Convolutional conditional neural processes for local climate downscaling

A new model is presented for multisite statistical downscaling of temperature and precipitation using convolutional conditional neural processes (convCNPs). ConvCNPs are a recently developed class of models that allow deep-learning techniques to be applied to off-the-grid spatio-temporal data. In contrast to existing methods that map from low-resolution model output to high-resolution predictions at a discrete set of locations, this model outputs a stochastic process that can be queried at an arbitrary latitude–longitude coordinate. The convCNP model is shown to outperform an ensemble of existing downscaling techniques over Europe for both temperature and precipitation taken from the VALUE intercomparison project. The model also outperforms an approach that uses Gaussian processes to interpolate single-site downscaling models at unseen locations. Importantly, substantial improvement is seen in the representation of extreme precipitation events. These results indicate that the convCNP is a robust downscaling model suitable for generating localised projections for use in climate impact studies.

Arctic sea level variability from high-resolution model simulations and implications for the Arctic observing system

Two high-resolution model simulations are used to investigate the spatiotemporal variability of the Arctic Ocean sea level. The model simulations reveal barotropic sea level variability at periods of < 30 d, which is strongly captured by bottom pressure observations. The seasonal sea level variability is driven by volume exchanges with the Pacific and Atlantic oceans and the redistribution of the water by the wind. Halosteric effects due to river runoff and evaporation minus precipitation ice melting/formation also contribute in the marginal seas and seasonal sea ice extent regions. In the central Arctic Ocean, especially the Canadian Basin, the decadal halosteric effect dominates sea level variability. The study confirms that satellite altimetric observations and Gravity Recovery and Climate Experiment (GRACE) could infer the total freshwater content changes in the Canadian Basin at periods longer than 1 year, but they are unable to depict the seasonal and subseasonal freshwater content changes. The increasing number of profiles seems to capture freshwater content changes since 2007, encouraging further data synthesis work with a more complicated interpolation method. Further, in situ hydrographic observations should be enhanced to reveal the freshwater budget and close the gaps between satellite altimetry and GRACE, especially in the marginal seas.

Modeling The Mysterious Tsunami Swirls In The Northeast Coast Of Japan During The March 2011 Tohoku Earthquake

The formation of tsunami swirls near the coast is an obvious oceanographic phenomenon during the occurrence of giant submarine earthquakes and mega-tsunamis. Several tsunami vortices were generated during the Asian tsunami of 2004 and the great Japan tsunami of March 2011 which lasted for several hours.New models of tsunami generation and propagation are hereby proposed and were used to investigate the tsunami inception, propagation and associated formation of swirls in the eastern coast of Japan. The proposed generation model assumes that the tsunami was driven by current oscillations at the seabed induced by the submarine earthquake. The major aim of this study is to develop a tsunami model to simulate the occurrence of tsunami swirls. Specifically, this study attempts to simulate and understand the formation of the mysterious tsunami swirls in the northeast coast of Japan. In addition, this study determines the vulnerability of the Philippines to destructive tsunami waves that originate near Japan. A coarse resolution model was therefore developed in a relatively large area encompassing Japan Sea and the eastern Philippine Sea. On the other hand, a fine-resolution model was implemented in a small area off Sendai coast near the epicenter. The model result was compared with the tsunami record obtained from the National Data Buoy Center with relatively good agreement as far as the height and period of the tsunami are concerned. Furthermore, the fine-resolution model was able to simulate the occurrence of tsunami vortices off Sendai coast with various sizes that lasted for several hours.

Analysis and Simulations of a Heavy Rainfall Event Associated with the Passage of a Shallow Front over Northern Taiwan on 2 June 2017

From 0200 to 1000 LST 2 June 2017, the shallow, East-West oriented Mei-Yu front (< 1 km) cannot move over the Yang-Ming Mountains (with peaks ∼ 1120 m) when it first arrives. The postfrontal cold air at the surface is deflected by the Yang-Ming Mountains and moves through the Keelung River and Tamsui River valleys into the Taipei Basin. The shallow northerly winds are anchored along the northern side of the Yang-Ming Mountains for 8 hours. In addition, the southwesterly barrier jet with maximum winds in the 900–950-hPa layer brings in abundant moisture and converges with the northwesterly flow in the southwestern flank of the Mei-Yu frontal cyclone. Therefore, torrential rain (> 600 mm) occurs over the northern side of the Yang-Ming Mountains. From 1100 to 1200 LST, with the gradual deepening of the postfrontal cold air, the front finally passes over the Yang-Ming Mountains and arrives at the Taipei Basin, which results in an E-W oriented rainband with the rainfall maxima over the northwestern coast and Taipei Basin. From 1300 to 1400 LST, the frontal rainband continues to move southward with rainfall over the northwestern slopes of the Snow Mountains. In the prefrontal southwesterly flow, the orographic lifting of the moisture-laden low-level winds results in heavy rainfall on the southwestern slopes of the Snow Mountains and the Central Mountain Range. With the terrain of the Yang-Ming Mountains removed in the high-resolution model, the Mei-Yu front moves quickly southward without a rainfall maximum over the northern tip of Taiwan.

