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Abstract
The 85-GHz polarization corrected temperature (PCT85) algorithm, using the V85 and H85 channels of the SSM/I sensor, is evaluated for estimation of midlatitude rainfall. The algorithm θ parameter and rain/no-rain thresholds are examined and found to be highly variable. Methods for automatic calibration, to amount for variable atmospheric and surface conditions, are presented. Derivation of θ and thresholds for each individual scene provides a marked improvement in rainfall identification accuracy over the equivalent monthly values. The algorithm is calibrated by comparison with radar data for the estimation of instantaneous rain rates. Detailed evaluation of a number of case studies suggest the relationship of PCT85 and rain rate is substantially different for frontal and mesoscale convective system rainfall. For most frontal conditions the PCT85 provides useful estimates of rain rates with sensitivity to rain intensities as low as 0.1 mm h−1. Overall, the PCT85 estimates of instantaneous rain rate at the footprint scale are to within ±75% of the radar quantity only 50% of the time. Systematic errors result from both the calibration process and from the inability of microwave scattering methods to identify warm rain processes, including orographically enhanced rainfall over land. The results show the need for improved empirical calibration of passive microwave algorithms to provide sensitivity to subsynoptic-scale surface and atmospheric conditions and rainfall processes.
Abstract
The 85-GHz polarization corrected temperature (PCT85) algorithm, using the V85 and H85 channels of the SSM/I sensor, is evaluated for estimation of midlatitude rainfall. The algorithm θ parameter and rain/no-rain thresholds are examined and found to be highly variable. Methods for automatic calibration, to amount for variable atmospheric and surface conditions, are presented. Derivation of θ and thresholds for each individual scene provides a marked improvement in rainfall identification accuracy over the equivalent monthly values. The algorithm is calibrated by comparison with radar data for the estimation of instantaneous rain rates. Detailed evaluation of a number of case studies suggest the relationship of PCT85 and rain rate is substantially different for frontal and mesoscale convective system rainfall. For most frontal conditions the PCT85 provides useful estimates of rain rates with sensitivity to rain intensities as low as 0.1 mm h−1. Overall, the PCT85 estimates of instantaneous rain rate at the footprint scale are to within ±75% of the radar quantity only 50% of the time. Systematic errors result from both the calibration process and from the inability of microwave scattering methods to identify warm rain processes, including orographically enhanced rainfall over land. The results show the need for improved empirical calibration of passive microwave algorithms to provide sensitivity to subsynoptic-scale surface and atmospheric conditions and rainfall processes.
Abstract
Long-term records of winter ice duration, formation, and breakup dates (1869–1996) and maximum thickness (1950–95) on Lake Baikal are analyzed to determine the nature of temporal trends and the relationship with the large-scale atmospheric circulation. There are highly significant trends of decreasing ice duration (and thickness) over the period, associated with later ice formation and earlier breakup dates. These trends are broadly in line with those of winter air temperatures in the region. Variability in Lake Baikal ice formation date, duration, and thickness is significantly related to winter temperatures over a wide area from the Caspian Sea to the Pacific and from northern India to the Kara Sea off the northern coast of Siberia. Thus, Lake Baikal ice cover is a robust indicator of continental-scale winter climate. Correlation and composite analysis of surface and upper-atmospheric fields reveal that interannual variability in ice cover is associated with a tripolar pattern of upper-level geopotential height anomalies. In years of high (low) ice duration and thickness, significant positive (negative) 700-hPa geopotential height anomalies occur over northern Siberia and the Arctic, complemented by negative (positive) anomalies over central-eastern Asia and southern Europe. This structure induces an anomalous meridional flow regime in eastern Siberia with cold (warm) temperature advection from the northeast (southwest) in years of high (low) ice duration and thickness. Analysis of the lower-tropospheric heat budget during years of extreme early and late ice onset indicates that horizontal temperature advection is largely responsible for the observed temperature anomalies. These circulation anomalies are associated with certain recognized patterns of Northern Hemisphere climate variability, notably the Scandinavian and Arctic Oscillation patterns. Significant correlations also occur between Lake Baikal ice cover and the Pacific–North American pattern in the previous winter. The component of variability in Lake Baikal ice cover unrelated to these modes of Northern Hemisphere climate variability is associated with the position and intensity of the Siberian high.
