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Küresel Navigasyon Uydu Sistemleri (GNSS) jeodezik ölçmeler, araç takip sistemleri, turizm, tarım, askeri, deformasyon ölçmeleri ve arama kurtarma çalışmaları gibi birçok farklı alana hizmet vermektedir. GNSS teknolojileri ayrıca güneş aktiviteleri, iyonosfer ve troposferin incelenmesine de olanak sağlamaktadır. Bu çalışmada, Hassas Nokta Konum Belirleme (PPP) yöntemi ile elde edilen troposferik gecikmelerden yararlanarak Yoğuşabilir Su Buharı (PWV:Precipitable Water Vapor) miktarının belirlenmesi ve elde edilen bu değerlerin günümüzde referans olarak kabul gören radyosondadan elde edilen PWV değerleri ile karşılaştırılması yapılmıştır. Çalışma kapsamında Türkiye’de bulunan Samsun, Erzurum, İstanbul ve İzmir radyosonda istasyonları ve yakın konumlarında bulunan SAM1, ERZR, ISTN ve IZMI CORS-TR istasyonlarının 2016-2020 yıllarına ait verileri kullanılmıştır. GNSS verilerinin değerlendirilmesinde CSRS-PPP servisinden yararlanılmıştır. Elde edilen sonuçlar incelendiğinde iki yöntemle elde edilen PWV değerlerinin uyum içerisinde olduğu ve çoğunlukla benzer trend gösterdiği görülmüştür., Global Navigation Satellite Systems (GNSS) serve many different fields such as geodetic measurements, vehicle tracking systems, tourism, agriculture, military, deformation measurements and search and rescue studies. GNSS technologies also allow the study of solar activities, ionosphere and troposphere. In this study, the amount of Precipitable Water Vapor (PWV) was determined by using the tropospheric delays obtained by the Precise Point Positioning (PPP) method and these values were compared with the PWV values obtained from the radiosonde, which is accepted as a reference today. Within the scope of the study, the data of Samsun, Erzurum, Istanbul and Izmir radiosonde stations in Turkey and SAM1, ERZR, ISTN and IZMI CORS-TR stations in their nearby locations for the years 2016-2020 were used. CSRS-PPP service was used to process GNSS data. When the results obtained were examined, it was seen that the PWV values obtained by the two methods were in harmony and mostly showed a similar trend.

The need for highly accurate atmospheric wind observations is a high priority in the science community, and in particular numerical weather prediction (NWP). To address this requirement, this study leverages Aeolus wind LIDAR Level-2B data provided by the European Space Agency (ESA) to better characterize atmospheric motion vector (AMV) bias and uncertainty, with the eventual goal of potentially improving AMV algorithms. AMV products from geostationary (GEO) and low-Earth polar orbiting (LEO) satellites are compared with reprocessed Aeolus horizontal line-of-sight (HLOS) global winds observed in August and September 2019. Winds from two of the four Aeolus observing modes are utilized for comparison with AMVs: Rayleigh-clear (derived from the molecular scattering signal) and Mie-cloudy (derived from particle scattering). For the most direct comparison, quality controlled (QC’d) Aeolus winds are collocated with quality controlled AMVs in space and time, and the AMVs are projected onto the Aeolus HLOS direction. Mean collocation differences (MCD) and standard deviation (SD) of those differences (SDCD) are determined from comparisons based on a number of conditions, and their relation to known AMV bias and uncertainty estimates is discussed. GOES-16 and LEO AMV characterizations based on Aeolus winds are described in more detail. Overall, QC’d AMVs correspond well with QC’d Aeolus HLOS wind velocities (HLOSV) for both Rayleigh-clear and Mie-cloudy observing modes, despite remaining biases in Aeolus winds after reprocessing. Comparisons with Aeolus HLOSV are consistent with known AMV bias and uncertainty in the tropics, NH extratropics, and in the Arctic, and at mid- to upper-levels in both clear and cloudy scenes. SH comparisons generally exhibit larger than expected SDCD, which could be attributed to height assignment errors in regions of high winds and enhanced vertical wind shear. GOES-16 water vapor clear-sky AMVs perform best relative to Rayleigh-clear winds, with small MCD (-0.6 m s-1 to 0.1 m s-1) and SDCD (5.4–5.6 m s-1) in the NH and tropics that fall within the accepted range of AMV error values relative to radiosonde winds. Compared to Mie-cloudy winds, AMVs exhibit similar MCD and smaller SDCD (~4.4–4.8 m s-1) throughout the troposphere. In polar regions, Mie-cloudy comparisons have smaller SDCD (5.2 m s-1 in the Arctic, 6.7 m s-1 in the Antarctic) relative to Rayleigh-clear comparisons, which are larger by 1–2 m s-1. The level of agreement between AMVs and Aeolus winds varies per combination of conditions including the Aeolus observing mode coupled with AMV derivation method, geographic region, and height of the collocated winds. It is advised that these stratifications be considered in future comparison studies and impact assessments involving 3D winds. Additional bias corrections to the Aeolus dataset are anticipated to further refine the results.

A dual thermistor radiosonde (DTR) comprising two (aluminium-coated and black) sensors with different emissivities was developed to correct the effects of solar radiation on temperature probes based on in situ radiation measurements. Herein, the DTR performance is characterised in terms of the uncertainty via a series of ground-based facilities and an intercomparison radiosounding test. The DTR characterisation procedure using laboratory facilities is as follows: individually calibrate the temperature of the thermistors in a climate chamber from −70 to 30 ∘C to evaluate the uncertainty of raw temperature measurement before radiation correction; test the effect of temperature on the resistance reading using radiosonde boards in the climate chamber from −70 to 20 ∘C to identify a potential source of errors owing to the boards, especially at cold temperatures; individually perform radiation tests on thermistors at room temperature to investigate the degree of heating of aluminium-coated and black sensors (the average ratio = 1 : 2.4) and use the result for obtaining unit-specific radiation correction formulas; and perform parameterisation of the radiation measurement and correction formulas with five representative pairs of sensors in terms of temperature, pressure, ventilation speed, and irradiance using an upper air simulator. These results are combined and applied to the DTR sounding test conducted in July 2021. Thereafter, the effective irradiance is measured using the temperature difference between the aluminium-coated and black sensors of the DTR. The measured irradiance is then used for the radiation correction of the DTR aluminium-coated sensor. The radiation-corrected temperature of the DTR is mostly consistent with that of a commercial radiosonde (Vaisala, RS41) within the expanded uncertainty (∼ 0.35 ∘C) of the DTR at the coverage factor k = 2. Furthermore, the components contributing to the uncertainty of the radiation measurement and correction are analysed. The DTR methodology can improve the accuracy of temperature measurement in the upper air within the framework of the traceability to the International System of Units.

Time and intensity-specific very short-term forecasting or nowcasting is the biggest challenge faced by an aviation meteorologist. Ground-based microwave radiometer (MWR) has been used for nowcasting convective activity and it was established that there is a good comparison between thermodynamic parameters derived from MWR and GPS radiosonde observations, indicating that MWR observations can be used to develop techniques for nowcasting severe convective activity. In this study, efforts have been made to bring out the efficacy of MWR in nowcasting thunderstorms and fog. Firstly, the observations of MWR located at Palam, New Delhi, India have been compared with the nearest radiosonde data to ascertain the variation in respective profiles. Large differences were found in relative humidity (RH), whereas temperatures from MWR were found to be close to radiosonde observed temperature up to 3.5 km. Subsequently, the scatter plots and correlation coefficients of thermodynamic indices/parameters indicated that most of the parameters are either not correlated or have moderate correlation only for 12:00 UTC profiles. The superepoch technique of lagged composite for various thermodynamic indices/parameters to obtain a combined picture of all the thunderstorm and dense fog cases on the time series could not determine any pattern to predict thunderstorm and dense fog with lead time of 2-4 hours. MWR profile for a case of occurrence of thunderstorm was analyzed. No significant variation was observed in most of the indices (as calculated from MWR observed parameters) prior to the occurrence of thunderstorm. RH at freezing level and between 950 and 700 hPa levels were the only parameters, which increased four hours prior to the occurrence.

