Surface Analysis Satellite Applications Facility Research Articles

A satellite view of the exceptionally warm summer of 2022 over Europe

Abstract. Summer heatwaves are becoming increasingly dangerous over Europe, and their close monitoring is essential for human activities. Typically, they are monitored using the 2 m temperature from meteorological weather stations or reanalysis datasets. In this study, the 2022 extremely warm summer over Europe is analysed using satellite land surface temperature (LST), specifically the LSA SAF (Land Surface Analysis Satellite Application Facility) all-sky LST product (available from 2004 onwards). Since climate applications of LST are still poorly explored, heatwave diagnostics derived from satellite observations are compared with those derived using ERA5/ERA5-Land reanalysis data. Results highlight the exceptionality of 2022 in different metrics such as the mean LST anomaly, area under extreme heat conditions, number of hot days and heatwave magnitude index. In all metrics, 2022 ranked first when compared with the remaining years. Compared to 2018 (next in all rankings), 2022 exceeded its LST anomaly by 0.7 °C and each pixel had on average 7 more hot days. Satellite LST complements reanalysis diagnostics, as higher LST anomalies occur over areas under severe drought, indicating a higher control and amplification of the heatwave by surface processes and vegetation stress. These cross-cutting diagnostics increase the confidence across satellite data records and reanalyses, fostering their usage in climate applications.

The Performance of ECMWF Ensemble Prediction System for European Extreme Fires: Portugal/Monchique in 2018

At the beginning of August 2018, Portugal experienced a severe heat episode over a few days that consequently increased the probability of wildfire events. Due to the advection of an anomalous very hot and dry air mass, severe fire-prone meteorological conditions were forecasted mainly over southern Portugal, in the Monchique region. Together with the significant fuel amount accumulated since the last extreme wildfire in August 2003, all the unfavorable conditions were set to drive a severe fire over this region. The Monchique fire started on 3 August 2018, being very hard to suppress and lasting for seven days, with a burnt area of 27,000 ha. Regarding the need to have operational early warning tools, this work aims to evaluate the reliability of fire probabilistic products, up to 72 h ahead, together with the use of fire radiative power products, as support tools in fire monitoring and resource activities. To accomplish this goal, we used the fire probabilistic products of the Ensemble Prediction System, provided by the Copernicus Atmosphere Monitoring Service. Among available fire danger rating systems, the Fire Weather Index and the Fine Fuels Moisture Code of the Canadian Forest Fire Weather Index System were selected to assess the meteorological fire danger. The assessment of the fire intensity was based on the Fire Radiative Energy released, considering the Fire Radiative Power, delivered in near real-time, by EUMETSAT Land Surface Analysis Satellite Applications Facility. The exceptional fire danger over southern Portugal that favors the ignition of the Monchique fire and its severity was essential driven by two important factors: (i) the anomalous fire weather danger, before and during the event; (ii) the accumulated fuel amount, since the last severe event occurred in 2003, over the region. Results show that the selected fire probabilistic products described the meteorological fire danger observed well, and the LSA-SAF products revealed the huge amount of fire energy emitted, in line with the difficulties faced by authorities to suppress the Monchique fire.

A new East African satellite data validation station: Performance of the LSA-SAF all-weather land surface temperature product over a savannah biome

We describe a new satellite data validation facility located in a savannah biome at the International Livestock Research Institute (ILRI) Kapiti Research Station (Kenya). The facility is focused on satellite land surface temperature (LST) and is equipped with multiple ground-viewing infrared radiometers across four sites. The in-situ LST observations are upscaled to match satellite LST products using a geometric illumination model. The in-situ sensor network represents a step-forward in LST validation in East Africa and savannah biomes. To our knowledge this is the first time that such an extensive network of LST radiometers and supporting measurements has been installed in sub-Saharan Africa, or a savannah. With this network we capture surface heterogeneity in a manner that has not previously been possible. The LST ground data from this station collected between October 2018 and March 2019 is used to evaluate the new Land Surface Analysis Satellite Application Facility (LSA-SAF) all-sky LST product (MLST-AS) that blends clear-sky infrared-retrieved LSTs with LSTs derived from a land surface energy balance model to fill gaps due to cloudy conditions. Comparison against the in-situ LSTs indicates overall accuracy, precision, and root-mean-square error (RMSE) of MLST-AS to be 2.02 K, 1.38 K and 3.64 K respectively. The infrared-retrieved LST component of MLST-AS under clear skies has an accuracy, precision and RMSE of 1.16 K, 0.8 K and 3.16 K respectively. The energy balance model-based component of MLST-AS has performance statistics of 3.02 K, 1.38 K and 4.16 K. The MLST-AS energy balance model component is observed to perform worse when surface moisture is present, underestimating night-time and daily maximum temperatures by between 2 and 4 K in the 24 h following surface water deposition as precipitation or dew.

