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Abstract
Systematic analyses of the daytime and nocturnal precipitation changes provide a better understand of the impact of global warming on the environment. In this study, the daytime and nocturnal precipitation across China from 1990 to 2019 was analyzed using observational data from 698 meteorological stations. Both daytime and nocturnal precipitation have increased in the western parts of China (including the Continental basin, headwaters of the Yangtze River basin, and Yellow River basin), whereas the trends in the eastern part are more complex. Climatological differences between daytime and nocturnal precipitation in summer were more significant than in other seasons. We developed a Z index to quantify the diurnal differences of precipitation. The annual mean Z index of China is about −2%, and its long-term change on an annual basis increased at a rate of 0.06% yr−1 (p < 0.1). The mean Z-index values during the year and seasons (except for summer) are negative and show an increasing trend. The intensity of the diurnal differences of precipitation has been decreasing in China since 1990. Topographic exposure and distance from the coast also influence the daytime and nocturnal precipitation changes. The Z index of the first-category stations (distance from the coast ≤ 100 km) was positively correlated with the distance from the coast (r = 0.39; p < 0.001) in summer, which may result from the superposition of the summer monsoon and sea-breeze effects.
Significance Statement
The diurnal cycle of precipitation is an important indicator for diagnosing the impact of global warming on the environment. There is a slight annual difference between daytime and nocturnal precipitation in China. The nocturnal precipitation maximum is in winter, spring, and autumn and the opposite occurs in summer. We define a precipitation index to quantifying the intensity of the diurnal differences of precipitation. The mean precipitation index is negative annually and seasonally (except for summer), with an increasing trend indicating that the intensity of the diurnal differences of precipitation has decreased in China from 1990 to 2019. These results are valuable for understanding the impact of recent warming on the diurnal differences of precipitation in China.
Abstract
Systematic analyses of the daytime and nocturnal precipitation changes provide a better understand of the impact of global warming on the environment. In this study, the daytime and nocturnal precipitation across China from 1990 to 2019 was analyzed using observational data from 698 meteorological stations. Both daytime and nocturnal precipitation have increased in the western parts of China (including the Continental basin, headwaters of the Yangtze River basin, and Yellow River basin), whereas the trends in the eastern part are more complex. Climatological differences between daytime and nocturnal precipitation in summer were more significant than in other seasons. We developed a Z index to quantify the diurnal differences of precipitation. The annual mean Z index of China is about −2%, and its long-term change on an annual basis increased at a rate of 0.06% yr−1 (p < 0.1). The mean Z-index values during the year and seasons (except for summer) are negative and show an increasing trend. The intensity of the diurnal differences of precipitation has been decreasing in China since 1990. Topographic exposure and distance from the coast also influence the daytime and nocturnal precipitation changes. The Z index of the first-category stations (distance from the coast ≤ 100 km) was positively correlated with the distance from the coast (r = 0.39; p < 0.001) in summer, which may result from the superposition of the summer monsoon and sea-breeze effects.
Significance Statement
The diurnal cycle of precipitation is an important indicator for diagnosing the impact of global warming on the environment. There is a slight annual difference between daytime and nocturnal precipitation in China. The nocturnal precipitation maximum is in winter, spring, and autumn and the opposite occurs in summer. We define a precipitation index to quantifying the intensity of the diurnal differences of precipitation. The mean precipitation index is negative annually and seasonally (except for summer), with an increasing trend indicating that the intensity of the diurnal differences of precipitation has decreased in China from 1990 to 2019. These results are valuable for understanding the impact of recent warming on the diurnal differences of precipitation in China.
