scholarly journals Assessment of High-Resolution Satellite-Based Rainfall Estimates over the Mediterranean during Heavy Precipitation Events

2013 ◽  
Vol 14 (5) ◽  
pp. 1500-1514 ◽  
Author(s):  
Dimitrios Stampoulis ◽  
Emmanouil N. Anagnostou ◽  
Efthymios I. Nikolopoulos
Keyword(s):  
High Resolution ◽  
Geographical Area ◽  
Heavy Precipitation ◽  
Type Systems ◽  
Economic Losses ◽  
Error Statistics ◽  
Radar Rainfall ◽  
Precipitation Estimation ◽  
Rainfall Estimates ◽  
Precipitation Events

Abstract Heavy precipitation events (HPE) can incur significant economic losses as well as losses of lives through catastrophic floods. Evidence of increasing heavy precipitation at continental and global scales clearly emphasizes the need to accurately quantify these phenomena. The current study focuses on the error analysis of two of the main quasi-global, high-resolution satellite products [Climate Prediction Center (CPC) morphing technique (CMORPH) and Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks (PERSIANN)], using rainfall data derived from high-quality weather radar rainfall estimates as a reference. This analysis is based on seven major flood-inducing HPEs that developed over complex terrain areas in northern Italy (Fella and Sessia regions) and southern France (Cevennes–Vivarais region). The storm cases were categorized as convective or stratiform based on their characteristics, including rainfall intensity, duration, and area coverage. The results indicate that precipitation type has an effect on the algorithm's ability to capture rainfall effectively. Convective storm cases exhibited greater rain rate retrieval errors, while low rain rates in stratiform-type systems are not well captured by the satellite algorithms investigated in this study, thus leading to greater missed rainfall volumes. Overall, CMORPH exhibited better error statistics than PERSIANN for the HPEs of this study. Similarities are also shown in the two satellite products' error characteristics for the HPEs that occurred in the same geographical area.

Using NWP Simulations in Satellite Rainfall Estimation of Heavy Precipitation Events over Mountainous Areas

2013 ◽  
Vol 14 (6) ◽  
pp. 1844-1858 ◽  
Author(s):  
Xinxuan Zhang ◽  
Emmanouil N. Anagnostou ◽  
Maria Frediani ◽  
Stavros Solomos ◽  
George Kallos
Keyword(s):  
Heavy Precipitation ◽  
Skill Score ◽  
Mountainous Areas ◽  
Error Statistics ◽  
Radar Rainfall ◽  
Adjustment Procedure ◽  
Heidke Skill Score ◽  
Satellite Rainfall ◽  
Rain Rates ◽  
Precipitation Events

Abstract In this study, the authors investigate the use of high-resolution simulations from the Weather Research and Forecasting Model (WRF) for evaluating satellite rainfall biases of flood-inducing storms in mountainous areas. A probability matching approach is applied to evaluate a power-law relationship between satellite-retrieved and WRF-simulated rain rates over the storm domain. Satellite rainfall in this study is from the NOAA Climate Prediction Center morphing technique (CMORPH). Results are presented based on analyses of five heavy precipitation events that induced flash floods in northern Italy and southern France complex terrain basins. The WRF-based adjusted CMORPH rain rates exhibited improved error statistics against independent radar rainfall estimates. The authors show that the adjustment procedure reduces the underestimation of high rain rates, thus moderating the magnitude dependence of CMORPH rainfall bias. The Heidke skill score for the WRF-based adjusted CMORPH was consistently higher for a range of rain rate thresholds. This is an indication that the adjustment procedure ameliorates the satellite rain rates to provide a better estimation. Results also indicate that the low rain detection of CMORPH technique is also identifiable in the WRF–CMORPH comparison; however, the adjustment procedure herein does not incorporate this effect on the satellite rainfall bias adjustment.

scholarly journals Advancing Precipitation Estimation and Streamflow Simulations in Complex Terrain with X-Band Dual-Polarization Radar Observations

2018 ◽  
Vol 10 (8) ◽  
pp. 1258 ◽  
Author(s):  
Marios Anagnostou ◽  
Efthymios Nikolopoulos ◽  
John Kalogiros ◽  
Emmanouil Anagnostou ◽  
Francesco Marra ◽  
...  
Keyword(s):  
Complex Terrain ◽  
Rain Gauge ◽  
Storm Events ◽  
Error Statistics ◽  
Radar Rainfall ◽  
Radar Observations ◽  
X Band ◽  
Precipitation Estimation ◽  
Rainfall Estimates

