2022 Vol. 39, No. 1
Display Method:
2022, 39(1): 1-8.
doi: 10.1007/s00376-021-1304-7
Abstract:
Fengyun-3E (FY-3E), the world’s first early-morning-orbit meteorological satellite for civil use, was launched successfully at the Jiuquan Satellite Launch Center on 5 July 2021. The FY-3E satellite will fill the vacancy of the global early-morning-orbit satellite observation, working together with the FY-3C and FY-3D satellites to achieve the data coverage of early morning, morning, and afternoon orbits. The combination of these three satellites will provide global data coverage for numerical weather prediction (NWP) at 6-hour intervals, effectively improving the accuracy and time efficiency of global NWP, which is of great significance to perfect the global earth observing system. In this article, the background and meteorological requirements for the early-morning-orbit satellite are reviewed, and the specifications of the FY-3E satellite, as well as the characteristics of the onboard instrumentation for earth observations, are also introduced. In addition, the ground segment and the retrieved geophysical products are also presented. It is believed that the NWP communities will significantly benefit from an optimal temporal distribution of observations provided by the early morning, mid-morning, and afternoon satellite missions. Further benefits are expected in numerous applications such as the monitoring of severe weather/climate events, the development of improved sampling designs of the diurnal cycle for accurate climate data records, more efficient monitoring of air quality by thermal infrared remote sensing, and the quasi-continuous monitoring of the sun for space weather and climate.
Fengyun-3E (FY-3E), the world’s first early-morning-orbit meteorological satellite for civil use, was launched successfully at the Jiuquan Satellite Launch Center on 5 July 2021. The FY-3E satellite will fill the vacancy of the global early-morning-orbit satellite observation, working together with the FY-3C and FY-3D satellites to achieve the data coverage of early morning, morning, and afternoon orbits. The combination of these three satellites will provide global data coverage for numerical weather prediction (NWP) at 6-hour intervals, effectively improving the accuracy and time efficiency of global NWP, which is of great significance to perfect the global earth observing system. In this article, the background and meteorological requirements for the early-morning-orbit satellite are reviewed, and the specifications of the FY-3E satellite, as well as the characteristics of the onboard instrumentation for earth observations, are also introduced. In addition, the ground segment and the retrieved geophysical products are also presented. It is believed that the NWP communities will significantly benefit from an optimal temporal distribution of observations provided by the early morning, mid-morning, and afternoon satellite missions. Further benefits are expected in numerous applications such as the monitoring of severe weather/climate events, the development of improved sampling designs of the diurnal cycle for accurate climate data records, more efficient monitoring of air quality by thermal infrared remote sensing, and the quasi-continuous monitoring of the sun for space weather and climate.
2022, (1): 1-1.
Abstract:
2022, 39(1): 9-21.
doi: 10.1007/s00376-021-1088-9
Abstract:
Satellite infrared (IR) sounder and imager measurements have become one of the main sources of data used by data assimilation systems to generate initial conditions for numerical weather prediction (NWP) models and atmospheric analysis/reanalysis. This paper reviews the development of satellite IR data assimilation in NWP in recent years, especially the assimilation of all-sky satellite IR observations. The major challenges and future directions are outlined and discussed.
Satellite infrared (IR) sounder and imager measurements have become one of the main sources of data used by data assimilation systems to generate initial conditions for numerical weather prediction (NWP) models and atmospheric analysis/reanalysis. This paper reviews the development of satellite IR data assimilation in NWP in recent years, especially the assimilation of all-sky satellite IR observations. The major challenges and future directions are outlined and discussed.
