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- Author or Editor: Lei Shi x
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Abstract
Backpropagation neural networks are applied to retrieve atmospheric temperature profiles and tropopause variables from the NOAA-15 Advanced Microwave Sounding Unit-A (AMSU-A) measurement based on two different data sources. The first case uses direct acquisition of 15-channel AMSU-A data over the eastern United States and western Atlantic Ocean for the months of July 1998 and January 1999, and the second case uses recorded global AMSU-A data for several days of January 2000. The corresponding global analysis data from the National Centers for Environmental Prediction are employed to build the neural network training sets. The retrievals yield excellent results in the atmospheric temperature profiles from the surface to the 10-hPa pressure level. For the more generalized global data retrieval case, the root-mean-square (rms) deviation of temperature retrieval is 3.2°C at the surface, only 1.0° to 1.2°C in the midtroposphere, less than 1.5°C around the tropopause, and between 1.0° and 1.5°C in the stratosphere. Simultaneous retrieval of tropopause temperature, height, and pressure yields the rms deviations of 1.9°C, 0.58 km, and 18.1 hPa, respectively, for these variables. Within the scope of regional data, the trained neural network results in smaller values of temperature profile rms deviations than those of the global-data case. When compared to a linear regression approach, the neural network retrieval yields significantly better results for all the atmospheric levels. The neural network with parameters obtained from the network training optimizations can be easily applied to AMSU-A retrieval operationally.
Abstract
Backpropagation neural networks are applied to retrieve atmospheric temperature profiles and tropopause variables from the NOAA-15 Advanced Microwave Sounding Unit-A (AMSU-A) measurement based on two different data sources. The first case uses direct acquisition of 15-channel AMSU-A data over the eastern United States and western Atlantic Ocean for the months of July 1998 and January 1999, and the second case uses recorded global AMSU-A data for several days of January 2000. The corresponding global analysis data from the National Centers for Environmental Prediction are employed to build the neural network training sets. The retrievals yield excellent results in the atmospheric temperature profiles from the surface to the 10-hPa pressure level. For the more generalized global data retrieval case, the root-mean-square (rms) deviation of temperature retrieval is 3.2°C at the surface, only 1.0° to 1.2°C in the midtroposphere, less than 1.5°C around the tropopause, and between 1.0° and 1.5°C in the stratosphere. Simultaneous retrieval of tropopause temperature, height, and pressure yields the rms deviations of 1.9°C, 0.58 km, and 18.1 hPa, respectively, for these variables. Within the scope of regional data, the trained neural network results in smaller values of temperature profile rms deviations than those of the global-data case. When compared to a linear regression approach, the neural network retrieval yields significantly better results for all the atmospheric levels. The neural network with parameters obtained from the network training optimizations can be easily applied to AMSU-A retrieval operationally.
Abstract
Measurements from the simultaneous nadir overpass (SNO) observations of the High Resolution Infrared Radiation Sounder (HIRS) are examined. The SNOs are the measurements taken at the orbital intersections of each pair of satellites viewing the same Earth target within a few seconds at high latitudes. The dataset includes satellites from NOAA-6 through NOAA-17 from 1981 to 2004. The authors found that for many channels, intersatellite biases vary significantly with respect to scene radiances. For a number of these channels, the change of the intersatellite bias within a channel can be larger than 1 mW (m2 sr cm−1)−1, which is approximately 1 K in brightness temperature, across the channel scene radiance ranges. Many of the channels with large variations of intersatellite biases are the tropospheric sounding channels centered along the sharp slope of the transmission line. These channels are particularly sensitive to the difference in spectral response functions from satellite to satellite. This radiance-dependency feature of the biases is an important factor to consider when performing intersatellite calibrations.
Abstract
Measurements from the simultaneous nadir overpass (SNO) observations of the High Resolution Infrared Radiation Sounder (HIRS) are examined. The SNOs are the measurements taken at the orbital intersections of each pair of satellites viewing the same Earth target within a few seconds at high latitudes. The dataset includes satellites from NOAA-6 through NOAA-17 from 1981 to 2004. The authors found that for many channels, intersatellite biases vary significantly with respect to scene radiances. For a number of these channels, the change of the intersatellite bias within a channel can be larger than 1 mW (m2 sr cm−1)−1, which is approximately 1 K in brightness temperature, across the channel scene radiance ranges. Many of the channels with large variations of intersatellite biases are the tropospheric sounding channels centered along the sharp slope of the transmission line. These channels are particularly sensitive to the difference in spectral response functions from satellite to satellite. This radiance-dependency feature of the biases is an important factor to consider when performing intersatellite calibrations.
