Abstract

Spaceborne precipitation radars, including the Tropical Rainfall Measuring Mission’s Precipitation Radar (PR) and the Global Precipitation Measurement Mission’s Dual-Frequency Precipitation Radar (DPR), measure not only precipitation echoes but surface echoes as well, the latter of which are used to estimate the path-integrated attenuation (PIA) in the surface reference technique (SRT). In our previous study based on analyzing PR measurements, we found that attenuation-free surface backscattering cross sections (denoted by ${\sigma }_e^0$ ) over land increased in the presence of precipitation. This behavior, called the soil moisture effect, causes an underestimate of the PIA by the SRT as the method does not explicitly consider this effect. In this study, measurements made by Ku-band Precipitation Radar (KuPR) and Ka-band Precipitation Radar (KaPR), which comprise the DPR, were analyzed to examine whether KuPR and KaPR exhibit similar dependencies on the soil moisture as does the PR. For both KuPR and KaPR, an increase in ${\sigma }_e^0$ was observed for a large portion of the land area, except for forests and deserts. Results from the Hitschfeld–Bordan (HB) method suggest that ${\sigma }_e^0$ increases with the surface precipitation rate for light precipitation events. Meanwhile, for heavy precipitation, owing to the degradation of the HB method, it is difficult to estimate ${\sigma }_e^0$ quantitatively. Thus, a correction method for PIA that considers the soil moisture effect was developed and implemented into the DPR standard algorithm. With this correction, the surface precipitation rate estimates increased by approximately 18% for KuPR and 15% for the normal scan of KaPR over land.

Abstract

Observationally, a major source of uncertainty in evaluation of climate models arises from the difficulty in obtaining globally distributed, fine-scale profiles of temperature, pressure, and water vapor that probe through convective precipitating clouds, from the boundary layer to the upper levels of the free troposphere. In this manuscript, a 2-yr analysis of data from the Radio Occultations through Heavy Precipitation (ROHP) polarimetric radio occultation (RO) demonstration mission onboard the Spanish Paz spacecraft is presented. ROHP measures the difference in the differential propagation phase delay (Δϕ) between two orthogonal polarization receive states that is induced from the presence of nonspherically shaped hydrometeors along the Global Navigation Satellite System (GNSS) propagation path, complementing the standard RO thermodynamic profile. Since Δϕ is a net path-accumulated depolarization and does not resolve the precipitation structure along the propagation path, orbital coincidences between ROHP and the Global Precipitation Measurement (GPM) constellation passive microwave (MW) radiometers are identified to provides three-dimensional precipitation context to the RO thermodynamic profile. Passive MW-derived precipitation profiles are used to simulate the Δϕ along the ROHP propagation paths. Comparison between the simulated and observed Δϕ are indicative of the ability of ROHP to detect threshold levels of ray-path-averaged condensed water content, as well as to suggest possible inferences on the average ice-phase hydrometeor nonsphericity. The use of the polarimetric RO vertical structure is demonstrated as a means to condition the lower-tropospheric humidity by the topmost height of the associated convective cloud structure.

Abstract

Previous studies showed that conical scanning radiometers greatly outperform cross-track scanning radiometers for precipitation retrieval over ocean. This study demonstrates a novel approach to improve precipitation rates at the cross-track scanning radiometers’ observation time by propagating the conical scanning radiometers’ retrievals to the cross-track scanning radiometers’ observation time. The improved precipitation rate is a weighted average of original cross-track radiometers’ retrievals and retrievals propagated from a conical scanning radiometer. The cross-track scanning radiometers include the Advanced Technology Microwave Sounder (ATMS) on board the SNPP satellite and four Microwave Humidity Sounders (MHSs). The conical scanning radiometers include the Advanced Microwave Scanning Radiometer 2 (AMSR2) and three Special Sensor Microwave Imager/Sounders (SSMISs), while the precipitation retrievals from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) are taken as the reference. Results show that the morphed precipitation rates agree much better with the reference. The degree of improvement depends on several factors, including the propagated precipitation source, the time interval between the cross-track scanning radiometer and the conical scanning radiometer, the precipitation type (convective versus stratiform), the precipitation events’ size, and the geolocation. The study has potential to greatly improve high-impact weather systems monitoring (e.g., hurricanes) and multisatellite precipitation products. It may also enhance the usefulness of future satellite missions with cross-track scanning radiometers on board.