2022, 39(1): 22-54.
doi: 10.1007/s00376-021-0408-4
Abstract:
This paper reviews the progress in our understanding of the atmospheric response to midlatitude oceanic fronts and eddies, emphasizing the Kuroshio-Oyashio Extension (KOE) region. Oceanic perturbations of interest consist of sharp oceanic fronts, temperature anomalies associated with mesoscale eddies, and to some extent even higher-frequency sub-mesoscale variability. The focus is on the free atmosphere above the boundary layer. As the midlatitude atmosphere is dominated by vigorous transient eddy activity in the storm track, the response of both the time-mean flow and the storm track is assessed. The storm track response arguably overwhelms the mean-flow response and makes the latter hard to detect from observations. Oceanic frontal impacts on the mesoscale structures of individual synoptic storms are discussed, followed by the role of oceanic fronts in maintaining the storm track as a whole. KOE fronts exhibit significant decadal variability and can therefore presumably modulate the storm track. Relevant studies are summarized and intercompared. Current understanding has advanced greatly but is still subject to large uncertainties arising from inadequate data resolution and other factors. Recent modeling studies highlighted the importance of mesoscale eddies and probably even sub-mesoscale processes in maintaining the storm track but confirmation and validation are still needed. Moreover, the atmospheric response can potentially provide a feedback mechanism for the North Pacific climate. By reviewing the above aspects, we envision that future research shall focus more upon the interaction between smaller-scale oceanic processes (fronts, eddies, submesoscale features) and atmospheric processes (fronts, extratropical cyclones etc.), in an integrated way, within the context of different climate background states.
This paper reviews the progress in our understanding of the atmospheric response to midlatitude oceanic fronts and eddies, emphasizing the Kuroshio-Oyashio Extension (KOE) region. Oceanic perturbations of interest consist of sharp oceanic fronts, temperature anomalies associated with mesoscale eddies, and to some extent even higher-frequency sub-mesoscale variability. The focus is on the free atmosphere above the boundary layer. As the midlatitude atmosphere is dominated by vigorous transient eddy activity in the storm track, the response of both the time-mean flow and the storm track is assessed. The storm track response arguably overwhelms the mean-flow response and makes the latter hard to detect from observations. Oceanic frontal impacts on the mesoscale structures of individual synoptic storms are discussed, followed by the role of oceanic fronts in maintaining the storm track as a whole. KOE fronts exhibit significant decadal variability and can therefore presumably modulate the storm track. Relevant studies are summarized and intercompared. Current understanding has advanced greatly but is still subject to large uncertainties arising from inadequate data resolution and other factors. Recent modeling studies highlighted the importance of mesoscale eddies and probably even sub-mesoscale processes in maintaining the storm track but confirmation and validation are still needed. Moreover, the atmospheric response can potentially provide a feedback mechanism for the North Pacific climate. By reviewing the above aspects, we envision that future research shall focus more upon the interaction between smaller-scale oceanic processes (fronts, eddies, submesoscale features) and atmospheric processes (fronts, extratropical cyclones etc.), in an integrated way, within the context of different climate background states.
2022, 39(1): 55-66.
doi: 10.1007/s00376-021-1167-y
Abstract:
State-of-the-art coupled general circulation models (CGCMs) are used to predict ocean heat uptake (OHU) and sea-level change under global warming. However, the projections of different models vary, resulting in high uncertainty. Much of the inter-model spread is driven by responses to surface heat perturbations. This study mainly focuses on the response of the ocean to a surface heat flux perturbation F, as prescribed by the Flux-Anomaly-Forced Model Intercomparison Project (FAFMIP). The results of ocean model were compared with those of a CGCM with the same ocean component. On the global scale, the changes in global mean temperature, ocean heat content (OHC), and steric sea level (SSL) simulated in the OGCM are generally consistent with CGCM simulations. Differences in changes in ocean temperature, OHC, and SSL between the two models primarily occur in the Arctic and Atlantic Oceans (AA) and the Southern Ocean (SO) basins. In addition to the differences in surface heat flux anomalies between the two models, differences in heat exchange between basins also play an important role in the inconsistencies in ocean climate changes in the AA and SO basins. These discrepancies are largely due to both the larger initial value and the greater weakening change of the Atlantic meridional overturning circulation (AMOC) in CGCM. The greater weakening of the AMOC in the CGCM is associated with the atmosphere–ocean feedback and the lack of a restoring salinity boundary condition. Furthermore, differences in surface salinity boundary conditions between the two models contribute to discrepancies in SSL changes.
