Upper-air Observations Research Articles

Enhancing Bright Band Prediction Through RadioSonde RadioWind for International Telecommunication Union- Radio Communications (ITU-R)

As a vital source of information on atmospheric conditions at different altitudes, upper air observations are critical for precise weather forecasting, climate monitoring, and atmospheric research. In coastal areas like Vishakhapatnam that are subject to tropical cyclones and monsoonal fluctuations, radiosondes provide precise vertical profiles that are essential for weather forecasting. They also measure temperature, humidity, and pressure. The addition of wind speed and direction readings via radiowind devices, often known as "rawinsondes," improves severe weather forecasts and our understanding of atmospheric dynamics. Forecasting floods, maintaining aviation safety, and managing water resources are all aided by the ability to predict the bright band, a radar indication of the precipitation layer melting. Weather models and readiness for disasters are much improved in Vishakhapatnam when radiosondes, rawinsondes, and bright band predictions are used in tandem.

Improving severe thunderstorm simulations over Bihar and West Bengal, India through assimilation of upper air observations using the 3DVAR of WRF model

Improving severe thunderstorm simulations over Bihar and West Bengal, India through assimilation of upper air observations using the 3DVAR of WRF model

Analysis of an intense O3 pollution episode on the Atlantic coast of the Iberian Peninsula using photochemical modeling: characterization of transport pathways and accumulation processes

Abstract. A tropospheric O3 pollution episode over the Atlantic coast of the Iberian Peninsula during 2–6 August 2018 has been analyzed. The episode was characterized by a permanent wind shear throughout the entire period, making the observed ozone surface distribution especially difficult to explain. A new methodology is described to analyze upper-level atmospheric parameters, such as temperature, wind direction, wind speed, and O3 concentrations, added to the traditional use of surface parameters, using the Weather Research and Forecasting (WRF) and Comprehensive Air Quality Model with Extensions (CAMx) models as well as available surface and upper-air observations. Results indicate that the episode was characterized by a first phase of a sudden increase in O3 concentrations produced by fumigation and inter-regional transport processes within the Iberian Peninsula, followed by continental O3 transport from Europe to the Atlantic coast. An Atlantic front produced the dissipation of the episode, generating an “ozone front” accompanying the cold front passage across the region.

Observational Study of Two Norman, Oklahoma Storms with Very Large, Damaging Hail in Long-Hodograph Environments

Two ≥3-in (7.6-cm) hailstorms affected Norman, Oklahoma in 2021: one on 28–29 April and the other on 10–11 October. Wind-driven hail associated with the April storm was estimated to have caused over two million dollars in damage just to county-owned buildings, with estimates of hundreds of millions in damage when including privately owned properties. The October storm caused further damage in approximately the same locations in Norman as the April storm. That these storms affected Norman in a single year was unusual, as only three other prior dates had ≥3-in (7.6-cm) hail reports in Norman from 1955–2021. Radar presentations, surface and upper-air observations, and environmental parameters are presented and intercompared. The April storm was situated behind a surface boundary, and was mostly sub-severe until it rapidly intensified upon approach to the boundary, while the October storm evolved near a surface boundary and had a history of tornadoes. A cursory comparison of model-derived and observed proximity soundings for the two 2021 hailstorms with those for the previous six ≥3-in (7.6-cm) hailstorm dates for Cleveland County (in which Norman is located), from 1955–2021, showed that the two 2021 storms had lower environmental buoyancy, but larger deep shear. This study supports both: 1) the idea that large shear or buoyancy can compensate for lack of the other for the production of significant hail, and 2) the association of long hodographs with large-hail events.

Convective weather event monitoring with multispectral image analysis of INSAT-3D/3DR over Indian domain

