Browse
Abstract
Observations of column water vapor in the tropics show significant variations in space and time, indicating that it is strongly influenced by the passage of weather systems. It is hypothesized that many of the influencing systems are moisture modes, systems whose thermodynamics are governed by moisture. On the basis of four objective criteria, results suggest that all oceanic convectively coupled tropical depression (TD)-like waves and equatorial Rossby waves are moisture modes. These modes occur where the horizontal column moisture gradient is steep and not where the column water vapor content is high. Despite geographical basic-state differences, the moisture modes are driven by the same mechanisms across all basins. The moist static energy (MSE) anomalies propagate westward by horizontal moisture advection by the trade winds. Their growth is determined by the advection of background moisture by the anomalous meridional winds and anomalous radiative heating. Horizontal maps of column moisture and 850-hPa streamfunction show that convection is partially collocated with the low-level circulation in nearly all the waves. Both this structure and the process of growth indicate that the moisture modes grow from moisture–vortex instability. Last, space–time spectral analysis reveals that column moisture and low-level meridional winds are coherent and exhibit a phasing that is consistent with a poleward latent energy transport. Collectively, these results indicate that moisture modes are ubiquitous across the tropics. That they occur in regions of steep horizontal moisture gradients and grow from moisture–vortex instability suggests that these gradients are inherently unstable and are subject to continuous stirring.
Significance Statement
Over the tropics, column water vapor has been found to be highly correlated with precipitation, especially in slowly evolving systems. These observations and theory support the hypothesis that moisture modes exist, a type of precipitating weather system that does not exist in dry theory. In this study, we found that all oceanic tropical depression (TD)-like waves and equatorial Rossby waves are moisture modes. These systems exist in regions where moisture varies greatly in space, and they grow by transporting air from the humid areas of the tropics toward their low pressure center. These results indicate that the climatological-mean distribution of moisture in the tropics is unstable and is subject to stirring by moisture modes.
Abstract
Observations of column water vapor in the tropics show significant variations in space and time, indicating that it is strongly influenced by the passage of weather systems. It is hypothesized that many of the influencing systems are moisture modes, systems whose thermodynamics are governed by moisture. On the basis of four objective criteria, results suggest that all oceanic convectively coupled tropical depression (TD)-like waves and equatorial Rossby waves are moisture modes. These modes occur where the horizontal column moisture gradient is steep and not where the column water vapor content is high. Despite geographical basic-state differences, the moisture modes are driven by the same mechanisms across all basins. The moist static energy (MSE) anomalies propagate westward by horizontal moisture advection by the trade winds. Their growth is determined by the advection of background moisture by the anomalous meridional winds and anomalous radiative heating. Horizontal maps of column moisture and 850-hPa streamfunction show that convection is partially collocated with the low-level circulation in nearly all the waves. Both this structure and the process of growth indicate that the moisture modes grow from moisture–vortex instability. Last, space–time spectral analysis reveals that column moisture and low-level meridional winds are coherent and exhibit a phasing that is consistent with a poleward latent energy transport. Collectively, these results indicate that moisture modes are ubiquitous across the tropics. That they occur in regions of steep horizontal moisture gradients and grow from moisture–vortex instability suggests that these gradients are inherently unstable and are subject to continuous stirring.
Significance Statement
Over the tropics, column water vapor has been found to be highly correlated with precipitation, especially in slowly evolving systems. These observations and theory support the hypothesis that moisture modes exist, a type of precipitating weather system that does not exist in dry theory. In this study, we found that all oceanic tropical depression (TD)-like waves and equatorial Rossby waves are moisture modes. These systems exist in regions where moisture varies greatly in space, and they grow by transporting air from the humid areas of the tropics toward their low pressure center. These results indicate that the climatological-mean distribution of moisture in the tropics is unstable and is subject to stirring by moisture modes.
