scholarly journals A New Technique for Estimation of Lower-Tropospheric Temperature and Water Vapor Profiles from Radio Occultation Refractivity

2009 ◽  
Vol 26 (6) ◽  
pp. 1075-1089 ◽  
Author(s):  
D. Jagadheesha ◽  
B. Simon ◽  
P-K. Pal ◽  
P. C. Joshi ◽  
A. Maheshwari
Keyword(s):  
Radio Occultation ◽  
Specific Humidity ◽  
New Technique ◽  
Radiosonde Data ◽  
Boreal Summer ◽  
Tropospheric Temperature ◽  
Lower Altitude ◽  
Temperature And Humidity ◽  
The Tropics ◽  
Humidity Profiles

Abstract An empirical technique is proposed to obtain temperature and humidity profiles over the tropics using radio occultation refractivity profiles and surface/available lower-altitude temperature and pressure measurements over humid tropical regions. The technique is tested on a large number of diverse radiosonde-derived refractivity profiles over the tropics (30°S–30°N) and selected Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) radio occultation refractivity profiles that have collocated radiosonde observations over the region 10°S–30°N during the boreal summer of 2006. In a number of cases, the results were in good agreement with the collocated radiosonde data. The error statistics of temperature and humidity profiles obtained from the proposed technique are discussed and compared with the previously published results from another technique and also with the results of a one-dimensional variational data assimilation (1DVAR) technique given with COSMIC data. It is found that the previously published results and proposed technique are marginally better (worse) in reproducing observed relative humidity (specific humidity) when compared to the 1DVAR technique. The proposed new technique is applied on COSMIC refractivity profiles over the Bay of Bengal during summer 2007 to derive changes in vertical thermal and moisture changes in the troposphere between active and break phases of the monsoon pattern and many of the observed features are captured reasonably well.

scholarly journals Assessments of the Retrieval of Atmospheric Profiles from GNSS Radio Occultation Data in Moist Tropospheric Conditions Using Radiosonde Data

2020 ◽  
Vol 12 (17) ◽  
pp. 2717
Author(s):  
Ying Li ◽  
Yunbin Yuan ◽  
Xiaoming Wang
Keyword(s):  
Radio Occultation ◽  
Satellite System ◽  
Specific Humidity ◽  
Radiosonde Data ◽  
Temperature Profiles ◽  
Standard Deviations ◽  
Mean Differences ◽  
June July ◽  
Global Navigation Satellite ◽  
Humidity Profiles

The Global Navigation Satellite System (GNSS) Radio Occultation (RO) retrieved temperature and specific humidity profiles can be widely used for weather and climate studies in troposphere. However, some aspects, such as the influences of background data on these retrieved moist profiles have not been discussed yet. This research evaluates RO retrieved temperature and specific humidity profiles from Wegener Center for Climate and Global Change (WEGC), Radio Occultation Meteorology Satellite Application Facility (ROM SAF) and University Corporation for Atmospheric Research (UCAR) Boulder RO processing centers by comparing with measurements from 10 selected Integrated Global Radiosonde Archive (IGRA) radiosonde stations in different latitudinal bands over 2007 to 2010. The background profiles used for producing their moist profiles are also compared with radiosonde. We found that RO retrieved temperature profiles from all centers agree well with radiosonde. Mean differences at polar, mid-latitudinal and tropical stations are varying within ±0.2 K, ±0.5 K and from −1 to 0.2 K, respectively, with standard deviations varying from 1 to 2 K for most pressure levels. The differences between RO retrieved and their background temperature profiles for WEGC are varying within ±0.5 K at altitudes above 300 hPa, and the differences for ROM SAF are within ±0.2 K, and that for UCAR are within 0.5 K at altitudes below 300 hPa. Both RO retrieved and background specific humidity above 600 hPa are found to have large positive differences (up to 40%) against most radiosonde measurements. Discrepancies of moist profiles among the three centers are overall minor at altitudes above 300 hPa for temperature and at altitudes above 700 hPa for specific humidity. Specific humidity standard deviations are largest at tropical stations in June July August months. It is expected that the outcome of this research can help readers to understand the characteristics of moist products among centers.

scholarly journals Estimation of atmospheric mixing layer height from radiosonde data

