Atmospheric Temperature Profiles Research Articles

Surface Energy Fluxes and Temperatures at Jezero Crater, Mars

AbstractDiurnal ground surface and air temperatures (Tg, Ta) and the five major surface energy budget fluxes are displayed as derived from M2020 mission observations and from column model simulations in two extreme cases (low and high diurnal Tg‐variation) along the Perseverance rover track in the Jezero crater. In both cases the fluxes and Tg are well modeled when using diurnally variable apparent ground thermal inertia I derived via a Fourier series method from the hourly observations. Hence the measurements, the diagnostic method and the model results are consistent with high‐ and low‐I nonhomogeneous terrain in the field‐of‐view (FOV) of the thermal infrared and solar sensors. In contrast less extreme values of I consistent with THEMIS retrievals are necessary for good simulations of observed Ta. We deduce that the measured Tg for the small ∼3 m2 FOV may not always be representative for the larger region around the rover, which controls the near‐surface atmospheric temperature profile.

Martian Atmospheric Temperature and Density Profiles During the First Year of NOMAD/TGO Solar Occultation Measurements

AbstractWe present vertical profiles of temperature and density from solar occultation (SO) observations by the “Nadir and Occultation for Mars Discovery” (NOMAD) spectrometer on board the Trace Gas Orbiter during its first operational year, which covered the second half of Mars Year 34. We used calibrated transmittance spectra in 380 scans, and apply an in‐house pre‐processing to clean data systematics. Temperature and CO2 profiles up to about 90 km, with consistent hydrostatic adjustment, are obtained, after adapting an Earth‐tested retrieval scheme to Mars conditions. Both pre‐processing and retrieval are discussed to illustrate their performance and robustness. Our results reveal the large impact of the MY34 Global Dust Storm (GDS), which warmed the atmosphere at all altitudes. The large GDS aerosols opacity limited the sounding of tropospheric layers. The retrieved temperatures agree well with global climate models (GCM) at tropospheric altitudes, but NOMAD mesospheric temperatures are wavier and globally colder by 10 K in the perihelion season, particularly during the GDS and its decay phase. We observe a warm layer around 80 km during the Southern Spring, especially in the Northern Hemisphere morning terminator, associated to large thermal tides, significantly stronger than in the GCM. Cold mesospheric pockets, close to CO2 condensation temperatures, are more frequently observed than in the GCM. NOMAD CO2 densities show oscillations upon a seasonal trend that track well the latitudinal variations expected. Results uncertainties and suggestions to improve future data re‐analysis are briefly discussed.

Extending the HIRS Data Record with IASI Measurements

The High-Resolution Infrared Radiation Sounder (HIRS) on the NOAA and the MetOp satellite series have provided global sounding measurements since the late 1970s, spanning over 40 years. These measurements have been useful in climate change detection, numerical weather prediction, and development of long-term climate data records of profiles of atmospheric temperature and humidity, cloud climatology, upper tropospheric water vapor, outgoing longwave radiation, etc. However, the HIRS instrument is being replaced by the new generation of sounders such as the hyperspectral Infrared Atmospheric Sounding Interferometer (IASI) on recently launched satellites. In order to continue and extend the HIRS record, we use IASI measurements to simulate and derive HIRS-like data for the 12 HIRS longwave channels. The MetOp satellite operated by EUMETSAT carries both the HIRS and the hyper-spectral IASI instrument with accurate spectral and radiometric calibration, providing a great opportunity to consistently calibrate the measurements. The IASI radiances are convolved with the HIRS spectral response functions to produce IASI-simulated HIRS (IHIRS) for the longwave channels. In the present work, IHIRS data are collocated and compared with HIRS observed radiances on the same satellite to develop a calibration table for each of the ascending/descending orbits and cloudy and clear categories. The resulting inter-instrument calibrated IHIRS data was found to agree with HIRS brightness temperatures within 0.05 K for all longwave channels.

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.

