Retrieval Of Atmospheric Parameters Research Articles

Accuracy assessment of ground-based microwave radiometer in the Mount Qomolangma region

Mount Qomolangma, known as the “Roof of the World,” holds significant importance in exploring the vertical characteristics of the atmosphere, providing insights into high-altitude atmospheric features, and improving numerical models for forecasting downstream regions. Ground-based microwave radiometers (GWR) enable continuous profiling of atmospheric conditions in the troposphere, complementing radiosonde and satellite observations. This study explores the performance of the MWP967KV GWR installed at Qomolangma Base Camp by comparison with concurrent radiosonde measurements. The assessment covers GWR's atmospheric radiative brightness temperatures (Tb) detection capability, accuracy of atmospheric parameter retrievals, and uncertainties in atmospheric parameters under different weather conditions. Using MonoRTM to calculate simulated Tb from radiosonde, the results indicate a high precision of GWR's Tb observations, showing a strong correlation (R ≈ 0.99) and a deviation of merely 2.4 K compared to simulated Tb. Moreover, GWR exhibits high reliability in temperature (R ≈ 0.98) and water vapor density (ρv) (R ≈ 0.91) observations, while the accuracy of relative humidity (RH) measurements requires improvement (R ≈ 0.55). The GWR observations are affected by clouds, precipitation, and surrounding Qomolangma mountain ridges, leading to significant uncertainties in atmospheric parameter retrievals between 2000 m to 6000 m above ground level. Additionally, GWR's sensitivity to temperature variations is limited, resulting in missed detections of temperature inversion. Despite these limitations, this study offers crucial insights into meteorological observations at high altitudes using GWR. These findings hold substantial implications for meteorological research and mountaineering activities in high-altitude regions.

Impact of Channel Selection with Different Bandwidths on Retrieval at 50–60 GHz

Microwave hyperspectral instruments represent one of the main atmospheric sounders of China’s next-generation Fengyun meteorological satellites. In order to better apply microwave hyperspectral observations in the fields of atmospheric parameter retrieval and data assimilation, this paper analyzes the sensitivity of trace gases to five selected bandwidth channels using a radiative transfer model based on the simulated data of microwave hyperspectral radiances at 50–60 GHz. This method uses information entropy and a weighting function to select channels and analyze the impact of this on the retrieval accuracy of atmospheric profiles before and after channel selection. The experimental results show that channel selection can reduce the number of channels by approximately 74.05% while maintaining a large amount of information content, and this retrieval effect is significantly better than that of MWTS-III. After channel selection, the 10 MHz, 30 MHz, and 50 MHz bandwidths have the best retrieval results in the stratosphere, whole atmosphere, and troposphere, respectively. When considering the number of channels, computational scale, and retrieval results comprehensively, the channel selection method is effective.

Retrieval of Atmospheric Temperature Profile from Historical Data and Ground-Based Observations by Using a Machine Learning Algorithm

The atmospheric temperature profile is an important parameter to describe the state of the atmosphere, and it is crucial to climate change research, weather forecasting, and atmospheric parameter retrieval. A machine learning algorithm that incorporates historical observations and ground-based measurements was developed in this study to retrieve the atmospheric temperature profile. Specifically, the deep learning network considered historical observations for the same period and temporally correlated temperature profiles. It combined multi-layer perceptron (MLP) and the convolutional neural network (CNN). MLP derived the features from the ground factors, and CNN captured the essential features associated with the temperature profiles at the current time from latent historical data. Then, the features of the two parts were concatenated to obtain the final network. The construction and parameters of the model were optimized to determine the best model configuration and retrieval performance. The results of the model were evaluated against those of other methods on the same dataset. The model showed a good retrieval precision, which was equivalent to a small retrieval bias, root-mean-square error, and mean absolute error at all altitudes. The analysis of the application of this model to the retrieval of atmospheric temperature profiles indicates that the method is feasible.

