Ship- and island-based soundings from the 2016 El Niño Rapid Response (ENRR) field campaign

Hartten, Leslie M.; Cox, Christopher; Johnston, Paul E.; Wolfe, D. E.; Abbott, Scott; McColl, H. Alex; Quan, Xiao-Wei; Winterkorn, Matthew G.

doi:10.5194/essd-10-1165-2018

Cited by 5 publications

(1 citation statement)

References 12 publications

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“…ENRR observation platforms include dropsondes (Vaisala RS-92) from 22 flights of the NOAA Gulfstream-IV aircraft (hereafter, G-IV) and 6 coordinated flights of Air Force C130s (NOAA 2018; UCAR/NCAR-Earth Observing Laboratory 1994). ENRR observations also include radiosondes (Vaisala RS92-SGP) launched from the NOAA ship Ronald H. Brown (RHB; Cox et al 2017) and a field campaign station on Kiritimati Island (Dole et al 2018;Hartten et al 2018aHartten et al , b, 2017. (Observation data from this campaign are available at https:// www.esrl.noaa.gov/psd/enso/rapid_response/data_pub/.)…”

Section: Data-denial Experimentsmentioning

confidence: 99%

What Is the Impact of Additional Tropical Observations on a Modern Data Assimilation System?

Slivinski

Compo

Whitaker

et al. 2019

Monthly Weather Review

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Given the network of satellite and aircraft observations around the globe, do additional in situ observations impact analyses within a global forecast system? Despite the dense observational network at many levels in the tropical troposphere, assimilating additional sounding observations taken in the eastern tropical Pacific Ocean during the 2016 El Niño Rapid Response (ENRR) locally improves wind, temperature, and humidity 6-h forecasts using a modern assimilation system. Fields from a 50-km reanalysis that assimilates all available observations, including those taken during the ENRR, are compared with those from an otherwise-identical reanalysis that denies all ENRR observations. These observations reveal a bias in the 200-hPa divergence of the assimilating model during a strong El Niño. While the existing observational network partially corrects this bias, the ENRR observations provide a stronger mean correction in the analysis. Significant improvements in the mean-square fit of the first-guess fields to the assimilated ENRR observations demonstrate that they are valuable within the existing network. The effects of the ENRR observations are pronounced in levels of the troposphere that are sparsely observed, particularly 500–800 hPa. Assimilating ENRR observations has mixed effects on the mean-square difference with nearby non-ENRR observations. Using a similar system but with a higher-resolution forecast model yields comparable results to the lower-resolution system. These findings imply a limited improvement in large-scale forecast variability from additional in situ observations, but significant improvements in local 6-h forecasts.

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Section: Data-denial Experimentsmentioning

confidence: 99%

What Is the Impact of Additional Tropical Observations on a Modern Data Assimilation System?

Slivinski

Compo

Whitaker

et al. 2019

Monthly Weather Review

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Central-Pacific surface meteorology from the 2016 El Niño Rapid Response (ENRR) field campaign

Hartten

Cox

Johnston

et al. 2018

Earth Syst. Sci. Data

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, continuous measurements of surface meteorology, sea surface temperature, and downwelling shortwave radiation were made by NOAA Ship Ronald H. Brown. These were largely done in support of the four to eight radiosondes launched each day as the ship travelled from Hawaii to TAO buoy locations along longitudes 140 and 125 • W and then back to port in San Diego, California. The rapid nature of these remote field deployments led to some specific challenges in addition to those common to many surface data collection efforts. This paper documents the two deployments as well as the steps taken to evaluate and process the data. The results are two multi-week surface meteorology data products and one accompanying set of surface fluxes, all collected in the core of the eastern-central Pacific's extremely warm waters. These data sets, plus metadata, are archived at the NOAA's National Centers for Environmental Information (NCEI) and are free for public access: surface meteorology from Kiritimati Island (https://doi.org/10.7289/V51Z42H4); surface meteorology and some surface fluxes from NOAA Ship Ronald H. Brown (https://doi.org/10.7289/V5SF2T80; https://doi.org/10.7289/V58050VP).

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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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Ship- and island-based soundings from the 2016 El Niño Rapid Response (ENRR) field campaign

Cited by 5 publications

References 12 publications

What Is the Impact of Additional Tropical Observations on a Modern Data Assimilation System?

What Is the Impact of Additional Tropical Observations on a Modern Data Assimilation System?

Central-Pacific surface meteorology from the 2016 El Niño Rapid Response (ENRR) field campaign

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

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