Historical observation impact assessments for EUMETNET using the ALADIN/HU limited area model

Two historical Observing System Experiment (OSE) studies using the ALADIN limited area model and its assimilation system are described. The first study, using an OSE scenario that minimizes the impacts of observations through the lateral boundary conditions, demonstrated the importance of each assimilated terrestrial (radiosonde, aircraft, and wind profiler) observations on the analyses and short-range forecasts of the ALADIN/HU model and proved evidence, that the role of conventional observations cannot be even partly taken over by satellite measurements without degradation of the forecast quality. The second study demonstrated that the assimilation of radiosonde observations remains indispensable even with a progressively increasing amount of aircraft measurements.

PERFORMA KONVERGENSI ANGIN PERMUKAAN DIURNAL MODEL REANALISIS ERA5 DI BENUA MARITIM INDONESIA

IntisariPada penelitian ini, penulis mengkaji uji performa kualitatif konvergensi angin permukaan model reanalisis ERA5 di BMI yang dibandingkan dengan hasil penelitian menggunakan limited area model (LAM) oleh Qian, Im dan Eltahir serta Alfahmi et al. Konvergensi angin permukaan dan anomali angin permukaan dihitung dengan menggunakan finite difference. Hasil penelitian menunjukkan bahwa model reanalisis ERA5 mampu mensimulasikan konvergensi anomali angin permukaan dengan baik terhadap model regional climate model (RegCM) maupun The MIT regional climate model (MRCM) resolusi 27 km di Pulau Jawa dan sekitarnya serta BMI bagian barat dengan nilai konvergensi yang lebih tinggi. Sedangkan terhadap model weather research forecast (WRF) 9 km di BMI bagian timur, model reanalisis ERA5 juga dapat mensimulasikan konvergensi angin permukaan, tetapi dengan nilai yang lebih rendah. Selain itu, model reanalisis ERA5 mensimulasikan konvergensi angin permukaan lebih cepat 2 jam di BMI bagian barat dan timur dibandingkan MRCM27 dan WRF. AbstractIn this study, we discuss the qualitative performance testing of ERA5 surface wind convergence over the Indonesia maritime continent (BMI) compared with research based on limited area model (LAM) by Qian, Im, and Eltahir and also Alfahmi et al. Wind surface convergence and wind surface anomalies convergence is calculated using finite-difference. The results show that the ERA5 reanalysis model can simulate convergence of surface wind anomalies compared with both regional climate model (RegCM) and 27 km MIT regional climate model (MRCM) over Java and also western BMI with higher convergence values. While ERA5 reanalysis model can also simulate convergence of surface winds, but with lower values compared to 9 km weather research forecast (WRF) model over eastern BMI. Besides, the ERA5 reanalysis model simulates convergence of surface winds, which is 2 hours faster over western and eastern BMI compared to MRCM27 and WRF.

Lateral boundary relaxation and large scale nudging in RCM runs

<p>Almost universally, in Regional Climate Modeling (RCM) integrations, Davies’ relaxation lateral boundary conditions are applied. They force variables in a number of rows around the boundary to conform to the driver global model values, completely at the boundary, and less and less toward the inside of the integration domain. Very often, in addition, investigators apply so-called large scale or spectral nudging inside the domain, forcing the integration variables not to depart much from those of the driver model.</p><p>It is pointed out that there is no scientific basis for these two practices. So why are they used? In particular for the former of these two, it is suggested that reasons must be either a belief that this is a practice RCM should follow, or a technique to address numerical issues of the limited area model used, or a combination of the two.  For the latter, a belief only.</p><p>Examples are shown that, in the absence of these two stratagems, the limited area model can improve on large scales inside its domain. This demonstrates that their use, aimed to force variables inside the domain not to depart much from the driver model data, should be detrimental, if possible numerical issues of the model used were to be remedied.</p>

Development of turbulent scheme in the FLEXPART-AROME v1.2.1 Lagrangian particle dispersion model

The FLEXible PARTicle dispersion model FLEXPART, first released in 1998, is a Lagrangian particle dispersion model developed to simulate atmospheric transport over large and mesoscale distances. Due to FLEXPART's success and its open source nature, different limited area model versions of FLEXPART were released making it possible to run FLEXPART simulations by ingesting WRF (Weather Research Forecasting model), COSMO (Consortium for Small-scale Modeling) or MM5 (mesoscale community model maintained by Penn State university) meteorological fields on top of the ECMWF (European Centre for Medium-Range Weather Forecasts) and GFS (Global Forecast System) meteorological fields. Here, we present a new FLEXPART limited area model that is compatible with the AROME mesoscale meteorological forecast model (the Applications of Research to Operations at Mesoscale model).1 FLEXPART-AROME was originally developed to study mesoscale transport around La Réunion, a small volcanic island in the southwest Indian Ocean with a complex orographic structure, which is not well represented in current global operational models. We present new turbulent modes in FLEXPART-AROME. They differ from each other by dimensionality, mixing length parameterization, turbulent transport constraint interpretation and time step configuration. A novel time step was introduced in FLEXPART-AROME. Performances of new turbulent modes are compared to the ones in FLEXPART-WRF by testing the conservation of well-mixedness by turbulence, the dispersion of a point release at the surface and the marine boundary layer evolution around Réunion. The novel time step configuration proved necessary to conserve the well-mixedness in the new turbulent modes. An adaptive vertical turbulence time step was implemented, allowing the model to adapt on a finer timescale when significant changes in the local turbulent state of the atmosphere occur.