Weather Noise Research Articles

Boosting power line inspection in bad weather: Removing weather noise with channel-spatial attention-based UNet

Boosting power line inspection in bad weather: Removing weather noise with channel-spatial attention-based UNet

Weather-aware object detection method for maritime surveillance systems

The development of machine learning-based maritime object detection technology aims to assist ship operators in maritime surveillance. However, as maritime environments can be quite complex, developing object detection models that can handle these situations is a challenging research problem, particularly when dealing with adverse weather conditions like rain and haze. While prior research has attempted to remove weather noise and improve object detection models under various weather conditions, they are limited by computing resources and hard to adapt to the constantly changing weather conditions of maritime environments. Preventing performance degradation as weather conditions shift is a significant challenge in maritime surveillance systems. To overcome these challenges, this paper proposes a weather-aware object detection method, Weather-OD, that employs an on-board edge and on-shore cloud-based system for maritime surveillance. It employs specialized machine learning models for object detection, which can be dynamically selected based on the weather conditions to ensure highly accurate object detection with low latency at sea. Weather-OD continuously improves accuracy by periodically training the models with newly collected datasets during voyages, and efficiently manages the life cycle of multiple object detection models, taking into account the constraints of limited edge computing resources. In addition, Weather-OD uses synthetic image data with weather noise to supplement the training data under different weather conditions. We conducted an evaluation of our weather-aware object detection models using a maritime benchmark dataset, the Singapore Maritime Dataset. Our experimental results demonstrated the feasibility of our mechanism with weather classification and a significant improvement in the mean Average Precision (mAP) of maritime object detection in rainy and hazy conditions. Additionally, our approach enables the continuous improvement of object detection accuracy through model retraining with small datasets.

Potential Predictability of Seasonal Global Precipitation Associated with ENSO and MJO

A covariance decomposition method is applied to a monthly global precipitation dataset to decompose the interannual variability in the seasonal mean time series into an unpredictable component related to “weather noise” and to a potentially predictable component related to slowly varying boundary forcing and low-frequency internal dynamics. The “potential predictability” is then defined as the fraction of the total interannual variance accounted for by the latter component. In tropical oceans (30° E–0° W, 30° S–30° N), the consensus is that the El Nino-Southern Oscillation (ENSO, with 4–8 year cycles) is a dominant driver of the potentially predictable component, while the Madden-Julian Oscillation (MJO, with 30–90 days cycles) is a dominant driver of the unpredictable component. In this study, the consensus is verified by using the Nino3-4 SST index and a popular MJO index. It is confirmed that Nino3-4 SST does indeed explain a significant part of the potential predictable component, but only limited variability of the unpredictable component is explained by the MJO index. This raises the question of whether the MJO is dominant in the variability of the unpredictable component of the precipitation, or the current MJO indexes do not represent MJO variability well.

Optimizing Extreme Learning Machine for Drought Forecasting: Water Cycle vs. Bacterial Foraging

Machine learning (ML) methods have shown noteworthy skill in recognizing environmental patterns. However, presence of weather noise associated with the chaotic characteristics of water cycle components restricts the capability of standalone ML models in the modeling of extreme climate events such as droughts. To tackle the problem, this article suggests two novel hybrid ML models based on combination of extreme learning machine (ELM) with water cycle algorithm (WCA) and bacterial foraging optimization (BFO). The new models, respectively called ELM-WCA and ELM-BFO, were applied to forecast standardized precipitation evapotranspiration index (SPEI) at Beypazari and Nallihan meteorological stations in Ankara province (Turkey). The performance of the proposed models was compared with those the standalone ELM considering root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), and graphical plots. The forecasting results for three- and six-month accumulation periods showed that the ELM-WCA is superior to its counterparts. The NSE results of the SPEI-3 forecasting in the testing period proved that the ELM-WCA improved drought modeling accuracy of the standalone ELM up to 72% and 85% at Beypazari and Nallihan stations, respectively. Regarding the SPEI-6 forecasting results, the ELM-WCA achieved the highest RMSE reduction percentage about 63% and 56% at Beypazari and Nallihan stations, respectively.