2014 ◽  
Vol 7 (6) ◽  
pp. 1701-1709 ◽  
Author(s):  
X. Y. Wang ◽  
K. C. Wang
Keyword(s):  
North America ◽  
Potential Temperature ◽  
Mixing Layer ◽  
Specific Humidity ◽  
Radiosonde Data ◽  
Consistent Estimate ◽  
Climate Data ◽  
Layer Height ◽  
Temperature And Humidity ◽  
Mixing Layer Height

Abstract. Mixing layer height (h) is an important parameter for understanding the transport process in the troposphere, air pollution, weather and climate change. Many methods have been proposed to determine h by identifying the turning point of the radiosonde profile. However, substantial differences have been observed in the existing methods (e.g. the potential temperature (θ), relative humidity (RH), specific humidity (q) and atmospheric refractivity (N) methods). These differences are associated with the inconsistency of the temperature and humidity profiles in a boundary layer that is not well mixed, the changing measurability of the specific humidity and refractivity with height, the measurement error of humidity instruments within clouds, and the general existence of clouds. This study proposes a method to integrate the information of temperature, humidity and cloud to generate a consistent estimate of h. We apply this method to high vertical resolution (~ 30 m) radiosonde data that were collected at 79 stations over North America during the period from 1998 to 2008. The data are obtained from the Stratospheric Processes and their Role in Climate Data Center (SPARC). The results show good agreement with those from N method as the information of temperature and humidity contained in N; however, cloud effects that are included in our method increased the reliability of our estimated h. From 1988 to 2008, the climatological h over North America was 1675 ± 303 m with a strong east–west gradient: higher values (generally greater than 1800 m) occurred over the Midwest US, and lower values (usually less than 1400 m) occurred over Alaska and the US West Coast.

scholarly journals Clustering a Global Field of Atmospheric Profiles by Mixture Decomposition of Copulas

2005 ◽  
Vol 22 (10) ◽  
pp. 1445-1459 ◽  
Author(s):  
Mathieu Vrac ◽  
Alain Chédin ◽  
Edwin Diday
Keyword(s):  
A Priori ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Specific Humidity ◽  
Vertical Profiles ◽  
Copula Functions ◽  
Mixture Density ◽  
Temperature And Humidity ◽  
Priori Information ◽  
Humidity Profiles

Abstract This work focuses on the clustering of a large dataset of atmospheric vertical profiles of temperature and humidity in order to model a priori information for the problem of retrieving atmospheric variables from satellite observations. Here, each profile is described by cumulative distribution functions (cdfs) of temperature and specific humidity. The method presented here is based on an extension of the mixture density problem to this kind of data. This method allows dependencies between and among temperature and moisture to be taken into account, through copula functions, which are particular distribution functions, linking a (joint) multivariate distribution with its (marginal) univariate distributions. After a presentation of vertical profiles of temperature and humidity and the method used to transform them into cdfs, the clustering method is detailed and then applied to provide a partition into seven clusters based, first, on the temperature profiles only; second, on the humidity profiles only; and, third, on both the temperature and humidity profiles. The clusters are statistically described and explained in terms of airmass types, with reference to meteorological maps. To test the robustness and the relevance of the method for a larger number of clusters, a partition into 18 classes is established, where it is shown that even the smallest clusters are significant. Finally, comparisons with more classical efficient clustering or model-based methods are presented, and the advantages of the approach are discussed.

scholarly journals Assessment of COSMIC radio occultation retrieval product using global radiosonde data

2012 ◽  
Vol 5 (6) ◽  
pp. 8405-8434
Author(s):  
B.-R. Wang ◽  
X.-Y. Liu ◽  
J.-K. Wang
Keyword(s):  
Water Vapor ◽  
Vapor Pressure ◽  
Relative Error ◽  
Radio Occultation ◽  
Water Vapor Pressure ◽  
Specific Humidity ◽  
Radiosonde Data ◽  
Cosmic Radio ◽  
Horizontal Distance ◽  
Matching Criteria