Abstract
Despite the enormous potential of precipitation forecasts to save lives and property in Africa, low skill has limited their uptake. To assess the skill and improve the performance of the forecast, validation and postprocessing should continuously be carried out. Here, we evaluate the quality of reforecasts from the European Centre for Medium-Range Weather Forecasts over equatorial East Africa (EEA) against satellite and rain gauge observations for the period 2001–18. The 24-h rainfall accumulations are analyzed from short- to medium-range time scales. Additionally, 48- and 120-h rainfall accumulations were also assessed. The skill was assessed using an extended probabilistic climatology (EPC) derived from the observations. Results show that the reforecasts overestimate rainfall, especially during the rainy seasons and over high-altitude areas. However, there is potential of skill in the raw forecasts up to 14-day lead time. There is an improvement of up to 30% in the Brier score/continuous ranked probability score relative to EPC in most areas, especially the higher-altitude regions, decreasing with lead time. Aggregating the reforecasts enhances the skill further, likely due to a reduction in timing mismatches. However, for some regions of the study domain, the predictive performance is worse than EPC, mainly due to biases. Postprocessing the reforecasts using isotonic distributional regression considerably improves skill, increasing the number of grid points with positive Brier skill score (continuous ranked probability skill score) by an average of 81% (91%) for lead times 1–14 days ahead. Overall, the study highlights the potential of the reforecasts, the spatiotemporal variation in skill, and the benefit of postprocessing in EEA.
Abstract
Despite the enormous potential of precipitation forecasts to save lives and property in Africa, low skill has limited their uptake. To assess the skill and improve the performance of the forecast, validation and postprocessing should continuously be carried out. Here, we evaluate the quality of reforecasts from the European Centre for Medium-Range Weather Forecasts over equatorial East Africa (EEA) against satellite and rain gauge observations for the period 2001–18. The 24-h rainfall accumulations are analyzed from short- to medium-range time scales. Additionally, 48- and 120-h rainfall accumulations were also assessed. The skill was assessed using an extended probabilistic climatology (EPC) derived from the observations. Results show that the reforecasts overestimate rainfall, especially during the rainy seasons and over high-altitude areas. However, there is potential of skill in the raw forecasts up to 14-day lead time. There is an improvement of up to 30% in the Brier score/continuous ranked probability score relative to EPC in most areas, especially the higher-altitude regions, decreasing with lead time. Aggregating the reforecasts enhances the skill further, likely due to a reduction in timing mismatches. However, for some regions of the study domain, the predictive performance is worse than EPC, mainly due to biases. Postprocessing the reforecasts using isotonic distributional regression considerably improves skill, increasing the number of grid points with positive Brier skill score (continuous ranked probability skill score) by an average of 81% (91%) for lead times 1–14 days ahead. Overall, the study highlights the potential of the reforecasts, the spatiotemporal variation in skill, and the benefit of postprocessing in EEA.
Abstract
Recently, Nakamura and Huang proposed a theory of blocking onset based on the budget of finite-amplitude local wave activity on the midlatitude waveguide. Blocks form in their idealized model due to a mechanism that also describes the emergence of traffic jams in traffic theory. The current work investigates the development of a winter European block in terms of finite-amplitude local wave activity to evaluate the possible relevance of the “traffic jam” mechanism for the flow transition. Two hundred members of a medium-range ensemble forecast of the blocking onset period are analyzed with correlation- and cluster-based sensitivity techniques. Diagnostic evidence points to a traffic jam onset on 17 December 2016. Block development is sensitive to upstream Rossby wave activity up to 1.5 days prior to its initiation and consistent with expectations from the idealized theory. Eastward transport of finite-amplitude local wave activity in the southern part of the block is suppressed by nonlinear flux modification from the large-amplitude blocking pattern, consistent with the expected obstruction in the traffic jam model. The relationship of finite-amplitude local wave activity and its zonal flux as mapped by the ensemble exhibits established characteristics of a traffic jam. This study suggests that the traffic jam mechanism may play an important role in some cases of blocking onset and more generally that applying finite-amplitude local wave activity diagnostics to ensemble data is a promising approach for the further examination of individual onset events in light of the Nakamura and Huang theory.
