Evaluation of Radar Precipitation Estimates from the National Mosaic and Multisensor Quantitative Precipitation Estimation System and the WSR-88D Precipitation Processing System over the Conterminous United States

Abstract This study evaluated 24-, 6-, and 1-h radar precipitation estimated from the National Mosaic and Multisensor Quantitative Precipitation Estimation System (NMQ) and the Weather Surveillance Radar-1988 Doppler (WSR-88D) Precipitation Processing System (PPS) over the conterminous United States (CONUS) for the warm season April–September 2009 and the cool season October 2009–March 2010. Precipitation gauge observations from the Automated Surface Observing System (ASOS) were used as the ground truth. Gridded StageIV multisensor precipitation estimates were applied for supplementary verification. The comparison of the two systems consisted of a series of analyses including the linear correlation coefficient (CC) and the root-mean-square error (RMSE) between the radar precipitation estimates and the gauge observations, large precipitation amount detection categorical scores, and the reliability of precipitation amount distribution. Data stratified for the 12 CONUS River Forecast Centers (RFCs) and for the cold rains events with bright-band effects were analyzed additionally. Major results are 1) the linear CC of NMQ versus ASOS are generally higher than that of PPS versus ASOS over CONUS, while the spatial variations stratified by the RFCs may switch with seasons; 2) compared to the precipitation distribution of ASOS, NMQ shows less deviation than PPS; 3) for the cold rains verified against ASOS, NMQ has higher CC and PPS has lower RMSE for 6-h and higher RMSE for 1-h cold rains; and 4) for the precipitation detection categorical scores, either NMQ or PPS can be superior, depending on the time interval and season. The verification against StageIV gridded precipitation estimates showed that NMQ consistently had higher correlations and lower biases than did PPS.

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Multi-Radar Multi-Sensor (MRMS) Severe Weather and Aviation Products: Initial Operating Capabilities

Bulletin of the American Meteorological Society ◽

10.1175/bams-d-14-00173.1 ◽

2016 ◽

Vol 97 (9) ◽

pp. 1617-1630 ◽

Cited By ~ 72

Author(s):

Travis M. Smith ◽

Valliappa Lakshmanan ◽

Gregory J. Stumpf ◽

Kiel L. Ortega ◽

Kurt Hondl ◽

...

Keyword(s):

United States ◽

Three Dimensional ◽

Severe Weather ◽

Severe Storms ◽

University Of Oklahoma ◽

Quantitative Precipitation Estimation ◽

Precipitation Estimation ◽

Estimation System ◽

Environmental Prediction ◽

The University

Abstract The Multi-Radar Multi-Sensor (MRMS) system, which was developed at the National Severe Storms Laboratory and the University of Oklahoma, was made operational in 2014 at the National Centers for Environmental Prediction. The MRMS system consists of the Warning Decision Support System–Integrated Information suite of severe weather and aviation products, and the quantitative precipitation estimation products created by the National Mosaic and Multi-sensor Quantitative Precipitation Estimation system. Products created by the MRMS system are at a spatial resolution of approximately 1 km, with 33 vertical levels, updating every 2 min over the conterminous United States and southern Canada. This paper describes initial operating capabilities for the severe weather and aviation products that include a three-dimensional mosaic of reflectivity; guidance for hail, tornado, and lightning hazards; and nowcasts of storm location, height, and intensity.

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Comparison of the GPCP 1DD Precipitation Product and NEXRAD Q2 Precipitation Estimates over the Continental United States

Journal of Hydrometeorology ◽

10.1175/jhm-d-15-0235.1 ◽

2016 ◽

Vol 17 (6) ◽

pp. 1837-1853 ◽

Cited By ~ 3

Author(s):

Wenjun Cui ◽

Xiquan Dong ◽

Baike Xi ◽

Ronald Stenz

Keyword(s):

