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Abstract
Multiple linear regression models were developed for 1–3-day lead forecasts of maximum and minimum temperature for two locations in the city of Lima, on the central coast of Peru (12°S), and contrasted with the operational forecasts issued by the National Meteorological and Hydrological Service—SENAMHI and the output of a regional numerical atmospheric model. We developed empirical models, fitted to data from the 2000–13 period, and verified their skill for the 2014–19 period. Since El Niño produces a strong low-frequency signal, the models focus on the high-frequency weather and subseasonal variability (60-day cutoff). The empirical models outperformed the operational forecasts and the numerical model. For instance, the high-frequency annual correlation coefficient and root-mean-square error (RMSE) for the 1-day lead forecasts were 0.37°–0.53° and 0.74°–1.76°C for the empirical model, respectively, but from around −0.05° to 0.24° and 0.88°–4.21°C in the operational case. Only three predictors were considered for the models, including persistence and large-scale atmospheric indices. Contrary to our belief, the model skill was lowest for the austral winter (June–August), when the extratropical influence is largest, suggesting an enhanced role of local effects. Including local specific humidity as a predictor for minimum temperature at the inland location substantially increased the skill and reduced its seasonality. There were cases in which both the operational and empirical forecast had similar strong errors and we suggest mesoscale circulations, such as the low-level cyclonic vortex over the ocean, as the culprit. Incorporating such information could be valuable for improving the forecasts.
Significance Statement
We wanted to compare the temperature of the operational forecast of the Meteorological and Hydrological Service, an atmospheric model, and persistence with the observed temperatures on the Peruvian central coast. In addition, we generated an empirical forecast model considering both atmospheric and local predictors. We got better results with this empirical model, considering the highest Pearson correlations and the lowest RMSE values. These results will allow us to use this empirical model as the main tool to automate the forecast on the central coast of Peru. Future work should be aimed at testing the skill of this model for forecasting in other cities of Peru.
Abstract
Multiple linear regression models were developed for 1–3-day lead forecasts of maximum and minimum temperature for two locations in the city of Lima, on the central coast of Peru (12°S), and contrasted with the operational forecasts issued by the National Meteorological and Hydrological Service—SENAMHI and the output of a regional numerical atmospheric model. We developed empirical models, fitted to data from the 2000–13 period, and verified their skill for the 2014–19 period. Since El Niño produces a strong low-frequency signal, the models focus on the high-frequency weather and subseasonal variability (60-day cutoff). The empirical models outperformed the operational forecasts and the numerical model. For instance, the high-frequency annual correlation coefficient and root-mean-square error (RMSE) for the 1-day lead forecasts were 0.37°–0.53° and 0.74°–1.76°C for the empirical model, respectively, but from around −0.05° to 0.24° and 0.88°–4.21°C in the operational case. Only three predictors were considered for the models, including persistence and large-scale atmospheric indices. Contrary to our belief, the model skill was lowest for the austral winter (June–August), when the extratropical influence is largest, suggesting an enhanced role of local effects. Including local specific humidity as a predictor for minimum temperature at the inland location substantially increased the skill and reduced its seasonality. There were cases in which both the operational and empirical forecast had similar strong errors and we suggest mesoscale circulations, such as the low-level cyclonic vortex over the ocean, as the culprit. Incorporating such information could be valuable for improving the forecasts.
Significance Statement
We wanted to compare the temperature of the operational forecast of the Meteorological and Hydrological Service, an atmospheric model, and persistence with the observed temperatures on the Peruvian central coast. In addition, we generated an empirical forecast model considering both atmospheric and local predictors. We got better results with this empirical model, considering the highest Pearson correlations and the lowest RMSE values. These results will allow us to use this empirical model as the main tool to automate the forecast on the central coast of Peru. Future work should be aimed at testing the skill of this model for forecasting in other cities of Peru.