Pre-monsoon season (March to May) is very challenging as convective activities prevails almost throughout the country. Most of the Rabi crops harvesting affected and sometimes suffer great losses due to sudden rain or high winds. INSAT-3D/3DR satellite images and derived products provides continuous support to the forecasters and end users in monitoring such events and thereafter significant value addition improves the prediction. This information was found to be very useful where actual ground based or upper air observations are limited or especially over data sparse or difficult terrain regions. In this work, we have examined three weather events at different Geographical locations (i) Rainfall over Bihar-24-26 June, 2020 (ii) Delhi & NCR region on 17 June, 2022 (iii) NE region activity in 16-18 June, 2022. The Real Time Analysis of Products and Information Dissemination (RAPID) web based tool was utilized in monitoring and diagnosing the convective weather events based on the brightness temperature & derived products like Outgoing longwave radiation, upper tropospheric humidity, insolation etc & RGB imagery composite in terms of day & night time microphysics daily operational products. The time series of the wind derived products for Delhi NCR rainfall and NE rainfall products also generated through RAPID. The synoptic model analysis provides valuable inputs for these mesoscale convective weather events. The southerly wind flow (at 925 hPa) and velocity convergence (at 500 hPa) analysis of European Centre for Medium Range Weather Forecasting (ECMWF) supports the severity of NE event occurred on 16-18 June, 2022. Therefore, utilization of near real time INSAT-3D/3DR products along with appropriate synoptic model analysis can help the forecasters to understand better about such mesoscale convective events & accurate forecast with sufficient lead time can save the life and property.

FESSTVaL: The Field Experiment on Submesoscale Spatio-Temporal Variability in Lindenberg

Abstract Numerical weather prediction models operate on grid spacings of a few kilometers, where deep convection begins to become resolvable. Around this scale, the emergence of coherent structures in the planetary boundary layer, often hypothesized to be caused by cold pools, forces the transition from shallow to deep convection. Yet, the kilometer-scale range is typically not resolved by standard surface operational measurement networks. The measurement campaign Field Experiment on Submesoscale Spatio-Temporal Variability in Lindenberg (FESSTVaL) aimed at addressing this gap by observing atmospheric variability at the hectometer-to-kilometer scale, with a particular emphasis on cold pools, wind gusts, and coherent patterns in the planetary boundary layer during summer. A unique feature was the distribution of 150 self-developed and low-cost instruments. More specifically, FESSTVaL included dense networks of 80 autonomous cold pool loggers, 19 weather stations, and 83 soil sensor systems, all installed in a rural region of 15-km radius in eastern Germany, as well as self-developed weather stations handed out to citizens. Boundary layer and upper-air observations were provided by eight Doppler lidars and four microwave radiometers distributed at three supersites; water vapor and temperature were also measured by advanced lidar systems and an infrared spectrometer; and rain was observed by a X-band radar. An uncrewed aircraft, multicopters, and a small radiometer network carried out additional measurements during a 4-week period. In this paper, we present FESSTVaL’s measurement strategy and show first observational results including unprecedented highly resolved spatiotemporal cold-pool structures, both in the horizontal as well as in the vertical dimension, associated with overpassing convective systems.

Causes and impact of extreme Low Level Wind Shear (LLWS) event at Soekarno-Hatta International Airport

The impact of low-level wind shear (LLWS) is still a critical issue in aviation. These days, as the aviation industry grows, it must follow safety improvement efforts and transportation management, one of which is through the accurate publishing of LLWS early warning. Therefore, it takes a high understanding related to phenomena, especially at airports with busy schedules. This study examined the causes and impacts of pilot-reported LLWS on April 5, 2018, from 13:15 to 16:00 UTC at Soekarno-Hatta International Airport which is the busiest airport in Indonesia. The data used are Doppler weather radar, automated weather observing system (AWOS), as well as synoptic and upper air observations. Based on the result of this research, linear convective clouds created from deep convection due to very unstable atmospheric circumstances are the cause of LLWS. A sizeable line of convective cloud with a 50-55 dBZ reflectivity accompanied by an outflow microburst (MBA) was detected crashing the airport. Heavy rains and strong winds up to 15-20 m/s speeds from the line of convective cloud triggered the formation of destructive LLWS. Subsequently, there was traffic build-up at 5 flight holding points before landing. Furthermore, 7 flights (Airbus, Boeing, and CRJX) underwent go-around, and 3 flights (Airbus and Boeing) were diverted.

Analysis of Stratospheric Ozone and Meteorological Parameters observed at Bharati station, Larsemann Hills, Antarctica during 37th Indian Scientific Expedition to Antarctica

India along with South America, Africa, Antarctica, and Australia was part of the supercontinent Gondwana some 140 million years ago. When the supercontinent split up, the Indian plat moved north-eastward while the Australian and African plates moved comparatively less distance and at much lower speeds. The Antarctica remained almost stationary. The 37th Indian Scientific Expedition to Antarctica was the fourth wintering of India Meteorological Department (IMD) members at Bharati station, Larsemann Hills, Antarctica. During the expedition recording of meteorological surface & upper air observations, vertical ozone measurement, continuous weather monitoring and forecasting were performed. All these meteorological parameters were thoroughly analyzed and studied. Temperature analysis showed that the coldest month was September and warmest was January. Strong and persistent north-easterly near-surface winds were observed throughout the year with May being the windiest month. During the period, 9 numbers of blizzards spanning a period of 15 days were observed with a maximum wind gust of 80 knots during a blizzard on 5th August 2018. During the expedition, a total of 32 balloon-borne GPS based ozone sonde ascents were taken and their analysis showed that the minimum thickness of ozone concentration is found between 15th September and 10th October. Also, the space weather, special meteorological phenomena and fauna found near Bharati station was observed and documented in this study.

