Integrated Multi-Satellite Evaluation for the Global Precipitation Measurement: Impact of Precipitation Types on Spaceborne Precipitation Estimation

Integrated multi-sensor assessment is proposed as a novel approach to advance satellite precipitation validation in order to provide users and algorithm developers with an assessment adequately coping with the varying performances of merged satellite precipitation estimates. Gridded precipitation rates retrieved from space sensors with quasi-global coverage feed numerous applications ranging from water budget studies to forecasting natural hazards caused by extreme events. Characterizing the error structure of satellite precipitation products is recognized as a major issue for the usefulness of these estimates. The Global Precipitation Measurement (GPM) mission aims at unifying precipitation measurements from a constellation of low-earth orbiting (LEO) sensors with various capabilities to detect, classify and quantify precipitation. They are used in combination with geostationary observations to provide gridded precipitation accumulations. The GPM Core Observatory satellite serves as a calibration reference for consistent precipitation retrieval algorithms across the constellation. The propagation of QPE uncertainty from LEO active/passive microwave (PMW) precipitation estimates to gridded QPE is addressed in this study, by focusing on the impact of precipitation typology on QPE from the Level-2 GPM Core Observatory Dual-frequency Precipitation Radar (DPR) to the Microwave Imager (GMI) to Level-3 IMERG precipitation over the Conterminous U.S. A high-resolution surface precipitation used as a consistent reference across scales is derived from the ground radar-based Multi-Radar/Multi-Sensor. While the error structure of the DPR, GMI and subsequent IMERG is complex because of the interaction of various error factors, systematic biases related to precipitation typology are consistently quantified across products. These biases display similar features across Level-2 and Level-3, highlighting the need to better resolve precipitation typology from space and the room for improvement in global-scale precipitation estimates. The integrated analysis and framework proposed herein applies more generally to precipitation estimates from sensors and error sources affecting low-earth orbiting satellites and derived gridded products

Global Precipitation Measurement

This chapter begins with a brief history and background of microwave precipitation sensors, with a discussion of the sensitivity of both passive and active instruments, to trace the evolution of satellite-based rainfall techniques from an era of inference to an era of physical measurement. Next, the highly successful Tropical Rainfall Measuring Mission will be described, followed by the goals and plans for the Global Precipitation Measurement (GPM) Mission and the status of precipitation retrieval algorithm development. The chapter concludes with a summary of the need for space-based precipitation measurement, current technological capabilities, near-term algorithm advancements and anticipated new sciences and societal benefits in the GPM era

The GPM GV Program

We present a detailed overview of the structure and activities associated with the NASA-led ground validation component of the NASA-JAXA Global Precipitation Measurement (GPM) mission. The overarching philosophy and approaches for NASAs GV program are presented with primary focus placed on aspects of direct validation and a summary of physical validation campaigns and results. We describe a spectrum of key instruments, methods, field campaigns and data products developed and used by NASAs GV team to verify GPM level-2 precipitation products in rain and snow. We describe the tools and analysis framework used to confirm that NASAs Level-1 science requirements for GPM are met by the GPM Core Observatory. Examples of routine validation activities related to verification of Integrated Multi-satellitE Retrievals for GPM (IMERG) products for two different regions of the globe (Korea and the U.S.) are provided, and a brief analysis related to IMERG performance in the extreme rainfall event associated with Hurricane Florence is discussed

Bayesian Correction Approach for Improving Dual-Frequency Precipitation Radar Rainfall Rate Estimates, A

Includes bibliographical references (pages 14-15).The accurate estimation of precipitation is an important objective for the Dual-frequency Precipitation Radar (DPR), which is located on board the Global Precipitation Measurement (GPM) satellite core observatory. In this study, a Bayesian correction (BC) approach is proposed to improve the DPR’s instantaneous rainfall rate product. Ground dual-polarization radar (GR) observations are used as references, and a log-transformed Gaussian distribution is assumed as the instantaneous rainfall process. Additionally, a generalized regression model is adopted in the BC algorithm. Rainfall intensities such as light, moderate, and heavy rain and their variable influences on the model’s performance are considered. The BC approach quantifies the predictive uncertainties associated with the Bayesiancorrected DPR (DPR_BC) rainfall rate estimates. To demonstrate the concepts developed in this study, data from the GPM overpasses of the Weather Service Surveillance Radar (WSR-88D), KHGX, in Houston, Texas, between April 2014 and June 2018 are used. Observation errors in the DPR instantaneous rainfall rate estimates are analyzed as a function of rainfall intensity. Moreover, the best-performing BC model is implemented in three GPM-overpass cases with heavy rainfall records across the southeastern United States. The results show that the DPR_BC rainfall rate estimates have superior skill scores and are in better agreement with the GR references than with the DPR estimates. This study demonstrates the potential of the proposed BC algorithm for enhancing the instantaneous rainfall rate product from spaceborne radar equipment

