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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.

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.

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.

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.

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.

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.

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

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.

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.

A newly upgraded German Weather Service (DWD) ozone and temperature lidar (HOH) located at the Hohenpeißenberg Meteorological Observatory (47.8∘ N, 11.0∘ E) has been evaluated through comparison with the travelling standard lidar operated by NASA's Goddard Space Flight Center (NASA GSFC Stratospheric Ozone (STROZ) lidar), satellite overpasses from the Microwave Limb Sounder (MLS), the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER), the Ozone Mapping and Profiler Suite (OMPS), meteorological radiosondes launched from Munich (65 km northeast), and locally launched ozonesondes. The “blind” evaluation was conducted under the framework of the Network for the Detection of Atmospheric Composition Change (NDACC) using 10 clear nights of measurements in 2018 and 2019. The campaign, referred to as the Hohenpeißenberg Ozone Profiling Study (HOPS), was conducted within the larger context of NDACC validation activities for European lidar stations. There was good agreement between all ozone lidar measurements in the range of 15 to 41 km with relative differences between co-located ozone profiles of less than ±10 %. Differences in the measured ozone number densities between the lidars and the locally launched ozone sondes were also generally less than 5 % below 30 km. The satellite ozone profiles demonstrated some differences with respect to the ground-based lidars which are due to sampling differences and geophysical variation. Both the original and new DWD lidars continue to meet the NDACC standard for lidar ozone profiles by exceeding 3 % accuracy between 16.5 and 43 km. Temperature differences for all instruments were less than ±5 K below 60 km, with larger differences present in the lidar–satellite comparisons above this region. Temperature differences between the DWD lidars met the NDACC accuracy requirements of ±1 K between 17 and 78 km. A unique cross-comparison between the HOPS campaign and a similar, recent campaign at Observatoire de Haute-Provence (Lidar Validation NDACC Experiment; LAVANDE) allowed for an investigation into potential biases in the NASA-STROZ reference lidar. The reference lidar may slightly underestimate ozone number densities above 43 km with respect to the French and German NDACC lidars. Below 20 km, the reference lidar temperatures profiles are 5 to 10 K cooler than the temperatures which are reported by the other instruments.

MAPM (Mapping Air Pollution eMissions) is a project whose goal is to develop a method to infer airborne particulate matter (PM) emissions maps from in situ PM concentration measurements. In support of MAPM, a winter field campaign was conducted in New Zealand in 2019 (June to September) to obtain the measurements required to test and validate the MAPM methodology. Two different types of instruments measuring PM were deployed: ES-642 remote dust monitors (17 instruments) and Outdoor Dust Information Nodes (ODINs; 50 instruments). The measurement campaign was bracketed by two intercomparisons where all instruments were co-located, with a permanently installed tapered element oscillating membrane (TEOM) instrument, to determine any instrument biases. Changes in biases between the pre- and post-campaign intercomparisons were used to determine instrument drift over the campaign period. Once deployed, each ES-642 was co-located with an ODIN. In addition to the PM measurements, meteorological variables (temperature, pressure, wind speed, and wind direction) were measured at three automatic weather station (AWS) sites established as part of the campaign, with additional data being sourced from 27 further AWSs operated by other agencies. Vertical profile measurements were made with 12 radiosondes during two 24 h periods and complimented measurements made with a mini micropulse lidar and ceilometer. Here we present the data collected during the campaign and discuss the correction of the measurements made by various PM instruments. We find that when compared to measurements made with a simple linear correction, a correction based on environmental conditions improves the quality of measurements retrieved from ODINs but results in over-fitting and increases the uncertainties when applied to the more sophisticated ES-642 instruments. We also compare PM2.5 and PM10 measured by ODINs which, in some cases, allows us to identify PM from natural and anthropogenic sources. The PM data collected during the campaign are publicly available from https://doi.org/10.5281/zenodo.4542559 (Dale et al., 2020b), and the data from other instruments are available from https://doi.org/10.5281/zenodo.4536640 (Dale et al., 2020a).