Abstract

A decades-long effort in observing precipitation from space has led to continuous improvements of satellite-derived passive microwave (PMW) large-scale precipitation products. However, due to a limited ability to relate observed radiometric signatures to precipitation type (convective and stratiform) and associated precipitation rate variability, PMW retrievals are prone to large systematic errors at instantaneous scales. The present study explores the use of deep learning approach in extracting the information content from PMW observation vectors to help identify precipitation types. A deep learning neural network model (DNN) is developed to retrieve the convective type in precipitating systems from PMW observations. A 12-month period of Global Precipitation Measurement mission Microwave Imager (GMI) observations is used as a dataset for model development and verification. The proposed DNN model is shown to accurately predict precipitation types for 85% of total precipitation volume. The model reduces precipitation rate bias associated with convective and stratiform precipitation in the GPM operational algorithm by a factor of 2 while preserving the correlation with reference precipitation rates, and is insensitive to surface type variability. Based on comparisons against currently used convective schemes, it is concluded that the neural network approach has the potential to address regime-specific PMW satellite precipitation biases affecting GPM operations.

Abstract

Atmospheric motion vectors (AMVs) are derived operationally at EUMETSAT from the AVHRR/3 instrument on the Polar System satellite MetOp-A since 2011. The launch of MetOp-B in 2012 allowed for doubling of the production of AMVs over the polar regions using both MetOp-A and MetOp-B satellite data. In addition to the single AVHRR polar wind product, in 2014 EUMETSAT developed a new global AVHRR wind product extracted from a pair of MetOp-A and MetOp-B images. This new product is extracted using the large overlap in the imagery data obtained from the tandem configuration of the two satellites on the same orbital plane but with a phase difference of about 50 min. The tandem configuration also provides the possibility to derive wind vectors over polar areas using a triplet of AVHRR images, keeping the same time period necessary to derive the single MetOp polar wind product but allowing for a temporal consistency check in the calculation of the AMV quality index. Three different AMV products are currently extracted from AVHRR imagery at EUMETSAT, using two or three images taken by one or two satellites having different coverage and time integration.

This paper describes the scientific concept of the AVHRR wind extraction algorithm developed at EUMETSAT and presents the performances of the various AVHRR wind products. Intercomparisons of these different products highlight the role of the temporal gap between the images used to extract the wind and the impact of the consistency check on the calculation of the quality index.

Abstract

The Dual-Frequency Precipitation Radar (DPR) on board the Global Precipitation Measurement (GPM) Core Observatory has reflectivity measurements at two different frequencies: Ku and Ka bands. The dual-frequency ratio from the measurements has been used to perform rain type classification and microphysics retrieval in the current DPR level 2 algorithm. The dual-frequency classification module is a new module in the GPM level 2 algorithm. The module performs rain type classification and melting region detection using the vertical profile of the dual-frequency ratio. This paper presents an evaluation of the performance of the GPM dual-frequency classification module after launch. The evaluation process includes a comparison between the dual-frequency classification results and the TRMM legacy single-frequency results, as well as validation with ground radars.

Abstract

In this paper, the operational Global Precipitation Measurement (GPM) mission combined radar–radiometer algorithm is thoroughly described. The operational combined algorithm is designed to reduce uncertainties in GPM Core Observatory precipitation estimates by effectively integrating complementary information from the GPM Dual-Frequency Precipitation Radar (DPR) and the GPM Microwave Imager (GMI) into an optimal, physically consistent precipitation product. Although similar in many respects to previously developed combined algorithms, the GPM combined algorithm has several unique features that are specifically designed to meet the GPM objectives of deriving, based on GPM Core Observatory information, accurate and physically consistent precipitation estimates from multiple spaceborne instruments, and ancillary environmental data from reanalyses. The algorithm features an optimal estimation framework based on a statistical formulation of the Gauss–Newton method, a parameterization for the nonuniform distribution of precipitation within the radar fields of view, a methodology to detect and account for multiple scattering in Ka-band DPR observations, and a statistical deconvolution technique that allows for an efficient sequential incorporation of radiometer information into DPR precipitation retrievals.

