GODAE Ocean View Activities in JMA (and Japan)

Transcription

1 GOVST VIII, Nov. 6 th, 2017, Bergen, Norway GODAE Ocean View Activities in JMA (and Japan) Yosuke Fujii 1, Norihisa Usui 1, Takahiro Toyoda 1, Nariaki Hirose 1, Hiromichi Igarashi 2, and Japan GODAE group ( 1 JMA/ Meteorological Research Institute, 2 JAMSTEC)

2 Targets of ODA Systems in JMA Ocean state monitoring and predictions For preventing natural disasters (e.g., high tides, rapid tides) For search and rescue. For predicting drift/advection of debris, oil spill, pollutant etc. For marine transportation and marine construction For fishery and sustaining marine eco-system For monitoring and understanding of climate variations Providing oceanic initial condition of the coupled model for subseasonal-seasonaldecadal predictions Currently, only seasonal predictions are conducted with the coupled model Coupled data assimilation will be required for seamless predictions Providing sea surface conditions of atmospheric models for weather predictions But observation-based analysis is used in weather predictions, currently Reproduction of the diurnal cycle of SST will be required for improvement of atmospheric predictions. JMA loosely collaborates with other Japanese agencies (e.g., JAMSTEC, JAXA) and universities. 2

3 Operational Systems in JMA MOVE/MRI.COM-G2 Tripolar Grid, 1º 0.5º 3DVAR-FGAT+ Bias Correction Seasonal and ENSO predictions (-7months by CGCM) MOVE/MRI.COM-NP/WNP MOVE/MRI.COM-WNP*/Seto G2 (0.5º 1º, Tripolar Grid) (semi-operation) NP(0.5º 0.5º) WNP(0.1º 0.1º) 3DVAR Kuroshio/Oyashio Monitoring Ocean Forecasting (1 week-1 month) WNP* (4DVAR) (0.1º 0.1º) Seto (2km 2km) Initialized with WNP* through IAU-DST (IAU as a Down-Scaling Technique). Monitoring of Coastal Ocean and abnormal tides Ocean Forecasting (-1 week)

4 2017 Kuroshio large meander (Prediction for 10/16) Actual Status (10/16, T200) T200 in WNP* (4DVAR) 9/7 Initial T200 in WNP (3DVAR) 9/28 Initial Predictions from 3DVAR tend to overestimate development and eastward advection of the meander, but predictions from 4DVAR adequately represent the large meander. 10/10 Initial The prediction is successful even the lead time is longer than 1 month.

5 Web Delivery for squid fishery (with JAMSTEC) satellite Since 2015 Japanese commercial fishing vessels cloud server HSI mapping web site m temperature SST,SSH, EKE,chl-a squid HSI map MOVE/MRI.COM-NP/WNP HSI map 200m-d temperature

6 4DVAR Ocean Reanalysis for the western North Pacific over 30 years (FORA-WNP30) Usui et al. 2016, doi: /s Ocean Reanalysis generated by MOVE/MRI.COM-WNP*. Period: Assimilated data: in situ TS profiles (0-1500m), JMA s gridded SST, and Satellite Sea Level Anomaly Available for free (see SST at Mar. 7 th, 2005 FORA-WNP30 reproduces the narrow southward intrusion of Oyashio to 37.2ºN observed by MODIS. Kuroshio extension is also well reproduced in FORA-WNP30. Satellite MODIS FORA-WNP30 6 From the web page of EORC:

7 Systems currently developed in JMA/MRI MOVE/MRI.COM-NP/JPN North Pacific 4DVAR System (NP*) 10km resolution (eddy-resolving) Japan-area high resolution model (JPN) 2km resolution (resolving coastal phenomena) Initialized with NP 4DVAR analysis fields by IAU-DST (IAU as a Down-Scaling Technique) MOVE/MRI.COM-NP/JPN 4DVAR model (10km) IAU-DST MOVE/MRI.COM-G3 Global 4DVAR System (G3A) Global, Tripolar grids, Resolution: 1º º Sea Ice 3DVAR (Sea Surf. T. is adjusted to SIC correction) Global forecast model (G3F) Global, Tripolar grids, Resolution: 1/4º 1/4º Initialized with G3A 4DVAR analysis fields by IAU-DST Japan-area model (2km) MRI-CDA1 Weakly coupled DA System (outer-loop coupling) Based on the global ODA system (MOVE/MRI.COM-G2), the atmospheric 4DVAR system, and CGCM used in JMA s operation 7

8 Comparison of SST fields (MOVE-MRI.COM-NP/JPN) NP* (10km, 4DVAR) JPN (2km, IAU-DST) Rapid mesoscale variability is well estimated by the 4DVAR scheme. Mesoscale features are well constrained Fine-scale structures can be seen. Satellite-based SST map (MGDSST ~25km) The variation is very slow because of time-smoothing effects of the objective analysis (OI). 8

9 Sea Ice 3DVAR Scheme with Surf. Air T Correction SIC Bias against observation data in March Exp.1: Assim. TS only Exp. 2: Assim. TS + SIC Exp. 3: TS+SIC+SAT Corr. SAT Correction in Exp. 3 Sea surface Atmospheric Temperature (SAT) is affected by Sea Ice Concentration (SIC) due to warmer temperature at the sea surface than on the ice top. On the other hand, SAT strongly constrains the SIC fields. Therefore, SAT should be corrected simultaneously when SIC is changed by DA. Introducing SAT correction according to SIC changes (Exp. 3) improves SIC fields over the experiment without SAT correction (Exp. 2). The correction of SAT is also consistent with other studies. From Toyoda et al. 2015

10 Japanese Data Assimilation Summer School (Since 1995) True First Guess Assimilation Observation 21 st Japanese Data Assimilation Summer School was held for 3 days in August of this year. Many students and early-carrier and senior researchers participated from Oceanography, Meteorology and other Geoscience fields The curriculum includes a lecture on DA basis, presentations on cutting-edge DA studies, and an excise of developing a data assimilation system for very simple models (Lorenz threeparameter model, 1-dimensional advective-diffusive model). The summer school really contributes to the development of Japanese data assimilation community.

