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Michael J. Erickson, Benjamin Albright, and James A. Nelson

Abstract

The Weather Prediction Center’s Excessive Rainfall Outlook (ERO) forecasts the probability of rainfall exceeding flash flood guidance within 40 km of a point. This study presents a comprehensive ERO verification between 2015 and 2019 using a combination of flooding observations and proxies. ERO spatial issuance frequency plots are developed to provide situational awareness for forecasters. Reliability of the ERO is assessed by computing fractional coverage of the verification within each probabilistic category. Probabilistic forecast skill is evaluated using the Brier skill score (BSS) and area under the relative operating characteristic (AUC). A “probabilistic observation” called practically perfect (PP) is developed and compared to the ERO as an additional measure of skill. The areal issuance frequency of the ERO varies spatially with the most abundant issuances spanning from the Gulf Coast to the Midwest and the Appalachians. ERO issuances occur most often in the summer and are associated with the Southwestern monsoon, mesoscale convective systems, and tropical cyclones. The ERO exhibits good reliability on average, although more recent trends suggest some ERO-defined probabilistic categories should be issued more frequently. AUC and BSS are useful bulk skill metrics, while verification against PP is useful in bulk and for shorter-term ERO evaluation. ERO forecasts are generally more skillful at shorter lead times in terms of AUC and BSS. There is no trend in ERO area size over 5 years, although ERO forecasts may be getting slightly more skillful in terms of critical success index when verified against the PP.

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Tom H. Zapotocny, W. Paul Menzel, James A. Jung, and James P. Nelson III

Abstract

The impact of in situ rawinsonde observations (raob), remotely sensed Geostationary Operational Environmental Satellite (GOES), and Polar-Orbiting Operational Environmental Satellite (POES) observations routinely used in NCEP’s Eta Data Assimilation/Forecast System (EDAS) is studied for extended-length time periods during four seasons. This work examines the contribution of nine individual components of the total observing system. The nine data types examined include rawinsonde mass and wind observations, GOES mass and wind observations, POES observations from the Microwave Sounding Unit (MSU), the Advanced Microwave Sounding Unit (AMSU-A and AMSU-B), the High Resolution Infrared Radiation Sounder (HIRS), and column total precipitable water and low-level wind observations from the Special Sensor Microwave Imager (SSM/I). The results are relevant for users of the Eta Model trying to compare/contrast the overall forecast impact of traditional, largely land-based rawinsonde observations against remotely sensed satellite observations, which are available domainwide.

The case studies chosen consist of 15-day periods during fall 2001, winter 2001/02, spring 2002, and summer 2002. Throughout these periods, a November 2001 32-km version of the EDAS is run 10 times at both 0000 and 1200 UTC. The 10 runs include a control run, which utilizes all data types routinely used in the EDAS, and 9 experimental runs in which one of the component data types noted above is denied. Differences between the experimental and control runs are then accumulated over the 15-day periods and analyzed to demonstrate the 00-h sensitivity and 24-h forecast impact of these individual data types in the EDAS. The diagnostics are computed over the entire horizontal model domain and a subsection covering the continental United States (CONUS) and adjacent coastal waters on isobaric surfaces extending into the lower stratosphere.

The 24-h forecast impact results show that a positive forecast impact is achieved from most of the nine component data sources during all four time periods. HIRS, MSU, and SSM/I wind observations yield only a slight positive forecast impact to all fields. Rawinsonde and GOES wind observations have the largest positive forecast impact for temperature over both the entire model domain and the extended CONUS. The same data types also provide the largest forecast impact to the u component of the wind, while GOES wind observations provide the largest forecast impact to moisture.

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Tom H. Zapotocny, W. Paul Menzel, James P. Nelson III, and James A. Jung

Abstract

The impact of 10 data types used in the Eta Data Assimilation/Forecast System (EDAS) is studied for extended-length time periods during three seasons. Five of the data types are remotely sensed satellite data, and the other five are in situ. The satellite data types include three-layer and vertically integrated precipitable water, temperature data down to cloud top, infrared cloud-drift winds, and water vapor cloud-top winds. The five in situ data types consist of two rawinsonde and two aircraft observation types along with surface land observations. The work described in this paper is relevant for Eta Model users trying to identify the impact of remotely sensed, largely maritime data types and in situ, largely land-based data types. The case studies chosen consist of 11-day periods during December 1998, April 1999, and July 1999. During these periods, 11 EDAS runs were executed twice daily. The 11 runs include a control run, which utilizes all data types used in the EDAS, and 10 experimental runs in which one of the data types is denied. Differences between the experimental and control runs are then accumulated and analyzed to demonstrate the 0-h sensitivity and 24-h forecast impact of these data types in the EDAS. Conventional meteorological terms evaluated include temperature, u component of the wind, and relative humidity on five pressure levels. These diagnostics are computed over the entire model domain and within a subsection centered on the continental United States (CONUS). The entire domain results show that a modest positive forecast impact is achieved from all 10 data types during all three time periods. Rawinsonde temperature and moisture observations and infrared cloud-drift wind observations have the largest positive impact season to season; however, both precipitable water data types provide significant positive forecast impact during the summer and transition seasons. Rawinsonde temperature and moisture, rawinsonde winds, aircraft winds, and infrared cloud-drift winds have the largest positive impact season to season over CONUS. The three-layer precipitable water data type produces large positive forecast impact over CONUS during July. In general, the forecast impacts are smaller for nearly all data types over CONUS than over the entire model domain. There are also more negative forecast impacts for both the in situ and remotely sensed data types over CONUS than over the entire domain.