The impact of observations in a data assimilation (DA) may depend on various factors, and one aspect that can affect the impact is the specification of the background error covariance matrix. The present study compares the impact of INSAT-3D atmospheric motion vector (AMV) observations in traditional three-dimensional variational (3DVAR) DA system and hybrid ensemble transform Kalman filter (ETKF)-3DVAR DA system (HYBRID) available in Weather Research and Forecast (WRF) modeling system. The objective of the study is to understand how the impact of INSAT-3D AMV observations differ when assimilated using 3DVAR and HYBRID DA systems. The DA experiments are conducted over a ~4-week period of Indian summer monsoon rainfall of July 2016. Four sets of experiments are performed with and without INSAT-3D AMV in both the DA systems. The domain-wide verification with respect to radiosonde observations reveals that forecasts in HYBRID experiments are more accurate than 3DVAR experiments, in general. Geographical distribution depicts the positive impacts of INSAT-3D AMV observations across the domain in both 3DVAR and HYBRID DA systems. The AMV observations show a larger relative impact in HYBRID than in 3DVAR. The relative improvement in HYBRID with AMV DA compared to 3DVAR is 77% and 71% for wind and tropical temperature. The skill scores for quantitative evaluation of precipitation forecast indicate a modest improvement in rainfall for HYBRID run, and incorporating the AMV observation does not considerably enhance the skill of 24-h and 48-h rainfall forecast.

Atmospheric water vapour plays an important role in phenomena related to the global hydrologic cycle and climate change. However, the rapid temporal–spatial variation in global tropospheric water vapour has not been well investigated due to a lack of long-term, high-temporal-resolution precipitable water vapour (PWV). Accordingly, this study generates an hourly PWV dataset for 272 ground-based International Global Navigation Satellite System (GNSS) Service (IGS) stations over the period of 2005–2016 using the zenith troposphere delay (ZTD) derived from global-scale GNSS observation. The root mean square (RMS) of the hourly ZTD obtained from the IGS tropospheric product is approximately 4 mm. A fifth-generation reanalysis dataset of the European Centre for Medium-range Weather Forecasting (ECMWF ERA5) is used to obtain hourly surface temperature (T) and pressure (P), which are first validated with GNSS synoptic station data and radiosonde data, respectively. Then, T and P are used to calculate the water vapour-weighted atmospheric mean temperature (Tm) and zenith hydrostatic delay (ZHD), respectively. T and P at the GNSS stations are obtained via an interpolation in the horizontal and vertical directions using the grid-based ERA5 reanalysis dataset. Here, Tm is calculated using a neural network model, whereas ZHD is obtained using an empirical Saastamoinen model. The RMS values of T and P at the collocated 693 radiosonde stations are 1.6 K and 3.1 hPa, respectively. Therefore, the theoretical error of PWV caused by the errors in ZTD, T and P is on the order of approximately 2.1 mm. A practical comparison experiment is performed using 97 collocated radiosonde stations and 23 GNSS stations equipped with meteorological sensors. The RMS and bias of the hourly PWV dataset are 2.87/−0.16 and 2.45/0.55 mm, respectively, when compared with radiosonde and GNSS stations equipped with meteorological sensors. Additionally, preliminary analysis of the hourly PWV dataset during the EI Nino event of 2014–2016 further indicates the capability of monitoring the daily changes in atmospheric water vapour. This finding is interesting and significant for further climate research.

The Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign (ATOMIC) took place from 7 January to 11 July 2020 in the tropical North Atlantic between the eastern edge of Barbados and 51∘ W, the longitude of the Northwest Tropical Atlantic Station (NTAS) mooring. Measurements were made to gather information on shallow atmospheric convection, the effects of aerosols and clouds on the ocean surface energy budget, and mesoscale oceanic processes. Multiple platforms were deployed during ATOMIC including the NOAA RV Ronald H. Brown (RHB) (7 January to 13 February) and WP-3D Orion (P-3) aircraft (17 January to 10 February), the University of Colorado's Robust Autonomous Aerial Vehicle-Endurant Nimble (RAAVEN) uncrewed aerial system (UAS) (24 January to 15 February), NOAA- and NASA-sponsored Saildrones (12 January to 11 July), and Surface Velocity Program Salinity (SVPS) surface ocean drifters (23 January to 29 April). The RV Ronald H. Brown conducted in situ and remote sensing measurements of oceanic and atmospheric properties with an emphasis on mesoscale oceanic–atmospheric coupling and aerosol–cloud interactions. In addition, the ship served as a launching pad for Wave Gliders, Surface Wave Instrument Floats with Tracking (SWIFTs), and radiosondes. Details of measurements made from the RV Ronald H. Brown, ship-deployed assets, and other platforms closely coordinated with the ship during ATOMIC are provided here. These platforms include Saildrone 1064 and the RAAVEN UAS as well as the Barbados Cloud Observatory (BCO) and Barbados Atmospheric Chemistry Observatory (BACO). Inter-platform comparisons are presented to assess consistency in the data sets. Data sets from the RV Ronald H. Brown and deployed assets have been quality controlled and are publicly available at NOAA's National Centers for Environmental Information (NCEI) data archive (https://www.ncei.noaa.gov/archive/accession/ATOMIC-2020, last access: 2 April 2021). Point-of-contact information and links to individual data sets with digital object identifiers (DOIs) are provided herein.

The addition of moisture observations via the Water Vapor Sensing System (WVSS) from about 150 aircraft available operationally through the World Meteorological Organization (WMO) Aircraft Meteorological Data Relay (AMDAR) program now provides highly reliable thermodynamic profiles of the troposphere. The nearly 900 profiles available daily provide greater temporal and spatial density than the operational radiosonde network over many parts of the United States. Previous studies comparing WVSS reports with specially collocated radiosondes have documented the quality and consistency of the WVSS observations. These studies, however, have been limited for short periods at a single location. This study expands on the earlier evaluations by using operational U.S. radiosondes from 2015 in a variety of locations, seasons, and climates. Comparison profiles at radiosonde sites were calculated in pressure layers and then interpolated to terrain-following sigma coordinates to account for the differences in elevations of comparison sites and provide a better means of integrating the higher vertical resolution of AMDAR observations taken in the boundary layer. Overall, systematic differences between the WVSS and radiosondes are smallest just above the surface, with the WVSS observations being slightly moister than the radiosondes aloft, with WVSS reports being moister during ascent than descent—possibly the result of small hysteresis effects. Standard deviations averaged 1.3 g kg−1 near the surface over the yearlong period. Differences varied by season and region. Overall, the results indicate that WVSS observations are compatible with radiosonde reports and can be used with high confidence to fill temporal and spatial data gaps.

The advanced Indian meteorological satellite INSAT-3DR was configured in rapid-scan mode during the formation of tropical cyclone ‘Fani’ over the North Indian Ocean. This study is carried out to explore the possibility of further enhancement in atmospheric motion vectors (AMVs) to understand the extreme weather event using rapid-scan observations. A novel experiment has been performed to retrieve rapid-scan AMVs (RS-AMVs) with different tracer sizes at two different temporal scales (10 and 15 min) using the Thermal Infrared (TIR)-1 channel of the INSAT-3DR satellite. The RS-AMVs are able to capture small-scale variations of winds in rapid-scan mode, compared to AMVs retrieved by the operational INSAT-3D satellite available at 30-min intervals. Initial quality assessment of retrieved RS-AMVs is performed using winds from NCEP final analysis, ERA-5 reanalysis, and radiosonde observations. The number of wind vectors are enhanced significantly in RS-AMVs against operational AMVs from INSAT-3DR. An improvement of about 12% in RS-AMVs relative to operational 30-min INSAT-3D-retrieved AMVs has been achieved. Moreover, a sensitivity study has been performed using four-dimensional variational data assimilation experiments to understand the impact of newly retrieved RS-AMVs against operational INSAT-3D-retrieved AMVs for tropical cyclone Fani. Results suggest noteworthy improvements in track prediction when RS-AMVs are used for assimilation as compared to operational AMVs. The model-simulated surface wind structures are also compared with satellite observations for the radius of the maximum winds $$(R_{\max } )$$ and the maximum radial extent of surface winds.

