2020 Vol. 37, No. 8
Display Method:
2020, 37(8): 800-816.
doi: 10.1007/s00376-020-9268-6
Abstract:
Regional coupled modeling is one of the frontiers of regional climate modeling, but intercomparison has not been well coordinated. In this study, a community regional climate model, WRF4, with a resolution of 15 km, was coupled with a high-resolution (0.1°) North Pacific Ocean model (LICOM_np). The performance of the regional coupled model, WRF4_LICOM, was compared to that of another regional coupled model, RegCM4_LICOM, which was a coupling of version 4 of the Regional Climate Model (RegCM4) with LICOM_np. The analysis focused on the 2005 western North Pacific summer monsoon rainfall. The results showed that the regional coupled models with either RegCM4 or WRF4 as their atmospheric model component simulated the broad features over the WNP reasonably well. Quantitative intercomparison of the regional coupled simulations exhibited different biases for different climate variables. RegCM4_LICOM exhibited smaller biases in its simulation of the averaged June–July–August SST and rainfall, while WRF4_LICOM better captured the tropical cyclone (TC) intensity, the percentage contributions of rainfall induced by TCs to the total rainfall, and the diurnal cycle of rainfall and stratiform percentages, especially over land areas. The different behaviors in rainfall simulated by the two models were partly ascribed to the behaviors in the simulated western North Pacific subtropical high (WNPSH). The stronger (weaker) WNPSH in WRF4_LICOM (RegCM4_LICOM) was driven by overestimated (underestimated) diabatic heating, which peaked at approximately 450 hPa over the region around the Philippines in association with different condensation–radiation processes. Coupling of WRF4 with LIOCM is a crucial step towards the development of the next generation of regional earth system models at the Chinese Academy of Sciences.
Regional coupled modeling is one of the frontiers of regional climate modeling, but intercomparison has not been well coordinated. In this study, a community regional climate model, WRF4, with a resolution of 15 km, was coupled with a high-resolution (0.1°) North Pacific Ocean model (LICOM_np). The performance of the regional coupled model, WRF4_LICOM, was compared to that of another regional coupled model, RegCM4_LICOM, which was a coupling of version 4 of the Regional Climate Model (RegCM4) with LICOM_np. The analysis focused on the 2005 western North Pacific summer monsoon rainfall. The results showed that the regional coupled models with either RegCM4 or WRF4 as their atmospheric model component simulated the broad features over the WNP reasonably well. Quantitative intercomparison of the regional coupled simulations exhibited different biases for different climate variables. RegCM4_LICOM exhibited smaller biases in its simulation of the averaged June–July–August SST and rainfall, while WRF4_LICOM better captured the tropical cyclone (TC) intensity, the percentage contributions of rainfall induced by TCs to the total rainfall, and the diurnal cycle of rainfall and stratiform percentages, especially over land areas. The different behaviors in rainfall simulated by the two models were partly ascribed to the behaviors in the simulated western North Pacific subtropical high (WNPSH). The stronger (weaker) WNPSH in WRF4_LICOM (RegCM4_LICOM) was driven by overestimated (underestimated) diabatic heating, which peaked at approximately 450 hPa over the region around the Philippines in association with different condensation–radiation processes. Coupling of WRF4 with LIOCM is a crucial step towards the development of the next generation of regional earth system models at the Chinese Academy of Sciences.
2020, 37(8): 817-831.
doi: 10.1007/s00376-020-9262-z
Abstract:
Formulating model uncertainties for a convection-allowing ensemble prediction system (CAEPS) is a much more challenging problem compared to well-utilized approaches in synoptic weather forecasting. A new approach is proposed and tested through assuming that the model uncertainty should reasonably describe the fast nonlinear error growth of the convection-allowing model, due to the fast developing character and strong nonlinearity of convective events. The Conditional Nonlinear Optimal Perturbation related to Parameters (CNOP-P) is applied in this study. Also, an ensemble approach is adopted to solve the CNOP-P problem. By using five locally developed strong convective events that occurred in pre-rainy season of South China, the most sensitive parameters were detected based on CNOP-P, which resulted in the maximum variations in precipitation. A formulation of model uncertainty is designed by adding stochastic perturbations into these sensitive parameters. Through comparison ensemble experiments by using all the 13 heavy rainfall cases that occurred in the flood season of South China in 2017, the advantages of the CNOP-P-based method are examined and verified by comparing with the well-utilized stochastically perturbed physics tendencies (SPPT) scheme. The results indicate that the CNOP-P-based method has potential in improving the under-dispersive problem of the current CAEPS.
