Ground-based Microwave Radiometer Research Articles

TP-PROFILE: Monitoring the Thermodynamic Structure of the Troposphere over the Third Pole

Ground-based microwave radiometers (MWRs) operating in the K- and V-bands (20–60 GHz) can help us obtain temperature and humidity profiles in the troposphere. Aside from some soundings from local meteorological observatories, the tropospheric atmosphere over the Tibetan Plateau (TP) has never been continuously observed. As part of the Chinese Second Tibetan Plateau Scientific Expedition and Research Program (STEP), the Tibetan Plateau Atmospheric Profile (TP-PROFILE) project aims to construct a comprehensive MWR troposphere observation network to study the synoptic processes and environmental changes on the TP. This initiative has collected three years of data from the MWR network. This paper introduces the data information, the data quality, and data downloading. Some applications of the data obtained from these MWRs were also demonstrated. Our comparisons of MWR against the nearest radiosonde observation demonstrate that the TP-PROFILE MWR system is adequate for monitoring the thermal and moisture variability of the troposphere over the TP. The continuous temperature and moisture profiles derived from the MWR data provide a unique perspective on the evolution of the thermodynamic structure associated with the heating of the TP. The TP-PROFILE project reveals that the low-temporal resolution instruments are prone to large uncertainties in their vapor estimation in the mountain valleys on the TP.

Impacts of Fengyun-4A and Ground-Based Observation Data Assimilation on the Forecast of Kaifeng’s Heavy Rainfall (2022) and Mechanism Analysis of the Event

The advancement of Numerical Weather Prediction (NWP) is pivotal for enhancing high-impact weather forecasting and warning systems. However, due to the high spatial and temporal inhomogeneity, the moisture field is difficult to describe by initial conditions in NWP models, which is the essential thermodynamic variable in the simulation of various physical processes. Data Assimilation techniques are central to addressing these challenges, integrating observational data with background fields to refine initial conditions and improve forecasting accuracy. This study evaluates the effectiveness of integrating observations from the Fengyun-4A (FY-4A) Advanced Geosynchronous Radiation Imager (AGRI) and ground-based microwave radiometer (MWR) in forecasts and mechanism analysis of a heavy rainfall event in the Kaifeng region of central China. Our findings reveal that jointly assimilating AGRI radiance and MWR data significantly enhances the model’s humidity profile accuracy across all atmospheric layers, resulting in improved heavy rainfall predictions. Analysis of the moisture sources indicates that the storm’s water vapor predominantly originates from westward air movement ahead of a high-altitude trough, with sustained channeling towards the rainfall zone, ensuring a continuous supply of moisture. The storm’s development is further facilitated by a series of atmospheric processes, including the interplay of high and low-level vorticity and divergence, vertical updrafts, the formation of a low-level jet, and the generation of unstable atmospheric energy. Additionally, this study examines the influence of Tai-hang Mountain’s terrain on precipitation patterns in the Kaifeng area. Our experiments, comparing a control setup (CTL) with varied terrain heights, demonstrate that reducing terrain height by 50–60% significantly decreases precipitation coverage and intensity. In contrast, increasing terrain height enhances precipitation, although this effect plateaus when the elevation increase exceeds 100%, closely mirroring the precipitation changes observed with a 75% terrain height increment.

Accuracy assessment of ground-based microwave radiometer in the Mount Qomolangma region