Upgrading Land-Cover and Vegetation Seasonality in the ECMWF Coupled System: Verification With FLUXNET Sites, METEOSAT Satellite Land Surface Temperatures, and ERA5 Atmospheric Reanalysis.

In this study, we show that limitations in the representation of land cover and vegetation seasonality in the European Centre for Medium‐Range Weather Forecasting (ECMWF) model are partially responsible for large biases (up to ∼10°C, either positive or negative depending on the region) on the simulated daily maximum land surface temperature (LST) with respect to satellite Earth Observations (EOs) products from the Land Surface Analysis Satellite Application Facility. The error patterns were coherent in offline land‐surface and coupled land‐atmosphere simulations, and in ECMWF's latest generation reanalysis (ERA5). Subsequently, we updated the ECMWF model's land cover characterization leveraging on state‐of‐the‐art EOs—the European Space Agency Climate Change Initiative land cover data set and the Copernicus Global Land Services leaf area index. Additionally, we tested a clumping parameterization, introducing seasonality to the effective low vegetation coverage. The updates reduced the overall daily maximum LST bias and unbiased root‐mean‐squared errors. In contrast, the implemented updates had a neutral impact on daily minimum LST. Our results also highlighted the complex regional heterogeneities in the atmospheric sensitivity to land cover and vegetation changes, particularly with issues emerging over eastern Brazil and northeastern Asia. These issues called for a re‐calibration of model parameters (e.g., minimum stomatal resistance, roughness length, rooting depth), along with a revision of several model assumptions (e.g., snow shading by high vegetation).

Statistical Gap-Filling of SEVIRI Land Surface Temperature

A reliable and practically useable method for gap filling in hourly Spinning Enhanced Visible and Infrared Imager (SEVIRI LST) data using ERA5 Land Skin Temperature (ERA5ST) co-variate and additional easily accessible data (elevation, time, solar radiation info) is proposed. The suggested approach provides estimates to all weather conditions and it is based on a probabilistic model via modern regression models. We have tested two classes of regression models of different complexity and flexibility, namely multiple linear regression (MLR), and generalized additive model (GAM). This analysis uses as main input the hourly LST data set over Romania, through 2016 and 2017, extracted from MSG-SEVIRI images, which is an operational product of the Land Surface Analysis–Satellite Application Facility (LSA-SAF). The comparison between the estimated LST and the original LST values shows that GAM model, that takes into account the distance between missing LST locations and the nearest non-missing locations (GAM2), provides the best results, hence this was used to fill the gaps from the analyzed remote sensing product. Considering the fact that the best covariate (ERA5ST) has global coverage and it is available at high spatial resolution and temporal resolution, the proposed approach could be also used to perform the gap-filling of other existing LST remote sensing products.

Scaling Point‐Scale (Pedo)transfer Functions to Seamless Large‐Domain Parameter Estimates for High‐Resolution Distributed Hydrologic Modeling: An Example for the Rhine River

