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Artykuły w czasopismach na temat "Calibration of climate model"
Kutschera, Ellynne, John B. Kim, G. Stephen Pitts i Ray Drapek. "“What’s Past Is Prologue”: Vegetation Model Calibration with and without Future Climate". Land 12, nr 6 (24.05.2023): 1121. http://dx.doi.org/10.3390/land12061121.
Pełny tekst źródłaLee, Jeonghoon, Jeonghyeon Choi, Jiyu Seo, Jeongeun Won i Sangdan Kim. "Exploring Climate Sensitivity in Hydrological Model Calibration". Water 15, nr 23 (25.11.2023): 4094. http://dx.doi.org/10.3390/w15234094.
Pełny tekst źródłaTett, Simon F. B., Jonathan M. Gregory, Nicolas Freychet, Coralia Cartis, Michael J. Mineter i Lindon Roberts. "Does Model Calibration Reduce Uncertainty in Climate Projections?" Journal of Climate 35, nr 8 (15.04.2022): 2585–602. http://dx.doi.org/10.1175/jcli-d-21-0434.1.
Pełny tekst źródłaO'Reilly, Christopher H., Daniel J. Befort i Antje Weisheimer. "Calibrating large-ensemble European climate projections using observational data". Earth System Dynamics 11, nr 4 (19.11.2020): 1033–49. http://dx.doi.org/10.5194/esd-11-1033-2020.
Pełny tekst źródłaBeltran, Cesar, N. R. Edwards, A. B. Haurie, J. P. Vial i D. S. Zachary. "Oracle-based optimization applied to climate model calibration". Environmental Modeling & Assessment 11, nr 1 (20.10.2005): 31–43. http://dx.doi.org/10.1007/s10666-005-9024-4.
Pełny tekst źródłaMeinshausen, M., S. C. B. Raper i T. M. L. Wigley. "Emulating coupled atmosphere-ocean and carbon cycle models with a simpler model, MAGICC6 – Part 1: Model description and calibration". Atmospheric Chemistry and Physics 11, nr 4 (16.02.2011): 1417–56. http://dx.doi.org/10.5194/acp-11-1417-2011.
Pełny tekst źródłaKoch, Julian, Mehmet Cüneyd Demirel i Simon Stisen. "Climate Normalized Spatial Patterns of Evapotranspiration Enhance the Calibration of a Hydrological Model". Remote Sensing 14, nr 2 (11.01.2022): 315. http://dx.doi.org/10.3390/rs14020315.
Pełny tekst źródłaGuzmán-Cruz, R., R. Castañeda-Miranda, J. J. García-Escalante, A. Lara-Herrera, I. Serroukh i L. O. Solis-Sánchez. "GENETIC ALGORITHMS FOR CALIBRATION OF A GREENHOUSE CLIMATE MODEL". Revista Chapingo Serie Horticultura XVI, nr 1 (styczeń 2010): 23–30. http://dx.doi.org/10.5154/r.rchsh.2010.16.003.
Pełny tekst źródłaKim, Daeha, Il Won Jung i Jong Ahn Chun. "A comparative assessment of rainfall–runoff modelling against regional flow duration curves for ungauged catchments". Hydrology and Earth System Sciences 21, nr 11 (15.11.2017): 5647–61. http://dx.doi.org/10.5194/hess-21-5647-2017.
Pełny tekst źródłaSteele, Katie, i Charlotte Werndl. "Climate Models, Calibration, and Confirmation". British Journal for the Philosophy of Science 64, nr 3 (1.09.2013): 609–35. http://dx.doi.org/10.1093/bjps/axs036.
Pełny tekst źródłaRozprawy doktorskie na temat "Calibration of climate model"
Raoult, Nina. "Calibration of plant functional type parameters using the adJULES system". Thesis, University of Exeter, 2017. http://hdl.handle.net/10871/29837.
Pełny tekst źródłaNiraula, Rewati. "Understanding the Hydrological Response of Changed Environmental Boundary Conditions in Semi-Arid Regions: Role of Model Choice and Model Calibration". Diss., The University of Arizona, 2015. http://hdl.handle.net/10150/594961.