In mountain basins, the use of long-range operational weather radars is often associated with poor quantitative precipitation estimation due to a number of challenges posed by the complexity of terrain. As a result, the applicability of radar-based precipitation estimates for hydrological studies is often limited over areas that are in close proximity to the radar. This study evaluates the advantages of using X-band polarimetric (XPOL) radar as a means to fill the coverage gaps and improve complex terrain precipitation estimation and associated hydrological applications based on a field experiment conducted in an area of Northeast Italian Alps characterized by large elevation differences. The corresponding rainfall estimates from two operational C-band weather radar observations are compared to the XPOL rainfall estimates for a near-range (10–35 km) mountainous basin (64 km2). In situ rainfall observations from a dense rain gauge network and two disdrometers (a 2D-video and a Parsivel) are used for ground validation of the radar-rainfall estimates. Ten storm events over a period of two years are used to explore the differences between the locally deployed XPOL vs. longer-range operational radar-rainfall error statistics. Hourly aggregate rainfall estimates by XPOL, corrected for rain-path attenuation and vertical reflectivity profile, exhibited correlations between 0.70 and 0.99 against reference rainfall data and 21% mean relative error for rainfall rates above 0.2 mm h−1. The corresponding metrics from the operational radar-network rainfall products gave a strong underestimation (50–70%) and lower correlations (0.48–0.81). For the two highest flow-peak events, a hydrological model (Kinematic Local Excess Model) was forced with the different radar-rainfall estimations and in situ rain gauge precipitation data at hourly resolution, exhibiting close agreement between the XPOL and gauge-based driven runoff simulations, while the simulations obtained by the operational radar rainfall products resulted in a greatly underestimated runoff response.

scholarly journals A Prototype Quantitative Precipitation Estimation Algorithm for Operational S-Band Polarimetric Radar Utilizing Specific Attenuation and Specific Differential Phase. Part II: Performance Verification and Case Study Analysis

2019 ◽  
Vol 20 (5) ◽  
pp. 999-1014 ◽  
Author(s):  
Stephen B. Cocks ◽  
Lin Tang ◽  
Pengfei Zhang ◽  
Alexander Ryzhkov ◽  
Brian Kaney ◽  
...  
Keyword(s):  
Real Time ◽  
Heavy Precipitation ◽  
Estimation Algorithm ◽  
Differential Phase ◽  
Specific Attenuation ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Bias Ratio ◽  
Using Data ◽  
Precipitation Events

Abstract The quantitative precipitation estimate (QPE) algorithm developed and described in Part I was validated using data collected from 33 Weather Surveillance Radar 1988-Doppler (WSR-88D) radars on 37 calendar days east of the Rocky Mountains. A key physical parameter to the algorithm is the parameter alpha α, defined as the ratio of specific attenuation A to specific differential phase KDP. Examination of a significant sample of tropical and continental precipitation events indicated that α was sensitive to changes in drop size distribution and exhibited lower (higher) values when there were lower (higher) concentrations of larger (smaller) rain drops. As part of the performance assessment, the prototype algorithm generated QPEs utilizing a real-time estimated and a fixed α were created and evaluated. The results clearly indicated ~26% lower errors and a 26% better bias ratio with the QPE utilizing a real-time estimated α as opposed to using a fixed value as was done in previous studies. Comparisons between the QPE utilizing a real-time estimated α and the operational dual-polarization (dual-pol) QPE used on the WSR-88D radar network showed the former exhibited ~22% lower errors, 7% less bias, and 5% higher correlation coefficient when compared to quality controlled gauge totals. The new QPE also provided much better estimates for moderate to heavy precipitation events and performed better in regions of partial beam blockage than the operational dual-pol QPE.

