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The meteorological sensor calibration facility is designed to test and assess radiosonde measurement quality through actual flights in the atmosphere. United States radiosonde temperature measurements are deficient in that they require correction for errors introduced by long- and short-wave radiation. The effect of not applying corrections results in a large bias between day time and night time measurements. This day/night bias has serious implications for users of radiosonde data, of which NASA is one. The derivation of corrections for the U.S. radiosonde is quite important. Determination of corrections depends on solving the heat transfer equation of the thermistor using laboratory measurements of the emissivity and absorptivity of the thermistor coating. The U.S. radiosonde observations from the World Meteorological Organization International Radiosonde Intercomparison were used as the data base to test whether the day/night height bias can be removed. Twenty-five noon time and 26 night time observations were used. Corrected temperatures were used to calculate new geopotentials. Day/night bias in the geopotentials decreased significantly when corrections were introduced. Some testing of thermal lag attendant with the standard carbon hygristor took place. Two radiosondes with small bead thermistors imbedded in the hygristor were flown. Detailed analysis was not accomplished; however, cursory examination of the data showed that the hygristor is at a higher temperature than the external thermistor indicates.
Two correlation studies of Total Ozone Mapping Spectrometer (TOMS) data with tropopause height from radiosondes performed over Europe showed a correlation coefficient of 0.94 and 0.96. As a result, the rms error in the prediction of tropopause height from total ozone was found to be 20 mb. Correlation between tropopause height and TOMS data was the highest of all the other correlations with variables directly derived from radiosondes or simulated thermal radiances over the location of radiosondes. Comparing the two dimensional fields of TOMS, tropopause height from radiosondes and tropopause height field from TIROS-N retrievals, we can say that the first field is much closer to the true field from radiosondes than the third. The correlation coefficient for a ten-day study between TOMS data and tropopause height from radiosondes is between 0.85 and 0.9 for 30-70N. Tropopause analysis provided by GLA model also shows a very high correlation with TOMS data.
A method has been devised to ensure stable, long-term calibration of Raman lidar measurements that are used to determine the altitude-dependent mixing ratio of water vapor in the upper troposphere and lower stratosphere. Because the lidar measurements yield a quantity proportional to the mixing ratio, rather than the mixing ratio itself, calibration is necessary to obtain the factor of proportionality. The present method involves the use of calibration data from two sources: (1) absolute calibration data from in situ radiosonde measurements made during occasional campaigns and (2) partial calibration data obtained by use, on a regular schedule, of a lamp that emits in a known spectrum determined in laboratory calibration measurements. In this method, data from the first radiosonde campaign are used to calculate a campaign-averaged absolute lidar calibration factor (t(sub 1)) and the corresponding campaign-averaged ration (L(sub 1)) between lamp irradiances at the water-vapor and nitrogen wavelengths. Depending on the scenario considered, this ratio can be assumed to be either constant over a long time (L=L(sub 1)) or drifting slowly with time. The absolutely calibrated water-vapor mixing ratio (q) obtained from the ith routine off-campaign lidar measurement is given by q(sub 1)=P(sub 1)/t(sub 1)=LP(sub 1)/P(sup prime)(sub 1) where P(sub 1) is water-vapor/nitrogen measurement signal ration, t(sub 1) is the unknown and unneeded overall efficiency ratio of the lidar receiver during the ith routine off-campaign measurement run, and P(sup prime)(sub 1) is the water-vapor/nitrogen signal ratio obtained during the lamp run associated with the ith routine off-campaign measurement run. If L is assumed constant, then the lidar calibration is routinely obtained without the need for new radiosonde data. In this case, one uses L=L(sub 1) = P(sup prime)(sub 1)/t(sub 1), where P(sub 1)(sup prime) is the water-vapor/nitrogen signal ratio obtained during the lamp run associated with the first radiosonde campaign. If L is assumed to drift slowly, then it is necessary to postpone calculation of a(sub 1) until after a second radiosonde campaign. In this case, one obtains a new value, L(sub 2), from the second radiosonde campaign, and for the ith routine off-campaign measurement run, one uses an intermediate value of L obtained by simple linear time interpolation between L(sub 1) and L(sub 2).