Enhancing MAX-DOAS atmospheric state retrievals by multispectral polarimetry – studies using synthetic data

Abstract. Ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) is a widely used measurement technique for the remote detection of atmospheric aerosol and trace gases. The technique relies on the analysis of ultra-violet and visible radiation spectra of scattered sunlight (skylight) to obtain information on different atmospheric parameters. From an appropriate set of spectra recorded under different viewing directions (typically a group of observations at different elevation angles) the retrieval of aerosol and trace gas vertical distributions is achieved through numerical inversion methods. It is well known that the polarisation state of skylight is particularly sensitive to atmospheric aerosol content as well as aerosol properties and that polarimetric measurements could therefore provide additional information for MAX-DOAS profile retrievals; however, such measurements have not yet been used for this purpose. To address this issue, we have developed the RAPSODI (Retrieval of Atmospheric Parameters from Spectroscopic Observations using DOAS Instruments) algorithm. In contrast to existing MAX-DOAS algorithms, it can process polarimetric information, and it can retrieve simultaneously profiles of aerosols and various trace gases at multiple wavelengths in a single retrieval step; a further advantage is that it contains a Mie scattering model, allowing for the retrieval of aerosol microphysical properties. The forward-model component in RAPSODI is based on a linearised vector radiative transfer model with Jacobian facilities, and we have used this model to create a database of synthetic measurements in order to carry out sensitivity analyses aimed at assessing the potential of polarimetric MAX-DOAS observations. We find that multispectral polarimetry significantly enhances the sensitivity, particularly to aerosol-related quantities. Assuming typical viewing geometries, the degrees of freedom for signal (DOFS) increase by about 50 % and 70 % for aerosol vertical distributions and aerosol properties, respectively, and by approximately 10 % for trace gas vertical profiles. For an idealised scenario with a horizontally homogeneous atmosphere, our findings predict an improvement in the inversion results' accuracy (root-mean-square deviations to the true values) of about 60 % for aerosol vertical column densities (VCDs) as well as for aerosol surface concentrations and by 40 % for aerosol properties. For trace gas VCDs, very little improvement is apparent, although the accuracy of trace gas surface concentrations improves by about 50 %.

Validation of Landsat 8 high resolution Sea Surface Temperature using surfers

Nearshore coastal waters are highly dynamic in both space and time. They can be difficult to sample using conventional methods due to their shallow depth, tidal variability, and the presence of strong currents and breaking waves. High resolution satellite sensors can be used to provide synoptic views of Surface Temperature (ST), but the performance of such ST products in the nearshore zone is poorly understood. Close to the shoreline, the ST pixels can be influenced by mixed composition of water and land, as a result of the sensor’s spatial resolution. This can cause thermal adjacency effects due to the highly different diurnal temperature cycles of water bodies and land. Previously, temperature data collected during surfing sessions has been proposed for validation of moderate resolution (1 km pixel size) satellite ST products. In this paper we use surfing temperature data to validate three high resolution (100 m resampled to 30 m pixel size) ST products derived from the Thermal InfraRed Sensor (TIRS) on board Landsat 8 (L8). ST was derived from Collection 1 and 2 Level 1 data (C1L1 and C2L1) using the Thermal Atmospheric Correction Tool (TACT), and was obtained from the standard Collection 2 Level 2 product (USGS C2L2). This study represents one of the first evaluations of the new C2 products, both L1 and L2, released by USGS at the end of 2020. Using automated matchup and image quality control, 88 matchups between L8/TIRS and surfers were identified, distributed across the North-Western semihemisphere. The unbiased Root Mean Squared Difference (uRMSD) between satellite and in situ measurements was generally ¡ 2 K, with warm biases (Mean Average Difference, MAD) of 1.7 K (USGS C2L2), 1.3 K (TACT C1L1) and 0.8 K (TACT C2L1). Large interquartile ranges of ST in 5 × 5 satellite pixels around the matchup location were found for several images, especially for the summer matchups around the Californian coast. By filtering on target stability the number of matchups reduced to 31, which halved the uRMSD across the three methods (to around 1.1K), MAD were much lower, i.e. 1.1 K (USGS C2L2), 0.6 K (TACT C1L1), and 0.2 K (TACT C2L1). The larger biases of the C2L2 product compared to TACT C2L1 are caused as a result of: (1) a lower emissivity value for water targets used in USGS C2L2, and (2) differences in atmospheric parameter retrieval, mainly from differences in upwelling atmospheric radiance and lower atmospheric transmittance retrieved by USGS C2L2. Additionally, tiling artefacts are present in the C2L2 product, which originate from a coarser atmospheric correction process. Overall, the L8/TIRS derived ST product compares well with in situ measurements made while surfing, and we found the best performing ST product for nearshore coastal waters to be the Collection 2 Level 1 data processed with TACT.

Peeking inside the Black Box: Interpreting Deep-learning Models for Exoplanet Atmospheric Retrievals

Abstract Deep-learning algorithms are growing in popularity in the field of exoplanetary science due to their ability to model highly nonlinear relations and solve interesting problems in a data-driven manner. Several works have attempted to perform fast retrievals of atmospheric parameters with the use of machine-learning algorithms like deep neural networks (DNNs). Yet, despite their high predictive power, DNNs are also infamous for being “black boxes.” It is their apparent lack of explainability that makes the astrophysics community reluctant to adopt them. What are their predictions based on? How confident should we be in them? When are they wrong, and how wrong can they be? In this work, we present a number of general evaluation methodologies that can be applied to any trained model and answer questions like these. In particular, we train three different popular DNN architectures to retrieve atmospheric parameters from exoplanet spectra and show that all three achieve good predictive performance. We then present an extensive analysis of the predictions of DNNs, which can inform us–among other things–of the credibility limits for atmospheric parameters for a given instrument and model. Finally, we perform a perturbation-based sensitivity analysis to identify to which features of the spectrum the outcome of the retrieval is most sensitive. We conclude that, for different molecules, the wavelength ranges to which the DNNs predictions are most sensitive do indeed coincide with their characteristic absorption regions. The methodologies presented in this work help to improve the evaluation of DNNs and to grant interpretability to their predictions.

Generalization of the complete data fusion to multi-target retrieval of atmospheric parameters and application to FORUM and IASI-NG simulated measurements

In the context of a growing need for innovatory techniques to take advantage of the largest amount of information from the great number of available remote sensing data, the Complete Data Fusion (CDF) algorithm was presented as a new method to combine independent measurements of the same vertical profile of an atmospheric parameter into a single estimate for a concise and complete characterization of the atmospheric state. The majority of the atmospheric composition measurements determine the altitude distribution of a great number of quantities: multi-target retrievals (MTRs) are increasingly applied to remote sensing observations to determine simultaneously atmospheric constituents with the purpose to reduce the systematic error caused by interfering species. In this work, we optimised the CDF for the application to MTR products. We applied the method to simulated retrievals in the thermal infrared and in the far infrared spectral ranges, considering the instrumental specifications and performances of IASI-NG (Infrared Atmospheric Sounding Interferometer New Generation) and FORUM (Far-Infrared Outgoing Radiation Understanding and Monitoring) instruments, respectively. The obtained results show that the CDF algorithm can cope with state vectors from MTRs, that must share at least one retrieved variable. In particular, the results show that the fused profile has the greatest number of degrees of freedom and the smallest error for all considered cases. The comparison between the CDF products and the synergistic retrieval ones shows the equivalence of the two methods when the linear approximation is adopted to simplify the treatment of the retrieval problem.

Application of the Deep Neural Network in Retrieving the Atmospheric Temperature and Humidity Profiles from the Microwave Humidity and Temperature Sounder Onboard the Feng-Yun-3 Satellite.

The shallow neural network (SNN) is a popular algorithm in atmospheric parameters retrieval from microwave remote sensing. However, the deep neural network (DNN) has a stronger nonlinear mapping capability compared to SNN and has great potential for applications in microwave remote sensing. The Microwave Humidity and Temperature Sounder (Beijing, China, MWHTS) onboard the Fengyun-3 (FY-3) satellite has the ability to independently retrieve atmospheric temperature and humidity profiles. A study on the application of DNN in retrieving atmospheric temperature and humidity profiles from MWHTS was carried out. Three retrieval schemes of atmospheric parameters in microwave remote sensing based on DNN were performed in the study of bias correction of MWHTS observation and the retrieval of the atmospheric temperature and humidity profiles using MWHTS observations. The experimental results show that, compared with SNN, DNN can obtain better bias-correction results when applied to MWHTS observation, and can obtain higher retrieval accuracy of temperature and humidity profiles in all three retrieval schemes. Meanwhile, DNN shows higher stability than SNN when applied to the retrieval of temperature and humidity profiles. The comparative study of DNN and SNN applied in different atmospheric parameter retrieval schemes shows that DNN has a more superior performance.

Retrieval of Land Surface Temperature, Emissivity, and Atmospheric Parameters From Hyperspectral Thermal Infrared Image Using a Feature-Band Linear-Format Hybrid Algorithm

Thermal infrared remote sensing can acquire large-scale land surface thermal radiance effectively. However, the observed data are affected by surface and atmospheric conditions. Traditional methods require some prior knowledge, such as emissivity in split-window algorithm and atmospheric correction in temperature–emissivity separation algorithm. This information is difficult to obtain directly and accurately. Hyperspectral thermal infrared data provide the possibility for simultaneous retrieval of atmospheric parameters, land surface temperature (LST), and emissivity because of their abundant band information. This study proposed a feature-band linear-format hybrid (FebLihy) algorithm by combining a deep neural network (DNN) model and a physical model with thermal airborne hyperspectral imager (TASI) data. The proposed algorithm was divided into three steps. First, the radiative transfer equation was converted into a linear form, and seven feature bands were chosen to reduce the unknowns. Second, the initial values of atmospheric and land surface parameters were estimated with the DNN model. Finally, least-squares optimization was used in the physical model to retrieve the final results. Results of the simulation data showed that the root-mean-square error (RMSE) of LST was 0.86 K, the RMSE of emissivity was less than 0.015, and the accuracy of atmospheric parameters was improved effectively by the physical model. The FebLihy algorithm was applied in a real TASI image in Fuyun County and verified with CE312 ground measurement data. Accurate results were achieved. The FebLihy algorithm will be optimized in terms of model and data in the future study.

Peeking inside the Black Box: Interpreting Deep Learning Models for Exoplanet Atmospheric Retrievals

Deep learning algorithms are growing in popularity in the field of exoplanetary science due to their ability to model highly non-linear relations and solve interesting problems in a data-driven manner. Several works have attempted to perform fast retrievals of atmospheric parameters with the use of machine learning algorithms like deep neural networks (DNNs). Yet, despite their high predictive power, DNNs are also infamous for being 'black boxes'. It is their apparent lack of explainability that makes the astrophysics community reluctant to adopt them. What are their predictions based on? How confident should we be in them? When are they wrong and how wrong can they be? In this work, we present a number of general evaluation methodologies that can be applied to any trained model and answer questions like these. In particular, we train three different popular DNN architectures to retrieve atmospheric parameters from exoplanet spectra and show that all three achieve good predictive performance. We then present an extensive analysis of the predictions of DNNs, which can inform us - among other things - of the credibility limits for atmospheric parameters for a given instrument and model. Finally, we perform a perturbation-based sensitivity analysis to identify to which features of the spectrum the outcome of the retrieval is most sensitive. We conclude that for different molecules, the wavelength ranges to which the DNN's predictions are most sensitive, indeed coincide with their characteristic absorption regions. The methodologies presented in this work help to improve the evaluation of DNNs and to grant interpretability to their predictions.

Simulating Multi-Directional Narrowband Reflectance of the Earth’s Surface Using ADAM (A Surface Reflectance Database for ESA’s Earth Observation Missions)

The ADAM (A Surface Reflectance Database for ESA’s Earth Observation Missions) product (a climatological database coupled to its companion calculation toolkit) enables users to simulate realistic hyperspectral and directional global Earth surface reflectances (i.e., top-of-canopy/bottom-of-atmosphere) over the 240–4000 nm spectral range (at 1-nm resolution) and in any illumination/observation geometry, at 0.1° × 0.1° spatial resolution for a typical year. ADAM aims to support the preparation of optical Earth observation missions as well as the design of operational processing chains for the retrieval of atmospheric parameters by characterizing the expected surface reflectance, accounting for its anisotropy. Firstly, we describe (1) the methods used in the development of the gridded monthly ADAM climatologies (over land surfaces: monthly means of normalized reflectances derived from MODIS observations in seven spectral bands for the year 2005; over oceans: monthly means over the 1999–2009 period of chlorophyll content from SeaWiFS and of wind speed from SeaWinds), and (2) the underlying modeling approaches of ADAM toolkit to simulate the spectro-directional variations of the reflectance depending on the assigned surface type. Secondly, we evaluate ADAM simulation performances over land surfaces. A comparison against POLDER multi-spectral/multi-directional measurements for year 2008 shows reliable simulation results with root mean square differences below 0.027 and R2 values above 0.9 for most of the 14 land cover IGBP classes investigated, with no significant bias identified. Only for the “Snow and ice” class is the performance lower pointing to a limitation of climatological data to represent actual snow properties. An evaluation of the modeled reflectance in the specific backscatter direction against CALIPSO data reveals that ADAM tends to overestimate (underestimate) the so-called “hot-spot” by a factor of about 1.5 (1.5 to 2) for barren (vegetated) surfaces.