Abstract
Despite many observational studies on the atmospheric boundary layer (ABL) depth zi
variability across various time scales (e.g., diurnal, seasonal, annual, and decadal), zi
variability before, during, and after frontal passages over land, or simply zi
variability as a function of weather patterns, has remained relatively unexplored. In this study, we provide an empirical framework using 5 years (2014–18) of daytime rawinsonde observations and surface analyses over 18 central and southeastern U.S. sites to report zi
variability across frontal boundaries. By providing systematic observations of front-relative contrasts in zi
(i.e., zi
differences between warm and cold sectors,
Significance Statement
The atmospheric boundary layer (ABL) is the lowermost part of the atmosphere adjacent to Earth’s surface. The irregular motion of air inside the ABL plays an essential role in relocating air near the surface to the free troposphere. Meteorologists use ABL depth in weather forecast models to determine the atmosphere’s ability to dilute or enrich tracers within the ABL. However, knowledge about the changes in ABL depth during stormy conditions remains incomplete. Here, we investigate how the ABL depth varies before and after cold-frontal passages. We found that ABL depths were much deeper before the cold-frontal passages than after. This knowledge will help us develop new approaches to consider how storms modify the ABL in weather forecast models.
Abstract
Despite many observational studies on the atmospheric boundary layer (ABL) depth zi
variability across various time scales (e.g., diurnal, seasonal, annual, and decadal), zi
variability before, during, and after frontal passages over land, or simply zi
variability as a function of weather patterns, has remained relatively unexplored. In this study, we provide an empirical framework using 5 years (2014–18) of daytime rawinsonde observations and surface analyses over 18 central and southeastern U.S. sites to report zi
variability across frontal boundaries. By providing systematic observations of front-relative contrasts in zi
(i.e., zi
differences between warm and cold sectors,
Significance Statement
The atmospheric boundary layer (ABL) is the lowermost part of the atmosphere adjacent to Earth’s surface. The irregular motion of air inside the ABL plays an essential role in relocating air near the surface to the free troposphere. Meteorologists use ABL depth in weather forecast models to determine the atmosphere’s ability to dilute or enrich tracers within the ABL. However, knowledge about the changes in ABL depth during stormy conditions remains incomplete. Here, we investigate how the ABL depth varies before and after cold-frontal passages. We found that ABL depths were much deeper before the cold-frontal passages than after. This knowledge will help us develop new approaches to consider how storms modify the ABL in weather forecast models.
Abstract
In this study, we investigate the tendencies of gamma parameters for particle size distributions (PSDs) containing snowflake aggregates in orographic, convective, and stratiform clouds, above snowstorms and above rainstorms, in temperatures ranging from 0° to −45°C. We find a strong relationship between μ and Λ but no dependence on temperature. Higher μ are observed during the experiments sampling winter snowstorms, and lower μ are observed during experiments sampling frozen clouds above convective and orographic storms. We find that a gamma function with a μ of −1.25 provides the best average representation of PSD shape and the most accurate representation of PSD moments related to mass and reflectivity. We also provide a lookup table of maximum particle size boundaries that can be used to parameterize incomplete gamma functions with negative μ values.
Significance Statement
In many weather models and satellite retrieval algorithms, frozen clouds and precipitation are governed by the same assumptions even though they develop through different growth processes. This paper provides recommendations for snowflake aggregate size distributions that reflect natural conditions, and these recommended assumptions are demonstrated to improve estimates of mass and radar reflectivity. We studied a variety of storms, such as thunderstorms, snow storms, and winter rainstorms, and we found that our model for snowflake aggregates was nearly identical in all observed conditions.
Abstract
In this study, we investigate the tendencies of gamma parameters for particle size distributions (PSDs) containing snowflake aggregates in orographic, convective, and stratiform clouds, above snowstorms and above rainstorms, in temperatures ranging from 0° to −45°C. We find a strong relationship between μ and Λ but no dependence on temperature. Higher μ are observed during the experiments sampling winter snowstorms, and lower μ are observed during experiments sampling frozen clouds above convective and orographic storms. We find that a gamma function with a μ of −1.25 provides the best average representation of PSD shape and the most accurate representation of PSD moments related to mass and reflectivity. We also provide a lookup table of maximum particle size boundaries that can be used to parameterize incomplete gamma functions with negative μ values.
