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Abstract
WRF-Solar is a numerical weather prediction model specifically designed to meet the increasing demand for accurate solar irradiance forecasting. The model provides flexibility in the representation of the aerosol–cloud–radiation processes. This flexibility can be argued to make it more difficult to improve the model’s performance because of the necessity of inspecting different configurations. To alleviate this situation, WRF-Solar has a reference configuration to use as a benchmark in sensitivity experiments. However, the scarcity of high-quality ground observations is a handicap to accurately quantify the model performance. An alternative to ground observations are satellite irradiance retrievals. Herein we analyze the adequacy of the National Solar Radiation Database (NSRDB) to validate the WRF-Solar performance using high-quality global horizontal irradiance (GHI) observations across the contiguous United States (CONUS). Based on the sufficient performance of NSRDB, we further analyze the WRF-Solar forecast errors across the CONUS, the growth of the forecasting errors as a function of the lead time, and sensitivities to the grid spacing and the representation of the radiative effects of unresolved clouds. Our results based on WRF-Solar forecasts spanning 2018 reveal a 7% median degradation of the mean absolute error (MAE) from the first to the second daytime period. Reducing the grid spacing from 9 to 3 km leads to a 4% improvement in the MAE, whereas activating the radiative effects of unresolved clouds is desirable over most of the CONUS even at 3 km of grid spacing. A systematic overestimation of the GHI is found. These results illustrate the potential of GHI retrievals to contribute to increasing the WRF-Solar performance.
Abstract
WRF-Solar is a numerical weather prediction model specifically designed to meet the increasing demand for accurate solar irradiance forecasting. The model provides flexibility in the representation of the aerosol–cloud–radiation processes. This flexibility can be argued to make it more difficult to improve the model’s performance because of the necessity of inspecting different configurations. To alleviate this situation, WRF-Solar has a reference configuration to use as a benchmark in sensitivity experiments. However, the scarcity of high-quality ground observations is a handicap to accurately quantify the model performance. An alternative to ground observations are satellite irradiance retrievals. Herein we analyze the adequacy of the National Solar Radiation Database (NSRDB) to validate the WRF-Solar performance using high-quality global horizontal irradiance (GHI) observations across the contiguous United States (CONUS). Based on the sufficient performance of NSRDB, we further analyze the WRF-Solar forecast errors across the CONUS, the growth of the forecasting errors as a function of the lead time, and sensitivities to the grid spacing and the representation of the radiative effects of unresolved clouds. Our results based on WRF-Solar forecasts spanning 2018 reveal a 7% median degradation of the mean absolute error (MAE) from the first to the second daytime period. Reducing the grid spacing from 9 to 3 km leads to a 4% improvement in the MAE, whereas activating the radiative effects of unresolved clouds is desirable over most of the CONUS even at 3 km of grid spacing. A systematic overestimation of the GHI is found. These results illustrate the potential of GHI retrievals to contribute to increasing the WRF-Solar performance.
Abstract
Western Equatorial Africa is one of the least sunny areas in the world. Yet, this has attracted little research so far. As in many other parts of Africa, light availability is mainly estimated using in situ measurements of sunshine duration (SDU). Therefore, this study conducts the first characterization of SDU evolution during the annual cycle for the region. It also evaluates the skill of satellite-based estimates of SDU from the Surface Solar Radiation Data Set–Heliosat, edition 2.1 (SARAH-2.1). Mean annual SDU levels are low: less than 5 h day−1 at the regional scale, with the sunniest stations in the northeast (Cameroon and Central African Republic) and the least sunny in an ∼150-km-wide coastal strip in Gabon and Republic of the Congo (RoC). For most of the stations except the southeast ones in the Democratic Republic of Congo, the lowest SDU levels are recorded in July–September, during the main dry season, with persistent overcast conditions. They are as low as 2.5 h day−1, especially on the windward slopes of the Massifs du Chaillu and du Mayombé, and of the Batéké Plateaus in Gabon and RoC. Although the mean annual and monthly spatial patterns are well reproduced in SARAH-2.1, SDU levels are systematically overestimated by 1–2 h day−1. The largest positive biases are recorded during the December–February dry season, especially at the northernmost stations. Analyses at the daily time scale show that SARAH-2.1 biases arise from a twofold problem: the number of dark days (SDU < 1 h day−1) is 50% lower than observed whereas that of sunny days (SDU > 9 h day−1) is 50% higher than observed.
