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

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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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

Journal of Hydrometeorology ◽

10.1175/jhm-d-11-064.1 ◽

2012 ◽

Vol 13 (3) ◽

pp. 1080-1093 ◽

Cited By ~ 25

Author(s):

Wanru Wu ◽

David Kitzmiller ◽

Shaorong Wu

Keyword(s):

United States ◽

Precipitation Amount ◽

Processing System ◽

Warm Season ◽

Distribution Data ◽

Time Interval ◽

Quantitative Precipitation Estimation ◽

Precipitation Estimation ◽

Precipitation Estimates ◽

Estimation System

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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NAM Model Forecasts of Warm-Season Quasi-Stationary Frontal Environments in the Central United States

Weather and Forecasting ◽

10.1175/2010waf2222394.1 ◽

2010 ◽

Vol 25 (4) ◽

pp. 1281-1292 ◽

Cited By ~ 3

Author(s):

Shih-Yu Wang ◽

Adam J. Clark

Keyword(s):

United States ◽

Mesoscale Model ◽

Convective Available Potential Energy ◽

Warm Season ◽

Correlation Coefficients ◽

Precipitable Water ◽

Wind Fields ◽

Vapor Flux ◽

Convective Systems ◽

Central United States

Abstract Using a composite procedure, North American Mesoscale Model (NAM) forecast and observed environments associated with zonally oriented, quasi-stationary surface fronts for 64 cases during July–August 2006–08 were examined for a large region encompassing the central United States. NAM adequately simulated the general synoptic features associated with the frontal environments (e.g., patterns in the low-level wind fields) as well as the positions of the fronts. However, kinematic fields important to frontogenesis such as horizontal deformation and convergence were overpredicted. Surface-based convective available potential energy (CAPE) and precipitable water were also overpredicted, which was likely related to the overprediction of the kinematic fields through convergence of water vapor flux. In addition, a spurious coherence between forecast deformation and precipitation was found using spatial correlation coefficients. Composite precipitation forecasts featured a broad area of rainfall stretched parallel to the composite front, whereas the composite observed precipitation covered a smaller area and had a WNW–ESE orientation relative to the front, consistent with mesoscale convective systems (MCSs) propagating at a slight right angle relative to the thermal gradient. Thus, deficiencies in the NAM precipitation forecasts may at least partially result from the inability to depict MCSs properly. It was observed that errors in the precipitation forecasts appeared to lag those of the kinematic fields, and so it seems likely that deficiencies in the precipitation forecasts are related to the overprediction of the kinematic fields such as deformation. However, no attempts were made to establish whether the overpredicted kinematic fields actually contributed to the errors in the precipitation forecasts or whether the overpredicted kinematic fields were simply an artifact of the precipitation errors. Regardless of the relationship between such errors, recognition of typical warm-season environments associated with these errors should be useful to operational forecasters.

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Statistical and Hydrological Evaluations of Multi-Satellite Precipitation Products over Fujiang River Basin in Humid Southeast China

Remote Sensing ◽

10.3390/rs10121898 ◽

2018 ◽

Vol 10 (12) ◽

pp. 1898 ◽

Cited By ~ 10

Author(s):

Weiwei Sun ◽

Jun Ma ◽

Gang Yang ◽

Weiyue Li

Keyword(s):