Abstract
In a midlatitude coastal region such as the New York Bight (NYB), the general thermodynamic structure and dynamics of the sea-breeze circulation is poorly understood. The NYB sea-breeze circulation is often amplified by and coterminous with other regional characteristics and phenomena such as complex coastal topology, a low-level jet (LLJ), and coastal upwelling. While typically considered a summertime phenomenon, the NYB sea-breeze circulation occurs year-round. This study creates a methodology to objectively identify sea-breeze days and their associated LLJs from 2010 to 2020. Filtering parameters include surface-based observations of sea level pressure (SLP) gradient and diurnal tendencies, afternoon wind speed and direction tendencies, air temperature gradient, and the dewpoint depression. LLJs associated with the sea-breeze circulation typically occur within 150–300 m MSL and are identified using a coastal New York State Mesonet (NYSM) profiler site. Along coastal Long Island, there are on average 32 sea-breeze days annually, featuring winds consistently backing to the south and strengthening at or around 1800 UTC (1400 EDT). The NYB LLJ is most frequent in the summer months. Sea-breeze days are classified into two categories: classic and hybrid. A classic sea breeze is driven primarily by both cross-shore pressure and temperature gradients, with light background winds; while a hybrid sea breeze occurs in combination with other larger-scale features, such as frontal systems. Both types of sea breeze are similarly distributed with a maximum frequency during July.
Abstract
In a midlatitude coastal region such as the New York Bight (NYB), the general thermodynamic structure and dynamics of the sea-breeze circulation is poorly understood. The NYB sea-breeze circulation is often amplified by and coterminous with other regional characteristics and phenomena such as complex coastal topology, a low-level jet (LLJ), and coastal upwelling. While typically considered a summertime phenomenon, the NYB sea-breeze circulation occurs year-round. This study creates a methodology to objectively identify sea-breeze days and their associated LLJs from 2010 to 2020. Filtering parameters include surface-based observations of sea level pressure (SLP) gradient and diurnal tendencies, afternoon wind speed and direction tendencies, air temperature gradient, and the dewpoint depression. LLJs associated with the sea-breeze circulation typically occur within 150–300 m MSL and are identified using a coastal New York State Mesonet (NYSM) profiler site. Along coastal Long Island, there are on average 32 sea-breeze days annually, featuring winds consistently backing to the south and strengthening at or around 1800 UTC (1400 EDT). The NYB LLJ is most frequent in the summer months. Sea-breeze days are classified into two categories: classic and hybrid. A classic sea breeze is driven primarily by both cross-shore pressure and temperature gradients, with light background winds; while a hybrid sea breeze occurs in combination with other larger-scale features, such as frontal systems. Both types of sea breeze are similarly distributed with a maximum frequency during July.
Abstract
A 16-member convective-scale ensemble prediction system (CEPS) developed at the Central Weather Bureau (CWB) of Taiwan is evaluated for probability forecasts of convective precipitation. To address the issues of limited predictability of convective systems, the CEPS provides short-range forecasts using initial conditions from a rapid-updated ensemble data assimilation system. This study aims to identify the behavior of the CEPS forecasts, especially the impact of different ensemble configurations and forecast lead times. Warm-season afternoon thunderstorms (ATs) from 30 June to 4 July 2017 are selected. Since ATs usually occur between 1300 and 2000 LST, this study compares deterministic and probabilistic quantitative precipitation forecasts (QPFs) launched at 0500, 0800, and 1100 LST. This study demonstrates that initial and boundary perturbations (IBP) are crucial to ensure good spread–skill consistency over the 18-h forecasts. On top of IBP, additional model perturbations have insignificant impacts on upper-air and precipitation forecasts. The deterministic QPFs launched at 1100 LST outperform those launched at 0500 and 0800 LST, likely because the most-recent data assimilation analyses enhance the practical predictability. However, it cannot improve the probabilistic QPFs launched at 1100 LST due to inadequate ensemble spreads resulting from limited error growth time. This study points out the importance of sufficient initial condition uncertainty on short-range probabilistic forecasts to exploit the benefits of rapid-update data assimilation analyses.