Impacts of Assimilating Future Clear-Air Radial Velocity Observations from Phased Array Radar on Convection Initiation Forecasts: An Observing System Simulation Experiment Study

Abstract Phased-array radar (PAR) technology can potentially provide high-quality clear-air radial velocity observations at a high spatiotemporal resolution, usually ∼1 min or less. These observations are hypothesized to partially fill the gaps in current operational observing systems with relatively coarse-resolution surface mesonet observations and the lack of high-resolution upper-air observations especially in planetary boundary layer. In this study, observing system simulation experiments are conducted to investigate the potential value of assimilating PAR observations of clear-air radial velocity to improve the forecast of convection initiation (CI) along small-scale boundary layer convergence zones. Both surface-based and elevated CIs driven by meso-γ-scale boundary layer convergence are tested. An ensemble Kalman filter method is used to assimilate synthetic surface mesonet observations and PAR clear-air radial velocity observations. Results show that assimilating only surface mesonet observations fails to predict either surface-based or elevated CI processes. Assimilating clear-air radial velocity observations in addition to surface mesonet observations can capture both surface-based and elevated CI processes successfully. Such an improvement benefits from the better analyses of boundary layer convergence, resulting from the assimilation of clear-air radial velocity observations. Additional improvement is observed with more frequent assimilation. Assimilating clear-air radial velocity observations only from the one radar results in analysis biases of cross-beam winds and CI location biases, and assimilating additional radial velocity observations from the second radar at an appropriate position can reduce these biases while sacrificing the CI timing. These results suggest the potential of assimilating clear-air radial velocity observations from PAR to improve the forecast of CI processes along boundary layer convergence zones.

Influence of Multi-Scale Meteorological Processes on PM2.5 Pollution in Wuhan, Central China

Heavy PM2.5 (particulate matter with an aerodynamics diameter less than 2.5 μm) pollution frequently happens in Wuhan under unfavorable meteorological conditions. To comprehensively understand the complex impact of both regional-scale synoptic forcing and local-scale processes within the planetary boundary layer (PBL) on air quality in Wuhan, this study analyzed long-term PM2.5 concentration measurement, near-surface and upper-air meteorological observations from March 2015 to February 2019, in combination with the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2). We found that in winter the day-to-day change of PM2.5 pollution level in Wuhan was governed by the synoptic warm/cold advection, in addition to the high emissions of pollutants. The synoptic condition can largely determine both the vertical development of PBL and horizontal transport of pollutants. When a high-pressure system is located to the north of Wuhan at the 900-hPa level, the induced warm advection above 1,000 m can enhance the thermal stability of lower troposphere and inhibit the development of daytime PBL, leading to a decreased dispersion volume for pollutants. Meanwhile, within the PBL the pollutants emitted from Henan, Shandong, and Anhui provinces can be transported to Wuhan, further worsening the pollution. Our results highlight the importance of coordinated pollution controls in Central China and adjacent north regions under the unfavorable synoptic condition.

The sensitivity of the WRF-4DVar data assimilation system to the control variables: A study on heavy rainfall events over India

The impact of different formulations of background error covariances (BECs) is examined for three heavy rainfall episodes over north India with a regional 4-dimensional variational (4DVar) data assimilation (DA) system. Three BEC formulations are analyzed, in which two of them employ stream function and velocity potential (ψ and χ) and the third one uses zonal and meridional velocity components (v and v) as momentum control variables. The uv-based formulation is completely univariate whereas, the correlations among the control variables are taken into account in the ψχ-based formulations through linear regression relations. Among the two ψχ-based BECs, one uses univariate relation and the other one uses multivariate relations for the moisture field. The multivariate relationship allows for impacting the moisture analysis through the assimilation of temperature or wind observations. Three experiments are carried out for each case with cyclic 4DVar assimilation. The conventional surface and upper-air observations are assimilated in combination with atmospheric motion vectors (AMVs) and ocean surface winds. Free forecast for 48 h is performed from respective final analysis fields for all the experiments. The results indicate that the uv-based analysis fields are closer to the observations. A comparative analysis of the 4DVar experiments with the 3DVar DA system provided a critical insight on the role of the 4DVar DA system on implicitly accounting for the multivariate correlations. The precipitation forecasts confirm the improved performance of the ψχ-based experiment, when multivariate nature of the humidity is incorporated. The time evolution of the intense rainfall episodes over the location of maximum rainfall are relatively well reproduced in the uv-based experiment. The results indicate that the inclusion of multivariate humidity variable in the BEC formulation does have a significant impact on suppressing the excessive overestimation in rainfall intensity.

