The shift from manual weather measurements to automation is almost inevitable. When switching to AWS (Automatic Weather Station), WMO requires parallel data testing between automatic and manual measurements to be performed. The purpose of this paper is to conduct a parallel test of AWS data using a simple statistical test that has been applied to three main weather parameters, namely temperature, pressure, humidity, rainfall, and wind direction and speed. The months of January and June were used as samples to represent the character of the wet and dry seasons in the Makassar monsoon area. The results of the analysis show that during the rainy season, only pressure and temperature are identical and homogeneous. Meanwhile, in the dry season, apart from these two parameters, humidity and wind speed are also homogeneous and rainfall is a non-homogeneous parameter in January and June. Both AWS and manual observations show that the influence of land-sea winds in Makassar is very strong. Considering that there are inhomogeneous parameters, it is highly recommended to test for a longer time, taking into account the season, the influence of other global phenomena, the effect of missing data and incorrect data testing various methods of homogeneity and characteristics in each place and their effect on forecasts.

Improving the accuracy of rainfall forecasts is related to the number of rain gauges needed in an area, so determining the optimal number of rain gauges is very important. This study aimed to determine the best method for calculating the optimal number of rain gauges. Generally, the calculation of the optimal number of rain gauges using the coefficient of variation only takes into account the accumulation of rainfall at the station. The distance between the location and height of the rain gauge is not taken into account. The phenomenon of rain that occurs in the tropics is very dynamic, where one place compared to another tends to have different rain intensity and duration. In addition, the height and distance factors also greatly affect the measured rainfall. Therefore, it is very important to know the best method to calculate the optimal number of rain gauges needed in a particular area. This study implements 3 methods to determine the appropriate method to be used in determining the optimal rain gauge number for urban areas: namely, World Meteorological Organization (WMO) criteria, coefficient of variation, and Kagan-Rodda. In this study, rainfall data from 2010 to 2019 at 5 locations in Makassar were used in calculating the optimal number of rain gauges required. The results showed that the optimal number of rain gauges in Makassar as an urban area following the WMO recommendation was 9–18, where small islands around it are not considered. Another result obtained is that if the rainfall data for the Sudiang area, which is located at the coordinates (119.522° E, 5.085° S), is not included in the calculation, it will greatly reduce the accuracy in determining the optimal number of rain gauges in the Makassar area.

In recent decades, abnormal rainfall and temperature patterns have significantly impacted the environment and human life, particularly in East Nusa Tenggara. The region is known for its low rainfall and high temperatures, making it vulnerable to drought events, which have their own complexities due to being random and changing over time. This study aimed to analyze the trend of short-term meteorological drought intensity in Timor Island, East Nusa Tenggara. The analysis was carried out by utilizing the standardized precipitation evapotranspiration index (SPEI) for a 1-month period to characterize drought in intensity, duration, and severity. A power law process approach was used to model the intensity of the event, which is inversely proportional to the magnitude of the drought event. Intensity parameters of the power law process were estimated using the maximum likelihood estimation (MLE) method to predict an increase in the intensity of drought events in the future. The probability of drought was calculated using the non-homogeneous Poisson process. The analysis showed that "extremely dry" events in Timor Island are less frequent than "very dry" and "dry" events. The power law process model's estimated intensity parameter showed a beta value greater than 1, indicating an increase in future drought events. In the next 12 months, two months of drought are expected in each region of Timor Island, East Nusa Tenggara, with the following probabilities for each region: 0.264 for Kupang City, 0.25 for Kupang, 0.265 for South Central Timor, 0.269 for North Central Timor, 0.265 for Malaka, and 0.266 for Belu. This research provides important insights into drought dynamics in vulnerable regions such as East Nusa Tenggara and its potential impact on future mitigation and adaptation planning.