The extreme variability of the cold point tropopause temperature (TCPT) and height (HCPT) are examined over a tropical station, Gadanki (13.45N, 79.2E) using high-resolution radiosonde data during ...

Visibility observed at different altitudes is favorable to understand the causes of air pollution. We conducted 4-years of observations of visibility at 2.8 and 60 m and particulate matter (PM) concentrations from 2015 to 2018 in Shenyang, a provincial city in Northeast China. The results indicated that visibility increased with the increasing height in winter (especially at night), and decreased with height in summer (especially at the daytime). PM concentration exhibited opposite vertical variation to visibility, reflecting that visibility degrades with the increase of aerosol concentration in the air. The radiosonde meteorological data showed that weak turbulence in the planetary boundary layer (PBL) in winter favored aerosols’ accumulation near the surface. Whereas in summer, unstable atmospheric conditions, upper-level moister environment, and regional transport of air pollutants resulted in the deterioration of upper-level visibility. Inter-annual variation in the two-level visibility indicated that the upper-level visibility improved more significantly than low-level visibility, much likely due to the reduction in emission of elevated point sources in Shenyang. Our study suggested that strengthening the control of surface non-point emissions is a promising control strategy to improve Shenyang air quality.

The physical mechanisms involved in the development and forecast of clouds and precipitation are both quite complex and dependent on the local atmospheric environment, especially when severe weather conditions are imminent. Research aimed at understanding the environmental mechanisms favorable to the different atmospheric scenarios can help operational weather forecasters’ empirical knowledge to issue warnings. In light of the above, this paper sought to provide a qualitative and quantitative contribution by employing radiosonde analyses, radar reflectivity, and numerical simulations to evaluate the formation of convective, stratiform, and nonprecipitating clouds over the metropolitan area of Rio de Janeiro, Brazil. The convective available potential energy (CAPE) and lifted index (LI), showed higher values on convective days (CAPE = 2600 J.Kg-1 and LI = -4 °C), followed by nonprecipitating (CAPE = 1500 J.Kg-1 and LI = -2 °C) and stratiform cloud days (CAPE = 1400 J.Kg-1 and LI = -1.5 °C). Highest precipitable water content (56 mm) was found to be associated with the stratiform days, suggesting the moisture importance to support the stratiform clouds analyzed. The dynamic variables presented the higher (absolute values) wind values in the lower (1000-850 hPa) and middle (850-700 hPa) atmospheric levels on convective days convergence (-16.5 s-1 and -9.6 s-1, respectively). On the other side, in the same atmospheric layers, winds divergence were observed on stratiform (6.4 s-1 and 6.9 s-1) and nonprecipitating (9.7 s-1 and 7.3 s-1) days. Higher wind divergence (8.3 s-1) was verified for the convective days in the upper atmospheric levels (300-200 hPa) in comparison with other analyzed days (3.2 s-1 (stratiform)and 2.8 s-1 (nonprecipitating)). In a general aspect, the results show a coupling of the winds convergence, moisture and energy in the lower atmospheric levels and divergence in the upper atmospheric levels on convective days. Despite the moisture availability observed on stratiform days and the thermodynamic energy on nonprecipitating days, the coupling between these conditions and dynamic triggers was not observed, respectively.

A physical retrieval approach based on the one-dimensional variational (1D-Var) algorithm is applied in this paper to simultaneously retrieve atmospheric temperature and humidity profiles under both clear-sky and partly cloudy conditions from FY-4A GIIRS (geostationary interferometric infrared sounder) observations. Radiosonde observations from upper-air stations in China and level 2 operational products from the Chinese National Satellite Meteorological Center (NSMC) during the periods from December 2019 to January 2020 (winter) and from July 2020 to August 2020 (summer) are used to validate the accuracies of the retrieved temperature and humidity profiles. Comparing the 1D-Var-retrieved profiles to radiosonde data, the accuracy of the temperature retrievals at each vertical level of the troposphere is characterized by a root mean square error (RMSE) within 2 K except for at the bottom level of the atmosphere under clear conditions. The RMSE slightly increases in the higher atmospheric layers, owing to the lack of temperature sounding channels. Under partly cloudy conditions, the temperature at each vertical level can be obtained, while the level-2 operational products obtain values only at altitudes above the cloud top. In addition, the accuracy of the retrieved temperature profiles is greatly improved compared with the accuracies of the operational products. With respect to the humidity retrievals, the mean RMSEs in the troposphere in winter and summer are both within 2 g/kg. Moreover, the retrievals performed better compared with the ERA5 reanalysis data between 800 hPa and 300 hPa both in summer and winter in the RMSE sense.

Radiosonde descent profiles have been available from tens of stations for several years now – mainly from Vaisala RS41 radiosondes. They have been compared with the ascent profiles, with ECMWF short-range forecasts and with co-located radio occultation retrievals. Over this time, our understanding of the data has grown, and the comparison has also shed some light on radiosonde ascent data. The fall rate is very variable and is an important factor, with high fall rates being associated with temperature biases, especially at higher altitudes. Ascent winds are affected by pendulum motion; on average, descent winds are less affected by pendulum motion and are smoother. It is plausible that the true wind variability in the vertical lies between that shown by ascent and descent profiles. This discrepancy indicates the need for reference wind measurements. With current processing, the best results are for radiosondes with parachutes and pressure sensors. Some of the wind, temperature and humidity data are now assimilated in the ECMWF forecast system.

This letter presents a novel method for instantaneous air temperature under cloudy conditions (Ta,cloudy) estimation using satellite-derived cloud top temperature (CTT), cloud top height (CTH), and Global Forecast System (GFS) forecasts. The radiosonde profiles were used to analyze the relationship between Ta,cloudy and CTT, CTH. The results showed that it is feasible to estimate Ta,cloudy using CTT and CTH, especially for low and middle cloud conditions. Linear and neural network (NN)-based Ta,cloudy estimation models were constructed and validated using the Visible Infrared Imaging Radiometer Suite (VIIRS) CTT, CTH, and GFS Tair for summer 2017 and 2018. The NN model performs better than the linear model, and GFS Tair can obviously improve the accuracy of Ta,cloudy estimation. The correlation coefficient (R), root-mean-square error (RMSE), and bias of the NN model with GFS Tair were 0.953, 1.950 °C, and -0.030 °C, respectively. The estimation model performed better under low and warm clouds than high and cold cloud conditions.

Profiles of Temperature (1000 hPa to 0.1 hPa at 40 pressure levels) retrieved by physical approach algorithm in cloud-free areas from Indian National Satellite-3D repeat (INSAT-3DR) satellite has been validated with ground-based in situ measurements from GPS based radiosonde measurements and then the same is compared with Atmospheric Infrared Sounder (onboard on NASA Aqua Satellite) derived profiles of temperature. The accuracy of these retrievals is a challenge and backbone of improvements in forecast accuracy. The statistical analysis is done by using INSAT-3DR and Atmospheric Infrared Sounder (onboard on NASA Aqua Satellite) data (September –December-2020). The retrieval performance and accuracy are examined in terms of biases, correlation coefficients (CC), and root means square error (RMSE) which is carried out over the land, hilly and coastal areas and found slight variations in the lower middle and upper levels of the troposphere. The key findings of this study INSAT −3DR vs AIRS are as follows: The correlation coefficient (CC) is greater than 0.7 above 15° N at all pressure levels except 150 hPa (CC ∼ 0.2 between 22° N to 35° N). The CC values are random (0.2 to 0.7) below 15° N to 7° N at all pressure levels. The warm bias of the order of (∼0.0 K to 2.5 K) from 1000 to 600 hPa over Indian land and ocean region except for some areas/, 25° N to 35° N have cold biases of the order of −2.0 K to −4.0 K. Himalayan region have a warm bias - ranges 0.0 to 4.0 K at all pressure levels. Similarly, the findings of INSAT-3R & GPS radiosonde are as follows: All the coastal stations show the random bias of the order of ∼ 3.0 K to + 3.0 K hPa above 100 hPa. RMSE value observed of ∼ 4.0 K for the stations (land, hilly, coastal) at each pressure level. The correlation coefficient (CC) of land and hilly regions is higher (0.7 to 1.0) as compared to coastal stations (0.5 to 0.6). INSAT-3DR derived temperature profiles from surface to 10.0 hPa have large bias overland (plain and desert) ranges from −3.0 K to + 3.0 K as compared to coastal and hilly regions −2.5 K to + 2.5 K, which may be attributed to the large surface emissivity values over land. In general, a positive bias of the order of (+1 to + 3 °K) from 1000 to 800 hPa, ± (1 to 2 °K) at 800 to 200 hPa & ± (1 to 3 °K) above 200 hPa have been observed with INSAT-3DR and radiosonde data. This study is found very useful in daily operational weather forecasting and analysis of weather systems at different temporal and spatial scales with more confidence.