Using Commercial Aircraft Meteorological Data to Assess the Heat Budget of the Convective Boundary Layer Over the Santiago Valley in Central Chile

The World Meteorological Organization Aircraft Meteorological Data Relay (AMDAR) programme refers to meteorological data gathered by commercial aircraft and made available to weather services. It has become a major source of upper-air observations whose assimilation into global models has greatly improved their performance. Near busy airports, AMDAR data generate semi-continuous vertical profiles of temperature and winds, which have been utilized to produce climatologies of atmospheric-boundary-layer (ABL) heights and general characterizations of specific cases. We analyze 2017–2019 AMDAR data for Santiago airport, located in the centre of a 40times 100 km^2 subtropical semi-arid valley in central Chile, at the foothills of the Andes. Profiles derived from AMDAR data are characterized and validated against occasional radiosondes launched in the valley and compared with routine operational radiosondes and with reanalysis data. The cold-season climatology of AMDAR temperatures reveals a deep nocturnal inversion reaching up to 700 m above ground level (a.g.l.) and daytime warming extending up to 1000 m a.g.l. Convective-boundary-layer (CBL) heights are estimated based on AMDAR profiles and the daytime heat budget of the CBL is assessed. The CBL warming variability is well explained by the surface sensible heat flux estimated with sonic anemometer measurements at one site, provided advection of the cool coastal ABL existing to the west is included. However, the CBL warming accounts for just half of the mean daytime warming of the lower troposphere, suggesting that rather intense climatological diurnal subsidence affects the dynamics of the daytime valley ABL. Possible sources of this subsidence are discussed.

Physical and Dynamical Characteristics of Thunderstorms Over Bangladesh Based on Radar, Satellite, Upper-Air Observations, and WRF Model Simulations

Physical and Dynamical Characteristics of Thunderstorms Over Bangladesh Based on Radar, Satellite, Upper-Air Observations, and WRF Model Simulations

Rapidly intensified, long duration North Indian Ocean tropical cyclones: Mesoscale downscaling and validation

High-resolution (6 km × 6 km) reanalyses (HRR) have been generated using the Advanced Research Weather Research and Forecasting model (WRF-ARW) and three-dimensional variational-Ensemble Kalman Filter (3DEnKF). The six-hourly cyclic downscaling methodology has been used to develop HRR for simulating the life cycle of three rapidly intensified long-life tropical cyclones (TCs) viz. Fani, Ockhi, and Luban over the North Indian Ocean (NIO). The available surface and upper-air observations, radiance data, and scatterometer/radiometer wind data have been assimilated. The simulated maximum surface wind (intensity), minimum sea level pressure (MSLP), and cyclone tracks have been compared to the International Best Track Archive For Climate Stewardship (IBTrACS) dataset. During the maximum intensity stage, the model successfully simulated the maximum surface wind variation and drop of MSLP. The surface winds validated using the Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction (RAMA) buoys observations showed a better correlation at the cyclonic stage and severe cyclonic stage compared to the very severe cyclonic stage. Comparing wind distribution at 850 hPa to the 5th Generation European Centre for Medium-Range Weather Forecasts Reanalysis (ERA5) showed that the analysis successfully captured more intense TC stages. The significant wind structure features in the surface, lower, middle, and upper tropospheric observed by Scatsat-1 and INSAT3D/3DR satellites have been simulated by analysis. The rapid intensifications (RI) of considered TCs were successfully simulated in the analysis. The simulated and the Global Precipitation Measurement (GPM) accumulated rainfall distribution was found to be collocated, especially over heavy rainfall regions.

