Atmospheric Temperature Profiles Research Articles

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

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

Assimilation of Ground-Based Microwave Radiometer on Heavy Rainfall Forecast in Beijing

Ground-based microwave radiometers (MWRPS) can provide continuous atmospheric temperature and relative humidity profiles for a weather prediction model. We investigated the impact of assimilation of ground-based microwave radiometers based on the rapid-refresh multiscale analysis and prediction system-short term (RMAPS-ST). In this study, five MWRP-retrieved profiles were assimilated for the precipitation enhancement that occurred in Beijing on 21 May 2020. To evaluate the influence of their assimilation, two experiments with and without the MWRPS assimilation were set. Compared to the control experiment, which only assimilated conventional observations and radar data, the MWRPS experiment, which assimilated conventional observations, the ground-based microwave radiometer profiles and the radar data, had a positive impact on the forecasts of the RMAPS-ST. The results show that in comparison with the control test, the MWRPS experiment reproduced the heat island phenomenon in the observation better. The MWRPS assimilation reduced the bias and RMSE of two-meter temperature and two-meter specific humidity forecasting in the 0–12 h of the forecast range. Furthermore, assimilating the MWRPS improved both the distribution and the intensity of the hourly rainfall forecast, as compared with that of the control experiment, with observations that predicted the process of the precipitation enhancement in the urban area of Beijing.

Radiative Transfer Model Simulations for Ground-Based Microwave Radiometers in North China

Ground-based microwave radiometer profilers (MWRPs) are widely used to provide high-temporal resolution atmospheric temperature and humidity profiles. The quality of the observed brightness temperature (TB) from MWRPs is key for retrieving accurate atmospheric profiles. In this study, TB simulations derived from a radiative transfer model (RTM) were used to assess the quality of TB observations. Two types of atmospheric profile data (conventional radiosonde and ERA5 reanalysis) were combined with the RTM to obtain TB simulations, then compared with corresponding observations from three MWRPs located in different places in North China to investigate the influence of input atmospheric profiles on TB simulations and evaluate the quality of TB observations from the three MWRPs. The comparisons of the matching samples under clear-sky conditions showed that TB simulations derived from both radiosonde and ERA5 profiles were very close to the TB observations from most of the MWRP channels; however, the correlation was lower and the bias was obvious at 51.26 GHz and 52.28 GHz, which indicates that the oxygen absorption component in the RTM needs to be improved for lower-frequency temperature channels. The difference in location of the radiosonde and MWRP sites affected the TB simulations for the water vapor channels, but had little impact on temperature channels that are insensitive to humidity. Comparisons of both simulations (ERA5 and Radiosonde) and the corresponding TB observations from the three sites indicated that the water vapor channels observation quality for the MWRP located in southern Beijing needs improvement. For the two types of profile data, ERA5 profiles have a more positive effect on TB simulations in the water vapor channels, such as enhanced consistence, reduced bias and standard deviation between simulations and observations for those MWRPs located away from the radiosonde station. Therefore, hourly ERA5 data are an optimal option in terms of compensating for limited radiosonde measurements and enhancing the monitoring quality of MWRP observations within 24 h.

A Hybrid Seasonal Autoregressive Integrated Moving Average and Denoising Autoencoder Model for Atmospheric Temperature Profile Prediction.

