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- Author or Editor: F. Chen x
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Abstract
Geostationary Operational Environmental Satellite (GOES) infrared data were used to study the effect of land use on the diurnal surface temperature fluctuation. Five major land use types in southern Florida: the sandy soil agricultural area; the Everglades Agricultural Area (EAA); the conservation areas; the Natural Everglades Area (NEA); and Lake Okeechobee; were observed. The average daytime and nocturnal surface temperatures of sandy soil in agricultural areas was lower than that of organic soil in agricultural areas. The average temperature of organic soil in agricultural areas was lower than that of organic soil in conservation areas. The surface temperature in the wet marsh area was much lower than that in a large water-storage lake. A land use change in the EAA, and an increase in the water storage in Lake Okeechobee and the conservation areas could influence the microclimate.
Abstract
Geostationary Operational Environmental Satellite (GOES) infrared data were used to study the effect of land use on the diurnal surface temperature fluctuation. Five major land use types in southern Florida: the sandy soil agricultural area; the Everglades Agricultural Area (EAA); the conservation areas; the Natural Everglades Area (NEA); and Lake Okeechobee; were observed. The average daytime and nocturnal surface temperatures of sandy soil in agricultural areas was lower than that of organic soil in agricultural areas. The average temperature of organic soil in agricultural areas was lower than that of organic soil in conservation areas. The surface temperature in the wet marsh area was much lower than that in a large water-storage lake. A land use change in the EAA, and an increase in the water storage in Lake Okeechobee and the conservation areas could influence the microclimate.
Abstract
In this study, the performance of two advanced land surface models (LSMs; Noah LSM and Pleim–Xiu LSM) coupled with the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5), version 3.7.2, in simulating the near-surface air temperature in the greater Göteborg area in Sweden is evaluated and compared using the GÖTE2001 field campaign data. Further, the effects of different planetary boundary layer schemes [Eta and Medium-Range Forecast (MRF) PBLs] for Noah LSM and soil moisture initialization approaches for Pleim–Xiu LSM are investigated. The investigation focuses on the evaluation and comparison of diurnal cycle intensity and maximum and minimum temperatures, as well as the urban heat island during the daytime and nighttime under the clear-sky and cloudy/rainy weather conditions for different experimental schemes. The results indicate that 1) there is an evident difference between Noah LSM and Pleim–Xiu LSM in simulating the near-surface air temperature, especially in the modeled urban heat island; 2) there is no evident difference in the model performance between the Eta PBL and MRF PBL coupled with the Noah LSM; and 3) soil moisture initialization is of crucial importance for model performance in the Pleim–Xiu LSM. In addition, owing to the recent release of MM5, version 3.7.3, some experiments done with version 3.7.2 were repeated to reveal the effects of the modifications in the Noah LSM and Pleim–Xiu LSM. The modification to longwave radiation parameterizations in Noah LSM significantly improves model performance while the adjustment of emissivity, one of the vegetation properties, affects Pleim–Xiu LSM performance to a larger extent. The study suggests that improvements both in Noah LSM physics and in Pleim–Xiu LSM initialization of soil moisture and parameterization of vegetation properties are important.
Abstract
In this study, the performance of two advanced land surface models (LSMs; Noah LSM and Pleim–Xiu LSM) coupled with the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5), version 3.7.2, in simulating the near-surface air temperature in the greater Göteborg area in Sweden is evaluated and compared using the GÖTE2001 field campaign data. Further, the effects of different planetary boundary layer schemes [Eta and Medium-Range Forecast (MRF) PBLs] for Noah LSM and soil moisture initialization approaches for Pleim–Xiu LSM are investigated. The investigation focuses on the evaluation and comparison of diurnal cycle intensity and maximum and minimum temperatures, as well as the urban heat island during the daytime and nighttime under the clear-sky and cloudy/rainy weather conditions for different experimental schemes. The results indicate that 1) there is an evident difference between Noah LSM and Pleim–Xiu LSM in simulating the near-surface air temperature, especially in the modeled urban heat island; 2) there is no evident difference in the model performance between the Eta PBL and MRF PBL coupled with the Noah LSM; and 3) soil moisture initialization is of crucial importance for model performance in the Pleim–Xiu LSM. In addition, owing to the recent release of MM5, version 3.7.3, some experiments done with version 3.7.2 were repeated to reveal the effects of the modifications in the Noah LSM and Pleim–Xiu LSM. The modification to longwave radiation parameterizations in Noah LSM significantly improves model performance while the adjustment of emissivity, one of the vegetation properties, affects Pleim–Xiu LSM performance to a larger extent. The study suggests that improvements both in Noah LSM physics and in Pleim–Xiu LSM initialization of soil moisture and parameterization of vegetation properties are important.
