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  • View in gallery

    Evolution of maximum temperature (Tmax) and minimum temperature (Tmin) at the station of Matam, Senegal, in 2019.

  • View in gallery

    Schematic of the heat wave forecasting process from the tools consisting of the predicted atmospheric circulation and the postprocessed mean temperature, maximum temperature, and heat index, and their verifications, to the heat hazards outlooks.

  • View in gallery

    Heat wave frequency based on heat index exceeding 40°C for April–May averaged over the period 1990–2019.

  • View in gallery

    Area under the relative operating characteristic (ROC) curve (AUC) for (a) Global Ensemble Forecast System (GEFS), (b) Climate Forecast System version 2 (CFSv2), and (c) area-averaged AUC for GEFS and CFSv2. AUC values of 0.5 suggest random forecasts. An AUC value of 1 reflects perfect forecast, while values below 0.5 are indicative of more false alarms in the forecast system.

  • View in gallery

    Tmax Heidke skill score (HSS) for (a) raw GEFS forecasts, (b) bias-corrected (BC) GEFS forecasts, and (c) area-averaged HSS for both raw and BC Tmax forecasts. Positive HSS values are indicative of more correct forecasts than missed forecasts and the higher the HSS, approaching 1 (perfect forecast), the more performing the forecasting system and the more confidence in the forecasts. Zero means no skill in the forecasts, and negative values suggest that a nonusable forecasting system as forecasts are incorrect most of the time.

  • View in gallery

    GEFS week-2 forecasts for (a) 10-m wind, (b) mean sea level pressure, (c) 500-hPa geopotential height (×10−1 gpm), and (d) 700-hPa divergence overlaid with wind.

  • View in gallery

    GEFS week-2 forecasts for (a) two-category probability heat wave forecasts based on Tmax and (b) as in (a), but for maximum heat index probability of exceedance of 41°C.

  • View in gallery

    Week-2 heat hazards outlooks, valid 29 Apr–5 May 2020 based on GEFS forecasting tools. Shaded in brown and yellow are areas that exhibit high and moderate risk of heat wave occurrence during the period.

  • View in gallery

    Schematic of the end-to-end process of heat–health early warning, from preparing the heat wave forecasts to assessing the impacts on health based on knowledge of population vulnerability to preparing and disseminating heat–health early warnings to public health intervention.

  • View in gallery

    Participants in the health sector breakout group during the workshop celebrating after completing the road map for translating the heat forecasts into early actions and early planning.

  • View in gallery

    (left) An environmental health scientist from the Direction Générale de la Santé Publique of Senegal and (right) a meteorologist from the Agence Nationale de la Météorologie et de l’Aviation Civile during training in the African Desk at the NOAA’s Climate Prediction Center, Feb–Jun 2020.

  • View in gallery

    Heat–health early warning bulletin, featuring the health alert due to heat on page 1, recommended actions to mitigate the impacts of heat on page 2, and the heat hazards as background information on page 3.

  • View in gallery

    African Desk heat information website: GEFS postprocessed heat guidance tools featuring ensemble probabilistic forecasts at week 1 and week 2 (www.cpc.ncep.noaa.gov/products/international/climatehealth/heat-health_forecasts.shtml).

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Toward Experimental Heat–Health Early Warning in Africa

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  • 1 Climate Prediction Center, National Centers for Environmental Prediction, National Oceanic and Atmospheric Administration, College Park, Maryland;
  • | 2 Climate Prediction Center, National Centers for Environmental Prediction, National Oceanic and Atmospheric Administration, College Park, Maryland, and University Corporation for Atmospheric Research, Boulder, Colorado;
  • | 3 Climate Prediction Center, National Centers for Environmental Prediction, National Oceanic and Atmospheric Administration, College Park, Maryland;
  • | 4 Agence Nationale de l’Aviation Civile et de la Météorologie, Dakar, Senegal;
  • | 5 Direction Générale de la Santé Publique, Dakar, Senegal
  • | 6 Agence Nationale de l’Aviation Civile et de la Météorologie, Dakar, Senegal;
  • | 7 Direction Générale de la Santé Publique, Dakar, Senegal
  • | 8 Agence Nationale de l’Aviation Civile et de la Météorologie, Dakar, Senegal;
  • | 9 Direction Générale de la Santé Publique, Dakar, Senegal
  • | 10 Agence Nationale de l’Aviation Civile et de la Météorologie, Dakar, Senegal;
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Abstract

Heat is one of the most serious hazards in the world as it affects human health and is extremely dangerous to vulnerable populations such as the elderly, people with preexisting cardiovascular or respiratory conditions, and even healthy people with prolonged sunlight exposure during heat waves. As the globe has warmed over the past several decades, extreme heat has become more frequent and intense than ever before, and Africa, especially the Sahel in West Africa, is one of the regions of the world where heat is a major public health concern exacerbated by livelihood activities during the heat season. Yet, there is a major gap in monitoring and forecasting heat waves in Africa. This paper describes NOAA’s role in enabling heat–health early warning in Africa, working with meteorological services and health professionals. Emphasis is on real-time heat wave forecasting at week 2, including the postprocessing of the NCEP model outputs, and providing the information to the meteorological services in Africa to serve as guidance in national heat wave forecasts. In addition, the paper describes the end-to-end process of heat hazard outlooks and translating the forecasts into early action and early planning to reduce heat risk to human health. Furthermore, the paper addresses the very important aspect of capacity development tailored at enhancing forecasters’ skills to prepare and issue heat wave forecasts and training of a cadre of health professionals to work with meteorologists to coproduce heat–health bulletins and to issue heat–health early warnings.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Wassila M. Thiaw, wassila.thiaw@noaa.gov