During the period of May 11 and 12, 1974, NASA conducted its second Atmospheric Variability Experiment (AVE II) over the eastern United States. In this time interval, two Nimbus 5 orbits crossed the AVE II area, providing a series of ITPR soundings as well as THIR data. Horizontal temperature mapping of the AVE II cloud field is examined using two grid print map scales. Implied cloud top heights are compared with maximum radar-echo top reports. In addition, shelter temperatures in areas of clear sky are compared with the surface temperatures as determined from 11.5 micrometer radiometer data of the THIR experiment. The ITPR sounding accuracy is evaluated using interpolated radiosonde temperatures at times nearly coincident with the ITPR soundings. It was found that mean differences between the two data sets were as small as 1.3 C near 500 mb and as large as 2.9 C near the tropopause. The differences between ITPR and radiosonde temperatures at constant pressure levels were sufficient to induce significant differences in the horizontal temperature gradient. Cross sections of geostrophic wind along the orbital tracks were developed using a thermal wind buildup based on the ITPR temperature data and the radiosonde temperature data. Differences between the radiosonde and ITPR geostrophic winds could be explained on the basis of differences in the ITPR and radiosonde temperature gradients.
Various combinations of temperature and moisture data from the VISSR Atmospheric Sounder (VAS), conventional radiosonde data, and National Meteorological Center (NMC) global analysis, were used in a successive-correction type of objective-analysis procedure to produce analyses for 1200 GMT. The NMC global analyses served as the first-guess field for all of the objective analysis procedures. The first-guess field was enhanced by radiosonde data alone, VAS data alone, both radiosonde and VAS data, or by neither data source. In addition, two objective analyses were used in a dynamic initialization: one included only radiosonde data and the other used both radiosonde and VAS data. The dependence of 12 hour forecast skill on data type and the methods by which the data were used in the analysis/initialization were then investigated. This was done by comparison of forecast and observed fields, of sea-level pressure, temperature, wind, moisture, and accumulated precipitation. The use of VAS data in the initial conditions had a slight positive impact upon forecast temperature and moisture but a negative impact upon forecast wind. This was true for both the static and dynamic initialization experiments. Precipitation forecasts from all of the model simulations were nearly the same.
Regular balloon ozone soundings with electrochemical sondes have been performed at Uccle since 1969. More than 450 ozone soundings between 1985 and 1989 were used to calculate the altitudes Zs from the VIZ radiosonde data and the altitudes Zr deduced from the tracking of the balloon train with a primary wind-finding radar. The values of Zs at fixed times appeared to be systematically too low as compared to Zr. The differences Zr-Zs increase with altitude; at 30 km the annual mean values of Zr-Zs (plus or minus standard deviation) vary between 590 plus or minus 910 m and 1410 plus or minus 1160 m, according to the pressure calibration of different manufacturing series of radiosondes. From these results it is found that around the 30 km level the ozone concentrations calculated from soundings with VIZ sondes are too low by 7.5 to 14 percent, depending upon the manufacturing series of radiosondes. At least part of the discrepancy which has often been found between ozone profiles from balloon soundings and from other techniques such as rocket observations or Umkehr measurements may be explained by this effect. An altitude correction would have important consequences as to the climatology of ozone in the middle stratosphere as adopted at the moment. About half of the day-to-day variability of ozone observed from soundings with VIZ radiosondes above the 30 km level, is induced by the variability of Zr-Zs. The agreement between altitudes calculated from radar data and Vaisala radiosondes is much better; from 34 comparative soundings a mean difference (plus or minus standard deviation) of about -300 plus/minus 180 m was found at 30 km.