Abstract
Long-term records of winter ice duration, formation, and breakup dates (1869–1996) and maximum thickness (1950–95) on Lake Baikal are analyzed to determine the nature of temporal trends and the relationship with the large-scale atmospheric circulation. There are highly significant trends of decreasing ice duration (and thickness) over the period, associated with later ice formation and earlier breakup dates. These trends are broadly in line with those of winter air temperatures in the region. Variability in Lake Baikal ice formation date, duration, and thickness is significantly related to winter temperatures over a wide area from the Caspian Sea to the Pacific and from northern India to the Kara Sea off the northern coast of Siberia. Thus, Lake Baikal ice cover is a robust indicator of continental-scale winter climate. Correlation and composite analysis of surface and upper-atmospheric fields reveal that interannual variability in ice cover is associated with a tripolar pattern of upper-level geopotential height anomalies. In years of high (low) ice duration and thickness, significant positive (negative) 700-hPa geopotential height anomalies occur over northern Siberia and the Arctic, complemented by negative (positive) anomalies over central-eastern Asia and southern Europe. This structure induces an anomalous meridional flow regime in eastern Siberia with cold (warm) temperature advection from the northeast (southwest) in years of high (low) ice duration and thickness. Analysis of the lower-tropospheric heat budget during years of extreme early and late ice onset indicates that horizontal temperature advection is largely responsible for the observed temperature anomalies. These circulation anomalies are associated with certain recognized patterns of Northern Hemisphere climate variability, notably the Scandinavian and Arctic Oscillation patterns. Significant correlations also occur between Lake Baikal ice cover and the Pacific–North American pattern in the previous winter. The component of variability in Lake Baikal ice cover unrelated to these modes of Northern Hemisphere climate variability is associated with the position and intensity of the Siberian high.
Abstract
The low-level jet (LLJ) over the Bodélé depression in northern Chad is a newly identified feature. Strong LLJ events are responsible for the emission of large quantities of mineral dust from the depression, the world’s largest single dust source, and its subsequent transport to West Africa, the tropical Atlantic, and beyond. Accurate simulation of this key dust-generating atmospheric feature is, therefore, an important requirement for dust models. The objectives of the present study are (i) to evaluate the ability of regional climate models (RCMs) and global analyses/reanalyses to represent this feature, and (ii) to determine the driving mechanisms of the LLJ and its strong diurnal cycle. Observational data obtained during the Bodélé Dust Experiment (BoDEx 2005) are utilized for comparison. When suitably configured, the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) RCM can represent very accurately many of the key features of the jet including the structure, diurnal cycle, and day-to-day variability. Surface winds are also well reproduced, including the peak winds, which activate dust emission. Model fidelity is, however, strongly dependent on the boundary layer parameterization scheme, surface roughness, and vertical resolution in the lowest layers. A model horizontal resolution of a few tens of kilometers is sufficient to resolve most of the key features of the LLJ, while in global analyses/reanalyses many features of the LLJ are not adequately represented. Idealized RCM simulations indicate that under strong synoptic forcing the surrounding orography of the Tibesti and Ennedi Mountains acts to focus the LLJ onto the Bodélé and to accelerate the jet by ∼40%. From the RCM experiments it is diagnosed that the pronounced diurnal cycle of the Bodélé LLJ is largely a result of varying eddy viscosity, with elevated heating/cooling over the Tibesti Mountains to the north as a second-order contribution.