Significance Statement
This study aims to understand the behavior of convective-scale short-range probabilistic forecasts in Taiwan and the surrounding area. Taiwan is influenced by diverse weather systems, including typhoons, mei-yu fronts, and local thunderstorms. During the past decade, there has been promising improvement in predicting mesoscale weather systems (e.g., typhoons and mei-yu fronts). However, it is still challenging to provide timely and accurate forecasts for rapid-evolving high-impact convection. This study provides a reference for the designation of convective-scale ensemble prediction systems; in particular, those with a goal to provide short-range probabilistic forecasts. While the findings cannot be extrapolated to all ensemble prediction systems, this study demonstrates that initial and boundary perturbations are the most important factors, while the model perturbation has an insignificant effect. This study suggests that in-depth studies are required to improve the convective-scale initial condition accuracy and uncertainty to provide reliable probabilistic forecasts within short lead times.
Abstract
A 16-member convective-scale ensemble prediction system (CEPS) developed at the Central Weather Bureau (CWB) of Taiwan is evaluated for probability forecasts of convective precipitation. To address the issues of limited predictability of convective systems, the CEPS provides short-range forecasts using initial conditions from a rapid-updated ensemble data assimilation system. This study aims to identify the behavior of the CEPS forecasts, especially the impact of different ensemble configurations and forecast lead times. Warm-season afternoon thunderstorms (ATs) from 30 June to 4 July 2017 are selected. Since ATs usually occur between 1300 and 2000 LST, this study compares deterministic and probabilistic quantitative precipitation forecasts (QPFs) launched at 0500, 0800, and 1100 LST. This study demonstrates that initial and boundary perturbations (IBP) are crucial to ensure good spread–skill consistency over the 18-h forecasts. On top of IBP, additional model perturbations have insignificant impacts on upper-air and precipitation forecasts. The deterministic QPFs launched at 1100 LST outperform those launched at 0500 and 0800 LST, likely because the most-recent data assimilation analyses enhance the practical predictability. However, it cannot improve the probabilistic QPFs launched at 1100 LST due to inadequate ensemble spreads resulting from limited error growth time. This study points out the importance of sufficient initial condition uncertainty on short-range probabilistic forecasts to exploit the benefits of rapid-update data assimilation analyses.
Significance Statement
This study aims to understand the behavior of convective-scale short-range probabilistic forecasts in Taiwan and the surrounding area. Taiwan is influenced by diverse weather systems, including typhoons, mei-yu fronts, and local thunderstorms. During the past decade, there has been promising improvement in predicting mesoscale weather systems (e.g., typhoons and mei-yu fronts). However, it is still challenging to provide timely and accurate forecasts for rapid-evolving high-impact convection. This study provides a reference for the designation of convective-scale ensemble prediction systems; in particular, those with a goal to provide short-range probabilistic forecasts. While the findings cannot be extrapolated to all ensemble prediction systems, this study demonstrates that initial and boundary perturbations are the most important factors, while the model perturbation has an insignificant effect. This study suggests that in-depth studies are required to improve the convective-scale initial condition accuracy and uncertainty to provide reliable probabilistic forecasts within short lead times.
Abstract
This article introduces an ensemble clustering tool developed at the Weather Prediction Center (WPC) to assist forecasters in the preparation of medium-range (3–7 day) forecasts. Effectively incorporating ensemble data into an operational forecasting process, like that used at WPC, can be challenging given time constraints and data infrastructure limitations. Often forecasters do not have time to view the large number of constituent members of an ensemble forecast, so they settle for viewing the ensemble’s mean and spread. This ignores the useful information about forecast uncertainty and the range of possible forecast outcomes that an ensemble forecast can provide. Ensemble clustering could be a solution to this problem as it can reduce a large ensemble forecast down to the most prevalent forecast scenarios. Forecasters can then quickly view these ensemble clusters to better understand and communicate forecast uncertainty and the range of possible forecast outcomes. The ensemble clustering tool developed at WPC is a variation of fuzzy clustering where operationally available ensemble members with similar 500-hPa geopotential height forecasts are grouped into four clusters. A representative case from 15 February 2021 is presented to demonstrate the clustering methodology and the overall utility of this new ensemble clustering tool. Cumulative verification statistics show that one of the four forecast scenarios identified by this ensemble clustering tool routinely outperforms all the available ensemble mean and deterministic forecasts.
