The vegetative development of grapevines is orchestrated by very specific meteorological conditions. In the wine industry vineyards demand diligent mo…The vegetative development of grapevines is orchestrated by very specific meteorological conditions. In the wine industry vineyards demand diligent monitoring, since quality and productivity are the backbone of the economic potential. Regional climate indicators and meteorological information are essential to winemakers to assure proper vineyard management. Satellite data are very useful in this process since they imply low costs and are easily accessible. This work proposes a statistical modelling approach based on parameters obtained exclusively from satellite data to simulate annual wine production. The study has been developed for the Douro Demarcated Region (DDR) due to its relevance in the winemaking industry. It is the oldest demarcated and controlled winemaking region of the world and listed as one of UNESCO’s World Heritage regions. Monthly variables associated with Land Surface Temperatures (LST) and Fraction of Absorbed Photosynthetic Active Radiation (FAPAR), which is representative of vegetation canopy health, were analysed for a 15-year period (2004 to 2018), to assess their relation to wine production. Results showed that high wine production years are associated with higher than normal FAPAR values during approximately the entire growing season and higher than normal values of surface temperature from April to August. A robust linear model was obtained using the most significant predictors, that includes FAPAR in December and maximum and mean LST values in March and July, respectively. The model explains 90% of the total variance of wine production and presents a correlation coefficient of 0.90 (after cross validation). The retained predictors’ anomalies for the investigated vegetative year (October to July) from 2017/2018 satellite data indicate that the ensuing wine production for the DDR is likely to be below normal, i.e., to be lower than what is considered a high-production year. This work highlights that is possible to estimate wine production at regional scale based solely on low-resolution remotely sensed observations that are easily accessible, free and available for numerous grapevines regions worldwide, providing a useful and easy tool to estimate wine production and agricultural monitoring.more
Abstract
The emerging signal of climate change is now clearly evident in Global Navigation Satellite System (GNSS) radio occultation (RO) …Abstract
The emerging signal of climate change is now clearly evident in Global Navigation Satellite System (GNSS) radio occultation (RO) data, matching predictions made by climate models 15 years ago. The observed RO trends represent well-understood responses to global warming, in particular the widespread cooling of the lower stratosphere and warming of the troposphere. This demonstrates the value of RO measurements for climate monitoring, consistent with their information content and their use in both weather forecasting and atmospheric reanalyses.more
The purpose of agrometeorological services conducted by various institutions around the world is to support decisions in the field of planning individ…The purpose of agrometeorological services conducted by various institutions around the world is to support decisions in the field of planning individual farmer works and agrotechnical treatments so as to fully enable the use of the prevailing weather and climatic conditions. However, the not always sufficient spatial distribution of ground measuring stations limits the possibility of the precise determination of meteorological conditions and the state of vegetation in a specific location. The solution may be the simultaneous use of both ground and satellite data, which can improve and enhance the final agrometeorological products. This paper presents examples of the use of meteorological products combining classical ground measurement and data from meteorological radars and satellites, applied in an agrometeorological service provided by the Institute of Meteorology and Water Management in Poland. Selected examples cover Wielkopolskie Province, which is a primarily agricultural region. An analysis of the course of the soil moisture index and HTC as well as differences in the PEI spatial distribution from ground and satellite data for the extremely dry growing season of 2018 are presented. The authors tried to demonstrate that combining data available from different sources may be a necessary condition for modern agriculture in the conditions of climate change.more
Abstract. A single coherent total ozone dataset, called the Multi Sensor Reanalysis (MSR), has been created from all available ozone column data measu…Abstract. A single coherent total ozone dataset, called the Multi Sensor Reanalysis (MSR), has been created from all available ozone column data measured by polar orbiting satellites in the near-ultraviolet Huggins band in the last thirty years. Fourteen total ozone satellite retrieval datasets from the instruments TOMS (on the satellites Nimbus-7 and Earth Probe), SBUV (Nimbus-7, NOAA-9, NOAA-11 and NOAA-16), GOME (ERS-2), SCIAMACHY (Envisat), OMI (EOS-Aura), and GOME-2 (Metop-A) have been used in the MSR. As first step a bias correction scheme is applied to all satellite observations, based on independent ground-based total ozone data from the World Ozone and Ultraviolet Data Center. The correction is a function of solar zenith angle, viewing angle, time (trend), and effective ozone temperature. As second step data assimilation was applied to create a global dataset of total ozone analyses. The data assimilation method is a sub-optimal implementation of the Kalman filter technique, and is based on a chemical transport model driven by ECMWF meteorological fields. The chemical transport model provides a detailed description of (stratospheric) transport and uses parameterisations for gas-phase and ozone hole chemistry. The MSR dataset results from a 30-year data assimilation run with the 14 corrected satellite datasets as input, and is available on a grid of 1× 1 1/2° with a sample frequency of 6 h for the complete time period (1978–2008). The Observation-minus-Analysis (OmA) statistics show that the bias of the MSR analyses is less than 1% with an RMS standard deviation of about 2% as compared to the corrected satellite observations used.more
Abstract
Most coupled model simulations substantially overestimate tropical tropospheric warming trends over the satellite era, underminin…Abstract
Most coupled model simulations substantially overestimate tropical tropospheric warming trends over the satellite era, undermining the reliability of model-projected future climate change. Here we show that the model-observation discrepancy over the satellite era has arisen in large part from multi-decadal climate variability and residual biases in the satellite record. Analyses indicate that although the discrepancy is closely linked to multi-decadal variability in the tropical Pacific sea surface temperatures, the overestimation remains over the satellite era in model simulations forced by observed time-varying sea surface temperatures with a La Niña-like pattern. Regarding moist thermodynamic processes governing tropical tropospheric warming, however, we find a broad model-observation consistency over a post-war period, suggesting that residual biases in the satellite record may contribute to model-observation discrepancy. These results underscore the importance of sustaining an accurate long-term observing system as well as constraining the model representation of tropical Pacific sea surface temperature change and variability.more
This article presents a novel artificial neural network technique for merging multi-sensor satellite data. Stacked neural networks (NNs) are used to l…This article presents a novel artificial neural network technique for merging multi-sensor satellite data. Stacked neural networks (NNs) are used to learn the temporal and spatial drifts between data from different satellite sensors. The resulting NNs are then used to sequentially adjust the satellite data for the creation of a global homogeneous long-term climate data record. The proposed technique has successfully been applied to the merging of ozone data from three European satellite sensors covering together a time period of more than 16 years. The resulting long-term ozone data record has an excellent long-term stability of 0.2 ± 0.2% per decade and can therefore be used for ozone and climate studies.more