Quantifying the value of redundant measurements at GCOS Reference Upper-Air Network sites

Madonna, Fabio; Rosoldi, Marco; Güldner, J.; Haefele, Alexander; Kivi, Rigel; Cadeddu, Maria; Sisterson, Douglas L; Pappalardo, Gelsomina

doi:10.5194/amt-7-3813-2014

Cited by 7 publications

(4 citation statements)

References 19 publications

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“…Further correlation approaches, such as the Shannon information index (Silva and Quiroz 2003;Madonna et al 2014) or directional information transfer index (Alfonso et al 2010), can measure the more general nonlinear information. It is computationally intensive for a large dataset and thus unsuitable for the application of global network assessment.…”

Section: Alternative Metrics For Evaluating a Networkmentioning

confidence: 99%

Spatial Coverage of Monitoring Networks: A Climate Observing System Simulation Experiment

Bodeker

Fassò

Chang

et al. 2017

Journal of Applied Meteorology and Climatology

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Observing systems consisting of a finite number of in situ monitoring stations can provide high-quality measurements with the ability to quality assure both the instruments and the data but offer limited information over larger geographic areas. This paper quantifies the spatial coverage represented by a finite set of monitoring stations by using global data-data that are possibly of lower resolution and quality. For illustration purposes, merged satellite temperature data from Microwave Sounding Units are used to estimate the representativeness of the Global Climate Observing System Reference Upper-Air Network (GRUAN). While many metrics exist for evaluating the representativeness of a site, the ability to have highly accurate monthly averaged data is essential for both trend detection and climatology evaluation. The calculated correlations of the monthly averaged upper-troposphere satellite-derived temperatures over the GRUAN stations with all other pixels around the globe show that the current 9 certified GRUAN stations have moderate correlations (r $ 0.7) for approximately 10% of the earth, but an expanded network incorporating another 15 stations would result in moderate correlations for just over 60% of the earth. This analysis indicates that the value of additional stations can be quantified by using historical, satellite, or model data and can be used to reveal critical gaps in current monitoring capabilities. Evaluating the value of potential additional stations and prioritizing their initiation can optimize networks. The expansion of networks can be evaluated in a manner that allows for optimal benefit on the basis of optimization theory and economic analyses.

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Section: Alternative Metrics For Evaluating a Networkmentioning

confidence: 99%

Spatial Coverage of Monitoring Networks: A Climate Observing System Simulation Experiment

Bodeker

Fassò

Chang

et al. 2017

Journal of Applied Meteorology and Climatology

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“…reduce the random uncertainties in applications using only one of these measurement techniques (Madonna et al, 2014).…”

Section: Radiosounding Activities At Ciao and Laboratory Equipment At Inrimmentioning

confidence: 99%

Intercomparison of Vaisala RS92 and RS41 radiosonde temperature sensors under controlled laboratory conditions

Rosoldi

Coppa

Merlone

et al. 2021

Preprint

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Abstract. Radiosounding profiles are essential for weather and climate applications, as well as for the calibration and validation of remote sensing measurements. Vaisala RS92 radiosondes have been widely used on a global scale until 2016, although in the fall of 2013 Vaisala introduced the RS41 model to progressively replace the RS92. To ensure homogeneity and the highest quality of data records following the transition from RS92 to RS41, intercomparisons of the two radiosonde models are needed. An intercomparison experiment has been performed where, for the first time and independently of the manufacturer, RS92 and RS41 radiosondes have been simultaneously tested and compared inside climatic chambers in order to characterize the noise, the calibration accuracy and the bias of their temperature measurements. A pair of RS41 and RS92 radiosondes has been tested at ambient pressure under very different temperature and humidity conditions. The results reveal that the temperature sensor of RS41 is less affected by noise and more accurate than that of RS92, with noise values less than 0.06 °C for RS41 and less than 0.1 °C for RS92. The error corrected by means of calibration, evaluated as the deviation from a reference value and referred as calibration error, is within ±0.1 °C for RS41 and the related uncertainty (hereafter with coverage factor k = 1) is less than 0.06 °C, while RS92 is affected by a cold bias in the calibration, which ranges from 0.1 °C up to a few tenths of a degree, with a calibration uncertainty less than 0.1 °C. Under conditions similar to those that radiosondes meet at the ground in nighttime radiosoundings, the temperature bias between RS41 and RS92 is within ±0.1 °C, while its uncertainty is less than 0.1 °C. The radiosondes have also been tested before and after fast (within ≈ 10 s) temperature changes of about ±20 °C, simulating a scenario similar to steep thermal changes that radiosondes may meet when passing from indoor to outdoor environment during the pre-launch phase. The results reveal that such thermal changes may increase the noise of temperature sensors during radiosoundings, up to 0.1 °C for the RS41 and up to 0.3 °C for the RS92, with a similar increase in the calibration uncertainty of temperature sensors, as well as an increase in the uncertainty of their bias up to 0.3 °C. However, the thermal changes do not appear to affect sensors’ calibration error and temperature bias.

