Atmospheric Temperature Profiles Research Articles

Muted Features in the JWST NIRISS Transmission Spectrum of Hot Neptune LTT 9779b

The hot Neptune desert is one of the most sparsely populated regions of the exoplanet parameter space, and atmosphere observations of its few residents can provide insights into how such planets have managed to survive in such an inhospitable environment. Here, we present transmission observations of LTT 9779 b, the only known hot Neptune to have retained a significant H/He-dominated atmosphere, taken with JWST NIRISS/SOSS. The 0.6–2.85 μm transmission spectrum shows evidence for muted spectral features, rejecting a perfectly flat line at >5σ. We explore water- and methane-dominated atmosphere scenarios for LTT 9779 b’s terminator, and retrieval analyses reveal a continuum of potential combinations of metallicity and cloudiness. Through comparisons to previous population synthesis works and our own interior structure modeling, we are able to constrain LTT 9779 b’s atmosphere metallicity to 20–850× solar. Within this range of metallicity, our retrieval analyses prefer solutions with clouds at millibar pressures, regardless of whether the atmosphere is water or methane dominated—though cloud-free atmospheres with metallicities >500× solar cannot be entirely ruled out. By comparing self-consistent atmosphere temperature profiles with cloud condensation curves, we find that silicate clouds can readily condense in the terminator region of LTT 9779 b. Advection of these clouds onto the dayside could explain the high dayside albedo previously inferred for this planet and be part of a feedback loop aiding the survival of LTT 9779 b’s atmosphere in the hot Neptune desert.

Atmospheric Temperature Measurements Using Microwave Hyper-Spectrum from Geostationary Satellite: Band Design, Weighting Functions and Information Content

A passive microwave instrument will be carried by China’s geostationary microwave satellite. A microwave hyper-spectral band included by the instrument ranges from 52.6 to 57.3 GHz, and totally has 89 channels in this spectral domain. The design of the hyper-spectral band is described from the aspects of scientific objectives and specifications. The weighting functions for each channel are calculated utilizing radiative transfer simulations under clear sky conditions. Then, the information content as well as the degree of freedom for signal are computed and analyzed to characterize this hyper-spectral sounding for atmospheric temperature profiling. Both the vertical distribution of the weighting functions and the width of retrieval averaging kernels indicate that the hyper-spectral band can provide more denser sampling for atmospheric temperature. The information content for the hyper-spectral band is approximately 46% higher than that of the ATMS-type channel 3 to 15, indicating that hyper-spectral measurement can improve the accuracy of retrieval. The most informative channels mainly locate near 57 GHz, having good consistency with the existing channels. The height range where the retrieval using the hyper-spectral observations is sensitive to the true profile, begins from about 800 to 1 hPa. Some channels can be considered as alternatives to each other since they have very similar information content and weighting functions. These results are expected to provide a valuable reference for future applications of the microwave hyper-spectral measurements.

Modeling Noncondensing Compositional Convection for Applications to Super-Earth and Sub-Neptune Atmospheres

Compositional convection is atmospheric mixing driven by density variations caused by compositional gradients. Previous studies have suggested that compositional gradients of atmospheric trace species within planetary atmospheres can impact convection and the final atmospheric temperature profile. In this work, we employ 3D convection-resolving simulations using Cloud Model 1 (CM1) to gain a fundamental understanding of how compositional variation influences convection and the final atmospheric state of exoplanet atmospheres. We perform 3D initial value problem simulations of noncondensing compositional convection for Earth-air, H2, and CO2 atmospheres. Conventionally, atmospheric convection is assumed to mix the atmosphere to a final, marginally stable state defined by a unique temperature profile. However, when there is compositional variation within an atmosphere, a continuous family of stable end states is possible, differing in the final state composition profile. Our CM1 simulations are used to determine which of the family of possible compositional end states is selected. Leveraging the results from our CM1 simulations, we develop a dry convective adjustment scheme for use in general circulation models (GCMs). This scheme relies on an energy analysis to determine the final adjusted atmospheric state. Our convection scheme produces results that agree with our CM1 simulations and can easily be implemented in GCMs to improve modeling of compositional convection in exoplanet atmospheres.