Significance Statement
Blocking is an occasional phenomenon in the mid- and high-latitude atmosphere characterized by the stalling of weather systems. Episodes of blocking are linked to extreme weather but their occurrence is not completely understood. A recent theory suggests that blocks may form in the jet stream like traffic jams on a highway. The onset mechanism contained in the theory could explain why forecasts of blocking are sometimes poor. In this work, we investigate the formation of a 2016 European winter block in the context of the traffic jam theory. Though questions remain regarding the implications for forecast uncertainty, our findings strongly support the notion of a traffic jam onset.
Abstract
Recently, Nakamura and Huang proposed a theory of blocking onset based on the budget of finite-amplitude local wave activity on the midlatitude waveguide. Blocks form in their idealized model due to a mechanism that also describes the emergence of traffic jams in traffic theory. The current work investigates the development of a winter European block in terms of finite-amplitude local wave activity to evaluate the possible relevance of the “traffic jam” mechanism for the flow transition. Two hundred members of a medium-range ensemble forecast of the blocking onset period are analyzed with correlation- and cluster-based sensitivity techniques. Diagnostic evidence points to a traffic jam onset on 17 December 2016. Block development is sensitive to upstream Rossby wave activity up to 1.5 days prior to its initiation and consistent with expectations from the idealized theory. Eastward transport of finite-amplitude local wave activity in the southern part of the block is suppressed by nonlinear flux modification from the large-amplitude blocking pattern, consistent with the expected obstruction in the traffic jam model. The relationship of finite-amplitude local wave activity and its zonal flux as mapped by the ensemble exhibits established characteristics of a traffic jam. This study suggests that the traffic jam mechanism may play an important role in some cases of blocking onset and more generally that applying finite-amplitude local wave activity diagnostics to ensemble data is a promising approach for the further examination of individual onset events in light of the Nakamura and Huang theory.
Significance Statement
Blocking is an occasional phenomenon in the mid- and high-latitude atmosphere characterized by the stalling of weather systems. Episodes of blocking are linked to extreme weather but their occurrence is not completely understood. A recent theory suggests that blocks may form in the jet stream like traffic jams on a highway. The onset mechanism contained in the theory could explain why forecasts of blocking are sometimes poor. In this work, we investigate the formation of a 2016 European winter block in the context of the traffic jam theory. Though questions remain regarding the implications for forecast uncertainty, our findings strongly support the notion of a traffic jam onset.
Abstract
Convectively coupled waves (CCWs) over the Western Hemisphere are classified based on their governing thermodynamics. It is found that only the tropical depressions (TDs; TD waves) satisfy the criteria necessary to be considered a moisture mode, as in the Rossby-like wave found in an earlier study. In this wave, water vapor fluctuations play a much greater role in the thermodynamics than temperature fluctuations. Only in the eastward-propagating inertio-gravity (EIG) wave does temperature govern the thermodynamics. Temperature and moisture play comparable roles in all the other waves, including the Madden–Julian oscillation over the Western Hemisphere (MJO-W). The moist static energy (MSE) budget of CCWs is investigated by analyzing ERA5 data and data from the 2014/15 observations and modeling of the Green Ocean Amazon (GoAmazon 2014/15) field campaign. Results reveal that vertical advection of MSE acts as a primary driver of the propagation of column MSE in westward inertio-gravity (WIG) wave, Kelvin wave, and MJO-W, while horizontal advection plays a central role in the mixed Rossby gravity (MRG) and TD wave. Results also suggest that cloud radiative heating and the horizontal MSE advection govern the maintenance of most of the CCWs. Major disagreements are found between ERA5 and GoAmazon. In GoAmazon, convection is more tightly coupled to variations in column MSE, and vertical MSE advection plays a more prominent role in the MSE tendency. These results along with substantial budget residuals found in ERA5 data suggest that CCWs over the tropical Western Hemisphere are not represented adequately in the reanalysis.
Significance Statement
In comparison to other regions of the globe, the weather systems that affect precipitation in the tropical Western Hemisphere have received little attention. In this study, we investigate the structure, propagation, and thermodynamics of convectively coupled waves that impact precipitation in this region. We found that slowly evolving tropical systems are “moisture modes,” i.e., moving regions of high humidity and precipitation that are maintained by interactions between clouds and radiation. The faster waves are systems that exhibit relatively larger fluctuations in temperature. Vertical motions are more important for the movement of rainfall in these waves. Last, we found that reanalysis and observations disagree over the importance of different processes in the waves that occurred over the Amazon region, hinting at potential deficiencies on how the reanalysis represents clouds in this region.