Abstract
Infrared digital data from geostationary satellites were used to demonstrate the usefulness of remotely sensed surface temperature data to delineate microscale and mesoscale climates. Nocturnal winter data (December-February) from 1976–77 to 1978–79 over Florida revealed noticeable contrasts in surface temperature patterns. Colder areas were associated with low soil moisture content in the upper layers of excessively-drained and well-drained sandy soils, whereas warmer areas were associated with bodies of water, wetlands, or poorly drained soils. An unexpected surface temperature pattern for one night where the north-central Florida climatic zone was colder than the north Florida climatic zone was found to be caused by differences in antecedent frontal rainfall. Differences in surface radiant energy fluxes over these two areas at 0200 EST 20 December 1977, based on average satellite-sensed surface temperatures, were compared with differences in soil heat fluxes that were computed from 1.5 m climatological temperatures and soil thermal properties by use of a simplified surface energy balance equation. The difference in computed soil heat fluxes was in reasonable agreement with the difference in radiant energy fluxes from the surface of the two areas. It was therefore concluded that this method could be used to compute differences in thermal inertia of the surface layer of different areas based on satellite and climatological temperature data.
Abstract
Infrared digital data from geostationary satellites were used to demonstrate the usefulness of remotely sensed surface temperature data to delineate microscale and mesoscale climates. Nocturnal winter data (December-February) from 1976–77 to 1978–79 over Florida revealed noticeable contrasts in surface temperature patterns. Colder areas were associated with low soil moisture content in the upper layers of excessively-drained and well-drained sandy soils, whereas warmer areas were associated with bodies of water, wetlands, or poorly drained soils. An unexpected surface temperature pattern for one night where the north-central Florida climatic zone was colder than the north Florida climatic zone was found to be caused by differences in antecedent frontal rainfall. Differences in surface radiant energy fluxes over these two areas at 0200 EST 20 December 1977, based on average satellite-sensed surface temperatures, were compared with differences in soil heat fluxes that were computed from 1.5 m climatological temperatures and soil thermal properties by use of a simplified surface energy balance equation. The difference in computed soil heat fluxes was in reasonable agreement with the difference in radiant energy fluxes from the surface of the two areas. It was therefore concluded that this method could be used to compute differences in thermal inertia of the surface layer of different areas based on satellite and climatological temperature data.
Abstract
Surface temperature patterns of drained organic soil farmland in the Everglades Agricultural Area south of Lake Okeechobee, Florida, were studied using GOES-1 satellite infrared digital data for the winters of 1976–77 and 1977–78. Local thermal features were explained in terms of soil types, soil depths and climate modification caused by organic soil drainage. A cold-prone area was delineated in the Everglades Agricultural Area with satellite data. The winter nighttime surface temperatures of this area were more characteristic of north central Florida, approximately 370 km further north, than of surrounding areas of mineral soil or undrained organic soil.
Abstract
Surface temperature patterns of drained organic soil farmland in the Everglades Agricultural Area south of Lake Okeechobee, Florida, were studied using GOES-1 satellite infrared digital data for the winters of 1976–77 and 1977–78. Local thermal features were explained in terms of soil types, soil depths and climate modification caused by organic soil drainage. A cold-prone area was delineated in the Everglades Agricultural Area with satellite data. The winter nighttime surface temperatures of this area were more characteristic of north central Florida, approximately 370 km further north, than of surrounding areas of mineral soil or undrained organic soil.
Abstract
Retrospective-analysis (or reanalysis) systems merge observations and models to provide global four-dimensional earth system data encompassing many physical and dynamical processes. Precipitation is one critical diagnostic that is not only sensitive to the observing system and model physics, but also reflects the general circulation. Climate records of observed precipitation through a merged satellite and gauge dataset provide a reference for comparison, though not without their own uncertainty. In this study, five reanalyses precipitation fields are compared with two observed data products to assess the strengths and weaknesses of the reanalyses. Taylor diagrams show the skill of the reanalyses relative to the reference dataset. While there is a general sense that the reanalyses precipitation data are improving in recent systems, it is not always the case. In some ocean regions, NCEP–NCAR reanalysis spatial patterns are closer to observed precipitation than NCEP–Department of Energy. The 40-yr ECMWF reanalysis (ERA-40) produces reasonable comparisons over Northern Hemisphere continents, but less so in the tropical oceans. On the other hand, the most recent reanalysis, the Japanese 25-yr reanalysis (JRA-25), shows good comparisons in both the Northern Hemisphere continents and the tropical oceans but contains distinct variation according to the available observing systems. The statistics and methods used are also tested on short experiments from a data assimilation system proposed to perform a satellite-era reanalysis.
