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

Abstract

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.

Tropical convection regimes in climate models: evaluation with satellite observations

“…RO data are also useful in the upper troposphere where specific humidity is small. Rieckh et al (2017) demonstrated that the detection of extremely dry atmospheric layers is possible with RO and showed agreement with data from multiple sources including, e.g., aircraft campaigns and reanalyses. Extensive validation of RO atmospheric profiles with independent observations, including radiosondes (e.g., Ladstädter et al, 2015) and satellite limb sounder data (e.g., Schwärz et al, 2016) has proved the high quality of RO variables.…”

“…Also, the value of water vapour information from RO has been demonstrated, e.g., for atmospheric dry layers (Rieckh et al, 2017) and for inferring water vapour feedback (Vergados et al, 2016). However, few studies exist on the evaluation of global climate model (GCM) data with RO; so far comparisons of geopotential height (Ao et al, 2015) and of dry temperature climatologies (Kishore et al, 2016;Schmidt et al, 2016) have been performed.…”

Increasing Frequency of Midtropospheric Dry Layers in the Pacific Intertropical Convergence Zone

“…Midtropospheric dry layers in tropical regions have been identified in several field campaigns using radiosonde observations (Jensen & Del Genio, ; Raymond et al, ) and aircraft profiles (Randel et al, ). More recently, satellite retrievals of water vapor profiles from hyperspectral profilers like the Atmospheric Infrared Sounder (AIRS; Chahine et al, ) and GPS radio occultation measurements (Rieckh et al, ) have been used to identify dry layers. Casey et al () developed a 5‐year climatology of midtropospheric dry layers using AIRS.…”

Fog Computing‐Based Differential Positioning Method for BDS

“…It can be seen from (1) that the pseudo-range value is related to tropospheric and ionospheric effects, which have a strong correlation with distance [27]. Although the atmospheric noise of the base station and mobile receiver can be equal by default in the case of medium or short baseline, shorter baselines have higher similarity [27]. Therefore, the weight of the base station closer to the mobile receiver should be higher.…”