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Abstract
The prevalence of low clouds significantly affects flight safety in Southwest China. However, relevant cloud parameters, especially low cloud-base height (LCBH), lack accurate forecasts. Based on the hourly atmospheric vertical profiles of ERA5 from 2008 to 2019, we developed a new algorithm for estimating LCBH by combining relative humidity (RH) threshold methods with convective condensation level (CCL) (RHs-CCL). To evaluate the performance of RHs-CCL, we use it to estimate the hourly LCBH of airports in Southwest China and compare the results with those based on the ground-based observations and the ERA5 CBH data. Using the observations as a ground truth, we compare the RHs-CCL algorithm with several existing algorithms with the following findings: 1) The correlation coefficient between RHs-CCL and observations reaches 0.5 on average, and the error of RHs-CCL is smaller than those of existing algorithms, with the minimum mean absolute error and root-mean-square error at the four airports studies being able to reach 243 and 321 m. 2) The bias score of RHs-CCL is 0.97 on average, and low clouds classification utilizing RHs-CCL attains the highest accuracy, up to 86%. 3) The errors of ERA5 CBH are the largest when compared with the others. 4) By implementing convective cloud occurrence condition and CCL, RHs-CCL has better applicability in regions of enhanced convective activity. These results suggest the potential of RHs-CCL as an algorithm moving forward for improvement of the LCBH estimates based upon high-resolution reanalysis products and for better predictions of the LCBH utilizing outputs from numerical weather prediction models.
Significance Statement
The new algorithm developed in this study can accurately estimate low cloud-base heights from vertical profiles of atmospheric variables. It provides us a much more computationally efficient approach for predicting low cloud-base height relative to running cloud models, which is critical for weather forecasting at locations lacking computational resources and/or cloud modeling capability. In areas such as Southwest China, low clouds are very common, and they pose major threats to aviation safety. The new algorithm has been successfully integrated into the daily operation at Guiyang Airport in Southwest China and demonstrated excellent skills in estimating cloud-base heights. The implementation of the algorithm in aviation forecasting over a broader region is on the horizon.
Abstract
The prevalence of low clouds significantly affects flight safety in Southwest China. However, relevant cloud parameters, especially low cloud-base height (LCBH), lack accurate forecasts. Based on the hourly atmospheric vertical profiles of ERA5 from 2008 to 2019, we developed a new algorithm for estimating LCBH by combining relative humidity (RH) threshold methods with convective condensation level (CCL) (RHs-CCL). To evaluate the performance of RHs-CCL, we use it to estimate the hourly LCBH of airports in Southwest China and compare the results with those based on the ground-based observations and the ERA5 CBH data. Using the observations as a ground truth, we compare the RHs-CCL algorithm with several existing algorithms with the following findings: 1) The correlation coefficient between RHs-CCL and observations reaches 0.5 on average, and the error of RHs-CCL is smaller than those of existing algorithms, with the minimum mean absolute error and root-mean-square error at the four airports studies being able to reach 243 and 321 m. 2) The bias score of RHs-CCL is 0.97 on average, and low clouds classification utilizing RHs-CCL attains the highest accuracy, up to 86%. 3) The errors of ERA5 CBH are the largest when compared with the others. 4) By implementing convective cloud occurrence condition and CCL, RHs-CCL has better applicability in regions of enhanced convective activity. These results suggest the potential of RHs-CCL as an algorithm moving forward for improvement of the LCBH estimates based upon high-resolution reanalysis products and for better predictions of the LCBH utilizing outputs from numerical weather prediction models.
Significance Statement
The new algorithm developed in this study can accurately estimate low cloud-base heights from vertical profiles of atmospheric variables. It provides us a much more computationally efficient approach for predicting low cloud-base height relative to running cloud models, which is critical for weather forecasting at locations lacking computational resources and/or cloud modeling capability. In areas such as Southwest China, low clouds are very common, and they pose major threats to aviation safety. The new algorithm has been successfully integrated into the daily operation at Guiyang Airport in Southwest China and demonstrated excellent skills in estimating cloud-base heights. The implementation of the algorithm in aviation forecasting over a broader region is on the horizon.
