A simple yet flexible and robust algorithm is described for fully partitioning an arbitrary dataset into compact, non-overlapping groups or classes, sorted by size, based entirely on a pairwise similarity matrix and a user-specified similarity threshold. Unlike many clustering algorithms, there is no assumption that natural clusters exist in the dataset, though clusters, when present, may be preferentially assigned to one or more classes. The method also does not require data objects to be compared within any coordinate system but rather permits the user to define pairwise similarity using almost any conceivable criterion. The method therefore lends itself to certain geoscientific applications for which conventional clustering methods are unsuited, including two non-trivial and distinctly different datasets presented as examples. In addition to identifying large classes containing numerous similar dataset members, it is also well-suited for isolating rare or anomalous members of a dataset. The method is inductive, in that prototypes identified in representative subset of a larger dataset can be used to classify the remainder.
This article introduces factors contributing significantly to climate change that have been largely neglected in both the scientific and popular press. These factors have immediate implications for public policy directed at slowing, halting and even reversing climate change and its effects. This article argues that in addition to the known contributions made by greenhouse gasses, climate change is also driven by shifts in the patterns of global atmospheric circulation which are influenced by persistent, large-scale vortices caused by the wake turbulence left by commercial air traffic. Because this traffic is highly concentrated along the most frequently traveled routes, the vortices aircraft create have transformed into semi-permanent atmospheric circulation which have widespread effects on how the atmosphere traps and releases heat. It is also possible that these changes alter the loss of water from the atmosphere. This would endanger all life on earth, not just the human population.
A new model validation and performance assessment tool is introduced, the sliding threshold of observation for numeric evaluation (STONE) curve. It is based on the relative operating characteristic (ROC) curve technique, but instead of sorting all observations in a categorical classification, the STONE tool uses the continuous nature of the observations. Rather than defining events in the observations and then sliding the threshold only in the classifier/model data set, the threshold is changed simultaneously for both the observational and model values, with the same threshold value for both data and model. This is only possible if the observations are continuous and the model output is in the same units and scale as the observations; the model is trying to exactly reproduce the data. The STONE curve has several similarities with the ROC curve – plotting probability of detection against probability of false detection, ranging from the (1,1) corner for low thresholds to the (0,0) corner for high thresholds, and values above the zero-intercept unity-slope line indicating better than random predictive ability. The main difference is that the STONE curve can be nonmonotonic, doubling back in both the x and y directions. These ripples reveal asymmetries in the data-model value pairs. This new technique is applied to modeling output of a common geomagnetic activity index as well as energetic electron fluxes in the Earth’s inner magnetosphere. It is not limited to space physics applications but can be used for any scientific or engineering field where numerical models are used to reproduce observations.
Current lightning predictions are uncertain because they either rely on empirical diagnostic relationships based on the present climate or use coarse-scale climate scenario simulations in which deep convection is parameterized. Previous studies demonstrated that simulations with convection-permitting resolutions (km-scale) improve lightning predictions compared to coarser-grid simulations using convection parameterization for different geographical locations but not over the boreal zone. In this study, lightning simulations with the NASA Unified-Weather Research and Forecasting (NU-WRF) model are evaluated over a 955x540 km2 domain including the Great Slave Lake in Canada for six lightning seasons. The simulations are performed at convection-parameterized (9 km) and convection-permitting (3 km) resolution using the Goddard 4ICE and the Thompson microphysics (MP) schemes. Four lightning indices are evaluated against observations from the Canadian Lightning Detection Network (CLDN), in terms of spatiotemporal frequency distribution, spatial pattern, daily climatology, and an event-based overall skill assessment. Concerning the model configuration, regardless of the spatial resolution, the Thompson scheme is superior to the Goddard 4ICE scheme in predicting the daily climatology but worse in predicting the spatial patterns of lightning occurrence. Several evaluation metrics indicate the benefit of working at a convection-permitting resolution. The relative performance of the different lightning indices depends on the evaluation criteria. Finally, this study demonstrates issues of the models to reproduce the observed spatial pattern of lightning well, which might be related to an insufficient representation of land surface heterogeneity in the study area.