Vertical distributions of temperature and humidity are two essential factors for understanding of atmospheric structure, extreme weather events, and regional and global climate. The ground-based MicroWave Radiometer (MWR), which acts as a passive sensor and operate continuously under all weather conditions, has an irreplaceable role in measuring the vertical information of the temperature and water content in the atmosphere. In this paper, we proposed a four-layer Back-Propagation Neural Network (BPNN) method to retrieve temperature and Relative Humidity (RH) profiles from the bright temperature measured by the MWR. In contrast to the traditional BPNN, this method has greater advantages in dealing with the problems of over-fitting, gradient disappearance, and gradient explosion in vertical atmospheric retrieval. By adding dropout layers, it can also help to describe the non-linear relationships for RH profiles. Results showed that the performance of the four-layer BPNN method was better than the quadratic regression method (QR, provided by MWR manufacturer) under both cloud and cloud-free conditions. Compared with measurements of radiosonde data, root-mean-square error of temperature and RH, BPNN achieves 1.88 K and 19.30 % under cloud conditions and 2.03 K and 15.10 % under cloud-free conditions, respectively. Whereas, the corresponding values by using QR method were only 3.07 K and 24.28 % under cloud conditions and 4.14 K and 18.96 % under cloud-free conditions, respectively. Temperature and RH profiles retrieval with high precision have increased the efficiency of the MWR observations and provided data foundation for further atmospheric climate research.

Accurately determining the height of the planetary boundary layer (PBL) is important since it can affect the climate, weather, and air quality. Ground-based infrared hyperspectral remote sensing is an effective way to obtain this parameter. Compared with radiosonde measurements, its temporal resolution is much higher. In this study, a method to retrieve the PBL height (PBLH) from the ground-based infrared hyperspectral radiance data is proposed based on machine learning. In this method, the channels that are sensitive to temperature and humidity profiles are selected as the feature vectors, and the PBLHs derived from radiosonde are taken as the true values. The support vector machine (SVM) is applied to train and test the data set, and the parameters are optimized in the process. The data set collected at the Atmospheric Radiation Measurement (ARM) program Southern Great Plains (SGP) from 2012 to 2015 is analyzed. The instruments used in this letter include Atmospheric Emitted Radiance Interferometer (AERI), Vaisala CL31 ceilometer, and radiosonde. It shows that the root mean square error (RMSE) between the PBLHs calculated by the proposed method using AERI data and those from radiosonde data can be within 370 m, and the square correlation coefficient (SCC) is greater than 0.7. Compared with the PBLHs derived from the ceilometer, it can be found that the new method is more stable and less affected by clouds.

The paper presents the Simulator for Investigation of Solar Temperature Error of Radiosondes (SISTER), a setup that was developed to quantify the solar heating of the temperature sensor of radiosondes under laboratory conditions by recreating as closely as possible the atmospheric and illumination conditions that are encountered during a daytime radiosounding ascent. SISTER controls the pressure (3 to 1020 hPa) and ventilation speed of the air inside the wind-tunnel-like setup to simulate the conditions between the surface and 35 km altitude, to determine the dependence of the radiation temperature error on the irradiance and the convective cooling. The radiosonde is mounted inside a quartz tube, while the complete sensor boom is illuminated by an external light source to include the conductive heat transfer between sensor and boom. A special feature of SISTER is that the radiosonde is rotated around its axis to imitate the spinning of the radiosonde in flight. The characterisation of the radiation temperature error is performed for various pressures, ventilation speeds, and illumination angles, yielding a 2D parameterisation of the radiation error for each illumination angle, with an uncertainty smaller than 0.2 K (k=2) for typical ascend speeds. This parameterisation is applied in the Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN) processing for radiosonde data, which relies on the extensive characterisation of the sensor properties to produce a traceable reference data product which is free of manufacturer-dependent effects. The GRUAN radiation correction model combines the laboratory characterisation with model calculations of the actual radiation field during the sounding to estimate the correction profile. In the second part of this paper it is described how this procedure was applied in the development of the GRUAN data product for the Vaisala RS41 radiosonde (version 1, RS41-GDP.1). The magnitude of the averaged correction profile increases gradually from 0.1 K at the surface to approximately 0.8 K at 35 km altitude. Comparisons between sounding data (N=154) that were GRUAN-processed and Vaisala-processed reveal that the daytime differences (GRUAN−Vaisala) are smaller than +0.1 K in the troposphere and increase above the tropopause steadily with altitude to +0.35 K at 35 km. These differences are just within the limits of the combined uncertainties (with coverage factor k=2) of both data products, meaning that the GRUAN processing and the Vaisala processing are in agreement.

High spatiotemporal resolution atmospheric water vapor can be retrieved using the Global Navigation Satellite System (GNSS) tomography technique, in which the remained ill-posed problem of the tomography system resulting from the acquisition geometry is a vital issue to be addressed. Remote sensing (RS) water vapor data, with high-resolution and global coverage, show great potential for retrieval of slant water vapor (SWV) observations to improve the tomographic geometrical distribution. In this article, we develop a GNSS-RS (GNSS combining RS) tomography model to fully exploit the value of observation signals from GNSS and RS measurements. The two key factors of retrieving the RS SWV are performed by calibrating the original precipitable water vapor (PWV) images and adding the tropospheric horizontal gradients. The results reveal that when introducing the RS SWV observations into the tomography model, the acquisition geometry is significantly improved, with the average rate of voxels crossed by rays from 62% to 95% and the mean number of observation signals from 395 to 508 during the tomographic periods. Independent radiosonde data are used to validate the tomographic water vapor fields. The mean root-mean-square error (RMSE) and bias of the water vapor profiles derived from GNSS-RS solutions are decreased by 28% and 45% with respect to the GNSS-only results, respectively. Such improvements highlight that GNSS-RS troposphere tomography has significant potential to improve the reconstruction of the atmospheric water vapor fields.

In this study, we devised a constraint method, called multi-model ensemble pattern (MEP), to estimate the refractive index structure parameter (Cn2) profiles based on observational data and multiple existing models. We verified this approach against radiosonde data from field campaigns in China’s eastern and northern coastal areas. Multi-dimensional statistical evaluations for the Cn2 profiles and integrated astronomical parameters have proved MEP’s relatively reliable performance in estimating optical turbulence in the atmosphere. The correlation coefficients of MEP and measurement overall Cn2 in two areas are up to 0.65 and 0.76. A much higher correlation can be found for a single radiosonde profile. Meanwhile, the difference evaluation of integrated astronomical parameters also shows its relatively robust performance compared to a single model. The prowess of this reliable approach allows us to carry out regional investigation on optical turbulence features with routine meteorological data soon.

The future of Earth-to-space communications relies on the use of higher radio-frequencies and optical frequencies. For such frequencies, tropospheric turbulence starts to have deleterious effects, leading to important phase variations and amplitude scintillation. Those effects can be described thanks to the knowledge of vertical profiles of the refractive index structure parameter Cn2. In this work, two approaches relying on radiosonde measurements are presented to obtain radio-frequency and optical Cn2. Theoretical developments highlighting the contribution of humidity to radio-frequency scintillation are presented. This contribution is also illustrated with high resolution radiosonde data at Trappes (France) and Hilo (HI, USA). Obtained Cn2 profiles are in agreement with the literature and will be validated with measurements at millimeter waves in the future.