Reconciling TRMM precipitation estimates related to El Niño Southern Oscillation variability

2017 Spring.Includes bibliographical references.Over the tropical oceans, large discrepancies in TRMM passive and active microwave rainfall retrievals become apparent during El Niño-Southern Oscillation (ENSO) events, where TMI retrievals exhibit a systematic shift in precipitation seemingly correlated with ENSO phase, while the PR does not. To investigate the causality of this relationship, this dissertation focuses, both spatially and temporally, on the evolution of precipitation organization between El Niño and La Niña conditions and their impacts on TRMM TMI and PR retrieved precipitation through the use of ground validation (GV) and satellite-based sources. The precipitation validation is performed as a function of convective organization through implementation of defined precipitation regimes, which have physical characteristics consistent across meteorological regimes. Before a full evaluation of TRMM retrieved rain rates is completed, an assessment of TRMM ground validation (GV) oceanic rain rate estimates is necessary. The robustness of radar-based GV rainfall estimates from the Kwajalein S-band KPOL radar are examined through comparisons with the Kwajalein rain gauge network. The TRMM-GV 2A53 rainfall product is found to heavily underestimate convective rain types, where prominent biases occur as precipitation becomes more organized. To further examine these rainfall biases, GV and polarimetrically-tuned rain rates are compared, where GV biases in both the 2A53 product and convective and stratiform Z-R relationships are minimized when the rain rate relationships are developed specifically as a function of precipitation regime. The results demonstrate that exploration into precipitation regimes should be considered when deriving and evaluating rain relationships to establish the source and range of uncertainties existing within different precipitating systems. TRMM radar (PR) and radiometer (TMI) rain rates are then evaluated though multiple case studies of collocated TRMM and KPOL rain rates at the 1°x1° and TMI footprint scale. The results of this study indicate that TRMM TMI and PR rainfall biases are best explained when derived as a function of organization and convective fraction. Large underestimates in both TMI and PR rain rates are associated with predominately convective rainfall across all regimes, where TMI rainfall underestimates both PR and GV rain rates. While PR rain rate estimates typically underestimate GV rainfall, TMI rain rates are heavily overestimated in rainfall regimes containing predominantly stratiform precipitation. Over the Kwajalein region, differences in TMI and PR rain rates seem to be driven by the occurrence of organized precipitation, where TMI-PR differences during El Niño conditions largely derive from MCS-like precipitating systems containing large stratiform precipitating regions. Application of the resultant biases helps mitigate the TMI-PR differences occurring between the ENSO phases and explain uncertainties introduced by the TMI Bayesian retrieval. Expanding the analysis tropics-wide, TRMM discrepancies directly relate to a shift from isolated deep convection during La Niña events toward organized precipitation during El Niño events with the largest variability occurring in the Pacific basins. During El Niño conditions, an increase in stratiform raining fraction leads to an increase in TMI rain rates that is less prevalent in PR rain rate retrievals. Reanalysis and AIRS data indicate that higher occurrences in organized systems are aided by increased mid- and upper-tropospheric moisture accompanied by more frequent deep convection. During La Niña events tropical rainfall is dominated by isolated deep convective regimes associated with drier mid-tropospheric conditions and strong mid- and upper level zonal wind shear. Application of the known TMI and PR biases yields increased consistency in PR rainfall with the radiometer-based TMI and GPCP rainfall estimates. The resultant satellite-based rainfall estimates are in general agreement when describing the response of tropical precipitation to ENSO induced variability in tropical SSTs

Distributed Disdrometer and Rain Gauge Measurement Infrastructure Developed for GPM Ground Validation

