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Abstract
In operational weather forecasting, it is effective to aggregate information on all members of an ensemble forecast through cluster analysis. The temporal coherence of ensemble members in each cluster is an important piece of information about the robustness of the forecast scenario given by clusters. This information is especially important for forecasts for which the target area is a city or prefecture, that is, an Eulerian framework, because the members that compose each cluster can change over time because of the small size of the target area. This study provided the temporal coherence of members in clusters by performing principal component analysis and cluster analysis on 3-hourly 500-hPa geopotential height forecasts and linking the clustering results in the time direction. The new method provided a consistently well-divided forecast scenario throughout the forecast period for Eulerian frame forecasts, as well as information on the temporal coherency of the members in the clusters, which was demonstrated to be effective through the experiment to preselect a cluster with small errors. The application of the new technique to improve precipitation forecasts was also discussed.
Significance Statement
Numerical weather forecasts always contain errors. Although the uncertainty of such forecasts cannot be obtained from the forecast itself, ensemble forecasts, which are aggregates of many forecasts, can be used to estimate the uncertainty of the forecast. In this study, a new method was developed to transfer the information contained in many ensemble forecasts into four forecasts by cluster analysis and to provide forecast information suitable for a small forecasting area such as a prefecture. The use of this method for improving precipitation forecasts was also examined.
Abstract
In operational weather forecasting, it is effective to aggregate information on all members of an ensemble forecast through cluster analysis. The temporal coherence of ensemble members in each cluster is an important piece of information about the robustness of the forecast scenario given by clusters. This information is especially important for forecasts for which the target area is a city or prefecture, that is, an Eulerian framework, because the members that compose each cluster can change over time because of the small size of the target area. This study provided the temporal coherence of members in clusters by performing principal component analysis and cluster analysis on 3-hourly 500-hPa geopotential height forecasts and linking the clustering results in the time direction. The new method provided a consistently well-divided forecast scenario throughout the forecast period for Eulerian frame forecasts, as well as information on the temporal coherency of the members in the clusters, which was demonstrated to be effective through the experiment to preselect a cluster with small errors. The application of the new technique to improve precipitation forecasts was also discussed.
Significance Statement
Numerical weather forecasts always contain errors. Although the uncertainty of such forecasts cannot be obtained from the forecast itself, ensemble forecasts, which are aggregates of many forecasts, can be used to estimate the uncertainty of the forecast. In this study, a new method was developed to transfer the information contained in many ensemble forecasts into four forecasts by cluster analysis and to provide forecast information suitable for a small forecasting area such as a prefecture. The use of this method for improving precipitation forecasts was also examined.
Abstract
A scale-dependent dynamic Smagorinsky model is implemented in the Met Office/NERC Cloud (MONC) model using two averaging flavors, along Lagrangian pathlines and local moving averages. The dynamic approaches were compared against the conventional Smagorinsky–Lilly scheme in simulating the diurnal cycle of shallow cumulus convection. The simulations spanned from the LES to the near-gray-zone and gray-zone resolutions and revealed the adaptability of the dynamic model across the scales and different stability regimes. The dynamic model can produce a scale- and stability-dependent profile of the subfilter turbulence length scale across the chosen resolution range. At gray-zone resolutions the adaptive length scales can better represent the early precloud boundary layer leading to temperature and moisture profiles closer to the LES compared to the standard Smagorinsky. As a result, the initialization and general representation of the cloud field in the dynamic model is in good agreement with the LES. In contrast, the standard Smagorinsky produces a less well-mixed boundary layer, which fails to ventilate moisture from the boundary layer, resulting in the delayed spinup of the cloud layer. Moreover, strong downgradient diffusion controls the turbulent transport of scalars in the cloud layer. However, the dynamic approaches rely on the resolved field to account for nonlocal transports, leading to overenergetic structures when the boundary layer is fully developed and the Lagrangian model is used. Introducing the local averaging version of the model or adopting a new Lagrangian time scale provides stronger dissipation without significantly affecting model behavior.
