Retrieval Of Temperature Profiles Research Articles

Deep Retrieval Architecture of Temperature and Humidity Profiles from Ground-Based Infrared Hyperspectral Spectrometer

Temperature and humidity profiles in the atmospheric boundary layer are essential for climate studies. The ground-based infrared hyperspectral spectrometer has the advantage of measuring radiances emitted from the atmosphere at a high temporal and moderate vertical resolution. In this article, the retrieval of temperature and humidity profiles from ground-based infrared hyperspectral observations is exploited. Although existing inversion algorithms based on physical models or statistical learning have made some progress, they still suffer from high computational complexity or poor performance. Motivated by the strength of the deep learning, we present a deep retrieval architecture (DReA) by skillfully designing a light-weight one-dimensional convolution neural network (CNN) to retrieve the temperature and humidity profiles. Experiments were conducted using atmospheric emitted radiance interferometer (AERI) and radiosonde data to demonstrate the superiority of the proposed DReA. The validation of the DReA with the radiosonde, using 802 profiles with 37 layers below 3 km, presents an excellent retrieval ability with a root mean square error (RMSE) of 0.87 K for the temperature and 1.06 g/kg for the water vapor mixing ratio. Furthermore, a thorough comparison with commonly used inversion methods such as the traditional back propagation (BP) and the eigenvector (EV) regression method, shows that our proposed DReA method obtains a leading solution in retrieving temperature and humidity profiles.

Stratospheric temperature measurements from nanosatellite stellar occultation observations of refractive bending

Abstract. Stellar occultation observations from space can probe the stratosphere and mesosphere at a fine vertical scale around the globe. Unlike other measurement techniques like radiosondes and aircraft, stellar occultation has the potential to observe the atmosphere above 30 km, and unlike radio occultation, stellar occultation probes fine-scale phenomena with potential to observe atmospheric turbulence. We imaged the refractive bending angle of a star centroid for a series of occultations by the atmosphere. Atmospheric refractivity, density, and then temperature are retrieved from the bending observations with the Abel transformation and Edlén's law, the hydrostatic equation, and the ideal gas law. The retrieval technique is applied to data collected by two nanosatellites operated by Terran Orbital. Measurements were primarily taken by the GEOStare SV2 mission, with a dedicated imaging telescope, supplemented with images captured by spacecraft bus sensors, namely the star trackers on other Terran Orbital missions. The bending angle noise floor is 10 and 30 arcsec for the star tracker and GEOStare SV2 data, respectively. The most significant sources of uncertainty are due to centroiding errors due to the fairly low-resolution stellar images and telescope pointing knowledge derived from noisy satellite attitude sensors. The former mainly affects the star tracker data, while the latter limits the GEOStare SV2 accuracy, with both providing low vertical resolution. This translates to a temperature profile retrieval up to roughly 20 km for both star tracker and GEOStare SV2 datasets. In preparation of an upcoming 2023 mission designed to correct these deficiencies, SOHIP, we simulated bending angle measurements with varying magnitudes of error. The expected maximum altitude of retrieved temperature is 41 km on average for these simulated measurements with a noise floor of 0.39 arcsec. Our work highlights the capabilities of stellar occultation observations from nanosatellites for atmospheric sounding. Future work will investigate high-frequency observations of atmospheric gravity waves and turbulence, mitigating the major uncertainties observed in these datasets.

Detail Enhancement of AIRS/AMSU Temperature and Moisture Profiles Using a 3D Deep Neural Network

Abstract In recent decades, spaceborne microwave and hyperspectral infrared sounding instruments have significantly benefited weather forecasting and climate science. However, existing retrievals of lower-troposphere temperature and humidity profiles have limitations in vertical resolution, and often cannot accurately represent key features such as the mixed-layer thermodynamic structure and the inversion at the planetary boundary layer (PBL) top. Because of the existing limitations in PBL remote sensing from space, there is a compelling need to improve routine, global observations of the PBL and enable advances in scientific understanding and weather and climate prediction. To address this, we have developed a new 3D deep neural network (DNN) that enhances detail and reduces noise in level 2 granules of temperature and humidity profiles from the Atmospheric Infrared Sounder (AIRS)/Advanced Microwave Sounding Unit (AMSU) sounder instruments aboard NASA’s Aqua spacecraft. We show that the enhancement improves accuracy and detail including key features such as capping inversions at the top of the PBL over land, resulting in improved accuracy in estimations of PBL height.