United States ◽

Warm Season ◽

Correlation Coefficients ◽

Heavy Rain ◽

Global Precipitation Climatology Project ◽

Cold Season ◽

Precipitation Estimation ◽

Precipitation Estimates ◽

Annual Means ◽

Mean Differences

Abstract This study compares the Global Precipitation Climatology Project (GPCP) 1 Degree Daily (1DD) precipitation estimates over the continental United States (CONUS) with National Mosaic and Multi-Sensor Quantitative Precipitation Estimation (NMQ) Next Generation (Q2) estimates. Spatial averages of monthly and yearly accumulated precipitation were computed based on daily estimates from six selected regions during the period 2010–12. Both Q2 and GPCP daily precipitation estimates show that precipitation amounts over southern regions (<40°N) are generally larger than northern regions (≥40°N). Correlation coefficients for daily estimates over selected regions range from 0.355 to 0.516 with mean differences (GPCP − Q2) varying from −0.86 to 0.99 mm. Better agreements are found in monthly estimates with the correlations varying from 0.635 to 0.787. For spatially averaged precipitation values averaged from grid boxes within selected regions, GPCP and Q2 estimates are well correlated, especially for monthly accumulated precipitation, with strong correlations ranging from 0.903 to 0.954. The comparisons between two datasets are also conducted for warm (April–September) and cold (October–March) seasons. During the warm season, GPCP estimates are 9.7% less than Q2 estimates, while during the cold season GPCP estimates exceed Q2 estimates by 6.9%. For precipitation over the CONUS, although annual means are close (978.54 for Q2 vs 941.79 mm for GPCP), Q2 estimates are much larger than GPCP over the central and southern United States and less than GPCP estimates in the northeastern United States. These results suggest that Q2 may have difficulties accurately estimating heavy rain and snow events, while GPCP may have an inability to capture some intense precipitation events, which warrants further investigation.

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Assessment of SCaMPR and NEXRAD Q2 Precipitation Estimates Using Oklahoma Mesonet Observations

Journal of Hydrometeorology ◽

10.1175/jhm-d-13-0199.1 ◽

2014 ◽

Vol 15 (6) ◽

pp. 2484-2500 ◽

Cited By ~ 10

Author(s):

Ronald Stenz ◽

Xiquan Dong ◽

Baike Xi ◽

Robert J. Kuligowski

Keyword(s):

Spatial Scales ◽

Retrieval Algorithm ◽

Strong Correlations ◽

Cloud Optical Depth ◽

Convective Systems ◽

Quantitative Precipitation Estimation ◽

Oklahoma Mesonet ◽

Precipitation Estimation ◽

Precipitation Estimates ◽

Estimation System

Abstract Although satellite precipitation estimates provide valuable information for weather and flood forecasts, infrared (IR) brightness temperature (BT)-based algorithms often produce large errors for precipitation detection and estimation during deep convective systems (DCSs). As DCSs produce greatly varying precipitation rates below similar IR BT retrievals, using IR BTs alone to estimate precipitation in DCSs is problematic. Classifying a DCS into convective-core (CC), stratiform (SR), and anvil cloud (AC) regions allows an evaluation of estimated precipitation distributions among DCS components to supplement typical quantitative precipitation estimate (QPE) evaluations and to diagnose these IR-based algorithm biases. This paper assesses the performance of the National Mosaic and Multi-Sensor Next Generation Quantitative Precipitation Estimation System (NMQ Q2), and a simplified version of the Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) algorithm, over the state of Oklahoma using Oklahoma Mesonet observations. While average annual Q2 precipitation estimates were about 35% higher than Mesonet observations, strong correlations exist between these two datasets for multiple temporal and spatial scales. Additionally, the Q2-estimated precipitation distribution among DCS components strongly resembled the Mesonet-observed distribution, indicating Q2 can accurately capture the precipitation characteristics of DCSs despite its wet bias. SCaMPR retrievals were typically 3–4 times higher than Mesonet observations, with relatively weak correlations during 2012. Overestimates from SCaMPR retrievals were primarily caused by precipitation retrievals from the anvil regions of DCSs when collocated Mesonet stations recorded no precipitation. A modified SCaMPR retrieval algorithm, employing both cloud optical depth and IR temperature, has the potential to make significant improvements to reduce the wet bias of SCaMPR retrievals over anvil regions of a DCS.