Comparison of Single- and Dual-Polarization–Based Rainfall Estimates Using NEXRAD Data for the NASA Iowa Flood Studies Project

2015 ◽  
Vol 16 (4) ◽  
pp. 1658-1675 ◽  
Author(s):  
Bong-Chul Seo ◽  
Brenda Dolan ◽  
Witold F. Krajewski ◽  
Steven A. Rutledge ◽  
Walter Petersen
Keyword(s):  
Rain Rate ◽  
Heavy Rain ◽  
Rain Gauge ◽  
Superior Performance ◽  
Dual Polarization ◽  
Radar Rainfall ◽  
Colorado State University ◽  
Precipitation Estimation ◽  
Improved Performance ◽  
Rainfall Estimates

Abstract This study compares and evaluates single-polarization (SP)- and dual-polarization (DP)-based radar-rainfall (RR) estimates using NEXRAD data acquired during Iowa Flood Studies (IFloodS), a NASA GPM ground validation field campaign carried out in May–June 2013. The objective of this study is to understand the potential benefit of the DP quantitative precipitation estimation, which selects different rain-rate estimators according to radar-identified precipitation types, and to evaluate RR estimates generated by the recent research SP and DP algorithms. The Iowa Flood Center SP (IFC-SP) and Colorado State University DP (CSU-DP) products are analyzed and assessed using two high-density, high-quality rain gauge networks as ground reference. The CSU-DP algorithm shows superior performance to the IFC-SP algorithm, especially for heavy convective rains. We verify that dynamic changes in the proportion of heavy rain during the convective period are associated with the improved performance of CSU-DP rainfall estimates. For a lighter rain case, the IFC-SP and CSU-DP products are not significantly different in statistical metrics and visual agreement with the rain gauge data. This is because both algorithms use the identical NEXRAD reflectivity–rain rate (Z–R) relation that might lead to substantial underestimation for the presented case.

Impact of FORMOSAT-7/COSMIC-2 RO on High-Resolution Hybrid 3DEnVar System at Central Weather Bureau of Taiwan

2020 ◽  
Author(s):  
Jing-Shan Hong ◽  
Wen-Jou Chen ◽  
Ying-Jhen Chen ◽  
Siou-Ying Jiang ◽  
Chin-Tzu Fong
Keyword(s):  
High Resolution ◽  
Weather Prediction ◽  
Heavy Precipitation ◽  
Precipitation Forecast ◽  
Preliminary Evaluation ◽  
Satellite Mission ◽  
Background Error ◽  
Weather Bureau ◽  
Central Weather Bureau ◽  
Precipitation Events

<p>The FORMOSAT-7/COSMIC-2 (simplified as FS-7/C-2 in the following descriptions) is the constellation of satellites for meteorology, ionosphere, climatology, and space weather research. FS-7/C-2 was a joint Taiwan-U.S. satellite mission that makes use of the radio occultation (RO) measurement technique. FORMOSAT-7 is the successor of FORMOSAT-3 which was launched in 2006. the FORMOSAT-3 RO data has been shown to be extremely valuable for numerical weather prediction, such as improving the prediction of tropical cyclogenesis and reducing the typhoon track error. The follow-on FS-7/C-2 mission was launched on 25 June 2019, and is currently going through preliminary testing and evaluation. After it is fully deployed, FS-7/C-2 is expected to provide 6,000 GNSS (Global Navigation Satellite System) RO profiles per day between 40S and 40N.  </p><p>In this study, we conduct a preliminary evaluation of FS-7/C-2 GNSS RO data on heavy precipitation events associated with typhoon and southwesterly monsoon flows based on the operational NWP system of the Central Weather Bureau (CWB) in Taiwan. The FS-7/C-2 GNSS RO data are assimilated using a dual-resolution hybrid 3DEnVare system with a 15-3 km nested-grid configuration. In the 15km resolution domain, flow-dependent background error covariances (BECs) derived from the perturbation of ensemble adjustment Kalman filter (EAKF), will be used to conduct hybrid 3DEnVar analysis. In the 3 km resolution domain, the 15 km resolution flow-dependent BECs will be inserted to the 3 km grid using a Dual-Resolution (DR) technique, and then combined with 3 km resolution static BECs, to perform the high-resolution 3DEnVar analysis. The performance of the CWB operational NWP system on quantitative precipitation forecast of significant precipitation events with and without the assimilation of FS-7/C-2 GNSS RO data will be evaluated.</p>

scholarly journals The Effect of Storm Life Cycle on Satellite Rainfall Estimation Error