Atmospheric parameter profile plays a vital role in the studies on meteorology and quantitative remote sensing. Besides measurement of the radiosonde and satellite remote sensing, the atmospheric temperature profile can be predicted by the time series model. However, the performance of the time series model is limited by the weak correlation of temperature data in the stratosphere. In this study, to predict monthly mean atmospheric temperature profile, a hybrid model named seasonal autoregressive integrated moving average and denoising autoencoder (SARIMA-DAE), which combines the traditional time series model with an artificial neural network (ANN), is proposed. The SARIMA is first used to predict the temperature at each altitude level independently; then, the DAE is employed to denoise the predicted temperature profile by SARIMA. The data set used is the L3 monthly mean gridded product of atmospheric infrared sounder, an instrument aboard NASA's Aqua satellite. Taking Maoming, a city in China, as an example, the absolute errors are within 1 K. The mean squared error and mean absolute percentage error on the test set are 0.12 and 0.0012, respectively. Compared with SARIMA, support vector machine regression, and ANN models, experimental results show that the SARIMA-DAE model improves the prediction accuracy at almost all altitude levels. Especially in the stratosphere, the advantage of the hybrid model is more pronounced. The model we proposed should, therefore, be of value to estimate missing data and predict atmospheric parameter profiles.

Temperature, water vapor and tropopause characteristics over the Tibetan Plateau in summer based on the COSMIC, ERA-5 and IGRA datasets

We established a set of atmospheric standard temperature and water vapor profiles and examined the characteristics of the tropopause over the Tibetan Plateau in summer using IGRA, COSMIC and ERA-5 datasets. We studied the temperature and water vapor profiles under clear sky, cloudy and “other” conditions and their diurnal variation. The atmosphere over the Central Tibetan Plateau is warmest and wettest in the lower to mid-troposphere, whereas the Western Tibetan Plateau is warmest and driest in the mid to high troposphere. The coldest and wettest air near the tropopause is over the Eastern Tibetan Plateau. The height of the tropopause decreases gradually from west to east over the plateau, although the difference in height is ≤0.5 km. Relative to clear sky conditions, cloudy conditions are characterized by warming and wetting anomalies in the lower to mid-troposphere and cooling and wetting anomalies in the tropopause and lower stratosphere. The largest vertical diurnal differences in temperature are at the surface and the difference decreases with increasing altitude from the surface to ~12 km. The difference between the six time periods of early morning, morning, noon, afternoon, evening and midnight are within −0.5 to 0.5 K above 12 km. The difference in water vapor between the six time periods is ≤0.2 g m−3. The lowest tropopause is at early morning. The height of the tropopause then increases followed by a decrease, with a sharp peak at noon. This establishment of a set of atmospheric standard profiles over Tibetan Plateau provides more accurate inputs for climate models.

РАЗВИТИЕ МЕТОДОВ И СРЕДСТВ ДИСТАНЦИОННОГО ТЕМПЕРАТУРНО-ВЛАЖНОСТНОГО ЗОНДИРОВАНИЯ ЗЕМНОЙ АТМОСФЕРЫ

The paper provides an overview of methods and technical devices for remote temperature and humidity sensing of the Earth’s atmosphere from satellites developed in Russia. A brief description of modern and forthcoming infrared and microwave atmospheric sounders installed on Meteor-M operational polar-orbiting weather satellites is given. The physical and mathematical base of interpreting measurements of atmospheric sounders (IKFS-2 infrared Fourier spectrometer and MTVZA-GYa microwave radiometer) is presented. The technologies for retrieving atmospheric temperature and humidity profiles are described. The results of retrieval validation as well as the prospects for further development of satellite remote temperature and humidity sensing systems are discussed.

Overview and Applications of the New York State Mesonet Profiler Network

AbstractVertical profiles of atmospheric temperature, moisture, wind, and aerosols are essential information for weather monitoring and prediction. Their availability, however, is limited in space and time due to the significant resources required to observe them. To fill this gap, the New York State Mesonet (NYSM) Profiler Network has been deployed as a national testbed to facilitate the research, development and evaluation of ground-based profiling technologies and applications. The testbed comprises 17 profiler stations across the state, forming a long-term regional observational network. Each Profiler station is comprised of a ground-based Doppler lidar, a microwave radiometer (MWR) and an environmental Sky Imaging Radiometer (eSIR). Thermodynamic profiles (temperature and humidity) from the MWR; wind and aerosol profiles from the Doppler lidar; and solar radiance and optical depth parameters from the eSIR are collected, processed, disseminated, and archived every 10 minutes. This paper introduces the NYSM Profiler Network and reviews the network design and siting, instrumentation, network operations and maintenance, data and products, and some example applications highlighting the benefits of the network. Some sample applications include improved situational awareness and monitoring of the sea/land breeze, long-range wildfire smoke transport, air quality (PM2.5and AOD) and boundary layer height. Ground-based profiling systems promise a path forward for filling a critical gap in the nation’s observing system with the potential to improve analysis and prediction for many weather-sensitive sectors, such as aviation, ground transportation, health, and wind energy.

