Cooperative Institute for Research in Environmental Sciences

Media Advisory: Highlights of CIRES science at AMS

Media Advisory: Highlights of CIRES science at AMS


Scientists from the NOAA Cooperative Institute for Research in Environmental Sciences (CIRES) at the University of Colorado will present new research at next week’s 93rd American Meteorological Society (AMS) Meeting in Austin, Texas.

Reporters are invited to attend our scientists’ scheduled talks and poster presentations. Among the issues our scientists will be focusing on are:

  • International weather and climate events of 2012
  • Impacts of the record Arctic sea ice minimum of 2012
  • Tools to facilitate offshore wind energy
  • Next-generation convective-scale forecast guidance
  • Using climatology forecasting for renewable energy resource assessments
  • Extreme precipitation events in the Southeast United States

For updates on the presentations, follow us on Facebook and Twitter (@theCIRESwire).

Below, find highlights of potential interest to journalists.

Monday, Jan. 7

Is global warming significantly affecting atmospheric circulation extremes?

Prashant D. Sardeshmukh, CIRES Fellow working at NOAA’s Earth System Research Laboratory
Session 1B
11:00–11:15 a.m.; Ballroom C (Austin Convention Center)

Although the anthropogenic influence on 20th century global warming is well established, the influence on the atmospheric circulation, especially on regional scales at which natural variability is relatively large, has proved harder to ascertain. And yet assertions are often made to this effect, especially in the media whenever an extreme warm or cold or dry or wet spell occurs and is tied to an apparent trend in the large-scale atmospheric circulation pattern. We are addressing this important issue using the longest currently available global atmospheric circulation dataset, an ensemble of 56 equally likely estimates of the atmospheric state within observational error bounds generated for every 6 hours from 1871 to the present in the 20th Century Reanalysis Project (20CR; Compo et al, QJRMS 2011). We previously presented evidence that long-term trends in the indices of several major modes of atmospheric circulation variability, including the North Atlantic Oscillation (NAO) and the tropical Pacific Walker Circulation (PWC), were weak or non-existent over the full period of record in the 20CR dataset. We have since investigated the possibility of a change in the probability density functions (PDFs) of the daily values of these indices, including changes in their tails, from the first to the second halves of the 20th century and found no statistically significant change. This was done taking into account the generally skewed and heavy-tailed character of these PDFs, and using both raw histograms and fitted “SGS” probability distributions (whose relevance in describing large-scale atmospheric variability was demonstrated in Sardeshmukh and Sura, J. Climate 2009) to assess the significance of any changes through extensive Monte Carlo simulations. We stress that without such an explicit accounting of departures from normal distributions, detection and attribution studies of changes in climate extremes may be seriously compromised and lead to wrong conclusions. Our finding of no significant change in the PDFs of the NAO and the PWC has important implications for how global warming is influencing atmospheric circulation variability and extreme anomaly statistics, and to what extent the CMIP5 models are correctly representing those influences.

The GOES-R Sudden Impulse Detection Algorithm

William Rowland, CIRES scientist working at NOAA’s Earth System Research Laboratory
GOES-R/JPSS Poster Session
2:30­–4:00 p.m.; Exhibit Hall 3 (Austin Convention Center)

The GOES-R Sudden Impulse (SI) Detection Algorithm will offer a powerful new tool to help forecasters and end users mitigate the effects of geomagnetic disturbances. Sudden Impulses often precede geomagnetic storms, which can cripple critical infrastructure such as the electrical grid. This new technique will therefore provide a way to help Space Weather forecasters prepare power companies, oil pipeline operators, and other affected parties with the opportunity to adapt their operations in such a way as to minimize impacts to the public.