Abstract
A scale-dependent dynamic Smagorinsky model is implemented in the Met Office/NERC Cloud (MONC) model using two averaging flavors, along Lagrangian pathlines and local moving averages. The dynamic approaches were compared against the conventional Smagorinsky–Lilly scheme in simulating the diurnal cycle of shallow cumulus convection. The simulations spanned from the LES to the near-gray-zone and gray-zone resolutions and revealed the adaptability of the dynamic model across the scales and different stability regimes. The dynamic model can produce a scale- and stability-dependent profile of the subfilter turbulence length scale across the chosen resolution range. At gray-zone resolutions the adaptive length scales can better represent the early precloud boundary layer leading to temperature and moisture profiles closer to the LES compared to the standard Smagorinsky. As a result, the initialization and general representation of the cloud field in the dynamic model is in good agreement with the LES. In contrast, the standard Smagorinsky produces a less well-mixed boundary layer, which fails to ventilate moisture from the boundary layer, resulting in the delayed spinup of the cloud layer. Moreover, strong downgradient diffusion controls the turbulent transport of scalars in the cloud layer. However, the dynamic approaches rely on the resolved field to account for nonlocal transports, leading to overenergetic structures when the boundary layer is fully developed and the Lagrangian model is used. Introducing the local averaging version of the model or adopting a new Lagrangian time scale provides stronger dissipation without significantly affecting model behavior.
Abstract
Recent studies suggest that the eddy kinetic energy is localized in the lee of significant topographic features in the Antarctic Circumpolar Current (ACC). Here we explore the importance of the local dynamics quantitatively using the outputs from the realistic ocean general circulation model hindcast with the aid of the modified Lorentz energy cycle. Results confirm the importance of energy transfer among reservoirs in the downstream region of standing meanders, showing that the major five standing meanders are responsible for more than 70% of the kinetic energy transfer to eddies and dissipation over the Antarctic Circumpolar Current region. The eddy kinetic energy is generated in the upper 3000-m depth downstream of the standing meanders and transported due to the vertical energy redistribution governed by the vertical pressure flux toward the deeper layer where the eddy energy is dissipated. Moreover, we also calculate the work done by the Ekman transport to confirm that the wind energy input works as the dominant energy source for the baroclinic energy pathway. The advantage of this quantity against the vertical mean density flux is that it is independent of the reference states defined arbitrarily. It is shown that the westerlies can supply sufficient energy locally to initiate baroclinic instability in the Indian and Pacific sectors of the ACC, whereas the nonlocal process is important in the Atlantic sector. Our results suggest that the five narrow regions associated with significant topography play key roles in the energy balance of the ACC region.
Significance Statement
The purpose of this study is to understand the eddy–mean flow interactions in the Antarctic Circumpolar Current from the energetic viewpoint. Our results show that the five narrow regions called “hotspots” in our study are responsible for the energy transfer from the mean flow to eddies. It is also found that the hotspots are important for the energy sink in the Southern Ocean. These findings suggest that the five hotspots are likely to play key roles in the responses of the Antarctic Circumpolar Current to the changes in westerlies in these decades.
Abstract
Recent studies suggest that the eddy kinetic energy is localized in the lee of significant topographic features in the Antarctic Circumpolar Current (ACC). Here we explore the importance of the local dynamics quantitatively using the outputs from the realistic ocean general circulation model hindcast with the aid of the modified Lorentz energy cycle. Results confirm the importance of energy transfer among reservoirs in the downstream region of standing meanders, showing that the major five standing meanders are responsible for more than 70% of the kinetic energy transfer to eddies and dissipation over the Antarctic Circumpolar Current region. The eddy kinetic energy is generated in the upper 3000-m depth downstream of the standing meanders and transported due to the vertical energy redistribution governed by the vertical pressure flux toward the deeper layer where the eddy energy is dissipated. Moreover, we also calculate the work done by the Ekman transport to confirm that the wind energy input works as the dominant energy source for the baroclinic energy pathway. The advantage of this quantity against the vertical mean density flux is that it is independent of the reference states defined arbitrarily. It is shown that the westerlies can supply sufficient energy locally to initiate baroclinic instability in the Indian and Pacific sectors of the ACC, whereas the nonlocal process is important in the Atlantic sector. Our results suggest that the five narrow regions associated with significant topography play key roles in the energy balance of the ACC region.