Pełny tekst źródłaDavies, Nicholas William. "The climate impacts of atmospheric aerosols using in-situ measurements, satellite retrievals and global climate model simulations". Thesis, University of Exeter, 2018. http://hdl.handle.net/10871/34544.
Pełny tekst źródłaBensouda, Nabil. "Extending and formalizing the energy signature method for calibrating simulations and illustrating with application for three California climates". Texas A&M University, 2004. http://hdl.handle.net/1969.1/1080.
Pełny tekst źródłaAndersson, Sara. "Mapping Uncertainties – A case study on a hydraulic model of the river Voxnan". Thesis, KTH, Mark- och vattenteknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-173848.
Pełny tekst źródłaLiang, Dong Cowles Mary Kathryn. "Issues in Bayesian Gaussian Markov random field models with application to intersensor calibration". Iowa City : University of Iowa, 2009. http://ir.uiowa.edu/etd/400.
Pełny tekst źródłaBen, Touhami Haythem. "Calibration Bayésienne d'un modèle d'étude d'écosystème prairial : outils et applications à l'échelle de l'Europe". Thesis, Clermont-Ferrand 2, 2014. http://www.theses.fr/2014CLF22444/document.
Pełny tekst źródłaGrasslands cover 45% of the agricultural area in France and 40% in Europe. Grassland ecosystems have a central role in the climate change context, not only because they are impacted by climate changes but also because grasslands contribute to greenhouse gas emissions. The aim of this thesis was to contribute to the assessment of uncertainties in the outputs of grassland simulation models, which are used in impact studies, with focus on model parameterization. In particular, we used the Bayesian statistical method, based on Bayes’ theorem, to calibrate the parameters of a reference model, and thus improve performance by reducing the uncertainty in the parameters and, consequently, in the outputs provided by models. Our approach is essentially based on the use of the grassland ecosystem model PaSim (Pasture Simulation model) already applied in a variety of international projects to simulate the impact of climate changes on grassland systems. The originality of this thesis was to adapt the Bayesian method to a complex ecosystem model such as PaSim (applied in the context of altered climate and across the European territory) and show its potential benefits in reducing uncertainty and improving the quality of model outputs. This was obtained by combining statistical methods (Bayesian techniques and sensitivity analysis with the method of Morris) and computing tools (R code -PaSim coupling and use of cluster computing resources). We have first produced a new parameterization for grassland sites under drought conditions, and then a common parameterization for European grasslands. We have also provided a generic software tool for calibration for reuse with other models and sites. Finally, we have evaluated the performance of the calibrated model through the Bayesian technique against data from validation sites. The results have confirmed the efficiency of this technique for reducing uncertainty and improving the reliability of simulation outputs
Battisti, Rafael. "Calibration, uncertainties and use of soybean crop simulation models for evaluating strategies to mitigate the effects of climate change in Southern Brazil". Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/11/11152/tde-03102016-162340/.