Mount Qomolangma, known as the “Roof of the World,” holds significant importance in exploring the vertical characteristics of the atmosphere, providing insights into high-altitude atmospheric features, and improving numerical models for forecasting downstream regions. Ground-based microwave radiometers (GWR) enable continuous profiling of atmospheric conditions in the troposphere, complementing radiosonde and satellite observations. This study explores the performance of the MWP967KV GWR installed at Qomolangma Base Camp by comparison with concurrent radiosonde measurements. The assessment covers GWR's atmospheric radiative brightness temperatures (Tb) detection capability, accuracy of atmospheric parameter retrievals, and uncertainties in atmospheric parameters under different weather conditions. Using MonoRTM to calculate simulated Tb from radiosonde, the results indicate a high precision of GWR's Tb observations, showing a strong correlation (R ≈ 0.99) and a deviation of merely 2.4 K compared to simulated Tb. Moreover, GWR exhibits high reliability in temperature (R ≈ 0.98) and water vapor density (ρv) (R ≈ 0.91) observations, while the accuracy of relative humidity (RH) measurements requires improvement (R ≈ 0.55). The GWR observations are affected by clouds, precipitation, and surrounding Qomolangma mountain ridges, leading to significant uncertainties in atmospheric parameter retrievals between 2000 m to 6000 m above ground level. Additionally, GWR's sensitivity to temperature variations is limited, resulting in missed detections of temperature inversion. Despite these limitations, this study offers crucial insights into meteorological observations at high altitudes using GWR. These findings hold substantial implications for meteorological research and mountaineering activities in high-altitude regions.

Satellite-based atmospheric characterization for sites of interest in millimeter and sub-millimeter astronomy

Context. Water vapor is the main source of atmospheric opacity for millimeter and sub-millimeter astronomy. Hence, several studies seek to effectively characterize it for site-testing purposes. In this task, reanalysis databases are quickly becoming a popular alternative to on-site measurements due to easy accessibility and the versatility of the data they provide. Aims. In the framework of validating the use of reanalysis data as a site-testing oriented tool, we perform a statistical comparison of atmospheric water vapor values obtainable from the MERRA-2 database with ground-based microwave radiometer measurements taken at two astronomical sites in Chile: Llano de Chajnantor, Atacama, and Cerro Paranal, Antofagasta. Methods. The MERRA-2 data were interpolated both vertically (across pressure levels) and geographically (latitude-longitude). For each site, different plots were generated: a direct temporal variation plot (to visually compare the data variation over time between both sources); a PWV versus PWV plot, fitting a linear fit through robust linear regression and calculating both the Pearson (r) and Spearman (ρ) correlation coefficients in order to look for correlations between both data sources; a histogram showing the distribution of the differences between the MERRA-2 data and the water vapor measurements (defined as APWV = PWVMERRA-2 − PWVsite), along with its standard deviation (σ), mean (µ), and median values, with the aim of better appreciating the similarities of the data sources over time; and a CDF plot to compare both data distributions disregarding time stamps. Finally, millimeter and sub-millimeter transmittance curves were created through the am atmospheric modeling software, which uses ozone and temperature data along with the verified water vapor data for the two studied sites as well as three other sites of interest for the next-generation Event Horizon Telescope: Las Campanas Observatory near La Serena, Chile; Valle Nevado, located near Santiago, Chile; and the General Bernardo O’Higgins base, located in Antarctica. Results. The interpolated MERRA-2 PWV values are highly correlated with the ground-based PWV values, with a Pearson coefficient greater than 0.9 and a Spearman coefficient higher than 0.85. However, their dependence is not linear, as PWVAPEX = m * PWV, with m being higher than 0.9 in both cases. The difference histograms show an almost zero-centered distribution for Llano de Chajnantor, with a µ value of −0.021 and a median value of −0.007. On the other hand, in Cerro Paranal, the difference histogram is slightly offset toward positive values, with µ value of 0.171 and a median value of 0.256. This offset is most likely due to the strong winds present in the site’s location, close to the Pacific Ocean. The transmittance curves show different performances depending on the site studied, with Cerro Chajnantor being the highest overall transmittance and Antarctica the lowest. Additionally, the transmittance profiles estimated for Cerro Chajnantor and Cerro Paranal were scaled using the PWV measurements, providing differences of less than 12% to the model data profiles. Results obtained at the Valle Nevado site suggest promising atmospheric conditions for stronomic observations in the millimeter and sub-millimeter range. Conclusions. The results we obtained show that the atmospheric water vapor estimation using MERRA-2 data can be used for site testing of new sites by evaluating the millimeter–sub-millimeter transmittance profile through vertical pressure correction and averaging the closest grid points to the site. This new method opens the door for future site-testing studies using MERRA-2 and potentially other reanalysis databases (e.g., ERA5) as reliable sources of information.