Abstract
Western Equatorial Africa is one of the least sunny areas in the world. Yet, this has attracted little research so far. As in many other parts of Africa, light availability is mainly estimated using in situ measurements of sunshine duration (SDU). Therefore, this study conducts the first characterization of SDU evolution during the annual cycle for the region. It also evaluates the skill of satellite-based estimates of SDU from the Surface Solar Radiation Data Set–Heliosat, edition 2.1 (SARAH-2.1). Mean annual SDU levels are low: less than 5 h day−1 at the regional scale, with the sunniest stations in the northeast (Cameroon and Central African Republic) and the least sunny in an ∼150-km-wide coastal strip in Gabon and Republic of the Congo (RoC). For most of the stations except the southeast ones in the Democratic Republic of Congo, the lowest SDU levels are recorded in July–September, during the main dry season, with persistent overcast conditions. They are as low as 2.5 h day−1, especially on the windward slopes of the Massifs du Chaillu and du Mayombé, and of the Batéké Plateaus in Gabon and RoC. Although the mean annual and monthly spatial patterns are well reproduced in SARAH-2.1, SDU levels are systematically overestimated by 1–2 h day−1. The largest positive biases are recorded during the December–February dry season, especially at the northernmost stations. Analyses at the daily time scale show that SARAH-2.1 biases arise from a twofold problem: the number of dark days (SDU < 1 h day−1) is 50% lower than observed whereas that of sunny days (SDU > 9 h day−1) is 50% higher than observed.
Abstract
Surface-based inversions (SBIs) are significant and common natural phenomena in the planetary boundary layer, and they play essential roles in weather and climate. This study used radiosonde data from 493 radiosonde stations worldwide from the Integrated Global Radiosonde Archive during 1989–2019 to investigate the variations in surface-based inversions from a global perspective. The results indicated that, from 1989 to 2019, the SBI frequency increased and the SBI strength variations with fluctuations and SBI depth decreased over the study period. However, the spatial distribution of frequency, strength, and depth did not have consistent trends. In comparison with the Southern Hemisphere, SBIs in the Northern Hemisphere occurred more frequently and were stronger and deeper. In terms of stations over land and the ocean, we found that the SBI frequency over the ocean has increased faster than that over land in the past 15 years and that the SBI strength over land was almost 2 times that of the ocean. The amplitudes of the annual cycle of SBI characteristics over land were greater than over the ocean in both hemispheres, and the frequency, strength, and depth were greater over land. This study investigated surface-based inversions from a global perspective and filled a gap in the current research on SBIs.
Abstract
Surface-based inversions (SBIs) are significant and common natural phenomena in the planetary boundary layer, and they play essential roles in weather and climate. This study used radiosonde data from 493 radiosonde stations worldwide from the Integrated Global Radiosonde Archive during 1989–2019 to investigate the variations in surface-based inversions from a global perspective. The results indicated that, from 1989 to 2019, the SBI frequency increased and the SBI strength variations with fluctuations and SBI depth decreased over the study period. However, the spatial distribution of frequency, strength, and depth did not have consistent trends. In comparison with the Southern Hemisphere, SBIs in the Northern Hemisphere occurred more frequently and were stronger and deeper. In terms of stations over land and the ocean, we found that the SBI frequency over the ocean has increased faster than that over land in the past 15 years and that the SBI strength over land was almost 2 times that of the ocean. The amplitudes of the annual cycle of SBI characteristics over land were greater than over the ocean in both hemispheres, and the frequency, strength, and depth were greater over land. This study investigated surface-based inversions from a global perspective and filled a gap in the current research on SBIs.