Studies on the atmospheric structure over the Antarctic Plateau are important for better understanding the weather and climate systems of polar regions. In the summer of 2017, an observational experiment was conducted at Dome-A, the highest station in Antarctica, with a total of 32 profiles obtained from global positioning system (GPS) radiosondes. Based on observational data, the atmospheric temperature, humidity and wind structures and their variations are investigated, and compared with those from four other stations inside the Antarctic circle. Distinguished thermal and dynamic structures were revealed over Dome-A, characterized by the lowest temperature, the highest tropopause, the largest lapse rate and the most frequent temperature and humidity inversion. During the experiment, a prominent blocking event was identified, with great influence on the atmospheric structure over Dome-A. The blocking high produced a strong anticyclone that brought warm and moist air to the hinterland of the Antarctic Plateau, causing a much warmer, wetter and windier troposphere over the Dome-A station. Meanwhile, a polar air mass was forced out of the Antarctic, formed a cold surge extending as far as Southern New Zealand and affected the weather there. Our results proved that there would be a direct interaction between the atmosphere over the hinterland of the Antarctic Plateau and middle latitudes with the action of a blocking high. Further studies are needed to explore the interaction between the atmospheric systems over the Antarctic and middle latitudes under intense synoptic disturbance, with long-term data and numerical modeling.

The planetary boundary layer (PBL) height (PBLH) is a key physical parameter of the PBL affected by numerous physical processes within the boundary layer. Specifically, the PBLH over land exhibits large spatial and temporal variation across different geographical regions. In this study, the Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) radio occultation (RO) profiles and high-resolution radiosonde profiles from 2007 to 2013 were analyzed to estimate the diurnal cycle of the PBLH over the Southern Great Plains (SGP) in the United States. Large variations in PBLH derived from radiosonde temperature, moisture, and refractivity are observed on seasonal scales. COSMIC RO is capable of observing diurnal and seasonal variations in the terrestrial PBLH over the SGP region. Annual mean diurnal amplitude of approximately 250 m in the terrestrial PBLH was observed, with maxima occurring at around 1500 local solar time (LST) in both the collocated radiosondes and COSMIC RO profiles. Seasonal changes in the PBLH diurnal cycles ranging from approximately 100 to 400 m were also observed. Such PBL diurnal and seasonal changes can be further incorporated into PBL parameterizations to help improve weather and climate model prediction. Significance Statement The atmospheric planetary boundary layer (PBL) and its height (PBLH) control many atmospheric processes that affect our everyday lives. Observations of the PBL are usually limited to radiosondes at limited time intervals. GNSS radio occultation (RO) provide high-vertical-resolution atmospheric observations that are ideal for PBL study. This study demonstrates that the GNSS RO is capable of capturing the diurnal and seasonal variations of the PBLH over the southern Great Plains (SGP) well as compared to the collocated radiosonde observations.

U.S. national research priorities include reducing the disruption caused by sonic booms, which hindered the economic viability of supersonic aircraft in decades past. However, shock waves are unavo...

The determination of zenith hydrostatic delay (ZHD) is a key step to obtain water vapor information using GNSS technique, and it can be used to separate zenith wet delay (ZWD) with high accuracy from GNSS measurements. Estimates of ZHD are generally of higher accuracy when obtained from accurate pressure information and using the Saastamoinen model rather than when obtained from climatology. To improve this model, we analyzed the differences between the integrated ZHD and the Saastamoinen modeled ZHD, explored the relationship between the ZHD differences and meteorological parameters, and proposed a calibration model based on a global 2.5°×2° grid using the 5 years of pressure levels from the ERA5 reanalysis data provided by European Center for Medium-Range Weather Forecasts (ECMWF). The performance of the calibration model was assessed against the Saastamoinen model using different data sources in 2016, i.e., the ERA5 data and globally distributed radiosonde data. The numerical results show that the calibration model outperforms the Saastamoinen model with a global 59%/89% improvement of RMSE and bias in the ECMWF comparison. In comparison with the radiosonde data, the mean bias/RMSE of the calibration model improves by 5.14/3.24, 1.73/1.47 and 3.15/2.84 mm against the Saastamoinen model in the latitude zones of 60°, respectively.

The rapid response of the atmosphere to the reduction in solar radiation during the annular solar eclipse on 26th December 2019 with maximum obscurity of 91.16% over Cochin, located in the southwest peninsular India, has been investigated. A suite of instruments encompassing an advanced VHF wind profiling Radar operating at 205 MHz, GPS radiosonde, and automatic weather station was employed to record the temporal and vertical variations in various meteorological parameters. Compared to the corresponding values averaged for two control days neighboring the eclipse day, the temperature, relative humidity, net radiation, pressure and wind speed at the surface were altered by −4.0 °C, +39.6%, −383.0 Wm−2, +0.62 mb, and −2.6 ms−1, respectively. The corresponding percentage variations are −13.6%, +71.0%, −67.0%, +0.06%, and −99.0% during the maximum phase of the eclipse, and approached close to the control-day values within about 2 h. Multiple temperature inversions as large as 1 °C were observed up to a height of about 7.3 km. The heights of mean planetary boundary layer (PBL) and tropopause were decreased by about 355 m (−50%) and 900 m (−5.5%), respectively during the peak of obscuration. Meanwhile, the vertical profiles of both the horizontal and vertical wind velocities demonstrate substantial reduction. The vertical wind dispersion, which characterizes the fluctuations in vertical air motion, was reduced by 34% in the PBL compared to its corresponding values on the control days. Subsequently, wavelet spectral analysis of the vertical wind yields a quasi-periodic oscillation indicating the presence of internal gravity waves with periodicities of 40–60 min in the lower altitudes up to 3 km during the eclipse day. Besides reporting the characteristic variations in various atmospheric parameters, the present study reveals the height-time variations in the vertical winds, a vital parameter of significance in the studies of convection, turbulence, and atmospheric waves.

Convective available potential energy (CAPE) is of strong interest in climate modeling because of its role in both severe weather and in model construction. Extreme levels of CAPE ($>$ 2000 J/kg) are associated with high-impact weather events, and CAPE is widely used in convective parametrizations to help determine the strength and timing of convection. However, to date no study has systematically evaluated CAPE biases in models in a climatological context, in an assessment large enough to characterize the high tail of the CAPE distribution. This work compares CAPE distributions in over 200,000 summertime proximity soundings from four sources: the observational radiosonde network (IGRA), 0.125 degree reanalysis (ERA-Interim and ERA5), and a 4 km convection-permitting regional WRF simulation driven by ERA-Interim. Both reanalyses and model consistently show too-narrow distributions of CAPE, with the high tail ($>$ 95th percentile) systematically biased low by up to 10% in surface-based CAPE and 20% at the most unstable layer. This "missing tail" corresponds to the most impacts-relevant conditions. CAPE bias in all datasets is driven by bias in surface temperature and humidity: reanalyses and model undersample observed cases of extreme heat and moisture. These results suggest that reducing inaccuracies in land surface and boundary layer models is critical for accurately reproducing CAPE., Submitted to AMS Journal of Climate (15 December 2020)

An effort is made to examine the cloud parameters over few stations in India for one year from October 2016 to September 2017. This study includes the investigation of cloud base height, cloud top height, zero degree isotherm height, number of cloud layers, cloud thickness, number of days cloud detected, cloud liquid water content and cloud attenuation at 6 GHz, 12.5 GHz and 24 GHz. Salonen Uppala (SU) model and Water Vapor Pressure-Nanyang Technological University (WVP-NTU) model are used for cloud detection using the radiosonde data at Bangalore, Hyderabad, Mumbai, Thiruvananthapuram and Chennai. The cloud parameters found using both the models are compared. WVP-NTU model shows enhanced values for most of the cloud parameters over SU model. Cloud base height calculated using these models are compared with METAR data and found that Cloud base height from WVP-NTU model matches well with METAR data. Cloud liquid water content and cloud attenuation values are calculated using the ITU-R model. The cumulative distributions (CDF) of cloud attenuation for one year are compared for the places considered. The seasonal CDFs of cloud attenuation using the WVP model are compared with the rain statistics. In this study, the maximum cloud attenuation of 1.67 dB/km is measured in Hyderabad by WVP-NTU combined ITU-R model at 24 GHz.

