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Model Output Statistics for KUANTAN AIRP. (48657)

DWD’s fully automatic MOSMIX product optimizes and interprets the forecast calculations of the NWP models ICON (DWD) and IFS (ECMWF), combines these and calculates statistically optimized weather forecasts in terms of point forecasts (PFCs). Thus, statistically corrected, updated forecasts for the next ten days are calculated for about 5400 locations around the world. Most forecasting locations are spread over Germany and Europe. MOSMIX forecasts (PFCs) include nearly all common meteorological parameters measured by weather stations. For further information please refer to: [in German: https://www.dwd.de/DE/leistungen/met_verfahren_mosmix/met_verfahren_mosmix.html ] [in English: https://www.dwd.de/EN/ourservices/met_application_mosmix/met_application_mosmix.html ]

Model Output Statistics for KUALA LUMPUR-SUBANG (48647)

DWD’s fully automatic MOSMIX product optimizes and interprets the forecast calculations of the NWP models ICON (DWD) and IFS (ECMWF), combines these and calculates statistically optimized weather forecasts in terms of point forecasts (PFCs). Thus, statistically corrected, updated forecasts for the next ten days are calculated for about 5400 locations around the world. Most forecasting locations are spread over Germany and Europe. MOSMIX forecasts (PFCs) include nearly all common meteorological parameters measured by weather stations. For further information please refer to: [in German: https://www.dwd.de/DE/leistungen/met_verfahren_mosmix/met_verfahren_mosmix.html ] [in English: https://www.dwd.de/EN/ourservices/met_application_mosmix/met_application_mosmix.html ]

Model Output Statistics for BAYAN LEPAS / PINANG ISL. (48601)

DWD’s fully automatic MOSMIX product optimizes and interprets the forecast calculations of the NWP models ICON (DWD) and IFS (ECMWF), combines these and calculates statistically optimized weather forecasts in terms of point forecasts (PFCs). Thus, statistically corrected, updated forecasts for the next ten days are calculated for about 5400 locations around the world. Most forecasting locations are spread over Germany and Europe. MOSMIX forecasts (PFCs) include nearly all common meteorological parameters measured by weather stations. For further information please refer to: [in German: https://www.dwd.de/DE/leistungen/met_verfahren_mosmix/met_verfahren_mosmix.html ] [in English: https://www.dwd.de/EN/ourservices/met_application_mosmix/met_application_mosmix.html ]

Model Output Statistics for SANDAKAN AIRP. / SABAH (96491)

DWD’s fully automatic MOSMIX product optimizes and interprets the forecast calculations of the NWP models ICON (DWD) and IFS (ECMWF), combines these and calculates statistically optimized weather forecasts in terms of point forecasts (PFCs). Thus, statistically corrected, updated forecasts for the next ten days are calculated for about 5400 locations around the world. Most forecasting locations are spread over Germany and Europe. MOSMIX forecasts (PFCs) include nearly all common meteorological parameters measured by weather stations. For further information please refer to: [in German: https://www.dwd.de/DE/leistungen/met_verfahren_mosmix/met_verfahren_mosmix.html ] [in English: https://www.dwd.de/EN/ourservices/met_application_mosmix/met_application_mosmix.html ]

Model Output Statistics for KOTA KINABALU / SABAH (96471)

DWD’s fully automatic MOSMIX product optimizes and interprets the forecast calculations of the NWP models ICON (DWD) and IFS (ECMWF), combines these and calculates statistically optimized weather forecasts in terms of point forecasts (PFCs). Thus, statistically corrected, updated forecasts for the next ten days are calculated for about 5400 locations around the world. Most forecasting locations are spread over Germany and Europe. MOSMIX forecasts (PFCs) include nearly all common meteorological parameters measured by weather stations. For further information please refer to: [in German: https://www.dwd.de/DE/leistungen/met_verfahren_mosmix/met_verfahren_mosmix.html ] [in English: https://www.dwd.de/EN/ourservices/met_application_mosmix/met_application_mosmix.html ]

Model Output Statistics for SITIAWAN (48620)