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Tom H. Zapotocny, W. Paul Menzel, James A. Jung, and James P. Nelson III

Abstract

The impact of in situ rawinsonde (raob) data, remotely sensed Geostationary Operational Environmental Satellite (GOES), and Polar Operational Environmental Satellite (POES) data routinely used in NCEP’s Eta Data Assimilation/Forecast System (EDAS) is studied for extended-length time periods during four seasons. The work described in this paper is relevant for users of the Eta Model trying to compare and contrast the overall forecast impact of traditional, mostly land-based rawinsonde data with remotely sensed data that are available domainwide.

The case studies chosen consist of 15-day periods during fall 2001, winter 2001/02, spring 2002, and summer 2002. During these periods, a 32-km/60-layer November 2001 version of the EDAS is run four times at both 0000 and 1200 UTC. The four runs include a control run, which utilizes all data types routinely used in the EDAS, and three experimental runs in which either all rawinsonde, GOES, or POES data are denied. Differences between the experimental and control runs are then accumulated over the 15-day periods and analyzed to demonstrate the 24- and 48-h forecast impact of these data types in the EDAS. Conventional meteorological terms evaluated include mean sea level pressure as well as temperature, both components of the wind, and relative humidity. Comparisons are made on seven pressure levels extending from near the earth’s surface to the lower stratosphere. The diagnostics are computed over both the entire horizontal model domain, and within a subsection covering the continental United States and adjacent coastal waters (extended CONUS).

The 24-h domainwide results show that a positive forecast impact is achieved from all three data sources during all four seasons. Cumulatively, the rawinsonde data have the largest positive impact over both the entire model domain and extended CONUS. However, GOES data have the largest contribution for several fields, especially moisture during summer and fall 2001. In general, GOES data also provide larger forecast impacts than POES data, especially for the wind components. All three data types demonstrate comparable forecast impact in terms of relative humidity. Finally, raob and POES data display a “spike” in positive forecast impact in the lower stratosphere during three of the four seasons.

Two additional findings from this study are also important. The first is that the forecast impact of all data types drops by at least a factor of 2 during all seasons between 24 and 48 h. The second is that GOES data show a preference for providing nearly equal improvement to the 0000 and 1200 UTC forecast cycles, while rawinsonde and especially POES data provide consistently larger forecast impacts at 1200 than at 0000 UTC.

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James A. Smith, Andrew J. Miller, Mary Lynn Baeck, Peter A. Nelson, Gary T. Fisher, and Katherine L. Meierdiercks

Abstract

The 9.1 km2 Moores Run watershed in Baltimore, Maryland, experiences floods with unit discharge peaks exceeding 1 m3 s−1 km−2 12 times yr−1, on average. Few, if any, drainage basins in the continental United States have a higher frequency. A thunderstorm system on 13 June 2003 produced the record flood peak (13.2 m3 s−1 km−2) during the 6-yr stream gauging record of Moores Run. In this paper, the hydrometeorology, hydrology, and hydraulics of extreme floods in Moores Run are examined through analyses of the 13 June 2003 storm and flood, as well as other major storm and flood events during the 2000–03 time period. The 13 June 2003 flood, like most floods in Moores Run, was produced by an organized system of thunderstorms. Analyses of the 13 June 2003 storm, which are based on volume scan reflectivity observations from the Sterling, Virginia, WSR-88D radar, are used to characterize the spatial and temporal variability of flash flood producing rainfall. Hydrology of flood response in Moores Run is characterized by highly efficient concentration of runoff through the storm drain network and relatively low runoff ratios. A detailed survey of high-water marks for the 13 June 2003 flood is used, in combination with analyses based on a 2D, depth-averaged open channel flow model (TELEMAC 2D) to examine hydraulics of the 13 June 2003 flood. Hydraulic analyses are used to examine peak discharge estimates for the 13 June flood peak, propagation of flood waves in the Moores Run channel, and 2D flow features associated with channel and floodplain geometry.

