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- Author or Editor: Wei Li x
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Abstract
The statistical analysis of innovation (observation minus forecast) vectors is one of the most commonly used techniques for estimating observation and forecast error covariances in large-scale data assimilation. Building on the work of Hollingsworth and Lönnberg, the height innovation data over North America from the Navy Operational Global Atmospheric Prediction System (NOGAPS) are analyzed. The major products of the analysis include (i) observation error variances and vertical correlation functions, (ii) forecast error autocovariances as functions of height and horizontal distance, (iii) their spectra as functions of height and horizontal wavenumber. Applying a multilevel least squares fitting method, which is simpler and more rigorously constrained than that of Hollingsworth and Lönnberg, a full-space covariance function was determined. It was found that removal of the large-scale horizontal component, which has only small variation in the vertical, reduces the nonseparability. The results were compared with those of Hollingsworth and Lönnberg, and show a 20% overall reduction in forecast errors and a 10% overall reduction in observation errors for the NOGAPS data in comparison with the ECMWF global model data 16 yr ago.
Abstract
The statistical analysis of innovation (observation minus forecast) vectors is one of the most commonly used techniques for estimating observation and forecast error covariances in large-scale data assimilation. Building on the work of Hollingsworth and Lönnberg, the height innovation data over North America from the Navy Operational Global Atmospheric Prediction System (NOGAPS) are analyzed. The major products of the analysis include (i) observation error variances and vertical correlation functions, (ii) forecast error autocovariances as functions of height and horizontal distance, (iii) their spectra as functions of height and horizontal wavenumber. Applying a multilevel least squares fitting method, which is simpler and more rigorously constrained than that of Hollingsworth and Lönnberg, a full-space covariance function was determined. It was found that removal of the large-scale horizontal component, which has only small variation in the vertical, reduces the nonseparability. The results were compared with those of Hollingsworth and Lönnberg, and show a 20% overall reduction in forecast errors and a 10% overall reduction in observation errors for the NOGAPS data in comparison with the ECMWF global model data 16 yr ago.
Abstract
This paper proposes a new method to properly define and accurately determine the vortex center of a model-predicted tropical cyclone (TC) from a dynamic perspective. Ideally, a dynamically determined TC vortex center should maximize the gradient wind balance or, equivalently, minimize the gradient wind imbalance measured by an energy norm over the TC vortex. In practice, however, such an energy norm cannot be used to easily and unambiguously determine the TC vortex center. An alternative yet practical approach is developed to dynamically and unambiguously define the TC vortex center. In this approach, the TC vortex core of near-solid-body rotation is modeled by a simple parametric vortex constrained by the gradient wind balance. Therefore, the modeled vortex can fit simultaneously the perturbation pressure and streamfunction of the TC vortex part (extracted from the model-predicted fields) over the TC vortex core area (within the radius of maximum tangential wind), while the misfit is measured by a properly defined cost function. Minimizing this cost function yields the desired dynamic optimality condition that can uniquely define the TC vortex center. Using this dynamic optimality condition, a new method is developed in the form of iterative least squares fit to accurately determine the TC vortex center. The new method is shown to be efficient and effective for finding the TC vortex center that accurately satisfies the dynamic optimality condition.
Abstract
This paper proposes a new method to properly define and accurately determine the vortex center of a model-predicted tropical cyclone (TC) from a dynamic perspective. Ideally, a dynamically determined TC vortex center should maximize the gradient wind balance or, equivalently, minimize the gradient wind imbalance measured by an energy norm over the TC vortex. In practice, however, such an energy norm cannot be used to easily and unambiguously determine the TC vortex center. An alternative yet practical approach is developed to dynamically and unambiguously define the TC vortex center. In this approach, the TC vortex core of near-solid-body rotation is modeled by a simple parametric vortex constrained by the gradient wind balance. Therefore, the modeled vortex can fit simultaneously the perturbation pressure and streamfunction of the TC vortex part (extracted from the model-predicted fields) over the TC vortex core area (within the radius of maximum tangential wind), while the misfit is measured by a properly defined cost function. Minimizing this cost function yields the desired dynamic optimality condition that can uniquely define the TC vortex center. Using this dynamic optimality condition, a new method is developed in the form of iterative least squares fit to accurately determine the TC vortex center. The new method is shown to be efficient and effective for finding the TC vortex center that accurately satisfies the dynamic optimality condition.
