Abstract
A novel high-resolution regional reanalysis is used to investigate the mesoscale processes that preceded the formation of Tropical Cyclone (TC) Mora (2017). Both satellite observations and the regional reanalysis show early morning mesoscale convective systems (MCSs) persistently initiated and organized in the downshear quadrant of the preexisting tropical disturbance a few days prior to the genesis of TC Mora. The diurnal MCSs gradually enhanced the meso-α-scale vortex near the center of the preexisting tropical disturbance through vortex stretching, providing a vorticity-rich and moist environment for the following burst of deep convection and enhancement of the meso-β-scale vortex. The regional reanalysis shows that the gravity waves that radiated from afternoon convection over the northern coast of the Bay of Bengal might play an important role in modulating the diurnal cycle of pregenesis MCSs. The diurnal convectively forced gravity waves increased the tropospheric stability, reduced the column saturation fraction, and suppressed deep convection within the preexisting tropical disturbance from noon to evening. A similar quasi-diurnal cycle of organized deep convection prior to TC genesis has also been observed over other basins. However, modeling studies are needed to conclusively demonstrate the relationships between the gravity waves and pregenesis diurnal MCSs. Also, whether diurnal gravity waves play a similar role in modulating the pregenesis deep convection in other TCs is worth future investigations.
Significance Statement
Tropical cyclogenesis is a process by which a less organized weather system in the tropics develops into a tropical cyclone (TC). Observations indicate that thunderstorms occurring prior to the tropical cyclogenesis often show a distinct quasi-diurnal cycle, while the related physical mechanisms are still unclear. In this study, we used a novel high-resolution dataset to investigate the diurnal thunderstorms occurring prior to the genesis of TC Mora (2017). We find that the pregenesis diurnal thunderstorms played a crucial role in spinning up the circulation of the atmosphere and provided a favorable environment for the rapid formation of Mora. It is likely that gravity waves emitted by afternoon thunderstorms over the inland region were responsible for regulating the diurnal variation of pregenesis thunderstorms over the ocean.
Abstract
A novel high-resolution regional reanalysis is used to investigate the mesoscale processes that preceded the formation of Tropical Cyclone (TC) Mora (2017). Both satellite observations and the regional reanalysis show early morning mesoscale convective systems (MCSs) persistently initiated and organized in the downshear quadrant of the preexisting tropical disturbance a few days prior to the genesis of TC Mora. The diurnal MCSs gradually enhanced the meso-α-scale vortex near the center of the preexisting tropical disturbance through vortex stretching, providing a vorticity-rich and moist environment for the following burst of deep convection and enhancement of the meso-β-scale vortex. The regional reanalysis shows that the gravity waves that radiated from afternoon convection over the northern coast of the Bay of Bengal might play an important role in modulating the diurnal cycle of pregenesis MCSs. The diurnal convectively forced gravity waves increased the tropospheric stability, reduced the column saturation fraction, and suppressed deep convection within the preexisting tropical disturbance from noon to evening. A similar quasi-diurnal cycle of organized deep convection prior to TC genesis has also been observed over other basins. However, modeling studies are needed to conclusively demonstrate the relationships between the gravity waves and pregenesis diurnal MCSs. Also, whether diurnal gravity waves play a similar role in modulating the pregenesis deep convection in other TCs is worth future investigations.
Significance Statement
Tropical cyclogenesis is a process by which a less organized weather system in the tropics develops into a tropical cyclone (TC). Observations indicate that thunderstorms occurring prior to the tropical cyclogenesis often show a distinct quasi-diurnal cycle, while the related physical mechanisms are still unclear. In this study, we used a novel high-resolution dataset to investigate the diurnal thunderstorms occurring prior to the genesis of TC Mora (2017). We find that the pregenesis diurnal thunderstorms played a crucial role in spinning up the circulation of the atmosphere and provided a favorable environment for the rapid formation of Mora. It is likely that gravity waves emitted by afternoon thunderstorms over the inland region were responsible for regulating the diurnal variation of pregenesis thunderstorms over the ocean.