Global Precipitation Mission (GPM)retrieval algorithm validation requires datasets characterizing the 4-D structure, variability, and correlation properties of hydrometeor particle size distributions (PSD) and accumulations over satellite fields of view (FOV;<10 km). Collection of this data provides a means to assess retrieval errors related to beam filling and algorithm PSD assumptions. Hence, GPM Ground Validation is developing a deployable network of precipitation gauges and disdrometers to provide fine-scale measurements of PSD and precipitation accumulation variability. These observations will be combined with dual-frequency, polarimetric, and profiling radar data in a bootstrapping fashion to extend validated PSD measurements to a large coverage domain. Accordingly, a total of 24 Parsivel disdrometers(PD), 5 3rd-generation 2D Video Disdrometers (2DVD), 70 tipping bucket rain gauges (TBRG),9 weighing gauges, 7 Hot-Plate precipitation sensors (HP), and 3 Micro Rain Radars (MRR) have been procured. In liquid precipitation the suite of TBRG, PD and 2DVD instruments will quantify a broad spectrum of rain rate and PSD variability at sub-kilometer scales. In the envisioned network configuration 5 2DVDs will act as reference points for 16 collocated PD and TBRG measurements. We find that PD measurements provide similar measures of the rain PSD as observed with collocated 2DVDs (e.g., D0, Nw) for rain rates less than 15 mm/hr. For heavier rain rates we will rely on 2DVDs for PSD information. For snowfall we will combine point-redundant observations of SWER distributed over three or more locations within a FOV. Each location will contain at least one fenced weighing gauge, one HP, two PDs, and a 2DVD. MRRs will also be located at each site to extend the measurement to the column. By collecting SWER measurements using different instrument types that employ different measurement techniques our objective is to separate measurement uncertainty from natural variability in SWER and PSD. As demonstrated using C3VP polarimetric radar, gauge, and 2DVD/PD datasets these measurements can be combined to bootstrap an area wide SWER estimate via constrained modification of density-diameter and radar reflectivity-snowfall relationships. These data will be combined with snowpack, airborne microphysics, radar, radiometer, and tropospheric sounding data to refine GPM snowfall retrievals. The gauge and disdrometer instruments are being developed to operate autonomously when necessary using solar power and wireless communications. These systems will be deployed in numerous field campaigns through 2016. Planned deployment of these systems include field campaigns in Finland (2010), Oklahoma (2011), Canada (2012) and North Carolina (2013). GPM will also deploy 20 pairs of TBRGs within a 25 km2 region along the Virginia coast under NASA NPOL radar coverage in order to quantify errors in point-area rainfall measurements

Exploring the limits of variational passive microwave retrievals

2017 Summer.Includes bibliographical references.Passive microwave observations from satellite platforms constitute one of the most important data records of the global observing system. Operational since the late 1970s, passive microwave data underpin climate records of precipitation, sea ice extent, water vapor, and more, and contribute significantly to numerical weather prediction via data assimilation. Detailed understanding of the observation errors in these data is key to maximizing their utility for research and operational applications alike. However, the treatment of observation errors in this data record has been lacking and somewhat divergent when considering the retrieval and data assimilation communities. In this study, some limits of passive microwave imager data are considered in light of more holistic treatment of observation errors. A variational retrieval, named the CSU 1DVAR, was developed for microwave imagers and applied to the GMI and AMSR2 sensors for ocean scenes. Via an innovative method to determine forward model error, this retrieval accounts for error covariances across all channels used in the iteration. This improves validation in more complex scenes such as high wind speed and persistently cloudy regimes. In addition, it validates on par with a benchmark dataset without any tuning to in-situ observations. The algorithm yields full posterior error diagnostics and its physical forward model is applicable to other sensors, pending intercalibration. This retrieval is used to explore the viability of retrieving parameters at the limits of the available information content from a typical microwave imager. Retrieval of warm rain, marginal sea ice, and falling snow are explored with the variational retrieval. Warm rain retrieval shows some promise, with greater sensitivity than operational GPM algorithms due to leveraging CloudSat data and accounting for drop size distribution variability. Marginal sea ice is also detected with greater sensitivity than a standard operational retrieval. These studies ultimately show that while a variational algorithm maximizes the effective signal to noise ratio of these observations, hard limitations exist due to the finite information content afforded by a typical microwave imager

Improving the quality of extreme precipitation estimates using satellite passive microwave rainfall retrievals

