Assessment of Heliosat-4 surface solar irradiance derived on the basis of SEVIRI-APOLLO cloud products

International audienceThe Heliosat-4 method developed by the MINES ParisTech and the German Aerospace Center (DLR), aims at estimating surface downwelling solar irradiance (SSI). It benefits from advanced products derived from recent Earth Observation missions, among which the cloud products are crucial for the assessment of SSI. The APOLLO cloud product provided by DLR includes abundant information about the cloud physical and optical properties. The performances of Heliosat-4 when using APOLLO product are evaluated for the period of 2004-2009. The estimated SSIs are compared to measurements made at six stations within the Baseline Surface Radiation Network. Extensive analysis of the discrepancies offers an in-depth view of the performance of Heliosat-4/APOLLO, an understanding of the advantages of this combination Heliosat-4/APOLLO when compared to existing methods and the identification of restrictions in both Heliosat-4 and the APOLLO product for future improvements

Use of OCA and APOLLO in Heliosat-4 method for the assessment of surface downwelling solar irradiance

International audienceWe test two cloud products: Optimal Cloud Analysis (OCA) of EUMETSAT, and APOLLO from the German Aerospace Center (DLR), for the assessment of surface downwelling solar irradiance (SSI). Each product is input to the Heliosat-4 method, and the SSI estimates are compared to accurate measurements performed in the Baseline Radiation Network (BSRN). The performances obtained by the two products are compared. The overall performance of Heliosat-4 method by using different cloud products is given and conclusions on the benefit of each product for an operational Heliosat-4 are drawn

Challenges of Harmonizing 40 Years of AVHRR Data: The TIMELINE Experience

Earth Observation satellite data allows for the monitoring of the surface of our planet at predefined intervals covering large areas. However, there is only one medium resolution sensor family in orbit that enables an observation time span of 40 and more years at a daily repeat interval. This is the AVHRR sensor family. If we want to investigate the long-term impacts of climate change on our environment, we can only do so based on data that remains available for several decades. If we then want to investigate processes with respect to climate change, we need very high temporal resolution enabling the generation of long-term time series and the derivation of related statistical parameters such as mean, variability, anomalies, and trends. The challenges to generating a well calibrated and harmonized 40-year-long time series based on AVHRR sensor data flown on 14 different platforms are enormous. However, only extremely thorough pre-processing and harmonization ensures that trends found in the data are real trends and not sensor-related (or other) artefacts. The generation of European-wide time series as a basis for the derivation of a multitude of parameters is therefore an extremely challenging task, the details of which are presented in this paper

Operational processing of AVHRR data at DFD

The German Remote Sensing Data Centre (DFD) of the German Aerospace Centre (DLR) has been receiving satellite data from the NOAA POES satellites in High Resolution Picture Transmission (HRPT) mode since November 1981 to serve the needs of the national and international user community. After reception of the AVHRR data a standardised preprocessing routine delivers calibrated and navigated data. These level 1 data are used for generating RGB - quicklooks overlaid with coastlines, and so-called media products showing wide parts of Central Europe in predefined annotated map projections. The level 1 data are also used for generating value-added products such as the “Normalised Difference Vegetation Index” of Europe, the sea surface temperature of European seas and the land surface temperature of Europe representing a day-time or night-time land surface temperature. For all value-added products different routines, such as automatic cloud masking using APOLLO software, precise geo-referencing, including land-sea mask and compositing, are necessary (level 2 data). The level 2 data are used for deriving daily, weekly and monthly level 3 products which are accessible by the internet using a user-friendly Web portal. More than 65,000 scenes (as of May 2003) have been received since 1981 at Oberpfaffenhofen covering the station's visibility. Depending on the actual track (eastern, central and western) being received, the scenes cover different areas, reaching from Spitzbergen in the north to the Northern Sahara in the south and from the Central Atlantic in the west to Central Asia in the east. This paper addresses aspects for daily operational processing, including the automatic supervision of the processing chain, for generating level 1 to 3 products , automatic failure identification and quality assurance. Future processing algorithms (automatic atmospheric correction, generation of cloud parameters and LAI time series), which are in a developmental stage, will also be discussed

APOLLO_NG – a probabilistic interpretation of the APOLLO legacy for AVHRR heritage channels