Abstract

Heat is one of the most serious hazards in the world as it affects human health and is extremely dangerous to vulnerable populations such as the elderly, people with preexisting cardiovascular or respiratory conditions, and even healthy people with prolonged sunlight exposure during heat waves. As the globe has warmed over the past several decades, extreme heat has become more frequent and intense than ever before, and Africa, especially the Sahel in West Africa, is one of the regions of the world where heat is a major public health concern exacerbated by livelihood activities during the heat season. Yet, there is a major gap in monitoring and forecasting heat waves in Africa. This paper describes NOAA’s role in enabling heat–health early warning in Africa, working with meteorological services and health professionals. Emphasis is on real-time heat wave forecasting at week 2, including the postprocessing of the NCEP model outputs, and providing the information to the meteorological services in Africa to serve as guidance in national heat wave forecasts. In addition, the paper describes the end-to-end process of heat hazard outlooks and translating the forecasts into early action and early planning to reduce heat risk to human health. Furthermore, the paper addresses the very important aspect of capacity development tailored at enhancing forecasters’ skills to prepare and issue heat wave forecasts and training of a cadre of health professionals to work with meteorologists to coproduce heat–health bulletins and to issue heat–health early warnings.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Wassila M. Thiaw, wassila.thiaw@noaa.gov

As extreme weather events have become more frequent, the demand for actionable real-time weather and climate information has increased significantly across all socioeconomic sectors, including health. Following a request from academia to engage NOAA in providing weather data to advance epidemiological modeling in Africa, and subsequent field campaigns in Niger to advance malaria modeling and forecasting, NOAA became increasingly engaged in helping advance climate-based health early warning systems. The long history of the African Desk at the Climate Prediction Center (CPC) (Thiaw and Kumar 2015) in providing real-time weather and climate information, enabling decision support services in food security, provides a basis for trying to fill this major gap. It is in this context that NOAA’s National Weather Service (NWS) initiated a project to help develop the capacity of meteorological services in Africa to provide reliable weather and climate information targeted at reducing risk to human health. At the beginning of the project, NWS funded the African Desk during two consecutive years in 2016 and 2017 to scope out opportunities to work collaboratively with meteorological services and Ministries of Health in Africa to advance weather-based health early warning systems. Two climate and health workshops conducted in West Africa and East Africa in 2016 and 2017 revealed that the health community was strongly interested in accessing weather and climate information relevant to this sector. However, understanding weather and climate data and translating this information to early planning and early actions remained a major gap. The workshops helped to identify two key priorities for the health sector in Africa: 1) infectious diseases (malaria) and 2) noncommunicable diseases [heat-related illnesses (HRI)] and considered the latter as a priority. The workshops recommended that 1) NOAA deliver training on the basics of weather and climate to educate the health sector on the usefulness of the training information and 2) NOAA provide the meteorological and health services in Africa with real-time information that could help advance health early warning. The successes of the workshops led to continued NOAA funding to support the climate and health initiative through the NWS–OAR Service Level Agreement (SLA). As part of the SLA, heat–health became a top priority to fulfill the needs of the health sector and for the obvious reason that air temperature continues to rise worldwide and the demand for reliable heat information is increasing. Indeed, recent observations show that the globe continues its warming trend as the year 2020 ranked as the second-hottest year on record for the planet behind 2016, according to NOAA, with the hottest years occurring during the last 5 years (NOAA/NCEI 2020). Under this substantial warming of the climate, heat waves frequency and intensity have been increasing, especially in the Sahel (Oueslati et al. 2017). Hence, there is increased concern among scientists that a significant number of heat-related deaths over the past several decades could be attributed to warming in the climate system (Vicedo-Cabrera et al. 2021). Africa is one of the regions of the world that is most vulnerable to heat hazards due to population increase and the prevalence of traditional livelihood outdoor practices such as farming, herding, water well drawing, and outdoor trading. Inadequate infrastructure to monitor and predict heat waves, to document and report HRI, and to mitigate the impacts of heat on health exacerbate health risks. The importance of the subseasonal time scale for informed socioeconomic decision-making with lead times of several weeks has been discussed (Vitart and Robertson 2018). More specifically, Batté et al. (2018) and White et al. (2017) studied the predictability of heat waves at the subseasonal time scale and found useful skill in the prediction system. Studying the July 2015 heat waves in western Europe, Ardilouze et al. (2017) found that models were able to depict elevated temperatures from several days’ up to 1-month lead time. Thus, clearly advances in numerical modeling and forecasting at subseasonal time scales provide an excellent opportunity for meteorologists to predict heat waves with sufficient lead time to allow health professionals to access the heat forecasts and to deploy contingency measures to mitigate heat impacts on human health. Deliberations from a French Institut de Recherche et Développement project on Sahel heat waves and impacts on health [Alerte aux Canicules Au Sahel et à leurs Impacts sur la Santé (ACASIS)] in Senegal in 2018, attended by various stakeholders in the Sahel, demonstrated that there was a clear demand for advancing heat–health early warning (HHEW) in the Sahel. The objectives of the heat–health project are 1) to evaluate the ability of NOAA models to depict and predict heat waves over Africa at week 2, 2) to take advantage of advances in predictions to develop tools for the forecasting of heat waves, 3) to provide meteorological services with access to the forecast tools, and 4) to facilitate the translation of the forecasts into actionable heat hazards to reduce disease risk. While our heat wave forecasting tools cover the entire continent of Africa, we recognize that the impacts of heat on health are region specific. This recognition led us to decide initially to focus our strategy for HHEW on the Sahel, one of the hottest and most vulnerable subregions of Africa to heat waves. We understand that procedures set in place for actionable heat wave forecasts in this region are transferrable to any other region of the world where heat has adverse impact on health, so long as forecasters can work with health professionals and decision-makers to enable early actions (Ebi 2007). At the beginning of this initiative, NOAA organized a workshop in Senegal in 2019 in collaboration with the Agence Nationale de l’Aviation Civile et de la Météorologie (ANACIM) of Senegal and the Ministry of Health (MoH) of Senegal. The workshop featured a large representation from the meteorological and health communities in the Sahel and helped shape our strategy for HHEW in Africa moving forward. During this workshop, participating government decision-makers from the health sector informed the meteorologists that accessing heat waves forecasts with a lead time of 7 days allows sufficient time to coordinate action with local communities to help reduce heat exposure and HRI. With advances in numerical modeling and predicting air temperature from the short-range, subseasonal to seasonal time scales, the meteorological community can now provide timely information to public health professionals, governments, and local authorities to take action early enough to mitigate impacts of heat waves on human health. In fact, a few meteorological services in the Sahel are now starting to develop heat wave forecasting tools. However, health professionals need to access, understand, and utilize this information to assess potential health risks and issue actionable warnings to the public that reduce adverse health outcomes. Achieving this goal requires fostering collaboration between meteorological services or climate institutions and health services. This paper reports on NOAA’s initiative on heat–health for Africa to help meteorological services develop capacity in heat wave forecasting and to work with Ministries of Health and other stakeholders to enable climate-based heat–health early warnings. In the following, we give a brief overview of the climatology of the Sahel. Then, we define heat waves and present the heat wave forecasts strategy including data and methodology. We then report on real-time heat wave forecasting tools using a case study for illustration and on the translation of the forecasts into health vulnerability risks. A summary follows this discussion.

