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Smoothing of generation variability, i.e. reduction of variance in the aggregate generation is crucial for grid integration of large-scale wind power plants. Prior studies of smoothing have focused on geographical smoothing, based on distance. In contrast, we propose a novel concept ‘diurnal smoothing’ that depends on spatial variations in the timing of seasonal-mean diurnal cycle peak. Considering the case of India, which experiences a strong diurnal cycle of wind-speed, we show how spatial heterogeneity in the wind diurnal cycle can be exploited to smooth wind power variability over and above geographical smoothing. For any given separation distance between sites, the hourly wind speed correlation is highly variable. Difference in timing of the diurnal cycle peak is an important factor for explaining this variability and we define smoothing from differently timed seasonal-mean diurnal cycle as ‘diurnal smoothing’. We show that apart from separation distance, the diurnal cycle is crucial for correlation among sites separated by 200 km or more with strong diurnal cycles (amplitude more than approximately 0.5 m s−1). Thus, diurnal smoothing is a vital factor in the aggregation of large wind power plants, and grid integration is benefited by considering (in addition to distance) new wind plant sites with largely separated diurnal cycles, especially those differing by roughly 12 h. Such diurnal smoothing is relevant for regions across the world with strong wind speed diurnal cycles. Ultimately grid integration depends on variations in total wind and solar generation and demand. Hence, their combined effects must be studied.

The seasonal transformation coefficient (SCOP) of a heat pump with a heat source outside air as a function of the temperature of the latter is studied. The bin method was used to determine the climatic data for the city of Varna. Outdoor air temperature data were used and statistical analysis of the data was performed. The average frequency and cumulative distribution of temperature bins values in the period from 2005 to 2019 in the city of Varna is calculated. The results are presented using graphs and tables. In the data stability assessment report, the standard deviation and coefficient of variation were used. The SCOP of a heat pump heating installation operating in Varna has been calculated. All results were compared with those of other studies of the authors under different conditions and with reference data.

The requirement for energy has expanded day by day due to excessive consumption, developing technology, and population growth, especially after the Industrial Revolution. Since the investments for promoting renewable energy sources, The Turkish energy sector has been progressing in the way of renewable energy. This inevitable change in the energy sector and variable power generation values in wind energy directly affect the energy markets. Accompanying with the structural transformation of the energy sector, Turkey had launched the Day-Ahead electricity marketing system in December 2011. The price per megawatt-hour is determined on the open market by the supply and demand. Hatay province was selected in this study because of its highest wind potential. Temperature and wind speed values, observed in Hatay Samandağı for October 2019, were determined. The data was configured with The Weather Research and Forecasting Model. The results indicate that the wind speed at high altitudes could reach above 7 meters per second on Turkey’s eastern Mediterranean coast, Hatay. The results were compared with the real-time data, and the error rates were determined. In the selected region, the electrical energy values generated using the model results were calculated the day before.

Extreme precipitation is ubiquitous in the Maritime Continent (MC) but poorly predicted numerical weather prediction (NWP) models. NWP evaluation against accurate measures of heavy precipitation is essential to improve their forecasting skill. Here we examine the potential utility of the Global Precipitation Measurement (GPM) Integrated Multi-Satellite Retrieval for GPM (IMERG) for NWP evaluation of extreme precipitation in the MC. For that purpose, we use radar data in Subang (Malaysia) and station data from the Global Historical Climatology Network (GHCN) in Malaysia and the Philippines. We find that earlier studies may have underestimated IMERG performances in the MC due to large spatial sampling errors of ground precipitation measurements, especially during extreme precipitation conditions. We recommend using the 95th percentile for NWP evaluation of extreme daily and sub-daily precipitation against IMERG. At higher percentiles, the IMERG rainfall rates tend to diverge from ground observation and may therefore be treated with caution.

The work of hybrid energy complexes of significant installed capacity based on photovoltaic and diesel generation in the northern regions of Yakutia is considered. The considerable remoteness of settlements from the country’s central energy system, the cargo delivery inaccessibility, as well as extreme climatic conditions determines the use of a hybrid energy complex with continuous diesel generation (without energy storage use).