Overview of spatial verification methods and their application to ensemble forecasting

State-of-the-art high-resolution NWP models simulate mesoscale systems with a high degree of detail, with large amplitudes and high gradients of fields of weather variables. Higher resolution leads to the spatial and temporal error growth and to a well-known double penalty problem. To solve this problem, the spatial verification methods have been developed over the last two decades, which ignore moderate errors (especially in the position), but can still evaluate the useful skill of a high-resolution model. The paper refers to the updated classification of spatial verification methods, briefly describes the main methods, and gives an overview of the international projects for intercomparison of the methods. Special attention is given to the application of the spatial approach to ensemble forecasting. Popular software packages are considered. The Russian translation is proposed for the relevant English terms. Keywords: high-resolution models, verification, double penalty, spatial methods, ensemble forecasting, object-based methods

Satellite data validation: a parametrization of the natural variability of atmospheric mixing ratios

High-resolution model data are used to estimate typical variabilities of mixing ratios of trace species as a function of spatial and temporal distance. These estimates can be used to explain that part of the differences between observations made with different observing systems that are due to less than perfect collocation of the measurements. The variability values are described by a two-parameter regression function. A reparametrization of the variabilities values as function of latitudinal graidents is proposed, and season-independence of linear approximation of such function is demonstrated.

Religions with or without Sense of Humor: A Psychological Perspective

The contrast between Homo Ridens and Homo Religiosus is launched and followed by the tug of war between the laugh of God and the sin of laughter. Funniness in jokes with religious content is explored through the incongruity-resolution model developed by Suls, a psychologist expert in artificial intelligence: among the faithful abound believers whom it deems inappropriate the hilarious endings invented, with ulterior motives, by humorists. The transgression model in graphic design, elaborated by Alvarez Junco, provides the frame of reference to discern the camouflage of four frescos and a sculpture by Michelangelo, who knew more than he appeared, and was a dissident, but not a heretic. Humor cannot be reduced to jokes, and the taxonomy created by Long and Grasser (cognitive and experimental psychologists) has been used to accentuate the nexus between witticism in daily life interactions with religious connotations: their eleven categories have been portrayed using literary narratives authored by well-known European and Asian writers. Efforts have been made to draft them with the sense of humor that corresponds to the heading. Psychologists pay attention mainly to individual or group experiences, that is, religiosity. Artists have relied on camouflage to ensure that inquisitive persons do not react by penalizing.

The Disproportionate Impact of COVID-19 among Undocumented Immigrants and Racial Minorities in the US

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for coronavirus disease 2019 (COVID-19), has had an unprecedented effect, especially among under-resourced minority communities. Surveillance of those at high risk is critical for preventing and controlling the pandemic. We must better understand the relationships between COVID-19-related cases or deaths and characteristics in our most vulnerable population that put them at risk to target COVID-19 prevention and management efforts. Population characteristics strongly related to United States (US) county-level data on COVID-19 cases and deaths during all stages of the pandemic were identified from the onset of the epidemic and included county-level socio-demographic and comorbidities data, as well as daily meteorological modeled observation data from the North American Regional Reanalysis (NARR), and the NARR high spatial resolution model to assess the environment. Advanced machine learning (ML) approaches were used to identify outbreaks (geographic clusters of COVID-19) and included spatiotemporal risk factors and COVID-19 vaccination efforts, especially among vulnerable and underserved communities. COVID-19 outcomes were found to be negatively associated with the number of people vaccinated and positively associated with age, the prevalence of cardiovascular disease, diabetes, and the minority population. There was also a strong positive correlation between unauthorized immigrants and the prevalence of COVID-19 cases and deaths. Meteorological variables were also investigated, but correlations with COVID-19 were relatively weak. Our findings suggest that COVID-19 has had a disproportionate impact across the US population among vulnerable and minority communities. Findings also emphasize the importance of vaccinations and tailored public health initiatives (e.g., mask mandates, vaccination) to reduce the spread of COVID-19 and the number of COVID-19 related deaths across all populations.