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This paper highlights detailed projected changes in rainfall over Thailand for the early (2011–2040), middle (2041–2070) and late (2071–2099) periods of the 21st century under the representative concentration pathways (RCP) 4.5 and RCP 8.5 using the high‐resolution multi‐model simulations of the Coordinated Regional Climate Downscaling Experiment (CORDEX) Southeast Asia. The ensemble mean is calculated based on seven members consisting of six general circulation models (GCMs) and three regional climate models (RCMs). Generally, the ensemble mean precipitation agrees reasonably well with observations, best represented by the Global Precipitation Climatology Center (GPCC) data, over Thailand during the historical period (1976–2005). However, inter‐model variations can be large among ensemble members especially during dry months (December to March) for northern‐central‐eastern parts, and throughout the year for the southern parts of Thailand. Similarly for future projection periods, inter‐model variations in the sign and magnitude of changes exist. The ensemble means of projected changes in rainfall for both RCPs during dry months show distinct contrast between the northern‐central‐eastern parts and the southern parts of Thailand with generally wetter and drier conditions, respectively. The magnitude of change can be as high as 15% of the historical period, which varies depending on the sub‐region, season, projection period, and RCP scenario. In contrast, generally drier conditions are projected during the wet season (June to September) throughout the country for both RCPs where the rainfall reduction can be as high as 10% in some areas. However, the magnitude of projected rainfall changes of some individual models can be much larger than the ensemble means, exceeding 40% in some cases. These projected changes are related to the changes in regional circulations associated with the winter and summer monsoons, which are projected to weaken. The drier (wetter) condition is associated with the enhanced subsidence (rising motion).

Informasi spasial curah hujan dibutuhkan oleh berbagai sektor namun karena keterbatasan pengamatan, proses interpolasi harus dilakukan. Metode interpolasi spasial terbaik untuk suatu tempat perlu ditentukan secara khusus. Penggunaan metode interpolasi Inverse Distance Weight (IDW) P=5 di Stasiun Klimatologi Malang perlu dikaji ulang. Tujuan penelitian ini adalah mencari justifikasi parameter interpolasi, membandingkan hasil interpolasi, dan pada akhirnya menentukan metode interpolasi terbaik untuk curah hujan bulanan Jawa Timur. Tiga metode yang diperbandingkan adalah IDW, Ordinary Kriging (OK), dan Regression Kriging (RK). Data curah hujan bulanan yang digunakan adalah 197 titik selama 204 bulan. Prediktor RK menggunakan ketinggian, kelerengan, dan estimasi curah hujan satelit. Parameter interpolasi seperti ukuran piksel, jumlah pencarian (NN), model variogram, dan power IDW dijustifikasi terlebih dahulu. Korelasi spasial digunakan untuk membandingkan hasil interpolasi. Validasi silang lipat sepuluh digunakan untuk menghasilkan galat. Galat interpolasi yang digunakan berupa nilai dan selisih kategori warna peta standar. RMSE dan MAE digunakan sebagai parameter validasi. Analisis waktu komputasi juga dilakukan. Piranti lunak R Statistics dan QGIS digunakan untuk membentuk bahan maupun mencari parameter interpolasi sedangkan interpolasi dilakukan menggunakan SAGA. Parameter interpolasi ditentukan sebagai berikut: ukuran piksel=0,01; NN=9; model variogram sperikal dengan Nugget=0, Sill=1, dan range bervariasi; power IDW=1,5. Hasil interpolasi RK jauh berbeda dari IDW maupun OK. Secara umum, IDW memiliki galat paling kecil (MAE kategori=0,871) dibandingkan OK (0,890) maupun RK (1,188).