Impact of assimilating Mode‐S EHS winds in the Met Office's high‐resolution NWP model

AbstractAircraft play an important role in providing in‐situ upper air observations for use in numerical weather prediction (NWP) models. Aircraft observations of wind, temperature and humidity have been routinely assimilated into NWP models for decades. More recently, the Met Office has installed a network of six receivers to collect Mode‐Select Enhanced Surveillance (Mode‐S EHS) broadcasts and derive wind and temperature information. The paper focuses on the assimilation of high‐frequency (sub‐hourly) Mode‐S winds into the Met Office's hourly cycling high‐resolution NWP model, which employs four‐dimensional‐variational data assimilation (4D‐Var). An observing system experiment (OSE) experiment was carried out to assess the impact of additionally assimilating Mode‐S wind data on the model forecasts in both summer and winter. It is shown that assimilating Mode‐S winds has a positive benefit on the forecast skill in the first 6 hr wind profiles, with an improvement of the mean bias and root mean square (RMS) errors of up to 0.5 m·s−1 in the first hour and 0.3 m·s−1 in the sixth hour. The assimilation of Mode‐S winds is also seen to be beneficial to the near‐surface‐focused UK NWP composite index, and to the hourly precipitation accumulations in fractions skill score (FSS) for forecasts up to 9 hr ahead.

A Revised Bourgouin Precipitation-Type Algorithm

AbstractUsing vertical temperature profiles obtained from upper-air observations or numerical weather prediction models, the Bourgouin technique calculates areas of positive melting energy and negative refreezing energy for determining precipitation type. Energies are proportional to the product of the mean temperature of a layer and its depth. Layers warmer than 0°C consist of positive energy; those colder than 0°C consist of negative energy. Sufficient melting or freezing energy in a layer can produce a phase change in a falling hydrometeor. The Bourgouin technique utilizes these energies to determine the likelihood of rain (RA) versus snow (SN) given a surface-based melting layer and ice pellets (PL) versus freezing rain (FZRA) or RA given an elevated melting layer. The Bourgouin approach was developed from a relatively small dataset but has been widely utilized by operational forecasters and in postprocessing of NWP output. Recent analysis with a larger dataset suggests ways to improve the original technique, especially when discriminating PL from FZRA or RA. This and several other issues are addressed by a modified version of the Bourgouin technique described in this article. Additional enhancements include use of the wet-bulb profile rather than temperature, a check for heterogeneous ice nucleation, and output that includes probabilities of four different weather types (RA, SN, FZRA, PL) rather than the single most likely type. Together these revisions result in improved performance and provide a more viable and valuable tool for precipitation-type forecasts. Several National Weather Service forecast offices have successfully utilized the revised tool in recent winters.

IMDAA: High Resolution Satellite-era Reanalysis for the Indian Monsoon Region

AbstractA high resolution regional reanalysis of the Indian Monsoon Data Assimilation and Analysis (IMDAA) project is made available to researchers for deeper understanding of the Indian monsoon and its variability. This 12 km resolution reanalysis covering the satellite-era from 1979 to 2018 using 4D-Var data assimilation method and the UK Met Unified Model is presently the highest resolution atmospheric reanalysis carried out for the Indian monsoon region. Conventional and satellite observations from different sources are used, including Indian surface and upper air observations, of which some were not used in any previous reanalyses. Various aspects of this reanalysis, like quality control and bias correction of observations, data assimilation system, land surface analysis, and verification of reanalysis products, are presented in this paper. Representation of important weather phenomena of each season over India in the IMDAA reanalysis verifies reasonably well against India Meteorological Department (IMD) observations and compares closely with ERA5. Salient features of the Indian summer monsoon are found to be well represented in the IMDAA reanalysis. Characteristics of major semi-permanent summer monsoon features (e.g., Low-level Jet and Tropical Easterly Jet) in IMDAA reanalysis are consistent with ERA5. The IMDAA reanalysis has captured the mean, inter-annual, and intra-seasonal variability of summer monsoon rainfall fairly well. IMDAA produces a slightly cooler winter and a hotter summer than the observations; the reverse for ERA5. IMDAA captured the fine-scale features associated with a notable heavy rainfall episode over complex terrain. In this study, the fine grid spacing nature of IMDAA is compromised due to the lack of comparable resolution observations for verification.