State-of-the-art coupled general circulation models (CGCMs) are used to predict ocean heat uptake (OHU) and sea-level change under global warming. However, the projections of different models vary, resulting in high uncertainty. Much of the inter-model spread is driven by responses to surface heat perturbations. This study mainly focuses on the response of the ocean to a surface heat flux perturbation F, as prescribed by the Flux-Anomaly-Forced Model Intercomparison Project (FAFMIP). The results of ocean model were compared with those of a CGCM with the same ocean component. On the global scale, the changes in global mean temperature, ocean heat content (OHC), and steric sea level (SSL) simulated in the OGCM are generally consistent with CGCM simulations. Differences in changes in ocean temperature, OHC, and SSL between the two models primarily occur in the Arctic and Atlantic Oceans (AA) and the Southern Ocean (SO) basins. In addition to the differences in surface heat flux anomalies between the two models, differences in heat exchange between basins also play an important role in the inconsistencies in ocean climate changes in the AA and SO basins. These discrepancies are largely due to both the larger initial value and the greater weakening change of the Atlantic meridional overturning circulation (AMOC) in CGCM. The greater weakening of the AMOC in the CGCM is associated with the atmosphere–ocean feedback and the lack of a restoring salinity boundary condition. Furthermore, differences in surface salinity boundary conditions between the two models contribute to discrepancies in SSL changes.
2022, 39(1): 67-79.
doi: 10.1007/s00376-021-1109-8
Abstract:
This study focuses on the temporal variation of dissimilarity in heat content (HC) anomalies in the upper 300 m of ocean (HC300A) in the equatorial Pacific (±10°N) and its response to the El Niño-Southern Oscillation (ENSO). The HC300A anomalies are derived from four ocean reanalyses that are commonly used in ENSO studies and are compared using a simple differencing method. The dissimilarity in HC300A is found to vary closely with the magnitude of ENSO (regardless of phase), meaning that it tends to be greater during strong ENSO events. However, the dissimilarity among ocean reanalyses persists after the event decays. This effect is more pronounced after strong events. The persistence of the dissimilarity after ENSO events is a result of a late maturation of the ENSO signal, its persistence, and the interruption of the signal decay due to follow-up ENSO events. The combined effect of these three factors slows down the decay of HC300A in the region and hence results in the slow decay of dissimilarity. It is also found that areas with a significant spread in vertical temperature profiles collocate with the ENSO signal during warm ENSO phases. Thus, differences in subsurface process reconstruction are a significant factor in the dissimilarity among ocean reanalyses during warm ENSO events.
This study focuses on the temporal variation of dissimilarity in heat content (HC) anomalies in the upper 300 m of ocean (HC300A) in the equatorial Pacific (±10°N) and its response to the El Niño-Southern Oscillation (ENSO). The HC300A anomalies are derived from four ocean reanalyses that are commonly used in ENSO studies and are compared using a simple differencing method. The dissimilarity in HC300A is found to vary closely with the magnitude of ENSO (regardless of phase), meaning that it tends to be greater during strong ENSO events. However, the dissimilarity among ocean reanalyses persists after the event decays. This effect is more pronounced after strong events. The persistence of the dissimilarity after ENSO events is a result of a late maturation of the ENSO signal, its persistence, and the interruption of the signal decay due to follow-up ENSO events. The combined effect of these three factors slows down the decay of HC300A in the region and hence results in the slow decay of dissimilarity. It is also found that areas with a significant spread in vertical temperature profiles collocate with the ENSO signal during warm ENSO phases. Thus, differences in subsurface process reconstruction are a significant factor in the dissimilarity among ocean reanalyses during warm ENSO events.
