Amélioration de la capabilité de modélisation et de mitigation du gel radiatif au milieu agricole

Le gel radiatif est une des conditions météorologiques sévère affect la production agricole dans de nombreuses région du monde. Les objectives de cette étude inclut deux innovations scientifiques liées aux dégâts causés par le gel radiatif : (1) l'amélioration de la capacité de prédiction du gel local (température nocturne minimale à une résolution de 30 mètres) grâce à un modèle d’échange énergétique entre la végétation et l’atmosphère, et (2) une nouvelle méthode de diminution des risques et de protection des cultures agricoles pendant les périodes de gel. La première innovation a été réalisée en suivant plusieurs objectifs spécifiques visant à améliorer les capacités d'un modèle de répartition spatiale météorologique (Micro-Met) via quatre sous-modèles : (i) estimation journalière du gradient thermique adiabatique de l'air, (ii) modification de l’équation de rayonnement des grandes longueurs d'onde en l’absence de nuage dans l’atmosphère, (iii) quantification des effets de l’écoulement de l’air froid sur la température de l’air, et (iv) quantifier l’effet de haies brise–vent sur la vitesse du vent. La deuxième innovation a été réalisée en mettant en œuvre et en testant une nouvelle méthode active basée sur le cycle thermodynamique. Le site d'étude se localise dans la région de Vallée de Coaticook de l’Estrie (Québec) subit les conséquences désastreuses du gel. Le premier sous-modèle utilise une combinaison de profils de température provenant du satellite AIRS et de stations météorologiques afin d’estimer quotidiennement et régionalement le gradient thermique de l’air. L'utilisation de valeurs journalières, au lieu de valeurs fixes, permet d’estimer plus précisément les conditions atmosphériques. Les résultats ont démontré l’utilité de l’utilisation de la température de l'air obtenue par AIRS (850 hPa et 700 hPa) pour l’estimation du gradient thermique. Le second sous-modèle utilise les données associées aux conditions synoptiques du gel radiatif pour obtenir une équation du rayonnement descendant localement ajustée. Alors que l’erreur aux moindres carrés (RMSE) de Micro-Met était de 176.95 Wm-2 avec une erreur absolue (MAE) moyenne de 176.40 Wm-2, la nouvelle équation génère une RMSE de 4.90 Wm-2 et une MAE de 4.00 Wm-2. Le troisième sous-modèle contient trois parties :la détection des vallées fermées, l’estimation de la rapidité de drainage de l’air, et l’intégration de la perte de chaleur sensible ainsi que le refroidissement radiatif en vallée durant la nuit. La comparaison entre les simulations Micro-Met et les mesures de la température de l’air montrent une MAE de 1.11 (°C) et une RMSE de 1.66 (°C). La comparaison avec le modèle amélioré indique un gain avec une MAE de 0.68 (°C) et une RMSE de 1.08 (°C). Le quatrième sous-modèle était construit sur des résultats expérimentaux de vitesse du vent générés en laboratoire par des simulations. Trois équations ont été proposées pour estimer la vitesse du vent. Les résultats indiquent un coefficient de corrélation (R2) de 71% pour une vitesse de vent en dessous de 6 ms-1. La version améliorée de Micro-Net fournit une nouvelle plateforme pour des modèles d’énergie végétation-atmosphère et permet de prévoir la température minimale nocturne. Les résultats des tests de prédiction de cette température minimum concordent avec les mesures in-situ. Ces mesures ont été prises dans 5 secteurs topographiques différents afin d’améliorer les modèles de prédiction et engendrent des erreurs pour des vallées fermées (RMSE = 1.34, MAE = 1.03), pour différentes pentes (RMAE = 0.93, MAE = 0.73), crêtes (RMSE = 1.02, MAE = 0.88), plaines (RMSE = 0.44, MAE = 0.40), et aux orées des forêts (RMSE = 0.58, MAE = 0.53). En plus des objectifs spécifiques précédents, cette étude a proposé une nouvelle méthode d'atténuation du gel basée sur la thermodynamique du transport de la vapeur d'eau d'une source humide à un puits sec. Nous avons ajouté au Selective Inverse System (SIS) déjà utilisé dans le milieu, un contenant d'eau chaude à sa base pour diffuser la vapeur d'eau dans l'air ambiant. Cette opération a augmenté l’humidité de l'air ambiant et augmenté l'entropie humide. Cet essai a été réalisé dans un verger. La méthode d'atténuation la plus courante se concentre sur la température de l'air. La méthode proposée repose plutôt sur les principes physiques de l'entropie humide, qui combinait à la fois la température et l'humidité de l'air et le contenu thermique représenté. Dans l'ensemble, pour ce projet de recherche, un modèle couplé a été conçu pour prévision la température minimale nocturne de l'air dans des terrains agricoles vallonnés. En particulier, en améliorant la précision des prévisions, nous avons élaboré et ajouté des sous-modèles pour estimer les baisses de température dues à la stagnation du drainage