AbstractMoving toward high‐resolution gridded hydrologic models asks for novel parametrization approaches. A high‐resolution conceptual hydrologic model (wflow_sbm) was parameterized for the Rhine basin in Europe based on point‐scale (pedo)transfer functions, without further calibration of effective model parameters on discharge. Parameters were estimated on the data resolution, followed by upscaling of parameter fields to the model resolution. The method was tested using a 6‐hourly time step at four model resolutions (1.2, 2.4, 3.6, and 4.8 km), followed by a validation with discharge observations and a comparison with actual evapotranspiration (ETact) estimates from an independent model (DMET Land Surface Analysis Satellite Application Facility). Additionally, the scalability of parameter fields and simulated fluxes was tested. Validation of simulated discharges yielded Kling‐Gupta Efficiency (KGE) values ranging from 0.6 to 0.9, except for the Alps where a volume bias caused lower performance. Catchment‐averaged temporal ETact dynamics were comparable with independent ET estimates (KGE ≈ 0.7), although wflow_sbm model simulations were on average 115 mm yr−1 higher. Spatially, the two models were less in agreement (SPAEF = 0.10), especially around the Rhine valley. Consistent parameter fields were obtained, and by running the model at the different resolutions, preserved ETact fluxes were found across the scales. For recharge, fluxes were less consistent with relative errors around 30% for regions with high drainage densities. However, catchment‐averaged fluxes were better preserved. Routed discharge in headwaters was not consistent across scales, although simulations for the main Rhine River were. Better processing (scale independent) of the river and drainage network may overcome this issue.

Spatial-Temporal Variability of Land Surface Dry Anomalies in Climatic Aspect: Biogeophysical Insight by Meteosat Observations and SVAT Modeling

The spatial-temporal variability of drought occurrence over Bulgaria is characterized based on long-term records (2007–2018) of Meteosat information and the SVAT model-derived soil moisture availability index (referred to root zone depth, SMAI). Land surface temperature according to the satellite-derived Land Surface Analysis Satellite Application Facility Land Surface Temperature (LSASAF LST) product and SMAI were used to designate land surface state dry anomalies. The utility of LST for drought assessment is tested by statistical comparative analyses, applying two approaches, site-scale quantitative comparison, and evaluation of spatial-temporal consistency between SMAI and LST variability. Pearson correlation and regression modeling techniques were applied. The main results indicate for a synchronized behavior between SMAI and LST during dry spells, as follows: opposite mean seasonal course (March–October); high to strong negative monthly correlation for different microclimate regimes. Negative linear regressions between the anomalies of SMAI and LST (monthly mean), with a strong correlation in their spatial-temporal variability. Qualitative evaluation of spatial-temporal variability dynamics is analyzed using color maps. Drought-prone areas were identified on the bases of LST maps (monthly mean), and it is illustrated they are more vulnerable to vegetation burning as detected by the Meteosat FRP-PIXEL product. The current study provides an advanced framework for using LST retrievals based on IR satellite observations from the geostationary MSG satellite as an alternative tool to SMAI, whose calculation requires the input of many parameters that are not always available.

Fire Activity and Fuel Consumption Dynamics in Sub-Saharan Africa

African landscape fires are widespread, recurrent and temporally dynamic. They burn large areas of the continent, modifying land surface properties and significantly affect the atmosphere. Satellite Earth Observation (EO) data play a pivotal role in capturing the spatial and temporal variability of African biomass burning, and provide the key data required to develop fire emissions inventories. Active fire observations of fire radiative power (FRP, MW) have been shown to be linearly related to rates of biomass combustion (kg s−1). The Meteosat FRP-PIXEL product, delivered in near real-time by the EUMETSAT Land Surface Analysis Satellite Applications Facility (LSA SAF), maps FRP at 3 km resolution and 15-min intervals and these data extend back to 2004. Here we use this information to assess spatio-temporal variations in fire activity across sub-Saharan Africa, and identify an overall trend of decreasing annual fire activity and fuel consumption, agreeing with the widely-used Global Fire Emissions Database (GFEDv4) based on burned area measures. We provide the first comprehensive assessment of relationships between per-fire FRE-derived fuel consumption (Tg dry matter, DM) and temporally integrated Moderate Resolution Imaging Spectroradiometer (MODIS) net photosynthesis (PSN) (Tg, which can be converted into pre-fire fuel load estimates). We find very strong linear relationships over southern hemisphere Africa (mean r = 0.96) that are partly biome dependent, though the FRE-derived fuel consumptions are far lower than those derived from the accumulated PSN, with mean fuel consumptions per unit area calculated as 0.14 kg DM m−2. In the northern hemisphere, FRE-derived fuel consumption is also far lower and characterized by a weaker linear relationship (mean r = 0.76). Differences in the parameterization of the biome look up table (BLUT) used by the MOD17 product over Northern Africa may be responsible but further research is required to reconcile these differences. The strong relationship between fire FRE and pre-fire fuel load in southern hemisphere Africa is encouraging and highlights the value of geostationary FRP retrievals in providing a metric that relates very well to fuel consumption and fire emission variations. The fact that the estimated fuel consumed is only a small fraction of the fuel available suggests underestimation of FRE by Spinning Enhanced Visible and Infrared Imager (SEVIRI) and/or that the FRE-to-fuel consumption conversion factor of 0.37 MJ kg−1 needs to be adjusted for application to SEVIRI. Future geostationary imaging sensors, such as on the forthcoming Meteosat Third Generation (MTG), will reduce the impact of this underestimation through its ability to detect even smaller and shorter-lived fires than can the current second generation Meteosat.

