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Abstract
Deep-layer mean temperatures from Microwave Sounding Unit (MSU) observations have been used by scientists to study trends and interannual variations of tropospheric and lower-stratospheric temperature. The spatial resolution of MSU deep-layer mean temperatures is rather poor for studying trends in localized regions. A method is developed in which infrared observations from the High-resolution InfraRed Sounder (HIRS) is used in combination with MSU to derive deep-layer mean temperatures with improved vertical and horizontal resolution. Even though the relationship between infrared radiance and temperature is not linear, the layer associated with the mean temperature is shown to be well defined with a small airmass dependency that is similar to MSU’s airmass dependency. Preliminary validation of HIRS–MSU-derived layer mean temperatures with radiosonde layer mean temperatures show similar precision when compared to MSU-only derived temperatures.
Abstract
Deep-layer mean temperatures from Microwave Sounding Unit (MSU) observations have been used by scientists to study trends and interannual variations of tropospheric and lower-stratospheric temperature. The spatial resolution of MSU deep-layer mean temperatures is rather poor for studying trends in localized regions. A method is developed in which infrared observations from the High-resolution InfraRed Sounder (HIRS) is used in combination with MSU to derive deep-layer mean temperatures with improved vertical and horizontal resolution. Even though the relationship between infrared radiance and temperature is not linear, the layer associated with the mean temperature is shown to be well defined with a small airmass dependency that is similar to MSU’s airmass dependency. Preliminary validation of HIRS–MSU-derived layer mean temperatures with radiosonde layer mean temperatures show similar precision when compared to MSU-only derived temperatures.
Abstract
A method for deriving a water vapor index is presented. An important feature of the index is the fact that it does not rely on radiosondes. Thus, it is not influenced by problems associated with radiosondes and the extent to which the horizontal variability of moisture invalidates the extrapolations from radiosonde measurements to satellite measurements. The index is derived by using channels that are insensitive to changes in moisture to predict a brightness temperature for one of the moisture channels and then by subtracting this predicted value from the observation. The predicted value represents the moisture value expected for the given temperature profile, and the difference between the predicted and measured values is the index. The subtraction removes the variability due to changes in atmospheric temperature from the moisture signal. This separation greatly enhances the ability to monitor atmospheric moisture patterns, especially near the ground and at high latitudes where some alternative methods have difficulties. The ability of the indices to display moisture patterns at all levels and latitudes is demonstrated.
Abstract
A method for deriving a water vapor index is presented. An important feature of the index is the fact that it does not rely on radiosondes. Thus, it is not influenced by problems associated with radiosondes and the extent to which the horizontal variability of moisture invalidates the extrapolations from radiosonde measurements to satellite measurements. The index is derived by using channels that are insensitive to changes in moisture to predict a brightness temperature for one of the moisture channels and then by subtracting this predicted value from the observation. The predicted value represents the moisture value expected for the given temperature profile, and the difference between the predicted and measured values is the index. The subtraction removes the variability due to changes in atmospheric temperature from the moisture signal. This separation greatly enhances the ability to monitor atmospheric moisture patterns, especially near the ground and at high latitudes where some alternative methods have difficulties. The ability of the indices to display moisture patterns at all levels and latitudes is demonstrated.
Abstract
The Weather Research and Forecasting model (WRF) double-moment 6-class microphysics scheme (WDM6) implements a double-moment bulk microphysical parameterization of clouds and precipitation and is applicable in mesoscale and general circulation models. WDM6 extends the WRF single-moment 6-class microphysics scheme (WSM6) by incorporating the number concentrations for cloud and rainwater along with a prognostic variable of cloud condensation nuclei (CCN) number concentration. Moreover, it predicts the mixing ratios of six water species (water vapor, cloud droplets, cloud ice, snow, rain, and graupel), similar to WSM6. This paper describes improving the computational performance of WDM6 by exploiting its inherent fine-grained parallelism using the NVIDIA graphics processing unit (GPU). Compared to the single-threaded CPU, a single GPU implementation of WDM6 obtains a speedup of 150× with the input/output (I/O) transfer and 206× without the I/O transfer. Using four GPUs, the speedup reaches 347× and 715×, respectively.
Abstract
The Weather Research and Forecasting model (WRF) double-moment 6-class microphysics scheme (WDM6) implements a double-moment bulk microphysical parameterization of clouds and precipitation and is applicable in mesoscale and general circulation models. WDM6 extends the WRF single-moment 6-class microphysics scheme (WSM6) by incorporating the number concentrations for cloud and rainwater along with a prognostic variable of cloud condensation nuclei (CCN) number concentration. Moreover, it predicts the mixing ratios of six water species (water vapor, cloud droplets, cloud ice, snow, rain, and graupel), similar to WSM6. This paper describes improving the computational performance of WDM6 by exploiting its inherent fine-grained parallelism using the NVIDIA graphics processing unit (GPU). Compared to the single-threaded CPU, a single GPU implementation of WDM6 obtains a speedup of 150× with the input/output (I/O) transfer and 206× without the I/O transfer. Using four GPUs, the speedup reaches 347× and 715×, respectively.