2022, 39(1): 80-96.
doi: 10.1007/s00376-021-1119-6
Abstract:
Anthropogenic emission inventory for aerosols and reactive gases is crucial to the estimation of aerosol radiative forcing and climate effects. Here, the anthropogenic emission inventory for AerChemMIP, endorsed by CMIP6, is briefly introduced. The CMIP6 inventory is compared with a country-level inventory (i.e., MEIC) over China from 1986 to 2015. Discrepancies are found in the yearly trends of the two inventories, especially after 2006. The yearly trends of the aerosol burdens simulated by CESM2 using the two inventories follow their emission trends and deviate after the mid-2000s, while the simulated aerosol optical depths (AODs) show similar trends. The difference between the simulated AODs is much smaller than the difference between model and observation. Although the simulated AODs agree with the MODIS satellite retrievals for country-wide average, the good agreement is an offset between the underestimation in eastern China and the overestimation in western China. Low-biased precursor gas of SO2, overly strong convergence of the wind field, overly strong dilution and transport by summer monsoon circulation, too much wet scavenging by precipitation, and overly weak aerosol swelling due to low-biased relative humidity are suggested to be responsible for the underestimated AOD in eastern China. This indicates that the influence of the emission inventory uncertainties on simulated aerosol properties can be overwhelmed by model biases of meteorology and aerosol processes. It is necessary for climate models to perform reasonably well in the dynamical, physical, and chemical processes that would influence aerosol simulations.
Anthropogenic emission inventory for aerosols and reactive gases is crucial to the estimation of aerosol radiative forcing and climate effects. Here, the anthropogenic emission inventory for AerChemMIP, endorsed by CMIP6, is briefly introduced. The CMIP6 inventory is compared with a country-level inventory (i.e., MEIC) over China from 1986 to 2015. Discrepancies are found in the yearly trends of the two inventories, especially after 2006. The yearly trends of the aerosol burdens simulated by CESM2 using the two inventories follow their emission trends and deviate after the mid-2000s, while the simulated aerosol optical depths (AODs) show similar trends. The difference between the simulated AODs is much smaller than the difference between model and observation. Although the simulated AODs agree with the MODIS satellite retrievals for country-wide average, the good agreement is an offset between the underestimation in eastern China and the overestimation in western China. Low-biased precursor gas of SO2, overly strong convergence of the wind field, overly strong dilution and transport by summer monsoon circulation, too much wet scavenging by precipitation, and overly weak aerosol swelling due to low-biased relative humidity are suggested to be responsible for the underestimated AOD in eastern China. This indicates that the influence of the emission inventory uncertainties on simulated aerosol properties can be overwhelmed by model biases of meteorology and aerosol processes. It is necessary for climate models to perform reasonably well in the dynamical, physical, and chemical processes that would influence aerosol simulations.
2022, 39(1): 97-116.
doi: 10.1007/s00376-021-1187-7
Abstract:
Idealized supercell storms are simulated with two aerosol-aware bulk microphysics schemes (BMSs), the Thompson and the Chen-Liu-Reisner (CLR), using the Weather Research and Forecast (WRF) model. The objective of this study is to investigate the parameterizations of aerosol effects on cloud and precipitation characteristics and assess the necessity of introducing aerosols into a weather prediction model at fine grid resolution. The results show that aerosols play a decisive role in the composition of clouds in terms of the mixing ratios and number concentrations of liquid and ice hydrometeors in an intense supercell storm. The storm consists of a large amount of cloud water and snow in the polluted environment, but a large amount of rainwater and graupel instead in the clean environment. The total precipitation and rain intensity are suppressed in the CLR scheme more than in the Thompson scheme in the first three hours of storm simulations. The critical processes explaining the differences are the auto-conversion rate in the warm-rain process at the beginning of storm intensification and the low-level cooling induced by large ice hydrometeors. The cloud condensation nuclei (CCN) activation and auto-conversion processes of the two schemes exhibit considerable differences, indicating the inherent uncertainty of the parameterized aerosol effects among different BMSs. Beyond the aerosol effects, the fall speed characteristics of graupel in the two schemes play an important role in the storm dynamics and precipitation via low-level cooling. The rapid intensification of storms simulated with the Thompson scheme is attributed to the production of hail-like graupel.
