scholarly journals Comparison of microwave satellite humidity data and radiosonde profiles: a survey of European stations

2005 ◽  
Vol 5 (2) ◽  
pp. 1529-1550 ◽  
Author(s):  
V. O. John ◽  
S. A. Buehler
Keyword(s):  
Systematic Difference ◽  
Radiosonde Data ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Comparison Results ◽  
Microwave Sounding Unit ◽  
Humidity Measurements ◽  
Upper Tropospheric Humidity ◽  
Microwave Sounding ◽  
The Difference

Abstract. A method to compare upper tropospheric humidity (UTH) from satellite and radiosonde data has been applied to the European radiosonde stations. The method uses microwave data as a benchmark for monitoring the performance of the stations. The present study utilizes three years (2002–2003) of data from channel 18 (183.31±1.00 GHz) of the Advanced Microwave Sounding Unit-B (AMSU-B) aboard the satellites NOAA-15 and NOAA-16. The comparison is done in the radiance space, the radiosonde data were transformed to the channel radiances using a radiative transfer model. The comparison results confirm that there is a dry bias in the UTH measured by the radiosondes. This bias is highly variable among the stations and the years. This variability is attributed mainly to the differences in the radiosonde humidity measurements. The results also hint at a systematic difference between the two satellites, the channel 18 brightness temperature of NOAA-15 is on average 1.0 K higher than that of NOAA-16. The difference of 1 K corresponds to approximately 7% relative error in UTH which is significant for climatological applications.

scholarly journals Comparison of microwave satellite humidity data and radiosonde profiles: A survey of European stations

2005 ◽  
Vol 5 (7) ◽  
pp. 1843-1853 ◽  
Author(s):  
V. O. John ◽  
S. A. Buehler
Keyword(s):  
Radiosonde Data ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Comparison Results ◽  
Microwave Sounding Unit ◽  
Humidity Measurements ◽  
Upper Tropospheric Humidity ◽  
Microwave Sounding ◽  
Dry Bias ◽  
Radiation Error

Abstract. A method to compare upper tropospheric humidity (UTH) from satellite and radiosonde data has been applied to the European radiosonde stations. The method uses microwave data as a benchmark for monitoring the performance of the stations. The present study utilizes three years (2001-2003) of data from channel 18 (183.31±1.00 GHz) of the Advanced Microwave Sounding Unit-B (AMSU-B) aboard the satellites NOAA-15 and NOAA-16. The comparison is done in the radiance space, the radiosonde data were transformed to the channel radiances using a radiative transfer model. The comparison results confirm that there is a dry bias in the UTH measured by the radiosondes. This bias is highly variable among the stations and the years. This variability is attributed mainly to the differences in the radiosonde humidity measurements. The analysis also shows a difference between daytime and nighttime soundings which is attributed to radiation error in the radiosonde data. The dry bias due to this error alone correspond to approximately 11% relative error in the UTH measurements.

scholarly journals Retrieval of upper tropospheric water vapor and upper tropospheric humidity from AMSU radiances

2005 ◽  
Vol 5 (8) ◽  
pp. 2019-2028 ◽  
Author(s):  
A. Houshangpour ◽  
V. O. John ◽  
S. A. Buehler
Keyword(s):  
Water Vapor ◽  
Synthetic Data ◽  
Weighted Average ◽  
Radiosonde Data ◽  
Atmospheric Conditions ◽  
Data Set ◽  
Microwave Sounding Unit ◽  
Upper Tropospheric Humidity ◽  
Microwave Sounding ◽  
Average Relative Humidity