de l'air froid et à l'effet des brise-vent forestiers sur la vitesse du vent. Pour réduire l'effet de gel, une nouvelle méthode de mitigation active respectueuse de l'environnement a été présentée. Cette étude a le potentiel d’aider les agriculteurs à réduire les dommages causés par le gel. De plus, elle peut être utile pour les services agricoles en termes de prise de décision, réduisant ainsi les dommages économiques.Abstract: The main objective of this study was related to radiation frost damage: (1) improving the forecasting capability of local frost, which was adapted to forecast nocturnal minimum temperature at a 30-meter resolution, using a vegetation atmosphere energy exchange framework, and (2) proposing a new mitigation approach to protect agricultural crops during frost periods. The first advance was achieved through several specific objectives to enhance the capabilities of a meteorological spatial distribution model (Micro-Met) on four sub-models: (i) estimating local air temperature lapse rate on a daily basis (ii) modifying downward longwave equation under clear sky condition, (iii) quantifying the effects of cold air drainage on air temperature, and (iv) quantifying the forest shelter effect on wind speed. The second advance advancement was accomplished by implementing and testing a new active method based on steam cycle thermodynamic. The first sub-model used AIRS (Atmosphere infrared sounder) air temperature profile and surface station data to estimate air temperature lapse rate on the daily and regional scale. The use of daily basis lapse rate, instead of the fixed value, allowed to present more accurate atmospheric condition. The results showed the potential of the AIRS air temperature profiles (850 hPa and 700 hPa) to estimate the temperature lapse rate. The second sub-model used observational data associated with synoptic conditions of radiation frost to present a locally adjusted downward longwave equation. The reported root means square error (RMSE) and mean absolute error (MAE) for the current version of Micro-Met were 176.95 (Wm-2) and 176.40 (Wm-2) respectively, while the results of the new equation led to an RMSE and MAE of 4.90 (Wm-2) and 4.00 (Wm-2) respectively. The third sub–model constituted three components: detected closed valley, estimated cold air drainage velocity, and integrated sensible heat loss and radiative cooling during the night on detected valleys. Comparison between the current Micro-Met simulation and the measured air temperature shows MAE of 1.11°C and RMSE of 1.66°C, while the comparison with the enhanced Micro-Met simulation indicated an improvement with MAE of 0.68 °C and RMSE of 1.08 °C. The fourth sub-model was based on experimental results of wind velocity produced in a laboratory with wind-tunnel models. Three separate equations were formulated for wind velocity estimation over the windward, through the shelterbelt, and leeward areas. The results indicated a coefficient of determination (R2) of 71% under the wind's velocity lower than 6ms-1. The Enhanced Micro-Met version provided a new platform to power vegetation-atmosphere energy model to forecast minimum nocturnal temperature. The performance test for forecasting minimum air temperatures indicated agreement with in-situ measurements. Measurements were taken on five topographic sectors in order to assess the improved modeled prediction and led to error assessment on closed valleys (RMSE=1.34, MAE = 1.03), different parts of slopes (RMAE = 0.93, MAE = 0.73), ridges (RMSE = 1.02, MAE = 0.88), flat areas (RMSE = 0.44, MAE = 0.40), and areas close to the forest (RMSE = 0.58, MAE = 0.53). In addition to previous specific objectives, this study proposed a new frost mitigation method based on the thermodynamics of water vapor transport from a moist source to dry sink. A vessel of warm water equipped with a Selective Inverted Sink (SIS) system was used to transport water vapor into the air, which ended up decreasing the air dryness and increasing moist entropy. This test was carried out in an orchard. The most common mitigation method focuses on air temperature. Instead, the proposed method was based on the physical principles of moist entropy, which combined both air temperature and humidity and depicted heat content. Overall, for this research project, a coupled model was designed to predict nocturnal minimum air temperature over hilly agricultural terrain. In particular, through improving prediction accuracy, we developed and added sub-models to estimate drops in temperature due to pooling and stagnation of cold air drainage and the effect of forest shelterbelt on wind velocity. To reduce frost effect, a new environmentally friendly active method was presented. This study served to help farmers reduce frost damages. Moreover, it can be useful for agricultural services in terms of decision-making, thereby, reducing economic damages