On the Applicability of Ground-Based Microwave Radiometers for Urban Boundary Layer Research.

Significant knowledge gaps exist in our understanding of urban boundary layer processes, particularly the hygrothermal state. The earth system community has successfully used microwave radiometers for several decades. However, the applicability in complex urban environments has never been adequately tested. Here, observations from a microwave radiometer are compared to radiosonde readings in a densely urbanized site in Houston, Texas. The site was influenced by both an urban heat island and the sea breeze phenomenon. The analysis showed significant disagreement between the virtual potential temperature predicted by the microwave radiometer and the radiosonde for all periods within the boundary layer. However, the values were reasonably comparable above the boundary layer. The microwave radiometer incorrectly predicted an inversion layer instead of a mixed layer during convective periods. The microwave radiometer measurements deviated from the radiosonde measurements throughout the lower troposphere for the relative humidity.

A Review of Remote Sensing of Atmospheric Profiles and Cloud Properties by Ground-Based Microwave Radiometers in Central China

Thermodynamic and liquid water profiles can be retrieved by a ground-based microwave radiometer (MWR) in nearly all weather conditions, which is useful for detecting mesoscale phenomena. This paper reviews the advances in remote sensing of atmospheric profiles and cloud properties by MWR in central China. Comparative studies indicate that MWR retrieval accuracy is different under various skies, especially those that decay under precipitation. The off-zenith method is proven to be capable of reducing the impact of precipitation and snow on MWR retrieval accuracy. Application studies demonstrate that MWR retrievals are helpful for early warning of rainstorms, hailstorms, and thunderstorms. Moreover, MWR retrievals provide a way to study cloud properties. The temporal variations of cloud occurrence frequency (COF) and liquid water path (LWP) are different for low, middle, and high clouds, and the vertical distribution of COF is also different in autumn and other seasons. Note that MWR can infer valid retrievals over the eastern Tibetan Plateau due to the weak precipitation over there. Also, cloud properties over the eastern Tibetan Plateau present differences from those over central China, and this is related to the different characteristics of atmospheric water vapor between these two regions. To bring more benefits for mechanism study and early warning of severe weather and numerical weather prediction, the decayed accuracy of MWR zenith retrievals under precipitation should be resolved. And combining MWR with other instruments is necessary for MWR application in detecting multi-layer clouds and ice clouds.