Abstract
Strong downslope windstorms can cause extensive property damage and extreme wildfire spread, so their accurate prediction is important. Although some early studies suggested high predictability for downslope windstorms, more recent analyses have found limited predictability for such winds. Nevertheless, there is a theoretical basis for expecting higher downslope-wind predictability in cases with a mean-state critical level, and this is supported by one previous effort to forecast actual events. To more thoroughly investigate downslope-windstorm predictability, we compare archived simulations from the NCAR ensemble, a 10-member mesoscale ensemble run at 3-km horizontal grid spacing over the entire contiguous United States, to observed events at 15 stations in the western United States susceptible to strong downslope winds. We assess predictability in three contexts: the average ensemble spread, which provides an estimate of potential predictability; a forecast evaluation based upon binary-decision criteria, which is representative of operational hazard warnings; and a probabilistic forecast evaluation using the continuous ranked probability score (CRPS), which is a measure of an ensemble’s ability to generate the proper probability distribution for the events under consideration. We do find better predictive skill for the mean-state-critical-level regime in comparison to other downslope-windstorm-generating mechanisms. Our downslope windstorm warning performance, calculated using binary-decision criteria from the bias-corrected ensemble forecasts, performed slightly worse for no-critical-level events, and slightly better for critical-level events, than National Weather Service high-wind warnings aggregated over all types of high-wind events throughout the US and annually averaged for each year between 2008 and 2019.
Abstract
Strong downslope windstorms can cause extensive property damage and extreme wildfire spread, so their accurate prediction is important. Although some early studies suggested high predictability for downslope windstorms, more recent analyses have found limited predictability for such winds. Nevertheless, there is a theoretical basis for expecting higher downslope-wind predictability in cases with a mean-state critical level, and this is supported by one previous effort to forecast actual events. To more thoroughly investigate downslope-windstorm predictability, we compare archived simulations from the NCAR ensemble, a 10-member mesoscale ensemble run at 3-km horizontal grid spacing over the entire contiguous United States, to observed events at 15 stations in the western United States susceptible to strong downslope winds. We assess predictability in three contexts: the average ensemble spread, which provides an estimate of potential predictability; a forecast evaluation based upon binary-decision criteria, which is representative of operational hazard warnings; and a probabilistic forecast evaluation using the continuous ranked probability score (CRPS), which is a measure of an ensemble’s ability to generate the proper probability distribution for the events under consideration. We do find better predictive skill for the mean-state-critical-level regime in comparison to other downslope-windstorm-generating mechanisms. Our downslope windstorm warning performance, calculated using binary-decision criteria from the bias-corrected ensemble forecasts, performed slightly worse for no-critical-level events, and slightly better for critical-level events, than National Weather Service high-wind warnings aggregated over all types of high-wind events throughout the US and annually averaged for each year between 2008 and 2019.
Abstract
Ensemble sensitivity analysis (ESA) is a numerical method by which the potential value of a single additional observation can be estimated using an ensemble numerical weather forecast. By performing ESA observation targeting on runs of the TTU WRF Ensemble from the Spring of 2016, a dataset of predicted variance reductions (hereafter referred to as target values) was obtained over approximately 6 weeks’ worth of convective forecasts for the central US. It was then ascertained from these cases that the geographic variation in target values is large for any one case, with local maxima often several standard deviations higher than the mean and surrounded by sharp gradients. Radiosondes launched from the surface, then, would need to take this variation into account to properly sample a specific target by launching upstream of where the target is located rather than directly underneath. In many cases, the difference between the maximum target value in the vertical and the actual target value observed along the balloon path was multiple standard deviations. This may help explain the lower-than-expected forecast improvements observed in previous ESA targeting experiments, especially the Mesoscale Predictability Experiment (MPEX). If target values are a good predictor of observation value, then it is possible that taking the balloon path into account in targeting systems for radiosonde deployment may substantially improve on the value added to the forecast by individual observations.
