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- Author or Editor: Min Chen x
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Abstract
The authors use a spatially explicit parameterization method and the Terrestrial Ecosystem Model (TEM) to quantify the carbon dynamics of forest ecosystems in the conterminous United States. Six key parameters that govern the rates of carbon and nitrogen dynamics in TEM are selected for calibration. Spatially explicit data for carbon and nitrogen pools and fluxes are used to calibrate the six key parameters to more adequately account for the spatial heterogeneity of ecosystems in estimating regional carbon dynamics. The authors find that a spatially explicit parameterization results in vastly different carbon exchange rates relative to a parameterization conducted for representative ecosystem sites. The new parameterization method estimates that the net ecosystem production (NEP), the annual gross primary production (GPP), and the net primary production (NPP) of the regional forest ecosystems are 61% (0.02 Pg C; 1 Pg = 1015 g) higher and 2% (0.11 Pg C) and 19% (0.45 Pg C) lower, respectively, than the values obtained using the traditional parameterization method for the period 1948–2000. The estimated vegetation carbon and soil organic carbon pool sizes are 51% (18.73 Pg C) lower and 29% (7.40 Pg C) higher. This study suggests that, to more adequately quantify regional carbon dynamics, spatial data for carbon and nitrogen pools and fluxes should be developed and used with the spatially explicit parameterization method.
Abstract
The authors use a spatially explicit parameterization method and the Terrestrial Ecosystem Model (TEM) to quantify the carbon dynamics of forest ecosystems in the conterminous United States. Six key parameters that govern the rates of carbon and nitrogen dynamics in TEM are selected for calibration. Spatially explicit data for carbon and nitrogen pools and fluxes are used to calibrate the six key parameters to more adequately account for the spatial heterogeneity of ecosystems in estimating regional carbon dynamics. The authors find that a spatially explicit parameterization results in vastly different carbon exchange rates relative to a parameterization conducted for representative ecosystem sites. The new parameterization method estimates that the net ecosystem production (NEP), the annual gross primary production (GPP), and the net primary production (NPP) of the regional forest ecosystems are 61% (0.02 Pg C; 1 Pg = 1015 g) higher and 2% (0.11 Pg C) and 19% (0.45 Pg C) lower, respectively, than the values obtained using the traditional parameterization method for the period 1948–2000. The estimated vegetation carbon and soil organic carbon pool sizes are 51% (18.73 Pg C) lower and 29% (7.40 Pg C) higher. This study suggests that, to more adequately quantify regional carbon dynamics, spatial data for carbon and nitrogen pools and fluxes should be developed and used with the spatially explicit parameterization method.
Abstract
The two types of wind observations, profiler and radar radial velocity, have been successfully assimilated into numerical weather prediction (NWP) systems. However, the added value of profiler data, especially from a densely deployed profiler network, is unknown when assimilated together with Doppler radar radial velocity. In this article, two combined assimilation strategies of profilers along with radar radial winds are compared within a convective-scale data assimilation (DA) framework. In strategy I, the profiler data are assimilated with conventional observations to generate an intermediate analysis that acts as a prior for radar data assimilation. In strategy II, both profiler and radar data are considered as storm-scale and assimilated within the same pass. Single- and dual-observation assimilation experiments indicate that for strategy I, the profiler DA improvement can be partly canceled by the potentially negative impact of the assimilation of single-radar radial velocity afterward, particularly when the radial wind is nearly orthogonal to the prevailing wind. For strategy II, important complements are provided when profilers are assimilated within the same pass along with radial winds. The diagnostics for a low-level jet case demonstrate that both strategies facilitate improved analyses and forecasts. But strategy II may bring more moderate analysis increments, which indicate mutual constraints of the profiler and radial winds when assimilated within the same pass. The results obtained in 1-month, retrospective cycling experiments also show that the strategy II outperforms the strategy I with slightly better wind and precipitation forecasts.