Case Analysis of Heavy Air Pollution Process in Xi'an Using Multi-source Data

Air pollution continues to be a serious problem in Xi'an. A heavy pollution process and formation mechanism were investigated in Xi'an in January 2019 using multi-source methods (such as material balance and sulfur/nitrogen oxidation rate (SOR/NOR)). The multi-source data included the concentrations of PM2.5, PM10, SO2, NO2, CO, and O3; the chemical components of PM2.5; the meteorological records of ground and vertical observations; the atmospheric reanalysis data. Three phases were obtained including the accumulation phase (P1), maintenance phase (P2), and dispersion phase (P3) during the pollution period. The pollution event was primarily attributed to the superposition of adverse weather conditions and feedback effects. During the periods of P1 and P2, the area of Xi'an was affected by blocking and zonal westerly airflow at 500 hPa (with flat westerly airflow) and uniform-distribution pressure at sea level with a limited pressure gradient and stable weather conditions, and the easterly wind was dominant at 925 hPa; not all of these factors were conducive to the pollutant diffusion. An interaction feedback mechanism between meteorological conditions and heavy pollution could be studied using the ground-based microwave radiometer. The correlations between PM2.5 and inversions of water vapor density, relative humidity, air temperature, and temperature inversion were significant with coefficients of 0.86, 0.62, 0.53, and 0.38, respectively. The feedback mechanism was primarily manifested as follows:with the pollutant accumulation, the radiative cooling effect could lead to or strengthen the occurrence and intensity of temperature inversion, decrease the mixed layer height, and cause moisture accumulation. High humidity could further maintain the pollution by accelerating the secondary formation and promoting the hygroscopic growth of aerosol particles. Therefore, the dominant chemical components to PM2.5were secondary inorganic ions (SO42-+NO3-+NH4+, SNA) and "other" components during the period of P2, with contributions of 43.2% and 23.1%, respectively. In addition, the peak values of PM2.5, SOR, NOR, and the light extinction coefficients all occurred on the same days (January 3 and 6), indicating that the effect of secondary formation was important for both heavy pollution events and visibility. The total contribution of NH4NO3, organic matter (OM), (NH4)2SO4, and EC to the light extinction coefficient was more than 85%. Limited variations in the proportion for components were observed in three phases. During the period of P3, the strong cold air in the mid-lower atmosphere was conducive to the dry and clean air sinking and the pressure gradient at sea level increasing. These were beneficial to the diffusion of air pollutants and water vapor.

Direct Assimilation of Ground-Based Microwave Radiometer Clear-Sky Radiance Data and Its Impact on the Forecast of Heavy Rainfall

Ground-based microwave radiometer (GMWR) data with high spatial and temporal resolution can improve the accuracy of weather forecasts when effectively assimilated into numerical weather prediction. Nowadays, the major method to assimilate these data is via indirect assimilation by assimilating the retrieved profiles, which introduces large retrieval errors and cannot easily be represented by an error covariance matrix. Direct assimilation, on the other hand, can avoid this issue. In this study, the ground-based version of the Radiative Transfer for the TIROS Operational Vertical Sounder (RTTOV-gb) was selected as the observation operator, and a direct assimilation module for GMWR radiance data was established in the Weather Research and Forecasting Model Data Assimilation (WRFDA). Then, this direct assimilation module was applied to assimilate GMWR data. The results were compared to the indirect assimilation experiment and demonstrated that direct assimilation can more effectively improve the model’s initial fields in terms of temperature and humidity than indirect assimilation while avoiding the influence of retrieval errors. In addition, direct assimilation performed better in the precipitation forecast than indirect assimilation, making the main precipitation center closer to the observation. In particular, the improvement in the precipitation forecast with a threshold of 60 mm/6 h was obvious, and the corresponding TS score was significantly enhanced.

Winter Precipitation Type from Microwave Radiometers in New York State Mesonet Profiler Network

Abstract Winter precipitation is a major cause of vehicle accidents, aviation delays, school and business closures, injuries through slips and falls, and adverse health impacts such as cardiac arrests and deaths. However, an improved ability to monitor and predict winter precipitation type (p-type) could significantly reduce and mitigate these adverse impacts. This study presents and evaluates a modified parcel thickness method to derive p-type from a microwave radiometer (MWR) every 10 min. The MWR-retrieved p-types from six selected New York State Mesonet (NYSM) profiler network sites are validated against reference observations from the Meteorological Phenomena Idenfication Near the Ground (mPING) and Automated Surface Observing System (ASOS). Between the two reference observations, the mPING reports are biased toward snow (SN) and sleet (SLT) and away from rain (RA) and freezing rain (FZR) compared to the ASOS. The MWR has the best Pierce skill score (PSS) for RA, followed by SN, FZR, and SLT, and consistently overforecasts FZR and underforecasts SLT compared to both mPING and ASOS. The MWR p-type retrievals are generally found to be in better agreement with ASOS than mPING. Continuous thermodynamic profiles and p-type estimates from across all 17 profiler sites are available at http://www.nysmesonet.org/networks/profiler. Having such thermodynamic information from across the state can be a valuable resource, with a significant advantage over twice daily NWS radiosondes, for monitoring and tracking hazardous winter weather in real time, for accurate forecasting, and for issuing timely warnings and alerts. Significance Statement Accurate prediction and monitoring of winter precipitation type (p-type) is important due to the adverse economic and health impacts posed by winter weather. However, complexities in understanding and modeling the processes that govern p-type and inadequate observational data limit accurate monitoring and prediction. To address these issues, a ground-based microwave radiometer (MWR) that provides thermodynamic profiles up to 10 km every 2 min, as deployed at 17 sites in the New York State Mesonet (NYSM) profiler network, can be a valuable tool. This study evaluates the accuracy of p-type estimates based on the parcel thickness method from the MWR data and its implementation to the NYSM real-time operations.