Reproducibility and replicability in analyzing data is one of the main requirements for the advancement of scientific fields that rely heavily on computational data analysis, such as atmospheric science. However, there are very few research activities that field in Indonesia that emphasize the principle of transparency of codes and data in the dissemination of the results. This issue is a major challenge for the Indonesian scientific community to verify the output of research activities from their peers. One common obstacle to the reproducibility of data-driven research is the portability issue of the computing environment used to reproduce the results. Therefore, in this article, we would like to offer a solution through Debian-based dockerized Jupyter Notebook that have been installed with several Python libraries that are often used in atmospheric science research. Through this containerized computing environment, we expect to overcome the portability and dependency constraints that often faced by atmospheric scientists and also to encourage the growth of research ecosystem in Indonesia through an open and replicable environment.
The observing system design of multi-disciplinary field measurements involves a variety of considerations on logistics, safety, and science objectives. Typically, this is done based on investigator intuition and designs of prior field measurements. However, there is potential for considerable increase in efficiency, safety, and scientific success by integrating numerical simulations in the design process. Here, we present a novel approach to observing system simulation experiments that aids surface-atmosphere synthesis at the interface of meso- and microscale meteorology. We used this approach to optimize the Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 (CHEESEHEAD19). During pre-field simulation experiments, we considered the placement of 20 eddy-covariance flux towers, operations for 72 hours of low-altitude flux aircraft measurements, and integration of various remote sensing data products. High-resolution Large Eddy Simulations generated a super-sample of virtual ground, airborne, and satellite observations to explore two specific design hypotheses. We then analyzed these virtual observations through Environmental Response Functions to yield an optimal aircraft flight strategy for augmenting a stratified random flux tower network in combination with satellite retrievals. We demonstrate how this novel approach doubled CHEESEHEAD19’s ability to explore energy balance closure and spatial patterning science objectives while substantially simplifying logistics. Owing to its extensibility, the approach lends itself to optimize observing system designs also for natural climate solutions, emission inventory validation, urban air quality, industry leak detection and multi-species applications, among other use cases.
Water storage plays an important role in mitigating heat and flooding in urban areas. Assessment of the capacity of cities to store water remains challenging due to the extreme heterogeneity of the urban surface. Traditionally, effective storage has been estimated from runoff. Here, we present a novel approach to estimate water storage capacity from recession rates of evaporation during precipitation-free periods. We test this approach for cities at neighborhood scale with eddy-covariance latent heat flux observations from thirteen contrasting sites with different local climate zones, vegetation cover and characteristics, and climates. We find effective water storage capacities to vary between 1.5 and 20 mm corresponding to e-folding timescales of 2.5 to 12 days. According to our results, urban water storage capacity is at least one order of magnitude smaller than the observed values for natural ecosystems, resulting in an evaporation regime characterised by extreme water limitation.
The spatiotemporal patterns of precipitation are critical for understanding the underlying mechanism of many hydrological and climate phenomena. Over the last decade, applications of the complex network theory as a data-driven technique has contributed significantly to study the intricate relationship between many variable in a compact way. In our work, we conduct a study to compare an extreme precipitation pattern in Ganga River Basin, by constructing the networks using two nonlinear methods - event synchronization (ES) and edit distance (ED). Event synchronization has been frequently used to measure the synchronicity between the climate extremes like extreme precipitation by calculating the number of synchronized events between two events like time series. Edit distance measures the similarity/dissimilarity between the events by reducing the number of operations required to convert one segment to another, that consider the events’ occurrence and amplitude. Here, we compare the extreme precipitation patterns obtained from both network construction methods based on different network’s characteristics. We used degree to understand network topology and identify important nodes in the networks. We also attempted to quantify the impact of precipitation seasonality and topography on extreme events. The study outcomes suggested that the degree is decreased in the southwest to the northwest direction and the timing of peak precipitation influences it. We also found an inverse relationship between elevation and timing of peak precipitation exists and the lower elevation greatly influences the connectivity of the stations. The study highlights that Edit distance better captures the network’s topology without getting affected by artificial boundaries.
