The stakes could hardly be higher. Indonesia’s tropical climate, with relatively consistent temperatures and distinct wet and dry seasons, has historically masked the growing threat of extreme heat. Yet heatwaves in Southeast Asia are becoming more frequent, with rising numbers of warm days and nights often compounded by high humidity, which intensifies physiological heat stress and impairs both physical and cognitive function. A 2024 report from Climate Central found that roughly 6.3 billion people, about 78 percent of the global population, experienced at least 31 days of extreme heat exceeding 90 percent of historical temperatures between 1991 and 2020. For Indonesia, the consequences are measurable in lost labor: the country ranks among those incurring the largest losses in work capacity, estimated at 4 to 6 percent of annual gross domestic product, with each worker losing an average of 71.8 hours in a single year, some 15 billion hours nationwide. More than half of Indonesia’s 270 million people already live in heat-exposed urban coastal areas, and by 2035 two-thirds of the population is projected to be urban.

At the heart of the problem lies a definitional bottleneck. Indonesia’s official extreme heat definition, codified in BMKG’s legal documentation, uses a single fixed threshold: a daily dry-bulb temperature anomaly exceeding the climatological average by 3 degrees Celsius. That metric has never been calibrated against health outcomes in the Indonesian context, and it overlooks several factors that matter enormously in the tropics. Warm nights, which are warming faster than daytime temperatures globally, are ignored. Humidity, which dramatically amplifies heat stress, is not captured. And Indonesia’s naturally low temperature variability means a 3-degree anomaly may be far rarer, and far more anomalous, than in temperate climates where such thresholds were first developed. International guidance from the World Meteorological Organization and World Health Organization recommends that heat early warning thresholds be grounded in epidemiological evidence linking temperature and humidity to mortality, hospital admissions, and heat illness, and that composite indices such as the Wet Bulb Globe Temperature or Universal Thermal Climate Index, which combine temperature, humidity, wind, and radiation, be used to capture physiological strain. Indonesia’s current approach does none of this.

The research team, led by Yoshua A. Nugroho of BMKG and the University of Copenhagen together with Emmanuel Raju, Agie W. Putra, and Carolina P. Marghidan, designed their study around a conceptual framework adapted from work on United States tornado warning decisions, translating it carefully to the very different physics and perception of heat. Twenty senior operational meteorologists with at least five years of service helped refine the survey through semi-structured discussions before it was distributed to 300 eligible forecasters in June 2024, one of Indonesia’s hottest months. The final sample of 140 valid responses closely matched BMKG’s workforce demographics, with 64.3 percent male participants, a majority aged 25 to 34, and over half working at local stations. The team then applied hierarchical ordinary least squares regression, entering predictors in blocks that mirrored the operational reasoning process: internal challenges first, then external challenges, then data-driven decisions, personal judgment, and communal judgment.

The results on dissemination challenges were striking in their selectivity. Of all the potential obstacles the researchers modeled, only two internal factors emerged as significant. The first was terminology ambiguity, meaning the lack of a universally accepted, context-appropriate definition of extreme heat for tropical settings, which showed medium-sized effects on both technical and non-technical challenges. Without a shared, credible definition, forecasters reinterpret guidance individually, producing inconsistent warnings and delayed action. The second was reliance on weather station data as the primary validation measure. Most stations have limited instrumentation, and urban neighborhoods, particularly informal settlements, are demonstrably hotter than what nearby stations record, meaning dangerous heat events can simply go unmeasured. External challenges, such as the absence of collaboration mechanisms with health departments and local governments, did not independently predict dissemination difficulties, but they mattered in a subtler and more troubling way.