The Hunga Tonga‐Hunga Ha'apai Volcanic Eruption as Seen in Satellite Microwave Observations and MiRS Temperature Retrievals

AbstractThe strongest volcanic eruption since the 19th century occurred on 15 January 2022 at Hunga Tonga‐Hunga Ha'apai, generating unprecedented atmospheric waves not seen before in observations. We used satellite microwave observations from (a) Advanced Technology Microwave Sounder (ATMS) on board the National Oceanic and Atmospheric Administration (NOAA)‐20 and the Suomi‐National Polar‐orbiting Partnership (SNPP) and (b) Advanced Microwave Sounding Unit (AMSU)‐A on board Meteorological operational satellite (MetOp)‐B/MetOp‐C to study these waves in the stratosphere immediately after the eruption. The NOAA Microwave Integrated Retrieval System (MiRS) was applied to these microwave observations to produce atmospheric temperature profiles. The atmospheric Lamb wave and fast‐traveling gravity waves are clearly revealed in both the brightness temperatures and the MiRS retrieved temperatures, revealing their vertical phase structures. This study is the first attempt to perform a detailed analysis of the stratospheric impact of the Tonga eruption on operational satellite microwave observations and the corresponding MiRS retrievals.

Aerosol absorption using in situ filter-based photometers and ground-based sun photometry in the Po Valley urban atmosphere

Abstract. Light-absorbing aerosols (LAAs) are short-lived climate forcers with a significant impact on Earth's radiative balance. LAAs include dust aerosols, black carbon (BC) and organic light-absorbing carbonaceous aerosol (collectively termed brown carbon, BrC), which have also been proven to be highly toxic. In this study, aerosol absorption at five wavelengths (ranging from ultraviolet to infrared) was monitored continuously using filter-based photometers during two winter seasons in 2020 and 2021 in the city of Modena (southern central Po Valley, northern Italy), at two regulatory air quality monitoring sites, along with other pollutants (coarse particulate matter, PM10; fine particulate matter, PM2.5; O3; NO; NO2; and C6H6) and the vehicular traffic rate. The aerosol optical depth (AOD) and other column aerosol optical properties were concurrently monitored at four wavelengths by an AErosol RObotic NETwork (AERONET) sun photometer under urban background conditions within Modena. In situ absorption levels were apportioned to both sources (fossil fuel and biomass burning) and species (BC and BrC), while columnar absorption was apportioned to BC, BrC and mineral dust. The combined analysis of the atmospheric aerosol and gas measurements and of the meteorological conditions (in situ and from the ERA5 reanalysis) identified the location of potential urban sources of BC and BrC, most likely related to traffic and biomass burning. In situ data show different diurnal/weekly patterns for BrC from biomass burning and BC from traffic, with minor differences between the background and the urban traffic conditions. AERONET version 3 absorption aerosol optical depth (AAOD) retrievals at four wavelengths allowed the estimation of the absorptive direct radiative effect due to LAAs over the same period under the reasonable assumption that the AOD signal is concentrated within the mixing layer. AERONET retrievals showed a modest correlation of columnar absorption with planetary boundary layer (PBL)-scaled in situ observations, although the correlation improved significantly during a desert dust transport event that affected both in situ aerosol and columnar absorption, particularly in the blue spectrum range. A low correlation occurred between the contribution of BrC to aerosol absorption for the in situ and the columnar observations, with the BrC contribution being generally larger for in situ observations. Finally, evidence of a highly layered atmosphere during the study period, featuring significant spatial mixing and modest vertical mixing, was shown by ERA5-based atmospheric temperature profiles and by the large correlation of concurrent AERONET AOD retrievals in Modena and in Ispra (on the northwestern side of the Po Valley, ca. 225 km from Modena).

Application of GOES-16 Atmospheric Temperature-Profile Data Assimilation in a Hurricane Forecast

This paper selects the case of the Atlantic hurricane “Michael” in 2018 to evaluate the accuracy of the GOES-16 atmospheric temperature profile during the hurricane and its effect on forecasting. Based on the weather research and forecasting (WRF) model, the assimilation of GOES-16 atmospheric temperature-profile products was achieved by using three-dimensional variational (3DVar) and the ensemble transform Kalman filter/three-dimensional variational (ETKF/3DVAR) hybrid system (Hybrid) systems. And the impact of geostationary satellite GOES-16 atmospheric temperature-profile data assimilation on a hurricane forecast is evaluated. The results show that, during the hurricane, the root mean square errors of the GOES-16 atmospheric temperature profile are all within 2 k at the height of 200–1000 hPa, and the quality of the data is generally good. Assimilating the GOES-16 atmospheric temperature-profile data can indeed effectively improve the analysis increment and improve the prediction results. The assimilation increment obtained by the hybrid system has obvious “flow-dependent” characteristics, which can reasonably improve the initial field of the model. Its temperature increment has an obvious spiral structure, which is in line with the characteristics of the hurricane, and the adjustment of the wind field and geopotential height field is also more beneficial to the development of the hurricane. It has a positive impact on the forecast of track, intensity, and precipitation, and the hybrid system is improved more obviously. In addition, from the RMSE of the analysis field and the forecast field relative to the observation data of different elements, the hybrid system is superior to the 3DVar system.

Preliminary Estimations of Mars Atmospheric and Ionospheric Profiles from Tianwen-1 Radio Occultation One-Way, Two-Way, and Three-Way Observations

The radio occultation method, one of the methods used to provide planetary atmospheric profiles with high vertical resolution, was applied to China’s first Mars mission, Tianwen-1. We carried out observations based on the Chinese Deep Space Network, and one-way, two-way, and three-way modes were used for Doppler observations from the Tianwen-1 spacecraft. We successfully obtained effective observations from Tianwen-1 on 22 and 25 March 2022. An inversion system developed for Tianwen-1 radio occultation observations enabled the derivation of neutral atmospheric density, pressure, temperature, and electron density profiles of Mars. Utilizing one-way tracking data, Martian ionospheric electron density profiles were retrieved at latitudes between 68.7 and 70.7 degrees (N). However, the presence of strong random walk noise in one-way tracking data led to poor inversion results. Meanwhile, Martian ionospheric electron density and neutral atmosphere profiles were extracted from two-way and three-way tracking data at latitudes between 55.1 and 57.0 degrees (S) on 22 March and at latitudes between 62.8 and 63.4 degrees (S) on 25 March. Importantly, our inversion results from Tianwen-1 maintained consistency with results from the Mars Express and the Chapman theory (mainly in the M2 layer). Through two days’ observation experiments, we established and verified the occultation solution system and prepared for the follow-up occultation plans.

Evaluation of Temperature and Humidity Profiles Retrieved from Fengyun-4B and Implications for Typhoon Assimilation and Forecasting

Fengyun-4B (FY-4B) is the first operational satellite from China’s latest generation of geostationary meteorological satellites. It is equipped with the Geostationary Interferometric Infrared Sounder (GIIRS), which is able to obtain highly accurate atmospheric temperature and humidity profiles through hyperspectral detection in long- and mid-wave infrared spectral bands. In this study, the accuracy of the FY-4B/GIIRS temperature and humidity profile retrievals over two months is evaluated using radiosonde observations and ERA5 reanalysis data. We go a step further to investigate the impact of the satellite retrievals on assimilation and forecasts for Typhoons Chaba and Ma-on in 2022. Results reveal that the root-mean-square difference (RMSD) for the FY-4B/GIIRS temperature and humidity profile retrievals were within 1 K and 1.5 g/kg, respectively, demonstrating high overall accuracy. Moreover, assimilating temperature and humidity profiles from FY-4B/GIIRS positively impacts model analysis and prediction, improving typhoon track and intensity forecasts. Additionally, improvements have been discovered in predicting precipitation, particularly with high-magnitude rainfall events.

Информационная инфраструктура, обеспечивающая своевременное обнаружение мезомасштабных конвективных комплексов

The results of the development of an information infrastructure that provides timely detection of mesoscale convective complexes (MCC) are described, automatic procedures that implement the basic functionality of this infrastructure are described. Along with the detection of MCC, the described procedures allow us to identify zones of deep convection, i.e. such MCC that develop according to the scenario of "daytime storms", i.e. intense thunderstorms, squalls and tornadoes. Information infrastructure, provides integration of satellite microwave radiometric sensing data of geostationary observations in the visible and IR range. This integration is designed to ensure the restoration of atmospheric temperature and humidity profiles in the conditions of emergency atmospheric situations caused by the MCC. The methodological feature of the developed approach is to minimize the probability of stopping the data analysis system when access to individual sources of a priori information is terminated. This technique is based on the use of an automatic system of adaptation to changes in access conditions and the application of the results of model forecasts of the development of atmospheric processes.

Drone-Based Vertical Atmospheric Temperature Profiling in Urban Environments

The accurate and detailed measurement of the vertical temperature, humidity, pressure, and wind profiles of the atmosphere is pivotal for high-resolution numerical weather prediction, the determination of atmospheric stability, as well as investigation of small-scale phenomena such as urban heat islands. Traditional approaches, such as weather balloons, have been indispensable but are constrained by cost, environmental impact, and data sparsity. In this article, we investigate uncrewed aerial systems (UASs) as an innovative platform for in situ atmospheric probing. By comparing data from a drone-mounted semiconductor temperature sensor (TMP117) with traditional radiosonde measurements, we spotlight the UAS-collected atmospheric data’s accuracy and such system suitability for atmospheric surface layer measurement. Our research encountered challenges linked with the inherent delays in achieving ambient temperature readings. However, by applying specific data processing techniques, including smoothing methodologies like the Savitzky–Golay filter, iterative smoothing, time shift, and Newton’s law of cooling, we have improved the data accuracy and consistency. In this article, 28 flights were examined and certain patterns between different methodologies and sensors were observed. Temperature differentials were assessed over a range of 100 m. The article highlights a notable accuracy achievement of 0.16 ± 0.014 °C with 95% confidence when applying Newton’s law of cooling in comparison to a radiosonde RS41’s data. Our findings demonstrate the potential of UASs in capturing accurate high-resolution vertical temperature profiles. This work posits that UASs, with further refinements, could revolutionize atmospheric data collection.

Correlated Atmospheric Variations Using Optimization of Dft Parameters for Monte Carlo Simulations Of Indian Space Missionscorrelated Atmospheric Variations Using Optimization of Dft Parameters for Monte Carlo Simulations Of Indian Space Missions

In space systems, atmospheric parameters are required for launch vehicle trajectory design and simulations, aerodynamic load characterization, Monte Carlo trajectory dispersion studies, re-entry of spent stages, safe recovery of reusable stages, etc. A need is felt to modify existing models over the Indian region to assimilate appropriate atmospheric dispersions. The correlated connectivity of the atmospheric profiles measured over various altitude regimes has to be ensured. A new methodology to generate any required number of atmospheric profiles is carried out in this paper. The dynamic processes of the lower and upper atmosphere are markedly different, leading to different mathematical models to generate the respective profiles. For the generation of lower altitude temperature profiles, Discrete Fourier Transform strategy is utilized; and for upper altitudes, Cumulative Distribution Function is used. Corresponding pressure and density profiles are generated using hypsometric equations. The variations in the atmospheric temperature profiles at different frequencies need to be captured to ascertain correct parameter bounds, particularly for density profiles. To generate any number of Monte Carlo atmospheric profiles replicating the observed variation and dispersion bounds in all frequency bands, a unique evolutionary optimization methodology is devised to ensure the required balance between convergence and diversification.

The Fusion of ERA5 and MERRA-2 Atmospheric Temperature Profiles with Enhanced Spatial Resolution and Accuracy

Accurate high-resolution atmospheric temperature profiles are essential for precisely characterizing the evolution of the atmosphere and developing numerical forecasts. Atmospheric datasets, such as ERA5 (the fifth-generation ECMWF Reanalysis) and MERRA-2 (the Modern-Era Retrospective Analysis for Research and Applications, Version 2), provide global and continuous temperature profiles, with fine vertical distribution and horizontal resolution. RAOB (Radiosonde Observation) sounding data have high confidence and representativeness and are usually used for data accuracy verification. Due to the difficulty of updating existing products, and the scarcity of research on mesospheric temperature profiles, this work maximizes the high observation accuracy of RAOB data, combines the benefits of ERA5’s horizontal resolution and MERRA-2’s vertical distribution, and employs the optimal interpolation method to combine the data, in order to produce a fused result with high spatial resolution. After converting all of the data to the same spatial distribution, the optimal interpolation method was used to combine the two datasets from separate places and different pressure layers in order to produce the fused results, which had a vertical distribution of 45 layers and a spatial resolution of 0.25°. The fused data’s RMSE and MAE were 6.0 K and 5.0 K lower than those of the MERRA-2 temperature profile data, respectively, and 0.3 K and 0.4 K lower than those of the ERA5 temperature profile data, respectively. The validation, using data from 2019, showed that the fused data exhibits better correlation and data accuracy than the other two datasets, which demonstrated that the fused algorithm can potentially be used to generate reliable datasets for future meteorological research.

Metrics for the emissions of F-gas refrigerants

This paper examines the metrics used to account for the contribution to global warming from fluorinated gases (f-gases) mainly used as refrigerants for cooling. F-gases are key to climate mitigation discussion as they a) are critical to delivering the surging demand for air conditioners which is expected to triple by 2050, b) have strong Global Warming Potential (GWP) as high as 12,000 kgCO2-eq (for e.g., HFC-23) and c) are targeted by international policy agreements such as the Montreal Protocol and Kigali Amendment. F-gases exacerbate atmospheric warming when leaked from cooling equipment, or during other phases of their life cycle. Thus far, the way these gases impact global warming is mostly reported based on their CO2-equivalent emissions with a time horizon of 100 years. However, the problem is that f-gases have significantly different lifespans and the GWP100 does not account for these variations. The debate on metrics to account for warming of other short-lived climate pollutants (SLCP), such as methane is already ongoing. Here, we provide the first step to open such debate for short-lived f-gases. We address this, first, with a critical review of the available metrics for carbon foot-printing of f-gases and present a gap analysis between the existing carbon foot printing metrics and the need to better understand warming from f-gases. Second, we use an atmospheric model to illustrate how the incumbent metric (CO2-equivalent calculated using GWP100), misrepresent the warming of an exemplary f-gas refrigerant (HFC-134a). The model outputs novel profiles of atmospheric concentration, radiative forcing, and temperature, in three scenarios. Scenario A models the response to a positive step change in emissions of the HFC, while Scenario B is the inverse. Scenario C models a reduction of 85% by 2036 of emissions according to targets for high-emitting countries set in the Kigali Amendment. The modelling results evidence that the commonly used CO2-equivalent with its GWP100 does not represent the atmospheric responses, and particularly the warming of the exemplary short-lived HFC. Through the literature review, however, we identify many other metrics available that could be applied for f-gases, and particularly GWP* is recommended to examine in future works. In summary, the paper offers insights into which metrics can best help to identify the effects of f-gases in terms of reducing global warming in a rapid timeframe, and how CO2-equivalents should not be used as proxy for atmospheric warming in policy discussions.

Evaluating the Impacts of COSMIC-2 GNSS RO Bending Angle Assimilation on Atlantic Hurricane Forecasts Using the HWRF Model

Abstract This study evaluates the impact of assimilating Global Navigation Satellite System (GNSS) radio occultation (RO) bending angles from Formosa Satellite Mission-7/Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2) receiver satellites on Hurricane Weather Research and Forecasting (HWRF) Model tropical cyclone (TC) forecasts. Launched in June 2019, the COSMIC-2 mission provides significantly higher tropics data coverage compared to its predecessor COSMIC constellation. GNSS RO measurements yield information about atmospheric pressure, temperature, and water vapor profiles. HWRF is cycled with and without COSMIC-2 bending angle data assimilation for six 2020 Atlantic hurricane cases. COSMIC-2 assimilation has little impact on HWRF track forecasts, consistent with HWRF’s design limiting cycled data assimilation impacts on surrounding large-scale flows; however, COSMIC-2 assimilation results in a statistically significant ∼8%–12% mean absolute forecast error reduction in minimum central sea level pressure for t = 36-, 54-, 60-, and 108–120-h lead times. Forecasts initialized from analyses assimilating COSMIC-2 observations also have a 1%–4% smaller 600–700-hPa specific humidity (SPFH) root-mean-squared deviation compared to radiosondes and dropwindsondes for most lead times. While not all HWRF intensity forecasts benefit from COSMIC-2 assimilation, a few show notable improvement. For example, assimilating two COSMIC-2 profiles within the inner core of developing Hurricane Hanna (2020) increases 800-hPa SPFH by up to 1 g kg−1 locally, helping to correct a dry bias. The forecast initialized from this analysis better captures Hanna’s observed intensification rate, likely because its moister inner core facilitates development of persistent deep convection near the TC center, where diabatic heating is more efficiently converted to cyclonic wind kinetic energy. Significance Statement Tropical cyclone (TC) intensification can be strongly sensitive to the lower-to-midtropospheric water vapor distribution near the storm. The COSMIC-2 GNSS radio occultation (RO) receiver satellite mission provides denser spatial coverage of atmospheric water vapor and temperature profiles over the tropics compared to other GNSS RO observation platforms. Herein, using six 2020 Atlantic TC cases, we evaluate the impacts of assimilating COSMIC-2 RO bending angles into a regional forecast model that already assimilates clear-sky satellite radiances. It is shown that COSMIC-2 assimilation yields a modest ∼10% intensity forecast skill improvement for several lead times, although more substantial intensity forecast improvement is found for a few forecasts where the COSMIC-2 observation assimilation helps correct a lower-to-midtropospheric water vapor bias.

Retrieval of atmospheric temperature and humidity profiles over a tropical coastal station from ground-based Microwave Radiometer using deep learning technique

Ground-based multi-frequency microwave radiometer Profiler (Microwave Radiometer Profiler-MRP) provides valuable information on the altitude distribution of atmospheric humidity and temperature in the troposphere (upto ∼10 km) with high temporal resolution (of ∼1 min) under all-weather condition. These information are vital inputs for characterization of the atmospheric boundary layer, convective cloud systems, local weather modification and for studying the atmospheric dynamics. Potential of such radiometers are well established over the mid-latitude region. Thiruvananthapuram, located in the Indian peninsular region, is one of the first stations in the equatorial region where such an instrument made continuously operational for seven years since 2010 at the tropical coastal station. The instrument measures the brightness temperatures (Tb in Kelvin) at Ka- and V-band frequencies with a temporal resolution of ∼1 min. A trained back-propagation neural network technique based on the radiosonde ascends data for more than one decade obtained by India Meteorological station at Thiruvananthapuram, was used to derive the atmospheric humidity and temperature profiles. This paper presents the implementation of another retrieval technique based on deep learning approach - batch normalization and neural network (BNN) to retrieve temperature and water vapour density (WVD) profiles from Tb values during clear sky conditions and validation of the retrieval accuracies. This inversion model, much faster in computation, consists of two hidden layers and the rectified linear unit (ReLU) is used as the activation function as it can overcome the problems of saturation and vanishing gradients. The Tbs observed by radiometer are corrected by reducing the bias between the simulated Tb, using forward radiative transfer model, and the observed Tb. The validation of retrieved profiles with the radiosonde demonstrates a good retrieval capability, showing a root-mean-square error of 1.8K for temperature, <2 g/m3 for WVD and ∼14% for RH.

Optical design of a reflective double-grating spectrometer to detect one branch in the pure rotational Raman spectrum of N2.

This paper presents the optical design of a high-resolution double-grating spectrometer for extracting the multiple lines in the Stokes or anti-Stokes branch of the pure rotational Raman spectra of nitrogen. The spectrometer is composed of collimating and focusing mirrors, two reflective gratings, and a linear detector. The structural parameters were calculated using geometric configuration, dispersion, and aberrational theory, and conditions for first-order correction of keystone distortion with divergent grating illumination were derived. Based on this method, we simulated a spectrometer with a 16-channel linear array photomultiplier tube, resulting in uniformly distributed single-branch lines on each detector channel. The resolution reached 0.225nm per channel, and the keystone distortion was less than 0.7%. The spectrometer avoids the interference of elastic signals by not detecting them, enabling the extraction of atmospheric temperature profiles via separated single-branch lines with high precision. Our design provides a promising solution to extract atmospheric temperature profiles for pure rotational Raman lidar.

Validation of FY-4A Temperature Profiles by Radiosonde Observations in Taklimakan Desert in China

The atmospheric temperature profiles (ATPs) retrieved through the geostationary Interferometric Infrared Sounder (GIIRS) onboard the FY-4A satellite (GIIRS/FY-4A) can effectively fill the gap of the scarce conventional sounding data in the Taklimakan Desert (TD), the second largest desert in the world, with an area of 330,000 square kilometers. In this study, we take the experimental radiosonde observations (RAOB) from one RAOB station in the hinterland of TD and seven conventional radiosondes in the oasis region around the desert as the true values and analyze the bias distribution characteristics of GIIRS/FY-4A ATPs with quality control (QC) flags 0 or 1 for this region. In addition, a bias comparison is made with GIIRS/FY-4A ATPs, and the fifth generation ECMWF atmospheric reanalysis of the global climate (ERA5) ATPs. The results show that (1) Missing measurements in GIIRS/FY-4A ATPs are the most frequent in the near-surface layer, accounting for more than 80% of all the retrieved grid points. The averaged total proportion of GIIRS/FY-4A ATPs with QC marks 0 or 1 is about 33.06%. (2) The root mean square error (RMSE) of GIIRS/FY-4A ATPs is less than 3 K, smaller than that of ERA5 ATPs. The RMSE of ERA5 ATPs can exceed 10 K in the desert hinterland. The absolute mean biases of GIIRS/FY-4A ATPs and ERA5 ATPs are, respectively, smaller than 3 K and 2 K, the former being slightly larger. The correlation coefficients of GIIRS/FY-4A ATPs with ERA5 ATPs and RAOB ATPs are higher than 0.98 and 0.99, respectively, and the correlation between GIIRS/FY-4A ATPs and RAOB ATPs is inferior to the latter. (3) The overall atmospheric temperature retrieved by GIIRS/FY-4A is 0.08 K higher than the temperature of RAOB, on average, while the overall temperature from ERA5 is 0.13 K lower than that of RAOB, indicating that the temperature profile obtained by integrating GIIRS/FY-4A ATPs and ERA5 ATPs may be much closer to RAOB ATPs. (4) The probability density of the GIIRS/FY-4A ATP biases in the TD region generally follows the Gaussian distribution so that it can be effectively assimilated in the 3-D variational assimilation modules. The probability density distribution characteristics of the GIIRS/FY-4A ATP biases in the desert hinterland and oasis are not much different. However, due to the fusion analysis of the relatively rich multi-source conventional observation data from the oasis stations, the probability density of ERA5 ATPs biases at the oasis stations is nearer to Gaussian distribution than that of the GIIRS/FY-4A ATPs. In the desert hinterland, where conventional observation is not enough, the probability density distributions of the ATPs biases from ERA5 and GIIRS/FY-4A are alike. Therefore, the GIIRS FY4A can contribute to a more accurate estimation of ERA5 ATPs in the TD region.