Significance Statement
In many weather models and satellite retrieval algorithms, frozen clouds and precipitation are governed by the same assumptions even though they develop through different growth processes. This paper provides recommendations for snowflake aggregate size distributions that reflect natural conditions, and these recommended assumptions are demonstrated to improve estimates of mass and radar reflectivity. We studied a variety of storms, such as thunderstorms, snow storms, and winter rainstorms, and we found that our model for snowflake aggregates was nearly identical in all observed conditions.
Abstract
This study used corn insurance data as a proxy for agricultural loss to better inform producers and decision-makers about resilience and mitigation. Building on previous research examining crop losses based on weather and climate perils, updates to the peril climatology, identification of peril hotspots, and the quantification of annual trends using inflation-adjusted indemnities for corn were performed over the period 1989–2020. Normalization techniques in loss cost and acreage loss at county-level spatial resolution were also calculated. Indemnity data showed drought and excess moisture as the two costliest and most frequent perils for corn in the United States, although changes in the socioeconomic landscape and frequency of extreme weather events in the recent decade have led to significant increases in corn indemnities for drought, heat, excess moisture, flood, hail, excess wind, and cold wet weather. Normalized losses also displayed significant trends but were dependent on the cause of loss and amount of spatial aggregation. Perhaps most notable were the documented robust increases in corn losses associated with excess moisture, especially considering future projections for increased mid and end-of-century extreme precipitation. Subtle decreasing trends in drought, hail, freeze/frost, and flood loss cost over the study period indicates hedging taking place to protect against these perils, especially in corn acreage outside the Corn Belt in high-risk production zones. The use of crop insurance as a proxy for agricultural loss highlights the importance for quantifying spatiotemporal trends by informing targeted adaption to certain hazards and operational management decisions.
Significance Statement
This study quantified the climatology and trends of weather and climate perils affecting corn in the United States. Robust increases in losses were noted with perils causing excess moisture, which is cause for further concern given projected increases in extreme rainfall under a warming climate.
Abstract
This study used corn insurance data as a proxy for agricultural loss to better inform producers and decision-makers about resilience and mitigation. Building on previous research examining crop losses based on weather and climate perils, updates to the peril climatology, identification of peril hotspots, and the quantification of annual trends using inflation-adjusted indemnities for corn were performed over the period 1989–2020. Normalization techniques in loss cost and acreage loss at county-level spatial resolution were also calculated. Indemnity data showed drought and excess moisture as the two costliest and most frequent perils for corn in the United States, although changes in the socioeconomic landscape and frequency of extreme weather events in the recent decade have led to significant increases in corn indemnities for drought, heat, excess moisture, flood, hail, excess wind, and cold wet weather. Normalized losses also displayed significant trends but were dependent on the cause of loss and amount of spatial aggregation. Perhaps most notable were the documented robust increases in corn losses associated with excess moisture, especially considering future projections for increased mid and end-of-century extreme precipitation. Subtle decreasing trends in drought, hail, freeze/frost, and flood loss cost over the study period indicates hedging taking place to protect against these perils, especially in corn acreage outside the Corn Belt in high-risk production zones. The use of crop insurance as a proxy for agricultural loss highlights the importance for quantifying spatiotemporal trends by informing targeted adaption to certain hazards and operational management decisions.
Significance Statement
This study quantified the climatology and trends of weather and climate perils affecting corn in the United States. Robust increases in losses were noted with perils causing excess moisture, which is cause for further concern given projected increases in extreme rainfall under a warming climate.