Significance Statement
The purpose of this study is to understand the eddy–mean flow interactions in the Antarctic Circumpolar Current from the energetic viewpoint. Our results show that the five narrow regions called “hotspots” in our study are responsible for the energy transfer from the mean flow to eddies. It is also found that the hotspots are important for the energy sink in the Southern Ocean. These findings suggest that the five hotspots are likely to play key roles in the responses of the Antarctic Circumpolar Current to the changes in westerlies in these decades.
Abstract
The energy and momentum balance of an abyssal overflow across a major sill in the Samoan Passage is estimated from two highly resolved towed sections, set 16 months apart, and results from a two-dimensional numerical simulation. Driven by the density anomaly across the sill, the flow is relatively steady. The system gains energy from divergence of horizontal pressure work
Abstract
The energy and momentum balance of an abyssal overflow across a major sill in the Samoan Passage is estimated from two highly resolved towed sections, set 16 months apart, and results from a two-dimensional numerical simulation. Driven by the density anomaly across the sill, the flow is relatively steady. The system gains energy from divergence of horizontal pressure work
Abstract
The high-resolution mooring observations reported here reveal a cascade process from internal solitary waves (ISWs) to turbulent mixing via high-frequency internal waves near the maximum local buoyancy frequency (near-N waves) in the deep water of the northern South China Sea (SCS). Riding on the parent ISW, near-N waves with a peak frequency of 20 cph emerged at the trough of the ISW and extended to the rear face of the ISW. Most of the near-N waves occurred around the thermocline, where the isothermal displacements induced by the near-N waves were largest with an amplitude of 12 m. The energy of near-N waves was 5% of that of the parent ISW, and instability investigations showed that due to the strong shear, Ri in the region of strong near-N waves was less than 1/4, suggesting that the near-N waves were unstable and might dissipate rapidly. Simulations based on the Korteweg–de Vries (KdV)–Burgers equation reproduced the formation of observed near-N waves due to the energy cascade from ISWs. Our observational results demonstrate a new energy cascade route from ISWs to turbulence in the deep water, deepening the understanding of the energy dissipation process of ISWs and their roles in the enhanced mixing in the northern SCS.
Abstract
The high-resolution mooring observations reported here reveal a cascade process from internal solitary waves (ISWs) to turbulent mixing via high-frequency internal waves near the maximum local buoyancy frequency (near-N waves) in the deep water of the northern South China Sea (SCS). Riding on the parent ISW, near-N waves with a peak frequency of 20 cph emerged at the trough of the ISW and extended to the rear face of the ISW. Most of the near-N waves occurred around the thermocline, where the isothermal displacements induced by the near-N waves were largest with an amplitude of 12 m. The energy of near-N waves was 5% of that of the parent ISW, and instability investigations showed that due to the strong shear, Ri in the region of strong near-N waves was less than 1/4, suggesting that the near-N waves were unstable and might dissipate rapidly. Simulations based on the Korteweg–de Vries (KdV)–Burgers equation reproduced the formation of observed near-N waves due to the energy cascade from ISWs. Our observational results demonstrate a new energy cascade route from ISWs to turbulence in the deep water, deepening the understanding of the energy dissipation process of ISWs and their roles in the enhanced mixing in the northern SCS.
