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Abstract
Three-dimensional cloud field morphology contributes to scene-averaged cloud reflectivity, but climate models do not currently incorporate methods of identifying situations where this contribution is substantial. This work represents an effort to identify atmospheric conditions conducive to the formation of cloud field configurations that significantly affect shortwave radiative fluxes. Once identified, these characteristics may form the basis of a parameterization that accounts for radiative impact of complex cloud fields. A k-means clustering algorithm is applied to observed cloud properties taken from the Atmospheric Radiation Measurement Program tropical western Pacific sites to identify specific cloud regimes. Results from a stand-alone stochastic model, which statistically represents shortwave radiative transfer through broken cloud fields, are compared with those of a plane-parallel model. The aggregate scenes in each regime are examined to measure the bias in shortwave flux calculations due to neglected cloud field morphology. The results from the model comparison and cluster analysis suggest that cloud fraction, vertical wind shear, and spacing between cloudy layers are all important indicators of complex cloud field geometry and that these criteria are most often met in cloud regimes characterized by moderate to strong convection. The cluster criteria are applied to output from the Community Climate System Model (version 3.0) and it is found that the presence of persistent high cirrus cloud in model simulations inhibits identification of specific cloud regimes.
Abstract
Three-dimensional cloud field morphology contributes to scene-averaged cloud reflectivity, but climate models do not currently incorporate methods of identifying situations where this contribution is substantial. This work represents an effort to identify atmospheric conditions conducive to the formation of cloud field configurations that significantly affect shortwave radiative fluxes. Once identified, these characteristics may form the basis of a parameterization that accounts for radiative impact of complex cloud fields. A k-means clustering algorithm is applied to observed cloud properties taken from the Atmospheric Radiation Measurement Program tropical western Pacific sites to identify specific cloud regimes. Results from a stand-alone stochastic model, which statistically represents shortwave radiative transfer through broken cloud fields, are compared with those of a plane-parallel model. The aggregate scenes in each regime are examined to measure the bias in shortwave flux calculations due to neglected cloud field morphology. The results from the model comparison and cluster analysis suggest that cloud fraction, vertical wind shear, and spacing between cloudy layers are all important indicators of complex cloud field geometry and that these criteria are most often met in cloud regimes characterized by moderate to strong convection. The cluster criteria are applied to output from the Community Climate System Model (version 3.0) and it is found that the presence of persistent high cirrus cloud in model simulations inhibits identification of specific cloud regimes.
Abstract
Satellite drift is a historical issue affecting the consistency of those few satellite records capable of being used for studies on climate time scales. Here, the authors address this issue for the Pathfinder Atmospheres Extended (PATMOS-x)/Advanced Very High Resolution Radiometer (AVHRR) cloudiness record, which spans three decades and 11 disparate sensors. A two-harmonic sinusoidal function is fit to a mean diurnal cycle of cloudiness derived over the course of the entire AVHRR record. The authors validate this function against measurements from Geostationary Operational Environmental Satellite (GOES) sensors, finding good agreement, and then test the stability of the diurnal cycle over the course of the AVHRR record. It is found that the diurnal cycle is subject to some interannual variability over land but that the differences are somewhat offset when averaged over an entire day. The fit function is used to generate daily averaged time series of ice, water, and total cloudiness over the tropics, where it is found that the diurnal correction affects the magnitude and even the sign of long-term cloudiness trends. A statistical method is applied to determine the minimum length of time required to detect significant trends, and the authors find that only recently have they begun generating satellite records of sufficient length to detect trends in cloudiness.