Abstract
Increases in the frequency of extreme rainfall occurrence have emerged as one of the more consistent climate trends in recent decades, particularly in the eastern United States. Such changes challenge the veracity of the conventional assumption of stationarity that has been applied in the published extreme rainfall analyses that are the foundation for engineering design assessments and resiliency planning. Using partial-duration series with varying record lengths, temporal changes in daily and hourly rainfall extremes corresponding to average annual recurrence probabilities ranging from 50% (i.e., the 2-yr storm) to 1% (i.e., the 100-yr storm) are evaluated. From 2000 through 2019, extreme rainfall amounts across a range of durations and recurrence probabilities have increased at 75% of the long-term precipitation observation stations in the mid-Atlantic region. At approximately one-quarter of the stations, increases in extreme rainfall have exceeded 5% from 2000 through 2019, with some stations experiencing increases in excess of 10% for both daily and hourly durations. At over 40% of the stations, the rainfall extremes based on the 1950–99 partial-duration series show a significant (p > 0.90) change in the 100-yr ARI relative to the 1950–2019 period. Collectively, the results indicate that, given recent trends in extreme rainfall, routine updates of extreme rainfall analyses are warranted on 20-yr intervals.
Significance Statement
Engineering design standards for drainage systems, dams, and other infrastructure rely on analyses of precipitation extremes. Often such structures are designed on the basis of the probability of exceeding a specified rainfall rate in a given year. The frequency of extreme rainfall events has increased in the mid-Atlantic region of the United States in recent decades, leading us to evaluate how these changes have affected these exceedance probabilities. From 2000 through 2019, there has been a consistent increase of generally 2.5%–5.0% in design rainfall amounts. The increase is similar across a range of rainfall durations from 1 h to 20 days and also annual exceedance probabilities ranging from 50% to 1% (i.e., from the “2-yr storm” to the “100-yr storm”). The work highlights the need to routinely update the climatological extreme-value analyses used in engineering design, with the results suggesting that a 20-yr cycle might be an appropriate update frequency.
Abstract
Increases in the frequency of extreme rainfall occurrence have emerged as one of the more consistent climate trends in recent decades, particularly in the eastern United States. Such changes challenge the veracity of the conventional assumption of stationarity that has been applied in the published extreme rainfall analyses that are the foundation for engineering design assessments and resiliency planning. Using partial-duration series with varying record lengths, temporal changes in daily and hourly rainfall extremes corresponding to average annual recurrence probabilities ranging from 50% (i.e., the 2-yr storm) to 1% (i.e., the 100-yr storm) are evaluated. From 2000 through 2019, extreme rainfall amounts across a range of durations and recurrence probabilities have increased at 75% of the long-term precipitation observation stations in the mid-Atlantic region. At approximately one-quarter of the stations, increases in extreme rainfall have exceeded 5% from 2000 through 2019, with some stations experiencing increases in excess of 10% for both daily and hourly durations. At over 40% of the stations, the rainfall extremes based on the 1950–99 partial-duration series show a significant (p > 0.90) change in the 100-yr ARI relative to the 1950–2019 period. Collectively, the results indicate that, given recent trends in extreme rainfall, routine updates of extreme rainfall analyses are warranted on 20-yr intervals.