Weather on Other Worlds. VI. Optical Spectrophotometry of Luhman 16B Reveals Large-amplitude Variations in the Alkali Lines

Abstract Using a novel wide-slit, multiobject approach with the GMOS spectrograph on the 8 m Gemini South telescope, we have obtained precise time-series spectrophotometry of the binary brown dwarf Luhman 16 at optical wavelengths over two full nights. The B component of this binary system is known to be variable in the red optical and near-infrared with a period of 5 hr and an amplitude of 5%–20%. Our observations probe its spectrally resolved variability in the 6000–10000 Å range. At wavelengths affected by the extremely strong, broadened spectral lines of the neutral alkali metals (the potassium doublet centered near 7682 Å and the sodium doublet at 5893 Å), we see photometric variations that differ strikingly from those of the 8000–10000 Å “red continuum” that dominates our detected flux. On UT 2014 February 24, these variations are anticorrelated with the red continuum, while on February 25 they have a large relative phase shift. The extent to which the wavelength-dependent photometric behavior diverges from that of the red continuum appears to correlate with the strength of the alkali absorption. We consider but ultimately reject models in which our observations are explained by lightning or auroral activity. A more likely cause is cloud-correlated, altitude-dependent variations in the gas-phase abundances of sodium and potassium, which are in chemical equilibrium with their chlorides in brown dwarf atmospheres. Clouds could influence these chemical equilibria by changing the atmospheric temperature profile and/or through cloud particles acting as chemical catalysts.

The Monitoring Nitrous Oxide Sources (MIN2OS) satellite project

The Monitoring Nitrous Oxide Sources (MIN2OS) satellite project aims at monitoring global-scale nitrous oxide (N2O) sources by retrieving N2O surface fluxes from the inversion of space-borne N2O measurements that are sensitive to the lowermost atmospheric layers under favorable conditions. MIN2OS will provide emission estimates of N2O at a horizontal resolution of 1° × 1° on the global scale and 10 × 10 km2 on the regional scale on a weekly to monthly basis depending on the application (e.g., agriculture, national inventories, policy, scientific research). Our novel approach is based on the development of: 1) a space-borne instrument operating in the Thermal InfraRed domain providing, in clear sky conditions, N2O mixing ratio in the lowermost atmosphere (900 hPa) under favorable conditions (summer daytime) over land and under favorable and unfavorable (winter nighttime) conditions over the ocean and 2) an atmospheric inversion framework to estimate N2O surface fluxes from the atmospheric satellite observations. After studying three N2O spectral bands (B1 at 1240–1350 cm−1, B2 at 2150–2260 cm−1 and B3 at 2400–2600 cm−1), a new TIR instrument will be developed, centered at 1250–1330 cm−1, with a resolution of 0.125 cm−1, a Full Width at Half Maximum of 0.25 cm−1 and a swath of 300 km. To optimally constrain the retrieval of N2O vertical profiles, the instrument will be on-board a platform at ~830 km altitude in a sun-synchronous orbit crossing the Equator in descending node at 09:30 local time in synergy with two other platforms (Metop-SG and Sentinel-2 NG) expected to fly in 2031–32 aiming at detecting surface properties, agricultural information on the field scale and vertical profiles of atmospheric constituents and temperature. The lifetime of the MIN2OS project would be 4–5 years to study the interannual variability of N2O surface fluxes. The spectral noise can be decreased by at least a factor of 5 compared to the lowest noise accessible to date with the Infrared Atmospheric Sounding Interferometer-New Generation (IASI-NG) mission. The N2O total error is expected to be less than ~1% (~3 ppbv) along the vertical. The preliminary design of the MIN2OS project results in a small instrument (payload of 90 kg, volume of 1200 × 600 × 300 mm3) with, in addition to the spectrometer, a wide field and 1-km resolution imager for cloud detection. The instruments could be hosted on a small platform, the whole satellite being largely compatible with a dual launch on VEGA-C. The MIN2OS project has been submitted to the European Space Agency Earth Explorer 11 mission ideas.

