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At least 1,693 records · Page 94

Error Modeling of Multibaseline Optical Truss: Part 1: Modeling of System Level Performance

Global astrometry is the measurement of stellar positions and motions. These are typically characterized by five parameters, including two position parameters, two proper motion parameters, and parallax. The Space Interferometry Mission (SIM) will derive these parameters for a grid of approximately 1300 stars covering the celestial sphere to an accuracy of approximately 4uas, representing a two orders of magnitude improvement over the most precise current star catalogues. Narrow angle astrometry will be performed to a 1uas accuracy. A wealth of scientific information will be obtained from these accurate measurements encompassing many aspects of both galactic (and extragalactic science. SIM will be subject to a number of instrument errors that can potentially degrade performance. Many of these errors are systematic in that they are relatively static and repeatable with respect to the time frame and direction of the observation. This paper and its companion define the modeling of the, contributing factors to these errors and the analysis of how they impact SIM's ability to perform astrometric science.

meterology modeling↗

Technical Note: Introduction of variance component analysis to setup error analysis in radiotherapy

Purpose: The purpose of this technical note is to introduce variance component analysis to the estimation of systematic and random components in setup error of radiotherapy. Methods: Balanced data according to the one-factor random effect model were assumed. Results: Analysis-of-variance (ANOVA)-based computation was applied to estimate the values and their confidence intervals (CIs) for systematic and random errors and the population mean of setup errors. The conventional method overestimates systematic error, especially in hypofractionated settings. The CI for systematic error becomes much wider than that for random error. The ANOVA-based estimation can be extended to a multifactor model considering multiple causes of setup errors (e.g., interpatient, interfraction, and intrafraction). Conclusions: Variance component analysis may lead to novel applications to setup error analysis in radiotherapy.

60 APPLIED LIFE SCIENCES↗

An investigation into the milliarcsecond attitude control errors of Hubble Space Telescope

An analysis of the fine pointing errors of the Hubble Space Telescope (HST), in the range from 1 to 15 milliarcsecs, is reported on. The emphasis is on the study of the performance characteristics of the pointing control system, fine guidance sensors and the optical telescope assembly, which produce relative attitude and astrometric measurement errors. Since the first servicing mission in December 1993, the HST relative short term position stability is of the order of 3 milliarcsecs RMS when averaged over 1 min intervals. At this level of accuracy, longer term systematic attitude errors in this range can have a noticeable impact on the telescope's observations. The various error sources are described, including: internal temperature effects; spacecraft structure temperature effects; calibration procedures, and computational inaccuracies.

Bradley, A. J.↗

Global Warming Estimation from MSU: Correction for Drift and Calibration Errors

Microwave Sounding Unit (MSU) radiometer observations in Ch 2 (53.74 GHz), made in the nadir direction from sequential, sun-synchronous, polar-orbiting NOAA morning satellites (NOAA 6, 10 and 12 that have about 7am/7pm orbital geometry) and afternoon satellites (NOAA 7, 9, 11 and 14 that have about 2am/2pm orbital geometry) are analyzed in this study to derive global temperature trend from 1980 to 1998. In order to remove the discontinuities between the data of the successive satellites and to get a continuous time series, first we have used shortest possible time record of each satellite. In this way we get a preliminary estimate of the global temperature trend of 0.21 K/decade. However, this estimate is affected by systematic time-dependent errors. One such error is the instrument calibration error. This error can be inferred whenever there are overlapping measurements made by two satellites over an extended period of time. From the available successive satellite data we have taken the longest possible time record of each satellite to form the time series during the period 1980 to 1998 to this error. We find we can decrease the global temperature trend by about 0.07 K/decade. In addition there are systematic time dependent errors present in the data that are introduced by the drift in the satellite orbital geometry arises from the diurnal cycle in temperature which is the drift related change in the calibration of the MSU. In order to analyze the nature of these drift related errors the multi-satellite Ch 2 data set is partitioned into am and pm subsets to create two independent time series. The error can be assessed in the am and pm data of Ch 2 on land and can be eliminated. Observations made in the MSU Ch 1 (50.3 GHz) support this approach. The error is obvious only in the difference between the pm and am observations of Ch 2 over the ocean. We have followed two different paths to assess the impact of the errors on the global temperature trend. In one path the entire error is placed in the am data while in the other it is placed in the pm data. Global temperature trend is increased or decreased by about 0.03 K/decade depending upon this placement. Taking into account all random errors and systematic errors our analysis of MSU observations leads us to conclude that a conservative estimate of the global warming is 0. 11 (+-) 0.04 K/decade during 1980 to 1998.