Abstract
Downbursts can produce severe damage in near-ground areas and can also pose serious threats to aircraft in flight. In this study, a high-resolution boundary layer model—the Boundary Layer Above Stationary, Inhomogeneous Uneven Surface (BLASIUS) model—is used to simulate the evolution of a downburst. The observational data collected in Tazhong, China, located in hinterland of the Taklimakan Desert, during the Boundary Layer Comprehensive Observational Experiment on 27 July 2016 are used as the thermodynamic initial field for the BLASIUS model. In addition, the impacts of the terrain on the structure, turbulence intensity, and maximum wind speed of the downburst are also investigated. The results show that the BLASIUS model can simulate the structure and evolution characteristics of downbursts. The cold pool becomes warm if an isolated hill is implanted in the model under the same model conditions. Both the movement speed of the head and the average wind speed of the downburst decrease, while the maximum wind speed increases. The scale of the hill affects the dynamic and thermodynamic structures of the downburst through obstruction and entrainment mixing. The maximum wind speeds occur on the windward slope, and the downburst passes over the hill in the various tests with a hill. The head of the cold pool becomes narrow and tall for larger hill width cases. The Froude number generally decreases as the height of the hill increases, and the downburst can pass over the hill. The results are helpful to improve our understanding of the effects that terrain blocking on downburst structure and near-ground wind shear.
Significance Statement
Downbursts have the potential to cause significant damage to building structures and agricultural production and to cause unpredictable serious disasters. It is particularly important to understand the structure and evolution of downbursts. In addition, the influence of the topography on the structure and intensity of turbulent vortices during a downburst remain unclear. The results show that the Boundary Layer Above Stationary, Inhomogeneous Uneven Surface (BLASIUS) model can simulate the structure and evolution characteristics of downbursts. The cold pool becomes warm if an isolated hill is implanted in the model. The scale of the hill affects the dynamic and thermodynamic structures of the downburst through obstruction and entrainment mixing. The Froude number generally decreases as the height of the hill increases, and the downburst can pass over the hill.
Abstract
Downbursts can produce severe damage in near-ground areas and can also pose serious threats to aircraft in flight. In this study, a high-resolution boundary layer model—the Boundary Layer Above Stationary, Inhomogeneous Uneven Surface (BLASIUS) model—is used to simulate the evolution of a downburst. The observational data collected in Tazhong, China, located in hinterland of the Taklimakan Desert, during the Boundary Layer Comprehensive Observational Experiment on 27 July 2016 are used as the thermodynamic initial field for the BLASIUS model. In addition, the impacts of the terrain on the structure, turbulence intensity, and maximum wind speed of the downburst are also investigated. The results show that the BLASIUS model can simulate the structure and evolution characteristics of downbursts. The cold pool becomes warm if an isolated hill is implanted in the model under the same model conditions. Both the movement speed of the head and the average wind speed of the downburst decrease, while the maximum wind speed increases. The scale of the hill affects the dynamic and thermodynamic structures of the downburst through obstruction and entrainment mixing. The maximum wind speeds occur on the windward slope, and the downburst passes over the hill in the various tests with a hill. The head of the cold pool becomes narrow and tall for larger hill width cases. The Froude number generally decreases as the height of the hill increases, and the downburst can pass over the hill. The results are helpful to improve our understanding of the effects that terrain blocking on downburst structure and near-ground wind shear.
Significance Statement
Downbursts have the potential to cause significant damage to building structures and agricultural production and to cause unpredictable serious disasters. It is particularly important to understand the structure and evolution of downbursts. In addition, the influence of the topography on the structure and intensity of turbulent vortices during a downburst remain unclear. The results show that the Boundary Layer Above Stationary, Inhomogeneous Uneven Surface (BLASIUS) model can simulate the structure and evolution characteristics of downbursts. The cold pool becomes warm if an isolated hill is implanted in the model. The scale of the hill affects the dynamic and thermodynamic structures of the downburst through obstruction and entrainment mixing. The Froude number generally decreases as the height of the hill increases, and the downburst can pass over the hill.
