National Oceanic And Atmospheric Administration Research Articles

Impact of heat exposure on health outcomes among older adults in India: an analysis across ten states.

This study investigates the impact of heat exposure on health outcomes among older adults in 10 Indian states, using data from the Longitudinal Aging Study of India (LASI-Wave I) and climate data from the National Oceanic and Atmospheric Administration (NOAA). Propensity score matching and ordered probit models were employed to control for demographic factors and isolate the effects of heat exposure. Our results show a significant association between heat exposure and worsening self-reported health and mental health, including increased feeling of depression, fatigue, fear, and decreased life satisfaction. The analysis indicates that access to cooling devices mitigates adverse health effects in rural areas, while access to onsite water benefits urban residents. These findings underscore the critical role of housing and environment factors in moderating the health impacts of heat exposure and suggest targeted interventions to protect vulnerable populations.

Ensemble Precipitation Forecast Postprocessing with Ensemble Coalescence and Quantile Mapping

Abstract Ensemble forecast systems are an integral part of the National Weather Service’s (NWS) program for producing skillful, reliable deterministic and probabilistic forecasts, but statistical postprocessing is often required to address known deficiencies. Here, we test improvements to the ensemble postprocessing procedure currently implemented at the NWS Meteorological Development Laboratory (MDL) that generates a deterministic quantitative precipitation forecast from multimodel ensemble means. The existing approach uses recent forecast and analysis data to estimate cumulative distributions of precipitation and then applies a quantile mapping between the mean forecast and analysis. In the current work, we use a revised version of this approach that, in addition, utilizes ensemble mean inputs improved by a coalescence procedure based on the feature alignment technique (FAT). This is a variational technique for determining the displacements needed to adjust precipitation features of individual ensemble members toward their positions in the raw ensemble mean. The performance of the combined coalescence–quantile mapping algorithm was evaluated over a 2-yr period of forecasts from version 12 of the NCEP Global Ensemble Forecast System (GEFSv12) after extensive testing of configurable parameters to ensure the physical reasonableness of the precipitation fields. The coalescence procedure by itself was shown to correct some of the raw ensemble mean deficiencies related to position errors in the individual ensemble forecasts, especially as lead times increase. The combined approach resulted in improved forecasts as measured by fractional bias and equitable threat scores. Implementation of the techniques described here for operational guidance is being considered by the National Oceanic and Atmospheric Administration (NOAA) and is applicable to other forecast models. Significance Statement Ensemble weather prediction systems provide information about the uncertainty in weather forecasts. However, the raw output from these models is voluminous and suffers from biases. Here, we combine two statistical approaches to correct for the biases and create a single deterministic forecast that is more manageable to use by stakeholders. The two techniques—coalescence and then quantile mapping—are applied in sequence and the resulting model forecast is compared against observations. Our approaches improve the statistical performance with precipitation fields that have more realistic extrema and a smaller extent of small precipitation amounts. The techniques are being evaluated for operational implementation by the National Weather Service.

Modeling the Inter-Arrival Time Between Severe Storms in the United States Using Finite Mixtures

When inter-arrival times between events follow an exponential distribution, this implies a Poisson frequency of events, as both models assume events occur independently and at a constant average rate. However, these assumptions are often violated in real-insurance applications. When the rate at which events occur changes over time, the exponential distribution becomes unsuitable. In this paper, we study the distribution of inter-arrival times of severe storms, which exhibit substantial variability, violating the assumption of a constant average rate. A new approach is proposed for modeling severe storm recurrence patterns using a finite mixture of log-normal distributions. This approach effectively captures both frequent, closely spaced storm events and extended quiet periods, addressing the inherent variability in inter-event durations. Parameter estimation is performed using the Expectation–Maximization algorithm, with model selection validated via the Bayesian information criterion (BIC). To complement the parametric approach, Kaplan–Meier survival analysis was employed to provide non-parametric insights into storm-free intervals. Additionally, a simulation-based framework estimates storm recurrence probabilities and assesses financial risks through probable maximum loss (PML) calculations. The proposed methodology is applied to the Billion-Dollar Weather and Climate Disasters dataset, compiled by the U.S. National Oceanic and Atmospheric Administration (NOAA). The results demonstrate the model’s effectiveness in predicting severe storm recurrence intervals, offering valuable tools for managing risk in the property and casualty insurance industry.