Jacobian matrix for near-infrared remote sensing based on vector radiative transfer model

Due to the polarization effects of the Earth’s surface reflection and atmospheric particles’ scattering, high-precision retrieval of atmospheric parameters from near-infrared satellite data requires accurate vector atmospheric radiative transfer simulations. This paper presents a near-infrared vector radiative transfer model based on the doubling and adding method. This new model utilizes approximate calculations of the atmospheric transmittance, reflection, and solar scattering radiance for a finitely thin atmospheric layer. To verify its accuracy, the results for four typical scenarios (single molecular layer with Rayleigh scattering, single aerosol layer scattering, multi-layer Rayleigh scattering, and true atmospheric with multi-layer molecular absorption, Rayleigh and aerosol scattering) were compared with benchmarks from a well-known model. The comparison revealed an excellent agreement between the results and the reference data, with accuracy within a few thousandths. Besides, to fulfill the retrieval algorithm, a numerical differentiation-based Jacobian calculation method is developed for the atmospheric and surface parameters. This is coupled with the adding and doubling process for the radiative transfer calculation. The Jacobian matrix produced by the new algorithm is evaluated by comparison with that from the perturbation method. The relative Jacobian matrix deviations between the two methods are within a few thousandths for carbon dioxide and less than 1.0×10−3% for aerosol optical depth. The two methods are consistent for surface albedo, with a deviation below 2.03×10−4%. All validation results suggest that the accuracy of the proposed radiative transfer model is suitable for inversion applications. This model exhibits the potential for simulating near-infrared measurements of greenhouse gas monitoring instruments.

Use of Double Channel Differences for Reducing the Surface Emissivity Dependence of Microwave Atmospheric Temperature and Humidity Retrievals

AbstractSurface emissivity has a significant impact on atmospheric parameter retrievals from microwave sounding instruments. To reduce the dependence of retrievals on surface emissivity, a double channel differences equation is deduced, and a corresponding retrieval scheme is constructed. Retrieval experiments are performed using Advanced Microwave Sounding Unit‐A (AMSU‐A) and Microwave Humidity Sounder (MHS) simulations and global measurements. Simulation experiments show that the double channel differences scheme can reduce the root mean square errors (RMSE) of the temperature and humidity profiles in the middle and lower atmosphere. Retrieval experiments based on AMSU‐A and MHS global measurements show that the proposed scheme can significantly reduce the RMSE of temperature profiles in the lower atmosphere and humidity profiles in the middle and lower atmosphere for cloudy and cloudless conditions, different surface types, and different scan angles, with maximum reduction values of 0.64 K and 9.03%, respectively. Regarding RMSE improvement, that of the cloudy condition is greater than that of the cloudless condition, that of the land is greater than that of the coast and the sea, and there is no significant dependence on the scan angles. The double channel differences scheme is very sensitive to initial near‐surface temperatures. Reducing the initial near‐surface temperature error can significantly improve the temperature retrieval accuracy below 900 hPa, with maximum reduction value of 3.25 K.

Sea Ice and Atmospheric Parameter Retrieval From Satellite Microwave Radiometers: Synergy of AMSR2 and SMOS Compared With the CIMR Candidate Mission

AbstractResearch on improving the prediction skill of climate models requires refining the quality of observational data used for initializing and tuning the models. This is especially true in the polar regions where uncertainties about the interactions between sea ice, ocean, and atmosphere are driving ongoing monitoring efforts. The Copernicus Imaging Microwave Radiometer (CIMR) is an European Space Agency (ESA) candidate mission which promises to offer high resolution, low uncertainty observation capabilities at the 1.4, 6.9, 10.65, 18.7, and 36.5 GHz frequencies. To assess the potential impact of CIMR for sea ice parameter retrieval, a comparison is made between retrievals based on present AMSR2 observations and a retrieval using future CIMR equivalent observations over a data set of validated sea ice concentration (SIC) values. An optimal estimation retrieval method (OEM) is used which can use input from different channel combinations to retrieve seven geophysical parameters (sea ice concentration, multi‐year ice fraction, ice surface temperature, columnar water vapor, liquid water path, over ocean wind speed, and sea surface temperature). An advantage of CIMR over existing radiometers is that it would provide higher spatial resolution observations at the lower frequency channels (6.9, 10.65, and 18.7 GHz) which are less sensitive to atmospheric influence. This enables the passive microwave based retrieval of SIC and other surface parameters with higher resolution and lower uncertainty than is currently possible. An information content analysis expands the comparison between AMSR2 and CIMR to all retrievable surface and atmospheric parameters. This analysis quantifies the contributions to the observed signal and highlights the differences between different input channel combinations. The higher resolution of the low frequency CIMR channels allow for unprecedented detail to be achieved in Arctic passive microwave sea ice retrievals. The presence of 1.4 GHz channels on board CIMR opens up the possibility for thin sea ice thickness (SIT) retrieval. A combination of collocated AMSR2 and SMOS observations is used to simulate a full CIMR suite of measurements, and the OEM is modified to include SIT as a retrieval parameter. The output from different retrieval configurations is compared with an operational SIT product. The CIMR instrument can provide increased accuracy for SIC retrieval at very high resolutions with a combination of the 18.7 and 36.5 GHz channels while also maintaining sensitivity for atmospheric water vapor retrieval. In combination with the 1.4 GHz channels, SIT can be added as an eighth retrieval parameter with performance on par with existing operational products.

