Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation: Initial Operating Capabilities

Abstract Rapid advancements of computer technologies in recent years made the real-time transferring and integration of high-volume, multisource data at a centralized location a possibility. The Multi-Radar Multi-Sensor (MRMS) system recently implemented at the National Centers for Environmental Prediction demonstrates such capabilities by integrating about 180 operational weather radars from the conterminous United States and Canada into a seamless national 3D radar mosaic with very high spatial (1 km) and temporal (2 min) resolution. The radar data can be integrated with high-resolution numerical weather prediction model data, satellite data, and lightning and rain gauge observations to generate a suite of severe weather and quantitative precipitation estimation (QPE) products. This paper provides an overview of the initial operating capabilities of MRMS QPE products.

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Impact of the Altitudinal Gradients of Precipitation on the Radar QPE Bias in the French Alps

Atmosphere ◽

10.3390/atmos10060306 ◽

2019 ◽

Vol 10 (6) ◽

pp. 306 ◽

Cited By ~ 1

Author(s):

Dominique Faure ◽

Guy Delrieu ◽

Nicolas Gaussiat

Keyword(s):

Ground Level ◽

Radar Data ◽

Rain Gauge ◽

Altitudinal Gradients ◽

French Alps ◽

Rain Gauges ◽

Quantitative Precipitation Estimation ◽

Precipitation Estimation ◽

Radar Data Processing

In the French Alps the quality of the radar Quantitative Precipitation Estimation (QPE) is limited by the topography and the vertical structure of precipitation. A previous study realized in all the French Alps, has shown a general bias between values of the national radar QPE composite and the rain gauge measurements: a radar QPE over-estimation at low altitude (+20% at 200 m a.s.l.), and an increasing underestimation at high altitudes (until −40% at 2100 m a.s.l.). This trend has been linked to altitudinal gradients of precipitation observed at ground level. This paper analyzes relative altitudinal gradients of precipitation estimated with rain gauges measurements in 2016 for three massifs around Grenoble, and for different temporal accumulations (yearly, seasonal, monthly, daily). Comparisons of radar and rain gauge accumulations confirm the bias previously observed. The parts of the current radar data processing affecting the bias value are pointed out. The analysis shows a coherency between the relative gradient values estimated at the different temporal accumulations. Vertical profiles of precipitation detected by a research radar installed at the bottom of the valley also show how the wide horizontal variability of precipitation inside the valley can affect the gradient estimation.

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3-hourly quantitative precipitation estimation over Central and Northern Europe from rain gauge and radar data

Atmospheric Research ◽

10.1016/j.atmosres.2009.05.005 ◽

2009 ◽

Vol 94 (4) ◽

pp. 544-554 ◽

Cited By ~ 8

Author(s):

Franz Rubel ◽

Katharina Brugger

Keyword(s):

Radar Data ◽

Rain Gauge ◽

Northern Europe ◽

Quantitative Precipitation Estimation ◽

Precipitation Estimation

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Near-Real-Time Applications of CloudSat Data

Journal of Applied Meteorology and Climatology ◽

10.1175/2007jamc1794.1 ◽

2008 ◽

Vol 47 (7) ◽

pp. 1982-1994 ◽

Cited By ~ 16

Author(s):

Cristian Mitrescu ◽

Steven Miller ◽

Jeffrey Hawkins ◽

Tristan L’Ecuyer ◽

Joseph Turk ◽

...

Keyword(s):

Real Time ◽

Weather Prediction ◽

Numerical Weather Prediction Model ◽

Research Field ◽

Naval Research ◽

Earth Observing System ◽

Quantitative Precipitation Estimation ◽

The Earth ◽

Precipitation Estimation ◽

Support Of Research

Abstract Within 2 months of its launch in April 2006 as part of the Earth Observing System A-Train satellite constellation, the National Aeronautics and Space Administration Earth System Science Pathfinder (ESSP) CloudSat mission began making significant contributions toward broadening the understanding of detailed cloud vertical structures around the earth. Realizing the potential benefit of CloudSat to both the research objectives and operational requirements of the U.S. Navy, the Naval Research Laboratory coordinated early on with the CloudSat Data Processing Center to receive and process first-look 94-GHz Cloud Profiling Radar datasets in near–real time (4–8 h latency), thereby making the observations more relevant to the operational community. Applications leveraging these unique data, described herein, include 1) analysis/validation of cloud structure and properties derived from conventional passive radiometers, 2) tropical cyclone vertical structure analysis, 3) support of research field programs, 4) validation of numerical weather prediction model cloud fields, and 5) quantitative precipitation estimation in light rainfall regimes.