Development of the next-generation air quality prediction system in the Unified Forecast System framework: Enhancing predictability of wildfire air quality impacts

Abstract The National Oceanic and Atmospheric Administration (NOAA) has developed an advanced regional air quality prediction system (AQPS) within the Unified Forecast System (UFS) framework to improve representations of wildfire emissions and their impacts on air quality predictions. This innovative system integrates the Environmental Protection Agency’s (EPA) Community Multiscale Air Quality (CMAQ) model as a column chemistry model with the UFS-based atmospheric model, operating in an online mode. The calculation of wildfire gas and particulate emissions relies on satellite-derived fire products, high-resolution Regional Hourly Advanced Baseline Imager (ABI) and Visible Infrared Imaging Radiometer Suite (VIIRS) Emissions (RAVE). A period in June and July 2023 with Quebec Canadian wildfires, which severely impacted air quality in the United States (US), was chosen as a case study to assess the predictive capability of the UFS-AQM system. The UFS-AQM predictions of fine particulate (PM2.5) and ozone (O3) were evaluated against AirNow observations from June 15 to July 14, 2023. The results indicate a substantial improvement in PM2.5 predictions when compared to the previous operational forecast. Meanwhile, the system demonstrates a strong ability of predicting O3 exceedance events during the dissipation phase of the wildfire. Furthermore, the online system shows more realistic predictions of aerosol optical depth (AOD) as compared to the previous operational forecast and satellite retrieval data. Finally, this study outlines a plan for further advancing a comprehensive regional AQPS at NOAA.

The Weather On-Demand Framework

This paper describes the Weather On-Demand (WOD) forecasting framework which is a software stack used to run operational and on-demand weather forecasts. The WOD framework is a distributed system for the following: (1) running the Weather Research and Forecast (WRF) model for data assimilation and forecasts by triggering either scheduled or on-demand jobs; (2) gathering upstream weather forecasts and observations from a wide variety of sources; (3) reducing output data file sizes for permanent storage; (4) making results available through Application Programming Interfaces (APIs); (5) making data files available to custom post-processors. Much effort is put into starting processing as soon as the required data become available, and in parallel where possible. In addition to being able to create short- to medium-range weather forecasts for any location on the globe, users are granted access to a plethora of both global and regional weather forecasts and observations, as well as seasonal outlooks from the National Oceanic and Atmospheric Administration (NOAA) in the USA through WOD integrated-APIs. All this information can be integrated with third-party software solutions via WOD APIs. The software is maintained in the Git distributed version control system and can be installed on suitable hardware, bringing the full flexibility and power of the WRF modelling system to the user in a matter of hours.

Temporal, Spatial Characteristics and Prediction of Precipitation in Guangzhou Based on the SARIMA Model

This article obtains nearly 18 years of precipitation data for Guangzhou from the National Centers for Environmental Information (NCEI) under the National Oceanic and Atmospheric Administration (NOAA) and conducts visualization analysis and modeling predictions using R software.Using the classic SARIMA model (the ARIMA model with seasonal components) as well as regression and residual ARIMA models to build and simulate the precipitation series.We also used Basic-bootstrap prediction, which involves resampling the residuals of the fitted model with replacement, using historical values to predict the future and obtain a model parameter, as well as Full-bootstrap prediction (which assumes that the residuals have uncertainty and that the estimated model coefficients themselves also have uncertainty).Therefore, not only are reasonable model residuals obtained through the bootstrap method, but multiple model parameters are also derived from this method to determine more reasonable model parameters, which are then used to forecast the next 12 periods for the fitted model. Comparing the results of the four prediction models, the Full-bootstrap model under the regression model showed the best predictive performance. The study found that there is no significant trend in precipitation changes over time, but short-term fluctuations still exist. Although some extreme values appeared, they remain within a predictable range.Therefore, the relevant city government departments prone to extreme rainfall can make reasonable adjustments to drainage facilities based on local precipitation forecasts