Precipitable water vapor (PWV) plays an important role in understanding the atmosphere and weather. The advancement in Global Navigation Satellite System (GNSS) technology provides the possibility of computing PWV in near real time. In this study, the PWV was computed using the Zenith Tropospheric Delay (ZTD) from Global Positioning System (GPS) data for two International GNSS Service (IGS) stations (IISC & HYDE) for monsoon period (June–September) of 2014 and 2015. The GPS-derived PWV has been validated with reference to radiosonde data and University NAVSTAR Consortium (UNAVCO) PWV products. The coefficient of determination between GPS-derived PWV and radiosonde PWV is 0.73–0.83 for IISC station and 0.76–0.91 for HYDE station. For IISC station, the coefficient of determination between GPS-derived PWV and UNAVCO-derived PWV is 0.80–0.89. Also, in this study, GPS-derived PWV for IISC station has been compared with the rainfall events of Indian National Satellite System (INSAT-3D). From the analysis, we observed a direct relation between PWV and rainfall events from Hydro Estimator Method (HEM), INSAT Multi spectral Rainfall (IMR) products of INSAT-3D. This study highlights the satellite-based observations from GNSS and INSAT satellite for regional weather forecasting in near real time.

To advance the understanding of the interplay among clouds, convection, and circulation, and its role in climate change, the Elucidating the role of clouds–circulation coupling in climate campaign (EUREC4A) and Atlantic Tradewind Ocean–Atmosphere Mesoscale Interaction Campaign (ATOMIC) collected measurements in the western tropical Atlantic during January and February 2020. Upper-air radiosondes were launched regularly (usually 4-hourly) from a network consisting of the Barbados Cloud Observatory (BCO) and four ships within 6–16∘ N, 51–60∘ W. From 8 January to 19 February, a total of 811 radiosondes measured wind, temperature, and relative humidity. In addition to the ascent, the descent was recorded for 82 % of the soundings. The soundings sampled changes in atmospheric pressure, winds, lifting condensation level, boundary layer depth, and vertical distribution of moisture associated with different ocean surface conditions, synoptic variability, and mesoscale convective organization. Raw (Level 0), quality-controlled 1 s (Level 1), and vertically gridded (Level 2) data in NetCDF format (Stephan et al., 2020) are available to the public at AERIS (https://doi.org/10.25326/137). The methods of data collection and post-processing for the radiosonde data set are described here.

In collaboration with 12 other institutions, the Meteorological Observation Center of the China Meteorological Administration undertook a comprehensive marine observation experiment in the South China Sea using the Yilong-10 high-altitude large unmanned aerial vehicle (UAV). The Yilong-10 UAV carried a self-developed dropsonde system and a millimeter-wave cloud radar system. In addition, a solar-powered unmanned surface vessel and two drifting buoys were used. The experiment was further supported by an intelligent, reciprocating horizontal drifting radiosonde system that was deployed from the Sansha Meteorological Observing Station, with the intent of producing a stereoscopic observation over the South China Sea. Comprehensive three-dimensional observations were collected using the system from 31 July to 2 August, 2020. This information was used to investigate the formation and development processes of Typhoon Sinlaku (2020). The data contain measurements of 21 oceanic and meteorological parameters acquired by the five devices, along with video footage from the UAV. The data proved very helpful in determining the actual location and intensity of Typhoon Sinlaku (2020). The experiment demonstrates the feasibility of using a high-altitude, large UAV to fill in the gaps between operational meteorological observations of marine areas and typhoons near China, and marks a milestone for the use of such data for analyzing the structure and impact of a typhoon in the South China Sea. It also demonstrates the potential for establishing operational UAV meteorological observing systems in the future, and the assimilation of such data into numerical weather prediction models.

