EUREC⁴A's <i>HALO</i>

As part of the EUREC4A (Elucidating the role of cloud–circulation coupling in climate) field campaign, the German research aircraft HALO (High Altitude and Long Range Research Aircraft), configured as a cloud observatory, conducted 15 research flights in the trade-wind region east of Barbados in January and February 2020. Narrative text, aircraft state data, and metadata describing HALO's operation during the campaign are provided. Each HALO research flight is segmented by timestamp intervals into standard elements to aid the consistent analysis of the flight data. Photographs from HALO's cabin and animated satellite images synchronized with flight tracks are provided to visually document flight conditions. As a comprehensive product from the remote sensing observations, a multi-sensor cloud mask product is derived and quantifies the incidence of clouds observed during the flights. In addition, to lower the threshold for new users of HALO's data, a collection of use cases is compiled into an online book, How to EUREC4A, included as an asset with this paper. This online book provides easy access to most of EUREC4A's HALO data through an intake catalogue. Code and data are freely available at the locations specified in Table 6.

VELOX – A new thermal infrared imager for airborne remote sensing of cloud and surface properties

The new airborne thermal infrared (TIR) imager VELOX (Video airbornE Longwave Observations within siX channels) is introduced. The commercial camera system of VELOX covers six spectral bands with center wavelengths between 7.7 µm and 12 µm. VELOX is currently applied on board the German High Altitude and Long Range Research Aircraft (HALO). It observes two-dimensional fields of upward terrestrial spectral radiance with a horizontal spatial resolution of approximately 10 m by 10 m at a target distance of 10 km. Atmospheric temperature values are rather low compared to the original application of the TIR imager system and range close to the detection limit of the sensor. This challenge requires additional calibration efforts to reduce the measurement uncertainties of VELOX. These calibration and correction procedures, including radiometric calibrations, non-uniform corrections, bad-pixel replacements, and window corrections for data collected by VELOX, are presented. First measurements acquired by VELOX during the EUREC4A (ElUcidating the RolE of Cloud-Circulation Coupling in ClimAte) campaign are presented, including an analyses of the cloud top brightness temperature, cloud mask/fraction, and cloud top altitude data. They reveal that the cloud top temperature can be resolved with a resolution of better than 0.1 K, which translates into a resolution of approximately 40 m with respect to cloud top altitude.

KappaMask: AI-Based Cloudmask Processor for Sentinel-2

The Copernicus Sentinel-2 mission operated by the European Space Agency (ESA) provides comprehensive and continuous multi-spectral observations of all the Earth’s land surface since mid-2015. Clouds and cloud shadows significantly decrease the usability of optical satellite data, especially in agricultural applications; therefore, an accurate and reliable cloud mask is mandatory for effective EO optical data exploitation. During the last few years, image segmentation techniques have developed rapidly with the exploitation of neural network capabilities. With this perspective, the KappaMask processor using U-Net architecture was developed with the ability to generate a classification mask over northern latitudes into the following classes: clear, cloud shadow, semi-transparent cloud (thin clouds), cloud and invalid. For training, a Sentinel-2 dataset covering the Northern European terrestrial area was labelled. KappaMask provides a 10 m classification mask for Sentinel-2 Level-2A (L2A) and Level-1C (L1C) products. The total dice coefficient on the test dataset, which was not seen by the model at any stage, was 80% for KappaMask L2A and 76% for KappaMask L1C for clear, cloud shadow, semi-transparent and cloud classes. A comparison with rule-based cloud mask methods was then performed on the same test dataset, where Sen2Cor reached 59% dice coefficient for clear, cloud shadow, semi-transparent and cloud classes, Fmask reached 61% for clear, cloud shadow and cloud classes and Maja reached 51% for clear and cloud classes. The closest machine learning open-source cloud classification mask, S2cloudless, had a 63% dice coefficient providing only cloud and clear classes, while KappaMask L2A, with a more complex classification schema, outperformed S2cloudless by 17%.

Global Land High-Resolution Cloud Climatology Based on an Improved MOD09 Cloud Mask

Clouds play an important role in the energy and moisture cycle of the earth–atmosphere system, which affects many important processes in nature and human societies. However, there are very few fine-grained and high-precision global cloud climatology data available for high-resolution models. In this paper, we produced a fine-grained (1 km resolution) global land cloud climatology (GLHCC) report based on MOD09 cloud masks from 2001 to 2016, with a temporal resolution of 10 days. The two improvements (short-wave infrared and Band 2/6 ratio threshold method) on the original MOD09 cloud mask have reduced the snow, ice, and bright areas mistakenly classified as clouds. The preliminary cloud products undergo the removal of orbital artifacts by Variational Stationary Noise Remover (VSNR) and the removal of abnormal albedo areas to generate the final cloud climatology data. The new product was directly validated by ground-based cloud observations collected from 3777 global weather stations. PATMOS-X from the Advanced Very High Resolution Radiometer (AVHRR) and MOD/MYD35 served as comparison products for consistency check of GLHCC. The assessment results show that GLHCC demonstrated a strong correlation with ground station observations, MOD/MYD35, and PATMOS-X. When the ground observations were taken as the truth value, GLHCC and MOD/MYD35 displayed higher accuracy than PATMOS-X. In most selected interested areas where the three behave differently, GLHCC matched the facts better than MOD/MYD35 and PATMOS-X. The GLHCC can well represent the cloud distribution over the past 16 years and will play an important role in the fine-grained demands of many aspects of nature and human society.