Floods drive dynamic and deeply uncertain risks for people and infrastructures. Uncertainty characterization is a crucial step in improving the predictive understanding of multi-sector dynamics and the design of risk-management strategies. Current approaches to estimate flood hazards often sample only a relatively small subset of the known unknowns, for example the uncertainties surrounding the model parameters. This approach neglects the impacts of key uncertainties on hazards and system dynamics. Here we mainstream a recently developed method for Bayesian inference to calibrate a computationally expensive distributed hydrologic model. We compare three different calibration approaches: (1) stepwise line search, (2) precalibration or screening, and (3) the new Fast Model Calibrations (FaMoS) approach. FaMoS deploys a particle-based approach that takes advantage of the massive parallelization afforded by modern high-performance computing systems. We quantify how neglecting parametric uncertainty and data discrepancy can drastically underestimate extreme flood events and risks. Precalibration improves prediction skill score over a stepwise line search. The Bayesian calibration improves the uncertainty characterization of model parameters and flood risk projections.
Reducing the model spread in free-tropospheric relative humidity (RH) and its response to warming is a crucial step towards reducing the uncertainty in clear-sky climate sensitivity, a step that is hoped to be taken with recently developed global storm-resolving models (GSRMs). In this study we quantify the inter-model differences in tropical present-day RH across GSRMs, making use of DYAMOND, a first 40-day intercomparison. We find that the inter-model spread in tropical mean free-tropospheric RH is reduced compared to conventional atmospheric models, except from the the tropopause region and the transition to the boundary layer. We estimate the reduction to approximately 50-70% in the upper troposphere and 25-50% in the mid troposphere. However, the remaining RH differences still result in a spread of 1.2 Wm-2 in tropical mean clear-sky outgoing longwave radiation (OLR). This spread is mainly caused by RH differences in the lower and mid free troposphere, whereas RH differences in the upper troposphere have a minor impact. By examining model differences in moisture space we identify two regimes with a particularly large contribution to the spread in tropical mean clear-sky OLR: rather moist regimes at the transition from deep convective to subsidence regimes and very dry subsidence regimes. Particularly for these regimes a better understanding of the processes controlling the RH biases is needed.
Soft x-ray and EUV radiation from the Sun is absorbed by and ionizes the atmosphere, creating both the ionosphere and thermosphere. Temporal changes in irradiance energy and spectral distribution can have drastic impacts on the ionosphere, impacting technologies such as satellite drag and radio communication. Because of this, it is necessary to estimate and predict changes in Solar EUV spectral irradiance. Ideally, this would be done by direct measurement but the high cost of solar EUV spectrographs makes this prohibitively expensive. Instead, scientists must use data driven models to predict the solar spectrum for a given irradiance measurement. In this study, we further develop the Synthetic Reference Spectral Irradiance Model (SynRef). The SynRef model, which uses broadband EUV irradiance data from EUVM at Mars, was created to mirror the SORCE XPS model which uses data from the TIMED SEE instrument and the SORCE XPS instrument at Earth. Both models superpose theoretical Active Region and Quiet Sun spectra generated by CHIANTI to match daily measured irradiance data, and output a modeled solar EUV spectrum for that day. By adjusting the weighting of Active Region and Quiet Sun spectra, we update the SynRef model to better agree with the FISM model and with spectral data collected from sounding rocket flights. We also use the broadband EUVM measurements to estimate AR temperature. This will allow us to select from a library of AR reference spectra with different temperatures. We present this updated SynRef model to more accurately characterize the Solar EUV and soft x-ray spectra.