Significance Statement
Ensemble forecasts could be used more effectively in medium-range (3–7 day) forecasting. Currently, the onus is put on forecasters to view and synthesize all of the data contained in an ensemble forecast. This is a task they often do not have time to adequately execute. This work proposes a solution to this problem. An automated tool was developed that would split the available ensemble members into four groups of broadly similar members. These groups were presented to forecasters as four potential forecast outcomes. Forecasters felt this tool helped them to better incorporate ensemble forecasts into their forecast process. Verification shows that presenting ensemble forecasts in this manner is an improvement on currently used ensemble forecast visualization techniques.
Abstract
This article introduces an ensemble clustering tool developed at the Weather Prediction Center (WPC) to assist forecasters in the preparation of medium-range (3–7 day) forecasts. Effectively incorporating ensemble data into an operational forecasting process, like that used at WPC, can be challenging given time constraints and data infrastructure limitations. Often forecasters do not have time to view the large number of constituent members of an ensemble forecast, so they settle for viewing the ensemble’s mean and spread. This ignores the useful information about forecast uncertainty and the range of possible forecast outcomes that an ensemble forecast can provide. Ensemble clustering could be a solution to this problem as it can reduce a large ensemble forecast down to the most prevalent forecast scenarios. Forecasters can then quickly view these ensemble clusters to better understand and communicate forecast uncertainty and the range of possible forecast outcomes. The ensemble clustering tool developed at WPC is a variation of fuzzy clustering where operationally available ensemble members with similar 500-hPa geopotential height forecasts are grouped into four clusters. A representative case from 15 February 2021 is presented to demonstrate the clustering methodology and the overall utility of this new ensemble clustering tool. Cumulative verification statistics show that one of the four forecast scenarios identified by this ensemble clustering tool routinely outperforms all the available ensemble mean and deterministic forecasts.
Significance Statement
Ensemble forecasts could be used more effectively in medium-range (3–7 day) forecasting. Currently, the onus is put on forecasters to view and synthesize all of the data contained in an ensemble forecast. This is a task they often do not have time to adequately execute. This work proposes a solution to this problem. An automated tool was developed that would split the available ensemble members into four groups of broadly similar members. These groups were presented to forecasters as four potential forecast outcomes. Forecasters felt this tool helped them to better incorporate ensemble forecasts into their forecast process. Verification shows that presenting ensemble forecasts in this manner is an improvement on currently used ensemble forecast visualization techniques.
Abstract
To mitigate the impacts associated with adverse weather conditions, meteorological services issue weather warnings to the general public. These warnings rely heavily on forecasts issued by underlying prediction systems. When deciding which prediction system(s) to utilize when constructing warnings, it is important to compare systems in their ability to forecast the occurrence and severity of high-impact weather events. However, evaluating forecasts for particular outcomes is known to be a challenging task. This is exacerbated further by the fact that high-impact weather often manifests as a result of several confounding features, a realization that has led to considerable research on so-called compound weather events. Both univariate and multivariate methods are therefore required to evaluate forecasts for high-impact weather. In this paper, we discuss weighted verification tools, which allow particular outcomes to be emphasized during forecast evaluation. We review and compare different approaches to construct weighted scoring rules, both in a univariate and multivariate setting, and we leverage existing results on weighted scores to introduce conditional probability integral transform (PIT) histograms, allowing forecast calibration to be assessed conditionally on particular outcomes having occurred. To illustrate the practical benefit afforded by these weighted verification tools, they are employed in a case study to evaluate probabilistic forecasts for extreme heat events issued by the Swiss Federal Office of Meteorology and Climatology (MeteoSwiss).