The discontinuities in historical Chinese radiosonde datasets are attributed to artificial errors. In order to reflect more realistically basic conditions of the atmosphere over China and provide more reasonable radiosonde data as input to climate change analysis and to atmospheric reanalysis data assimilation systems, this paper proposes a scheme to identify breakpoints and adjust biases in daily radiosonde observations. The ongoing ECMWF ReAnalysis-Interim (ERA-Interim) 12-h forecasts are used as reference series in the scheme, complemented by the ECMWF Twentieth Century Reanalysis (ERA-20C). A series of breakpoint identification schemes are developed and combined with metadata to detect breakpoints. The Quantile-Matching (QM) method is applied to test and adjust daily radiosonde data on 12 mandatory pressure levels collected at 80 sounding stations during 1979–2013. The adjusted temperatures on mandatory levels are interpolated to significant levels for temperature adjustment on these levels. The adjustment scheme not only solves the data discontinuity problem caused by changes in observational instruments and bias correction methods, but also solves the discontinuity problem in the 1200 minus 0000 UTC temperature time series on mandatory levels at individual sounding stations. Before the adjustment, obvious discontinuities can be found in the deviation field between the raw radiosonde data and ERA-Interim reanalysis with relatively large deviations before 2001. The deviation discontinuity is mainly attributed to the nationwide upgrade of the radiosonde system in China around 2001. After the adjustment, the time series of deviations becomes more continuous. In addition, compared with the adjusted temperature data on mandatory levels over 80 radiosonde stations in China contained in the Radiosonde Observation Correction Using Reanalyses (RAOBCORE) 1.5, the dataset adjusted by the method proposed in the present study exhibits higher quality than RAOBCORE 1.5, while discontinuities still exist in the time series of temperature at 0000, 1200, and 1200 minus 0000 UTC in RAOBCORE 1.5.

Currently, radar and radionavigation systems for aerological sounding of the atmosphere with the help of aerological radiosondes launched into the atmosphere are widely used to provide operational weather and climate forecasts. Meteorological radar systems for various purposes have been developed to obtain operational data on the free atmosphere. The systems are operated, providing measurement of meteorological parameters by noncontact methods. One of the most important meteorological parameters is the vertical wind profile. Specialized radars have been created to determine the distribution of wind direction and speed by altitude. They allow measuring the dynamic parameters of the wind in real time. The article considers the possibility of creating a radar for the wind parameters measuring the lower troposphere based on the modernization of a serial domestic aerological radar. Technical characteristics of the radar such as the pulse transmitting power, the sensitivity of the receiver, the ability to control the antenna radiation pattern accurately by angular coordinates make it possible to provide fundamental wind measurement in the lower troposphere. The main issues of modernization are related to the need of implementation of the coherent operation mode into the radar receiver and transmitter and ensuring the coherent energy storage of the reflected signal during the interval of statistical stability of the atmosphere. The information about the wind at the measured altitude is estimated from the Doppler spectrum of the signal reflected from meteorological inhomogeneities. The ability to accurately point the antenna during the sounding session allows determination of the vertical wind profile in the altitude range, even in clear sky conditions. Bukrin I. V., Gusev A. V., Kudinov S. I., Ivanov V. E., Plokhikh O. V., Shabunin S. N. Application of aerological radar to determine the wind profile in the lower troposphere. Ural Radio Engineering Journal. 2021;5(1):32–48. (In Russ.) DOI: 10.15826/urej.2021.5.1.003.

The large-scale monitoring of rainfall is of great significance in the research of meteorology, hydrology, and atmospheric measurement science. In recent years, with the quick development of communication satellite constellation, the use of earth-space link (ESL) to measure rainfall in the atmosphere is expected to be a potential approach for the large-scale monitoring of global rainfall. In this article, to verify the long-term performance of rainfall measurement using ESL, the data of an ESL at the Ku band and a Thies Laser Precipitation Monitor (LPM) in Nanjing were collected. The rainfall inversion model using ESL was optimized according to the height of 0 °C-layer from the radiosonde data of ten years, and the inversion results in the different types of rainfall were discussed. The results show that the rainfall inversed by the optimized ESL model are in good agreement with the rainfall measured by LPM (correlative coefficient is 0.985), the relative errors of rain intensity inversed by ESL in light rain, moderate rain, heavy rain, and extreme rain are 20%, 15.17%, 8.93%, and 8.99%, respectively. The average relative errors of rain intensity measured by the ESL in convective rainfall and stratiform rainfall are 16.01% and 26.59%, respectively.