Significance Statement
Engineering design standards for drainage systems, dams, and other infrastructure rely on analyses of precipitation extremes. Often such structures are designed on the basis of the probability of exceeding a specified rainfall rate in a given year. The frequency of extreme rainfall events has increased in the mid-Atlantic region of the United States in recent decades, leading us to evaluate how these changes have affected these exceedance probabilities. From 2000 through 2019, there has been a consistent increase of generally 2.5%–5.0% in design rainfall amounts. The increase is similar across a range of rainfall durations from 1 h to 20 days and also annual exceedance probabilities ranging from 50% to 1% (i.e., from the “2-yr storm” to the “100-yr storm”). The work highlights the need to routinely update the climatological extreme-value analyses used in engineering design, with the results suggesting that a 20-yr cycle might be an appropriate update frequency.
Abstract
Clouds and precipitation play critical roles in wet removal of aerosols and soluble gases in the atmosphere, and hence their accurate prediction largely influences accurate prediction of air pollutants. In this study, the impacts of clouds and precipitation on wet scavenging and long-range transboundary transport of pollutants are examined during the 2016 Korea–United States Air Quality (KORUS-AQ) field campaign using the Weather Research and Forecasting Model coupled with chemistry. Two simulations—one in which atmospheric moisture is constrained and one in which it is not—are performed and evaluated against surface and airborne observations. The simulation with moisture constraints is found to better reproduce precipitation as well as surface PM2.5, whereas the areal extent and amount of precipitation are overpredicted in the simulation without moisture constraints. As a results of overpredicted clouds and precipitation and consequently overpredicted wet scavenging, PM2.5 concentration is generally underpredicted across the model domain in the simulation without moisture constraints. The effects are significant not only in the precipitating region (upwind region, southern China in this study) but also in the downwind region (South Korea) where no precipitation is observed. The difference in upwind precipitation by 77% on average between the two simulations leads to the difference in PM2.5 by ∼39% both in the upwind and downwind regions. The transboundary transport of aerosol precursors, especially nitric acid, has a considerable impact on ammonium-nitrate aerosol formation in the ammonia-rich downwind region. This study highlights that skillful prediction of atmospheric moisture can have ultimate potential to skillful prediction of aerosols across regions.
Abstract
Clouds and precipitation play critical roles in wet removal of aerosols and soluble gases in the atmosphere, and hence their accurate prediction largely influences accurate prediction of air pollutants. In this study, the impacts of clouds and precipitation on wet scavenging and long-range transboundary transport of pollutants are examined during the 2016 Korea–United States Air Quality (KORUS-AQ) field campaign using the Weather Research and Forecasting Model coupled with chemistry. Two simulations—one in which atmospheric moisture is constrained and one in which it is not—are performed and evaluated against surface and airborne observations. The simulation with moisture constraints is found to better reproduce precipitation as well as surface PM2.5, whereas the areal extent and amount of precipitation are overpredicted in the simulation without moisture constraints. As a results of overpredicted clouds and precipitation and consequently overpredicted wet scavenging, PM2.5 concentration is generally underpredicted across the model domain in the simulation without moisture constraints. The effects are significant not only in the precipitating region (upwind region, southern China in this study) but also in the downwind region (South Korea) where no precipitation is observed. The difference in upwind precipitation by 77% on average between the two simulations leads to the difference in PM2.5 by ∼39% both in the upwind and downwind regions. The transboundary transport of aerosol precursors, especially nitric acid, has a considerable impact on ammonium-nitrate aerosol formation in the ammonia-rich downwind region. This study highlights that skillful prediction of atmospheric moisture can have ultimate potential to skillful prediction of aerosols across regions.
Abstract
Atmospheric aerosols originating from natural and anthropogenic sources have important implications for modeling atmospheric phenomena, but aerosol conditions can change significantly and rapidly because of their dependence on local geography and atmospheric conditions. In this work, we applied a computational k-means clustering algorithm to a global set of data obtained from the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), to yield a set of 25 clusters that discriminate on the basis of land type, elevation, and atmospheric conditions to predict statistical aerosol optical depth (AOD) information. We considered different subsets of MERRA-2 data, consisting of all the data averaged over a single year (2016) as well as data averaged by meteorological season over a span of five years (2012–16), arriving at five separate sets of 25 clusters. We make the clustered AOD information available with decision trees, qualitative cluster descriptions, and color-coded cluster maps to assist in identifying which cluster to use in retrieving AOD information. The results of this analysis have applications in atmospheric modeling where knowledge of approximate or typical aerosol conditions is needed in lookup-table form without requiring access to large atmospheric databases or computationally intensive aerosol models; such applications could include quick-turnaround or large-volume analyses of atmospheric conditions required to inform decision-making that affects national security, such as in modeling remote sensing and estimating upper and lower bounds for visible and infrared photon transport.