Climatology of the Sahel

The Sahel is defined here as the land band between the Sahara Desert and the Gulf of Guinea region, roughly 10°–20°N, 18°W–15°E, encompassing the countries from Senegal on the Atlantic coast eastward to Niger. The Sahel climate is semiarid with two marked seasons. The rainfall season typically starts in late May in the southern areas of the Sahel, then the rains expand northward as the West Africa monsoon season strengthen and peaks in August, then the rains decline gradually southward to bring the rainfall season to an end in mid- to late October. As the rainfall season ends, a long dry season that lasts about 7–8 months from November to May in the southern areas of the Sahel or June in the northern areas settles in. During the early months of the dry season, November to February, air temperature is typically low from night to the early morning, with temperatures averaging less than 10°C in some localities. However, the diurnal cycle is very strong and temperatures can exceed 40°C in the afternoon in some areas. The premonsoon season between March and May can be extremely hot. Maximum temperatures (Tmax) can rise up to 48°C or even higher in areas away from the Atlantic coast. The observed daily maximum and minimum temperatures for the Matam station (15.66°N, 13.26°W) in Senegal during 2019 are shown in Fig. 1. The lowest temperature of 13°C was observed on 22 January, with a maximum temperature 32°C the same day. Maximum temperatures were above 45°C during 32 days from April to June, with the highest maximum temperature of 48°C occurring on 22 May. Minimum temperatures also exceeded 30°C for 21 days between April and June. The hot months of March–May coincide with livelihood activities, many of which are outdoors, including buying and selling goods in open markets, transporting herds to watering holes, and working in the fields to prepare land for the next crop season. Sustained heat exposure during this time can lead to adverse health outcomes. Access to timely heat alerts can help reduce increases in morbidity and mortality among the vulnerable populations.

Fig. 1.
Fig. 1.

Evolution of maximum temperature (Tmax) and minimum temperature (Tmin) at the station of Matam, Senegal, in 2019.

Citation: Bulletin of the American Meteorological Society 103, 8; 10.1175/BAMS-D-20-0140.1

Forecast strategy

Meteorological forecasting requires several steps to be taken to complete the process, including an understanding of observational data, the forecasting tools generated from the numerical models, the systematic or random model errors, and the atmospheric circulation that drive the forecasts. In the interest of space, Fig. 2 summarizes some of the elements of our heat wave forecast process. We provide more details about the forecasts in the following.

Fig. 2.
Fig. 2.

Schematic of the heat wave forecasting process from the tools consisting of the predicted atmospheric circulation and the postprocessed mean temperature, maximum temperature, and heat index, and their verifications, to the heat hazards outlooks.

Citation: Bulletin of the American Meteorological Society 103, 8; 10.1175/BAMS-D-20-0140.1

Data and methodology

Observational and model data.

The NCEP Global Ensemble Forecast System (GEFS) (Toth and Kalnay 1997) and Climate Forecast System Version 2 (CFSv2) (Saha et al. 2014) data are used to generate week-1 and week-2 exceedance probabilities of extreme heat events. The real-time GEFS model used here has 21 forecasts (also known as ensemble members) that are valid through 384 h. GEFS version 2 (GEFSv2) reforecasts with 10 ensemble members (Hamill et al. 2013) serve to compute model climatology for the period 1999–2018. For the CFSv2, the combination of initial conditions from 2 consecutive days makes up 32 ensemble members in the CFSv2 forecasts. The model hindcasts, period 1999–2018, are used to calculate the model climatology for each of the eight members. The GEFS and CFSv2 models’ spatial resolution is 1° × 1°. For observations, we use the NCEP Global Data Assimilation System (GDAS) and the CPC gridded daily 2-m air temperature dataset, hereafter referred to as CPCGTA. The latter dataset contains maximum and minimum temperature, hereafter referred to as Tmax and Tmin, respectively, making it suitable to define heat waves in terms of Tmax and/or Tmin. Horizontal resolution is 0.5° latitude–longitude. These data are used to calibrate the forecasts and to define climatological references in generating forecast probabilities of exceedance. GDAS contains mean temperature (Tmean) and relative humidity (RH), which are the basis for defining heat waves in terms of the NOAA Heat Index (HI) that combines Tmean and RH such that a prolonged exposure to elevated HI can cause the human body to respond with excessive sweat, exhaustion, cramps, seizures, and even heatstroke.

