scholarly journals On the Propagation of Satellite Precipitation Estimation Errors: From Passive Microwave to Infrared Estimates

2020 ◽  
Vol 21 (6) ◽  
pp. 1367-1381 ◽  
Author(s):  
Shruti A. Upadhyaya ◽  
Pierre-Emmanuel Kirstetter ◽  
Jonathan J. Gourley ◽  
Robert J. Kuligowski
Keyword(s):  
Complex Terrain ◽  
Passive Microwave ◽  
Calibration Data ◽  
Estimation Errors ◽  
Budget Analysis ◽  
Stratiform Precipitation ◽  
Precipitation Estimation ◽  
Ground Validation ◽  
Detection Score ◽  
Systematic And Random Error

ABSTRACTThe launch of NOAA’s latest generation of geostationary satellites known as the Geostationary Operational Environmental Satellite (GOES)-R Series has opened new opportunities in quantifying precipitation rates. Recent efforts have strived to utilize these data to improve space-based precipitation retrievals. The overall objective of the present work is to carry out a detailed error budget analysis of the improved Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) algorithm for GOES-R and the passive microwave (MW) combined (MWCOMB) precipitation dataset used to calibrate it with an aim to provide insights regarding strengths and weaknesses of these products. This study systematically analyzes the errors across different climate regions and also as a function of different precipitation types over the conterminous United States. The reference precipitation dataset is Ground-Validation Multi-Radar Multi-Sensor (GV-MRMS). Overall, MWCOMB reveals smaller errors as compared to SCaMPR. However, the analysis indicated that that the major portion of error in SCaMPR is propagated from the MWCOMB calibration data. The major challenge starts with poor detection from MWCOMB, which propagates in SCaMPR. In particular, MWCOMB misses 90% of cool stratiform precipitation and the overall detection score is around 40%. The ability of the algorithms to quantify precipitation amounts for the Warm Stratiform, Cool Stratiform, and Tropical/Stratiform Mix categories is poor compared to the Convective and Tropical/Convective Mix categories with additional challenges in complex terrain regions. Further analysis showed strong similarities in systematic and random error models with both products. This suggests that the potential of high-resolution GOES-R observations remains underutilized in SCaMPR due to the errors from the calibrator MWCOMB.

scholarly journals Operational C-Band Dual-Polarization Radar QPE for the Subtropical Complex Terrain of Taiwan

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Yadong Wang ◽  
Jian Zhang ◽  
Pao-Liang Chang ◽  
Carrie Langston ◽  
Brian Kaney ◽  
...  
Keyword(s):  
Complex Terrain ◽  
Vertical Profile ◽  
Differential Phase ◽  
Quantitative Precipitation Estimation ◽  
Stratiform Precipitation ◽  
Precipitation Estimation ◽  
Orographic Enhancement ◽  
Vertical Variations ◽  
Heavy Rains ◽  
Precipitation Events

Complex terrain poses significant challenges to the radar based quantitative precipitation estimation (QPE) because of blockages to the lower tilts of radar observations. The blockages often force the use of higher tilts data to estimate precipitation at the ground and result in errors due to vertical variations of the radar variables. To obtain accurate radar QPEs in the subtropical complex terrain of Taiwan, a vertically corrected composite algorithm (VCCA) was developed for two C-band polarimetric radars. The new algorithm corrects higher tilt radar variables with the vertical profile of reflectivity (VPR) or vertical profile of specific differential phase (VPSDP) and estimates rainfall rate at the ground through an automated combination ofR-ZandR-KDPrelations. The VCCA was assessed with three precipitation cases of different regimes including typhoon, mei-yu, and summer stratiform precipitation events. The results showed that a combination ofR-ZandR-KDPrelations provided more accurate QPEs than each alone becauseR-Zprovides better rainfall estimates for light rains andR-KDPrelation is more suitable for heavy rains. The vertical profile corrections for reflectivity and specific differential phase significantly reduced radar QPE errors caused by inadequate sampling of the orographic enhancement of precipitation near the ground.

scholarly journals Application of Attenuation Correction to Quantitative Precipitation Estimation on C-Band Weather Radar in Bengkulu

2021 ◽  
Vol 893 (1) ◽  
pp. 012054
Author(s):  
M F Handoyo ◽  
M P Hadi ◽  
S Suprayogi
Keyword(s):  
Attenuation Correction ◽  
Estimation Error ◽  
Weather Radar ◽  
Radar Data ◽  
Signal Interference ◽  
Active System ◽  
Estimation Errors ◽  
Rain Gauges ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation

Abstract A weather radar is an active system remote sensing tool that observes precipitation indirectly. Weather radar has an advantage in estimating precipitation because it has a high spatial resolution (up to 0.5 km). Reflectivity generated by weather radar still has signal interference caused by attenuation factors. Attenuation causes the Quantitative Precipitation Estimation (QPE) by the C-band weather radar to underestimate. Therefore attenuation correction on C-band weather radar is needed to eliminate precipitation estimation errors. This study aims to apply attenuation correction to determine Quantitative Precipitation Estimation (QPE) on the c-band weather radar in Bengkulu in December 2018. Gate-by-gate method attenuation correction with Kraemer approach has applied to c-band weather radar data from the Indonesian Agency for Meteorology and Geophysics (BMKG) weather radar network Bengkulu. This method uses reflectivity as the only input. Quantitative Precipitation Estimation (QPE) has obtained by comparing weather radar-based rain estimates to 10 observation rain gauges over a month with the Z-R relation equation. Root Mean Square Error (RMSE) is used to calculate the estimation error. Weather radar data are processed using Python-based libraries Wradlib and ArcGIS 10.5. As a result, the calculation between the weather radar estimate precipitation and the observed rainfall obtained equation Z=2,65R1,3. The attenuation correction process with Kreamer's approach on the c-band weather radar has reduced error in the Qualitative Precipitation Estimation (QPE). Corrected precipitation has a smaller error value (r = 0.88; RMSE = 8.38) than the uncorrected precipitation (r = 0.83; RMSE = 11.70).

scholarly journals Analysis and Estimation of Geographical and Topographic Influencing Factors for Precipitation Distribution over Complex Terrains: A Case of the Northeast Slope of the Qinghai–Tibet Plateau

Atmosphere ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 349 ◽  
Author(s):  
Weicheng Liu ◽  
Qiang Zhang ◽  
Zhao Fu ◽  
Xiaoyan Chen ◽  
Hong Li
Keyword(s):  
Complex Terrain ◽  
Multivariate Regression ◽  
Estimation Method ◽  
Precipitation Forecast ◽  
Tibet Plateau ◽  
Mean Annual Precipitation ◽  
Observation Data ◽  
Precipitation Distribution ◽  
Precipitation Estimation ◽  
Qinghai Tibet Plateau

Due to the complex terrain, sparse precipitation observation sites, and uneven distribution of precipitation in the northeastern slope of the Qinghai–Tibet Plateau, it is necessary to establish a precipitation estimation method with strong applicability. In this study, the precipitation observation data from meteorological stations in the northeast slope of the Qinghai–Tibet Plateau and 11 geographical and topographic factors related to precipitation distribution were used to analyze the main factors affecting precipitation distribution. Based on the above, a multivariate linear regression precipitation estimation model was established. The results revealed that precipitation is negatively related to latitude and elevation, but positively related to longitude and slope for stations with an elevation below 1700 m. Meanwhile, precipitation shows positive correlations with both latitude and longitude, and negative correlations with elevation for stations with elevations above 1700 m. The established multivariate regression precipitation estimating model performs better at estimating the mean annual precipitation in autumn, summer, and spring, and is less accurate in winter. In contrast, the multivariate regression mode combined with the residual error correction method can effectively improve the precipitation forecast ability. The model is applicable to the unique natural geographical features of the northeast slope of the Qinghai–Tibet Plateau. The research results are of great significance for analyzing the temporal and spatial distribution pattern of precipitation in complex terrain areas.