Forced signal and predictability in a prototype climate model: Implications for fingerprinting based detection in the presence of multidecadal natural variability.

Advanced numerical models used for climate prediction are known to exhibit biases in their simulated climate response to variable concentrations of the atmospheric greenhouse gases and aerosols that force a non-uniform, in space and time, secular global warming. We argue here that these biases can be particularly pronounced due to misrepresentation, in these models, of the multidecadal internal climate variability characterized by large-scale, hemispheric-to-global patterns. This point is illustrated through the development and analysis of a prototype climate model comprised of two damped linear oscillators, which mimic interannual and multidecadal internal climate dynamics and are set into motion via a combination of stochastic driving, representing weather noise, and deterministic external forcing inducing a secular climate change. The model time series are paired with pre-specified patterns in the physical space and form, conceptually, a spatially extended time series of the zonal-mean near-surface temperature, which is further contaminated by a spatiotemporal noise simulating the rest of climate variability. The choices of patterns and model parameters were informed by observations and climate-model simulations of the 20th century near-surface air temperature. Our main finding is that the intensity and spatial patterns of the internal multidecadal variability associated with the slow-oscillator model component greatly affect (i) the ability of modern pattern-recognition/fingerprinting methods to isolate the forced response of the climate system in the 20th century ensemble simulations and (ii) climate-system predictability, especially decadal predictability, as well as the estimates of this predictability using climate models in which the internal multidecadal variability is underestimated or otherwise misrepresented.

The 2021 “Complex Systems” Nobel Prize: The Climate, With and Without Geocomplexity

AbstractOne half of 2021s Nobel Physics prize was awarded to statistical physicist Giorgio Parisi and the other—the first in geophysics in 75 years—to climate scientists Syukoro Manabe and Klaus Hasselmann, the former for pioneering General Circulation Models and the latter (primarily) for proposing a statistical model explaining the climate as a slowly varying state driven by random weather noise. However, the Nobel committee recognized the climate laureates' work almost exclusively from the 1960s and 1970s. We update their report with the contributions from nonlinear geophysics and discuss the implications for the unity of geoscience, and for future climate modeling.

Trabzon ili orman rekreasyon bölgelerini kullanan Arap turistlerin tüketici profili ve rekreasyonel talep yapısı

Trabzon province attracts foreign tourists from Arab countries, and forest recreation areas are used extensively. This study aimed to determine the consumer profile, recreational use of forest resources, user and recreational demand characteristics for recreational areas, and consumer preferences and expectations of Arab tourists coming to Trabzon. In the study, a face-to-face survey was conducted with the random sampling method for 400 Arab tourists using 10 forest recreation areas in Trabzon. In the survey, questions such as visitor characteristics of foreign tourists, use of forest resources, recreational suitability, deficiencies, and reasons for site preference were included. The obtained data were evaluated with descriptive statistics, analysis of variance, and the Duncan test. According to the analyzes and evaluations, it was found that approximately 83% of Arab tourists coming to Trabzon for forest recreation tourism came from Kuwait, Jordan, United Arab Emirates, Oman, and Iraq. The most common activities in forest recreation areas were determined as walking on forest roads, chilling, watching the scenery, taking fresh air, and taking pictures. The reasons considered very important for the preference of forest recreation areas by Arab tourists were determined natural and aesthetic values, the beauty of forests, and clean and cold water to drink, respectively. The most important variables affecting the utilization of forest recreation areas are inadequate facilities and services (4 points), environmental pollution (3.41 points), crowdedness (3.38 points), adverse weather conditions (3.34 points), noise pollution (3,12 points), security (2.86 points), transportation problem (2.59 points), and inadequate pandemic measures (1.35 points). In addition, the demand for forest recreation areas differs at 95% confidence level according to age, gender, education, family size, natural and aesthetic values, clean and cold water, the beauty of forests, tour program, friend recommendation, desire to be alone with nature, pandemic measures, lack of another place to go around the city, pollution situation, promotion and facility, and service adequacy. In the study, some suggestions were made for the sustainable management of forest recreation areas, increasing the quality of recreational services, tourist satisfaction, and tourism development for Arab tourists.