Real Time ◽

River Basin ◽

Local Community ◽

Pearson Correlation ◽

Warm Season ◽

Correlation Coefficients ◽

Cold Season ◽

Optimal Interpolation ◽

Distributed Model ◽

Satellite Precipitation

The purpose of the paper is to evaluate the quality and hydrological utility of four popular satellite precipitation products, including the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) product (3B42V7), near real-time product (3B42RT), and the Climate Prediction Center (CPC) MORPHing technique (CMORPH) satellite–gauge merged product (CMORPH BLD) and bias-corrected product (CMORPH CRT) over Fujiang River basin, China. First, we provided a statistical assessment of the four precipitation products at multiple spatiotemporal scales. The results show that: (1) all the products except 3B42RT capture the spatial pattern of annual precipitation fairly well; (2) in general, CMORPH BLD benefits from the application of the probability density function-optimal interpolation (PDF-OI) gauge adjustment algorithm and performs best among all the products with Pearson correlation coefficients (CC) of 0.84 and 0.94, equitable threat score (ETS) of 0.56 and 0.63 in grid and basin scales, respectively, followed by 3B42V7 and CMORPH CRT; whereas 3B42RT performs worst across all the metrics; (3) according to the occurrence frequencies of rainfall, satellite estimates mainly fall into the bin of 0–1 mm/day and tend to underestimate light precipitation. In addition, the performance of all the products in warm season is much better than in cold season in both grid and basin scales. Subsequently, a physically based distributed model is established to further evaluate the hydrological utility of different precipitation products. The results reveal that: (1) the errors in precipitation products mainly propagate into hydrological simulations, resulting in the best hydrological performance in CMORPH BLD in both daily and monthly scales after recalibrating the model, while 3B42RT shows limited skills in reproducing the daily observed hydrograph; (2) after recalibrating the model with the respective satellite data, significant improvements are observed for all the products; (3) CMORPH BLD no longer shows its superiority during near-real-time monitoring of floods. There is still a great challenge for the application of current satellite-based estimates into local flood monitoring. This study could be used as guidance for choosing alternative satellite precipitation products for hydrological applications in a local community, particularly in basins in which rainfall gauges are scarce.

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Assessment of bias-adjusted PM<sub>2.5</sub> air quality forecasts over the continental United States during 2007

Geoscientific Model Development Discussions ◽

10.5194/gmdd-2-1375-2009 ◽

2009 ◽

Vol 2 (2) ◽

pp. 1375-1406 ◽

Cited By ~ 3

Author(s):

D. Kang ◽

R. Mathur ◽

S. Trivikrama Rao

Keyword(s):

United States ◽

Kalman Filter ◽

Air Quality ◽

Recursive Algorithm ◽

Warm Season ◽

Correlation Coefficients ◽

Rocky Mountain ◽

Meteorological Model ◽

Bias Adjustment ◽

Air Quality Forecast

Abstract. To develop fine particular matter (PM2.5) air quality forecasts, a National Air Quality Forecast Capability (NAQFC) system, which linked NOAA's North American Mesoscale (NAM) meteorological model with EPA's Community Multiscale Air Quality (CMAQ) model, was deployed in the developmental mode over the continental United States during 2007. This study investigates the operational use of a bias-adjustment technique called the Kalman Filter Predictor approach for improving the accuracy of the PM2.5 forecasts at monitoring locations. The Kalman Filter Predictor bias-adjustment technique is a recursive algorithm designed to optimally estimate bias-adjustment terms using the information extracted from previous measurements and forecasts. The bias-adjustment technique is found to improve PM2.5 forecasts (i.e. reduced errors and increased correlation coefficients) for the entire year at almost all locations. The NAQFC tends to overestimate PM2.5 during the cool season and underestimate during the warm season in the eastern part of the continental US domain, but the opposite is true for the pacific coast. In the Rocky Mountain region, the NAQFC system overestimates PM2.5 for the whole year. The bias-adjustment forecasts can quickly (after 2–3 days' lag) adjust to reflect the transition from one regime to the other. The modest computational requirements and systematical improvements in forecast results across all seasons suggest that this technique can be easily adapted to perform bias-adjustment for real-time PM2.5 air quality forecasts.

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Assessment of bias-adjusted PM<sub>2.5</sub> air quality forecasts over the continental United States during 2007

Geoscientific Model Development ◽

10.5194/gmd-3-309-2010 ◽

2010 ◽

Vol 3 (1) ◽

pp. 309-320 ◽

Cited By ~ 25

Author(s):

D. Kang ◽

R. Mathur ◽

S. Trivikrama Rao

Keyword(s):

United States ◽

Kalman Filter ◽

Air Quality ◽

Recursive Algorithm ◽

Fine Particulate Matter ◽

Warm Season ◽

Correlation Coefficients ◽

Meteorological Model ◽

Bias Adjustment ◽

Air Quality Forecast

Abstract. To develop fine particulate matter (PM2.5) air quality forecasts for the US, a National Air Quality Forecast Capability (NAQFC) system, which linked NOAA's North American Mesoscale (NAM) meteorological model with EPA's Community Multiscale Air Quality (CMAQ) model, was deployed in the developmental mode over the continental United States during 2007. This study investigates the operational use of a bias-adjustment technique called the Kalman Filter Predictor approach for improving the accuracy of the PM2.5 forecasts at monitoring locations. The Kalman Filter Predictor bias-adjustment technique is a recursive algorithm designed to optimally estimate bias-adjustment terms using the information extracted from previous measurements and forecasts. The bias-adjustment technique is found to improve PM2.5 forecasts (i.e. reduced errors and increased correlation coefficients) for the entire year at almost all locations. The NAQFC tends to overestimate PM2.5 during the cool season and underestimate during the warm season in the eastern part of the continental US domain, but the opposite is true for the Pacific Coast. In the Rocky Mountain region, the NAQFC system overestimates PM2.5 for the whole year. The bias-adjusted forecasts can quickly (after 2–3 days' lag) adjust to reflect the transition from one regime to the other. The modest computational requirements and systematic improvements in forecast outputs across all seasons suggest that this technique can be easily adapted to perform bias adjustment for real-time PM2.5 air quality forecasts.

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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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Precipitation Extremes: Trends and Relationships with Average Precipitation and Precipitable Water in the Contiguous United States

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-19-0185.1 ◽

2020 ◽

Vol 59 (1) ◽

pp. 125-142 ◽

Cited By ~ 5

Author(s):

Kenneth E. Kunkel ◽

Thomas R. Karl ◽

Michael F. Squires ◽

Xungang Yin ◽

Steve T. Stegall ◽

...

Keyword(s):

United States ◽

Rocky Mountains ◽

Extreme Precipitation ◽

Warm Season ◽

Precipitable Water ◽

Cold Season ◽

Total Precipitation ◽

Average Return ◽

Long Duration ◽

Average Precipitation

AbstractTrends of extreme precipitation (EP) using various combinations of average return intervals (ARIs) of 1, 2, 5, 10, and 20 years with durations of 1, 2, 5, 10, 20, and 30 days were calculated regionally across the contiguous United States. Changes in the sign of the trend of EP vary by region as well as by ARI and duration, despite the statistically significant upward trends for all combinations of EP thresholds when area averaged across the contiguous United States. Spatially, there is a pronounced east-to-west gradient in the trends of the EP with strong upward trends east of the Rocky Mountains. In general, upward trends are larger and more significant for longer ARIs, but the contribution to the trend in total seasonal and annual precipitation is significantly larger for shorter ARIs because they occur more frequently. Across much of the contiguous United States, upward trends of warm-season EP are substantially larger than those for the cold season and have a substantially greater effect on the annual trend in total precipitation. This result occurs even in areas where the total precipitation is nearly evenly divided between the cold and warm seasons. When compared with short-duration events, long-duration events—for example, 30 days—contribute the most to annual trends. Coincident statistically significant upward trends of EP and precipitable water (PW) occur in many regions, especially during the warm season. Increases in PW are likely to be one of several factors responsible for the increase in EP (and average total precipitation) observed in many areas across the contiguous United States.

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Cold-Season Tornadoes: Climatological and Meteorological Insights

Weather and Forecasting ◽

10.1175/waf-d-17-0120.1 ◽

2018 ◽

Vol 33 (3) ◽

pp. 671-691 ◽

Cited By ~ 15

Author(s):

Samuel J. Childs ◽

Russ S. Schumacher ◽

John T. Allen

Keyword(s):

United States ◽

Public Awareness ◽

Warm Season ◽

Cold Season ◽

Reanalysis Data ◽

The Arctic ◽

Current State ◽

The Arctic Oscillation ◽

Bull's Eye ◽

Convective Mode

Abstract Tornadoes that occur during the cold season, defined here as November–February (NDJF), pose many societal risks, yet less attention has been given to their climatological trends and variability than their warm-season counterparts, and their meteorological environments have been studied relatively recently. This study aims to advance the current state of knowledge of cold-season tornadoes through analysis of these components. A climatology of all (E)F1–(E)F5 NDJF tornadoes from 1953 to 2015 across a domain of 25°–42.5°N, 75°–100°W was developed. An increasing trend in cold-season tornado occurrence was found across much of the southeastern United States, with a bull’s-eye in western Tennessee, while a decreasing trend was found across eastern Oklahoma. Spectral analysis reveals a cyclic pattern of enhanced NDJF counts every 3–7 years, coincident with the period of ENSO. La Niña episodes favor enhanced NDJF counts, but a stronger relationship was found with the Arctic Oscillation (AO). From a meteorological standpoint, the most-tornadic and least-tornadic NDJF seasons were compared using NCEP–NCAR reanalysis data of various severe weather and tornado parameters. The most-tornadic cold seasons are characterized by warm and moist conditions across the Southeast, with an anomalous mean trough across the western United States. In addition, analysis of the convective mode reveals that NDJF tornadoes are common in both discrete and linear storm modes, yet those associated with discrete supercells are more deadly. Taken together, the perspectives presented here provide a deeper understanding of NDJF tornadoes and their societal impacts, an understanding that serves to increase public awareness and reduce human casualty.

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Reprocessed, Bias-Corrected CMORPH Global High-Resolution Precipitation Estimates from 1998

Journal of Hydrometeorology ◽

10.1175/jhm-d-16-0168.1 ◽

2017 ◽

Vol 18 (6) ◽

pp. 1617-1641 ◽

Cited By ~ 116

Author(s):

Pingping Xie ◽

Robert Joyce ◽

Shaorong Wu ◽

Soo-Hyun Yoo ◽

Yelena Yarosh ◽

...

Keyword(s):

High Resolution ◽

Global Precipitation Climatology Project ◽

Temporal Variations ◽

Cold Season ◽

Superior Performance ◽

Climate Data ◽

Integration Algorithm ◽

Satellite Precipitation ◽

Precipitation Estimates ◽

Global Precipitation

Abstract The Climate Prediction Center (CPC) morphing technique (CMORPH) satellite precipitation estimates are reprocessed and bias corrected on an 8 km × 8 km grid over the globe (60°S–60°N) and in a 30-min temporal resolution for an 18-yr period from January 1998 to the present to form a climate data record (CDR) of high-resolution global precipitation analysis. First, the purely satellite-based CMORPH precipitation estimates (raw CMORPH) are reprocessed. The integration algorithm is fixed and the input level 2 passive microwave (PMW) retrievals of instantaneous precipitation rates are from identical versions throughout the entire data period. Bias correction is then performed for the raw CMORPH through probability density function (PDF) matching against the CPC daily gauge analysis over land and through adjustment against the Global Precipitation Climatology Project (GPCP) pentad merged analysis of precipitation over ocean. The reprocessed, bias-corrected CMORPH exhibits improved performance in representing the magnitude, spatial distribution patterns, and temporal variations of precipitation over the global domain from 60°S to 60°N. Bias in the CMORPH satellite precipitation estimates is almost completely removed over land during warm seasons (May–September), while during cold seasons (October–April) CMORPH tends to underestimate the precipitation due to the less-than-desirable performance of the current-generation PMW retrievals in detecting and quantifying snowfall and cold season rainfall. An intercomparison study indicated that the reprocessed, bias-corrected CMORPH exhibits consistently superior performance than the widely used TRMM 3B42 (TMPA) in representing both daily and 3-hourly precipitation over the contiguous United States and other global regions.

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