Abstract
An atmospheric general circulation model (AGCM) is coupled to three different land surface schemes (LSSs), both individually and in combination (i.e., the LSSs receive the same AGCM forcing each time step and the averaged upward surface fluxes are passed back to the AGCM), to study the uncertainty of simulated climatologies and variabilities caused by different LSSs. This tiling of the LSSs is done to study the uncertainty of simulated mean climate and climate variability caused by variations between LSSs. The three LSSs produce significantly different surface fluxes over most of the land, no matter whether they are coupled individually or in combination. Although the three LSSs receive the same atmospheric forcing in the combined experiment, the inter-LSS spread of latent heat flux can be larger or smaller than the individually coupled experiment, depending mostly on the evaporation regime of the schemes in different regions. Differences in precipitation are the main reason for the different latent heat fluxes over semiarid regions, but for sensible heat flux, the atmospheric differences and LSS differences have comparable contributions. The influence of LSS uncertainties on the simulation of surface temperature is strongest in dry seasons, and its influence on daily maximum temperature is stronger than on minimum temperature. Land–atmosphere interaction can dampen the impact of LSS uncertainties on surface temperature in the tropics, but can strengthen their impact in middle to high latitudes. Variations in the persistence of surface heat fluxes exist among the LSSs, which, however, have little impact on the global pattern of precipitation persistence. The results provide guidance to future diagnosis of model uncertainties related to LSSs.
Abstract
An atmospheric general circulation model (AGCM) is coupled to three different land surface schemes (LSSs), both individually and in combination (i.e., the LSSs receive the same AGCM forcing each time step and the averaged upward surface fluxes are passed back to the AGCM), to study the uncertainty of simulated climatologies and variabilities caused by different LSSs. This tiling of the LSSs is done to study the uncertainty of simulated mean climate and climate variability caused by variations between LSSs. The three LSSs produce significantly different surface fluxes over most of the land, no matter whether they are coupled individually or in combination. Although the three LSSs receive the same atmospheric forcing in the combined experiment, the inter-LSS spread of latent heat flux can be larger or smaller than the individually coupled experiment, depending mostly on the evaporation regime of the schemes in different regions. Differences in precipitation are the main reason for the different latent heat fluxes over semiarid regions, but for sensible heat flux, the atmospheric differences and LSS differences have comparable contributions. The influence of LSS uncertainties on the simulation of surface temperature is strongest in dry seasons, and its influence on daily maximum temperature is stronger than on minimum temperature. Land–atmosphere interaction can dampen the impact of LSS uncertainties on surface temperature in the tropics, but can strengthen their impact in middle to high latitudes. Variations in the persistence of surface heat fluxes exist among the LSSs, which, however, have little impact on the global pattern of precipitation persistence. The results provide guidance to future diagnosis of model uncertainties related to LSSs.
Abstract
Based on daily meteorological observation data in South China (SC) from 1967 to 2018, the spatiotemporal characteristics of the precipitation in SC over the past 52 years were studied. Only 8% of the stations showed a significant increase in annual rainfall, and there was no significant negative trend at any weather stations at a confidence level of 90%. Monthly rainfall showed the most significant decreasing and increasing trends in April and November, respectively. During the entire flooding season from April to September, the monthly rainfall at the weather stations in the coastal areas showed almost no significant change. The annual rainfall gradually decreased toward the inland area with the central and coastal areas of Guangdong Province as the high-value rainfall center. By using the empirical orthogonal function decomposition method, it was found that the two main monthly rainfall modes had strong annual signals. The first modal spatial distribution was basically consistent with the average annual rainfall distribution. Based on the environmental background analysis, it was found that during the flooding season the main water vapor to SC was transported by the East Asian summer monsoon and the Indian summer monsoon. In late autumn and winter, the prevailing wind from northeastern China could not bring much water vapor to SC and led to little precipitation in these two seasons. The spatial distribution of precipitation in SC during summer was more consistent with the moisture flux divergence distribution of the bottom layer from 925 to 1000 hPa rather than that of the layer from 700 to 1000 hPa.