Pełny tekst źródłaO déficit hídrico é o principal fator causador de perda de produtividade para a soja no Centro-Sul do Brasil e tende a aumentar com as mudanças climáticas. Alternativas de mitigação podem ser avaliadas usando modelos de simulação de cultura, os quais diferem em nível de complexidade e desempenho. Baseado nisso, os objetivos desse estudo foram: avaliar cinco modelos de simulação para a soja e a média desses modelos; avaliar a sensibilidade dos modelos a mudança sistemática do clima; avaliar características adaptativas da soja ao déficit hídrico para o clima atual e futuro; e avaliar a resposta produtiva de manejos da soja para o clima atual e futuro. Os modelos utilizados foram FAO - Zona Agroecológica, AQUACROP, DSSAT CSM-CROPGRO-Soybean, APSIM Soybean e MONICA. Os modelos foram calibrados a partir de dados experimentais obtidos na safra 2013/2014 em diferentes locais e datas de semeadura sob condições irrigadas e de sequeiro. Na análise de sensibilidade foram modificadas a temperatura do ar, [CO2], chuva e radiação solar. Para as características de tolerância ao déficit hídrico foram manipulados, apenas no modelo DSSAT CSMCROPGRO- Soybean, a distribuição do sistema radicular, biomassa divergida para crescimento radicular sob déficit hídrico, redução antecipada da transpiração, limitação da transpiração em função do déficit de pressão de vapor, fixação de N2 sob déficit hídrico e redução da aceleração do ciclo devido ao déficit hídrico. Os manejos avaliados foram irrigação, data de semeadura, ciclo de cultivar e densidade de semeadura. A produtividade estimada obteve raiz do erro médio quadrático (REMQ) variando entre 553 kg ha-1 e 650 kg ha-1, com índice d acima de 0.90 para todos os modelos. O melhor desempenho foi obtido utilizando a média de todos os modelos, com REMQ de 262 kg ha-1. Os modelos obtiveram diferentes níveis de sensibilidade aos cenários climáticos, reduzindo a produtividade com aumento da temperatura, maior taxa de redução da produtividade com menor quantidade de chuva do que aumento de produtividade com maior quantidade de chuva, diferentes respostas com a mudança da radiação solar em função do clima local e do modelo, e resposta positiva assimptótica para o aumento da concentração de [CO2]. Quando combinado as mudanças dos cenários, a produtividade foi afetada principalmente pela redução da chuva (aumento da radiação solar), enquanto a mudança na temperatura e [CO2] mostrou compensação nas perdas e ganhos. A distribuição do sistema radicular foi o mecanismo de tolerância ao déficit hídrico com maior ganho de produtividade, representando ganho total na produção de 3,3 % e 4,0% para a região, respectivamente, para o clima atual e futuro. Para os manejos não se observou melhores resultados com a mudança do manejo para o futuro em relação a melhor condição para o clima atual. Desta forma, os modelos mostraram diferentes desempenho, em que a parametrização e a estrutura do modelo afetaram a resposta das alternativas avaliadas para mudanças climáticas. Apesar das incertezas, os modelos de cultura são uma importante ferramenta para avaliar o impacto e alternativas de mitigação as mudanças climáticas.
Dinh, Thi Lan Anh. "Crop yield simulation using statistical and machine learning models. From the monitoring to the seasonal and climate forecasting". Electronic Thesis or Diss., Sorbonne université, 2022. http://www.theses.fr/2022SORUS425.
Pełny tekst źródłaWeather and climate strongly impact crop yields. Many studies based on different techniques have been done to measure this impact. This thesis focuses on statistical models to measure the sensitivity of crops to weather conditions based on historical records. When using a statistical model, a critical difficulty arises when data is scarce, which is often the case with statistical crop modelling. There is a high risk of overfitting if the model development is not done carefully. Thus, careful validation and selection of statistical models are major concerns of this thesis. Two statistical approaches are developed. The first one uses linear regression with regularization and leave-one-out cross-validation (or LOO), applied to Robusta coffee in the main coffee-producing area of Vietnam (i.e. the Central Highlands). Coffee is a valuable commodity crop, sensitive to weather, and has a very complex phenology due to its perennial nature. Results suggest that precipitation and temperature information can be used to forecast the yield anomaly with 3–6 months' anticipation depending on the location. Estimates of Robusta yield at the end of the season show that weather explains up to 36 % of historical yield anomalies. The first approach using LOO is widely used in the literature; however, it can be misused for many reasons: it is technical, misinterpreted, and requires experience. As an alternative, the “leave-two-out nested cross-validation” (or LTO) approach, is proposed to choose the suitable model and assess its true generalization ability. This method is sophisticated but straightforward; its benefits are demonstrated for Robusta coffee in Vietnam and grain maize in France. In both cases, a simpler model with fewer