Flippin’
χ
SOLO, an Upper-Ocean Autonomous Turbulence-Profiling FloatAbstract
A new autonomous turbulence profiling float has been designed, built, and tested in field trials off Oregon. Flippin’ χSOLO (FχS) employs a SOLO-II buoyancy engine that not only changes but also shifts ballast to move the center of mass to positions on either side of the center of buoyancy, thus causing FχS to flip. FχS is outfitted with a full suite of turbulence sensors—two shear probes, two fast thermistors, and pitot tube, as well as a pressure sensor and three-axis linear accelerometers. FχS descends and ascends with turbulence sensors leading, thereby permitting measurement through the sea surface. The turbulence sensors are housed antipodal from communication antennas so as to eliminate flow disturbance. By flipping at the sea surface, antennas are exposed for communications. The mission of FχS is to provide intensive profiling measurements of the upper ocean from 240 m and through the sea surface, particularly during periods of extreme surface forcing. While surfaced, accelerometers provide estimates of wave height spectra and significant wave height. From 3.5 day field trials, here we evaluate (i) the statistics from two FχS units and our established shipboard profiler, Chameleon, and (ii) FχS-based wave statistics by comparison to a nearby NOAA wave buoy.
Significance Statement
The oceanographic fleet of Argo autonomous profilers yields important data that define the state of the ocean’s interior. Continued deployments over time define the evolution of the ocean’s interior. A significant next step will be to include turbulence measurements on these profilers, leading to estimates of thermodynamic mixing rates that predict future states of the ocean’s interior. An autonomous turbulence profiler that employs the buoyancy engine, mission logic, and remote communication of one particular Argo float is described herein. The Flippin’ χSOLO is an upper-ocean profiler tasked with rapid and continuous profiling of the upper ocean during weather conditions that preclude shipboard profiling and that includes the upper 10 m that is missed by shipboard turbulence profilers.
Abstract
A new autonomous turbulence profiling float has been designed, built, and tested in field trials off Oregon. Flippin’ χSOLO (FχS) employs a SOLO-II buoyancy engine that not only changes but also shifts ballast to move the center of mass to positions on either side of the center of buoyancy, thus causing FχS to flip. FχS is outfitted with a full suite of turbulence sensors—two shear probes, two fast thermistors, and pitot tube, as well as a pressure sensor and three-axis linear accelerometers. FχS descends and ascends with turbulence sensors leading, thereby permitting measurement through the sea surface. The turbulence sensors are housed antipodal from communication antennas so as to eliminate flow disturbance. By flipping at the sea surface, antennas are exposed for communications. The mission of FχS is to provide intensive profiling measurements of the upper ocean from 240 m and through the sea surface, particularly during periods of extreme surface forcing. While surfaced, accelerometers provide estimates of wave height spectra and significant wave height. From 3.5 day field trials, here we evaluate (i) the statistics from two FχS units and our established shipboard profiler, Chameleon, and (ii) FχS-based wave statistics by comparison to a nearby NOAA wave buoy.
Significance Statement
The oceanographic fleet of Argo autonomous profilers yields important data that define the state of the ocean’s interior. Continued deployments over time define the evolution of the ocean’s interior. A significant next step will be to include turbulence measurements on these profilers, leading to estimates of thermodynamic mixing rates that predict future states of the ocean’s interior. An autonomous turbulence profiler that employs the buoyancy engine, mission logic, and remote communication of one particular Argo float is described herein. The Flippin’ χSOLO is an upper-ocean profiler tasked with rapid and continuous profiling of the upper ocean during weather conditions that preclude shipboard profiling and that includes the upper 10 m that is missed by shipboard turbulence profilers.