Abstract. A regression method was developed to retrieve upper tropospheric water vapor (UTWV in kg/m2) and upper tropospheric humidity (UTH in % RH) from radiances measured by the Advanced Microwave Sounding Unit (AMSU). In contrast to other UTH retrieval methods, UTH is defined as the average relative humidity between 500 and 200hPa, not as a Jacobian weighted average, which has the advantage that the UTH altitude does not depend on the atmospheric conditions. The method uses AMSU channels 6-10, 18, and 19, and should achieve an accuracy of 0.48 kg/m2 for UTWV and 6.3% RH for UTH, according to a test against an independent synthetic data set. This performance was confirmed for northern mid-latitudes by a comparison against radiosonde data from station Lindenberg in Germany, which yielded errors of 0.23 kg/m2 for UTWV and 6.1% RH for UTH.

scholarly journals Retrieval of upper tropospheric water Retrieval of upper tropospheric water Retrieval of upper tropospheric water

2005 ◽  
Vol 5 (2) ◽  
pp. 1551-1584
Author(s):  
A. Houshangpour ◽  
V. O. John ◽  
S. A. Buehler
Keyword(s):  
Synthetic Data ◽  
Weighted Average ◽  
Regression Method ◽  
Radiosonde Data ◽  
Atmospheric Conditions ◽  
Data Set ◽  
Microwave Sounding Unit ◽  
Upper Tropospheric Humidity ◽  
Microwave Sounding ◽  
Average Relative Humidity

Abstract. A regression method was developed to retrieve upper tropospheric water vapor (UTWV in kg/m2) and upper tropospheric humidity (UTH in %RH) from radiances measured by the Advanced Microwave Sounding Unit (AMSU). In contrast to other UTH retrieval methods, UTH is defined as the average relative humidity between 500 and 200 hPa, not as a Jacobian weighted average, which has the advantage that the UTH altitude does not depend on the atmospheric conditions. The method uses AMSU channels 6–10, 18, and 19, and should achieve an accuracy of 0.48 kg/m2 for UTWV and 6.3%RH for UTH, according to a test against an independent synthetic data set. This performance was confirmed for northern mid-latitudes by a comparison against radiosonde data from station Lindenberg in Germany, which yielded errors of 0.23 kg/m2 for UTWV and 6.1%RH for UTH.

scholarly journals Calibration and Validation of Antenna and Brightness Temperatures from Metop-C Advanced Microwave Sounding Unit-A (AMSU-A)

2020 ◽  
Vol 12 (18) ◽  
pp. 2978
Author(s):  
Banghua Yan ◽  
Junye Chen ◽  
Cheng-Zhi Zou ◽  
Khalil Ahmad ◽  
Haifeng Qian ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Data Quality ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Model Simulations ◽  
Calibration And Validation ◽  
Microwave Sounding Unit ◽  
Data Record ◽  
Microwave Sounding ◽  
Advanced Microwave Sounding Unit

This study carries out the calibration and validation of Antenna Temperature Data Record (TDR) and Brightness Temperature Sensor Data Record (SDR) data from the last National Oceanic and Atmospheric Administration (NOAA) Advanced Microwave Sounding Unit-A (AMSU-A) flown on the Meteorological Operational satellite programme (MetOp)-C satellite. The calibration comprises the selection of optimal space view positions for the instrument and the determination of coefficients in calibration equations from the Raw Data Record (RDR) to TDR and SDR. The validation covers the analyses of the instrument noise equivalent differential temperature (NEDT) performance and the TDR and SDR data quality from the launch until 15 November 2019. In particular, the Metop-C data quality is assessed by comparing to radiative transfer model simulations and observations from Metop-A/B AMSU-A, respectively. The results demonstrate that the on-orbit instrument NEDTs have been stable since launch and continue to meet the specifications at most channels except for channel 3, whose NEDT exceeds the specification after April 2019. The quality of the Metop-C AMSU-A data for all channels except channel 3 have been reliable since launch. The quality at channel 3 is degraded due to the noise exceeding the specification. Compared to its TDR data, the Metop-C AMSU-A SDR data exhibit a reduced and more symmetric scan angle-dependent bias against radiative transfer model simulations, demonstrating the great performance of the TDR to SDR conversion coefficients. Additionally, the Metop-C AMSU-A data quality agrees well with Metop-A/B AMSU-A data, with an averaged difference in the order of 0.3 K, which is confirmed based on Simultaneous Nadir Overpass (SNO) inter-sensor comparisons between Metop-A/B/C AMSU-A instruments via either NOAA-18 or NOAA-19 AMSU-A as a transfer.

