Analysis of the Global Microwave Polarization Data of Clouds

Abstract Information about the characteristics of ice particles in clouds is necessary for improving our understanding of the states, processes, and subsequent modeling of clouds and precipitation for numerical weather prediction and climate analysis. Two NASA passive microwave radiometers, the satellite-borne Global Precipitation Measurement (GPM) Microwave Imager (GMI) and the aircraft-borne Conical Scanning Millimeter-Wave Imaging Radiometer (CoSMIR), measure vertically and horizontally polarized microwaves emitted by clouds (including precipitating particles) and Earth’s surface below. In this paper, GMI (or CoSMIR) data are analyzed with CloudSat (or aircraft-borne radar) data to find polarized difference (PD) signals not affected by the surface, thereby obtaining the information on ice particles. Statistical analysis of 4 years of GMI and CloudSat data, for the first time, reveals that optically thick clouds contribute positively to GMI PD at 166 GHz over all the latitudes and their positive magnitude of 166-GHz GMI PD varies little with latitude. This result suggests that horizontally oriented ice crystals in thick clouds are common from the tropics to high latitudes, which contrasts the result of Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) that horizontally oriented ice crystals are rare in optically thin ice clouds.

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Microwave and submillimeter wave scattering of oriented ice particles

Atmospheric Measurement Techniques ◽

10.5194/amt-13-2309-2020 ◽

2020 ◽

Vol 13 (5) ◽

pp. 2309-2333 ◽

Cited By ~ 1

Author(s):

Manfred Brath ◽

Robin Ekelund ◽

Patrick Eriksson ◽

Oliver Lemke ◽

Stefan A. Buehler

Keyword(s):

Tilt Angle ◽

Incidence Angle ◽

Dipole Approximation ◽

Ice Crystals ◽

Scattering Data ◽

Random Orientation ◽

Ice Particles ◽

Microwave Imager ◽

Global Precipitation ◽

Snow And Ice

Abstract. Microwave (1–300 GHz) dual-polarization measurements above 100 GHz are so far sparse, but they consistently show polarized scattering signals of ice clouds. Existing scattering databases of realistically shaped ice crystals for microwaves and submillimeter waves (>300 GHz) typically assume total random orientation, which cannot explain the polarized signals. Conceptual models show that the polarization signals are caused by oriented ice particles. Only a few works that consider oriented ice crystals exist, but they are limited to microwaves only. Assuming azimuthally randomly oriented ice particles with a fixed but arbitrary tilt angle, we produced scattering data for two particle habits (51 hexagonal plates and 18 plate aggregates), 35 frequencies between 1 and 864 GHz, and 3 temperatures (190, 230 and 270 K). In general, the scattering data of azimuthally randomly oriented particles depend on the incidence angle and two scattering angles, in contrast to total random orientation, which depends on a single angle. The additional tilt angle further increases the complexity. The simulations are based on the discrete dipole approximation in combination with a self-developed orientation averaging approach. The scattering data are publicly available from Zenodo (https://doi.org/10.5281/zenodo.3463003). This effort is also an essential part of preparing for the upcoming Ice Cloud Imager (ICI) that will perform polarized observations at 243 and 664 GHz. Using our scattering data radiative transfer simulations with two liquid hydrometeor species and four frozen hydrometeor species of polarized Global Precipitation Measurement (GPM) Microwave Imager (GMI) observations at 166 GHz were conducted. The simulations recreate the observed polarization patterns. For slightly fluttering snow and ice particles, the simulations show polarization differences up to 11 K using plate aggregates for snow, hexagonal plates for cloud ice and totally randomly oriented particles for the remaining species. Simulations using strongly fluttering hexagonal plates for snow and ice show similar polarization signals. Orientation, shape and the hydrometeor composition affect the polarization. Ignoring orientation can cause a negative bias for vertically polarized observations and a positive bias for horizontally polarized observations.