potential predictors and inputs is more appropriate. Using only the LOO method, without any regularization, can be highly misleading as it encourages choosing a model that overfits the data in an indirect way. The LTO approach is also useful in seasonal forecasting applications. The end-of-season grain maize yield estimates suggest that weather can account for more than 40 % of the variability in yield anomaly. Climate change's impacts on coffee production in Brazil and Vietnam are also studied using climate simulations and suitability models. Climate data are, however, biased compared to the real-world climate. Therefore, many “bias correction” methods (called here instead “calibration”) have been introduced to correct these biases. An up-to-date review of the available methods is provided to better understand each method's assumptions, properties, and applicative purposes. The climate simulations are then calibrated by a quantile-based method before being used in the suitability models. The suitability models are developed based on census data of coffee areas, and potential climate variables are based on a review of previous studies using impact models for coffee and expert recommendations. Results show that suitable arabica areas in Brazil could decrease by about 26 % by the mid-century in the high-emissions scenario, while the decrease is surprisingly high for Vietnamese Robusta coffee (≈ 60 %). Impacts are significant at low elevations for both coffee types, suggesting potential shifts in production to higher locations. The used statistical approaches, especially the LTO technique, can contribute to the development of crop modelling. They can be applied to a complex perennial crop like coffee or more industrialized annual crops like grain maize. They can be used in seasonal forecasts or end-of-season estimations, which are helpful in crop management and monitoring. Estimating the future crop suitability helps to anticipate the consequences of climate change on the agricultural system and to define adaptation or mitigation strategies. Methodologies used in this thesis can be easily generalized to other cultures and regions worldwide
Martínez, Asensio Adrián. "Impact of large-scale atmospheric variability on sea level and wave climate". Doctoral thesis, Universitat de les Illes Balears, 2015. http://hdl.handle.net/10803/371456.
Pełny tekst źródłaEsta tesis caracteriza cuantitativamente la variabilidad climática reciente (las últimas décadas) y futura del clima marino en el Mar Mediterráneo y en el Océano Atlántico Norte. Concretamente, se centra en el nivel del mar y en el oleaje, ya que éstas son las variables con un mayor impacto potencial en ecosistemas e infraestructuras costeras. En primer lugar, utilizamos datos de boyas y altimetría para calibrar un hindcast de oleaje de 50 años en el Mediterráneo Occidental, con el objetivo de obtener la mejor caracterización climática del oleaje sobre esta región. La minimización de las diferencias con respecto a las observaciones a través de una transformación no lineal de las Funciones Empíricas Ortogonales de los campos modelados se traduce en una mejora del hindcast, de acuerdo al test de validación llevado a cabo con observaciones independientes. Luego nos centramos en las relaciones entre el forzamiento atmosférico de gran escala y nuestras variables de interés. En concreto, cuantificamos y exploramos las relaciones causa-efecto entre los modos de variabilidad atmosférica más importantes del Atlántico Norte y Europa (la Oscilación del Atlántico Norte, el patrón del Atlántico Oriental, el patrón del Atlántico Oriental/Rusia Occidental y el patrón Escandinavo) y el nivel del mar del Mediterráneo y el oleaje del Atlántico Norte. Para ello, usamos datos de diferentes conjuntos de observaciones y modelos numéricos, incluyendo mareógrafos, boyas de oleaje, altimetría, hidrografía y simulaciones numéricas. Nuestros resultados señalan la Oscilación del Atlántico Norte como el modo de mayor impacto, tanto en el nivel del mar del Mediterráneo (debido a la influencia local y remota en su componente atmosférica) como en el oleaje del Atlántico Norte (debido a su efecto en las componentes de mar de viento y de mar de fondo). Otros índices climáticos tienen contribuciones más pequeñas pero todavía significativas; e.g. el patrón del Atlántico Oriental juega un papel importante en la variabilidad del oleaje a través de su impacto en la componente de mar de fondo. Finalmente, exploramos la capacidad de los modelos estadísticos de proyectar el clima futuro del oleaje sobre el Atlántico Norte bajo escenarios de calentamiento global, incluyendo los modos climáticos de gran escala como predictores junto con otras variables como la presión atmosférica y la velocidad del viento. En particular, destacamos que el uso de la velocidad del viento como predictor estadístico es esencial para reproducir las tendencias a largo plazo proyectadas de por los modelos dinámicos.
Książki na temat "Calibration of climate model"
Zapata, C. E. Calibration and validation of the enhanced integrated climatic model for pavement design. Washington, D.C: Transportation Research Board, 2008.