Abstract
Satellite drift is a historical issue affecting the consistency of those few satellite records capable of being used for studies on climate time scales. Here, the authors address this issue for the Pathfinder Atmospheres Extended (PATMOS-x)/Advanced Very High Resolution Radiometer (AVHRR) cloudiness record, which spans three decades and 11 disparate sensors. A two-harmonic sinusoidal function is fit to a mean diurnal cycle of cloudiness derived over the course of the entire AVHRR record. The authors validate this function against measurements from Geostationary Operational Environmental Satellite (GOES) sensors, finding good agreement, and then test the stability of the diurnal cycle over the course of the AVHRR record. It is found that the diurnal cycle is subject to some interannual variability over land but that the differences are somewhat offset when averaged over an entire day. The fit function is used to generate daily averaged time series of ice, water, and total cloudiness over the tropics, where it is found that the diurnal correction affects the magnitude and even the sign of long-term cloudiness trends. A statistical method is applied to determine the minimum length of time required to detect significant trends, and the authors find that only recently have they begun generating satellite records of sufficient length to detect trends in cloudiness.
Abstract
The emergence of satellite-based cloud records of climate length and quality hold tremendous potential for climate model development, climate monitoring, and studies on global water cycling and its subsequent energetics. This article examines the more than 30-yr Pathfinder Atmospheres–Extended (PATMOS-x) Advanced Very High Resolution Radiometer (AVHRR) cloudiness record over North America and assesses its suitability as a climate-quality data record. A loss of ~4.2% total cloudiness is observed between 1982 and 2012 over a North American domain centered over the contiguous United States. While ENSO can explain some of the observed change, a weather state clustering analysis identifies shifts in weather patterns that result in loss of water cloud over the Great Lakes and cirrus over southern portions of the United States. The radiative properties of the shifting weather states are characterized, and the results suggest that extended cloud satellite records may prove useful tools for increasing knowledge of cloud feedbacks, a long-standing issue in the climate change community.
Abstract
The emergence of satellite-based cloud records of climate length and quality hold tremendous potential for climate model development, climate monitoring, and studies on global water cycling and its subsequent energetics. This article examines the more than 30-yr Pathfinder Atmospheres–Extended (PATMOS-x) Advanced Very High Resolution Radiometer (AVHRR) cloudiness record over North America and assesses its suitability as a climate-quality data record. A loss of ~4.2% total cloudiness is observed between 1982 and 2012 over a North American domain centered over the contiguous United States. While ENSO can explain some of the observed change, a weather state clustering analysis identifies shifts in weather patterns that result in loss of water cloud over the Great Lakes and cirrus over southern portions of the United States. The radiative properties of the shifting weather states are characterized, and the results suggest that extended cloud satellite records may prove useful tools for increasing knowledge of cloud feedbacks, a long-standing issue in the climate change community.
Abstract
A dataset is generated from a method to retrieve distributions of cloud liquid water path over partially cloudy scenes. The method was introduced in a 2011 paper by Foster and coauthors that described the theory and provided test cases. Here it has been applied to Moderate Resolution Imaging Spectroradiometer (MODIS) collection-5 and collection-6 cloud products, resulting in a value-added dataset that contains adjusted distributions of cloud liquid water path for more than 10 years for marine liquid cloud for both Aqua and Terra. This method adjusts horizontal distributions of cloud optical properties to be more consistent with observed visible reflectance and is especially useful in areas where cloud optical retrievals fail or are considered to be of low quality. Potential uses of this dataset include validation of climate and radiative transfer models and facilitation of studies that intercompare satellite records. Results show that the fit method is able to reduce bias between observed visible reflectance and that derived from optical retrievals by up to an average improvement of 3%. The level of improvement is dependent on several factors, including seasonality, viewing geometry, cloud fraction, and cloud heterogeneity. Applications of this dataset are explored through a satellite intercomparison with PATMOS-x and Global Change Observation Mission–First Water (GCOM-W1; “SHIZUKU”) AMSR-2 and use of a Monte Carlo radiative transfer model. From the 3D Monte Carlo model simulations, albedo biases are found when the method is applied, with seasonal averages that range over 0.02–0.06.