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Daily Rainfall Estimate by Emissivity Temporal Variation from 10 Satellites

Journal of Hydrometeorology ◽

10.1175/jhm-d-20-0195.1 ◽

2021 ◽

Author(s):

Yalei You ◽

S. Joseph Munchak ◽

Christa Peters-Lidard ◽

Sarah Ringerud

Keyword(s):

Temporal Variation ◽

Low Frequency ◽

Daily Rainfall ◽

Passive Microwave ◽

Advanced Technology ◽

Precipitation Measurement ◽

Comparable Performance ◽

Microwave Imager ◽

Microwave Radiometers ◽

Global Precipitation

AbstractRainfall retrieval algorithms for passive microwave radiometers often exploits the brightness temperature depression due to ice scattering at high frequency channels (≥ 85 GHz) over land. This study presents an alternate method to estimate the daily rainfall amount using the emissivity temporal variation (i.e., Δe) under rain-free conditions at low frequency channels (19, 24 and 37 GHz). Emissivity is derived from 10 passive microwave radiometers, including the Global Precipitation Measurement (GPM) Microwave Imager (GMI), the Advanced Microwave Scanning Radiometer 2 (AMSR2), three Special Sensor Microwave Imager/Sounder (SSMIS), the Advanced Technology Microwave Sounder (ATMS), and four Advanced Microwave Sounding Unit-A (AMSU-A). Four different satellite combination schemes are used to derive the Δe for daily rainfall estimates. They are all-10-satellites, 5-imagers, 6-satellites with very different equator crossing times, and GMI-only. Results show that Δe from all-10-satellites has the best performance with a correlation of 0.60 and RMSE of 6.52 mm, comparing with the integrated multi-satellite retrievals (IMERG) final run product. The 6-satellites scheme has comparable performance with all-10-satellites scheme. The 5-imagers scheme performs noticeably worse with a correlation of 0.49 and RMSE of 7.28 mm, while the GMI-only scheme performs the worst with a correlation of 0.25 and RMSE of 11.36 mm. The inferior performance from the 5-imagers and GMI-only schemes can be explained by the much longer revisit time, which cannot accurately capture the emissivity temporal variation.

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Assessment of New Satellite Missions within the Framework of Numerical Weather Prediction

Remote Sensing ◽

10.3390/rs12101580 ◽

2020 ◽

Vol 12 (10) ◽

pp. 1580 ◽

Cited By ~ 3

Author(s):

Stuart Newman ◽

Fabien Carminati ◽

Heather Lawrence ◽

Niels Bormann ◽

Kirsti Salonen ◽

...

Keyword(s):

Numerical Weather Prediction ◽

Gap Analysis ◽

Weather Prediction ◽

Data Sets ◽

Next Generation ◽

Numerical Weather ◽

Surface Emission ◽

Precipitation Measurement ◽

Microwave Imager ◽

Global Precipitation

Confidence in the use of Earth observations for monitoring essential climate variables (ECVs) relies on the validation of satellite calibration accuracy to within a well-defined uncertainty. The gap analysis for integrated atmospheric ECV climate monitoring (GAIA-CLIM) project investigated the calibration/validation of satellite data sets using non-satellite reference data. Here, we explore the role of numerical weather prediction (NWP) frameworks for the assessment of several meteorological satellite sensors: the advanced microwave scanning radiometer 2 (AMSR2), microwave humidity sounder-2 (MWHS-2), microwave radiation imager (MWRI), and global precipitation measurement (GPM) microwave imager (GMI). We find departures (observation-model differences) are sensitive to instrument calibration artefacts. Uncertainty in surface emission is identified as a key gap in our ability to validate microwave imagers quantitatively in NWP. The prospects for NWP-based validation of future instruments are considered, taking as examples the microwave sounder (MWS) and infrared atmospheric sounding interferometer-next generation (IASI-NG) on the next generation of European polar-orbiting satellites. Through comparisons with reference radiosondes, uncertainties in NWP fields can be estimated in terms of equivalent top-of-atmosphere brightness temperature. We find NWP-sonde differences are consistent with a total combined uncertainty of 0.15 K for selected temperature sounding channels, while uncertainties for humidity sounding channels typically exceed 1 K.

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Microphysical Properties of Frozen Particles Inferred from Global Precipitation Measurement (GPM) Microwave Imager (GMI) Polarimetric Measurements

10.5194/acp-2016-787 ◽

2016 ◽

Author(s):

Jie Gong ◽

Dong L. Wu

Keyword(s):

Dual Frequency ◽

Melting Layer ◽

Bell Curve ◽

Microphysical Properties ◽

Precipitation Measurement ◽

Wave Imaging ◽

Microwave Imager ◽

The Tropics ◽

Global Precipitation Measurement ◽

Global Precipitation

Abstract. Scattering differences induced by frozen particle microphysical properties are investigated, using the vertically (V) and horizontally (H) polarized radiances from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) 89 and 166 GHz channels. It is the first study on global frozen particle microphysical properties that uses the dual-frequency microwave polarimetric signals. From the ice cloud scenes identified by the 183.3 ± 3 GHz channel brightness temperature (TB), we find that the scatterings of frozen particles are highly polarized with V-H polarimetric differences (PD) being positive throughout the tropics and the winter hemisphere mid-latitude jet regions, including PDs from the GMI 89 and 166 GHz TBs, as well as the PD at 640 GHz from the ER-2 Compact Scanning Submillimeter-wave Imaging Radiometer (CoSSIR) during the TC4 campaign. Large polarization dominantly occurs mostly near convective outflow region (i.e., anvils or stratiform precipitation), while the polarization signal is small inside deep convective cores as well as at the remote cirrus region. Neglecting the polarimetric signal would result in as large as 30 % error in ice water path retrievals. There is a universal "bell-curve" in the PD – TB relationship, where the PD amplitude peaks at ~ 10 K for all three channels in the tropics and increases slightly with latitude. Moreover, the 166 GHz PD tends to increase in the case where a melting layer is beneath the frozen particles aloft in the atmosphere, while 89 GHz PD is less sensitive than 166 GHz to the melting layer. This property creates a unique PD feature for the identification of the melting layer and stratiform rain with passive sensors. Horizontally oriented non-spherical frozen particles are thought to produce the observed PD because of different ice scattering properties in the V and H polarizations. On the other hand, changes in the ice microphysical habitats or orientation due to turbulence mixing can also lead to a reduced PD in the deep convective cores. The current GMI polarimetric measurements themselves cannot fully disentangle the possible mechanisms.

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Validation of GPM IMERG extreme precipitation in the Peninsular Malaysia and Philippines by station and radar data

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/893/1/012020 ◽

2021 ◽

Vol 893 (1) ◽

pp. 012020

Author(s):

Nicolas A Da Silva ◽

Benjamin G M Webber ◽

Adrian J Matthews ◽

Matthew M Feist ◽

Thorwald H M Stein ◽

...

Keyword(s):

Extreme Precipitation ◽

Weather Prediction ◽

Heavy Precipitation ◽

Radar Data ◽

The Philippines ◽

Peninsular Malaysia ◽

Sampling Errors ◽

Precipitation Measurement ◽

Global Precipitation Measurement ◽

Global Precipitation

Abstract Extreme precipitation is ubiquitous in the Maritime Continent (MC) but poorly predicted numerical weather prediction (NWP) models. NWP evaluation against accurate measures of heavy precipitation is essential to improve their forecasting skill. Here we examine the potential utility of the Global Precipitation Measurement (GPM) Integrated Multi-Satellite Retrieval for GPM (IMERG) for NWP evaluation of extreme precipitation in the MC. For that purpose, we use radar data in Subang (Malaysia) and station data from the Global Historical Climatology Network (GHCN) in Malaysia and the Philippines. We find that earlier studies may have underestimated IMERG performances in the MC due to large spatial sampling errors of ground precipitation measurements, especially during extreme precipitation conditions. We recommend using the 95th percentile for NWP evaluation of extreme daily and sub-daily precipitation against IMERG. At higher percentiles, the IMERG rainfall rates tend to diverge from ground observation and may therefore be treated with caution.