This study aims to evaluate the impact of the National Economic Recovery Program—Pemulihan Ekonomi Nasional (PEN) and digitalization on micro, small, and medium enterprises’ (MSMEs) resilience during the COVID-19 pandemic. This research is based on primary data from a survey of 6009 Bank Rakyat Indonesia customers conducted from March–June 2021. Using the generalized ordered logistic regression technique, this study found that a combination of new loans, credit restructuring, and/or interest subsidies was the most successful PEN for enhancing MSME resilience. Meanwhile, providing new loans merely improved liquidity, not sales or profitability. However, just providing a restructuring program weakened resiliency. This research also discovered that MSMEs that have been digitalizing for more than a year are more resilient than those that have not. This study highlights the necessity of offering several interventions for MSMEs and assisting MSMEs in going digital to improve MSME resilience during the COVID-19 pandemic.

This study presents the application of the aquila optimizer (AO) algorithm to determine the parameters of the proportional integral derivative (PID) controller to control the speed of a dc motor. The AO method is inspired by the most popular bird of prey in the northern hemisphere named Aquila. Initially, the proposed AO algorithm is applied to unimodal and multimodal benchmark optimization problems. To get the performance of the AO method, the controller is compared with other methods, namely Seagull optimization algorithm (SOA), marine predators algorithm, giza pyramids construction (GPC), and chimp optimization algorithm (ChOA). The results represent that the AO is promising and shows the effectiveness. Determination of PID parameters using the AO method for dc motor speed control system shows superior performance.

Indonesia has two seasons and witnesses three rainfall patterns throughout the year. Although Aceh and North Sumatra experience low rainfall, the underlying causes of this condition are unknown. Unfortunately, studies on drought in these regions are very limited. This study uses the Effective Drought Index (EDI) to assess drought in these regions using daily rainfall data from 1985–2019 (35 years) from the meteorology, climatology, and geophysics agency stations. These stations are Sultan Iskandar Muda, Malikussaleh, Deli Serdang, and FL Tobing. In this study, the Ocean Niño Index (ONI) and the Dipole Mode Index (DMI) were used to identify El Niño years and positive and negative phases of the Indian Ocean Dipole (IOD). These indices were used to analyze drought-related climatic phenomena. The results obtained indicated that some drought events were not associated with a positive Indian Ocean Dipole or El Niño, as is typically the case. These include the extreme drought in 1989/90 and moderate drought in 1999 at Sultan Iskandar Muda, moderate drought at Malikussaleh (March to June 2008), moderate drought at Deli Serdang in 2010, as well as the drought from January to June 2006 at FL Tobing. Analysis of spatial patterns revealed that moderate droughts were more prevalent than severe and extreme droughts. These drought assessment results are essential for the mitigation of natural catastrophes.

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Graphic abstract On March 2, 2020, the first Coronavirus Disease (COVID-19) case was reported in Jakarta, Indonesia. One and a half months later (15/05/2020), the cumulative number of infection cases was 16,496, with a total of 1076 mortalities. This study investigates the possible role of weather in the early cases of COVID-19 in six selected cities in Indonesia. Daily temperature and relative humidity data from weather stations nearby in each city were collected from March 3 to April 30, 2020, corresponding with COVID-19 incidence. Correlation tests and regression analysis were performed to examine the association of those two data series. Moreover, we analyzed the distribution of COVID-19 referring the weather data to estimate the effective range of weather data supporting the COVID-19 incidence. Our result reveals that weather data is generally associated with COVID-19 incidence. The daily average temperature (T-ave) and relative humidity (RH) present significant positive and negative correlation with COVID-19 data, respectively. However, the correlation coefficients are weak, with the strongest correlations found at the 5-day lag, i.e., 0.37 (− 0.41) for T-ave (RH). The regression analysis consistently confirmed this relation. The distribution analysis reveals that most COVID-19 cases in Indonesia occurred in the daily temperature range of 25–31 °C and relative humidity of 74–92%. Our findings suggest that COVID-19 incidence in Indonesia has a weak association with weather conditions. Therefore, non-meteorological factors seem to play a more prominent role and should be given greater consideration in preventing the spread of COVID-19. Supplementary Information The online version contains supplementary material available at 10.1007/s42398-021-00202-9.