The planetary boundary layer (PBL) is fundamental to numerous atmospheric processes, including aerosol mixing and transport, cloud evolution, and precipitation formation. A critical parameter in these studies is the PBL height (PBLHT). This vertical depth is essential for characterizing PBL structures in numerical simulations and serves as a primary metric for estimating flux exchanges between the Earth’s surface and the atmosphere. Radiosonde (SONDE) observations provide high-vertical-resolution measurements of temperature and moisture profiles and are widely used to estimate PBLHT (Liu and Liang 2010, Seidel et al. 2010). The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) User Facility’s PBLHT value-added product (VAP) for radiosonde measurements, known as PBLHTSONDE, applies three commonly used methods—the Heffter (1980) method, the Liu and Liang (2010) method, and the bulk Richardson number approach (Seibert et al. 2000)—to derive PBLHT. The PBLHTSONDE VAP operates routinely at ARM observatories and mobile facilities, with data available from the ARM Data Center shortly after sounding observations are collected (Sivaraman et al. 2013). However, radiosonde observations are limited by their low temporal resolution. Most stations launch soundings only twice daily, which constrains the ability to investigate and characterize the temporal evolution of the PBL using radiosonde data alone. The use of continuous remote-sensing observations provides high temporal resolution of PBLHT estimates. These observations include aerosol lidars (Dang et al. 2019, Su et al. 2020), Doppler lidar (DL; Tucker et al. 2009, Krishnamurthy et al. 2021), and water vapor and/or temperature lidars and radiometers (Turner et al. 2014). These observations provide valuable data on the PBL’s thermodynamic properties (e.g., water vapor and/or temperature lidars and radiometers), dynamic properties (e.g., DL), and distribution of tracer substances (e.g., aerosol lidars), all of which can be used to estimate PBLHT. ARM developed PBLHT estimates from the micropulse lidar (MPL; PBLHTMPL), Doppler lidar (PBLHTDL), and combined Raman lidar (RL)/atmospheric emitted radiance interferometer (AERI) thermodynamic profiles (PBLHTTHERMO). Each estimate captures different physical characteristics of the boundary layer—aerosol tracers, vertical velocity turbulence, and thermodynamic structure—and exhibits distinct strengths and limitations depending on the PBL regime and time of day. In addition, the ARM ceilometer (CEIL) provides three potential PBLHT candidates derived from the vendor's built-in algorithm. Building on these individual retrievals, ARM developed the PBLHTBEML VAP, which combines the four remote-sensing-based estimates with ancillary meteorological variables using the machine learning approach of Zhang et al. (2025) to produce a best-estimate PBLHT at 10-minute resolution.
An Atmospheric Variability Experiment (AVE) was conducted over the central U.S. in the spring of 1982, collecting radiosonde date to verify mesoscale soundings from the VISSR Atmospheric Sounder (VAS) on the GOES satellite. Previously published VAS/AVE comparisons for the 6 March 1982 case found that the satellite retrievals scarcely detected a low level temperature inversion or a mid-tropospheric cold pool over a special mesoscale radiosonde verification network in north central Texas. The previously published regression and physical retrieval algorithms did not fully utilize VAS' sensitivity to important subsynoptic thermal features. Therefore, the 6 March 1982 case was reprocessed adding two enhancements to the VAS regression retrieval algorithm: (1) the regression matrix was determined using AVE profile data obtained in the region at asynoptic times, and (2) more optimistic signal-to-noise statistical conditioning factors were applied to the VAS temperature sounding channels. The new VAS soundings resolve more of the low level temperature inversion and mid-level cold pool. Most of the improvements stems from the utilization of asynoptic radiosonde observations at NWS sites. This case suggests that VAS regression soundings may require a ground-based asynoptic profiler network to bridge the gap between the synoptic radiosonde network and the high resolution geosynchronous satellite observations during the day.
