The Value of Meteorological Data in Optimizing the Pattern of Physical Load—A Forecast Model of Rowing Pacing Strategy

Mastering the information of arena environment is the premise for athletes to optimize their patterns of physical load. Therefore, improving the forecast accuracy of the arena conditions is an urgent task in competitive sports. This paper excavates the meteorological features that have great influence on outdoor events such as rowing and their influence on the pacing strategy. We selected the meteorological data of Tokyo from 1979 to 2020 to forecast the meteorology during the Tokyo 2021 Olympic Games, analyzed the athletes’ pacing choice under different temperatures, humidity and sports levels, and then recommend the best pacing strategy for rowing teams of China. The model proposed in this paper complements the absence of meteorological features in the arena environment assessment and provides an algorithm basis for improving the forecast performance of pacing strategies in outdoor sports.

Modeling of Residual GNSS Station Motions through Meteorological Data in a Machine Learning Approach

Long-term Global Navigation Satellite System (GNSS) height residual time series contain signals that are related to environmental influences. A big part of the residuals can be explained by environmental surface loadings, expressed through physical models. This work aims to find a model that connects raw meteorological parameters with the GNSS residuals. The approach is to train a Temporal Convolutional Network (TCN) on 206 GNSS stations in central Europe, after which the resulting model is applied to 68 test stations in the same area. When comparing the Root Mean Square (RMS) error reduction of the time series reduced by physical models, and, by the TCN model, the latter reduction rate is, on average, 0.8% lower. In a second experiment, the TCN is utilized to further reduce the RMS of the time series, of which the loading models were already subtracted. This yields additional 2.7% of RMS reduction on average, resulting in a mean RMS reduction of 28.6% overall. The results suggests that a TCN, using meteorological features as input data, is able to reconstruct the reductions almost on the same level as physical models. Trained on the residuals, reduced by environmental loadings, the TCN is still able to slightly increase the overall reduction of variations in the GNSS station position time series.

Meteorological features associated with Indian drought in 2002

lkj & fiNys 14 o"kksZa ds nkSjku yxkrkj gqbZ vPNh ekulwu o"kkZ&_rq ds ckn Hkkjr esa o"kZ 2002 esa Hkh"k.k lw[kk iM+kA ;gk¡ rd fd ekfld le; eku ij Hkh 19 oha 'krkCnh ds e/; ls ysdj vc rd ds fjdkMZ ds bfrgkl esa tqykbZ dk eghuk o"kkZ dh n`f"V ls cgqr gh [kjkc eghuk jgk ftlesa vf[ky Hkkjrh; iSekus ij o"kkZ ds izfr’kr dk varj lkekU; ls 51-5 izfr’kr de jgkA ,d vU; egRoiw.kZ fo’ks"krk ;g jgh fd fiNys 133 o"kksaZ esa igyh ckj lEiw.kZ nf{k.kh if’peh ekulwu _rq ds nkSjku ,d Hkh vonkc vFkok pØokrh rwQku ugha cukA Hkkjr esa ekulwu dbZ&dbZ fnuksa dh vo:)rk ds lkFk yxkrkj vkxs c<+kA 1960 ds ckn ls igyh ckj ,slk gqvk gS fd lqnwj mÙkjh&if’peh Hkkjr esa ekulwu] _rq ds iwok)Z esa ugha igq¡pkA bl 'kks/k&i= esa o"kZ 2002 ds nkSjku ekulwu ds fofHkUu y{k.kksa dh fo’ks"krkvksa tSls fd ekulwu dk vkjEHk] mldk vkxs c<+uk] :duk] fofHkUu flukWfIVd vkSj v)Z LFkk;h y{k.kksa rFkk nf{k.kh if’peh ekulwu o"kkZ _rq dh fo’ks"krkvksa dk foospu fd;k x;k gSA bu fo’ks"krkvksa dh rqyuk igys iMs+ lw[ks ds o"kksZa dh fo’ks"krkvksa ds lkFk dh xbZ gSA tqykbZ 2002 ds nkSjku ekulwu o"kkZ dh Hkh"k.k deh ds laHkkfor dkj.kksa dk irk yxkus ds fy, fgan & iz’kkar ¼baMksislsfQd½ {ks= esa c`grLrjh; vkSlr ekfld vlkekU; egklkxjh; vkSj ok;qeaMyh; fLFkfr;ksa dh tk¡p dh xbZ gSA bl v/;;u ls izkIr gq, ifj.kkeksa ls ;g irk pyrk gS fd cgqr lh vlkekU; vkSj fof’k"V izdkj dh fo’ks"krkvksa ds dkj.k o"kZ 2002 ds nkSjku iwjs Hkkjr esa lw[kk iM+kA bl v/;;u ls ;g Hkh irk pyrk gS fd vuqdwy varjk&ekSleh {ks=h; fo’ks"krkvksa tSls fd ekulwu fo{kksHkksa vkSj v)Z LFkk;h ra=ksa] vR;ar ean yks ysoy tsV dh fo|ekurk] izcy e/; v{kka’kh; if’peh gokvksa ds izHkko] {ks= esa pØokr cuus dh vR;kf/kd vko`fr ds lkFk ekulwu _rq ds eghuksa ds nkSjku iz’kkar egklkxjh; fuuksa 4 {ks= esa vR;kf/kd m".k rhozrk ds lkFk ean ls lkekU; ,y fuuksa dk cuuk ,sls eq[; dkj.k gSa ftuds ifj.kkeLo:i tqykbZ ds eghus esa o"kkZ dh vR;kf/kd deh gqbZ gSA India experienced severe drought in the year 2002 after 14 consecutive years of good monsoon. On the monthly time scale, July had the worst rainfall in the recorded history of monsoon dating back to middle of nineteenth century when the country as a whole registered rainfall deficiency of 51.5%. Another notable feature was that for the first time in the last 133 years, not a single depression or cyclonic storm formed during the whole southwest monsoon season. The advance of monsoon over India was accompanied with frequent as well as prolonged stagnations. The monsoon failed to arrive for the first time in extreme northwest India during the first half of the season since 1960. In the present study, various features of monsoon such as onset, progress, stagnation, different synoptic and semi-permanent features and characteristics of rainfall of southwest monsoon in 2002 over India have been discussed. A comparison of these features with those in the earlier drought years has been made. Large-scale mean monthly anomalous ocean and atmospheric conditions over Indo-Pacific region have also been investigated to find out the possible causes for drastic failure of the monsoon during July 2002. Results show that many abnormal and unique features during 2002 have resulted into all India drought. Study also shows that absence of favourable regional intra-seasonal features like monsoon disturbances and semi-permanent systems, presence of very weak low level jet, penetration of strong mid-latitude westerlies, weak to moderate El-Nino with most intense warming over Nino 4 region of Pacific Ocean during monsoon months together with higher frequency of typhoon formation over the region are the main causes that led to one of the highly pronounced rainfall deficiencies in the month of July.

