The link between stratiform precipitation microphysics and multifrequency radar observables is thoroughly investigated by exploiting simultaneous airborne radar and in situ observations collected from two aircraft during the OLYMPEX/RADEX (Olympic Mountain Experiment/Radar Definition Experiment 2015) field campaign. Above the melting level, in situ images and triple‐frequency radar signatures both indicate the presence of moderately rimed aggregates. Various mass‐size relationships of ice particles and snow scattering databases are used to compute the radar reflectivity from the in situ particle size distribution. At Ku and Ka band, the best agreement with radar observations is found when using the self‐similar Rayleigh‐Gans approximation for moderately rimed aggregates. At W band, a direct comparison is challenging because of the non‐Rayleigh effects and of the probable attenuation due to ice aggregates and supercooled liquid water between the two aircraft. A variational method enables the retrieval of the full precipitation profile above and below the melting layer, by combining the observations from the three radars. Even with three radar frequencies, the retrieval of rain properties is challenging over land, where the integrated attenuation is not available. Otherwise, retrieved mean volume diameters and water contents of both solid and liquid precipitation are in agreement with in situ observations and indicate local changes of the degree of riming of ice aggregates, on the scale of 5 km. Finally, retrieval results are analyzed to explore the validity of using continuity constraints on the water mass flux and diameter within the melting layer in order to improve retrievals of ice properties.

This study presents, for the first time ever, occulting signals of the Global Navigation Satellite Systems (GNSSs) acquired at two polarizations from a Low Earth Orbiter, and it shows that they sense heavy precipitation. The data sets are obtained from early stages of the Radio Occultation and Heavy Precipitation experiment aboard the PAZ satellite, launched in February 2018 and activated in May 2018. Preliminary calibration algorithms are applied to remove other systematic effects, and the resulting vertical profiles of polarimetric phase shift observations are compared to precipitation information from other missions. The analysis of the data shows consistency between Radio Occultation and Heavy Precipitation experiment aboard the PAZ satellite polarimetric phase shift measurements and presence of hydrometeors, with strong signatures from heavy precipitation. The polarimetric measurements also capture vertical features consistent with the vertical structures of precipitation.

The tropical Northwest Pacific (TNWP) is a receptor for pollution sources throughout Asia and is highly susceptible to climate change, making it imperative to understand long-range transport in this complex aerosol-meteorological environment. Measurements from the NASA Cloud, Aerosol, and Monsoon Processes Philippines Experiment (CAMP²Ex; 24 August to 5 October 2019) and back trajectories from the National Oceanic and Atmospheric Administration Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT) were used to examine transport into the TNWP from the Maritime Continent (MC), peninsular Southeast Asia (PSEA), East Asia (EA), and the West Pacific (WP). A mid-campaign monsoon shift on 20 September 2019 led to distinct transport patterns between the southwest monsoon (SWM; before 20 September) and monsoon transition (MT; after 20 September). During the SWM, long-range transport was a function of southwesterly winds and cyclones over the South China Sea. Low- (high-) altitude air generally came from MC (PSEA), implying distinct aerosol processing related to convection and perhaps wind shear. The MT saw transport from EA and WP, driven by Pacific northeasterly winds, continental anticyclones, and cyclones over the East China Sea. Composition of transported air differed by emission source and accumulated precipitation along trajectories (APT). MC air was characterized by biomass burning tracers while major components of EA air pointed to Asian outflow and secondary formation. Convective scavenging of PSEA air was evidenced by considerable vertical differences between aerosol species but not trace gases, as well as notably higher APT and smaller particles than other regions. Finally, we observed a possible wet scavenging mechanism acting on MC air aloft that was not strictly linked to precipitation. These results are important for understanding the transport and processing of air masses with further implications for modeling aerosol lifecycles and guiding international policymaking to public health and climate, particularly during the SWM and MT.

The Global Precipitation Measurement (GPM) Dual-Frequency Precipitation Radar (DPR) (Ku- and Ka-band, or 14 and 35 GHz) provides the capability to resolve the precipitation structure under moderate to heavy precipitation conditions. In this manuscript, the use of near-coincident observations between GPM and the CloudSat Profiling Radar (CPR) (W-band, or 94 GHz) are demonstrated to extend the capability of representing light rain and cold-season precipitation from DPR and the GPM passive microwave constellation sensors. These unique triple-frequency data have opened up applications related to cold-season precipitation, ice microphysics, and light rainfall and surface emissivity effects.

This paper presents the calibration and validation studies for the Radio Occultation and Heavy Precipitation experiment aboard the PAZ satellite. These studies, necessary to assess and characterize the noise level and robustness of the differential phase shift (ΔΦ) observable of polarimetric radio occultations (PROs), confirm the good performance of the experiment and the capability of this technique in sensing precipitation. It is shown how all the predicted effects that could have an impact into the PRO observables (e.g., effect of metallic structures nearby the antenna, the Faraday rotation at the ionosphere, signal impurities in the transmission, and altered cross-polarization isolation) are effectively calibrated and corrected, and they have a negligible effect on the final observable. The on-orbit calibration, performed using an extensive dataset of free-of-rain and low-ionospheric activity observations, is successfully used to correct all the collected observations, which are further validated against independent precipitation observations confirming the sensitivity of the observables to the presence of hydrometeors. The validation results also show how vertically averaged ΔΦ can be used as a proxy for precipitation.

Global Navigation Satellite System radio occultation (GNSS-RO) and microwave radiometry (MWR) are two of the most impactful spaceborne remote sensing techniques for numerical weather prediction (NWP). These two techniques provide complementary information about atmospheric temperature and water vapor structure. GNSS-RO provides high vertical resolution measurements with cloud penetration capability, but the temperature and moisture are coupled in the GNSS-RO retrieval process and their separation requires the use of a priori information or auxiliary observations. On the other hand, the MWR measures brightness temperature (T_b) in numerous frequency bands related to the temperature and water vapor structure but is limited by poor vertical resolution (> 2 km) and precipitation. In this study, we combine these two technologies in an optimal estimation approach, 1D variation method (1DVar), to improve the characterization of the complex thermodynamic structures in the lower troposphere. This study employs both simulated and operational observations. GNSS-RO bending angle and MWR T_b observations are used as inputs to the joint retrieval, where bending can be modeled by an Abel integral and T_b can be modeled by a radiative transfer model (RTM) that takes into account atmospheric absorption, as well as surface reflection and emission. By incorporating the forward operators into the 1DVar method, the strength of both techniques can be combined to bridge individual weaknesses. Applying 1DVar to the data simulated from large eddy simulation (LES) is shown to reduce GNSS-RO temperature and water vapor retrieval biases at the lower troposphere while simultaneously capturing the fine-scale variability that MWR cannot resolve. A sensitivity analysis is also conducted to quantify the impact of the a priori information and error covariance used in different retrieval scenarios. The applicability of 1DVar joint retrieval to the actual GNSS-RO and MWR observations is also demonstrated through combining collocated COSMIC-2 and Suomi-NPP (National Polar-orbiting Partnership) measurements.