Retrieval Of Atmospheric Temperature Profiles Research Articles

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.

Improving typhoon predictions by assimilating the retrieval of atmospheric temperature profiles from the FengYun-4A's Geostationary Interferometric Infrared Sounder (GIIRS)

The Geostationary Interferometric Infrared Sounder (GIIRS) onboard China's FengYun-4A geostationary satellite provides an unprecedented opportunity to observe the three-dimensional thermodynamic structure of typhoons in the western North Pacific Ocean with high spatiotemporal resolutions. Field campaigns of targeting observation based on the conditional nonlinear optimal perturbation (CNOP) sensitivity were carried out utilizing the GIIRS on FY-4A for five typhoons: Chan-hom, Maysak, and Higos in 2020 and typhoons Chanthu and Conson in 2021 and collected temporally continuous observations for the thermodynamic structure of typhoons and their environments.This study investigated the impact of atmospheric temperature profile retrieved from the collected GIIRS radiance on typhoon analysis and prediction in a regional Hurricane WRF (HWRF) model using the ensemble-variational data assimilation scheme. Despite the case dependence, the assimilation of the additional satellite retrieval generally improves the typhoon track forecasts relative to those with simply the operational observations assimilated. The improvement mainly occurs beyond two days and the average reduction of track errors reaches up to 100 km at about day 3. Diagnostics show that the improvement of initial temperature condition influences the geopotential height and wind fields roughly through the hydrostatic relationship. The assimilation of temperature retrieval also shows some potential in improving wind forecasts in the near-coast areas at upper and lower levels and the precipitation forecasts on land.

Improving Typhoon Predictions by Assimilating the Retrieval of Atmospheric Temperature Profiles from the Fengyun-4a's Geostationary Interferometric Infrared Sounder (Giirs)

Improving Typhoon Predictions by Assimilating the Retrieval of Atmospheric Temperature Profiles from the Fengyun-4a's Geostationary Interferometric Infrared Sounder (Giirs)

Improving Typhoon Predictions by Assimilating the Retrieval of Atmospheric Temperature Profiles from the Fengyun-4a's Geostationary Interferometric Infrared Sounder (Giirs)

Improving Typhoon Predictions by Assimilating the Retrieval of Atmospheric Temperature Profiles from the Fengyun-4a's Geostationary Interferometric Infrared Sounder (Giirs)

Improving Typhoon Predictions by Assimilating the Retrieval of Atmospheric Temperature Profiles from the Fengyun-4a's Geostationary Interferometric Infrared Sounder (Giirs)

Improving Typhoon Predictions by Assimilating the Retrieval of Atmospheric Temperature Profiles from the Fengyun-4a's Geostationary Interferometric Infrared Sounder (Giirs)

An Improved Method Combining ANN and 1D-Var for the Retrieval of Atmospheric Temperature Profiles from FY-4A/GIIRS Hyperspectral Data

In our study, a retrieval method of temperature profiles is proposed which combines an improved one-dimensional variational algorithm (1D-Var) and artificial neural network algorithm (ANN), using FY-4A/GIIRS (Geosynchronous Interferometric Infrared Sounder) infrared hyperspectral data. First, according to the characteristics of the FY-4A/GIIRS observation data using the conventional 1D-Var, we introduced channel blacklists and discarded the channels that have a large negative impact on retrieval, then used the information capacity method for channel selection and introduced a neural network to correct the satellite observation data. The improved 1D-Var effectively used the observation information of 1415 channels, reducing the impact of the error of the satellite observation and radiative transfer model, and realizing the improvement of retrieval accuracy. We subsequently used the improved 1D-Var and ANN algorithms to retrieve the temperature profiles, respectively, from the GIIRS data. The results showed that the accuracy when using ANN is better than using improved 1D-Var in situations where the pressure ranges from 800 hPa to 1000 hPa. Therefore, we combined the improved 1D-Var and ANN method to retrieve temperature profiles for different pressure levels, calculating the error by taking sounding data published by the University of Wyoming as the true values. The results show that the average error of the retrieved temperature profiles is smaller than 2 K when using our method, this method makes the accuracy of the retrieved temperature profiles superior to the accuracy of the GIIRS products from 10 hPa to 575 hPa. All in all, through the combination of the physical retrieval method and the machine learning retrieval method, this paper can certainly provide a reference for improving the accuracy of products.