Definition of heat waves.

Several definitions have been proposed in the scientific literature (Guigma et al. 2021; Batté et al. 2018; Smith et al. 2013). However, there is general agreement that for a heat wave to occur, Tmax or HI must exceed a critical threshold value for a few consecutive days. In our real-time forecasting of heat waves, we define heat wave events as one of the following events:

  • HI values exceeding Xt°C for at least 3 consecutive days, with Xt ranging between 38° and 42°C depending on the area of interest.

  • Tmax greater than the nth percentile for at least 3 consecutive days in the historical record 1999–2018, where n can be 80, 85, 90, 95, or 99.

  • Tmax greater than varying threshold values ranging between 38° and 45°C for at least 3 consecutive days.

Figure 3 shows the average heat wave frequency over the Sahel based on HI over the period 1999–2019, suggesting that most areas in the Sahel experience on average more than three heat waves during the peak of the warm season April–May, and HWs are even more prominent in the central part of the region encompassing the southern areas of Burkina Faso and Niger southward into the northern sectors of the Gulf of Guinea region.

Fig. 3.
Fig. 3.

Heat wave frequency based on heat index exceeding 40°C for April–May averaged over the period 1990–2019.

Citation: Bulletin of the American Meteorological Society 103, 8; 10.1175/BAMS-D-20-0140.1

Our choice for varying HI and Tmax threshold values and Tmax ranking percentiles stems from the fact that heat and impact on health is geographically dependent. According to the participants in the heat–health workshop in Senegal in 2019, even in West Africa, where sustained Tmax at 35°C can be a problem, for example, in western Sahel along the Atlantic coast, other regions such as interior Sahel will require over 40°C for many consecutive days to cause discomfort and risk to the health of vulnerable populations. Batté et al. (2018) used Tmax greater than the 90th percentile for at least 3 consecutive days. We expanded this range to include the 80th–99th percentiles to capture moderate and severe heat events.

Forecasting tools.

The development and persistence of heat waves are dependent on a number of factors, including the state of the atmosphere. Thus, predictions of heat waves rely on a set of tools that include the predicted temperatures and the associated circulation anomalies, with the latter providing useful information that can add values to the forecasts.

The above definitions of HW are applied to the NCEP GDAS and the CPC gridded temperature analysis (CPCGTA) data to construct historical weekly frequency of HWs based on observed daily maximum HI and Tmax. HI values are calculated from GDAS for the period 2015–19. Due the fact that RH was not available in the GEFS hindcasts in prior years, we decided that since we are dealing with weekly forecasts, the 5-yr period yielded a time series long enough to allow for robust results. Calculation of Tmax percentiles is based on the 1999–2019 base period. We prepare experimental week-2 heat wave forecasts based on the NCEP GEFS (21 members) and CFSv2 (32 members). The week-2 forecasts are valid for a 7-day period with a lead time of 8 days. The forecasts are expressed in probabilistic terms to convey uncertainty. Forecast quality is assessed using two verification metrics: the Heidke skill score (HSS) and the area under the ROC curve (AUC). While the latter allows for the assessment of the ability of the model to distinguish heat waves from non–heat waves, HSS helps to compare the proportion of correct forecasts to a no skill or random forecast. AUC values range between 0 and 1, with 0.5 indicating random forecasts and a value of 1 reflecting a perfect forecast. Scores above 0.5 are indicative of more correct forecasts or hits than false alarms in the forecasting system. The higher the AUC values, the more confident the forecasts. Scores below 0.5 are indicative of more false alarms than hits in the forecasting system. HSS ranges between −∞ and 1. Positive HSS values are indicative of more correct forecasts than missed forecasts and the higher the HSS approaching 1 (perfect forecast), the more performing the forecasting system and the more confidence in the forecasts. Zero means no skill in the forecasts, and negative values suggest a nonusable forecasting system as forecasts are incorrect most of the time. The CPCGTA data are used to verify week-2 bias-corrected and non-bias-corrected HW forecasts based on Tmax, while GDAS is used in the verification of HW forecasts based on HI. In the following, we evaluate the performance of the GEFS and CFSv2 in predicting HWs at week 2 and provide information on the bias correction applied to the forecasts.

Temperature and heat index forecasts and skill assessment

Heat wave forecasts based on heat index.

NCEP GEFS and the CFSv2 forecast skills based on HI are displayed in Figs. 4a and 4b and suggest relatively high predictability. The eastern half of Senegal and central Sahel exhibit impressive skills in the GEFS forecasts of HWs based on HI at week 2, with AUC values averaging 0.7–0.8. There is consistency between CFSv2 and GEFS forecasts over eastern Senegal. However, the highest scores in the GEFS HI week-2 forecasts over central Sahel (eastern Burkina Faso, western Niger) are weaker and slightly shifted to the north to include southeastern Mali in CFSv2. Figure 4c shows AUC values for week-2 HI forecasts from the GEFS and the CFSv2 averaged over the entire Sahel domain during the period January 2017 to December 2019, confirming the high predictability of heat waves as defined by HI, with GEFS slightly outperforming CFSv2. Given the focus of this paper on week 2, we favor the use of the GEFS for the remainder of our analysis.

Fig. 4.
Fig. 4.

Area under the relative operating characteristic (ROC) curve (AUC) for (a) Global Ensemble Forecast System (GEFS), (b) Climate Forecast System version 2 (CFSv2), and (c) area-averaged AUC for GEFS and CFSv2. AUC values of 0.5 suggest random forecasts. An AUC value of 1 reflects perfect forecast, while values below 0.5 are indicative of more false alarms in the forecast system.

Citation: Bulletin of the American Meteorological Society 103, 8; 10.1175/BAMS-D-20-0140.1

Heat wave forecasts based on maximum temperature.

