Dual-Scale Neighboring Ensemble Variational Assimilation for a Cloud-Resolving Model Dual-Scale Neighboring Ensemble Variational Assimilation for a Cloud-Resolving Model Kazumasa Aonashi, Kozo Okamoto, and Seiji Origuchi Meteorological Research Institute (MRI) / Japan Meteorological Agency (JMA) 3. Analyses of CRM Ensemble forecast error Acknowledgements: This study is supported by the 7th Precipitation Measurement Mission (PMM) Japanese Research Announcement of JAXA. 1. Introduction The goal of the present study is to assimilate precipitation-related observations, such as satellite microwave radiometer (MWR) brightness temperature (TB) into Cloud-Resolving Models (CRM). To handle the nonlinearity of precipitation-related observations, we have been developing an ensemble-based variational data assimilation (EnVA) method (Zupanski 2005, Aonashi and Eito 2011). However, EnVA suffered from sampling errors of CRM ensemble forecasts. The problem became serious especially for precipitation related variables. We developed a sampling error damping method that used dual-scale neighboring ensembles. To examine this method, we performed OSSEs and assimilated simulated MWR TBs for heavy rain cases. 1. Introduction The goal of the present study is to assimilate precipitation-related observations, such as satellite microwave radiometer (MWR) brightness temperature (TB) into Cloud-Resolving Models (CRM). To handle the nonlinearity of precipitation-related observations, we have been developing an ensemble-based variational data assimilation (EnVA) method (Zupanski 2005, Aonashi and Eito 2011). However, EnVA suffered from sampling errors of CRM ensemble forecasts. The problem became serious especially for precipitation related variables. We developed a sampling error damping method that used dual-scale neighboring ensembles. To examine this method, we performed OSSEs and assimilated simulated MWR TBs for heavy rain cases. x xx 3. Improved EnVA including sampling error damping methods 3-1. Neighboring ensemble (NE) based on Spectral Localization (SL) in addition to an adaptive spatial localization SL in the spatial domain express the correlation C between locations x1 and x2 using a spatially shift s as This shows that creating spatially shifted versions of ensemble members to calculate the forecast error covariance (Buehner and Charron, 2007). The study uses NE within up to s=5 grids (=5x5 ensemble), successfully reducing average length with high correlation of precipitation (Fig.1) Variable separation dependent on horizontal scale Horizontal correlation varies for different variables and precipitation situation (Fig.2). Analysis variables are separated into large-scale and local variables. The separation enables reasonable calculation of forecast error correlation dependent on variables while modest scaled correlations for all variables are computed without the separation (Fig.3) Initial tests of new EnVA using OSSE The new EnVA including the above sampling error damping methods was tested by assimilating simulated RAOB and MWR surface precipitation. Analysis increment of U at 1460 m height shows expected scale variation according to precipitation conditions (Fig.4). Precipitation analysis well agrees with pseudo-truth. 3. Improved EnVA including sampling error damping methods 3-1. Neighboring ensemble (NE) based on Spectral Localization (SL) in addition to an adaptive spatial localization SL in the spatial domain express the correlation C between locations x1 and x2 using a spatially shift s as This shows that creating spatially shifted versions of ensemble members to calculate the forecast error covariance (Buehner and Charron, 2007). The study uses NE within up to s=5 grids (=5x5 ensemble), successfully reducing average length with high correlation of precipitation (Fig.1) Variable separation dependent on horizontal scale Horizontal correlation varies for different variables and precipitation situation (Fig.2). Analysis variables are separated into large-scale and local variables. The separation enables reasonable calculation of forecast error correlation dependent on variables while modest scaled correlations for all variables are computed without the separation (Fig.3) Initial tests of new EnVA using OSSE The new EnVA including the above sampling error damping methods was tested by assimilating simulated RAOB and MWR surface precipitation. Analysis increment of U at 1460 m height shows expected scale variation according to precipitation conditions (Fig.4). Precipitation analysis well agrees with pseudo-truth. 4. Results of OSSE 4-1. Experiment Meteorological case We applied the assimilation method to incorporate TMI TBs around Typhoon Conson (TY0404) at 22 UTC 9 th June Simulated MWR TBs We regarded one of 100-ensemble members as the ‘truth’. Instead of the real TMI data, we used the simulated TBs (10v,19v,21v, 37v, & 85v) that RTM-calculated from the ‘truth’ and averaged over 25 km x 25km Preliminary results Displacement in precipitation distribution and MWI TBs between observation and the first guess (mean of ensemble forecast). Assimilation of MWI TBs reduced the displacement error, in particular, to the north of the Typhoon. While TBs calculated from the analysis agreed well with the observation, the analysis underestimated concentrated heavy precipitation around the Typhoon center. 4. Results of OSSE 4-1. Experiment Meteorological case We applied the assimilation method to incorporate TMI TBs around Typhoon Conson (TY0404) at 22 UTC 9 th June Simulated MWR TBs We regarded one of 100-ensemble members as the ‘truth’. Instead of the real TMI data, we used the simulated TBs (10v,19v,21v, 37v, & 85v) that RTM-calculated from the ‘truth’ and averaged over 25 km x 25km Preliminary results Displacement in precipitation distribution and MWI TBs between observation and the first guess (mean of ensemble forecast). Assimilation of MWI TBs reduced the displacement error, in particular, to the north of the Typhoon. While TBs calculated from the analysis agreed well with the observation, the analysis underestimated concentrated heavy precipitation around the Typhoon center. 5. Summary The new methods (Section 3-1 and 3-2) effectively reduced sampling errors by suppressing spurious correlation. OSSE demonstrated the EnVA method reduced the displacement error of precipitation. The analysis underestimated heavy precipitation bands around Typhoon. 5. Summary The new methods (Section 3-1 and 3-2) effectively reduced sampling errors by suppressing spurious correlation. OSSE demonstrated the EnVA method reduced the displacement error of precipitation. The analysis underestimated heavy precipitation bands around Typhoon. Fig.1: Averaged distance of horizontal correlation of Prec > 0.5 in the case of typhoon. The area is 2000kmx2000km. Fig.2: Horizontal correlation of ensemble forecast error for U (m/s), RHW (%), W (m/s) [km] Weak rain (1~3mm/h) Heavy rain (>10mm/h) No rain w/o NE w NE [km] Fig.4: Analysis increment of U (m/s) generated by assimilating pseudo-RAOB. The area is 1000km X 1000km Weak Rain Heavy rain Fig.3: Forecast error correlation of Prec and U without separation (left 2 panels) and with separation (right 2 panels). The area is 500km X 500km. Precip w sep. U w sep. Prec w/o sep. U w/o sep. km km Prec with sep. U with sep Fig.5: Surface Weather Map 00 UTC 10 June 2004 Fig. 7: Surface Precipitation (mm/hr) around T0404 at 22 UTC 9 June Truth(left), 1 st guess (center), and analysis of EnVA (right) Fig.6: MWI TB19v around T0404 at 22UTC 9 June Observation (left), 1 st guess (center), and analysis of EnVA (right)