That subtlety surfaced in the analysis of perceived effectiveness. The interaction between heavy reliance on station data and weak external collaboration was the strongest predictor of whether forecasters believed the current system was working well. In agencies with few ties to health authorities or municipal governments, forecasters who leaned on station data were significantly more likely to view the existing threshold-based system as adequate. The authors interpret this as institutional self-reinforcement: the system appears effective not because it truly captures heat risk, but because it aligns with operational norms under constraint, and forecasters’ views are rarely corrected because they seldom engage with the agencies that see heat’s health impacts firsthand. The researchers also argue that the apparently insignificant effect of missing vulnerability data reflects insufficient operationalization rather than irrelevance. Indonesia already holds census data, disaster agency risk maps, and community health profiles, but none are integrated into heat warning operations as decision-support tools.

The decision-making findings carry the sharpest implications. The full model explained 48 percent of the variance in whether a forecaster chose to issue a heat warning. Data-driven factors dominated, contributing 23 percent of explained variance, with the forecaster’s own interpretation of meteorological analyses, such as model outputs and forecast trends, showing a significant medium effect. Field reports from weather observers did not matter statistically. Among personal judgment factors, the most influential single predictor, with the largest effect in the entire model, was recognition of past experiences with successful warnings. In practice, forecasters consult a mental library of previous events, recalling which combinations of temperature, humidity, and wind once produced impactful outcomes, and match current conditions against that archive. This is what cognitive scientists of forecasting call tacit expertise, built over hundreds of hours of operational work, and the study shows it functioning as the primary decision substrate precisely where formal guidance is weakest.

The second significant personal factor was warning philosophy. Forecasters who adopt a liberal approach, issuing warnings more readily than colleagues, were significantly more likely to warn, a pattern the authors link to ambiguous terminology forcing individual adaptation and to defensive, self-protective behavior aimed at avoiding blame. The liberal philosophy minimizes the risk of failing to warn, but it inflates the false alarm ratio, and repeated false alarms erode public urgency in the phenomenon known as the cry wolf effect. Notably, communal judgment, such as seeking second opinions from colleagues, and interaction effects between human judgment and meteorological input showed no significant influence, suggesting that heat warning decisions in Indonesia remain largely solitary rather than collaborative. For a hazard whose impacts unfold across health systems, labor markets, and urban infrastructure, that individualization of risk is itself a structural vulnerability.

The authors close with concrete recommendations for BMKG and policymakers. They call for co-developing adaptive, locally calibrated heat thresholds with epidemiologists, urban planners, and public health experts so that exposure, sensitivity, and adaptive capacity enter the warning criteria directly. They urge integration of real-time health surveillance streams into the impact-based forecasting platform, deployment of additional measurement tools in dense urban areas, and contextual vulnerability data as reference layers for forecasters. They also recommend operational training built on experiential learning, reflective practice, and scenario-based simulation to strengthen the demonstrated interplay between data and experience. Theoretically, the study extends the sociology of forecasting by showing that when formal guidance and context-specific thresholds are underdeveloped, tacit expertise ceases to be a supplementary refinement of data and becomes the foundation of the warning itself. As extreme heat accelerates across the tropics, the study suggests that the most important upgrade to any heat early warning system may not be a new algorithm, but the institutional scaffolding that lets human judgment, good data, and cross-agency trust reinforce one another.

Subject of Research: Operational challenges and decision-making in heat early warning systems in Indonesia

Article Title: Heat Risk Communication by Operational Meteorologists in Indonesia: Current Operational Challenges and Decision-Making Dilemmas in Heat Early Warning System

Article References: Nugroho, Y. A., Raju, E., Putra, A. W., & Marghidan, C. P. (2026). Heat Risk Communication by Operational Meteorologists in Indonesia: Current Operational Challenges and Decision-Making Dilemmas in Heat Early Warning System. International Journal of Disaster Risk Science. https://doi.org/10.1007/s13753-026-00760-8

Image Credits: AI Generated

DOI: 10.1007/s13753-026-00760-8

Keywords: heat early warning systems, operational meteorologists, Indonesia, BMKG, extreme heat, heat thresholds, risk communication, decision making, impact-based forecasting, tacit expertise, climate change, disaster risk science