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Surface-Based Microwave Humidity Retrievals over the Equatorial Indian Ocean: Applications and Challenges


The Madden–Julian oscillation (MJO) is a large-scale circulation that is more strongly defined by variations in humidity than temperature (e.g., Sobel et al. 2001; Zhang 2005). The time scales at which cloud structures interact with the humidity field are important for distinguishing the mechanisms encouraging Madden–Julian oscillation initiation, development, and propagation. One line of argument holds that clouds respond to externally imposed changes, primarily the horizontal advection of moisture (Hohenegger and Stevens 2013; Chikira 2014; Hannah et al. 2016). Another line of argument attributes the large-scale column moistening preceding the active phase of the MJO more directly to the vertical transport of moisture by clouds (Bladé and Hartmann 1993; Benedict and Randall 2007; Powell and Houze 2013; Xu and Rutledge 2016). A radiative feedback by which the vertical and horizontal moisture transport constructively act together has also been postulated (e.g., Ciesielski et al. 2017). This feedback focuses more on the interaction of the largescale circulation with convective variability at smaller scales, such as through shallow-to-deep convective transitions (e.g., Xu and Rutledge 2016) and through the diurnal cycle (Ruppert and Johnson 2015).
Accurate estimates of the moisture field and its variability at a range of time scales are essential for studying convection and its two-way interaction with humidity. The continuing number of studies devoted to understanding tropical moisture–convection–radiation interactions (e.g., Holloway et al. 2017; Mapes et al. 2017; Chandra et al. 2018, manuscript submitted to J. Geophys. Res. Atmos., and references therein) argues for more techniques with which to assess the horizontal and vertical distribution of moisture. Radiosondes accurately estimate the atmospheric state, and radiosonde networks have demonstrated their value for understanding and constraining equatorial moisture and heat budgets (Johnson et al. 2015, and references therein). Such networks possess a 3-hourly temporal resolution at best, and the spatial sampling is sparse enough that significant moisture filaments can be missed (Hannah et al. 2016). Satellite measurements of column water vapor path provide improved spatial resolution, but they have more difficulty resolving moisture variations in the lower free troposphere because of contributing surface emission, especially over land.
Another approach, explored within this study, is that of surface-based microwave radiometry. Microwave radiometers have a long history of accurate retrievals of column-integrated water vapor path (WVP) and liquid water path (LWP) in nonprecipitating to lightly precipitating conditions (e.g., Zuidema et al. 2005; Turner et al. 2007), including in tropical environments (Holloway and Neelin 2009; Kuo et al. 2018), and possess the advantages of being autonomous, cloud penetrating, and relatively maintenance-free (Cadeddu et al. 2013). More ambitious studies have also demonstrated some potential for microwave radiometers to retrieve vertically resolved temperature and humidity profiles (Hewison 2007; Löhnert et al. 2007; Blumberg et al. 2015). Löhnert et al. (2009) examined retrievals based on simulated clear-sky conditions from Darwin, Australia, and found their humidity retrieval provided approximately 3 vertical degrees of freedom, with a mean humidity bias of 0.5 g m23 . This vertical resolution is far coarser than that available from radiosondes but nevertheless captures the vertical moisture variability that is most important for tropical convective development (Sherwood 1999; Holloway and Neelin 2009), especially because the radiometer is preferentially sensitive to the moisture variations in the lower free troposphere.
Few surface-based microwave radiometry assessments have been made to date of the moisture structure in the tropics. Exceptions include Löhnert et al. (2009) and Raju et al. (2013); the latter is a case study of the water vapor field surrounding a tropical water spout from a scanning microwave radiometer. We extend this research area into the equatorial Indian Ocean and adopt two approaches for evaluating surface-based microwave radiometry humidity profiling. One approach examines the retrieval accuracy itself, whereas the second approach assesses radiometry’s ability to capture equatorial humidity variability at a range of time scales. Examined time scales encompass daily resolutions of the MJO 30–50-day intraseasonal time scale, the diurnal cycle during more convectively suppressed conditions, and the more challenging subdaily time scales characteristic of individual convective events. The shorter time scales build on the strength of the radiometer to continually sample every minute.
As part of the Dynamics of the Madden–Julian Oscillation (DYNAMO) campaign (Yoneyama et al. 2013), the University of Miami’s 22–30-GHz radiometric microwave radiometer (MWR) was deployed to Gan Island of the Addu Atoll in the Maldives (0.78S, 73.28E), where it was collocated with the NCAR S-band/ Ka-band dual-polarization, dual-wavelength Doppler (S-PolKa) radar (Sahoo et al. 2015). The data for this study were collected from 18 October 2011 to 14 January 2012. The radiometer’s frequency range is arguably the most commonly applied in deployed radiometers worldwide, but it excludes the temperature-sensitive oxygen absorption band at 60 GHz. Radiosonde information is integral to both the retrieval and its assessment. These are provided by the Atmospheric Radiation Measurement (ARM) MJO Investigation Experiment (AMIE; Long et al. 2011), which brought the ARM Mobile Facility 2 (AMF2; Miller et al. 2016) to a location that is approximately 8.5 km southeast of the radiometer.
A detailed description of the instruments and the radiative transfer model is provided in section 2, augmented by the appendix. The retrieval algorithms are introduced in section 3. Section 4 contains several quantitative evaluations of the accuracy of the profile retrievals, including a bias analysis and sensitivity tests. In section 5, the radiometer-derived moisture structure and its variability at different time scales (diurnal, convective scale, daily mean, and intraseasonal) are evaluated by comparing with radiosonde-derived values. Section 6 provides a conclusive summary of the lessons learned from this study and an outlook for the potential roles of MWRs in future field campaigns.

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