Abstract
Atmospheric aerosols originating from natural and anthropogenic sources have important implications for modeling atmospheric phenomena, but aerosol conditions can change significantly and rapidly because of their dependence on local geography and atmospheric conditions. In this work, we applied a computational k-means clustering algorithm to a global set of data obtained from the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), to yield a set of 25 clusters that discriminate on the basis of land type, elevation, and atmospheric conditions to predict statistical aerosol optical depth (AOD) information. We considered different subsets of MERRA-2 data, consisting of all the data averaged over a single year (2016) as well as data averaged by meteorological season over a span of five years (2012–16), arriving at five separate sets of 25 clusters. We make the clustered AOD information available with decision trees, qualitative cluster descriptions, and color-coded cluster maps to assist in identifying which cluster to use in retrieving AOD information. The results of this analysis have applications in atmospheric modeling where knowledge of approximate or typical aerosol conditions is needed in lookup-table form without requiring access to large atmospheric databases or computationally intensive aerosol models; such applications could include quick-turnaround or large-volume analyses of atmospheric conditions required to inform decision-making that affects national security, such as in modeling remote sensing and estimating upper and lower bounds for visible and infrared photon transport.
Abstract
A novel algorithm is developed for detecting and classifying the Chesapeake Bay breeze and similar water-body breezes in output from mesoscale numerical weather prediction (NWP) models. To assess the generality of the new model-based detection algorithm (MBDA), it is tested on simulations from the Weather Research and Forecasting (WRF) Model and on analyses and forecasts from the High-Resolution Rapid Refresh (HRRR) model. The MBDA outperforms three observation-based detection algorithms (OBDAs) when applied to the same model output. In addition, by defining the onshore wind directions on the basis of model land-use data and not on the actual geography of the region of interest, performance of the OBDAs with model output can be improved. Although simulations by the WRF Model were used to develop the new MBDA, it performed best when applied to HRRR analyses. The generality of the MBDA is promising, and additional tuning of its parameters might improve it further.
Abstract
A novel algorithm is developed for detecting and classifying the Chesapeake Bay breeze and similar water-body breezes in output from mesoscale numerical weather prediction (NWP) models. To assess the generality of the new model-based detection algorithm (MBDA), it is tested on simulations from the Weather Research and Forecasting (WRF) Model and on analyses and forecasts from the High-Resolution Rapid Refresh (HRRR) model. The MBDA outperforms three observation-based detection algorithms (OBDAs) when applied to the same model output. In addition, by defining the onshore wind directions on the basis of model land-use data and not on the actual geography of the region of interest, performance of the OBDAs with model output can be improved. Although simulations by the WRF Model were used to develop the new MBDA, it performed best when applied to HRRR analyses. The generality of the MBDA is promising, and additional tuning of its parameters might improve it further.