DWD’s fully automatic MOSMIX product optimizes and interprets the forecast calculations of the NWP models ICON (DWD) and IFS (ECMWF), combines these and calculates statistically optimized weather forecasts in terms of point forecasts (PFCs). Thus, statistically corrected, updated forecasts for the next ten days are calculated for about 5400 locations around the world. Most forecasting locations are spread over Germany and Europe. MOSMIX forecasts (PFCs) include nearly all common meteorological parameters measured by weather stations. For further information please refer to: [in German: https://www.dwd.de/DE/leistungen/met_verfahren_mosmix/met_verfahren_mosmix.html ] [in English: https://www.dwd.de/EN/ourservices/met_application_mosmix/met_application_mosmix.html ]

Model Output Statistics for KUCHING AIRP. / SARAWAK (96413)

DWD’s fully automatic MOSMIX product optimizes and interprets the forecast calculations of the NWP models ICON (DWD) and IFS (ECMWF), combines these and calculates statistically optimized weather forecasts in terms of point forecasts (PFCs). Thus, statistically corrected, updated forecasts for the next ten days are calculated for about 5400 locations around the world. Most forecasting locations are spread over Germany and Europe. MOSMIX forecasts (PFCs) include nearly all common meteorological parameters measured by weather stations. For further information please refer to: [in German: https://www.dwd.de/DE/leistungen/met_verfahren_mosmix/met_verfahren_mosmix.html ] [in English: https://www.dwd.de/EN/ourservices/met_application_mosmix/met_application_mosmix.html ]

Innovative Ansätze zur Verbesserung des Kohlenstoffspeicherpotenzials von Vegetationsküstenökosystemen, Vorhaben: Fernerkundungsbasiertes Monitoring von Küstenökosystemen

Bioökonomie International 2024: LiBiPIUs - Lignocellulose- Biorefinement durch Plasma-Ultraschall- Behandlung

Pollen and environmental reconstruction, Holocene dynamics of tropical rainforest, climate, fire, human impact and land use in Sulawesi and Sumatra, Indonesia

The present-day configuration of Indonesia and SE Asia is the results of a long history of tectonic movements, volcanisms and global eustatic sea-level changes. Not indifferent to these dynamics, fauna and flora have been evolving and dispersing following a complicate pattern of continent-sea changes to form what are today defined as Sundaland and Wallacea biogeographical regions. The modern intraannual climate of Indonesia is generally described as tropical, seasonally wet with seasonal reversals of prevailing low-level winds (Asian-Australian monsoon). However at the interannual scale a range of influences operating over varying time scales affect the local climate in respect of temporal and spatial distribution of rainfall. Vegetation generally reflects climate and to simplify it is possible to distinguish three main ecological elements in the flora of Malaysia: everwet tropical, seasonally dry tropical (monsoon) and montane. Within those major ecological groups, a wide range of specific local conditions caused a complex biogeography which has and still attract the attention of botanists and biogeographers worldwide. Being one of the richest regions in the Worlds in terms of species endemism and biodiversity, Indonesia has recently gone through intensive transformation of previously rural/natural lands for intensive agriculture (oil palm, rubber, cocoa plantations and rice fields). Climate change represents an additional stress. Projected climate changes in the region include strengthening of monsoon circulation and increase in the frequency and magnitude of extreme rainfall and drought events. The ecological consequences of these scenarios are hard to predict. Within the context of sustainable management of conservation areas and agro-landscapes, Holocene palaeoecological and palynological studies provide a valuable contribution by showing how the natural vegetation present at the location has changed as a consequence of climate variability in the long-term (e.g. the Mid-Holocene moisture maximum, the modern ENSO onset, Little Ice Age etc.). The final aim of my PhD research is to compare the Holocene history of Jambi province and Central Sulawesi. In particular: - Reconstructing past vegetation, plant diversity and climate dynamics in the two study areas Jambi (Sumatra) and Lore Lindu National Park (Sulawesi) - Comparing the ecological responses of lowland monsoon swampy rainforest (Sumatra) and everwet montane rainforests (Sulawesi) to environmental variability (vulnerability/resilience) - Investigating the history of human impact on the landscape (shifting cultivation, slash and burn, crop cultivation, rubber and palm oil plantation) - Assessing the impact and role of droughts (El Niño) and fires - Adding a historical perspective to the evaluation of current and future changes.

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