Weather-induced crop failure events under climate change: a storyline approach

Unfavourable weather is a common cause for crop failures all over the world. Whilst extreme weather conditions may cause extreme impacts, crop failure commonly is induced by the occurrence of multiple and combined anomalous meteorological drivers. For these cases, the explanation of conditions leading to crop failure is complex, as the links connecting weather and crop yield can be multiple and non-linear. Furthermore, climate change is likely to perturb the meteorological conditions, possibly altering the occurrences of crop failures or leading to unprecedented drivers of extreme impacts. The goal of this study is to identify important meteorological drivers that cause crop failures and to explore changes in crop failures due to global warming. For that, we focus on a historical failure event, the extreme low soybean production during the 2012 season in the Midwest US. We first train a random forest model to identify the most relevant meteorological drivers of historical crop failures and to predict crop failure probabilities. Second, we explore the influence of global warming on crop failures and on the structure of compound drivers. We use large ensembles from the EC-Earth global climate model, corresponding to present day, pre-industrial +2 °C and 3 °C warming respectively, to isolate the global warming component. Finally, we explore the meteorological conditions inductive for the 2012 crop failure, and construct analogues of these failure conditions in future climate settings. Unlike present-day conditions, future warming may increase the probability of crop failures resulting from univariate meteorological features, reducing the importance of compound failure drivers. Impact-analogues show a significant increase under global warming, with changes in the corresponding drivers. This has implications for risk assessment, as changing drivers of extreme impact events are highly relevant.

Surveillance of airborne plant disease dissemination at continental scale using air mass trajectory analysis and network theory

Sustainable management of plant disease outbreaks in agriculture is one of the main challenges of the next years to restore economic and environmental viability of farming practices. Improving early-detection capabilities and disease surveillance is increasingly seen as an obligate step to design appropriate and effective prophylactic measures. In this context, plant diseases caused by wind-dispersed pathogens represent a peculiar case of study, since they are particularly complex and hard to observe directly, especially if compared to other dissemination means, and demand for a multidisciplinary approach to be dealt with. Wind dispersal could imply a geographic differentiation in pathogens spreading potential, due to the emerging of local meteorological features. In this work we analyze the spatio-temporal patterns of wind connectivity in Europe and the Mediterranean basin in order to identify possible pathways of Puccinia graminis spores, the causal agent of stem rust of wheat. By running backwards Lagrangian simulations merging a biological layer coupled with a pathogen viability model, we investigate possible long-distance connections between regions in the study area across different seasons. We characterized these regions in terms of network centrality indicators to identify possible spreaders of stem rust of wheat, founding that Central and Western European regions appears to provide highest connectivity for the spread of P. graminis.

