Improving weather forecasting by assimilation of water vapor isotopes.
Ontology highlight
ABSTRACT:
SUBMITTER: Tada M
PROVIDER: S-EPMC8440787 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:General circulation models (GCMs) predict that the global hydrological cycle will change in response to anthropogenic warming. However, these predictions remain uncertain, in particular for precipitation [IPCC, 2013]. Held and Soden [2006] suggest that as lower-tropospheric water vapor concentration increases in a warming climate, the atmospheric circulation and convective mass fluxes will weaken. Unfortunately, this process is difficult to constrain, as convective mass fluxes are poorly observed and incompletely simulated in GCMs. Here, we demonstrate that stable hydrogen isotope ratios in tropical atmospheric water vapor can trace changes in temperature, atmospheric circulation and convective mass flux in a warming world. We evaluate changes in temperature, the distribution of water vapor, vertical velocity (?) and advection, and water isotopes in vapor (?D V ) in water isotopeenabled GCM experiments for modern vs. high CO 2 atmospheres to identify spatial patterns of circulation change over the tropical Pacific. We find that slowing circulation in the tropical Pacific moistens the lower troposphere and weakens convective mass flux, both of which impact the ?D of water vapor in the mid-troposphere. Our findings constitute a critical demonstration of how water isotope ratios in the tropical Pacific respond to changes in radiative forcing and atmospheric warming. Moreover, as changes in ?D V can be observed by satellites, our results develop new metrics for the detection of global warming impacts to the hydrological cycle and, specifically, the strength of the Walker Circulation.
| S-EPMC6242291 | biostudies-literature
Project description:Anthropogenic modification of the water cycle involves a diversity of processes, many of which have been studied intensively using models and observations. Effective tools for measuring the contribution and fate of combustion-derived water vapor in the atmosphere are lacking, however, and this flux has received relatively little attention. We provide theoretical estimates and a first set of measurements demonstrating that water of combustion is characterized by a distinctive combination of H and O isotope ratios. We show that during periods of relatively low humidity and/or atmospheric stagnation, this isotopic signature can be used to quantify the concentration of water of combustion in the atmospheric boundary layer over Salt Lake City. Combustion-derived vapor concentrations vary between periods of atmospheric stratification and mixing, both on multiday and diurnal timescales, and respond over periods of hours to variations in surface emissions. Our estimates suggest that up to 13% of the boundary layer vapor during the period of study was derived from combustion sources, and both the temporal pattern and magnitude of this contribution were closely reproduced by an independent atmospheric model forced with a fossil fuel emissions data product. Our findings suggest potential for water vapor isotope ratio measurements to be used in conjunction with other tracers to refine the apportionment of urban emissions, and imply that water vapor emissions associated with combustion may be a significant component of the water budget of the urban boundary layer, with potential implications for urban climate, ecohydrology, and photochemistry.
| S-EPMC4371996 | biostudies-literature
Project description:Rapid Arctic warming is associated with important water cycle changes: sea ice loss, increasing atmospheric humidity, permafrost thaw, and water-induced ecosystem changes. Understanding these complex modern processes is critical to interpreting past hydrologic changes preserved in paleoclimate records and predicting future Arctic changes. Cyclones are a prevalent Arctic feature and water vapor isotope ratios during these events provide insights into modern hydrologic processes that help explain past changes to the Arctic water cycle. Here we present continuous measurements of water vapor isotope ratios (?(18)O, ?(2)H, d-excess) in Arctic Alaska from a 2013 cyclone. This cyclone resulted in a sharp d-excess decrease and disproportional ?(18)O enrichment, indicative of a higher humidity open Arctic Ocean water vapor source. Past transitions to warmer climates inferred from Greenland ice core records also reveal sharp decreases in d-excess, hypothesized to represent reduced sea ice extent and an increase in oceanic moisture source to Greenland Ice Sheet precipitation. Thus, measurements of water vapor isotope ratios during an Arctic cyclone provide a critical processed-based explanation, and the first direct confirmation, of relationships previously assumed to govern water isotope ratios during sea ice retreat and increased input of northern ocean moisture into the Arctic water cycle.
| S-EPMC4650601 | biostudies-literature
Project description:The isotopic composition of water vapour provides integrated perspectives on the hydrological histories of air masses and has been widely used for tracing physical processes in hydrological and climatic studies. Over the last two decades, the infrared laser spectroscopy technique has been used to measure the isotopic composition of water vapour near the Earth's surface. Here, we have assembled a global database of high temporal resolution stable water vapour isotope ratios (δ18O and δD) observed using this measurement technique. As of March 2018, the database includes data collected at 35 sites in 15 Köppen climate zones from the years 2004 to 2017. The key variables in each dataset are hourly values of δ18O and δD in atmospheric water vapour. To support interpretation of the isotopologue data, synchronized time series of standard meteorological variables from in situ observations and ERA5 reanalyses are also provided. This database is intended to serve as a centralized platform allowing researchers to share their vapour isotope datasets, thus facilitating investigations that transcend disciplinary and geographic boundaries.