GEFS HSS in the prediction of heat waves as defined by Tmax is shown in Fig. 5. Skill in the GEFS raw forecasts (Fig. 5a) is relatively low, with scores marginally above 0.1 in most areas over the western part of the Sahel. Skill improves to 0.3–0.6 in portions of central Sahel in the areas encompassing Burkina Faso, Niger, and northern Nigeria. The relative low forecast skill in the raw forecasts is partially due to systematic errors in the Tmax forecasts (Toth et al. 2003). Thus, the postprocessing of model outputs is required to remove systematic errors (Cui et al. 2012). Both raw and bias-corrected GEFS forecasts are updated four times daily and valid from 0 to 384 h. To remove the lead-time-dependent bias from a model grid, bias-corrected GEFS forecasts are generated by applying several methodologies. In this paper, we use the mean bias removal technique (Fan and van den Dool 2011), which consists of computing the average bias during the past 14 days to correct the systematic errors in real-time forecasts. The mean bias (mb) calculated as a function of lead time is defined as
mb=1Ni=1N(FiOi),
where N is the number of days in the recent past forecasts, Fi is forecast on day i, and Oi is the corresponding observation on day i. The value N can be any number of days, and we chose 14 days consistent with Fan and van den Dool (2011).
Fig. 5.
Fig. 5.

Tmax Heidke skill score (HSS) for (a) raw GEFS forecasts, (b) bias-corrected (BC) GEFS forecasts, and (c) area-averaged HSS for both raw and BC Tmax forecasts. Positive HSS values are indicative of more correct forecasts than missed forecasts and the higher the HSS, approaching 1 (perfect forecast), the more performing the forecasting system and the more confidence in the forecasts. Zero means no skill in the forecasts, and negative values suggest that a nonusable forecasting system as forecasts are incorrect most of the time.

Citation: Bulletin of the American Meteorological Society 103, 8; 10.1175/BAMS-D-20-0140.1

The bias-corrected forecast (bcf) with respect to the current raw forecast (rf) is computed as
bcf=rfmb.

An evaluation of the GEFS heat wave forecasts based on Tmax after bias correction is displayed in Fig. 5b and shows higher HSS values compared to the raw forecasts. Area-averaged HSS over the entire Sahel domain shown in Fig. 5c further suggests that bias correction improves the forecasting of heat waves as defined by Tmax.

An additional forecasting tool (not illustrated in this manuscript) is the calibrated Tmean forecasts using ensemble regression calibration (Unger et al. 2009), where a linear regression relationship between forecast f for each member of the model and observation y is expressed as
y=mf+b.
where m is the regression coefficient, y is the observed temperature anomaly, and f is predicted temperature anomaly in the hindcast period. Regression coefficients and standard deviations are used to correct the raw forecast anomalies, which are then converted into a probabilistic forecast, using the normal cumulative density function (CDF). Thus, the ensemble regression calibration serves to generate two-category temperature forecasts that are used as additional tools in the heat wave outlooks and in the search for convergence of evidence between the circulation forecasts tools and the postprocessed temperature and HI forecasts, and between the postprocessed temperature tools as well.

Circulation forecasts.

Atmospheric circulation at all levels contributes to driving heat wave events. We examine selected predicted meteorological fields in the preparation of real-time HW forecasts. At the surface, the predicted mean sea level pressure (MSLP) totals and anomalies support the monitoring of the evolution of the heat low with the understanding that a deepening of the heat low in the Sahel is indicative of increased surface heating and increased chances for extreme heat events. Surface wind speed and direction are important in predicting the likelihood of extreme heat events. The direction of the wind can help locate areas that are likely to undergo warm-air advection. In addition, reduced wind speed may be conducive to suppressed ventilation and increased likelihood of extreme heat events. In contrast, high wind speed and the associated ventilation act to favor fewer HW events. At the midatmospheric level, 700 and 500 hPa, for instance, the dynamics that lead to a stable atmosphere include areas of divergence and anticyclonic ridges. For instance, at 500 hPa, midlevel anticyclonic ridges and associated high geopotential heights contribute to the suppression of clouds, to favor clear sky, more solar radiation reaching the surface, and higher chances for HW to occur. An examination of the coupling between the lower and upper levels of the atmosphere together with model postprocessed forecasts helps build confidence in the forecasts where there clearly is convergence of evidence between the different tools. For instance, if model postprocessed forecasts suggest a high probability for Tmax or HI to exceed given threshold values in areas of predicted upper-level convergence or cyclonic circulation, coupled with midtropospheric high geopotential height, this would indicate a high confidence for a HW event to occur during the validity period.

Heat wave forecasts and impacts on human health

As in any real-time forecasting of weather and or climate patterns, the forecasting of heat waves requires a careful examination of a set of forecasting tools to help decide on the most probable outcomes. The tools discussed in the previous section help identify convergence of evidence among the various products. For example, the presence of a deep heat low or thermal low (low pressure area that results from intense heating), a wind flow with a warm-air advection at 850 hPa, divergence at the 700-hPa level, higher geopotential height at 500 hPa, and upper-level convergence suggest a stable atmosphere that leads to a clear sky and heating of the surface. If, in addition to these conditions, the GEFS guidance suggests a high probability (about 70% or higher) of Tmax exceeding the 80th percentile or HI exceeding 40°C for 3 consecutive days, then the forecast is more likely tilted toward a high risk for an HW to occur. Thus, real-time heat waves forecasts are expressed in terms of high or moderate risk. Forecasts are not issued for areas at low risk, or where there is no convergence of evidence that a heat wave may occur. The forecast calls for a high risk of an HW event occurring when there is a strong agreement between the predicted circulation anomaly and the Tmax or HI forecasts. The forecast downgrades to a moderate risk when one or two of the dynamical factors are not in agreement with the Tmax or HI tools. We use a Geographical Information System (GIS) to draw forecast polygons in areas where HWs may occur. Shapefiles and raster files serve for overlaying the heat wave forecasts with health data to issue heat–health warnings. Prior to finalizing the forecasts, a briefing of the forecasts prepared by the African meteorologists and CPC scientists takes place to discuss the rationale for the forecasts with participating health professionals for feedback and to make adjustments to the forecasts as deemed necessary.

