Waves to Weather (W2W)
Description:
This special collection comprises the results of the Collaborative Research Center “Waves to Weather” (W2W), which is funded by the Deutsche Forschungsgemeinschaft (German Research Foundation) for a period of 4 years with possible extensions up to 12 years. The main topic of W2W is predictability and prediction of weather. The current scientific themes of W2W are "Upscale error growth", "Cloud-scale uncertainties", and "Predictability of local weather". It includes theoretical studies, numerical modeling, and process studies based in part on cutting edge statistical methods and visualization tools, NWP models and data collected during the field campaign NAWDEX.
The aim of W2W is to identify the limits of predictability of weather and to produce the best forecasts that are physically possible. The focus of W2W is on the most important causes of remaining uncertainties in weather prediction, which include:
- the quick upscale growth of forecast errors from insufficiently resolved or represented processes like convection or boundary layer mixing, which modify synoptic-scale waves,
- our limited understanding of processes in clouds, and
- the influence of local factors on weather that influence the predictability associated with larger-scale wave disturbances.
W2W addresses these three areas in a concerted effort involving contributions from the disciplines of atmospheric dynamics, cloud physics, statistics, inverse methods and visualization.
W2W uses, and further develops a broad range of tools, including numerical models with detailed treatment of cloud processes and aerosols, and ensemble forecasts with sophisticated statistical post-processing to describe uncertainty. Improved insight has already been gained through the development of new interactive visualization methods, that enable rapid exploration of forecast ensembles to identify the sources and evolution of uncertainty in meteorologically significant features, as well as through the unprecedented dataset collected during the international field campaign NAWDEX.
W2W currently consist of eighteen individual scientific projects located in Germany (Ludwig-Maximilians University of Munich, Karlsruhe Institute of Technology, Johannes Gutenberg University in Mainz, German Aerospace Center (DLR) Oberpfaffenhofen, and University of Heidelberg).
Collection organizers:
Audine Laurian and George C. Craig, Meteorological Institute, Ludwig-Maximilians University, Munich, Germany
Waves to Weather (W2W)
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).
Abstract
Warm conveyor belts (WCBs) associated with extratropical cyclones transport air from the lower troposphere into the tropopause region and contribute to upper-level ridge building and the formation of blocking anticyclones. Recent studies indicate that this constitutes an important source and magnifier of forecast uncertainty and errors in numerical weather prediction (NWP) models. However, a systematic evaluation of the representation of WCBs in NWP models has yet to be determined. Here, we employ the logistic regression models developed in Part I to identify the inflow, ascent, and outflow stages of WCBs in the European Centre for Medium-Range Weather Forecasts (ECMWF) subseasonal reforecasts for Northern Hemisphere winter in the period January 1997 to December 2017. We verify the representation of these WCB stages in terms of systematic occurrence frequency biases, forecast reliability, and forecast skill. Systematic WCB frequency biases emerge already at early lead times of around 3 days with an underestimation for the WCB outflow over the North Atlantic and eastern North Pacific of around 40% relative to climatology. Biases in the predictor variables of the logistic regression models can partially explain these biases in WCB inflow, ascent, or outflow. Despite an overconfidence in predicting high WCB probabilities, skillful WCB forecasts are on average possible up to a lead time of 8–10 days with more skill over the North Pacific compared to the North Atlantic region. Our results corroborate that the current limited forecast skill for the large-scale extratropical circulation on subseasonal time scales beyond 10 days might be tied to the representation of WCBs and associated upscale error growth.
Abstract
Warm conveyor belts (WCBs) associated with extratropical cyclones transport air from the lower troposphere into the tropopause region and contribute to upper-level ridge building and the formation of blocking anticyclones. Recent studies indicate that this constitutes an important source and magnifier of forecast uncertainty and errors in numerical weather prediction (NWP) models. However, a systematic evaluation of the representation of WCBs in NWP models has yet to be determined. Here, we employ the logistic regression models developed in Part I to identify the inflow, ascent, and outflow stages of WCBs in the European Centre for Medium-Range Weather Forecasts (ECMWF) subseasonal reforecasts for Northern Hemisphere winter in the period January 1997 to December 2017. We verify the representation of these WCB stages in terms of systematic occurrence frequency biases, forecast reliability, and forecast skill. Systematic WCB frequency biases emerge already at early lead times of around 3 days with an underestimation for the WCB outflow over the North Atlantic and eastern North Pacific of around 40% relative to climatology. Biases in the predictor variables of the logistic regression models can partially explain these biases in WCB inflow, ascent, or outflow. Despite an overconfidence in predicting high WCB probabilities, skillful WCB forecasts are on average possible up to a lead time of 8–10 days with more skill over the North Pacific compared to the North Atlantic region. Our results corroborate that the current limited forecast skill for the large-scale extratropical circulation on subseasonal time scales beyond 10 days might be tied to the representation of WCBs and associated upscale error growth.