The dynamics of interactions between the environmental and the meteorological variables in an urban region is extremely complex due to continuously evolving coupled human-natural processes in an urban setting We attempt to understand the same with the networks of variables using information theory, known as process network We monitored local meteorological variables at half-hourly scale using an eddy covariance observation system combined with available concentration of pollutants from other sources Both the datasets are for Powai, Mumbai, India, from January to April 2020 that includes pre-lockdown and lockdown periods associated with interventions in response to COVID-19 Analysis of the weekly process networks developed with the same data shows that they are more dominated by memory during the lockdown period We find that a high concentration of pollutants under no-lockdown scenarios, during specific work commute hours, interrupts the memory of the network A seasonal transition in temperature during the pre-lockdown period failed to make any major changes Our analysis shows that the dynamics of pollutant concentration drives the interaction between the variables of urban environmental meteorological system © 2020 The Author(s) Published by IOP Publishing Ltd

Trabajo presentado a Science of Making Torque from Wind 2020, TORQUE 2020 Synthetic wind-speed generators can provide a detailed characterisation of the wind variability at different time scales. A keen interest in the availability of synthetic wind speeds has recently risen in wind power modelling applications. In particular, a proper simulation of the diurnal and annual variability of the wind speed is sought that can lead to a more efficient grid integration of this renewable source. This paper proposes a statistical model for generating synthetic wind speeds consistent with both the probability density function and the spectral density function of a measured wind-speed dataset and that simulates accurately its average diurnal variation. To test the proposed methodology, multiple synthetic time series are generated using three long-term wind-speed time series recorded at a meteorological site in the Netherlands. The accuracy in terms of the statistical descriptors of the generated time series and their average diurnal variation is assessed with respect to the target data. We show that the average diurnal cycles present in all the three measured time series are always reproduced accurately, and that the statistical descriptors of the target dataset are constantly matched with high accuracy. Possible advantages of the present approach in terms of power system modelling are discussed.

People spend major part of their time inside places such as homes and offices, so it is very important to know the indoor and outdoor pollution in this type of studies. The atmospheric dispersion model WRF/Chem is used to know the outdoor pollution and meteorological conditions with high spatial (1 km) and temporal (1-hour) resolution and the building energy model EnergyPlus to simulate the indoor contaminants. EnergyPlus model is used to investigate the dynamic behaviour of pollutants with a single package using a multizone approach. 2016 year is used for the simulations with hourly outputs. Outdoor and indoor pollutions are linked to through the simulated infiltration process. The evaluation of outdoor, indoor air quality and human health effects was carried out considering different exposure profiles, for people working and living in an office and house located in the same building in the Madrid city center. The study takes into account different ventilation modes in the building and indoor emission scenarios (oven for heating, cooking, photocopy machine, smoke cigarettes). Health impact assessment considered mortality and hospital admissions, associated with exposure to PM2.5 and NO2 taking into account the differences between the exposure profiles, which have been used to describe the time activity patterns of the people. The health impacts of emitting sources are highest in the warm months due to the operation of the air conditioning system. The health impact of indoor emission sources is higher than the outdoor pollution. People in the zone where the emitting sources are located would experience a mortality and morbidity of 2.5 times more than in the non-emitting zones.

In both meteorological and metrological applications, it is well-known that air temperature sensors are susceptible to radiative errors, both from direct irradiation from the Sun and lighting sources, and from thermal sources such as the walls of a room, an enclosure or a human body. The key insight reported in this paper is that the radiative error measured by an air temperature sensor in flowing air depends upon sensor diameter, with smaller sensors reporting values closer to true air temperature. This is not a dynamic effect caused by low sensor mass, but a geometrical fluid-dynamical effect arising from sensor size. Although this result has been published [1] and is in meteorology text books [2], it does not appear to be widely known, and as a consequence air temperature can be - and probably is being - widely mis-estimated. In metrological applications, air temperature is required in the estimation of refractive index for dimensional measurements and in the estimation of air density for buoyancy corrections in precision weighing. In these applications, errors on the order of 0.1 °C are significant, representing limiting uncertainties in their respective fields. In this paper we show that errors of this magnitude can easily be present even inside well-controlled metrological laboratories. We then describe how to detect such errors, and how to minimise and correct for them. In meteorological applications, air temperature is the most commonly reported measurand and it is known to be subject to radiative errors both from direct insolation and thermal radiation even within a thermometer `screen'. The significance of the error is also known to be correlated with low wind speeds. However the diameter dependence of the correction is not widely appreciated. Understanding of the diameter dependence allows direct detection and assessment of the magnitude of the radiative error and, with proper design, it also enables minimisation of the error.