Significance Statement
Due to the high spatial–temporal wind information provided by profiler and radar radial velocity measurements, their combined assimilation would be expected to improve wind analysis. To fully utilize dense profiler data and radar radial wind in future operational applications, this study proposes a suitable assimilation strategy. If the profilers are defined as synoptic-scale observations, the profiler and Doppler radar data must be assimilated in different passes to adopt different length and variance scales. Whereas it is more reasonable to use a small background correlation length consistent with the radial velocity and, therefore, assimilate in the same pass if the profiler data are considered to better sample storm-scale features. Single- and dual-observation experiments indicate that profiler data provide important complements, while the assimilation of single-radar radial wind may yield analyzed wind results that do not depict the ground truth. A low-level jet case and a 1-month impact study further show that the combined assimilation strategy of assimilating both profiler and Doppler radar using smaller background correlation lengths enhances the analysis and forecasting of wind, resulting in more accurate accumulated precipitation forecasts.
Abstract
The two types of wind observations, profiler and radar radial velocity, have been successfully assimilated into numerical weather prediction (NWP) systems. However, the added value of profiler data, especially from a densely deployed profiler network, is unknown when assimilated together with Doppler radar radial velocity. In this article, two combined assimilation strategies of profilers along with radar radial winds are compared within a convective-scale data assimilation (DA) framework. In strategy I, the profiler data are assimilated with conventional observations to generate an intermediate analysis that acts as a prior for radar data assimilation. In strategy II, both profiler and radar data are considered as storm-scale and assimilated within the same pass. Single- and dual-observation assimilation experiments indicate that for strategy I, the profiler DA improvement can be partly canceled by the potentially negative impact of the assimilation of single-radar radial velocity afterward, particularly when the radial wind is nearly orthogonal to the prevailing wind. For strategy II, important complements are provided when profilers are assimilated within the same pass along with radial winds. The diagnostics for a low-level jet case demonstrate that both strategies facilitate improved analyses and forecasts. But strategy II may bring more moderate analysis increments, which indicate mutual constraints of the profiler and radial winds when assimilated within the same pass. The results obtained in 1-month, retrospective cycling experiments also show that the strategy II outperforms the strategy I with slightly better wind and precipitation forecasts.
Significance Statement
Due to the high spatial–temporal wind information provided by profiler and radar radial velocity measurements, their combined assimilation would be expected to improve wind analysis. To fully utilize dense profiler data and radar radial wind in future operational applications, this study proposes a suitable assimilation strategy. If the profilers are defined as synoptic-scale observations, the profiler and Doppler radar data must be assimilated in different passes to adopt different length and variance scales. Whereas it is more reasonable to use a small background correlation length consistent with the radial velocity and, therefore, assimilate in the same pass if the profiler data are considered to better sample storm-scale features. Single- and dual-observation experiments indicate that profiler data provide important complements, while the assimilation of single-radar radial wind may yield analyzed wind results that do not depict the ground truth. A low-level jet case and a 1-month impact study further show that the combined assimilation strategy of assimilating both profiler and Doppler radar using smaller background correlation lengths enhances the analysis and forecasting of wind, resulting in more accurate accumulated precipitation forecasts.
Abstract
In this paper several configurations of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5), which is implemented at Beijing Institute of Urban Meteorology in China, are used to demonstrate the initial noise problem caused either by interpolating global model fields onto an MM5 grid or by using MM5 objective analysis schemes. An implementation of a digital filter initialization (DFI) package to MM5 is then documented. A heavy rain case study and intermittent data assimilation experiments are used to assess the impact of DFI on MM5 forecasts. It is shown that DFI effectively filters out the noise and produces a balanced initial model state. It is also shown that DFI improves the spinup aspects for precipitation, leading to better scores for short-range precipitation forecasts. The issues related to the initialization of variables that are not observed and/or analyzed, in particular those for nonhydrostatic quantities, are discussed.
Abstract
In this paper several configurations of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5), which is implemented at Beijing Institute of Urban Meteorology in China, are used to demonstrate the initial noise problem caused either by interpolating global model fields onto an MM5 grid or by using MM5 objective analysis schemes. An implementation of a digital filter initialization (DFI) package to MM5 is then documented. A heavy rain case study and intermittent data assimilation experiments are used to assess the impact of DFI on MM5 forecasts. It is shown that DFI effectively filters out the noise and produces a balanced initial model state. It is also shown that DFI improves the spinup aspects for precipitation, leading to better scores for short-range precipitation forecasts. The issues related to the initialization of variables that are not observed and/or analyzed, in particular those for nonhydrostatic quantities, are discussed.
