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

Challenges and Opportunities for Ecosystem-Based Management and Marine Spatial Planning in the Irish Sea

Ecosystem-Based Management (EBM) integrates the connections between land, air, water and all living things including human beings and their institutions. The location of the Irish Sea, between major historical industrial centres, its history of use and exploitation, combined with its hydrographic characteristics, have led to the current patterns of use. EBM efforts have been ongoing for over a decade but political boundaries have led to fragmented governance. The forthcoming UK exit from the European Union (EU) may pose further challenges. This chapter examines articulations between political boundaries, spatial scales of Marine Spatial Planning and nested social-ecological systems including the gyre in the western Irish Sea, and Dublin Bay. Examples of emerging best practices are provided and the challenges of data availability for ecosystem services are considered

Fernerkundungen von Wolken über dem Ozean bei Nacht mit dem AVHRR DES NOAA - 7

Mit den drei IR - Kanälen des AVHRR an Bord des NOAA-7 Satelliten werden Wolken bei Nacht über dem Ozean entdeckt und klassifiziert. Im Gegensatz zu früheren automatischen Wolkenerkennungs- und Klassifizierungsverfahren, die im übrigen nur bei Tag anwendbar sind, wird hier auf die räumliche Information ganz verzichtet und nur die spektrale Information aus den drei Kanälen genutzt. So ist es möglich, die Bildelemente nacheinander und in voller räumlicher Auflösung mit einem Algorithmus zu behandeln. Die Entwicklung des zugehörigen Algorithmus' gelingt am besten mit der Modellierung der Strahlungsübertragung in Wolken fUür die NOAA-7 Kanäle. Dazu wird die Strahlungsübertragung in Wolken in neuer Art und Weise bearbeitet. Frühere Modellierungen hatten nämlich fast immer integrale Parameter zum Ziel, wie zum Beispiel den langwelligen Strahlungshaushalt. Hier nun wird in hoher spektraler Auflösung sowohl am Rande, als auch in der Mitte des terrestrischen IR die Strahlungsübertragung betrachtet. Dabei stellt sich heraus, daß das Verhältnis von Streuung zu Extinktion der beherrschende Parameter ist. Etwas geringer ist der Einfluß der spektralen optischen Dicke. Die unterschiedliche Anisotropie der Streuung einzelner Wolkentypen spielt keine große Rolle. Geeignete Größen zur Klassifizierung von Wolken sind die Strahlungstemperatur in Kanal 5 (Wellenlänge 12.0µm) und die Differenz der Strahlungstemperaturen von Kanal 3 (Wellenlänge 3.7 µm) und Kanal 4 (Wellenlänge 10.8 µm). Die Strahlungstemperatur in Kanal 5 ist der wahren Temperatur in der extingierenden Schicht von allen 3 Temperaturen am nächsten, während die Differenz von den spektralen Unterschieden der Wolken gesteuert wird. Das Schema der Klassifizierung, gewonnen aus 139 Modellwolken, für den Spezialfall der Standardatmosphäre in 45° Nord wird auf aktuelle Satellitendaten angewendet

An All-Weather Land Surface Temperature Product Based on MSG/SEVIRI Observations

A new all-weather land surface temperature (LST) product derived at the Satellite Application Facility on Land Surface Analysis (LSA-SAF) is presented. It is the first all-weather LST product based on visible and infrared observations combining clear-sky LST retrieved from the Spinning Enhanced Visible and Infrared Imager on Meteosat Second Generation (MSG/SEVIRI) infrared (IR) measurements with LST estimated with a land surface energy balance (EB) model to fill gaps caused by clouds. The EB model solves the surface energy balance mostly using products derived at LSA-SAF. The new product is compared with in situ observations made at 3 dedicated validation stations, and with a microwave (MW)-based LST product derived from Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) measurements. The validation against in-situ LST indicates an accuracy of the new product between -0.8 K and 1.1 K and a precision between 1.0 K and 1.4 K, generally showing a better performance than the MW product. The EB model shows some limitations concerning the representation of the LST diurnal cycle. Comparisons with MW LST generally show higher LST of the new product over desert areas, and lower LST over tropical regions. Several other imagers provide suitable measurements for implementing the proposed methodology, which oers the potential to obtain a global, nearly gap-free LST product