Assessment of a youth climate empowerment program: Climate READY

Abstract. This article presents an in-depth assessment of a youth, climate empowerment program, called Climate READY – Climate Resilience Education and Action for Dedicated Youth. It was developed by the Florida Atlantic University Pine Jog Environmental Education Center (FAU Pine Jog) and funded by the National Oceanic and Atmospheric Administration (NOAA) Environmental Literacy Program. The program built climate literacy and community resilience through a three-semester dual-enrollment program (financed by grant no. NOAA-SEC-OED-2020-2006190). Most student participants (∼80 %) were from Title 1 high schools in low socio-economic communities vulnerable to extreme weather and environmental hazards in Palm Beach County, Florida (see definition in Appendix A). The main objectives of this program were to increase knowledge of south Florida's changing climate systems; teach and promote environmentally responsible behavior, which results in the stewardship of healthy ecosystems and a reduction in carbon consumption to mitigate future environmental risks; and empower students to act as agents of change within the community by teaching community members about local climate impacts and resilience strategies for extreme weather events. Important characteristics of the program included the following: Students ages 15 to 17 years old registered for the Climate READY Ambassador Institute (summer semester 1) built climate knowledge, explored NOAA Science on a Sphere technology, engaged with scientists and resilience experts, developed communication and advocacy skills, and learned about local resilience solutions. At the end of the course, these students were given completion certificates and the title Climate READY Ambassadors (CRAs). An after-school mentorship (fall semester 2) component paired new Climate READY Ambassadors with fourth- and fifth-grade after-school students ages 9 to 11 years old to build community resilience awareness through four structured lessons and the creation of storybooks. Lastly, community outreach (spring semester 3) provided ways for Climate READY Ambassadors to share local resilience strategies at public events and promoted civic engagement in climate solutions. Data were collected from all students in the form of pre- and post-assessment questionnaires during the 2022–2023 academic year. Summative statistics were analyzed for climate science knowledge, self-identity, self-efficacy, and sense of place. Climate READY Ambassadors felt more prepared, confident, and able to communicate within their communities about climate change, and many demonstrated a significantly better understanding of climate science concepts. After-school students showed a better understanding of climate change and were able to identify ways to help reduce the effects of climate change. Both groups of students benefitted from the Climate READY Ambassador mentorship, demonstrating learning by doing and learning by storytelling.

Large-Scale Surface Air Temperature Bias in Summer over the CONUS and Its Relationship to Tropical Central Pacific Convection in the UFS Prototype 8

Abstract This study aims to understand the source of surface air temperature bias over the contiguous United States (CONUS) during boreal summer (June–September) in the Unified Forecast System (UFS) coupled model prototype 8 (P8), developed by the National Centers for Environmental Prediction (NCEP) and the National Oceanic and Atmospheric Administration (NOAA). The focus is on the subseasonal variability defined as a weekly average in weeks 2–5 of forecast leads (total 224 cases; 4 weeks × 2 initialization dates × 4 months × 7 years). The large-scale surface air temperature bias pattern is extracted using the empirical orthogonal function (EOF) analysis. The associated principal component describes the variability of bias for each reforecasting week throughout the 2011–17 reforecasting period. The leading EOF of surface air temperature bias exhibits an east–west dipole pattern over the CONUS, explaining 31.6% of the total variability of weekly temperature bias. This bias pattern is strongly related to the upper-level Rossby wave induced by a bias in convection over the central tropical Pacific. Furthermore, the mean bias of background flow in the extratropics degrades the representation of teleconnections from the tropics to the midlatitudes. UFS P8 has weaker zonal wind over the North Pacific with stronger vertical wind shear than the ERA5 reanalysis. The weak zonal wind hampers the Rossby wave’s propagation, while strong vertical shear reduces its amplitude.