Abstract
Retrospective-analysis (or reanalysis) systems merge observations and models to provide global four-dimensional earth system data encompassing many physical and dynamical processes. Precipitation is one critical diagnostic that is not only sensitive to the observing system and model physics, but also reflects the general circulation. Climate records of observed precipitation through a merged satellite and gauge dataset provide a reference for comparison, though not without their own uncertainty. In this study, five reanalyses precipitation fields are compared with two observed data products to assess the strengths and weaknesses of the reanalyses. Taylor diagrams show the skill of the reanalyses relative to the reference dataset. While there is a general sense that the reanalyses precipitation data are improving in recent systems, it is not always the case. In some ocean regions, NCEP–NCAR reanalysis spatial patterns are closer to observed precipitation than NCEP–Department of Energy. The 40-yr ECMWF reanalysis (ERA-40) produces reasonable comparisons over Northern Hemisphere continents, but less so in the tropical oceans. On the other hand, the most recent reanalysis, the Japanese 25-yr reanalysis (JRA-25), shows good comparisons in both the Northern Hemisphere continents and the tropical oceans but contains distinct variation according to the available observing systems. The statistics and methods used are also tested on short experiments from a data assimilation system proposed to perform a satellite-era reanalysis.
Abstract
Cloud vertical profile measurements from the CALIPSO and CloudSat active sensors are used to improve top-of-atmosphere (TOA) shortwave (SW) broadband (BB) irradiance computations. The active sensor measurements, which occasionally miss parts of the cloud columns because of the full attenuation of sensor signals, surface clutter, or insensitivity to a certain range of cloud particle sizes, are adjusted using column-integrated cloud optical depth derived from the passive MODIS sensor. Specifically, we consider two steps in generating cloud profiles from multiple sensors for irradiance computations. First, cloud extinction coefficient and cloud effective radius (CER) profiles are merged using available active and passive measurements. Second, the merged cloud extinction profiles are constrained by the MODIS visible scaled cloud optical depth, defined as a visible cloud optical depth multiplied by (1 − asymmetry parameter), to compensate for missing cloud parts by active sensors. It is shown that the multisensor-combined cloud profiles significantly reduce positive TOA SW BB biases, relative to those with MODIS-derived cloud properties only. The improvement is more pronounced for optically thick clouds, where MODIS ice CER is largely underestimated. Within the SW BB (0.18–4 μm), the 1.04–1.90-μm spectral region is mainly affected by the CER, where both the cloud absorption and solar incoming irradiance are considerable.
Significance Statement
The purpose of this study is to improve shortwave irradiance computations at the top of the atmosphere by using combined cloud properties from active and passive sensor measurements. Relative to the simulation results with passive sensor cloud measurements only, the combined cloud profiles provide more accurate shortwave simulation results. This is achieved by more realistic profiles of cloud extinction coefficient and cloud particle effective radius. The benefit is pronounced for optically thick clouds composed of large ice particles.
Abstract
Cloud vertical profile measurements from the CALIPSO and CloudSat active sensors are used to improve top-of-atmosphere (TOA) shortwave (SW) broadband (BB) irradiance computations. The active sensor measurements, which occasionally miss parts of the cloud columns because of the full attenuation of sensor signals, surface clutter, or insensitivity to a certain range of cloud particle sizes, are adjusted using column-integrated cloud optical depth derived from the passive MODIS sensor. Specifically, we consider two steps in generating cloud profiles from multiple sensors for irradiance computations. First, cloud extinction coefficient and cloud effective radius (CER) profiles are merged using available active and passive measurements. Second, the merged cloud extinction profiles are constrained by the MODIS visible scaled cloud optical depth, defined as a visible cloud optical depth multiplied by (1 − asymmetry parameter), to compensate for missing cloud parts by active sensors. It is shown that the multisensor-combined cloud profiles significantly reduce positive TOA SW BB biases, relative to those with MODIS-derived cloud properties only. The improvement is more pronounced for optically thick clouds, where MODIS ice CER is largely underestimated. Within the SW BB (0.18–4 μm), the 1.04–1.90-μm spectral region is mainly affected by the CER, where both the cloud absorption and solar incoming irradiance are considerable.
Significance Statement
The purpose of this study is to improve shortwave irradiance computations at the top of the atmosphere by using combined cloud properties from active and passive sensor measurements. Relative to the simulation results with passive sensor cloud measurements only, the combined cloud profiles provide more accurate shortwave simulation results. This is achieved by more realistic profiles of cloud extinction coefficient and cloud particle effective radius. The benefit is pronounced for optically thick clouds composed of large ice particles.