Week-2 heat wave forecasts valid 29 April–5 May 2020.

The premonsoonal season, March–May, in the Sahel is the hottest time of the year, and Tmax can easily approach 50°C in some areas in the Sahel. This is the time when work in the fields begins as farmers prepare land for the upcoming growing season. Increased exposure to heat can aggravate underlying illnesses with severe consequences. Given that near-surface air temperature at 2-m height is one of the most predictable weather variables and the fact that predictability extends to the week-2 time scale that is forecasts with 8 days’ lead time, there is a good opportunity to provide stakeholders with timely and actionable heat information that could help reduce health risk due to prolonged exposure. In this section, we discuss the end-to-end process of preparing the forecasts and mapping the associated health risks. We illustrate this with the heat wave forecasts for 29 April–5 May 2020, when the heat wave season is at its peak. This is also one of the events where the forecasts signals were the most prominent. Model analysis and forecasts initialized on 21 April 2020 are used to prepare HW forecasts valid 29 April–5 May 2020. The circulation forecasts displayed in Fig. 6 consist of predicted variables including MSLP, geopotential height at 500 hPa, surface winds, and winds overlaid with divergence anomaly at 700 and 200 hPa. Surface winds (Fig. 6a) indicate a northerly flow across the Sahel converging with a southwesterly flow just about 12°N in the western part of the Sahel, and with a southerly flow at about 15°N in the eastern part of the Sahel. Wind speed is relatively low at about 1–3 m s−1 in the southern part of the Sahel. The MSLP forecasts (Fig. 6b) exhibit a thermal heat low indicated by 1008 hPa that covers the entire Sahel band, where 500-hPa geopotential heights (Fig. 6c) are quite prominent and indicative of a strong subsidence as evidenced in the 700-hPa divergence field overlaid with streamlines (Fig. 6d). The presence of large-scale divergence across the Sahel suggests suppressed rainfall and conditions conducive to the occurrence of extreme heat. The upper levels (not shown) featured an anomalous cyclonic flow centered over the Atlantic and expanding into West Africa. The flow is associated with convergence aloft and acts to reinforce subsidence, clear sky, and favorable conditions for temperature rise.

Fig. 6.
Fig. 6.

GEFS week-2 forecasts for (a) 10-m wind, (b) mean sea level pressure, (c) 500-hPa geopotential height (×10−1 gpm), and (d) 700-hPa divergence overlaid with wind.

Citation: Bulletin of the American Meteorological Society 103, 8; 10.1175/BAMS-D-20-0140.1

The GEFS heat wave forecasts based on the Tmax and HI definitions, expressed in terms of probability forecasts, are displayed in Fig. 7. The two-category probability forecasts (Fig. 7a) indicate more than 70% chance of heat wave occurrence across the central Sahel in the area encompassing western Mali, Burkina Faso, and southern Niger. The probabilities were lower over central Senegal between 55% and 70%. A heat wave was less likely to occur along the coastal areas from Guinea to Mauritania. The GEFS heat wave forecasting tool based on the probability of the HI exceeding 41°C is shown in Fig. 7b and reveals more than 90% chance of heat wave occurrence over central Senegal, the southern areas of Burkina Faso, the southwestern and southeastern tips of Niger, and the northern areas of the Gulf of Guinea. Probabilities of heat wave occurrence were lower east of Senegal and across 15°N over Mali and Niger.

Fig. 7.
Fig. 7.

GEFS week-2 forecasts for (a) two-category probability heat wave forecasts based on Tmax and (b) as in (a), but for maximum heat index probability of exceedance of 41°C.

Citation: Bulletin of the American Meteorological Society 103, 8; 10.1175/BAMS-D-20-0140.1

Heat hazards outlooks are prepared by examining various forecasting tools including Tmax, HI, and the circulation forecasts, and are expressed in terms of risk of heat wave occurrence. Confidence in the outlooks is based on convergence of evidence between the predicted circulation fields and the model postprocessed Tmax and HI forecasts. The heat hazards outlooks for 29 April–5 May 2020 are displayed in Fig. 8. For this period, the prediction of an expansive thermal low across the Sahel associated with a prominent anticyclonic flow and divergence, combined with high probabilities of heat wave occurrence in the Tmax two-category probability forecasts and the HI probability of exceedance, provides high confidence in the occurrence of a heat wave in the area encompassing western Mali and Senegal. The confidence in the forecasts is rather moderate over much of western Mali, eastern Burkina Faso, and southern Niger along the border with Nigeria.

Fig. 8.
Fig. 8.

Week-2 heat hazards outlooks, valid 29 Apr–5 May 2020 based on GEFS forecasting tools. Shaded in brown and yellow are areas that exhibit high and moderate risk of heat wave occurrence during the period.

Citation: Bulletin of the American Meteorological Society 103, 8; 10.1175/BAMS-D-20-0140.1

Health vulnerability risk forecasts.