This work presents ship-based measurements of fog off St John’s, Newfoundland, on 13 September 2018 during the Coastal Fog field campaign. The measurements included cloud-particle spectra, cloud-base height and aerosol backscatter, radiation, turbulence, visibility, and sea-surface temperature. Radiosonde soundings were made at intervals of less than 2 h. Fog occurred in two episodes during the passage of an eastward-moving synoptic low-pressure system. The boundary-layer structure during the first fog episode consisted of three layers, separated by two saturated temperature inversions, and capped by a subsidence inversion. The lowest layer was fog, and the upper layers were cloud. The second fog episode consisted of one well-mixed fog layer capped by a subsidence inversion. Low wind speeds and stable stratification maintained low surface-layer turbulence during fog. Droplet size distributions had typical bimodal distributions. The visibility correlated with the droplet number concentration and liquid water content. The air temperature was higher than the sea-surface temperature for the first 30 min of the first fog episode but was lower than the sea for the remainder of all fog. The sensible heat flux was upward, from sea to air, for the first 62% of the first fog episode and then reversed to downward, from air to sea, for the remainder of the first fog episode and the second fog episode. The counter-gradient heat fluxes observed (i.e., opposite to what is expected from the instantaneous air–sea temperature difference) appear to be related to turbulence, entrainment, and stratification in the fog layer that overwhelmed the influence of the air–sea temperature difference. While the synoptic-scale dynamics preconditioned the area for fog formation, the final step of fog appearance in this case was nuanced by stratification–turbulence interactions, local advective processes, and microphysical environment.

The diurnal cycle is the most prominent mode of rainfall variability in the tropics, governed mainly by the strong solar heating and land–sea interactions that trigger convection. Over the western Maritime Continent, complex orographic and coastal effects can also play an important role. Weather and climate models often struggle to represent these physical processes, resulting in substantial model biases in simulations over the region. For numerical weather prediction, these biases manifest themselves in the initial conditions, leading to phase and amplitude errors in the diurnal cycle of precipitation. Using a tropical convective-scale data assimilation system, we assimilate 3-hourly radiosonde data from the pilot field campaign of the Years of Maritime Continent, in addition to existing available observations, to diagnose the model biases and assess the relative impacts of the additional wind, temperature, and moisture information on the simulated diurnal cycle of precipitation over the western coast of Sumatra. We show how assimilating such high-frequency in situ observations can improve the simulated diurnal cycle, verified against satellite-derived precipitation, radar-derived precipitation, and rain gauge data. The improvements are due to a better representation of the sea breeze and increased available moisture in the lowest 4 km prior to peak convection. Assimilating wind information alone was sufficient to improve the simulations. We also highlight how during the assimilation, certain multivariate background error constraints and moisture addition in an ad hoc manner can negatively impact the simulations. Other approaches should be explored to better exploit information from such high-frequency observations over this region.

In Taiwan, the frequency of afternoon convection increases in summer (July and August), and the peak hour of afternoon convection occurs at 1500–1600 local solar time (LST). Afternoon convection events are forecasted based on the atmospheric stability index, as computed from the 0800 LST radiosonde data. However, the temporal and spatial resolution and forecast precision are not satisfactory. This study used the observation data of Aqua satellite overpass near Taiwan around 1–3 h before the occurrence of afternoon convection. Its advantages are that it improves the prediction accuracy and increases the data coverage area, which means that more airports can use results of this research, especially those without radiosondes. In order to determine the availability of Atmospheric Infrared Sounder (AIRS) in Taiwan, 2010–2016 AIRS and radiosonde-sounding data were used to determine the accuracy of AIRS. This study also used 2017–2018 AIRS data to establish K index (KI) and total precipitable water (TPW) thresholds for the occurrence of afternoon convection of four airports in Taiwan. Finally, the KI and TPW were calculated using the independent AIRS atmospheric sounding (2019–2020) to forecast the occurrence of afternoon convection at each airport. The average predictive accuracy rate of the four airports is 84%. Case studies at Hualien Airport show the average predictive accuracy rate of this study is 81.8%, which is 9.1% higher than that of the traditional sounding forecast (72.7%) during the same period. Research results show that using AIRS data to predict afternoon convection in this study could not only increase data coverage area but also improve the accuracy of the prediction effectively.

Clouds and precipitation play a critical role in the Earth's water cycle and energy budget. We present ground-level observations of snowfall coinciding with radiosonde launches in Sodankylä, Finland (67.367∘ N, 26.629∘ E) through a period of 8 cold months (October–April) in 2019 and 2020. They comprise 7401 depositing snow particles detected by a snowflake camera and 468 radiosonde profiles. Our results show that precipitating clouds were extending from ground to at least 2.7 km in altitude. Approximately one-quarter of them were mixed phase and the rest were likely fully glaciated. Estimations of the cloud top temperatures indicate that in roughly half of the snowfall events, ice might have been initiated through heterogeneous freezing. For such cases, the predicted ice-nucleating particle concentrations active at cloud top temperatures could explain the analysed ice crystal particle concentrations observed near ground in approximately one- to two-thirds of the cases. For the rest, ice multiplication was likely active. In a warmer climate, the relative proportion of solid to liquid cloud particles will probably decrease, with implications on the radiation balance.

Continuous monitoring of the planetary boundary layer (PBL) height was carried out using simultaneous observations of the micro-pulse lidar (MPL) at Kattankulathur (12.83°N, 80.04°E) and radiosonde and surface meteorological observations at Meenambakkam (13.0°N, 80.18°E) during different seasons in the year 2016 over Chennai, in the Indian east coast region. These observations provide excellent data to evaluate the performance of the five different PBL parameterization schemes from the Weather Research and Forecasting (WRF) model simulation of PBL characteristics over the coastal environment. The simulation experiments were run using Asymmetrical Convective Model version 2 (ACM2), Yonsei University (YSU), Mellor-Yamada-Janjic (MYJ), Mellor-Yamada Nakanishi and Nino level 2.5 (MYNN2), and Bougeault–Lacarrere (BouLac) schemes. The first two schemes are the first-order closure schemes, while the last three schemes are the turbulent kinetic energy (TKE) closure schemes. We have conducted the experiments for dry and wet surface conditions on clear sky days during different seasons. Under the dry surface conditions, the comparison of the simulated PBL height with observation reveals that all the schemes perform well during the daytime. However, during night-time, all the schemes except MYNN2 underestimate the observation. MYNN2 simulates the similar PBL diurnal pattern as detected by MPL. The potential temperature and relative humidity (RH) profiles from radiosonde observations at 00Z and 12Z and their surface observations at every 1-h interval from automatic weather station are also compared with simulated results. In the case of surface parameters, the WRF model underestimates the surface temperature by ~ 2 K and surface relative humidity by ~ 10–20% for the dry surface condition. However, under the wet surface condition, simulated PBL height and surface temperature (RH) overestimate (underestimate) the observation first 8 h after the rainfall.

A low-level-jet (LLJ) event that occurred over a coastal area in complex terrain is analyzed to investigate its effect on the dispersion of potential air pollutants released in the area. The atmospheric model RAMS (Regional Atmospheric Modeling System) is employed with a high vertical resolution close to the surface, adopting a rarely used nesting approach, in order to allow a detailed analysis of the flow and to characterize the specific features of the LLJ. After a comparison with meteorological variables measured by radiosondes, numerical experiments are performed adding a scalar tracer in the simulation. As a first test, the tracer is distributed uniformly throughout the domain to follow the dynamics of the LLJ and its effect on the tracer dispersion. Then, continuous releases from virtual point sources are simulated to address their possible impact in the area under LLJ conditions. This allows the identification of “hotspots” of pollutant accumulation due to very local circulations and convective cells that develop from the combined effect of terrain-induced flow and the interaction of the LLJ flow with complex topography. An original mass analysis is applied on the dispersion results for an advanced exploration of the LLJ impact on the tracer. The RAMS model provides reliable results demonstrating that with which atmospheric numerical models are useful tools with which to study LLJ dynamics and their effect on local circulation and pollutant dispersion.