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Toward a Polarimetric Radar Classification Scheme for Coalescence-Dominant Precipitation: Application to Complex Terrain

Journal of Hydrometeorology ◽

10.1175/jhm-d-17-0016.1 ◽

2017 ◽

Vol 18 (12) ◽

pp. 3199-3215 ◽

Cited By ~ 5

Author(s):

Leonardo Porcacchia ◽

P. E. Kirstetter ◽

J. J. Gourley ◽

V. Maggioni ◽

B. L. Cheong ◽

...

Keyword(s):

Complex Terrain ◽

Classification Scheme ◽

Flash Flood ◽

Radar Data ◽

High Elevation ◽

Rain Gauge ◽

Polarimetric Radar ◽

Quantitative Precipitation Estimation ◽

X Band ◽

Precipitation Estimation

Abstract Accurate quantitative precipitation estimation over mountainous basins is of great importance because of their susceptibility to natural hazards. It is generally difficult to obtain reliable precipitation information over complex areas because of the scarce coverage of ground observations, the limited coverage from operational radar networks, and the high elevation of the study sites. Warm-rain processes have been observed in several flash flood events in complex terrain regions. While they lead to high rainfall rates from precipitation growth due to collision–coalescence of droplets in the cloud liquid layer, their characteristics are often difficult to identify. X-band mobile dual-polarization radars located in complex terrain areas provide fundamental information at high-resolution and at low atmospheric levels. This study analyzes a dataset collected in North Carolina during the 2014 Integrated Precipitation and Hydrology Experiment (IPHEx) field campaign over a mountainous basin where the NOAA/National Severe Storm Laboratory’s X-band polarimetric radar (NOXP) was deployed. Polarimetric variables are used to isolate collision–coalescence microphysical processes. This work lays the basis for classification algorithms able to identify coalescence-dominant precipitation by merging the information coming from polarimetric radar measurements. The sensitivity of the proposed classification scheme is tested with different rainfall-rate retrieval algorithms and compared to rain gauge observations. Results show the inadequacy of rainfall estimates when coalescence identification is not taken into account. This work highlights the necessity of a correct classification of collision–coalescence processes, which can lead to improvements in quantitative precipitation estimation. Future studies will aim at generalizing this scheme by making use of spaceborne radar data.

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Updates on the Radar Data Quality Control in the MRMS Quantitative Precipitation Estimation System

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-19-0165.1 ◽

2020 ◽

Vol 37 (9) ◽

pp. 1521-1537

Author(s):

Lin Tang ◽

Jian Zhang ◽

Micheal Simpson ◽

Ami Arthur ◽

Heather Grams ◽

...

Keyword(s):

Quality Control ◽

Data Quality ◽

Wind Farm ◽

Critical Role ◽

Radar Data ◽

Data Quality Control ◽

Quantitative Precipitation Estimation ◽

Precipitation Estimation ◽

Estimation System ◽

Environmental Prediction

AbstractThe Multi-Radar-Multi-Sensor (MRMS) system was transitioned into operations at the National Centers for Environmental Prediction in the fall of 2014. It provides high-quality and high-resolution severe weather and precipitation products for meteorology, hydrology, and aviation applications. Among processing modules, the radar data quality control (QC) plays a critical role in effectively identifying and removing various nonhydrometeor radar echoes for accurate quantitative precipitation estimation (QPE). Since its initial implementation in 2014, the radar QC has undergone continuous refinements and enhancements to ensure its robust performance across seasons and all regions in the continental United States and southern Canada. These updates include 1) improved melting-layer delineation, 2) clearance of wind farm contamination, 3) mitigation of corrupt data impacts due to hardware issues, 4) mitigation of sun spikes, and 5) mitigation of residual ground/lake/sea clutter due to sidelobe effects and anomalous propagation. This paper provides an overview of the MRMS radar data QC enhancements since 2014.

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Radar-Based Quantitative Precipitation Estimation for the Identification of Debris-Flow Occurrence over Earthquake affected Region in Sichuan, China

10.5194/nhess-2017-308 ◽

2017 ◽

Cited By ~ 1

Author(s):

Zhao Shi ◽

Fangqiang Wei ◽

Chandrasekar Venkatachalam

Keyword(s):

Debris Flow ◽

Radar Data ◽

Rain Gauge ◽

Small Scale ◽

Mountainous Area ◽

Radar Rainfall ◽

Quantitative Precipitation Estimation ◽

Precipitation Estimation ◽

New Generation ◽

Frequentist Method

Abstract. Both of Ms8.0 Wenchuan earthquake on May 12, 2008 and Ms7.0 Lushan earth quake on April 20, 2013 occurred in Sichuan Province of China. In the earthquake affected mountainous area, a large amount of loose material caused a high occurrence of debris flow during the rainy season. In order to evaluate the rainfall Intensity–Duration (I-D) threshold of the debris flow in the earthquake-affected area, and for filling up the observational gaps caused by the relatively scarce and low altitude deployment of rain gauges in this area, raw data from two S-band China New Generation Doppler weather radar (CINRAD) were captured for six rainfall events which triggered 519 debris flows between 2012 and 2014. Due to the challenges of radar quantitative precipitation estimation (QPE) over mountainous area, a series of improving measures are considered including the hybrid scan mode, the vertical reflectivity profile (VPR) correction, the mosaic of reflectivity, a merged rainfall-reflectivity(R-Z) relationship for convective and stratiform rainfall and rainfall bias adjustment with Kalman filter (KF). For validating rainfall accumulation over complex terrains, the study areas are divided into two kinds of regions by the height threshold of 1.5 km from the ground. Three kinds of radar rainfall estimates are compared with rain gauge measurements. It is observed that the normalized mean bias (NMB) is decreased by 39 % and the fitted linear ratio between radar and rain gauge observation reaches at 0.98. Furthermore, the radar-based I-D threshold derived by the Frequentist method is I = 10.1D−0.52, and it's also found that the I-D threshold is underestimated by uncorrected raw radar data. In order to verify the impacts on observations due to spatial variation, I-D thresholds are identified from the nearest rain gauge observations and radar observations at the rain gauge locations. It is found that both kinds of observations have similar I-D threshold and likewise underestimate I-D thresholds owing to under shooting at the core of convective rainfall. It is indicated that improvement of spatial resolution and measuring accuracy of radar observation will lead to the improvement of identifying debris flow occurrence, especially for events triggered by the small-scale strong rainfall process in the study area.

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Radar-based quantitative precipitation estimation for the identification of debris flow occurrence over earthquake-affected regions in Sichuan, China

Natural Hazards and Earth System Science ◽

10.5194/nhess-18-765-2018 ◽

2018 ◽

Vol 18 (3) ◽

pp. 765-780 ◽

Cited By ~ 9

Author(s):

Zhao Shi ◽

Fangqiang Wei ◽

Venkatachalam Chandrasekar

Keyword(s):

Debris Flow ◽

Radar Data ◽

Rain Gauge ◽

Small Scale ◽

Mountainous Area ◽

Radar Rainfall ◽

Quantitative Precipitation Estimation ◽

Precipitation Estimation ◽

Improvement Measures ◽

Frequentist Method

Abstract. Both Ms 8.0 Wenchuan earthquake on 12 May 2008 and Ms 7.0 Lushan earthquake on 20 April 2013 occurred in the province of Sichuan, China. In the earthquake-affected mountainous area, a large amount of loose material caused a high occurrence of debris flow during the rainy season. In order to evaluate the rainfall intensity–duration (I–D) threshold of the debris flow in the earthquake-affected area, and to fill up the observational gaps caused by the relatively scarce and low-altitude deployment of rain gauges in this area, raw data from two S-band China New Generation Doppler Weather Radar (CINRAD) were captured for six rainfall events that triggered 519 debris flows between 2012 and 2014. Due to the challenges of radar quantitative precipitation estimation (QPE) over mountainous areas, a series of improvement measures are considered: a hybrid scan mode, a vertical reflectivity profile (VPR) correction, a mosaic of reflectivity, a merged rainfall–reflectivity (R − Z) relationship for convective and stratiform rainfall, and rainfall bias adjustment with Kalman filter (KF). For validating rainfall accumulation over complex terrains, the study areas are divided into two kinds of regions by the height threshold of 1.5 km from the ground. Three kinds of radar rainfall estimates are compared with rain gauge measurements. It is observed that the normalized mean bias (NMB) is decreased by 39 % and the fitted linear ratio between radar and rain gauge observation reaches at 0.98. Furthermore, the radar-based I–D threshold derived by the frequentist method is I = 10.1D−0.52 and is underestimated by uncorrected raw radar data. In order to verify the impacts on observations due to spatial variation, I–D thresholds are identified from the nearest rain gauge observations and radar observations at the rain gauge locations. It is found that both kinds of observations have similar I–D thresholds and likewise underestimate I–D thresholds due to undershooting at the core of convective rainfall. It is indicated that improvement of spatial resolution and measuring accuracy of radar observation will lead to the improvement of identifying debris flow occurrence, especially for events triggered by the strong small-scale rainfall process in the study area.