Abstract
Inland flooding and mudslides from tropical cyclone (TC) rainstorms are among the most destructive natural hazards in China, resulting in considerable direct economic losses and large numbers of fatalities. In this paper, a TC precipitation model (TCPM) is improved by incorporating the effects of complex terrain through a set of new parameters (e.g., slope, roughness, and attenuation distance) for a more accurate assessment of TC rainfall hazards in China. Moreover, by introducing parameterized spiral rainbands, the model could more accurately capture the intensity of extreme precipitation. The model comprehensively considers dynamic and thermodynamic precipitation factors and is adept at capturing the climate characteristics of TC precipitation and the probability distribution of extreme TC precipitation in China. The model is verified by providing two comparisons. One is analysis including detailed results of three typical TC cases, and the other uses empirical cumulative distribution functions for extreme observations and simulations of historical landfalling TCs in China during the period 1960–2018. The comparisons reveal that the TCPM shows impressive performance for strong TCs with heavy precipitation within 200–300 km of the TC center. Moreover, both the modeled extreme hourly and total TC precipitation probability distributions are consistent with the observations. However, the model needs to be further improved for TCs with dispersive or long-distance precipitation.
Significance Statement
In this paper, an optimized and physics-based model for the simulation of tropical cyclone precipitation is described and used to estimate the risk of TC rainfall hazards in China. The work is innovative in that it considers the effect of complex terrain from three perspectives, including slope, roughness, and attenuation distance. The simulations demonstrated that the model is adept at capturing the main climate characteristics of TC precipitation and the probability distribution of extreme TC precipitation in China, which is simple to run several hundred thousand times, with bright application prospects in catastrophe risk assessment.
Abstract
Inland flooding and mudslides from tropical cyclone (TC) rainstorms are among the most destructive natural hazards in China, resulting in considerable direct economic losses and large numbers of fatalities. In this paper, a TC precipitation model (TCPM) is improved by incorporating the effects of complex terrain through a set of new parameters (e.g., slope, roughness, and attenuation distance) for a more accurate assessment of TC rainfall hazards in China. Moreover, by introducing parameterized spiral rainbands, the model could more accurately capture the intensity of extreme precipitation. The model comprehensively considers dynamic and thermodynamic precipitation factors and is adept at capturing the climate characteristics of TC precipitation and the probability distribution of extreme TC precipitation in China. The model is verified by providing two comparisons. One is analysis including detailed results of three typical TC cases, and the other uses empirical cumulative distribution functions for extreme observations and simulations of historical landfalling TCs in China during the period 1960–2018. The comparisons reveal that the TCPM shows impressive performance for strong TCs with heavy precipitation within 200–300 km of the TC center. Moreover, both the modeled extreme hourly and total TC precipitation probability distributions are consistent with the observations. However, the model needs to be further improved for TCs with dispersive or long-distance precipitation.
Significance Statement
In this paper, an optimized and physics-based model for the simulation of tropical cyclone precipitation is described and used to estimate the risk of TC rainfall hazards in China. The work is innovative in that it considers the effect of complex terrain from three perspectives, including slope, roughness, and attenuation distance. The simulations demonstrated that the model is adept at capturing the main climate characteristics of TC precipitation and the probability distribution of extreme TC precipitation in China, which is simple to run several hundred thousand times, with bright application prospects in catastrophe risk assessment.
Abstract
The southwest vortex (SWV) is a critical weather system in China, but our knowledge of this system remains incomplete. Here, we investigate the cloud properties in the SWV. First, we search for the SWVs with time steps and center locations that are consistent between the SWV yearbook and ERA-Interim reanalysis data. Second, we supplement these SWVs’ life spans and movement paths. Third, we relocate the Fengyun (FY) satellite FY-4A cloud retrievals in the 10° × 10° region centered on each SWV and analyze the cloud occurrence frequency (COF), cloud-top height (CTH), and cloud optical thickness (COT). A distribution mode of cloud types is summarized from the COFs, with water clouds, supercooled clouds, mixed clouds, ice clouds, cirrus clouds, and overlap clouds occurring sequentially from west to east. The CTH probability density (PD) distribution features a significant north–south difference. In addition, the COT PD distributions exhibit a common trend: with increasing COT, the PD increases rapidly and then slowly before peaking, whereupon the PD decreases abruptly. From spring to summer, the region with the highest convective COF shifts from the northeast to the northwest, and an east–west gradient of the convective COF appears in autumn and winter. Furthermore, we investigate the cloud properties during SWV-related heavy rainfall. Heavy rain occurs mainly in the west of the SWV, and convective clouds are mainly in the northwest, partly in the southwest and near the SWV center. The average CTH in heavy rainfall is generally higher than 6 km, and the average COT is greater than 20.