Abstract
Prediction of weather is a main goal of atmospheric science. Its importance to society is growing continuously due to factors such as vulnerability to natural disasters, the move to renewable energy sources, and the risks of climate change. But prediction is also a major scientific challenge due to the inherently limited predictability of a chaotic atmosphere, and has led to a revolution in forecasting methods as we have moved to probabilistic prediction. These changes provide the motivation for Waves to Weather (W2W), a major national research program in Germany with three main university partners in Munich, Mainz, and Karlsruhe. We are currently in the second 4-yr phase of our planned duration of 12 years and employ 36 doctoral and postdoctoral scientists. In the context of this large program, we address what we have identified to be the most important and challenging scientific questions in predictability of weather, namely, upscale error growth, errors associated with cloud processes, and probabilistic prediction of high-impact weather. This paper presents some key results of the first phase of W2W and discusses how they have influenced our understanding of predictability. The key role of interdisciplinary research linking atmospheric scientists with experts in visualization, statistics, numerical analysis, and inverse methods will be highlighted. To ensure a lasting impact on research in our field in Germany and internationally, we have instituted innovative programs for training and support of early-career scientists, and to support education, equal opportunities, and outreach, which are also described here.
Abstract
Prediction of weather is a main goal of atmospheric science. Its importance to society is growing continuously due to factors such as vulnerability to natural disasters, the move to renewable energy sources, and the risks of climate change. But prediction is also a major scientific challenge due to the inherently limited predictability of a chaotic atmosphere, and has led to a revolution in forecasting methods as we have moved to probabilistic prediction. These changes provide the motivation for Waves to Weather (W2W), a major national research program in Germany with three main university partners in Munich, Mainz, and Karlsruhe. We are currently in the second 4-yr phase of our planned duration of 12 years and employ 36 doctoral and postdoctoral scientists. In the context of this large program, we address what we have identified to be the most important and challenging scientific questions in predictability of weather, namely, upscale error growth, errors associated with cloud processes, and probabilistic prediction of high-impact weather. This paper presents some key results of the first phase of W2W and discusses how they have influenced our understanding of predictability. The key role of interdisciplinary research linking atmospheric scientists with experts in visualization, statistics, numerical analysis, and inverse methods will be highlighted. To ensure a lasting impact on research in our field in Germany and internationally, we have instituted innovative programs for training and support of early-career scientists, and to support education, equal opportunities, and outreach, which are also described here.
Abstract
The waveguidability of an upper-tropospheric zonal jet quantifies its propensity to duct Rossby waves in the zonal direction. This property has played a central role in previous attempts to explain large wave amplitudes and the subsequent occurrence of extreme weather. In these studies, waveguidability was diagnosed with the help of ray tracing arguments using the zonal average of the observed flow as the relevant background state. Here, it is argued that this method is problematic both conceptually and mathematically. The issue is investigated in the framework of the nondivergent barotropic model. This model allows the straightforward computation of an alternative “zonalized” background state, which is obtained through conservative symmetrization of potential vorticity contours and that is argued to be superior to the zonal average. Using an idealized prototypical flow configuration with large-amplitude eddies, it is shown that the two different choices for the background state yield very different results; in particular, the zonal-mean background state diagnoses a zonal waveguide, while the zonalized background state does not. This result suggests that the existence of a waveguide in the zonal-mean background state is a consequence of, rather than a precondition for, large wave amplitudes, and it would mean that the direction of causality is opposite to the usual argument. The analysis is applied to two heatwave episodes from summer 2003 and 2010, yielding essentially the same result. It is concluded that previous arguments about the role of waveguidability for extreme weather need to be carefully reevaluated to prevent misinterpretation in the future.
