DTM2018 IN THE FRAMEWORK OF THE H2020 PROJECT SWAMI

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Gregory Kelley
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This project has received funding from the European Union s Horizon 2020 research and innovation programme under grant agreement No 776287 DTM2018 IN THE

1 This project has received funding from the European Union s Horizon 2020 research and innovation programme under grant agreement No DTM2018 IN THE FRAMEWORK OF THE H2020 PROJECT SWAMI Sean Bruinsma CNES - Space Geodesy Office Toulouse, France 15 th European Space Weather Week Session 9 (7 November 2018)

2 SWAMI project objectives 2/14 Space Weather Atmosphere Model and Indices To develop a model of the whole atmosphere (MOWA) with a science as well as operations-focused approach (MCM). Two existing models of the atmosphere, the UM and the DTM, will be extended and blended to produce this unique new whole atmosphere model, which shall provide estimates of both climatology and space weather variability. To provide new high-cadence geomagnetic indices, Hpxx, including its nowcast and predictions to be used in the UM and DTM. To develop steps, including provision of software, model output, or data sharing facilities, to transition the improved model system into operations.

3 SWAMI: project diagram 3/14 1) Develop new index 2) Develop MOWA Geomagnetic activity: New algorithm for High-cadence Hp index Thermosphere observations Thermosphere DTM: update & improve This presentation Solar activity: F30 radio flux + Surface to LT 4) Develop Kp forecast model Poster session 10 Atmosphere observations UM: Multi-year run Geomagnetic activity: High-cadence Hp & Nowcast and Forecast Solar activity: F30 radio flux & Nowcast and Forecast Poster (this session) MOWA Climatology & Weather 3) Develop MCM MCM ( km) Climatology and weather re-analysis and predictions: - Temperature - Density + composition - Winds

4 SWAMI: DTM2018 4/14 DTM2018 is an intermediate model that, compared with DTM2013, is based on: Same algorithm and drivers (DTM2019: Hp xx geomagnetic index) More data, different preprocessing (no scaling) Better data correction and editing Iterative model development

5 Data: CHAMP, GRACE and GOCE CHAMP ( ): STAR resolution: m/s 2 /Hz 0.5 GPS and SLR inclination: 87 Altitude: km Fluxgate magnetometers and star sensors Overhauser magnetometer GRACE ( ): Total density data SuperSTAR resolution: m/s 2 /Hz 0.5 GPS and SLR inclination: 90 Digital ion driftmeter and Langmuir probe Altitude: km Star sensor GPS POD antenna STAR accelerometer (at CoM) S-band antenna 5/14 Stella (POE) ( ) Swarm ( GPS Acc ) GOCE ( ): Acc. resolution: m/s 2 /Hz 0.5 ion propulsion GPS and SLR inclination: 96.5 Altitude: km (7/2014 7/2017)

6 Total density data 6/14 Mean altitude and 81-day mean F30 (red; right axis) Thermosphere = 4D BUT sparse data: > 2017 < 2002 > 600 km altitude < 300 km altitude (and LST, high flux etc) (km) Stella Cryosat2 GRACE CHAMP GOCE SwarmA (sfu) year

7 Scale of the data 7/14 In DTM2013, data were scaled to the USAF model HASDM. Example: GOCE (Easy case, because direct comparison) (g/cm3) GOCE GOCEx1.25 Daily mean densities Determination of scale factors between satellite datasets is necessary but complicated due to: Altitude Epoch LST Days since 1/1950 (27/03/ /07/2013)

8 Scaling of data 8/14 Data scaling applied in this version: GOCE V1_5 (ESA / E. Doornbos) (1.00) CHAMP (E. Doornbos) (1.00) SwarmA (ESA / E. Doornbos) (1.00) GRACE scaled to GOCE, CHAMP, and SwarmA : : (<9/10) : 0.70 NB: Data rejected for 10/2007-9/2009 9/ : 0.70 Stella What do you mean, scale of density data?! Aerodynamic coefficient a drag = 1 2 C D A m ρv 2 (1.00) Surface in ram direction

9 Results of the preliminary DTM2018 model 9/14 The model was elaborated in 3 iterations: A-priori Estimate Data Result DTM2013 O and N2 GOCE, CHAMP & DTM_ON2 DTM_ON2 He GRACE, Stella, SwarmA DTM_ON2He DTM_ON2He T, O, N2, He All DTM2018

10 Reference results: DTM /14 DTM2013 with data scaled to the HASDM densities DTM2013 with data scaled to the ESA densities mean O/C StD R GOCE (O/C all data) % CHAMP (annual O/C) % GRACE (annual O/C) % SwarmA (O/C all data) 0.78* % Stella (annual O/C) % * Not assimilated in DTM2013

11 Results DTM /14 DTM2013 with data scaled to the HASDM densities DTM2013 with data scaled to the ESA densities DTM2018 with the ( TU Delft scaled) datasets: mean O/C StD R GOCE (O/C all data) % 10.3% CHAMP (annual O/C) % 17.5% GRACE (annual O/C) % 21.2% SwarmA (O/C all data) 0.78* % 24.9% Stella (annual O/C) % 18.8% * Not assimilated in DTM2013

12 Results of the preliminary DTM2018 model 12/ Annual O/C DTM2013 (solid) and DTM2018 (open symbols) 1.1 DTM2013 stella: 0.97 / 6.9% DTM2018 stella: 1.01 / 5.9% Annual O/C DTM2013 (solid) and DTM2018 (open symbols) - NB: different data scaling DTM2013 grace: 1.06 / 7.4% stella: 0.97 / 6.9% DTM2018 grace: 0.98 / 5.4% stella: 1.01 / 5.9% year year

13 Summary and outlook 13/14 DTM2018 is an intermediate model: More data were assimilated, and CHAMP, SwarmA & GOCE were used without scaling, More precise than DTM2013, Model predicts lower densities; this needs thought in view of anticipated user reactions. Next: Benchmark DTM2013 storm performance and elaborate metrics, Implement algorithm for high-cadence geomagnetic activity indices (Hp xx ), Add 1-2 years of additional data (Swarm, Stella, Cryosat-2, ), Compare with UM densities in the blending region ( km), corrections if required.

14 H2020 SWAMI website 14/14

Atmospheric density calibration using satellite drag observations

Atmospheric density calibration using satellite drag observations Eelco Doornbos (TU Delft) Heiner Klinkrad (ESA/ESOC) Second European Space Weather Week Tuesday, 15 November, 2005 Outline Applications