Formulating model uncertainties for a convection-allowing ensemble prediction system (CAEPS) is a much more challenging problem compared to well-utilized approaches in synoptic weather forecasting. A new approach is proposed and tested through assuming that the model uncertainty should reasonably describe the fast nonlinear error growth of the convection-allowing model, due to the fast developing character and strong nonlinearity of convective events. The Conditional Nonlinear Optimal Perturbation related to Parameters (CNOP-P) is applied in this study. Also, an ensemble approach is adopted to solve the CNOP-P problem. By using five locally developed strong convective events that occurred in pre-rainy season of South China, the most sensitive parameters were detected based on CNOP-P, which resulted in the maximum variations in precipitation. A formulation of model uncertainty is designed by adding stochastic perturbations into these sensitive parameters. Through comparison ensemble experiments by using all the 13 heavy rainfall cases that occurred in the flood season of South China in 2017, the advantages of the CNOP-P-based method are examined and verified by comparing with the well-utilized stochastically perturbed physics tendencies (SPPT) scheme. The results indicate that the CNOP-P-based method has potential in improving the under-dispersive problem of the current CAEPS.
2020, 37(8): 832-846.
doi: 10.1007/s00376-020-9176-9
Abstract:
The diurnal variation in the vertical structure of the raindrop size distribution (RSD) associated with stratiform rain at Kototabang, West Sumatra (0.20°S, 100.32°E), was investigated using micro rain radar (MRR) observations from January 2012 to August 2016. Along with the MRR data, the RSD from an optical disdrometer and vertical profile of precipitation from the Tropical Rainfall Measuring Mission were used to establish the microphysical characteristics of diurnal rainfall. Rainfall during 0000–0600 LST and 1800–2400 LST had a lower concentration of small drops and a higher concentration of large drops when compared to rainfall during the daytime (0600–1800 LST). The RSD stratified on the basis of rain rate (R) showed a lower total concentration of drops and higher mass-weighted mean diameter in 0000–0600 LST and 1800–2400 LST than in the daytime. During the daytime, the RSD is likely governed by a riming process that can be seen from a weak bright band (BB). On the other hand, during 0000–0600 LST and 1800–2400 LST, the BB was stronger and the rainfall was associated with a higher concentration of midsize and large drops, which could be attributed to more active aggregation right above the melting layer with minimal breakup. Diurnal variation in the vertical profile of RSD led to a different radar reflectivity (Z)–R relationship in the rain column, in which Z during the periods 0000–0600 LST and 1800–2400 LST was larger than at the other times, for the same R.
The diurnal variation in the vertical structure of the raindrop size distribution (RSD) associated with stratiform rain at Kototabang, West Sumatra (0.20°S, 100.32°E), was investigated using micro rain radar (MRR) observations from January 2012 to August 2016. Along with the MRR data, the RSD from an optical disdrometer and vertical profile of precipitation from the Tropical Rainfall Measuring Mission were used to establish the microphysical characteristics of diurnal rainfall. Rainfall during 0000–0600 LST and 1800–2400 LST had a lower concentration of small drops and a higher concentration of large drops when compared to rainfall during the daytime (0600–1800 LST). The RSD stratified on the basis of rain rate (R) showed a lower total concentration of drops and higher mass-weighted mean diameter in 0000–0600 LST and 1800–2400 LST than in the daytime. During the daytime, the RSD is likely governed by a riming process that can be seen from a weak bright band (BB). On the other hand, during 0000–0600 LST and 1800–2400 LST, the BB was stronger and the rainfall was associated with a higher concentration of midsize and large drops, which could be attributed to more active aggregation right above the melting layer with minimal breakup. Diurnal variation in the vertical profile of RSD led to a different radar reflectivity (Z)–R relationship in the rain column, in which Z during the periods 0000–0600 LST and 1800–2400 LST was larger than at the other times, for the same R.