“Dust in the wind...”, deep learning application to wind energy time series forecasting

To balance electricity production and demand, it is required to use different prediction techniques extensively. Renewable energy, due to its intermittency, increases the complexity and uncertainty of forecasting, and the resulting accuracy impacts all the different players acting around the electricity systems around the world like generators, distributors, retailers, or consumers. Wind forecasting can be done under two major approaches, using meteorological numerical prediction models or based on pure time series input. Deep learning is appearing as a new method that can be used for wind energy prediction. This work develops several deep learning architectures and shows their performance when applied to wind time series. The models have been tested with the most extensive wind dataset available, the National Renewable Laboratory Wind Toolkit, a dataset with 126,692 wind points in North America. The architectures designed are based on different approaches, Multi-Layer Perceptron Networks (MLP), Convolutional Networks (CNN), and Recurrent Networks (RNN). These deep learning architectures have been tested to obtain predictions in a 12-h ahead horizon, and the accuracy is measured with the coefficient of determination, the R² method. The application of the models to wind sites evenly distributed in the North America geography allows us to infer several conclusions on the relationships between methods, terrain, and forecasting complexity. The results show differences between the models and confirm the superior capabilities on the use of deep learning techniques for wind speed forecasting from wind time series data.Peer ReviewedPostprint (published version

Estimating particulate matter using sattellite based aerosol optical depth and meteorological parameters in Malaysia

The insufficient number of ground-based stations for measuring Particulate Matter less than 10µm (PM10), especially in the developing countries hinders PM10 monitoring at a regional scale. The present study aims to develop empirical models for PM10 estimates from space over Malaysia using Aerosol Optical Depth (AOD550) retrieval from Moderate Resolution Imaging Spectroradiometer (MODIS), Medium Resolution Imaging Spectrometer/Advanced Along-Track Scanner Radiometer (MERIS/AATSR) synergy algorithm and meteorological data that include surface temperature, relative humidity and atmospheric stability from 2007-2011. Accuracy of meteorological parameters that have been used in the estimation of PM10 are examined. The estimated relative humidity and surface temperature using satellite data agree well with ground data where coefficient of determination (R2) = 0.78 and 0.49 and Root Mean Square Error (RMSE) = 5.14% and 2.68?C for relative humidity and surface temperature respectively. Multiple Linear Regressions (MLR) and Artificial Neural Network (ANN) techniques are utilized to develop the empirical models. The models were developed using PM10 data measured at 29 stations over Malaysia. Result of the research reveals that the ANN using MODIS AOD550 provide higher accuracy with R2 = 0.71 and RMSE = 11.61?gm-3 compared to the MLR method where R2 = 0.66 and RMSE = 12.39?gm-3 or models that use MERIS/AATSR AOD data. Stepwise regression analysis performed on the MLR method reveals that the MODIS AOD550 is the most important parameter for PM10 predictions where R2 = 0.59 and RMSE = 13.61?gm-3. However, the inclusion of the meteorological parameters in the MLR increases the accuracy of the PM10 estimations. The significance of the meteorological parameters in prediction of PM10 concentrations is in the order of (i) atmospheric stability, (ii) relative humidity and (iii) surface temperature. The estimated PM10 concentrations are validated against another 16 stations dataset of measured PM10 with the ANN model to result in higher accuracy (R2= 0.58, RMSE = 10.16?gm-3) compared to the MLR technique (R2 = 0.56, RMSE = 10.58?gm-3). The higher accuracy that has been attained in PM10 estimations from space allows (i) to map the PM10 distribution at large spatial and temporal scales and (ii) permits for future estimates of PM2.5 concentrations from space for monitoring of the Environmental Performance Index (EPI)