Abstract
Convectively coupled waves (CCWs) over the Western Hemisphere are classified based on their governing thermodynamics. It is found that only the tropical depressions (TDs; TD waves) satisfy the criteria necessary to be considered a moisture mode, as in the Rossby-like wave found in an earlier study. In this wave, water vapor fluctuations play a much greater role in the thermodynamics than temperature fluctuations. Only in the eastward-propagating inertio-gravity (EIG) wave does temperature govern the thermodynamics. Temperature and moisture play comparable roles in all the other waves, including the Madden–Julian oscillation over the Western Hemisphere (MJO-W). The moist static energy (MSE) budget of CCWs is investigated by analyzing ERA5 data and data from the 2014/15 observations and modeling of the Green Ocean Amazon (GoAmazon 2014/15) field campaign. Results reveal that vertical advection of MSE acts as a primary driver of the propagation of column MSE in westward inertio-gravity (WIG) wave, Kelvin wave, and MJO-W, while horizontal advection plays a central role in the mixed Rossby gravity (MRG) and TD wave. Results also suggest that cloud radiative heating and the horizontal MSE advection govern the maintenance of most of the CCWs. Major disagreements are found between ERA5 and GoAmazon. In GoAmazon, convection is more tightly coupled to variations in column MSE, and vertical MSE advection plays a more prominent role in the MSE tendency. These results along with substantial budget residuals found in ERA5 data suggest that CCWs over the tropical Western Hemisphere are not represented adequately in the reanalysis.
Significance Statement
In comparison to other regions of the globe, the weather systems that affect precipitation in the tropical Western Hemisphere have received little attention. In this study, we investigate the structure, propagation, and thermodynamics of convectively coupled waves that impact precipitation in this region. We found that slowly evolving tropical systems are “moisture modes,” i.e., moving regions of high humidity and precipitation that are maintained by interactions between clouds and radiation. The faster waves are systems that exhibit relatively larger fluctuations in temperature. Vertical motions are more important for the movement of rainfall in these waves. Last, we found that reanalysis and observations disagree over the importance of different processes in the waves that occurred over the Amazon region, hinting at potential deficiencies on how the reanalysis represents clouds in this region.
Abstract
In the last few years, central Europe faced a number of severe, record-breaking heatwaves. Previous studies focused on predictability of heatwaves on medium-range to subseasonal time scales (5–30 days). However, also short-range (3-day) forecasts of maximum temperature (Tmax) can exhibit substantial errors even on larger spatial scales. This study investigates the causes of short-range forecast errors in Tmax over central Europe for the summers of 2015–20 using the 50-member ensemble of the operational ECMWF-IFS (ECMWF-ENS). The 3-day forecast errors, individually calculated for each ensemble member with respect to a 0–18-h control forecast, are fed into a multivariate linear regression model to study the relative importance of different error sources. Outside of heatwaves, errors in Tmax forecasts are predominantly caused by incorrectly predicted downwelling shortwave radiation, mainly due to errors in low cloud cover. During heatwaves, ECMWF-ENS exhibits a systematic underestimation of Tmax (−0.4 K), which is exacerbated under clear-sky and low wind conditions, and other error sources gain importance: the second most important error source is over- or underestimation of nocturnal temperatures in the residual layer. Additional Lagrangian trajectory analysis for the years 2018–20 (due to limited data availability) suggests a link to accumulating errors in near-surface diabatic heating of air masses associated with forecast errors in residence time over land and cloud cover. Regionally, other physical processes can be of dominant importance during heatwaves. Coastal regions are influenced by errors in near-surface wind whereas errors in soil moisture are more important in southeastern parts of central Europe.