Pre-Seismic Irregularities during the 2020 Samos (Greece) Earthquake (M = 6.9) as Investigated from Multi-Parameter Approach by Ground and Space-Based Techniques

We present a comprehensive analysis of pre-seismic anomalies as computed from the ground and space-based techniques during the recent Samos earthquake in Greece on 30 October 2020, with a magnitude M = 6.9. We proceed with a multi-parametric approach where pre-seismic irregularities are investigated in the stratosphere, ionosphere, and magnetosphere. We use the convenient methods of acoustics and electromagnetic channels of the Lithosphere–Atmosphere–Ionosphere-Coupling (LAIC) mechanism by investigating the Atmospheric Gravity Wave (AGW), magnetic field, electron density, Total Electron Content (TEC), and the energetic particle precipitation in the inner radiation belt. We incorporate two ground-based IGS GPS stations DYNG (Greece) and IZMI (Turkey) for computing the TEC and observed a significant enhancement in daily TEC variation around one week before the earthquake. For the space-based observation, we use multiple parameters as recorded from Low Earth Orbit (LEO) satellites. For the AGW, we use the SABER/TIMED satellite data and compute the potential energy of stratospheric AGW by using the atmospheric temperature profile. It is found that the maximum potential energy of such AGW is observed around six days before the earthquake. Similar AGW is also observed by the method of wavelet analysis in the fluctuation in TEC values. We observe significant energetic particle precipitation in the inner radiation belt over the earthquake epicenter due to the conventional concept of an ionospheric-magnetospheric coupling mechanism by using an NOAA satellite. We first eliminate the particle count rate (CR) due to possible geomagnetic storms and South Atlantic Anomaly (SAA) by the proper choice of magnetic field B values. After the removal of the statistical background CRs, we observe a significant enhancement of CR four and ten days before the mainshock. We use Swarm satellite outcomes to check the magnetic field and electron density profile over a region of earthquake preparation. We observe a significant enhancement in electron density one day before the earthquake. The parameters studied here show an overall pre-seismic anomaly from a duration of ten days to one day before the earthquake.

Measurement of Solar Absolute Brightness Temperature Using a Ground-Based Multichannel Microwave Radiometer

Ground-based multichannel microwave radiometers (GMRs) can observe the atmospheric microwave radiation brightness temperature at K-bands and V-bands and provide atmospheric temperature and humidity profiles with a relatively high temporal resolution. Currently, microwave radiometers are operated in many countries to observe the atmospheric temperature and humidity profiles. However, a theoretical analysis showed that a radiometer can be used to observe solar radiation. In this work, we improved the control algorithm and software of the antenna servo control system of the GMR so that it could track and observe the sun and we use this upgraded GMR to observe solar microwave radiation. During the observation, the GMR accurately tracked the sun and responded to the variation in solar radiation. Furthermore, we studied the feasibility for application of the GMR to measure the absolute brightness temperature (TB) of the sun. The results from the solar observation data at 22.235, 26.235, and 30.000 GHz showed that the GMR could accurately measure the TB of the sun. The derived solar TB measurements were 9950 ± 334, 10,351 ± 370, and 9217 ± 375 K at three frequencies. In a comparison with previous studies, we obtained average percentage deviations of 9.1%, 5.3%, and 4.5% at 22.235, 26.235, and 30.0 GHz, respectively. The results demonstrated that the TB of the sun retrieved from the GMR agreed well with the previous results in the literature. In addition, we also found that the GMR responded to the variation in sunspots and a positive relationship existed between the solar TB and the sunspot number. According to these results, it was demonstrated that the solar observation technique can broaden the field usage of GMR.