A Quality Control Method for Wind Profiler Observations toward Assimilation ApplicationsAbstract
A wind profiler network with a total of 65 profiling radar systems was operated by the China Meteorological Observation Center (MOC) of the China Meteorological Administration (CMA) until July 2015. In this study, a quality control procedure is constructed to incorporate the profiler data from the wind-profiling network into the local data assimilation and forecasting systems. The procedure applies a blacklisting check that removes stations with gross errors and an outlier check that rejects data with large deviations from the background. As opposed to the biweight method, which has been commonly implemented in outlier elimination for univariate observations, the outlier elimination method is developed based on the iterated reweighted minimum covariance determinant (IRMCD) for multivariate observations, such as wind profiler data. A quality control experiment is performed separately for subsets containing profiler data tagged with/without rain flags in parallel every 0000 and 1200 UTC from 20 June to 30 September 2015. The results show that with quality control, the frequency distributions of the differences between the observations and the model background meet the requirements of a Gaussian distribution for data assimilation. A further intensive assessment of each quality control step reveals that the stations rejected by the blacklisting contained poor data quality and that the IRMCD rejects outliers in a robust and physically reasonable manner. Detailed comparisons between the IRMCD and the biweight method are performed, and the IRMCD is demonstrated to be more efficient and more comprehensive regarding the dataset used in this study.
Abstract
A wind profiler network with a total of 65 profiling radar systems was operated by the China Meteorological Observation Center (MOC) of the China Meteorological Administration (CMA) until July 2015. In this study, a quality control procedure is constructed to incorporate the profiler data from the wind-profiling network into the local data assimilation and forecasting systems. The procedure applies a blacklisting check that removes stations with gross errors and an outlier check that rejects data with large deviations from the background. As opposed to the biweight method, which has been commonly implemented in outlier elimination for univariate observations, the outlier elimination method is developed based on the iterated reweighted minimum covariance determinant (IRMCD) for multivariate observations, such as wind profiler data. A quality control experiment is performed separately for subsets containing profiler data tagged with/without rain flags in parallel every 0000 and 1200 UTC from 20 June to 30 September 2015. The results show that with quality control, the frequency distributions of the differences between the observations and the model background meet the requirements of a Gaussian distribution for data assimilation. A further intensive assessment of each quality control step reveals that the stations rejected by the blacklisting contained poor data quality and that the IRMCD rejects outliers in a robust and physically reasonable manner. Detailed comparisons between the IRMCD and the biweight method are performed, and the IRMCD is demonstrated to be more efficient and more comprehensive regarding the dataset used in this study.
Abstract
The eigenvalue problems for the original Eady model and a modified Eady model (the G model) are examined with no friction, Ekman friction only, and both Ekman and interior friction. When both Ekman and interior friction are included in the models, normal modes show little additional change when compared to the case with Ekman friction only, whereas the relevant “continuum modes” have large negative growth rates. Interior friction has a much greater effect on the continuum modes than on the normal modes because inviscid continuum modes have a delta-function vertical profile of potential vorticity q. In contrast, normal modes have much smoother profiles of q in the interior. Streamfunction profiles for the continuum modes are notably different in the two models. The continuum modes in the more realistic G model have sharp peak amplitudes that are not as broad in the vertical as in the Eady model.
Abstract
The eigenvalue problems for the original Eady model and a modified Eady model (the G model) are examined with no friction, Ekman friction only, and both Ekman and interior friction. When both Ekman and interior friction are included in the models, normal modes show little additional change when compared to the case with Ekman friction only, whereas the relevant “continuum modes” have large negative growth rates. Interior friction has a much greater effect on the continuum modes than on the normal modes because inviscid continuum modes have a delta-function vertical profile of potential vorticity q. In contrast, normal modes have much smoother profiles of q in the interior. Streamfunction profiles for the continuum modes are notably different in the two models. The continuum modes in the more realistic G model have sharp peak amplitudes that are not as broad in the vertical as in the Eady model.