Unique Observational Constraints on the Seasonal and Longitudinal Variability of the Earth’s Planetary Albedo and Cloud Distribution Inferred From EPIC Measurements

Thorough comparison to observations is key to developing a credible climate model forecasting capability. Deep Space Climate Observatory (DSCOVR) measurements of Earth’s reflected solar and emitted thermal radiation provide a unique observational perspective that permits a more reliable model/data comparison than is possible with the otherwise available satellite data. The uniqueness is in the DSCOVR satellite’s viewing geometry, which enables continuous viewing of the Earth’s sunlit hemisphere from its Lissajous orbit around the Lagrangian L1 point. The key instrument is the Earth Polychromatic Imaging Camera (EPIC), which views the Earth’s sunlit hemisphere with 1024-by-1024-pixel imagery in 10 narrow spectral bands from 317 to 780 nm, acquiring up to 22 high spatial resolution images per day. The additional feature is that the frequency of EPIC image acquisition is nearly identical to that of the climate GCM data generation scheme where climate data for the entire globe are ‘instantaneously’ calculated at 1-h radiation time-step intervals. Implementation of the SHS (Sunlit Hemisphere Sampling) EPIC-view geometry for the in-line GCM output data sampling establishes a precise self-consistency in the space-time data sampling between EPIC observational and GCM output data generation and sampling. The remaining problem is that the GCM generated data are radiative fluxes, while the EPIC measurements are backscatter-dependent radiances. Radiance to flux conversion is a complex problem with no simple way to convert GCM radiative fluxes into spectral radiances. The more expedient approach is to convert the EPIC spectral radiances into broadband radiances by MODIS/CERES-based regression relationships and then into solar radiative fluxes using the CERES angular distribution models. Averaging over the sunlit hemisphere suppresses the meteorological weather noise, but preserves the intra-seasonal larger scale variability. Longitudinal slicing by the Earth’s rotation permits a self-consistent model/data comparison of the longitudinal model/data differences in the variability of the reflected solar radiation. Ancillary EPIC Composite data provide additional cloud property information for climate model diagnostics. Comparison of EPIC-derived seasonal and longitudinal variability of the Earth’s planetary albedo with the GISS ModelE2 results shows systematic overestimate of cloud reflectivity over the Pacific Ocean with corresponding underestimates over continental land areas.

Unique NISTAR-Based Climate GCM Diagnostics of the Earth’s Planetary Albedo and Spectral Absorption Through Longitudinal Data Slicing

Deep Space Climate Observatory (DSCOVR) measurements of Earth’s reflected solar and emitted thermal radiation permit a unique model/data comparison perspective that is not readily available from other satellite data. The key factor is the unique Lissajous orbital viewing geometry from the Lagrangian L1 point, which enables a continuous view of Earth’s sunlit hemisphere. The National Institute of Standards and Technology Advanced Radiometer (NISTAR) is the DSCOVR Mission energy budget instrument, which views the reflected and emitted radiation of the Earth’s sunlit hemisphere by means of single pixel active cavity full-spectrum (Band-A, 0.2–100 μm) and filtered solar wavelength (Band-B, 0.2–4.0 μm; and Band-C, 0.7–4.0 μm) radiometer measurements. An additional solar wavelength photodiode channel (0.3–1.1 μm) provides a calibration reference. The objective of this study is the assessment of climate GCM performance via direct model/data comparisons. Such comparisons are difficult due to quasi-chaotic natural variability present in real-world observational data and in climate GCM simulations. This is where the unique DSCOVR viewing geometry makes possible the longitudinal data slicing methodology for more direct model/data comparison. The key point of the longitudinal slicing approach is that data integration over the entire sunlit hemisphere eliminates the quasi-chaotic meteorological weather-scale noise, while preserving intra-seasonal and planetary-scale variability. The rotation of the Earth that retrieves this climate-style, large-scale longitudinal and seasonal variability. The hemispheric averaging is accomplished automatically in NISTAR measurements with its single-pixel view of the Earth. For climate GCMs, this requires implementing the Sunlit Hemisphere Sampling (SHS) scheme to operate on the GCM run-time output data, utilizing the DSCOVR Satellite Ephemeris data to assure precise viewing geometry between NISTAR measurements and GCM output data, while averaging out the meteorological weather noise. However, GCM generated data are radiative fluxes, while NISTAR (and EPIC) measurements are near-backscattered radiances. Conversing NISTSR measurements into radiative fluxes cannot be accomplished using NISTAR data alone, even with detailed support from conventional satellite data. But the identical viewing geometry of Earth’s sunlit hemisphere, and synergistic analyses of EPIC data make it feasible for this conversion of NISTAR near-backscatter radiances into radiative fluxes.