Using radiosonde observations at five stations in the tropical western Pacific and reanalysis data for the 15 years from 2005 to 2019, we report an extremely negative anomaly in atmospheric water vapor during the super El Niño winter of 2015/16 and compare the anomaly with that in the other three El Niño winters of the period. A strong specific humidity anomaly is concentrated below 8 km of the troposphere with a peak at 2.5–3.5 km, and a column-integrated water vapor mass anomaly over the five radiosonde sites has a large negative correlation coefficient of −0.63 with the oceanic Niño3.4 index but with a lag of about 2–3 months. In general, the tropical circulation anomaly in the El Niño winter is characterized by divergence (convergence) in the lower troposphere over the tropical western (eastern) Pacific; thus, the water vapor decreases over the tropical western Pacific as upward motion is suppressed. The variability of the Hadley circulation is quite small and has little influence on the observed water vapor anomaly. The anomaly of the Walker circulation makes a considerable contribution to the total anomaly in all four El Niño winters, especially in the 2006/07 and 2015/16 eastern Pacific (EP) El Niño events. The monsoon circulation shows a remarkable change from one event to another, and its anomaly is large in the 2009/10 and 2018/19 central Pacific (CP) El Niño winters and small in the two EP El Niño winters. The observed water vapor anomaly is caused mainly by the Walker circulation anomaly in the super EP event of 2015/16 but is caused by the monsoon circulation anomaly in the strong CP event of 2009/10. The roles of the Hadley, Walker, and monsoon circulations in the EP and CP events are confirmed by the composite EP and CP El Niños based on the reanalysis data for 41 years. Owing to the anomalous decrease in upward transport of water vapor during the El Niño winter, lower cloud amounts and more outgoing longwave radiation over the five stations are clearly presented in satellite observation. In addition, a detailed comparison of water vapor in the reanalysis, radiosonde, and satellite data shows a fine confidence level for the datasets; nevertheless, the reanalysis seems to slightly underestimate the water vapor over the five stations in the 2009/10 winter.

The impact of using wind observations from the Aeolus satellite in a limited-area numerical weather prediction (NWP) system is being investigated using the limited-area NWP model Harmonie–Arome over the Nordic region. We assimilate the horizontal line-of-sight (HLOS) winds observed by Aeolus using 3D-Var data assimilation for two different periods, one in September–October 2018 when the satellite was recently launched and a later period in April–May 2020 to investigate the updated data processing of the HLOS winds. We find that the quality of the Aeolus observations has degraded between the first and second experiment period over our domain. However, observations from Aeolus, in particular the Mie winds, have a clear impact on the analysis of the NWP model for both periods, whereas the forecast impact is neutral when compared against radiosondes. Results from evaluation of observation minus background and observation minus analysis departures based on Desroziers diagnostics show that the observation error should be increased for Aeolus data in our experiments, but the impact of doing so is small. We also see that there is potential improvement in using 4D-Var data assimilation, which generates flow-dependent analysis increments, with the Aeolus data.

Weather radar is used by forecasters for identifying storms and estimating its corresponding precipitation. Anomalous propagation of the radar beam may lead to misinterpretation of the weather events and associated errors in precipitation estimates. As the weather radar transmits electromagnetic waves, it is affected by the refractive index of the atmosphere which depends on the temperature, pressure and water vapor content. It is important to understand the refractive index of the atmosphere and how it affects the beam propagation of the radar to interpret the echoes better. Meteorological conditions causing anomalous propagation is well described in literature by Battan (1973), Doviak and Zrnik (2006) and Rinehart (2001). The vertical refractivity gradient (VRG) affects the propagation of radio waves in the atmosphere (Gossard, 1977). These anomalous propagation cause clutter to be displayed in the radar images. The intensity of the clutter was differentiated into various groups by the amount of clutter present in the radar image. Refractivity parameters at various heights and the height of the temperature inversion layer were calculated using radiosonde observational data at the Visakhapatnam (VSK) station. The observed values from the radiosonde data were compared with the intensity groups and it was found that three parameters were influential in determining the intensity of the clutter which is the presence of the temperature inversion layer above the radar, the VRG of the temperature inversion layer above the radar and the VRG from the radar to a height of 1 km from sea level.

Recent reports have highlighted the need for improved observations of the atmosphere boundary layer. In this study, we explore the combination of ground-based active and passive remote sensors deployed for thermodynamic profiling to analyze various boundary-layer observation strategies. Optimal-estimation retrievals of thermodynamic profiles from Atmospheric Emitted Radiance Interferometer (AERI) observed spectral radiance are compared with and without the addition of active sensor observations from a May–June 2017 observation period at the Atmospheric Radiation Measurement Southern Great Plains site. In all, three separate thermodynamic retrievals are considered here: retrievals including AERI data only, retrievals including AERI data and Vaisala water vapour differential-absorption lidar data, and retrievals including AERI data and Raman lidar data. First, the three retrievals are compared to each other and to reference radiosonde data over the full observation period to obtain a bulk understanding of their differences and characterize the impact of clouds on these retrieved profiles. These analyses show that the most significant differences are in the water vapour field, where the active sensors are better able to represent the moisture gradient in the entrainment zone near the boundary-layer top. We also explore how differences in retrievals may affect results of applied analyses including land–atmosphere coupling, convection indices, and severe storm environmental characterization. Overall, adding active sensors to the optimal-estimation retrieval shows some added information, particularly in the moisture field. Given the costs of such platforms, the value of that added information must be weighed for the application at hand.

The goal of this work is to summarize synoptic meteorological conditions during the Coastal Fog (C-FOG) field project that took place onshore and offshore of the Avalon Peninsula, Newfoundland, from 25 August until 8 October 2018. Visibility was measured at three locations at the Ferryland supersite that are about 1 km from each other, and at two additional sites 66 and 76 km to the north. Supporting meteorological measurements included surface winds, air temperature, humidity, pressure, radiation, cloud-base height, and atmospheric thermodynamic profiles from radiosonde soundings. Statistics are presented for surface measurements during fog events including turbulence kinetic energy, net longwave radiation, visibility, and precipitation. Eleven fog events are observed at Ferryland. Each significant fog event is related to a large-scale cyclonic system. The longest fog event is due to interaction of a northern deep low and a tropical cyclone. Fog occurrence is also examined across Atlantic Canada by including Sable Island, Yarmouth, Halifax, and Sydney. It is concluded that at Ferryland, all significant fog events occur under a cyclonic system while at Sable Island all significant fog events occur under both cyclonic and anticyclonic systems. The fog-formation mechanism involves cloud lowering and stratus broadening or only stratus broadening for the cyclonic systems while for the anticyclonic systems it is stratus broadening or radiation. Although widely cited as the main cause of fog in Atlantic Canada, advection fog is not found to be the primary or sole fog type in the events examined.

In this paper, we examine convective overshooting and its effects on the thermal structure of the troposphere and lower stratosphere in the Tibetan Plateau in summer by matching the Tropical Rainfall Measuring Mission (TRMM) with Integrated Global Radiosonde Archive (IGRA), Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC), European Centre for Medium-Range Weather Forecasts 5th Reanalysis (ERA-5), the Japanese Meteorological Association 55-year reanalysis (JRA-55) and the National Aeronautics and Space Administration Modern-Era Retrospective analysis for Research and Applications, Versions2 (MERRA-2). It was found that convective overshooting mainly occurs in the central and eastern part of the Tibetan Plateau, and its frequency varies from 0.01 × 10−4 to 0.91 × 10−4. The convective overshooting warms the low middle tropopause and cools the tropopause nearby significantly, which can also makes air get wetter. The tropopause of the convective overshooting is substantially lower than the mean tropopause. Statistical results calculated from the five datasets are generally consistent; however, each dataset has its own strengths and weaknesses. The high spatiotemporal resolution temperature profiles from ERA-5 along with the high vertical resolution temperature profiles from COSMIC can be combined to accurately study convective overshooting in the Tibetan Plateau.

A cloud particle sensor (CPS) sonde is an observing system attached with a radiosonde sensor to observe the vertical structure of cloud properties. The signals obtained from CPS sondes are related to the phase, size, and number of cloud particles. The system offers economic advantages including human resource and simple operation costs compared with aircraft measurements and land-/satellite-based remote sensing. However, the observed information should be appropriately corrected because of several uncertainties. Here we made field experiments in the Arctic region by launching approximately 40 CPS sondes between 2018 and 2020. Using these data sets, a better practical correction method was proposed to exclude unreliable data, estimate the effective cloud water droplet radius, and determine a correction factor for the total cloud particle count. We apply this method to data obtained in October 2019 over the Arctic Ocean and March 2020 at Ny-Ålesund, Svalbard, Norway, to compare with a particle counter aboard a tethered balloon and liquid water content retrieved by a microwave radiometer. The estimated total particle count and liquid water content from the CPS sondes generally agree with those data. Although further development and validation of CPS sondes based on dedicated laboratory experiments would be required, the practical correction approach proposed here would offer better advantages in retrieving quantitative information on the vertical distribution of cloud microphysics under the condition of a lower number concentration.