Abstract
The low-level jet (LLJ) over the Bodélé depression in northern Chad is a newly identified feature. Strong LLJ events are responsible for the emission of large quantities of mineral dust from the depression, the world’s largest single dust source, and its subsequent transport to West Africa, the tropical Atlantic, and beyond. Accurate simulation of this key dust-generating atmospheric feature is, therefore, an important requirement for dust models. The objectives of the present study are (i) to evaluate the ability of regional climate models (RCMs) and global analyses/reanalyses to represent this feature, and (ii) to determine the driving mechanisms of the LLJ and its strong diurnal cycle. Observational data obtained during the Bodélé Dust Experiment (BoDEx 2005) are utilized for comparison. When suitably configured, the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) RCM can represent very accurately many of the key features of the jet including the structure, diurnal cycle, and day-to-day variability. Surface winds are also well reproduced, including the peak winds, which activate dust emission. Model fidelity is, however, strongly dependent on the boundary layer parameterization scheme, surface roughness, and vertical resolution in the lowest layers. A model horizontal resolution of a few tens of kilometers is sufficient to resolve most of the key features of the LLJ, while in global analyses/reanalyses many features of the LLJ are not adequately represented. Idealized RCM simulations indicate that under strong synoptic forcing the surrounding orography of the Tibesti and Ennedi Mountains acts to focus the LLJ onto the Bodélé and to accelerate the jet by ∼40%. From the RCM experiments it is diagnosed that the pronounced diurnal cycle of the Bodélé LLJ is largely a result of varying eddy viscosity, with elevated heating/cooling over the Tibesti Mountains to the north as a second-order contribution.
Abstract
Future projections of precipitation at regional scales are vital to inform climate change adaptation activities. Therefore, is it important to quantify projected changes and associated uncertainty, and understand model processes responsible. This paper addresses these challenges for southern Africa and the adjacent Indian Ocean focusing on the local wet season. Precipitation projections for the end of the twenty-first century indicate a pronounced dipole pattern in the CMIP5 multimodel mean. The dipole indicates future wetting (drying) to the north (south) of the climatological axis of maximum rainfall, implying a northward shift of the ITCZ and south Indian Ocean convergence zone that is not consistent with a simple “wet get wetter” pattern. This pattern is most pronounced in early austral summer, suggesting a later and shorter wet season over much of southern Africa. Using a decomposition method we determine physical mechanisms underlying this dipole pattern of projected change, and the associated intermodel uncertainty. The projected dipole pattern is largely associated with the dynamical component of change indicative of shifts in the location of convection. Over the Indian Ocean, this apparent northward shift in the ITCZ may reflect the response to changes in the north–south SST gradient over the Indian Ocean, consistent with a “warmest get wetter” mechanism. Over land subtropical drying is relatively robust, particularly in the early wet season. This has contributions from dynamical shifts in the location of convection, which may be related to regional SST structures in the southern Indian Ocean, and the thermodynamic decline in relative humidity. Implications for understanding and potentially constraining uncertainty in projections are discussed.
Abstract
Future projections of precipitation at regional scales are vital to inform climate change adaptation activities. Therefore, is it important to quantify projected changes and associated uncertainty, and understand model processes responsible. This paper addresses these challenges for southern Africa and the adjacent Indian Ocean focusing on the local wet season. Precipitation projections for the end of the twenty-first century indicate a pronounced dipole pattern in the CMIP5 multimodel mean. The dipole indicates future wetting (drying) to the north (south) of the climatological axis of maximum rainfall, implying a northward shift of the ITCZ and south Indian Ocean convergence zone that is not consistent with a simple “wet get wetter” pattern. This pattern is most pronounced in early austral summer, suggesting a later and shorter wet season over much of southern Africa. Using a decomposition method we determine physical mechanisms underlying this dipole pattern of projected change, and the associated intermodel uncertainty. The projected dipole pattern is largely associated with the dynamical component of change indicative of shifts in the location of convection. Over the Indian Ocean, this apparent northward shift in the ITCZ may reflect the response to changes in the north–south SST gradient over the Indian Ocean, consistent with a “warmest get wetter” mechanism. Over land subtropical drying is relatively robust, particularly in the early wet season. This has contributions from dynamical shifts in the location of convection, which may be related to regional SST structures in the southern Indian Ocean, and the thermodynamic decline in relative humidity. Implications for understanding and potentially constraining uncertainty in projections are discussed.
