We previously produced the local ensemble transform Kalman filter (LETKF)-based Ocean Research Analysis (LORA) version 1.0 datasets for the western North Pacific and Maritime Continent regions (LORA-WNP and LORA-MC, respectively) during the period from August 2015 to January 2024. However, these limited domains and periods constrain their applicability. Therefore, we developed a new eddy-permitting LETKF-based ocean data assimilation system and produced LORA version 2.0 for a quasi-global domain (LORA-QG) from June 2002, when the Advanced Microwave Scanning Radiometer (AMSR) series, a series of space-borne microwave imagers, began providing sea surface temperature observations and the Argo program substantially expanded in situ temperature and salinity measurements. We validated LORA-QG using observations from surface drifter buoys, tide gauges, and ocean climate stations, and compared the results with those of three eddy-permitting global ocean reanalysis datasets (GLORYS2V4, ORAS5, and C-GLORSv7). Although these observations are independent of LORA-QG, they may not be entirely independent of the other three reanalysis datasets. The validation results show that LORA-QG agrees well with the observations and has the second-highest accuracy among the four datasets in terms of overall root-mean-square deviations relative to the observations, thus achieving sufficient accuracy for geoscientific research and practical applications. LORA-QG provides features unavailable in conventional global reanalysis products, including ensemble-based uncertainty estimates and individual terms of the heat and salinity budget equations. These features make LORA-QG a valuable dataset for ensemble-based ocean forecasting and process-based studies. However, room for improvement remains, as LORA-QG exhibits significant warm biases in the tropics, particularly in the western tropical Pacific, and its sea surface salinity representation is likely limited due to relatively strong salinity nudging toward a climatological dataset in the mixed layer.