Idealized supercell storms are simulated with two aerosol-aware bulk microphysics schemes (BMSs), the Thompson and the Chen-Liu-Reisner (CLR), using the Weather Research and Forecast (WRF) model. The objective of this study is to investigate the parameterizations of aerosol effects on cloud and precipitation characteristics and assess the necessity of introducing aerosols into a weather prediction model at fine grid resolution. The results show that aerosols play a decisive role in the composition of clouds in terms of the mixing ratios and number concentrations of liquid and ice hydrometeors in an intense supercell storm. The storm consists of a large amount of cloud water and snow in the polluted environment, but a large amount of rainwater and graupel instead in the clean environment. The total precipitation and rain intensity are suppressed in the CLR scheme more than in the Thompson scheme in the first three hours of storm simulations. The critical processes explaining the differences are the auto-conversion rate in the warm-rain process at the beginning of storm intensification and the low-level cooling induced by large ice hydrometeors. The cloud condensation nuclei (CCN) activation and auto-conversion processes of the two schemes exhibit considerable differences, indicating the inherent uncertainty of the parameterized aerosol effects among different BMSs. Beyond the aerosol effects, the fall speed characteristics of graupel in the two schemes play an important role in the storm dynamics and precipitation via low-level cooling. The rapid intensification of storms simulated with the Thompson scheme is attributed to the production of hail-like graupel.
2022, 39(1): 117-130.
doi: 10.1007/s00376-021-0438-y
Abstract:
Before 2008, the number of surface observation stations in China was small. Thus, the surface observation data were too sparse to effectively support the High-resolution China Meteorological Administration's Land Assimilation System (HRCLDAS) which ultimately inhibited the output of high-resolution and high-quality gridded products. This paper proposes a statistical downscaling model based on a deep learning algorithm in super-resolution to research the above problem. Specifically, we take temperature as an example. The model is used to downscale the 0.0625° × 0.0625°, 2-m temperature data from the China Meteorological Administration's Land Data Assimilation System (CLDAS) to 0.01° × 0.01°, named CLDASSD. We performed quality control on the paired data from CLDAS and HRCLDAS, using data from 2018 and 2019. CLDASSD was trained on the data from 31 March 2018 to 28 February 2019, and then tested with the remaining data. Finally, extensive experiments were conducted in the Beijing-Tianjin-Hebei region which features complex and diverse geomorphology. Taking the HRCLDAS product and surface observation data as the “true values” and comparing them with the results of bilinear interpolation, especially in complex terrain such as mountains, the root mean square error (RMSE) of the CLDASSD output can be reduced by approximately 0.1°C, and its structural similarity (SSIM) was approximately 0.2 higher. CLDASSD can estimate detailed textures, in terms of spatial distribution, with greater accuracy than bilinear interpolation and other sub-models and can perform the expected downscaling tasks.
Before 2008, the number of surface observation stations in China was small. Thus, the surface observation data were too sparse to effectively support the High-resolution China Meteorological Administration's Land Assimilation System (HRCLDAS) which ultimately inhibited the output of high-resolution and high-quality gridded products. This paper proposes a statistical downscaling model based on a deep learning algorithm in super-resolution to research the above problem. Specifically, we take temperature as an example. The model is used to downscale the 0.0625° × 0.0625°, 2-m temperature data from the China Meteorological Administration's Land Data Assimilation System (CLDAS) to 0.01° × 0.01°, named CLDASSD. We performed quality control on the paired data from CLDAS and HRCLDAS, using data from 2018 and 2019. CLDASSD was trained on the data from 31 March 2018 to 28 February 2019, and then tested with the remaining data. Finally, extensive experiments were conducted in the Beijing-Tianjin-Hebei region which features complex and diverse geomorphology. Taking the HRCLDAS product and surface observation data as the “true values” and comparing them with the results of bilinear interpolation, especially in complex terrain such as mountains, the root mean square error (RMSE) of the CLDASSD output can be reduced by approximately 0.1°C, and its structural similarity (SSIM) was approximately 0.2 higher. CLDASSD can estimate detailed textures, in terms of spatial distribution, with greater accuracy than bilinear interpolation and other sub-models and can perform the expected downscaling tasks.