Abstract
Southern Africa is characterized by a high degree of rainfall variability, affecting agriculture and hydrology, among other sectors. This paper aims to investigate such variability and to identify stable relationships with its potential drivers in the climate system; such relationships may be used as the basis for the statistical downscaling of climate model outputs, for example. The analysis uses generalized linear models (GLMs). The GLMs are fitted to twentieth-century observational data for the period 1957–2006 to characterize the dependence of monthly precipitation occurrences and amounts upon the climate indicators of interest. In contrast with many of the analyses that have previously been used to investigate controls on precipitation in the region, GLMs allow for the investigation of the relationships between different components of the climate system (geographical and climatic drivers) simultaneously. Six climate factors were found to drive part of the rainfall variability in the region, and their modeled effect upon rainfall occurrences and amounts resulted in general agreement with previous studies. Among the retained indices, relative humidity and El Niño accounted for the highest degree of explained variability. The location and intensity of the jet stream were also found to have a statistically significant and physically meaningful effect upon rainfall variability.
Abstract
Southern Africa is characterized by a high degree of rainfall variability, affecting agriculture and hydrology, among other sectors. This paper aims to investigate such variability and to identify stable relationships with its potential drivers in the climate system; such relationships may be used as the basis for the statistical downscaling of climate model outputs, for example. The analysis uses generalized linear models (GLMs). The GLMs are fitted to twentieth-century observational data for the period 1957–2006 to characterize the dependence of monthly precipitation occurrences and amounts upon the climate indicators of interest. In contrast with many of the analyses that have previously been used to investigate controls on precipitation in the region, GLMs allow for the investigation of the relationships between different components of the climate system (geographical and climatic drivers) simultaneously. Six climate factors were found to drive part of the rainfall variability in the region, and their modeled effect upon rainfall occurrences and amounts resulted in general agreement with previous studies. Among the retained indices, relative humidity and El Niño accounted for the highest degree of explained variability. The location and intensity of the jet stream were also found to have a statistically significant and physically meaningful effect upon rainfall variability.
Abstract
There are numerous applications in climatology and hydrology where accurate information at scales smaller than the existing monthly/2.5° products would be invaluable. Here, a new microwave/infrared rainfall algorithm is introduced that combines satellite passive microwave (PMW) and infrared (IR) data to account for limitations in both data types. Rainfall estimates are produced at the high spatial resolution and temporal frequency of the IR data using rainfall information from the PMW data. An IRTb–rain rate relationship, variable in space and time, is derived from coincident observations of IRTb and PMW rain rate (accumulated over a calibration domain) using the probability matching method. The IRTb–rain rate relationship is then applied to IR imagery at full temporal resolution.
MIRA estimates of rainfall are evaluated over a range of spatial and temporal scales. Over the global Tropics and subtropics, optimum IR thresholds and IRTb–rain rate relationships are highly variable, reflecting the complexity of dominant cloud microphysical processes. As a result, MIRA shows sensitivity to these variations, resulting in potentially useful improvements in estimate accuracy at small scales in comparison to the Geostationary Operational Environmental Satellite Precipitation Index (GPI) and the PMW-calibrated Universally Adjusted GPI (UAGPI). Unlike some existing PMW/IR techniques, MIRA can successfully capture variability in rain rates at the smallest possible scales. At larger scales MIRA and UAGPI produce very similar improvements over the GPI. The results demonstrate the potential for a new high-resolution rainfall climatology from 1987 onward, using International Satellite Cloud Climatology Project DX and Special Sensor Microwave Imager data. For real-time regional or quasi-global applications, a temporally “rolling” calibration window is suggested.