Application of the Deep Neural Network in Retrieving the Atmospheric Temperature and Humidity Profiles from the Microwave Humidity and Temperature Sounder Onboard the Feng-Yun-3 Satellite.

The shallow neural network (SNN) is a popular algorithm in atmospheric parameters retrieval from microwave remote sensing. However, the deep neural network (DNN) has a stronger nonlinear mapping capability compared to SNN and has great potential for applications in microwave remote sensing. The Microwave Humidity and Temperature Sounder (Beijing, China, MWHTS) onboard the Fengyun-3 (FY-3) satellite has the ability to independently retrieve atmospheric temperature and humidity profiles. A study on the application of DNN in retrieving atmospheric temperature and humidity profiles from MWHTS was carried out. Three retrieval schemes of atmospheric parameters in microwave remote sensing based on DNN were performed in the study of bias correction of MWHTS observation and the retrieval of the atmospheric temperature and humidity profiles using MWHTS observations. The experimental results show that, compared with SNN, DNN can obtain better bias-correction results when applied to MWHTS observation, and can obtain higher retrieval accuracy of temperature and humidity profiles in all three retrieval schemes. Meanwhile, DNN shows higher stability than SNN when applied to the retrieval of temperature and humidity profiles. The comparative study of DNN and SNN applied in different atmospheric parameter retrieval schemes shows that DNN has a more superior performance.

IMK/IAA MIPAS temperature retrieval version 8: nominal measurements

Abstract. A new global set of atmospheric temperature profiles is retrieved from recalibrated radiance spectra recorded with the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS). Changes with respect to previous data versions include a new radiometric calibration considering the time dependency of the detector nonlinearity and a more robust frequency calibration scheme. Temperature is retrieved using a smoothing constraint, while tangent altitude pointing information is constrained using optimal estimation. ECMWF ERA-Interim is used as a priori temperature below 43 km. Above, a priori data are based on data from the Whole Atmosphere Community Climate Model Version 4 (WACCM4). Bias-corrected fields from specified dynamics runs, sampled at the MIPAS times and locations, are used, blended with ERA-Interim between 43 and 53 km. Horizontal variability of temperature is considered by scaling an a priori 3D temperature field in the orbit plane in a way that the horizontal structure is provided by the a priori while the vertical structure comes from the measurements. Additional microwindows with better sensitivity at higher altitudes are used. The background continuum is jointly fitted with the target parameters up to 58 km altitude. The radiance offset correction is strongly regularized towards an empirically determined vertical offset profile. In order to avoid the propagation of uncertainties of O3 and H2O a priori assumptions, the abundances of these species are retrieved jointly with temperature. The retrieval is based on HITRAN 2016 spectroscopic data, with a few amendments. Temperature-adjusted climatologies of vibrational populations of CO2 states emitting in the 15 µm region are used in the radiative transfer modeling in order to account for non-local thermodynamic equilibrium. Numerical integration in the radiative transfer model is now performed at higher accuracy. The random component of the temperature uncertainty typically varies between 0.4 and 1 K, with occasional excursions up to 1.3 K above 60 km altitude. The leading sources of the random component of the temperature error are measurement noise, gain calibration uncertainty, spectral shift, and uncertain CO2 mixing ratios. The systematic error is caused by uncertainties in spectroscopic data and line shape uncertainties. It ranges from 0.2 K at 20 km altitude for northern midlatitude summer conditions to 2.3 K at 12 km for tropical conditions. The estimated total uncertainty amounts to values between 0.6 K at 20 km for midlatitude summer conditions to 2.5 K at 12–15 km for tropical conditions. The vertical resolution varies around 3 km for altitudes below 50 km. The long-term drift encountered in the previous temperature product has been largely reduced. The consistency between high spectral resolution results from 2002 to 2004 and the reduced spectral resolution results from 2005 to 2012 has been largely improved. As expected, most pronounced temperature differences between version 8 and previous data versions are found in elevated stratopause situations. The fact that the phase of temperature waves seen by MIPAS is not locked to the wave phase found in ECMWF analyses demonstrates that our retrieval provides independent information and does not merely reproduce the prior information.