Znajdź pełny tekst źródłaWise, Jacquelyn A. Thermometer calibration: A model for state calibration laboratories. Washington: U.S. Dept. of Commerce, National Bureau of Standards, 1986.
Znajdź pełny tekst źródłaWise, Jacquelyn A. Thermometer calibration: A model for state calibration laboratories. Gaithersburg, MD: U.S. Dept. of Commerce, National Bureau of Standards, 1986.
Znajdź pełny tekst źródłaSun, Ne-Zheng, i Alexander Sun. Model Calibration and Parameter Estimation. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2323-6.
Pełny tekst źródłaKemp, Malcolm H. D. Market consistency: Model calibration in imperfect markets. Hoboken, NJ: Wiley, 2009.
Znajdź pełny tekst źródłaMarket consistency: Model calibration in imperfect markets. Chichester, U.K: Wiley, 2009.
Znajdź pełny tekst źródłaAssociates, Dick Conway &. Puget Sound subarea forecasts: Model calibration and forecasts. [Seattle?: Puget Sound Regional Council?, 1992.
Znajdź pełny tekst źródłaDawkins, Christina. New directions in applied general equilibrium model calibration. [s.l.]: typescript, 1999.
Znajdź pełny tekst źródłaHackl, Christoph. Calibration and Parameterization Methods for the Libor Market Model. Wiesbaden: Springer Fachmedien Wiesbaden, 2014. http://dx.doi.org/10.1007/978-3-658-04688-0.
Pełny tekst źródłaR, McNew-Cartwright Elizabeth, i Geological Survey (U.S.), red. Calibration of a ground-water-flow model by regression. Coram, N.Y: U.S. Dept. of the Interior, U.S. Geological Survey, 1996.
Znajdź pełny tekst źródłaCzęści książek na temat "Calibration of climate model"
Nandi, Saswata, i M. Janga Reddy. "Multiobjective Automatic Calibration of a Physically Based Hydrologic Model Using Multiobjective Self-Adaptive Differential Evolution Algorithm". W Climate Change Impacts on Water Resources, 435–48. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-64202-0_37.
Pełny tekst źródłaOladapo, Olukunle Olaonipekun, Leonard Kofitse Amekudzi, Olatunde Micheal Oni, Abraham Adewale Aremu i Marian Amoakowaah Osei. "Climate Change Impact on Soil Moisture Variability: Health Effects of Radon Flux Density Within Ogbomoso, Nigeria". W African Handbook of Climate Change Adaptation, 437–52. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-45106-6_201.
Pełny tekst źródłaPapadelis, Sotiris, i Alexandros Flamos. "An Application of Calibration and Uncertainty Quantification Techniques for Agent-Based Models". W Understanding Risks and Uncertainties in Energy and Climate Policy, 79–95. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03152-7_3.
Pełny tekst źródłaNoilhan, Joel, Pierre Lacarrère, Florence Habets i Richard J. Harding. "Use of Field Experiments in Improving the Land-surface Description in Atmospheric Models: Calibration, Aggregation and Scaling". W Vegetation, Water, Humans and the Climate, 221–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-18948-7_21.
Pełny tekst źródłaEngler, Camila, Carlos Marcelo Pais, Silvina Saavedra, Emanuel Juarez i Hugo Leonardo Rufiner. "Prediction of the Impact of the End of year Festivities on the Local Epidemiology of COVID-19 Using Agent-Based Simulation with Hidden Markov Models". W Computational Science and Its Applications – ICCSA 2022, 61–75. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10522-7_5.
Pełny tekst źródłaBelarbi, Halima, Bénina Touaibia, Nadir Boumechra, Chérifa Abdelbaki i Sakina Amiar. "Analysis of the Hydrological Behavior of Watersheds in the Context of Climate Change (Northwestern Algeria)". W Natural Disaster Science and Mitigation Engineering: DPRI reports, 143–79. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2904-4_5.
Pełny tekst źródłaMamalakis, Antonios, Imme Ebert-Uphoff i Elizabeth A. Barnes. "Explainable Artificial Intelligence in Meteorology and Climate Science: Model Fine-Tuning, Calibrating Trust and Learning New Science". W xxAI - Beyond Explainable AI, 315–39. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04083-2_16.