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“…The Global Satellite Navigation System (GNSS) has become a unique data source for the retrieval of IWV [8] with outstanding features like high accuracy, high temporal resolution, and all-weather capability [4]. These advantages allow GNSS IWV to be used for a broad range of applications, including validating radiosonde, satellite and reanalysis data [9][10][11][12], studying diurnal variations and trends of IWV [4,9,[13][14][15], regional drought monitoring [16], monitoring climate change [17] and validating climate models [18][19][20]. Monitoring the long-term trends in IWV [21] may contribute to confirming the warming of the atmosphere, thus giving ancillary information on climate change.…”

Section: Introductionmentioning

confidence: 99%

The Novel Copernicus Global Dataset of Atmospheric Total Water Vapour Content with Related Uncertainties from GNSS Observations

Rannat,

Keernik,

Madonna

2023

Remote Sensing

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A novel algorithm has been designed and implemented in the Climate Data Store (CDS) frame of the Copernicus Climate Change Service (C3S) with the main goal of providing high-quality GNSS-based integrated water vapour (IWV) datasets for climate research and applications. For this purpose, the related CDS GNSS datasets were primarily obtained from GNSS reprocessing campaigns, given their highest quality in adjusting systematic effects due to changes in instrumentation and data processing. The algorithm is currently applied to the International GNSS Service (IGS) tropospheric products, which are consistently extended in near real-time and date back to 2000, and to the results of a reprocessing campaign conducted by the EUREF Permanent GNSS Network (EPN repro2), covering the period from 1996 to 2014. The GNSS IWV retrieval employs ancillary meteorological data sourced from ERA5. Moreover, IWV estimates are provided with associated uncertainty, using an approach similar to that used for the Global Climate Observing System Reference Upper-Air Network (GRUAN) GNSS data product. To assess the quality of the newly introduced GNSS IWV datasets, a comparison is made against the radiosonde data from GRUAN and the Radiosounding HARMonization (RHARM) dataset as well as with the IGS repro3, which will be the next GNSS-based extension of IWV time series at CDS. The comparison indicates that the average difference in IWV among the reprocessed GNSS datasets is less than 0.1 mm. Compared to RHARM and GRUAN IWV values, a small dry bias of less than 1 mm for the GNSS IWV is detected. Additionally, the study compares GNSS IWV trends with the corresponding values derived from RHARM at selected radiosonde sites with more than ten years of data. The trends are mostly statistically significant and in good agreement.

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Quantifying the value of redundant measurements at GCOS Reference Upper-Air Network sites

Cited by 7 publications

References 19 publications

Spatial Coverage of Monitoring Networks: A Climate Observing System Simulation Experiment

Spatial Coverage of Monitoring Networks: A Climate Observing System Simulation Experiment

Intercomparison of Vaisala RS92 and RS41 radiosonde temperature sensors under controlled laboratory conditions

The Novel Copernicus Global Dataset of Atmospheric Total Water Vapour Content with Related Uncertainties from GNSS Observations

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