Evaluation of GPROF-SSMI/S rainfall estimates over land during the Brazilian CHUVA-VALE campaign

Oliveira, Rômulo; Braga, Ramon Campos; Vila, Daniel; Morales, Carlos A.

doi:10.1016/j.atmosres.2014.11.010

Cited by 8 publications

(7 citation statements)

References 54 publications

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“…Several evaluation studies of passive microwave retrievals have revealed heterogeneous performances depending on geographical areas, seasons, and types of precipitation systems (Tang et al 2014;Oliveira et al 2015;Liu et al 2017;Petković and Kummerow 2017;Guilloteau et al 2017). The passive microwave retrieval over oceans generally outperforms the retrieval over land.…”

Section: Introductionmentioning

confidence: 99%

Resolving Surface Rain from GMI High-Frequency Channels: Limits Imposed by the Three-Dimensional Structure of Precipitation

Guilloteau

Foufoula‐Georgiou

Kummerow

et al. 2018

Journal of Atmospheric and Oceanic Technology

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The scattering of microwaves at frequencies between 50 and 200 GHz by ice particles in the atmosphere is an essential element in the retrieval of instantaneous surface precipitation from spaceborne passive radiometers. This paper explores how the variable distribution of solid and liquid hydrometeors in the atmospheric column over land surfaces affects the brightness temperature (TB) measured by GMI at 89 GHz through the analysis of Dual-Frequency Precipitation Radar (DPR) reflectivity profiles along the 89-GHz beam. The objective is to refine the statistical relations between observed TBs and surface precipitation over land and to define their limits. As GMI is scanning with a 53° Earth incident angle, the observed atmospheric volume is actually not a vertical column, which may lead to very heterogeneous and seemingly inconsistent distributions of the hydrometeors inside the beam. It is found that the 89-GHz TB is mostly sensitive to the presence of ice hydrometeors several kilometers above the 0°C isotherm, up to 10 km above the 0°C isotherm for the deepest convective systems, but is a modest predictor of the surface precipitation rate. To perform a precise mapping of atmospheric ice, the altitude of the individual ice clusters must be known. Indeed, if variations in the altitude of ice are not accounted for, then the high incident angle of GMI causes a horizontal shift (parallax shift) between the estimated position of the ice clusters and their actual position. We show here that the altitude of ice clusters can be derived from the 89-GHz TB itself, allowing for correction of the parallax shift.

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Section: Introductionmentioning

confidence: 99%

Resolving Surface Rain from GMI High-Frequency Channels: Limits Imposed by the Three-Dimensional Structure of Precipitation

Guilloteau

Foufoula‐Georgiou

Kummerow

et al. 2018

Journal of Atmospheric and Oceanic Technology

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“…The DPSRI algorithm uses a combination of reflectivity (Z), specific differential propagation phase (Kdp) and rain rate (R) variables (a Z-Kdp-R relation), for X-band radars at a specific Constant Altitude Plan Position Indicator (CAPPI) level. A detailed description of the precipitation retrievals technique used by the X-band CHUVA radar and its performance in representing surface precipitation compared to rain gauges can be found in Gematronik [36] and Oliveira et al [17].…”

Section: Radar Rainfall Estimatesmentioning

confidence: 99%

“…In order to study the satellite precipitation error distributions, we looked at volumetric (PDFv) and occurrence (PDFc) probability density function analyses [15][16][17][18]53]. The PDFv represents the relative contribution of each rain rate bin to the total rainfall volume.…”