2009 ◽  
Vol 26 (4) ◽  
pp. 769-777 ◽  
Author(s):  
Alemu Tadesse ◽  
Emmanouil N. Anagnostou
Keyword(s):  
Life Cycle ◽  
Estimation Error ◽  
Rain Gauge ◽  
Life Cycles ◽  
Error Statistics ◽  
Radar Rainfall ◽  
Convective Systems ◽  
Satellite Retrieval ◽  
Maturity Stages ◽  
Precipitation Estimation

Abstract The study uses storm tracking information to evaluate error statistics of satellite rain estimation at different maturity stages of storm life cycles. Two satellite rain retrieval products are used for this purpose: (i) NASA’s Multisatellite Precipitation Analysis–Real Time product available at 25-km/hourly resolution (3B41-RT) and (ii) the University of California (Irvine) Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) product available at 4-km–hourly resolution. Both algorithms use geostationary satellite infrared (IR) observations calibrated to an array of passive microwave (PM) earth-orbiting satellite sensor rain retrievals. The techniques differ in terms of algorithmic structure and in the way they use the PM rainfall to calibrate the IR rain algorithms. The satellite retrievals are evaluated against rain gauge–calibrated radar rainfall estimates over the continental United States. Error statistics of hourly rain volumes are determined separately for thunderstorm and shower-type convective systems and for different storm life durations and stages of maturity. The authors show distinct differences between the two satellite retrieval error characteristics. The most notable difference is the strong storm life cycle dependence of 3B41-RT relative to the nearly independent PERSIANN behavior. Another is in the algorithm performance between thunderstorms and showers; 3B41-RT exhibits significant bias increase at longer storm life durations. PERSIANN exhibits consistently improved correlations relative to the 3B41-RT for all storm life durations and maturity stages. The findings of this study support the hypothesis that incorporating cloud type information into the retrieval (done by the PERSIANN algorithm) can help improve the satellite retrieval accuracy.

On the role of CO2 in enhancing the temporal clustering of heavy precipitation across Europe

2020 ◽  
Author(s):  
Zhiqi Yang ◽  
Gabriele Villarini
Keyword(s):  
Climate Model ◽  
Cox Regression ◽  
Heavy Precipitation ◽  
The Arctic ◽  
Cmip5 Models ◽  
Economic Losses ◽  
Climate Modes ◽  
Temporal Clustering ◽  
Simulated Precipitation ◽  
Precipitation Events

<p>Heavy precipitation has increased across many areas of the world, not only in terms of amounts but also of intensity and frequency, causing billions of dollars in economic losses and numerous fatalities. Our ability to prepare for and adapt to these events is tied to our understanding of the physical processes responsible for these events, and how they may respond to changes in anthropogenic forcings. Here we focus on the temporal clustering of heavy precipitation across Europe, highlight what the major climate drivers responsible for it are, and how it may change in response to changes in the concentration of greenhouse gasses. More specifically, we use a peak over threshold approach to identify heavy precipitation events, and Cox regression to relate the occurrence of these events to four climate modes that have been connected with the occurrence of heavy precipitation across Europe: the Arctic Oscillation (AO), the North Atlantic Oscillation (NAO), the East Atlantic (EA) pattern, and the Scandinavia pattern (SCAND). We use outputs from the Coupled Model Intercomparison Project Phase 5 (CMIP5), and experiments that allow us to focus on the response to CO<sub>2</sub> (pre-industrial, 1pctCO<sub>2</sub>, abrupt4×CO<sub>2</sub>). To further detect the effects of downscaling on model-simulated precipitation, we also considered the accuracy of the EURO-CORDEX regional climate model (RCM) on capturing the temporal clustering in heavy precipitation across Europe. We find that: 1) the CMIP5 models can capture the temporal clustering in heavy precipitation across Europe as a function of these four climate modes; 2) the increases in CO<sub>2</sub> are expected to lead to a strengthening of the relationship between the climate modes and the occurrence of heavy precipitation events; 3) the response to an abrupt increase in CO<sub>2</sub> is generally stronger compared to a more gradual one.</p>

scholarly journals The New French Operational Radar Rainfall Product. Part II: Validation