Abstract
Based on daily meteorological observation data in South China (SC) from 1967 to 2018, the spatiotemporal characteristics of the precipitation in SC over the past 52 years were studied. Only 8% of the stations showed a significant increase in annual rainfall, and there was no significant negative trend at any weather stations at a confidence level of 90%. Monthly rainfall showed the most significant decreasing and increasing trends in April and November, respectively. During the entire flooding season from April to September, the monthly rainfall at the weather stations in the coastal areas showed almost no significant change. The annual rainfall gradually decreased toward the inland area with the central and coastal areas of Guangdong Province as the high-value rainfall center. By using the empirical orthogonal function decomposition method, it was found that the two main monthly rainfall modes had strong annual signals. The first modal spatial distribution was basically consistent with the average annual rainfall distribution. Based on the environmental background analysis, it was found that during the flooding season the main water vapor to SC was transported by the East Asian summer monsoon and the Indian summer monsoon. In late autumn and winter, the prevailing wind from northeastern China could not bring much water vapor to SC and led to little precipitation in these two seasons. The spatial distribution of precipitation in SC during summer was more consistent with the moisture flux divergence distribution of the bottom layer from 925 to 1000 hPa rather than that of the layer from 700 to 1000 hPa.
Abstract
Correlation scales have been used in the traditional scheme of three-dimensional variational data assimilation (3DVAR) to estimate the background (or first guess) error covariance matrix (the 𝗕 matrix in brief) for the numerical forecast and reanalysis of ocean for decades. However, it is challenging to implement this scheme. On the one hand, determining the correlation scales accurately can be difficult. On the other hand, the positive definite of the 𝗕 matrix cannot be guaranteed unless the correlation scales are sufficiently small. Xie et al. indicated that a traditional 3DVAR only corrects certain wavelength errors, and its accuracy depends on the accuracy of the 𝗕 matrix. Generally speaking, the shortwave error cannot be sufficiently corrected until the longwave error is corrected. An inaccurate 𝗕 matrix may mistake longwave errors as shortwave ones, resulting in erroneous analyses.
A new 3DVAR data assimilation scheme, called a multigrid data assimilation scheme, is proposed in this paper for quickly minimizing longwave and shortwave errors successively. By assimilating the sea surface temperature and temperature profile observations into a numerical model of the China Seas, this scheme is applied to a retroactive real-time forecast experiment and favorable results are obtained. Compared to the traditional scheme of 3DVAR, this new scheme has higher forecast accuracy and lower root-mean-square errors. Note that the new scheme demonstrates greatly improved numerical efficiency in the analysis procedure.
Abstract
Correlation scales have been used in the traditional scheme of three-dimensional variational data assimilation (3DVAR) to estimate the background (or first guess) error covariance matrix (the 𝗕 matrix in brief) for the numerical forecast and reanalysis of ocean for decades. However, it is challenging to implement this scheme. On the one hand, determining the correlation scales accurately can be difficult. On the other hand, the positive definite of the 𝗕 matrix cannot be guaranteed unless the correlation scales are sufficiently small. Xie et al. indicated that a traditional 3DVAR only corrects certain wavelength errors, and its accuracy depends on the accuracy of the 𝗕 matrix. Generally speaking, the shortwave error cannot be sufficiently corrected until the longwave error is corrected. An inaccurate 𝗕 matrix may mistake longwave errors as shortwave ones, resulting in erroneous analyses.
A new 3DVAR data assimilation scheme, called a multigrid data assimilation scheme, is proposed in this paper for quickly minimizing longwave and shortwave errors successively. By assimilating the sea surface temperature and temperature profile observations into a numerical model of the China Seas, this scheme is applied to a retroactive real-time forecast experiment and favorable results are obtained. Compared to the traditional scheme of 3DVAR, this new scheme has higher forecast accuracy and lower root-mean-square errors. Note that the new scheme demonstrates greatly improved numerical efficiency in the analysis procedure.