Monthly layer-mean TOVS retrieved temperatures for NOAA-6 and NOAA-7 from the tropics to the midlatitudes for 1980-1983 are compared to monthly layer-mean radiosonde temperature data. The amplitude of the January/July mean temperatures and the amplitude of the annual temperature cycle in the 100-70 and the 700-500 hPa layers are analyzed. It is observed that in the 100-70 hPa layer the TOVS data underestimate the annual cycle by about 3-4 K and the NOAA-6 data are closer to the radiosonde temperatures than the NOAA-7; in the 700-500 hPa layer the NOAA-6 is within + or - 1 K of the radiosonde measurements and the NOAA-7 data are within 0.5-1 K of the radiosonde data.
Collocated measurements of radiosonde and TIROS Operational Vertical Sounder (TOVS) data are compared to determine whether significant latitudinal, seasonal, or instrumental biases exist. Long-wave structure determined by each system is being examined for correlation. Because U.S. radiosonde data are archived uncorrected, an effort to correct the measurements is being considered. These corrections will be applied to 7 years of measurements. New comparisons will then be made using corrected radiosonde and TOVS data. A regression method is used to retrieve temperatures, and radiosonde zonal means are used to update the regression coefficients. Preliminary results of auto-correlation analysis indicate that detection of long-wave structure in each set of data is slightly different with some levels and locations experiencing different wave periods or amplitudes. Scatter diagrams and linear regression for each layer show bias in the mean temperatures. Winter-time measurements show poorer correlations due to noisy measurements.
VAS satellite retrievals and derived parameters used in forecasting severe local storms were evaluated against corresponding radiosonde values. VAS products were also compared with the first-guess input to the retrieval algorithm. The evaluation methodology was to pair each radiosonde observation with the closest VAS retrieval within 50 km during a four-month period in 1986. VAS temperatures agreed closely with radiosonde values; dewpoints showed less agreement. Sounding differences were poorly correlated with the number of pixels from which the retrievals were prepared. VAS discrepancies usually were not well correlated with first-guess errors. Horizontal gradients of VAS products were stronger than those from radiosondes. VAS precipitable water agreed better with ground truth than did dewpoints at individual levels, but VAS thicknesses were not much improved. Results for severe storms forecasting parameters indicated that discrepancies increased with the amount of manipulation required during computation. The lifted index showed the best agreement; positive bouyant energy showed poor comparisons.
This paper reviews observations of stratospheric temperatures that have been made over a period of several decades. Those observed temperatures have been used to assess variations and trends in stratospheric temperatures. A wide range of observation datasets have been used, comprising measurements by radiosonde (1940s to the present), satellite (1979 - present), lidar (1979 - present) and rocketsonde (periods varying with location, but most terminating by about the mid-1990s). In addition, trends have also been assessed from meteorological analyses, based on radiosonde and/or satellite data, and products based on assimilating observations into a general circulation model. Radiosonde and satellite data indicate a cooling trend of the annual-mean lower stratosphere since about 1980. Over the period 1979-1994, the trend is 0.6K/decade. For the period prior to 1980, the radiosonde data exhibit a substantially weaker long-term cooling trend. In the northern hemisphere, the cooling trend is about 0.75K/decade in the lower stratosphere, with a reduction in the cooling in mid-stratosphere (near 35 km), and increased cooling in the upper stratosphere (approximately 2 K per decade at 50 km). Model simulations indicate that the depletion of lower stratospheric ozone is the dominant factor in the observed lower stratospheric cooling. In the middle and upper stratosphere both the well-mixed greenhouse gases (such as CO) and ozone changes contribute in an important manner to the cooling.