Abstract
A new set of CMIP6 data downscaled using the localized constructed analogs (LOCA) statistical method has been produced, covering central Mexico through southern Canada at 6-km resolution. Output from 27 CMIP6 Earth system models is included, with up to 10 ensemble members per model and 3 SSPs (245, 370, and 585). Improvements from the previous CMIP5 downscaled data result in higher daily precipitation extremes, which have significant societal and economic implications. The improvements are accomplished by using a precipitation training dataset that better represents daily extremes and by implementing an ensemble bias correction that allows a more realistic representation of extreme high daily precipitation values in models with numerous ensemble members. Over southern Canada and the CONUS exclusive of Arizona (AZ) and New Mexico (NM), seasonal increases in daily precipitation extremes are largest in winter (∼25% in SSP370). Over Mexico, AZ, and NM, seasonal increases are largest in autumn (∼15%). Summer is the outlier season, with low model agreement except in New England and little changes in 5-yr return values, but substantial increases in the CONUS and Canada in the 500-yr return value. One-in-100-yr historical daily precipitation events become substantially more frequent in the future, as often as once in 30–40 years in the southeastern United States and Pacific Northwest by the end of the century under SSP 370. Impacts of the higher precipitation extremes in the LOCA version 2 downscaled CMIP6 product relative to the LOCA downscaled CMIP5 product, even for similar anthropogenic emissions, may need to be considered by end-users.
Abstract
A new set of CMIP6 data downscaled using the localized constructed analogs (LOCA) statistical method has been produced, covering central Mexico through southern Canada at 6-km resolution. Output from 27 CMIP6 Earth system models is included, with up to 10 ensemble members per model and 3 SSPs (245, 370, and 585). Improvements from the previous CMIP5 downscaled data result in higher daily precipitation extremes, which have significant societal and economic implications. The improvements are accomplished by using a precipitation training dataset that better represents daily extremes and by implementing an ensemble bias correction that allows a more realistic representation of extreme high daily precipitation values in models with numerous ensemble members. Over southern Canada and the CONUS exclusive of Arizona (AZ) and New Mexico (NM), seasonal increases in daily precipitation extremes are largest in winter (∼25% in SSP370). Over Mexico, AZ, and NM, seasonal increases are largest in autumn (∼15%). Summer is the outlier season, with low model agreement except in New England and little changes in 5-yr return values, but substantial increases in the CONUS and Canada in the 500-yr return value. One-in-100-yr historical daily precipitation events become substantially more frequent in the future, as often as once in 30–40 years in the southeastern United States and Pacific Northwest by the end of the century under SSP 370. Impacts of the higher precipitation extremes in the LOCA version 2 downscaled CMIP6 product relative to the LOCA downscaled CMIP5 product, even for similar anthropogenic emissions, may need to be considered by end-users.
Abstract
This paper introduces a new tool for verifying tropical cyclone (TC) forecasts. Tropical cyclone forecasts made by operational centers and by numerical weather prediction (NWP) models have been objectively verified for decades. Typically, the mean absolute error (MAE) and/or MAE skill are calculated relative to values within the operational center’s best track. Yet, the MAE can be strongly influenced by outliers and yield misleading results. Thus, this paper introduces an assessment of consistency between the MAE skill as well as two other measures of forecast performance. This “consistency metric” objectively evaluates the forecast-error evolution as a function of lead time based on thresholds applied to the 1) MAE skill; 2) the frequency of superior performance (FSP), which indicates how often one forecast outperforms another; and 3) median absolute error (MDAE) skill. The utility and applicability of the consistency metric is validated by applying it to four research and forecasting applications. Overall, this consistency metric is a helpful tool to guide analysis and increase confidence in results in a straightforward way. By augmenting the commonly used MAE and MAE skill with this consistency metric and creating a single scorecard with consistency metric results for TC track, intensity, and significant-wind-radii forecasts, the impact of observing systems, new modeling systems, or model upgrades on TC-forecast performance can be evaluated both holistically and succinctly. This could in turn help forecasters learn from challenging cases and accelerate and optimize developments and upgrades in NWP models.
Significance Statement
Evaluating the impact of observing systems, new modeling systems, or model upgrades on TC forecasts is vital to ensure more rapid and accurate implementations and optimizations. To do so, errors between model forecasts and observed TC parameters are calculated. Historically, analyzing these errors heavily relied on using one or two metrics: mean absolute errors (MAE) and/or MAE skill. Yet, doing so can lead to misleading conclusions if the error distributions are skewed, which often occurs (e.g., a poorly forecasted TC). This paper presents a new, straightforward way to combine useful information from several different metrics to enable a more holistic assessment of forecast errors when assessing the MAE and MAE skill.