Abstract
The waveguidability of an upper-tropospheric zonal jet quantifies its propensity to duct Rossby waves in the zonal direction. This property has played a central role in previous attempts to explain large wave amplitudes and the subsequent occurrence of extreme weather. In these studies, waveguidability was diagnosed with the help of ray tracing arguments using the zonal average of the observed flow as the relevant background state. Here, it is argued that this method is problematic both conceptually and mathematically. The issue is investigated in the framework of the nondivergent barotropic model. This model allows the straightforward computation of an alternative “zonalized” background state, which is obtained through conservative symmetrization of potential vorticity contours and that is argued to be superior to the zonal average. Using an idealized prototypical flow configuration with large-amplitude eddies, it is shown that the two different choices for the background state yield very different results; in particular, the zonal-mean background state diagnoses a zonal waveguide, while the zonalized background state does not. This result suggests that the existence of a waveguide in the zonal-mean background state is a consequence of, rather than a precondition for, large wave amplitudes, and it would mean that the direction of causality is opposite to the usual argument. The analysis is applied to two heatwave episodes from summer 2003 and 2010, yielding essentially the same result. It is concluded that previous arguments about the role of waveguidability for extreme weather need to be carefully reevaluated to prevent misinterpretation in the future.
Abstract
Current numerical weather prediction models show limited skill in predicting low-latitude precipitation. To aid future improvements, be it with better dynamical or statistical models, we propose a well-defined benchmark forecast. We use the arguably best available high-resolution, gauge-calibrated, gridded precipitation product, the Integrated Multisatellite Retrievals for GPM (IMERG) “final run” in a ±15-day window around the date of interest to build an empirical climatological ensemble forecast. This window size is an optimal compromise between statistical robustness and flexibility to represent seasonal changes. We refer to this benchmark as extended probabilistic climatology (EPC) and compute it on a 0.1° × 0.1° grid for 40°S–40°N and the period 2001–19. To reduce and standardize information, a mixed Bernoulli–Gamma distribution is fitted to the empirical EPC, which hardly affects predictive performance. The EPC is then compared to 1-day ensemble predictions from the European Centre for Medium-Range Weather Forecasts (ECMWF) using standard verification scores. With respect to rainfall amount, ECMWF performs only slightly better than EPS over most of the low latitudes and worse over high-mountain and dry oceanic areas as well as over tropical Africa, where the lack of skill is also evident in independent station data. For rainfall occurrence, EPC is superior over most oceanic, coastal, and mountain regions, although the better potential predictive ability of ECMWF indicates that this is mostly due to calibration problems. To encourage the use of the new benchmark, we provide the data, scripts, and an interactive web tool to the scientific community.
Abstract
Current numerical weather prediction models show limited skill in predicting low-latitude precipitation. To aid future improvements, be it with better dynamical or statistical models, we propose a well-defined benchmark forecast. We use the arguably best available high-resolution, gauge-calibrated, gridded precipitation product, the Integrated Multisatellite Retrievals for GPM (IMERG) “final run” in a ±15-day window around the date of interest to build an empirical climatological ensemble forecast. This window size is an optimal compromise between statistical robustness and flexibility to represent seasonal changes. We refer to this benchmark as extended probabilistic climatology (EPC) and compute it on a 0.1° × 0.1° grid for 40°S–40°N and the period 2001–19. To reduce and standardize information, a mixed Bernoulli–Gamma distribution is fitted to the empirical EPC, which hardly affects predictive performance. The EPC is then compared to 1-day ensemble predictions from the European Centre for Medium-Range Weather Forecasts (ECMWF) using standard verification scores. With respect to rainfall amount, ECMWF performs only slightly better than EPS over most of the low latitudes and worse over high-mountain and dry oceanic areas as well as over tropical Africa, where the lack of skill is also evident in independent station data. For rainfall occurrence, EPC is superior over most oceanic, coastal, and mountain regions, although the better potential predictive ability of ECMWF indicates that this is mostly due to calibration problems. To encourage the use of the new benchmark, we provide the data, scripts, and an interactive web tool to the scientific community.