Abstract
A dataset is generated from a method to retrieve distributions of cloud liquid water path over partially cloudy scenes. The method was introduced in a 2011 paper by Foster and coauthors that described the theory and provided test cases. Here it has been applied to Moderate Resolution Imaging Spectroradiometer (MODIS) collection-5 and collection-6 cloud products, resulting in a value-added dataset that contains adjusted distributions of cloud liquid water path for more than 10 years for marine liquid cloud for both Aqua and Terra. This method adjusts horizontal distributions of cloud optical properties to be more consistent with observed visible reflectance and is especially useful in areas where cloud optical retrievals fail or are considered to be of low quality. Potential uses of this dataset include validation of climate and radiative transfer models and facilitation of studies that intercompare satellite records. Results show that the fit method is able to reduce bias between observed visible reflectance and that derived from optical retrievals by up to an average improvement of 3%. The level of improvement is dependent on several factors, including seasonality, viewing geometry, cloud fraction, and cloud heterogeneity. Applications of this dataset are explored through a satellite intercomparison with PATMOS-x and Global Change Observation Mission–First Water (GCOM-W1; “SHIZUKU”) AMSR-2 and use of a Monte Carlo radiative transfer model. From the 3D Monte Carlo model simulations, albedo biases are found when the method is applied, with seasonal averages that range over 0.02–0.06.
Abstract
A new method of deriving statistical moments related to the distribution of liquid water path over partially cloudy scenes is tested using a satellite cloud climatology. The method improves the ability to reconstruct total-scene visible reflectance when compared with an approach that relies on valid liquid water path retrievals, and thus it maintains physical consistency with the primary satellite observations when deriving cloud climatologies. A global application of the new method finds a mean bias of −0.008 ± 0.017 when reconstructing total-scene reflectance from liquid water path distributions, as compared with a bias of 0.05 ± 0.047 when using a conventional approach. Application of the method to a multidecadal cloud climatology suggests that this may provide a means of identifying data artifacts that could affect long-term cloud property trends. The conservation of reflectance plus the ease of applicability to various satellite datasets makes this method a valuable tool for model validation and comparison of satellite climatologies. Gaussian and gamma functions are used to approximate the distribution of horizontal subgrid-scale liquid water path for 1° × 1° scenes, and while both functions perform well for the majority of atmospheric conditions, it is found that the Gaussian distribution generates a negative bias for cases in which visible reflectance is very high and that neither function is able to represent liquid water path well in the few cases in which the observed distribution is bi- or multimodal.
Abstract
A new method of deriving statistical moments related to the distribution of liquid water path over partially cloudy scenes is tested using a satellite cloud climatology. The method improves the ability to reconstruct total-scene visible reflectance when compared with an approach that relies on valid liquid water path retrievals, and thus it maintains physical consistency with the primary satellite observations when deriving cloud climatologies. A global application of the new method finds a mean bias of −0.008 ± 0.017 when reconstructing total-scene reflectance from liquid water path distributions, as compared with a bias of 0.05 ± 0.047 when using a conventional approach. Application of the method to a multidecadal cloud climatology suggests that this may provide a means of identifying data artifacts that could affect long-term cloud property trends. The conservation of reflectance plus the ease of applicability to various satellite datasets makes this method a valuable tool for model validation and comparison of satellite climatologies. Gaussian and gamma functions are used to approximate the distribution of horizontal subgrid-scale liquid water path for 1° × 1° scenes, and while both functions perform well for the majority of atmospheric conditions, it is found that the Gaussian distribution generates a negative bias for cases in which visible reflectance is very high and that neither function is able to represent liquid water path well in the few cases in which the observed distribution is bi- or multimodal.