Abstract
This paper introduces a new tool for verifying tropical cyclone (TC) forecasts. Tropical cyclone forecasts made by operational centers and by numerical weather prediction (NWP) models have been objectively verified for decades. Typically, the mean absolute error (MAE) and/or MAE skill are calculated relative to values within the operational center’s best track. Yet, the MAE can be strongly influenced by outliers and yield misleading results. Thus, this paper introduces an assessment of consistency between the MAE skill as well as two other measures of forecast performance. This “consistency metric” objectively evaluates the forecast-error evolution as a function of lead time based on thresholds applied to the 1) MAE skill; 2) the frequency of superior performance (FSP), which indicates how often one forecast outperforms another; and 3) median absolute error (MDAE) skill. The utility and applicability of the consistency metric is validated by applying it to four research and forecasting applications. Overall, this consistency metric is a helpful tool to guide analysis and increase confidence in results in a straightforward way. By augmenting the commonly used MAE and MAE skill with this consistency metric and creating a single scorecard with consistency metric results for TC track, intensity, and significant-wind-radii forecasts, the impact of observing systems, new modeling systems, or model upgrades on TC-forecast performance can be evaluated both holistically and succinctly. This could in turn help forecasters learn from challenging cases and accelerate and optimize developments and upgrades in NWP models.
Significance Statement
Evaluating the impact of observing systems, new modeling systems, or model upgrades on TC forecasts is vital to ensure more rapid and accurate implementations and optimizations. To do so, errors between model forecasts and observed TC parameters are calculated. Historically, analyzing these errors heavily relied on using one or two metrics: mean absolute errors (MAE) and/or MAE skill. Yet, doing so can lead to misleading conclusions if the error distributions are skewed, which often occurs (e.g., a poorly forecasted TC). This paper presents a new, straightforward way to combine useful information from several different metrics to enable a more holistic assessment of forecast errors when assessing the MAE and MAE skill.
Abstract
The sea surface temperature anomaly (SSTA) plays a key role in climate change and extreme weather processes. Usually, SSTA forecast methods consist of numerical and conventional statistical models, and the former can be seriously influenced by the uncertainty of physical parameterization schemes, the nonlinearity of ocean dynamic processes, and the nonrobustness of numerical discretization algorithms. Recently, deep learning has been explored to address forecast issues in the field of oceanography. However, existing deep learning models for ocean forecasting are mainly site specific, which were designed for forecasting on a single point or for an independent variable. Moreover, few special deep learning networks have been developed to deal with SSTA field forecasts under typhoon conditions. In this study, a multivariable convolutional neural network (MCNN) is proposed, which can be applied for synoptic-scale SSTA forecasting in the South China Sea. In addition to the SSTA itself, the surface wind speed and the surface current velocity are regarded as input variables for the prediction networks, effectively reflecting the influences of both local atmospheric dynamic forcing and nonlocal oceanic thermal advection. Experimental results demonstrate that MCNN exhibits better performance than a single-variable convolutional neural network (SCNN), especially for the SSTA forecast during the typhoon passage. While forecast results deteriorate rapidly in the SCNN during the passage of a typhoon, forecast errors in the MCNN can be effectively restrained to slowly increase over the forecast time due to the introduction of the surface wind speed in this network.