11 Future Plans and Visions Short Term Plans The new ocean monitoring/prediction system (10-km North Pacific 4DVAR system + 2-km Japan-area model) will be introduced in operation from The new seasonal forecasting system, which includes the global ocean DA system (1-1/2- deg. 4DVAR + ¼-deg. forecast model) will be introduced in operation from Long Term Visions Develop multiple-very-high-resolution coastal models. Provide SST and the sea ice state for weather predictions - Improving the method of assimilating satellite SST (Direct assimilation, removing biases) - Aim to reproduce the diurnal cycle Continue to develop the coupled data assimilation system - Aim to introduce it in operational subseasonal predictions or reanalysis. JMA will also contribute to the GODAE community by sharing results and experiences in operation and development of ODA systems, and through evaluation of observing systems. 11

12 Thank you

13 2017 Kuroshio Large Meander (Actual Status) T200 in WNP* (4DVAR) Satellite SST (Himawari) 9/18 32oN 9/30 32oN 10/12 32oN T200 in WNP (3DVAR)

14 Future Outlook (Forecasts from 10/15) T200 in WNP* (4DVAR) T200 in WNP (3DVAR) 10/16 10/30 11/15

15 New System for Ocean Forecasting MOVE/MRI.COM-NP/JPN Constituted of a North Pacific 4DVAR model (NP) and a Japan-area high-resolution model (JPN) North Pacific 4DVAR Model (NP) 10km resolution (eddy-resolving) Nonlinear 4DVAR assimilation scheme (The model operator in the cost function is not linearized.) 4DVAR optimization starts from 3DVAR results Target: Mesoscale Variability Japan-area high-resolution model (JPN) 2km (resolving coastal phenomena) Embedded in NP by two-way nesting Tidal components are also simulated. Initialized with NP 4DVAR analysis fields by IAU-DST (IAU as a Down-Scaling Technique) Target: Coastal Phenomena, Abnormal Tides 4DVAR model (10km) IAU-DST Japan-area model (2km) 15

16 Comparison of SST snapshot with NOAA/AVHRR Satellite SST image (NOAA/AVHRR) /sui/kaikyo/detail.htm NP* (10km, 4DVAR) JPN (2km, IAU-DST) 16

17 Comparison with Tide gages Forecast model JPN-assim (with IAU-DST) JPN-free (without IAU-DST) Observation tide gauge data 48 hour tide killer filter (Hanawa and Mitsudera, 1985) Daily mean (2009/1/1~2009/12/31) Kuroshio path is well reproduced Hachijo-jima Kobe Seto Inland Sea (coastal area) JPN-assim reproduces the sea level variations mostly better than JPN-free

18 New System for Seasonal Forecasting MOVE/MRI.COM-G3 Constituted of the analysis model (G3A) and the forecasts model (G3F) (similar to a inner-model-outer-model system which uses an incremental method) Analysis Model (G3A) Global, Tripolar, Resolution: 1º º Upgrade from 3DVAR to nonlinear 4DVAR assimilation scheme for the ocean state 4DVAR optimization starts from 3DVAR results New Sea Ice 3DVAR scheme Separated from the 4DVAR Surf. Air Temp. is modified. Forecast model (G3F) Global, Tripolar Resolution: 0.25º 0.25º Initialized with G3A through IAU-DST. SIC data are directly assimilated through SI 3DVAR. For seasonal predictions as a part of a coupled model. 18

19 SST deviations from observational data (28Oct-01Nov, 2012) First Guess (G3A) - Obs Pre-3DVAR result (G3A) - Obs First Guess (G3F) - Obs 4DVAR result (G3A)- Obs 3DVAR result (G2) - Obs Assimilation (G3F) - Obs The first guess fields of G3A and G3F has a similar accuracy to the current operational system (G2). In G3A, RMSEs are reduced by pre-3dvar, and further reduced by 4DVAR. In G3F, RMSEs are slightly reduced by IAU-DST using G3A fields, although RMSEs are slightly larger than in G3A.

20 Comparison of Sea Ice Concentration Fields Arctic Region (30Jul-03Aug, 2012) Antarctic Region (30Jul-03Aug, 2012) FG (New System) An (New System) FG FG (New (New System) An An (New (New System) System) An (Old System) Observation An An (Old (Old System) System) Observation Sea Ice data are not assimilated in the current operational system By assimilating Sea Ice concentration data, the distribution of the sea ice field is much improved in the new system. The extension of the sea ice area in red circles becomes much improved. And it is effectively modified in the analysis step. The distribution in the Antarctic Pacific basin (in blue squares) is also effectively improved.

21 Development of a Coupled DA System Weekly coupled DA system (MRI-CDA1) Based on the operational ocean DA system, MOVE/MRI.COM- G2, as well as the operational atmosphere DA systems and the operational coupled model. The coupled model is used as the outer model for the atmospheric 4DVAR. Comparison of the coupled reanalysis with the uncoupled version of it indicates that precipitation fields are slightly improved by coupled DA. Comparison of 5-day averaged precipitation scores against CMAP for Dec Nov between the coupled reanalysis (CDA) and uncoupled version (UCPL) Absolute Bias (UCPL-CDA) RMSE (UCPL-CDA) ACC (UCPL-CDA)