Abstract
An incremental analysis update (IAU) scheme is successfully implemented into a WRF/WRFDA-based hourly cycling data assimilation system with the goal to reduce the imbalance introduced by the high-frequency intermittent data assimilation, especially when radar data is included. With the application of IAU, the analysis increment is smoothly introduced into the model integration over a time window centered at the analysis time. As in digital filter initialization (DFI), the IAU scheme is able to limit large shocks in the early part of a model forecast. Compared to DFI, IAU does better in hydrometeor spin-up and produces more continuous precipitation forecasts from cycle to cycle. The run with IAU is shown to improve the precipitation forecast skills (10+% for CSI scores) compared to the regular cycling forecasts without IAU. The data assimilation system with IAU is also able to accept more observations due to balanced first-guess fields. Comparable results are obtained in IAU tests when the time-varying weights are used versus constant weights. Because of its better property, the IAU with the time-varying weights is implemented in the operational system.
Abstract
An incremental analysis update (IAU) scheme is successfully implemented into a WRF/WRFDA-based hourly cycling data assimilation system with the goal to reduce the imbalance introduced by the high-frequency intermittent data assimilation, especially when radar data is included. With the application of IAU, the analysis increment is smoothly introduced into the model integration over a time window centered at the analysis time. As in digital filter initialization (DFI), the IAU scheme is able to limit large shocks in the early part of a model forecast. Compared to DFI, IAU does better in hydrometeor spin-up and produces more continuous precipitation forecasts from cycle to cycle. The run with IAU is shown to improve the precipitation forecast skills (10+% for CSI scores) compared to the regular cycling forecasts without IAU. The data assimilation system with IAU is also able to accept more observations due to balanced first-guess fields. Comparable results are obtained in IAU tests when the time-varying weights are used versus constant weights. Because of its better property, the IAU with the time-varying weights is implemented in the operational system.
A Modified Nonstationary Tidal Harmonic Analysis Model for the Yangtze Estuarine TidesAbstract
Influenced by river discharge, the tidal properties of estuarine tides can be more complex than those of oceanic tides, which makes the tidal prediction less accurate when using a classical tidal harmonic analysis approach, such as the T_TIDE model. Although the nonstationary tidal harmonic analysis model NS_TIDE can improve the accuracy for the analysis of tides in a river-dominated estuary, it becomes less satisfactory when applying the NS_TIDE model to a mesotidal estuary like the Yangtze estuary. Through the error source analysis, it is found that the main errors originate from the low frequency of tidal fluctuation. The NS_TIDE model is then modified by replacing the stage model with the frequency-expanded tidal–fluvial model so that more subtidal constituents, especially the “atmospheric tides,” can be taken into account. The results show that the residuals from tidal harmonic analysis are significantly reduced by using the modified NS_TIDE model, with the yearly root-mean-square-error values being only 0.04–0.06 m for the Yangtze estuarine tides.
Abstract
Influenced by river discharge, the tidal properties of estuarine tides can be more complex than those of oceanic tides, which makes the tidal prediction less accurate when using a classical tidal harmonic analysis approach, such as the T_TIDE model. Although the nonstationary tidal harmonic analysis model NS_TIDE can improve the accuracy for the analysis of tides in a river-dominated estuary, it becomes less satisfactory when applying the NS_TIDE model to a mesotidal estuary like the Yangtze estuary. Through the error source analysis, it is found that the main errors originate from the low frequency of tidal fluctuation. The NS_TIDE model is then modified by replacing the stage model with the frequency-expanded tidal–fluvial model so that more subtidal constituents, especially the “atmospheric tides,” can be taken into account. The results show that the residuals from tidal harmonic analysis are significantly reduced by using the modified NS_TIDE model, with the yearly root-mean-square-error values being only 0.04–0.06 m for the Yangtze estuarine tides.