A solar irradiation GIS as decision support tool for the Province of Salta, Argentina

The province of Salta is characterized by its solar energy high potential. The use of solar resource would improve living conditions in the area, diversify the energy matrix, promote more sustainable production systems and reduce greenhouse gases emissions. However, there are only a few studies that describe in high spatial resolution the variability of the solar resource in Argentina. Multidimensional tools, that consider the environment and the socio-economic situation, have to be considered for adequate support decision-making, such as solar collector location assessment and photovoltaic potential. In this sense, a deep evaluation of the solar resource is needed first, as solar irradiation is an essential input variable for the design and evaluation of solar application systems. In this paper, we detail the methodology used to elaborate a GIS tool to support decisions related to renewable energy policies and solar technology design. A comparison between global solar irradiation measurements in situ, empirical models, and data provided by Land Surface Analysis Satellite Applications Facility (LSA-SAF), is performed in daily, monthly and annual basis for a seven-year period. This analysis validates the use of this satellite data for the determination of solar irradiation in the region.

Electricity self-sufficiency of single-family houses in Germany and the Czech Republic

Motivated by a research project that studies the future of the energy system in rural areas at the border between Germany and the Czech Republic, and by the publication of the COSMO-REA high-resolution regional reanalysis data sets for Europe in 2017, this study presents a methodology for generating maps indicating minimum battery and photovoltaics sizes for self-sufficient single-family houses. The methodology consists of three subsequent parts: First, spatiotemporal data sets of electricity demand for single-family houses in rural and low-density urban areas are generated. Second, spatiotemporal data sets of photovoltaics potential are computed based on (a) a technical photovoltaics model, (b) two decades of hourly solar irradiance and temperature data, and (c) snow cover data from the Land Surface Analysis Satellite Applications Facility. Third, a linear optimization model serves to define photovoltaics and battery systems sizes and to generate the corresponding maps. The resulting maps cover Germany and the Czech Republic and are generated for 18 technical and weather-dependent scenarios. The results show how challenging it could be to achieve complete independence from the grid in certain locations. Especially relevant for the sizing of the systems are long periods (several days in a row) of low photovoltaic energy generation due to overcast sky conditions or snow cover of the panels. Furthermore, the results offer a scientifically based source of information for sizing photovoltaics and battery systems in the two countries.

Assessing reference evapotranspiration at regional scale based on remote sensing, weather forecast and GIS tools

Reference evapotranspiration (ETo) is a key component in efficient water management, especially in arid and semi-arid environments. However, accurate ETo assessment at the regional scale is complicated by the limited number of weather stations and the strict requirements in terms of their location and surrounding physical conditions for the collection of valid weather data. In an attempt to overcome this limitation, new approaches based on the use of remote sensing techniques and weather forecast tools have been proposed.Use of the Land Surface Analysis Satellite Application Facility (LSA SAF) tool and Geographic Information Systems (GIS) have allowed the design and development of innovative approaches for ETo assessment, which are especially useful for areas lacking available weather data from weather stations. Thus, by identifying the best-performing interpolation approaches (such as the Thin Plate Splines, TPS) and by developing new approaches (such as the use of data from the most similar weather station, TS, or spatially distributed correction factors, CITS), errors as low as 1.1% were achieved for ETo assessment. Spatial and temporal analyses reveal that the generated errors were smaller during spring and summer as well as in homogenous topographic areas.The proposed approaches not only enabled accurate calculations of seasonal and daily ETo values, but also contributed to the development of a useful methodology for evaluating the optimum number of weather stations to be integrated into a weather station network and the appropriateness of their locations. In addition to ETo, other variables included in weather forecast datasets (such as temperature or rainfall) could be evaluated using the same innovative methodology proposed in this study.

