The Multivariate and Minimum Residual （MMR） cloud detection and retrieval algorithm,previously developed and tested on simulated observations and Advanced Infrared Sounder radiance,was explored and validated using v...The Multivariate and Minimum Residual （MMR） cloud detection and retrieval algorithm,previously developed and tested on simulated observations and Advanced Infrared Sounder radiance,was explored and validated using various radiances from multiple sensors.For validation,the cloud retrievals were compared to independent cloud products from CloudSat,MODIS （Moderate Resolution Imaging Spectroradiometer）,and GOES （Geostationary Operational Environmental Satellites）.We found good spatial agreement within a single instrument,although the cloud fraction on each pixel was estimated independently.The retrieved cloud properties showed good agreement using radiances from multiple satellites,especially for the vertically integrated cloud mask.The accuracy of the MMR scheme in detecting mid-level clouds was found to be higher than for higher and lower clouds.The accuracy in retrieving cloud top pressures and cloud profiles increased with more channels from observations.For observations with fewer channels,the MMR solution was an ＂overly smoothed＂ estimation of the true vertical profile,starting from a uniform clear guess.Additionally,the retrieval algorithm showed some meaningful skill in simulating the cloudy radiance as a linear observation operator,discriminating between numerical weather prediction （NWP） error and cloud effects.The retrieval scheme was also found to be robust when different radiative transfer models were used.The potential application of the MMR algorithm in NWP with multiple radiances is also discussed.展开更多
Passive Fourier transform infrared （FTIR） remote sensing measurement of chemical gas cloud is a vital technology. It takes an important part in many fields for the detection of released gases. The principle of conce...Passive Fourier transform infrared （FTIR） remote sensing measurement of chemical gas cloud is a vital technology. It takes an important part in many fields for the detection of released gases. The principle of concentration measurement is based on the Beer-Lambert law. Unlike the active measurement, for the passive remote sensing, in most cases, the difference between the temperature of the gas cloud and the brightness temperature of the background is usually a few kelvins. The gas cloud emission is almost equal to the background emission, thereby the emission of the gas cloud cannot be ignored. The concentration retrieval algorithm is quite different from the active measurement. In this paper, the concentration retrieval algorithm for the passive FTIR remote measurement of gas cloud is presented in detail, which involves radiative transfer model, radiometric calibration, absorption coefficient calculation, et al. The background spectrum has a broad feature, which is a slowly varying function of frequency. In this paper, the background spectrum is fitted with a polynomial by using the Levenberg-Marquardt method which is a kind of nonlinear least squares fitting algorithm. No background spectra are required. Thus, this method allows mobile, real-time and fast measurements of gas clouds.展开更多
This paper established a geophysical retrieval algorithm for sea surface wind vector, sea surface temperature, columnar atmospheric water vapor, and columnar cloud liquid water from WindSat, using the measured brightn...This paper established a geophysical retrieval algorithm for sea surface wind vector, sea surface temperature, columnar atmospheric water vapor, and columnar cloud liquid water from WindSat, using the measured brightness temperatures and a matchup database. To retrieve the wind vector, a chaotic particle swarm approach was used to determine a set of possible wind vector solutions which minimize the difference between the forward model and the WindSat observations. An adjusted circular median filtering function was adopted to remove wind direction ambiguity. The validation of the wind speed, wind direction, sea surface temperature, columnar atmospheric water vapor, and columnar liquid cloud water indicates that this algorithm is feasible and reasonable and can be used to retrieve these atmospheric and oceanic parameters. Compared with moored buoy data, the RMS errors for wind speed and sea surface temperature were 0.92 m s^(-1) and 0.88℃, respectively. The RMS errors for columnar atmospheric water vapor and columnar liquid cloud water were 0.62 mm and 0.01 mm, respectively, compared with F17 SSMIS results. In addition, monthly average results indicated that these parameters are in good agreement with AMSR-E results. Wind direction retrieval was studied under various wind speed conditions and validated by comparing to the Quik SCAT measurements, and the RMS error was 13.3?. This paper offers a new approach to the study of ocean wind vector retrieval using a polarimetric microwave radiometer.展开更多