2022, 39(1): 131-144.
doi: 10.1007/s00376-021-1110-2
Abstract:
Through multi-order structure function analysis and singularity measurement, the Hurst index and intermittent parameter are obtained to quantitatively describe the characteristics of atmospheric disturbance based on the round-trip intelligent sounding system (RTISS) in the lower-middle stratosphere. According to the third-order structure function, small-scale gravity waves are classified into three states: stable, unstable, and accompanied by turbulence. The evolution of gravity waves is reflected by the variation of the third-order structure function over time, and the generation of turbulence is also observed. The atmospheric disturbance intensity parameter RT is defined in this paper and contains both wave disturbance (\begin{document}$ {H}_{1} $\end{document} ![]()
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Through multi-order structure function analysis and singularity measurement, the Hurst index and intermittent parameter are obtained to quantitatively describe the characteristics of atmospheric disturbance based on the round-trip intelligent sounding system (RTISS) in the lower-middle stratosphere. According to the third-order structure function, small-scale gravity waves are classified into three states: stable, unstable, and accompanied by turbulence. The evolution of gravity waves is reflected by the variation of the third-order structure function over time, and the generation of turbulence is also observed. The atmospheric disturbance intensity parameter RT is defined in this paper and contains both wave disturbance (
2022, 39(1): 145-160.
doi: 10.1007/s00376-021-1035-9
Abstract:
In this paper, a scheme of dual-Doppler radar wind analysis based on a three-dimensional variational method is proposed and performed in two steps. First, the horizontal wind field is simultaneously recovered through minimizing a cost function defined as a radial observation term with the standard conjugate gradient method, avoiding a weighting parameter specification step. Compared with conventional dual-Doppler wind synthesis approaches, this variational method minimizes errors caused by interpolation from radar observation to analysis grid in the iterative solution process, which is one of the main sources of errors. Then, through the accelerated Liebmann method, the vertical velocity is further re-estimated as an extra step by solving the Poisson equation with impermeable conditions imposed at the ground and near the tropopause. The Poisson equation defined by the second derivative of the vertical velocity is derived from the mass continuity equation. Compared with the method proposed by O'Brien, this method is less sensitive to the uncertainty of the boundary conditions and has better stability and reliability. Furthermore, the method proposed in this paper is applied to Doppler radar observation of a squall line process. It is shown that the retrieved vertical wind profile agrees well with the vertical profile obtained with the velocity–azimuth display (VAD) method, and the retrieved radial velocity as well as the analyzed positive and negative velocity centers and horizontal wind shear of the squall line are in accord with radar observations. There is a good correspondence between the divergence field of the derived wind field and the vertical velocity. And, the horizontal and vertical circulations within and around the squall line, as well as strong updrafts, the associated downdrafts, and associated rear inflow of the bow echo, are analyzed well. It is worth mentioning that the variational method in this paper can be applied to simultaneously synthesize the three-dimensional wind field from multiple-Doppler radar observations.
In this paper, a scheme of dual-Doppler radar wind analysis based on a three-dimensional variational method is proposed and performed in two steps. First, the horizontal wind field is simultaneously recovered through minimizing a cost function defined as a radial observation term with the standard conjugate gradient method, avoiding a weighting parameter specification step. Compared with conventional dual-Doppler wind synthesis approaches, this variational method minimizes errors caused by interpolation from radar observation to analysis grid in the iterative solution process, which is one of the main sources of errors. Then, through the accelerated Liebmann method, the vertical velocity is further re-estimated as an extra step by solving the Poisson equation with impermeable conditions imposed at the ground and near the tropopause. The Poisson equation defined by the second derivative of the vertical velocity is derived from the mass continuity equation. Compared with the method proposed by O'Brien, this method is less sensitive to the uncertainty of the boundary conditions and has better stability and reliability. Furthermore, the method proposed in this paper is applied to Doppler radar observation of a squall line process. It is shown that the retrieved vertical wind profile agrees well with the vertical profile obtained with the velocity–azimuth display (VAD) method, and the retrieved radial velocity as well as the analyzed positive and negative velocity centers and horizontal wind shear of the squall line are in accord with radar observations. There is a good correspondence between the divergence field of the derived wind field and the vertical velocity. And, the horizontal and vertical circulations within and around the squall line, as well as strong updrafts, the associated downdrafts, and associated rear inflow of the bow echo, are analyzed well. It is worth mentioning that the variational method in this paper can be applied to simultaneously synthesize the three-dimensional wind field from multiple-Doppler radar observations.