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Michael J. Erickson, Joshua S. Kastman, Benjamin Albright, Sarah Perfater, James A. Nelson, Russ S. Schumacher, and Gregory R. Herman

Abstract

The Flash Flood and Intense Rainfall (FFaIR) Experiment developed within the Hydrometeorology Testbed (HMT) of the Weather Prediction Center (WPC) is a pseudo-operational platform for participants from across the weather enterprise to test emerging flash flood forecasting tools and issue experimental forecast products. This study presents the objective verification portion of the 2017 edition of the experiment, which examines the performance from a variety of guidance tools (deterministic models, ensembles, and machine-learning techniques) and the participants’ forecasts, with occasional reference to the participants’ subjective ratings. The skill of the model guidance used in the FFaIR Experiment is evaluated using performance diagrams verified against the Stage IV analysis. The operational and FFaIR Experiment versions of the excessive rainfall outlook (ERO) are evaluated by assessing the frequency of issuances, probabilistic calibration, Brier skill score (BSS), and area under relative operating characteristic (AuROC). An ERO first-guess field called the Colorado State University Machine-Learning Probabilities method (CSU-MLP) is also evaluated in the FFaIR Experiment. Among convection-allowing models, the Met Office Unified Model generally performed optimally throughout the FFaIR Experiment when using performance diagrams (at the 0.5- and 1-in. thresholds; 1 in. = 25.4 mm), whereas the High-Resolution Rapid Refresh (HRRR), version 3, performed best subjectively. In terms of subjective and objective ensemble scores, the HRRR ensemble scored optimally. The CSU-MLP overpredicted lower risk categories and underpredicted higher risk categories, but it shows future promise as an ERO first-guess field. The EROs issued by the FFaIR Experiment forecasters had improved BSS and AuROC relative to the operational ERO, suggesting that the experimental guidance may have aided forecasters.

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Tom H. Zapotocny, Steven J. Nieman, W. Paul Menzel, James P. Nelson III, James A. Jung, Eric Rogers, David F. Parrish, Geoffrey J. DiMego, Michael Baldwin, and Timothy J. Schmit

Abstract

A case study is utilized to determine the sensitivity of the Eta Data Assimilation System (EDAS) to all operational observational data types used within it. The work described in this paper should be of interest to Eta Model users trying to identify the impact of each data type and could benefit other modelers trying to use EDAS analyses and forecasts as initial conditions for other models.

The case study chosen is one characterized by strong Atlantic and Pacific maritime cyclogenesis, and is shortly after the EDAS began using three-dimensional variational analysis. The control run of the EDAS utilizes all 34 of the operational data types. One of these data types is then denied for each of the subsequent experimental runs. Differences between the experimental and control runs are analyzed to demonstrate the sensitivity of the EDAS system to each data type for the analysis and subsequent 48-h forecasts. Results show the necessity of various nonconventional observation types, such as aircraft data, satellite precipitable water, and cloud drift winds. These data types are demonstrated to have a significant impact, especially observations in maritime regions.

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Sarah A. Tessendorf, Roelof T. Bruintjes, Courtney Weeks, James W. Wilson, Charles A. Knight, Rita D. Roberts, Justin R. Peter, Scott Collis, Peter R. Buseck, Evelyn Freney, Michael Dixon, Matthew Pocernich, Kyoko Ikeda, Duncan Axisa, Eric Nelson, Peter T. May, Harald Richter, Stuart Piketh, Roelof P. Burger, Louise Wilson, Steven T. Siems, Michael Manton, Roger C. Stone, Acacia Pepler, Don R. Collins, V. N. Bringi, M. Thurai, Lynne Turner, and David McRae

As a response to extreme water shortages in southeast Queensland, Australia, brought about by reduced rainfall and increasing population, the Queensland government decided to explore the potential for cloud seeding to enhance rainfall. The Queensland Cloud Seeding Research Program (QCSRP) was conducted in the southeast Queensland region near Brisbane during the 2008/09 wet seasons. In addition to conducting an initial exploratory, randomized (statistical) cloud seeding study, multiparameter radar measurements and in situ aircraft microphysical data were collected. This comprehensive set of observational platforms was designed to improve the physical understanding of the effects of both ambient aerosols and seeding material on precipitation formation in southeast Queensland clouds. This focus on gaining physical understanding, along with the unique combination of modern observational platforms utilized in the program, set it apart from previous cloud seeding research programs. The overarching goals of the QCSRP were to 1) determine the characteristics of local cloud systems (i.e., weather and climate), 2) document the properties of atmospheric aerosol and their microphysical effects on precipitation formation, and 3) assess the impact of cloud seeding on cloud microphysical and dynamical processes to enhance rainfall. During the course of the program, it became clear that there is great variability in the natural cloud systems in the southeast Queensland region, and understanding that variability would be necessary before any conclusions could be made regarding the impact of cloud seeding. This article presents research highlights and progress toward achieving the goals of the program, along with the challenges associated with conducting cloud seeding research experiments

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