Significance Statement
The southwest vortex (SWV) is an important weather system in China. However, we do not yet comprehensively know this weather system. The cloud properties can indicate the structures of weather systems and are key parameters in numerical weather prediction (NWP) models. Thus, investigating cloud properties is necessary and meaningful to understand the SWV and accurately predict SWV-related precipitation in NWP models. In this paper, a typical distribution mode of six cloud types in the SWV is summarized from the cloud occurrence frequency, and the distribution features of convective clouds, cloud-top height, and cloud optical thickness in the SWV are analyzed. Furthermore, the cloud properties in SWV-related heavy rain are also studied.
Abstract
The southwest vortex (SWV) is a critical weather system in China, but our knowledge of this system remains incomplete. Here, we investigate the cloud properties in the SWV. First, we search for the SWVs with time steps and center locations that are consistent between the SWV yearbook and ERA-Interim reanalysis data. Second, we supplement these SWVs’ life spans and movement paths. Third, we relocate the Fengyun (FY) satellite FY-4A cloud retrievals in the 10° × 10° region centered on each SWV and analyze the cloud occurrence frequency (COF), cloud-top height (CTH), and cloud optical thickness (COT). A distribution mode of cloud types is summarized from the COFs, with water clouds, supercooled clouds, mixed clouds, ice clouds, cirrus clouds, and overlap clouds occurring sequentially from west to east. The CTH probability density (PD) distribution features a significant north–south difference. In addition, the COT PD distributions exhibit a common trend: with increasing COT, the PD increases rapidly and then slowly before peaking, whereupon the PD decreases abruptly. From spring to summer, the region with the highest convective COF shifts from the northeast to the northwest, and an east–west gradient of the convective COF appears in autumn and winter. Furthermore, we investigate the cloud properties during SWV-related heavy rainfall. Heavy rain occurs mainly in the west of the SWV, and convective clouds are mainly in the northwest, partly in the southwest and near the SWV center. The average CTH in heavy rainfall is generally higher than 6 km, and the average COT is greater than 20.
Significance Statement
The southwest vortex (SWV) is an important weather system in China. However, we do not yet comprehensively know this weather system. The cloud properties can indicate the structures of weather systems and are key parameters in numerical weather prediction (NWP) models. Thus, investigating cloud properties is necessary and meaningful to understand the SWV and accurately predict SWV-related precipitation in NWP models. In this paper, a typical distribution mode of six cloud types in the SWV is summarized from the cloud occurrence frequency, and the distribution features of convective clouds, cloud-top height, and cloud optical thickness in the SWV are analyzed. Furthermore, the cloud properties in SWV-related heavy rain are also studied.
Abstract
Given their potentially severe impacts, understanding how freezing rain events may change as the climate changes is of great importance to stakeholders including electrical utility companies and local governments. Identification of freezing rain in climate models requires the use of precipitation-type algorithms, and differences between algorithms may lead to differences in the types of precipitation identified for a given thermodynamic profile. We explore the uncertainty associated with algorithm selection by applying four algorithms (Cantin and Bachand, Baldwin, Ramer, and Bourgouin) offline to an ensemble of simulations of the fifth-generation Canadian Regional Climate Model (CRCM5) at 0.22° grid spacing. First, we examine results for the CRCM5 driven by ERA-Interim reanalysis to analyze how well the algorithms reproduce the recent climatology of freezing rain and how results vary depending on algorithm parameters and the characteristics of available model output. We find that while the Ramer and Baldwin algorithms tend to be better correlated with observations than Cantin and Bachand or Bourgouin, their results are highly sensitive to algorithm parameters and to the number of pressure levels used. We also apply the algorithms to four CRCM5 simulations driven by different global climate models (GCMs) and find that the uncertainty associated with algorithm selection is generally similar to or greater than that associated with choice of driving GCM for the recent past climate. Our results provide guidance for future studies on freezing rain in climate simulations and demonstrate the importance of accounting for uncertainty between algorithms when identifying precipitation type from climate model output.