In this study, I show that microwave signals emitted from satellites and received by the GNSS system can be utilized for meteorological purposes, such as heavy rain in Indonesia, using open-source software goGPS. Comparison of the gradient of the North and East atmospheric delays between goGPS, IGS, and UNR. The correlation of the results of goGPS with those from IGS and UNR is relatively high. The gradient of the late goGPS and IGS, and UNR products show good consistency. This means that the process of goGPS is good enough to get an accurate value. The standard deviation and RMS difference between goGPS and IGS are 16.99 and -1.32 (in mm), respectively. The ones for the goGPS-UNR pair are 18.33 and -1.08. goGPS software works very well at different times and stations. This means that the goGPS software package can be an alternative to commercial software systems for ZTD estimation. The correlation of PWV values from GPS and radiosonde for the dry and rainy seasons are 0.84 and 0.73, respectively. PWV values during this dry period show a downward trend and an upward trend during the wet season period.

Between 14 and 20 July 2018, small unmanned aircraft systems (UASs) were deployed to the San Luis Valley of Colorado (USA) alongside surface-based remote sensors, in situ sensors, and radiosonde systems as part of the Lower Atmospheric Profiling Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE). The measurements collected as part of LAPSE-RATE targeted quantities related to enhancing our understanding of boundary layer structure, cloud and aerosol properties and surface–atmosphere exchange and provide detailed information to support model evaluation and improvement work. Additionally, intensive intercomparison between the different unmanned aircraft platforms was completed. The current paper describes the observations obtained using three different types of surface-based mobile observing vehicles. These included the University of Colorado Mobile UAS Research Collaboratory (MURC), the National Oceanic and Atmospheric Administration National Severe Storms Laboratory Mobile Mesonet, and two University of Nebraska Combined Mesonet and Tracker (CoMeT) vehicles. Over the 1-week campaign, a total of 143 h of data were collected using this combination of vehicles. The data from these coordinated activities provide detailed perspectives on the spatial variability of atmospheric state parameters (air temperature, humidity, pressure, and wind) throughout the northern half of the San Luis Valley. These datasets have been checked for quality and published to the Zenodo data archive under a specific “community” setup for LAPSE-RATE (https://zenodo.org/communities/lapse-rate/, last access: 21 January 2021) and are accessible at no cost by all registered users. The primary dataset DOIs are https://doi.org/10.5281/zenodo.3814765 (CU MURC measurements; de Boer et al., 2020d), https://doi.org/10.5281/zenodo.3738175 (NSSL MM measurements; Waugh, 2020), and https://doi.org/10.5281/zenodo.3838724 (UNL CoMeT measurements; Houston and Erwin, 2020).

This paper studies precipitable water vapor (PWV) in 2015-2016, an El Nino-Southern Oscillation episode (ENSO), and causes drought in Indonesia. Using the Global Positioning System (GPS), we obtained the PWV value. Expected can detect ENSO phenomenon through PWV value. We processed the GPS observation data by Precise Point Positioning (PPP) method using the goGPS software. The obtained values of Zenith Troposphere Delay (ZTD) and PWV were validated by comparing with the ZTD values from four IGS stations (BAKO, PIMO, NTUS, ALIC), i.e., there was a mean difference of -3.48, the average standard deviation is 13.36 mm, and the correlation coefficient was 0.94. The variation between the IGS ZTD and PPP ZTD estimates is within the measurement error. Our results can be used to study PWV behaviors for meteorological and climatological purposes in Indonesia. The values of PWV obtained from station GPS CMAK and CBIT were compared with PWV by radiosonde observations. The results showed a good agreement with the coefficient correlation is 0.86 at CMAK Station and 0.85 at CBIT station. The PWV values at CBIT stations varied between 37.08 and 76.01 mm with an average of 57.05 mm. For CTOL, CMAK, and CKEN stations, their average PWV is 61.17, 50.84, and 51.75 mm. The standard deviation (STD) value of the PWV at CMAK stations was 11.79 mm, higher than the other stations. During the El Nino event, the correlation between SSTa and PWV became weak at the four stations.