Aura Microwave Limb Sounder (MLS) measurements show that chemical processing was critical to the observed record-low Arctic stratospheric ozone in spring 2020. The 16-year MLS record indicates more denitrification and dehydration in 2019/2020 than in any Arctic winter except 2015/2016. Chlorine activation and ozone depletion began earlier than in any previously observed winter, with evidence of chemical ozone loss starting in November. Active chlorine then persisted as late into spring as it did in 2011. Empirical estimates suggest maximum chemical ozone losses near 2.8 ppmv by late March in both 2011 and 2020. However, peak chlorine activation, and thus peak ozone loss, occurred at lower altitudes in 2020 than in 2011, leading to the lowest Arctic ozone values ever observed at potential temperature levels from ~400–480 K, with similar ozone values to those in 2011 at higher levels.
An outstanding challenge in modeling the radiative properties of stratiform rain systems is an accurate representation of the mixed-phase hydrometeors present in the melting layer. The use of ice spheres coated with meltwater or mixed-dielectric spheroids have been used as rough approximations, but more realistic shapes are needed to improve the accuracy of the models. Recently, realistically structured synthetic snowflakes have been computationally generated, with radiative properties that were shown to be consistent with coincident airborne radar and microwave radiometer observations. However, melting such finely-structured ice hydrometeors is a challenging problem, and most of the previous efforts have employed heuristic approaches. In the current work, physical laws governing the melting process are applied to the melting of synthetic snowflakes using a meshless-Lagrangian computational approach henceforth referred to as the Snow Meshless Lagrangian Technique (SnowMeLT). SnowMeLT is capable of scaling across large computing clusters, and a collection of synthetic aggregate snowflakes from NASA’s OpenSSP database with diameters ranging from 2–10.5 mm are melted as a demonstration of the method. To properly capture the flow of meltwater, the simulations are carried out at relatively high resolution (15 μm), and a new analytic approximation is developed to simulate heat transfer from the environment without the need to simulate the atmosphere explicitly.
More recurrent heatwaves and extreme ozone episodes are likely to occur during the next decades and a key question is about the concurrence of those hazards, the atmospheric patterns behind their appearance and their joint effect on human health. In this work, we use surface maximum temperature and O3 observations during extended summers in two cities from Morocco: Casablanca and Marrakech, between 2010 and 2019. We assess the connection between these data and climate indexes (North Atlantic Oscillation (NAO), Mediterranean Oscillation (MO) and Saharan Oscillation (SaOI)). We then identify concurrent heatwaves and ozone episodes, the weather type behind this concurrence and the combined health risks. Our findings show that the concurrence of heatwaves and O3 episodes depends both on the specific city and the large-scale atmospheric circulation. The likely identified synoptic pattern is when the country is under the combined influence of an anticyclonic area in the north and the Saharan trough extending the depression centered in the south. This pattern generates a warm flow and may foster photochemical pollution. Our study is the first step towards the establishment of an alert system. It will help to provide recommendations for coping with concurrent heatwaves and air pollution episodes.
Few studies have utilized machine learning techniques to predict or understand the Madden-Julian oscillation (MJO), a key source of subseasonal variability and predictability. Here we present a simple framework for real-time MJO prediction using shallow artificial neural networks (ANNs). We construct two ANN architectures, one deterministic and one probabilistic, that predict a real-time MJO index using maps of tropical variables. These ANNs make skillful MJO predictions out to ~17 days in October-March and ~10 days in April-September, outperforming conventional linear models and efficiently capturing aspects of MJO predictability found in more complex, dynamical models. The flexibility and explainability of simple ANN frameworks is highlighted through varying model input and applying ANN explainability techniques that reveal sources and regions important for ANN prediction skill. The accessibility, performance, and efficiency of this simple machine learning framework is more broadly applicable to predict and understand other Earth system phenomena.