Significance Statement
Freezing rain events and ice storms can have major consequences, including power outages and dangerous road conditions. It is therefore important to understand how climate change might affect the frequency and severity of these events. One source of uncertainty in climate studies of these events is related to the choice of algorithm used to detect freezing rain in model output. We compare the frequency of freezing rain identified using four different algorithms and find sometimes large differences depending on the algorithm chosen over some regions. Our findings highlight the importance of taking this source of uncertainty into account and will provide researchers with guidance as to which algorithms are best suited for climate studies of freezing rain.
Abstract
Given their potentially severe impacts, understanding how freezing rain events may change as the climate changes is of great importance to stakeholders including electrical utility companies and local governments. Identification of freezing rain in climate models requires the use of precipitation-type algorithms, and differences between algorithms may lead to differences in the types of precipitation identified for a given thermodynamic profile. We explore the uncertainty associated with algorithm selection by applying four algorithms (Cantin and Bachand, Baldwin, Ramer, and Bourgouin) offline to an ensemble of simulations of the fifth-generation Canadian Regional Climate Model (CRCM5) at 0.22° grid spacing. First, we examine results for the CRCM5 driven by ERA-Interim reanalysis to analyze how well the algorithms reproduce the recent climatology of freezing rain and how results vary depending on algorithm parameters and the characteristics of available model output. We find that while the Ramer and Baldwin algorithms tend to be better correlated with observations than Cantin and Bachand or Bourgouin, their results are highly sensitive to algorithm parameters and to the number of pressure levels used. We also apply the algorithms to four CRCM5 simulations driven by different global climate models (GCMs) and find that the uncertainty associated with algorithm selection is generally similar to or greater than that associated with choice of driving GCM for the recent past climate. Our results provide guidance for future studies on freezing rain in climate simulations and demonstrate the importance of accounting for uncertainty between algorithms when identifying precipitation type from climate model output.
Significance Statement
Freezing rain events and ice storms can have major consequences, including power outages and dangerous road conditions. It is therefore important to understand how climate change might affect the frequency and severity of these events. One source of uncertainty in climate studies of these events is related to the choice of algorithm used to detect freezing rain in model output. We compare the frequency of freezing rain identified using four different algorithms and find sometimes large differences depending on the algorithm chosen over some regions. Our findings highlight the importance of taking this source of uncertainty into account and will provide researchers with guidance as to which algorithms are best suited for climate studies of freezing rain.
Abstract
This research utilized NASA–Prediction of Worldwide Energy Resource (POWER) datasets to quantify and map long-term annual and growing-season mean maximum, minimum, and mean air temperatures (T max, T min, and T avg); diurnal temperature range (DTR); growing degree-days (thermal unit) (GDD); seasonal total precipitation; mean daily precipitation; incoming shortwave radiation Rs ; relative humidity (RH); wind speed u 2; saturation and actual vapor pressures (es and ea ); vapor pressure deficit (VPD); grass- and alfalfa-reference evapotranspiration (ET o and ET r ); and aridity index (AI) over the nine agricultural zones of Türkiye (Turkey). In addition to the latitudinal influence, the influence of continentality and oceanity effects and physiographic features were evident in the spatial patterns of all climate indicators. T min was the most spatially variable indicator at both scales, which can have significant impact on nighttime respiration, potentially reducing agroecosystem productivity. At the annual scale, climate indicators that showed coefficient of variation (CV) greater than 0.20 were GDD, T avg, VPD, AI, and ea . Growing-season mean indicators with CV > 0.20 were GDD, AI, VPD, total precipitation, mean daily precipitation, and ea . The Rs showed the least spatial variability at both scales. Annual-scale mean CV (0.21) was 7% greater than that the growing season (0.19). To the best of our knowledge, the analyses, information, and resources presented here are the first to quantify agricultural-zone-specific climate indicators during the growing season in Türkiye and are invaluable for decision-making on issues at the intersection of meteorology, agriculture, water resources, and hydrology.
