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

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:

Thank you!!