scholarly journals Long-Term Observations of Microwave Brightness Temperatures over a Metropolitan Area: Comparison of Radiometric Data and Spectra Simulated with the Use of Radiosonde Measurements

2021 ◽  
Vol 13 (11) ◽  
pp. 2061
Author(s):  
Mikhail V. Belikovich ◽  
Mikhail Yu. Kulikov ◽  
Dmitry S. Makarov ◽  
Natalya K. Skalyga ◽  
Vitaly G. Ryskin ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Elevation Angle ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Systematic Bias ◽  
Tropospheric Temperature ◽  
Absorption Model ◽  
60 Ghz ◽  
Error Budget ◽  
The Difference

Ground-based microwave radiometers are increasingly used in operational meteorology and nowcasting. These instruments continuously measure the spectra of downwelling atmospheric radiation in the range 20–60 GHz used for the retrieval of tropospheric temperature and water vapor profiles. Spectroscopic uncertainty is an important part of the retrieval error budget, as it leads to systematic bias. In this study, we analyze the difference between observed and simulated microwave spectra obtained from more than four years of microwave and radiosonde observations over Nizhny Novgorod (56.2° N, 44° E). We focus on zenith-measured and elevation-scanning data in clear-sky conditions. The simulated spectra are calculated by a radiative transfer model with the use of radiosonde profiles and different absorption models, corresponding to the latest spectroscopy research. In the case of zenith-measurements, we found a systematic bias (up to ~2 K) of simulated spectra at 51–54 GHz. The sign of bias depends on the absorption model. A thorough investigation of the error budget points to a spectroscopic nature of the observed differences. The dependence of the results on the elevation angle and absorption model can be explained by the basic properties of radiative transfer and by cloud contamination at elevation angles.

scholarly journals A Satellite-Based Assessment of Upper-Tropospheric Water Vapor Measurements during AFWEX

2009 ◽  
Vol 48 (11) ◽  
pp. 2284-2294 ◽  
Author(s):  
Eui-Seok Chung ◽  
Brian J. Soden
Keyword(s):  
Water Vapor ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Raman Lidar ◽  
Test Bed ◽  
Upper Troposphere ◽  
Brightness Temperatures ◽  
Radiation Test ◽  
Humidity Measurements ◽  
Atmospheric Radiation Measurement

Abstract Consistency of upper-tropospheric water vapor measurements from a variety of state-of-the-art instruments was assessed using collocated Geostationary Operational Environmental Satellite-8 (GOES-8) 6.7-μm brightness temperatures as a common benchmark during the Atmospheric Radiation Measurement Program (ARM) First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment (FIRE) Water Vapor Experiment (AFWEX). To avoid uncertainties associated with the inversion of satellite-measured radiances into water vapor quantity, profiles of temperature and humidity observed from in situ, ground-based, and airborne instruments are inserted into a radiative transfer model to simulate the brightness temperature that the GOES-8 would have observed under those conditions (i.e., profile-to-radiance approach). Comparisons showed that Vaisala RS80-H radiosondes and Meteolabor Snow White chilled-mirror dewpoint hygrometers are systemically drier in the upper troposphere by ∼30%–40% relative to the GOES-8 measured upper-tropospheric humidity (UTH). By contrast, two ground-based Raman lidars (Cloud and Radiation Test Bed Raman lidar and scanning Raman lidar) and one airborne differential absorption lidar agree to within 10% of the GOES-8 measured UTH. These results indicate that upper-tropospheric water vapor can be monitored by these lidars and well-calibrated, stable geostationary satellites with an uncertainty of less than 10%, and that correction procedures are required to rectify the inherent deficiencies of humidity measurements in the upper troposphere from these radiosondes.