Prabhakara, C.↗

Improved Analysis of GW150914 Using a Fully Spin-Precessing Waveform Model

This paper presents updated estimates of source parameters for GW150914, a binary black-hole coalescence event detected by the Laser Interferometer Gravitational-wave Observatory (LIGO) in 2015 [Abbott et al. Phys. Rev. Lett. 116, 061102 (2016).]. Abbott et al. [Phys. Rev. Lett. 116, 241102 (2016).] presented parameter estimation of the source using a 13-dimensional, phenomenological precessing-spin model (precessing IMRPhenom) and an 11-dimensional nonprecessing effective-one-body (EOB) model calibrated to numerical-relativity simulations, which forces spin alignment (nonprecessing EOBNR). Here, we present new results that include a 15-dimensional precessing-spin waveform model (precessing EOBNR) developed within the EOB formalism. We find good agreement with the parameters estimated previously [Abbott et al. Phys. Rev. Lett. 116, 241102 (2016).], and we quote updated component masses of 35(+5)(-3) solar M; and 30(+3)(-4) solar M; (where errors correspond to 90 symmetric credible intervals). We also present slightly tighter constraints on the dimensionless spin magnitudes of the two black holes, with a primary spin estimate is less than 0.65 and a secondary spin estimate is less than 0.75 at 90% probability. Abbott et al. [Phys. Rev. Lett. 116, 241102 (2016).] estimated the systematic parameter-extraction errors due to waveform-model uncertainty by combining the posterior probability densities of precessing IMRPhenom and nonprecessing EOBNR. Here, we find that the two precessing-spin models are in closer agreement, suggesting that these systematic errors are smaller than previously quoted.

Abbott, B. P.↗

Global Warming Estimation from MSU: Correction for Drift and Calibration Errors

Microwave Sounding Unit (MSU) radiometer observations in Ch 2 (53.74 GHz), made in the nadir direction from sequential, sun-synchronous, polar-orbiting NOAA morning satellites (NOAA 6, 10 and 12 that have approximately 7am/7pm orbital geometry) and. afternoon satellites (NOAA 7, 9, 11 and 14 that have approximately 2am/2pm orbital geometry) are analyzed in this study to derive global temperature trend from 1980 to 1998. In order to remove the discontinuities between the data of the successive satellites and to get a continuous time series, first we have used shortest possible time record of each satellite. In this way we get a preliminary estimate of the global temperature trend of 0.21 K/decade. However, this estimate is affected by systematic time-dependent errors. One such error is the instrument calibration error eo. This error can be inferred whenever there are overlapping measurements made by two satellites over an extended period of time. From the available successive satellite data we have taken the longest possible time record of each satellite to form the time series during the period 1980 to 1998 to this error eo. We find eo can decrease the global temperature trend by approximately 0.07 K/decade. In addition there are systematic time dependent errors ed and ec present in the data that are introduced by the drift in the satellite orbital geometry. ed arises from the diurnal cycle in temperature and ec is the drift related change in the calibration of the MSU. In order to analyze the nature of these drift related errors the multi-satellite Ch 2 data set is partitioned into am and pm subsets to create two independent time series. The error ed can be assessed in the am and pm data of Ch 2 on land and can be eliminated. Observation made in the MSU Ch 1 (50.3 GHz) support this approach. The error ec is obvious only in the difference between the pm and am observations of Ch 2 over the ocean. We have followed two different paths to assess the impact of the error ec on the global temperature trend. In one path the entire error ec is placed in the am data while in the other it is placed in the pm data. Global temperature trend is increased or decreased by approximately 0.03 K/decade depending upon this placement. Taking into account all random errors and systematic errors our analysis of MSU observations leads us to conclude that a conservative estimate of the global warming is 0. 11 (+/-) 0.04 K/decade during 1980 to 1998.

