how do model errors and localization approaches affects model parameter estimation juan ruiz,...
TRANSCRIPT
How do model errors and localization approaches affects model parameter
estimation
Juan Ruiz, Takemasa Miyoshi and Masaru Kunii
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
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).
WRFqq FF
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
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:
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
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.
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.
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
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
Precipitation forecast (compared with CMORPH)
24 hr 48 hr 72 hr
ET
SB
IAS
Precipitation forecast improved ETS. Precipitation frequency decreases .
Real world experiments:
Impact upon TC forecast
Some cases shows a consistent improvement while others shows a consistent degradation...
Real world experiments:
Forecast degradedForecast improved
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:
Sensitivity to localization strategy:
2D estimation 0D 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.
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.
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
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: