scholarly journals Validation of brightness and physical temperature from two scanning microwave radiometers in the 60 GHz O<sub>2</sub>-band using radiosonde measurements

2016 ◽  
Author(s):  
Francisco Navas-Guzmán ◽  
Niklaus Kämpfer ◽  
Alexander Haefele
Keyword(s):  
Brightness Temperature ◽  
Microwave Radiometer ◽  
Estimation Method ◽  
Weather Conditions ◽  
Radiative Transfer Model ◽  
Inversion Method ◽  
Transfer Model ◽  
Tropospheric Temperature ◽  
60 Ghz ◽  
Inversion Algorithm

Abstract. In this paper, we address the assessment of the tropospheric performance of a new temperature radiometer (TEMPERA) at 60 GHz.With this goal, an intercomparison campaign was carried out at the aerological station of MeteoSwiss in Payerne (Swizerland). The brightness temperature and the tropospheric temperature were assessed by means of a comparison with simultaneous and collocated radiosondes which are launched twice a day at this station. In addition, the TEMPERA performances are compared with the ones from a commercial microwave radiometer (HATPRO) which has some different instrumental characteristics and uses a different inversion algorithm. Brightness temperatures from both radiometers were compared with the ones simulated using a radiative transfer model and atmospheric profiles from radiosondes. A total of 532 cases were analyzed under all weather conditions and evidenced larger brightness temperature deviations between the two radiometers and the radiosondes for the most transparent channels. Two different retrievals for the TEMPERA radiometer were implemented in order to evaluate the effect of the different channels on the temperature retrievals. The comparison with radiosondes evidenced better results and very similar to the ones from HATPRO when the 8 more opaques channels were used. The study shows the good performance of TEMPERA to retrieve temperature profiles in the troposphere. The inversion method of TEMPERA is based on the Optimal Estimation Method. The main advantage of this algorithm is that there is no necessity for radiosonde information to achieve good results in contrast to conventional methods as neuronal networks or lineal regression. Finally, an assessment of the effect of instrumental characteristics as the filter response and the antenna pattern on the brightness temperature showed that they can have an important impact on the most transparent channels.

scholarly journals Validation of brightness and physical temperature from two scanning microwave radiometers in the 60 GHz O<sub>2</sub> band using radiosonde measurements

2016 ◽  
Vol 9 (9) ◽  
pp. 4587-4600 ◽  
Author(s):  
Francisco Navas-Guzmán ◽  
Niklaus Kämpfer ◽  
Alexander Haefele
Keyword(s):  
Brightness Temperature ◽  
Microwave Radiometer ◽  
Estimation Method ◽  
Optimal Estimation ◽  
Weather Conditions ◽  
Radiative Transfer Model ◽  
Inversion Method ◽  
Transfer Model ◽  
Tropospheric Temperature ◽  
Inversion Algorithm

Abstract. In this paper, we address the assessment of the tropospheric performance of a new temperature radiometer (TEMPERA) at 60 GHz. With this goal, an intercomparison campaign was carried out at the aerological station of MeteoSwiss in Payerne (Switzerland). The brightness temperature and the tropospheric temperature were assessed by means of a comparison with simultaneous and collocated radiosondes that are launched twice a day at this station. In addition, the TEMPERA performances are compared with the ones from a commercial microwave radiometer (HATPRO), which has some different instrumental characteristics and uses a different inversion algorithm. Brightness temperatures from both radiometers were compared with the ones simulated using a radiative transfer model and atmospheric profiles from radiosondes. A total of 532 cases were analyzed under all weather conditions and evidenced larger brightness temperature deviations between the two radiometers and the radiosondes for the most transparent channels. Two different retrievals for the TEMPERA radiometer were implemented in order to evaluate the effect of the different channels on the temperature retrievals. The comparison with radiosondes evidenced better results very similar to the ones from HATPRO, when the eight more opaque channels were used. The study shows the good performance of TEMPERA to retrieve temperature profiles in the troposphere. The inversion method of TEMPERA is based on the optimal estimation method. The main advantage of this algorithm is that there is no necessity for radiosonde information to achieve good results in contrast to conventional methods as neural networks or lineal regression. Finally, an assessment of the effect of instrumental characteristics as the filter response and the antenna pattern on the brightness temperature showed that they can have an important impact on the most transparent channels.

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 RTTOV-gb – Adapting the fast radiative transfer model RTTOV for the assimilation of ground-based microwave radiometer observations

2016 ◽  
Author(s):  
Francesco De Angelis ◽  
Domenico Cimini ◽  
James Hocking ◽  
Pauline Martinet ◽  
Stefan Kneifel
Keyword(s):  
Radiative Transfer ◽  
Liquid Water ◽  
Weather Prediction ◽  
Microwave Radiometer ◽  
Software Tool ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
60 Ghz ◽  
Variational Assimilation ◽  
Temperature And Humidity

Abstract. Ground-based microwave radiometers (MWR) offer a new capability to provide continuous observations of the atmospheric thermodynamic state in the planetary boundary layer. Thus, they are potential candidates to supplement radiosonde network and satellite data to improve numerical weather prediction (NWP) models through a variational assimilation of their data. However in order to assimilate MWR observations a fast radiative transfer model is required and such a model is not currently available. This is necessary for going from the model state vector space to the observation space at every observation point. The fast radiative transfer model RTTOV is well accepted in the NWP community, though it was developed to simulate satellite observations only. In this work, the RTTOV code has been modified to allow for simulations of ground-based upward looking microwave sensors. In addition, the Tangent Linear, Adjoint, and K-modules of RTTOV have been adapted to provide Jacobians (i.e. the sensitivity of observations to the atmospheric thermodynamical state) for ground-based geometry. These modules are necessary for the fast minimization of the cost function in a variational assimilation scheme. The proposed ground-based version of RTTOV, called RTTOV-gb, has been validated against accurate and less time-efficient line-by-line radiative transfer models. In the frequency range commonly used for temperature and humidity profiling (22–60 GHz), root-mean-square brightness temperature differences are smaller than typical MWR uncertainties (~ 0.5 K) at all channels used in this analysis. Brightness temperatures (TB) computed with RTTOV-gb from radiosonde profiles have been compared with nearly simultaneous and colocated ground-based MWR observations. Differences between simulated and measured TB are below 0.5 K for all channels except for the water vapor band, where most of the uncertainty comes from instrumental errors. The Jacobians calculated with the K-module of RTTOV-gb have been compared with those calculated with the brute force technique and those from the line-by-line model ARTS. Jacobians are found to be almost identical, except for liquid water content Jacobians for which a 10 % difference between ARTS and RTTOV-gb at transparent channels around 450 hPa is attributed to differences in liquid water absorption models. Finally, RTTOV-gb has been applied as the forward model operator within a 1-Dimensional Variational (1D-Var) software tool in an Observing-System Simulation Experiment (OSSE). For both temperature and humidity profiles, the 1D-Var with RTTOV-gb improves the retrievals with respect to NWP model in the first few kilometers from the ground.

scholarly journals First Three Years of the Microwave Radiometer aboard Envisat: In-Flight Calibration, Processing, and Validation of the Geophysical Products

2006 ◽  
Vol 23 (6) ◽  
pp. 802-814 ◽  
Author(s):  
E. Obligis ◽  
L. Eymard ◽  
N. Tran ◽  
S. Labroue ◽  
P. Femenias
Keyword(s):  
Microwave Radiometer ◽  
Radiative Transfer Model ◽  
Altimeter Data ◽  
Transfer Model ◽  
Path Delay ◽  
Inversion Algorithm ◽  
Weather Forecasts ◽  
Brightness Temperatures ◽  
Medium Range ◽  
Satellite Altimeter Data