Fog causes severe hazards in the fields of aviation, transportation, agriculture and public health over Dhaka, Bangladesh during the winter season every year. The characterization of fog occurrences, its onset, duration and dissipation time over Hazrat Shahjalal International Airport, Dhaka are the topics of interest in the present study. Attempts have therefore been made to investigate the climatological perspectives of fog over Dhaka, Bangladesh by conducting two selected dense fog events occurred during 24-25 December 2019 and 14-15 January 2020 using WRF-ARW model. The model performance is evaluated by analyzing different meteorological parameters namely visibility, relative humidity, temperature, and wind. The model outputs have been compared with METAR data from Dhaka Airport, Sounding data and INSAT 3D satellite images for validation purpose. Considering RMSE, the model underestimates of relative humidity. Model simulations are good for other meteorological parameters. Thermodynamic analysis reveals that calm wind persists at surface level during fog formation, southwesterly dry wind was over Dhaka and inversion layer is found to persist in the lower troposphere over Dhaka during the event dates. It is observed in the satellite images that fog/low-level cloud was present over Dhaka during the fog events. The Dhaka University Journal of Earth and Environmental Sciences, Vol. 9(1), 2020, P 39-47

The satellite-based quantification of cloud radiative forcing remains poorly understood, due largely to the limitation or uncertainties in characterizing cloud-base height (CBH). Here, we use the CBH data from radiosonde measurements over China in combination with the collocated cloud-top height (CTH) and cloud properties from MODIS/Aqua to quantify the impact of CBH on shortwave cloud radiative forcing (SWCRF). The climatological mean SWCRF at the surface (SWCRFSUR), at the top of the atmosphere (SWCRFTOA), and in the atmosphere (SWCRFATM) are estimated to be −97.14, −84.35, and 12.79 W m−2, respectively for the summers spanning 2010 to 2018 over China. To illustrate the role of the cloud base, we assume four scenarios according to vertical profile patterns of cloud optical depth (COD). Using the CTH and cloud properties from MODIS alone results in large uncertainties for the estimation of SWCRFATM, compared with those under scenarios that consider the CBH. Furthermore, the biases of the CERES estimation of SWCRFATM tend to increase in the presence of thick clouds with low CBH. Additionally, the discrepancy of SWCRFATM relative to that calculated without consideration of CBH varies according to the vertical profile of COD. When a uniform COD vertical profile is assumed, the largest SWCRF discrepancies occur during the early morning or late afternoon. By comparison, the two-point COD vertical distribution assumption has the largest uncertainties occurring at noon when the solar irradiation peaks. These findings justify the urgent need to consider the cloud vertical structures when calculating the SWCRF which is otherwise neglected.

Water in all of its forms takes part nearly in all meteorological activities. Therefore, in climate and atmospheric studies, water vapor plays a central role. Precipitable Water Vapor (PWV) is hard to obtain since its distribution in the troposphere is heterogeneous and anisotropic. Radiosonde observations provide data for mapping the PWV for local to regional scale at low spatial and temporal resolution. Global Positioning System (GPS) provides an alternative option for gathering high temporal and spatial resolution PWV data. In this paper, seasonal PWV variations are studied. Data from six Turkish radiosonde stations were used to validate the performance of GPS estimated PWV. The computed root mean square error (RMSE) of PWV differences between GPS and radiosonde stations is about 1.40–1.82 mm. After assessing the performance of GPS estimated PWV, trends and seasonal variations are computed from a decade worth of 90 GPS stations in Turkey (Jan. 2010–Dec. 2019). These trends at GPS stations were validated using the Mann-Kendall trend test. The test suggests that the trends are statistically significant for all the stations. It has been found that the distinct seasonal cycles are in summer with a maximum water vapor and in winter with a minimum water vapor. The higher amplitudes are located in higher latitudes and lower latitudes (9–15 ± 0.1 mm), and at middle latitudes, amplitudes are considerably lower with 5–7 ± 0.1 mm.

A macro-vertical structure is closely related to weather evolution and the energy budget balance of the atmospheric system of the Earth. In this study, radiosonde data were used to identify a cloud vertical structure (CVS) using the adjusted relative humidity threshold method. To evaluate the reliability and stability of this method, the results obtained based on the spatiotemporal matching criteria established in this study were compared with Ka-band millimetre-wave cloud radar (MMCR) observation data. This comparison showed that both devices exhibit high consistency in low-level cloud detection. With the increase in the cloud height, the frequency of the cloud appearance detection by the radiosonde became higher than that by the MMCR. In spring, the results of the CVS detection by the two devices were in good agreement. Specifically, the determination coefficients of the modified degrees of freedom (adjusted R-square) of the cloud base height (CBH) and cloud top height (CTH) detected by the two devices were 0.934 and 0.879, respectively. The horizontal drift of the radiosonde was the smallest in summer, and the adj. R-square values of the CBH and CTH were 0.814 and 0.852, respectively. The CVS observation results by the radiosonde and the MMCR were significantly different in autumn (the adj. R-Square values of the CBH and CTH were 0.715 and 0.629, respectively). In winter, the adj. R-Square values of the CBH and CTH observed by the radiosonde and the MMCR were 0.958 and 0.710, respectively. The statistics and analysis of the results of the distribution characteristics of the CVSs using radiosonde data from 2019 to 2021 from Xi’an showed that the average CTH and CBH were at 7–10 km and 3–5 km, respectively. The frequencies of the cloud absence, rainfall, and two- and three-layer clouds were the highest in the winter (34.36%), autumn (12.99%), and summer, respectively.

Accurate simulation of meteorological variables is a prerequisite for numerous downstream applications such as air quality modeling and weather forecasting. Weather Research and Forecasting (WRF) model is widely utilized to simulate various prognostic meteorological variables across multiple spatial scales. The suitability of the WRF-v3.9 model in simulation of surface meteorological variables and vertical thermodynamic profile is tested with available in situ surface and radiosonde observations collected from the locations representing rural, semi-urban, and urban environments of the central Indian region. Nested domains with 12 and 4 km grid spacing having 28 vertical layers are set up during the fair weather days of January and March 2018. The model sensitivity is tested by varying two non-local (Yonsei University, YSU and Asymmetric Convective Model, ACM2) and one local (Mellor-Yamada Eta, MY-E) closure Planetary Boundary Layer (PBL) schemes. Results indicate that no particular PBL scheme simulates best for all meteorological variables at different land uses. Overall, thermodynamic variables (temperature and relative humidity) are more accurately simulated than the dynamic variables (wind speed and direction). YSU and MY-E schemes have relatively better accuracy in simulating surface temperature in rural and semi-urban locations, while ACM2 performed better in the urban location. MY-E is relatively better in simulating relative humidity and wind speed in rural and semi-urban locations, while it poorly performed in the urban location. The vertical thermodynamic profile is perfectly correlated with radiosonde observations over the urban location during January and with a reasonably good fit during March. The study provides a comprehensive evaluation of boundary-layer meteorological variables simulated by the WRF model in Central India.

