1. How do model errors and localization approaches affects model parameter estimation Juan Ruiz, Takemasa Miyoshi and Masaru Kunii jruiz@cima.fcen.uba.ar Centro de Investigaciones del Mar y la Atmósfera- CONICET University of Buenos Aires Advanced Institute for Computational Science - RIKEN World Weather Open Science Conference. Montreal, Canada, 16 to 21 August 2014

2. Several works showed that surface exchange parameters have a large impact upon model performance These parameters might be estimated using data assimilation based parameter estimation (Ito et al. 2010, Kang et al. 2012, Green and Zhang 2014). Simple parameter estimation approach: a multiplicative correction factor is introduced and is estimated using the LETKF-WRF system. In this work we evaluate a simple approach for data assimilation based parameter estimation using the LETKF-WRF system (Miyoshi and Kunii 2012). Experiments goes from ideal to real observations tests

3. More sensitive (latent heat exchange) Less sensitive (heat exchange) Ruiz, Miyoshi and Kunii (2014, in preparation) TC Sinlaku (2008) Given the stronger impact of latent heat fluxes we test the methodology focusing on these fluxes. Model sensitivity to surface fluxes:

4. OSSE experiments: Realistic observation distribution quasi perfect model and boundary conditions. Estimated parameter is identifiable. Observation network seems to be adequate for the estimation of the parameter. -> OSSE experiments are successful

5. OSSE experiments: Realistic observation distribution, prefect BC but imperfect model Estimated parameter is seems to converge to a different value Error reduction is not as large as in the perfect model scenario but improvements can be found in all variables.

6. Estimated model parameters as a function of time Estimated parameters are below one indicating that surface moisture flux is reduced in the parameter estimation experiment. Real world experiments: Horizontal distribution is quite homogeneous particularly over the tropical ocean where the model sensitivity to the parameter is stronger.

7. Low level biases are removed in almost all variables. Upper level biases are increased. RMSE improved for wind. Moisture and temperature shows mixed behaviour Impact upon the analysis (compared with GDAS) Real world experiments: BIAS RMSE relative improvement

8. Impact upon the forecast (compared with GDAS) 40 member ensemble forecast Real world experiments: Wind improved at almost all levels Temperature and moisture improved at low levels but degraded at middle and upper levels. IMPROVEMENT DEGRADATION PS

9. Precipitation forecast (compared with CMORPH) 24 hr 48 hr 72 hr ETS BIAS Precipitation forecast improved ETS. Precipitation frequency decreases. Real world experiments:

10. Impact upon TC forecast Some cases shows a consistent improvement while others shows a consistent degradation... Real world experiments: Forecast degraded Forecast improved

11. Impact upon TC forecast The mean track error is slightly better for the parameter estimation experiment. The sample is too small to have robust results. Real world experiments:

12. Sensitivity to localization strategy: 2D estimation0D estimation Without vertical localization With vertical localization Without vertical localization Large biases near the surface might significantly affect the estimated parameter values Sensitivity to the parameters not necessarily confined to low levels Three experiments have been conducted to explore the sensitivity of the estimated parameters to the localization strategy.

13. Impact upon the estimated parameters All strategies estimate parameter values that are below the default. 0D estimation produces noisier results. Experiment with vertical localization produce lower parameter values. Sensitivity to localization strategy: Estimated parameters as a function of time.

14. Impact upon the estimated parameters 0D strategy seems to provide the best results for wind and temperature (although larger degradation is introduce in the moisture field) Similar results are obtained for the forecast Sensitivity to localization strategy: Vertical profile of RMSE improvement

15. Parameters are successfully estimated using the LETKF-WRF system. In all the experiments parameters indicate that moisture surface fluxes are too strong and are possible responsible for the moist biases at low levels. Parameter estimation impact upon the forecast is positive in some variables including precipitation. Impact upon the TC forecast is still unclear although results suggest that estimated parameters can potentially improve TC forecasting. Localization has an impact upon the estimated parameters. Best results has been obtained with 0D parameters (maybe because of small spatial variability of the estimated parameter). Conclusions:

16. Thank you!!