Impact of AMSU-A Radiances in a Column Ensemble Kalman Filter

2018 ◽  
Vol 146 (12) ◽  
pp. 3949-3976 ◽  
Author(s):  
Herschel L. Mitchell ◽  
P. L. Houtekamer ◽  
Sylvain Heilliette
Keyword(s):  
Covariance Matrix ◽  
Background Error ◽  
Error Covariance Matrix ◽  
Background Error Covariance ◽  
Error Covariance ◽  
Microwave Sounding Unit ◽  
The Matrix ◽  
Microwave Sounding ◽  
Background Temperature ◽  
The Difference

Abstract A column EnKF, based on the Canadian global EnKF and using the RTTOV radiative transfer (RT) model, is employed to investigate issues relating to the EnKF assimilation of Advanced Microwave Sounding Unit-A (AMSU-A) radiance measurements. Experiments are performed with large and small ensembles, with and without localization. Three different descriptions of background temperature error are considered: 1) using analytical vertical modes and hypothetical spectra, 2) using the vertical modes and spectrum of a covariance matrix obtained from the global EnKF after 2 weeks of cycling, and 3) using the vertical modes and spectrum of the static background error covariance matrix employed to initiate a global data assimilation cycle. It is found that the EnKF performs well in some of the experiments with background error description 1, and yields modest error reductions with background error description 3. However, the EnKF is virtually unable to reduce the background error (even when using a large ensemble) with background error description 2. To analyze these results, the different background error descriptions are viewed through the prism of the RT model by comparing the trace of the matrix , where is the RT model and is the background error covariance matrix. Indeed, this comparison is found to explain the difference in the results obtained, which relates to the degree to which deep modes are, or are not, present in the different background error covariances. The results suggest that, after 2 weeks of cycling, the global EnKF has virtually eliminated all background error structures that can be “seen” by the AMSU-A radiances.

scholarly journals MINERAL ABUNDANCE AND PARTICLE SIZE DISTRIBUTION DERIVED FROM IN-SITU SPECTRA MEASUREMENTS OF YUTU ROVER OF CHANG’E-3

2017 ◽  
Vol XLII-3/W1 ◽  
pp. 85-89
Author(s):  
H. Lin ◽  
X. Zhang ◽  
Y. Yang ◽  
X. Wu ◽  
D. Guo
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Landing Site ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Particle Sizes ◽  
The Mean ◽  
The Difference ◽  
Yutu Rover

From geologic perspective, understanding the types, abundance, and size distributions of minerals allows us to address what geologic processes have been active on the lunar and planetary surface. The imaging spectrometer which was carried by the Yutu Rover of Chinese Chang’E-3 mission collected the reflectance at four different sites at the height of ~ 1 m, providing a new insight to understand the lunar surface. The mineral composition and Particle Size Distribution (PSD) of these four sites were derived in this study using a Radiative Transfer Model (RTM) and Sparse Unmixing (SU) algorithm. The endmembers used were clinopyroxene, orthopyroxene, olivine, plagioclase and agglutinate collected from the lunar sample spectral dataset in RELAB. The results show that the agglutinate, clinopyroxene and olivine are the dominant minerals around the landing site. In location Node E, the abundance of agglutinate can reach up to 70 %, and the abundances of clinopyroxene and olivine are around 10 %. The mean particle sizes and the deviations of these endmembers were retrieved. PSDs of all these endmembers are close to normal distribution, and differences exist in the mean particle sizes, indicating the difference of space weathering rate of these endmembers.

scholarly journals Refinement of the ice absorption spectrum in the visible using radiance profile measurements in Antarctic snow