Abstract
High-latitude ocean surface air temperature and humidity derived from intersatellite-calibrated High-Resolution Infrared Radiation Sounder (HIRS) measurements are examined. A neural network approach is used to develop retrieval algorithms. HIRS simultaneous nadir overpass observations from high latitudes are used to intercalibrate observations from different satellites. Investigation shows that if HIRS observations were not intercalibrated, then it could lead to intersatellite biases of 1°C in the air temperature and 1–2 g kg−1 in the specific humidity for high-latitude ocean surface retrievals. Using a full year of measurements from a high-latitude moored buoy site as ground truth, the instantaneous (matched within a half-hour) root-mean-square (RMS) errors of HIRS retrievals are 1.50°C for air temperature and 0.86 g kg−1 for specific humidity. Compared to a large set of operational moored and drifting buoys in both northern and southern oceans greater than 50° latitude, the retrieval instantaneous RMS errors are within 2.6°C for air temperature and 1.4 g kg−1 for specific humidity. Compared to 5 yr of International Maritime Meteorological Archive in situ data, the HIRS specific humidity retrievals show less than 0.5 g kg−1 of differences over the majority of northern high-latitude open oceans.
Abstract
High-latitude ocean surface air temperature and humidity derived from intersatellite-calibrated High-Resolution Infrared Radiation Sounder (HIRS) measurements are examined. A neural network approach is used to develop retrieval algorithms. HIRS simultaneous nadir overpass observations from high latitudes are used to intercalibrate observations from different satellites. Investigation shows that if HIRS observations were not intercalibrated, then it could lead to intersatellite biases of 1°C in the air temperature and 1–2 g kg−1 in the specific humidity for high-latitude ocean surface retrievals. Using a full year of measurements from a high-latitude moored buoy site as ground truth, the instantaneous (matched within a half-hour) root-mean-square (RMS) errors of HIRS retrievals are 1.50°C for air temperature and 0.86 g kg−1 for specific humidity. Compared to a large set of operational moored and drifting buoys in both northern and southern oceans greater than 50° latitude, the retrieval instantaneous RMS errors are within 2.6°C for air temperature and 1.4 g kg−1 for specific humidity. Compared to 5 yr of International Maritime Meteorological Archive in situ data, the HIRS specific humidity retrievals show less than 0.5 g kg−1 of differences over the majority of northern high-latitude open oceans.
Abstract
Cloud properties derived from the whole-sky infrared cloud-measuring system (WSIRCMS) are analyzed in relation to measurements of visual observations and a ceilometer during the period July–August 2010 at the Chinese Meteorological Administration Yangjiang Station, Guangdong Province, China. The comparison focuses on the performance and features of the WSIRCMS as a prototype instrument for automatic cloud observations. Cloud cover derived from the WSIRCMS cloud algorithm compares quite well with cloud cover derived from visual observations. Cloud cover differences between WSIRCMS and visual observations are within ±1 octa in 70.83% and within ±2 octa in 82.44% of the cases. For cloud-base height from WSIRCMS data and Vaisala ceilometer CL51, the comparison shows a generally good correspondence in the lower and midtroposphere up to the height of about 6 km, with some systematic difference due to different detection methods. Differences between the resulting cloud-type classifications derived from the WSIRCMS and from visual observations show that cumulus and cirrus are classified with high accuracy, but that stratocumulus and altocumulus are not. Stratocumulus and altocumulus are suggested to be treated as waveform cloud for classification purposes. In addition, it is considered an intractable problem for automatic cloud-measurement instruments to do cloud classification when the cloud amount is less than 2 octa.
Abstract
Cloud properties derived from the whole-sky infrared cloud-measuring system (WSIRCMS) are analyzed in relation to measurements of visual observations and a ceilometer during the period July–August 2010 at the Chinese Meteorological Administration Yangjiang Station, Guangdong Province, China. The comparison focuses on the performance and features of the WSIRCMS as a prototype instrument for automatic cloud observations. Cloud cover derived from the WSIRCMS cloud algorithm compares quite well with cloud cover derived from visual observations. Cloud cover differences between WSIRCMS and visual observations are within ±1 octa in 70.83% and within ±2 octa in 82.44% of the cases. For cloud-base height from WSIRCMS data and Vaisala ceilometer CL51, the comparison shows a generally good correspondence in the lower and midtroposphere up to the height of about 6 km, with some systematic difference due to different detection methods. Differences between the resulting cloud-type classifications derived from the WSIRCMS and from visual observations show that cumulus and cirrus are classified with high accuracy, but that stratocumulus and altocumulus are not. Stratocumulus and altocumulus are suggested to be treated as waveform cloud for classification purposes. In addition, it is considered an intractable problem for automatic cloud-measurement instruments to do cloud classification when the cloud amount is less than 2 octa.