Abstract
The evolution of the average freezing depth and maximum freezing depth of seasonal frozen soil and their correlations with the average winter half-year temperature in Heilongjiang Province in China are analyzed. Linear regression, the Mann–Kendall test, and kriging interpolation are applied to freezing depth data from 20 observation stations in Heilongjiang Province from 1972 to 2016 and daily average temperature data from 34 national meteorological stations collected in the winters of 1972–2020. The results show that the average freezing depth decreases at a rate of 4.8 cm (10 yr)−1 and that the maximum freezing depth decreases at a rate of 10.1 cm (10 yr)−1. The winter half-year average temperature generally shows a fluctuating upward trend in Heilongjiang Province, increasing at a rate of 0.3°C (10 yr)−1. The correlations between the average and maximum freezing depths and the winter half-year average temperature are −0.53 and −0.49, respectively. For every 1°C increase in the average temperature during the winter half of the year, the average freezing depth decreases by 3.85 cm and the maximum freezing depth decreases by 7.84 cm. The average freezing depth sequence mutated in 1987, and the maximum freezing depth sequence mutated in 1988. The average temperature in the winter half-year displayed multiple abrupt changes from 1972 to 2020. The spatial variations in the average and maximum freezing depths are basically consistent with those in the average winter half-year temperature. These research results provide a theoretical basis for the design and site selection of hydraulic structures in cold areas and for regional development and agricultural planning.
Significance Statement
The freeze–thaw balance in the frozen soil environment has been disrupted in recent years, and various degrees of degradation have occurred in the frozen soil. The degradation of frozen soil will further aggravate the greenhouse effect, which in turn will affect the accumulation of water in the soil and will have a significant impact on local agricultural production. This article uses Heilongjiang Province in China as an example. The results show that 1) the temperature in the winter half-year has exhibited an upward trend in recent years, 2) the temperature in the winter half-year has a considerable impact on the frozen soil environment, and 3) the response of the spatial distribution of frozen soil to temperature changes in the winter half-year is revealed.
Abstract
The evolution of the average freezing depth and maximum freezing depth of seasonal frozen soil and their correlations with the average winter half-year temperature in Heilongjiang Province in China are analyzed. Linear regression, the Mann–Kendall test, and kriging interpolation are applied to freezing depth data from 20 observation stations in Heilongjiang Province from 1972 to 2016 and daily average temperature data from 34 national meteorological stations collected in the winters of 1972–2020. The results show that the average freezing depth decreases at a rate of 4.8 cm (10 yr)−1 and that the maximum freezing depth decreases at a rate of 10.1 cm (10 yr)−1. The winter half-year average temperature generally shows a fluctuating upward trend in Heilongjiang Province, increasing at a rate of 0.3°C (10 yr)−1. The correlations between the average and maximum freezing depths and the winter half-year average temperature are −0.53 and −0.49, respectively. For every 1°C increase in the average temperature during the winter half of the year, the average freezing depth decreases by 3.85 cm and the maximum freezing depth decreases by 7.84 cm. The average freezing depth sequence mutated in 1987, and the maximum freezing depth sequence mutated in 1988. The average temperature in the winter half-year displayed multiple abrupt changes from 1972 to 2020. The spatial variations in the average and maximum freezing depths are basically consistent with those in the average winter half-year temperature. These research results provide a theoretical basis for the design and site selection of hydraulic structures in cold areas and for regional development and agricultural planning.
Significance Statement
The freeze–thaw balance in the frozen soil environment has been disrupted in recent years, and various degrees of degradation have occurred in the frozen soil. The degradation of frozen soil will further aggravate the greenhouse effect, which in turn will affect the accumulation of water in the soil and will have a significant impact on local agricultural production. This article uses Heilongjiang Province in China as an example. The results show that 1) the temperature in the winter half-year has exhibited an upward trend in recent years, 2) the temperature in the winter half-year has a considerable impact on the frozen soil environment, and 3) the response of the spatial distribution of frozen soil to temperature changes in the winter half-year is revealed.