Remote Sensing of Precipitation: Volume 2

Precipitation is a well-recognized pillar in global water and energy balances. An accurate and timely understanding of its characteristics at the global, regional, and local scales is indispensable for a clearer understanding of the mechanisms underlying the Earth’s atmosphere–ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change. In its various forms, precipitation comprises a primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage. Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne

Physics-Informed Deep Learning to Reduce the Bias in Joint Prediction of Nitrogen Oxides

Atmospheric nitrogen oxides (NOx) primarily from fuel combustion have recognized acute and chronic health and environmental effects. Machine learning (ML) methods have significantly enhanced our capacity to predict NOx concentrations at ground-level with high spatiotemporal resolution but may suffer from high estimation bias since they lack physical and chemical knowledge about air pollution dynamics. Chemical transport models (CTMs) leverage this knowledge; however, accurate predictions of ground-level concentrations typically necessitate extensive post-calibration. Here, we present a physics-informed deep learning framework that encodes advection-diffusion mechanisms and fluid dynamics constraints to jointly predict NO2 and NOx and reduce ML model bias by 21-42%. Our approach captures fine-scale transport of NO2 and NOx, generates robust spatial extrapolation, and provides explicit uncertainty estimation. The framework fuses knowledge-driven physicochemical principles of CTMs with the predictive power of ML for air quality exposure, health, and policy applications. Our approach offers significant improvements over purely data-driven ML methods and has unprecedented bias reduction in joint NO2 and NOx prediction

Multisensor Fusion Remote Sensing Technology For Assessing Multitemporal Responses In Ecohydrological Systems