The algorithm will work by combining measurements taken by the magnetometers aboard GOES satellites, ground magnetometers, and possibly measurements of the solar wind and magnetic field taken upstream of Earth at the L1 Lagrangian location by the Advanced Composition Explorer (ACE) or Deep Space Climate Observatory (DSCOVR). The algorithm then searches for a rapid change in these observations in a short time period. Two different methods are currently being employed to analyze the results for a relevant disturbance. The basic difference is that the first approach identifies time periods when an individual magnetometer is experiencing a rapid change, then counts how many magnetometers are affected within a certain time window to identify an SI. The other tries to develop a global picture of the change in the geomagnetic field first, then determines whether this global field proxy is changing rapidly to identify an SI. Identification of regional changes, for example a rapidly changing field in the magnetic longitudes spanned by the United States in the absence of a global sudden impulse, is also under consideration.

Each method has strengths and weaknesses which will be discussed in some detail. Each method also has a certain amount of scalability, which should mean that as the forecast center obtains access to additional magnetometers these data can be added to the algorithm, permitting results to improve throughout the life cycle of the algorithm. Ultimately, selection of the method for implementation will be based upon scoring the results of each algorithm versus a truth dataset.

Validation has been initiated on each algorithm using over a year's worth of high temporal resolution data provided by NOAA and the USGS. We plan to utilize data from a full solar cycle for the final validation and scoring. This extensive validation, combined with regular feedback from forecasters throughout the development cycle, should help to ensure that the end product substantively improves operators' abilities to protect the interests of the public.

Tuesday, Jan. 8

International weather and climate events of 2012

Klaus Wolter, CIRES Scientist working at NOAA’s Earth System Research Laboratory
Session 1
8:30–9:00 a.m.; Ballroom E (Austin Convention Center)

This talk gives an overview of noteworthy large-scale weather and climate anomalies in 2012, with a discussion of the resultant seasonal temperature and precipitation anomalies around the Globe. Weather and climate events include severe drought conditions, heat waves, wildfire seasons, major tropical cyclones and extratropical storms, flooding rains, snow storms, sea ice conditions, as well as cold waves. Where possible, these are related to the ENSO conditions of last year, as well as to expected impacts due to anthropogenic climate change.

Impacts of the record Arctic sea ice minimum of 2012

Mark C. Serreze, CIRES Fellow
Session 1
9:15–9:30 a.m.; Ballroom E (Austin Convention Center)

On 16 September, 2012, Arctic sea ice extent dropped to the lowest level recorded over the satellite era, which at 3.49 million square km was 18% lower than the previous record low extent set in September 2007. The summer of 2007 featured unusually high sea level pressure centered north of the Beaufort Sea and Greenland, paired with unusually low pressure along northern Eurasia, bringing in warm southerly winds along the shores of the East Siberian and Chukchi seas, favoring strong ice melt in these sectors and pushing the ice away from the coast, leaving open water. The pressure pattern also favored the transport of ice out of the Arctic Ocean and into the North Atlantic through Fram Strait. By sharp contrast, apart from an unusually strong low pressure system in the first week of August centered over the northern Beaufort Sea, weather patterns during the summer of 2012 were unremarkable. While evaluations are ongoing as this abstract is written, it appears that in response to a warming Arctic over the past several decades, the spring ice cover is now so thin that large parts of the sea ice cover are now simply unable to survive the summer melt season. Through the summer of 2012, the Arctic Ocean absorbed a great deal of solar energy in dark open water areas. The release of this stored heat to the atmosphere during the autumn and winter, manifested as strong positive anomalies in surface and lower tropospheric temperatures, serves as an exclamation point on the ongoing process of Arctic amplification – the observed outsized rise in air temperatures over the Arctic compared to the globe as a whole. Whether this outsized warming will influence autumn and winter weather patterns beyond the Arctic region, as has been argued to have been the case in other recent years with low end-of-summer sea ice extent, remains to be seen. What is clear is that the events of 2012 have further raised awareness of the economic and strategic importance of the Arctic through its growing accessibility to marine shipping and extraction of natural resources.