Landfalling tropical cyclones (TCs) frequently occur with strong intensity in most coastal areas, and storm surges are likely to occur in response to extreme sea level (ESL) growth. However, the level of ESL growth under various wind conditions, coastline geometries and tide-surge interactions has not been clarified. In the Pearl River Estuary and Daya Bay, observations of landfalling TCs have indicated an increasing frequency of intense and rapid landfalls in the 2010s as compared to the 2000s, accompanied by a noteworthy increase in storm surge. Based on a large ensemble (∼0.5 million storm surge events with various tracks, maximum wind speeds, maximum wind radiuses, translation speeds and tidal conditions) obtained from well-validated model simulations, the ESL growth in the study area is further quantified as follows: (a) ESL growth is more sensitive to the acceleration effect of landfalling TCs than to the strengthening effect of landfalling TCs since the effect of low acceleration (+3 m s−1) is comparable to that under notable strengthening (+10 m s−1); (b) ESL growth is strongly modulated by coastline geometry, especially in flared or arching coastline areas. ESL growth mainly occurs along flared coastline areas when landfalling TCs strengthen into severe TCs or typhoons but can also occur along arching coastline areas for stronger landfalling TCs, such as severe typhoons or supertyphoons; and (c) ESL growth could be increased or decreased by approximately 10% under the effect of tide-surge interactions. Both the large-ensemble method and the above ESL growth characteristics are worthy of attention in risk assessment and rapid prediction of storm surges in shallow waters.

Radon is a natural radioactive gas that may cumulate in indoor environments. The impacts of weather events on indoor radon concentration had been explored in different places around the world but only marginally in Switzerland. Using basic statistical analysis, this study investigates different meteorological parameters’ influence on indoor radon concentrations and shows that outdoor air temperature is the most influential beyond others. Finally, this paper highlights the importance of radon dynamic in buildings, a topic often overlooked by construction professionals and the broader public in Switzerland.

One of the important factors in weather and climate dynamics that can trigger precipitation on the coast and the surrounding area is a sea breeze. Sea breeze occurs because of differences in the surface temperature between land and sea due to solar heating which then forms a pressure gradient that leads to a land called the sea breeze circulation. An important part of sea breeze circulation is the Sea Breeze Front (SBF). SBF is a boundary area where wind from the sea direction meets the wind from the land direction, which is marked by significant changes in temperature, humidity, wind and can trigger convective activity. This study aims to determine the characteristics of the SBF on the north coast of Banten-Jakarta in 2018 which were identified using a Doppler weather radar Plan Position Indicator (PPI) product and convective activity using the Coloumn Maximum (CMAX) product. Qualitative and quantitative methods are used to determine the SBF parameters such as frequency of occurrence, onset time, duration, length, column depth, and SBF penetration, and convective activity during the occurrence of SBF. The results showed that SBF was detected more in the rainy season January, February, and December 2018, and occur between 08:08 LT and 15:20 LT. SBF can trigger the occurrence of convective clouds and affect the temperature and humidity conditions around the SBF.

This study aimed to analyze the spatial and temporal lightning distribution in North Sulawesi. The general meteorological condition of North Sulawesi has also been considered to identify the cause of the lightning occurrence. Lightning activity over North Sulawesi has been investigated using lightning data from Winangun Geophysical Station during 2019-2020. The result shows that in the land area of North Sulawesi, the highest lightning density occurred in the Tomohon regions due to its topographical features. Overall, the flash density over the land area is higher than the sea area due to its high atmospheric instability. The maximum flash density does not occur during the wet periods, but it occurs during September - October – November, which has a high surface temperature over this region. It is also known that shearline and low-pressure areas contribute to the high lightning occurrence in October 2019, while La Nina in the Pacific Ocean influences lightning activity in July 2020