Abstract
This paper describes a time-sensitive approach to climate change projections that was developed as part of New York City’s climate change adaptation process and that has provided decision support to stakeholders from 40 agencies, regional planning associations, and private companies. The approach optimizes production of projections given constraints faced by decision makers as they incorporate climate change into long-term planning and policy. New York City stakeholders, who are well versed in risk management, helped to preselect the climate variables most likely to impact urban infrastructure and requested a projection range rather than a single “most likely” outcome. The climate projections approach is transferable to other regions and is consistent with broader efforts to provide climate services, including impact, vulnerability, and adaptation information. The approach uses 16 GCMs and three emissions scenarios to calculate monthly change factors based on 30-yr average future time slices relative to a 30-yr model baseline. Projecting these model mean changes onto observed station data for New York City yields dramatic changes in the frequency of extreme events such as coastal flooding and dangerous heat events. On the basis of these methods, the current 1-in-10-year coastal flood is projected to occur more than once every 3 years by the end of the century and heat events are projected to approximately triple in frequency. These frequency changes are of sufficient magnitude to merit consideration in long-term adaptation planning, even though the precise changes in extreme-event frequency are highly uncertain.
Abstract
This paper describes a time-sensitive approach to climate change projections that was developed as part of New York City’s climate change adaptation process and that has provided decision support to stakeholders from 40 agencies, regional planning associations, and private companies. The approach optimizes production of projections given constraints faced by decision makers as they incorporate climate change into long-term planning and policy. New York City stakeholders, who are well versed in risk management, helped to preselect the climate variables most likely to impact urban infrastructure and requested a projection range rather than a single “most likely” outcome. The climate projections approach is transferable to other regions and is consistent with broader efforts to provide climate services, including impact, vulnerability, and adaptation information. The approach uses 16 GCMs and three emissions scenarios to calculate monthly change factors based on 30-yr average future time slices relative to a 30-yr model baseline. Projecting these model mean changes onto observed station data for New York City yields dramatic changes in the frequency of extreme events such as coastal flooding and dangerous heat events. On the basis of these methods, the current 1-in-10-year coastal flood is projected to occur more than once every 3 years by the end of the century and heat events are projected to approximately triple in frequency. These frequency changes are of sufficient magnitude to merit consideration in long-term adaptation planning, even though the precise changes in extreme-event frequency are highly uncertain.
Abstract
The Advanced Microwave Sounding Unit (AMSU) has better horizontal resolution and vertical temperature sounding abilities than its predecessor, the Microwave Sounding Unit (MSU). Those improved capabilities are demonstrated with observations of two cyclonic weather systems located in the South Pacific Ocean on 1 March 1999. These weather systems appear quite similar in conventional infrared satellite imagery, suggesting that they are comparable in structure and intensity. However, an analysis using temperature retrievals created from the AMSU shows that their vertical thermal structure is quite different.
This is just one example of an application highlighting the improved sounding capabilities available with the AMSU instrument suite. A preliminary look at what the AMSU can provide in data-void regions and a discussion of future plans to create AMSU-based products to better diagnose synoptic-scale weather systems are presented.
Abstract
The Advanced Microwave Sounding Unit (AMSU) has better horizontal resolution and vertical temperature sounding abilities than its predecessor, the Microwave Sounding Unit (MSU). Those improved capabilities are demonstrated with observations of two cyclonic weather systems located in the South Pacific Ocean on 1 March 1999. These weather systems appear quite similar in conventional infrared satellite imagery, suggesting that they are comparable in structure and intensity. However, an analysis using temperature retrievals created from the AMSU shows that their vertical thermal structure is quite different.
This is just one example of an application highlighting the improved sounding capabilities available with the AMSU instrument suite. A preliminary look at what the AMSU can provide in data-void regions and a discussion of future plans to create AMSU-based products to better diagnose synoptic-scale weather systems are presented.
Abstract
Soil moisture is an important variable in the climate system that integrates the combined influence of the atmosphere, land surface, and soil. Soil moisture is frequently used for drought monitoring and climate forecasting. However, in situ soil moisture observations are not systematically archived and there are relatively few national soil moisture networks. The lack of observed soil moisture data makes it difficult to characterize long-term soil moisture variability and trends. The North American Soil Moisture Database (NASMD) is a new high-quality observational soil moisture database. It includes over 1,800 monitoring stations in the United States, Canada, and Mexico, making it the largest collections of in situ soil moisture observations in North America. Data are collected from multiple sources, quality controlled, and integrated into an online database (soilmoisture.tamu.edu). Here we describe the development of the database, including quality control/quality assurance, standardization, and collection of metadata. The utility of the NASMD is demonstrated through an analysis of the inter- and intraannual variability of soil moisture from multiple networks. The NASMD is a useful tool for drought monitoring and forecasting, calibrating/validating satellites and land surface models, and documenting how soil moisture influences the climate system on seasonal to interannual time scales.