Abstract
Ensemble sensitivity analysis (ESA) is a numerical method by which the potential value of a single additional observation can be estimated using an ensemble numerical weather forecast. By performing ESA observation targeting on runs of the TTU WRF Ensemble from the Spring of 2016, a dataset of predicted variance reductions (hereafter referred to as target values) was obtained over approximately 6 weeks’ worth of convective forecasts for the central US. It was then ascertained from these cases that the geographic variation in target values is large for any one case, with local maxima often several standard deviations higher than the mean and surrounded by sharp gradients. Radiosondes launched from the surface, then, would need to take this variation into account to properly sample a specific target by launching upstream of where the target is located rather than directly underneath. In many cases, the difference between the maximum target value in the vertical and the actual target value observed along the balloon path was multiple standard deviations. This may help explain the lower-than-expected forecast improvements observed in previous ESA targeting experiments, especially the Mesoscale Predictability Experiment (MPEX). If target values are a good predictor of observation value, then it is possible that taking the balloon path into account in targeting systems for radiosonde deployment may substantially improve on the value added to the forecast by individual observations.
Abstract
The pan-African Great Green Wall for the Sahara and the Sahel initiative (GGW) is a reforestation program to reverse the degradation of land. We investigate characteristics of mean precipitation due to proposed land-use changes to woody savannah with three hypothetical courses of the GGW, with an area between 0.8 and 1.25 million km2, and between the 100- and 400-mm isohyets. The global Model for Prediction Across Scales (MPAS) was applied for this investigation, employing ensembles with 40 members for the rainy season from June to September and 50 members for August when precipitation is at its peak. In comparison with the observational reference, the results show that a wet bias on the order of 33% in the eastern Sahel and a moderate dry bias of −41% in the western Sahel are present in the MPAS simulations. Our simulations do not provide any significant evidence for GGW-induced changes in the characteristics of the summer precipitation, for positive changes within the Sahel supporting the forestation activities, or for potentially adverse changes in the neighboring regions. Changes are present at the regional scale, but they are not significant at the 5% level. Also, changes simulated for further hydrometeorological variables such as temperature, radiation fluxes, or runoff are comparatively small.
Abstract
The pan-African Great Green Wall for the Sahara and the Sahel initiative (GGW) is a reforestation program to reverse the degradation of land. We investigate characteristics of mean precipitation due to proposed land-use changes to woody savannah with three hypothetical courses of the GGW, with an area between 0.8 and 1.25 million km2, and between the 100- and 400-mm isohyets. The global Model for Prediction Across Scales (MPAS) was applied for this investigation, employing ensembles with 40 members for the rainy season from June to September and 50 members for August when precipitation is at its peak. In comparison with the observational reference, the results show that a wet bias on the order of 33% in the eastern Sahel and a moderate dry bias of −41% in the western Sahel are present in the MPAS simulations. Our simulations do not provide any significant evidence for GGW-induced changes in the characteristics of the summer precipitation, for positive changes within the Sahel supporting the forestation activities, or for potentially adverse changes in the neighboring regions. Changes are present at the regional scale, but they are not significant at the 5% level. Also, changes simulated for further hydrometeorological variables such as temperature, radiation fluxes, or runoff are comparatively small.
Abstract
Parameters of the normalized gamma particle size distribution (PSD) have been retrieved from the Precipitation Image Package (PIP) snowfall observations collected during the International Collaborative Experiment–PyeongChang Olympic and Paralympic winter games (ICE-POP 2018). Two of the gamma PSD parameters, the mass-weighted particle diameter Dmass and the normalized intercept parameter NW, have median values of 1.15–1.31 mm and 2.84–3.04 log(mm−1 m−3), respectively. This range arises from the choice of the relationship between the maximum versus equivalent diameter, Dmx–Deq, and the relationship between the Reynolds and Best numbers, Re–X. Normalization of snow water equivalent rate (SWER) and ice water content W by NW reduces the range in NW, resulting in well-fitted power-law relationships between SWER/NW and Dmass and between W/NW and Dmass. The bulk descriptors of snowfall are calculated from PIP observations and from the gamma PSD with values of the shape parameter μ ranging from −2 to 10. NASA’s Global Precipitation Measurement (GPM) mission, which adopted the normalized gamma PSD, assumes μ = 2 and 3 in its two separate algorithms. The mean fractional bias (MFB) of the snowfall parameters changes with μ, where the functional dependence on μ depends on the specific snowfall parameter of interest. The MFB of the total concentration was underestimated by 0.23–0.34 when μ = 2 and by 0.29–0.40 when μ = 3, whereas the MFB of SWER had a much narrower range (from −0.03 to 0.04) for the same μ values.