A novel re-compositing approach to create continuous and consistent cross-sensor/cross-production global NDVI datasets

ABSTRACT The longest Normalized Difference Vegetation Index (NDVI) time series produced from the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) has ended in 2017. At some point in the near future, all AVHRR sensors will be retired. To maintain continuity and consistency of this global data set, it is imperative to extend NDVI from other sensors, especially the operational Visible Infrared Imaging Radiometer Suite (VIIRS), which planned to maintain continuity at least through 2038. NDVI could be de-composited into two components: (1) the multi-year climatology and (2) the vegetation condition index (VCI). The former contains climate information and a majority of sensor noise, and the latter contains weather information and residual sensor noise. With the assumption that VCI from different sensors are similar, we re-composited the cross-sensor/cross-production NDVI with original VCI and the cross-sensor/cross-production climatology, and compared various cross-converted datasets with the three base NDVI datasets: two NDVI productions derived from AVHRR observation and another from VIIRS observation. As a result, the re-composited NDVI agrees well with the target base NDVI spatially and temporally, with an accuracy of 0.02 NDVI unit at a global scale. The comparison with several regression approaches distinguish the superiority of the new re-compositing approach.

Using regional scaling for temperature forecasts with the Stochastic Seasonal to Interannual Prediction System (StocSIPS)

Over time scales between 10 days and 10–20 years—the macroweather regime—atmospheric fields, including the temperature, respect statistical scale symmetries, such as power-law correlations, that imply the existence of a huge memory in the system that can be exploited for long-term forecasts. The Stochastic Seasonal to Interannual Prediction System (StocSIPS) is a stochastic model that exploits these symmetries to perform long-term forecasts. It models the temperature as the high-frequency limit of the (fractional) energy balance equation, which governs radiative equilibrium processes when the relevant equilibrium relaxation processes are power law, rather than exponential. They are obtained when the order of the relaxation equation is fractional rather than integer and they are solved as past value problems rather than initial value problems. StocSIPS was first developed for monthly and seasonal forecast of globally averaged temperature. In this paper, we extend it to the prediction of the spatially resolved temperature field by treating each grid point as an independent time series. Compared to traditional global circulation models (GCMs), StocSIPS has the advantage of forcing predictions to converge to the real-world climate. It extracts the internal variability (weather noise) directly from past data and does not suffer from model drift. Here we apply StocSIPS to obtain monthly and seasonal predictions of the surface temperature and show some preliminary comparison with multi-model ensemble (MME) GCM results. For 1 month lead time, our simple stochastic model shows similar—but somewhat higher—values of the skill scores than the much more complex deterministic models.

Colorado River water supply is predictable on multi-year timescales owing to long-term ocean memory

Skillful multi-year climate forecasts provide crucial information for decision-makers and resource managers to mitigate water scarcity, yet such forecasts remain challenging due to unpredictable weather noise and the lack of dynamical model capability. Here we demonstrate that the annual water supply of the Colorado River is predictable up to several years in advance by a drift-free decadal climate prediction system using a fully coupled climate model. Observational analyses and model experiments show that prolonged shortages of water supply in the Colorado River are significantly linked to sea surface temperature precursors including tropical Pacific cooling, North Pacific warming, and southern tropical Atlantic warming. In the Colorado River basin, the water deficits can reduce crop yield and increase wildfire potential. Thus, a multi-year prediction of severe water shortages in the Colorado River basin could be useful as an early indicator of subsequent agricultural loss and wildfire risk.