Abstract
In the last few years, central Europe faced a number of severe, record-breaking heatwaves. Previous studies focused on predictability of heatwaves on medium-range to subseasonal time scales (5–30 days). However, also short-range (3-day) forecasts of maximum temperature (Tmax) can exhibit substantial errors even on larger spatial scales. This study investigates the causes of short-range forecast errors in Tmax over central Europe for the summers of 2015–20 using the 50-member ensemble of the operational ECMWF-IFS (ECMWF-ENS). The 3-day forecast errors, individually calculated for each ensemble member with respect to a 0–18-h control forecast, are fed into a multivariate linear regression model to study the relative importance of different error sources. Outside of heatwaves, errors in Tmax forecasts are predominantly caused by incorrectly predicted downwelling shortwave radiation, mainly due to errors in low cloud cover. During heatwaves, ECMWF-ENS exhibits a systematic underestimation of Tmax (−0.4 K), which is exacerbated under clear-sky and low wind conditions, and other error sources gain importance: the second most important error source is over- or underestimation of nocturnal temperatures in the residual layer. Additional Lagrangian trajectory analysis for the years 2018–20 (due to limited data availability) suggests a link to accumulating errors in near-surface diabatic heating of air masses associated with forecast errors in residence time over land and cloud cover. Regionally, other physical processes can be of dominant importance during heatwaves. Coastal regions are influenced by errors in near-surface wind whereas errors in soil moisture are more important in southeastern parts of central Europe.
Abstract
This study investigates the transition from current practical predictability of midlatitude weather to its intrinsic limit. For this purpose, estimates of the current initial condition uncertainty of 12 real cases are reduced in several steps from 100% to 0.1% and propagated in time with a global numerical weather prediction model (ICON at 40 km resolution) that is extended by a stochastic convection scheme to better represent error growth from unresolved motions. With the provision that the perfect model assumption is sufficiently valid, it is found that the potential forecast improvement that could be obtained by perfecting the initial conditions is 4–5 days. This improvement is essentially achieved with an initial condition uncertainty reduction by 90% relative to current conditions, at which point the dominant error growth mechanism changes: With respect to physical processes, a transition occurs from rotationally driven initial error growth to error growth dominated by latent heat release in convection and due to the divergent component of the flow. With respect to spatial scales, a transition from large-scale up-amplitude error growth to a very rapid initial error growth on small scales is found. Reference experiments with a deterministic convection scheme show a 5%–10% longer predictability, but only if the initial condition uncertainty is small. These results confirm that planetary-scale predictability is intrinsically limited by rapid error growth due to latent heat release in clouds through an upscale-interaction process, while this interaction process is unimportant on average for current levels of initial condition uncertainty.
Significance Statement
Weather predictions provide high socioeconomic value and have been greatly improved over the last decades. However, it is widely believed that there is an intrinsic limit to how far into the future the weather can be predicted. Using numerical simulations with an innovative representation of convection, we are able to confirm the existence of this limit and to demonstrate which physical processes are responsible. We further provide quantitative estimates for the limit and the remaining improvement potential. These results make clear that our current weather prediction capabilities are not yet maxed out and could still be significantly improved with advancements in atmospheric observation and simulation technology in the upcoming decades.
Abstract
This study investigates the transition from current practical predictability of midlatitude weather to its intrinsic limit. For this purpose, estimates of the current initial condition uncertainty of 12 real cases are reduced in several steps from 100% to 0.1% and propagated in time with a global numerical weather prediction model (ICON at 40 km resolution) that is extended by a stochastic convection scheme to better represent error growth from unresolved motions. With the provision that the perfect model assumption is sufficiently valid, it is found that the potential forecast improvement that could be obtained by perfecting the initial conditions is 4–5 days. This improvement is essentially achieved with an initial condition uncertainty reduction by 90% relative to current conditions, at which point the dominant error growth mechanism changes: With respect to physical processes, a transition occurs from rotationally driven initial error growth to error growth dominated by latent heat release in convection and due to the divergent component of the flow. With respect to spatial scales, a transition from large-scale up-amplitude error growth to a very rapid initial error growth on small scales is found. Reference experiments with a deterministic convection scheme show a 5%–10% longer predictability, but only if the initial condition uncertainty is small. These results confirm that planetary-scale predictability is intrinsically limited by rapid error growth due to latent heat release in clouds through an upscale-interaction process, while this interaction process is unimportant on average for current levels of initial condition uncertainty.