Abstract
Droughts are widespread disasters worldwide and are concurrently influenced by multiple large-scale climate signals. This is particularly true over Japan, where drought has strong heterogeneity due to multiple factors such as monsoon, topography, and ocean circulations. Regional heterogeneity poses challenges for drought prediction and management. To overcome this difficulty, this study provides a comprehensive analysis of teleconnection between climate signals and homogeneous drought zones over Japan. First, droughts are characterized by simulated soil moisture from a land surface model during 1958–2012. The Mclust toolkit, distinct empirical orthogonal function, and wavelet coherence analysis are used, respectively, to investigate the homogeneous drought zone, principal component of each homogeneous zone, and teleconnection between climate signals and drought. Results indicate that nine homogeneous drought zones with different characteristics are defined and quantified. Among these nine zones, zone 1 is dominated by extreme drought events. Zones 2 and 6 are typical representatives of spring droughts, whereas zone 7 is wet for most of the period. The Hokkaido region is divided into wetter zone 4 and drier zone 9. Zones 3, 5, and 8 are distinguished by the topography. The analyses also reveal almost all nine zones have a high level of homogeneity, with more than 60% explained variance. Also, these nine zones are dominated by different large-scale climate signals: the Arctic Oscillation has the strongest impact on zones 1, 7, and 8; the influence of the North Atlantic Oscillation on zones 3, 4, and 6 is significant; zones 2 and 9 are both dominated by the Pacific decadal oscillation; and El Niño–Southern Oscillation dominates zone 5. The results will be valuable for drought management and drought prevention.
Abstract
Droughts are widespread disasters worldwide and are concurrently influenced by multiple large-scale climate signals. This is particularly true over Japan, where drought has strong heterogeneity due to multiple factors such as monsoon, topography, and ocean circulations. Regional heterogeneity poses challenges for drought prediction and management. To overcome this difficulty, this study provides a comprehensive analysis of teleconnection between climate signals and homogeneous drought zones over Japan. First, droughts are characterized by simulated soil moisture from a land surface model during 1958–2012. The Mclust toolkit, distinct empirical orthogonal function, and wavelet coherence analysis are used, respectively, to investigate the homogeneous drought zone, principal component of each homogeneous zone, and teleconnection between climate signals and drought. Results indicate that nine homogeneous drought zones with different characteristics are defined and quantified. Among these nine zones, zone 1 is dominated by extreme drought events. Zones 2 and 6 are typical representatives of spring droughts, whereas zone 7 is wet for most of the period. The Hokkaido region is divided into wetter zone 4 and drier zone 9. Zones 3, 5, and 8 are distinguished by the topography. The analyses also reveal almost all nine zones have a high level of homogeneity, with more than 60% explained variance. Also, these nine zones are dominated by different large-scale climate signals: the Arctic Oscillation has the strongest impact on zones 1, 7, and 8; the influence of the North Atlantic Oscillation on zones 3, 4, and 6 is significant; zones 2 and 9 are both dominated by the Pacific decadal oscillation; and El Niño–Southern Oscillation dominates zone 5. The results will be valuable for drought management and drought prevention.
Abstract
A new amalgamation of weather stations in and around Joshua Tree National Park in southeastern California has allowed for objective climate analysis regionalization at a much finer scale than past studies. First, it sets a baseline for many regions within the park’s boundaries that were not subject to direct observations. Second, these new observations are key to understanding shifting microclimate regimes in a desert ecosystem prone to the effects of climate change. Principal component analysis was used to regionalize the climate network based on monthly temperature and precipitation climate observations and standardized anomalies. Both the observation values and standardized climate anomalies identified regional boundaries. In general, these boundaries align with traditional ideas and past studies of the Mojave and Sonoran Deserts based on elevation (specifically the 1000-m contour) for the National Park Service. Standardized anomaly values identified a boundary based on seasonal precipitation, whereas observation values identified a boundary based on elevation. The boundary line within the park is similar for both data approaches, with the boundary running along the higher western one-third of the park. Conversely, the two methods differ significantly in the Coachella Valley, where low elevations and low precipitation meet winter-dominated seasonal precipitation. This study highlights the importance and opportunity of field observations to create climatological and ecological regionalization, and it also constructs a baseline to monitor and manage shifting desert regions in the future.