A Novel Method for Regional Short-Term Forecasting of Water Level

The water level forecasting system represented by the hydrodynamic model relies too much on the input data and the forecast value of the boundary, therefore introducing uncertainty in the prediction results. Tide tables ignore the effect of the residual water level, which is usually significant. Therefore, to solve this problem, a water level forecasting method for the regional short-term (3 h) is proposed in this study. First, a simplified MIKE21 flow model (FM) was established to construct the regional major astronomical tides after subdividing the model residuals into stationary constituents (surplus astronomical tides, simulation deviation) and nonstationary constituents (residual water level). Harmonic analysis (HA) and long short-term memory (LSTM) were adopted to forecast these model residuals, respectively. Finally, according to different spatial background information, the prediction for each composition was corrected by the inverse distance weighting (IDW) algorithm and its improved IDW interpolation algorithm based on signal energy and the spatial distance (IDWSE) from adjacent observation stations to nonmeasured locations. The developed method was applied to Narragansett Bay in Rhode Island. Compared with the assimilation model, the root-mean-square error (RMSE) of the proposed method decreased from 12.3 to 5.0 cm, and R2 increased from 0.932 to 0.988. The possibility of adding meteorological features into the LSTM network was further explored as an extension of the prediction of the residual water level. The results show that the accuracy was limited to a moderate level, which is related to the difficulty presented by using only wind features to completely characterize the regional dynamic energy equilibrium process.

The influence of mesoscale atmospheric convection on local and regional infrasound propagation

&lt;p&gt;Infrasound can propagate over a variety of spatiotemporal ranges and is therefore affected by spatiotemporally diverse atmospheric conditions.&amp;#160; However, studies of the influence of meteorology on infrasound propagation have historically utilized weather data that rely on point sources or coarser spatiotemporal resolutions, which often gloss over the effects of mesoscale meteorological phenomena.&amp;#160; In light of this knowledge gap, this study examines the influence of mesoscale meteorological features on infrasound propagation on local and regional scales.&amp;#160; To accomplish this task, output from simulations using the Weather Research and Forecasting (WRF) meteorological model is fed into an infrasound propagation model to generate infrasound predictions using realistic meteorological conditions.&amp;#160; The WRF simulations covered a range of horizontal resolutions &amp;#8211; from 1 to 15 km &amp;#8211; enabling an analysis of the sensitivity of the infrasound predictions to the horizontal resolution of the WRF output.&amp;#160; The main result is that convective precipitation events can appreciably alter the transmission loss patterns emanating from infrasonic sources, which is especially evident at finer horizontal resolutions.&amp;#160; This demonstrates that high-resolution weather data may be necessary to correctly simulate local to regional infrasound propagation, especially within warm-season, convective environments.&lt;/p&gt;&lt;p&gt;(This work was funded by the Assistant Secretary of the Army for Acquisition, Logistics, and Technology [ASA{ALT}] under 0602784/T40/46 and 0602146/AR9/01.)&amp;#160;&lt;/p&gt;&lt;p&gt;Approved for public release: distribution is unlimited.&lt;/p&gt;

Towards improving current estimates of CO2 emissions and sinks in the Aix-Marseille metropolis area, France, and developing virtuous CO2 mitigation scenarios in link with local stakeholders and socio-economic actors.