Abstract
This research utilized NASA–Prediction of Worldwide Energy Resource (POWER) datasets to quantify and map long-term annual and growing-season mean maximum, minimum, and mean air temperatures (T max, T min, and T avg); diurnal temperature range (DTR); growing degree-days (thermal unit) (GDD); seasonal total precipitation; mean daily precipitation; incoming shortwave radiation Rs ; relative humidity (RH); wind speed u 2; saturation and actual vapor pressures (es and ea ); vapor pressure deficit (VPD); grass- and alfalfa-reference evapotranspiration (ET o and ET r ); and aridity index (AI) over the nine agricultural zones of Türkiye (Turkey). In addition to the latitudinal influence, the influence of continentality and oceanity effects and physiographic features were evident in the spatial patterns of all climate indicators. T min was the most spatially variable indicator at both scales, which can have significant impact on nighttime respiration, potentially reducing agroecosystem productivity. At the annual scale, climate indicators that showed coefficient of variation (CV) greater than 0.20 were GDD, T avg, VPD, AI, and ea . Growing-season mean indicators with CV > 0.20 were GDD, AI, VPD, total precipitation, mean daily precipitation, and ea . The Rs showed the least spatial variability at both scales. Annual-scale mean CV (0.21) was 7% greater than that the growing season (0.19). To the best of our knowledge, the analyses, information, and resources presented here are the first to quantify agricultural-zone-specific climate indicators during the growing season in Türkiye and are invaluable for decision-making on issues at the intersection of meteorology, agriculture, water resources, and hydrology.
Abstract
The southeastern interior of the Iberian Peninsula (Spain) is characterized by a complex orography, which determines a pronounced altitudinal gradient, significant slopes, and marked valleys with important temperature inversion processes. In this work, to analyze the yearly and seasonal evolution of minimum temperatures, six indicators related to minimum temperatures were used: the frost days (FD) and number of days with minimum temperature below −2°C (TNltm2), 10th temperature minimum percentile (TN10p), absolute minimum temperatures (TNn), average minimum temperatures (TNm), and cold-spell duration index (CSDI). For this, the Spearman nonparametric statistical test was used to analyze data from a total of 22 meteorological stations (1950–2020), using a daily resolution of minimum temperatures. Significant changes during the study period were revealed, especially between the 1960s and 1990s. In most cases, there has been a statistically significant increase in the minimum temperatures in the study area, except in the western (most mountainous) part, where the dynamics differed from the rest of the interior of the southeast of the peninsula. Nine large global teleconnection patterns were analyzed in relation to the average minimum temperatures in the study area. These are well-characterized indices for the Northern Hemisphere, which show a very high correlation of the average minimum temperatures with the temporal evolution of the global climate pattern of the east Atlantic (EA) index, especially in the Mediterranean region of the Iberian Peninsula, where it seems to have a very marked influence.
Significance Statement
We analyzed variation in observed minimum temperatures during 1950–2020 in inland southeastern Spain. Temporal analysis of “east Atlantic index” cycles and their interrelation with frost days and minimum temperatures is very relevant for seasonal meteorological prediction in the study area, where agroindustry represents 20% of GDP. The results show a significant decrease in frost days and an increase in average minimum temperature, especially in coastal and prelittoral areas. The variations are less important mountainous western areas, where frost days have even increased. This is explained by a change in the atmospheric dynamics in midlatitudes of the Atlantic Ocean, quantified by the east Atlantic global teleconnection index, which has a high statistical correlation with Spanish minimum temperatures, especially in winter.
Abstract
The southeastern interior of the Iberian Peninsula (Spain) is characterized by a complex orography, which determines a pronounced altitudinal gradient, significant slopes, and marked valleys with important temperature inversion processes. In this work, to analyze the yearly and seasonal evolution of minimum temperatures, six indicators related to minimum temperatures were used: the frost days (FD) and number of days with minimum temperature below −2°C (TNltm2), 10th temperature minimum percentile (TN10p), absolute minimum temperatures (TNn), average minimum temperatures (TNm), and cold-spell duration index (CSDI). For this, the Spearman nonparametric statistical test was used to analyze data from a total of 22 meteorological stations (1950–2020), using a daily resolution of minimum temperatures. Significant changes during the study period were revealed, especially between the 1960s and 1990s. In most cases, there has been a statistically significant increase in the minimum temperatures in the study area, except in the western (most mountainous) part, where the dynamics differed from the rest of the interior of the southeast of the peninsula. Nine large global teleconnection patterns were analyzed in relation to the average minimum temperatures in the study area. These are well-characterized indices for the Northern Hemisphere, which show a very high correlation of the average minimum temperatures with the temporal evolution of the global climate pattern of the east Atlantic (EA) index, especially in the Mediterranean region of the Iberian Peninsula, where it seems to have a very marked influence.