Temperature and Relative Humidity Profile Retrieval from Fengyun-3D/VASS in the Arctic Region Using Neural Networks

In this study, a new technique is proposed to retrieve temperature and relative humidity profiles under clear sky conditions in the Arctic region based on the artificial neural network (ANN) algorithm using Fengyun-3D (FY-3D) vertical atmospheric sounder suit (VASS: HIRAS, MWTS-II, and MWHS-II) observations. This technology combines infrared (IR) and microwave (MW) observations to improve retrieval accuracy in the middle and low troposphere by reducing the sensitivity of the neural networks (NNs) to cloud coverage. The approach was compared against other methods available in the literature on retrieving profiles only from FY-3D/HIRAS data. Furthermore, its retrieval performance was tested by comparing the NNs’ prediction accuracy versus the corresponding FY-3D/VASS and Aqua/AIRS L2 products. The results showed that: (1) NNs retrieval accuracy is higher during the warm season and over the ocean; (2) the retrieval accuracy of NNs has been significantly improved compared with satellite L2 products; (3) referring to radiosonde observations, the retrieval accuracy of NNs below 600 hPa is effectively improved by adding the information of the MW channel, especially on land where cloud clearing is more difficult. The root mean square error (RMSE) of temperature and relative humidity in the cold season were reduced by 0.3 K and 2%, respectively. The advanced NNs proposed herein offer a more stable retrieval performance compared with NNs built only by FY-3D/HIRAS data. The study results indicated the potential value in time and space domain of the NN algorithm in retrieving temperature and relative humidity profiles of the Arctic region from FY-3D/VASS observations under clear-sky conditions. All in all, this work enhances our knowledge towards improving operational use of FY-3D satellite data in the Arctic region.

A Bayesian neural network approach for tropospheric temperature retrievals from a lidar instrument

ABSTRACT We have constructed a Bayesian neural network able of retrieving tropospheric temperature profiles from rotational Raman-scatter measurements of nitrogen and oxygen and applied it to measurements taken by the RAman Lidar for Meteorological Observations (RALMO) in Payerne, Switzerland. We give a detailed description of using a Bayesian method to retrieve temperature profiles including estimates of the uncertainty due to the network weights and the statistical uncertainty of the measurements. We trained our model using lidar measurements under different atmospheric conditions, and we tested our model using measurements not used for training the network. The computed temperature profiles extend over the altitude range of 0.7 km to 6 km. The mean bias estimate of our temperatures relative to the MeteoSwiss standard processing algorithm does not exceed 0.05 K at altitudes below 4.5 km, and does not exceed 0.08 K in an altitude range of 4.5 km to 6 km. This agreement shows that the neural network estimated temperature profiles are in excellent agreement with the standard algorithm. The method is robust and is able to estimate the temperature profiles with high accuracy for both clear and cloudy conditions. Moreover, the trained model can provide the statistical and model uncertainties of the estimated temperature profiles. Thus, the present study is a proof of concept that the trained NNs are able to generate temperature profiles along with a full-budget uncertainty. We present case studies showcasing the Bayesian neural network estimations for day and night measurements, as well as in clear and cloudy conditions. We have concluded that the proposed Bayesian neural network is an appropriate method for the statistical retrieval of temperature profiles.

Evaluating the Value of CrIS Shortwave-Infrared Channels in Atmospheric-Sounding Retrievals

The Cross-track Infrared Sounder (CrIS), in low Earth orbit since 2011, makes measurements of the top of atmosphere radiance for input into data assimilation (DA) systems as well as the retrieval of geophysical state variables. CrIS measurements have 2211 narrow infrared channels ranging between 650 and 2550 cm−1 (~3.9–15.4 μm) and capture the variation in profiles of atmospheric temperature, water vapor, and numerous trace gas species. DA systems derive atmospheric temperature by assimilating CO2-sensitive channels in the CrIS longwave (LW) band (650–1095 cm−1). Here, we investigate if CO2-sensitive channels in the shortwave (SW) band (2155–2550 cm−1) can similarly be applied. We first evaluated the information content of the CrIS bands followed by an assessment of the performance degradation of retrievals due to the loss of individual CrIS bands. We found that temperature profile retrievals derived from the CrIS SW band were statistically both well-behaved and as accurate as a retrieval utilizing the CrIS LW band. The one caveat, however, is that the higher CrIS instrument noise in the SW band limited its performance under certain conditions. We conclude with a discussion on the implications our results have for channel selection in retrieval and DA systems as well as the design of future space instruments.

Long-term validation of MIPAS ESA operational products using MIPAS-B measurements

Abstract. The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) was a limb-viewing infrared Fourier transform spectrometer that operated from 2002 to 2012 aboard the Environmental Satellite (ENVISAT). The final re-processing of the full MIPAS mission Level 2 data was performed with the ESA operational version 8 (v8) processor. This MIPAS dataset includes not only the retrieval results of pressure–temperature and the standard species H2O, O3, HNO3, CH4, N2O, and NO2 but also vertical profiles of volume mixing ratios of the more difficult-to-retrieve molecules N2O5, ClONO2, CFC-11, CFC-12 (included since v6 processing), HCFC-22, CCl4, CF4, COF2, and HCN (included since v7 processing). Finally, vertical profiles of the species C2H2, C2H6, COCl2, OCS, CH3Cl, and HDO were additionally retrieved by the v8 processor. The balloon-borne limb-emission sounder MIPAS-B was a precursor of the MIPAS satellite instrument. Several flights with MIPAS-B were carried out during the 10-year operational phase of ENVISAT at different latitudes and seasons, including both operational periods when MIPAS measured with full spectral resolution (FR mode) and with optimised spectral resolution (OR mode). All MIPAS operational products (except HDO) were compared to results inferred from dedicated validation limb sequences of MIPAS-B. To enhance the statistics of vertical profile comparisons, a trajectory match method has been applied to search for MIPAS coincidences along the 2 d forward and backward trajectories running from the MIPAS-B measurement geolocations. This study gives an overview of the validation results based on the ESA operational v8 data comprising the MIPAS FR and OR observation periods. This includes an assessment of the data agreement of both sensors, taking into account the combined errors of the instruments. The differences between the retrieved temperature profiles of both MIPAS instruments generally stays within ±2 K in the stratosphere. For most gases – namely H2O, O3, HNO3, CH4, N2O, NO2, N2O5, ClONO2, CFC-11, CFC-12, HCFC-22, CCl4, CF4, COF2, and HCN – we find a 5 %–20 % level of agreement for the retrieved vertical profiles of both MIPAS instruments in the lower stratosphere. For the species C2H2, C2H6, COCl2, OCS, and CH3Cl, however, larger differences (within 20 %–50 %) appear in this altitude range.

Performance Analysis of the Temperature and Humidity Profiles Retrieval for FY-3D/MWTHS in Arctic Regions

The special geographical location of the polar regions increases the difficulty of modeling surface emissivity, thus the physical retrieval algorithms of the temperature and humidity profiles for microwave radiometers mainly focus on the regions between 60°S and 60°N. In this paper, the deep neural networks (DNN) and long short-term memory (LSTM) models are first implemented to retrieve atmospheric temperature and humidity profiles in real time from FY-3D/MWHTS in Arctic regions and are compared with the physical retrieval algorithm. The hyperparameters of the machine learning models are determined using the grid search and 10-fold cross-validation. Results show that, compared with the physical retrieval algorithm, the retrieval accuracies of the atmospheric temperature and humidity profiles of the DNN and LSTM models in June 2021 are higher over sea ice, and the maximum retrieval accuracies are improved by about 3.5 K and 42%. Over land, the retrieval accuracies of the atmospheric temperature profiles for the DNN and LSTM models in June 2021 are improved by about 5 K. The retrieved humidity results for these two models are not compared with the physical retrieval algorithm, which fails for the humidity profile retrieval over land. In addition, the retrieval results of the DNN-based and LSTM-based models using the independent validation data in February, April, and September are also evaluated over different surface types. The RMSEs of the retrieved temperature profiles for the two models are within 4 K, except for the near-surface, and the humidity profiles are within 25%, except for in February. The temperature profiles in September and the humidity profiles in February are somewhat reduced compared to other months because of the highly variable emissivity properties in autumn and winter. Overall results show that the machine learning method can well-evaluate the retrieval capability of FY-3D/MWHTS of the atmospheric temperature and humidity profiles in Arctic regions.

Synergistic Retrieval of Temperature and Humidity Profiles from Space-Based and Ground-Based Infrared Sounders Using an Optimal Estimation Method

The atmospheric temperature and humidity profiles of the troposphere are generally measured by radiosondes and satellites, which are essential for analyzing and predicting weather. Nevertheless, the insufficient observation frequencies and low detection accuracy of the boundary layer restricts the description of atmospheric state changes by the temperature and humidity profiles. Therefore, this work focus on retrieving the temperature and humidity profiles using observations of the FengYun-4 (FY-4) Geostationary Interferometric Infrared Sounder (GIIRS) combined with ground-based infrared spectral observations from the Atmospheric Emitted Radiance Interferometer (AERI), which are more accurate than space-based individual retrieval results and have a wider effective retrieval range than ground-based individual retrieval results. Based on the synergistic observations, which are made by matching the space-based and ground-based data with those of different spatial and temporal resolutions, a synergistic retrieval process is proposed to obtain the temperature and humidity profiles at a high frequency under clear-sky conditions based on the optimal estimation method. In this research, using the line-by-line radiative transfer model (LBLRTM) as the forward model for observing simulations, a retrieval experiment was carried out in Qingdao, China, where an AERI is situated. Taking radiosonde data as a reference for comparing the retrieval results of the temperature and humidity profiles of the troposphere, the root-mean-square error (RMSE) of the synergistic retrieval algorithm below 400 hPa is within 2 K for temperature and within 12% for relative humidity. Compared with the GIIRS individual retrieval, the RMSE of temperature and relative humidity for the synergistic method is reduced by 0.13–1.5 K and 2.7–4.4% at 500 hPa, and 0.13–2.1 K and 2.5–7.2% at 900 hPa. Moreover, the forecast index (FI) calculated from the retrieval results shows reasonable consistency with the FIs calculated from the ERA5 reanalysis and from radiosonde data. The synergistic retrieval results have higher temporal resolution than space-based retrieval results and can reflect the changes in the atmospheric state more accurately. Overall, the results demonstrated the promising potential of the synergistic retrieval of temperature and humidity profiles at high accuracy and high temporal resolution under clear-sky conditions from FY-4/GIIRS and AERI.

An optimal estimation algorithm for the retrieval of fog and low cloud thermodynamic and micro-physical properties

Abstract. A new generation of cloud radars, with the ability to make observations close to the surface, presents the possibility of observing fog properties with better insight than was previously possible. The use of these instruments as part of an operational observation network could improve the prediction of fog events, something which is still a problem for even high-resolution numerical weather prediction models. However, the retrieval of liquid water content (LWC) profiles from radar reflectivity alone is an under-determined problem, something which ground-based microwave radiometer observations can help to constrain. In fact, microwave radiometers are not only sensitive to temperature and humidity profiles but are also known to be instruments of reference for the liquid water path. By providing the thermodynamic state of the atmosphere, to which the formation and evolution of fog events are highly sensitive, in addition to accurate liquid water path, which can be used to constrain the LWC retrieval from the cloud radar alone, combining microwave radiometers with cloud radars seems a natural next step to better understand and forecast fog events. To that end, a newly developed one-dimensional variational (1D-Var) algorithm designed for the retrieval of temperature, specific humidity and liquid water content profiles with both cloud radar and microwave radiometer (MWR) observations is presented in this study. The algorithm was developed to evaluate the capability of cloud radar and MWR to provide accurate LWC profiles in addition to temperature and humidity in view of assimilating the retrieved profiles into a 3D- and 4D-Var operational assimilation system. The algorithm is firstly tested on a synthetic dataset, which allows the evaluation of the developed algorithm in idealised conditions. This dataset was constructed by perturbing a high-resolution forecast dataset of fog and low-cloud cases by its expected errors. The algorithm is then tested with real data from the recent field campaign SOFOG-3D, carried out with the use of LWC measurements made from a tethered balloon platform. As expected, results from the synthetic dataset study were found to contain lower errors than those found from the retrievals on the dataset of real observations. It was found that LWC can be retrieved in idealised conditions with an uncertainty of less than 0.04 g m−3. With real data, as expected, retrievals with a good correlation (0.7) to in situ measurements were found but with a higher uncertainty than the synthetic dataset of around 0.06 g m−3 (41 %). This was reduced to 0.05 g m−3 (35 %) when an accurate droplet number concentration could be prescribed to the algorithm. A sensitivity study was conducted to discuss the impact of different settings used in the 1D-Var algorithm and the forward operator. Additionally, retrievals of LWC from a real fog event observed during the SOFOG-3D field campaign were found to significantly improve the operational background profiles of the AROME (Application of Research to Operations at MEsoscale) model, showing encouraging results for future improvement of the AROME model initial state during fog conditions.

Martian Atmospheric Thermal Structure and Dust Distribution During the MY 34 Global Dust Storm From ACS TIRVIM Nadir Observations

AbstractThe Thermal InfraRed channel in honor of professor Vassili Ivanovich Moroz (TIRVIM) of the Atmospheric Chemistry Suite onboard ExoMars Trace Gas Orbiter has continuously monitored the Martian atmosphere from 13 March 2018 until 2 December 2019, covering almost a complete Martian Year (MY). In the nadir mode of observations, infrared spectra obtained by TIRVIM in the spectral range 600–1,300 cm−1 permit retrievals of vertical temperature profiles from the surface up to 60 km of altitude, surface temperatures and column aerosol optical depths. Here we report the retrieved atmospheric thermal structure and the column dust content during the global dust storm (GDS) of MY 34 monitored from Ls = 182.2° to Ls = 211.8° (Solar Longitude), capturing the evolution of the GDS and the response of the atmospheric thermal structure to the changing dust loading. The global storm caused asymmetric atmosphere heating, predominantly in the southern hemisphere, and changed diurnal contrast of atmospheric thermal structure. We also observe a reduced diurnal contrast of surface temperatures at the peak of the GDS.

A Uniform Retrieval Analysis of Ultra-cool Dwarfs. IV. A Statistical Census from 50 Late-T Dwarfs

The spectra of brown dwarfs are key to exploring the chemistry and physics that take place in their atmospheres. Late-T dwarf spectra are particularly diagnostic, due to their relatively cloud-free atmospheres and deep molecular bands. With the use of powerful atmospheric retrieval tools applied to the spectra of these objects, direct constraints on molecular/atomic abundances, gravity, and vertical thermal profiles can be obtained, enabling a broad exploration of the chemical/physical mechanisms operating in their atmospheres. We present a uniform retrieval analysis on low-resolution Infrared Telescope Facility SpeX near-infrared spectra for a sample of 50 T dwarfs, including new observations as part of a recent volume-limited survey. This analysis more than quadruples the sample of T dwarfs with retrieved temperature profiles and abundances (H2O, CH4, NH3, K, and subsequent C/O and metallicities). We are generally able to constrain the effective temperatures to within 50 K, the volume mixing ratios for major species to within 0.25 dex, the atmospheric metallicities [M/H] to within 0.2, and the C/O ratios to within 0.2. We compare our retrieved constraints on the thermal structures, chemistry, and gravities of these objects with predictions from self-consistent radiative-convective equilibrium models and find, in general, though with substantial scatter, consistency with solar composition chemistry and the thermal profiles of the neighboring stellar FGK population. Objects with notable discrepancies between the two modeling techniques and potential mechanisms for their differences, be they related to the modeling approach or physically motivated, are discussed more thoroughly in the text.

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.

A Neural Network Based Single Footprint Temperature Retrieval for Atmospheric Infrared Sounder Measurements and Its Application to Study on Stratospheric Gravity Wave

Satellite hyperspectral infrared sounder measurements have better horizontal resolution than other sounding techniques as it boasts the stratospheric gravity wave (GW) analysis. To accurately and efficiently derive the three- dimensional structure of the stratospheric GWs from the single-field-of-view (SFOV) Atmospheric InfraRed Sounder (AIRS) observations, this paper firstly focuses on the retrieval of the atmospheric temperature profiles in the altitude range of 20-60 km with an artificial neural network approach (ANN). The simulation experiments show that the retrieval bias is less than 0.5 K, and the root mean square error (RMSE) ranges from 1.8 to 4 K. Moreover, the retrieval results from 20 granules of the AIRS observations with the trained neural network (AIRS_SFOV) and the corresponding operational AIRS products (AIRS_L2) as well as the dual-regression results from the Cooperative Institute for Meteorological Satellite Studies (CIMSS) (AIRS_DR) are compared respectively with ECMWF T799 data. The comparison indicates that the standard deviation of the ANN retrieval errors is significantly less than that of the AIRS_DR. Furthermore, the analysis of the typical GW events induced by the mountain Andes and the typhoon "Soulik" using different data indicates that the AIRS_SFOV results capture more details of the stratospheric gravity waves in the perturbation amplitude and pattern than the operational AIRS products do.

First Observations of Mars Atmosphere and Ionosphere with Tianwen-1 Radio-Occultation Technique on 5 August 2021

The radio-occultation technique can provide vertical profiles of planetary ionospheric and atmospheric parameters, which merit the planetary-climate and space-weather scientific research so far. The Tianwen-1 one-way single-frequency radio-occultation technique was developed to retrieve Mars ionospheric and atmospheric parameters. The first radio-occultation event observation experiment was conducted on 5 August 2021. The retrieved excess Doppler frequency, bending angle, refractivity, electron density, neutral mass density, pressure and temperature profiles are presented. The Mars ionosphere M1 (M2) layer peak height is at 140 km (105 km) with a peak density of about 3.7 × 1010 el/m3 (5.3 × 1010 el/m3) in the retrieved electron-density profile. A planetary boundary layer (−2.35 km~5 km), a troposphere (temperature decreases with height) and a stratosphere (24 km–40 km) clearly appear in the retrieved temperature profile below 50 km. Results show that Tianwen-1 radio occultation data are scientifically reliable and useful for further Mars climate and space-weather studies.

