a rate of 0.076 mm day−1 decade−1. This study provides sufficient data support for water resource operations and management, and regional water planning.

Monetary Flood Damage Prediction Based On Machine Learning Models

Flooding has increased by 50%, and for the past 25 years, the United States has experienced a flooding event every 2 to 3 days [1]. The Federal Emergency Management Agency (FEMA) is the only resource people utilize to determine whether their homes are at risk for flooding. FEMA maps are costly, outdated, and lack predictive accuracy, leaving homeowners unaware and unprepared for potential dangers [2]. Additional tools are needed to ascertain accurate estimates of flood damages, but computational methods require rich datasets, which are difficult to obtain for flooding events [3]. We create a model that predicts monetary damages using various factors related to the flood event, such as duration, location, and cause. We utilize a flood dataset from the National Oceanic and Atmospheric Administration (NOAA) and data pre-processing techniques to handle missing values and remove irrelevant features [4]. We generate aggregate predictions by leveraging linear regression, random forest regression, XGBoost regression, and neural networks. Our findings show we can effectively use upsampling techniques to combat limited flood data. We show that floods with higher monetary damages are easier to predict, which is important because these floods inflict greater hardship on communities. The results improve preparedness for flood-related risks, property value assessments, and the accuracy of insurance policy underwriting [2]. Ultimately, the model provides a preliminary study on how individuals can make better-informed decisions and prepare for the impact of flooding in their communities. We hope this work encourages further machine learning applications to help prepare citizens for natural disasters.

Scoring the invisible

This article discusses the computational and material interplays embedded in the making of [re]capture, a research–creation project combining a bio-inspired installation that materialises particulate matter, together with outdoor sensing instruments that collect atmospheric data in at-risk neighbourhoods (Montreal, Canada). With impacts on health and the environment, habitual and slow forms of exposure to atmospheric pollution (Hsu 2016) outline the relationality of air and the porosity of bodies, both human and more-than-human (Nieuwenhuis 2016; Albano 2022). What kind of technical objects, and material-esthetics can “negotiate a rapprochement” (Gissen 2009, 22) with the invisible materiality of air? At the intersection of critical and bio-design, mechanical engineering, and computer science, [re]capture delves into this question through the lens of ‘filtration,’ simultaneously envisioned as a physical process for attending to atmospheric pollution, and as a generative concept for interpolating technology, materiality, and the city. While the artwork iterates a virtual testing model (Blender and ossia score) with physical prototyping, the article examines how to compose with air through digital simulation and scoring to create new alliances between porous meshes, bioindicators, data, particulate matter, light, wind, and electronics. It also asks How to design installations that embody and materialise the affective properties of air? Attending the speculative trajectory of this process, the article draws on feedback from computer-aided simulation techniques and collaborative experiments in residency spaces to investigate the ‘scoring’ of [im]materiality and explore the spatio-temporality of air.

Hydroclimatic Vulnerability of Wetlands to Upwind Land Use Changes

AbstractDespite their importance, wetland ecosystems protected by the Ramsar Convention are under pressure from climate change and human activities. These drivers are altering water availability in these wetlands, changing water levels or surface water extent, in some cases, beyond historical variability. Attribution of the effects of human and climate activities is usually focused on changes within the wetlands or their upstream surface and groundwater inputs. However, the reliance of wetland water availability on upwind atmospheric moisture supply is less understood. Here, we assess the vulnerability of 40 Ramsar wetlands to precipitation changes caused by land use and hydroclimatic change occurring in their upwind moisture‐supplying regions. We use moisture flows from a Lagrangian tracking model, atmospheric reanalysis data, and historical land use change (LUC) data to assess and quantify these changes. Our analyses show that historical LUC has decreased precipitation and terrestrial moisture recycling in most wetland hydrological basins, decreasing surface water availability (precipitation minus evaporation). The most substantial effects on wetland water availability occurred in the tropic subtropical regions of Central Europe and Asia. Overall, we found wetlands in Central Asia and South America to be the most vulnerable by a combination of LUC‐driven effects on runoff, high terrestrial precipitation recycling, and recent decreases in surface water availability. This study stresses the need to incorporate upwind effects of land use changes in the restoration, management, and conservation of the world's wetlands.