Simulated Clear-Sky Water Vapor and Temperature Retrievals from PREFIRE Measurements

Abstract The Polar Radiant Energy in the Far Infrared Experiment (PREFIRE) mission will measure Earth’s emission at wavelengths ranging from 3 to 54 μm. The prelaunch clear-sky retrieval algorithm, evaluated with simulated test data, indicates that PREFIRE measurements will be valuable for retrieving atmospheric water vapor and temperature profiles. Far infrared measurements provide unique retrieval information, indicated by the high ranking of select FIR channels as primary contributors to the total degrees of freedom for signal (DFS). In utilizing all the PREFIRE channels, the average total DFS of 4 test regions ranges from 1.90 to 4.71. The information content increases with higher column water vapor and in the presence of near-surface temperature inversions. Using the DFS profiles for guidance, the retrieval concentrates information into 7 distinct layers to reduce the retrieval uncertainty per layer. Sensitivity tests indicate forward model error due to surface emissivity uncertainty results in about a 9% increase in column water vapor uncertainty. The clear-sky retrieval is sensitive to the presence of undetected ice clouds, especially those with optical depths larger than 0.2. Hence, in addition to a separate PREFIRE cloud mask, optimal estimation retrieval metrics are explored as possible indicators of cloudy scenes.

Inferring the vertical distribution of CO and CO2 from TCCON total column values using the TARDISS algorithm

Abstract. We describe an approach for determining limited information about the vertical distribution of carbon monoxide (CO) and carbon dioxide (CO2) from total column ground-based Total Carbon Column Observation Network (TCCON) observations. For CO and CO2, it has been difficult to retrieve information about their vertical distribution from spectral line shapes because of the errors in the spectroscopy and the atmospheric temperature profile that mask the effects of variations in their mixing ratio with altitude. For CO2 the challenge is especially difficult given that these variations are typically 2 % or less. Nevertheless, if sufficient accuracy can be obtained, such information would be highly valuable for evaluation of retrievals from satellites and more generally for improving the estimate of surface sources and sinks of these trace gases. We present here the Temporal Atmospheric Retrieval Determining Information from Secondary Scaling (TARDISS) retrieval algorithm. TARDISS uses several simultaneously obtained total column observations of the same gas from different absorption bands with distinctly different vertical averaging kernels. The different total column retrievals are combined in TARDISS using a Bayesian approach where the weights and temporal covariance applied to the different retrievals include additional constraints on the diurnal variation in the vertical distribution for these gases. We assume that the near-surface part of the column varies rapidly over the course of a day (from surface sources and sinks, for example) and that the upper part of the column has a larger temporal covariance over the course of a day. Using measurements from the five North American TCCON sites, we find that the retrieved lower partial column (between the surface and ∼ 800 hPa) of the CO and CO2 dry mole fractions (DMFs) have slopes of 0.999 ± 0.002 and 1.001 ± 0.003 with respect to lower column DMF from integrated in situ data measured directly from aircraft and in AirCores. The average error for our lower column CO retrieval is 1.51 ppb (∼ 2 %) while the average error for our CO2 retrieval is 5.09 ppm (∼ 1.25 %). Compared with classical line-shape-derived vertical profile retrievals, our algorithm reduces the influence of forward model errors such as imprecision in spectroscopy (line shapes and intensities) and in the instrument line shape. In addition, because TARDISS uses the existing retrieved column abundances from TCCON (which themselves are computationally much less intensive than profile retrieval algorithms), it is very fast and processes years of data in minutes. We anticipate that this approach will find broad application for use in carbon cycle science.

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.