Real-Time Retrieval of Precipitable Water Vapor From Galileo Observations by Using the MGEX Network

The rapid development of the European Galileo system brings a great opportunity for the real-time retrieval of atmospheric parameters. In this contribution, Galileo observations are employed to retrieve real-time water vapor based on the precise point positioning (PPP) technique, where the benefit of ambiguity resolution on water vapor sensing is also investigated. The obtained atmospheric parameters, including zenith tropospheric delay (ZTD) and precipitable water vapor (PWV), are validated with respect to the postprocessing Global Positioning System (GPS) ZTD products and the PWV products derived from the European Centre for Medium-Range Weather Forecasts (ECMWF). The results show that the real-time ZTDs, derived from the Galileo PPP solutions, agree well with the postprocessing GPS ZTDs. An averaged root-mean-square (rms) value of 8.5 mm for the ZTD differences is achieved for the float solution after an averaged initialization process of about 27.4 min. In terms of the fixed solution, the averaged rms value is decreased to 7 mm and the initialization time is shortened to 20.8 min, showing an improvement of 17.6% and 24.1%, respectively, when compared to the float solution. Furthermore, the derived Galileo-PWVs display good agreement with the ECMWF PWVs, with an accuracy of 1.9 and 1.7 mm for the float and the fixed solution, respectively.

Application of Sentinel-2 MSI in Arctic Research: Evaluating the Performance of Atmospheric Correction Approaches Over Arctic Sea Ice

Multispectral remote sensing may be a powerful tool for areal retrieval of biogeophysical parameters in the Arctic sea ice. The MultiSpectral Instrument on board the Sentinel-2 (S-2) satellites of the European Space Agency offers new possibilities for Arctic research; S-2A and S-2B provide 13 spectral bands between 443 and 2202 nm and spatial resolutions between 10 and 60 m, which may enable the monitoring of large areas of Arctic sea ice. For an accurate retrieval of parameters such as surface albedo, the elimination of atmospheric influences in the data is essential. We therefore provide an evaluation of five currently available atmospheric correction processors for S-2 (ACOLITE, ATCOR, iCOR, Polymer, and Sen2Cor). We evaluate the results of the different processors using in situ spectral measurements of ice and snow and open water gathered north of Svalbard during RV Polarstern cruise PS106.1 in summer 2017. We used spectral shapes to assess performance for ice and snow surfaces. For open water, we additionally evaluated intensities. ACOLITE, ATCOR and iCOR performed well over sea ice and Polymer generated the best results over open water. ATCOR, iCOR and Sen2Cor failed in the image-based retrieval of atmospheric parameters (aerosol optical thickness, water vapor). ACOLITE estimated AOT within the uncertainty range of AERONET measurements. Parameterization based on external data, therefore, was necessary to obtain reliable results. To illustrate consequences of processor selection on secondary products we computed average surface reflectance of six bands and normalized difference melt index (NDMI) on an image subset. Medians of average reflectance and NDMI range from 0.80 to 0.97 and 0.12 to 0.18 while medians for TOA are 0.75 and 0.06, respectively.