Abstract
A recursive filter or parameterized curve fitting technique is usually used in a three-dimensional variational data assimilation (3DVAR) scheme to approximate the background error covariance, which can only represent the errors of an ocean field over a predetermined scale. Without an accurate flow-dependent error covariance that is also local and time dependent, a 3DVAR system may not provide good analyses because it is optimal only under the assumption of an accurate covariance. In this study, a sequential 3DVAR (S3DVAR) is formulated in model grid space to examine if there is useful information that can be extracted from the observation. This formulation is composed of a series of 3DVARs, each of which uses recursive filters with different length scales. It can provide an inhomogeneous and anisotropic analysis for the wavelengths that can be resolved by the observation network, just as with the conventional Barnes analysis or successive corrections. Being a variational formulation, S3DVAR can deal with data globally with an explicit specification of the observation errors; explicit physical balances or constraints; and advanced datasets, such as satellite and radar. Even though the S3DVAR analysis can be viewed as a set of isotropic functions superpositioned together, this superposition is not prespecified as in a single 3DVAR approach but is determined by the information that can be resolved by observation. The S3DVAR is adopted in a global sea surface temperature (SST) data assimilation system, into which the shipboard SSTs and the 4-km Advanced Very High Resolution Radiometer (AVHRR) Pathfinder daily SSTs are assimilated, respectively. The results demonstrate that the proposed S3DVAR works better in practice than a single 3DVAR.
Abstract
A recursive filter or parameterized curve fitting technique is usually used in a three-dimensional variational data assimilation (3DVAR) scheme to approximate the background error covariance, which can only represent the errors of an ocean field over a predetermined scale. Without an accurate flow-dependent error covariance that is also local and time dependent, a 3DVAR system may not provide good analyses because it is optimal only under the assumption of an accurate covariance. In this study, a sequential 3DVAR (S3DVAR) is formulated in model grid space to examine if there is useful information that can be extracted from the observation. This formulation is composed of a series of 3DVARs, each of which uses recursive filters with different length scales. It can provide an inhomogeneous and anisotropic analysis for the wavelengths that can be resolved by the observation network, just as with the conventional Barnes analysis or successive corrections. Being a variational formulation, S3DVAR can deal with data globally with an explicit specification of the observation errors; explicit physical balances or constraints; and advanced datasets, such as satellite and radar. Even though the S3DVAR analysis can be viewed as a set of isotropic functions superpositioned together, this superposition is not prespecified as in a single 3DVAR approach but is determined by the information that can be resolved by observation. The S3DVAR is adopted in a global sea surface temperature (SST) data assimilation system, into which the shipboard SSTs and the 4-km Advanced Very High Resolution Radiometer (AVHRR) Pathfinder daily SSTs are assimilated, respectively. The results demonstrate that the proposed S3DVAR works better in practice than a single 3DVAR.
Abstract
A new, fully conserved minimal adjustment scheme with temperature and salinity (T, S) coherency is presented for eliminating false static instability generated from analyzing and assimilating stable ocean (T, S) profiles data, that is, from generalized averaging over purely observed data (data analysis) or over modeled/observed data (data assimilation). This approach consists of a variational method with (a) fully (heat, salt, and potential energy) conserved conditions, (b) minimal adjustment, and (c) (T, S) coherency. Comparison with three existing schemes (minimal adjustment, conserved minimal adjustment, and convective adjustment) using observational profiles and a simple one-dimensional ocean mixed layer model shows the superiority of this new scheme.
Abstract
A new, fully conserved minimal adjustment scheme with temperature and salinity (T, S) coherency is presented for eliminating false static instability generated from analyzing and assimilating stable ocean (T, S) profiles data, that is, from generalized averaging over purely observed data (data analysis) or over modeled/observed data (data assimilation). This approach consists of a variational method with (a) fully (heat, salt, and potential energy) conserved conditions, (b) minimal adjustment, and (c) (T, S) coherency. Comparison with three existing schemes (minimal adjustment, conserved minimal adjustment, and convective adjustment) using observational profiles and a simple one-dimensional ocean mixed layer model shows the superiority of this new scheme.
Abstract
No Abstract available.