Abstract
In this work, we investigate the effect of areawide building retrofitting on summertime, street-level outdoor temperatures in an urban district in Berlin, Germany. We perform two building-resolving, weeklong large-eddy simulations: one with nonretrofitted buildings and the other with retrofitted buildings in the entire domain to meet today’s energy efficiency standards. The comparison of the two simulations reveals that the mean outdoor temperatures are higher with retrofitted buildings during daytime conditions. This behavior is caused by the much smaller inertia of the outermost roof/wall layer in the retrofitting case, which is thermally decoupled from the inner roof/wall layers by an insulation layer. As a result, the outermost layer heats up more rigorously during the daytime, leading to increased sensible heat fluxes into the atmosphere. During the nighttime, the outermost layer’s temperature drops down faster, resulting in cooling of the atmosphere. However, as the simulation progresses, the cooling effect becomes smaller and the warming effect becomes larger. After 1 week, we find the mean temperatures to be 4 K higher during the daytime while the cooling effects become negligible.
Significance Statement
Building retrofitting is taking place in Europe and other continents as a measure to reduce energy consumption. The change in the building envelope directly influences the urban atmosphere. Our study reveals that areawide retrofitting in a German city district can have negative effects on the outdoor microclimate in summer by causing higher air temperatures.
Abstract
In this work, we investigate the effect of areawide building retrofitting on summertime, street-level outdoor temperatures in an urban district in Berlin, Germany. We perform two building-resolving, weeklong large-eddy simulations: one with nonretrofitted buildings and the other with retrofitted buildings in the entire domain to meet today’s energy efficiency standards. The comparison of the two simulations reveals that the mean outdoor temperatures are higher with retrofitted buildings during daytime conditions. This behavior is caused by the much smaller inertia of the outermost roof/wall layer in the retrofitting case, which is thermally decoupled from the inner roof/wall layers by an insulation layer. As a result, the outermost layer heats up more rigorously during the daytime, leading to increased sensible heat fluxes into the atmosphere. During the nighttime, the outermost layer’s temperature drops down faster, resulting in cooling of the atmosphere. However, as the simulation progresses, the cooling effect becomes smaller and the warming effect becomes larger. After 1 week, we find the mean temperatures to be 4 K higher during the daytime while the cooling effects become negligible.
Significance Statement
Building retrofitting is taking place in Europe and other continents as a measure to reduce energy consumption. The change in the building envelope directly influences the urban atmosphere. Our study reveals that areawide retrofitting in a German city district can have negative effects on the outdoor microclimate in summer by causing higher air temperatures.
Abstract
The exponential growth in solar radiation measuring stations across the conterminous United States permits the generation of gridded solar irradiance data that capture the spatiotemporal variability of solar irradiance far more accurately than was previously possible from ground-based observations. Taking advantage of these observations, we generated a 30-yr climatology (1991–2020) of mean monthly global irradiance at a resolution of 30 arc s (∼800 m) on both a horizontal surface and a sloped ground surface. This paper describes the methods used to generate the gridded data, which include extensive quality control of station data, spatial interpolation of effective cloud transmittance using the “PRISM” method, and simulation of the effects of elevation, shading, and reflection from nearby terrain on solar irradiance. A comparison of the new dataset with several other solar radiation products reveals some spatial features in solar radiation that are either lacking or underresolved in some or all of the other datasets. Examples of these features include strong gradients near foggy coastlines and along mountain ranges where there is persistent orographically driven cloud formation. The workflow developed to create the long-term means will be used as a template for generating time series of monthly and daily solar radiation grids up to the present.
Abstract
The exponential growth in solar radiation measuring stations across the conterminous United States permits the generation of gridded solar irradiance data that capture the spatiotemporal variability of solar irradiance far more accurately than was previously possible from ground-based observations. Taking advantage of these observations, we generated a 30-yr climatology (1991–2020) of mean monthly global irradiance at a resolution of 30 arc s (∼800 m) on both a horizontal surface and a sloped ground surface. This paper describes the methods used to generate the gridded data, which include extensive quality control of station data, spatial interpolation of effective cloud transmittance using the “PRISM” method, and simulation of the effects of elevation, shading, and reflection from nearby terrain on solar irradiance. A comparison of the new dataset with several other solar radiation products reveals some spatial features in solar radiation that are either lacking or underresolved in some or all of the other datasets. Examples of these features include strong gradients near foggy coastlines and along mountain ranges where there is persistent orographically driven cloud formation. The workflow developed to create the long-term means will be used as a template for generating time series of monthly and daily solar radiation grids up to the present.