Comparison of satellite imagery based data, reanalysis data and statistical methods for mapping global solar radiation in the Lerma Valley (Salta, Argentina)

Global solar radiation is the most important factor to determine the energy generation potential of photovoltaic (PV) plants. In Argentina only a small number of ground measured data of global solar radiation exists. Researchers and PV developers have to use statistical methods or satellite imagery to handle this lack of information and provide an estimation of global solar radiation for a particular location. In this paper global solar radiation data sources for the Lerma valley are explored in order to assess which data set is best suitable for future studies of photovoltaic energy generation potential at the best possible spatial and temporal resolution. We evaluate global solar radiation from the reanalysis data set provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) and the Meteosat second generation derived data set provided by Land Surface Analysis Satellite Applications Facility (LSA-SAF). These are compared with each other, with data generated using statistical methods and with data from a pyranometer. The results show that the data from LSA-SAF fit best to the ground measurements. This together with a high spatial and temporal resolution makes the LSA-SAF data a satisfactory alternative to compensate the lack of global solar radiation data available for the study area.

Long Term Validation of Land Surface Temperature Retrieved from MSG/SEVIRI with Continuous in-Situ Measurements in Africa

Since 2005, the Land Surface Analysis Satellite Application Facility (LSA SAF) operationally retrieves Land Surface Temperature (LST) for the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board Meteosat Second Generation (MSG). The high temporal resolution of the Meteosat satellites and their long term availability since 1977 make their data highly valuable for climate studies. In order to ensure that the LSA SAF LST product continuously meets its target accuracy of 2 °C, it is validated with in-situ measurements from four dedicated LST validation stations. Three stations are located in highly homogenous areas in Africa (semiarid bush, desert, and Kalahari semi-desert) and typically provide thousands of monthly match-ups with LSA SAF LST, which are used to perform seasonally resolved validations. An uncertainty analysis performed for desert station Gobabeb yielded an estimate of total in-situ LST uncertainty of 0.8 ± 0.12 °C. Ignoring rainy seasons, the results for the period 2009–2014 show that LSA SAF LST consistently meets its target accuracy: the highest mean root-mean-square error (RMSE) for LSA SAF LST over the African stations was 1.6 °C while mean absolute bias was 0.1 °C. Nighttime and daytime biases were up to 0.7 °C but had opposite signs: when evaluated together, these partially compensated each other.

LSA SAF Meteosat FRP products – Part 1: Algorithms, product contents, and analysis

Abstract. Characterizing changes in landscape fire activity at better than hourly temporal resolution is achievable using thermal observations of actively burning fires made from geostationary Earth Observation (EO) satellites. Over the last decade or more, a series of research and/or operational "active fire" products have been developed from geostationary EO data, often with the aim of supporting biomass burning fuel consumption and trace gas and aerosol emission calculations. Such Fire Radiative Power (FRP) products are generated operationally from Meteosat by the Land Surface Analysis Satellite Applications Facility (LSA SAF) and are available freely every 15 min in both near-real-time and archived form. These products map the location of actively burning fires and characterize their rates of thermal radiative energy release (FRP), which is believed proportional to rates of biomass consumption and smoke emission. The FRP-PIXEL product contains the full spatio-temporal resolution FRP data set derivable from the SEVIRI (Spinning Enhanced Visible and Infrared Imager) imager onboard Meteosat at a 3 km spatial sampling distance (decreasing away from the west African sub-satellite point), whilst the FRP-GRID product is an hourly summary at 5° grid resolution that includes simple bias adjustments for meteorological cloud cover and regional underestimation of FRP caused primarily by underdetection of low FRP fires. Here we describe the enhanced geostationary Fire Thermal Anomaly (FTA) detection algorithm used to deliver these products and detail the methods used to generate the atmospherically corrected FRP and per-pixel uncertainty metrics. Using SEVIRI scene simulations and real SEVIRI data, including from a period of Meteosat-8 "special operations", we describe certain sensor and data pre-processing characteristics that influence SEVIRI's active fire detection and FRP measurement capability, and use these to specify parameters in the FTA algorithm and to make recommendations for the forthcoming Meteosat Third Generation operations in relation to active fire measures. We show that the current SEVIRI FTA algorithm is able to discriminate actively burning fires covering down to 10−4 of a pixel and that it appears more sensitive to fire than other algorithms used to generate many widely exploited active fire products. Finally, we briefly illustrate the information contained within the current Meteosat FRP-PIXEL and FRP-GRID products, providing example analyses for both individual fires and multi-year regional-scale fire activity; the companion paper (Roberts et al., 2015) provides a full product performance evaluation and a demonstration of product use within components of the Copernicus Atmosphere Monitoring Service (CAMS).