Spatial variability of marine winds as studied by Doppler lidar

Yelena Pichugina, CIRES scientist working at NOAA’s Earth System Research Laboratory
Session 2
11:00–11:15 a.m.; Room 18C (Austin Convention Center)

Accurate, high-resolution vertical profiles of the horizontal wind and other wind information in the lowest several hundred meters of the atmosphere are essential for many applications, such as transport of air pollutants and other airborne trace species, numerical model verification and improvement, research into meteorological factors affecting the flows over the ocean, and more recently, offshore wind energy. Because information is difficult to obtain above the surface, users will have to rely on remote sensing systems, such as Doppler lidar, to obtain the needed data. Many significant challenges are involved in obtaining accurate wind data over the sea from moving platforms, such as removing the various motions of the platform from the wind estimates. ESRL has adapted its scanning, pulsed, coherent Doppler lidar system, the High Resolution Doppler Lidar (HRDL) to operate from a moving ship by developing a sophisticated motion compensation system that allows the winds to be measured to high accuracy. The paper will descript the measurement system and present results related to wind energy issues such as temporal and spatial variability of marine winds, distributions of wind speed and wind direction at the heights of modern turbine rotors. Presented wind flow characteristics were obtained off the New England coast, when HRDL was deployed on the research vessel Ronald Brown. This datasets was chosen because the waters off the New England coast is the region planned for development of wind farms in the near future. Analysis of wind and turbulence characteristics over a wide range of heights, variations of wind shear in time during strong and calm wind nights, along with examples of error in the actual and predicted wind resources will be given. These results will illustrate of the kind of information available from remote sensing instruments for wind energy research and show the value of the existing offshore datasets to gain greater insight into the characteristics of offshore flows at turbine heights for better understanding of the range of marine atmospheric conditions.

Independent confirmation of global land warming without the use of station temperatures

Gilbert P. Compo, CIRES scientist working at NOAA’s Earth System Research Laboratory
Session 5A
11:30–11:45 a.m.; Ballroom B (Austin Convention Center)

Confidence in estimates of anthropogenic climate change is limited by known issues with air temperature observations from land stations. We test those observations using a completely different approach to investigate global land warming over the 20th century. We have ignored all land temperature observations and instead inferred the temperature from global observations of barometric pressure, sea surface temperature, and sea-ice concentration using a physically-based data assimilation system called the 20th Century Reanalysis. This independent dataset reproduces both annual variations and centennial trends in the observation-based land surface temperature datasets, demonstrating the robustness of previous conclusions regarding global warming.

A long-term hydrologically based dataset of land surface fluxes and states for the conterminous U.S.: Update and extensions

Ben Livneh, CIRES scientist
Session 6A
2:00–2:15 p.m.; Ballroom B (Austin Convention Center)

We describe a publicly available, long-term (1915 – 2010), hydrologically consistent data set for the conterminous United States, intended to aid in studies of water and energy exchanges at the land surface. These data are gridded at a spatial resolution of 1/16 degree latitude-longitude and are derived from daily temperature and precipitation observations from approximately 20,000 NOAA Cooperative Observer (Co-op) stations. The available meteorological data include temperature, precipitation, and wind, as well as derived humidity and downwelling solar and infared radiation estimated via algorithms that index these quantities to the daily mean temperature, temperature range, and precipitation, and disaggregate them to three-hourly time steps. Furthermore, we employ the Variable Infiltration Capacity (VIC) model to produce three-hourly estimates of soil moisture, snow water equivalent, discharge, and surface heat fluxes. Relative to an earlier similar data set by Maurer and others, we have: a) extended the period of analysis (1915-2010 versus 1950-2000), b) increased the spatial resolution from 1/8° to 1/16°, and c) used an updated version of VIC. The previous data set has been widely used in water and energy budget studies, climate change assessments, drought reconstructions, and for many other purposes. We anticipate that the spatial refinement and temporal extension will be of interest to a wide cross-section of the scientific community.