The need for adequate rainfall data in all regions of Indonesia cannot be achieved only by relying on ground observation tools. This work aims to evaluate the application of spatial satellite rainfall data in characterizing rainfall associated with climatic condition over Indonesia. This study applied an Integrated Multi-satellite Retrievals for GPM (IMERG) data using a double correlation method (DCM). The analysis was carried out in the period April 2014 to March 2019. Before regionalization, IMERG V06 data were validated using observed rainfall data from the Agency for Meteorology, Climatology, and Geophysics of the Republic of Indonesia (BMKG). The results showed that 96% of 154 total validation locations have a high correlation score between IMERG and rain gauges (r = 0.5 – 0.97). IMERG was also able to identify monthly and annual rainfall patterns in Indonesia. Based on DCM, we obtained four rainfall regions in Indonesia. Region A has the monsoonal characteristic, covers central and south Indonesia from south Sumatra to Nusa Tenggara, south parts of Kalimantan, some areas of Sulawesi, and parts of Papua. Region B has an equatorial pattern (semi-monsoonal), located in the equatorial area of Indonesia and covers the west and east part of Sumatra and the north-central part of Kalimantan. Region C, with an anti-monsoonal pattern, covers Maluku, western-central Papua, and parts of Sulawesi. Region D is influenced by monsoon and cold surge characteristics, located in the north part of Sumatera and a small portion of northern Kalimantan to the South China Sea region. Besides the new region D, this research also showed five other differences between IMERG-based map and gridded rain gauges’ data-based map (2003). The regionalization results based on IMERG reveal that there is a possibility of updating areas with certain rainfall characters in Indonesia related to resolution, density, and updates data sources.

Storm is defined as a disturbance of the atmosphere marked by winds and usually by rain. Coastal storms must comprise a maritime component, such as waves, currents and/or water levels. Coastal storm detection is necessary so the number of casualties and losses caused by these events can be reduced. The method used in this system is the Sugianto wave forecasting method with standardization of coastal storms using the Beaufort scale. The purpose of this study is to built up an internet of things based system to observe coastal storm information and wave forecasting data from wind speed data that obtained in Timbulsloko, Demak, Central Java, Indonesia. The tidal data is processed using the Admiralty method. This system was built using Arduino Uno equipped with anemometer JL-FS2 to measure wind and waves parameters. The power source from 100 wp solar panels stored in a 40 Ah accumulator. Data from field instrument is stored to the IoT MAPID database using NodeMCU ESP8266. This system is placed in Timbulsloko, Demak. The results of field observation then validated using BMKG. This method could be applied in other location along the north coast of Java. The results of field observation showed an average wind speed 3.9848 m/s; significant wave height 0.4632 m; significant wave period 3.8641 s; wave energy 493.90 J/m2; wind energy 116.74 W/m2.

The influence of hybrid sigma coordinate is better to represent turbulence in America than basic sigma coordinate. Therefore, it is necessary to search the effect of these coordinates on turbulence simulations in Indonesia due to the analysis of different atmospheric conditions from America. In this research, two experiments are performed using two different vertical coordinates with a case study flight turbulence from Batik Airlines on October 24, 2017. The two different vertical coordinates are the hybrid sigma coordinate and basic sigma coordinate. The data used are NCEP-FNL, Himawari-8 satellite image data, and sounding data. Based on the result of this research, simulation using hybrid sigma coordinate shows isentropic lines that have the potential turbulence during and after turbulence event. Richardson number value about 0.1 – 0.2 and intensity of the energy dissipation rate is 0.06 m 2/3s-1. According to the Richardson number value and intensity of the energy dissipation rate, the hybrid sigma coordinate simulation shows turbulence potential more significant than the basic sigma coordinate.