Abstract
The standardized precipitation index (SPI) measures meteorological drought relative to historical climatology by normalizing accumulated precipitation. Longer record lengths improve parameter estimates, but these longer records may include signals of anthropogenic climate change and multidecadal natural climate fluctuations. Historically, climate nonstationarity has either been ignored or incorporated into the SPI using a quasi-stationary reference period, such as the WMO 30-yr period. This study introduces and evaluates a novel nonstationary SPI model based on Bayesian splines, designed to both improve parameter estimates for stationary climates and to explicitly incorporate nonstationarity. Using synthetically generated precipitation, this study directly compares the proposed Bayesian SPI model with existing SPI approaches based on maximum likelihood estimation for stationary and nonstationary climates. The proposed model not only reproduced the performance of existing SPI models but improved upon them in several key areas: reducing parameter uncertainty and noise, simultaneously modeling the likelihood of zero and positive precipitation, and capturing nonlinear trends and seasonal shifts across all parameters. Further, the fully Bayesian approach ensures all parameters have uncertainty estimates, including zero precipitation likelihood. The study notes that the zero precipitation parameter is too sensitive and could be improved in future iterations. The study concludes with an application of the proposed Bayesian nonstationary SPI model for nine gauges across a range of hydroclimate zones in the United States. Results of this experiment show that the model is stable and reproduces nonstationary patterns identified in prior studies, while also indicating new findings, particularly for the shape and zero precipitation parameters.
Significance Statement
We typically measure how bad a drought is by comparing it with the historical record. With long-term changes in climate or other factors, however, a typical drought today may not have been typical in the recent past. The purpose of this study is to build a model that measures drought relative to a changing climate. Our results confirm that the model is accurate and captures previously noted climate change patterns—a drier western United States, a wetter eastern United States, earlier summer weather, and more extreme wet seasons. This is significant because this model can improve drought measurement and identify recent changes in drought.
Abstract
The standardized precipitation index (SPI) measures meteorological drought relative to historical climatology by normalizing accumulated precipitation. Longer record lengths improve parameter estimates, but these longer records may include signals of anthropogenic climate change and multidecadal natural climate fluctuations. Historically, climate nonstationarity has either been ignored or incorporated into the SPI using a quasi-stationary reference period, such as the WMO 30-yr period. This study introduces and evaluates a novel nonstationary SPI model based on Bayesian splines, designed to both improve parameter estimates for stationary climates and to explicitly incorporate nonstationarity. Using synthetically generated precipitation, this study directly compares the proposed Bayesian SPI model with existing SPI approaches based on maximum likelihood estimation for stationary and nonstationary climates. The proposed model not only reproduced the performance of existing SPI models but improved upon them in several key areas: reducing parameter uncertainty and noise, simultaneously modeling the likelihood of zero and positive precipitation, and capturing nonlinear trends and seasonal shifts across all parameters. Further, the fully Bayesian approach ensures all parameters have uncertainty estimates, including zero precipitation likelihood. The study notes that the zero precipitation parameter is too sensitive and could be improved in future iterations. The study concludes with an application of the proposed Bayesian nonstationary SPI model for nine gauges across a range of hydroclimate zones in the United States. Results of this experiment show that the model is stable and reproduces nonstationary patterns identified in prior studies, while also indicating new findings, particularly for the shape and zero precipitation parameters.
Significance Statement
We typically measure how bad a drought is by comparing it with the historical record. With long-term changes in climate or other factors, however, a typical drought today may not have been typical in the recent past. The purpose of this study is to build a model that measures drought relative to a changing climate. Our results confirm that the model is accurate and captures previously noted climate change patterns—a drier western United States, a wetter eastern United States, earlier summer weather, and more extreme wet seasons. This is significant because this model can improve drought measurement and identify recent changes in drought.