The High-Resolution Rapid Refresh (HRRR): Accessibility of next generation convective-scale forecast guidance from research to operations

Curtis R. Alexander, CIRES scientist working at NOAA’s Earth System Research Laboratory
Session 4
2:30–2:45 p.m.; Room 11AB (Austin Convention Center)

The High-Resolution Rapid Refresh (HRRR) is a CONUS 3-km convection permitting atmospheric prediction system run hourly in real-time at the NOAA Earth System Research Laboratory. The HRRR uses a specially configured version of the Advanced Research WRF (ARW) model (including Thompson microphysics, MYJ PBL, and RUC LSM). The HRRR is run out to fifteen hours over a domain covering the entire coterminous United States using initial and boundary conditions from an hourly-cycled 13-km mesoscale model, the WRF-ARW-based Rapid Refresh (RAP). The RAP assimilates many novel and most conventional observation types including satellite observations on an hourly basis using Gridpoint Statistical Interpolation (GSI) and includes a procedure for initializing ongoing precipitation systems from observed radar reflectivity data using a digital filter, a cloud analysis system to initialize stable layer clouds, and special techniques to enhance retention of surface observation information.

The HRRR provides unique convective-scale forecast guidance with high spatial and temporal resolution leveraging both hourly updates and a sub-hourly output interval. In this presentation we will provide an overview of the HRRR forecast system including background on its inception, evolution to the current configuration with key milestones, and the path forward to operational implementation at the National Centers for Environmental Prediction (NCEP). We will provide examples of the diverse set of current HRRR forecast products, applications and users including the aviation, severe weather and renewable energy communities (both public and private) with use by the National Weather Service (NWS) including the Storm Prediction Center (SPC), and collaborative projects such as the Federal Aviation Administration-sponsored CoSPA and the Wind Forecast Improvement Project (WFIP). We will also describe challenges and infrastructure associated with maintaining a reliable, but non-operational, real-time system in terms of scalability and redundancy for user demands with data production on the order of one terabyte per day.

Wednesday, Jan. 9

Understanding forecast errors in extreme precipitation events in the Southeast U.S
Exhibit Hall 3 (Austin Convention Center)

Kelly M. Mahoney, CIRES scientist working at NOAA’s Earth System Research Laboratory
Poster Session
2:30–4:00 p.m.; Exhibit Hall 3 (Austin Convention Center)

The NOAA Hydrometeorology Testbed (HMT) aims to foster the transition of research advances into forecasting operations based on observation- and model-based studies of precipitation and meteorological conditions that can lead to flooding. The Southeast U.S. is the location of the HMT's newest regional field program.

The objective of this work is to elucidate the salient challenges in forecasting extreme precipitation events in the Southeast U.S. for both numerical weather prediction (NWP) models and human forecasters. While human forecasters rely on NWP model guidance for many aspects of a weather forecast, it is the human recognition of local conditions, model error and bias, and past experience that is often most critical to successful forecasts of high-impact events. Therefore, improving both NWP guidance and forecaster awareness is key to improving the precipitation forecast.

The Southeast U.S. experiences extreme precipitation from a number of different phenomena, making quantitative precipitation forecasting (QPF) in this region especially challenging. As an initial step toward improving predictive capabilities, preliminary model-based experiments have been conducted on select heavy rainfall events in this region. Analysis of these experiments focuses on improved understanding of the forecast errors for events with the lowest skill, and also examines possible connections between specific forecast challenges and key environmental fields (e.g., CAPE, shear, precipitable water) and event characteristics (e.g., system size, duration, strong/weak moisture transport).

Simulations are generated in two ways. First, extreme event composite fields serve as initial conditions in order to examine a “generalized” extreme event environment. Second, select case studies are simulated and examined in more detail to diagnose operational forecast successes and challenges. Specifically, the flooding that affected the Atlanta, GA region in 2009 and the Nashville, TN region in 2010 will be highlighted, and key features and forecast challenges associated with each event will be contrasted.

The results of these experiments are intended to facilitate forecaster identification and understanding of particularly challenging forecast scenarios, and also to better understand existing NWP model challenges associated with such scenarios. The transition of this research to operations will be made through both standard, ongoing discussion and documentation, and also via more innovative R2O techniques such as realtime and/or retrospective forecaster experiments. Findings will also be useful toward improving and refining NWP numerical models in development.