Bandung is one of big cities in Indonesia with high activities on industrial and transportation that will increase the air pollutant emission and causes adversely affect the public health. Based on that matter, monitoring of air pollutant concentration is urgently needed to predict the direction of pollutant dispersion and to analyze which locations are vulnerable to maximum exposure of the pollutant. Field monitoring of air pollutant concentration needs much time and high cost, but modeling could help for this. One of the models that can be used to predict the direction of pollutant distribution is the Weather Research Forecasting/Chemistry (WRF-Chem) model, which is a model that combines meteorological models with air quality models. The output of the WRF-Chem running model on July and October 2018, which has been analyzed visually, showed the dispersion pattern of PM10 and PM2.5 is spread mostly to the west, northwest, and north following the wind direction. According to the output of the WRF-Chem model, Bandung Kulon is the most polluted subdistrict by PM10 and PM2.5 with an exposure frequency of 22 hours (PM10), 24 hours (PM2.5) on July 2018 and 19 Hours (PM10), 14 hours (PM2.5) on October 2018. The correlation value for meteorological parameters is quite high in July 2018 (R = 0.9 for wind speed and R = 0.82 for air temperature). So based on the meteorological factor, WRF-Chem model can be used to predict the direction of pollutant distribution.

Gravity Waves (GWs) are believed to play important role in the generation of the driving force of the stratospheric Quasi-Biennial Oscillation (QBO). Deep convection in the equatorial region can generate large amount of GW with short vertical wavelength (λzzfrom 0.5 to 3.5 km in the UTLS region of 20-27 km heights are identified by calculating the GW potential energy (Ep). Correlation between GW activities and QBO phases is examined using 50 hPa zonal wind as the QBO index. The results show that during both easterly and westerly QBO phases, the GWEpvalue increases gradually with time and reaches its peak in the transition periods. This pattern is seen inEpwith all vertical wavelengths between 0.5-3.5 km but the percentage value ofEpfor λzEpvalues exhibit downward propagation with the QBO phase but there are also discernible upward propagations of GW activities below 24 km height and intersect those two bring large changes in QBO phases. Additionally, higher percentage ofEpwith λz

Heavy rainfall inducing other catastrophic events are frequently experienced globally. Understanding the mechanisms of moisture transport during such events will help in furthering our knowledge about such systems. In the current study, estimation of most likely moisture trajectoriesis performed using back trajectory analyses. Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model available from National Oceanic and Atmospheric Administration’s (NOAA) Air Resources Laboratory (ARL) is used for the purpose. A preliminary analysis is conducted by calculating the frequencies of back trajectories from two locations in Kerala for three heavy rainfall cases. The analysis indicates that both the locations have similar pattern of moisture trajectories during the cases occurring in south west monsoon and pre monsoon periods. However, a change in the behaviour of the trajectories for the two locations is observed for the case during the north east monsoon period. Since this study involved only individual cases, robust conclusions cannot be made based on this for the dynamics of moisture transport for these locations. More detailed analysis will follow this preliminary study in future for the purpose.

Climate change due to architecture occurs as a result of technological developments that support the development of materials, electrical mechanics, structures, and building shapes that play a role in increasing emission levels in the air. One type of building in Indonesia that contributes to increasing emissions is the residential building known as rumah susun. This research employs the case study method, observing the Rumah Susun Jatinegara Barat, located in East Jakarta. The case study shows that the use of prototypes that are not environmentally friendly makes a building’s performance worse. The use of precast, which resulted in monotonous window dimensions, is considered the main factor causing the failure of this Rumah Susun Jatinegara Barat to adapt to the surrounding environment. This problem occurred because the openings in the building façades had a monotonous dimension while the wind intensity that hit the building was increasing. The final result shows that the windows on the façades of the case study were not functioning except if all the openings in the residential unit are open. These results prove that the height of a building is an essential factor in planning high-rise flats, especially in Jakarta.

The integration of the availability and processing of The Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) data by the Google Earth Engine (GEE) platform is used in this study to extract the estimated monthly rainfall in South Sulawesi. Several areas are selected based on the characteristics of the rainy period cycle representing South Sulawesi, namely Makassar, Masamba, Wajo, and Bone. Monthly rainfall estimation data of CHIRPS in the year 2019 were validated by monthly observed rainfall at the same period showing the CHIRPS rainfall estimation has not been maximized with correlation coefficient values are 0.94, 0.63, 0.65, 0.75, and RMSE percentage 54%, 52%, 95%, 64% for each of the study areas. Then the increase in rainfall estimation performance is carried out by applying multiple linear regression method and considering both monthly observed and estimated rainfall during 30 years from 1989 to 2018, latitude and longitude point as well as elevation in every location. The results show an increase of correlation coefficient to 0.95, 0.74, 0.74, and 0.87 and a general decrease of RMSE percentage to 53%, 39%, 80%, and 67%. Thus, monthly rainfall estimation performance improvement is successfully achieved in various rainy period cycles of the study area.