Ozone and water vapor variability in the polar middle atmosphere observed with ground-based microwave radiometers

Abstract. Leveraging continuous ozone and water vapor measurements with the two ground-based radiometers GROMOS-C and MIAWARA-C at Ny-Ålesund, Svalbard (79∘ N, 12∘ E) that started in September 2015 and combining MERRA-2 and Aura-MLS datasets, we analyze the interannual behavior and differences in ozone and water vapor and compile climatologies of both trace gases describing the annual variation of ozone and water vapor at polar latitudes. A climatological comparison of the measurements from our ground-based radiometers with reanalysis and satellite data was performed. Overall differences between GROMOS-C and Aura-MLS ozone volume mixing ratio (VMR) climatology are mainly within ±7 % throughout the middle and upper stratosphere and exceed 10 % in the lower mesosphere (1–0.1 hPa) in March and October. For the water vapor climatology, the average 5 % agreement is between MIAWARA-C and Aura-MLS water vapor VMR values throughout the stratosphere and mesosphere (100–0.01 hPa). The comparison to MERRA-2 yields an agreement that reveals discrepancies larger than 50 % above 0.2 hPa depending on the implemented radiative transfer schemes and other model physics. Furthermore, we perform a conjugate latitude comparison by defining a virtual station in the Southern Hemisphere at the geographic coordinate (79∘ S, 12∘ E) to investigate interhemispheric differences in the atmospheric compositions. Both trace gases show much more pronounced interannual and seasonal variability in the Northern Hemisphere than in the Southern Hemisphere. We estimate the effective water vapor transport vertical velocities corresponding to upwelling and downwelling periods driven by the residual circulation. In the Northern Hemisphere, the water vapor ascent rate (5 May to 20 June in 2015, 2016, 2017, 2018, and 2021 and 15 April to 31 May in 2019 and 2020) is 3.4 ± 1.9 mm s−1 from MIAWARA-C and 4.6 ± 1.8 mm s−1 from Aura-MLS, and the descent rate (15 September to 31 October in 2015–2021) is 5.0 ± 1.1 mm s−1 from MIAWARA-C and 5.4 ± 1.5 mm s−1 from Aura-MLS at the altitude range of about 50–70 km. The water vapor ascent (15 October to 30 November in 2015–2021) and descent rates (15 March to 30 April in 2015–2021) in the Southern Hemisphere are 5.2 ± 0.8 and 2.6 ± 1.4 mm s−1 from Aura-MLS, respectively. The water vapor transport vertical velocities analysis further reveals a higher variability in the Northern Hemisphere and is suitable to monitor and characterize the evolution of the northern and southern polar dynamics linked to the polar vortex as a function of time and altitude.