scholarly journals An Improved Dual-Polarization Radar Rainfall Algorithm (DROPS2.0): Application in NASA IFloodS Field Campaign

2017 ◽  
Vol 18 (4) ◽  
pp. 917-937 ◽  
Author(s):  
Haonan Chen ◽  
V. Chandrasekar ◽  
Renzo Bechini
Keyword(s):  
Spatial Coherence ◽  
Dual Polarization ◽  
Radar Rainfall ◽  
Field Campaign ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Ground Validation ◽  
Single Polarization ◽  
The Impact ◽  
Self Aggregation

Abstract Compared to traditional single-polarization radar, dual-polarization radar has a number of advantages for quantitative precipitation estimation because more information about the drop size distribution and hydrometeor type can be gleaned. In this paper, an improved dual-polarization rainfall methodology is proposed, which is driven by a region-based hydrometeor classification mechanism. The objective of this study is to incorporate the spatial coherence and self-aggregation of dual-polarization observables in hydrometeor classification and to produce robust rainfall estimates for operational applications. The S-band dual-polarization data collected from the NASA Polarimetric (NPOL) radar during the GPM Iowa Flood Studies (IFloodS) ground validation field campaign are used to demonstrate and evaluate the proposed rainfall algorithm. Results show that the improved rainfall method provides better performance than a few single- and dual-polarization algorithms in previous studies. This paper also investigates the impact of radar beam broadening on various rainfall algorithms. It is found that the radar-based rainfall products are less correlated with ground disdrometer measurements as the distance from the radar increases.

scholarly journals Characterization of precipitation product errors across the US using multiplicative Triple Collocation

2015 ◽  
Vol 12 (2) ◽  
pp. 2527-2559 ◽  
Author(s):  
S. H. Alemohammad ◽  
K. A. McColl ◽  
A. G. Konings ◽  
D. Entekhabi ◽  
A. Stoffelen
Keyword(s):  
Mean Squared Error ◽  
The United States ◽  
Error Model ◽  
Estimation Errors ◽  
Spatial Error ◽  
Wide Range ◽  
The Us ◽  
Precipitation Estimation ◽  
Precipitation Estimates

Abstract. Validation of precipitation estimates from various products is a challenging problem, since the true precipitation is unknown. However, with the increased availability of precipitation estimates from a wide range of instruments (satellite, ground-based radar, and gauge), it is now possible to apply the Triple Collocation (TC) technique to characterize the uncertainties in each of the products. Classical TC takes advantage of three collocated data products of the same variable and estimates the mean squared error of each, without requiring knowledge of the truth. In this study, triplets among NEXRAD-IV, TRMM 3B42, GPCP and GPI products are used to quantify the associated spatial error characteristics across a central part of the continental US. This is the first study of its kind to explore precipitation estimation errors using TC across the United States (US). A multiplicative (logarithmic) error model is incorporated in the original TC formulation to relate the precipitation estimates to the unknown truth. For precipitation application, this is more realistic than the additive error model used in the original TC derivations, which is generally appropriate for existing applications such as in the case of wind vector components and soil moisture comparisons. This study provides error estimates of the precipitation products that can be incorporated into hydrological and meteorological models, especially those used in data assimilation. Physical interpretations of the error fields (related to topography, climate, etc) are explored. The methodology presented in this study could be used to quantify the uncertainties associated with precipitation estimates from each of the constellation of GPM satellites. Such quantification is prerequisite to optimally merging these estimates.