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Optimization of Cloud-Radiation Databases for Passive Microwave Precipitation Retrievals over Ocean

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-15-0198.1 ◽

2016 ◽

Vol 33 (8) ◽

pp. 1649-1671 ◽

Cited By ~ 5

Author(s):

Eun-Kyoung Seo ◽

Sung-Dae Yang ◽

Mircea Grecu ◽

Geun-Hyeok Ryu ◽

Guosheng Liu ◽

...

Keyword(s):

Tropical Rainfall Measuring Mission ◽

Optimization Method ◽

Passive Microwave ◽

Brightness Temperatures ◽

Precipitation Measurement ◽

Microwave Imager ◽

Microwave Radiometers ◽

Trmm Microwave Imager ◽

Global Precipitation Measurement ◽

Global Precipitation

AbstractUsing 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.

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Combined Radar and Radiometer Analysis of Precipitation Profiles for a Parametric Retrieval Algorithm

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech1751.1 ◽

2005 ◽

Vol 22 (7) ◽

pp. 909-929 ◽

Cited By ~ 69

Author(s):

Hirohiko Masunaga ◽

Christian D. Kummerow

Keyword(s):

A Priori ◽

Three Dimensional ◽

Tropical Rainfall Measuring Mission ◽

Dimensional Structure ◽

Retrieval Algorithm ◽

Brightness Temperatures ◽

Precipitation Measurement ◽

Microwave Imager ◽

Trmm Microwave Imager ◽

Global Precipitation

Abstract A methodology to analyze precipitation profiles using the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and precipitation radar (PR) is proposed. Rainfall profiles are retrieved from PR measurements, defined as the best-fit solution selected from precalculated profiles by cloud-resolving models (CRMs), under explicitly defined assumptions of drop size distribution (DSD) and ice hydrometeor models. The PR path-integrated attenuation (PIA), where available, is further used to adjust DSD in a manner that is similar to the PR operational algorithm. Combined with the TMI-retrieved nonraining geophysical parameters, the three-dimensional structure of the geophysical parameters is obtained across the satellite-observed domains. Microwave brightness temperatures are then computed for a comparison with TMI observations to examine if the radar-retrieved rainfall is consistent in the radiometric measurement space. The inconsistency in microwave brightness temperatures is reduced by iterating the retrieval procedure with updated assumptions of the DSD and ice-density models. The proposed methodology is expected to refine the a priori rain profile database and error models for use by parametric passive microwave algorithms, aimed at the Global Precipitation Measurement (GPM) mission, as well as a future TRMM algorithms.

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An IMERG-Based Optimal Extended Probabilistic Climatology (EPC) as a Benchmark Ensemble Forecast for Precipitation in the Tropics and Subtropics

Weather and Forecasting ◽

10.1175/waf-d-20-0233.1 ◽

2021 ◽

Author(s):

Eva–Maria Walz ◽

Marlon Maranan ◽

Roderick van der Linden ◽

Andreas H. Fink ◽

Peter Knippertz

Keyword(s):

Prediction Models ◽

Weather Prediction ◽

Predictive Ability ◽

Window Size ◽

Predictive Performance ◽

Ensemble Forecast ◽

High Mountain ◽

Rainfall Occurrence ◽

Precipitation Measurement ◽

Global Precipitation

AbstractCurrent numerical weather prediction models show limited skill in predicting low-latitude precipitation. To aid future improvements, be it with better dynamical or statistical models, we propose a well-defined benchmark forecast. We use the arguably best currently high-resolution, gauge-calibrated, gridded precipitation product, the Integrated Multi-Satellite Retrievals for GPM (Global Precipitation Measurement) (IMERG) “final run” in a ± 15-day window around the date of interest to build an empirical climatological ensemble forecast. This window size is an optimal compromise between statistical robustness and flexibility to represent seasonal changes. We refer to this benchmark as Extended Probabilistic Climatology (EPC) and compute it on a 0.1°×0.1° grid for 40°S–40°N and the period 2001–2019. In order to reduce and standardize information, a mixed Bernoulli-Gamma distribution is fitted to the empirical EPC, which hardly affects predictive performance. The EPC is then compared to 1-day ensemble predictions from the European Centre for Medium-Range Weather Forecasts (ECMWF) using standard verification scores. With respect to rainfall amount, ECMWF performs only slightly better than EPS over most of the low latitudes and worse over high-mountain and dry oceanic areas as well as over tropical Africa, where the lack of skill is also evident in independent station data. For rainfall occurrence, EPC is superior over most oceanic, coastal, and mountain regions, although the better potential predictive ability of ECMWF indicates that this is mostly due to calibration problems. To encourage the use of the new benchmark, we provide the data, scripts, and an interactive webtool to the scientific community.