In Situ VTOL Drone-Borne Observations of Temperature and Relative Humidity over Dome C, Antarctica

The Antarctic atmosphere is rapidly changing, but there are few observations available in the interior of the continent to quantify this change due to few ground stations and satellite measurements. The Concordia station is located on the East Antarctic Plateau (75° S, 123° E, 3233 m above mean sea level), one of the driest and coldest places on Earth. Several remote sensing instruments are available at the station to probe the atmosphere, together with operational meteorological sensors. In order to observe in situ clouds, temperature, relative humidity and supercooled liquid water (SLW) at a high vertical resolution, a new project based on the use of an unmanned aerial vehicle (drone) vertical take-off and landing from the DeltaQuad Company has been set up at Concordia. A standard Vaisala pressure, temperature and relative humidity sensor was installed aboard the drone coupled to an Anasphere SLW sensor. A total of thirteen flights were conducted from 24 December 2022 to 17 January 2023: nine technology flights and four science flights (on 2, 10, 11 and 13 January 2023). Drone-based temperature and relative humidity profiles were compared to (1) the balloon-borne meteorological observations at 12:00 UTC, (2) the ground-based microwave radiometer HAMSTRAD and (3) the outputs from the numerical weather prediction models ARPEGE and AROME. No SLW clouds were present during the period of observations. Despite technical issues with drone operation due to the harsh environments encountered (altitude, temperature and geomagnetic field), the drone-based observations were consistent with the balloon-borne observations of temperature and relative humidity. The radiometer showed a systematic negative bias in temperature of 2 °C, and the two models were, in the lowermost troposphere, systematically warmer (by 2–4 °C) and moister (by 10–30%) than the drone-based observations. Our study shows the great potential of a drone to probe the Antarctic atmosphere in situ at very high vertical resolution (a few meters).

Machine Learning Model-Based Retrieval of Temperature and Relative Humidity Profiles Measured by Microwave Radiometer

The accuracy of temperature and relative humidity (RH) profiles retrieved by the ground-based microwave radiometer (MWR) is crucial for meteorological research. In this study, the four-year measurements of brightness temperature measured by the microwave radiometer from Huangpu meteorological station in Guangzhou, China, and the radiosonde data from the Qingyuan meteorological station (70 km northwest of Huangpu station) during the years from 2018 to 2021 are compared with the sonde data. To make a detailed comparison on the performance of machine learning models in retrieving the temperature and RH profiles, four machine learning algorithms, namely Deep Learning (DL), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost) and Random Forest (RF), are employed and verified. The results show that the DL model performs the best in temperature retrieval (with the root-mean-square error and the correlation coefficient of 2.36 and 0.98, respectively), while the RH of the four machine learning methods shows different excellence at different altitude levels. The integrated machine learning (ML) RH method is proposed here, in which a certain method with the minimum RMSE is selected from the four methods of DL, GBM, XGBoost and RF for a certain altitude level. Two cases on 29 January 2021 and on 10 February 2021 are used for illustration. The case on 29 January 2021 illustrates that the DL model is suitable for temperature retrieval and the ML model is suitable for RH retrieval in Guangzhou. The case on 10 February 2021 shows that the ML RH method reaches over 85% before precipitation, implying the application of the ML RH method in pre-precipitation warnings.

Improvement of research methodology mesostructures of atmospheric fronts on the basis of ground­based microwave radiometry

The paper considers the some results of experimental studies of the mesoscale structure of atmospheric fronts using a ground-based microwave water vapor radiometer. A methodology for studying the mesoscale structure of atmospheric fronts is described, which includes two stages: the stage of syn- optic (frontological) analysis and the stage of mesoscale analysis based on measurements of atmospheric moisture and cloud liquid water content using a microwave water vapor radiometer. The application of the methodology is demonstrated by the example of the analysis of the results of observation of the case of the passage of warm and cold fronts through the Leningrad region on September 9—14, 2020. Based on the analysis of the results of previous studies, the features of changes in the characteristics of moisture content during the passage of atmospheric fronts are generalized. The growth of the atmospheric water vapor and cloud liquid water content is delayed relative to the appearance of harbingers of a warm cloud front of the upper tier. The growth of moisture content characteristics has a wave-like character, which can be explained by the manifestation of the strip mesostructure of the front. Estimates of the characteristic scales of spatial heterogeneities in the structure of atmospheric fronts for the area under consideration differ from similar characteristics obtained by P. Hobbs for the northeastern coast of North America. In the course of mesoscale analysis, the type of atmospheric front crossing the observation point can be clarified. The results obtained determine the prospects for improving the methods of diagnostics and current forecasting of dangerous weather phenomena during the passage of atmospheric fronts using ground-based microwave radiometers

