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Autor: Carlo Montes
Daily weather data for International Wheat Improvement Network (IWIN) locations based on AgERA5
Carlo Montes Urs Schulthess (2021)
Daily weather data from 1979 to 2019. The variables included are precipitation (mm), relative humidity max, relative humidity min, short wave radiation (MJ/m2/d), temperature max (°C), temperature min (°C), vapor pressure deficit max (kPa), wind speed 2m (m/s) and wind speed 10m (m/s). Data are world wide with a resolution of 0.1° x 0.1°.
Dataset
Estimating wheat canopy temperature from meteorological data: a multi-location approach
Carlo Montes Azam Lashkari Urs Schulthess (2021)
Objeto de congreso
CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CANOPY WHEAT TEMPERATURE METEOROLOGICAL OBSERVATIONS
An ERA5-based global dataset of vapor pressure deficit at maximum air temperature over land
Carlo Montes Urs Schulthess Azam lashkari (2021)
Global data set of daily vapor pressure deficit calculated at time of maximum temperture from hourly ECMWF ERA5 air temperature and dewpoint temperature. The dataset has a global coverage over land, daily time step, from 1979 through 2020, and files are provided yearly.
Dataset
Carlo Montes Tek Sapkota Balwinder-Singh (2022)
Artículo
Biomass Burning Emission Inventory Active Fires CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA AIR QUALITY BIOMASS BURNING EMISSION FIRES
Calibrated multi-model ensemble seasonal prediction of Bangladesh summer monsoon rainfall
Nachiketa Acharya Carlo Montes Timothy Joseph Krupnik (2023)
Bangladesh summer monsoon rainfall (BSMR), typically from June through September (JJAS), represents the main source of water for multiple sectors. However, its high spatial and interannual variability makes the seasonal prediction of BSMR crucial for building resilience to natural disasters and for food security in a climate-risk-prone country. This study describes the development and implementation of an objective system for the seasonal forecasting of BSMR, recently adopted by the Bangladesh Meteorological Department (BMD). The approach is based on the use of a calibrated multi-model ensemble (CMME) of seven state-of-the-art general circulation models (GCMs) from the North American Multi-Model Ensemble project. The lead-1 (initial conditions of May for forecasting JJAS total rainfall) hindcasts (spanning 1982–2010) and forecasts (spanning 2011–2018) of seasonal total rainfall for the JJAS season from these seven GCMs were used. A canonical correlation analysis (CCA) regression is used to calibrate the raw GCMs outputs against observations, which are then combined with equal weight to generate final CMME predictions. Results show, compared to individual calibrated GCMs and uncalibrated MME, that the CCA-based calibration generates significant improvements over individual raw GCM in terms of the magnitude of systematic errors, Spearman's correlation coefficients, and generalised discrimination scores over most of Bangladesh areas, especially in the northern part of the country. Since October 2019, the BMD has been issuing real-time seasonal rainfall forecasts using this new forecast system.
Artículo
Multi-Model Ensemble Seasonal Forecasting CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CLIMATE SERVICES FORECASTING MONSOONS
Daily wheat canopy temperature and meteorological data for IWIN locations
Carlo Montes Urs Schulthess Azam lashkari (2021)
Dataset of daily canopy temperature and meteorological data from the ECMWF’s AgERA5 product for the period 1979 though 2020, and for 785 points belonging to the International Wheat Improvement Network (IWIN). Wheat canopy temperature was estimated from a linear model using maximum air temperature, vapor pressure deficit, and solar radiation as inputs. The model was calibrated using multiple measurements of wheat canopy temperature.
Dataset
Carlo Montes Anton Urfels Eunjin Han Balwinder-Singh (2023)
Artículo
Rainy Season TIMESAT APSIM Agricultural Production Systems Simulator Climate Adaptation CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA RICE WHEAT MONSOONS WET SEASON CROP MODELLING CLIMATE CHANGE ADAPTATION
Variaciones en un transecto profundo frente a la costa de Nayarit, México
Emilio Palacios Hernández Luis Brito Castillo LAURA ELENA CARRILLO BIBRIEZCA CARLOS EDUARDO CABRERA RAMOS JORGE MANUEL MONTES ARECHIGA (2022)
"Six oceanographic cruises in a NE-SW transect were made nearshore of southern Sinaloa and Nayarit from March 2006 through May 2008, where no in situ hydrographic data are available. Applying the Thermodynamic Equation of Seawater 2010 (TEOS-10) to the observations, the hydrography and geostrophic currents of the region were characterized. Results indicate that surface variability (0-50 m) emerged mainly from seasonal atmospheric forcing. A relative salinity maximum was present during all cruises below this surface layer, which is attributed to a water mass intrusion of Subtropical Subsurface Water that could be associated with the Mexican Coastal Current. Another water mass intrusion is from the California Current. Samples from the 2007-2008 La Niña produced an uncommon circulation, where water flowing from the Gulf of California along the coast of Sinaloa was observed, opposite to what is commonly known as a mean circulation. This uncommon circulation matches the generation of anticyclonic eddies around the Islas Marias archipelago."
Artículo
Gulf of California, Mexican Coastal Current, Nayarit Coast, seasonal variation, La Niña CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA CIENCIAS DE LA TIERRA Y DEL ESPACIO OCEANOGRAFÍA OCEANOGRAFÍA DESCRIPTIVA OCEANOGRAFÍA DESCRIPTIVA
Replication Data for: Genome-based prediction of multiple wheat quality traits in multiple years
Maria Itria Ibba Jose Crossa Osval Antonio Montesinos-Lopez Philomin Juliana Carlos Guzman Susanne Dreisigacker Jesse Poland (2020)
The use of genomic prediction could greatly help to increase the efficiency of selecting for wheat quality traits by reducing the cost and time required for this analysis. This study contains data used to evaluate the prediction performances of 13 wheat quality traits under two multi-trait models [Bayesian multi-trait multi-environment (BMTME) and multi-trait ridge regression (MTR)]. Separate files are provided for each year of data. An additional supplemental data file provides R code for running the analyses as well as a table describing the Average Pearson´s correlation (APC) and mean arctangent absolute percentage error (MAAPE) for the testing sets for each dataset and trait.
Dataset
Replication data for: Increased ranking change in wheat breeding under climate change
Wei Xiong Matthew Paul Reynolds Jose Crossa Urs Schulthess Kai Sonder Carlo Montes Nicoletta Addimando Ravi Singh Karim Ammar Bruno Gerard Thomas Payne (2022)
A standard quantitative genetic model was used to examine how genotype-environment interactions have changed over the past decades from four spring wheat trial data sets. The variability of cross interactions for yield from one year to another is explained in more than 70% by climatic factors.
Dataset