Abstract
The sea surface temperature anomaly (SSTA) plays a key role in climate change and extreme weather processes. Usually, SSTA forecast methods consist of numerical and conventional statistical models, and the former can be seriously influenced by the uncertainty of physical parameterization schemes, the nonlinearity of ocean dynamic processes, and the nonrobustness of numerical discretization algorithms. Recently, deep learning has been explored to address forecast issues in the field of oceanography. However, existing deep learning models for ocean forecasting are mainly site specific, which were designed for forecasting on a single point or for an independent variable. Moreover, few special deep learning networks have been developed to deal with SSTA field forecasts under typhoon conditions. In this study, a multivariable convolutional neural network (MCNN) is proposed, which can be applied for synoptic-scale SSTA forecasting in the South China Sea. In addition to the SSTA itself, the surface wind speed and the surface current velocity are regarded as input variables for the prediction networks, effectively reflecting the influences of both local atmospheric dynamic forcing and nonlocal oceanic thermal advection. Experimental results demonstrate that MCNN exhibits better performance than a single-variable convolutional neural network (SCNN), especially for the SSTA forecast during the typhoon passage. While forecast results deteriorate rapidly in the SCNN during the passage of a typhoon, forecast errors in the MCNN can be effectively restrained to slowly increase over the forecast time due to the introduction of the surface wind speed in this network.
Abstract
Subseasonal forecasts have recently attracted widespread interest yet remain a challenging issue. A statistical Kalman filter pattern projection method (KFPPM), which absorbs the projection conception of the raw covariance pattern projection (COVPPM) and the adaptive adjustments of the Kalman filter, is proposed to calibrate the single-model forecasts of the daily maximum and minimum temperatures (Tmax and Tmin) for lead times of 8–42 days over East Asia in 2018 derived from the UKMO control (CTL) forecast. The Kalman filter–based gridly calibration (KFGC) is carried out in parallel as a benchmark, which could improve the forecast skills to a certain extent. The COVPPM effectively calibrates the temperature forecasts at the early stage and displays better performances than the CTL and KFGC. However, with the growing lead times, it shows speedily decreasing skills and can no longer produce positive adjustments over the areas outside the plateaus. By contrast, the KFPPM consistently outperforms the other calibrations and reduces the forecast errors by almost 1.0° and 0.5°C for Tmax and Tmin, respectively, both retaining superiorities to the random climatology benchmark till the lead time of 24 days. The optimization of KFPPM maintains throughout the whole range of the subseasonal time scale, showing the most conspicuous improvements distributed over the Tibetan Plateau and its surroundings. Though the postprocessing procedures are more skillful in calibrating Tmax forecasts than Tmin forecasts, the Tmax forecasts are still characterized by lower skills than the latter. Case experiments further demonstrate the abovementioned features and imply the potential capability of KFPPM in improving forecast skills and disaster preventions for extreme temperature events.
Abstract
Subseasonal forecasts have recently attracted widespread interest yet remain a challenging issue. A statistical Kalman filter pattern projection method (KFPPM), which absorbs the projection conception of the raw covariance pattern projection (COVPPM) and the adaptive adjustments of the Kalman filter, is proposed to calibrate the single-model forecasts of the daily maximum and minimum temperatures (Tmax and Tmin) for lead times of 8–42 days over East Asia in 2018 derived from the UKMO control (CTL) forecast. The Kalman filter–based gridly calibration (KFGC) is carried out in parallel as a benchmark, which could improve the forecast skills to a certain extent. The COVPPM effectively calibrates the temperature forecasts at the early stage and displays better performances than the CTL and KFGC. However, with the growing lead times, it shows speedily decreasing skills and can no longer produce positive adjustments over the areas outside the plateaus. By contrast, the KFPPM consistently outperforms the other calibrations and reduces the forecast errors by almost 1.0° and 0.5°C for Tmax and Tmin, respectively, both retaining superiorities to the random climatology benchmark till the lead time of 24 days. The optimization of KFPPM maintains throughout the whole range of the subseasonal time scale, showing the most conspicuous improvements distributed over the Tibetan Plateau and its surroundings. Though the postprocessing procedures are more skillful in calibrating Tmax forecasts than Tmin forecasts, the Tmax forecasts are still characterized by lower skills than the latter. Case experiments further demonstrate the abovementioned features and imply the potential capability of KFPPM in improving forecast skills and disaster preventions for extreme temperature events.