Significance Statement
Weather predictions provide high socioeconomic value and have been greatly improved over the last decades. However, it is widely believed that there is an intrinsic limit to how far into the future the weather can be predicted. Using numerical simulations with an innovative representation of convection, we are able to confirm the existence of this limit and to demonstrate which physical processes are responsible. We further provide quantitative estimates for the limit and the remaining improvement potential. These results make clear that our current weather prediction capabilities are not yet maxed out and could still be significantly improved with advancements in atmospheric observation and simulation technology in the upcoming decades.
Abstract
Postprocessing ensemble weather predictions to correct systematic errors has become a standard practice in research and operations. However, only a few recent studies have focused on ensemble postprocessing of wind gust forecasts, despite its importance for severe weather warnings. Here, we provide a comprehensive review and systematic comparison of eight statistical and machine learning methods for probabilistic wind gust forecasting via ensemble postprocessing that can be divided in three groups: state-of-the-art postprocessing techniques from statistics [ensemble model output statistics (EMOS), member-by-member postprocessing, isotonic distributional regression], established machine learning methods (gradient-boosting extended EMOS, quantile regression forests), and neural network–based approaches (distributional regression network, Bernstein quantile network, histogram estimation network). The methods are systematically compared using 6 years of data from a high-resolution, convection-permitting ensemble prediction system that was run operationally at the German weather service, and hourly observations at 175 surface weather stations in Germany. While all postprocessing methods yield calibrated forecasts and are able to correct the systematic errors of the raw ensemble predictions, incorporating information from additional meteorological predictor variables beyond wind gusts leads to significant improvements in forecast skill. In particular, we propose a flexible framework of locally adaptive neural networks with different probabilistic forecast types as output, which not only significantly outperform all benchmark postprocessing methods but also learn physically consistent relations associated with the diurnal cycle, especially the evening transition of the planetary boundary layer.
Abstract
Postprocessing ensemble weather predictions to correct systematic errors has become a standard practice in research and operations. However, only a few recent studies have focused on ensemble postprocessing of wind gust forecasts, despite its importance for severe weather warnings. Here, we provide a comprehensive review and systematic comparison of eight statistical and machine learning methods for probabilistic wind gust forecasting via ensemble postprocessing that can be divided in three groups: state-of-the-art postprocessing techniques from statistics [ensemble model output statistics (EMOS), member-by-member postprocessing, isotonic distributional regression], established machine learning methods (gradient-boosting extended EMOS, quantile regression forests), and neural network–based approaches (distributional regression network, Bernstein quantile network, histogram estimation network). The methods are systematically compared using 6 years of data from a high-resolution, convection-permitting ensemble prediction system that was run operationally at the German weather service, and hourly observations at 175 surface weather stations in Germany. While all postprocessing methods yield calibrated forecasts and are able to correct the systematic errors of the raw ensemble predictions, incorporating information from additional meteorological predictor variables beyond wind gusts leads to significant improvements in forecast skill. In particular, we propose a flexible framework of locally adaptive neural networks with different probabilistic forecast types as output, which not only significantly outperform all benchmark postprocessing methods but also learn physically consistent relations associated with the diurnal cycle, especially the evening transition of the planetary boundary layer.