Abstract
A climatology of the diurnal cycles of HIRS clear-sky brightness temperatures was developed based on measurements over the period 2002–07. This was done by fitting a Fourier series to monthly gridded brightness temperatures of HIRS channels 1–12. The results show a strong land–sea contrast with stronger diurnal cycles over land, and extending from the surface up to HIRS channel 6 or 5, with regional maxima over the subtropics. Over seas, the diurnal cycles are generally small and therefore challenging to detect. A Monte Carlo uncertainty analysis showed that more robust results are reached by aggregating the data zonally before applying the fit. The zonal fits indicate that small diurnal cycles do exist over sea. The results imply that for a long-lived satellite such as NOAA-14, drift in the overpass time can cause a diurnal sampling bias of more than 5 K for channel 8 (surface and lower troposphere).
Abstract
A climatology of the diurnal cycles of HIRS clear-sky brightness temperatures was developed based on measurements over the period 2002–07. This was done by fitting a Fourier series to monthly gridded brightness temperatures of HIRS channels 1–12. The results show a strong land–sea contrast with stronger diurnal cycles over land, and extending from the surface up to HIRS channel 6 or 5, with regional maxima over the subtropics. Over seas, the diurnal cycles are generally small and therefore challenging to detect. A Monte Carlo uncertainty analysis showed that more robust results are reached by aggregating the data zonally before applying the fit. The zonal fits indicate that small diurnal cycles do exist over sea. The results imply that for a long-lived satellite such as NOAA-14, drift in the overpass time can cause a diurnal sampling bias of more than 5 K for channel 8 (surface and lower troposphere).
An Evaluation of HIRS Near-Surface Air Temperature Product in the Arctic with SHEBA DataAbstract
The accuracy of cloud-screened 2-m air temperatures derived from the intersatellite-calibrated brightness temperatures based on the High Resolution Infrared Radiation Sounder (HIRS) measurements on board the National Oceanic and Atmospheric Administration (NOAA) Polar-Orbiting Operational Environmental Satellite (POES) series is evaluated by comparing HIRS air temperatures to 1-yr quality-controlled measurements collected during the Surface Heat Budget of the Arctic Ocean (SHEBA) project (October 1997–September 1998). The mean error between collocated HIRS and SHEBA 2-m air temperature is found to be on the order of 1°C, with a slight sensitivity to spatial and temporal radii for collocation. The HIRS temperatures capture well the temporal variability of SHEBA temperatures, with cross-correlation coefficients higher than 0.93, all significant at the 99.9% confidence level. More than 87% of SHEBA temperature variance can be explained by linear regression of collocated HIRS temperatures. The analysis found a strong dependency of mean temperature errors on cloud conditions observed during SHEBA, indicating that availability of an accurate cloud mask in the region is essential to further improve the quality of HIRS near-surface air temperature products. This evaluation establishes a baseline of accuracy of HIRS temperature retrievals, providing users with information on uncertainty sources and estimates. It is a first step toward development of a new long-term 2-m air temperature product in the Arctic that utilizes intersatellite-calibrated remote sensing data from the HIRS instrument.
Abstract
The accuracy of cloud-screened 2-m air temperatures derived from the intersatellite-calibrated brightness temperatures based on the High Resolution Infrared Radiation Sounder (HIRS) measurements on board the National Oceanic and Atmospheric Administration (NOAA) Polar-Orbiting Operational Environmental Satellite (POES) series is evaluated by comparing HIRS air temperatures to 1-yr quality-controlled measurements collected during the Surface Heat Budget of the Arctic Ocean (SHEBA) project (October 1997–September 1998). The mean error between collocated HIRS and SHEBA 2-m air temperature is found to be on the order of 1°C, with a slight sensitivity to spatial and temporal radii for collocation. The HIRS temperatures capture well the temporal variability of SHEBA temperatures, with cross-correlation coefficients higher than 0.93, all significant at the 99.9% confidence level. More than 87% of SHEBA temperature variance can be explained by linear regression of collocated HIRS temperatures. The analysis found a strong dependency of mean temperature errors on cloud conditions observed during SHEBA, indicating that availability of an accurate cloud mask in the region is essential to further improve the quality of HIRS near-surface air temperature products. This evaluation establishes a baseline of accuracy of HIRS temperature retrievals, providing users with information on uncertainty sources and estimates. It is a first step toward development of a new long-term 2-m air temperature product in the Arctic that utilizes intersatellite-calibrated remote sensing data from the HIRS instrument.