Evaluating 11 years of quantitative precipitation forecast performance for extreme events

Ellen Sukovich, CIRES scientist working at NOAA’s Earth System Research Laboratory
Poster Session
2:30–4:00 p.m.; Exhibit Hall 3 (Austin Convention Center)

Extreme precipitation events (i.e., events associated with the tail end of the precipitation probability distribution) are high impact events that can cause loss of life and significant disruption to local, regional, and even national economies. There are many communities (e.g., water resources management, agriculture, transportation, emergency management), which require accurate forecasts of extreme events for decision-making, preparation, and management; however, accurately forecasting such events remains one of meteorology's most difficult challenges. Since verification provides both a way to measure improvement in quantitative precipitation forecasts (QPF) and a method by which forecast errors can be identified, the Hydrometeorology Testbed (HMT) has identified QPF verification as an integral component to improving extreme QPFs.

This study examines national QPF performance for extreme events over an 11 year period (January 2001 through December 2011) using regionally defined extreme precipitation thresholds. Data for this analysis include 32-km gridded QPFs from the National Centers for Environmental Prediction's (NCEP) Hydrometeorological Prediction Center (HPC) and 4-km gridded Stage IV data from the National Weather Service (NWS) River Forecast Centers (RFC). Regional extreme precipitation thresholds were quantitatively defined as the 99th and 99.9th percentile precipitation values of all “wet-site” days (i.e., ≥ 0.01 in 24 h-1 at each grid point) for each RFC region. Five verification metrics [probability of detection (POD), false alarm ratio (FAR), threat score, mean absolute error (MAE) and bias] were calculated by aggregating all regional extreme wet-site days. The results of these metrics were compared to the current NOAA Government Performance and Results Act (GPRA) precipitation threshold (≥ 1.0 in 24 h-1) to determine a baseline performance.

Results from this study indicate that national 32-km extreme QPFs have improved over the last 11 years, although the yearly threat scores of the baselined extreme precipitation are approximately half of the GPRA threat scores. In addition, extreme QPF threat scores appear to be improving slightly faster (~10-15%) than the GPRA threat scores (~9%) between 2001 and 2011. Further examination has also shown that extreme precipitation amounts tend to be consistently under predicted. Seasonally, national extreme QPFs show highest skill during the winter months (i.e., December, January, February) and lower skill during the summer months (i.e., June, July, August) although a significant increase in QPF skill is observed during the month of September, most likely due to landfalling hurricanes and tropical cyclones.

A key challenge of this verification work is the smaller sample size of the extreme events, which tend to occur less frequently and over smaller areas. The results of this study provide feedback to operations at NCEP/HPC regarding extreme QPF performance for the last 11 years. Finally, the method and framework applied in this study to define and verify extreme events can be applied to any gridded dataset, and extreme QPF baseline performance can be established for that dataset.

Renewable energy resource assessments from a climatology of short-range High-Resolution Rapid Refresh forecasts

Eric P. James, CIRES scientist working at NOAA’s Earth System Research Laboratory
Session 14
4:45–5:00 p.m.; Room 6A (Austin Convention Center)

The High Resolution Rapid Refresh (HRRR) experimental model is being run hourly at 3km horizontal resolution in real-time at the Global Systems Division (GSD) of the National Oceanic and Atmospheric Administration (NOAA)/Earth System Research Laboratory (ESRL). Each hour, the HRRR model is run out to a duration of 15 hours over a domain covering the entire conterminous United States (CONUS). Its 3-km resolution allows explicit treatment of convective storms. Initial and boundary conditions for the HRRR are obtained from the coarser 13km hourly updated Rapid Refresh (RAP).

While many users refer to HRRR output for applications such as severe weather forecasting, aviation, and energy, NOAA/ESRL also recognizes the HRRR's potential as a tool for building a long-term climatology of wind and solar resources based on its very short-range forecasts. Such a climatology, on the 3-km scale of the HRRR grid, would be able to resolve many small-scale orographic effects in complex terrain and coastal regions, but still remain well-tethered in 2-3h forecasts to very recent observations. We anticipate this resource will be of particular interest to the renewable energy community. In order to facilitate additional work, we have initiated a long-term effort to create climatological averages of some renewable energy related variables from a year-long history of HRRR runs.

