Gotowe bibliografie tematyczne / AgMERRA

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Gotowa bibliografia na temat „AgMERRA”

Autor: Grafiati
Data publikacji: 3 czerwca 2025
Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "AgMERRA"

1

Tang, Xiongpeng, Jianyun Zhang, Chao Gao, Gebdang Ruben, and Guoqing Wang. "Assessing the Uncertainties of Four Precipitation Products for Swat Modeling in Mekong River Basin." Remote Sensing 11, no. 3 (2019): 304. http://dx.doi.org/10.3390/rs11030304.

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Using hydrological simulation to evaluate the accuracy of satellite-based and reanalysis precipitation products always suffer from a large uncertainty. This study evaluates four widely used global precipitation products with high spatial and temporal resolutions [i.e., AgMERRA (AgMIP modern-Era Retrospective Analysis for Research and Applications), MSWEP (Multi-Source Weighted-Ensemble Precipitation), PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record), and TMPA (Tropical Rainfall Measuring Mission 3B42 Version7)] agains
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Ceglar, Andrej, Andrea Toreti, Gianpaolo Balsamo, and Shinya Kobayashi. "Precipitation over Monsoon Asia: A Comparison of Reanalyses and Observations." Journal of Climate 30, no. 2 (2017): 465–76. http://dx.doi.org/10.1175/jcli-d-16-0227.1.

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Reanalysis products represent a valuable source of information for different impact modeling and monitoring activities over regions with sparse observational data. It is therefore essential to evaluate their behavior and their intrinsic uncertainties. This study focuses on precipitation over monsoon Asia, a key agricultural region of the world. Four reanalysis datasets are evaluated, namely ERA-Interim, ERA-Interim/Land, AgMERRA (an agricultural version of MERRA), and JRA-55. APHRODITE and the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) dataset are the two gridded obser
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3

Salehnia, Nasrin, Amin Alizadeh, Hossein Sanaeinejad, Mohammad Bannayan, Azar Zarrin, and Gerrit Hoogenboom. "Estimation of meteorological drought indices based on AgMERRA precipitation data and station-observed precipitation data." Journal of Arid Land 9, no. 6 (2017): 797–809. http://dx.doi.org/10.1007/s40333-017-0070-y.

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4

Yaghoubi, Fatemeh, Mohammad Bannayan, and Ghorban-Ali Asadi. "Performance of predicted evapotranspiration and yield of rainfed wheat in the northeast Iran using gridded AgMERRA weather data." International Journal of Biometeorology 64, no. 9 (2020): 1519–37. http://dx.doi.org/10.1007/s00484-020-01931-y.

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Bender, Fabiani Denise, and Paulo Cesar Sentelhas. "Solar Radiation Models and Gridded Databases to Fill Gaps in Weather Series and to Project Climate Change in Brazil." Advances in Meteorology 2018 (July 5, 2018): 1–15. http://dx.doi.org/10.1155/2018/6204382.

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The quantification of climate change impacts on several human activities depends on reliable weather data series, without gaps and long enough to build up future climate. Based on that, this study aimed to evaluate the performance of temperature-based models for estimating global solar radiation and gridded databases (AgCFSR, AgMERRA, NASA/POWER, and XAVIER) as alternative ways for filling gaps in historical weather series (1980–2009) in Brazil and to project climate change scenarios based on measured and gridded weather data. Projections for mid- and end-of-century periods (2040–2069 and 2070
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6

Yaghoubi, Fatemeh, Mohammad Bannayan, and Ghorban-Ali Asadi. "Correction to: Changes in spatio-temporal distribution of AgMERRA-derived agro-climatic indices and agro-climatic zones for wheat crops in the northeast Iran." International Journal of Biometeorology 66, no. 3 (2021): 447. http://dx.doi.org/10.1007/s00484-021-02219-5.

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7

Gorgin Paveh, Faraz, Hadi Ramezani Etedali, and Brian Collins. "Evaluation of CRU TS, GPCC, AgMERRA, and AgCFSR meteorological datasets for estimating climate and crop variables: A case study of maize in Qazvin Province, Iran." Journal of Arid Land 14, no. 12 (2022): 1361–76. http://dx.doi.org/10.1007/s40333-022-0108-7.

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8

Tang, Xiongpeng, Jianyun Zhang, Guoqing Wang, et al. "Error Correction of Multi-Source Weighted-Ensemble Precipitation (MSWEP) over the Lancang-Mekong River Basin." Remote Sensing 13, no. 2 (2021): 312. http://dx.doi.org/10.3390/rs13020312.

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The demand for accurate long-term precipitation data is increasing, especially in the Lancang-Mekong River Basin (LMRB), where ground-based data are mostly unavailable and inaccessible in a timely manner. Remote sensing and reanalysis quantitative precipitation products provide unprecedented observations to support water-related research, but these products are inevitably subject to errors. In this study, we propose a novel error correction framework that combines products from various institutions. The NASA Modern-Era Retrospective Analysis for Research and Applications (AgMERRA), the Asian P
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9

Bagula, Espoir M., Jackson Gilbert M. Majaliwa, Gustave N. Mushagalusa, et al. "Climate Change Effect on Water Use Efficiency under Selected Soil and Water Conservation Practices in the Ruzizi Catchment, Eastern D.R. Congo." Land 11, no. 9 (2022): 1409. http://dx.doi.org/10.3390/land11091409.

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Concerns have been raised on the effectiveness and sustainability of Soil and Water Conservation (SWC) practices as adaptation options to climate change and high intra– and inter–annual rainfall variabilities in eastern Democratic Republic of Congo (DRC). This study was conducted in the Ruzizi Plain, a dryland area, to assess the performance of maize (Zea mays L.) under two Representative Concentration Pathways (RCP 4.5 and 8.5) and two SWC practices (tied ridges and conventional tillage). The AgMIP’s Regional Integrated Assessment (RIA) approach was used to simulate Water Use Efficiency (WUE)
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10

Charles, Bwalya Chisanga, Phiri Elijah, and R. N. Chinene Vernon. "Statistical Bias Correction of Fifth Coupled Model Intercomparison Project Data from the CGIAR Research Program on Climate Change, Agriculture and Food Security - Climate Portal for Mount Makulu, Zambia." British Journal of Applied Science & Technology 21, no. 4 (2017): 1–16. https://doi.org/10.9734/BJAST/2017/33531.

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Although Global Climate Models (GCMs) are regarded as the best tools available for future climate projections, there are biases in simulating precipitation and temperature due to their coarse spatial resolution and cannot be used directly to assess the impact of projected climate change. The study objective was to investigate how bias correction methods impact the modelled future climate change under Representative Concentration Pathway 8.5 (RCP8.5) for 2020-2050.Reanalysisdata (1980-2000) and bias correction approaches (change factor [CF], nudging and Quantile Mapping [QM]) were used to calib
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