Effects of CO2 Changes on Hyperspectral Infrared Radiances and Its Implications on Atmospheric Temperature Profile Retrieval and Data Assimilation in NWP

Although atmospheric CO2 is a trace gas, it has seasonal variations and has increased over the last decade. Its seasonal variation and increase have substantial radiative effects on hyperspectral infrared (IR) radiance calculations in both longwave (LW) and shortwave (SW) CO2 absorption spectral regions that are widely used for weather and climate applications. The effects depend on the spectral coverage and spectral resolution. The radiative effect caused by the increase of CO2 has been calculated to be greater than 0.5 K within 5 years, whereas a radiative effect of 0.1–0.5 K is introduced by the seasonal variation in some CO2 absorption spectral regions. It is important to take into account the increasing trend and seasonal variation of CO2 in retrieving the atmospheric temperature profile from hyperspectral IR radiances and in the radiance assimilation in numerical weather prediction (NWP) models. The simulation further indicates that it is very difficult to separate atmospheric temperature and CO2 information from hyperspectral IR sounder radiances because the atmospheric temperature signal is much stronger than that of CO2 in the CO2 absorption IR spectral regions.

A New Temperature Channel Selection Method Based on Singular Spectrum Analysis for Retrieving Atmospheric Temperature Profiles from FY-4A/GIIRS

Hyperspectral data have important research and application value in the fields of meteorology and remote sensing. With the goal of improving retrievals of atmospheric temperature profiles, this paper outlines a novel temperature channel selection method based on singular spectrum analysis (SSA) for the Geostationary Interferometric Infrared Sounder (GIIRS), which is the first infrared sounder operating in geostationary orbit. The method possesses not only the simplicity and rapidity of the principal component analysis method, but also the interpretability of the conventional channel selection method. The novel SSA method is used to decompose the GIIRS observed infrared brightness temperature spectrum (700–1130 cm−1), and the reconstructed grouped components can be obtained to reflect the energy variations in the temperature-sensitive waveband of the respective sequence. At 700–780 cm−1, the channels selected using our method perform better than IASI (Infrared Atmospheric Sounding Interferometer) and CrIS (Cross-track Infrared Sounder) temperature channels when used as inputs to the neural network retrieval model.

Neural Network Based Retrieval of Atmospheric Temperature Profile Using AMSU-A Observations

The present study describes artificial neural network (ANN) based approach for the retrieval of atmospheric temperature profiles from AMSU-A microwave temperature sounder. The nonlinear relationship between the temperature profiles and satellite brightness temperatures dictates the use of ANN, which is inherently nonlinear in nature. Since latitudinal variation of temperature is dominant one in the Earth’s atmosphere, separate network configurations have been established for different latitudinal belts, namely, tropics, mid-latitudes, and polar regions. Moreover, as surface emissivity in the microwave region of electromagnetic spectrum significantly influences the radiance (or equivalently the brightness temperature) at the satellite altitude, separate algorithms have been developed for land and ocean for training the networks. Temperature profiles from National Center for Environmental Prediction (NCEP) analysis and brightness temperature observations of AMSU-A onboard NOAA-19 for the year 2010 have been used for training of the networks. Further, the algorithm has been tested on the independent dataset comprising several months of 2012 AMSU-A observations. Finally, an error analysis has been performed by comparing retrieved profiles with collocated temperature profiles from NCEP. Errors in the tropical region are found to be less than those in the mid-latitude and polar regions. Also, in each region the errors over ocean are less than the corresponding ones over land.