Ground-Based Microwave Measurements of Mesospheric Ozone Variations over Moscow Region during the Solar Eclipses of 20 March 2015 and 25 October 2022

An increase in the ozone content in the mesosphere over the Moscow region during the solar eclipses of 20 March 2015 and 25 October 2022 was observed by means of a ground-based microwave radiometer operated at frequencies of the ozone spectral line of 142.175 GHz. Changes in ozone mixing ratio (OMR) at altitudes of 90 km and 65 km were estimated and compared with diurnal ozone variations measured on the dates closest to the events. It was found that the observed increase in the OMR at 90 km during the 20 March 2015 eclipse was almost two times greater than during the 25 October 2022 eclipse, although the maximum Sun’s obscurations of these eclipses were close to each other (0.625 and 0.646). Most likely, this difference can be explained by the difference in concentration of atomic hydrogen, which plays an important role in ozone destruction at altitudes of around 90 km and above.

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

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

Effects of joint assimilation of FY-4A AGRI and ground-based microwave radiometer on heavy rainfall prediction

As the latest generation of Chinese Geostationary Weather Satellites, Fengyun-4 carries the Advanced Geosynchronous Radiation Imager (AGRI), which has more spectral bands and higher temporal and spatial resolution than the Visible Infrared Spin-Scan Radiometer (VISSR) onboard geostationary satellite FY-2. Direct assimilation of the FY-4A AGRI datasets has been proved to be an efficient way to improve heavy rainfall simulation. We aim to assess the joint assimilation of AGRI infrared radiance and ground-based MWR (Microwave Radiometer) data on short-duration heavy rainfall prediction. RTTOV (Radiative Transfer for the TIROS Operational Vertical Sounder) is used as the observational operator for FY-4A AGRI data assimilation. The data assimilation interface is built in WRFDA 4.3 to achieve direct assimilation of FY4A AGRI radiance. The forecasting effectiveness of the joint assimilation for a typical heavy rainfall event over northern China is analyzed with four simulation experiments. The main conclusions are: 1) Assimilating MWR data can improve the initial humidity condition in the middle-lower layers, while AGRI radiance assimilation favors the initial humidity correction in the middle-upper layers. The joint assimilation of two datasets can remarkably improve the initial humidity condition in the entire column. 2) Data assimilation effectively improves the 6-h accumulated rainfall simulation. The joint assimilation of AGRI radiance and MWR data is superior to assimilating either of them. The joint assimilation significantly improves the rainfall forecast over the Beijing area, where the seven MWRs are distributed. 3) Data assimilation experiments present similar effects on predicted and initial humidity conditions. The MWR_DA experiment (only assimilate MWR data) markedly improves the humidity forecast in the middle-lower layers, while AGRI_DA (only assimilate AGRI data) is effective in the middle-upper layers. The joint assimilation of AGRI radiance and MWR data could skillfully correct the humidity distribution in the entire layers, allowing for more accurate heavy rainfall prediction. This paper provides a valuable basis for further improving the application of FY-4A AGRI radiance in numerical weather models.