Mapping essential fish ‘hardbottom’ habitat using sonar techniques: A local reef off Masonboro Island, North Carolina, USA

This study investigates an outcropping submarine reef, known as John Creek Hardbottom, located on the shoreface off Masonboro Island, North Carolina. The exposure is likely associated with the high-relief Pleistocene coquina calcarenite hardbottom reefs, which are prevalent off southeastern North Carolina. The reefs provide essential ecosystem services, which include habitat for a wide range of biological communities, in addition to acting as an important sand source for local beaches and providing substantial recreational fishing and diving opportunities. Due to their local importance, the National Oceanic and Atmospheric Administration (NOAA) has classified the reefs as Essential Fish Habitat (EFH). In this study, a range of different hydrographic survey techniques, including sidescan sonar and single-beam echosounding, were collected to produce a geo-referenced mosaic and bathymetric grid of the reef. The reef’s various morphological features are classified based on terminology from previous studies, and upon further examination, a new term for local hardbottom reef morphology, known as Importantly, John Creek’s unique morphology differs from other nearby hardbottoms, allowing this study to provide a wider understanding of local hardbottom characteristics. Due to their protected status, in addition to natural and anthropogenic threats they face, mapping and monitoring southeastern North Carolina’s reefs provides critical information for all stakeholders involved in sustainable coastal management decision-making.

Differential Analysis Based on Different Decomposition Methods of Time Series

A detailed analysis of four popular time series decomposition methods: Classical, X11, SEATS, and STL is provided in this study, with a focus on their applications, strengths, and limitations. Understanding seasonal patterns in time series data is an important aspect in several fields such as economics or environmental science. The R package seasonal was employed to conduct the decompositions on the official data of National Oceanic and Atmospheric Administration (NOAA) Climate Data from January 1980 till July 2024. It offers a detailed analysis of the performance of each method in isolating trends, seasonal components, and residuals. Findings reveal that although all methods can be used for time series data decomposition, their success is influenced by data nature and particular analytical tasks. For instance, SEATS is robust enough to handle irregularities while Classical decomposition wins when it comes to simple understanding. Similarly, X11 methods were known for lack of flexibility as well as recurring seasonal changes sensitivity, where STL is particularly efficient for varying seasonal patterns datasets with nonlinearity trends. From the findings, it can be concluded that it is important to choose the appropriate decomposition method to increase reliability and make appropriate adjustments in the forecasts incorporating time series data.

Development of a Wave Model Component in the First Coupled Global Ensemble Forecast System at NOAA

Abstract We describe the development of the wave component in the first global-scale coupled operational forecast system using the Unified Forecasting System (UFS) at the National Oceanic and Atmospheric Administration (NOAA), part of the U.S. National Weather Service (NWS) operational forecasting suite. The operational implementation of the atmosphere–wave coupled Global Ensemble Forecast System, version 12 (GEFSv12), in September 2020 was a critical step in NOAA’s transition to the broader community-based UFS framework. GEFSv12 represents a significant advancement, extending forecast ranges and empowering the NWS to deliver advanced weather predictions with extended lead times for high-impact events. The integration of a coupled wave component with higher spatial and temporal resolution and optimized physics parameterizations notably enhanced forecast skill and predictability, particularly benefiting winter storm predictions of wave heights and peak wave periods. This successful endeavor encountered challenges that were addressed by the simultaneous development of new features that enhanced wave model forecast skill and product quality and facilitated by a multidisciplinary team collaborating with NOAA’s operational forecasting centers. The GEFSv12 upgrade marks a pivotal shift in NOAA’s global forecasting capabilities, setting a new standard in wave prediction. We also describe the coupled GEFSv12-Wave component impacts on NOAA operational forecasts and ongoing experimental enhancements, which altogether represent a substantial contribution to NOAA’s transition to the fully coupled UFS framework.