2007 ◽  
Vol 22 (3) ◽  
pp. 409-427 ◽  
Author(s):  
P. Tabary ◽  
J. Desplats ◽  
K. Do Khac ◽  
F. Eideliman ◽  
C. Gueguen ◽  
...  
Keyword(s):  
Radar Data ◽  
Rain Gauge ◽  
Radiosonde Data ◽  
Radar Rainfall ◽  
Freezing Level ◽  
Ground Clutter ◽  
Precipitation Estimation ◽  
Upper Level ◽  
Quality Indexes ◽  
Precipitation Events

Abstract A new operational radar-based rainfall product has been developed at Météo-France and is currently being deployed within the French operational network. The new quantitative precipitation estimation (QPE) product is based entirely on radar data and includes a series of modules aimed at correcting for ground clutter, partial beam blocking, and vertical profile of reflectivity (VPR) effects, as well as the nonsimultaneity of radar measurements. The surface rainfall estimation is computed as a weighted mean of the corrected tilts. In addition to the final QPE, a map of quality indexes is systematically generated. This paper is devoted to the validation of the new radar QPE. The VPR identification module has been specifically validated by analyzing 489 precipitation events observed over 1 yr by a representative eight-radar subset of the network. The conceptual model of VPR used in the QPE processing chain is shown to be relevant. A climatology of the three shape parameters of the conceptual VPR (brightband peak, brightband thickness, and upper-level decreasing rate) is established and the radar-derived freezing-level heights are shown to be in good agreement with radiosonde data. A total of 27 precipitation events observed by three S-band radars of the network during the winter of 2005 and the autumns of 2002 and 2003 are used to compare the new radar QPE to the old one. Results are stratified according to the distance to the radar and according to the height of the freezing level. The Nash criterion is increased from 0.23 to 0.62 at close range (below 50 km) and from 0.35 to 0.42 at long range (between 100 and 150 km). The relevance of the proposed quality indexes is assessed by examining their statistical relationship with long-term radar–rain gauge statistics. Mosaics of QPE and quality indexes are also illustrated.

The Diurnal Cycle of Clouds and Precipitation along the Sierra Madre Occidental Observed during NAME-2004: Implications for Warm Season Precipitation Estimation in Complex Terrain

2008 ◽  
Vol 9 (4) ◽  
pp. 728-743 ◽  
Author(s):  
Stephen W. Nesbitt ◽  
David J. Gochis ◽  
Timothy J. Lang
Keyword(s):  
High Resolution ◽  
Diurnal Cycle ◽  
Complex Terrain ◽  
Deep Convection ◽  
Sierra Madre Occidental ◽  
Convective Systems ◽  
Sierra Madre ◽  
Precipitation Estimation ◽  
Rainfall Estimates ◽  
Tier I

Abstract This study examines the spatial and temporal variability in the diurnal cycle of clouds and precipitation tied to topography within the North American Monsoon Experiment (NAME) tier-I domain during the 2004 NAME enhanced observing period (EOP, July–August), with a focus on the implications for high-resolution precipitation estimation within the core of the monsoon. Ground-based precipitation retrievals from the NAME Event Rain Gauge Network (NERN) and Colorado State University–National Center for Atmospheric Research (CSU–NCAR) version 2 radar composites over the southern NAME tier-I domain are compared with satellite rainfall estimates from the NOAA Climate Prediction Center Morphing technique (CMORPH) and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) operational and Tropical Rainfall Measuring Mission (TRMM) 3B42 research satellite estimates along the western slopes of the Sierra Madre Occidental (SMO). The rainfall estimates are examined alongside hourly images of high-resolution Geostationary Operational Environmental Satellite (GOES) 11-μm brightness temperatures. An abrupt shallow to deep convective transition is found over the SMO, with the development of shallow convective systems just before noon on average over the SMO high peaks, with deep convection not developing until after 1500 local time on the SMO western slopes. This transition is shown to be contemporaneous with a relative underestimation (overestimation) of precipitation during the period of shallow (deep) convection from both IR and microwave precipitation algorithms due to changes in the depth and vigor of shallow clouds and mixed-phase cloud depths. This characteristic life cycle in cloud structure and microphysics has important implications for ice-scattering microwave and infrared precipitation estimates, and thus hydrological applications using high-resolution precipitation data, as well as the study of the dynamics of convective systems in complex terrain.

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