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A vertically pointing Ka-band radar (Metek MIRA-35C) installed at the Instituto Geofísico del Perú, Atmospheric Microphysics and Radiation Laboratory (LAMAR) Huancayo Observatory, which is located at an elevation of 3.3 km MSL in the Andes Mountains of Peru, is used to investigate the effects of terrain on satellite-based precipitation measurement in the Andes. We compare the vertical structure of precipitation observed by the MIRA-35C with Ka-band radar measurements from the Dual-Frequency Precipitation Radar (DPR) on board the Global Precipitation Measurement (GPM) mission core satellite using an approach based on Taylor’s hypothesis of frozen turbulence that attempts to reduce the impact of spatiotemporal offsets between these two radar measurements. From 3 April 2014 to 20 May 2018, the DPR measured precipitation near LAMAR during 15 of its 157 coincident overpasses. There were six simultaneous observations with MIRA-35C. We found that the average of the DPR’s lowest clutter-free bin is 1.62 km AGL, but the presence of precipitation worsens the situation, causing a 0.4-km-deeper algorithm-detected blind zone for the DPR at the Huancayo Observatory. In the study area, the depth of the clutter layer observed with DPR often extends above the melting layer but can be highly variable, extending even as high as 5 km AGL. These results suggest that DPR estimates of stratiform precipitation over the Andes Mountains are likely underestimated because of the terrain effects on the satellite measurements and problems in its blind zone detection algorithms, highlighting the difficulty in estimating precipitation in mountainous terrain from spaceborne radar.

A multisensor snowfall observational suite has been deployed at the Marquette, Michigan, National Weather Service Weather Forecast Office (KMQT) since 2014. Micro Rain Radar (MRR; profiling radar), Precipitation Imaging Package (PIP; snow particle imager), and ancillary ground-based meteorological observations illustrate the unique capabilities of these combined instruments to document radar and concomitant microphysical properties associated with northern Great Lakes snowfall regimes. Lake-effect, lake-orographic, and transition event case studies are presented that illustrate the variety of snowfall events that occur at KMQT. Case studies and multiyear analyses reveal the ubiquity of snowfall produced by shallow events. These shallow snowfall features and their distinctive microphysical fingerprints are often difficult to discern with conventional remote sensing instruments, thus highlighting the scientific and potential operational value of MRR and PIP observations. The importance of near-surface lake-orographic snowfall enhancement processes in extreme snowfall events and regime-dependent snow particle microphysical variability controlled by regime and environmental factors are also highlighted.

As a component of the National Aeronautics and Space Administration's (NASA's) Weather Focus Area and Global Precipitation Measurement (GPM) Ground Validation participation in the International Collaborative Experiments for the PyeongChang 2018 Olympic and Paralympic Winter Games' (ICE-POP 2018) field research and forecast demonstration programs, hourly ocean surface meteorology properties were retrieved from the GPM microwave observations for January–March 2018. In this study, the retrieved ocean surface meteorological products – 2 m temperature, 2 m specific humidity, and 10 m wind speed – were assimilated into a regional numerical weather prediction (NWP) framework. This explored the application of these observations for two heavy snowfall events during the ICE-POP 2018, on 27–28 February and 7–8 March 2018. The Weather Research and Forecasting (WRF) model and the community Gridpoint Statistical Interpolation (GSI) were used to conduct high-resolution simulations and data assimilation experiments. The results indicate that the data assimilation has a large influence on surface thermodynamic and wind fields in the model initial condition for both events. With cycled data assimilation, a significantly positive influence of the retrieved surface observation was found for the March case, with improved quantitative precipitation forecasts and reduced errors in temperature forecasts. A slightly smaller yet positive impact was also found in the forecast for the February case.