Stakeholder engagement is essential to producing weather and climate information relevant to sector needs and requirements (Kruk et al. 2017). Indeed, effective translation of heat wave forecasts into actionable knowledge and establishing HHEW systems requires a cadre of meteorologists with expertise in forecasting of extreme heat and active engagement of an informed and well-trained user community. Institutional commitments through multiagency partnerships are essential to foster multidisciplinary collaboration leading to effective and integrated HHWSs and action plans into national public health policies to mitigate impacts of extreme heat on human health (Casanueva et al. 2019; Kotharkar and Arch 2022). Consistent with the definition of heat–health warning system used by Kovats and Ebi (2006) and the WMO–WHO Heat–Health Action Plan (McGregor et al. 2015), we illustrate the end-to-end process of our HHEW in Fig. 9. The development of the heat wave forecasting tools and forecasts by the meteorologists are the basis of our real-time HHEW. Health professionals contribute livelihood data that lead to knowledge of population vulnerability. As in Ebi (2007), factors that exacerbate the impact of heat on health in Africa include age, nutrition and dehydration, and prolonged heat exposure. In addition, in Africa a significant number of people suffer from undiagnosed cardiovascular and respiratory diseases and yet are unaware of the dangers of heat exposure. This knowledge of population vulnerability is integrated into the heat wave forecasts to coproduce the health risk map that feeds into the HHEW bulletin. The ultimate goal is to translate the HHEW bulletin into several languages and to share it with government authorities, NGOs, and community organizers to take action based on the warnings. Training of all the stakeholders including the forecasters, the health professionals, the decision-makers, and the populations is essential to enabling effective HHEW. For this, at the outset of our heat–health project, we engaged the MoH of Senegal and ANACIM to develop capacity in HHEW in Senegal. To achieve this goal, we organized a workshop “Heat Waves and Impacts on Health,” on 3–5 December 2019. The goal of the workshop was to demonstrate the readiness to take advantage of recent progress in weather modeling and predictions to advance HHEW in the Sahel. The workshop brought together 40 meteorologists and health professionals from the Sahel countries of Burkina Faso, Mali, Niger, and Senegal to learn how to use heat wave forecasts prepared by the meteorologists to issue outlooks of impacts on human health and prepare heat wave early warning to mitigate the impacts on human health. The workshop updated the participants on 1) the effects of heat waves on human health, including an increase in heat-related illnesses and mortality in vulnerable areas and 2) the basic understanding of heat waves with a proposed definition similar to that described in this paper and serving as a basis for the forecasting of heat waves and health impacts. This update was followed by two invited science presentations on climate, heat, and the brain, and heat impacts on respiratory and cardiovascular diseases. Then, the workshop addressed the problem of heat waves in the Sahel. The National Framework for Climate Services (NFCS) was discussed, and work currently being done toward developing early warning systems in various socioeconomic sectors was outlined. The workshop provided an opportunity to show that heat wave forecasts at least 8 days in advance are reliable enough and suitable for developing HHEW systems. Following the plenary presentations, the workshop participants organized into two breakout groups. The first group consisted of meteorologists, and they reviewed the mechanisms associated with past heat wave events, their forecasts, and verifications. The group then reviewed forecasting tools for two selected historical heat wave events and prepared retrospective forecasts at week 2. The second group consisted of professionals from the various sectors within the health community, including clinical and research medical doctors, environmentalists, social workers, communication specialists, health economists, demographers, pharmaceutical managers, etc. (Fig. 10). They worked to develop a prototype legend for the health impacts risk maps and a heat wave information flyer to convey heat information to the public in a way that is easily understandable. In a sense, the health professionals prepared for what to make of the heat wave forecasts once released by the meteorologists. Following the breakouts, participants organized into two mixed groups (meteorologists and health experts) to work toward issuing health impact outlooks based on the heat wave forecasts. These outlooks then served as a basis for issuing a HHEW in the form of a bulletin. A prototype three-page heat–health bulletin, featuring the health impact risk map on the first page, a description of potential health impacts on page 2, and a brief presentation of the heat hazards outlooks on page 3 was proposed. The bulletin, a coproduction between the meteorological services and the health services is intended to guide the government, local authorities, and community organizers on actions to take to mitigate impacts of heat on health. Thus, the workshop demonstrated the feasibility of initiating experimental HHEWs. Clearly at the end of the workshop, the meteorologists were able to analyze forecasting tools and prepared heat wave forecasts. The health professionals were able to access the forecasts, prepared to interpret them, and to coproduce the heat impact forecasts with the meteorologists based on their understanding of livelihood conditions in the areas where heat waves were predicted to occur. Challenges, opportunities, and strategies to implement an effective heat–health early warning system were discussed. In many of the Sahel countries, the existing NFCS ratified by various government agencies provides an excellent opportunity to form a climate and health subworking group to enable HHEW. In addition, the workshop recommended that NOAA pairs at the African Desk a meteorologist from ANACIM with a health professional from the Senegal MoH to work on the forecasting of heat waves and impacts on health, collaboratively with the CPC staff. This training took place from February to June 2020 and provided an excellent opportunity to prepare experimental heat hazards outlooks and health impacts outlooks during the heat wave season of 2020 (Fig. 11).

Fig. 9.
Fig. 9.

Schematic of the end-to-end process of heat–health early warning, from preparing the heat wave forecasts to assessing the impacts on health based on knowledge of population vulnerability to preparing and disseminating heat–health early warnings to public health intervention.

Citation: Bulletin of the American Meteorological Society 103, 8; 10.1175/BAMS-D-20-0140.1

Fig. 10.
Fig. 10.

Participants in the health sector breakout group during the workshop celebrating after completing the road map for translating the heat forecasts into early actions and early planning.

Citation: Bulletin of the American Meteorological Society 103, 8; 10.1175/BAMS-D-20-0140.1

Fig. 11.
Fig. 11.

(left) An environmental health scientist from the Direction Générale de la Santé Publique of Senegal and (right) a meteorologist from the Agence Nationale de la Météorologie et de l’Aviation Civile during training in the African Desk at the NOAA’s Climate Prediction Center, Feb–Jun 2020.