Abstract
During the spring of 1995, an operational forecast experiment using a three-dimensional cloud model was carried out for the north Texas region. Gridpoint soundings were obtained from the daily operational numerical weather prediction models run at the National Centers for Environmental Prediction, and these soundings were then used to initialize a limited-domain cloud-resolving model in an attempt to predict convective storm type and morphology in a timely manner. The results indicate that this type of convective forecast may be useful in the operational environment, despite several limitations associated with this methodology. One interesting result from the experiment is that while the gridpoint soundings obtained from the NCEP models generally overforecast instability and vertical wind shear, the resulting convective storm evolution and morphology in the cloud model was often similar to that of the observed storms. Therefore the “overforecast” of mesoscale environment’s instability and vertical wind shear still resulted in a thunderstorm-scale forecast that provided useful information to operational forecasters.
Abstract
During the spring of 1995, an operational forecast experiment using a three-dimensional cloud model was carried out for the north Texas region. Gridpoint soundings were obtained from the daily operational numerical weather prediction models run at the National Centers for Environmental Prediction, and these soundings were then used to initialize a limited-domain cloud-resolving model in an attempt to predict convective storm type and morphology in a timely manner. The results indicate that this type of convective forecast may be useful in the operational environment, despite several limitations associated with this methodology. One interesting result from the experiment is that while the gridpoint soundings obtained from the NCEP models generally overforecast instability and vertical wind shear, the resulting convective storm evolution and morphology in the cloud model was often similar to that of the observed storms. Therefore the “overforecast” of mesoscale environment’s instability and vertical wind shear still resulted in a thunderstorm-scale forecast that provided useful information to operational forecasters.
Abstract
The naive Bayesian methodology has been applied to the challenging problem of cloud detection with NOAA’s Advanced Very High Resolution Radiometer (AVHRR). An analysis of collocated NOAA-18/AVHRR and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO)/Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) observations was used to automatically and globally derive the Bayesian classifiers. The resulting algorithm used six Bayesian classifiers computed separately for seven surface types. Relative to CALIPSO, the final results show a probability of correct detection of roughly 90% over water, deserts, and snow-free land; 82% over the Arctic; and below 80% over the Antarctic. This technique is applied within the NOAA Pathfinder Atmosphere’s Extended (PATMOS-x) climate dataset and the Clouds from AVHRR Extended (CLAVR-x) real-time product generation system. Comparisons of the PATMOS-x results with those from International Satellite Cloud Climatology Project (ISCCP) and Moderate Resolution Imaging Spectroradiometer (MODIS) indicate close agreement with zonal mean differences in cloud amount being less than 5% over most zones. Most areas of difference coincided with regions where the Bayesian cloud mask reported elevated uncertainties. The ability to report uncertainties is a critical component of this approach.
Abstract
The naive Bayesian methodology has been applied to the challenging problem of cloud detection with NOAA’s Advanced Very High Resolution Radiometer (AVHRR). An analysis of collocated NOAA-18/AVHRR and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO)/Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) observations was used to automatically and globally derive the Bayesian classifiers. The resulting algorithm used six Bayesian classifiers computed separately for seven surface types. Relative to CALIPSO, the final results show a probability of correct detection of roughly 90% over water, deserts, and snow-free land; 82% over the Arctic; and below 80% over the Antarctic. This technique is applied within the NOAA Pathfinder Atmosphere’s Extended (PATMOS-x) climate dataset and the Clouds from AVHRR Extended (CLAVR-x) real-time product generation system. Comparisons of the PATMOS-x results with those from International Satellite Cloud Climatology Project (ISCCP) and Moderate Resolution Imaging Spectroradiometer (MODIS) indicate close agreement with zonal mean differences in cloud amount being less than 5% over most zones. Most areas of difference coincided with regions where the Bayesian cloud mask reported elevated uncertainties. The ability to report uncertainties is a critical component of this approach.