In this study, we develop statistical relationships between radar observables and drop size distribution properties in different latitude bands to inform radar rainfall retrieval techniques and understand underpinning microphysical reasons for differences reported in the literature between satellite mean zonal rainfall products at high latitudes (up to a factor 2 between products over ocean). A major assumption in satellite retrievals is the attenuation-reflectivity relationships for convective and stratiform precipitation. They are found to systematically produce higher attenuation than our relationships with all latitudes included or within individual latitude bands (except in the tropics). The scatter around fitted curves approximating the radar reflectivity-mass-weighted diameter Dm relationship and the dual-frequency ratio (ratio of Ka- to Ku-band reflectivities)-Dm relationships is found to be large and of the same magnitude. This result suggests that the added value of two radar frequencies to improve the Dm retrieval from space seems limited. In contrast, the relationship between Dm and the attenuation/reflectivity ratio is robust and not dependent on latitude. Direct relationships between rainfall and either reflectivity or attenuation are also found to be very robust. Attenuation-reflectivity, Dm-reflectivity, and rainfall rate-reflectivity relationships in the Southern Hemisphere high latitude and Northern Hemisphere polar latitude bands are fundamentally different from those at other latitude bands, producing smaller attenuation, much larger Dm, and lower rainfall rates. This implies that specific relationships need to be used for these latitude bands in radar rainfall retrieval techniques using such relationships. © 2019 Australian Bureau of Meteorology, Commonwealth of Australia.

In this study, we analyze an in situ shipboard global ocean drop size distribution (DSD) 8-year database to understand the underpinning microphysical reasons for discrepancies between satellite oceanic rainfall products at high latitudes reported in the literature. The natural, latitudinal, and convective-stratiform variability of the DSD is found to be large, with a substantially lower drop concentration with diameter smaller than 3 mm in the Southern hemisphere high latitude (S-highlat, south of 45°S) and Northern Hemisphere polar latitude (N-polar, north of 67.5°S) bands, which is where satellite rainfall products most disagree. In contrast, the latitudinal variability of the normalized oceanic DSD is small, implying that the functional form of the normalized DSD can be assumed constant and accurately parameterized using proposed fits. The S-highlat and N-polar latitude bands stand out as regions with oceanic rainfall properties different from other latitudes, highlighting fundamental differences in rainfall processes at different latitudes and associated specific challenges for satellite rainfall retrieval techniques. The most salient differences in DSD properties between these two regions and the other latitude bands are: (1) a systematically higher (lower) frequency of occurrence of rainfall rates below (above) 1 mm h-1, (2) much lower drop concentrations, (3) very different values of the DSD shape parameter (μ0) from what is currently assumed in satellite radar rainfall algorithms, and (4) very different DSD properties in both the convective and stratiform rainfall regimes. Overall, this study provides insights into how DSD assumptions in satellite radar rainfall retrieval techniques could be refined. ©2019. American Geophysical Union. All Rights Reserved.

Surface precipitation phase is a fundamental meteorological property with immense importance. Accurate classification of phase from satellite remotely sensed observations is difficult. This study demonstrates the ability of the Precipitation Imaging Package (PIP), a ground-based, in situ precipitation imager, to distinguish precipitation phase. The PIP precipitation phase identification capabilities are compared to observer records from the National Weather Service (NWS) office in Marquette, Michigan, as well as co-located observations from profiling and scanning radars, disdrometer data, and surface meteorological measurements. Examined are 13 events with at least one precipitation phase transition. The PIP-determined onsets and endings of the respective precipitation phase periods agree to within 15 min of NWS observer records for the vast majority of the events. Additionally, the PIP and NWS liquid water equivalent accumulations for 12 of the 13 events were within 10%. Co-located observations from scanning and profiling radars, as well as reanalysis-derived synoptic and thermodynamic conditions, support the accuracy of the precipitation phases identified by the PIP. PIP observations for the phase transition events are compared to output from a parameterization based on wet bulb and near-surface lapse rates to produce a probability of solid precipitation. The PIP phase identification and the parameterization output are consistent. This work highlights the ability of the PIP to properly characterize hydrometeor phase and provide dependable precipitation accumulations under complicated mixed-phase and rain and snow (or vice versa) transition events.

The Integrated Multi-satellitE Retrievals for GPM (IMERG) is a global gridded precipitation product that unifies measurements from a network of satellites in the GPM constellation. Multi-temporal evaluation of IMERG products for a specific region is essential to the algorithm improvement and product applications. This study evaluates the Version-06B IMERG Early, Late and Final Runs using ground-based Korean Quantitative Precipitation Estimation (QPE).