Long-term radiocarbon variation studies in the air and tree rings of Slovakia

Fifty-five years of radiocarbon variation studies are reviewed with an emphasis on a better understanding of the impacts of the Bohunice nuclear power plant and fossil fuel CO2 on the atmosphere and biosphere of Slovakia. The maximum Δ14C levels in the air up to about 1200‰ were observed during the 1970s at the Žlkovce monitoring station, which after 2005 decreased to <30‰. A relative decrease in the atmospheric Δ14C levels due to increasing levels of fossil CO2 in the atmosphere has also been significant, for example, in Bratislava down to about −330‰, but after 2005 they were only <50‰ below the Jungfraujoch European clean-air level. The tree-ring data, averaging the annual Δ14C levels for several stations in Slovakia, have been in agreement with the atmospheric data, as well as with the newly established clean-air station at Jasná in central Slovakia. Future 14C levels will depend strongly on fossil CO2 levels in the atmosphere, which will change the bomb 14C era to the fossil CO2 era. New investigations of 14C variations in the atmosphere-biosphere-hydrosphere compartments represent a great challenge for radiocarbon science, important for better understanding of environmental processes, climate change, and impacts of human activities on the total environment. This new era of radiocarbon research will also need new developments in radiocarbon analytical technologies, as further progress in accuracy and precision of results (<1‰) will be needed to meet the new radiocarbon challenges.

Explosive Cyclone Impact on the Power Distribution Grid in Rio Grande do Sul, Brazil

Southern Brazil is a region strongly influenced by the occurrence of extratropical cyclones. Some of them go through a rapid and intense deepening and are known as explosive cyclones. These cyclones are associated with severe weather conditions such as heavy rainfall, strong winds, and lightning, leading to various natural disasters and causing socioeconomic losses. This study investigated the interaction between atmospheric and oceanic conditions that contributed to the rapid intensification of the cyclone that occurred near the coast of South Brazil from 29 June to 3 July 2020, causing significant havoc. Hourly atmospheric and oceanic data from the ERA5 reanalysis were employed in this analysis. The results showed that warm air and moisture transportation were key contributors to these phenomena. In addition, the interaction between the jet stream and the cyclone’s movement played a crucial role in cyclone formation and intensification. Positive sea surface temperature anomalies also fueled the cyclone’s intensification. These anomalies increased the surface heat fluxes, making the atmosphere more unstable and promoting a strong upward motion. Due to the strong winds and the heavy rainfall, the explosive cyclone caused substantial impacts on the power services, resulting in widespread power outages, damaged infrastructure, and interruptions in energy distribution. This work describes in detail the cyclone development and intensification and aims at the understanding of these storms, which is crucial for minimizing their aftermaths, especially on energy distribution.

Impact of Temperature and Atmospheric Pressure on Hospitalizations of Patients Presenting With Acute Coronary Syndrome.

This study aims to investigatethe impact of temperature and atmospheric pressure on hospitalizations of patients with acute coronary syndrome (ACS). This is a retrospective, observational, analytical study conducted in a single center, University Hospital Center "Mother Teresa," Tirana, Albania, in the period January-December 2018. This study included 1,165 patients with ACS, who performed urgent coronary angiography, from January 2018 to December 2018. Patients were diagnosed withACS based on clinical and examination findings. The data were collected retrospectively using patient files. Baseline demographic, clinical, and procedural characteristics were collected. Data on atmospheric parameters, measured at the weather monitoring station, were obtained from the National Meteorological Service database. Measurements from the meteorological service provided values ​​for each parameter: average daily temperature and atmospheric pressure in each country district. Atmospheric data measurements were taken for the day under review. The number of inhabitants for the respective districts is taken from the National Institute of Statistics (INSTAT). The study involved 1,165 patients, with a mean age of 63.1 years, ranging from 27 years to 89 years old. The majority of patients (78.6%) were male, while 21.4% were female. A statistically significant relationship was observed between seasonal changes in temperature and atmospheric pressure concerning the number of cases with ACS; the autumn season prevails with 27.9% of the total cases, followed by the spring season with 25.6%, the summer season with 24.2%, and winter season with 22.3% (p = 0.04). Additionally, significant changes in the average monthly values ​​of temperature and atmospheric pressure were accompanied by a statistically significant increase in the number of cases as occurred in March-April and October-November (p ≤ 0.05). Most cases in the cold period (November-March) occurred on days with a change in temperature or atmospheric pressure with a statistically significant value of p < 0.05. An important relationship between seasonal, monthly, and daily changes in temperature and atmospheric pressure concerning the frequency of cases with ACS was observed.

Optimum Power Forecasting Technique for Hybrid Renewable Energy Systems Using Deep Learning

Power forecasting in large-scale electrical systems, comprising photovoltaic (PV), solar, and wind power, faces challenges due to geographical diffusion and temporal variations. Despite numerous studies, the disparity between predicted and actual generation remains a significant issue. This study utilizes historical power and atmospheric data from diverse plants, employing preprocessing techniques to enhance quality and reduce noise. K-Means clustering is applied to the dataset, optimizing deep learning training periods and increasing accuracy. A resilient hybrid deep learning model is proposed for microgrid (MG) power forecasting, encompassing preprocessing, model training, and assessment stages. Mathematical models for PV systems, battery storage, and wind systems, along with a K-means clustering algorithm, contribute to accurate forecasting. The recurrent neural network based on gated recurrent unit architecture outperforms traditional algorithms, demonstrating superior accuracy, and reduced errors in extensive experimental analyses. Pearson coefficients reveal associations between different power production forms, emphasizing the potential of hybrid renewable energy clusters to enhance forecasting. Case studies illustrate the partial controllability of concentrated solar power production, reducing overall renewable energy cluster unpredictability. The proposed method showcases the efficacy of the hybrid model in addressing challenges and improving accuracy in large-scale power forecasting.