Continuous temperature soundings at the stratosphere and lower mesosphere with a ground-based radiometer considering the Zeeman effect

Abstract. Continuous temperature observations at the stratosphere and lower mesosphere are rare. Radiometry opens the possibility of observing microwave emissions from two oxygen lines to retrieve temperature profiles at all altitudes. In this study, we present observations performed with a temperature radiometer (TEMPERA) at the MeteoSwiss station at Payerne for the period from 2014 to 2017. We reanalyzed these observations with a recently developed and improved retrieval algorithm accounting for the Zeeman line splitting in the line center of both oxygen emission lines at 52.5424 and 53.0669 GHz. The new temperature retrievals were validated against MERRA2 reanalysis and the meteorological analysis NAVGEM-HA. The comparison confirmed that the new algorithm yields an increased measurement response up to an altitude of 53–55 km, which extends the altitude coverage by 8–10 km compared to previous retrievals without the Zeeman effect. Furthermore, we found correlation coefficients comparing the TEMPERA temperatures with MERRA2 and NAVGEM-HA for monthly mean profiles to be in the range of 0.8–0.96. In addition, mean temperature biases of 1 and −2 K were found between TEMPERA and both models (MERRA2 and NAVGEM-HA), respectively. We also identified systematic altitude-dependent cold and warm biases compared to both model data sets.

Improving thermodynamic profile retrievals from microwave radiometers by including radio acoustic sounding system (RASS) observations

Abstract. Thermodynamic profiles are often retrieved from the multi-wavelength brightness temperature observations made by microwave radiometers (MWRs) using regression methods (linear, quadratic approaches), artificial intelligence (neural networks), or physical iterative methods. Regression and neural network methods are tuned to mean conditions derived from a climatological dataset of thermodynamic profiles collected nearby. In contrast, physical iterative retrievals use a radiative transfer model starting from a climatologically reasonable profile of temperature and water vapor, with the model running iteratively until the derived brightness temperatures match those observed by the MWR within a specified uncertainty. In this study, a physical iterative approach is used to retrieve temperature and humidity profiles from data collected during XPIA (eXperimental Planetary boundary layer Instrument Assessment), a field campaign held from March to May 2015 at NOAA's Boulder Atmospheric Observatory (BAO) facility. During the campaign, several passive and active remote sensing instruments as well as in situ platforms were deployed and evaluated to determine their suitability for the verification and validation of meteorological processes. Among the deployed remote sensing instruments were a multi-channel MWR as well as two radio acoustic sounding systems (RASSs) associated with 915 and 449 MHz wind profiling radars. In this study the physical iterative approach is tested with different observational inputs: first using data from surface sensors and the MWR in different configurations and then including data from the RASS in the retrieval with the MWR data. These temperature retrievals are assessed against co-located radiosonde profiles. Results show that the combination of the MWR and RASS observations in the retrieval allows for a more accurate characterization of low-level temperature inversions and that these retrieved temperature profiles match the radiosonde observations better than the temperature profiles retrieved from only the MWR in the layer between the surface and 3 km above ground level (a.g.l.). Specifically, in this layer of the atmosphere, both root mean square errors and standard deviations of the difference between radiosonde and retrievals that combine MWR and RASS are improved by mostly 10 %–20 % compared to the configuration that does not include RASS observations. Pearson correlation coefficients are also improved. A comparison of the temperature physical retrievals to the manufacturer-provided neural network retrievals is provided in Appendix A.

One-Dimensional Variational Retrieval of Temperature and Humidity Profiles from the FY4A GIIRS

One-Dimensional Variational Retrieval of Temperature and Humidity Profiles from the FY4A GIIRS

Development of a Polarimetric 50-GHz Spectrometer for Temperature Sounding in the Middle Atmosphere

This paper addresses the further development of the groundbased temperature radiometer TEMPERA which measures atmospheric microwave radiation in one linear polarization in order to retrieve temperature profiles up to an altitude of 50 km [1]. The latest innovation is a new polarimetric receiver, which allows observing the atmosphere simultaneously in left- and right circular polarization. In combination with an adapted inversion method, the fully polarimetric analysis can improve the accuracy and extends the vertical upper limit of retrieved temperature profiles. Comparisons between single polarization and fully polarimetric retrievals with simulated atmospheric spectra are presented and the influence of the Earth's magnetic field is analysed. In addition, we propose a simple calibration method for fully polarimetric radiometers and present first atmospheric spectra measured with the new TEMPERA-C instrument.

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)