Abstract
Cold-air pools (CAPs), or stable atmospheric boundary layers that form within topographic basins, are associated with poor air quality, hazardous weather, and low wind energy output. Accurate prediction of CAP dynamics presents a challenge for mesoscale forecast models in part because CAPs occur in regions of complex terrain, where traditional turbulence parameterizations may not be appropriate. This study examines the effects of the planetary boundary layer (PBL) scheme and horizontal diffusion treatment on CAP prediction in the Weather Research and Forecasting (WRF) Model. Model runs with a one-dimensional (1D) PBL scheme and Smagorinsky-like horizontal diffusion are compared with runs that use a new three-dimensional (3D) PBL scheme to calculate turbulent fluxes. Simulations are completed in a nested configuration with 3-km/750-m horizontal grid spacing over a 10-day case study in the Columbia River basin, and results are compared with observations from the Second Wind Forecast Improvement Project. Using event-averaged error metrics, potential temperature and wind speed errors are shown to decrease both with increased horizontal grid resolution and with improved treatment of horizontal diffusion over steep terrain. The 3D PBL scheme further reduces errors relative to a standard 1D PBL approach. Error reduction is accentuated during CAP erosion, when turbulent mixing plays a more dominant role in the dynamics. Last, the 3D PBL scheme is shown to reduce near-surface overestimates of turbulence kinetic energy during the CAP event. The sensitivity of turbulence predictions to the master length-scale formulation in the 3D PBL parameterization is also explored.
Significance Statement
In this article, we demonstrate how a new framework for modeling atmospheric turbulence improves cold pool predictions, using a case study from January 2017 in the Columbia River basin (U.S. Pacific Northwest). Cold pools are regions of cold, stagnant air that form within valleys or basins, and improved forecasts could help to mitigate the risks they pose to air quality, transportation, and wind energy production. For the chosen case study, our tests show a reduction in temperature and wind speed errors by up to a factor of 2–3 relative to standard model options. These results strongly motivate continued development of the framework as well as its application to other complex weather events.
Abstract
Cold-air pools (CAPs), or stable atmospheric boundary layers that form within topographic basins, are associated with poor air quality, hazardous weather, and low wind energy output. Accurate prediction of CAP dynamics presents a challenge for mesoscale forecast models in part because CAPs occur in regions of complex terrain, where traditional turbulence parameterizations may not be appropriate. This study examines the effects of the planetary boundary layer (PBL) scheme and horizontal diffusion treatment on CAP prediction in the Weather Research and Forecasting (WRF) Model. Model runs with a one-dimensional (1D) PBL scheme and Smagorinsky-like horizontal diffusion are compared with runs that use a new three-dimensional (3D) PBL scheme to calculate turbulent fluxes. Simulations are completed in a nested configuration with 3-km/750-m horizontal grid spacing over a 10-day case study in the Columbia River basin, and results are compared with observations from the Second Wind Forecast Improvement Project. Using event-averaged error metrics, potential temperature and wind speed errors are shown to decrease both with increased horizontal grid resolution and with improved treatment of horizontal diffusion over steep terrain. The 3D PBL scheme further reduces errors relative to a standard 1D PBL approach. Error reduction is accentuated during CAP erosion, when turbulent mixing plays a more dominant role in the dynamics. Last, the 3D PBL scheme is shown to reduce near-surface overestimates of turbulence kinetic energy during the CAP event. The sensitivity of turbulence predictions to the master length-scale formulation in the 3D PBL parameterization is also explored.
Significance Statement
In this article, we demonstrate how a new framework for modeling atmospheric turbulence improves cold pool predictions, using a case study from January 2017 in the Columbia River basin (U.S. Pacific Northwest). Cold pools are regions of cold, stagnant air that form within valleys or basins, and improved forecasts could help to mitigate the risks they pose to air quality, transportation, and wind energy production. For the chosen case study, our tests show a reduction in temperature and wind speed errors by up to a factor of 2–3 relative to standard model options. These results strongly motivate continued development of the framework as well as its application to other complex weather events.