This study undertook verification of the applicability and accuracy of wind data measured using a WindCube V2 Doppler Wind Lidar (DWL). The data were collected as part of a field experiment in Zhoushan, Zhejiang Province (China), which was conducted by Shanghai Typhoon Institute of China Meteorological Administration during the passage of Super Typhoon Lekima (2019). The DWL measurements were compared with balloon-borne GPS radiosonde (GPS sonde) data, which were acquired using balloons launched from the DWL location. Results showed that wind speed measured by GPS sonde at heights of 100 m) was found for DWL-measured wind speed time-averaged during the ascent of the GPS sonde from the ground surface to the height of 270 m (correlation coefficient: 0.82; root mean square (RMS): 2.19). Analysis revealed that precipitation intensity (PI) exerts considerable influence on both the carrier-to-noise ratio and the rate of missing DWL data; however, PI has minimal effect on the wind speed bias of DWL measurements. Specifically, the rate of missing DWL data increased with increasing measurement height and PI. For PI classed as heavy rain or less (PI 90 mm·h−1), only data below 100 m were valid. Up to the height of 300 m, the RMS of the DWL measurements was nearly half that of wind profile radar (WPR) estimates (4.32 m·s−1), indicating that DWL wind data are more accurate than WPR data under typhoon conditions.

This paper presents a wind profile estimation method for test flight of a short-range air-to-surface missile. The method integrates the telemetry data (e.g., navigation data, fin control history), empirical regional wind profile data, and a high-fidelity flight dynamics simulator to infer on the vertical wind profile that the missile has experienced during the flight. The inference procedure works in a sequential fashion: (a) starting from the launching altitude, random winds are sampled from seasonal rawinsonde data, (b) the wind that matches best to the simulated trajectory using the telemetry data is chosen, (c) the updated wind distribution at the altitude is used as the prior information for the next altitude layer, and (d) this process is repeated. Simulation studies with high-fidelity simulator are performed to validate the proposed estimation method, highlighting its robustness against variation in wind profiles and flight conditions.

Hundreds of supercell proximity soundings obtained for field programs over the central United States are analyzed to reconcile differences in recent studies and to refine our knowledge of supercell environments. The large, storm-centric observation-based dataset and high vertical resolution of the sounding data provide an unprecedented look at supercell environments. Not surprisingly, storm-relative environmental helicity (SRH) is found to be larger in tornadic soundings than in nontornadic soundings. The primary finding that departs from previous studies is that storm-relative winds contribute substantially to the larger SRH. Stronger ground-relative winds and more rightward-deviant storm motions contribute to the larger storm-relative winds for the tornadic soundings. Spatial analyses of the soundings reveal lower near-ground pressure perturbations and stronger low- to midlevel cyclonic flow for the tornadic soundings, which suggests stronger mesocyclones, perhaps explaining the more rightward-deviant motions. Differences in the mean critical angle between the tornadic and nontornadic soundings are small and do not contribute to the larger mean SRH, but the tornadic soundings do have fewer instances of smaller (smaller 0–3 km lapse rates in tornadic environments related to weaker/shallower capping inversions, and larger 0–3 km CAPE in near-field environments.

Observations of the air vertical velocities (wair) in supercell updrafts are presented, including uncertainty estimates, from radiosonde GPS measurements in two supercells. These in situ observations were collected during the Colorado State University Convective Cloud Outflows and Updrafts Experiment (C3LOUD-Ex) in moderately unstable environments in Colorado and Wyoming. Based on the radiosonde accelerations, instances when the radiosonde balloon likely bursts within the updraft are determined, and adjustments are made to account for the subsequent reduction in radiosonde buoyancy. Before and after these adjustments, the maximum estimated wair values are 36.2 and 49.9 m s−1, respectively. Radar data are used to contextualize the in situ observations and suggest that most of the radiosonde observations were located several kilometers away from the most intense vertical motions. Therefore, the radiosonde-based wair values presented likely underestimate the maximum values within these storms due to these sampling biases, as well as the impacts from hydrometeors, which are not accounted for. When possible, radiosonde-based wair values were compared to estimates from dual-Doppler methods and from parcel theory. When the radiosondes observed their highest wair values, dual-Doppler methods generally produced 15–20 m s−1 lower wair for the same location, which could be related to the differences in the observing systems’ resolutions. In situ observations within supercell updrafts, which have been limited in recent decades, can be used to improve our understanding and modeling of storm dynamics. This study provides new in situ observations, as well as methods and lessons that could be applied to future field campaigns.