Surface-based temperature inversion characteristics and impact on surface air temperatures in northwestern Canada from radiosonde data between 1990 and 2016

Assumptions of linear lapse rates in regions prone to surface-based inversions (SBIs) can generate biases in the prediction of surface air temperature. Although studies of Arctic inversions are common, few regional studies of their characteristics exist in high-latitude regions with mountainous topography. To address this gap, vertical atmospheric temperature profiles for five sites in northwestern Canada were analysed using archived radiosonde data from 1990 to 2016. We present monthly, seasonal, and annual SBI characteristics including the occurrence of transient and persistent SBIs. A novel metric, surface-based inversion impact (SBIimp), was developed by combining the traditional inversion characteristics of depth, strength, and frequency, and was used to quantify the impact of SBIs on cooling the surface air temperature. SBIimp values of >5 °C yr−1 and ∼10 °C winter−1 occur locally. A weak linear relationship between sea ice coverage in the Beaufort Sea and SBIimp manifests across parts of the study area, though this relationship does not persist after detrending the datasets. Topographic analysis of areas surrounding each radiosonde location reveals highly variable SBIimp in complex mountain areas and more consistent SBIimp across areas of low relief. Our results can help interpret the role of inversions in climatic conditions maintaining cryospheric elements such as permafrost.

Impacts of springtime biomass burning in Southeast Asia on atmospheric carbonaceous components over the Beibu Gulf in China: Insights from aircraft observations.

Limited by the scarcity of in situ vertical observation data, the influences of biomass burning in Southeast Asia on major atmospheric carbonaceous compositions in downwind regions have not been thoroughly studied. In this study, aircraft observations were performed to obtain high time-resolved in situ vertical distributions of black carbon (BC) as well as carbon monoxide (CO) and carbon dioxide (CO2). Four types of profiles were revealed: Mode I (from 2000 to 3000 m, the BC, CO and CO2 concentrations were enhanced), Mode II (with increasing altitude, the BC, CO and CO2 concentrations almost decreased), Mode III (inhomogeneous vertical BC, CO and CO2 profiles with BC peaks were observed from 2500 to 3000 m) and Mode IV (the BC, CO and CO2 concentrations increased above 1500 m). Furthermore, simulations were conducted to calculate radiative forcing (RF) caused by BC and study the heating rate (HR) of BC in combination with the vertical BC profiles. A larger BC distribution in the atmosphere resulted in a sharp RF change from negative to positive values, imposing a nonnegligible influence on the atmospheric temperature profile, with maximum HR values ranging from 0.4 to 5.8 K/day. The values of the absorption Ångström exponent (AAE) were 1.46 ± 0.11 and 1.48 ± 0.17 at altitudes from 1000 to 2000 and 2000-3000 m, respectively. The average BC light absorption coefficient at the 370 nm wavelength (α BC (370)) accounted for 50.3 %-76.8 % of the α (370), while the brown carbon (BrC) light absorption coefficient at the 370 nm wavelength (α BrC (370)) contributed 23.2 %-49.7 % to the α (370) at altitudes of 1000-2000 m. At altitudes of 2000-3000 m, α BC (370) and α BrC (370) contributed 43.8 %-88.2 % and 11.8 %-56.2 % to the α (370), respectively. These findings show that calculations that consider the surface BC concentration but ignore the vertical BC distribution could result in massive uncertainties in estimating the RF and HR caused by BC. This study helped achieve a deeper understanding of the influences of biomass burning over the region of Southeast Asia on the profiles of atmospheric carbonaceous compositions and atmospheric BC absorption and its warming effect.

A Fast Piecewise-Defined Neural Network Method to Retrieve Temperature and Humidity Profile for the Vertical Atmospheric Sounding System of FengYun-3E Satellite

A fast piecewise-defined neural network (PDNN) method is presented to produce accurate atmospheric temperature and humidity profiles from satellite hyperspectral infrared and microwave observations in all-sky conditions. The PDNN method relies on a novel classification approach and a principal component-based neural network function to better capture the nonlinear relationship between the spectral radiances and atmospheric state vectors. The algorithm was designed to only rely on satellite measurements. Large datasets were used for network training to make the retrieval robust to random errors in the reference data. For each retrieved profile, an effective quality indicator was obtained by training against the absolute value of the retrieval error. In addition, a reliable rain cloud flag was generated based on the scattering difference of cloud particles between the 50 and 118 GHz channels. Besides the independent reanalysis field data, the algorithm’s performance was also evaluated using radiosonde measurements. Preliminary validation shows that the best temperature retrieval occurred in the mid-troposphere (around 1.0 K). Depending on the reference data, errors in the boundary layer typically ranged from 1.8 K to 2.5 K. For water vapor, the retrieval error was less than 22% in the low-troposphere and less than 35% in the mid-and upper-troposphere when validated against the reanalysis field. The humidity error was approximately 10% lower when compared to radiosondes. The PDNN is used operationally to produce atmospheric temperature and moisture soundings from the Vertical Atmospheric Sounding System of the FengYun-3E satellite, an early-morning-orbit meteorological satellite launched in 2021.

