The study focused on three wards in Nyanga District—Tangwena, Samanyika, and Kute—that were selected for their documented exposure to the storm and established vulnerability profiles. The researchers drew on eight focus group discussions, 27 in-depth interviews with affected households and key informants from the Department of Civil Protection, UNDP, the Zimbabwe Red Cross Society, and the Meteorological Services Department, alongside field observations and government documents. Conducted primarily in Shona and analyzed thematically using NVivo, the qualitative approach was designed to capture the social and institutional dynamics that quantitative damage assessments often miss. The result is a granular portrait of a disaster that struck households already struggling with poor roads, settlement on steep slopes and floodplains, and the lingering memory of Idai.

The physical impacts of Cyclone Ana were substantial for a moderate storm. Field assessments documented 126 affected houses, 70 of which were rendered uninhabitable, displacing families into temporary shelters and overcrowded public facilities. Flooding damaged boreholes and latrines, forcing some residents into open defecation and others into longer journeys for safe water; one female-headed household reported that daily water collection increased by roughly 3.5 kilometers, adding about two hours to caregiving duties. The Murozi footbridge, which provides access to nine villages and serves approximately 135 schoolchildren, was destroyed, while sections of the Troutbeck-Nyafaru road became impassable, delaying emergency assistance. Because the cyclone hit shortly before the main harvest, maize, bean, and vegetable fields were inundated, irrigation schemes and dip tanks were destroyed, and small livestock were lost during evacuations—triggering cascading effects that included food insecurity, school dropouts, and mounting psychological strain.

Perhaps the most striking finding concerns preparedness. Seventy-six percent of participants were unaware of any formal emergency preparedness plans, and roughly 64 percent said they had never been consulted during risk assessments or disaster plan development. Ward-level disaster risk management plans drafted in 2018 with external support were intended to guide local action until 2024, but document analysis showed they remained heavily response-oriented: only 18 percent of their content addressed preparedness, compared with 65 percent devoted to response. Capacity assessments of 45 disaster risk management committee members revealed deep deficits—35 lacked contingency planning experience, 40 had never been trained in post-disaster needs assessment, and about 70 percent reported no refresher training in the previous three years. Only 24 percent of the district’s committees met regularly, while nearly a third were largely inactive.

The researchers trace these weaknesses to structural and legal roots. At the time of Cyclone Ana, Zimbabwe’s disaster framework was still anchored in the Civil Protection Act of 1989, a hazard-focused, response-oriented law that gives limited weight to prevention and preparedness and specifies contingency planning mechanisms only vaguely. Donor funding cycles reinforce the imbalance, mobilizing resources after disasters strike rather than sustaining risk reduction between events. As one official told the researchers, communities ask politicians about new boreholes and clinics, not about disaster preparedness—until the cyclone hits. The result is a governance system in which decentralization exists on paper but budgets, technical support, and decision-making authority remain concentrated at levels far removed from the villages where floods and landslides actually occur.

Early warning performance during Ana was equally revealing. The Meteorological Services Department issued regular bulletins through print, broadcast, and social media in coordination with civil protection authorities, and around 70 percent of participants reported receiving some form of warning. But the messages were technical and generalized, referring broadly to thundery showers, downpours, and strong winds across several provinces without specifying local risks or recommended actions. Terms such as tropical depression and overland depression were poorly understood, particularly among older residents, and warnings rarely addressed secondary hazards like landslides or infrastructure failure. Roughly 30 percent of participants received no warning at all, hindered by unstable mobile networks, poor radio reception in mountainous terrain, and damaged roads that impeded door-to-door communication. Even where 31 evacuation centers had been designated, the district lacked clear evacuation protocols, transport plans, and pre-positioned supplies—so warnings did not consistently translate into protective action.

Social and cognitive factors compounded the problem. More than half of participants believed the storm would not directly affect them, often citing past forecasts they perceived as exaggerated. Others prioritized livestock and crops over personal safety, and some expressed skepticism toward government information. The researchers interpret these patterns through established frameworks such as Protective Action Decision Theory, noting that residents filtered official warnings through prior experience and local knowledge—so-called disaster scripts that normalized risk. One farmer recounted how traditional signs, from the behavior of birds to the color of the evening sky, prompted her family to move their goats to higher ground before dawn, even as the same knowledge could not save their maize crop. Indigenous early warning, the study suggests, is a genuine asset, but one with limits under extreme conditions and in need of integration with technical forecasting rather than substitution for it.