Abstract
Parameters of the normalized gamma particle size distribution (PSD) have been retrieved from the Precipitation Image Package (PIP) snowfall observations collected during the International Collaborative Experiment–PyeongChang Olympic and Paralympic winter games (ICE-POP 2018). Two of the gamma PSD parameters, the mass-weighted particle diameter Dmass and the normalized intercept parameter NW, have median values of 1.15–1.31 mm and 2.84–3.04 log(mm−1 m−3), respectively. This range arises from the choice of the relationship between the maximum versus equivalent diameter, Dmx–Deq, and the relationship between the Reynolds and Best numbers, Re–X. Normalization of snow water equivalent rate (SWER) and ice water content W by NW reduces the range in NW, resulting in well-fitted power-law relationships between SWER/NW and Dmass and between W/NW and Dmass. The bulk descriptors of snowfall are calculated from PIP observations and from the gamma PSD with values of the shape parameter μ ranging from −2 to 10. NASA’s Global Precipitation Measurement (GPM) mission, which adopted the normalized gamma PSD, assumes μ = 2 and 3 in its two separate algorithms. The mean fractional bias (MFB) of the snowfall parameters changes with μ, where the functional dependence on μ depends on the specific snowfall parameter of interest. The MFB of the total concentration was underestimated by 0.23–0.34 when μ = 2 and by 0.29–0.40 when μ = 3, whereas the MFB of SWER had a much narrower range (from −0.03 to 0.04) for the same μ values.
Abstract
Both the frequency and intensity of hot temperature extremes are expected to increase in the coming decades, challenging various socio-economic sectors including public health. Thereby, societal attention data available in real time, such as Google search attention, could help monitor heat wave impacts in domains with lagged data availability. Here, we jointly analyze societal attention and health impacts of heat waves in Germany at weekly time scales. We find that Google search attention responds similar to hot temperatures as indicators of public health impacts; represented by excess mortality and hospitalizations. This emerges from piecewise linear relationships of Google search attention and health impacts to temperature. We can then determine temperature thresholds above which both attention and public health are affected by heat. More generally, given the clear and similar response of societal indicators to heat, we conclude that heat waves can and should be defined from a joint societal and meteorological perspective, whereby temperatures are compared with thresholds established using societal data. A better joint understanding of societal attention and health impacts offers the potential to better manage future heat waves.
Abstract
Both the frequency and intensity of hot temperature extremes are expected to increase in the coming decades, challenging various socio-economic sectors including public health. Thereby, societal attention data available in real time, such as Google search attention, could help monitor heat wave impacts in domains with lagged data availability. Here, we jointly analyze societal attention and health impacts of heat waves in Germany at weekly time scales. We find that Google search attention responds similar to hot temperatures as indicators of public health impacts; represented by excess mortality and hospitalizations. This emerges from piecewise linear relationships of Google search attention and health impacts to temperature. We can then determine temperature thresholds above which both attention and public health are affected by heat. More generally, given the clear and similar response of societal indicators to heat, we conclude that heat waves can and should be defined from a joint societal and meteorological perspective, whereby temperatures are compared with thresholds established using societal data. A better joint understanding of societal attention and health impacts offers the potential to better manage future heat waves.