Abstract The Envisat microwave radiometer is designed to correct the satellite altimeter data for the excess path delay resulting from tropospheric humidity. Neural networks have been used to formulate the inversion algorithm to retrieve this quantity from the measured brightness temperatures. The learning database has been built with European Centre for Medium-Range Weather Forecasts (ECMWF) analyses and simulated brightness temperatures by a radiative transfer model. The in-flight calibration has been performed in a consistent way by adjusting measurements on simulated brightness temperatures. Finally, coincident radiosonde measurements are used to validate the Envisat wet-tropospheric correction, and this comparison shows the good performances of the method.

scholarly journals Effects of Various parameters on Radiative Transfer Theory Based Microwave Emission Model

2017 ◽  
Vol 27 (1) ◽  
pp. 95-107
Author(s):  
Mazen E. Assiri Mazen E. Assiri
Keyword(s):  
Radiative Transfer ◽  
Brightness Temperature ◽  
Microwave Radiometer ◽  
Microwave Emission ◽  
Radiative Transfer Model ◽  
Stem Volume ◽  
Transfer Model ◽  
Emission Model ◽  
Radiative Transfer Theory ◽  
Transfer Theory

This paper outlines research that is currently being carried out to model the interaction of electromagnetic radiation with earth and atmosphere. Among many others, passive microwave (PM) imagery represents a useful source of data for mapping Earth features. Since, signal of a microwave radiometer is composed of surface and atmospheric contributions, for proper interpretation of the data these effects should be quantified. This research presents analysis of radiative transfer model contributors, which include; the ground based parameters, forest area, water area, and meteorological parameters. The principal objective of this study is to analyze the degree to which brightness temperature can be affected by various earth and atmospheric features. A sensitivity analysis is performed to test the contributing effects of various parameters in radiative transfer theory based microwave emission model. The results of the study show that soil temperature and forest stem volume are the main contributing parameters in estimating brightness temperature values. The results further show that both the earthly located features and atmospheric parameters are important factors that must be taken into account in the development and application of radiative transfer theory based models

Dependence of Simulation Biases at AHI Surface-Sensitive Channels on Land Surface Emissivity over China

2018 ◽  
Vol 35 (6) ◽  
pp. 1283-1298 ◽  
Author(s):  
X. Zhuge ◽  
X. Zou ◽  
F. Weng ◽  
M. Sun
Keyword(s):  
Brightness Temperature ◽  
Spectral Resolution ◽  
Land Surface ◽  
High Spectral Resolution ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Surface Emissivity ◽  
Bare Rock ◽  
Land Surface Emissivity ◽  
M Channels

AbstractThis study compares the simulation biases of Advanced Himawari Imager (AHI) brightness temperature to observations made at night over China through the use of three land surface emissivity (LSE) datasets. The University of Wisconsin–Madison High Spectral Resolution Emissivity dataset, the Combined Advanced Spaceborne Thermal Emission and Reflection Radiometer and Moderate Resolution Imaging Spectroradiometer Emissivity database over Land High Spectral Resolution Emissivity dataset, and the International Geosphere–Biosphere Programme (IGBP) infrared LSE module, as well as land skin temperature observations from the National Basic Meteorological Observing stations in China are used as inputs to the Community Radiative Transfer Model. The results suggest that the standard deviations of AHI observations minus background simulations (OMBs) are largely consistent for the three LSE datasets. Also, negative biases of the OMBs of brightness temperature uniformly occur for each of the three datasets. There are no significant differences in OMB biases estimated with the three LSE datasets over cropland and forest surface types for all five AHI surface-sensitive channels. Over the grassland surface type, significant differences (~0.8 K) are found at the 10.4-, 11.2-, and 12.4-μm channels if using the IGBP dataset. Over nonvegetated surface types (e.g., sandy land, gobi, and bare rock), the lack of a monthly variation in IGBP LSE introduces large negative biases for the 3.9- and 8.6-μm channels, which are greater than those from the two other LSE datasets. Thus, improvements in simulating AHI infrared surface-sensitive channels can be made when using spatially and temporally varying LSE estimates.

scholarly journals Modelling the L-Band Snow-Covered Surface Emission in a Winter Canadian Prairie Environment