Significance Statement
We analyzed variation in observed minimum temperatures during 1950–2020 in inland southeastern Spain. Temporal analysis of “east Atlantic index” cycles and their interrelation with frost days and minimum temperatures is very relevant for seasonal meteorological prediction in the study area, where agroindustry represents 20% of GDP. The results show a significant decrease in frost days and an increase in average minimum temperature, especially in coastal and prelittoral areas. The variations are less important mountainous western areas, where frost days have even increased. This is explained by a change in the atmospheric dynamics in midlatitudes of the Atlantic Ocean, quantified by the east Atlantic global teleconnection index, which has a high statistical correlation with Spanish minimum temperatures, especially in winter.
Abstract
Jakarta, a megacity in Indonesia, experiences recurrent floods associated with heavy rainfall. Characteristics of subdaily rainfall and the local factors influencing rainfall around Jakarta have not been thoroughly investigated, primarily because of data limitations. In this study, we examine the frequency and intensity of hourly and daily rain rate, including spatial characteristics and variations across time scales. We use 6-min C-band Doppler radar and 1-min in situ data during 2009–12 to resolve spatial rain-rate characteristics at higher resolution than previous studies. A reflectivity–rain rate (Z–R) relationship is derived (Z = 102.7R 1.75) and applied to estimate hourly rain rate. Our results show that rain rate around Jakarta is spatially inhomogeneous. In the rainy season [December–February (DJF)], rain rate exhibits statistical properties markedly different from other seasons, with much higher frequency of rain, but, on average, less intense rain rate. In all seasons, there is a persistent higher hourly and daily mean rain rate found over mountainous areas, indicating the importance of local orographic effects. In contrast, for hourly rain-rate extremes, peaks are observed mostly over the coastal land and lowland areas. For the diurnal cycle of mean rain rate, a distinct afternoon peak is found developing earlier in DJF and later in the dry season. This study has implications for other analyses of mesoscale rain-rate extremes in areas of complex topography and suggests that coarse-grain products may miss major features of the rain-rate variability identified in our study.
Significance Statement
For many years, Jakarta and its surrounding regions have been repeatedly inundated by flooding triggered by short-duration heavy rainfall or rainfall accumulated over multiple days. Little is known about the distribution of local rainfall and how it differs between seasons. In this study, we used high-resolution C-band Doppler radar during 2009–12 to understand the characteristics of rainfall over this complex topography. The results demonstrate that the rainfall features vary spatially and seasonally. In the wet season, rainfall is more frequent but, on average, lighter relative to other seasons. In all seasons, the highest hourly and daily mean rain rate persistently occurs over the mountains, indicating the vital role of topography in generating rainfall in the region.
Abstract
Jakarta, a megacity in Indonesia, experiences recurrent floods associated with heavy rainfall. Characteristics of subdaily rainfall and the local factors influencing rainfall around Jakarta have not been thoroughly investigated, primarily because of data limitations. In this study, we examine the frequency and intensity of hourly and daily rain rate, including spatial characteristics and variations across time scales. We use 6-min C-band Doppler radar and 1-min in situ data during 2009–12 to resolve spatial rain-rate characteristics at higher resolution than previous studies. A reflectivity–rain rate (Z–R) relationship is derived (Z = 102.7R 1.75) and applied to estimate hourly rain rate. Our results show that rain rate around Jakarta is spatially inhomogeneous. In the rainy season [December–February (DJF)], rain rate exhibits statistical properties markedly different from other seasons, with much higher frequency of rain, but, on average, less intense rain rate. In all seasons, there is a persistent higher hourly and daily mean rain rate found over mountainous areas, indicating the importance of local orographic effects. In contrast, for hourly rain-rate extremes, peaks are observed mostly over the coastal land and lowland areas. For the diurnal cycle of mean rain rate, a distinct afternoon peak is found developing earlier in DJF and later in the dry season. This study has implications for other analyses of mesoscale rain-rate extremes in areas of complex topography and suggests that coarse-grain products may miss major features of the rain-rate variability identified in our study.
Significance Statement
For many years, Jakarta and its surrounding regions have been repeatedly inundated by flooding triggered by short-duration heavy rainfall or rainfall accumulated over multiple days. Little is known about the distribution of local rainfall and how it differs between seasons. In this study, we used high-resolution C-band Doppler radar during 2009–12 to understand the characteristics of rainfall over this complex topography. The results demonstrate that the rainfall features vary spatially and seasonally. In the wet season, rainfall is more frequent but, on average, lighter relative to other seasons. In all seasons, the highest hourly and daily mean rain rate persistently occurs over the mountains, indicating the vital role of topography in generating rainfall in the region.
