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Model Output Statistics for MANDALAY (48042)

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 RANGOON (48097)

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 SITTWE (AKYAB) (48062)

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 ]

New Sericini from Myanmar (Coleoptera: Scarabaeidae: Sericinae)

<p>Distribution data of the taxonomic paper: New Sericini from Myanmar (Coleoptera: Scarabaeidae: Sericinae)</p>

Reports of inversions of pseudo-dynamic rupture model and centroid moment tensor inversion of 28 March 2025 MW 7.7 Myanmar earthquake and moment tensor inversion of selected aftershocks

This dataset containts supplementary information for the publication "Supershear Rupture Along the Sagaing Fault Seismic Gap: The 2025 Myanmar Earthquake”, published in The Seismic Record. Specifically, it contains detailed html reports for the mainshock moment tensor inversion, all analysed aftershock moment tensor inversions, and the Pseudo-Dynamic Rupture inversion of the mainshock. Those reports include waveform misfit plots and figures showing uncertainties and parameter trade-offs. All source models were inverted using the Python-based tool Grond (Heimann et al., 2018) from the Pyrocko seismology software (Heimann et al., 2017). Grond is an open-source software for the characterization of earthquake sources based on seismic waveforms, waveform attributes, and/or geodetic observations like InSAR and GNSS. The HTML reports include information on the inversion setups, the best-fitting results, as well as bootstrap-based uncertainties, allowing for detailed insight into the result quality. To open the reports, please follow the instructions provided in the README files within the zip directories.

NUR: Management multipler Risiken bei Extremereignissen in schnell wachsenden (Mega)Städten Myanmars (RiskUrbMyanmar)

Update Myanmar

The GRDC has updated the Global Runoff Database for 10 stations from Myanmar with daily discharge data. The data were kindly provided by the Department of Meteorology and Hydrology of Myanmar. Access to the data: GRDC Data Portal

Myanmar subduction to collision imaging array (MySCOLAR)

The temporary seismic array of MySCOLAR in northern Myanmar consists of 30 broadband stations. The overall scientific goals are to understand the transition from continental collision to oceanic subduction, to quantify the partitioning of deformation in the accretionary prism, in the Burma Plate and along the strike-slip Sagaing fault system and to image the subducting Indian Plate beneath Myanmar and southwest China. The seismological analysis methods applied to this dataset will include location of local earthquakes and determining their focal mechanisms, surface wave tomography from ambient noise and earthquake data, body wave tomography from local and teleseismic earthquakes, full waveform inversion for Earth structure, receiver functions, and shear wave splitting. A subset of the stations was transmitting data in real time, and these stations contributed to real-time earthquake analysis by the Department of Meteorology and Hydrology (DMH) in Myanmar and the GEOFON earthquake monitoring service. Waveform data are available from the GEOFON data centre, under network code 6C.

Provenance and pressure-temperature-time-deformation evolution of Indian crust in central Myanmar

Other

Globaler Klima-Risiko-Index 2017 vorgestellt

Mit Mosambik (Rang 1), Malawi (3), Ghana und Madagaskar (beide 8) gehörten im vergangenen Jahr gleich vier afrikanische Staaten zu den zehn Ländern, die am härtesten von Wetterextremen getroffen wurden. Dies ist ein Kernergebnis der 12. Auflage des Globalen Klima-Risiko-Index, den die Umwelt- und Entwicklungsorganisation Germanwatch am 8. November 2016 beim Klimagipfel in Marrakesch vorgestellt hat. Weltweit betrachtet haben Hitzewellen 2015 die meisten Todesopfer gefordert. Betroffen waren sowohl Entwicklungs- und Schwellenländer - ein Beispiel ist Indien mit mehr als 4300 Todesfällen - als auch Industrienationen, zum Beispiel Frankreich (3300 Todesopfer). Die Menschen litten auch unter fehlenden Schutzmaßnahmen und unzureichender Katastrophenvorsorge in armen Staaten. Alle zehn am meisten betroffenen Länder in den vergangenen 20 Jahren bis 2015 sind Entwicklungsländer, neun davon gehören zur Gruppe der Staaten mit niedrigem oder unterem mittleren Einkommen. Sie gehören zu den Staaten, die am wenigsten zum Klimawandel beigetragen und sehr wenig Mittel für Anpassungs- und Schutzmaßnahmen haben. Die am stärksten betroffenen Länder dieser längerfristigen Betrachtung - also seit 1996 - sind Honduras, Myanmar und Haiti. Weltweit forderten in den vergangenen 20 Jahren rund 11.000 Extremwetterereignisse fast 530.000 Menschenleben. Die direkten materiellen Verluste addierten sich auf knapp 3,1 Billionen US-Dollar, gerechnet in Kaufkraftparitäten (PPP).

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