2018 ◽  
Vol 10 (9) ◽  
pp. 1451 ◽  
Author(s):  
Alexandre Roy ◽  
Marion Leduc-Leballeur ◽  
Ghislain Picard ◽  
Alain Royer ◽  
Peter Toose ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Brightness Temperature ◽  
Frozen Soil ◽  
Microwave Emission ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Emission Model ◽  
Canadian Prairie ◽  
Seasonal Snow ◽  
L Band

Detailed angular ground-based L-band brightness temperature (TB) measurements over snow covered frozen soil in a prairie environment were used to parameterize and evaluate an electromagnetic model, the Wave Approach for LOw-frequency MIcrowave emission in Snow (WALOMIS), for seasonal snow. WALOMIS, initially developed for Antarctic applications, was extended with a soil interface model. A Gaussian noise on snow layer thickness was implemented to account for natural variability and thus improve the TB simulations compared to observations. The model performance was compared with two radiative transfer models, the Dense Media Radiative Transfer-Multi Layer incoherent model (DMRT-ML) and a version of the Microwave Emission Model for Layered Snowpacks (MEMLS) adapted specifically for use at L-band in the original one-layer configuration (LS-MEMLS-1L). Angular radiometer measurements (30°, 40°, 50°, and 60°) were acquired at six snow pits. The root-mean-square error (RMSE) between simulated and measured TB at vertical and horizontal polarizations were similar for the three models, with overall RMSE between 7.2 and 10.5 K. However, WALOMIS and DMRT-ML were able to better reproduce the observed TB at higher incidence angles (50° and 60°) and at horizontal polarization. The similar results obtained between WALOMIS and DMRT-ML suggests that the interference phenomena are weak in the case of shallow seasonal snow despite the presence of visible layers with thicknesses smaller than the wavelength, and the radiative transfer model can thus be used to compute L-band brightness temperature.

scholarly journals Efficient radiative transfer model for thermal infrared brightness temperature simulation in cloudy atmospheres

2020 ◽  
Vol 28 (18) ◽  
pp. 25730
Author(s):  
Wenwen Li ◽  
Feng Zhang ◽  
Yi-Ning Shi ◽  
Hironobu Iwabuchi ◽  
Mingwei Zhu ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Brightness Temperature ◽  
Thermal Infrared ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Infrared Brightness

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.

scholarly journals Evaluation of MWHS-2 Using a Co-located Ground-Based Radar Network for Improved Model Assimilation

2019 ◽  
Vol 11 (20) ◽  
pp. 2338 ◽  
Author(s):  
Liu ◽  
Chu ◽  
Yin ◽  
Liu
Keyword(s):  
Radiative Transfer ◽  
Brightness Temperature ◽  
Weather Prediction ◽  
Order Relation ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Radar Network ◽  
Intense Precipitation ◽  
Standard Deviations ◽  
Light Precipitation

Accurate precipitation detection is one of the most important factors in satellite data assimilation, due to the large uncertainties associated with precipitation properties in radiative transfer models and numerical weather prediction (NWP) models. In this paper, a method to achieve remote sensing of precipitation and classify its intensity over land using a co-located ground-based radar network is described. This method is intended to characterize the O−B biases for the microwave humidity sounder -2 (MWHS-2) under four categories of precipitation: precipitation-free (0–5 dBZ), light precipitation (5–20 dBZ), moderate precipitation (20–35 dBZ), and intense precipitation (>35 dBZ). Additionally, O represents the observed brightness temperature (TB) of the satellite and B is the simulated TB from the model background field using the radiative transfer model. Thresholds for the brightness temperature differences between channels, as well as the order relation between the differences, exhibited a good estimation of precipitation. It is demonstrated that differences between observations and simulations were predominantly due to the cases in which radar reflectivity was above 15 dBZ. For most channels, the biases and standard deviations of O−B increased with precipitation intensity. Specifically, it is noted that for channel 11 (183.31 ± 1 GHz), the standard deviations of O−B under moderate and intense precipitation were even smaller than those under light precipitation and precipitation-free conditions. Likewise, abnormal results can also be seen for channel 4 (118.75 ± 0.3 GHz).

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