Abstract
The Pearl River Delta (PRD) region, located in the southern part of Guangdong Province in China, is one of the most rapidly developing regions in the world. The evolution of local and regional sea-breeze circulation (SBC) is believed to be responsible for forming meteorological conditions for high air-pollution episodes in the PRD. To understand better the impacts of urbanization and its associated urban heat island (UHI) on the local- and regional-scale atmospheric circulations over PRD, a number of high-resolution numerical experiments, with different approaches to treat the land surface and urban processes, have been conducted using the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5). The results show that an accurate urban land-use dataset and a proper urban land-use parameterization are critical for the mesoscale model to capture the major features of the observed UHI effect and land–sea-breeze circulations in the PRD. Stronger UHI in the PRD increases the differential temperature gradient between urbanized areas and nearby ocean surface and hence enhances the mesoscale SBC. The SBC front consequently penetrates farther inland to overcome the prevailing easterly flow in the western part of inland Hong Kong. Additional sensitivity studies indicate that further industrial development and urbanization will strengthen the daytime SBC as well as increase the air temperature in the lowest 2 km of the atmosphere.
Abstract
The Pearl River Delta (PRD) region, located in the southern part of Guangdong Province in China, is one of the most rapidly developing regions in the world. The evolution of local and regional sea-breeze circulation (SBC) is believed to be responsible for forming meteorological conditions for high air-pollution episodes in the PRD. To understand better the impacts of urbanization and its associated urban heat island (UHI) on the local- and regional-scale atmospheric circulations over PRD, a number of high-resolution numerical experiments, with different approaches to treat the land surface and urban processes, have been conducted using the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5). The results show that an accurate urban land-use dataset and a proper urban land-use parameterization are critical for the mesoscale model to capture the major features of the observed UHI effect and land–sea-breeze circulations in the PRD. Stronger UHI in the PRD increases the differential temperature gradient between urbanized areas and nearby ocean surface and hence enhances the mesoscale SBC. The SBC front consequently penetrates farther inland to overcome the prevailing easterly flow in the western part of inland Hong Kong. Additional sensitivity studies indicate that further industrial development and urbanization will strengthen the daytime SBC as well as increase the air temperature in the lowest 2 km of the atmosphere.
Abstract
Cloud macrophysical changes over the Pacific Ocean from 2007 to 2017 are examined by combining CALIPSO and CloudSat (CALCS) active-sensor measurements, and these are compared with MODIS passive-sensor observations. Both CALCS and MODIS capture well-known features of cloud changes over the Pacific associated with meteorological conditions during El Niño–Southern Oscillation (ENSO) events. For example, midcloud (cloud tops at 3–10 km) and high cloud (cloud tops at 10–18 km) amounts increase with relative humidity (RH) anomalies. However, a better correlation is obtained between CALCS cloud volume and RH anomalies, confirming more accurate CALCS cloud boundaries than MODIS. Both CALCS and MODIS show that low cloud (cloud tops at 0–3 km) amounts increase with EIS and decrease with SST over the eastern Pacific, consistent with earlier studies. It is also further shown that the low cloud amounts do not increase with positive EIS anomalies if SST anomalies are positive. While similar features are found between CALCS and MODIS low cloud anomalies, differences also exist. First, relative to CALCS, MODIS shows stronger anticorrelation between low and mid/high cloud anomalies over the central and western Pacific, which is largely due to the limitation in detecting overlapping clouds from passive MODIS measurements. Second, relative to CALCS, MODIS shows smaller impacts of mid- and high clouds on the low troposphere (<3 km). The differences are due to the underestimation of MODIS cloud layer thicknesses of mid- and high clouds.
Abstract
Cloud macrophysical changes over the Pacific Ocean from 2007 to 2017 are examined by combining CALIPSO and CloudSat (CALCS) active-sensor measurements, and these are compared with MODIS passive-sensor observations. Both CALCS and MODIS capture well-known features of cloud changes over the Pacific associated with meteorological conditions during El Niño–Southern Oscillation (ENSO) events. For example, midcloud (cloud tops at 3–10 km) and high cloud (cloud tops at 10–18 km) amounts increase with relative humidity (RH) anomalies. However, a better correlation is obtained between CALCS cloud volume and RH anomalies, confirming more accurate CALCS cloud boundaries than MODIS. Both CALCS and MODIS show that low cloud (cloud tops at 0–3 km) amounts increase with EIS and decrease with SST over the eastern Pacific, consistent with earlier studies. It is also further shown that the low cloud amounts do not increase with positive EIS anomalies if SST anomalies are positive. While similar features are found between CALCS and MODIS low cloud anomalies, differences also exist. First, relative to CALCS, MODIS shows stronger anticorrelation between low and mid/high cloud anomalies over the central and western Pacific, which is largely due to the limitation in detecting overlapping clouds from passive MODIS measurements. Second, relative to CALCS, MODIS shows smaller impacts of mid- and high clouds on the low troposphere (<3 km). The differences are due to the underestimation of MODIS cloud layer thicknesses of mid- and high clouds.