2020, 37(8): 847-860.
doi: 10.1007/s00376-020-0028-4
Abstract:
After the strong 2015/16 El Niño event, cold conditions prevailed in the tropical Pacific with the second-year cooling of the 2017/18 La Niña event. Many coupled models failed to predict the cold SST anomalies (SSTAs) in 2017. By using the ERA5 and GODAS (Global Ocean Data Assimilation System) products, atmospheric and oceanic factors were examined that could have been responsible for the second-year cooling, including surface wind and the subsurface thermal state. A time sequence is described to demonstrate how the cold SSTAs were produced in the central-eastern equatorial Pacific in late 2017. Since July 2017, easterly anomalies strengthened in the central Pacific; in the meantime, wind stress divergence anomalies emerged in the far eastern region, which strengthened during the following months and propagated westward, contributing to the development of the second-year cooling in 2017. At the subsurface, weak negative temperature anomalies were accompanied by upwelling in the eastern equatorial Pacific, which provided the cold water source for the sea surface. Thereafter, both the cold anomalies and upwelling were enhanced and extended westward in the centraleastern equatorial Pacific. These changes were associated with the seasonally weakened EUC (the Equatorial Undercurrent) and strengthened SEC (the South Equatorial Current), which favored more cold waters being accumulated in the central-equatorial Pacific. Then, the subsurface cold waters stretched upward with the convergence of the horizontal currents and eventually outcropped to the surface. The subsurface-induced SSTAs acted to induce local coupled air–sea interactions, which generated atmospheric–oceanic anomalies developing and evolving into the second-year cooling in the fall of 2017.
After the strong 2015/16 El Niño event, cold conditions prevailed in the tropical Pacific with the second-year cooling of the 2017/18 La Niña event. Many coupled models failed to predict the cold SST anomalies (SSTAs) in 2017. By using the ERA5 and GODAS (Global Ocean Data Assimilation System) products, atmospheric and oceanic factors were examined that could have been responsible for the second-year cooling, including surface wind and the subsurface thermal state. A time sequence is described to demonstrate how the cold SSTAs were produced in the central-eastern equatorial Pacific in late 2017. Since July 2017, easterly anomalies strengthened in the central Pacific; in the meantime, wind stress divergence anomalies emerged in the far eastern region, which strengthened during the following months and propagated westward, contributing to the development of the second-year cooling in 2017. At the subsurface, weak negative temperature anomalies were accompanied by upwelling in the eastern equatorial Pacific, which provided the cold water source for the sea surface. Thereafter, both the cold anomalies and upwelling were enhanced and extended westward in the centraleastern equatorial Pacific. These changes were associated with the seasonally weakened EUC (the Equatorial Undercurrent) and strengthened SEC (the South Equatorial Current), which favored more cold waters being accumulated in the central-equatorial Pacific. Then, the subsurface cold waters stretched upward with the convergence of the horizontal currents and eventually outcropped to the surface. The subsurface-induced SSTAs acted to induce local coupled air–sea interactions, which generated atmospheric–oceanic anomalies developing and evolving into the second-year cooling in the fall of 2017.
2020, 37(8): 861-872.
doi: 10.1007/s00376-020-9264-x
Abstract:
Electrical characteristics of an isolated supercell storm observed on 13 June 2014 over Beijing were investigated using lightning data obtained from the Beijing Lightning Network, radar reflectivity, and hydrometeor retrievals during the 6-h lifetime. Positive cloud-to-ground (+CG) lightning took a high percentage of CG lightning. Before and during a hail event, +CG lightning was more frequent than negative cloud-to-ground (−CG) lightning, except that +CG lightning took a high percentage at the beginning and in the dissipating stage. After the hail event ended, −CG lightning dominated and reached its maximum value. An analysis of hydrometeors retrieved by X-band polarimetric radar revealed that the discharge concentrated in the convective region with graupel particles and hailstones, whereas graupel, snow and ice crystals in the stratiform region. Lightning radiation sources were located mainly in the convective region, some of which were distributed along a gradient of radar reflectivity from the convective region to the stratiform region. The indication is that the supercell demonstrated an inverted tripole charge structure before the hail event, which converted to a normal tripole structure after the hail event.
