63 research outputs found
Bias of area counted from sub-pixel map:Origin and correction
Get PDFWith the increasingly widespread use of sub-pixel mapping techniques in land cover/use mapping, more accurate area information is often required for a specific land cover type in a particular study region. However, the bias of area counted from sub-pixel maps (called area bias below), and the inadequate understanding of the area bias's origin and influential factors pose a challenge to using this information accurately. Traditional model-assisted estimators combining the map and the reference sample showed unreliable performances in the case of small sample sizes collected in target regions. This work presented a theoretical analysis of the origin of area bias. It then proposed a novel bias-adjusted estimator which can effectively deal with the small sample sizes. The theoretical analysis illustrated that area bias mainly originates from two terms, i.e., the abundance-dependent error and the probability distribution of abundances. We next developed a stratified bias-adjusted area estimator named the two-term method (TTM) by incorporating the sub-pixel map and a reference sample obtained from both target and external regions. We validated the effects of different sub-pixel mapping methods, different spatial resolutions, the varying spatial structures of statistical units on area bias, and the performance of TTM in correcting the biased areas in multiple cases. The results showed that area bias varied from zero to approximately 20% with the variation of three influential factors. TTM effectively corrected the biased area values to nearly the true values, showing approximate equivalence with the traditional stratified regression estimator (STRE) when adequate reference samples are collected sorely inside target regions. However, in cases of small samples from target regions, TTM showed significant superiority over STRE in reducing the variance and MSE due to the incorporation of external reference samples. We conclude that the theoretical analysis resulted in a better understanding of area bias counted from sub-pixel maps and an improved area estimator for dealing with the cases of small sample sizes inside target regions.</p
Earlier Vegetation Green-Up Has Reduced Spring Dust Storms
Get PDFThe observed decline of spring dust storms in Northeast Asia since the 1950s has been attributed to surface wind stilling. However, spring vegetation growth could also restrain dust storms through accumulating above ground biomass and increasing surface roughness. To investigate the impacts of vegetation spring growth on dust storms, we examine the relationships between recorded spring dust storm outbreaks and satellite-derived vegetation green-up date in Inner Mongolia, Northern China from 1982 to 2008. We find a significant dampening effect of advanced vegetation growth on spring dust storms (r = 0.49, p = 0.01), with a one-day earlier green-up date corresponding to a decrease in annual spring dust storm outbreaks by 3%. Moreover, the higher correlation (r = 0.55, p \u3c 0.01) between green-up date and dust storm outbreak ratio (the ratio of dust storm outbreaks to times of strong wind events) indicates that such effect is independent of changes in surface wind. Spatially, a negative correlation is detected between areas with advanced green-up dates and regional annual spring dust storms (r = −0.49, p = 0.01). This new insight is valuable for understanding dust storms dynamics under the changing climate. Our findings suggest that dust storms in Inner Mongolia will be further mitigated by the projected earlier vegetation green-up in the warming world
Are We at Risk of Losing the Current Generation of Climate Researchers to Data Science?
Get PDFClimate model output has progressively increased in size over the past decades and is expected to continue to rise in the future. Consequently, the research time expended by Early Career Researchers (ECRs) on data-intensive activities is displacing the time spent in fostering novel scientific ideas and expanding the frontiers of climate sciences. Here, we highlight an urgent need for a better balance between data-intensive and foundational climate science activities, more open-ended research opportunities that reinforce the scientific freedom of the ECRs, and strong coordinated action to provide infrastructure and resources to the ECRs working in under-resourced environments
Improving the reproducibility in geoscientific papers: lessons learned from a Hackathon in climate science
Get PDFIn this paper, we explore the crucial role and challenges of computational reproducibility in geosciences, drawing insights from the Climate Informatics Reproducibility Challenge (CICR) in 2023. The competition aimed at (1) identifying common hurdles to reproduce computational climate science; and (2) creating interactive reproducible publications for selected papers of the Environmental Data Science journal. Based on lessons learned from the challenge, we emphasize the significance of open research practices, mentorship, transparency guidelines, as well as the use of technologies such as executable research objects for the reproduction of geoscientific published research. We propose a supportive framework of tools and infrastructure for evaluating reproducibility in geoscientific publications, with a case study for the climate informatics community. While the recommendations focus on future CIRCs, we expect they would be beneficial for wider umbrella of reproducibility initiatives in geosciences
Are We at Risk of Losing the Current Generation of Climate Researchers to Data Science?
Get PDFClimate model output has progressively increased in size over the past decades and is expected to continue to rise in the future. Consequently, the research time expended by Early Career Researchers (ECRs) on data-intensive activities is displacing the time spent in fostering novel scientific ideas and expanding the frontiers of climate sciences. Here, we highlight an urgent need for a better balance between data-intensive and foundational climate science activities, more open-ended research opportunities that reinforce the scientific freedom of the ECRs, and strong coordinated action to provide infrastructure and resources to the ECRs working in under-resourced environments
Are We at Risk of Losing the Current Generation of Climate Researchers to Data Science?