Abstract
Given the rapid increase in the use of operational mesoscale models to satisfy different specialized needs, it is important for the community to share ideas and solutions for meeting the many associated challenges that encompass science, technology, education, and training. As a contribution toward this objective, this paper begins a series that reports on the characteristics and performance of an operational mesogamma-scale weather analysis and forecasting system that has been developed for use by the U.S. Army Test and Evaluation Command. During the more than five years that this four-dimensional weather system has been in use at seven U.S. Army test ranges, valuable experience has been gained about the production and effective use of high-resolution model products for satisfying a variety of needs. This paper serves as a foundation for the rest of the papers in the series by describing the operational requirements for the system, the data assimilation and forecasting system characteristics, and the forecaster training that is required for the finescale products to be used effectively.
Abstract
Given the rapid increase in the use of operational mesoscale models to satisfy different specialized needs, it is important for the community to share ideas and solutions for meeting the many associated challenges that encompass science, technology, education, and training. As a contribution toward this objective, this paper begins a series that reports on the characteristics and performance of an operational mesogamma-scale weather analysis and forecasting system that has been developed for use by the U.S. Army Test and Evaluation Command. During the more than five years that this four-dimensional weather system has been in use at seven U.S. Army test ranges, valuable experience has been gained about the production and effective use of high-resolution model products for satisfying a variety of needs. This paper serves as a foundation for the rest of the papers in the series by describing the operational requirements for the system, the data assimilation and forecasting system characteristics, and the forecaster training that is required for the finescale products to be used effectively.
Abstract
A large number of urban surface energy balance models now exist with different assumptions about the important features of the surface and exchange processes that need to be incorporated. To date, no comparison of these models has been conducted; in contrast, models for natural surfaces have been compared extensively as part of the Project for Intercomparison of Land-surface Parameterization Schemes. Here, the methods and first results from an extensive international comparison of 33 models are presented. The aim of the comparison overall is to understand the complexity required to model energy and water exchanges in urban areas. The degree of complexity included in the models is outlined and impacts on model performance are discussed. During the comparison there have been significant developments in the models with resulting improvements in performance (root-mean-square error falling by up to two-thirds). Evaluation is based on a dataset containing net all-wave radiation, sensible heat, and latent heat flux observations for an industrial area in Vancouver, British Columbia, Canada. The aim of the comparison is twofold: to identify those modeling approaches that minimize the errors in the simulated fluxes of the urban energy balance and to determine the degree of model complexity required for accurate simulations. There is evidence that some classes of models perform better for individual fluxes but no model performs best or worst for all fluxes. In general, the simpler models perform as well as the more complex models based on all statistical measures. Generally the schemes have best overall capability to model net all-wave radiation and least capability to model latent heat flux.
Abstract
A large number of urban surface energy balance models now exist with different assumptions about the important features of the surface and exchange processes that need to be incorporated. To date, no comparison of these models has been conducted; in contrast, models for natural surfaces have been compared extensively as part of the Project for Intercomparison of Land-surface Parameterization Schemes. Here, the methods and first results from an extensive international comparison of 33 models are presented. The aim of the comparison overall is to understand the complexity required to model energy and water exchanges in urban areas. The degree of complexity included in the models is outlined and impacts on model performance are discussed. During the comparison there have been significant developments in the models with resulting improvements in performance (root-mean-square error falling by up to two-thirds). Evaluation is based on a dataset containing net all-wave radiation, sensible heat, and latent heat flux observations for an industrial area in Vancouver, British Columbia, Canada. The aim of the comparison is twofold: to identify those modeling approaches that minimize the errors in the simulated fluxes of the urban energy balance and to determine the degree of model complexity required for accurate simulations. There is evidence that some classes of models perform better for individual fluxes but no model performs best or worst for all fluxes. In general, the simpler models perform as well as the more complex models based on all statistical measures. Generally the schemes have best overall capability to model net all-wave radiation and least capability to model latent heat flux.