Abstract
Rain gauge data sparsity over Africa is known to impede the assessments of hydrometeorological risks and of the skill of numerical weather prediction models. Satellite rainfall estimates (SREs) have been used as surrogate fields for a long time and are continuously replaced by more advanced algorithms and new sensors. Using a unique daily rainfall dataset from 36 stations across equatorial East Africa for the period 2001–18, this study performs a multiscale evaluation of gauge-calibrated SREs, namely, IMERG, TMPA, CHIRPS, and MSWEP (v2.2 and v2.8). Skills were assessed from daily to annual time scales, for extreme daily precipitation, and for TMPA and IMERG near-real-time (NRT) products. Results show that 1) the SREs reproduce the annual rainfall pattern and seasonal rainfall cycle well, despite exhibiting biases of up to 9%; 2) IMERG is the best for shorter temporal scales while MSWEPv2.2 and CHIRPS perform best at the monthly and annual time steps, respectively; 3) the performance of all the SREs varies spatially, likely due to an inhomogeneous degree of gauge calibration, with the largest variation seen in MSWEPv2.2; 4) all the SREs miss between 79% (IMERG-NRT) and 98% (CHIRPS) of daily extreme rainfall events recorded by the rain gauges; 5) IMERG-NRT is the best regarding extreme event detection and accuracy; and 6) for return values of extreme rainfall, IMERG, and MSWEPv2.2 have the least errors while CHIRPS and MSWEPv2.8 cannot be recommended. The study also highlights improvements of IMERG over TMPA, the decline in performance of MSWEPv2.8 compared to MSWEPv2.2, and the potential of SREs for flood risk assessment over East Africa.
Abstract
Rain gauge data sparsity over Africa is known to impede the assessments of hydrometeorological risks and of the skill of numerical weather prediction models. Satellite rainfall estimates (SREs) have been used as surrogate fields for a long time and are continuously replaced by more advanced algorithms and new sensors. Using a unique daily rainfall dataset from 36 stations across equatorial East Africa for the period 2001–18, this study performs a multiscale evaluation of gauge-calibrated SREs, namely, IMERG, TMPA, CHIRPS, and MSWEP (v2.2 and v2.8). Skills were assessed from daily to annual time scales, for extreme daily precipitation, and for TMPA and IMERG near-real-time (NRT) products. Results show that 1) the SREs reproduce the annual rainfall pattern and seasonal rainfall cycle well, despite exhibiting biases of up to 9%; 2) IMERG is the best for shorter temporal scales while MSWEPv2.2 and CHIRPS perform best at the monthly and annual time steps, respectively; 3) the performance of all the SREs varies spatially, likely due to an inhomogeneous degree of gauge calibration, with the largest variation seen in MSWEPv2.2; 4) all the SREs miss between 79% (IMERG-NRT) and 98% (CHIRPS) of daily extreme rainfall events recorded by the rain gauges; 5) IMERG-NRT is the best regarding extreme event detection and accuracy; and 6) for return values of extreme rainfall, IMERG, and MSWEPv2.2 have the least errors while CHIRPS and MSWEPv2.8 cannot be recommended. The study also highlights improvements of IMERG over TMPA, the decline in performance of MSWEPv2.8 compared to MSWEPv2.2, and the potential of SREs for flood risk assessment over East Africa.
Abstract
In this study, the relationship between ENSO and winter synoptic temperature variability (STV) over the Asian–Pacific–American region is examined in 26 CMIP5/6 model outputs. Compared to observations, most models fail to simulate the correct ENSO–STV relationship in historical simulations. To investigate the possible bias in the ENSO–STV simulations, two possible processes for the connection between ENSO and winter STV are examined in high pattern score (HPS) models and low pattern score (LPS) models, respectively. On the one hand, both HPS and LPS models can overall reproduce a reasonable relationship between STV and the mean-flow conditions supporting extratropical eddy development. On the other hand, only HPS models can well capture the relationship between ENSO and the development of extratropical eddies, while LPS models fail to simulate this feature, indicating that the bias in the simulated ENSO–STV relationship among CMIP5/6 models can be traced back to ENSO simulation. Furthermore, the bias of the ENSO simulation is characterized by an unreasonable SST pattern bias, with an excessive westward extension of warm SST anomalies over the western Pacific and weak warm SST anomalies over the equatorial central-eastern Pacific, resulting in the underestimation of the zonal SST anomaly gradient among models. Therefore, the ENSO pattern bias induces an unrealistic circulation and temperature gradient over the Asian–Pacific–American region, affecting the simulations of the ENSO–STV connection. In addition, the ENSO–STV relationship over the Asian–Pacific–American region is still robust in future projections based on HPS models, providing implications for the selection of future climate predictors.