Get PDFClimate model output has progressively increased in size over the past decades and is expected to continue to rise in the future. Consequently, the research time expended by Early Career Researchers (ECRs) on data-intensive activities is displacing the time spent in fostering novel scientific ideas and expanding the frontiers of climate sciences. Here, we highlight an urgent need for a better balance between data-intensive and foundational climate science activities, more open-ended research opportunities that reinforce the scientific freedom of the ECRs, and strong coordinated action to provide infrastructure and resources to the ECRs working in under-resourced environments
ClimateBench v1.0: A benchmark for data-driven climate projections
Get PDFMany different emission pathways exist that are compatible with the Paris climate agreement, and many more are possible that miss that target. While some of the most complex Earth System Models have simulated a small selection of Shared Socioeconomic Pathways, it is impractical to use these expensive models to fully explore the space of possibilities. Such explorations therefore mostly rely on one-dimensional impulse response models, or simple pattern scaling approaches to approximate the physical climate response to a given scenario. Here we present ClimateBench - a benchmarking framework based on a suite of CMIP, AerChemMIP and DAMIP simulations performed by a full complexity Earth System Model, and a set of baseline machine learning models that emulate its response to a variety of forcers. These emulators can predict annual mean global distributions of temperature, diurnal temperature range and precipitation (including extreme precipitation) given a wide range of emissions and concentrations of carbon dioxide, methane and aerosols, allowing them to efficiently probe previously unexplored scenarios. We discuss the accuracy and interpretability of these emulators and consider their robustness to physical constraints such as total energy conservation. Future opportunities incorporating such physical constraints directly in the machine learning models and using the emulators for detection and attribution studies are also discussed. This opens a wide range of opportunities to improve prediction, consistency and mathematical tractability. We hope that by laying out the principles of climate model emulation with clear examples and metrics we encourage others to tackle this important and demanding challenge
The Simons Observatory: Design, integration, and testing of the small aperture telescopes
Full text linkThe Simons Observatory (SO) is a cosmic microwave background (CMB) survey
experiment that includes small-aperture telescopes (SATs) observing from an
altitude of 5,200 m in the Atacama Desert in Chile. The SO SATs will cover six
spectral bands between 27 and 280 GHz to search for primordial B-modes to a
sensitivity of , with quantified systematic errors well below
this value. Each SAT is a self-contained cryogenic telescope with a 35
field of view, 42 cm diameter optical aperture, 40 K half-wave plate, 1 K
refractive optics, and TES detectors.
We describe the nominal design of the SATs and present details about the
integration and testing for one operating at 93 and 145 GHz
Global, regional, and national incidence of six major immune-mediated inflammatory diseases: findings from the global burden of disease study 2019
Get PDFBACKGROUND: The causes for immune-mediated inflammatory diseases (IMIDs) are diverse and the incidence trends of IMIDs from specific causes are rarely studied. The study aims to investigate the pattern and trend of IMIDs from 1990 to 2019. METHODS: We collected detailed information on six major causes of IMIDs, including asthma, inflammatory bowel disease, multiple sclerosis, rheumatoid arthritis, psoriasis, and atopic dermatitis, between 1990 and 2019, derived from the Global Burden of Disease study in 2019. The average annual percent change (AAPC) in number of incidents and age standardized incidence rate (ASR) on IMIDs, by sex, age, region, and causes, were calculated to quantify the temporal trends. FINDINGS: In 2019, rheumatoid arthritis, atopic dermatitis, asthma, multiple sclerosis, psoriasis, inflammatory bowel disease accounted 1.59%, 36.17%, 54.71%, 0.09%, 6.84%, 0.60% of overall new IMIDs cases, respectively. The ASR of IMIDs showed substantial regional and global variation with the highest in High SDI region, High-income North America, and United States of America. Throughout human lifespan, the age distribution of incident cases from six IMIDs was quite different. Globally, incident cases of IMIDs increased with an AAPC of 0.68 and the ASR decreased with an AAPC of −0.34 from 1990 to 2019. The incident cases increased across six IMIDs, the ASR of rheumatoid arthritis increased (0.21, 95% CI 0.18, 0.25), while the ASR of asthma (AAPC = −0.41), inflammatory bowel disease (AAPC = −0.72), multiple sclerosis (AAPC = −0.26), psoriasis (AAPC = −0.77), and atopic dermatitis (AAPC = −0.15) decreased. The ASR of overall and six individual IMID increased with SDI at regional and global level. Countries with higher ASR in 1990 experienced a more rapid decrease in ASR. INTERPRETATION: The incidence patterns of IMIDs varied considerably across the world. Innovative prevention and integrative management strategy are urgently needed to mitigate the increasing ASR of rheumatoid arthritis and upsurging new cases of other five IMIDs, respectively. FUNDING: The Global Burden of Disease Study is funded by the Bill and Melinda Gates Foundation. The project funded by Scientific Research Fund of Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital (2022QN38)
Robust estimation of bacterial cell count from optical density
Get PDFOptical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data
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