Abstract
Soil moisture is an important variable in the climate system that integrates the combined influence of the atmosphere, land surface, and soil. Soil moisture is frequently used for drought monitoring and climate forecasting. However, in situ soil moisture observations are not systematically archived and there are relatively few national soil moisture networks. The lack of observed soil moisture data makes it difficult to characterize long-term soil moisture variability and trends. The North American Soil Moisture Database (NASMD) is a new high-quality observational soil moisture database. It includes over 1,800 monitoring stations in the United States, Canada, and Mexico, making it the largest collections of in situ soil moisture observations in North America. Data are collected from multiple sources, quality controlled, and integrated into an online database (soilmoisture.tamu.edu). Here we describe the development of the database, including quality control/quality assurance, standardization, and collection of metadata. The utility of the NASMD is demonstrated through an analysis of the inter- and intraannual variability of soil moisture from multiple networks. The NASMD is a useful tool for drought monitoring and forecasting, calibrating/validating satellites and land surface models, and documenting how soil moisture influences the climate system on seasonal to interannual time scales.
The first Advanced Microwave Sounding Unit (AMSU) was launched aboard the NOAA-15 satellite on 13 May 1998. The AMSU is well suited for the observation of tropical cyclones because its measurements are not significantly affected by the ice clouds that cover tropical storms. In this paper, the following are presented: 1) upper-tropospheric thermal anomalies in tropical cyclones retrieved from AMSU data, 2) the correlation of maximum temperature anomalies with maximum wind speed and central pressure, 3) winds calculated from the temperature anomaly field, 4) comparison of AMSU data with GOES and AVHRR imagery, and 5) tropical cyclone rainfall potential. The AMSU data appear to offer substantial opportunities for improvement in tropical cyclone analysis and forecasting.
The first Advanced Microwave Sounding Unit (AMSU) was launched aboard the NOAA-15 satellite on 13 May 1998. The AMSU is well suited for the observation of tropical cyclones because its measurements are not significantly affected by the ice clouds that cover tropical storms. In this paper, the following are presented: 1) upper-tropospheric thermal anomalies in tropical cyclones retrieved from AMSU data, 2) the correlation of maximum temperature anomalies with maximum wind speed and central pressure, 3) winds calculated from the temperature anomaly field, 4) comparison of AMSU data with GOES and AVHRR imagery, and 5) tropical cyclone rainfall potential. The AMSU data appear to offer substantial opportunities for improvement in tropical cyclone analysis and forecasting.
The Global Space-based Inter-Calibration System (GSICS) is a new international program to assure the comparability of satellite measurements taken at different times and locations by different instruments operated by different satellite agencies. Sponsored by the World Meteorological Organization and the Coordination Group for Meteorological Satellites, GSICS will intercalibrate the instruments of the international constellation of operational low-earth-orbiting (LEO) and geostationary earth-orbiting (GEO) environmental satellites and tie these to common reference standards. The intercomparability of the observations will result in more accurate measurements for assimilation in numerical weather prediction models, construction of more reliable climate data records, and progress toward achieving the societal goals of the Global Earth Observation System of Systems. GSICS includes globally coordinated activities for prelaunch instrument characterization, onboard routine calibration, sensor intercomparison of near-simultaneous observations of individual scenes or overlapping time series, vicarious calibration using Earth-based or celestial references, and field campaigns. An initial strategy uses high-accuracy satellite instruments, such as the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) and Atmospheric Infrared Sounder (AIRS) and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)'s Centre National d'Études Spatiales (CNES) Infrared Atmospheric Sounding Interferometer (IASI), as space-based reference standards for intercalibrating the operational satellite sensors. Examples of initial intercalibration results and future plans are presented. Agencies participating in the program include the Centre National d'Études Spatiales, China Meteorological Administration, EUMETSAT, Japan Meteorological Agency, Korea Meteorological Administration, NASA, National Institute of Standards and Technology, and NOAA.
The Global Space-based Inter-Calibration System (GSICS) is a new international program to assure the comparability of satellite measurements taken at different times and locations by different instruments operated by different satellite agencies. Sponsored by the World Meteorological Organization and the Coordination Group for Meteorological Satellites, GSICS will intercalibrate the instruments of the international constellation of operational low-earth-orbiting (LEO) and geostationary earth-orbiting (GEO) environmental satellites and tie these to common reference standards. The intercomparability of the observations will result in more accurate measurements for assimilation in numerical weather prediction models, construction of more reliable climate data records, and progress toward achieving the societal goals of the Global Earth Observation System of Systems. GSICS includes globally coordinated activities for prelaunch instrument characterization, onboard routine calibration, sensor intercomparison of near-simultaneous observations of individual scenes or overlapping time series, vicarious calibration using Earth-based or celestial references, and field campaigns. An initial strategy uses high-accuracy satellite instruments, such as the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) and Atmospheric Infrared Sounder (AIRS) and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)'s Centre National d'Études Spatiales (CNES) Infrared Atmospheric Sounding Interferometer (IASI), as space-based reference standards for intercalibrating the operational satellite sensors. Examples of initial intercalibration results and future plans are presented. Agencies participating in the program include the Centre National d'Études Spatiales, China Meteorological Administration, EUMETSAT, Japan Meteorological Agency, Korea Meteorological Administration, NASA, National Institute of Standards and Technology, and NOAA.