Instantaneous three-dimensional thermal structure of the South Polar Vortex of Venus

The Venus thermal radiation spectrum exhibits the signature of CO2 absorption bands. By means of inversion techniques, those bands enable the retrieval of atmospheric temperature profiles. We have analyzed VIRTIS-M-IR night-side data obtaining high-resolution thermal maps of the Venus south polar region between 55 and 85km altitudes. This analysis is specific to three Venus Express orbits where the vortex presents different dynamical configurations. The cold collar is clearly distinguishable centered at ∼62km (∼100mbar) altitude level. On average, the cold collar is more than 15K colder than the pole, but its specific temperature varies with time. In the three orbits under investigation the South Polar Vortex appears as a vertically extended hot region close to the pole and squeezed by the cold collar between altitudes 55 and 67km but spreading equatorwards at about 74km. Both the instantaneous temperature maps and their zonal averages show that the top altitude limit of the thermal signature from the vortex is at ∼80km altitude, at least on the night-side of the planet. The upper part of the atmosphere (67–85km) is more homogeneous and has long-scale horizontal temperature differences of about 25K over horizontal distances of ∼2000km. The lower part (55–67km) shows more fine-scale structure, creating the vortex morphology, with thermal differences of up to about 50K over the same altitude range and ∼500km horizontal distances. This lower part of the atmosphere is highly affected by the upper cloud deck, leading to stronger local temperature variations and larger uncertainties in the retrieval. From the temperature maps, we also study the vertical stability of different atmospheric layers for the three vortex configurations. The static stability is always positive (ST>0) in the considered altitude range (55–85km) and in the whole polar vortex. The cold collar is the most vertically stable structure at polar latitudes, while the vortex and sub-polar latitudes show lower stability values. Furthermore, the hot filaments present within the vortex exhibit lower stability values than their surroundings. The layer between 62 and 67km resulted to be the most stable. These results are in good agreement with conclusions from previous radio occultation analyses.

Ensemble retrieval of atmospheric temperature profiles from AIRS

Satellite-based observations provide great opportunities for improving weather forecasting. Physical retrieval of atmospheric profiles from satellite observations is sensitive to the uncertainty of the first guess and other factors. In order to improve the accuracy of the physical retrieval, an ensemble methodology was developed with an emphasis on perturbing the first guess. In the methodology, a normal probability density function (PDF) is used to select the optimal profile from the ensemble retrievals. The ensemble retrieval algorithm contains four steps: (1) regression retrieval for original first guess; (2) perturbation of the original first guess to generate new first guesses (ensemble first guesses); (3) using the ensemble first guesses and nonlinear iterative physical retrieval to generate ensemble physical results; and (4) the final optimal profile is selected from the ensemble physical results by using PDF. Temperature eigenvectors (EVs) were used to generate the perturbation and generate the ensemble first guess. Compared with the regular temperature profile retrievals from the Atmospheric InfraRed Sounder (AIRS), the ensemble retrievals RMSE of temperature profiles selected by the PDF was reduced between 150 and 320 hPa and below 400 hPa, with a maximum improvement of 0.3 K at 400 hPa. The bias was also reduced in many layers, with a maximum improvement of 0.69 K at 460 hPa. The combined optimal (CombOpt) profile and a mean optimal (MeanOpt) profile of all ensemble physical results were improved below 150 hPa. The MeanOpt profile was better than the CombOpt profile, and was regarded as the final optimal (FinOpt) profile. This study lays the foundation for improving temperature retrievals from hyper-spectral infrared radiance measurements.

Self-consistent retrieval of temperature profiles and cloud structure in the northern hemisphere of Venus using VIRTIS/VEX and PMV/VENERA-15 radiation measurements