2022, 39(1): 161-177.
doi: 10.1007/s00376-021-1007-0
Abstract:
Zonda wind is a typical downslope windstorm over the eastern slopes of the Central Andes in Argentina, which produces extremely warm and dry conditions and creates substantial socioeconomic impacts. The aim of this work is to obtain an index for predicting the probability of Zonda wind occurrence. The Principal Component Analysis (PCA) is applied to the vertical sounding data on both sides of the Andes. Through the use of a binary logistic regression, the PCA is applied to discriminate those soundings associated with Zonda wind events from those that are not, and a probabilistic forecasting tool for Zonda occurrence is obtained. This index is able to discriminate between Zonda and non-Zonda events with an effectiveness close to 91%. The best model consists of four variables from each side of the Andes. From an event-based statistical perspective, the probability of detection of the mixed model is above 97% with a probability of false detection lower than 7% and a missing ratio below 1%. From an alarm-based perspective, models exhibit false alarm rate below 7%, a missing alarm ratio lower than 1.5% and higher than 93% for the correct alarm ratio. The zonal component of the wind on both sides of the Andes and the windward temperature are the key variables in class discrimination. The vertical structure of Zonda wind includes two wind maximums and an unstable lapse rate at midlevels on the lee side and a wind maximum at 700 hPa accompanied by a relatively stable layer near the mountain top.
Zonda wind is a typical downslope windstorm over the eastern slopes of the Central Andes in Argentina, which produces extremely warm and dry conditions and creates substantial socioeconomic impacts. The aim of this work is to obtain an index for predicting the probability of Zonda wind occurrence. The Principal Component Analysis (PCA) is applied to the vertical sounding data on both sides of the Andes. Through the use of a binary logistic regression, the PCA is applied to discriminate those soundings associated with Zonda wind events from those that are not, and a probabilistic forecasting tool for Zonda occurrence is obtained. This index is able to discriminate between Zonda and non-Zonda events with an effectiveness close to 91%. The best model consists of four variables from each side of the Andes. From an event-based statistical perspective, the probability of detection of the mixed model is above 97% with a probability of false detection lower than 7% and a missing ratio below 1%. From an alarm-based perspective, models exhibit false alarm rate below 7%, a missing alarm ratio lower than 1.5% and higher than 93% for the correct alarm ratio. The zonal component of the wind on both sides of the Andes and the windward temperature are the key variables in class discrimination. The vertical structure of Zonda wind includes two wind maximums and an unstable lapse rate at midlevels on the lee side and a wind maximum at 700 hPa accompanied by a relatively stable layer near the mountain top.
2022, 39(1): 178-188.
doi: 10.1007/s00376-021-0398-2
Abstract:
A thunderstorm tracking algorithm is proposed to nowcast the possibility of lightning activity over an area of concern by using the total lightning data and neighborhood technique. The lightning radiation sources observed from the Beijing Lightning Network (BLNET) were used to obtain information about the thunderstorm cells, which are significantly valuable in real-time. The boundaries of thunderstorm cells were obtained through the neighborhood technique. After smoothing, these boundaries were used to track the movement of thunderstorms and then extrapolated to nowcast the lightning approaching in an area of concern. The algorithm can deliver creditable results prior to a thunderstorm arriving at the area of concern, with accuracies of 63%, 80%, and 91% for lead times of 30, 15, and 5 minutes, respectively. The real-time observations of total lightning appear to be significant for thunderstorm tracking and lightning nowcasting, as total lightning tracking could help to fill the observational gaps in radar reflectivity due to the attenuation by hills or other obstacles. The lightning data used in the algorithm performs well in tracking the active thunderstorm cells associated with lightning activities.