We compared zonal mean specific humidity derived from the 21 June-4 July 1995 Global Positioning System (GPS)/MET occultation observations with that derived from the European Center for Medium-Range Weather Forecasts (ECMWF) global analyses. The GPS/MET results indicate a drier troposphere, especially near the subtropical tradewind inversion. A small, moist bias in the GPS/MET upper northern-hemisphere troposphere compared to ECMWF may be due to a small radiosonde temperature bias. A diagram shows the difference (g/kg) between the GPS/MET zonal mean specific humidity and that for June-August derived from 1963-1973 radiosondes. Although the observing period is short, GPS and ECMWF results both indicate a significantly wetter boundary layer at most latitudes consistent with decadal trends observed in radiosonde data. GPS/MET results exhibit higher tropical convective available potential energy (CAPE), suggesting a more vigorous tropical Hadley circulation. Drier, free troposphere air in the descending branches of the Hadley circulation is due in part to a moist radiosonde bias but may also reflect some negative moisture feedback. Using 1992-1997 ground GPS observations and recent advancements in GPS technology, we removed an apparent altimetric drift (-1.2 +/- 0.4 mm/yr) due to columnar water vapor from the Topography (Ocean) Experiment (TOPEX) microwave radiometer, which brought the TOPEX mean sea level change estimates into better agreement with historical tide gauge records, suggesting global mean sea level is rising at a rate of 1.5-2.0 mm/yr. We can also discern a statistically significant increase of 0.2 +/- 0.1 kg/square m/yr in mean columnar water vapor over the ocean from 1992-1997. Optimal fingerprinting can be used for the detection and attribution of tropospheric warming due to an anthropogenic greenhouse. Optimal fingerprinting distinguishes between different types of signals according to their spatial and temporal patterns, while minimizing the influence of natural climate variability. S. Leroy concludes that the signal-to-noise ratio of global warming detection increases by unity approximately every 10 years if a single oceanic region is chosen. Less time for detection is likely when many global regions are considered simultaneously. GPS occultation constellations allow the possibility of detecting small changes in upper air temperature with inconsequential calibration errors, making occultation an ideal data type for global warming detection studies. Our initial study of a 22-GHz satellite-satellite occultation system predicts upper troposphere moisture sensitivities of 3-5 ppmv and 1-2 percent in the middle and lower troposphere. Additional information contained in original.
SAGE water vapor data has proven valuable for addressing several of the important issues in climate change research. It has been used to investigate how the upper troposphere water vapor responds to warming and convection, a key question in understanding the water vapor feedback to anthropogenic global warming. In the case of summer versus winter differences, SAGE results showed that the upper tropospheric relative humidity remained approximately constant; this result was in general agreement with how a GCM handled the seasonal difference, and gave credence to the argument that the GCM was not overestimating the water vapor feedback associated with convection. In addition, the convection-water vapor relationship was investigated further using SAGE water vapor and ISCCP cloud data. The results showed that upper tropospheric drying did appear to occur simultaneously with deep convective events in the tropics, only to be replaced by moistening a few hours later, associated (most likely) with the reevaporation of moisture from anvil clouds. The total effect was, again, a moistening of the upper troposphere associated with convection. Calculation of the actual trends in upper tropospheric moisture is a potential goal for SAGE data usage; trends calculated with radiosonde data, or instruments calibrated with radiosonde data have the problem of the effect of changing radiosonde instruments. SAGE data have in effect been used to compare different radiosondes through comparisons, and could continue to do so. SAGE 3 should also help clarify the absolute accuracy of SAGE retrievals in the troposphere. and its consequences. Model results show that water vapor increases can help explain the observations of stratospheric cooling, along with increasing C02 and ozone reduction. SAGE has been shown to provide trends similar to those of some other satellite and in situ retrievals, with increasing water vapor over time. However, SAGE is impacted by aerosol contamination which must be removed from the data; approaches used in the past will be reviewed. The SAGE water vapor errors, if taken literally, would make it hard to justify using SAGE data for such trends, and the error bars must be investigated. Stratospheric water vapor increases, like tropospheric increases, would lead to noticeable warming at the surface, and their changes must be quantified.