Pełny tekst źródłaXu, Jie. "Model Calibration". W Simulation Foundations, Methods and Applications, 27–46. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64182-9_3.
Pełny tekst źródłaMermoud, Grégory. "Model Calibration". W Stochastic Reactive Distributed Robotic Systems, 99–108. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-02609-1_7.
Pełny tekst źródłaVieux, Baxter E. "Distributed Model Calibration". W Water Science and Technology Library, 189–209. Dordrecht: Springer Netherlands, 2016. http://dx.doi.org/10.1007/978-94-024-0930-7_10.
Pełny tekst źródłaStreszczenia konferencji na temat "Calibration of climate model"
Jahn, Patrick, Gerrit Lassahn i Kang Qiu. "Model-Based Calibration of an Automotive Climate Control System". W WCX SAE World Congress Experience. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2020. http://dx.doi.org/10.4271/2020-01-1253.
Pełny tekst źródłaHadjrioua, Farid, N. Belhaouas, A. Aissaoui, F. Mehareb i K. Bakria. "Outdoor PV Module Characterization and Sandia Model Calibration in Local Climate". W 2022 International Conference of Advanced Technology in Electronic and Electrical Engineering (ICATEEE). IEEE, 2022. http://dx.doi.org/10.1109/icateee57445.2022.10093701.
Pełny tekst źródłaSteinschneider, S., C. Brown, R. N. Palmer i D. Ahlfeld. "Hydrology Models for Climate Change Assessment: Inter-Decadal Climate Variability and Parameter Calibration". W World Environmental and Water Resources Congress 2011. Reston, VA: American Society of Civil Engineers, 2011. http://dx.doi.org/10.1061/41173(414)428.
Pełny tekst źródłaOsypov, Valeriy, Nataliia Osadcha, Andrii Bonchkovskyi, Oleksandr Kostetskyi, Viktor Nikoriak, Yurii Ahafonov, Yevhenii Matviienko, Herman Mossur i Volodymyr Osadchyi. "Hydrological model of Ukraine: setup, calibration, and web interface". W International Conference of Young Scientists on Meteorology, Hydrology and Environmental Monitoring. Ukrainian Hydrometeorological Institute, 2023. http://dx.doi.org/10.15407/icys-mhem.2023.013.
Pełny tekst źródła"�Na�ve� inclusion of diverse climates in calibration is not sufficient to improve model reliability under future climate uncertainty". W 24th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, 2021. http://dx.doi.org/10.36334/modsim.2021.j8.trotter.
Pełny tekst źródłaSong, Mohan, Andrea E. Brookfield i Alan E. Fryar. "INTEGRATED HYDROLOGIC MODEL CALIBRATION UNDER NON-STATIONARY CLIMATES". W GSA Connects 2023 Meeting in Pittsburgh, Pennsylvania. Geological Society of America, 2023. http://dx.doi.org/10.1130/abs/2023am-392564.
Pełny tekst źródłaWang, Shuyuan, Dennis C. Flanagan i Bernard A. Engel. "Calibration, Validation, and Evaluation of the Water Erosion Prediction Project (WEPP) Model for Hillslopes with Natural Runoff Plot Data". W Soil Erosion Research Under a Changing Climate, January 8-13, 2023, Aguadilla, Puerto Rico, USA. St. Joseph, MI: American Society of Agricultural and Biological Engineers, 2023. http://dx.doi.org/10.13031/soil.2023022.
Pełny tekst źródłaWang, Shuyuan, Dennis C. Flanagan i Bernard A. Engel. "Calibration, Validation, and Evaluation of the Water Erosion Prediction Project (WEPP) Model for Hillslopes with Natural Runoff Plot Data". W Soil Erosion Research Under a Changing Climate, January 8-13, 2023, Aguadilla, Puerto Rico, USA. St. Joseph, MI: American Society of Agricultural and Biological Engineers, 2023. http://dx.doi.org/10.13031/soil.23022.