A thunderstorm tracking algorithm is proposed to nowcast the possibility of lightning activity over an area of concern by using the total lightning data and neighborhood technique. The lightning radiation sources observed from the Beijing Lightning Network (BLNET) were used to obtain information about the thunderstorm cells, which are significantly valuable in real-time. The boundaries of thunderstorm cells were obtained through the neighborhood technique. After smoothing, these boundaries were used to track the movement of thunderstorms and then extrapolated to nowcast the lightning approaching in an area of concern. The algorithm can deliver creditable results prior to a thunderstorm arriving at the area of concern, with accuracies of 63%, 80%, and 91% for lead times of 30, 15, and 5 minutes, respectively. The real-time observations of total lightning appear to be significant for thunderstorm tracking and lightning nowcasting, as total lightning tracking could help to fill the observational gaps in radar reflectivity due to the attenuation by hills or other obstacles. The lightning data used in the algorithm performs well in tracking the active thunderstorm cells associated with lightning activities.
2022, 39(1): 189-200.
doi: 10.1007/s00376-021-1066-2
Abstract:
The surface energy budget is closely related to freeze-thaw processes and is also a key issue for land surface process research in permafrost regions. In this study, in situ data collected from 2005 to 2015 at the Tanggula site were used to analyze surface energy regimes, the interaction between surface energy budget and freeze-thaw processes. The results confirmed that surface energy flux in the permafrost region of the Qinghai-Tibetan Plateau exhibited obvious seasonal variations. Annual average net radiation (Rn) for 2010 was 86.5 W m−2, with the largest being in July and smallest in November. Surface soil heat flux (G0) was positive during warm seasons but negative in cold seasons with annual average value of 2.7 W m−2. Variations in Rn and G0 were closely related to freeze-thaw processes. Sensible heat flux (H) was the main energy budget component during cold seasons, whereas latent heat flux (LE) dominated surface energy distribution in warm seasons. Freeze-thaw processes, snow cover, precipitation, and surface conditions were important influence factors for surface energy flux. Albedo was strongly dependent on soil moisture content and ground surface state, increasing significantly when land surface was covered with deep snow, and exhibited negative correlation with surface soil moisture content. Energy variation was significantly related to active layer thaw depth. Soil heat balance coefficient K was > 1 during the investigation time period, indicating the permafrost in the Tanggula area tended to degrade.
The surface energy budget is closely related to freeze-thaw processes and is also a key issue for land surface process research in permafrost regions. In this study, in situ data collected from 2005 to 2015 at the Tanggula site were used to analyze surface energy regimes, the interaction between surface energy budget and freeze-thaw processes. The results confirmed that surface energy flux in the permafrost region of the Qinghai-Tibetan Plateau exhibited obvious seasonal variations. Annual average net radiation (Rn) for 2010 was 86.5 W m−2, with the largest being in July and smallest in November. Surface soil heat flux (G0) was positive during warm seasons but negative in cold seasons with annual average value of 2.7 W m−2. Variations in Rn and G0 were closely related to freeze-thaw processes. Sensible heat flux (H) was the main energy budget component during cold seasons, whereas latent heat flux (LE) dominated surface energy distribution in warm seasons. Freeze-thaw processes, snow cover, precipitation, and surface conditions were important influence factors for surface energy flux. Albedo was strongly dependent on soil moisture content and ground surface state, increasing significantly when land surface was covered with deep snow, and exhibited negative correlation with surface soil moisture content. Energy variation was significantly related to active layer thaw depth. Soil heat balance coefficient K was > 1 during the investigation time period, indicating the permafrost in the Tanggula area tended to degrade.
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