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Abstract
A variational method is used to estimate wave-affected parameters in a two-equation turbulence model with assimilation of temperature data into an ocean boundary layer model. Enhancement of turbulent kinetic energy dissipation due to breaking waves is considered. The Mellor–Yamada level 2.5 turbulence closure scheme (MY2.5) with the two uncertain wave-affected parameters (wave energy factor α and Charnock coefficient β) is selected as the two-equation turbulence model for this study. Two types of experiments are conducted. First, within an identical synthetic experiment framework, the upper-layer temperature “observations” in summer generated by a “truth” model are assimilated into a biased simulation model to investigate if (α, β) can be successfully estimated using the variational method. Second, real temperature profiles from Ocean Weather Station Papa are assimilated into the biased simulation model to obtain the optimal wave-affected parameters. With the optimally estimated parameters, the upper-layer temperature can be well predicted. Furthermore, the horizontal distribution of the wave-affected parameters employed in a high-order turbulence closure scheme can be estimated optimally by using the four-dimensional variational method that assimilates the upper-layer available temperature data into an ocean general circulation model.
Abstract
A variational method is used to estimate wave-affected parameters in a two-equation turbulence model with assimilation of temperature data into an ocean boundary layer model. Enhancement of turbulent kinetic energy dissipation due to breaking waves is considered. The Mellor–Yamada level 2.5 turbulence closure scheme (MY2.5) with the two uncertain wave-affected parameters (wave energy factor α and Charnock coefficient β) is selected as the two-equation turbulence model for this study. Two types of experiments are conducted. First, within an identical synthetic experiment framework, the upper-layer temperature “observations” in summer generated by a “truth” model are assimilated into a biased simulation model to investigate if (α, β) can be successfully estimated using the variational method. Second, real temperature profiles from Ocean Weather Station Papa are assimilated into the biased simulation model to obtain the optimal wave-affected parameters. With the optimally estimated parameters, the upper-layer temperature can be well predicted. Furthermore, the horizontal distribution of the wave-affected parameters employed in a high-order turbulence closure scheme can be estimated optimally by using the four-dimensional variational method that assimilates the upper-layer available temperature data into an ocean general circulation model.
Abstract
A close relationship between sea level pressure (SLP) over East Asia and precipitation indices (PIs) in eastern China was observed in the summers (June–August) of 1850–2008 using singular value decomposition (SVD) analysis. To investigate this relationship over a longer period, the SLP fields were reconstructed back to 1470 based on a mathematical model and the historical precipitation indices of eastern China. A cross-validation test of independent samples suggests that the reconstructed SLPs are statistically acceptable. According to the first three predominant SVD modes of the SLP field, three SLP index series (SLPI1–SLPI3) were developed to quantify the thermodynamic differences among the critical SLP centers of East Asia. Both SLPI1 and SLPI2 are highly correlated with the East Asian summer monsoon index, whereas SLPI3 is related to the index of Eurasian meridional atmospheric circulation. The temporal scales of SLP indices were examined during 1470–2008 using the wavelet power spectra. Results indicate that there is significant variance at a 2–5-yr band in the power spectra of the three SLP indices, suggesting SLPI1–SLPI3 have evident interannual variability. Moreover, the wavelet power spectra of SLPI1 and SLPI2 show significantly higher power at the 8–12-yr scale from 1470 to 1750 and at the 60–90-yr scale after 1750. For SLPI3, besides the interannual variability, it has additional periodical variability of 6–11 and 23–33 yr.
Abstract
A close relationship between sea level pressure (SLP) over East Asia and precipitation indices (PIs) in eastern China was observed in the summers (June–August) of 1850–2008 using singular value decomposition (SVD) analysis. To investigate this relationship over a longer period, the SLP fields were reconstructed back to 1470 based on a mathematical model and the historical precipitation indices of eastern China. A cross-validation test of independent samples suggests that the reconstructed SLPs are statistically acceptable. According to the first three predominant SVD modes of the SLP field, three SLP index series (SLPI1–SLPI3) were developed to quantify the thermodynamic differences among the critical SLP centers of East Asia. Both SLPI1 and SLPI2 are highly correlated with the East Asian summer monsoon index, whereas SLPI3 is related to the index of Eurasian meridional atmospheric circulation. The temporal scales of SLP indices were examined during 1470–2008 using the wavelet power spectra. Results indicate that there is significant variance at a 2–5-yr band in the power spectra of the three SLP indices, suggesting SLPI1–SLPI3 have evident interannual variability. Moreover, the wavelet power spectra of SLPI1 and SLPI2 show significantly higher power at the 8–12-yr scale from 1470 to 1750 and at the 60–90-yr scale after 1750. For SLPI3, besides the interannual variability, it has additional periodical variability of 6–11 and 23–33 yr.