Windows of Opportunity for Skillful Forecasts Subseasonal to Seasonal and Beyond

AbstractThere is high demand and a growing expectation for predictions of environmental conditions that go beyond 0–14-day weather forecasts with outlooks extending to one or more seasons and beyond. This is driven by the needs of the energy, water management, and agriculture sectors, to name a few. There is an increasing realization that, unlike weather forecasts, prediction skill on longer time scales can leverage specific climate phenomena or conditions for a predictable signal above the weather noise. Currently, it is understood that these conditions are intermittent in time and have spatially heterogeneous impacts on skill, hence providing strategic windows of opportunity for skillful forecasts. Research points to such windows of opportunity, including El Niño or La Niña events, active periods of the Madden–Julian oscillation, disruptions of the stratospheric polar vortex, when certain large-scale atmospheric regimes are in place, or when persistent anomalies occur in the ocean or land surface. Gains could be obtained by increasingly developing prediction tools and metrics that strategically target these specific windows of opportunity. Across the globe, reevaluating forecasts in this manner could find value in forecasts previously discarded as not skillful. Users’ expectations for prediction skill could be more adequately met, as they are better aware of when and where to expect skill and if the prediction is actionable. Given that there is still untapped potential, in terms of process understanding and prediction methodologies, it is safe to expect that in the future forecast opportunities will expand. Process research and the development of innovative methodologies will aid such progress.

Decomposition of the Atlantic Multidecadal Variability in a Historical Climate Simulation

AbstractThe Atlantic multidecadal variability (AMV) modulates various climate features worldwide with enormous societal and economic implications, including variations in hurricane activity in the Atlantic, sea level, West African and Indian monsoon rainfall, European climate, and hemispheric-scale surface temperature. Leading hypotheses regarding the nature and origin of AMV focus primarily on its links with oceanic and coupled ocean–atmosphere internal variability, and on its response to external forcing. The role of another possible process, that of atmospheric noise forcing of the ocean, has received less attention. This is addressed here by means of historical coupled simulations and diagnostic experiments, which isolate the influences of external and atmospheric noise forcings. Our findings show that external forcing is an important driver of the simulated AMV. They also demonstrate that weather noise is key in driving the simulated internal AMV in the southern part (0°–60°N) of the AMV region, and that weather noise forcing is responsible for up to 10%–20% of the multidecadal internal SST variability in some isolated areas of the subpolar gyre region. Ocean dynamics independent from the weather noise forcing is found to be the dominant cause of multidecadal SST in the northern part of the AMV region.

Impact of Model Resolution on Tropical Cyclone Simulation Using the HighResMIP–PRIMAVERA Multimodel Ensemble

AbstractA multimodel, multiresolution set of simulations over the period 1950–2014 using a common forcing protocol from CMIP6 HighResMIP have been completed by six modeling groups. Analysis of tropical cyclone performance using two different tracking algorithms suggests that enhanced resolution toward 25 km typically leads to more frequent and stronger tropical cyclones, together with improvements in spatial distribution and storm structure. Both of these factors reduce typical GCM biases seen at lower resolution. Using single ensemble members of each model, there is little evidence of systematic improvement in interannual variability in either storm frequency or accumulated cyclone energy as compared with observations when resolution is increased. Changes in the relationships between large-scale drivers of climate variability and tropical cyclone variability in the Atlantic Ocean are also not robust to model resolution. However, using a larger ensemble of simulations (of up to 14 members) with one model at different resolutions does show evidence of increased skill at higher resolution. The ensemble mean correlation of Atlantic interannual tropical cyclone variability increases from ~0.5 to ~0.65 when resolution increases from 250 to 100 km. In the northwestern Pacific Ocean the skill keeps increasing with 50-km resolution to 0.7. These calculations also suggest that more than six members are required to adequately distinguish the impact of resolution within the forced signal from the weather noise.