The response phase exposed both institutional fragility and remarkable community resilience. Detailed damage assessments in the worst-hit villages took nearly two weeks, hampered by impassable roads, fuel shortages, and limited vehicles; of the 7.3 million dollars allocated nationally for disaster risk management in 2022, only about 28 percent supported operational logistics. Residents were repeatedly surveyed by different agencies yet received little assistance, and coordination gaps produced fragmented vulnerability data. Meanwhile, only 20 of the 126 affected households received government-prefabricated shelters, with allocations that sometimes ignored household size—one family of more than 20 members received a single unit. In the vacuum, communities organized themselves: youth cleared debris from blocked paths, churches provided food, and families whose homes survived sheltered those who had lost everything. The authors argue these grassroots efforts were not merely a stopgap but a core, under-recognized component of community-based disaster risk management that formal systems should support, resource, and scale.

Recovery, supported in part by a crisis modifier under the Zimbabwe Resilience Building Fund and technical backing from UNDP, produced genuine gains alongside persistent inequities. Forty houses received reconstruction or rehabilitation support guided by Build Back Better principles, and a piped water supply installed at Dazi School and Clinic allowed services to resume—what the authors call a recovery surplus, improving conditions beyond pre-disaster baselines. Roads and footbridges along the Troutbeck-Dazi-Nyafaru corridor were rehabilitated, roughly 600 farmers received short-cycle seed inputs, and 126 local leaders and officials received disaster risk reduction training. Yet many households were left patching roofs with plastic sheeting and salvaged debris, and eligibility criteria excluded some affected farmers from livelihood support entirely. Short funding cycles and narrow targeting, the study concludes, can entrench the very vulnerabilities recovery is meant to reduce.

The broader significance of the Nyanga case lies in what it establishes as a baseline for reform. Since Cyclone Ana, Zimbabwe has pursued a new Disaster Risk Management and Civil Protection Bill, impact-based forecasting, anticipatory action frameworks, and strengthened contingency planning—developments the authors welcome as real progress. But their analysis identifies a critical implementation gap between policy commitments and local practice, visible in gaps of interpretation, coordination, financing, and first-mile communication. Closing it, they argue, requires aligning legislative reform, sustainable financing, and institutional capacity with genuinely community-centered approaches that embed local knowledge and participation across every stage of risk governance. As cyclones such as Freddy, Filipo, and Chido continue to test the region, the lesson from Nyanga is that effective disaster management depends not only on better forecasts and laws, but on sustained investment in the institutions, trust, and collective capacities of the communities who face the storm first.

Subject of Research: Disaster preparedness, response, and recovery from Tropical Cyclone Ana in Nyanga District, Zimbabwe

Article Title: Assessing Disaster Preparedness and Recovery from Tropical Cyclones in Zimbabwe: Insights from Cyclone Ana in Nyanga

Article References: Mabumbo, D., & Ngulube, N. K. (2026). Assessing Disaster Preparedness and Recovery from Tropical Cyclones in Zimbabwe: Insights from Cyclone Ana in Nyanga. International Journal of Disaster Risk Science. https://doi.org/10.1007/s13753-026-00759-1

Image Credits: AI Generated

DOI: 10.1007/s13753-026-00759-1

Keywords: tropical cyclones, Zimbabwe, Cyclone Ana, disaster risk management, early warning systems, community-based disaster risk management, Nyanga District, Cyclone Idai, build back better, anticipatory action, disaster recovery, impact-based forecasting

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

The review, led by Zecheng Li and Chng Saun Fong of the Institute for Advanced Studies at Universiti Malaya, together with colleagues spanning software engineering, public health, and chemical engineering, synthesised sixty-nine studies on how compound environmental events are defined, forecast, and communicated within early warning system frameworks. The team followed established systematic review methodology, including PRISMA reporting guidelines, and mapped every study against a purpose-built taxonomy that organises the field into three connected stages: how compound events are operationally defined, how they are jointly forecast, and how the resulting risk reaches the public as an actionable warning.

The first stage, event definition, turns out to be far more consequential than it might appear. Most existing systems identify a compound event simply by overlapping fixed or percentile-based thresholds, for example declaring a compound hot-and-polluted episode when both temperature and ozone or particulate matter exceed chosen cut-offs simultaneously. This approach is transparent and easy to automate, but the review shows it is also arbitrary: studies of mortality in Wuhan, Montreal, Beijing, and dozens of other cities demonstrate that the health impact of a given pollutant concentration shifts substantially with temperature, meaning a threshold defined without reference to measured health outcomes can systematically misclassify genuinely dangerous days. Impact-based interaction models, which use epidemiological relationships to weigh the joint effect of heat and pollution, offer a more defensible alternative, and the review documents a rapidly growing body of work fitting distributed lag non-linear models and machine learning methods to hospital admissions and mortality records to derive compound definitions grounded in actual harm.