Abstract
Variability and trends in total cloud cover for 1982–2009 across the contiguous United States from the International Satellite Cloud Climatology Project (ISCCP), AVHRR Pathfinder Atmospheres–Extended (PATMOS-x), and EUMETSAT Satellite Application Facility on Climate Monitoring Clouds, Albedo and Radiation from AVHRR Data Edition 1 (CLARA-A1) satellite datasets are assessed using homogeneity-adjusted weather station data. The station data, considered as “ground truth” in the evaluation, are generally well correlated with the ISCCP and PATMOS-x data and with the physically related variables diurnal temperature range, precipitation, and surface solar radiation. Among the satellite products, overall, the PATMOS-x data have the highest interannual correlations with the weather station cloud data and those other physically related variables. The CLARA-A1 daytime dataset generally shows the lowest correlations, even after trends are removed. For the U.S. mean, the station dataset shows a negative but not statistically significant trend of −0.40% decade−1, and satellite products show larger downward trends ranging from −0.55% to −5.00% decade−1 for 1984–2007. PATMOS-x 1330 local time trends for U.S. mean cloud cover are closest to those in the station data, followed by the PATMOS-x diurnally corrected dataset and ISCCP, with CLARA-A1 having a large negative trend contrasting strongly with the station data. These results tend to validate the usefulness of weather station cloud data for monitoring changes in cloud cover, and they show that the long-term stability of satellite cloud datasets can be assessed by comparison to homogeneity-adjusted station data and other physically related variables.
Abstract
Variability and trends in total cloud cover for 1982–2009 across the contiguous United States from the International Satellite Cloud Climatology Project (ISCCP), AVHRR Pathfinder Atmospheres–Extended (PATMOS-x), and EUMETSAT Satellite Application Facility on Climate Monitoring Clouds, Albedo and Radiation from AVHRR Data Edition 1 (CLARA-A1) satellite datasets are assessed using homogeneity-adjusted weather station data. The station data, considered as “ground truth” in the evaluation, are generally well correlated with the ISCCP and PATMOS-x data and with the physically related variables diurnal temperature range, precipitation, and surface solar radiation. Among the satellite products, overall, the PATMOS-x data have the highest interannual correlations with the weather station cloud data and those other physically related variables. The CLARA-A1 daytime dataset generally shows the lowest correlations, even after trends are removed. For the U.S. mean, the station dataset shows a negative but not statistically significant trend of −0.40% decade−1, and satellite products show larger downward trends ranging from −0.55% to −5.00% decade−1 for 1984–2007. PATMOS-x 1330 local time trends for U.S. mean cloud cover are closest to those in the station data, followed by the PATMOS-x diurnally corrected dataset and ISCCP, with CLARA-A1 having a large negative trend contrasting strongly with the station data. These results tend to validate the usefulness of weather station cloud data for monitoring changes in cloud cover, and they show that the long-term stability of satellite cloud datasets can be assessed by comparison to homogeneity-adjusted station data and other physically related variables.
The Advanced Very High Resolution Radiometer (AVHRR) Pathfinder Atmospheres–Extended (PATMOS-x) dataset offers over three decades of global observations from the NOAA Polar-orbiting Operational Environmental Satellite (POES) project and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) [Meteorological Operational (MetOp)] satellite series. The AVHRR has flown since 1978 and continues to provide radiometrically consistent observations with a spatial resolution of roughly 4 km and a temporal resolution of an ascending and descending node per satellite per day, achieving global coverage. The AVHRR PATMOS-x data provide calibrated AVHRR observations in addition to properties about tropospheric clouds and aerosols, Earth's surface, Earth's radiation budget, and relevant ancillary data. To provide three decades of data in a convenient format, PATMOS-x generates mapped and sampled results with a spatial resolution of 0.1° on a global latitude–longitude grid. This format avoids spatial or temporal averaging of data, thus maintaining the flexibility to conduct multidimensional analysis. Comparison of this format against the unsampled record demonstrates the ability to reproduce the pixel distribution to a high level of accuracy. AVHRR PATMOS-x is composed of data from 17 different sensors. An examination of cloud amount and total-sky albedo time series demonstrates that intersatellite biases are less than 2%. The comparison of the cloud amount time series to the Interim European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-Interim) demonstrates a high degree of correlation, indicating that sensor-to-sensor differences are also not contributing significantly to the observed climate variability in PATMOS-x. AVHRR PATMOS-x data are hosted by the National Climatic Data Center (NCDC) (available at www.ncdc.noaa.gov/cdr/operationalcdrs.html).