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Error Analysis of Satellite Precipitation Products in Mountainous Basins

Journal of Hydrometeorology ◽

10.1175/jhm-d-13-0194.1 ◽

2014 ◽

Vol 15 (5) ◽

pp. 1778-1793 ◽

Cited By ~ 99

Author(s):

Yiwen Mei ◽

Emmanouil N. Anagnostou ◽

Efthymios I. Nikolopoulos ◽

Marco Borga

Keyword(s):

Error Analysis ◽

Warm Season ◽

Tropical Rainfall Measuring Mission ◽

Heavy Precipitation ◽

Rain Gauge ◽

Satellite Precipitation ◽

Quantitative Precipitation Estimation ◽

Precipitation Regimes ◽

Precipitation Estimation ◽

High Precipitation

Abstract Accurate quantitative precipitation estimation over mountainous basins is of great importance because of their susceptibility to hazards such as flash floods, shallow landslides, and debris flows, triggered by heavy precipitation events (HPEs). In situ observations over mountainous areas are limited, but currently available satellite precipitation products can potentially provide the precipitation estimation needed for hydrological applications. In this study, four widely used satellite-based precipitation products [Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42, version 7 (3B42-V7), and in near–real time (3B42-RT); Climate Prediction Center (CPC) morphing technique (CMORPH); and Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks (PERSIANN)] are evaluated with respect to their performance in capturing the properties of HPEs over different basin scales. Evaluation is carried out over the upper Adige River basin (eastern Italian Alps) for an 8-yr period (2003–10). Basin-averaged rainfall derived from a dense rain gauge network in the region is used as a reference. Satellite precipitation error analysis is performed for warm (May–August) and cold (September–December) season months as well as for different quantile ranges of basin-averaged precipitation accumulations. Three error metrics and a score system are introduced to quantify the performances of the various satellite products. Overall, no single precipitation product can be considered ideal for detecting and quantifying HPE. Results show better consistency between gauges and the two 3B42 products, particularly during warm season months that are associated with high-intensity convective events. All satellite products are shown to have a magnitude-dependent error ranging from overestimation at low precipitation regimes to underestimation at high precipitation accumulations; this effect is more pronounced in the CMORPH and PERSIANN products.

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Assessment of Precipitation Estimation from the NWP Models and Satellite Products for the Spring 2019 Severe Floods in Iran

Remote Sensing ◽

10.3390/rs11232741 ◽

2019 ◽

Vol 11 (23) ◽

pp. 2741 ◽

Cited By ~ 3

Author(s):

Aminyavari ◽

Saghafian ◽

Sharifi

Keyword(s):

Weather Prediction ◽

Precipitation Amount ◽

Early Warning Systems ◽

Flood Impacts ◽

Spatial Coverage ◽

Basin Scale ◽

Precipitation Estimation ◽

Precipitation Estimates ◽

Precipitation Threshold ◽

Precipitation Thresholds

Precipitation monitoring and early warning systems are required to reduce negative flood impacts. In this study, the performance of ensemble precipitation forecasts of three numerical weather prediction (NWP) models within the THORPEX interactive grand global ensemble (TIGGE) as well as the integrated multi-satellite retrievals for global precipitation measurement (GPM), namely IMERG, for precipitation estimates were evaluated in recent severe floods in Iran over the March–April 2019 period. The evaluations were conducted in three aspects: spatial distribution of precipitation, mean areal precipitation in three major basins hard hit by the floods, and the dichotomous evaluation in four precipitation thresholds (25, 50, 75, and 100 mm per day). The results showed that the United Kingdom Met Office (UKMO) model, in terms of spatial coverage and satellite estimates as well as the precipitation amount, were closer to the observations. Moreover, with regard to mean precipitation at the basin scale, UKMO and European Center for Medium-Range Weather Forecasts (ECMWF) models in the Gorganrud Basin, ECMWF in the Karkheh Basin and UKMO in the Karun Basin performed better than others in flood forecasting. The National Centers for Environmental Forecast (NCEP) model performed well at low precipitation thresholds, while at high thresholds, its performance decreased significantly. On the contrary, the accuracy of IMERG improved when the precipitation threshold increased. The UKMO had better forecasts than the other models at the 100 mm/day precipitation threshold, whereas the ECMWF had acceptable forecasts in all thresholds and was able to forecast precipitation events with a lower false alarm ratio and better detection when compared to other models.