ПЕРСПЕКТИВЫ ПРИМЕНЕНИЯ МЕТОДОВ НАЗЕМНОЙ МИКРОВОЛНОВОЙ РАДИОМЕТРИИ ДЛЯ СИНОПТИЧЕСКОГО АНАЛИЗА АТМОСФЕРНЫХ ФРОНТОВ И ПРОГНОЗА ОПАСНЫХ ЯВЛЕНИЙ ПОГОДЫ

A brief description of the method of ground-based microwave radiometry for determining the characteristics of atmospheric moisture content is given. The results of experimental studies of total precipitable water and cloud liquid water content using ground-based microwave radiometers, in particular, during the development of severe weather events associated with the development of convective clouds, thunderstorm processes are reported. The capabilities of microwave radiometry in improving the diagnosis of synoptic and mesoscale structure of atmospheric fronts are discussed. The prospects for using radiophysical facilities of the Mozhaisky Military Space Academy Geophysical Observatory for experimental studies of the atmosphere, clouds, and precipitation in the Leningrad region, which are aimed at improving methods for forecasting severe weather events associated with the development of convective clouds, thunderstorms, and precipitation, are considered.

Harmonized retrieval of middle atmospheric ozone from two microwave radiometers in Switzerland

Abstract. We present new harmonized ozone time series from two ground-based microwave radiometers in Switzerland: GROMOS and SOMORA. Both instruments have measured hourly ozone profiles in the middle atmosphere (20–75 km) for more than 2 decades. As inconsistencies in long-term trends derived from these two instruments were detected, a harmonization project was initiated in 2019. The goal was to fully harmonize the data processing of GROMOS and SOMORA to better understand and possibly reduce the discrepancies between the two data records. The harmonization has been completed for the data from 2009 until 2022 and has been successful at reducing the differences observed between the two time series. It also explains the remaining differences between the two instruments and flags their respective anomalous measurement periods in order to adapt their consideration for future trend computations. We describe the harmonization and the resulting time series in detail. We also highlight the improvements in the ozone retrievals with respect to the previous data processing. In the stratosphere and lower mesosphere, the seasonal ozone relative differences between the two instruments are now within 10 % and show good correlation (R &gt; 0.7) (except during summertime). We also perform a comparison of these new data series against measurements from the Microwave Limb Sounder (MLS) and Solar Backscatter Ultraviolet Radiometer (SBUV) satellite instruments over Switzerland. Seasonal mean differences with MLS and SBUV are within 10 % in the stratosphere and lower mesosphere up to 60 km and increase rapidly above that point.

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

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

Characteristics Analysis of the Multi-Channel Ground-Based Microwave Radiometer Observations during Various Weather Conditions

Ground-based multi-channel microwave radiometers (MWRs) can continuously detect atmospheric profiles in the tropospheric atmosphere. This makes MWR an ideal tool to supplement radiosonde and satellite observations in monitoring the thermodynamic evolution of the atmosphere and improving numerical weather prediction (NWP) through data assimilation. The analysis of product characteristics of MWR is the basis for applying its data to real-time monitoring and assimilation. In this paper, observations from the latest generation of ground-based multi-channel MWR RPG-HATPRO-G5 installed in Shanghai, China, are compared with the radiosonde observations (RAOB) observed in the same location. The detection performance, characteristics of various channels, and the accuracy of the retrieval profile products of the MWR RPG are comprehensively evaluated during various weather conditions. The results show that the brightness temperatures (BTs) observed by the ground-based MWR RPG during precipitation conditions were high, which affected its detection performance. The bias and the standard deviation (SD) between the BT observed by MWR RPG and the simulated BT during clear and cloudy sky conditions were slight and large, respectively, and the coefficient of determination (R2) was high and low, respectively. However, when the cloud liquid water (CLW) information was added when simulating BT, the bias and the SD of the observed BT and the simulated BT during cloudy days were reduced and the R2 value improved, which indicated that CLW information should be taken into account when simulating BT during cloudy conditions. The temperature profiles of the MWR retrieval had the same accuracy of RMSEs (root-mean-square error) with heights during both clear-sky and cloudy sky conditions, where the RMSEs were below 2 K when the heights were below 4 km. In addition, the MWR RPG has the potential ability to retrieve the temperature inversion in the boundary layer, which has important application value for fog and air pollution monitoring.