Abstract
To mitigate the impacts associated with adverse weather conditions, meteorological services issue weather warnings to the general public. These warnings rely heavily on forecasts issued by underlying prediction systems. When deciding which prediction system(s) to utilize when constructing warnings, it is important to compare systems in their ability to forecast the occurrence and severity of high-impact weather events. However, evaluating forecasts for particular outcomes is known to be a challenging task. This is exacerbated further by the fact that high-impact weather often manifests as a result of several confounding features, a realization that has led to considerable research on so-called compound weather events. Both univariate and multivariate methods are therefore required to evaluate forecasts for high-impact weather. In this paper, we discuss weighted verification tools, which allow particular outcomes to be emphasized during forecast evaluation. We review and compare different approaches to construct weighted scoring rules, both in a univariate and multivariate setting, and we leverage existing results on weighted scores to introduce conditional probability integral transform (PIT) histograms, allowing forecast calibration to be assessed conditionally on particular outcomes having occurred. To illustrate the practical benefit afforded by these weighted verification tools, they are employed in a case study to evaluate probabilistic forecasts for extreme heat events issued by the Swiss Federal Office of Meteorology and Climatology (MeteoSwiss).
Abstract
This study analyzes the potential of deep learning using probabilistic artificial neural networks (ANNs) for postprocessing ensemble precipitation forecasts at four observation locations. We split the precipitation forecast problem into two tasks: estimating the probability of precipitation and predicting the hourly precipitation. We then compare the performance with classical statistical postprocessing (logistical regression and GLM). ANNs show a higher performance at three of the four stations for estimating the probability of precipitation and at all stations for predicting the hourly precipitation. Further, two more general ANN models are trained using the merged data from all four stations. These general ANNs exhibit an increase in performance compared to the station-specific ANNs at most stations. However, they show a significant decay in performance at one of the stations at estimating the hourly precipitation. The general models seem capable of learning meaningful interactions in the data and generalizing these to improve the performance at other sites, which also causes the loss of local information at one station. Thus, this study indicates the potential of deep learning in weather forecasting workflows.
Abstract
This study analyzes the potential of deep learning using probabilistic artificial neural networks (ANNs) for postprocessing ensemble precipitation forecasts at four observation locations. We split the precipitation forecast problem into two tasks: estimating the probability of precipitation and predicting the hourly precipitation. We then compare the performance with classical statistical postprocessing (logistical regression and GLM). ANNs show a higher performance at three of the four stations for estimating the probability of precipitation and at all stations for predicting the hourly precipitation. Further, two more general ANN models are trained using the merged data from all four stations. These general ANNs exhibit an increase in performance compared to the station-specific ANNs at most stations. However, they show a significant decay in performance at one of the stations at estimating the hourly precipitation. The general models seem capable of learning meaningful interactions in the data and generalizing these to improve the performance at other sites, which also causes the loss of local information at one station. Thus, this study indicates the potential of deep learning in weather forecasting workflows.
Abstract
The extended-range forecast with a lead time of 10–30 days is the gap between weather (<10 days) and climate (>30 days) predictions. Improving the forecast skill of extreme weather events at the extended range is crucial for risk management of disastrous events. In this study, three deep learning (DL) models based on the methods of convolutional neural networks and gate recurrent units are constructed to predict the rainfall anomalies and associated extreme events in East China at lead times of 1–6 pentads. All DL models show skillful prediction of the temporal variation of rainfall anomalies (in terms of temporal correlation coefficient skill) over most regions in East China beyond 4 pentads, outperforming the dynamical models from the China Meteorological Administration (CMA) and the European Centre for Medium-Range Weather Forecasts (ECMWF). The spatial distribution of the rainfall anomalies is also better predicted by the DL models than the dynamical models; and the DL models show higher pattern correlation coefficients than the dynamical models at lead times of 3–6 pentads. The higher skill of DL models in predicting the rainfall anomalies will help to improve the accuracy of extreme-event predictions. The Heidke skill scores of the extreme rainfall event forecast performed by the DL models are also superior to those of the dynamical models at a lead time beyond about 4 pentads. Heat map analysis for the DL models shows that the predictability sources are mainly the large-scale factors modulating the East Asian monsoon rainfall.