| S-EPMC6343514 | biostudies-literature
Project description:Stable Hydrogen and Oxygen isotopic composition of water vapor, rainwater and surface seawater show a distinct trend across the latitude over the Southern Indian Ocean. Our observations on isotopic composition of surface seawater, water vapor and rainwater across a transect covering the tropical Indian Ocean to the regions of the Southern Ocean showed a strong latitudinal dependency; characterized by the zonal process of evaporation and precipitation. The sampling points were spread across diverse zones of SST, wind speed and rainfall regimes. The observed physical parameters such as sea surface temperature, wind speed and relative humidity over the oceanic regions were used in a box model calculation across the latitudes to predict the isotopic composition of water vapor under equilibrium and kinetic conditions, and compared with results from isotope enabled global spectral model. Further, we obtained the average fraction of recycled moisture across the oceanic transect latitudes as 13.4 ± 7.7%. The values of recycled fraction were maximum at the vicinity of the Inter Tropical Convergence Zone (ITCZ), while the minimum values were recorded over the region of subsidence and evaporation, at the Northern and Southern latitudes of the ITCZ. These estimates are consistent with the earlier reported recyling values.
| S-EPMC5954022 | biostudies-literature
Project description:Type 2 diabetes leads to premature death and reduced quality of life for 8% of Americans. Nutrition management is critical to maintaining glycemic control, yet it is difficult to achieve due to the high individual differences in glycemic response to nutrition. Anticipating glycemic impact of different meals can be challenging not only for individuals with diabetes, but also for expert diabetes educators. Personalized computational models that can accurately forecast an impact of a given meal on an individual's blood glucose levels can serve as the engine for a new generation of decision support tools for individuals with diabetes. However, to be useful in practice, these computational engines need to generate accurate forecasts based on limited datasets consistent with typical self-monitoring practices of individuals with type 2 diabetes. This paper uses three forecasting machines: (i) data assimilation, a technique borrowed from atmospheric physics and engineering that uses Bayesian modeling to infuse data with human knowledge represented in a mechanistic model, to generate real-time, personalized, adaptable glucose forecasts; (ii) model averaging of data assimilation output; and (iii) dynamical Gaussian process model regression. The proposed data assimilation machine, the primary focus of the paper, uses a modified dual unscented Kalman filter to estimate states and parameters, personalizing the mechanistic models. Model selection is used to make a personalized model selection for the individual and their measurement characteristics. The data assimilation forecasts are empirically evaluated against actual postprandial glucose measurements captured by individuals with type 2 diabetes, and against predictions generated by experienced diabetes educators after reviewing a set of historical nutritional records and glucose measurements for the same individual. The evaluation suggests that the data assimilation forecasts compare well with specific glucose measurements and match or exceed in accuracy expert forecasts. We conclude by examining ways to present predictions as forecast-derived range quantities and evaluate the comparative advantages of these ranges.
| S-EPMC5409456 | biostudies-literature
Project description:We introduce a minimalist outbreak forecasting model that combines data-driven parameter estimation with variational data assimilation. By focusing on the fundamental components of nonlinear disease transmission and representing data in a domain where model stochasticity simplifies into a process with independent increments, we design an approach that only requires four core parameters to be estimated. We illustrate this novel methodology on COVID-19 forecasts. Results include case count and deaths predictions for the US and all of its 50 states, the District of Columbia, and Puerto Rico. The method is computationally efficient and is not disease- or location-specific. It may therefore be applied to other outbreaks or other countries, provided case counts and/or deaths data are available.
| S-EPMC8132228 | biostudies-literature
Project description:ObjectivesIn this study a prototype of a new health forecasting alert system is developed, which is aligned to the approach used in the Met Office's (MO) National Severe Weather Warning Service (NSWWS). This is in order to improve information available to responders in the health and social care system by linking temperatures more directly to risks of mortality, and developing a system more coherent with other weather alerts. The prototype is compared to the current system in the Cold Weather and Heatwave plans via a case-study approach to verify its potential advantages and shortcomings.MethodThe prototype health forecasting alert system introduces an "impact vs likelihood matrix" for the health impacts of hot and cold temperatures which is similar to those used operationally for other weather hazards as part of the NSWWS. The impact axis of this matrix is based on existing epidemiological evidence, which shows an increasing relative risk of death at extremes of outdoor temperature beyond a threshold which can be identified epidemiologically. The likelihood axis is based on a probability measure associated with the temperature forecast. The new method is tested for two case studies (one during summer 2013, one during winter 2013), and compared to the performance of the current alert system.ConclusionsThe prototype shows some clear improvements over the current alert system. It allows for a much greater degree of flexibility, provides more detailed regional information about the health risks associated with periods of extreme temperatures, and is more coherent with other weather alerts which may make it easier for front line responders to use. It will require validation and engagement with stakeholders before it can be considered for use.
| S-EPMC4592211 | biostudies-literature
Project description:Dryland ecosystems are characterised by rainfall variability and strong vegetation response to changes in water availability over a range of timescales. Forecasting dryland vegetation condition can be of great value in planning agricultural decisions, drought relief, land management and fire preparedness. At monthly to seasonal time scales, knowledge of water stored in the system contributes more to predictability than knowledge of the climate system state. However, realising forecast skill requires knowledge of the vertical distribution of moisture below the surface and the capacity of the vegetation to access this moisture. Here, we demonstrate that contrasting satellite observations of water presence over different vertical domains can be assimilated into an eco-hydrological model and combined with vegetation observations to infer an apparent vegetation-accessible water storage (hereafter called accessible storage). Provided this variable is considered explicitly, skilful forecasts of vegetation condition are achievable several months in advance for most of the world's drylands.
| S-EPMC6349931 | biostudies-literature
Project description:[This corrects the article DOI: 10.1371/journal.pcbi.1005232.].
| S-EPMC8378709 | biostudies-literature