Prabhakara, C.↗

Data Fusion of In-Flight Aerothermodynamic Heating Measurements Using Kalman Filtering

On February 18th, 2021, the Mars 2020 entry system successfully delivered the Perseverance rover to the surface of Mars at Jezero Crater. The entry capsule carried instrumentation installed on the heatshield and backshell, named the Mars Entry, Descent, and Landing Instrumentation 2. The instruments included pressure transducers, thermocouples, heat flux gauges, and a radiometer to measure the aerodynamic and aerothermodynamic performance of the entry vehicle. Three of these sensors, a thermocouple plug, heat flux gauge, and a radiometer, were co-located on the backshell. The sensors were exposed to roughly the same aerodynamic heating, but measured these environments in different ways, each with its own set of modeling and measurement error complications. This paper develops a method for blending each of these measurements together in a single algorithm to produce estimates of the aerothermodynamic environments at that backshell location. The approach makes use of the Kalman-Schmidt filter/smoother methodology, where systematic measurement error parameters are modeled as multiplicative states that are estimated by the filter along with the aerothermal states. The results indicate peak convective and radiative heating values of 0.86 and 5.16 W/cm2, respectively, compared to the filter predictive model values of 0.67 and 4.83 W/cm2.

Christopher D Karlgaard↗

Data Fusion of In-Flight Aerothermodynamic Heating Measurements Using Kalman Filtering

On February 18th, 2021, the Mars 2020 entry system successfully delivered the Perseverance rover to the surface of Mars at Jezero Crater. The entry capsule carried instrumentation installed on the heatshield and backshell, named the Mars Entry, Descent, and Landing Instrumentation 2. The instruments included pressure transducers, thermocouples, heat flux gauges, and a radiometer to measure the aerodynamic and aerothermodynamic performance of the entry vehicle. Three of these sensors, a thermocouple plug, heat flux gauge, and a radiometer, were co-located on the backshell. The sensors were exposed to roughly the same aerodynamic heating, but measured these environments in different ways, each with its own set of modeling and measurement error complications. This paper develops a method for blending each of these measurements together in a single algorithm to produce estimates of the aerothermodynamic environments at that backshell location. The approach makes use of the Kalman-Schmidt filter/smoother methodology, where systematic measurement error parameters are modeled as multiplicative states that are estimated by the filter along with the aerothermal states. The results indicate peak convective and radiative heating values of 0.86 and 5.16 W/cm$^2$, respectively, compared to the filter predictive model values of 0.67 and 4.83 W/cm$^2$.