Abstract
There are numerous applications in climatology and hydrology where accurate information at scales smaller than the existing monthly/2.5° products would be invaluable. Here, a new microwave/infrared rainfall algorithm is introduced that combines satellite passive microwave (PMW) and infrared (IR) data to account for limitations in both data types. Rainfall estimates are produced at the high spatial resolution and temporal frequency of the IR data using rainfall information from the PMW data. An IRTb–rain rate relationship, variable in space and time, is derived from coincident observations of IRTb and PMW rain rate (accumulated over a calibration domain) using the probability matching method. The IRTb–rain rate relationship is then applied to IR imagery at full temporal resolution.
MIRA estimates of rainfall are evaluated over a range of spatial and temporal scales. Over the global Tropics and subtropics, optimum IR thresholds and IRTb–rain rate relationships are highly variable, reflecting the complexity of dominant cloud microphysical processes. As a result, MIRA shows sensitivity to these variations, resulting in potentially useful improvements in estimate accuracy at small scales in comparison to the Geostationary Operational Environmental Satellite Precipitation Index (GPI) and the PMW-calibrated Universally Adjusted GPI (UAGPI). Unlike some existing PMW/IR techniques, MIRA can successfully capture variability in rain rates at the smallest possible scales. At larger scales MIRA and UAGPI produce very similar improvements over the GPI. The results demonstrate the potential for a new high-resolution rainfall climatology from 1987 onward, using International Satellite Cloud Climatology Project DX and Special Sensor Microwave Imager data. For real-time regional or quasi-global applications, a temporally “rolling” calibration window is suggested.
Abstract
As part of the U.S. Agency for International Development/National Oceanic and Atmospheric Administration project to develop an improved monitoring, forecasting, and simulation system for the river Nile, the Remote Sensing Unit of the University of Bristol has been investigating and developing satellite infrared techniques for small-scale estimation of rainfall over the region of the upper Nile basin. In this paper, the need for variable IR rain/no-rain temperature thresholds as a basis for reliable satellite identification of rain areas over small scales is explained, and the spatially and temporally variable nature of optimum IR rain/no-rain threshold temperatures is examined.
Meteosat IR data covering a period of 17 months have been analyzed along with daily rain gauge reports for calibration and validation. Analyses have been carried out on a monthly basis. Optimum IR rain/no rain threshold temperatures over the study area in the east Africa region are shown to have exhibited a marked seasonal trend, with an annual variation approaching 40 K. Minimum threshold temperature values were found at the onset of the summer wet season, and maximum threshold temperature values during the driest winter months. Generally, summer threshold temperatures were low, around 230 K, and winter thresholds high, in the range of 240–260 K.
During the wet season, optimum IR rain/no-rain threshold temperatures exhibited a distinct pattern of spatial variation. This was modeled as a function of pixel latitude, longitude, and surface elevation. This threshold temperature model was then used to generate threshold temperature estimates at the pixel scale from an independent Meteosat dataset for 1992. Compared with the performance of spatially uniform threshold methods, marked improvements in rain-area classification accuracy were obtained. Optimum IR rain/no-rain threshold temperature variation is therefore seen to be a result of a complex interaction of climatology, meteorology, and topography, and as such the implications of this for the design and use of regional-scale rainfall monitoring techniques are discussed.
Abstract
As part of the U.S. Agency for International Development/National Oceanic and Atmospheric Administration project to develop an improved monitoring, forecasting, and simulation system for the river Nile, the Remote Sensing Unit of the University of Bristol has been investigating and developing satellite infrared techniques for small-scale estimation of rainfall over the region of the upper Nile basin. In this paper, the need for variable IR rain/no-rain temperature thresholds as a basis for reliable satellite identification of rain areas over small scales is explained, and the spatially and temporally variable nature of optimum IR rain/no-rain threshold temperatures is examined.
Meteosat IR data covering a period of 17 months have been analyzed along with daily rain gauge reports for calibration and validation. Analyses have been carried out on a monthly basis. Optimum IR rain/no rain threshold temperatures over the study area in the east Africa region are shown to have exhibited a marked seasonal trend, with an annual variation approaching 40 K. Minimum threshold temperature values were found at the onset of the summer wet season, and maximum threshold temperature values during the driest winter months. Generally, summer threshold temperatures were low, around 230 K, and winter thresholds high, in the range of 240–260 K.