Abstract
Nocturnal warming events (NWEs) are abrupt interruptions in the typical cooling of surface temperatures at night. Using temperature time series from the high-resolution Vancouver Island School-Based Weather Station Network (VWSN) in British Columbia, Canada, we investigate temporal and spatial characteristics of NWEs. In this coastal region, NWEs are more frequently detected in winter than in summer, with a seasonal shift from slowly warming NWEs dominating the winter months to rapidly warming NWEs dominating the summer months. Slow-warming NWEs are of relatively small amplitude and exhibit slow cooling rates after the temperature peaks. In contrast, fast-warming NWEs have a temperature increase of several kelvins with shorter-duration temperature peaks. The median behavior of these distinct NWE classes at individual stations is similar across the entire set of stations. The spatial synchronicity of NWEs across the VWSN (determined by requiring NWEs at station pairs to occur within given time windows) decreases with distance, including substantial variability at nearby stations that reflects local influences. Fast-warming NWEs are observed to occur either simultaneously across a number of stations or in isolation at one station. Spatial synchronicity values are used to construct undirected networks to investigate spatial connectivity structures of NWEs. We find that, independent of individual seasons or NWE classes, the networks are largely unstructured, with no clear spatial connectivity structures related to local topography or direction.
Abstract
Nocturnal warming events (NWEs) are abrupt interruptions in the typical cooling of surface temperatures at night. Using temperature time series from the high-resolution Vancouver Island School-Based Weather Station Network (VWSN) in British Columbia, Canada, we investigate temporal and spatial characteristics of NWEs. In this coastal region, NWEs are more frequently detected in winter than in summer, with a seasonal shift from slowly warming NWEs dominating the winter months to rapidly warming NWEs dominating the summer months. Slow-warming NWEs are of relatively small amplitude and exhibit slow cooling rates after the temperature peaks. In contrast, fast-warming NWEs have a temperature increase of several kelvins with shorter-duration temperature peaks. The median behavior of these distinct NWE classes at individual stations is similar across the entire set of stations. The spatial synchronicity of NWEs across the VWSN (determined by requiring NWEs at station pairs to occur within given time windows) decreases with distance, including substantial variability at nearby stations that reflects local influences. Fast-warming NWEs are observed to occur either simultaneously across a number of stations or in isolation at one station. Spatial synchronicity values are used to construct undirected networks to investigate spatial connectivity structures of NWEs. We find that, independent of individual seasons or NWE classes, the networks are largely unstructured, with no clear spatial connectivity structures related to local topography or direction.
Abstract
Reanalysis datasets have been widely used in meteorological research, including studies of Northeast China cold vortices (NCCVs), where these datasets act as effective substitutes for observations. However, to date, no studies have focused on their performance in reproducing NCCVs. To address this knowledge gap, we adopted an automatic three-step identification algorithm (TIA) and used it to detect NCCVs from ERA5 and MERRA-2 reanalysis datasets spanning 39 warm seasons (May–September) during the period from 1980 to 2018. A comparative method was employed for a rough verification of the characteristics of the reproduced NCCVs. Moreover, a dataset derived from 1370 Chinese ground-based observational stations was used to verify the performance of the reanalysis models in reproducing the precipitation and air temperature associated with NCCVs. The results show that the TIA identified the majority of NCCVs, with an accuracy of approximately 90% from ERA5 or MERRA-2. Both reanalysis models can reproduce the characteristics of NCCVs (including location, strength, and duration), and both replicate air temperature better than precipitation. ERA5 and MERRA-2 showed strong consistency in reproducing the central longitude, central latitude, central height, and range of NCCVs, with correlation coefficients of 0.974, 0.972, 0.996, and 0.919, respectively, at the 99.9% significance level. The daily average 2-m temperatures in both reanalysis datasets were in good agreement with observations; however, overestimations of approximately 7°–8°C arose in steep high-altitude regions. In addition, both models tended to overestimate light rain (≤5 mm day−1) by approximately 1.2 mm and underestimate heavy rain (≥20 mm day−1) by over 6.7 mm.