A Study on the Retrieval of Temperature and Humidity Profiles Based on FY-3D/HIRAS Infrared Hyperspectral Data

Satellite infrared hyperspectral instruments can obtain a wealth of atmospheric spectrum information. In order to obtain high-precision atmospheric temperature and humidity profiles, we used the traditional One-Dimensional Variational (1D-Var) retrieval algorithm, combined with the information capacity-weight function coverage method to select the spectrum channel. In addition, an Artificial Neural Network (ANN) algorithm was introduced to correct the satellite observation data error and compare it with the conventional error correction method. Finally, to perform the temperature and humidity profile retrieval calculation, we used the FY-3D satellite HIRAS (Hyperspectral Infrared Atmospheric Sounder) infrared hyperspectral data and combined the RTTOV (Radiative Transfer for TOVS) radiative transfer model to build an atmospheric temperature and humidity profile retrieval system. We used data on the European region from July to August 2020 to carry out the training and testing of the retrieval system, respectively, and used the balloon-retrieved sounding data of temperature and humidity published by the University of Wyoming as standard truth values to evaluate the retrieval accuracy. Our preliminary research results show that, compared with the retrieval results of conventional deviation correction, the introduction of ANN algorithm error correction can improve the retrieval accuracy of the retrieval system effectively and the RMSE (Root-Mean-Square Error) of the temperature and humidity has a maximum accuracy of improvement of about 0.5 K (The K represents the thermodynamic temperature unit) and 5%, respectively. The temperature and humidity results obtained by the retrieval system were compared with Global Forecast System (GFS) forecast data. The retrieved temperature RMSE was less than 1.5 K on average, which was better than that for the GFS; the humidity RMSE was less than 15% as a whole, and better than the forecast profile between 100 hpa (1 hpa is 100 pa, the pa represents the air pressure unit) and 600 hpa. Compared with AIRS (Atmospheric Infrared Sounder) products, the result of the retrieval system also had a higher accuracy. The main improvement of the temperature was at 200 hpa and 800 hpa, with maximum accuracy improvements of 2 K and 1.5 K, respectively. The RMSE of the humidity retrieved by the system was also better than the AIRS humidity products at most pressure levels, and the error of maximum difference could reach 15%. After combining the two algorithms, the FY-3D/HIRAS infrared hyperspectral retrieval system could obtain higher-precision temperature and humidity profiles, and relevant results could provide a reference for improving the accuracy of business products.

Impact of dust in PMIP-CMIP6 mid-Holocene simulations with the IPSL model

Abstract. We investigate the climate impact of reduced dust during the mid-Holocene using simulations with the IPSL model. We consider simulations where dust is either prescribed from an IPSL PI simulation or from CESM simulations (Albani et al., 2015). In addition, we also consider an extreme mid-Holocene case where dust is suppressed. We focus on the estimation of the dust radiative effects and the relative responses of the African and Indian monsoon, showing how local dust forcing or orography affect atmospheric temperature profiles, humidity and precipitation. The simulated mid-Holocene climate is statistically different in many regions compared to previous mid-Holocene simulations with the IPSL models. However, it translates to only minor improvements compared to palaeoclimate reconstructions, and the effect of dust has little impact on mid-Holocene model skill over large regions. Our analyses confirm the peculiar role of dust radiative effect over bright surfaces such as African deserts compared to other regions, brought about by the change of sign of the dust radiative effect at the top of atmosphere for high surface albedo. We also highlight a strong dependence of results on the dust pattern. In particular, the relative dust forcing between West Africa and the Middle East impacts the relative climate response between India and Africa and between Africa, the western tropical Atlantic and the Atlantic meridional circulation. It also affects the feedback on the Atlantic Ocean thermohaline circulation. Dust patterns should thus be better constrained to fully understand the changes in the dust cycle and forcing during the mid-Holocene, which also informs on the potential changes in key dust feedbacks in the future.

Improving the Retrieval of Cloudy Atmospheric Profiles from Brightness Temperatures Observed with a Ground-Based Microwave Radiometer

Atmospheric temperature and humidity retrievals from ground-based microwave remote sensing are useful in a variety of meteorological and environmental applications. Though the influence of clouds is usually considered in current retrieval algorithms, the resulting temperature and humidity estimates are still biased high in overcast conditions compared to radiosonde observations. Therefore, there is a need to improve the quality of retrievals in cloudy conditions. This paper presents an approach to make brightness temperature (TB) correction for cloud influence before the data can be used in the inversion of vertical profiles of atmospheric temperature and humidity. A three-channel method is proposed to make cloud parameter estimation, i.e., of the total 22 channels of the ground-based radiometer, three are adopted to set up a relationship between cloud parameters and brightness temperatures, so that the observations from the three channels can be used to estimate cloud thickness and water content and complete the cloud correction for the rest of the channels used in the retrieval. Based on two years of data from the atmosphere in Beijing, a comparison of the retrievals with radiosonde observations (RAOB) shows: (1) the temperature retrievals from this study have a higher correlation with RAOB and are notably better than in the vendor-provided LV2. The bias of the temperature retrievals from this study is close to zero at all heights, and the RMSE is greatly reduced from >5 °C to <2 °C in the layer, from about 1.5 km up to 5 km. The temperature retrievals from this study have higher correlation with RAOB data compared to the vendor-provided LV2, especially at and above a 2 km height. (2) The bias of the water vapor density profile from this study is near to zero, while the LV2 has a positive bias as large as 4 g/m3. The RMSE of the water vapor density profile from this study is <2 g/m3, while the RMSE for LV2 is as large as 10 g/m3. That is, both the bias and RMSE from this study are evidently less than the LV2, with a greater improvement in the lower troposphere below 5 km. Correlation with RAOB is improved even more for the water vapor density. The correlation of the retrievals from this study increases to one within the boundary layer, but the correlation of LV2 with RAOB is only 0.8 at 0.5 km height, 0.7 at 1 km, and even less than 0.5 at 2 km. (3) A parameter named the Cloud Impact index, determined by cloud water concentration and cloud thickness, together with the cloud base height, has been defined to show that both BIAS and RMSE of “high-CI subsample” are larger than those of the “low-CI subsample”, indicating that high-CI cloud has a higher impact on the retrievals and the correction for cloud influence is more necessary.

Evaluation and Improvement of the Quality of Ground-Based Microwave Radiometer Clear-Sky Data