2016 ◽  
Author(s):  
Ghislain Picard ◽  
Quentin Libois ◽  
Laurent Arnaud
Keyword(s):  
Absorption Coefficient ◽  
Fiber Optics ◽  
Estimation Method ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Snow Layer ◽  
Antarctic Snow ◽  
Order Of Magnitude ◽  
The Difference ◽  
The Antarctic

Abstract. Ice is a highly transparent material in the visible. According to the most widely used database (Warren and Brandt, 2008; IA2008), the ice absorption coefficient reaches values lower than 10−3 m−1 around 400 nm. These values were obtained from a radiance profile measured in a single snow layer at Dome C in Antarctica. We reproduced this experiment using a fiber optics inserted in the snow to record 56 profiles from which 70 homogeneous layers were identified. Applying the same estimation method on every layer yields 70 ice absorption spectra with a significant variability and overall larger than IA2008 by one order of magnitude. We devise another estimation method based on Bayesian inference. It reduces the statistical variability and confirms the higher absorption, around 2 × 10−2 m−1 near the minimum at 440 nm. We explore potential instrumental artifacts by developing a 3D radiative transfer model able to explicitly account for the presence of the fiber in the snow. The simulation results show that the radiance profile is indeed perturbed by the fiber intrusion but the error on the ice absorption estimate is not larger than a factor 2. This is insufficient to explain the difference between our new estimate and IA2008. Nevertheless, considering the number of profiles acquired for this study and other estimates from the Antarctic Muon and Neutrino Detector Array (AMANDA), we estimate that ice absorption values around 10−2 m−1 at the minimum are more likely than under 10−3 m−1. We provide a new estimate in the range 400–600 nm for future modeling of snow, cloud, and sea-ice optical properties. Most importantly we recommend that modeling studies take into account the large uncertainty of the ice absorption coefficient in the visible.

The Effect of Hydrometeors on MSU/AMSU Temperature Observations over the Tropical Ocean

2018 ◽  
Vol 35 (5) ◽  
pp. 1141-1150 ◽  
Author(s):  
Hamid A. Pahlavan ◽  
Qiang Fu ◽  
John M. Wallace
Keyword(s):  
Regression Coefficient ◽  
Temperature Fields ◽  
Tropospheric Temperature ◽  
Tropical Ocean ◽  
Microwave Sounding Unit ◽  
Mean Fields ◽  
Microwave Sounding ◽  
Temperature Observations ◽  
The Difference ◽  
Grid Points

AbstractThe temperature of Earth’s atmosphere has been monitored continuously since late 1978 by the Microwave Sounding Unit (MSU) and the Advanced Microwave Sounding Unit (AMSU) flown on polar-orbiting weather satellites. It is well known that these measurements are affected by the scattering and emission from hydrometeors, including cloud water, precipitation, and ice particles. In this study the hydrometeor effects on MSU/AMSU temperature observations are investigated by comparing satellite-observed temperature of the middle troposphere (TMT) with synthetic TMT constructed using temperature fields from ECMWF Interim [ERA-Interim (ERA-I)]. Precipitation data have been used to estimate how much of the difference between these two TMT fields is due to hydrometeor contamination effects. It is shown that there exists a robust linear proportionality between TMT deficit (i.e., the measured TMT minus the synthetic TMT) and precipitation at individual grid points in monthly mean fields. The linear correlation is even stronger in the annual mean and seasonally varying climatology and also in the spatial pattern of ENSO-related anomalies. The linear regression coefficient obtained in all of these analyses is virtually identical: −0.042 K (mm day−1)−1. The channel that senses lower-tropospheric temperature (TLT) is more sensitive to precipitation than the TMT channel: the regression coefficient is −0.059 K (mm day−1)−1. It is shown that correcting the TMT or TLT monthly anomalies by removing the hydrometeor contamination does not significantly influence estimates of tropical mean temperature trends, but it could affect the pattern of temperature trend over the tropical oceans.

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