LSA SAF Meteosat FRP products – Part 2: Evaluation and demonstration for use in the Copernicus Atmosphere Monitoring Service (CAMS)

Abstract. Characterising the dynamics of landscape-scale wildfires at very high temporal resolutions is best achieved using observations from Earth Observation (EO) sensors mounted onboard geostationary satellites. As a result, a number of operational active fire products have been developed from the data of such sensors. An example of which are the Fire Radiative Power (FRP) products, the FRP-PIXEL and FRP-GRID products, generated by the Land Surface Analysis Satellite Applications Facility (LSA SAF) from imagery collected by the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) series of geostationary EO satellites. The processing chain developed to deliver these FRP products detects SEVIRI pixels containing actively burning fires and characterises their FRP output across four geographic regions covering Europe, part of South America and Northern and Southern Africa. The FRP-PIXEL product contains the highest spatial and temporal resolution FRP data set, whilst the FRP-GRID product contains a spatio-temporal summary that includes bias adjustments for cloud cover and the non-detection of low FRP fire pixels. Here we evaluate these two products against active fire data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) and compare the results to those for three alternative active fire products derived from SEVIRI imagery. The FRP-PIXEL product is shown to detect a substantially greater number of active fire pixels than do alternative SEVIRI-based products, and comparison to MODIS on a per-fire basis indicates a strong agreement and low bias in terms of FRP values. However, low FRP fire pixels remain undetected by SEVIRI, with errors of active fire pixel detection commission and omission compared to MODIS ranging between 9–13 % and 65–77 % respectively in Africa. Higher errors of omission result in greater underestimation of regional FRP totals relative to those derived from simultaneously collected MODIS data, ranging from 35 % over the Northern Africa region to 89 % over the European region. High errors of active fire omission and FRP underestimation are found over Europe and South America and result from SEVIRI's larger pixel area over these regions. An advantage of using FRP for characterising wildfire emissions is the ability to do so very frequently and in near-real time (NRT). To illustrate the potential of this approach, wildfire fuel consumption rates derived from the SEVIRI FRP-PIXEL product are used to characterise smoke emissions of the 2007 "mega-fire" event focused on Peloponnese (Greece) and used within the European Centre for Medium-Range Weather Forecasting (ECMWF) Integrated Forecasting System (IFS) as a demonstration of what can be achieved when using geostationary active fire data within the Copernicus Atmosphere Monitoring Service (CAMS). Qualitative comparison of the modelled smoke plumes with MODIS optical imagery illustrates that the model captures the temporal and spatial dynamics of the plume very well, and that high temporal resolution emissions estimates such as those available from a geostationary orbit are important for capturing the sub-daily variability in smoke plume parameters such as aerosol optical depth (AOD), which are increasingly less well resolved using daily or coarser temporal resolution emissions data sets. Quantitative comparison of modelled AOD with coincident MODIS and AERONET (Aerosol Robotic Network) AOD indicates that the former is overestimated by ~ 20–30 %, but captures the observed AOD dynamics with a high degree of fidelity. The case study highlights the potential of using geostationary FRP data to drive fire emissions estimates for use within atmospheric transport models such as those implemented in the Monitoring Atmospheric Composition and Climate (MACC) series of projects for the CAMS.

