Atmospheric Temperature Profiles Research Articles

Improving Atmospheric Temperature and Relative Humidity Profiles Retrieval Based on Ground-Based Multichannel Microwave Radiometer and Millimeter-Wave Cloud Radar

Obtaining temperature and humidity profiles with high vertical resolution is essential for describing and predicting atmospheric motion, and, in particular, for understanding the evolution of medium- and small-scale weather processes, making short-range and near-term weather forecasting, and implementing weather modifications (artificial rainfall, artificial rain elimination, etc.). Ground-based microwave radiometers can acquire vertical tropospheric atmospheric data with high temporal and spatial resolution. However, the accuracy of temperature and relative humidity retrieval is still not as accurate as that of radiosonde data, especially in cloudy conditions. Therefore, improving the observation and retrieval accuracy is a major challenge in current research. The focus of this study was to further improve the accuracy of atmospheric temperature and humidity profile retrieval and investigate the specific effects of cloud information (cloud-base height and cloud thickness) on temperature and humidity profile retrieval. The observation data from the ground-based multichannel microwave radiometer (GMR) and the millimeter-wave cloud radar (MWCR) were incorporated into the retrieval process of the atmospheric temperature and relative humidity profiles. The retrieval was performed using the backpropagation neural network (BPNN). The retrieval results were quantified using the mean absolute error (MAE) and root mean square error (RMSE). The statistical results showed that the temperature profiles were less affected by the cloud information compared with the relative humidity profiles. Cloud thickness was the main factor affecting the retrieval of relative humidity profiles, and the retrieval with cloud information was the best retrieval method. Compared with the retrieval profiles without cloud information, the MAE and RMSE values of most of the altitude layers were reduced to different degrees after adding cloud information, and the relative humidity (RH) errors of some altitude layers were reduced by approximately 50%. The maximum reduction in the RMSE and MAE values for the retrieval of temperature profiles with cloud information was about 1.0 °C around 7.75 km, and the maximum reduction in RMSE and MAE values for the relative humidity profiles was about 10%, which was obtained around 2 km.

Impact of atmospheric vertical profile on hyperspectral simulations over bright desert pseudo-invariant calibration site

ABSTRACT This paper investigates how atmospheric vertical profiles of pressure, temperature, and concentration affect molecular absorption calculation. This sensitivity analysis is performed in preparation to the upcoming TRUTHS mission, anticipated to provide hyperspectral TOA BRF records with a radiometric accuracy better than 1%. Two methods for characterizing the atmospheric vertical profile are compared: rescaling the H2O and O3 concentrations of an AFGL U.S. Standard vertical profile, and using customized profiles based on CAMS data. The study investigates the effects of those methods on multi-spectral observations in key spectral regions affected, respectively, by water vapour, ozone and methane, as well as on hyperspectral observations covering the visible to SWIR region. Results show that when molecular transmittance exceeds 97%, the choice of method has minimal impact, with less than 1% uncertainty. When the molecular transmittance decreases from 97% to 75%, the corresponding uncertainty on the TOA BRF simulation increases from 1% up to 5%. For transmittance below 75%, using CAMS data for vertical profile characterization is recommended. The study also highlights how pressure and temperature profiles influence Rayleigh optical thickness estimation, particularly affecting TOA BRF in the blue spectral region.

Prediction of Tropical Pacific Rain Rates with Overparameterized Neural Networks

Abstract The prediction of tropical rain rates from atmospheric profiles poses significant challenges, mainly due to the heavy-tailed distribution exhibited by tropical rainfall. This study introduces overparameterized neural networks not only to forecast tropical rain rates but also to explain their heavy-tailed distribution. The investigation is separately conducted for three rain types (stratiform, deep convective, and shallow convective) observed by the Global Precipitation Measurement satellite radar over the west and east Pacific regions. Atmospheric profiles of humidity, temperature, and zonal and meridional winds from the MERRA-2 reanalysis are considered as features. Although overparameterized neural networks are well known for their “double descent phenomenon,” little has been explored about their applicability to climate data and capability of capturing the tail behavior of data. In our results, overparameterized neural networks accurately estimate the rain-rate distributions and outperform other machine learning methods. Spatial maps show that overparameterized neural networks also successfully describe the spatial patterns of each rain type across the tropical Pacific. In addition, we assess the feature importance for each overparameterized neural network to provide insight into the key factors driving the predictions, with low-level humidity and temperature variables being the overall most important. These findings highlight the capability of overparameterized neural networks in predicting the distribution of the rain rate and explaining extreme values. Significance Statement This study aims to introduce the capability of overparameterized neural networks, a type of neural network with more parameters than data points, in predicting the distribution of tropical rain rates from gridscale environmental variables and explaining their tail behavior. Rainfall prediction has been a topic of importance, yet it remains a challenging problem for its heavy-tailed nature. Overparameterized neural networks correctly captured rain-rate distributions and the spatial patterns and heterogeneity of the observed rain rates for multiple rain types, which could not be achieved by any other previous statistical or machine learning frameworks. We find that overparameterized neural networks can play a key role in general prediction tasks, with potential expanded applicability to other domains with heavy-tailed data distribution.

Research on Validation Method on Retrieval of Atmospheric Temperature and Humidity Profile Using a Microwave Sounder

Research on Validation Method on Retrieval of Atmospheric Temperature and Humidity Profile Using a Microwave Sounder

Atmospheric muons and their variations with temperature

Seasonal variations of atmospheric muons are traditionally interpreted in terms of an effective temperature that relates the atmospheric temperature profile at a given time to the dependence of muon production on atmospheric depth. This paper aims to review and generalize the treatment of muon production and effective temperature that has been used to interpret seasonal variations of atmospheric muons by many experiments. The formalism is developed both in integral form – for application to compact detectors at a fixed depth that record all muons with Eμ>Eμmin – and in differential form — for application to extended detectors like IceCube, KM3NeT, and Baikal-GVD, where the rates are proportional to energy-dependent effective areas.

A Two‐Column Model Parameterization for Subgrid Surface Heterogeneity Driven Circulations

AbstractEarth system models currently struggle to account for the complex effects that land surface heterogeneity can have on land‐atmosphere interactions. There have been attempts to include the impact of this heterogeneity on the atmosphere, but they ignore the development of coherent circulations that can be driven by spatial differential surface heating. A wealth of literature, particularly large‐eddy simulation (LES) based studies, shows that these circulations have significant impacts on the development and organization of clouds. In this work, we describe a two‐column model with a parameterized circulation driven by atmospheric virtual potential temperature profiles, differences in near surface temperature between the two columns, patterns of surface heterogeneity, and the mean background wind. Key aspects of the proposed model structure are compared with LES output, and the model is then implemented between two otherwise independent single column models. While some avenues for improvement exist, when the circulations are parameterized, we see increased cloud development and realistic changes to the mean profiles of temperature and moisture. The proposed model qualitatively matches expectations from the literature and LES, and points to the potential success of its future implementation in coarse grid models.

Refined Short‐Term Forecasting Atmospheric Temperature Profiles in the Stratosphere Based on Operators Learning of Neural Networks

AbstractThe efficacious forecasting of single‐station atmospheric temperature profiles can provide essential support for the structural design and flight missions of spacecrafts in near space. However, empirical models and reference atmospheric models most are calculations of the average state of the atmosphere profiles. Numerical assimilation models require expensive computational costs to improve the accuracy for medium and long‐term forecasting. It has been still a challenge to refined predict short‐term temperature profiles of near space at a low‐cost. We present a temperature profile operator method for refined modeling in the stratosphere by fusing the ability of Long Short‐Term Memory (LSTM) networks or its variants‐ bidirectional LSTM (BiLSTM) to exploit time series correlated information and deep operator networks (DeepONets) to approximate the solution operator of temperature profiles. It consists of three subnetworks. The first subnetwork is used to approximate the discrete temperature profile function, the second net is applied to represent the spatial information of pressure heights, and the third branch is utilized to encode the time domain of the temperature profile operator. We first use the hourly low latitude temperature data (20–50 km) from ERA5 for training, verification and iterative testing in the next 48 hr. The results denote that the temperature profile operator network has a stable and low error of cumulative generalization, and the BiLSTM operator significantly outperform the other models. We also apply two scenarios to testing the refined applicability of the high latitude temperature profile operator and the mid latitude wind profile operator in the stratosphere. This work provides a novel perspective for us to study the refined single‐station modeling of the upper and middle atmosphere.

Wind Profile Retrieval Based on LSTM Algorithm and Mobile Observation of Brightness Temperature over the Tibetan Plateau

Stationary or mobile microwave radiometers (MRs) can measure atmospheric temperature, relative humidity, and water vapor density profiles with high spatio-temporal resolution, but cannot obtain the vertical variations of wind field. Based on a dataset of brightness temperatures (TBs) measured with a mobile MR over the Three-River-Source Region of the Tibetan Plateau from 18 to 30 July 2021, we develop a direct retrieval method for the wind profile (WP) based on the Long Short-Term Memory (LSTM) network technique, and obtain the reliable dynamic variation characteristics of the WP in the region. Furthermore, the ground-based radiative transfer model for TOVS (RTTOV-gb) was employed to validate the reliability of the TB observation, and we analyzed the impact of weather conditions, altitude, observational mode, and TB diurnal variation on the accuracy of the TB measurement and the retrieval of the WP. Results show that the TB from the mobile observation (MOTB) on clear and cloudy days are close to those of the simulated TB with the RTTOV-gb model, while TB measurements on rainy days are far larger than the modeled TBs. When compared with radiosonde observations, the WPs retrieved with the LSTM algorithm are better than the ERA5 reanalysis data, especially below 350 hPa, where the root mean square errors for both wind speed and wind direction are smaller than those of ERA5. The major factors influencing WP retrieval include the weather conditions, altitude, observational mode, and TB diurnal variation. Under clear-sky and cloudy conditions, the LSTM retrieval method can reproduce the spatio-temporal evolution of wind field and vertical wind shear characteristics. The findings of this study help to improve our understanding of meso-scale atmospheric dynamic structures, characteristics of vertical wind shear, atmospheric boundary layer turbulence, and enhance the assessment and forecasting accuracy of wind energy resources.

On the conditions for absolute minimum fuel burn for turbofan powered, civil transport aircraft and a simple model for wave drag

Abstract In a recent series of papers, Poll and Schumann have been developing a simple model for estimating fuel burn for turbofan powered, civil transport aircraft for a given mass, Mach number and flight level and in a specified ambient temperature profile for all phases of flight. This paper focuses upon the combination of Mach number and flight level at which an aircraft cruises with the absolute minimum fuel burn. For a given aircraft type, the information necessary to determine these conditions must be specified and this poses a challenge. An initial attempt to obtain these data has been described previously by the first author. In this paper, the optimum conditions are found using a completely different approach. Starting from first principles and using established theory, the equations governing the situation where engine overall efficiency and airframe lift-to-drag ratio both have local maxima at the same flight condition are developed. This special case is termed the “design optimum” condition and, for a specified aircraft mass and a specified atmospheric temperature versus pressure profile, it gives the lowest possible fuel burn for any aircraft and engine combination. The design optimum occurs at a particular Mach number and Reynolds number, and it is a fixed characteristic of the aircraft. The analysis reveals the significance of Reynolds number variations, wave drag, including its derivatives with respect to both lift coefficient and Mach number, and the atmospheric properties. Whilst wave drag is notoriously difficult to determine accurately, it is found that solutions to the equations are not particularly sensitive to the accuracy of this quantity. Consequently, a simple, physically realistic model can give good results. An appropriate model is developed and a complete, approximate solution is obtained. Taking the International Standard Atmosphere as the design atmosphere, results are presented for the 53 aircraft types previously considered by Poll and Schumann. Relative to the design optimum conditions, when Reynolds number is constant and wave drag is zero, compressibility alone reduces L/D by about 5%, reduces lift coefficient by about 1.5% and increases drag coefficient by about 3.5%. Reynolds number variation has little effect upon L/D, but it reduces lift coefficient and drag coefficient by a further 7% and 8% respectively. The reduction in lift coefficient has a significant impact on the optimum cruise flight level. In general, an aircraft’s operating optimum will not coincide with its design optimum, but deviations are expected to be small. Therefore, using the design optimum solution as a reference point, an improved version of the operating optimum estimation method described by Poll and Schumann in previous work is developed. This allows the estimation of the conditions for absolute minimum fuel burn for an aircraft of given mass flying thorough any atmosphere. Updated coefficients for the 53 aircraft types are given.

A Convolutional Neural Network and Attention-Based Retrieval of Temperature Profile for a Satellite Hyperspectral Microwave Sensor

As numerical weather forecasting advances, there is a growing demand for higher-quality atmospheric data. Hyperspectral instruments can capture more atmospheric information and increase vertical resolution, but there has been limited research into retrieval algorithms for obtaining hyperspectral microwaves in the future. This study proposes an atmospheric temperature profile detection algorithm based on Convolutional Neural Networks (CNN) and Local Attention Mechanisms for local feature extraction, applied to hyperspectral microwave sensors. The study utilizes the method of information entropy to extract more effective channels in the vicinities of 60 GHz, 118 GHz, and 425 GHz. The algorithm uses the brightness temperature as the input of the network. The algorithm addresses common issues encountered in conventional networks, such as overfitting, gradient explosion, and gradient vanishing. Additionally, this method isolates the three oxygen-sensitive frequency bands for modularized local feature extraction training, thereby avoiding abrupt changes in brightness temperature between adjacent frequency bands. More importantly, the algorithm considers the correlation between multiple channels and information redundancy, focusing on variations in local information. This enhances the effectiveness of hyperspectral microwave channel information extraction. We simulated the brightness temperatures of the selected channels through ARTS and divided them into training, validation, and test sets. The retrieval capability of the proposed method is validated on a test dataset, achieving a root mean square error of 1.46 K and a mean absolute error of 1.4 K for temperature profile. Detailed comparisons are also made between this method and other commonly used networks for atmospheric retrieval. The results demonstrate that the proposed method significantly improves the accuracy of temperature profile retrieval, particularly in capturing fine details, and is more adaptable to complex environments. The model also exhibits scalability, extending from one-dimensional (pressure level) to three-dimensional space. The error for each pressure level is controlled within 0.7 K and the average error is within 0.4 K, demonstrating effectiveness across different scales with impressive results. The computational efficiency and accuracy have both been improved when handling a large amount of radiation data.

Enhanced prediction of premonsoon thunderstorms over eastern India through assimilation of INSAT‐3D sounding data

AbstractThe present study assesses the impact of Indian National Satellite 3D (INSAT‐3D)‐derived atmospheric (temperature and relative humidity) profiles on the simulation of severe thunderstorms over eastern India during the premonsoon season. Two sets of numerical experiments, without assimilation (CNTL) and with assimilation of INSAT‐3D profiles (INSAT) have been conducted using the three‐dimensional variational data assimilation system of the Weather Research and Forecasting model for 11 thunderstorm events. Analysis increments are positive with INSAT profile assimilation in the lower to middle atmosphere in the thunderstorm region. The errors in the initial fields have been reduced by approx. 50% after the assimilation. After assimilation, the predictions of the surface fields (2‐m temperature, 2‐m relative humidity, and 10‐m wind speed) have been improved at the mature stage (by 37%, 26%, and 15%) compared to the CNTL. Similar improvements are noticed in the vertical profiles of relative humidity and wind speed in the INSAT experiment. The time and magnitude of updrafts and downdrafts improved in INSAT. After assimilation, rainfall intensity improved in nine cases, and time improved in four cases out of 11. The Critical Success Index (False Alarm Rate) of the INSAT ranges from approx. 0.6–0.15 (∼0.37–0.48) for all thresholds, and are higher (lower) than the CNTL. The rainfall area is improved by 25% for light rainfall, 11% for moderate rainfall, and 2% for heavy rainfall in the INSAT over the CNTL experiment. Overall thunderstorm prediction has been improved with the assimilation of INSAT‐3D data. Thus, the study highlights the viability of INSAT‐3D profiles in improving thunderstorm predictions over eastern India.

Orthogonal retrieval algorithm of atmospheric temperature profiles from pure rotational Raman lidar signals.

Aimed at the stability of calibration coefficients in a general non-orthogonal retrieval algorithm (NRA) of pure rotational Raman lidars (PRRLs), an orthogonal retrieval algorithm (ORA) of atmospheric temperature profiles based on the orthogonal basis function is proposed. This algorithm eliminates the correlation between the calibration coefficients in the NRA to reduce the influence of the number of calibration points and the selection scheme on the calibration coefficients. In this paper, the stabilities of calibration coefficients in the NRA and ORA are compared and analyzed, and the data analysis for atmospheric temperature profiles with a time resolution of minute-level are given, based on the developed Cloud Precipitation Potential Evaluation (CPPV) lidar data and the parallel radiosonde temperature data. The analysis results show that coefficients of variation (CVs) of ORA calibration coefficients are one order of magnitude smaller than those of NRA coefficients. The mean deviation of the ORA retrieval results is roughly reduced by 16.1% compared with the NRA, and the root-mean-square deviation is roughly reduced by 15.0% compared with the NRA. Therefore, the temperature retrieval performance of the ORA is better than that of the NRA.

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.