Methane emissions from natural gas are of ever-increasing importance as we struggle to reach Paris climate targets. Locating and measuring emissions f…Methane emissions from natural gas are of ever-increasing importance as we struggle to reach Paris climate targets. Locating and measuring emissions from natural gas can be particularly difficult as they are often widely distributed across supply chains. Satellites are increasingly used to measure these emissions, with some such as TROPOMI giving daily coverage worldwide, making locating and quantifying these emissions easier. However, there is little understanding of the real-world detection limits of TROPOMI, which can cause emissions to go undetected or be misattributed. This paper uses TROPOMI and meteorological data to calculate, and create a map of, the minimum detection limits of the TROPOMI satellite sensor across North America for different campaign lengths. We then compared these to emission inventories to determine the quantity of emissions that can be captured by TROPOMI. We find that minimum detection limits vary from 500–8800 kg/h/pixel in a single overpass to 50–1200 kg/h/pixel for a yearlong campaign. This leads to 0.04 % of a year's emissions being captured in a single (day) measurement to 14.4 % in a 1-year measurement campaign. Assuming gas sites contain super-emitters, emissions of between 4.5 % - 10.1 % from a single measurement and 35.6 % - 41.1 % for a yearlong campaign are captured.more
Demand for electricity is constantly increasing, and production is facing new constraints due to the current world situation. An alternative to standa…Demand for electricity is constantly increasing, and production is facing new constraints due to the current world situation. An alternative to standard energy production methodologies is based on the use of renewable sources; however, these methodologies do not produce energy consistently due to weather factors. This results in a significant commitment of the user who must appropriately distribute loads in the most productive time slots. In this paper, a comparison is made between two methods of predicting solar energy production, one statistical and the other meteorological. For this work, a system capable of presenting the scheduling of household appliances is tested. The system is able to predict the energy consumption of the users and the energy production of the solar system. The system is tested using data from three different users, and the mean percentage of consumption reduction is about 77.73%. This is achieved through optimized programming of appliance use that also considers user comfort.more
The spatial quantification of solar resources is necessary for the deployment of solar systems and must consider the local specificities of territorie…The spatial quantification of solar resources is necessary for the deployment of solar systems and must consider the local specificities of territories, such as complex topography in mountainous areas. This paper presents a methodology for obtaining solar cadastres, based on the Solar Energy on Building Envelopes (SEBE) model incorporated in QGIS and applied to French municipalities. The differences in solar potential between plain and mountain villages are analyzed through the simulation of 92 carefully selected villages located in these two types of regions. The distributions of annual rooftop irradiation per building are obtained for each studied village and approximated with a Johnson’s SU density function. From this arises the definition of two statistical indicators: the mode and the spread at one-third maximum. Main results include a mean decrease in the mode of 189 kWh/m2 and a higher dispersion of 69 kWh/m2 between mountain and plain villages. Two physical indicators, the Sky View Index (SVI) and the Diffuse Fraction Index (DFI), are defined to explain these differences in the shape of the distributions. Higher cloud covers (high DFI) and the presence of distant shading effects (low SVI), caused by terrain relief, explains respectively the smaller modes and the higher dispersion observed in mountainous areas. SVI, DFI and latitude are fed to a multiple linear regression model, allowing the estimation of distributions with smaller computational costs than the developed methodology. Overall, this analysis demonstrates that the characteristics of mountainous environments greatly influence solar resources and should be considered in energy planning.more
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