Ultra Rapid Data Assimilation for Real Time Weather Walter Acevedo, - PowerPoint PPT Presentation

Ultra Rapid Data Assimilation for Real Time Weather Walter Acevedo, Zoi Paschalidi, Christian Welzbacher & Roland Potthast January 2019 ISDA 2019, Kobe

Motivation Autonomous driving depends strongly on weather conditions! page 2

Outline • Motivation • New observation sources • Ultra-Rapid DA (URDA) Algorithm • Experiments with KENDA system • Conclusions and perspective Source: AUDI AG

New observation sources Car observations Source: AUDI AG page 4

New observation sources Car observations Source: AUDI AG page 5

New observation sources Road Weather Stations Measured Variables § Road temperature § Road condition § Air temperature § Dew point § Temperature at 30 cm depth § Precipitation type § Precipitation intensity § Wind direction § Wind velocity § Visibility Source: Bavarian Administration page 6

New observation sources Road Weather Stations Coverage for Bavaria on January 2017 (285 stations) Source: Bavarian Administration page 7

Normal sequential DA Analysis Cycle: Forecast Ensemble-Transformation New Forecast Analysis New Analysis 𝑢 # 𝑢 "$% 𝑢 " Model Dynamics • Forecast step • Assimilation step page 8

Ultra-Rapid DA (URDA) „Preemptive Forecasting“ : • 2007: 𝑢 # 𝑢 "$% 𝑢 " • 2015: • 2018: page 9

Ultra-Rapid DA (URDA) Properties: • Equivalent to Normal sequential DA for linear Model and observation operator [Potthast & Welzbacher 2018]: • Applicable to several assimilation steps from different time intervals: • No model reinitialization necessary. • No need to update the whole model state. A reduced set of selected variables and gridpoints can be updated: page 10

Experiments with KENDA system KENDA (Km-scale ENsemble DA) system: COSMO-D2: • Limited-area short-range convection- permitting numerical model weather prediction model D x @ 2.2 km / 65 vertical layers • • Explicit deep convection Local Ensemble Transform Kalman Filter: • LETKF implementation following Hunt et al., 2007 • Rapid Update cycle (RUC) of 1 hour • 40 members + 1 deterministic run • adaptive horizontal localisation • adaptive multiplicative inflation + RTPP • additive covariance inflation page 11

URDA-KENDA Experiment Forecast time : „Model state“ 15 min 10 min 5 min Free run RUC-run W W W W W W W URDA run RUC run Observation 00:00 01:00 02:00 * RUC = rapid update cycle – 1h assimilation cycle • Three conventional observational sources: 1. Synoptic stations 2. Radiosondes 3. Aircraft observations page 12

URDA-KENDA Experiment Pressure Differences (ground layer) regarding the free forecast Pa Pa RUC minus Free run URDA minus Free run page 13

URDA-KENDA Experiment Pressure Differences (ground layer) regarding the free forecast Unbalance Stable introduced behaviour Pa Pa RUC minus Free run URDA minus Free run page 13

URDA-KENDA Experiment Pressure Differences (ground layer) regarding the free forecast Spinup waves Stable spread out behaviour Pa Pa RUC minus Free run URDA minus Free run page 13

URDA-KENDA Experiment Temperature Differences (ground layer) regarding the free forecast Stable Spinup-Effect behaviour C° C° RUC minus Free run URDA minus Free run page 14

URDA-KENDA Experiment Forecast time : „Model state“ 15 min 10 min 5 min Free run RUC-run W W W W W W W Unbalance model state URDA-run RUC-run Observation Spinup-Periode 00:00 01:00 02:00 * RUC = rapid update cycle – 1h assimilation cycle • Model re-initialization introduces imbalances • URDA can beat RUC for short forecast times page 15

URDA-KENDA Experiment Root Mean Square Error (RMSE) vs Leadtime Pressure improved Assimilating Pressure observations ONLY page 16

URDA-KENDA Experiment Root Mean Square Error (RMSE) vs Leadtime U-wind improved V-wind improved Pressure improved Assimilating Pressure, Temperature and U-V Wind observations page 17

Conclusions and Perspective 1. URDA shows great potential for short leadtimes: • Reduced computational cost • No spinup effect 2. Observation operator for car observations under development • Quality control for a moving weather station • Modelling of dependency between meteorological state and auto-microclimate • Auto-dependent bias correction • Time- and spatial aggregation • Data anonymization page 18

References 1. B. J. Etherton. Preemptive forecasts using an ensemble kalman filter. Monthly Weather Review,135(10):3484–3495, 2007. 2. L. E. Madaus and G. J. Hakim. Rapid, short-term ensemble forecast adjustment through offline data assimilation. Quarterly Journal of the Royal Meteorological Society, 141(692):2630–2642,2015. 3. R. Potthast and C.A. Welzbacher. Ultra Rapid Data Assimilation Based on Ensemble Filters. Front. Appl. Math. Stat. 4:45, 2018. 4. Hunt BR, Kostelich EJ, and Szunyogh I. 2007. Efficient data assimilation for spa-tiotemporal chaos: A local ensemble transform Kalman Filter .Physica D,230: 112-126. page 19

Thank you! Vielen Dank! Eυχαριστώ! Gracias! どうもありがとう

Ultra Rapid Data Assimilation for Real Time Weather Walter Acevedo, - PowerPoint PPT Presentation

Ultra Rapid Data Assimilation for Real Time Weather Walter Acevedo, Zoi Paschalidi, Christian Welzbacher & Roland Potthast January 2019 ISDA 2019, Kobe Motivation Autonomous driving depends strongly on weather conditions! page 2

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Ultra Rapid Data Assimilation for Real Time Weather Walter Acevedo, - PowerPoint PPT Presentation

Ultra Rapid Data Assimilation for Real Time Weather Walter Acevedo, Zoi Paschalidi, Christian Welzbacher & Roland Potthast January 2019 ISDA 2019, Kobe Motivation Autonomous driving depends strongly on weather conditions! page 2

Land surface data assimilation for Numerical Weather Prediction P. de Rosnay , J. Muoz Sabater,

Data Assimilation for Numerical Weather Prediction [NWP] Project: LETKF, Efficient DA Filtering

REAL TIME WEATHER RADAR DATA PROCESSING FOR URBAN HYDROLOGY IN NANCY FAURE Dominique,

A guide to real time weather in computer games Krsito Mnna Intent of this presentation

Space Weather Forecasting with a Multimodel Ensemble Prediction System (MEPS) of Data Assimilation

NOAA nowCOAST GIS-Web Mapping Portal to Near-Real-Time Weather &amp; Ocean Data, Warnings, and

Data Assimilation for Numerical Weather Prediction [NWP] Project Ahmed Attia Statistical and

Data assimilation and observing Data assimilation and observing strategies working group

NESDIS Data System Readiness Mitch Goldberg NESDIS/ORA/CRAD February 14, 2002 Near- real-time

A coupled weather and wildland fire forecasting system with assimilation of satellite remote

MATHEMATICAL PROBLEMS ASSOCIATED WITH MATHEMATICAL PROBLEMS ASSOCIATED WITH ATMOSPHERIC DATA

Rapid Method for Enumeration of Microbial Counts - Real Time Air Monitoring Roni Cohen, PhD Head

Assimilation of AIRS Data at NRL Assimilation of AIRS Data at NRL Benjamin Ruston, Clay

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