Abstract
The Beijing 2008 Forecast Demonstration Project (B08FDP) included a variety of nowcasting systems from China, Australia, Canada, and the United States. A goal of the B08FDP was to demonstrate state-of-the-art nowcasting systems within a mutual operational setting. The nowcasting systems were a mix of radar echo extrapolation methods, numerical models, techniques that blended numerical model and extrapolation methods, and systems incorporating forecaster input. This paper focuses on the skill of the nowcasting systems to forecast convective storms that threatened or affected the Summer Olympic Games held in Beijing, China. The topography surrounding Beijing provided unique challenges in that it often enhanced the degree and extent of storm initiation, growth, and dissipation, which took place over short time and space scales. The skill levels of the numerical techniques were inconsistent from hour to hour and day to day and it was speculated that without assimilation of real-time radar reflectivity and Doppler velocity fields to support model initialization, particularly for weakly forced convective events, it would be very difficult for models to provide accurate forecasts on the nowcasting time and space scales. Automated blending techniques tended to be no more skillful than extrapolation since they depended heavily on the models to provide storm initiation, growth, and dissipation. However, even with the cited limitations among individual nowcasting systems, the Chinese Olympic forecasters considered the B08FDP human consensus forecasts to be useful. Key to the success of the human forecasts was the development of nowcasting rules predicated on the character of Beijing convective weather realized over the previous two summers. Based on the B08FDP experience, the status of nowcasting convective storms and future directions are presented.
Abstract
The Beijing 2008 Forecast Demonstration Project (B08FDP) included a variety of nowcasting systems from China, Australia, Canada, and the United States. A goal of the B08FDP was to demonstrate state-of-the-art nowcasting systems within a mutual operational setting. The nowcasting systems were a mix of radar echo extrapolation methods, numerical models, techniques that blended numerical model and extrapolation methods, and systems incorporating forecaster input. This paper focuses on the skill of the nowcasting systems to forecast convective storms that threatened or affected the Summer Olympic Games held in Beijing, China. The topography surrounding Beijing provided unique challenges in that it often enhanced the degree and extent of storm initiation, growth, and dissipation, which took place over short time and space scales. The skill levels of the numerical techniques were inconsistent from hour to hour and day to day and it was speculated that without assimilation of real-time radar reflectivity and Doppler velocity fields to support model initialization, particularly for weakly forced convective events, it would be very difficult for models to provide accurate forecasts on the nowcasting time and space scales. Automated blending techniques tended to be no more skillful than extrapolation since they depended heavily on the models to provide storm initiation, growth, and dissipation. However, even with the cited limitations among individual nowcasting systems, the Chinese Olympic forecasters considered the B08FDP human consensus forecasts to be useful. Key to the success of the human forecasts was the development of nowcasting rules predicated on the character of Beijing convective weather realized over the previous two summers. Based on the B08FDP experience, the status of nowcasting convective storms and future directions are presented.
Abstract
Tropical cyclone (TC) translation speed (TCS) over the western North Pacific (WNP) has experienced long-term decreasing trend. To date, however, little is known about the multidecadal variability of TCS and its possible influence on this trend. This study investigated the multidecadal variability of the WNP TCS and the underlying physical mechanisms. Results show that the WNP TCS presents robust multidecadal variability during the past seven decades, which is dominated by the TCS over the extratropics. Further analysis shows that the Atlantic multidecadal oscillation (AMO) is responsible for the TCS multidecadal variability. AMO positive (negative) phases lead to favorable (unfavorable) large-scale environmental conditions for maintaining TCs over the extratropics, which results in longer (shorter) residence time for TCs been accelerated by the mid-latitude westerlies, thus, leading to higher (lower) TCS. The TCS phase shift strongly offsets its slowdown trend, leading to the inconsistent trends during past decades. This inconsistency may also relate to the influence of extratropical transitioned cyclones without been totally excluded. These cyclones may be inhomogenously recorded due to the absence of satellite observation before the 1980s. Our results indicate that internal variation such as AMO may dominate TCS low frequency variations over the past several decades. Previous studies have attributed the inconsistent trends of TCS during different subperiods to data inhomogeneity. This study shows that AMO can modulate the TCS trends in different subperiods with phase shift, thus providing new evidence for the recent controversial TCS slowdown.