Abstract
Convective variability is used to diagnose different pathways towards convective self-aggregation (CSA) in radiative-convective equilibrium simulations with two cloud-resolving models, SCALE and VVM. The results show that convection undergoes gradual growth in SCALE and fast transition in VVM, which is associated with different mechanisms between the two models. In SCALE, strong radiative cooling associated with a dry environment drives the circulation from the dry region, and the dry environment results from strong subsidence and insufficient surface flux supply. The circulation driven by the radiative cooling then pushes convection aggregating, which is the dry-radiation pathway. In VVM, CSA develops due to the rapid strengthening of circulation driven by convective systems in the moist region, which is the convection-upscaling pathway. The different pathways of CSA development can be attributed to the upscale process of convective structures identified by the cloud size spectrum. The upscaling of large-size convective systems can enhance circulation from the moist region in VVM. In SCALE, the infrequent appearance of large convective systems is insufficient to generate circulation, as compensating subsidence can occur within the moist region even in the absence of convective systems. This study shows that the convective variabilities between models can lead to different pathways of CSA, and mechanism-denial experiments also support our analyses.
Abstract
Convective variability is used to diagnose different pathways towards convective self-aggregation (CSA) in radiative-convective equilibrium simulations with two cloud-resolving models, SCALE and VVM. The results show that convection undergoes gradual growth in SCALE and fast transition in VVM, which is associated with different mechanisms between the two models. In SCALE, strong radiative cooling associated with a dry environment drives the circulation from the dry region, and the dry environment results from strong subsidence and insufficient surface flux supply. The circulation driven by the radiative cooling then pushes convection aggregating, which is the dry-radiation pathway. In VVM, CSA develops due to the rapid strengthening of circulation driven by convective systems in the moist region, which is the convection-upscaling pathway. The different pathways of CSA development can be attributed to the upscale process of convective structures identified by the cloud size spectrum. The upscaling of large-size convective systems can enhance circulation from the moist region in VVM. In SCALE, the infrequent appearance of large convective systems is insufficient to generate circulation, as compensating subsidence can occur within the moist region even in the absence of convective systems. This study shows that the convective variabilities between models can lead to different pathways of CSA, and mechanism-denial experiments also support our analyses.
Abstract
The export of Antarctic Bottom Water (AABW) supplies the bottom cell of the global overturning circulation and plays a key role in regulating climate. This AABW outflow must cross, and is therefore mediated by, the Antarctic Circumpolar Current (ACC). Previous studies present widely-varying conceptions of the role of the ACC in directing AABW across the Southern Ocean, suggesting either that AABW may be zonally recirculated by the ACC, or that AABW may flow northward within deep western boundary currents (DWBC) against bathymetry. In this study the authors investigate how the forcing and geometry of the ACC influences the transport and transformation of AABW using a suite of process-oriented model simulations. The model exhibits a strong dependence on the elevation of bathymetry relative to AABW layer thickness: higher meridional ridges suppress zonal AABW exchange, increase the strength of flow in the DWBC, and reduce the meridional variation in AABW density across the ACC. Furthermore, the transport and transformation vary with density within the AABW layer, with denser varieties of AABW being less efficiently transported between basins. These findings indicate that changes in the thickness of the AABW layer, for example due to changes in Antarctic shelf processes, and tectonic changes in the sea floor shape may alter the pathways and transformation of AABW across the ACC.
Abstract
The export of Antarctic Bottom Water (AABW) supplies the bottom cell of the global overturning circulation and plays a key role in regulating climate. This AABW outflow must cross, and is therefore mediated by, the Antarctic Circumpolar Current (ACC). Previous studies present widely-varying conceptions of the role of the ACC in directing AABW across the Southern Ocean, suggesting either that AABW may be zonally recirculated by the ACC, or that AABW may flow northward within deep western boundary currents (DWBC) against bathymetry. In this study the authors investigate how the forcing and geometry of the ACC influences the transport and transformation of AABW using a suite of process-oriented model simulations. The model exhibits a strong dependence on the elevation of bathymetry relative to AABW layer thickness: higher meridional ridges suppress zonal AABW exchange, increase the strength of flow in the DWBC, and reduce the meridional variation in AABW density across the ACC. Furthermore, the transport and transformation vary with density within the AABW layer, with denser varieties of AABW being less efficiently transported between basins. These findings indicate that changes in the thickness of the AABW layer, for example due to changes in Antarctic shelf processes, and tectonic changes in the sea floor shape may alter the pathways and transformation of AABW across the ACC.