A unique capability of the Global Precipitation Measurement (GPM) mission is its ability to better estimate the raindrop size distribution (DSD) on a global scale. To validate the GPM DSD retrievals, a network of more than 100 ground-based polarimetric radars from across the globe are utilized within the broader context of the GPM Validation Network (VN) processing architecture. The GPM VN ensures quality controlled dual-polarimetric radar moments for use in providing reference estimates of the DSD. The VN DSD estimates are carefully geometrically matched with the GPM core satellite measurements for evaluation of the GPM algorithms. We use the GPM VN to compare the DSD retrievals from the GPM&rsquo, s Dual-frequency Precipitation Radar (DPR) and combined DPR&ndash, GPM Microwave Imager (GMI) Level-2 algorithms. Results suggested that the Version 06A GPM core satellite algorithms provide estimates of the mass-weighted mean diameter (Dm) that are biased 0.2 mm too large when considered across all precipitation types. In convective precipitation, the algorithms tend to overestimate Dm by 0.5&ndash, 0.6 mm, leading the DPR algorithm to underestimate the normalized DSD intercept parameter (Nw) by a factor of two, and introduce a significant bias to the DPR retrievals of rainfall rate for DSDs with large Dm. The GPM Combined algorithm performs better than the DPR algorithm in convection but provides a severely limited range of Nw estimates, highlighting the need to broaden its a priori database in convective precipitation.

The stability and accuracy of weather radar reflectivity calibration are imperative for quantitative applications, such as rainfall estimation, severe weather monitoring and nowcasting, and assimilation in numerical weather prediction models. Various radar calibration and monitoring techniques have been developed, but only recently have integrated approaches been proposed, that is, using different calibration techniques in combination. In this paper the following three techniques are used: 1) ground clutter monitoring, 2) comparisons with spaceborne radars, and 3) the self-consistency of polarimetric variables. These techniques are applied to a C-band polarimetric radar (CPOL) located in the Australian tropics since 1998. The ground clutter monitoring technique is applied to each radar volumetric scan and provides a means to reliably detect changes in calibration, relative to a baseline. It is remarkably stable to within a standard deviation of 0.1 dB. To obtain an absolute calibration value, CPOL observations are compared to spaceborne radars on board TRMM and GPM using a volume-matching technique. Using an iterative procedure and stable calibration periods identified by the ground echoes technique, we improve the accuracy of this technique to about 1 dB. Finally, we review the self-consistency technique and constrain its assumptions using results from the hybrid TRMM–GPM and ground echo technique. Small changes in the self-consistency parameterization can lead to 5 dB of variation in the reflectivity calibration. We find that the drop-shape model of Brandes et al. with a standard deviation of the canting angle of 12° best matches our dataset.

Precipitation represents a life-critical energy and hydrologic exchange between the Earth's atmosphere and its surface. As such, knowledge of where, when, and how much rain and snow falls is essential for scientific research and societal applications. Building on the 17-year success of the Tropical Rainfall Measurement Mission (TRMM), the Global Precipitation Measurement (GPM) Core Observatory (GPM-CO) is the first U.S. National Aeronautical and Space Administration (NASA) satellite mission specifically designed with sensors to observe the structure and intensities of both rain and falling snow. The GPM-CO has proved to be a worthy successor to TRMM, extending and improving high-quality active and passive microwave observations across all times of day. The GPM-CO launched in early 2014, is a joint mission between NASA and the Japanese Aerospace Exploration Agency (JAXA), with sensors that include the NASA-provided GPM Microwave Imager and the JAXA-provided Dual-frequency Precipitation Radar. These sensors were devised with high accuracy standards enabling them to be used as a reference for inter-calibrating a constellation of partner satellite data. These intercalibrated partner satellite retrievals are used with infrared data to produce merged precipitation estimates at temporal scales of 30 minutes and spatial scales of 0.1° × 0.1°. Precipitation estimates from the GPM-CO and partner constellation satellites, provided in near real time and later reprocessed with all ancillary data, are an indispensable source of precipitation data for operational and scientific users. Advances have been made using GPM data, primarily in improving sensor calibration, retrieval algorithms, and ground validation measurements, and used to further our understanding of the characteristics of liquid and frozen precipitation and the science of water and hydrological cycles for climate/weather forecasting. These advances have extended to societal benefits related to water resources, operational numerical weather prediction, hurricane monitoring, prediction, and disaster response, extremes, and disease.