The cloud processing scheme APOLLO (AVHRR Processing scheme Over cLouds, Land and Ocean) has been in use for cloud detection and cloud property retrieval since the late 1980s. The physics of the APOLLO scheme still build the backbone of a range of cloud detection algorithms for AVHRR (Advanced Very High Resolution Radiometer) heritage instruments. The APOLLO_NG (APOLLO_NextGeneration) cloud processing scheme is a probabilistic interpretation of the original APOLLO method. It builds upon the physical principles that have served well in the original APOLLO scheme. Nevertheless, a couple of additional variables have been introduced in APOLLO_NG. Cloud detection is no longer performed as a binary yes/no decision based on these physical principles. It is rather expressed as cloud probability for each satellite pixel. Consequently, the outcome of the algorithm can be tuned from being sure to reliably identify clear pixels to conditions of reliably identifying definitely cloudy pixels, depending on the purpose. The probabilistic approach allows retrieving not only the cloud properties (optical depth, effective radius, cloud top temperature and cloud water path) but also their uncertainties. APOLLO_NG is designed as a standalone cloud retrieval method robust enough for operational near-realtime use and for application to large amounts of historical satellite data. The radiative transfer solution is approximated by the same two-stream approach which also had been used for the original APOLLO. This allows the algorithm to be applied to a wide range of sensors without the necessity of sensorspecific tuning. Moreover it allows for online calculation of the radiative transfer (i.e., within the retrieval algorithm) giving rise to a detailed probabilistic treatment of cloud variables. This study presents the algorithm for cloud detection and cloud property retrieval together with the physical principles from the APOLLO legacy it is based on. Furthermore a couple of example results from NOAA-18 are presented

Verification of Sectoral Cloud Motion Based Direct Normal Irradiance Nowcasting from Satellite Imagery

The successful integration of solar electricity from photovoltaics or concentrating solar power plants into the existing electricity supply requires an electricity production forecast for 48 hours, while any improved surface irradiance forecast over the next upcoming hours is relevant for an optimized operation of the power plant. While numerical weather prediction has been widely assessed and is in commercial use, the short-term nowcasting is still a major field of development. European Commission’s FP7 DNICast project is especially focusing on this task and this paper reports about parts of DNICast results. A nowcasting scheme based on Meteosat Second Generation cloud imagery and cloud movement tracking has been developed for Southern Spain as part of a solar production forecasting tool (CSP-FoSyS). It avoids the well-known, but not really satisfying standard cloud motion vector approach by using a sectoral approach and asking the question at which time any cloud structure will affect the power plant. It distinguishes between thin cirrus clouds and other clouds, which typically occur in different heights in the atmosphere and move in different directions. Also, their optical properties are very different - especially for the calculation of direct normal irradiances as required by concentrating solar power plants. Results for Southern Spain show a positive impact of up to 8 hours depending of the time of the day and a RMSD reduction of up to 10% in hourly DNI irradiation compared to day ahead forecasts. This paper presents the verification of this scheme at other locations in Europe and Northern Africa (BSRN and EnerMENA stations) with different cloud conditions. Especially for Jordan and Tunisia as the most relevant countries for CSP in this station list, we also find a positive impact of up to 8 hour

Identifying Changing Snow Cover Characteristics in Central Asia between 1986 and 2014 from Remote Sensing Data

Central Asia consists of the five former Soviet States Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan, therefore comprising an area of ~4 Mio km2. The continental climate is characterized by hot and dry summer months and cold winter seasons with most precipitation occurring as snowfall. Accordingly, freshwater supply is strongly depending on the amount of accumulated snow as well as the moment of its release after snowmelt. The aim of the presented study is to identify possible changes in snow cover characteristics, consisting of snow cover duration, onset and offset of snow cover season within the last 28 years. Relying on remotely sensed data originating from medium resolution imagers, these snow cover characteristics are extracted on a daily basis. The resolution of 500–1000 m allows for a subsequent analysis of changes on the scale of hydrological sub-catchments. Long-term changes are identified from this unique dataset, revealing an ongoing shift towards earlier snowmelt within the Central Asian Mountains. This shift can be observed in most upstream hydro catchments within Pamir and Tian Shan Mountains and it leads to a potential change of freshwater availability in the downstream regions, exerting additional pressure on the already tensed situation

Retrieval of PAR-estimates using remote sensing data and radiation transfer models

A new method is proposed for retrieving the photosynthetically active radiation (PAR) using remote sensing data (AVHRR) and a radiation transfer model (libRadtran). The approach applicable for clear and cloudy sky conditions takes into account atmospheric parameters affecting the radiation transfer through the atmosphere, namely, extinction by aerosols and clouds. Due to the high temporal variability of cloud distribution and cloud properties, a geostationary satellite as e.g. the future Meteosat Second Generation (MSG) with SEVIRI on board is proposed to derive inter-daily variation of cloud parameters. The upcoming SEVIRI sensor will deliver spectral information of clouds and atmosphere every 15 minutes. The spectral information content of SEVIRI is simulated by the existing polar orbiting sensor Advanced Very High Resolution Radiometer (AVHRR) on board of the NOAA satellites. Our first results are validated with ground truth data from the European Light Dosimeter Network (ELDONET). Up to 8 stations distributed over Europe are available for validation purposes. It is shown that for heterogeneous atmospheric conditions a good correlation exist between measured and estimated PAR values. Processing more AVHRR data over a longer time period and fine-adjustment of the algorithms combined with extended validation will consolidate our findings in the future