During the wet season, optimum IR rain/no-rain threshold temperatures exhibited a distinct pattern of spatial variation. This was modeled as a function of pixel latitude, longitude, and surface elevation. This threshold temperature model was then used to generate threshold temperature estimates at the pixel scale from an independent Meteosat dataset for 1992. Compared with the performance of spatially uniform threshold methods, marked improvements in rain-area classification accuracy were obtained. Optimum IR rain/no-rain threshold temperature variation is therefore seen to be a result of a complex interaction of climatology, meteorology, and topography, and as such the implications of this for the design and use of regional-scale rainfall monitoring techniques are discussed.
Abstract
The development of a combined infrared and passive microwave satellite rainfall estimation technique is outlined. Infrared data from geostationary satellites are combined with polar-orbiting passive microwave estimates to provide 30-min rainfall estimates. Collocated infrared and passive microwave values are used to generate a database, which is accessed by a cumulative histogram matching approach to generate an infrared temperature–rain-rate relationship. The technique produces initial estimates at 30-min and 12-km resolution ready to be aggregated to the user requirements. A 4-month case study over Africa has been chosen to compare the results from this technique with those of some existing rainfall techniques. The results indicate that the technique outlined here has statistical scores that are similar to other infrared/passive microwave combined algorithms. Comparison with the Geostationary Operational Environmental Satellite (GOES) precipitation index shows that while these algorithms result in lower correlation scores, areal statistics are significantly better than either the infrared or passive microwave techniques alone.
Abstract
The development of a combined infrared and passive microwave satellite rainfall estimation technique is outlined. Infrared data from geostationary satellites are combined with polar-orbiting passive microwave estimates to provide 30-min rainfall estimates. Collocated infrared and passive microwave values are used to generate a database, which is accessed by a cumulative histogram matching approach to generate an infrared temperature–rain-rate relationship. The technique produces initial estimates at 30-min and 12-km resolution ready to be aggregated to the user requirements. A 4-month case study over Africa has been chosen to compare the results from this technique with those of some existing rainfall techniques. The results indicate that the technique outlined here has statistical scores that are similar to other infrared/passive microwave combined algorithms. Comparison with the Geostationary Operational Environmental Satellite (GOES) precipitation index shows that while these algorithms result in lower correlation scores, areal statistics are significantly better than either the infrared or passive microwave techniques alone.
Abstract
Equatorial East Africa (EEA) suffers from significant flood risks. These can be mitigated with preemptive action; however, currently available early warnings are limited to a few days’ lead time. Extending warnings using subseasonal climate forecasts could open a window for more extensive preparedness activity. However, before these forecasts can be used, the basis of their skill and relevance for flood risk must be established. Here we demonstrate that subseasonal forecasts are particularly skillful over EEA. Forecasts can skillfully anticipate weekly upper-quintile rainfall within a season, at lead times of 2 weeks and beyond. We demonstrate the link between the Madden–Julian oscillation (MJO) and extreme rainfall events in the region, and confirm that leading forecast models accurately represent the EEA teleconnection to the MJO. The relevance of weekly rainfall totals for fluvial flood risk in the region is investigated using a long record of streamflow from the Nzoia River in western Kenya. Both heavy rainfall and high antecedent rainfall conditions are identified as key drivers of flood risk, with upper-quintile weekly rainfall shown to skillfully discriminate flood events. We additionally evaluate GloFAS global flood forecasts for the Nzoia basin. Though these are able to anticipate some flooding events with several weeks lead time, analysis suggests action based on these would result in a false alarm more than 50% of the time. Overall, these results build on the scientific evidence base that supports the use of subseasonal forecasts in EEA, and activities to advance their use are discussed.