Abstract
In this study, the relationship between ENSO and winter synoptic temperature variability (STV) over the Asian–Pacific–American region is examined in 26 CMIP5/6 model outputs. Compared to observations, most models fail to simulate the correct ENSO–STV relationship in historical simulations. To investigate the possible bias in the ENSO–STV simulations, two possible processes for the connection between ENSO and winter STV are examined in high pattern score (HPS) models and low pattern score (LPS) models, respectively. On the one hand, both HPS and LPS models can overall reproduce a reasonable relationship between STV and the mean-flow conditions supporting extratropical eddy development. On the other hand, only HPS models can well capture the relationship between ENSO and the development of extratropical eddies, while LPS models fail to simulate this feature, indicating that the bias in the simulated ENSO–STV relationship among CMIP5/6 models can be traced back to ENSO simulation. Furthermore, the bias of the ENSO simulation is characterized by an unreasonable SST pattern bias, with an excessive westward extension of warm SST anomalies over the western Pacific and weak warm SST anomalies over the equatorial central-eastern Pacific, resulting in the underestimation of the zonal SST anomaly gradient among models. Therefore, the ENSO pattern bias induces an unrealistic circulation and temperature gradient over the Asian–Pacific–American region, affecting the simulations of the ENSO–STV connection. In addition, the ENSO–STV relationship over the Asian–Pacific–American region is still robust in future projections based on HPS models, providing implications for the selection of future climate predictors.
Abstract
While previous research on subseasonal tropical cyclone (TC) occurrence has mostly focused on either the validation of numerical weather prediction (NWP) models, or the development of statistical models trained on past data, the present study combines both approaches to a statistical–dynamical (hybrid) model for probabilistic forecasts in the North Atlantic basin. Although state-of-the-art NWP models have been shown to lack predictive skill with respect to subseasonal weekly TC occurrence, they may predict the environmental conditions sufficiently well to generate predictors for a statistical model. Therefore, an extensive predictor set was generated, including predictor groups representing the climatological seasonal cycle (CSC), oceanic, and tropical conditions, tropical wave modes, as well as extratropical influences, respectively. The developed hybrid forecast model is systematically validated for the Gulf of Mexico and central main development region (MDR) for lead times up to 5 weeks. Moreover, its performance is compared against a statistical approach trained on past data, as well as against different climatological and NWP benchmarks. For subseasonal lead times, the CSC models are found to outperform the NWP models, which quickly lose skill within the first two forecast weeks, even in case of recalibration. The statistical models trained on past data increase skill over the CSC models, whereas even greater improvements in skill are gained by the hybrid approach out to week 5. The vast majority of the additional subseasonal skill in the hybrid model, relative to the CSC model, could be attributed to the tropical (oceanic) conditions in the Gulf of Mexico (central MDR).
Abstract
While previous research on subseasonal tropical cyclone (TC) occurrence has mostly focused on either the validation of numerical weather prediction (NWP) models, or the development of statistical models trained on past data, the present study combines both approaches to a statistical–dynamical (hybrid) model for probabilistic forecasts in the North Atlantic basin. Although state-of-the-art NWP models have been shown to lack predictive skill with respect to subseasonal weekly TC occurrence, they may predict the environmental conditions sufficiently well to generate predictors for a statistical model. Therefore, an extensive predictor set was generated, including predictor groups representing the climatological seasonal cycle (CSC), oceanic, and tropical conditions, tropical wave modes, as well as extratropical influences, respectively. The developed hybrid forecast model is systematically validated for the Gulf of Mexico and central main development region (MDR) for lead times up to 5 weeks. Moreover, its performance is compared against a statistical approach trained on past data, as well as against different climatological and NWP benchmarks. For subseasonal lead times, the CSC models are found to outperform the NWP models, which quickly lose skill within the first two forecast weeks, even in case of recalibration. The statistical models trained on past data increase skill over the CSC models, whereas even greater improvements in skill are gained by the hybrid approach out to week 5. The vast majority of the additional subseasonal skill in the hybrid model, relative to the CSC model, could be attributed to the tropical (oceanic) conditions in the Gulf of Mexico (central MDR).