Robust relation of streamwise velocity autocorrelation in atmospheric surface layers based on an autoregressive moving average model

We construct an autoregressive moving average (ARMA) model consisting of the history and random effects for the streamwise velocity fluctuation in boundary-layer turbulence. The distance to the wall and the boundary-layer thickness determine the time step and the order of the ARMA model, respectively. Based on the autocorrelation's analytical expression of the ARMA model, we obtain a global analytical expression for the second-order structure function, which asymptotically captures the inertial, dynamic and large-scale ranges. Specifically, the exponential autocorrelation of the ARMA model arises from the autoregressive coefficients and is modified to logarithmic behaviour by the moving-average coefficients. The asymptotic expressions enable us to determine model coefficients by existing parameters, such as the Kolmogorov and the Townsend–Perry constants. A consequent double-log expression for the characteristic length scale is derived and is justified by direct numerical simulation data with $Re_\tau \approx 5200$ and field-measured neutral atmospheric surface layer data with $Re_\tau \sim O(10^6)$ from the Qingtu Lake Observation Array site. This relation is robust because it applies to $Re_\tau$ from $O(10^4)$ to $O(10^6)$ , and even when the statistics of natural ASL deviate from those of canonical boundary-layer turbulence, e.g. in the case of imbalance in energy production and dissipation, and when the Townsend–Perry constant deviates from traditional values.

A Convolutional Neural Network and Attention-Based Retrieval of Temperature Profile for a Satellite Hyperspectral Microwave Sensor

As numerical weather forecasting advances, there is a growing demand for higher-quality atmospheric data. Hyperspectral instruments can capture more atmospheric information and increase vertical resolution, but there has been limited research into retrieval algorithms for obtaining hyperspectral microwaves in the future. This study proposes an atmospheric temperature profile detection algorithm based on Convolutional Neural Networks (CNN) and Local Attention Mechanisms for local feature extraction, applied to hyperspectral microwave sensors. The study utilizes the method of information entropy to extract more effective channels in the vicinities of 60 GHz, 118 GHz, and 425 GHz. The algorithm uses the brightness temperature as the input of the network. The algorithm addresses common issues encountered in conventional networks, such as overfitting, gradient explosion, and gradient vanishing. Additionally, this method isolates the three oxygen-sensitive frequency bands for modularized local feature extraction training, thereby avoiding abrupt changes in brightness temperature between adjacent frequency bands. More importantly, the algorithm considers the correlation between multiple channels and information redundancy, focusing on variations in local information. This enhances the effectiveness of hyperspectral microwave channel information extraction. We simulated the brightness temperatures of the selected channels through ARTS and divided them into training, validation, and test sets. The retrieval capability of the proposed method is validated on a test dataset, achieving a root mean square error of 1.46 K and a mean absolute error of 1.4 K for temperature profile. Detailed comparisons are also made between this method and other commonly used networks for atmospheric retrieval. The results demonstrate that the proposed method significantly improves the accuracy of temperature profile retrieval, particularly in capturing fine details, and is more adaptable to complex environments. The model also exhibits scalability, extending from one-dimensional (pressure level) to three-dimensional space. The error for each pressure level is controlled within 0.7 K and the average error is within 0.4 K, demonstrating effectiveness across different scales with impressive results. The computational efficiency and accuracy have both been improved when handling a large amount of radiation data.

OPTIMAL SIZING OF SOLAR-WIND HYBRID MICROGRID USING IMPROVED GREY WOLF OPTIMIZATION ALGORITHM A CASE STUDY OF KADUNA - NIGERIA

This paper presents an improved grey wolf optimization algorithm (IGWOA) for optimal sizing of an isolated photovoltaic (PV), wind turbine (WT), and battery energy storage (BES) hybrid microgrid. To demonstrate the effectiveness of the proposed approach, atmospheric data sets comprising of wind, solar, and temperature of Kaduna International Airport were collected from Nigerian Meteorological Agency while the load demand data was collected from Kaduna International Airport Electricity Distribution Center. The microgrid optimal sizing was formulated as a constrained single objective optimization problem. Constraints including, loss of power supply probability (LPSP), power balance, generation limits and battery state of charge (SOC) were imposed. Three simulation scenarios were considered. Firstly, the target allowable maximum LPSP was fixed at 25% and the algorithm was able to determine the optimal sizing of the hybrid microgrid components and minimize the initial cost from 169,880.00 USD to 112,356.40 USD per annum resulting in 34% savings in cost. Secondly, the effect of the target allowable maximum LPSP variation was investigated, and it was found that the total installed capacity of the system decreases with increase in LPSP thereby decreasing the total cost. Additionally, a novel electricity price index (EPI) was introduced in order to quantify the degree of optimality of the solution. The EPI was found to increase exponentially with increase in LPSP, resulting in an EPI of &lt; 0.05USD/kWh at 20% LPSP. Lastly, to validate the proposed approach, a comparative analysis between the IGWOA and other algorithms was carried out, and the proposed IGWOA proved applicable.