Improved radiative transfer simulation and multi-window retrieval procedures (MWR) are described that are applied to investigate thermal structure and cloud features in the nightside atmosphere of Venus over the northern hemisphere. Comparative analyses of spectroscopic data, which were recorded independently in different parts of the infrared spectrum by the Visible and InfraRed Thermal Imaging Spectrometer (VIRTIS-M-IR) aboard ESA's Venus Express space probe and the Profile Measuring Instrument for Venus (PMV, Fourier spectrometer FS-1/4) during the earlier Soviet Venera-15 experiment, are combined with self-consistent temperature profile and cloud parameter retrievals. MWR performance is studied using synthetic spectra at different latitudes and for different atmospheric temperature profiles and cloud parameters. VIRTIS and PMV retrieval result comparisons are used to determine constraints on physical state parameter variations, especially on applicability of different cloud models. An analytically parameterized initial model of four-modal cloud altitude distributions is proposed. Together with retrieved cloud parameters, which encompass individual mode factors and cloud upper altitude boundary, it permits optimum fits of measured radiances and brightness temperatures in the 4.3 and 15µm CO2 bands utilized for atmospheric temperature profile retrievals. A new multi-spectrum retrieval (MSR) method (Kappel et al., 2012, Kappel, in press) provides deep atmosphere CO2 opacity correction parameters, which affect cloud parameter retrievals from short-wavelength emission windows.The main features of retrieved latitude and altitude-dependent temperature fields (zonal averages) are in good quantitative accordance with earlier obtained results. Prominent structures like ‘cold collar’ and ‘hot dipole’ are re-examined. The cold inversion layer is centered at about 62–66km and 55 and 75°N. Below the collar, temperature usually decreases with latitude at the same altitude, while it typically increases toward the pole above 70km. Absolute temperature differences between zonal averages of VIRTIS and PMV results are usually below 3K between 58 and 80km, while a 9K lower VIRTIS temperature is only observed at 75°N near 60km. Both VIRTIS and PMV retrievals reveal a slow decrease of cloud top altitude from about 71km at the equator to about 70km at mid latitudes. Northward of 55°N, it quickly descends down to about 61.5km at polar latitudes in accordance with earlier results. Cloud particle size and total cloud optical depth exhibit a minimum at 50°N and increase towards the equator and the North Pole. The hemispheric average of cloud opacity derived from VIRTIS data is 34.7 at 1µm.

Hurricane Sandy warm‐core structure observed from advanced Technology Microwave Sounder

The warm‐core structures of Hurricane Sandy and other nine tropical cyclones (TCs) are studied using the temperatures retrieved from Advanced Technology Microwave Sounder (ATMS). A new algorithm is developed for the retrieval of atmospheric temperature profiles from the ATMS radiances. Since ATMS observation has a higher spatial resolution and better coverage than its predecessor, Advanced Microwave Sounding Unit‐A, the retrieved temperature field explicitly resolves TC warm core throughout troposphere and depicts the cold temperature anomalies in the eyewall and spiral rainbands. Unlike a typical TC, the height of maximum warm core of Hurricane Sandy is very low, but the storm size is quite large. Based on the analysis of 10 TCs in 2012, close correlations are found between ATMS‐derived warm core and the TC maximum sustained wind (MSW) or minimum sea level pressure (MSLP). The estimation errors of MSW and MSLP from ATMS‐retrieved warm core are 13.5 mph and 13.1 hPa, respectively.

New technique for retrieval of atmospheric temperature profiles from Rayleigh-scatter lidar measurements using nonlinear inversion

The conventional method of calculating atmospheric temperature profiles using Rayleigh-scattering lidar measurements has limitations that necessitate abandoning temperatures retrieved at the greatest heights, due to the assumption of a pressure value required to initialize the integration at the highest altitude. An inversion approach is used to develop an alternative way of retrieving nightly atmospheric temperature profiles from the lidar measurements. Measurements obtained by the Purple Crow lidar facility located near The University of Western Ontario are used to develop and test this new technique. Our results show temperatures can be reliably retrieved at all heights where measurements with adequate signal-to-noise ratio exist. A Monte Carlo technique was developed to provide accurate estimates of both the systematic and random uncertainties for the retrieved nightly average temperature profile. An advantage of this new method is the ability to seed the temperature integration from the lowest rather than the greatest height, where the variability of the pressure is smaller than in the mesosphere or lower thermosphere and may in practice be routinely measured by a radiosonde, rather than requiring a rocket or satellite-borne measurement. Thus, this new technique extends the altitude range of existing Rayleigh-scatter lidars 10-15km, producing the equivalent of four times the power-aperture product.