To assess the quality of the retrieved products from ground-based microwave radiometers, the “clear-sky” Level-2 data (LV2) products (profiles of atmospheric temperature and humidity) filtered through a radiometer in Beijing during the 24 months from January 2010 to December 2011 were compared with radiosonde data. Evident differences were revealed. Therefore, this paper investigated an approach to calibrate the observed brightness temperatures by using the model-simulated brightness temperatures as a reference under clear-sky conditions. The simulation was completed with a radiative transfer model and National Centers for Environmental Prediction final analysis (NCEP FNL) data that are independent of the radiometer system. Then, the least-squares method was used to invert the calibrated brightness temperatures to the atmospheric temperature and humidity profiles. A comparison between the retrievals and radiosonde data showed that the calibration of the brightness temperature observations is necessary, and can improve the inversion of temperature and humidity profiles compared with the original LV2 products. Specifically, the consistency with radiosonde was clearly improved: the correlation coefficients are increased, especially, the correlation coefficient for water vapor density increased from 0.2 to 0.9 around the 3 km height; the bias decreased to nearly zero at each height; the RMSE (root of mean squared error) for temperature profile was decreased by more than 1 degree at most heights; the RMSE for water vapor density was decreased from greater than 4 g/m3 to less than 1.5 g/m3 at 1 km height; and the decrease at all other heights were also noticeable. In this paper, the evolution of a temperature inversion process is given as an example, using the high-temporal-resolution brightness temperature after quality control to obtain a temperature and humidity profile every two minutes. Therefore, the characteristics of temperature inversion that cannot be seen by conventional radiosonde data (twice daily) were obtained by radiometer. This greatly compensates for the limited temporal coverage of radiosonde data. The approach presented by this paper is a valuable reference for the reprocessing of the historical observations, which have been accumulated for years by less-calibrated radiometers.

Connecting Land–Atmosphere Interactions to Surface Heterogeneity in CHEESEHEAD19

AbstractThe Chequamegon Heterogeneous Ecosystem Energy-Balance Study Enabled by a High-Density Extensive Array of Detectors 2019 (CHEESEHEAD19) is an ongoing National Science Foundation project based on an intensive field campaign that occurred from June to October 2019. The purpose of the study is to examine how the atmospheric boundary layer (ABL) responds to spatial heterogeneity in surface energy fluxes. One of the main objectives is to test whether lack of energy balance closure measured by eddy covariance (EC) towers is related to mesoscale atmospheric processes. Finally, the project evaluates data-driven methods for scaling surface energy fluxes, with the aim to improve model–data comparison and integration. To address these questions, an extensive suite of ground, tower, profiling, and airborne instrumentation was deployed over a 10 km × 10 km domain of a heterogeneous forest ecosystem in the Chequamegon–Nicolet National Forest in northern Wisconsin, United States, centered on an existing 447-m tower that anchors an AmeriFlux/NOAA supersite (US-PFa/WLEF). The project deployed one of the world’s highest-density networks of above-canopy EC measurements of surface energy fluxes. This tower EC network was coupled with spatial measurements of EC fluxes from aircraft; maps of leaf and canopy properties derived from airborne spectroscopy, ground-based measurements of plant productivity, phenology, and physiology; and atmospheric profiles of wind, water vapor, and temperature using radar, sodar, lidar, microwave radiometers, infrared interferometers, and radiosondes. These observations are being used with large-eddy simulation and scaling experiments to better understand submesoscale processes and improve formulations of subgrid-scale processes in numerical weather and climate models.

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

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

Inversion of Temperature and Humidity Profile of Microwave Radiometer Based on BP Network

In this paper, the inversion method of atmospheric temperature and humidity profiles via ground-based microwave radiometer is studied. Using the three-layer BP neural network inversion algorithm, four BP neural network models (temperature and humidity models with and without cloud information) are established using L-band radiosonde data obtained from the Atmospheric Exploration base of the China Meteorological Administration from July 2018 to June 2019. Microwave radiometer level 1 data and cloud radar data from July to September 2019 are used to evaluate the model. The four models are compared with the measured sounding data, and the inversion accuracy and the influence of cloud information on the inversion are subsequently analyzed. The results show the following: the average errors of temperature and humidity profiles for the model without cloud information are 1.18°C and 11.7%, while the average errors of temperature and humidity profiles for the model with cloud information are 0.71°C and 6.09%. Compared with the profiles that lack cloud information, the RMSE of most altitudes is reduced to some extent after cloud information is added, which is particularly obvious at layers where cloud is present.