Abstract
Drought is a recurrent natural phenomenon, but there is concern that climate change may increase the frequency or severity of drought in Alaska. Because most common drought indices were designed for lower latitudes, it is unclear how effectively they characterize drought in Alaska’s diverse, high-latitude climates. Here, we compare three commonly used meteorological drought indices [the standardized precipitation index (SPI), the standardized precipitation evapotranspiration index (SPEI), and the self-calibrating Palmer drought severity index (scPDSI)] with each other and with streamflow across Alaska’s 13 climate divisions. All of the drought indices identify major droughts, but the severity of the drought varies depending on the index used. The SPI and the SPEI are more flexible and often better correlated with streamflow than the scPDSI, and we recommend using them. Although SPI and SPEI are very similar in energy-limited climates, the drought metrics do diverge in drier locations in recent years, and consideration of the impact of temperature on drought may grow more important in the coming decades. Hargreaves potential evapotranspiration (PET) estimates appeared more physically realistic than the more commonly used Thornthwaite equation and are equally easy to calculate, so we suggest using the Hargreaves equation when PET is estimated from temperature. This study, one of the first to evaluate drought indices for high-latitude regions, has the potential to improve drought monitoring and representation within the U.S. Drought Monitor, leading to more informed decision-making during drought in Alaska, and it improves our ability to track changes in drought driven by rising temperatures.
Significance Statement
Tracking drought at high latitudes is challenging because we have not adequately studied drought impacts in cold climates, and the primary meteorological drought indices were designed for lower latitudes and may not accurately estimate evaporative demand and the influence of snow. We investigate three common drought indices and recommend using the standardized precipitation index (SPI) or the standardized precipitation evapotranspiration index (SPEI) because they can track short and long droughts. The SPEI may be more useful because comparisons between the SPI and SPEI show that, in recent decades, temperature has made noticeable contributions to drought in drier parts of Alaska. If using the SPEI, we suggest the Hargreaves potential evapotranspiration rather than the Thornthwaite because it is more physically realistic.
Abstract
Drought is a recurrent natural phenomenon, but there is concern that climate change may increase the frequency or severity of drought in Alaska. Because most common drought indices were designed for lower latitudes, it is unclear how effectively they characterize drought in Alaska’s diverse, high-latitude climates. Here, we compare three commonly used meteorological drought indices [the standardized precipitation index (SPI), the standardized precipitation evapotranspiration index (SPEI), and the self-calibrating Palmer drought severity index (scPDSI)] with each other and with streamflow across Alaska’s 13 climate divisions. All of the drought indices identify major droughts, but the severity of the drought varies depending on the index used. The SPI and the SPEI are more flexible and often better correlated with streamflow than the scPDSI, and we recommend using them. Although SPI and SPEI are very similar in energy-limited climates, the drought metrics do diverge in drier locations in recent years, and consideration of the impact of temperature on drought may grow more important in the coming decades. Hargreaves potential evapotranspiration (PET) estimates appeared more physically realistic than the more commonly used Thornthwaite equation and are equally easy to calculate, so we suggest using the Hargreaves equation when PET is estimated from temperature. This study, one of the first to evaluate drought indices for high-latitude regions, has the potential to improve drought monitoring and representation within the U.S. Drought Monitor, leading to more informed decision-making during drought in Alaska, and it improves our ability to track changes in drought driven by rising temperatures.
Significance Statement
Tracking drought at high latitudes is challenging because we have not adequately studied drought impacts in cold climates, and the primary meteorological drought indices were designed for lower latitudes and may not accurately estimate evaporative demand and the influence of snow. We investigate three common drought indices and recommend using the standardized precipitation index (SPI) or the standardized precipitation evapotranspiration index (SPEI) because they can track short and long droughts. The SPEI may be more useful because comparisons between the SPI and SPEI show that, in recent decades, temperature has made noticeable contributions to drought in drier parts of Alaska. If using the SPEI, we suggest the Hargreaves potential evapotranspiration rather than the Thornthwaite because it is more physically realistic.