ProbSevere Version 3: Improved Exploitation of Data Fusion and Machine Learning for Nowcasting Severe Weather

Abstract National Oceanic and Atmospheric Administration (NOAA)/Cooperative Institute for Meteorological Satellite Studies (CIMSS) ProbSevere, or the “probability of severe” models, provide next-hour probabilistic guidance on severe weather in the United States (large hail, strong wind gusts, and tornadoes) using machine learning and meteorological data from remotely sensed platforms and short-term numerical weather models. ProbSevere has been widely used by NOAA’s National Weather Service (NWS) since 2016. ProbSevere version 3 employs multiplatform, multiscale storm object identification and tracking to collect storm features and then uses gradient boosting decision trees to make predictions of convective hazards from the extracted predictors. ProbSevere v3 was trained, validated, and tested on data from 2018 to 2023. Results demonstrate improved performance over ProbSevere v2 for the majority of the United States across all seasons, spanning a wide range of meteorological regimes. The greatest improvements are for the hail, wind, and any severe models, whereas the tornado model demonstrated modest improvement. ProbSevere v3 models were evaluated by NWS forecasters in simulated real-time experiments from 2021 to 2023. The majority of forecasters preferred ProbSevere v3 over v2 due to improved confidence in predictions and greater lead times for the issuance of severe weather warnings. An analysis of predictor importance revealed several radar, lightning, satellite, and numerical model fields that strongly contribute to the model predictions, which is consistent with past research and knowledge of severe thunderstorm forecasting. This reinforces the importance of environmental data fusion for nowcasting severe weather associated with midlatitude convection. Significance Statement This study details the updates made to a U.S.-wide, real-time, probabilistic severe-weather guidance system aimed to improve forecaster confidence and the accuracy of the U.S. National Weather Service severe weather warnings. An evaluation of the system demonstrated improved performance over the operational predecessor, which was driven by radar, ground-based lightning, satellite, and numerical model data sources.

Enhanced Retrospective Forecasting in Dissipative Dynamical Systems Using Transformer and Multi-Scale ESRGAN Models

Accurate characterization of complex dynamical systems is crucial for understanding their intrinsic behavior, and retrospective prediction provides a promising solution. However, traditional methods often fail to effectively predict dissipative terms, which are key in dissipative dynamical systems. This study introduces a deep learning method (DLM) that combines a Transformer model with a multiscale enhanced super-resolution generative adversarial network (MS-ESRGAN) to improve retrospective predictions by extracting implicit information from temporal evolution data. The Transformer excels at capturing past dynamics, while MS-ESRGAN refines the predicted fields, achieving resolutions on par with ground truth data. The effectiveness of the DLM is demonstrated using two canonical flow cases: forced isotropic turbulence and a transitional boundary layer. The model closely matches the velocity fields of the ground truth, with only minor deviations attributed to the nonlinearity of the governing equations and the inherent difficulty in resolving small-scale structures. In addition, the DLM has been applied to National Oceanic and Atmospheric Administration (NOAA) sea surface temperature (SST) data, demonstrating its practical utility for climate science. Despite the challenges associated with capturing small-scale structures, the data-driven DLM outperforms traditional numerical methods that either neglect the dissipative term or utilize negative dissipative coefficients, representing a significant advancement in retrospective predictions.