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Inter-calibrating SMMR brightness temperatures over continental surfaces

10.5194/amt-2019-493 ◽

2020 ◽

Author(s):

Samuel Favrichon ◽

Carlos Jimenez ◽

Catherine Prigent

Keyword(s):

Land Surface ◽

Microwave Remote Sensing ◽

Calibration Method ◽

Diurnal Cycles ◽

Brightness Temperatures ◽

Linear Correction ◽

Long Time ◽

Microwave Imager ◽

Microwave Radiometers ◽

Global Precipitation

Abstract. Microwave remote sensing can be used to monitor the time evolution of some key parameters over land, such as land surface temperature or surface water extent. Observations are made with instrument such as the Scanning Microwave Multichannel Radiometer (SMMR) before 1987, the Special Sensor Microwave/Imager (SSM/I) and the following Special Sensor Microwave Imager/Sounder (SSMIS) from 1987 and still operating, to the more recent Global Precipitation Mission Microwave Imager (GMI). As these instruments differ on some of their characteristics and use different calibration schemes, they need to be inter-calibrated before long time series products can be derived from the observations. Here an inter-calibration method is designed to remove major inconsistencies between the SMMR and other microwave radiometers for the 18 GHz and 37 GHz channels over continental surfaces. Because of a small overlap in observations and a ~6 h difference in overpassing times between SMMR and SSM/I, GMI was chosen as a reference despite the lack of a common observing period. The diurnal cycles from three years of GMI brightness temperatures are first calculated, and then used to evaluate SMMR differences. Based on a statistical analysis of the differences, a simple linear correction is implemented to calibrate SMMR on GMI. This correction is shown to also reduce the biases between SMMR and SSM/I, and can then be applied to SMMR observations to make them more coherent with existing data record of microwave brightness temperatures over continental surfaces.

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Validation of the First Years of GPM Operation over Cyprus

Remote Sensing ◽

10.3390/rs10101520 ◽

2018 ◽

Vol 10 (10) ◽

pp. 1520 ◽

Cited By ~ 13

Author(s):

Adrianos Retalis ◽

Dimitris Katsanos ◽

Filippos Tymvios ◽

Silas Michaelides

Keyword(s):

High Resolution ◽

Prediction Models ◽

Weather Prediction ◽

Monthly Basis ◽

Rain Gauges ◽

Precipitation Measurement ◽

Precipitation Estimates ◽

First Results ◽

Global Precipitation Measurement ◽

Global Precipitation

Global Precipitation Measurement (GPM) high-resolution product is validated against rain gauges over the island of Cyprus for a three-year period, starting from April 2014. The precipitation estimates are available in both high temporal (half hourly) and spatial (10 km) resolution and combine data from all passive microwave instruments in the GPM constellation. The comparison performed is twofold: first the GPM data are compared with the precipitation measurements on a monthly basis and then the comparison focuses on extreme events, recorded throughout the first 3 years of GPM’s operation. The validation is based on ground data from a dense and reliable network of rain gauges, also available in high temporal (hourly) resolution. The first results show very good correlation regarding monthly values; however, the correspondence of GPM in extreme precipitation varies from “no correlation” to “high correlation”, depending on case. This study aims to verify the GPM rain estimates, since such a high-resolution dataset has numerous applications, including the assimilation in numerical weather prediction models and the study of flash floods with hydrological models.

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