Significance Statement
Improving the forecast skill for extreme weather events at the extended range (10–30 days in advance), particularly over populated regions such as East China, is crucial for risk management. This study aims to develop skillful models of the rainfall anomalies and associated extreme heavy rainfall events using deep learning techniques. The models constructed here benefit from the capability of deep learning to identify the predictability sources of rainfall variability, and outperform the current operational models, including the ECMWF and the CMA models, at forecast lead times beyond 3–4 pentads. These results reveal the promising application prospect of deep learning techniques in the extended-range forecast.
Abstract
The extended-range forecast with a lead time of 10–30 days is the gap between weather (<10 days) and climate (>30 days) predictions. Improving the forecast skill of extreme weather events at the extended range is crucial for risk management of disastrous events. In this study, three deep learning (DL) models based on the methods of convolutional neural networks and gate recurrent units are constructed to predict the rainfall anomalies and associated extreme events in East China at lead times of 1–6 pentads. All DL models show skillful prediction of the temporal variation of rainfall anomalies (in terms of temporal correlation coefficient skill) over most regions in East China beyond 4 pentads, outperforming the dynamical models from the China Meteorological Administration (CMA) and the European Centre for Medium-Range Weather Forecasts (ECMWF). The spatial distribution of the rainfall anomalies is also better predicted by the DL models than the dynamical models; and the DL models show higher pattern correlation coefficients than the dynamical models at lead times of 3–6 pentads. The higher skill of DL models in predicting the rainfall anomalies will help to improve the accuracy of extreme-event predictions. The Heidke skill scores of the extreme rainfall event forecast performed by the DL models are also superior to those of the dynamical models at a lead time beyond about 4 pentads. Heat map analysis for the DL models shows that the predictability sources are mainly the large-scale factors modulating the East Asian monsoon rainfall.
Significance Statement
Improving the forecast skill for extreme weather events at the extended range (10–30 days in advance), particularly over populated regions such as East China, is crucial for risk management. This study aims to develop skillful models of the rainfall anomalies and associated extreme heavy rainfall events using deep learning techniques. The models constructed here benefit from the capability of deep learning to identify the predictability sources of rainfall variability, and outperform the current operational models, including the ECMWF and the CMA models, at forecast lead times beyond 3–4 pentads. These results reveal the promising application prospect of deep learning techniques in the extended-range forecast.
Abstract
A new probabilistic tornado detection algorithm was developed to potentially replace the operational tornado detection algorithm (TDA) for the WSR-88D radar network. The tornado probability algorithm (TORP) uses a random forest machine learning technique to estimate a probability of tornado occurrence based on single-radar data, and is trained on 166 145 data points derived from 0.5°-tilt radar data and storm reports from 2011 to 2016, of which 10.4% are tornadic. A variety of performance evaluation metrics show a generally good model performance for discriminating between tornadic and nontornadic points. When using a 50% probability threshold to decide whether the model is predicting a tornado or not, the probability of detection and false alarm ratio are 57% and 50%, respectively, showing high skill by several metrics and vastly outperforming the TDA. The model weaknesses include false alarms associated with poor-quality radial velocity data and greatly reduced performance when used in the western United States. Overall, TORP can provide real-time guidance for tornado warning decisions, which can increase forecaster confidence and encourage swift decision-making. It has the ability to condense a multitude of radar data into a concise object-based information readout that can be displayed in visualization software used by the National Weather Service, core partners, and researchers.
Significance Statement
This study describes the tornado probability algorithm (TORP) and its performance. Operational forecasters can use TORP as real-time guidance when issuing tornado warnings, causing increased confidence in warning decisions, which in turn can extend tornado warning lead times.