Forecasting energy poverty using different machine learning techniques for Missouri

Energy poverty in Missouri was analyzed using the four quadrant approach using both state and county level data sets for two separate definitions of the grid. Predictions used machine learning techniques including decision trees, random forest, extreme gradient boosting and support vector machines. It was determined that the extreme gradient boosting performed the best when compared to all the other models after the hyperparameters were tuned. The F1 scores for the county level data sets were higher than for the state levels thus indicating greater predictability for the National Oceanic and Atmospheric Administration (NOAA) climatological regional runs. For the county level data, the F1 score was the highest for region 1, which coincided with one of the highest expenditure risk values whilst regions 2-4 were the lowest scoring area. In grid 2, the largest class distribution changed from grid 1’s expenditure risk to the no risk category. This grid had more variability in terms of the double risk class when compared to grid 1 and, as such, its predictability in terms of its F1 scores was reduced. There were similarities in the ranking of the prediction scores for the regions for both grids as regions 1 and 6 incurred the largest F1 values. Thus energy poverty can be classified and predicted for Missouri, which in turn may aid policy makers via quantitative regional risk analysis. This data-driven informed policy making can lead to the development and implementation of laws and social programs to help ameliorate energy poverty.

Impact of the ITF Relationship and the Onset of Rainfall on Precipitation in the Republic of Guinea

Republic of Guinea is one of the West African countries which share form a border with the southern. The rainfall onset in this country is one of the major problems that the farmers face. For it, we determined with more precision, the rainy season onset in eight synoptic stations in Republic of Guinea: Boke, Conakry, Faranah, Kankan, Koundara, Labe, Mamou and NZerekore. Then, we studied the InterTropical Front (ITF) position compared to each station at the onset date. This work better analyses the atmospheric dynamics leading to rainfall particularity. We used the daily rainfall data provided by the National Meteorological Service of Guinea for (1991-2020) years and the dekadal (10-day) ITF position data produced by the National Oceanic and Atmospheric Administration (NOAA) for (1990-2021) years. Using these daily rain of the eight stations and the ITF data, we calculated, the dekadal, monthly and annual rain amount, then we established a relation between the ITF position and the precipitation for each station. Results obtained show that the rainy season begins on May 18th, April 20th, May 4th, May 14th, June 8th, May 12th, April 28th and March 20th in Boké, Conakry, Faranah, Kankan, Koundara, Labe, Mamou and NZerekore regions, respectively. Compared to these dates, the ITF is located 2247.77 km north of Boke, 2136.13 km north of Conakry, 2121.24 km north of Faranah, 2136.13 km north of Kankan, 2236.61 km north of Koundara, 2247.77 km north of Labe, 2236.61 km north of Mamou and 2136.13 km north of NZerekore. The good correlation is obtained between the ITF position and the monthly precipitations for eight regions. This study will allow farmers to know with more precision the rainfall onset in Republic of Guinea.

Risk Assessment of Radiological Dispersal Device Terrorism in Malaysia Cities

Research has revealed that a terror attack in Malaysia, a developing nation, is a cause for alarm as it could create panic among its citizens. To address this, a study was conducted to investigate the dispersion of radionuclides after a hypothetical explosion, considering multiple cities in Malaysia as potential targets, including Kuala Lumpur, Kuching, and Kota Kinabalu. The primary aim of the research was to analyze the dose concentrations released from the RDD explosion over a 24-hour period and to assess the associated health risks in terms of morbidity and mortality per 100,000 people in these cities. The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) software was employed to simulate the trajectory and dispersion of air particles, taking into account parameters such as time, altitude, and potential target cities, based on meteorological data from the Global Data Assimilation System (GDAS) of the National Oceanic and Atmospheric Administration (NOAA). The research focused on the analysis of Cs-137 at 50 TBq and was conducted with simulations on December 22, 2020. Results from the simulations revealed that the dose concentration in the air within 24 hours was 3.3 mSv, 1.8 mSv, and 7.7 mSv for Kuala Lumpur, Kuching, and Kota Kinabalu, respectively, while the dose deposited on the ground was 150 mSv, 71 mSv, and 310 mSv for the same cities. Furthermore, it was found that the dose concentration was most significant within the first four hours after the release of Cs-137, peaking between 30 to 90 minutes at altitudes of 60 to 80 meters. In terms of risk assessment, the research indicated that, for individuals located at this altitude during the timeframe of 0045 hours to 0100 hours after the dispersion of Cs-137, there could be 88 fatalities and 128 injuries per 100,000 residents in the city. The simulation offers valuable insights and guidance for governmental efforts to improve radioactive waste management and formulating or enhancing protocols to address such incidents promptly. It is essential to take swift and effective actions in order to protect the lives of the population in the event of a radiological terrorist threat.