Thermal comfort is one of the standard assessments of building thermal environment. Air movement is an important parameter for in a naturally ventilated to achieve thermal comfort by accelerating the evaporative cooling process on the human body. Aceh House has a standard of thermal comfort with a vernacular architecture with a natural ventilation system. This vernacular architectural building has a fairly high harmonization of the environment because it has undergone a process of adaptation. In this study, observations were made at the Original House (OH), the Adaptive Reuse House (ARH), and the Aceh Modified House (AMH). By using the method of assessing changes in environmental comfort, using Wet Bulb Temperature Index (WBGT) method, the minimum and maximum temperature ranges are 25°C and 30°C. In the WBGT thermal rating, AMH has the higher thermal and is followed by ARH and OH respectively. Thus, OH has lower thermal compared to other Aceh houses.

Convective cloud monitoring since its growth stage primarily related to location and time of the first convective cloud initiated, called convective initiation (CI), could be the primary key in providing an earlier heavy rainfall event prediction. This study aimed to assess the accuracy and lead time of CI nowcasting using Satellite Convection Analysis and Tracking (SATCAST) algorithm in predicting the CI event within 0-60 minutes over Surabaya and surrounding area using Himawari-8 satellite during June-July-August (JJA) period in 2018. Three main processes used in this study were cloud masking, cloud object tracking, and CI nowcasting. Twelve interest fields were utilized as predictors based on six bands of Himawari-8 satellite, which represented cloud physics attributes such as cloud-top height, glaciation, or cooling rate. The verification was conducted by comparing CI prediction to CI location and time based on Surabaya weather radar within the next 0-60 minutes. The algorithm resulted that the prediction could achieve 87.3% of accuracy from the 3449 cloud objects. The prediction had POD, FAR, and CSI scores of 57.1%, 52.2%, and 35.2%, respectively. The 32.3 minutes of averaged lead time prediction indicated that CI nowcasting could detect growing cumulus about 30 minutes prior to the CI event.

Solar Energy is the most popular among several clean energies. As a tropical country, Indonesia has big opportunity to develop solar power, particularly in East Kalimantan which spans around the equator. Solar energy generation, however, is influenced by weather parameters which give uncertain values of the amount of the captured energy. Therefore, this research is conducted to overcome the effect of weather towards solar energy. The aim of this research is to examine the model for sun power forecasting based on the data. The Artificial Neural Network (ANN) and Multiple Linear Regression have taken as the approach models to determine energy forecasting. This study used five input variables; temperature, precipitation level, humidity, wind speed, and surface pressure, while the solar radiation was taken as the output variable. Moreover, the daily solar power and weather data from East Kalimantan has been taken along the period of 27th July 2018 – 28th July 2021. The result of this study showed that the RMSE of ANN was slightly similar with the multiple linear regression methods which were calculated by 160.26 and 160.46 respectively. However, the ANN is preferable to use in the solar energy forecasting since the tendency of nonlinearity of the climate data.

High-Frequency (HF) Radar is an instrument using radio waves to measure ocean currents and waves remotely. This technology has many advantages, including has unprecedented spatial and temporal resolution, can operate in any weather condition, and is not dangerous for the environment. However, HF Radar's research is still limited in Indonesia. This research aimed to analyze the tidal and residual current in the Bali Strait in July 2020. Radial velocity from two HF Radar sites is combined to obtain the total currents. Current data from HF Radar were compared with Acoustic Doppler Current Profiler (ADCP) data to investigate its accuracy. Surface current data were analyzed using harmonic analysis to separate tidal and residual currents. Comparison between HF Radar and ADCP data are in good agreement for meridional current with a very high correlation of 0.813 and a small RMSE value of 0.22 m/s. Harmonic analysis shows that the dominant currents are tidal currents. The current direction was northward (southward) at flood (ebb), with maximum northward (southward) velocities are 2.17 m/s (2.97 m/s), respectively. The residual current has a random pattern, slightly faster northward than southward, and has similar spectral with the wind.