Abstract
The physical and dynamical processes associated with warm conveyor belts (WCBs) importantly affect midlatitude dynamics and are sources of forecast uncertainty. Moreover, WCBs modulate the large-scale extratropical circulation and can communicate and amplify forecast errors. Therefore, it is desirable to assess the representation of WCBs in numerical weather prediction (NWP) models in particular on the medium to subseasonal forecast range. Most often, WCBs are identified as coherent bundles of Lagrangian trajectories that ascend in a time interval of 2 days from the lower to the upper troposphere. Although this Lagrangian approach has advanced the understanding of the involved processes significantly, the calculation of trajectories is computationally expensive and requires NWP data at a high spatial [
Abstract
The physical and dynamical processes associated with warm conveyor belts (WCBs) importantly affect midlatitude dynamics and are sources of forecast uncertainty. Moreover, WCBs modulate the large-scale extratropical circulation and can communicate and amplify forecast errors. Therefore, it is desirable to assess the representation of WCBs in numerical weather prediction (NWP) models in particular on the medium to subseasonal forecast range. Most often, WCBs are identified as coherent bundles of Lagrangian trajectories that ascend in a time interval of 2 days from the lower to the upper troposphere. Although this Lagrangian approach has advanced the understanding of the involved processes significantly, the calculation of trajectories is computationally expensive and requires NWP data at a high spatial [
Abstract
Precipitation forecasts are of large societal value in the tropics. Here, we compare 1–5-day ensemble predictions from the European Centre for Medium-Range Weather Forecasts (ECMWF, 2009–17) and the Meteorological Service of Canada (MSC, 2009–16) over 30°S–30°N with an extended probabilistic climatology based on the Tropical Rainfall Measuring Mission 3 B42 gridded dataset. Both models predict rainfall occurrence better than the reference only over about half of all land points, with a better performance by MSC. After applying the postprocessing technique ensemble model output statistics, this fraction increases to 87% (ECMWF) and 82% (MSC). For rainfall amount there is skill in many tropical areas (about 60% of land points), which can be increased by postprocessing to 97% (ECMWF) and 88% (MSC). Forecasts for extremes (>20 mm) are only marginally worse than those of occurrence but do not improve as much through postprocessing, particularly over dry areas. Forecast performance is generally best over arid Australia and worst over oceanic deserts, the Andes and Himalayas, as well as over tropical Africa, where models misrepresent the high degree of convective organization, such that even postprocessed forecasts are hardly better than climatology. Skill of 5-day accumulated forecasts often exceeds that of shorter ranges, as timing errors matter less. An increase in resolution and major model update in 2010 has significantly improved ECMWF predictions. Especially over tropical Africa new techniques such as convection-permitting models or combined statistical-dynamical forecasts may be needed to generate skill beyond the climatological reference.
Abstract
Precipitation forecasts are of large societal value in the tropics. Here, we compare 1–5-day ensemble predictions from the European Centre for Medium-Range Weather Forecasts (ECMWF, 2009–17) and the Meteorological Service of Canada (MSC, 2009–16) over 30°S–30°N with an extended probabilistic climatology based on the Tropical Rainfall Measuring Mission 3 B42 gridded dataset. Both models predict rainfall occurrence better than the reference only over about half of all land points, with a better performance by MSC. After applying the postprocessing technique ensemble model output statistics, this fraction increases to 87% (ECMWF) and 82% (MSC). For rainfall amount there is skill in many tropical areas (about 60% of land points), which can be increased by postprocessing to 97% (ECMWF) and 88% (MSC). Forecasts for extremes (>20 mm) are only marginally worse than those of occurrence but do not improve as much through postprocessing, particularly over dry areas. Forecast performance is generally best over arid Australia and worst over oceanic deserts, the Andes and Himalayas, as well as over tropical Africa, where models misrepresent the high degree of convective organization, such that even postprocessed forecasts are hardly better than climatology. Skill of 5-day accumulated forecasts often exceeds that of shorter ranges, as timing errors matter less. An increase in resolution and major model update in 2010 has significantly improved ECMWF predictions. Especially over tropical Africa new techniques such as convection-permitting models or combined statistical-dynamical forecasts may be needed to generate skill beyond the climatological reference.