Annual evapotranspiration retrieved from satellite vegetation indices for the eastern Mediterranean at 250 m spatial resolution

Abstract. We present a model to retrieve actual evapotranspiration (ET) from satellites' vegetation indices (Parameterization of Vegetation Indices for ET estimation model, or PaVI-E) for the eastern Mediterranean (EM) at a spatial resolution of 250 m. The model is based on the empirical relationship between satellites' vegetation indices (normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) from MODIS) and total annual ET (ETAnnual) estimated at 16 FLUXNET sites, representing a wide range of plant functional types and ETAnnual. Empirical relationships were first examined separately for (a) annual vegetation systems (i.e. croplands and grasslands) and (b) systems with combined annual and perennial vegetation (i.e. woodlands, forests, savannah and shrublands). Vegetation indices explained most of the variance in ETAnnual in those systems (71 % for annuals, and 88 % for combined annual and perennial systems), while adding land surface temperature data in a multiple-variable regression and a modified version of the Temperature and Greenness model did not result in better correlations (p > 0.1). After establishing empirical relationships, PaVI-E was used to retrieve ETAnnual for the EM from 2000 to 2014. Models' estimates were highly correlated (R = 0.92, p < 0.01) with ETAnnual calculated from water catchment balances along rainfall gradient of the EM. They were also comparable to the coarser-resolution ET products of the Land Surface Analysis Satellite Applications Facility (LSA-SAF MSG ETa, 3.1 km) and MODIS (MOD16, 1 km) at 148 EM basins with R of 0.75 and 0.77 and relative biases of 5.2 and −5.2 %, respectively (p < 0.001 for both). In the absence of high-resolution (< 1 km) ET models for the EM the proposed model is expected to contribute to the hydrological study of this region, assisting in water resource management, which is one of the most valuable resources of this region.

On the Importance of Aerosol Composition for Estimating Incoming Solar Radiation: Focus on the Western African Stations of Dakar and Niamey during the Dry Season

The article investigates the impact of aerosol composition on the estimation of the downwelling surface shortwave flux (DSSF). This initiative forms part of the efforts to improve the DSSF distributed by the Land Surface Analysis Satellite Application Facility (LSA-SAF). This operational product assumes invariant aerosol properties under clear sky conditions, which can be inaccurate for some regions of the world. This is the case of West Africa, where aerosol activity is not only highly variable due to frequent dust storms but also rich because of the coexistence of different aerosol species. This study was carried out over the West African stations of Dakar and Niamey, which represent different aerosol scenarios. Several dates during the dry season of 2006 were selected and classified into four different day types according to aerosol activity: standard, clean, mixture and dusty days. The diurnal evolution of DSSF and its direct and diffuse components were estimated for the selected dates by the current LSA-SAF algorithm and two other approaches using aerosol measurements from the Aerosol Robotic Network (AERONET) as input. The first alternative approach took the diurnal evolution of the total aerosol optical depth (AOD) into account, assuming a default desert aerosol type. Experiments with this method showed a significant improvement in the estimated DSSF compared to the current LSA-SAF algorithm. For example, root mean square error (RMSE) improved from 170 W/m2 to 50 W/m2 for dusty days in Dakar and from 73 W/m2 to 21 W/m2 for mixture days in Niamey. This improvement resulted from the consideration of a time-varying AOD, which accounted for the rapidly changing aerosol load for these two day types. The second alternative approach tested included consideration of the diurnal variation of the aerosol load and composition. Again, this was done using AERONET data on the fine and coarse aerosol modes, which may be associated with different sized dust particles, sea salt, or soot from biomass burning (depending on the date). This enhanced consideration of the aerosol composition greatly improved the estimation of the diffuse component of the DSSF, further reducing the RMSE during mixture days from 50 W/m2 to less than 10 W/m2. This improvement mainly came from consideration of the right scattering properties of the aerosol particles, which may be significantly different for each aerosol type.

Using SEVIRI fire observations to drive smoke plumes in the CMAQ air quality model: a case study over Antalya in 2008