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Product-Error-Driven Uncertainty Model for Probabilistic Quantitative Precipitation Estimation with NEXRAD Data

Journal of Hydrometeorology ◽

10.1175/2007jhm814.1 ◽

2007 ◽

Vol 8 (6) ◽

pp. 1325-1347 ◽

Cited By ~ 163

Author(s):

Grzegorz J. Ciach ◽

Witold F. Krajewski ◽

Gabriele Villarini

Keyword(s):

Agricultural Research ◽

Central Element ◽

Processing System ◽

Rain Gauge ◽

Random Errors ◽

Radar Rainfall ◽

Quantitative Precipitation Estimation ◽

Uncertainty Model ◽

Precipitation Estimation ◽

The U.S

Abstract Although it is broadly acknowledged that the radar-rainfall (RR) estimates based on the U.S. national network of Weather Surveillance Radar-1988 Doppler (WSR-88D) stations contain a high degree of uncertainty, no methods currently exist to inform users about its quantitative characteristics. The most comprehensive characterization of this uncertainty can be achieved by delivering the products in a probabilistic rather than the traditional deterministic form. The authors are developing a methodology for probabilistic quantitative precipitation estimation (PQPE) based on weather radar data. In this study, they present the central element of this methodology: an empirically based error structure model for the RR products. The authors apply a product-error-driven (PED) approach to obtain a realistic uncertainty model. It is based on the analyses of six years of data from the Oklahoma City, Oklahoma, WSR-88D radar (KTLX) processed with the Precipitation Processing System algorithm of the NEXRAD system. The modeled functional-statistical relationship between RR estimates and corresponding true rainfall consists of two components: a systematic distortion function and a stochastic factor quantifying remaining random errors. The two components are identified using a nonparametric functional estimation apparatus. The true rainfall is approximated with rain gauge data from the Oklahoma Mesonet and the U.S. Department of Agriculture (USDA) Agricultural Research Service Micronet networks. The RR uncertainty model presented here accounts for different time scales, synoptic regimes, and distances from the radar. In addition, this study marks the first time in which results on RR error correlation in space and time are presented.

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Changes in Extreme Precipitation in the Northeast United States: 1979–2014

Journal of Hydrometeorology ◽

10.1175/jhm-d-18-0155.1 ◽

2019 ◽

Vol 20 (4) ◽

pp. 673-689 ◽

Cited By ~ 9

Author(s):

Macy E. Howarth ◽

Christopher D. Thorncroft ◽

Lance F. Bosart

Keyword(s):

United States ◽

Extreme Events ◽

Extreme Precipitation ◽

Precipitation Amount ◽

Warm Season ◽

Daily Maximum ◽

Northeast United States ◽

Extreme Precipitation Events ◽

Increasing Trend ◽

Increasing Trends

Abstract Extreme precipitation can have significant adverse impacts on infrastructure and property, human health, and local economies. This paper examines recent changes in extreme precipitation in the northeast United States. Daily station data from 58 stations missing less than 5% of days for the years 1979–2014 from the U.S. Historical Climatology Network were used to analyze extreme precipitation, defined as the top 1% of days with precipitation. A statistically significant (95% confidence level) increasing trend of the threshold for the top 1% of extreme precipitation events was found (0.3 mm yr−1). This increasing trend was due to both an increase in the frequency of extreme events and the magnitude of extreme events. Rainfall events ≥ 150 mm (24-h accumulation) increased in frequency from 6 events between 1979 and 1996 to 25 events between 1997 and 2014, a 317% increase. The annual daily maximum precipitation, or the highest recorded precipitation amount in a given year, increased by an average of 1.6 mm yr−1, a total increase of 58.0 mm. Decreasing trends in extreme precipitation were observed east of Lake Erie during the warm season. Increasing trends in extreme precipitation were most robust during the fall months of September, October, and November, and particularly at locations further inland. The analysis showed that increases in events that were tropical in nature, or associated with tropical moisture, led to the observed increase in extreme precipitation during the fall months.

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A Dual-Polarization Radar Synthetic QPE for Operations

Journal of Hydrometeorology ◽

10.1175/jhm-d-19-0194.1 ◽

2020 ◽

Vol 21 (11) ◽

pp. 2507-2521 ◽

Cited By ~ 4

Author(s):

Jian Zhang ◽

Lin Tang ◽

Stephen Cocks ◽

Pengfei Zhang ◽

Alexander Ryzhkov ◽

...