Retrieval of water vapor mixing ratio using the Howard University Raman Lidar is presented with emphasis on three aspects: i) performance of the lidar against collocated radiosondes and Raman lidar, ii) investigation of the atmospheric state variables when poor agreement between lidar and radiosondes values occurred and iii) a comparison with satellite-based measurements. The measurements were acquired during the Water Vapor Validation Experiment Sondes/Satellites 2006 field campaign. Ensemble averaging of water vapor mixing ratio data from ten night-time comparisons with Vaisala RS92 radiosondes shows on average an agreement within 10 % up to approx. 8 km. A similar analysis of lidar-to-lidar data of over 700 profiles revealed an agreement to within 20 % over the first 7 km (10 % below 4 km). A grid analysis, defined in the temperature - relative humidity space, was developed to characterize the lidar - radiosonde agreement and quantitatively localizes regions of strong and weak correlations as a function of altitude, temperature or relative humidity. Three main regions of weak correlation emerge: i) regions of low relative humidity and low temperature, ii) moderate relative humidity at low temperatures and iii) low relative humidity at moderate temperatures. Comparison of Atmospheric InfraRed Sounder and Tropospheric Emission Sounder satellites retrievals of moisture with that of Howard University Raman Lidar showed a general agreement in the trend but the formers miss a lot of the details in atmospheric structure due to their low resolution. A relative difference of about 20 % is usually found between lidar and satellites measurements.
In a recent publication, LeBlanc and McDermid proposed a hybrid calibration technique for Raman water vapor lidar involving a tungsten lamp and radiosondes. Measurements made with the lidar telescope viewing the calibration lamp were used to stabilize the lidar calibration determined by comparison with radiosonde. The technique provided a significantly more stable calibration constant than radiosondes used alone. The technique involves the use of a calibration lamp in a fixed position in front of the lidar receiver aperture. We examine this configuration and find that such a configuration likely does not properly sample the full lidar system optical efficiency. While the technique is a useful addition to the use of radiosondes alone for lidar calibration, it is important to understand the scenarios under which it will not provide an accurate quantification of system optical efficiency changes. We offer examples of these scenarios.
Reanalyses have become important sources of data in weather and climate research. While observations are the most crucial component of the systems, few research projects consider carefully the multitudes of assimilated observations and their impact on the results. This is partly due to the diversity of observations and their individual complexity, but also due to the unfriendly nature of the data formats. Here, we discuss the NASA Modern-Era Retrospective analysis for Research and Applications (MERRA) and a companion dataset, the Gridded Innovations and Observations (GIO). GIO is simply a post-processing of the assimilated observations and their innovations (forecast error and analysis error) to a common spatio-temporal grid, following that of the MERRA analysis fields. This data includes in situ, retrieved and radiance observations that are assimilated and used in the reanalysis. While all these disparate observations and statistics are in a uniform easily accessible format, there are some limitations. Similar observations are binned to the grid, so that multiple observations are combined in the gridding process. The data is then implicitly thinned. Some details in the meta data may also be lost (e.g. aircraft or station ID). Nonetheless, the gridded observations should provide easy access to all the observations input to the reanalysis. To provide an example of the GIO data, a case study evaluating observing systems over the United States and statistics is presented, and demonstrates the evaluation of the observations and the data assimilation. The GIO data is used to collocate 200mb Radiosonde and Aircraft temperature measurements from 1979-2009. A known warm bias of the aircraft measurements is apparent compared to the radiosonde data. However, when larger quantities of aircraft data are available, they dominate the analysis and the radiosonde data become biased against the forecast. When AMSU radiances become available the radiosonde and aircraft analysis and forecast error take on an annual cycle. While this supports results of previous work that recommend bias corrections for the aircraft measurements, the interactions with AMSU radiances will also require further investigation. This also provides an example for reanalysis users in examining the available observations and their impact on the analysis. GIO data is presently available alongside the MERRA reanalysis.