Abstract. Among the atmospheric emission sources, wildfires are episodic events characterized by large spatial and temporal variability. Therefore, accurate information on gaseous and aerosol emissions from fires for specific regions and seasons is critical for air quality forecasts. The Spinning Enhanced Visible and Infrared Imager (SEVIRI) in geostationary orbit provides fire observations over Africa and the Mediterranean with a temporal resolution of 15 min. It thus resolves the complete fire life cycle and captures the fires' peak intensities, which is not possible in Moderate Resolution Imaging Spectroradiometer (MODIS) fire emission inventories like the Global Fire Assimilation System (GFAS). We evaluate two different operational fire radiative power (FRP) products derived from SEVIRI, by studying a large forest fire in Antalya, Turkey, in July–August 2008. The EUMETSAT Land Surface Analysis Satellite Applications Facility (LSA SAF) has higher FRP values during the fire episode than the Wildfire Automated Biomass Burning Algorithm (WF_ABBA). It is also in better agreement with the co-located, gridded MODIS FRP. Both products miss small fires that frequently occur in the region and are detected by MODIS. Emissions are derived from the FRP products. They are used along-side GFAS emissions in smoke plume simulations with the Weather Research and Forecasting (WRF) model and the Community Multiscale Air Quality (CMAQ) model. In comparisons with MODIS aerosol optical thickness (AOT) and Infrared Atmospheric Sounding Interferometer (IASI), CO and NH3 observations show that including the diurnal variability of fire emissions improves the spatial distribution and peak concentrations of the simulated smoke plumes associated with this large fire. They also show a large discrepancy between the currently available operational FRP products, with the LSA SAF being the most appropriate.

Uncertainty in estimating reference evapotranspiration using remotely sensed and forecasted weather data under the climatic conditions of Southern Spain

ABSTRACTIn this study, a detailed spatial analysis of the reference evapotranspiration (ETo) estimates provided by the MAK‐Adv approach and the well‐known Penman‐Monteith equation (PM‐FAO56) has been carried out. MAK‐Adv approach considers a regionally calibrated Makkink equation based on a combination of remotely sensed solar radiation, Rs, provided by the Land Surface Analysis Satellite Applications Facility (LSA SAF), and numerical weather forecasts of near‐surface air temperature, T2m, provided by the European Centre for Medium‐range Weather Forecasts (ECMWF). This analysis evaluated the performance of these approaches with data measured by 57 weather stations under different weather conditions within a semi‐arid region, Andalusia, located at Southern Spain, and determined the role of certain physical and climate variables, such as wind speed and sensible heat advection, on the quality of the ETo estimation.For three years (2007–2009), daily Rs and T2m provided by LSA SAF and ECMWF were compared with measured data showing very good estimates, with RMSD equal to 1.47 W m−2 and 1.53 °C, respectively. Equally, ETo using MAK‐Adv approach with data provided by LSA SAF and ECMWF was compared with ETo provided by the PM‐FAO56 approach with measured data, obtaining RMSD = 0.69 mm, linear regression slope = 0.94, and R2 = 0.92. Although ETo estimates using both approaches were similar between them, the small differences in terms of estimation ETo were mainly correlated with the wind speed (u) and the advection index (Ia), depicting the critical role of these factors in the ETo estimations under semi‐arid conditions, and increasing the uncertainty in ETo estimation when wind speed and/or Ia are high.The comparison of MAK‐Adv and PM‐FAO56 approaches has proved the usefulness in providing ETo values at a regional scale and detecting those areas where the ETo estimation had higher uncertainty, contributing to an improvement in water management.

Integrated fusion of multi-scale polar-orbiting and geostationary satellite observations for the mapping of high spatial and temporal resolution land surface temperature

Land surface temperature (LST) and its diurnal variation are important when evaluating climate change, the land–atmosphere energy budget, and the global hydrological cycle. However, the available satellite LST products have either a coarse spatial resolution or a low temporal resolution, which constrains their potential applications. This paper proposes a spatio-temporal integrated temperature fusion model (STITFM) for the retrieval of high temporal and spatial resolution LST from multi-scale polar-orbiting and geostationary satellite observations. Compared with the traditional fusion methods for LST with two different sensors, the proposed method is able to fuse the LST from arbitrary sensors in a unified framework. The model was tested using LST with fine, moderate, and coarse-resolutions. Data from the Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) sensors, the Moderate Resolution Imaging Spectroradiometer (MODIS), the Geostationary Operational Environmental Satellite (GOES) Imager, and the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) were used. The fused LST values were evaluated with in situ LST obtained from the Surface Radiation Budget Network (SURFRAD) and the Land Surface Analysis Satellite Application Facility (LSA SAF) project. The final validation results indicate that the STITFM is accurate to within about 2.5K.