Abstract. The radio occultation retrieval product of the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) radio occultation sounding system was verified using the global radiosonde from 2007 to 2010. 4 yr of samples were used to collect quantities of data using much stricter matching criteria than previous studies to obtain more accurate results. The horizontal distance between the radiosonde station and the occultation event is within 100 km, and the time window is 1 h. The comparison was performed from 925 hPa to 10 hPa. The results indicated that the COSMIC's temperature data agreed well with the radiosonde data. The global mean temperature bias was −0.09 K, with a standard deviation (SD) of 1.72 K. The water vapor pressure of COSMIC showed a systematic bias in relation to radiosonde in higher layers. The mean specific humidity bias of 925–200 hPa is about −0.011 g kg−1, with a SD of about 0.662 g kg−1. The COSMIC quality control process could not detect some abnormal extremely small humidity data which occured frequently in subtropical zone. Despite the large relative error of water vapor pressure, the relative error of refractivity is small. This paper also provides a comparison of eight radiosonde types with COSMIC product. Because the retrieval product is affected by the background error which differed between different regions, the COSMIC retrieval product could be used as a benchmark if the precision requirement is not strict.

scholarly journals Assessment of COSMIC radio occultation retrieval product using global radiosonde data

2013 ◽  
Vol 6 (4) ◽  
pp. 1073-1083 ◽  
Author(s):  
B.-R. Wang ◽  
X.-Y. Liu ◽  
J.-K. Wang
Keyword(s):  
Water Vapor ◽  
Vapor Pressure ◽  
Relative Error ◽  
Radio Occultation ◽  
Water Vapor Pressure ◽  
Specific Humidity ◽  
Radiosonde Data ◽  
Cosmic Radio ◽  
The Mean ◽  
Matching Criteria

Abstract. The radio occultation retrieval product of the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) Radio Occultation sounding system was verified using the global radiosonde data from 2007 to 2010. Samples of 4 yr were used to collect quantities of data using much stricter matching criteria than previous studies to obtain more accurate results. The horizontal distance between the radiosonde station and the occultation event is within 100 km, and the time window is 1 h. The comparison was performed from 925 hPa to 10 hPa. The results indicated that the COSMIC's temperature data agreed well with the radiosonde data. The global mean temperature bias was −0.09 K, with a standard deviation (SD) of 1.72 K. According to the data filtration used in this paper, the mean specific humidity bias of 925–200 hPa is −0.012 g kg−1, with a SD of 0.666 g kg−1, and the mean relative error of water vapor pressure is about 33.3%, with a SD of 107.5%. The COSMIC quality control process failed to detect some of the abnormal extremely small humidity data which occurred frequently in subtropical zone. Despite the large relative error of water vapor pressure, the relative error of refractivity is small. This paper also provides a comparison of eight radiosonde types with COSMIC product. Because the retrieval product is affected by the background error which differed between different regions, the COSMIC retrieval product could be used as a benchmark if the precision requirement is not strict.

scholarly journals Climate intercomparison of GPS radio occultation, RS90/92 radiosondes and GRUAN over 2002 to 2013

2014 ◽  
Vol 7 (11) ◽  
pp. 11735-11769
Author(s):  
F. Ladstädter ◽  
A. K. Steiner ◽  
M. Schwärz ◽  
G. Kirchengast
Keyword(s):  
Radio Occultation ◽  
Global Climate ◽  
Time Frame ◽  
Specific Humidity ◽  
Radiosonde Data ◽  
Gps Radio Occultation ◽  
Long Term Stability ◽  
High Vertical Resolution ◽  
Global Coverage

Abstract. Observations from the GPS radio occultation (GPSRO) satellite technique and from the newly established GCOS Reference Upper Air Network (GRUAN) are both candidates to serve as reference observations in the Global Climate Observing System (GCOS). Such reference observations are key to decrease existing uncertainties in upper-air climate research. There are now more than 12 years of data available from GPSRO, with the recognized properties high accuracy, global coverage, high vertical resolution, and long-term stability. These properties make GPSRO a suitable choice for comparison studies with other upper-air observational systems. The GRUAN network consists of reference radiosonde ground stations (16 at present), which adhere to the GCOS climate monitoring principles. In this study, we intercompare GPSRO temperature and humidity profiles and Vaisala RS90/92 data from the "standard" global radiosonde network over the whole 2002 to 2013 time frame. Additionally, we include the first years of GRUAN data (using Vaisala RS92), available since 2009. GPSRO profiles which occur within 3 h and 300 km of radiosonde launches are used. Very good agreement is found between all three datasets with temperature differences usually less than 0.2 K. In the stratosphere above 30 hPa, temperature differences are larger but still within 0.5 K. Day/night comparisons with GRUAN data reveal small deviations likely related to a warm bias of the radiosonde data at high altitudes, but also residual errors from the GPSRO retrieval process might play a role. Vaisala RS90/92 specific humidity exhibits a dry bias of up to 40% in the upper troposphere, with a smaller bias at lower altitudes within 15%. GRUAN shows a marked improvement in the bias characteristics, with less than 5% difference to GPSRO up to 300 hPa. GPSRO dry temperature and physical temperature are validated using radiosonde data as reference. We find that GPSRO provides valuable long-term stable reference observations with well-defined error characteristics for climate applications and for anchoring other upper-air measurements.