Electrical characteristics of an isolated supercell storm observed on 13 June 2014 over Beijing were investigated using lightning data obtained from the Beijing Lightning Network, radar reflectivity, and hydrometeor retrievals during the 6-h lifetime. Positive cloud-to-ground (+CG) lightning took a high percentage of CG lightning. Before and during a hail event, +CG lightning was more frequent than negative cloud-to-ground (−CG) lightning, except that +CG lightning took a high percentage at the beginning and in the dissipating stage. After the hail event ended, −CG lightning dominated and reached its maximum value. An analysis of hydrometeors retrieved by X-band polarimetric radar revealed that the discharge concentrated in the convective region with graupel particles and hailstones, whereas graupel, snow and ice crystals in the stratiform region. Lightning radiation sources were located mainly in the convective region, some of which were distributed along a gradient of radar reflectivity from the convective region to the stratiform region. The indication is that the supercell demonstrated an inverted tripole charge structure before the hail event, which converted to a normal tripole structure after the hail event.
2020, 37(8): 873-892.
doi: 10.1007/s00376-020-9274-8
Abstract:
We applied the multigrid nonlinear least-squares four-dimensional variational assimilation (MG-NLS4DVar) method in data assimilation and prediction experiments for Typhoon Haikui (2012) using the Weather Research and Forecasting (WRF) model. Observation data included radial velocity (Vr) and reflectivity (Z) data from a single Doppler radar, quality controlled prior to assimilation. Typhoon prediction results were evaluated and compared between the NLS-4DVar and MG-NLS4DVar methods. Compared with a forecast that began with NCEP analysis data, our radar data assimilation results were clearly improved in terms of structure, intensity, track, and precipitation prediction for Typhoon Haikui (2012). The results showed that the assimilation accuracy of the NLS-4DVar method was similar to that of the MG-NLS4DVar method, but that the latter was more efficient. The assimilation of Vr alone and Z alone each improved predictions of typhoon intensity, track, and precipitation; however, the impacts of Vr data were significantly greater that those of Z data. Assimilation window-length sensitivity experiments showed that a 6-h assimilation window with 30-min assimilation intervals produced slightly better results than either a 3-h assimilation window with 15-min assimilation intervals or a 1-h assimilation window with 6-min assimilation intervals.
We applied the multigrid nonlinear least-squares four-dimensional variational assimilation (MG-NLS4DVar) method in data assimilation and prediction experiments for Typhoon Haikui (2012) using the Weather Research and Forecasting (WRF) model. Observation data included radial velocity (Vr) and reflectivity (Z) data from a single Doppler radar, quality controlled prior to assimilation. Typhoon prediction results were evaluated and compared between the NLS-4DVar and MG-NLS4DVar methods. Compared with a forecast that began with NCEP analysis data, our radar data assimilation results were clearly improved in terms of structure, intensity, track, and precipitation prediction for Typhoon Haikui (2012). The results showed that the assimilation accuracy of the NLS-4DVar method was similar to that of the MG-NLS4DVar method, but that the latter was more efficient. The assimilation of Vr alone and Z alone each improved predictions of typhoon intensity, track, and precipitation; however, the impacts of Vr data were significantly greater that those of Z data. Assimilation window-length sensitivity experiments showed that a 6-h assimilation window with 30-min assimilation intervals produced slightly better results than either a 3-h assimilation window with 15-min assimilation intervals or a 1-h assimilation window with 6-min assimilation intervals.
2020, 37(8): 893-911.
doi: 10.1007/s00376-020-9269-5
Abstract:
This study investigated the regime-dependent predictability using convective-scale ensemble forecasts initialized with different initial condition perturbations in the Yangtze and Huai River basin (YHRB) of East China. The scale-dependent error growth (ensemble variability) and associated impact on precipitation forecasts (precipitation uncertainties) were quantitatively explored for 13 warm-season convective events that were categorized in terms of strong forcing and weak forcing. The forecast error growth in the strong-forcing regime shows a stepwise increase with increasing spatial scale, while the error growth shows a larger temporal variability with an afternoon peak appearing at smaller scales under weak forcing. This leads to the dissimilarity of precipitation uncertainty and shows a strong correlation between error growth and precipitation across spatial scales. The lateral boundary condition errors exert a quasi-linear increase on error growth with time at the larger scale, suggesting that the large-scale flow could govern the magnitude of error growth and associated precipitation uncertainties, especially for the strong-forcing regime. Further comparisons between scale-based initial error sensitivity experiments show evident scale interaction including upscale transfer of small-scale errors and downscale cascade of larger-scale errors. Specifically, small-scale errors are found to be more sensitive in the weak-forcing regime than those under strong forcing. Meanwhile, larger-scale initial errors are responsible for the error growth after 4 h and produce the precipitation uncertainties at the meso-β-scale. Consequently, these results can be used to explain under-dispersion issues in convective-scale ensemble forecasts and provide feedback for ensemble design over the YHRB.