Keyword(s):

United States ◽

Scattered Light ◽

Warm Season ◽

Dual Polarization ◽

Specific Attenuation ◽

Central United States ◽

Precipitation Estimation ◽

Systematic Biases ◽

Differential Reflectivity ◽

Light Precipitation

AbstractA new dual-polarization (DP) radar synthetic quantitative precipitation estimation (QPE) product was developed using a combination of specific attenuation A, specific differential phase KDP, and reflectivity Z to calculate the precipitation rate R. Specific attenuation has advantages of being insensitive to systematic biases in Z and differential reflectivity ZDR due to partial beam blockage, attenuation, and calibration while more linearly related to R than other radar variables. However, the R(A) relationship is not applicable in areas containing ice. Therefore, the new DP QPE applies R(A) in areas where radar is observing pure rain, R(KDP) in regions potentially containing hail, and R(Z) elsewhere. Further, an evaporation correction was applied to minimize false light precipitation related to virga. The new DP QPE was evaluated in real time over the conterminous United States and showed significant improvements over previous radar QPE techniques that were based solely on R(Z) relationships. The improvements included reduced dry biases in heavy to extreme precipitation during the warm season. The new DP QPE also reduced errors and spatial discontinuities in regions impacted by partial beam blockage. Further, the new DP QPE reduced wet bias for scattered light precipitation in the southwest and north central United States where there is significant boundary layer evaporation.

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Organization of Flash-Flood-Producing Precipitation in the Northeast United States

Weather and Forecasting ◽

10.1175/waf-d-11-00026.1 ◽

2012 ◽

Vol 27 (2) ◽

pp. 345-361 ◽

Cited By ~ 9

Author(s):

Stephen M. Jessup ◽

Stephen J. Colucci

Keyword(s):

United States ◽

Flash Flood ◽

Warm Season ◽

Heavy Precipitation ◽

Flash Flooding ◽

Radar Signature ◽

Long Duration ◽

Central United States ◽

Linear Types ◽

Precipitation Estimates

Abstract Heavy precipitation and flash flooding have been extensively studied in the central United States, but less so in the Northeast. This study examines 187 warm-season flash flood events identified in Storm Data to better understand the structure of the precipitation systems that cause flash flooding in the Northeast. Based on the organization and movement of these systems on radar, the events are classified into one of four categories—back-building, linear, multiple, and other/size—and then further classified into subtypes for each category. Eight of these subtypes were not previously recognized in the literature. The back-building events were the most common, followed by the multiple, other/size, and linear types. The linear event types appear to produce flash flooding less commonly in the Northeast than in other regions. In general, the subtypes producing the highest precipitation estimates are those whose structures are most conducive to a long duration of sustained moderate to heavy rainfall. The event types were found to differ from those in the central United States in that the events were more often found to be more disorganized in the Northeast. One event type in particular, back-building with merging features, while not more disorganized than the previously recognized event types, offers promise for improved forecasting because its radar signature makes the duration of sustained heavy precipitation potentially easier to predict.

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Assimilation of weather radar and binary ubiquitous sensor measurements for quantitative precipitation estimation

Journal of Hydroinformatics ◽

10.2166/hydro.2015.072 ◽

2015 ◽

Vol 17 (4) ◽

pp. 598-613 ◽

Cited By ~ 7

Author(s):

David J. Hill

Keyword(s):

Weather Radar ◽

Simulation Studies ◽

Heterogeneous Sensors ◽

Intensity Measurements ◽

Quantitative Precipitation Estimation ◽

Markov Random ◽

Precipitation Estimation ◽

Precipitation Estimates ◽

Temporal Measurement ◽

Quantitative Precipitation Estimates

Assimilation of data from heterogeneous sensors and sensor networks is critical for achieving accurate measurements of environmental processes at the time and space scales necessary to improve forecasting and decision-making. Owing to different measurement accuracies and types of spatial and/or temporal measurement support of the component sensors, it is often unclear how best to combine these data. This study explores the utility of ubiquitous sensors producing categorical wet/dry rainfall measurements for improving the resolution of areal quantitative precipitation estimates through fusion with weather radar observations. The model developed in this study employs a Markov random field model to compute the probability of rainfall at sub-grid pixels. These likelihoods are used to ‘unmix’ the cell-averaged rainfall rate measured by the radar. Simulation studies using synthetic and known rainfall fields reveal that the model can improve remotely sensed quantitative rainfall intensity measurements by 40% using networks of ubiquitous sensors with a density of 56 sensors per square kilometer, and for denser networks, the accuracy can increase by as much as 50%.

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