Citation: Bulletin of the American Meteorological Society 103, 8; 10.1175/BAMS-D-20-0140.1

The experimental HHEW bulletin including the heat-related illnesses risk map for Senegal and heat hazards outlooks is displayed in Fig. 12, consistent with the prototype described above. The health risk map was derived based on the heat wave forecasts and knowledge of local livelihood information available to the workshop participants in the health community, including demographic data, field activities, and access to water. The trainees shared the heat wave forecasts and the early warning bulletin with the team of meteorologists and health professionals in Senegal to help initiate experimental HHEWs in Senegal. After the return of the trainees to their respective home institutions, we organized a virtual workshop in January 2021 to assess our experimental HHEW during the 2020 heat season and to discuss a strategy forward for the heat season of 2021 and to move toward gradual implementation of a HHEW in Senegal. The MoH of Senegal renewed their interest in working with the health community and ANACIM to make the heat wave forecasts actionable, but also to engage more actively the medical districts in Senegal located in areas where heat has the highest impacts to start documenting threshold temperatures that trigger HRI. A road map (not shown) has been prepared and steps are being taken to implement despite resource constraints due to the unfolding COVID-19 pandemic. A meeting between NOAA, ANACIM, the MoH, and other stakeholders including Environment Senegal and NGOs is planned for October 2021 to discuss the heat early warning strategy for 2022. To sustain this effort and to promote the initiation of HHEW in Africa, NOAA is working with meteorological services in West Africa to help implement heat wave forecasts. We are providing real time access to heat forecasting products through the website displayed in Fig. 13 featuring week-1 and week-2 probability of exceedance of the 90th and 95th percentiles of persisting raw and bias-corrected Tmax forecasts, HI, and maximum HI, as well as deterministic forecasts of selected atmospheric parameters relevant to heat. These tools provide guidance to meteorological services in Africa to prepare national heat wave forecasts and to work with their health organizations to translate these forecasts into health early planning and early actions. An interesting development of this initiative is the interest of the WMO Global Heat Health Information Network (GHHIN) to promote our approach to HHEW. A report of the Dakar workshop was published in the GHHIN Newsletter and they are following this work closely. In addition, the Pan American Health Organization, a regional office of the World Health Organization for the Americas, is interested in using our strategy to advance HHEW in Latin America, and we are planning to develop this partnership further.

Fig. 12.
Fig. 12.

Heat–health early warning bulletin, featuring the health alert due to heat on page 1, recommended actions to mitigate the impacts of heat on page 2, and the heat hazards as background information on page 3.

Citation: Bulletin of the American Meteorological Society 103, 8; 10.1175/BAMS-D-20-0140.1

Fig. 13.
Fig. 13.

African Desk heat information website: GEFS postprocessed heat guidance tools featuring ensemble probabilistic forecasts at week 1 and week 2 (www.cpc.ncep.noaa.gov/products/international/climatehealth/heat-health_forecasts.shtml).

Citation: Bulletin of the American Meteorological Society 103, 8; 10.1175/BAMS-D-20-0140.1

Summary

Extreme heat has been a real burden on human health in Africa, especially in the Sahel, a semiarid region of West Africa, increasing morbidity and mortality in many vulnerable populations. Advances in numerical modeling and forecasting of temperature provide an excellent opportunity for meteorologists to make real-time forecasts that can translate into health-impact-based decision services. This paper has proposed an approach for end-to-end heat–health early warning (HHEW) in Africa. We define heat waves as 3 consecutive days for the HI value to exceed varying threshold values or for the maximum temperature (Tmax) to exceed the 80th percentile at least. The NCEP Global Ensemble Forecast System (GEFS) is employed to predict heat waves at week 2 with 8 days lead. An evaluation of the performance of the GEFS suggests significant skill at week 2 in predicting heat waves as defined by HI. Skill achieved when Tmax is used to predict heat waves is lower. However, bias correction applied to the GEFS Tmax raw forecasts increases skill substantially, making both HI and Tmax forecasts complementary tools to predict heat wave events at week 2. An examination of the GEFS predicted circulation at week 2 combined with the temperature forecasts help determine consistency among the tools, and to assess confidence in heat hazards outlooks. The 8-day lead provides ample time for the government, local authorities, nongovernmental organizations, and community organizers to access the heat impact forecasts and to take actions in order to minimize health risks. Capacity development is essential to enable HHEW systems. This includes

  • sustained training of meteorologists to access heat wave forecasting tools and to prepare the heat wave forecasts;

  • training of health professionals to know how to locate vulnerable populations, understand the heat wave forecasts, and integrate these into aggravating risk factors such as access to shelters, medical facilities, and safe drinking water;

  • training of meteorologists and health professionals to enable coproduction of the health-impact-based heat forecasts; and

  • training of local governments, community organizers, and the people in understanding heat risks, early planning, and early actions to reduce impacts of heat on human health.

NOAA’s policy of open access to weather data, including forecasts, is a catalyst for evaluating model performance and for implementing real-time forecasts of extreme events in meteorological services around the world. In addition, NOAA’s long history of helping develop the capacity of meteorological services in Africa and our experience working with stakeholders to enable the translation of forecasts into food-security decision-making represents a foundation for advancing the agenda of weather and climate-based health early warning systems in Africa and around the world. Ultimately, institutional commitments through interagency agreements bringing multiple stakeholders to work across disciplines relevant to heat–health is the foundation of an effective and sustainable HHEW system.

Acknowledgments.

This work was initiated through an NWS grant. The authors thank Dr. Louis W. Uccellini, Director of NWS, for his unwavering support for advancing weather-based health early warning systems in Africa. The grant continued through an NWS–OAR Service Level Agreement. We recognize CPO and WPO for their support for this project. The authors thank Valerie Were, Jieshun Zhou, and Douglas LeComte for their constructive critique of the manuscript. We appreciate and recognize the efforts of three anonymous reviewers and the editor. Their comments helped improve the paper and we are grateful to them.

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