Investigation of the relationship between differential phase shift and path integrated attenuation in the melting layer of precipitation of X-band radar

2021 ◽  
Author(s):  
Anil Kumar Khanal ◽  
Guy Delrieu ◽  
Brice Boudevillain ◽  
Frédéric Cazenave ◽  
Nan Yu
Keyword(s):  
Phase Shift ◽  
Complex Terrain ◽  
High Mountain ◽  
Differential Phase Shift ◽  
Melting Layer ◽  
Differential Phase ◽  
Quantitative Precipitation Estimation ◽  
X Band ◽  
Precipitation Estimation ◽  
The Relationship

<p>The RadAlp experiment at the Grenoble region in the French Alps aims to advance the radar remote sensing techniques of precipitation in high mountain regions. Since 2016, two dual-polarimetric X-band radars, one on top of Mt Moucherotte (1901 m asl) and another in the Grenoble valley (220 m asl) are operated by Metro France and IGE respectively. High spatio-temporal variability of precipitation (e.g. intensity and phase) in the complex terrain requires high-resolution observations. X-band radar provides high spatial and temporal resolution imagery which makes it ideal for use in complex terrain but also comes with significant attenuation problems during heavy precipitation and in the melting layer (ML). The development of polarimetric techniques, especially differential phase shift (ϕDP) has helped to mitigate the power signal attenuation problem to a certain extent. The ϕDP is immune to attenuation due to rainfall, radar calibration errors and partial beam blockage, making it an attractive parameter for quantitative precipitation estimation (QPE) through attenuation correction of the reflectivity (Z). The ϕDP, however, is quite noisy and requires regularization. An iterative algorithm based on maximum allowed step sizes provides a robust solution in ϕDP regularization. In this study, we aim to understand the relationship between differential phase shift (ϕDP) and path integrated attenuation (PIA) at X-band. This relationship is crucial for quantitative precipitation estimation (QPE) using polarimetric techniques. Furthermore, this relationship is still poorly documented within the melting layer due to the complexity of the hydrometeors' distributions in terms of phase, size, shape and density. We use the mountain reference technique (MRT) for direct PIA estimations associated with the decrease in returns from mountain targets during precipitation events as compared to dry periods. The quasi-vertical profiles from the valley-based radar (XPORT) help to identify, characterize and follow the evolution of the melting layer. For the mountaintop radar (MOUC) stratiform events (59 days between Nov 2016 to Dec 2019) where the O° elevation angle beam passes through the melting layer are considered.  The PIA/ ϕDP ratios at different strata of the ML, snow-ML interface and ML-rain interface are studied. Initial results show that the PIA/ ϕDP ratio peaks at the levels of cross-correlation coefficient (ρHV) minima, remains strong in the upper part of the ML and tends to 0 towards the top of ML. Additionally, its value in rain (0.32 dB per deg) below the ML matches closely with the specific attenuation vs specific phase (k-KDP) relationship (0.29) derived from the disdrometer at ground level.  Its value increases steadily in the lower part of ML (peaks around 0.70 dB per deg), remains strong in the upper part of ML (0.5 - 0.6 dB per degree), and decreases rapidly to 0.13 dB per degree above the ML (in snow).</p>

scholarly journals Advances in the Stochastic Modeling of Satellite-Derived Rainfall Estimates Using a Sparse Calibration Dataset

2014 ◽  
Vol 15 (5) ◽  
pp. 1810-1831 ◽  
Author(s):  
Helen Greatrex ◽  
David Grimes ◽  
Tim Wheeler
Keyword(s):  
Complex Terrain ◽  
Calibration Procedure ◽  
Calibration Data ◽  
New Developments ◽  
Simulation Methodology ◽  
Satellite Technology ◽  
Calibration Dataset ◽  
Satellite Rainfall ◽  
Rainfall Estimates

Abstract As satellite technology develops, satellite rainfall estimates are likely to become ever more important in the world of food security. It is therefore vital to be able to identify the uncertainty of such estimates and for end users to be able to use this information in a meaningful way. This paper presents new developments in the methodology of simulating satellite rainfall ensembles from thermal infrared satellite data. Although the basic sequential simulation methodology has been developed in previous studies, it was not suitable for use in regions with more complex terrain and limited calibration data. Developments in this work include the creation of a multithreshold, multizone calibration procedure, plus investigations into the causes of an overestimation of low rainfall amounts and the best way to take into account clustered calibration data. A case study of the Ethiopian highlands has been used as an illustration.

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