The Advanced Very High Resolution Radiometer (AVHRR) Pathfinder Atmospheres–Extended (PATMOS-x) dataset offers over three decades of global observations from the NOAA Polar-orbiting Operational Environmental Satellite (POES) project and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) [Meteorological Operational (MetOp)] satellite series. The AVHRR has flown since 1978 and continues to provide radiometrically consistent observations with a spatial resolution of roughly 4 km and a temporal resolution of an ascending and descending node per satellite per day, achieving global coverage. The AVHRR PATMOS-x data provide calibrated AVHRR observations in addition to properties about tropospheric clouds and aerosols, Earth's surface, Earth's radiation budget, and relevant ancillary data. To provide three decades of data in a convenient format, PATMOS-x generates mapped and sampled results with a spatial resolution of 0.1° on a global latitude–longitude grid. This format avoids spatial or temporal averaging of data, thus maintaining the flexibility to conduct multidimensional analysis. Comparison of this format against the unsampled record demonstrates the ability to reproduce the pixel distribution to a high level of accuracy. AVHRR PATMOS-x is composed of data from 17 different sensors. An examination of cloud amount and total-sky albedo time series demonstrates that intersatellite biases are less than 2%. The comparison of the cloud amount time series to the Interim European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-Interim) demonstrates a high degree of correlation, indicating that sensor-to-sensor differences are also not contributing significantly to the observed climate variability in PATMOS-x. AVHRR PATMOS-x data are hosted by the National Climatic Data Center (NCDC) (available at www.ncdc.noaa.gov/cdr/operationalcdrs.html).
Using Sounder Data to Improve Cirrus Cloud Height Estimation from Satellite ImagersAbstract
Modern polar-orbiting meteorological satellites provide both imaging and sounding observations simultaneously. Most imagers, however, do not have H2O and CO2 absorption bands and therefore struggle to accurately estimate the height of optically thin cirrus clouds. Sounders provide these needed observations, but at a spatial resolution that is too coarse to resolve many important cloud structures. This paper presents a technique to merge sounder and imager observations with the goal of maintaining the details offered by the imager’s high spatial resolution and the accuracy offered by the sounder’s spectral information. The technique involves deriving cloud temperatures from the sounder observations, interpolating the sounder temperatures to the imager pixels, and using the sounder temperatures as an additional constraint in the imager cloud height optimal estimation approach. This technique is demonstrated using collocated VIIRS and Cross-track Infrared Sounder (CrIS) observations with the impact of the sounder observations validated using coincident CALIPSO/CALIOP cloud heights These comparisons show significant improvement in the cloud heights for optically thin cirrus. The technique should be generally applicable to other imager/sounder pairs.
Abstract
Modern polar-orbiting meteorological satellites provide both imaging and sounding observations simultaneously. Most imagers, however, do not have H2O and CO2 absorption bands and therefore struggle to accurately estimate the height of optically thin cirrus clouds. Sounders provide these needed observations, but at a spatial resolution that is too coarse to resolve many important cloud structures. This paper presents a technique to merge sounder and imager observations with the goal of maintaining the details offered by the imager’s high spatial resolution and the accuracy offered by the sounder’s spectral information. The technique involves deriving cloud temperatures from the sounder observations, interpolating the sounder temperatures to the imager pixels, and using the sounder temperatures as an additional constraint in the imager cloud height optimal estimation approach. This technique is demonstrated using collocated VIIRS and Cross-track Infrared Sounder (CrIS) observations with the impact of the sounder observations validated using coincident CALIPSO/CALIOP cloud heights These comparisons show significant improvement in the cloud heights for optically thin cirrus. The technique should be generally applicable to other imager/sounder pairs.