Abstract

The Global Precipitation Measurement (GPM) Dual-Frequency Precipitation Radar (DPR) algorithms consist of modules. This paper describes version 4 (V4) of GPM DPR level 2 (L2) classification (CSF) modules, which consist of two single-frequency (SF) modules—that is, Ku-only and Ka-only modules—and a dual-frequency (DF) module. Each CSF module detects bright band (BB) and classifies rain into three major types, that is, stratiform, convective, and other. The Ku-only and Ka-only CSF modules use algorithms that are similar to the Tropical Rainfall Measuring Mission (TRMM) rain type classification algorithm 2A23. The DF CSF module uses a new method called the measured dual-frequency ratio (DFRm) method for the rain type classification and the detection of BB. It is shown that the Ku-only CSF module and the DF CSF module produce almost indistinguishable rain type counts in a statistical sense. It is also shown that the DFRm method in the DF CSF module improves the detection of BB.

Abstract

Using Tropical Rainfall Measuring Mission (TRMM) observations from storms collected over the oceans surrounding East Asia, during summer, a method of creating physically consistent cloud-radiation databases to support satellite radiometer retrievals is introduced. In this method, vertical profiles of numerical model-simulated cloud and precipitation fields are optimized against TRMM radar and radiometer observations using a hybrid empirical orthogonal function (EOF)–one-dimensional variational (1DVAR) approach.The optimization is based on comparing simulated to observed radar reflectivity profiles and the corresponding passive microwave observations at the frequencies of the TRMM Microwave Imager (TMI) instrument. To minimize the discrepancies between the actual and the synthetic observations, the simulated cloud and precipitation profiles are optimized by adjusting the contents of the hydrometeors. To reduce the dimension of the hydrometeor content profiles in the optimization, multivariate relations among hydrometeor species are used.

After applying the optimization method to modify the simulated clouds, the optimized cloud-radiation database has a joint distribution of reflectivity and associated brightness temperatures that is considerably closer to that observed by TRMM PR and TMI, especially at 85 GHz. This implies that the EOF–1DVAR approach can generate profiles with realistic distributions of frozen hydrometeors, such as snow and graupel. This approach may be similarly adapted to operate with the variety and capabilities of the passive microwave radiometers that compose the Global Precipitation Measurement (GPM) constellation. Furthermore, it can be extended to other oceanic regions and seasons.

Abstract

The joint National Aeronautics and Space Administration (NASA) and Japanese Aerospace Exploration Agency (JAXA) Global Precipitation Measurement (GPM) is a constellation mission, centered upon observations from the core satellite dual-frequency precipitation radar (DPR) and its companion passive microwave (MW) GPM Microwave Imager (GMI). One of the key challenges for GPM is how to link the information from the single DPR across all passive MW sensors in the constellation, to produce a globally consistent precipitation product. Commonly, the associated surface emissivity and environmental conditions at the satellite observation time are interpolated from ancillary data, such as global forecast models and emissivity climatology, and are used for radiative transfer simulations and cataloging/indexing the brightness temperature (TB) observations and simulations within a common MW precipitation retrieval framework.

In this manuscript, the feasibility of an update to the surface emissivity state at or near the satellite observation time, regardless of surface type, is examined for purposes of assisting these algorithms with specification of the surface and environmental conditions. Since the constellation MW radiometers routinely observe many more nonprecipitating conditions than precipitating conditions, a principal component analysis is developed from the noncloud GMI–DPR observations as a means to characterize the emissivity state vector and to consistently track the surface and environmental conditions. The method is demonstrated and applied over known complex surface conditions to probabilistically separate cloud and cloud-free scenes. The ability of the method to globally identify “self-similar” surface locations from the TB observations without requiring any ancillary knowledge of geographical location or time is demonstrated.