Abstract
Equatorial East Africa (EEA) suffers from significant flood risks. These can be mitigated with preemptive action; however, currently available early warnings are limited to a few days’ lead time. Extending warnings using subseasonal climate forecasts could open a window for more extensive preparedness activity. However, before these forecasts can be used, the basis of their skill and relevance for flood risk must be established. Here we demonstrate that subseasonal forecasts are particularly skillful over EEA. Forecasts can skillfully anticipate weekly upper-quintile rainfall within a season, at lead times of 2 weeks and beyond. We demonstrate the link between the Madden–Julian oscillation (MJO) and extreme rainfall events in the region, and confirm that leading forecast models accurately represent the EEA teleconnection to the MJO. The relevance of weekly rainfall totals for fluvial flood risk in the region is investigated using a long record of streamflow from the Nzoia River in western Kenya. Both heavy rainfall and high antecedent rainfall conditions are identified as key drivers of flood risk, with upper-quintile weekly rainfall shown to skillfully discriminate flood events. We additionally evaluate GloFAS global flood forecasts for the Nzoia basin. Though these are able to anticipate some flooding events with several weeks lead time, analysis suggests action based on these would result in a false alarm more than 50% of the time. Overall, these results build on the scientific evidence base that supports the use of subseasonal forecasts in EEA, and activities to advance their use are discussed.
Abstract
The subseasonal-to-seasonal (S2S) predictive time scale, encompassing lead times ranging from 2 weeks to a season, is at the frontier of forecasting science. Forecasts on this time scale provide opportunities for enhanced application-focused capabilities to complement existing weather and climate services and products. There is, however, a “knowledge–value” gap, where a lack of evidence and awareness of the potential socioeconomic benefits of S2S forecasts limits their wider uptake. To address this gap, here we present the first global community effort at summarizing relevant applications of S2S forecasts to guide further decision-making and support the continued development of S2S forecasts and related services. Focusing on 12 sectoral case studies spanning public health, agriculture, water resource management, renewable energy and utilities, and emergency management and response, we draw on recent advancements to explore their application and utility. These case studies mark a significant step forward in moving from potential to actual S2S forecasting applications. We show that by placing user needs at the forefront of S2S forecast development—demonstrating both skill and utility across sectors—this dialogue can be used to help promote and accelerate the awareness, value, and cogeneration of S2S forecasts. We also highlight that while S2S forecasts are increasingly gaining interest among users, incorporating probabilistic S2S forecasts into existing decision-making operations is not trivial. Nevertheless, S2S forecasting represents a significant opportunity to generate useful, usable, and actionable forecast applications for and with users that will increasingly unlock the potential of this forecasting time scale.
Abstract
The subseasonal-to-seasonal (S2S) predictive time scale, encompassing lead times ranging from 2 weeks to a season, is at the frontier of forecasting science. Forecasts on this time scale provide opportunities for enhanced application-focused capabilities to complement existing weather and climate services and products. There is, however, a “knowledge–value” gap, where a lack of evidence and awareness of the potential socioeconomic benefits of S2S forecasts limits their wider uptake. To address this gap, here we present the first global community effort at summarizing relevant applications of S2S forecasts to guide further decision-making and support the continued development of S2S forecasts and related services. Focusing on 12 sectoral case studies spanning public health, agriculture, water resource management, renewable energy and utilities, and emergency management and response, we draw on recent advancements to explore their application and utility. These case studies mark a significant step forward in moving from potential to actual S2S forecasting applications. We show that by placing user needs at the forefront of S2S forecast development—demonstrating both skill and utility across sectors—this dialogue can be used to help promote and accelerate the awareness, value, and cogeneration of S2S forecasts. We also highlight that while S2S forecasts are increasingly gaining interest among users, incorporating probabilistic S2S forecasts into existing decision-making operations is not trivial. Nevertheless, S2S forecasting represents a significant opportunity to generate useful, usable, and actionable forecast applications for and with users that will increasingly unlock the potential of this forecasting time scale.
