Evaluation of Temperature and Humidity Profiles of Unified Model and ECMWF Analyses Using GRUAN Radiosonde Observations

Noh, Young-Chan; Sohn, Byung-Ju; Kim, Yoonjae; Joo, Sangwon; Bell, William

doi:10.3390/atmos7070094

Cited by 22 publications

(16 citation statements)

References 23 publications

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“…Reanalysis outputs based on past radiosonde data, also assimilating satellite data when available, offer multiple-level, globally gridded, synopticscale moisture fields up to four times daily from a beginning year (e.g., 1948 in NCEP/NCAR Reanalysis 1; 1979 in NCEP/NCAR Reanalysis 2 and ECMWF's ERA-Interim) to present time -even though radiosonde observations are scarce over the ocean, unevenly spaced over land, and taken normally twice a day, with significant differences in vertical coverage. Naturally, since air moisture is highly variable in time and space, humidity data from different reanalysis models show discrepancies and can differ significantly from the collocated radiosonde data (e.g., Noh et al, 2016). Therefore, the radiosonde archives represent the primary source of information on the short-and long-term distribution of moisture in the troposphere, despite various data inhomogeneities.…”

Section: Introductionmentioning

confidence: 99%

Completeness of radiosonde humidity observations based on the Integrated Global Radiosonde Archive

Ferreira

Nieto

Gimeno

2019

Earth Syst. Sci. Data

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Abstract. Radiosonde measurements from the 1930s to present give unique information on the distribution and variability of water vapor in the troposphere. The sounding data from the Integrated Global Radiosonde Archive (IGRA) Version 2 are examined here until the end of 2016, aiming to describe the completeness of humidity observations (simultaneous measurements of pressure, temperature, and humidity) in different times and locations. Upon finding the stations with a non-negligible number of radiosonde observations in their period of record, thus removing pilot-balloon stations from IGRA, the selected set (designated IGRA-RS) comprises 1723 stations, including 1300 WMO stations, of which 178 belong to the current GCOS Upper-Air Network (GUAN) and 16 to the GCOS Reference Upper-Air Network (GRUAN). Completeness of humidity observations for a radiosonde station and a full year is herein defined by five basic parameters: number of humidity soundings, fraction of days with humidity data, average vertical resolution, average atmospheric pressure and altitude at the highest measuring level, and maximum number of consecutive days without data. The observations eligible for calculating precipitable water vapor – i.e., having adequate vertical sampling between the surface and 500 hPa – are particularly studied. The present study presents the global coverage of humidity data and an overall picture of the temporal and vertical completeness parameters over time. This overview indicates that the number of radiosonde stations potentially useful for climate studies involving humidity depends not only on their record length, but also on the continuity, regularity, and vertical sampling of the humidity time series. Additionally, a dataset based on IGRA is described with the purpose of helping climate and environmental scientists to select radiosonde data according to various completeness criteria – even if differences in instrumentation and observing practices require extra attention. This dataset consists of two main subsets: (1) statistical metadata for each IGRA-RS station and year within the period of record; and (2) metadata for individual observations from each station. These are complemented by (3) a list of the stations represented in the whole dataset, along with the observing periods for humidity (relative humidity or dew-point depression) and the corresponding counts of observations. The dataset is to be updated on a 2-year basis, starting in 2019, and is available at https://doi.org/10.5281/zenodo.1332686.

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Section: Introductionmentioning

confidence: 99%

Completeness of radiosonde humidity observations based on the Integrated Global Radiosonde Archive

Ferreira

Nieto

Gimeno

2019

Earth Syst. Sci. Data

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“…However, the accuracy of model analysis differs by operational centers depending on the models and quality control schemes that the centers employ for the assimilations [27]. In addition, the accuracy of radiosonde measurements greatly depends on radiosonde types and stations [29,30], which will, in turn, affect the accuracy of NWP models that employ radiosonde data for the assimilations.…”

Section: Validation Resultsmentioning

confidence: 99%

“…The diurnal variation of O-B values in the surface-sensitive channels may be attributed to NWP model skin temperatures, which are not representative of the surface temperature but of a deeper layer and held fixed over the ocean during the day [13]. The diurnal variations shown in the water vapor channels, which are not sensitive to the surface nor to clouds, may also be attributed to NWP models which are known to have diurnal variations of humidity in the upper troposphere over convective regions and have the patterns that are substantially different from that of satellite observations [26][27][28]. After the retrieval (Figure 10b,d), however, the statistics are very stable with small bias within the observation error range throughout the analyzed time period.…”

Section: Tb Departuresmentioning

confidence: 98%

Atmospheric Profile Retrieval Algorithm for Next Generation Geostationary Satellite of Korea and Its Application to the Advanced Himawari Imager

2017

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Abstract:In preparation for the 2nd geostationary multi-purpose satellite of Korea with a 16-channel Advanced Meteorological Imager; an algorithm has been developed to retrieve clear-sky vertical profiles of temperature (T) and humidity (Q) based on a nonlinear optimal estimation method. The performance and characteristics of the algorithm have been evaluated using the measured data of the Advanced Himawari Imager (AHI) on board the Himawari-8 of Japan, launched in 2014. Constraints for the optimal estimation solution are provided by the forecasted T and Q profiles from a global numerical weather prediction model and their error covariance. Although the information contents for temperature is quite low due to the limited number of channels used in the retrieval; the study reveals that useful moisture information (2~3 degrees of freedom for signal) is provided from the three water vapor channels; contributing to the increase in the moisture retrieval accuracy upon the model forecast. The improvements are consistent throughout the tropospheric atmosphere with almost zero mean bias and 9% (relative humidity) of root mean square error between 100 and 1000 hPa when compared with the quality-controlled radiosonde data from 2016 August.

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“…Noh et al . () used one‐year radiosonde observations from the Global Climate Observing System Reference Upper‐Air Network (GRUAN) to verify the Global UM analysis. Moist bias (approximately 3% in RH) was only found in the upper troposphere, much smaller than that in this study.…”

Section: Resultsmentioning

confidence: 99%

Model moist bias in the middle and upper troposphere during DEEPWAVE

Yang

Moore

Uddstrom

et al. 2017

Atmospheric Science Letters

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Data from 279 dropsonde profiles collected during the Deep Propagating Gravity Wave Experiment (DEEPWAVE) over New Zealand between 4 June and 20 July 2014 were used to verify the relative humidity (RH) fields simulated by regional configurations of the UK Met Office Unified Model (MetUM) in the troposphere. Significant RH biases (predictions up to 28% too high) were found in the middle and upper troposphere during this period. This RH bias was found to be mainly caused by the errors in the simulated-specific humidity. It is demonstrated here that evaporation from the lower boundary (mainly sea surface) is not a factor leading to the moist bias. A similar magnitude of moist bias was also found in the Global UM (the global configuration of the MetUM) and from a preliminary inspection is also very likely to occur in ERA-interim and NCEP-GFS reanalyses. This study suggests that the moist bias is very likely not a regional or a model specific issue.

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Evaluation of Temperature and Humidity Profiles of Unified Model and ECMWF Analyses Using GRUAN Radiosonde Observations

Cited by 22 publications

References 23 publications

Completeness of radiosonde humidity observations based on the Integrated Global Radiosonde Archive

Completeness of radiosonde humidity observations based on the Integrated Global Radiosonde Archive

Atmospheric Profile Retrieval Algorithm for Next Generation Geostationary Satellite of Korea and Its Application to the Advanced Himawari Imager

Model moist bias in the middle and upper troposphere during DEEPWAVE

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