Understanding climate change impacts on wildland fire activity has been constrained by the high uncertainty embedded in the prediction of fire size (FS), annual number of fires (ANF), and annual area burned (AAB). While there has been a sustained effort to make connections between fire weather and fire activity, most studies have focused on individual parameters instead of treating them as a connected group. This study explores the intrinsic relationships among the major parameters of fire activity and how they relate to fire-conducive weather conditions to determine optimal prediction models. We found maximum number of fire spread days and maximum FS best predict ANF and AAB, respectively. Assessing the robustness of these relationships across Canada’s ecozones showed they are stronger in the Cordillera than in the Shields and Plains and more universal for AAB than for ANF. We also found skewness of FS distributions may play an important role in relationship strength. These relationships provide a unique way to model future fire activities under changing climate conditions.

All data, including air temperature data, must be verified by conducting quality control using the step check method. Step check quality control is carried out by looking at the difference of a parameter in a certain period compared to the threshold value that was already determined. Therefore before carrying out step check quality control, it is necessary to determine the ceiling and floor boundaries of the difference in air temperature data every hour. The data used in this study are hourly air temperature data and hourly present weather data from weather observations at the South Tangerang Climatological Station during 2016 - 2020. In determining the threshold for air temperature step check quality control, the air temperature data is paired with weather condition data to obtain a threshold value according to rain and no rain conditions. The threshold conducted in this study is based on a check for unusual climatological values, where the limits for an unusual and impossible jump in hourly air temperature changes are determined based on a certain percentage of the data distribution. This study uses percentile analysis to determine the threshold, where 5% in the lower and upper part of the data distribution are used as the threshold. The results show various thresholds every hour. The increase in temperature dominates the changes of hourly air temperature in no-rain conditions. The highest threshold for temperature increase occurs at 00.00 – 01.00 UTC at 3.2°C and continues to decrease over time. The highest threshold for temperature decrease occurs at 09.00 UTC - 10.00 UTC at 2.2°C. In rain conditions, the increase in temperature can still occur. However, the decrease in temperature mainly occurs. The highest threshold for temperature increase during rainy conditions is 1.8°C at 01.00 - 02.00 UTC, while the highest threshold for the temperature decrease is 5.8°C at 06.00 UTC – 07.00 UTC. With these results, observers can first carry out quality control with the Step Check method before filling in the data into the system database. Thus, any suspect data either from reading errors or tool errors can be minimized and finally produce a valid dataset.

Rainfall characteristics of Indonesia's tropical climate have high variability according to space and time, so to determine the rainfall pattern of a location, an in situ rainfall measuring instrument (AWS = automatic weather station) is needed with high density. The existence of AWS also requires relatively high maintenance costs and a standard placement location (according to the rules of WMO = World Meteorological Organization) which is relatively broad and is not obstructed by other objects that can make the result of rainfall data is not representative. With the concept of computer vision, research will be carried out to estimate the rainfall condition from the CCTV cameras. The CCTV camera data which have qualitative characteristic into rainfall data which have quantitative characteristics. This research is also motivated by the large number of CCTVs that are placed in a lot of locations by local governments along with the Smart City program in districts and cities throughout Indonesia. The preliminary research was conducted in Center for Atmospheric Science and Technology office in Bandung. Rainfall data from AWS was used to validate CCTV data which placed in same location. The process of converting CCTV data into rainfall data goes through 6 stages. The first is reading the image mapping data and AWS (in rainfall accumulation data form). Second, read the image data in grayscale. Third, extract the features. Fourth, split the reference and sample data. Fifth, conducts the K-NN Mapping Reference Image and rainfall accumulation data. Sixth is to praise K-NN Testing. The accuracy is calculate with comparing the estimated number of CCTV cameras that are correct with the total sample size. The evaluation result states that the highest accuracy is obtained with K = 1. When K=1, the accuracy percentage reaching 94.8%. Accuracy decreases with increasing value of K and drastically decreases with K> 2. In the 1-10 days reference data, the highest accuracy is obtained by the number of reference data for 10 days, which is around 97%, stable until the value of K = 8. While the lowest accuracy is obtained when the reference data is 1 day with an accuracy value of about 43%. Based on the results of this study, it can be concluded that rain data from CCTV can be used to estimate the rainfall data. The best result happened when K-value is equal to 1.