This study describes the generation and testing of a reference rainfall product created from field campaign datasets collected during the NASA Global Precipitation Measurement (GPM) mission Ground Validation Iowa Flood Studies (IFloodS) experiment. The study evaluates ground-based radar rainfall (RR) products acquired during IFloodS in the context of building the reference rainfall product. The purpose of IFloodS was not only to attain a high-quality ground-based reference for the validation of satellite rainfall estimates but also to enhance understanding of flood-related rainfall processes and the predictability of flood forecasting. We assessed the six RR estimates (IFC, Q2, CSU-DP, NWS-DP, Stage IV, and Q2-Corrected) using data from rain gauge and disdrometer networks that were located in the broader field campaign area of central and northeastern Iowa. We performed the analyses with respect to time scales ranging from 1 h to the entire campaign period in order to compare the capabilities of each RR product and to characterize the error structure at scales that are frequently used in hydrologic applications. The evaluation results show that the Stage IV estimates perform superior to other estimates, demonstrating the need for gauge-based bias corrections of radar-only products. This correction should account for each product’s algorithm-dependent error structure that can be used to build unbiased rainfall products for the campaign reference. We characterized the statistical error structures (e.g., systematic and random components) of each RR estimate and used them for the generation of a campaign reference rainfall product. To assess the hydrologic utility of the reference product, we performed hydrologic simulations driven by the reference product over the Turkey River basin. The comparison of hydrologic simulation results demonstrates that the campaign reference product performs better than Stage IV in streamflow generation.

Researchers now have the benefit of an unprecedented suite of space- and ground-based sensors that provide multidimensional and multiparameter precipitation information. Motivated by NASA’s Global Precipitation Measurement (GPM) mission and ground validation objectives, the System for Integrating Multiplatform Data to Build the Atmospheric Column (SIMBA) has been developed as a unique multisensor precipitation data fusion tool to unify field observations recorded in a variety of formats and coordinate systems into a common reference frame. Through platform-specific modules, SIMBA processes data from native coordinates and resolutions only to the extent required to set them into a user-defined three-dimensional grid. At present, the system supports several ground-based scanning research radars, NWS NEXRAD radars, profiling Micro Rain Radars (MRRs), multiple disdrometers and rain gauges, soundings, the GPM Microwave Imager and Dual-Frequency Precipitation Radar on board the Core Observatory satellite, and Multi-Radar Multi-Sensor system quantitative precipitation estimates. SIMBA generates a new atmospheric column data product that contains a concomitant set of all available data from the supported platforms within the user-specified grid defining the column area in the versatile netCDF format. Key parameters for each data source are preserved as attributes. SIMBA provides a streamlined framework for initial research tasks, facilitating more efficient precipitation science. We demonstrate the utility of SIMBA for investigations, such as assessing spatial precipitation variability at subpixel scales and appraising satellite sensor algorithm representation of vertical precipitation structure for GPM Core Observatory overpass cases collected in the NASA Wallops Precipitation Science Research Facility and the GPM Olympic Mountain Experiment (OLYMPEX) ground validation field campaign in Washington State.

During May and June 2014, NOAA X-Pol (NOXP), the National Severe Storms Laboratory’s dual-polarized X-band mobile radar, was deployed to the Pigeon River basin in the Great Smoky Mountains of North Carolina as part of the NASA Integrated Precipitation and Hydrology Experiment. Rain gauges and disdrometers were positioned within the basin to verify precipitation estimates from various radar and satellite precipitation algorithms. First, the performance of the Self-Consistent Optimal Parameterization–Microphysics Estimation (SCOP-ME) algorithm for NOXP was examined using ground instrumentation as validation and was found to perform similarly to or slightly outperform other precipitation algorithms over the course of the intensive observation period (IOP). Radar data were also used to examine ridge–valley differences in radar and microphysical parameters for a case of stratiform precipitation passing over the mountains. Inferred coalescence microphysical processes were found to dominate within the upslope region, while a combination of processes were present as the system propagated over the valley. This suggests that enhanced updrafts aided by orographic lift sustain convection over the upslope regions, leading to larger median drop diameters.

The advent of global precipitation data sets with increasing temporal span has made it possible to use them for validating climate models. In order to fulfill the requirement of global coverage, existing products integrate satellite-derived retrievals from many sensors with direct ground observations (gauges, disdrometers, radars), which are used as reference for the satellites. While the resulting product can be deemed as the best-available source of quality validation data, awareness of the limitations of such data sets is important to avoid extracting wrong or unsubstantiated conclusions when assessing climate model abilities. This paper provides guidance on the use of precipitation data sets for climate research, including model validation and verification for improving physical parameterizations. The strengths and limitations of the data sets for climate modeling applications are presented, and a protocol for quality assurance of both observational databases and models is discussed. The paper helps elaborating the recent IPCC AR5 acknowledgment of large observational uncertainties in precipitation observations for climate model validation.