Christopher D Karlgaard↗

Planck 2013 results. V. LFI calibration

We discuss the methods employed to photometrically calibrate the data acquired by the Low Frequency Instrument on Planck. Our calibration is based on a combination of the orbital dipole plus the solar dipole, caused respectively by the motion of the Planck spacecraft with respect to the Sun and by motion of the solar system with respect to the cosmic microwave background (CMB) rest frame. The latter provides a signal of a few mK with the same spectrum as the CMB anisotropies and is visible throughout the mission. In this data releasewe rely on the characterization of the solar dipole as measured by WMAP. We also present preliminary results (at 44 GHz only) on the study of the Orbital Dipole, which agree with the WMAP value of the solar system speed within our uncertainties. We compute the calibration constant for each radiometer roughly once per hour, in order to keep track of changes in the detectors’ gain. Since non-idealities in the optical response of the beams proved to be important, we implemented a fast convolution algorithm which considers the full beam response in estimating the signal generated by the dipole. Moreover, in order to further reduce the impact of residual systematics due to sidelobes, we estimated time variations in the calibration constant of the 30 GHz radiometers (the ones with the largest sidelobes) using the signal of an internal reference load at 4 K instead of the CMB dipole. We have estimated the accuracy of the LFI calibration following two strategies: (1) we have run a set of simulations to assess the impact of statistical errors and systematic effects in the instrument and in the calibration procedure; and (2) we have performed a number of internal consistency checks on the data and on the brightness temperature of Jupiter. Errors in the calibration of this Planck/LFI data release are expected to be about 0.6% at 44 and 70 GHz, and 0.8% at 30 GHz. Both these preliminary results at low and high ℓ are consistent with WMAP results within uncertainties and comparison of power spectra indicates good consistency in the absolute calibration with HFI (0.3%) and a 1.4σ discrepancy with WMAP (0.9%).

79 ASTRONOMY AND ASTROPHYSICS↗

Errors in retarding potential analyzers caused by nonuniformity of the grid-plane potential.

One aspect of the degradation in performance of retarding potential analyzers caused by potential depressions in the retarding grid is quantitatively estimated from laboratory measurements and theoretical calculations. A simple expression is obtained that permits the use of laboratory measurements of grid properties to make first-order corrections to flight data. Systematic positive errors in ion temperature of approximately 16% for the Ogo 4 instrument and 3% for the Ogo 6 instrument are deduced. The effects of the transverse electric fields arising from the grid potential depressions are not treated.

Hanson, W. B.↗

Impact of point spread function higher moments error on weak gravitational lensing – II. A comprehensive study

ABSTRACT Weak lensing is one of the most powerful probes for dark matter and dark energy science, although it faces increasing challenges in controlling systematic uncertainties as the statistical errors become smaller. The point spread function (PSF) needs to be precisely modeled to avoid systematic error on the weak lensing measurements. The weak lensing biases induced by errors in the PSF model second moments, i.e. its size and shape, are well-studied. However, Zhang et al. showed that errors in the higher moments of the PSF may also be a significant source of systematics for upcoming weak lensing surveys. Therefore, this work comprehensively investigate the modelling quality of PSF moments from the 3rd to 6th order, and propagate the PSFEx higher moments modelling error in the HSC survey data set to the weak lensing shear–shear correlation functions and their cosmological analyses. The overall multiplicative shear bias associated with errors in PSF higher moments can cause an ∼0.1σ shift on the cosmological parameters for LSST Y10, while the associated additive biases can induce 1σ uncertainties in cosmology parameter inference for LSST Y10, if not accounted. We compare the PSFEx model with PSF in Full FOV, and find similar performance in modelling the PSF higher moments. We conclude that PSF higher moment errors of the future PSF models should be reduced from those in current methods, otherwise needed to be explicitly modeled in the weak lensing analysis.

79 ASTRONOMY AND ASTROPHYSICS↗

Land Surface Temperature Measurements form EOS MODIS Data

We have developed a physics-based land-surface temperature (LST) algorithm for simultaneously retrieving surface band-averaged emissivities and temperatures from day/night pairs of MODIS (Moderate Resolution Imaging Spectroradiometer) data in seven thermal infrared bands. The set of 14 nonlinear equations in the algorithm is solved with the statistical regression method and the least-squares fit method. This new LST algorithm was tested with simulated MODIS data for 80 sets of band-averaged emissivities calculated from published spectral data of terrestrial materials in wide ranges of atmospheric and surface temperature conditions. Comprehensive sensitivity and error analysis has been made to evaluate the performance of the new LST algorithm and its dependence on variations in surface emissivity and temperature, upon atmospheric conditions, as well as the noise-equivalent temperature difference (NE(Delta)T) and calibration accuracy specifications of the MODIS instrument. In cases with a systematic calibration error of 0.5%, the standard deviations of errors in retrieved surface daytime and nighttime temperatures fall between 0.4-0.5 K over a wide range of surface temperatures for mid-latitude summer conditions. The standard deviations of errors in retrieved emissivities in bands 31 and 32 (in the 10-12.5 micrometer IR spectral window region) are 0.009, and the maximum error in retrieved LST values falls between 2-3 K. Several issues related to the day/night LST algorithm (uncertainties in the day/night registration and in surface emissivity changes caused by dew occurrence, and the cloud cover) have been investigated. The LST algorithms have been validated with MODIS Airborne Simulator (MAS) dada and ground-based measurement data in two field campaigns conducted in Railroad Valley playa, NV in 1995 and 1996. The MODIS LST version 1 software has been delivered.

Wan, Zhengming↗

Noise Reduction for Solar-Induced Fluorescence Retrievals Using Machine Learning and Principal Component Analysis: Simulations and Applications to GOME-2 Satellite Retrievals

We use a spectral-based approach that employs principal component analysis along with a relatively shallow artificial neural network (NN) to substantially reduce noise and other artifacts in terrestrial chlorophyll solar-induced fluorescence (SIF) retrievals. SIF is a very small emission at red and far-red wavelengths that is difficult to measure and is highly sensitive to random errors and systematic artifacts. Our approach relies upon an assumption that a trained NN can effectively reconstruct the total SIF signal from a relatively small number of leading principal components of the satellite-observed far-red radiance spectra without using information from the trailing modes that contain most of the random errors. We test the approach with simulated reflectance spectra produced with a full atmospheric and surface radiative transfer model using different observing and geophysical parameters and various noise levels. Resulting noisy and noise-reduced retrieved SIF values are compared with true values to assess performance. We then apply our noise reduction approach to SIF derived from two different satellite spectrometers. For evaluation, since the truth in this case is unknown, we compare SIF retrievals from two independent sensors with each other. We also compare the noise-reduced SIF temporal variations with those from an independent gross primary product (GPP) product that should display similar variations. Results show that our noise reduction approach improves capture of SIF seasonal and inter-annual variability. Our approach should be applicable to many noisy data products derived from spectral measurements. Our methodology does not replace the original retrieval algorithms; rather the original noisy retrievals are needed as the target for the NN training process.

noise reduction, solar-induced fluorescence, SIF, ↗

The Surface Water and Ocean Topography Satellite Mission - An Assessment of Swath Altimetry Measurements of River Hydrodynamics

The Surface Water and Ocean Topography (SWOT) satellite mission, scheduled for launch in 2020 with development commencing in 2015, will provide a step-change improvement in the measurement of terrestrial surface water storage and dynamics. In particular, it will provide the first, routine two-dimensional measurements of water surface elevations, which will allow for the estimation of river and floodplain flows via the water surface slope. In this paper, we characterize the measurements which may be obtained from SWOT and illustrate how they may be used to derive estimates of river discharge. In particular, we show (i) the spatia-temporal sampling scheme of SWOT, (ii) the errors which maybe expected in swath altimetry measurements of the terrestrial surface water, and (iii) the impacts such errors may have on estimates of water surface slope and river discharge, We illustrate this through a "virtual mission" study for a approximately 300 km reach of the central Amazon river, using a hydraulic model to provide water surface elevations according to the SWOT spatia-temporal sampling scheme (orbit with 78 degree inclination, 22 day repeat and 140 km swath width) to which errors were added based on a two-dimension height error spectrum derived from the SWOT design requirements. Water surface elevation measurements for the Amazon mainstem as may be observed by SWOT were thereby obtained. Using these measurements, estimates of river slope and discharge were derived and compared to those which may be obtained without error, and those obtained directly from the hydraulic model. It was found that discharge can be reproduced highly accurately from the water height, without knowledge of the detailed channel bathymetry using a modified Manning's equation, if friction, depth, width and slope are known. Increasing reach length was found to be an effective method to reduce systematic height error in SWOT measurements.