&lt;p&gt;Most of the world population leaves in urbanized areas, and this is expected to expand rapidly in the next decades. Cities and their industrial facilities are estimated to emit more than 70% of fossil fuel CO&lt;sub&gt;2&lt;/sub&gt;. Still, these estimates, mostly based on bottom-up emission inventories, need to be verified at the city scale. Atmospheric top-down approaches are a tool of choice in this sense. They rely mostly on continuous atmospheric CO&lt;sub&gt;2&lt;/sub&gt; measurements inside and outside of the studied urbanized area to catch the urban plume and its variability (either from in-situ, remote sensing or airborne instrumentation), on the use of emission tracers such as carbon monoxide and black carbon for combustion processes, of volatile organic compounds and of carbon isotopes to distangle the contribution of natural, modern and fossil fluxes, on mass balance approaches &amp;#160;which needs measurements of the atmospheric boundary layer height, and on direct and inverse modeling frameworks. Furthermore, as they represent the main anthropogenic CO&lt;sub&gt;2&lt;/sub&gt; emission sector, cities and industrial facilities are strategic places where actions on mitigating CO&lt;sub&gt;2&lt;/sub&gt; emissions should be undertaken in priority.&lt;/p&gt;&lt;p&gt;The Aix-Marseille metropolis (AMm), located in the south-east of France, is the second most populated area of France (1.8 M inhabitants). It is also much industrialized, and is located in the SUD-PACA region, which is strongly exposed to the risks of Climate Change. Since 2017, two top-down research projects have been funded by the LABEX OT-MED (AMC project, 2016-2019) and by the French National Research Agency ANR (COoL-AMmetropolis project, 2020-2024) to fullfill the following objectives&amp;#160;: 1/ assessing the spatio-temporal variability of atmospheric CO&lt;sub&gt;2&lt;/sub&gt; in the AMm area&amp;#160;; 2/ characterizing the different sources and sinks that control CO&lt;sub&gt;2&lt;/sub&gt; through the use of tracers and carbon isotopes&amp;#160;; 3/ verifying independently the high-resolved CO&lt;sub&gt;2&lt;/sub&gt; emission inventory delivered by the regional air quality agency ATMOSUD&amp;#160;; 4/ developing a direct modeling framework, facing challenges such as the complex AMm topography, coastal boundary layer dynamics, and some specific meteorological features that are mistral and land/sea breezes&amp;#160;; and 5/ developing scenarios to the horizon 2035 for mitigating AMm CO&lt;sub&gt;2&lt;/sub&gt; emissions and find the most effective way to integrate vertuous scenarios, defined in&amp;#160;interaction with stakeholders, into legal and urban planning schemes, tools, charters or practices. A synthesis of the results obtained until now from these two projects will be presented.&lt;/p&gt;

Integrating Spectral Information and Meteorological Data to Monitor Wheat Yellow Rust at a Regional Scale: A Case Study

Wheat yellow rust has a severe impact on wheat production and threatens food security in China; as such, an effective monitoring method is necessary at the regional scale. We propose a model for yellow rust monitoring based on Sentinel-2 multispectral images and a series of two-stage vegetation indices and meteorological data. Sensitive spectral vegetation indices (single- and two-stage indices) and meteorological features for wheat yellow rust discrimination were selected using the random forest method. Wheat yellow rust monitoring models were established using three different classification methods: linear discriminant analysis (LDA), support vector machine (SVM), and artificial neural network (ANN). The results show that models based on two-stage indices (i.e., those calculated using images from two different days) significantly outperform single-stage index models (i.e., those calculated using an image from a single day), the overall accuracy improved from 63.2% to 78.9%. The classification accuracies of models combining a vegetation index with meteorological feature are higher than those of pure vegetation index models. Among them, the model based on two-stage vegetation indices and meteorological features performs best, with a classification accuracy exceeding 73.7%. The SVM algorithm performed best for wheat yellow rust monitoring among the three algorithms; its classification accuracy (84.2%) was ~10.5% and 5.3% greater than those of LDA and ANN, respectively. Combined with crop growth and environmental information, our model has great potential for monitoring wheat yellow rust at a regional scale. Future work will focus on regional-scale monitoring and forecasting of crop disease.

Natural ice in the debris flow basins of the Middle and Southern Sakhalin

The article deals with the ice mounds formation processes in the mudflow basins. Small natural frosts are widespread on the territory of Middle and Southern Sakhalin within the slope and small valley debris flow basins, along with dangerous slope exogenous processes (debris flows, landslides, erosion, etc.), often having a paragenetic nature of the current. The mechanism of ice data formation is due to the hydro/meteorological features of the territory, as well as disturbance of the thermal, hydrological or hydrogeological regime of the debris flow basin. The main reason for the formation of natural ice mounds in the valley debris flow basins is disturbance of the hydrological regime of the catchment basin during debris flow formation, which mainly contributes to the annual solid runoff of first order watercourses. Scum of slope debris flow basins are formed in case of violation of the thermal regime of the surface and soil of the debris flow basin due to climatic anomalies, as well as damage to natural heat-insulating coverings: snow and soil-vegetable. It is also possible to form sloping ice as a result of a violation of the hydrogeological regime of the catchment basin under anthropogenic impact or the development of dangerous exogenous slope processes with a sufficient depth of rock capture. Cryogenic processes of ice formation affect debris flow and channel processes.