Data Assimilation for Rainfall-Runoff Prediction Based on Coupled Atmospheric-Hydrologic Systems with Variable Complexity

The data assimilation technique is an effective method for reducing initial condition errors in numerical weather prediction (NWP) models. This paper evaluated the potential of the weather research and forecasting (WRF) model and its three-dimensional data assimilation (3DVar) module in improving the accuracy of rainfall-runoff prediction through coupled atmospheric-hydrologic systems. The WRF model with the assimilation of radar reflectivity and conventional surface and upper-air observations provided the improved initial and boundary conditions for the hydrological process; subsequently, three atmospheric-hydrological systems with variable complexity were established by coupling WRF with a lumped, a grid-based Hebei model, and the WRF-Hydro modeling system. Four storm events with different spatial and temporal rainfall distribution from mountainous catchments of northern China were chosen as the study objects. The assimilation results showed a general improvement in the accuracy of rainfall accumulation, with low root mean square error and high correlation coefficients compared to the results without assimilation. The coupled atmospheric-hydrologic systems also provide more accurate flood forecasts, which depend upon the complexity of the coupled hydrological models. The grid-based Hebei system provided the most stable forecasts regardless of whether homogeneous or inhomogeneous rainfall was considered. Flood peaks before assimilation were underestimated more in the lumped Hebei model relative to the other coupling systems considered, and the model seems more applicable for homogeneous temporal and spatial events. WRF-Hydro did not exhibit desirable predictions of rapid flood process recession. This may reflect increasing infiltration due to the interaction of atmospheric and land surface hydrology at each integration, resulting in mismatched solutions for local runoff generation and confluence.

An Evaluation of the Performance of the Twentieth Century Reanalysis Version 3

AbstractThe performance of a new historical reanalysis, the NOAA–CIRES–DOE Twentieth Century Reanalysis version 3 (20CRv3), is evaluated via comparisons with other reanalyses and independent observations. This dataset provides global, 3-hourly estimates of the atmosphere from 1806 to 2015 by assimilating only surface pressure observations and prescribing sea surface temperature, sea ice concentration, and radiative forcings. Comparisons with independent observations, other reanalyses, and satellite products suggest that 20CRv3 can reliably produce atmospheric estimates on scales ranging from weather events to long-term climatic trends. Not only does 20CRv3 recreate a “best estimate” of the weather, including extreme events, it also provides an estimate of its confidence through the use of an ensemble. Surface pressure statistics suggest that these confidence estimates are reliable. Comparisons with independent upper-air observations in the Northern Hemisphere demonstrate that 20CRv3 has skill throughout the twentieth century. Upper-air fields from 20CRv3 in the late twentieth century and early twenty-first century correlate well with full-input reanalyses, and the correlation is predicted by the confidence fields from 20CRv3. The skill of analyzed 500-hPa geopotential heights from 20CRv3 for 1979–2015 is comparable to that of modern operational 3–4-day forecasts. Finally, 20CRv3 performs well on climate time scales. Long time series and multidecadal averages of mass, circulation, and precipitation fields agree well with modern reanalyses and station- and satellite-based products. 20CRv3 is also able to capture trends in tropospheric-layer temperatures that correlate well with independent products in the twentieth century, placing recent trends in a longer historical context.

The Impact of the Observation Data Assimilation on Atmospheric Reanalyses over Tibetan Plateau and Western Yunnan-Guizhou Plateau

Three modern atmospheric reanalyses with different input observation data (NOAA–CIRES 20th Century Reanalysis (20CR), Japanese 55-year Reanalysis (JRA-55), and JRA-55C) were compared against the independent radiosonde observations over the Tibetan Plateau (TP) and the western Yunnan–Guizhou Plateau (YGP) from the China-Japan Meteorological Disaster Reduction Cooperation (JICA/Tibet) Center Project in the summer of 2018 to investigate the effects of the assimilation of the observation data on the quality and accuracy of the reanalyses in the troposphere. The results indicate that the mean biases and mean root-mean-square errors of horizontal wind, temperature, and specific humidity significantly decreased when comparing the 20CR reanalysis (assimilating only surface pressure) to the JRA-55C (assimilating conventional surface and upper-air observations) and the JRA-55 (assimilating conventional and satellite observations), and the bias spreads of these aboveground variables in JRA-55C and JRA-55 were cut to almost half of those observed in 20CR. However, the mean biases and uncertainties varied little from JRA-55C to JRA-55. This means that the assimilation of conventional observation data plays a vital role in the quality of reanalyses for the troposphere over these data-sparse plateaus. It was also found that the temperature and specific humidity near the ground over TP showed larger mean biases and bias spans than those over YGP, likely due to the sparser surface observation over TP.