Abstract
Reanalysis datasets have been widely used in meteorological research, including studies of Northeast China cold vortices (NCCVs), where these datasets act as effective substitutes for observations. However, to date, no studies have focused on their performance in reproducing NCCVs. To address this knowledge gap, we adopted an automatic three-step identification algorithm (TIA) and used it to detect NCCVs from ERA5 and MERRA-2 reanalysis datasets spanning 39 warm seasons (May–September) during the period from 1980 to 2018. A comparative method was employed for a rough verification of the characteristics of the reproduced NCCVs. Moreover, a dataset derived from 1370 Chinese ground-based observational stations was used to verify the performance of the reanalysis models in reproducing the precipitation and air temperature associated with NCCVs. The results show that the TIA identified the majority of NCCVs, with an accuracy of approximately 90% from ERA5 or MERRA-2. Both reanalysis models can reproduce the characteristics of NCCVs (including location, strength, and duration), and both replicate air temperature better than precipitation. ERA5 and MERRA-2 showed strong consistency in reproducing the central longitude, central latitude, central height, and range of NCCVs, with correlation coefficients of 0.974, 0.972, 0.996, and 0.919, respectively, at the 99.9% significance level. The daily average 2-m temperatures in both reanalysis datasets were in good agreement with observations; however, overestimations of approximately 7°–8°C arose in steep high-altitude regions. In addition, both models tended to overestimate light rain (≤5 mm day−1) by approximately 1.2 mm and underestimate heavy rain (≥20 mm day−1) by over 6.7 mm.
Abstract
Remote sensing snowfall retrievals are powerful tools for advancing our understanding of global snow accumulation patterns. However, current satellite-based snowfall retrievals rely on assumptions about snowfall particle shape, size, and distribution that contribute to uncertainty and biases in their estimates. Vertical radar reflectivity profiles provided by the vertically pointing X-band radar (VertiX) instrument in Egbert, Ontario, Canada, are compared with in situ surface snow accumulation measurements from January to March 2012 as a part of the Global Precipitation Measurement (GPM) Cold Season Precipitation Experiment (GCPEx). In this work, we train a random forest (RF) machine learning model on VertiX radar profiles and ERA5 atmospheric temperature estimates to derive a surface snow accumulation regression model. Using event-based training–testing sets, the RF model demonstrates high predictive skill in estimating surface snow accumulation at 5-min intervals with a low mean-square error of approximately 1.8 × 10−3 mm2 when compared with collocated in situ measurements. The machine learning model outperformed other common radar-based snowfall retrievals (Ze –S relationships) that were unable to accurately capture the magnitudes of peaks and troughs in observed snow accumulation. The RF model also displayed consistent skill when applied to unseen data at a separate experimental site in South Korea. An estimate of predictor importance from the RF model reveals that combinations of multiple reflectivity measurement bins in the boundary layer below 2 km were the most significant features in predicting snow accumulation. Nonlinear machine learning–based retrievals like those explored in this work can offer new, important insights into global snow accumulation patterns and overcome traditional challenges resulting from sparse in situ observational networks.
Abstract
Remote sensing snowfall retrievals are powerful tools for advancing our understanding of global snow accumulation patterns. However, current satellite-based snowfall retrievals rely on assumptions about snowfall particle shape, size, and distribution that contribute to uncertainty and biases in their estimates. Vertical radar reflectivity profiles provided by the vertically pointing X-band radar (VertiX) instrument in Egbert, Ontario, Canada, are compared with in situ surface snow accumulation measurements from January to March 2012 as a part of the Global Precipitation Measurement (GPM) Cold Season Precipitation Experiment (GCPEx). In this work, we train a random forest (RF) machine learning model on VertiX radar profiles and ERA5 atmospheric temperature estimates to derive a surface snow accumulation regression model. Using event-based training–testing sets, the RF model demonstrates high predictive skill in estimating surface snow accumulation at 5-min intervals with a low mean-square error of approximately 1.8 × 10−3 mm2 when compared with collocated in situ measurements. The machine learning model outperformed other common radar-based snowfall retrievals (Ze –S relationships) that were unable to accurately capture the magnitudes of peaks and troughs in observed snow accumulation. The RF model also displayed consistent skill when applied to unseen data at a separate experimental site in South Korea. An estimate of predictor importance from the RF model reveals that combinations of multiple reflectivity measurement bins in the boundary layer below 2 km were the most significant features in predicting snow accumulation. Nonlinear machine learning–based retrievals like those explored in this work can offer new, important insights into global snow accumulation patterns and overcome traditional challenges resulting from sparse in situ observational networks.