Wilson, Matthew D.↗

“Plug-and-Play” potentials: Investigating quantum effects in (H{sub 2}){sub 2}–Li{sup +}–benzene

Quantum and anharmonic effects are investigated in (H{sub 2}){sub 2}–Li{sup +}–benzene, a model for hydrogen adsorption in metal-organic frameworks and carbon-based materials, using rigid-body diffusion Monte Carlo (RBDMC) simulations. The potential-energy surface (PES) is calculated as a modified Shepard interpolation of M05-2X/6-311+G(2df,p) electronic structure data. The RBDMC simulations yield zero-point energies (ZPE) and probability density histograms that describe the ground-state nuclear wavefunction. Binding a second H{sub 2} molecule to the H{sub 2}–Li{sup +}–benzene complex increases the ZPE of the system by 5.6 kJ mol{sup −1} to 17.6 kJ mol{sup −1}. This ZPE is 42% of the total electronic binding energy of (H{sub 2}){sub 2}–Li{sup +}–benzene and cannot be neglected. Our best estimate of the 0 K binding enthalpy of the second H{sub 2} to H{sub 2}–Li{sup +}–benzene is 7.7 kJ mol{sup −1}, compared to 12.4 kJ mol{sup −1} for the first H{sub 2} molecule. Anharmonicity is found to be even more important when a second (and subsequent) H{sub 2} molecule is adsorbed; use of harmonic ZPEs results in significant error in the 0 K binding enthalpy. Probability density histograms reveal that the two H{sub 2} molecules are found at larger distance from the Li{sup +} ion and are more confined in the θ coordinate than in H{sub 2}–Li{sup +}–benzene. They also show that both H{sub 2} molecules are delocalized in the azimuthal coordinate, ϕ. That is, adding a second H{sub 2} molecule is insufficient to localize the wavefunction in ϕ. Two fragment-based (H{sub 2}){sub 2}–Li{sup +}–benzene PESs are developed. These use a modified Shepard interpolation for the Li{sup +}–benzene and H{sub 2}–Li{sup +}–benzene fragments, and either modified Shepard interpolation or a cubic spline to model the H{sub 2}–H{sub 2} interaction. Because of the neglect of three-body H{sub 2}, H{sub 2}, Li{sup +} terms, both fragment PESs lead to overbinding of the second H{sub 2} molecule by 1.5 kJ mol{sup −1}. Probability density histograms, however, indicate that the wavefunctions for the two H{sub 2} molecules are effectively identical on the “full” and fragment PESs. This suggests that the 1.5 kJ mol{sup −1} error is systematic over the regions of configuration space explored by our simulations. Notwithstanding this, modified Shepard interpolation of the weak H{sub 2}–H{sub 2} interaction is problematic and we obtain more accurate results, at considerably lower computational cost, using a cubic spline interpolation. Indeed, the ZPE of the fragment-with-spline PES is identical, within error, to the ZPE of the full PES. This fragmentation scheme therefore provides an accurate and inexpensive method to study higher hydrogen loading in this and similar systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

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

An 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/Multi­Sensor. 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 global­scale 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.

Pierre-Emmanuel Kirstette↗

Online Tools for Uncovering Data Quality (DQ) Issues in Satellite-Based Global Precipitation Products

Data quality (DQ) has many attributes or facets (i.e., errors, biases, systematic differences, uncertainties, benchmark, false trends, false alarm ratio, etc.)Sources can be complicated (measurements, environmental conditions, surface types, algorithms, etc.) and difficult to be identified especially for multi-sensor and multi-satellite products with bias correction (TMPA, IMERG, etc.) How to obtain DQ info fast and easily, especially quantified info in ROI Existing parameters (random error), literature, DIY, etc.How to apply the knowledge in research and applications.Here, we focus on online systems for integration of products and parameters, visualization and analysis as well as investigation and extraction of DQ information.

precipitation↗

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.

Rainfall↗