Earth ecosystems and environment have been changing rapidly due to the advanced technologies and developments of humans. Impacts caused by human activities and developments are difficult to acquire for evaluations due to the rapid changes. Remote sensing (RS) technology has been implemented for environmental managements. A new and promising trend in remote sensing for environment is widely used to measure and monitor the earth environment and its changes. RS allows large-scaled measurements over a large region within a very short period of time. Continuous and repeatable measurements are the very indispensable features of RS. Soil moisture is a critical element in the hydrological cycle especially in a semiarid or arid region. Point measurement to comprehend the soil moisture distribution contiguously in a vast watershed is difficult because the soil moisture patterns might greatly vary temporally and spatially. Space-borne radar imaging satellites have been popular because they have the capability to exhibit all weather observations. Yet the estimation methods of soil moisture based on the active or passive satellite imageries remain uncertain. This study aims at presenting a systematic soil moisture estimation method for the Choke Canyon Reservoir Watershed (CCRW), a semiarid watershed with an area of over 14,200 km2 in south Texas. With the aid of five corner reflectors, the RADARSAT-1 Synthetic Aperture Radar (SAR) imageries of the study area acquired in April and September 2004 were processed by both radiometric and geometric calibrations at first. New soil moisture estimation models derived by genetic programming (GP) technique were then developed and applied to support the soil moisture distribution analysis. The GP-based nonlinear function derived in the evolutionary process uniquely links a series of crucial topographic and geographic features. Included in this process are slope, aspect, vegetation cover, and soil permeability to compliment the well-calibrated SAR data. Research indicates that the novel application of GP proved useful for generating a highly nonlinear structure in regression regime, which exhibits very strong correlations statistically between the model estimates and the ground truth measurements (volumetric water content) on the basis of the unseen data sets. In an effort to produce the soil moisture distributions over seasons, it eventually leads to characterizing local- to regional-scale soil moisture variability and performing the possible estimation of water storages of the terrestrial hydrosphere. A new evolutionary computational, supervised classification scheme (Riparian Classification Algorithm, RICAL) was developed and used to identify the change of riparian zones in a semi-arid watershed temporally and spatially. The case study uniquely demonstrates an effort to incorporating both vegetation index and soil moisture estimates based on Landsat 5 TM and RADARSAT-1 imageries while trying to improve the riparian classification in the Choke Canyon Reservoir Watershed (CCRW), South Texas. The CCRW was selected as the study area contributing to the reservoir, which is mostly agricultural and range land in a semi-arid coastal environment. This makes the change detection of riparian buffers significant due to their interception capability of non-point source impacts within the riparian buffer zones and the maintenance of ecosystem integrity region wide. The estimation of soil moisture based on RADARSAT-1 Synthetic Aperture Radar (SAR) satellite imagery as previously developed was used. Eight commonly used vegetation indices were calculated from the reflectance obtained from Landsat 5 TM satellite images. The vegetation indices were individually used to classify vegetation cover in association with genetic programming algorithm. The soil moisture and vegetation indices were integrated into Landsat TM images based on a pre-pixel channel approach for riparian classification. Two different classification algorithms were used including genetic programming, and a combination of ISODATA and maximum likelihood supervised classification. The white box feature of genetic programming revealed the comparative advantage of all input parameters. The GP algorithm yielded more than 90% accuracy, based on unseen ground data, using vegetation index and Landsat reflectance band 1, 2, 3, and 4. The detection of changes in the buffer zone was proved to be technically feasible with high accuracy. Overall, the development of the RICAL algorithm may lead to the formulation of more effective management strategies for the handling of non-point source pollution control, bird habitat monitoring, and grazing and live stock management in the future. Soil properties, landscapes, channels, fault lines, erosion/deposition patches, and bedload transport history show geologic and geomorphologic features in a variety of watersheds. In response to these unique watershed characteristics, the hydrology of large-scale watersheds is often very complex. Precipitation, infiltration and percolation, stream flow, plant transpiration, soil moisture changes, and groundwater recharge are intimately related with each other to form water balance dynamics on the surface of these watersheds. Within this chapter, depicted is an optimal site selection technology using a grey integer programming (GIP) model to assimilate remote sensing-based geo-environmental patterns in an uncertain environment with respect to some technical and resources constraints. It enables us to retrieve the hydrological trends and pinpoint the most critical locations for the deployment of monitoring stations in a vast watershed. Geo-environmental information amassed in this study includes soil permeability, surface temperature, soil moisture, precipitation, leaf area index (LAI) and normalized difference vegetation index (NDVI). With the aid of a remote sensing-based GIP analysis, only five locations out of more than 800 candidate sites were selected by the spatial analysis, and then confirmed by a field investigation. The methodology developed in this remote sensing-based GIP analysis will significantly advance the state-of-the-art technology in optimum arrangement/distribution of water sensor platforms for maximum sensing coverage and information-extraction capacity. Effective water resources management is a critically important priority across the globe. While water scarcity limits the uses of water in many ways, floods also have caused so many damages and lives. To more efficiently use the limited amount of water or to resourcefully provide adequate time for flood warning, the results have led us to seek advanced techniques for improving streamflow forecasting. The objective of this section of research is to incorporate sea surface temperature (SST), Next Generation Radar (NEXRAD) and meteorological characteristics with historical stream data to forecast the actual streamflow using genetic programming. This study case concerns the forecasting of stream discharge of a complex-terrain, semi-arid watershed. This study elicits microclimatological factors and the resultant stream flow rate in river system given the influence of dynamic basin features such as soil moisture, soil temperature, ambient relative humidity, air temperature, sea surface temperature, and precipitation. Evaluations of the forecasting results are expressed in terms of the percentage error (PE), the root-mean-square error (RMSE), and the square of the Pearson product moment correlation coefficient (r-squared value). The developed models can predict streamflow with very good accuracy with an r-square of 0.84 and PE of 1% for a 30-day prediction