Swimming kinematics of deep-sea fishes.

Although the deep oceans represent Earth's largest habitat, the challenges of studying deep-sea organisms in situ have limited our understanding of adaptation, ecology, and behaviour in these important ecosystems. One fundamental trait of fishes that remains largely unexplored in the deep ocean is swimming, a vital process for movement, migration, and dispersal in marine habitats. Deep-sea conditions such as temperature, pressure, and food availability could each impact the speed and efficiency of swimming in fishes. To investigate swimming kinematics of fishes with increasing depth, we analysed in situ video of bony fishes across a 6000-m depth gradient. We compared open-source videos of fishes from National Oceanic and Atmospheric Administration (NOAA)Ocean Exploration with tank-based recordings of shallow-water relatives from Puget Sound, Washington, USA to understand how both habitat depth and phylogeny influence swimming in fishes. We analysed kinematics in four dominant demersal fish groups, the orders Anguilliformes, Gadiformes, Ophidiiformes, and Perciformes. Deep-sea fishes swam consistently slowly. Swimming kinematics varied across temperature, oxygen, body elongation, and depth. These results suggest that swimming kinematics do not change linearly with increasing habitat depth in fishes and that the impacts of deep-sea conditions such as low temperatures, high pressures, and low nutrient availability on swimming behaviour need to be considered independently of one another. These findings provide insight into the evolution of fish form and function in the deep ocean.

Identifying time patterns of highland and lowland air temperature trends in Italy and the UK across monthly and annual scales

Abstract. This paper presents a statistical analysis of air temperature data from 32 stations in Italy and the UK up to 2000 m above sea level from 2002 to 2021. The data came from both highland and lowland areas in order to evaluate the differences due to both location and elevation. The analysis focused on detecting trends at annual and monthly timescales, employing ordinary least-squares (OLS), robust S-estimator regression, and Mann–Kendall (MK) and Sen's slope methods. Hierarchical clustering (HCA) using dynamic time warping (DTW) was then applied to the monthly data to analyze the intra-annual pattern similarity of trends within and across the groups. Two different regions of Europe were chosen because of the different climate and temperature trends – namely, the northern UK (smaller trends) and the northwest Italian Alps (larger trends). The main novelty of the work is to show that stations with similar locations and altitudes have similar monthly slopes by quantifying them using DTW and clustering. These results reveal the nonrandomness of different trends throughout the year and between different parts of Europe, with a modest influence of altitude in wintertime. The findings revealed that group average trends were close to the National Oceanic and Atmospheric Administration (NOAA) values for the areas in Italy and the UK, confirming the validity of analyzing a small number of stations. More interestingly, intra-annual patterns were detected commonly at the stations of each of the groups and are clearly different between them. Confirming the different climates, most highland and lowland stations in Italy exhibit statistically significant positive trends, while in the UK, both highland and lowland stations show statistically nonsignificant negative trends. Hierarchical clustering in combination with DTW showed consistent similarity between monthly patterns of means and trends within the group of stations and inconsistent similarity between patterns across groups. The use of the 12 distance correlation matrices (dcor) (one for each month) also contributes to what is the main result of the paper, which is to clearly show the different temporal patterns in relation to location and (in some months) altitude. The anomalous behaviors detected at 3 of the 32 stations, namely Valpelline, Fossano, and Aonoch Mòr, can be attributed, respectively, to the facts that Valpelline is the lowest-elevation station in its group; Fossano is the southernmost of the Italian stations, with some sublittoral influence; and Aonoch Mòr has a large number of missing values. In conclusion, these results improve our understanding of temperature spatio-temporal dynamics in two very different regions of Europe and emphasize the importance of consistent analysis of data to assess the ongoing effects of climate change. The intra-annual time patterns of temperature trends could also be compared with climate model results.