2017 Summer.Includes bibliographical references.Satellite rainfall estimates are invaluable in assessing global precipitation. As a part of the Global Precipitation Measurement (GPM) mission, a constellation of orbiting sensors, dominated by passive microwave imagers, provides a full coverage of the planet approximately every 2-3 hours. Several decades of development have resulted in passive microwave rainfall retrievals that are indispensable in addressing global precipitation climatology. However, this prominent achievement is often overshadowed by the retrieval's performance at finer spatial and temporal scales, where large variability in cloud morphology poses an obstacle for accurate rainfall measurements. This is especially true over land, where rainfall estimates are based on an observed mean relationship between high frequency (e.g., 89 GHz) brightness temperature (Tb) depression (i.e., the ice-scattering signature) and rainfall rate. In the first part of this study, an extreme precipitation event that caused historical flooding over south-east Europe is analyzed using the GPM constellation. Performance of the rainfall retrieval is evaluated against ground radar and gage reference. It is concluded that satellite observations fully address the temporal evolution of the event but greatly underestimate total rainfall accumulation (by factor of 2.5). A primary limitation of the rainfall algorithm is found to be its inability to recognize variability in precipitating system structure. This variability is closely related to the structure of the precipitation regime and the large-scale environment. To address this influence of rainfall physics on the overall retrieval bias, the second part of this study utilizes TRMM radar (PR) and radiometer (TMI) observations to first confirm that the Tb-to-rain-rate relationship is governed by the amount of ice in the atmospheric column. Then, using the Amazon and Central African regions as testbeds, it demonstrates that the amount of ice aloft is strongly linked to a precipitation regime. A correlation found between the large-scale environment and precipitation regimes is then further examined. Variables such as Convective Available Potential Energy (CAPE), Cloud Condensation Nuclei (CCN), wind shear, and vertical humidity profiles are found to be capable of predicting a precipitation regime and explaining up to 40% of climatological biases. Dry over moist air conditions are favorable for developing intense, well organized systems such as MCSs in West Africa and the Sahel. These systems are characterized by strong Tb depressions and above average amounts of ice aloft. As a consequence, microwave retrieval algorithms misinterpret these non-typical systems assigning them unrealistically high rainfall rates. The opposite is true in the Amazon region, where observed raining systems exhibit relatively little ice while producing high rainfall rates. Based on these findings, in the last part of the study, the GPM operational retrieval (GPROF) for the GMI sensor is modified to offer additional information on atmospheric conditions to its Bayesian-based algorithm. When forming an estimate, the modified algorithm is allowed to use this ancillary information to filter out a priori states that do not match the general environmental condition relevant to the observation and thus reduce the difference between the assumed and observed variability in ice-to-rain ratio. The results are compared to the ground Multi-Radar Multi-Sensor (MRMS) network over the US at various spatial and temporal scales demonstrating outstanding potentials in improving the accuracy of rainfall estimates from satellite-borne passive microwave sensors over land

Global Precipitation Measurement Cold Season Precipitation Experiment (GCPEx): For Measurement Sake Let it Snow

As a component of the Earth's hydrologic cycle, and especially at higher latitudes,falling snow creates snow pack accumulation that in turn provides a large proportion of the fresh water resources required by many communities throughout the world. To assess the relationships between remotely sensed snow measurements with in situ measurements, a winter field project, termed the Global Precipitation Measurement (GPM) mission Cold Season Precipitation Experiment (GCPEx), was carried out in the winter of 2011-2012 in Ontario, Canada. Its goal was to provide information on the precipitation microphysics and processes associated with cold season precipitation to support GPM snowfall retrieval algorithms that make use of a dual-frequency precipitation radar and a passive microwave imager on board the GPM core satellite,and radiometers on constellation member satellites. Multi-parameter methods are required to be able to relate changes in the microphysical character of the snow to measureable parameters from which precipitation detection and estimation can be based. The data collection strategy was coordinated, stacked, high-altitude and in-situ cloud aircraft missions with three research aircraft sampling within a broader surface network of five ground sites taking in-situ and volumetric observations. During the field campaign 25 events were identified and classified according to their varied precipitation type, synoptic context, and precipitation amount. Herein, the GCPEx fieldcampaign is described and three illustrative cases detailed