On the forecasting side, the technological picture has changed dramatically in just a few years. Coupled meteorology-chemistry models such as WRF-Chem and operational systems built on the NOAA Global Forecast System can now simulate how heat, boundary-layer dynamics, and atmospheric chemistry interact, capturing for instance how a heat dome suppresses ventilation and allows pollutants to accumulate. More striking still is the arrival of artificial intelligence in numerical weather prediction. The review highlights machine learning systems that have achieved skillful medium-range global forecasts and, more recently, probabilistic ensemble forecasts generated entirely by neural networks, alongside hybrid approaches that combine physical chemical transport models with machine learning bias correction to deliver high-resolution particulate matter predictions. Interpretable models such as random forests equipped with SHAP explainability are also being used to unpick which environmental variables drive ozone formation and health outcomes, giving forecasters both accuracy and a measure of transparency.

These forecasting advances feed into a third strand of technology: unified risk indices. Rather than issuing a heat alert and an air quality alert independently, several research groups have constructed combined indices that merge temperature and multiple pollutants into a single health-relevant number, validated against mortality in places as varied as Monterrey, Taiwan, and Beijing. Studies building graded heat-health risk forecasts with full-season coverage across China demonstrate that such integrated products can be produced at national scale. The review treats these indices as a crucial bridge between the raw machinery of forecasting and the blunt reality of public communication, because a single number with a clear protective message is far easier to act on than two parallel warnings that may never be reconciled.

The final stage, risk communication and warning triggers, is where the review finds both encouraging innovation and stubborn weaknesses. Impact-based forecasting, now promoted by the World Meteorological Organization, shifts the emphasis from describing what the weather will be to describing what it will do, and evaluation studies from South Korea and New Zealand show that warnings framed around expected impacts measurably improve risk perception and protective behaviour. Probabilistic trigger rules drawn from forecast-based financing allow humanitarian agencies to release funds before a disaster strikes, and research on visual and verbal communication of uncertainty shows that well-designed probabilistic messages help decision makers rather than confuse them. Newer experiments, including digital heat warning platforms for older adults and generative AI chatbots tailored to multi-lingual communities, hint at how compound warnings might one day reach vulnerable individuals directly.

Yet despite this rapid progress at every stage, the review’s central finding is a persistent integration gap. Definitions are developed by epidemiologists, forecasts by atmospheric modellers, and warning triggers by disaster management agencies, usually without shared targets or common validation. A compound event may be defined one way in a research paper, forecast another way in an operational model, and trigger a warning under a third set of rules entirely. Uncertainty estimates produced by ensemble forecasts rarely survive the journey into the public warning product. The authors argue that this fragmentation means the impressive technical capabilities documented across the literature are not being converted into coherent, end-to-end systems, and that the missing ingredient is a shared compound-risk target that all three stages are explicitly designed to serve.

To remedy this, the review proposes both a taxonomy and a research agenda. The taxonomy classifies compound-event generation methods by how they combine hazards, whether through threshold overlap, statistical interaction models, coupled physics-based simulation, or AI-driven fusion, and by how they connect to impacts and triggers. The research agenda calls for uncertainty-aware model fusion so that probabilistic information flows unbroken from forecast to warning; for impact-based warning thresholds calibrated against health and service outcomes rather than convenient percentiles; and for end-to-end validation that tests not whether a model predicted a hazard accurately, but whether the resulting warning changed behaviour and reduced harm. The authors also stress equity considerations documented in the underlying literature, including evidence that the health effects of compound heat and pollution fall disproportionately on historically marginalised neighbourhoods, and that people-centred design remains essential even as the technology becomes more sophisticated.

The timing of this synthesis is significant. Under the United Nations Early Warnings for All initiative, governments worldwide are being pressed to expand multi-hazard early warning coverage, and climate change is steadily increasing the frequency with which extreme heat and air pollution coincide over populated regions, from the Pearl River Delta to California, Delhi, and London. The review’s message to policymakers is that simply adding more hazards to existing single-hazard systems will not suffice; the architecture itself must become compound-aware. If the integration gap can be closed, the authors conclude, the technological ingredients for warnings that genuinely reflect the compound risks people face already exist. What remains is the harder work of connecting them.

Subject of Research: Technological advances in generating compound heat and air pollution events within early warning system frameworks

Article Title: A systematic review of technological advances in compound-event generation within early warning system frameworks

Article References: Li, Z., Fong, C. S., Ab Hamid, S. H., Aghamohammadi, N., Jamali, S. N., & Sulaiman, N. M. (2026). A systematic review of technological advances in compound-event generation within early warning system frameworks. Air Quality, Atmosphere & Health, 19(9), Article 204. https://doi.org/10.1007/s11869-026-02091-5

Image Credits: AI Generated

DOI: 10.1007/s11869-026-02091-5

Keywords: compound events, early warning systems, extreme heat, air pollution, impact-based forecasting, machine learning, risk communication, public health, climate risk, heat-health warning, coupled meteorology-chemistry models, systematic review