Atmospheric profiles are key inputs in correcting the atmospheric effects of thermal infrared (TIR) remote sensing data for estimating land surface temperature (LST). This study is a first insight into the feasibility of using the weather research and forecasting (WRF) model to provide high-resolution vertical profiles for LST retrieval. WRF numerical simulations were performed to downscaling NCEP climate forecast system version 2 (CFSv2) reanalysis profiles, using two nested grids with horizontal resolutions of 12 km (G12) and 3 km (G03). We investigated the use of these profiles in the atmospheric correction of TIR data applying the radiative transfer equation (RTE) inversion single-channel approach. The MODerate resolution atmospheric TRANsmission (MODTRAN) model and Landsat 8 TIRS10 (10.6–11.2 µm) band were taken for the method application. The accuracy evaluation was performed using in situ radiosondes in Southern Brazil. We included in the comparative analysis the atmospheric correction parameter calculator (ACPC; NASA) web tool and profiles directly from the NCEP CFSv2 reanalysis. The atmospheric correction parameters from ACPC, followed by CFSv2, had better agreement with the ones calculated using in situ radiosondes. When applied into the RTE to retrieve LST, the best results (RMSE) were, in descending order: CSFv2 (0.55 K), ACPC (0.56 K), WRF G12 (0.79 K), and WRF G03 (0.82 K). The findings suggest that increasing the horizontal resolution of reanalysis profiles does not particularly improve the accuracy of RTE-based LST retrieval. However, the WRF results are yet satisfactory and promising, encouraging further assessments. We endorse the use of the well-known ACPC and also recommend the NCEP CFSv2 reanalysis profiles for TIR remote sensing atmospheric correction and LST single-channel retrieval.

[EN] Abstract. The ability of two downscaling experiments to correctly simulate thermodynamic conditions over the Iberian Peninsula (IP) is compared in this paper. To do so, three parameters used to evaluate the unstable conditions in the atmosphere are evaluated: the total totals index (TT), convective available potential energy (CAPE), and convective inhibition (CIN). The Weather and Research Forecasting (WRF) model is used for the simulations. The N experiment is driven by ERA-Interim’s initial and boundary conditions. The D experiment has the same configuration as N, but the 3DVAR data assimilation step is additionally run at 00:00, 06:00, 12:00, and 18:00 UTC. Eight radiosondes are available over the IP, and the vertical temperature and moisture profiles from the radiosondes provided by the University of Wyoming and the Integrated Global Radiosonde Archive (IGRA) were used to calculate three parameters commonly used to represent atmospheric instability by our own methodology using the R package aiRthermo. According to the validation, the correlation, standard deviation (SD), and root mean squared error (RMSE) obtained by the D experiment for all the variables at most of the stations are better than those for N. The different methods produce small discrepancies between the values for TT, but these are larger for CAPE and CIN due to the dependency of these quantities on the initial conditions assumed for the calculation of a lifted air parcel. Similar results arise from the seasonal analysis concerning both WRF experiments: N tends to overestimate or underestimate (depending on the parameter) the variability of the reference values of the parameters, but D is able to capture it in most of the seasons. In general, D is able to produce more reliable results due to the more realistic values of dew point temperature and virtual temperature profiles over the IP. The heterogeneity of the studied variables is highlighted in the mean maps over the IP. According to those for D, the unstable air masses are found along the entire Atlantic coast during winter, but in summer they are located particularly over the Mediterranean coast. The convective inhibition is more extended towards inland at 00:00 UTC in those areas. However, high values are also observed near the southeastern corner of the IP (near Murcia) at 12:00 UTC. Finally, no linear relationship between TT, CAPE, or CIN was found, and consequently, CAPE and CIN should be preferred for the study of the instability of the atmosphere as more atmospheric layers are employed during their calculation than for the TT index. This research has been supported by the Ministry of Economy and Competitiveness (MINECO) (grant no. CGL2016-76561-R) and the University of the Basque Country (UPV/EHU) (grant no. GIU 20/008). Santos J. González-Rojí was further supported by the Oeschger Centre for Climate Change Research (OCCR) and by the Ministry of Economy and Competitiveness (MINECO) (FPI predoctoral research grant no. BES-2014-069977) during his PhD.

High-altitude electric fields are a critical parameter of electricity in the atmosphere. Due to such drawbacks as their bulk and high cost, traditional electric field mills are not available for daily atmospheric sounding purposes. This paper introduces a highly sensitive and robust electric field sounding sensor based on an existent micro-electro-mechanical systems chip and a new protective package that augments sensor sensitivity. To gain high sensitivity, an innovative external metal electrode connects to the electric field microchip’s package cap, hangs down out of the guardian foam, and effectively enhances the local electric field near the microchip due to the distortion effect. This distortion regularity was studied during a series of finite element simulations, calibrations, and comparison experiments. With a 1.5-meter-long external electrode, the sensitivity of the sensor reached 465 mV/kV/m with a resolution that was better than 10 V/m in an actual long-term ground comparison test. Furthermore, the microsensor chip and its circuit are protected inside a foam while the metal electrode is exposed outside, therefore gaining a robust stability during harsh environments such as low temperature, rainfall, etc. To examine the performances, we built an electric field sounding system, including the electric field sensor, a radiosonde, and a receiving antenna, and measured high-altitude electric fields in various weather conditions. Intended for atmospheric electricity research, our innovative electric field sounding sensor would be especially propitious if integrated with a radiosonde and applicable to daily sounding services.

Upper-air data form the backbone of weather analysis and reanalysis products, particularly in the pre-satellite era. However, they are particularly prone to errors and uncertainties, especially data from the early days of aerology. Information that allows to better characterize the errors of radiosonde data is important. This paper reports on an attempt to collect data from historical upper-air intercomparisons and from historical error assessments reaching back to the 1930s. The digitised numerical data will be made available through Copernicus Climate Change Services; here we publish the full information that includes images, literature, and other metadata that may be relevant and can be used to inform homogenization approaches or reanalysis production. The data collection described in this paper is available on PANGAEA https://www.pangaea.de/tok/9e5b464ba828e931103d3794d9ae1d4576f7a03d (Imfeld et al, in review).

Precipitable water vapor (PWV) can be assimilated into a numerical weather model (NWM) to improve the prediction accuracy of numerical weather prediction. In this study, taking GNSS data for the Beijing Fangshan station (BJFS) as an example, based on the method of Pearson correlation coefficient combined with quantitative analysis, GNSS datasets are used to study the relationships between GNSS-derived PWV (GNSS PWV_Met) and its influencing factors, including the internal influencing factors zenith troposphere delay (ZTD), zenith hydrostatic delay (ZHD), zenith wet delay (ZWD), and surface temperature (Ts), and the external influencing factor haze (mainly PM2.5). Firstly, based on the strong correlation between PWV_Met and ZTD hourly sequences from the International GNSS Service Network’s BJFS station for DOYS 182–212, 2015, the results of experiment prove that the reliability of GNSS ZTD is used to forecast PWV_Met in short-term forecasting. Secondly, based on hourly data of BJFS in 2016, the correlation between PWV_Met and ZTD, ZWD, ZHD, pressure (P) and Ts is analyzed, and then, with the rate of ZTD variation as the main factor, ZTD variation as auxiliary factor, the prediction success rate is 88.24% from hourly data of precipitation event for DOYs 183–213 in Beijing. The experiment indicates that ZTD can help forecast short-term precipitation. Thirdly, based on data from three hazy periods with relatively stable weather conditions, no heavy rainfall, and relatively continuous data in the past three years, the correlation between GNSS PWV_Met/ZTD and PM2.5 hourly series is analyzed. The results of the experiments suggests that GNSS ZTD should be considered to assist in haze monitoring. So in the absence of radiosonde stations and meteorological elements, ZTDs on retrieval of GNSS stations have more application value in short-term forecast.

We evaluate temperature and humidity profiles retrieved from an INSAT-3DR sounder against radiosonde measurements for one year during 2017–2018. This evaluation is carried out in terms of slope, intercept, bias, Root Mean Square Error (RMSE), and correlation coefficient in temperature and relative humidity profiles. This comparison provided first-hand information about the performance of INSAT-3DR sounder retrievals. This validation exercise is unique in terms of comparing with the high resolution (1 Hz) radiosonde measurements performed in the afternoon time when there is maximum convection driven by solar radiation. 276 pairs of co-located temperatures have been compared. INSAT-3DR temperature retrievals are strongly linearly correlated to radiosonde measurements, with the slope as 1.001 and correlation coefficient of 0.99, while the mean temperature bias between INSAT-3DR and radiosonde is -0.23°C with RMSE of 1.9°C. Comparison of Relative humidity retrieved from INSAT-3DR to the radiosonde data results in RMSE of 9.8% with a slope of 0.99 with a correlation coefficient of 0.77. The collocation of data points for validation is done matching in space [Latitude, Longitude, Pressure level (height)] and time (±30 minutes). The effect of relaxing the space and time window on the validation statistics is also studied for the first time.