Cikkek nyilvánosan hozzáférhető megbízással - Roland PotthastTovábbi információ
Sehol sem hozzáférhető: 3
The evaluation of EnVar method including hydrometeors analysis variables for assimilating cloud liquid/ice water path on prediction of rainfall events
D Meng, Y Chen, H Wang, Y Gao, R Potthast, Y Wang
Atmospheric Research 219, 1-12, 2019
Megbízások: National Natural Science Foundation of China
Assimilating Precipitation Features Based on the Fractions Skill Score: An Idealized Study with an Intermediate AGCM
S Otsuka, T Awazu, CA Welzbacher, R Potthast, T Miyoshi
Numerical Weather Prediction: East Asian Perspectives, 283-294, 2023
Megbízások: Japan Science and Technology Agency
Dynamic inverse scattering
RWE Potthast
Direct and Inverse Problems in Wave Propagation and Applications 14, 233, 2013
Megbízások: UK Engineering and Physical Sciences Research Council
Valahol hozzáférhető: 19
On the representation error in data assimilation
T Janjić, N Bormann, M Bocquet, JA Carton, SE Cohn, SL Dance, ...
Quarterly Journal of the Royal Meteorological Society 144 (713), 1257-1278, 2018
Megbízások: US National Aeronautics and Space Administration, German Research Foundation …
Particle filters for high‐dimensional geoscience applications: A review
PJ Van Leeuwen, HR Künsch, L Nerger, R Potthast, S Reich
Quarterly Journal of the Royal Meteorological Society 145 (723), 2335-2365, 2019
Megbízások: German Research Foundation, UK Natural Environment Research Council …
Inverse modeling: an introduction to the theory and methods of inverse problems and data assimilation
G Nakamura, R Potthast
IOP Publishing, 2015
Megbízások: German Research Foundation, Volkswagen Foundation, UK Engineering and …
How can existing ground-based profiling instruments improve European weather forecasts?
AJ Illingworth, D Cimini, A Haefele, M Haeffelin, M Hervo, S Kotthaus, ...
Bulletin of the American Meteorological Society 100 (4), 605-619, 2019
Megbízások: European Commission
Tutorial on neural field theory
S Coombes, P beim Graben, R Potthast
Neural fields: theory and applications, 1-43, 2014
Megbízások: German Research Foundation, UK Engineering and Physical Sciences Research …
WIVERN: A new satellite concept to provide global in-cloud winds, precipitation, and cloud properties
AJ Illingworth, A Battaglia, J Bradford, M Forsythe, P Joe, P Kollias, ...
Bulletin of the American Meteorological Society 99 (8), 1669-1687, 2018
Megbízások: US National Aeronautics and Space Administration, UK Natural Environment …
Nonlinear bias correction for satellite data assimilation using Taylor series polynomials
JA Otkin, R Potthast, AS Lawless
Monthly Weather Review 146 (1), 263-285, 2018
Megbízások: US National Oceanic and Atmospheric Administration
Regional atmospheric reanalysis activities at Deutscher Wetterdienst: review of evaluation results and application examples with a focus on renewable energy
F Kaspar, D Niermann, M Borsche, S Fiedler, J Keller, R Potthast, T Rösch, ...
Advances in Science and Research 17, 115-128, 2020
Megbízások: Federal Ministry of Education and Research, Germany
Assessment of progress and status of data assimilation in numerical weather prediction
IH Kwon, S English, W Bell, R Potthast, A Collard, B Ruston
Bulletin of the American Meteorological Society 99 (5), ES75-ES79, 2018
Megbízások: US National Oceanic and Atmospheric Administration
Observation error statistics for Doppler radar radial wind superobservations assimilated into the DWD COSMO-KENDA system
JA Waller, E Bauernschubert, SL Dance, NK Nichols, R Potthast, ...
Monthly Weather Review 147 (9), 3351-3364, 2019
Megbízások: UK Engineering and Physical Sciences Research Council, UK Natural …
Bayesian inference for fluid dynamics: a case study for the stochastic rotating shallow water model
O Lang, PJ Van Leeuwen, D Crisan, R Potthast
Frontiers in Applied Mathematics and Statistics 8, 949354, 2022
Megbízások: UK Engineering and Physical Sciences Research Council, European Commission
A local particle filter and its Gaussian mixture extension implemented with minor modifications to the LETKF
S Kotsuki, T Miyoshi, K Kondo, R Potthast
Geoscientific Model Development Discussions 2022, 1-38, 2022
Megbízások: Japan Science and Technology Agency
Near-realtime quantitative precipitation estimation and prediction (RealPEP)
S Trömel, C Chwala, C Furusho-Percot, CC Henken, J Polz, R Potthast, ...
Bulletin of the American Meteorological Society 102 (8), E1591-E1596, 2021
Megbízások: German Research Foundation
Targeted covariance inflation for 3D‐volume radar reflectivity assimilation with the LETKF
K Vobig, K Stephan, U Blahak, K Khosravian, R Potthast
Quarterly Journal of the Royal Meteorological Society 147 (740), 3789-3805, 2021
Megbízások: German Research Foundation
Universal neural field computation
P beim Graben, R Potthast
Neural fields: theory and applications, 299-318, 2014
Megbízások: German Research Foundation
Data assimilation of nowcasted observations
R Potthast, K Vobig, U Blahak, C Simmer
Monthly Weather Review 150 (5), 969-980, 2022
Megbízások: German Research Foundation
Classification and fault detection methods for fuel cell monitoring and quality control
NLH Lowery, MM Vahdati, RWE Potthast, W Holderbaum
Journal of fuel cell science and technology 10 (2), 021002, 2013
Megbízások: UK Engineering and Physical Sciences Research Council
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