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Eight scenarios rapid update cycle effect on weather radar data assimilation WRF toward rainfall prediction in Palembang (cases study of flood events on November 12th, 2018)

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/893/1/012038 ◽

2021 ◽

Vol 893 (1) ◽

pp. 012038

Author(s):

L M W Paksi ◽

A H Saputra ◽

I Fitrianti

Keyword(s):

Data Assimilation ◽

Weather Prediction ◽

Wrf Model ◽

Weather Radar ◽

Radar Data ◽

Rain Gauge ◽

Numerical Weather Prediction Model ◽

Observation Data ◽

Initial State ◽

Better Than

Abstract Weather Research and Forecasting (WRF) is an open source numerical weather prediction model that can be used for high resolution rainfall predictions. Besides these advantages, WRF output accuracy can be affected by the initial condition. The accuration of WRF model can be improved by data assimilation. Data assimilation is combining observation data with model data to improve the initial state of atmospheric flow. This study aims to investigate the effect of assimilation weather radar in models using WRF for predictions rainfall events in Palembang region on November 12th, 2018. This study uses radar radial velocity data as input data for assimilation. The assimilation technique uses the 3DVAR with rapid update cycle (RUC) procedure 1 hour, 3 hours, 6 hours with spin up 12 and 6 hours. The output of the model verified using Global Satellite Mapping of Precipitation (GSMaP) data and using rain gauge data for point verification. The results of this study indicate that the output of the assimilation model, especially in the spin-up 12 hours skenario implementation of the 1-hour RUC is better than the model without assimilation. From the eight scenario models implemented, it can be concluded that the 12 hours spin up is better than the 6 hours spin up.

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Rainfall-induced attenuation correction for two operational dual-polarization C-band radars in the Netherlands

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-20-0113.1 ◽

2021 ◽

Author(s):

Aart Overeem ◽

Hylke de Vries ◽

Hassan Al Sakka ◽

Remko Uijlenhoet ◽

Hidde Leijnse

Keyword(s):

Attenuation Correction ◽

Weather Prediction ◽

Rain Gauge ◽

Numerical Weather Prediction Model ◽

Dual Polarization ◽

Radar Rainfall ◽

Freezing Level ◽

Weather Radars ◽

Data Archives ◽

Hourly Rainfall

AbstractThe Royal Netherlands Meteorological Institute (KNMI) operates two operational dual-polarization C-band weather radars providing 2-D radar rainfall products. Attenuation can result in severe underestimation of rainfall amounts, particularly in convective situations that are known to have high impact on society. In order to improve the radar-based precipitation estimates, two attenuation correction methods are evaluated and compared: 1) Modified Kraemer (MK) method, i.e. Hitschfeld-Bordan where parameters of the power-law Zh–kh relation are adjusted such that reflectivities in the entire dataset do not exceed 59 dBZh and attenuation correction is limited to 10 dB; 2) using two-way path-integrated attenuation computed from the dual-polarization moment specific differential phase Kdp (Kdp method). In both cases the open-source Python library wradlib is employed for the actual attenuation correction. A radar voxel only contributes to the computed path-integrated attenuation if its height is below the forecasted freezing level height from the numerical weather prediction model HARMONIE-AROME. The methods are effective in improving hourly and daily quantitative precipitation estimation (QPE), where the Kdp method performs best. The verification against rain gauge data shows that the underestimation diminishes from 55% to 37% for hourly rainfall for the Kdp method when the gauge indicates more than 10 mm of rain in that hour. The improvement for the MK method is less pronounced, with a resulting underestimation of 40%. The stability of the MK method holds a promise for application to data archives from single-polarization radars.

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