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

DEVELOPMENT OF SMART CEMENT FOR REAL TIME MONITORING OF ULTRA DEEPWATER OIL WELL CEMENTING

On a Data Assimilation Method coupling Kalman Filtering, MCRE Concept and PGD Model Reduction for

4D-Var data assimilation of atmospheric CO 2 from infrared satellite sounders Richard Engelen

Acquisition of semantic relations between terms: how far can we get with standard NLP tools? Ina

Designing a Pipelined Processor Computer System Architecture Go back and examine your

Atomic COTS G. Hampson, D. Humphrey, G.Jourjon, J. Bunton, K. Bengston, A.Bolin, Y. Chen,

SRNS: Supporting the Delivery of Strategic Materials Proposed Savannah River Plutonium Processing

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Geometrical Consistency in Processing of Helical Filaments Pawel A. Penczek The University of

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