The monthly inflow and outflow of money from an area is one of the important concerns in the economic life of a region. This study aims to model and predict the monthly cash inflow and outflow of Kediri, East Java Province, Indonesia using the Hybrid Seasonal Autoregressive Integrated Moving Average – Feedforward Neural Network (SARIMA-FFNN) model. Seasonal time series data from monthly cash inflow and outflow of Kediri are used to test the forecasting accuracy of the proposed hybrid model. First, both variables are modeled using the SARIMA model. Then, non-linearity testing was carried out on the best SARIMA model for each variable and the results showed that only cash inflow was non-linear. Therefore, only cash inflow could be continued with the FFNN model. The best selected model was the FFNN model with the input SARIMA(0,0,0)(1,0,0)12 with five hidden layers. The input of FFNN modeling was based on the best SARIMA model with only the autoregressive order which for non-seasonal and seasonal. The sum of hidden layers was chosen by the smallest values of MAPE and RMSE. Forecasting results with the hybrid SARIMA-FFNN model on data testing followed the actual data pattern.

This paper was done by using the HF Radar data from 2018-2019 to study the characteristics of Sea Surface Current (SSC) in the Bali Strait. The data processing method was done by calculating the speed and SSC direction of the zonal and meridional components. Furthermore, SSC analysis was performed every hour and month by calculating the average of all data at the same hour and month. It was found that the unique SSC pattern in the Bali Strait occurred on the western side of Bali Island and the eastern side of Java Island. On the west side of the Bali Island, there was a decrease in SSC speed at 0.00-7.00 and 13.00-18.00, as well as a two-fold increase at 8.00-12.00 and 19.00-2.00, both of which were in a fluctuating speed range from 0-140 cm s-1 in the direction of dominant towards the south. On the eastern side of Java Island, SSC speed ranges from 0 to 40 cm s-1 all the time with the dominant direction heading from east to southeast. The monthly SSC pattern was also seen more clearly in this study, meanwhile during December-March the SSC rate was lower than during June-September, ranging from 0 to 20 cm s-1 and from 40 to 140 cm s-1, respectively. Furthermore, the two SSC patterns above can be simplified into two periods, namely periods of relaxation and agitation. This study also applies the device to ship accidents that occurred in the Bali Strait as case studies.

Pedestrian wind environment assessment is becoming an essential part of the urban design process especially in dense urban areas due to its ability to address the wind comfort/safety/health concerns in an early phase. In this paper, high-fidelity computational fluid dynamics (CFD) simulations, validated with experimental data, are performed on eight different designs in a generic urban layout to study the impact of volume distribution on pedestrian wind environment in high-rise urban districts. The results show that the blockage effects of the high-rise buildings decelerates the wind in the streets parallel to the flow while accelerating the flow in the streets perpendicular to the flow. This effect is evident up to a two block distance upstream of the high-rises. Furthermore, it is shown that consequent rows of high-rises in the downstream of the first row facing the wind flow have little effect on the upstream pedestrian wind; however, they have a significant role in the extent of affected areas downstream. The findings of this study provide further understanding about the impact of different volume distributions on pedestrian wind environment in high-rise urban districts and clarify their effect on wind safety and comfort.

Aiming at the problem of wind power interval prediction, a short-term wind power interval prediction model based on VMD and improved BLS is proposed. Firstly, the complex wind power time series are decomposed by variational mode decomposition to reduce the non stationarity of wind power. Then an improved broad learning system (BLS) is established to predict the power and error of each component, and a weight is given to the prediction error of each component. The sparrow search algorithm (SSA) is used to optimize the weight, and the width of the prediction interval is obtained by adding the power and error prediction values. The experimental data show that the proposed model improves the accuracy of prediction interval.