Section: Probability Distributions By Rainfall Volumes and Occurrencesmentioning

confidence: 99%

Characteristics and Diurnal Cycle of GPM Rainfall Estimates over the Central Amazon Region

et al. 2016

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Abstract:Studies that investigate and evaluate the quality, limitations and uncertainties of satellite rainfall estimates are fundamental to assure the correct and successful use of these products in applications, such as climate studies, hydrological modeling and natural hazard monitoring. Over regions of the globe that lack in situ observations, such studies are only possible through intensive field measurement campaigns, which provide a range of high quality ground measurements, e.g., CHUVA (Cloud processes of tHe main precipitation systems in Brazil: A contribUtion to cloud resolVing modeling and to the GlobAl Precipitation Measurement) and GoAmazon (Observations and Modeling of the Green Ocean Amazon) over the Brazilian Amazon during 2014/2015. This study aims to assess the characteristics of Global Precipitation Measurement (GPM) satellite-based precipitation estimates in representing the diurnal cycle over the Brazilian Amazon. The Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) and the Goddard Profiling Algorithm-Version 2014 (GPROF2014) algorithms are evaluated against ground-based radar observations. Specifically, the S-band weather radar from the Amazon Protection National System (SIPAM), is first validated against the X-band CHUVA radar and then used as a reference to evaluate GPM precipitation. Results showed satisfactory agreement between S-band SIPAM radar and both IMERG and GPROF2014 algorithms. However, during the wet season, IMERG, which uses the GPROF2014 rainfall retrieval from the GPM Microwave Imager (GMI) sensor, significantly overestimates the frequency of heavy rainfall volumes around 00:00-04:00 UTC and 15:00-18:00 UTC. This overestimation is particularly evident over the Negro, Solimões and Amazon rivers due to the poorly-calibrated algorithm over water surfaces. On the other hand, during the dry season, the IMERG product underestimates mean precipitation in comparison to the S-band SIPAM radar, mainly due to the fact that isolated convective rain cells in the afternoon are not detected by the satellite precipitation algorithm.

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“…Therefore, it is critical to quantitatively evaluate the performance of SPDs both for the algorithm developer finding the source of the errors and for guiding external users in selecting suitable SPD for their application(s) [21,22]. Conceptually, retrieving precipitation from satellite observations mainly consist of two steps, including rain/no-rain areas delineation and rainfall rate estimation, and each step is the source of the errors in SPDs.…”

Section: Introductionmentioning

confidence: 99%

Tracing the Source of the Errors in Hourly IMERG Using a Decomposition Evaluation Scheme

Shen

2016

Atmosphere

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Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) is an important satellite precipitation product of Global Precipitation Measurement (GPM) mission. Quantitative information about the errors of IMERG has great significance for the data developers and end users. In order to investigate the characteristics and the source of the errors contained in IMERG, a bias-decomposition scheme was employed to evaluate the hourly IMERG over the eastern part of Mainland China during the warm season. First, the total bias of IMERG before and after calibration (termed as precipitationUncal and precipitationCal) was calculated using rain gauge measurements as reference. Then the bias was decomposed into three independent components including false bias, missed bias, and hit bias. Finally, the hit bias was further decomposed according to the rainfall intensity measured by rain gauges. The results indicate that (1) the bias of precipitationUncal over the north part is dominated by hit bias and false bias, leading to the serious overestimation for the precipitation over this area, but it underestimates the precipitation over the south part with the false bias and missed bias acting as major contributors; (2) the precipitationCal overestimates the precipitation over more than 80% of the study areas mainly as a result of a large amplitude of false bias; (3) the calibration algorithm used by IMERG could not reduce the missed bias and enlarges the false bias over some regions, revealing a shortcoming of this algorithm in that it could not effectively alleviate the bias resulting from the rain areas delineation; (4) the hit bias of IMERG is strongly related with the rainfall intensity of rain gauge measurements, which should be beneficial for reducing the errors of IMERG. This study provides a deep insight into the characteristics and sources of the biases inherent in IMERG, which is significant for its utilization and possible correction in future.

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Evaluation of GPROF-SSMI/S rainfall estimates over land during the Brazilian CHUVA-VALE campaign

Cited by 8 publications

References 54 publications

Resolving Surface Rain from GMI High-Frequency Channels: Limits Imposed by the Three-Dimensional Structure of Precipitation

Resolving Surface Rain from GMI High-Frequency Channels: Limits Imposed by the Three-Dimensional Structure of Precipitation

Characteristics and Diurnal Cycle of GPM Rainfall Estimates over the Central Amazon Region

Tracing the Source of the Errors in Hourly IMERG Using a Decomposition Evaluation Scheme

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