This study investigated the regime-dependent predictability using convective-scale ensemble forecasts initialized with different initial condition perturbations in the Yangtze and Huai River basin (YHRB) of East China. The scale-dependent error growth (ensemble variability) and associated impact on precipitation forecasts (precipitation uncertainties) were quantitatively explored for 13 warm-season convective events that were categorized in terms of strong forcing and weak forcing. The forecast error growth in the strong-forcing regime shows a stepwise increase with increasing spatial scale, while the error growth shows a larger temporal variability with an afternoon peak appearing at smaller scales under weak forcing. This leads to the dissimilarity of precipitation uncertainty and shows a strong correlation between error growth and precipitation across spatial scales. The lateral boundary condition errors exert a quasi-linear increase on error growth with time at the larger scale, suggesting that the large-scale flow could govern the magnitude of error growth and associated precipitation uncertainties, especially for the strong-forcing regime. Further comparisons between scale-based initial error sensitivity experiments show evident scale interaction including upscale transfer of small-scale errors and downscale cascade of larger-scale errors. Specifically, small-scale errors are found to be more sensitive in the weak-forcing regime than those under strong forcing. Meanwhile, larger-scale initial errors are responsible for the error growth after 4 h and produce the precipitation uncertainties at the meso-β-scale. Consequently, these results can be used to explain under-dispersion issues in convective-scale ensemble forecasts and provide feedback for ensemble design over the YHRB.
2020, 37(8): 912-924.
doi: 10.1007/s00376-020-9270-z
Abstract:
Millimeter-wave cloud radar (MMCR) provides the capability of detecting the features of micro particles inside clouds and describing the internal microphysical structure of the clouds. Therefore, MMCR has been widely applied in cloud observations. However, due to the influence of non-meteorological factors such as insects, the cloud observations are often contaminated by non-meteorological echoes in the clear air, known as clear-air echoes. It is of great significance to automatically identify the clear-air echoes in order to extract effective meteorological information from the complex weather background. The characteristics of clear-air echoes are studied here by combining data from four devices: an MMCR, a laser-ceilometer, an L-band radiosonde, and an all-sky camera. In addition, a new algorithm, which includes feature extraction, feature selection, and classification, is proposed to achieve the automatic identification of clear-air echoes. The results show that the recognition algorithm is fairly satisfied in both simple and complex weather conditions. The recognition accuracy can reach up to 95.86% for the simple cases when cloud echoes and clear-air echoes are separate, and 88.38% for the complicated cases when low cloud echoes and clear-air echoes are mixed.
Millimeter-wave cloud radar (MMCR) provides the capability of detecting the features of micro particles inside clouds and describing the internal microphysical structure of the clouds. Therefore, MMCR has been widely applied in cloud observations. However, due to the influence of non-meteorological factors such as insects, the cloud observations are often contaminated by non-meteorological echoes in the clear air, known as clear-air echoes. It is of great significance to automatically identify the clear-air echoes in order to extract effective meteorological information from the complex weather background. The characteristics of clear-air echoes are studied here by combining data from four devices: an MMCR, a laser-ceilometer, an L-band radiosonde, and an all-sky camera. In addition, a new algorithm, which includes feature extraction, feature selection, and classification, is proposed to achieve the automatic identification of clear-air echoes. The results show that the recognition algorithm is fairly satisfied in both simple and complex weather conditions. The recognition accuracy can reach up to 95.86% for the simple cases when cloud echoes and clear-air echoes are separate, and 88.38% for the complicated cases when low cloud echoes and clear-air echoes are mixed.
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