Retrieval of meteorological parameters from ROSA on-board Megha Tropiques satellite: evaluation with COSMIC GPS RO, ERA-Interim and radiosonde data

ABSTRACT The Indian Space Research Organization (ISRO), in collaboration with Centre National d’Etudes Spatiales, France, launched radio occultation sounder for atmosphere (ROSA) on-board Megha Tropiques (MT) satellite. In this article, the retrieval of atmospheric parameters from ROSA and its evaluation with a network of in situ (radiosonde), similar satellite (Constellation Observational System for Meteorology, Ionosphere and Climate (COSMIC) GPS RO), and re-analysis (ERA-Interim) data sets are presented. The refractivity is retrieved from the bending angle as a function of impact parameter, and pressure and temperature profiles are derived from the profiles of refractivity. The algorithm has been successfully applied to COSMIC data. We have applied and validated this algorithm for the first time to ROSAon board MT. The algorithm retrieves the refractivity and pressure information from surface to 40 km whereas temperature profiles are restricted between 10 and 40 km. The ROSA algorithm refractivity, pressure, and temperature profiles compare well with independent data sets mentioned above. The mean fractional difference between ROSA and COSMIC, ERA-Interim and radiosonde refractivity and the temperature is found to be 0.1% with a standard deviation of 0.5%. It is shown that the algorithm derives the atmospheric parameters with good accuracies from the ROSA instrument and provides the atmospheric community new data from GPS RO at the verge of existing COSMIC GPS RO and in between upcoming GPS RO missions.

Real-time tropospheric delay retrieval with GPS, GLONASS, Galileo and BDS data

The precise point positioning (PPP) is a promising technology for the real-time retrieval of atmospheric parameters with a single receiver in anywhere, all-weather and any time. The real-time atmospheric parameters can be applied to the time-critical meteorology, such as the severe weather nowcasting. The PPP is a satellite-based technology. Multi-constellation integration can enhance satellite geometry and increase measurement redundancy so that the solutions of atmospheric parameters are expected to be improved. Currently, the Global Navigation Satellite System (GNSS) family includes recovered GLONASS and modernized GPS as well as the emerging Galileo and BDS. A week of GNSS observations from 160 stations are processed to retrieve the tropospheric zenith total delay (ZTD) in real time. The four-constellation mixed real-time precise products including satellite orbit and clock corrections are adopted, and their quality is evaluated. The performance of ZTD estimates is assessed in terms of accuracy and convergence time by comparing with final tropospheric ZTD products provided by two analysis centers. The ZTDs retrieved from different constellation combinations (i.e., GPS/GLONASS/Galileo/BDS, GPS/GLONASS, and GPS-only), different processing models for ionospheric delays (i.e., ionospheric-free (IF) combined PPP, and uncombined (UC) PPP), and different modes (i.e., real-time mode, and post-processing mode) are compared.

Level 1b error budget for MIPAS on ENVISAT

Abstract. The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) is a Fourier transform spectrometer measuring the radiance emitted from the atmosphere in limb geometry in the thermal infrared spectral region. It was operated onboard the ENVISAT satellite from 2002 to 2012. Calibrated and geolocated spectra, the so-called level 1b data, are the basis for the retrieval of atmospheric parameters. In this paper we present the error budget for the level 1b data of the most recent data version 8 in terms of radiometric, spectral, and line of sight accuracy. The major changes of version 8 compared to older versions are also described. The impact of the different error sources on the spectra is characterized in terms of spectral, vertical, and temporal correlation because these correlations have an impact on the quality of the retrieved quantities. The radiometric error is in the order of 1 % to 2.4 %, the spectral accuracy is better than 0.3 ppm, and the line of sight accuracy at the tangent point is around 400 m. All errors are well within the requirements, and the achieved accuracy allows atmospheric parameters to be retrieved from the measurements with high quality.

Retrieval of Atmospheric Parameters and Surface Reflectance from Visible and Shortwave Infrared Imaging Spectroscopy Data

Remote imaging spectroscopy in the 0.4–2.5-μm visible and shortwave infrared (VSWIR) range captures the majority of solar-reflected energy and enables a wide range of earth surface studies. This spectral range is also influenced by atmospheric effects including absorption from atmospheric gases and aerosols, Rayleigh scattering, and particle scattering. Globally consistent surface measurements must compensate for these atmospheric effects. This article reviews the physical and mathematical foundations of modern VSWIR atmospheric retrieval, focusing on imaging spectrometers. We assess sensitivity of the retrieval to errors in atmospheric state estimation. Finally, we describe some promising avenues of future research to support the next generation of orbital imaging spectrometers.