scholarly journals A New Algorithm for the Retrieval of Atmospheric Profiles from GNSS Radio Occultation Data in Moist Air and Comparison to 1DVar Retrievals

2019 ◽  
Vol 11 (23) ◽  
pp. 2729 ◽  
Author(s):  
Li ◽  
Kirchengast ◽  
Scherllin-Pirscher ◽  
Schwaerz ◽  
Nielsen ◽  
...  
Keyword(s):  
Radio Occultation ◽  
Optimal Estimation ◽  
Satellite System ◽  
Temporal Variations ◽  
Uncertainty Estimation ◽  
Retrieval Algorithm ◽  
Climate Data ◽  
Moist Air ◽  
Temperature And Humidity ◽  
Humidity Profiles

The Global Navigation Satellite System (GNSS) Radio Occultation (RO) is a key technique for obtaining thermodynamic profiles of temperature, humidity, pressure, and density in the Earth’s troposphere. However, due to refraction effects of both the dry air and water vapor at low altitudes, retrieval of accurate profiles is challenging. Here we introduce a new moist air retrieval algorithm aiming to improve the quality of RO-retrieved profiles in moist air and including uncertainty estimation in a clear sequence of steps. The algorithm first uses RO dry temperature and pressure and background temperature/humidity and their uncertainties to retrieve humidity/temperature and their uncertainties. These temperature and humidity profiles are then combined with their corresponding background profiles by optimal estimation employing inverse-variance weighting. Finally, based on the optimally estimated temperature and humidity profiles, pressure and density profiles are computed using hydrostatic and equation-of-state formulas. The input observation and background uncertainties are dynamically estimated, accounting for spatial and temporal variations. We show results from applying the algorithm on test datasets, deriving insights from both individual profiles and statistical ensembles, and from comparison to independent 1D-Variational (1DVar) algorithm-derived moist air retrieval results from Radio Occultation Meteorology Satellite Application Facility Copenhagen (ROM-SAF) and University Corporation for Atmospheric Research (UCAR) Boulder RO processing centers. We find that the new scheme is comparable in its retrieval performance and features advantages in the integrated uncertainty estimation that includes both estimated random and systematic uncertainties and background bias correction. The new algorithm can therefore be used to obtain high-quality tropospheric climate data records including uncertainty estimation.

scholarly journals Accuracy of retrieving temperature and humidity profiles by ground-based microwave radiometry in truly complex terrain

2015 ◽  
Vol 8 (8) ◽  
pp. 3355-3367 ◽  
Author(s):  
G. Massaro ◽  
I. Stiperski ◽  
B. Pospichal ◽  
M. W. Rotach
Keyword(s):  
Complex Terrain ◽  
Microwave Radiometer ◽  
Radiosonde Data ◽  
Alpine Valley ◽  
Topographic Slope ◽  
Retrieval Algorithms ◽  
Temperature And Humidity ◽  
Upper Level ◽  
The One ◽  
Humidity Profiles

Abstract. Within the Innsbruck Box project, a ground-based microwave radiometer (RPG-HATPRO) was operated in the Inn Valley (Austria), in very complex terrain, between September 2012 and May 2013 to obtain temperature and humidity vertical profiles of the full troposphere with a specific focus on the valley boundary layer. In order to assess its performance in a deep alpine valley, the profiles obtained by the radiometer with different retrieval algorithms based on different climatologies are compared to local radiosonde data. A retrieval that is improved with respect to the one provided by the manufacturer, based on better resolved data, shows a significantly smaller root mean square error (RMSE), both for the temperature and humidity profiles. The improvement is particularly substantial at the heights close to the mountaintop level and in the upper troposphere. Lower-level inversions, common in an alpine valley, are resolved to a satisfactory degree. On the other hand, upper-level inversions (above 1200 m) still pose a significant challenge for retrieval. For this purpose, specialized retrieval algorithms were developed by classifying the radiosonde climatologies into specialized categories according to different criteria (seasons, daytime, nighttime) and using additional regressors (e.g., measurements from mountain stations). The training and testing on the radiosonde data for these specialized categories suggests that a classification of profiles that reproduces meaningful physical characteristics can yield improved targeted specialized retrievals. A novel and very promising method of improving the profile retrieval in a mountainous region is adding further information in the retrieval, such as the surface temperature at fixed levels along a topographic slope or from nearby mountaintops.