基金sponsored by the 973 Program (Grant No. 2013CB430102)the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)+3 种基金and the Air Force Weather Agencysupport from Craig S. SCHWARTZ, Allegrino Americo SAMUEL, and Gael DESCOMBES are greatly appreciatedsponsored by the National Science Foundationthe National Science Foundation
文摘The Multivariate and Minimum Residual （MMR） cloud detection and retrieval algorithm,previously developed and tested on simulated observations and Advanced Infrared Sounder radiance,was explored and validated using various radiances from multiple sensors.For validation,the cloud retrievals were compared to independent cloud products from CloudSat,MODIS （Moderate Resolution Imaging Spectroradiometer）,and GOES （Geostationary Operational Environmental Satellites）.We found good spatial agreement within a single instrument,although the cloud fraction on each pixel was estimated independently.The retrieved cloud properties showed good agreement using radiances from multiple satellites,especially for the vertically integrated cloud mask.The accuracy of the MMR scheme in detecting mid-level clouds was found to be higher than for higher and lower clouds.The accuracy in retrieving cloud top pressures and cloud profiles increased with more channels from observations.For observations with fewer channels,the MMR solution was an ＂overly smoothed＂ estimation of the true vertical profile,starting from a uniform clear guess.Additionally,the retrieval algorithm showed some meaningful skill in simulating the cloudy radiance as a linear observation operator,discriminating between numerical weather prediction （NWP） error and cloud effects.The retrieval scheme was also found to be robust when different radiative transfer models were used.The potential application of the MMR algorithm in NWP with multiple radiances is also discussed.
基金Project supported by the National Natural Science Foundation of China (Grant No 083H311501)the National High Technology Research and Development Program of China (Grant No 073H3f1514)
文摘Passive Fourier transform infrared （FTIR） remote sensing measurement of chemical gas cloud is a vital technology. It takes an important part in many fields for the detection of released gases. The principle of concentration measurement is based on the Beer-Lambert law. Unlike the active measurement, for the passive remote sensing, in most cases, the difference between the temperature of the gas cloud and the brightness temperature of the background is usually a few kelvins. The gas cloud emission is almost equal to the background emission, thereby the emission of the gas cloud cannot be ignored. The concentration retrieval algorithm is quite different from the active measurement. In this paper, the concentration retrieval algorithm for the passive FTIR remote measurement of gas cloud is presented in detail, which involves radiative transfer model, radiometric calibration, absorption coefficient calculation, et al. The background spectrum has a broad feature, which is a slowly varying function of frequency. In this paper, the background spectrum is fitted with a polynomial by using the Levenberg-Marquardt method which is a kind of nonlinear least squares fitting algorithm. No background spectra are required. Thus, this method allows mobile, real-time and fast measurements of gas clouds.
基金supported by the National Natural Science Foundation of China (Grant Nos.41205013 and 41105012)
文摘This paper established a geophysical retrieval algorithm for sea surface wind vector, sea surface temperature, columnar atmospheric water vapor, and columnar cloud liquid water from WindSat, using the measured brightness temperatures and a matchup database. To retrieve the wind vector, a chaotic particle swarm approach was used to determine a set of possible wind vector solutions which minimize the difference between the forward model and the WindSat observations. An adjusted circular median filtering function was adopted to remove wind direction ambiguity. The validation of the wind speed, wind direction, sea surface temperature, columnar atmospheric water vapor, and columnar liquid cloud water indicates that this algorithm is feasible and reasonable and can be used to retrieve these atmospheric and oceanic parameters. Compared with moored buoy data, the RMS errors for wind speed and sea surface temperature were 0.92 m s^(-1) and 0.88℃, respectively. The RMS errors for columnar atmospheric water vapor and columnar liquid cloud water were 0.62 mm and 0.01 mm, respectively, compared with F17 SSMIS results. In addition, monthly average results indicated that these parameters are in good agreement with AMSR-E results. Wind direction retrieval was studied under various wind speed conditions and validated by comparing to the Quik SCAT measurements, and the RMS error was 13.3?. This paper offers a new approach to the study of ocean wind vector retrieval using a polarimetric microwave radiometer.