Pełny tekst źródłaBin Masood, Junaid, Sajid Hussain, Ali AlAlili, Sara Zaidan i Ebrahim Al Hajri. "Detailed Dynamic Model of an Institutional Building in Hot and Humid Climate Conditions". W ASME 2017 11th International Conference on Energy Sustainability collocated with the ASME 2017 Power Conference Joint With ICOPE-17, the ASME 2017 15th International Conference on Fuel Cell Science, Engineering and Technology, and the ASME 2017 Nuclear Forum. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/es2017-3582.
Pełny tekst źródłaKnoppová, Kateřina, Daniel Marton i Petr Štěpánek. "APPLICATION OF RAINFALL-RUNOFF MODEL: CLIMATE CHANGE IMPACTS ON RESERVOIR INFLOW". W XXVII Conference of the Danubian Countries on Hydrological Forecasting and Hydrological Bases of Water Management. Nika-Tsentr, 2020. http://dx.doi.org/10.15407/uhmi.conference.01.11.
Pełny tekst źródłaRaporty organizacyjne na temat "Calibration of climate model"
Ellingson, R., W. Wiscombe, D. Murcray, W. Smith i R. Strauch. ICRCCM Phase 2: Verification and calibration of radiation codes in climate models. Office of Scientific and Technical Information (OSTI), styczeń 1992. http://dx.doi.org/10.2172/7162458.
Pełny tekst źródłaEllingson, R. G., W. J. Wiscombe, D. Murcray, W. Smith i R. Strauch. ICRCCM Phase 2: Verification and calibration of radiation codes in climate models. Office of Scientific and Technical Information (OSTI), styczeń 1991. http://dx.doi.org/10.2172/6165998.
Pełny tekst źródłaEllingson, R. G., W. J. Wiscombe, D. Murcray, W. Smith i R. Strauch. ICRCCM (InterComparison of Radiation Codes used in Climate Models) Phase 2: Verification and calibration of radiation codes in climate models. Office of Scientific and Technical Information (OSTI), styczeń 1990. http://dx.doi.org/10.2172/6232336.
Pełny tekst źródłaEllingson, R. G., W. J. Wiscombe, D. Murcray, W. Smith i R. Strauch. ICRCCM Phase 2: Verification and calibration of radiation codes in climate models. Technical report, 1 November 1991--1 December 1992. Office of Scientific and Technical Information (OSTI), grudzień 1992. http://dx.doi.org/10.2172/10105305.
Pełny tekst źródłaEllingson, R. G., W. J. Wiscombe, D. Murcray, W. Smith i R. Strauch. ICRCCM phase II: Verification and calibration of radiation codes in climate models. Final report, 1 May 1990--30 April 1993. Office of Scientific and Technical Information (OSTI), grudzień 1993. http://dx.doi.org/10.2172/569119.
Pełny tekst źródłaEllingson, R. G. ICRCCM Phase II: Verification and calibration of radiation codes in climate models. Final report, May 1, 1993--June 30, 1997. Office of Scientific and Technical Information (OSTI), grudzień 1997. http://dx.doi.org/10.2172/563327.
Pełny tekst źródłaFinkelstein-Shapiro, Alan, i Victoria Nuguer. Climate Policies, Labor Markets, and Macroeconomic Outcomes in Emerging Economies. Inter-American Development Bank, kwiecień 2023. http://dx.doi.org/10.18235/0004844.
Pełny tekst źródłaMenikoff, Ralph. SURF Model Calibration Strategy. Office of Scientific and Technical Information (OSTI), marzec 2017. http://dx.doi.org/10.2172/1346849.
Pełny tekst źródłaMenikoff, Ralph. SURF model calibration strategy. Office of Scientific and Technical Information (OSTI), styczeń 2020. http://dx.doi.org/10.2172/1581567.
Pełny tekst źródłaOhring, George, Bruce Wielicki, Roy Spencer, Bill Emery i Raju Datla. Satellite instrument calibration for measuring global climate change. Gaithersburg, MD: National Institute of Standards and Technology, 2004. http://dx.doi.org/10.6028/nist.ir.7047.
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