Bangladesh is a small country of South Asia which is considered as one of the most vulnerable countries in the world to climate change and it is affected by severe weather and climate events. In this paper, the climatic change and variability over Bangladesh has been studied. The time series analysis was applied to investigate the variability and trend over three zone [(Northern (North West & North East), Middle and Southern (Coastal, Island, Hilly)] of Bangladesh. The study was based upon the regional differences of climatic trends for the country and tested it for each region against other regions via a t-test. Result suggest that as a topical country, there is small temperature variation in Bangladesh. But there is regional variation between northwest and south zone. In winter there exists a north-south temperature gradient which reverses during summer (pre-monsoon and monsoon). There is an extreme large seasonal variation from winter- to summer- monsoon in Bangladesh. In all seasons the difference of maximum and minimum temperature is higher in the north zone than in the south zone. A remarkable correlation was discovered between this temperature range and the rainfall occurrence. All seasons the mean maximum temperature is increasing except in winter for the northwest and middle zone (-0.004 °C/year and –0.0069 °C/year). Hilly region is showing highest increasing rates of mean maximum Temperature in all season (+0.0754 °C/year in winter, +0.0635 °C/year in Pre monsoon, +0.0554 °C/year in monsoon, +0.0679 °C/year in Post monsoon); Where as in all season mean minimum temperature is decreasing only hilly region. Overall the temperature is increasing in whole of the country. We also find a positive trend in the rainfall, especially large during the summer monsoon and in the southern zone (in Hilly region, increasing rate of rainfall +17.784 mm/year). The northwest area on the contrary is characterized by decreasing rainfall values both in winter and summer. So we can conclude that the northwest is clearly becoming more arid. May be this is enhanced by the large scale deforestation processes going on.
Atmospheric rivers (ARs) affect surface hydrometeorology in the US West Coast and Midwest. We systematically sought optimal AR indices for expressing surface precipitation impacts within the Atmospheric River Tracking Method Intercomparison Project (ARTMIP) framework. We adopted a multifactorial approach. Four factors—moisture fields, climatological thresholds, shape criteria, and temporal thresholds—collectively generated 81 West Coast AR indices and 81 Midwest indices from January 1980 to June 2017. Two moisture fields were extracted from the MERRA-2 data for ARTMIP: integrated water vapor transport (IVT) and integrated water vapor (IWV). Metrics for precipitation effects included two-way summary statistics relating the concurrence of AR and that of precipitation, per-event averaged precipitation rate, and per-event precipitation accumulation. We found that an optimal AR index for precipitation depends on the types of impact to be addressed, associated physical mechanisms in the affected regions, timing, and duration. In West Coast and Midwest, IWV-based AR indices identified the most abundant AR event time steps, most accurately associated AR to days with precipitation, and represented the presence of precipitation the best. With a lower climatological threshold, they detected the most accumulated precipitation with the longest event duration. Longer duration thresholds also led to higher accumulated precipitation, holding other factors constant. IWV-based indices are the overall choice for Midwest ARs under varying seasonal precipitation drivers. IVT-based indices suitably capture the accumulation of intense orographic precipitation on the West Coast. Indices combining IVT and IWV identify the fewest, shortest, but most intense AR precipitation episodes.
In its three-dimensional (3-D) characterization, drought is approached as an event whose spatial extent changes over time. Each drought event has an onset and end time, a location, a magnitude, and a spatial trajectory. These characteristics help to analyze and describe how drought develops in space and time, i.e., drought dynamics. Methodologies for 3-D characterization of drought include a 3-D clustering technique to extract the drought events from the hydrometeorological data. The application of the clustering method yields small ‘artifact’ droughts. These small clusters are removed from the analysis with the use of a cluster size filter. However, according to the literature, the filter parameters are usually set arbitrarily, so this study concentrated on a method to calculate the optimal cluster size filter for the 3-D characterization of drought. The effect of different drought indicator thresholds to calculate drought is also analyzed. The approach was tested in South America with data from the Latin American Flood and Drought Monitor (LAFDM) for 1950–2017. Analysis of the spatial trajectories and characteristics of the most extreme droughts is also included. Calculated droughts are compared with information reported at a country scale and a reasonably good match is found.