In this study, we introduce a newly developed upper-air observational instrument for atmospheric research. The “Storm Tracker” is an ultra-lightweight (about 20 g including battery), multi-channel simultaneous capable radiosonde designed by the Department of Atmospheric Sciences at National Taiwan University. Developed in 2016, the Storm Tracker aims to provide an alternative for observing atmospheric vertical profiles with a high temporal resolution, especially the lower-level atmosphere under severe weather conditions such as extreme thunderstorms and tropical cyclones. Field experiments were conducted as trial runs in Wu-Chi, Taichung, Taiwan, to examine the ability of the Storm Tracker to observe the boundary layer, in addition to the intercomparison between the Storm Tracker and the widely used Vaisala RS41-SGP radiosonde. Among the co-launches of the Storm Tracker and Vaisala RS41 radiosondes, the measurements of pressure, wind speed, and wind direction are highly consistent between the measurements of the Storm Tracker and the Vaisala RS41-SGP. However, a significant daytime warm bias in the Storm Tracker was found due to solar heating. A metal shield specifically for the Storm Tracker was thus installed and showed mitigation for the warm biases and the overall variance. With the much lower costs of the radiosondes and the simultaneous multi-channel receiver, the Storm Tracker system has shown great potential for high-frequency observational needs in atmospheric research.

The Paso Del Norte (PdN) region comprises the city of El Paso, TX, Ciudad Juarez, Mexico, and some neighboring cities in the state of New Mexico. Developing a regional weather model for this specific region has always been challenging due to its complex terrain. To obtain more accurate weather and pollution forecasting for the PdN region, the results of the downscaled WRF (Weather Research and Forecast) model were intercompared with meteorological satellite data, with ground and radiosonde dataset. In addition, it is critical to analyze the distributions of ozone concentrations to better understand atmospheric aerosol concentrations and predict them both more accurately. Hence, in this study the ozone results of CAMx (Comprehensive Air Quality Model with Extensions) were extensively intercompared with ozonesonde data. The radiosonde/ozonesonde data were obtained throughout a campaign conducted during the summer of 2017 in the PdN region. Different meteorological variables such as temperature, pressure, relative humidity, wind speed, and ozone concentrations were used for comparison at several locations in the PdN region. The TCEQ (Texas Commission of Environment Quality) data from different CAMS (Continuous Ambient Monitoring Stations) were used for ground data intercomparison with the WRF results. The meteorological satellite sounding data were retrieved using an in-house satellite antenna receiver. The results of this research paper will not only provide better pollution forecasting capability for the PdN region but also for other regions with similar topography and terrain.

The significance of planetary boundary layer (PBL) height detection is apparent in various fields, especially in air pollution dispersion assessments. Numerical weather models produce a high spatial and temporal resolution of PBL heights; however, their performance requires validation. This necessity is addressed here by an array of eight ceilometers; a radiosonde; and two models – the Integrated Forecast System (IFS) global model and COnsortium for Small-scale MOdeling (COSMO) regional model. The ceilometers were analyzed with the wavelet covariance transform method, and the radiosonde and models with the parcel method and the bulk Richardson method. Good agreement for PBL height was found between the ceilometer and the adjacent Bet Dagan radiosonde (33 m a.s.l.) at 11:00 UTC launching time (N=91 d, ME =4 m, RMSE =143 m, R=0.83). The models' estimations were then compared to the ceilometers' results in an additional five diverse regions where only ceilometers operate. A correction tool was established based on the altitude (h) and distance from shoreline (d) of eight ceilometer sites in various climate regions, from the shoreline of Tel Aviv (h=5 m a.s.l., d=0.05 km) to eastern elevated Jerusalem (h=830 m a.s.l., d=53 km) and southern arid Hazerim (h=200 m a.s.l., d=44 km). The tool examined the COSMO PBL height approximations based on the parcel method. Results from a 14 August 2015 case study, between 09:00 and 14:00 UTC, showed the tool decreased the PBL height at the shoreline and in the inner strip of Israel by ∼100 m and increased the elevated sites of Jerusalem and Hazerim up to ∼400 m, and ∼600 m, respectively. Cross-validation revealed good results without Bet Dagan. However, without measurements from Jerusalem, the tool underestimated Jerusalem's PBL height by up to ∼600 m.