Significance Statement
This study identifies a high-resolution climate boundary zone in Joshua Tree National Park between the Sonoran and Mojave Deserts. The new transition zone presents the seasonal and elevational temperature and precipitation components of the two deserts, connecting with the unique ecology of the deserts. This finding highlights just one study opportunity of new field observation networks in arid or topographically diverse regions. It also provides a baseline for climate change as a resource for environmental management groups to better understand and preserve our natural spaces.
Abstract
A new amalgamation of weather stations in and around Joshua Tree National Park in southeastern California has allowed for objective climate analysis regionalization at a much finer scale than past studies. First, it sets a baseline for many regions within the park’s boundaries that were not subject to direct observations. Second, these new observations are key to understanding shifting microclimate regimes in a desert ecosystem prone to the effects of climate change. Principal component analysis was used to regionalize the climate network based on monthly temperature and precipitation climate observations and standardized anomalies. Both the observation values and standardized climate anomalies identified regional boundaries. In general, these boundaries align with traditional ideas and past studies of the Mojave and Sonoran Deserts based on elevation (specifically the 1000-m contour) for the National Park Service. Standardized anomaly values identified a boundary based on seasonal precipitation, whereas observation values identified a boundary based on elevation. The boundary line within the park is similar for both data approaches, with the boundary running along the higher western one-third of the park. Conversely, the two methods differ significantly in the Coachella Valley, where low elevations and low precipitation meet winter-dominated seasonal precipitation. This study highlights the importance and opportunity of field observations to create climatological and ecological regionalization, and it also constructs a baseline to monitor and manage shifting desert regions in the future.
Significance Statement
This study identifies a high-resolution climate boundary zone in Joshua Tree National Park between the Sonoran and Mojave Deserts. The new transition zone presents the seasonal and elevational temperature and precipitation components of the two deserts, connecting with the unique ecology of the deserts. This finding highlights just one study opportunity of new field observation networks in arid or topographically diverse regions. It also provides a baseline for climate change as a resource for environmental management groups to better understand and preserve our natural spaces.
Abstract
Several urban canopy models now incorporate urban vegetation to represent local urban cooling related to natural soil and plant evapotranspiration. Nevertheless, little is known about the realism of simulating these processes and turbulent exchanges within the urban canopy. Here, the coupled modeling of thermal and hydrological exchanges was investigated for a lawn located in an urban environment and for which soil temperature and water content measurements were available. The ISBA diffusive (ISBA-DF) surface–vegetation–atmosphere transfer model is inline coupled to the Town Energy Balance urban canopy model to model mixed urban environments. For the present case study, ISBA-DF was applied to the lawn and first evaluated in its default configuration. Particular attention was then paid to the parameterization of turbulent exchanges above the lawn and to the description of soil characteristics. The results highlighted the importance of taking into account local roughness related to surrounding obstacles for computing the turbulent exchanges over the lawn and simulating realistic surface and soil temperatures. The soil nature and texture vertical heterogeneity are also key properties for simulating the soil water content evolution and water exchanges.
Abstract
Several urban canopy models now incorporate urban vegetation to represent local urban cooling related to natural soil and plant evapotranspiration. Nevertheless, little is known about the realism of simulating these processes and turbulent exchanges within the urban canopy. Here, the coupled modeling of thermal and hydrological exchanges was investigated for a lawn located in an urban environment and for which soil temperature and water content measurements were available. The ISBA diffusive (ISBA-DF) surface–vegetation–atmosphere transfer model is inline coupled to the Town Energy Balance urban canopy model to model mixed urban environments. For the present case study, ISBA-DF was applied to the lawn and first evaluated in its default configuration. Particular attention was then paid to the parameterization of turbulent exchanges above the lawn and to the description of soil characteristics. The results highlighted the importance of taking into account local roughness related to surrounding obstacles for computing the turbulent exchanges over the lawn and simulating realistic surface and soil temperatures. The soil nature and texture vertical heterogeneity are also key properties for simulating the soil water content evolution and water exchanges.