Forecasting the regional fire radiative power for regularly ignited vegetation fires

This paper presents a phenomenological framework for forecasting the area-integrated fire radiative power from wildfires. In the method, a region of interest is covered with a regular grid, which cells are uniquely and independently parameterized with regard to the fire intensity according to (i) the fire incidence history, (ii) the retrospective meteorological information, and (iii) remotely-sensed high temporal resolution fire radiative power taken together with (iv) consistent cloud mask data. The parameterization is realized by fitting the predetermined functions for diurnal and annual profiles of fire radiative power to the remote-sensing observations. After the parametrization, the input for the fire radiative power forecast is the meteorological data alone, i.e., the weather forecast. The method is tested retrospectively for south-central African savannah areas with grid cell size of 1.5° × 1.5°. The input data included ECMWF ERA5 meteorological reanalysis and SEVIRI/MSG Fire Radiative Power and Cloud Mask. It has been found that in the areas with large numbers of wildfires regularly ignited on a daily basis during dry seasons from year to year, the temporal fire radiative power evolution is quite predictable, whereas the areas with irregular fire behaviour predictability was low. The predictive power of the method is demonstrated by comparing the predicted fire radiative power patterns and fire radiative energy values against the corresponding remote-sensing observations. The current method showed good skills for the considered African regions and was useful in understanding the challenges in predicting the wildfires in a more general case.

Assessment of the Reprocessed Suomi NPP VIIRS Enterprise Cloud Mask Product

The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite continually provides global observations used to retrieve over 20 VIIRS Environmental Data Record (EDR) products. Among them, the cloud mask product is essential for many other VIIRS EDR products such as aerosols, ocean color, and active fire. The reprocessed S-NPP VIIRS Sensor Data Record (SDR) data produced by NOAA/Center for Satellite Applications and Research (STAR) have shown improved stability and consistency. Recently, the VIIRS Enterprise Cloud Mask (ECM) has been reprocessed using the reprocessed VIIRS SDR data. This study assesses the reprocessed ECM product by comparing the reprocessed cloud mask types and cloud probability with those from the operational VIIRS ECM product. It found that the overall differences are small. Most of the discrepancies occur between neighboring types at the cloud edge. These findings help lay the foundation for the user community to understand the reprocessed ECM product. In addition, due to the better quality of the reprocessed VIIRS SDR data that are utilized to generate the reprocessed ECM product, it is expected that the reprocessed ECM product will have better stability and consistency compared to the operational ECM products. Therefore, the reprocessed ECM product is a useful benchmark for the user community.

SENTINEL-2 SURFACE REFLECTANCE PRODUCTS GENERATED BY CNES AND DLR: METHODS, VALIDATION AND APPLICATIONS

To allow for a robust and automatic exploitation of Sentinel-2 data, Analysis Ready Data (ARD) products are requested by most users. The processors of ARD products take care of the common burdens necessary for most applications, that include precise orthorectification, cloud detection and atmospheric correction steps, as well as the generation of periodic syntheses of cloud free surface reflectances. The French Theia land data center, and the German Earth Observation Center (EOC) started delivering Sentinel-2 surface reflectance products to users in 2016 in France and 2019 in Germany respectively. Both centers produce and distribute these data sets in near real time, over large regions requested by French users such as Western Europe, Maghreb, Sahel, Madagascar… Theia’s and EOC products include an instantaneous surface reflectance product (Level-2A), and a monthly cloud free synthesis of surface reflectance (Level-3A). This article shortly describes the methods used to generate the Level-2A products with the MAJA processor, and the Level-3A products with theWASP processor. The MAJA processor is based on multi-temporal methods, that use the slow variation of surface reflectance to detect clouds and estimate aerosol depth, while WASP, thanks to the quality of MAJA cloud mask, calculates a weighted average of all the cloud free observations over 45 days, every month. The article also provides validation results for Level-2A and Level-3A products, resulting from comparison with in-situ data and with other methods. A last section gives first insights from the monitoring of user uptake of the distributed products.

GERMANYS FIRST CLOUD-BASED WEB SERVICE FOR LAND MONITORING USING COPERNICUS SENTINEL-2 DATA

The research and development project named Landscape Change Detection Service (German abbreviation: LaVerDi) was initiated by the German Federal Agency for Cartography and Geodesy (BKG). Within the scope of the project a monitoring service for landscape changes was developed and implemented using free Copernicus satellite data for an automated derivation of potential land cover change. This change indication is meant to be used to update or continue BKG in-house products, such as the Digital Land Cover Model Germany (LBM-DE), in a comprehensive and uniform quality. The results can be further used for numerous applications or as change information for administration and planning, and for the compilation of spatial statistics. It satisfies the users' need for a national service for open data on land cover changes and thus represents the first automatic and verified national satellite product for land cover changes in Germany. As input data the service uses pre-processed Sentinel-2 data from the European Copernicus satellite program, as well as an image segmentation approach to extract change objects. Using an improved cloud mask algorithm, Sentinel-2 tiles with up to maximum cloud coverage of 60% can be used for analysis. The service (data processing, change detection, visualisation) runs on the German “Copernicus Data and Utilization Platform” (CODE-DE). As of December 2020, the INSPIRE-compliant LaVerDi web service is operational. The thematic accuracy of the generated change layers is above the given requirements (minimum of 80%), considering the 95% confidence interval for all relevant land cover classes in certain test areas. The transferability of the methodology has been successfully shown by a prototypic nationwide demonstrator in early 2020 and is therefore expected to reliably detect both long-term and seasonal changes.