Sensitivity analysis for air temperature profile estimation methods around the tropopause using simulated Aqua/AIRS data

A sensitivity analysis is performed to investigate potential improvements to the accuracy of air temperature profile retrievals near the tropopause. A simple inversion method is employed to identify and remove redundant spectral channels from the retrievals using simulated data for the high-spectral resolution sounder AIRS (Atmospheric Infrared Sounder) on the Aqua satellite. Bayesian optimal theory and inverse technique are applied for the atmospheric temperature profile retrievals, and the 15 μm CO 2 absorption bands (620–750 cm −1) are chosen for this study. Sequentially elimination of redundant channels is directly integrated into the inverse scheme for the temperature profile, in order to accurately retain the valuable channels and remove all the redundant channels, for accurate retrieval of the temperature profile. Also, the tropopause and troposphere are treated differently in the inverse scheme to improve the retrieval accuracy in the tropopause. Results of a sensitivity analysis based on this method, for the Tropical and Middle-Latitude Summer models simulated by MODTRAN4.0, show that the estimated accuracies are improved by 2 K around the tropopause, and are only changed by less than 0.2 K in the troposphere.

Neural network solution for temperature profile retrieval from infrared spectra with high spectral resolution

A method based on artificial neural network for atmospheric temperature profile retrieval from spectra measured by nadir viewing high resolution Fourier transform spectrometers is presented. Very fast retrieval of temperature profile is approached. Impact of instrument noise on the retrieval results is considered.

Radiometric profiling of temperature using algorithm based on neural networks

The retrieval of atmospheric temperature profiles from microwave radiometer brightness temperatures requires the solution of a nonlinear inversion problem. An inversion technique based on neural networks (NN) is developed. The NN technique, compared with the classical inversion methods, exhibits better results in terms of retrieval accuracy, vertical resolution and elaboration time.

A Regularized Neural Net Approach for Retrieval of Atmospheric and Surface Temperatures with the IASI Instrument

In this paper, a fast atmospheric and surface temperature retrieval algorithm is developed for the high-resolution Infrared Atmospheric Sounding Interferometer (IASI) spaceborne instrument. This algorithm is constructed on the basis of a neural network technique that has been regularized by introduction of information about the solution of the problem that is in addition to the information contained in the problem (a priori information). The performance of the resulting fast and accurate inverse radiative transfer model is presented for a large diversified dataset of radiosonde atmospheres that includes rare events. Two configurations are considered: a tropical-airmass specialized scheme and an all-airmasses scheme. The surface temperature for tropical situations yields an rms error of 0.4 K for instantaneous retrievals. Results for atmospheric temperature profile retrievals are close to the specifications of the World Meteorological Organization, namely, 1-K rms error for the instantaneous temperature retrieval with 1-km vertical resolution.

An Assessment of the Impact of Satellite Microwave Sounder Incidence Angle and Scan Geometry on the Accuracy of Atmospheric Temperature Profile Retrievals

Abstract Cross-track and conical scan microwave sounder designs are compared with respect to temperature profile retrieval accuracy in nonprecipitating atmospheres and with respect to the beamfilling effect of precipitation. The conical design shows slightly better accuracy at pressure levels of 3 hPa or less, while the cross-track design performs slightly better at pressure levels of 850 hPa or greater. Under the assumption that precipitation-contaminated fields of view would be rejected, consideration of beamfilling by rain cells indicates that retrieval yield would be higher for the cross-track design.

An Extended Kalman-Bucy Filter for Atmospheric Temperature Profile Retrieval with a Passive Microwave Sounder

An extended Kalman-Bucy filter has been implemented for atmospheric temperature profile retrievals from observations made using the Scanned Microwave Spectrometer (SCAMS) instrument carried on the Nimbus 6 satellite. This filter has the advantage that it requires neither stationary statistics in the underlying processes nor linear production of the observed variables from the variables to be estimated. This extended Kalman-Bucy filter has yielded significant performance improvement relative to multiple regression retrieval methods. A multi-spot extended Kalman-Bucy filter has also been developed in which the temperature profiles at a number of scan angles in a scanning instrument are retrieved simultaneously. These multi-spot retrievals are shown to outperform the single-spot Kalman retrievals.