scholarly journals Biases in Stratospheric and Tropospheric Temperature Trends Derived from Historical Radiosonde Data

2006 ◽  
Vol 19 (10) ◽  
pp. 2094-2104 ◽  
Author(s):  
William J. Randel ◽  
Fei Wu
Keyword(s):  
Lower Stratosphere ◽  
Radiosonde Data ◽  
Tropospheric Temperature ◽  
Satellite Measurements ◽  
Upper Troposphere ◽  
Step Functions ◽  
Temperature Trends ◽  
Microwave Sounding Unit ◽  
Microwave Sounding ◽  
The Tropics

Abstract Temperature trends derived from historical radiosonde data often show substantial differences compared to satellite measurements. These differences are especially large for stratospheric levels, and for data in the Tropics, where results are based on relatively few stations. Detailed comparisons of one radiosonde dataset with collocated satellite measurements from the Microwave Sounding Unit reveal time series differences that occur as step functions or jumps at many stations. These jumps occur at different times for different stations, suggesting that the differences are primarily related to problems in the radiosonde data, rather than in the satellite record. As a result of these jumps, the radiosondes exhibit systematic cooling biases relative to the satellites. A large number of the radiosonde stations in the Tropics are influenced by these biases, suggesting that cooling in the tropical lower stratosphere is substantially overestimated in these radiosonde data. Comparison of trends from stations with larger and smaller biases suggests the cooling bias extends into the tropical upper troposphere. Significant biases are observed in both daytime and nighttime radiosonde measurements.

scholarly journals Tropospheric dry layers in the tropical western Pacific: comparisons of GPS radio occultation with multiple data sets

2017 ◽  
Vol 10 (3) ◽  
pp. 1093-1110 ◽  
Author(s):  
Therese Rieckh ◽  
Richard Anthes ◽  
William Randel ◽  
Shu-Peng Ho ◽  
Ulrich Foelsche
Keyword(s):  
Radio Occultation ◽  
Western Pacific ◽  
Radiosonde Data ◽  
Data Sets ◽  
Vertical Resolution ◽  
Gps Radio Occultation ◽  
Tropical Western Pacific ◽  
Multiple Data ◽  
Multiple Data Sets ◽  
The Tropics

Abstract. We use GPS radio occultation (RO) data to investigate the structure and temporal behavior of extremely dry, high-ozone tropospheric air in the tropical western Pacific during the 6-week period of the CONTRAST (CONvective TRansport of Active Species in the Tropics) experiment (January and February 2014). Our analyses are aimed at testing whether the RO method is capable of detecting these extremely dry layers and evaluating comparisons with in situ measurements, satellite observations, and model analyses. We use multiple data sources as comparisons, including CONTRAST research aircraft profiles, radiosonde profiles, AIRS (Atmospheric Infrared Sounder) satellite retrievals, and profiles extracted from the ERA (ERA-Interim reanalysis) and the GFS (US National Weather Service Global Forecast System) analyses, as well as MTSAT-2 satellite images. The independent and complementary radiosonde, aircraft, and RO data provide high vertical resolution observations of the dry layers. However, they all have limitations. The coverage of the radiosonde data is limited by having only a single station in this oceanic region; the aircraft data are limited in their temporal and spatial coverage; and the RO data are limited in their number and horizontal resolution over this period. However, nearby observations from the three types of data are highly consistent with each other and with the lower-vertical-resolution AIRS profiles. They are also consistent with the ERA and GFS data. We show that the RO data, used here for the first time to study this phenomenon, contribute significant information on the water vapor content and are capable of detecting layers in the tropics and subtropics with extremely low humidity (less than 10 %), independent of the retrieval used to extract moisture information. Our results also verify the quality of the ERA and GFS data sets, giving confidence to the reanalyses and their use in diagnosing the full four-dimensional structure of the dry layers.

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