Abstract
A new probabilistic tornado detection algorithm was developed to potentially replace the operational tornado detection algorithm (TDA) for the WSR-88D radar network. The tornado probability algorithm (TORP) uses a random forest machine learning technique to estimate a probability of tornado occurrence based on single-radar data, and is trained on 166 145 data points derived from 0.5°-tilt radar data and storm reports from 2011 to 2016, of which 10.4% are tornadic. A variety of performance evaluation metrics show a generally good model performance for discriminating between tornadic and nontornadic points. When using a 50% probability threshold to decide whether the model is predicting a tornado or not, the probability of detection and false alarm ratio are 57% and 50%, respectively, showing high skill by several metrics and vastly outperforming the TDA. The model weaknesses include false alarms associated with poor-quality radial velocity data and greatly reduced performance when used in the western United States. Overall, TORP can provide real-time guidance for tornado warning decisions, which can increase forecaster confidence and encourage swift decision-making. It has the ability to condense a multitude of radar data into a concise object-based information readout that can be displayed in visualization software used by the National Weather Service, core partners, and researchers.
Significance Statement
This study describes the tornado probability algorithm (TORP) and its performance. Operational forecasters can use TORP as real-time guidance when issuing tornado warnings, causing increased confidence in warning decisions, which in turn can extend tornado warning lead times.
Abstract
A hybrid three-dimensional ensemble–variational (En3D-Var) data assimilation system has been developed to explore incorporating information from an 11-member regional ensemble prediction system, which is dynamically downscaled from a global ensemble system, into a 3-hourly cycling convective-scale data assimilation system over the western Maritime Continent. From the ensemble, there exists small-scale ensemble perturbation structures associated with positional differences of tropical convection, but these structures are well represented only after the downscaled ensemble forecast has evolved for at least 6 h due to spinup. There was also a robust moderate negative correlation between total specific humidity and potential temperature background errors, presumably because of incorrect vertical motion in the presence of clouds. Time shifting of the ensemble perturbations, by using those available from adjacent cycles, helped to ameliorate the sampling error prevalent in their raw autocovariances. Monthlong hybrid En3D-Var trials were conducted using different weights assigned to the ensemble-derived and climatological background error covariances. The forecast fits to radiosonde relative humidity and wind observations were generally improved with hybrid En3D-Var, but in all experiments, the fits to surface observations were degraded compared to the baseline 3D-Var configuration. Over the Singapore radar domain, there was a general improvement in the precipitation forecasts, especially when the weighting toward the climatological background error covariance was larger, and with the additional application of time-shifted ensemble perturbations. Future work involves consolidating the ensemble prediction and deterministic system, by centering the ensemble prediction system on the hybrid analysis, to better represent the analysis and forecast uncertainties.
Abstract
A hybrid three-dimensional ensemble–variational (En3D-Var) data assimilation system has been developed to explore incorporating information from an 11-member regional ensemble prediction system, which is dynamically downscaled from a global ensemble system, into a 3-hourly cycling convective-scale data assimilation system over the western Maritime Continent. From the ensemble, there exists small-scale ensemble perturbation structures associated with positional differences of tropical convection, but these structures are well represented only after the downscaled ensemble forecast has evolved for at least 6 h due to spinup. There was also a robust moderate negative correlation between total specific humidity and potential temperature background errors, presumably because of incorrect vertical motion in the presence of clouds. Time shifting of the ensemble perturbations, by using those available from adjacent cycles, helped to ameliorate the sampling error prevalent in their raw autocovariances. Monthlong hybrid En3D-Var trials were conducted using different weights assigned to the ensemble-derived and climatological background error covariances. The forecast fits to radiosonde relative humidity and wind observations were generally improved with hybrid En3D-Var, but in all experiments, the fits to surface observations were degraded compared to the baseline 3D-Var configuration. Over the Singapore radar domain, there was a general improvement in the precipitation forecasts, especially when the weighting toward the climatological background error covariance was larger, and with the additional application of time-shifted ensemble perturbations. Future work involves consolidating the ensemble prediction and deterministic system, by centering the ensemble prediction system on the hybrid analysis, to better represent the analysis and forecast uncertainties.