Investigation of historical severe storms and storm tides in the German Bight with century reanalysis data

Abstract. Century reanalysis models offer a possibility to investigate extreme events and gain further insights into their impact through numerical experiments. This paper is a comprehensive summary of historical hazardous storm tides in the German Bight (southern North Sea) with the aim of comparing and evaluating the potential of different century reanalysis data to be used for the reconstruction of extreme water levels. The composite analysis of historical water level extremes, underlying atmospheric situations and their uncertainties may further support decision-making on coastal protection and risk assessment. The analysis is done based on the results of the regional hydrodynamic model simulations forced by atmospheric century reanalysis data, e.g. 20th Century Reanalysis Project (20CR) ensembles, ERA5 and UERRA–HARMONIE. The eight selected historical storms lead either to the highest storm tide extremes for at least one of three locations around the German Bight or to extreme storm surge events during low tide. In general, extreme storm tides could be reproduced, and some individual ensemble members are suitable for the reconstruction of respective storm tides. However, the highest observed water level in the German Bight could not be simulated with any considered forcing. The particular weather situations with corresponding storm tracks are analysed to better understand their different impact on the peak storm tides, their variability and their predictability. Storms with more northerly tracks generally show less variability in wind speed and a better agreement with the observed extreme water levels for the German Bight. The impact of two severe historical storms that peaked at low tide is investigated with shifted tides. For Husum in the eastern German Bight this results in a substantial increase in the peak water levels reaching a historical maximum.

Airborne lidar measurements of atmospheric CO2 column concentrations to cloud tops made during the 2017 ASCENDS/ABoVE campaign

Abstract. We measured the column-averaged atmospheric CO2 mixing ratio (XCO2) to a variety of cloud tops with an airborne pulsed multi-wavelength integrated path differential absorption (IPDA) lidar during NASA's 2017 ASCENDS/ABoVE airborne campaign. Measurements of height-resolved atmospheric backscatter profiles allow this lidar to retrieve XCO2 to cloud tops, as well as to the ground, with accurate knowledge of the photon path length. We validated these measurements with those from an onboard in situ CO2 sensor during spiral-down maneuvers. These lidar measurements were 2–3 times better than those from previous airborne campaigns due to our using a wavelength step-locked laser transmitter and a high-efficiency detector for this campaign. Precisions of 0.6 parts per million (ppm) were achieved for 10 s average measurements to mid-level clouds and 0.9 ppm to low-level clouds at the top of the planetary boundary layer. This study demonstrates the lidar's capability to fill in XCO2 measurement gaps in cloudy regions and to help resolve the vertical and horizontal distributions of atmospheric CO2. Future airborne campaigns and spaceborne missions with this capability can be used to improve atmospheric transport modeling, flux estimation and carbon data assimilation.

CAMELS-Chem: augmenting CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) with atmospheric and stream water chemistry data

Abstract. Large sample datasets are transforming the catchment sciences, but there are few off-the-shelf stream water chemistry datasets with complementary atmospheric deposition, streamflow, meteorology, and catchment physiographic attributes. The existing CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) dataset includes data on topography, climate, streamflow, land cover, soil, and geology across the continental US. With CAMELS-Chem, we pair these existing attribute data for 516 catchments with atmospheric deposition data from the National Atmospheric Deposition Program and water chemistry and instantaneous discharge data from the US Geological Survey over the period from 1980 through 2018 in a relational database and corresponding dataset. The data include 18 common stream water chemistry constituents: Al, Ca, Cl, dissolved organic carbon, total organic carbon, HCO3, K, Mg, Na, total dissolved N, total organic N, NO3, dissolved oxygen, pH (field and lab), Si, SO4, and water temperature. Annual deposition loads and concentrations include hydrogen, NH4, NO3, total inorganic N, Cl, SO4, Ca, K, Mg, and Na. We demonstrate that CAMELS-Chem water chemistry data are sampled effectively across climates, seasons, and discharges for trend analysis and highlight the coincident sampling of stream constituents for process-based understanding. To motivate their use by the larger scientific community across a variety of disciplines, we show examples of how these publicly available datasets can be applied to trend detection and attribution, biogeochemical process understanding, and new hypothesis generation via data-driven techniques.