Scenario forecast of changes of the water balance components in the Ob-Irtysh river basin in connection with potential climate change

An intergovernmental group of experts on climate control obtained the forecast options of the dynamics for the weather components of the Ob-Irtysh river basin in the XXI century for four global climatic scenarios of the family “Special Report on Emissions Scenarios” complying with the set scenarios of economic, technological, political, and demographic development of the civilization, which served as the basin for the estimation of the options of possible changes of the water balance components of the specified river basic up to the middle of the XXI century. The calculation technique is based on using the physically based model of heat and mass exchange between the land surface and atmosphere SWAP and the climatic scenarios generator MAGICC/SCENGEN. The changes of the annual flow of the Ob river due to potential global climate changes were compared herein with its natural fluctuations caused by the weather noise.

Scenario Forecasting of Changes in Water Balance Components in the Ob–Irtysh Basin in the Context of Possible Climate Change

Four global climate scenarios of the family “Special Report on Emissions Scenarios” of the Intergovernmental Panel on Climate Change, which correspond to the specified scenarios of economic, technological, political, and demographic development of the human civilization were used to develop forecast versions of meteorological forcing data dynamics in the Ob–Irtysh basin in the XXI century. The obtained data were used to simulate the variants of possible changes in water balance components in this river basin up to mid-XXI century. The calculation technique is based on the use of land surface model SWAP and a generator of climate scenarios MAGICC/SCENGEN. The changes in the annual runoff of the Ob, governed by possible global climate change are compared with its natural variations caused by weather noise.

Designing and evaluating mobile self-reporting techniques: crowdsourcing for citizen science

In recent years, mobile phone technology has taken tremendous leaps and bounds to enable all types of sensing applications and interaction methods, including mobile journaling and self-reporting to add metadata and to label sensor data streams. Mobile self-report techniques are used to record user ratings of their experiences during structured studies, instead of traditional paper-based surveys. These techniques can be timely and convenient when data are collected “in the wild”. This paper proposes three new viable methods for mobile self-reporting projects and in real-life settings such as recording weather information or urban noise mapping. These techniques are Volume Buttons control, NFC-on-Body, and NFC-on-Wall. This work also provides an experimental and comparative analysis of various self-report techniques regarding user preferences and submission rates based on a series of user experiments. The statistical analysis of our data showed that pressing screen buttons and screen touch allowed for higher labelling rates, while Volume Buttons proved to be more valuable when users engaged in other activities, e.g. while walking. Similarly, based on participants’ preferences, we found that NFC labelling was also an easy and intuitive technique when used in the context of self-reporting and place-tagging. Our hope is that by reviewing current self-reporting interfaces and user requirements, we will be able to enable new forms of self-reporting technologies that were not possible before.

Modelling Water Balance Components of River Basins Located in Different Regions of the Globe

Three river basins—the Lena, Ganges, and Darling—were selected to study the possibility of reproducing water balance components of river basins, located in different regions of the globe under a wide variety of natural conditions, with the use of the land surface model SWAP and global data sets. Input data including meteorological forcings and land surface parameters were prepared on the basis of the WATCH and ECOCLIMAP global data sets, respectively. Long-term variations of the water balance components of the Lena, Ganges, and Darling river basins were simulated by the SWAP model. The results of simulations were compared with observations. In addition, the natural variability of river runoff caused by the weather noise of atmospheric characteristics was estimated.

Possible Climate Change Impact on River Runoff in the Different Regions of the Globe

The possibility ofassessing changes in river runofftill 2100 for a number oflarge river basins of the world for a wide range of natural conditions is investigated. The assessment is based on the SWAP (Soil Water–Atmosphere–Plants) model using meteorological data as inputs which were simulated with different general atmosphere–ocean circulation models in accordance with the RCP climate change scenarios. The possible climatic changes in annual runoff for some rivers by the end of the 21st century are compared with the natural interannual variability of river runoff caused by weather noise.