Abstract
Three levels of process-oriented model diagnostics are applied to evaluate the Global Ensemble Forecast System version 12 (GEFSv12) reforecasts. The level-1 diagnostics are focused on model systematic errors, which reveals that precipitation onset over tropical oceans occurs too early in terms of column water vapor accumulation. Since precipitation acts to deplete water vapor, this results in prevailing negative biases of precipitable water in the tropics. It is also associated with overtransport of moisture into the mid- and upper troposphere, leading to a dry bias in the lower troposphere and a wet bias in the mid–upper troposphere. The level-2 diagnostics evaluate some major predictability sources on the extended-range time scale: the Madden–Julian oscillation (MJO) and North American weather regimes. It is found that the GEFSv12 can skillfully forecast the MJO up to 16 days ahead in terms of the Real-time Multivariate MJO indices (bivariate correlation ≥ 0.6) and can reasonably represent the MJO propagation across the Maritime Continent. The weakened and less coherent MJO signals with increasing forecast lead times may be attributed to humidity biases over the Indo-Pacific warm pool region. It is also found that the weather regimes can be skillfully predicted up to 12 days ahead with persistence comparable to the observation. In the level-3 diagnostics, we examined some high-impact weather systems. The GEFSv12 shows reduced mean biases in tropical cyclone genesis distribution and improved performance in capturing tropical cyclone interannual variability, and midlatitude blocking climatology in the GEFSv12 also shows a better agreement with the observations than in the GEFSv10.
Significance Statement
The latest U.S. operational weather prediction model—Global Ensemble Forecast System version 12—is evaluated using a suite of physics-based diagnostic metrics from a climatic perspective. The foci of our study consist of three levels: 1) systematic biases in physical processes, 2) tropical and extratropical extended-range predictability sources, and 3) high-impact weather systems like hurricanes and blockings. Such process-oriented diagnostics help us link the model performance to the deficiencies of physics parameterization and thus provide useful information on future model improvement.
Abstract
Three levels of process-oriented model diagnostics are applied to evaluate the Global Ensemble Forecast System version 12 (GEFSv12) reforecasts. The level-1 diagnostics are focused on model systematic errors, which reveals that precipitation onset over tropical oceans occurs too early in terms of column water vapor accumulation. Since precipitation acts to deplete water vapor, this results in prevailing negative biases of precipitable water in the tropics. It is also associated with overtransport of moisture into the mid- and upper troposphere, leading to a dry bias in the lower troposphere and a wet bias in the mid–upper troposphere. The level-2 diagnostics evaluate some major predictability sources on the extended-range time scale: the Madden–Julian oscillation (MJO) and North American weather regimes. It is found that the GEFSv12 can skillfully forecast the MJO up to 16 days ahead in terms of the Real-time Multivariate MJO indices (bivariate correlation ≥ 0.6) and can reasonably represent the MJO propagation across the Maritime Continent. The weakened and less coherent MJO signals with increasing forecast lead times may be attributed to humidity biases over the Indo-Pacific warm pool region. It is also found that the weather regimes can be skillfully predicted up to 12 days ahead with persistence comparable to the observation. In the level-3 diagnostics, we examined some high-impact weather systems. The GEFSv12 shows reduced mean biases in tropical cyclone genesis distribution and improved performance in capturing tropical cyclone interannual variability, and midlatitude blocking climatology in the GEFSv12 also shows a better agreement with the observations than in the GEFSv10.
Significance Statement
The latest U.S. operational weather prediction model—Global Ensemble Forecast System version 12—is evaluated using a suite of physics-based diagnostic metrics from a climatic perspective. The foci of our study consist of three levels: 1) systematic biases in physical processes, 2) tropical and extratropical extended-range predictability sources, and 3) high-impact weather systems like hurricanes and blockings. Such process-oriented diagnostics help us link the model performance to the deficiencies of physics parameterization and thus provide useful information on future model improvement.