Abstract
Tropical cyclone (TC) translation speed (TCS) over the western North Pacific (WNP) has experienced long-term decreasing trend. To date, however, little is known about the multidecadal variability of TCS and its possible influence on this trend. This study investigated the multidecadal variability of the WNP TCS and the underlying physical mechanisms. Results show that the WNP TCS presents robust multidecadal variability during the past seven decades, which is dominated by the TCS over the extratropics. Further analysis shows that the Atlantic multidecadal oscillation (AMO) is responsible for the TCS multidecadal variability. AMO positive (negative) phases lead to favorable (unfavorable) large-scale environmental conditions for maintaining TCs over the extratropics, which results in longer (shorter) residence time for TCs been accelerated by the mid-latitude westerlies, thus, leading to higher (lower) TCS. The TCS phase shift strongly offsets its slowdown trend, leading to the inconsistent trends during past decades. This inconsistency may also relate to the influence of extratropical transitioned cyclones without been totally excluded. These cyclones may be inhomogenously recorded due to the absence of satellite observation before the 1980s. Our results indicate that internal variation such as AMO may dominate TCS low frequency variations over the past several decades. Previous studies have attributed the inconsistent trends of TCS during different subperiods to data inhomogeneity. This study shows that AMO can modulate the TCS trends in different subperiods with phase shift, thus providing new evidence for the recent controversial TCS slowdown.
Abstract
Soil moisture (SM) links the water and energy cycles over the land–atmosphere interface and largely determines ecosystem functionality, positioning it as an essential player in the Earth system. Despite its importance, accurate estimation of large-scale SM remains a challenge. Here we leverage the strength of neural network (NN) and fidelity of long-term measurements to develop a daily multilayer cropland SM dataset for China from 1981 to 2013, implemented for a range of different cropping patterns. The training and testing of the NN for the five soil layers (0–50 cm, 10-cm depth each) yield R2 values of 0.65–0.70 and 0.64–0.69, respectively. Our analysis reveals that precipitation and soil properties are the two dominant factors determining SM, but cropping pattern is also crucial. In addition, our simulations of alternative cropping patterns indicate that winter wheat followed by fallow will largely alleviate the SM depletion in most parts of China. On the other hand, cropping patterns of fallow in the winter followed by maize/soybean seem to further aggravate SM decline in the Huang-Huai-Hai region and southwestern China, relative to prevalent practices of double cropping. This may be due to their low soil porosity, which results in more soil water drainage, as opposed to the case that winter crop roots help maintain SM. This multilayer cropland SM dataset with granularity of cropping patterns provides an important alternative and is complementary to modeled and satellite-retrieved products.
Abstract
Soil moisture (SM) links the water and energy cycles over the land–atmosphere interface and largely determines ecosystem functionality, positioning it as an essential player in the Earth system. Despite its importance, accurate estimation of large-scale SM remains a challenge. Here we leverage the strength of neural network (NN) and fidelity of long-term measurements to develop a daily multilayer cropland SM dataset for China from 1981 to 2013, implemented for a range of different cropping patterns. The training and testing of the NN for the five soil layers (0–50 cm, 10-cm depth each) yield R2 values of 0.65–0.70 and 0.64–0.69, respectively. Our analysis reveals that precipitation and soil properties are the two dominant factors determining SM, but cropping pattern is also crucial. In addition, our simulations of alternative cropping patterns indicate that winter wheat followed by fallow will largely alleviate the SM depletion in most parts of China. On the other hand, cropping patterns of fallow in the winter followed by maize/soybean seem to further aggravate SM decline in the Huang-Huai-Hai region and southwestern China, relative to prevalent practices of double cropping. This may be due to their low soil porosity, which results in more soil water drainage, as opposed to the case that winter crop roots help maintain SM. This multilayer cropland SM dataset with granularity of cropping patterns provides an important alternative and is complementary to modeled and satellite-retrieved products.