Atmospheric characterization of the ultra-hot Jupiter WASP-33b

Ultra-hot Jupiters are highly irradiated gas giant exoplanets on close-in orbits around their host stars. The dayside atmospheres of these objects strongly emit thermal radiation due to their elevated temperatures, making them prime targets for characterization by emission spectroscopy. We analyzed high-resolution spectra from CARMENES, HARPS-N, and ESPaDOnS taken over eight observation nights to study the emission spectrum of WASP-33b and draw conclusions about its atmosphere. By applying the cross-correlation technique, we detected the spectral signatures of Ti i, V i, and a tentative signal of Ti ii for the first time via emission spectroscopy. These detections are an important finding because of the fundamental role of Ti- and V-bearing species in the planetary energy balance. Moreover, we assessed and confirm the presence of OH, Fe i, and Si i from previous studies. The spectral lines are all detected in emission, which unambiguously proves the presence of an inverted temperature profile in the planetary atmosphere. By performing retrievals on the emission lines of all the detected species, we determined a relatively weak atmospheric thermal inversion extending from approximately 3400 to 4000 K. We infer a supersolar metallicity close to 1.5 dex in the planetary atmosphere, and find that its emission signature undergoes significant line broadening with a Gaussian full width at half maximum of about 4.5 km s−1. Also, we find that the atmospheric temperature profile retrieved at orbital phases far from the secondary eclipse is about 300 to 700 K cooler than that measured close to the secondary eclipse, which is consistent with different day- and nightside temperatures. Moreover, retrievals performed on the emission lines of the individual chemical species lead to consistent results, which gives additional confidence to our retrieval method. Increasing the number of species included in the retrieval and expanding the set of retrieved atmospheric parameters will further advance our understanding of exoplanet atmospheres.

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.

Atmospheric GNSS RO 1D-Var in Use at UCAR: Description and Validation

This paper describes, along with some validation results, the one-dimensional variational method (1D-Var) that is in use at the University Corporation for Atmospheric Research (UCAR) to retrieve atmospheric profiles of temperature, pressure, and humidity from the observation of the Global Navigation Satellite System (GNSS) radio occultation (RO). The retrieved profiles are physically consistent among the variables and statistically optimal as regards to a priori error statistics. Tests with idealized data demonstrate that the 1D-Var is highly effective in spreading the observational information and confirm that the method works as designed and expected, provided that correct input data are given. Tests for real-world data sets show that the retrieved profiles agree remarkably well with global weather analyses and collocated high vertical resolution radiosonde observations, and that the 1D-Var can produce value-added retrievals with respect to a priori profiles. We also find that the retrieved profiles are of exceptional long-term stability, suggesting that the 1D-Var can provide an excellent climate data record.

Processing and Validation of the STAR COSMIC-2 Temperature and Water Vapor Profiles in the Neutral Atmosphere

The global navigation satellite system (GNSS) radio occultation (RO) is becoming an essential component of National Oceanic and Atmospheric Administration (NOAA) observation systems. The constellation observing system for meteorology, ionosphere, and climate (COSMIC) 2 mission and the Formosa satellite mission 7, a COSMIC follow-on mission, is now the NOAA’s backbone RO mission. The NOAA’s dedicated GNSS RO SAtellite processing and science Application Center (RO-SAAC) was established at the Center for Satellite Applications and Research (STAR). To better quantify how the observation uncertainty from clock error and geometry determination may propagate to bending angle and refractivity profiles, STAR has developed the GNSS RO data processing and validation system. This study describes the COSMIC-2 neutral atmospheric temperature and moisture profile inversion algorithms at STAR. We used RS41 and ERA5, and UCAR 1D-Var products (wetPrf2) to validate the accuracy and uncertainty of the STAR 1D-Var thermal profiles. The STAR-RS41 temperature differences are less than a few tenths of 1 K from 8 km to 30 km altitude with a standard deviation (std) of 1.5–2 K. The mean STAR-RS41 water vapor specific humidity difference and the standard deviation are −0.35 g/kg and 1.2 g/kg, respectively. We also used the 1D-Var-derived temperature and water vapor profiles to compute the simulated brightness temperature (BTs) for advanced technology microwave sounder (ATMS) and cross-track infrared sounder (CrIS) channels and compared them to the collocated ATMS and CrIS measurements. The BT differences of STAR COSMIC-2-simulated BTs relative to SNPP ATMS are less than 0.1 K over all ATMS channels.

Оперативное обнаружение и мониторинг МКК

The results of the development of an information system providing the integration of satellite microwave radiometric sensing data of geostationary observations in the visible and IR range are described. This integration is designed to ensure the restoration of atmospheric temperature and humidity profiles in the conditions of emergency atmospheric situations caused by mesoscale convective complexes (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.

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.

Comparison of Three Convolution Neural Network Schemes to Retrieve Temperature and Humidity Profiles from the FY4A GIIRS Observations

FY4A/GIIRS (Geostationary Interferometric Infrared Sounder) is the first infrared hyperspectral atmospheric vertical sounder onboard a geostationary satellite. It can achieve observations of atmospheric temperature and humidity profiles with high vertical and temporal resolutions. Presently, convolutional neural network algorithms are relatively less used in the field of atmospheric profile retrieval, and different convolutional neural network approaches have different characteristics. The one-dimensional convolutional neural network scheme 1D-Net and two three-dimensional retrieval schemes U-Net 1 and U-Net 2 are used to achieve atmospheric temperature and humidity profiles under all skies based on GIIRS-observed brightness temperatures in this paper. After validation with test training data, the retrievals of different schemes derived from actual GIIRS observations and level 2 operational products were verified with ERA5 reanalysis data and radiosonde measurements in summer and winter respectively. The retrieved three-dimensional temperature and humidity fields from U-Net 1 and U-Net 2 are closer to the ERA5 reanalysis field in both distribution and value than the retrievals from the 1D-Net scheme and level 2 operational products. In particular, the inversion field of the U-Net 2 scheme is more continuous in space. Compared with radiosonde observations, the accuracy of the level 2 temperature product is the highest when the field of view is completely clear both in winter and summer month. The root mean square error (RMSE) of temperature retrieval of the two U-Net schemes is the second highest, and the RMSE and bias of the 1D-Net scheme are both large. Two U-Net schemes overestimate the temperature and humidity slightly in winter and underestimate it in summer in both clear and all sky cases. Under all sky conditions, the temperature retrieval RMSE and bias of the two U-Net schemes above 800 hPa are lower than those of the level 2 products, especially the U-Net 2 scheme with an RMSE of approximately 2.5 K. The U-Net 2 scheme bias is the smallest, with a value of approximately 0.5 K in winter. Since the level 2 product only provides the atmospheric temperature above the cloud top, it indicates that its temperature product accuracy is very low when the field of view is influenced by clouds. The humidity retrieval RMSEs of the two U-Net schemes is within 2 g/kg, better than that of the 1D-Net scheme. The retrieval accuracy of the U-Net 2 scheme is approximately 0.3 g/kg better than that of the U-Net 1 scheme below 600 hPa in winter. Level 2 does not provide humidity products. The summer humidity retrieval is worse than in winter. In general, among the three deep machine learning algorithms, 1D-Net has a large retrieval error, and the temperature and humidity from U-Net 2 have the highest accuracy. The retrieval speeds of the two U-Net schemes are nearly the same, and both are faster than that of scheme 1D-Net.

A New Model of Solar Ultraviolet Irradiance Variability With 0.1–0.5 nm Spectral Resolution

AbstractObservations of solar irradiance made from space since 2003 with 0.1 nm spectral resolution at wavelengths from 115 to 310 nm and 0.5 nm spectral resolution at wavelengths from 260 to 500 nm are used to construct a new model, NRLSSI2h, of solar irradiance variability with higher spectral resolution than the 1 nm NRLSSI2 model used to specify the NOAA Solar Irradiance Climate Data Record. The new model better resolves irradiance variability in specific emission and absorption features that are directly attributable to atoms and molecules in the Sun’s atmosphere. Singularly prominent is spectral irradiance variability at 379–389 nm, dominated by the CN molecular band system; irradiance in this 10 nm band increased 0.078 W m−2 during solar cycle 23, contributing 4.6% of the 1.7 W m−2 concurrent total solar irradiance increase. Irradiance variability at wavelengths from 300 to 400 nm, a region dominated by multiple spectral features, is a factor of 2–5 smaller in the new model than estimated by semiempirical models that use radiative transfer codes to calculate the contrasts of faculae and sunspots, which alter the temperature‐dependent densities of these species relative to the surrounding continuum. Solar atmosphere temperature and composition profiles in radiative transfer models may therefore not be realistic or their atomic and molecular databases may be incomplete. Improved colocation of spectral features in solar irradiance and the absorption cross sections of molecular oxygen and ozone with the new model may allow higher fidelity calculations of energy deposition in Earth’s atmosphere.

Assimilation of Temperatures and Column Dust Opacities Measured by ExoMars TGO‐ACS‐TIRVIM During the MY34 Global Dust Storm

AbstractWe assimilate atmospheric temperature profiles and column dust optical depth observations from the ExoMars Trace Gas Orbiter Atmospheric Chemistry Suite thermal infrared channel (TIRVIM) into the Mars Planetary Climate Model. The assimilation period is Mars Year 34 Ls = 182.3°–211.4°, covering the onset and peak of the 2018 global dust storm. We assimilated observations using the Local Ensemble Transform Kalman Filter with 36 ensemble members and adaptive inflation; our nominal configuration assimilated TIRVIM temperature profiles to update temperature and dust profiles, followed by dust column optical depths to update the total column dust abundance. The observation operator for temperature used the averaging kernels and prior profile from the TIRVIM retrievals. We verified our analyses against in‐sample TIRVIM observations and independent Mars Climate Sounder (MCS) temperature and dust density‐scaled opacity profiles. When dust observations were assimilated, the root‐mean‐square temperature error verified against MCS fell by 50% during the onset period of the storm, compared with assimilating temperature alone. At the peak of the storm the analysis reproduced the location and magnitude of the peak in the nighttime MCS dust distribution, along with the surface pressure diurnal cycle measured by Curiosity with a bias of less than 10 Pa. The analysis winds showed that, at the peak of the storm, the meridional circulation strengthened, a 125 m s−1 asymmetry developed in the midlatitude zonal jets, the diurnal tide weakened near the equator and strengthened to 10–15 K at midlatitudes, and the semi‐diurnal tide strengthened almost everywhere, particularly in the equatorial lower atmosphere.

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.

Hyperspectral Infrared Atmospheric Sounder (HIRAS) Atmospheric Sounding System

Accurate atmospheric temperature and moisture profiles are essential for weather forecasts and research. Satellite-based hyperspectral infrared observations are meaningful in detecting atmospheric profiles, especially over oceans where conventional observations can seldom be used. In this study, a HIRAS (Hyperspectral Infrared Atmospheric Sounder) Atmospheric Sounding System (HASS) was introduced, which retrieves atmospheric temperature and moisture profiles using a one-dimension variational scheme based on HIRAS observations. A total of 274 channels were optimally selected from the entire HIRAS spectrum through information entropy analyses, and a group of retrieval experiments were independently performed for different HIRAS fields of views (FOVs). Compared with the ECMWF reanalysis data version-5 (ERA5), the RMSEs of temperature (relative humidity) for low-, mid-, and high-troposphere layers were 1.5 K (22.3%), 1.0 K (33.2%), and 1.3 K (38.5%), respectively, which were similar in magnitude to those derived from other hyperspectral infrared sounders. Meanwhile, the retrieved temperature RMSEs with respect to the satellite radio occultation (RO) products increased to 1.7 K, 1.8 K, and 1.9 K for the low-, mid-, and high-troposphere layers, respectively, which could be attributed to the accurate RO temperature products in the upper atmospheres. It was also found that the RMSE varied with the FOVs and latitude, which may be caused by the current angle-dependent bias correction and unique background profiles.

EMIRS Observations of the Aphelion‐Season Mars Atmosphere

AbstractThermal infrared spectra taken by the Emirates Mars Infrared Spectrometer (EMIRS) on‐board the Emirates Mars Mission (EMM) spacecraft are well suited for the retrieval of surface temperatures, the atmospheric temperature profile from the surface to ∼40 km, and the column abundance of dust aerosols, water ice clouds, and water vapor. A constrained linear inversion retrieval routine that includes multiple scattering has been developed and optimized for this purpose. Here, we present a brief overview of the retrieval algorithm and first atmospheric science results from observations taken by EMIRS over the first Earth year of EMM Science Phase operations. These retrievals show extensive water ice clouds, typical for the aphelion season of these observations, and the expected north polar summer maximum and subsequent equatorward transport of water vapor is well documented. An unusually strong and early regional dust storm and its associated thermal response were also observed.

The Effect of the Solar Eclipse of March 29, 2006 on Atmospheric Pressure Fluctuations and Surface Temperature Profiles

The Effect of the Solar Eclipse of March 29, 2006 on Atmospheric Pressure Fluctuations and Surface Temperature Profiles