There is a large amount of documented weather information all over the world, including Asia (e.g., old diaries, log books, etc.). The ultimate goal of this study is to reconstruct historical weather by deriving total cloud cover (TCC) from historically documented weather records and to assimilate them using a general circulation model and a data assimilation scheme. Two experiments are performed using the Global Spectral Model and an ensemble Kalman filter: 1) a reanalysis data experiment and 2) a ground observation data experiment, for 18 synthesized observation stations in Japan according to the Historical Weather Data Base. By assuming that weather records can be converted into three TCC categories, the synthetic observation data of daily TCC are created from reanalysis data, with a large observation error of 30%, and by classifying ground observation data into the three categories. Compared with the simulation without assimilation of any observation, the results of the reanalysis data experiment show improvements, not only in TCC but also in other meteorological variables (e.g., humidity, precipitation, precipitable water, wind, and pressure). For specific humidity at 2 m above the surface, the monthly averaged root-mean-square error is reduced by 18%–22% downstream of the assimilated region. The results of the ground observation data experiment are not as successful as a result of additional error sources, indicating the bias needs to be handled correctly. By showing improvements with the loosely classified cloud information, the feasibility of the developed model to be applied for historical weather reconstruction is confirmed.

Dynamical downscaling of General Circulation Model (GCM) data for any region has been made possible due to a set of physics options and model dynamics within the Weather Research and Forecasting (WRF) model. This study evaluated the performance of an ensemble of physics options in simulating rainfall during wet and dry seasons of Lao PDR. The model evaluation criteria focused on identifying the optimum physics options for a range of scenarios. No single combination of physics options performed well in all scenarios reflecting the importance of using different parameterizations according to the geographic location and the intended application of the results. For the dry season, none of the ensemble members performed satisfactorily for the southern region of Lao PDR, while all the ensemble members performed well for the northern and central regions. While almost all the WRF simulations overestimated the rainfall during the wet season, BMJ for cumulus physics performed better in the northern and central regions, and KF performed better in the south region. The YSU scheme performed best as the planetary boundary layer for both wet and dry seasons, while WSM5 for the wet season and Lin for the dry season gave the best model performance as the microphysics option.

Removal of suspended solids from raw water is one of the essential processes in water and wastewater treatment plants. The conventional sedimentation tanks in the water treatment plants occupy a large area and become expensive in urban areas. The use of plate settlers or tube settlers in sedimentation tanks to increase the efficiency and hence to reduce the footprint of sedimentation tanks is an economical solution in the water treatment industry. This study investigated the effectiveness of plate and tube settlers compared to the plain settlers in a water treatment plant. A three-dimensional computational fluid dynamics (CFD) model was set up using ANSYS-CFX 17.2. Seven cases of plain settlers, plate settlers, and tube settlers were analysed for the comparison of tube settler performances. The maximum removal efficiencies of all solid classes were approximately equal in both plate settlers and tube settlers, and they are around 100, 67, 28, and 9% for the solid classes with particle diameters of 41, 17, 9.5, and 5.0 μm, respectively. The settling efficiency remained unchanged with the increase of the plate settling area beyond 60% of the plain settler area. The tube cross section shape does not affect the particle removal efficiency of a tube settler.

Extreme rainfall events leading to severe hydrological impacts warrant an accurate prediction of such events not only on time but also in magnitude. Sri Lanka is a South Asian country that is frequently affected by severe tropical storms. The primary aim of this study was to improve heavy rainfall events forecast during the North-east monsoon over the Badulu Oya catchment, Sri Lanka. This aim was accomplished by simulating precipitation for two extreme North-East monsoon rainfall events using the Weather Research and Forecasting (WRF-ARW) model. A detailed comparison was made between the 24-h spatial distribution of model rainfall and observations obtained from rainfall gauges. Verification was evaluated based on three deterministic approaches. Each rainfall event was simulated multiple times using 15 different parameterization scheme combinations including six microphysics and four cumulus schemes at a 3 km grid resolution. The filtered best model combinations were validated using observations from another two heavy North-East monsoon rainfall events. The key finding from these evaluations was that model configurations with WSM5, WSM6, Kessler and WDM6 microphysics, and KF, BMJ and MKF cumulus schemes displayed the overall best performances. Therefore, these combinations have a good potential for operational use in numerical weather prediction over the said catchment.