Improving Rainfall Prediction Accuracy in the USA Using Advanced Machine Learning Techniques

The key aim of this research project is to design and evaluate advanced machine learning models for increasing accuracy in rainfall forecasting over the USA. We intended to investigate nonlinear relationships typical of the atmospheric variables using state-of-the-art ML methods for more accurate rainfall predictions. For this research project on rainfall forecasting in the USA, we utilized an extensive dataset that comprises historical rainfall data collected from the National Oceanic and Atmospheric Administration (NOAA) and other meteorological agencies. The main dataset we use in this paper consists of daily rainfall measurements across various geographical locations of the USA, thus capturing the wide-ranging historical data necessary for both training and validation of the model. Besides measuring rainfall, we included meteorological data from sources such as NOAA's Global Historical Climatology Network and NASA's Modern-Era Retrospective Analysis for Research and Applications. These datasets further provided key variables that are known to affect rain, including temperature, humidity, wind speed, and atmospheric pressure. The performance metrics used in this research work for the models considered include accuracy, precision, recall, and F1 score. The above table shows that the Random Forest Classifier outperformed the other models, achieving perfect accuracy. That indicated that it rightly classified all the instances in the test set. The Logistic Regression and Support Vector Machine models gave a quite good performance by giving above average accuracy but had lower precision and recall for the rainfall prediction. Accurate rainfall forecasting has direct consequences on agriculture, greatly empowering farmers and agricultural planners to make more effective decisions regarding planting, harvesting, and crop management. The forecasts of rainfall are also of critical importance in disaster management regarding planning for flood emergencies. Moreover, precise forecasting of rainfall, particularly in sustainable water resources management, presents the most important data in planning for and conserving these resources.

Empirical Evaluation and Simulation of the Impact of Global Navigation Satellite System Solutions on Uncrewed Aircraft System–Structure from Motion for Shoreline Mapping and Charting

Uncrewed aircraft systems (UASs) and structure-from-motion/multi-view stereo (SfM/MVS) photogrammetry are efficient methods for mapping terrain at local geographic scales. Traditionally, indirect georeferencing using ground control points (GCPs) is used to georeference the UAS image locations before further processing in SfM software. However, this is a tedious practice and unsuitable for surveying remote or inaccessible areas. Direct georeferencing is a plausible alternative that requires no GCPs. It relies on global navigation satellite system (GNSS) technology to georeference the UAS image locations. This research combined field experiments and simulation to investigate GNSS-based post-processed kinematic (PPK) as a means to eliminate or reduce reliance on GCPs for shoreline mapping and charting. The study also conducted a brief comparison of real-time network (RTN) and precise point positioning (PPP) performances for the same purpose. Ancillary experiments evaluated the effects of PPK base station distance and GNSS sample rate on the accuracy of derived 3D point clouds and digital elevation models (DEMs). Vertical root mean square errors (RMSEz), scaled to the 95% confidence interval using an assumption of normally-distributed errors, were desired to be within 0.5 m to satisfy National Oceanic and Atmospheric Administration (NOAA) requirements for nautical charting. Simulations used a Monte Carlo approach and empirical tests to examine the influence of GNSS performance on the quality of derived 3D point clouds. RTN and PPK results consistently yielded RMSEz values within 10 cm, thus satisfying NOAA requirements for nautical charting. PPP did not meet the accuracy requirements but showed promising results that prompt further investigation. PPK experiments using higher GNSS sample rates did not always provide the best accuracies. GNSS performance and model accuracies were enhanced when using base stations located within 30 km of the survey site. Results without using GCPs observed a direct relationship between point cloud accuracy and GNSS performance, with R2 values reaching up to 0.97.