This talk will present the methodology and some preliminary results of this ongoing work. We will describe and present various measures of the model representation of the 80m wind field during 2012, with a focus on thresholding to identify regions (over land and offshore) of high potential for wind energy development. In addition, a similar analysis of downwelling solar radiation during 2012 will be presented. Statistics will also be broken down by time of day and season.

Thursday, Jan. 10

High-Resolution Rapid Refresh (HRRR) model and production advancements for 2013 with targeted improvements for reliable convective weather guidance in the national airspace system

Curtis R. Alexander, CIRES scientist working at NOAA’s Earth System Research Laboratory
Session 9
8:45–9:00 a.m.; Room 17A (Austin Convention Center)

The High-Resolution Rapid Refresh (HRRR) is a CONUS 3-km convection permitting atmospheric prediction system run hourly in real-time at the NOAA Earth System Research Laboratory. The HRRR uses a specially configured version of the Advanced Research WRF (ARW) model (including Thompson microphysics, MYJ PBL, and RUC LSM). The HRRR is run out to fifteen hours over a domain covering the entire coterminous United States using initial and boundary conditions from an hourly-cycled 13-km mesoscale model, the WRF-ARW-based Rapid Refresh (RAP). The RAP assimilates many novel and most conventional observation types including satellite observations on an hourly basis using Gridpoint Statistical Interpolation (GSI) and includes a procedure for initializing ongoing precipitation systems from observed radar reflectivity data using a digital filter, a cloud analysis system to initialize stable layer clouds, and special techniques to enhance retention of surface observation information.

In this presentation we will review the performance of 2012 HRRR forecasts with an emphasis on warm-season convection in real-time and retrospective runs. We will document the reduction in moist bias of soil moisture, dewpoints, precipitation and convective initiation, particularly in the first few forecast hours of each model cycle, and show improved development and maintenance of mesoscale convective systems. We will also present an improvement in the HRRR echo top height forecasts that was applied in July 2012.

We will also preview the development of the 2013 HRRR forecast system with a focus on four areas including (1) establishment of data assimilation (including radar observations) at the 3-km scale to further reduce convective-scale “spin-up” in the first few forecast hours, (2) enhancement in model dynamics and physics including shallow convective parameterization to improve the timing of convective initiation in weakly-forced weather regimes, (3) reduction of latency in HRRR model forecast production through an accelerated 3-km analysis and more efficient post-processing, and (4) improved reliability and availability of HRRR forecasts through redundant high performance computer systems hosted in Boulder, CO and Fairmont, WV. We will also update progress on other anticipated changes in the cloud analysis and ensemble data assimilation in an hourly update cycle that will improve year-round performance of the HRRR. Finally, we will discuss the development of time-lagged ensemble convective probabilities produced from HRRR runs.

NOAA’s hydrometeorological testbed: A decade of research and its impact on operational decision making

David W. Reynolds, CIRES scientist working at NOAA’s Earth System Research Laboratory
Session 7
4:00–4:15 p.m.; Ballroom A (Austin Convention Center)

The Hydrometeorological Testbed (HMT) is a NOAA research program aimed at accelerating the research, development, and infusion of new technologies, models, and scientific results from the research community into daily forecasting operations of the National Weather Service (NWS) Weather Forecast Offices (WFOs), River Forecast Centers (RFCs), and the National Centers for Environmental Prediction (NCEP) Hydrometeorological Prediction Center (HPC). In addition, the USGS, US Army Corps, the NWS National Water Center (NWC), and state water management agencies (e.g. the California Department of Water Resources) will benefit as these data and information provide improved decision support to meet their missions.

The first phase of HMT was an outgrowth of NOAA's CALJET and PACJET projects from 1997–2003 on the West Coast. HMT-West targeted California's flood-vulnerable American River Basin as the first full-scale deployment of highly sophisticated instrumentation, deployed during the period from 2005 through 2011. Preliminary, small-scale tests of HMT facilities were conducted in California's Coast Range in 2004 (HMT- 04), and the HMT was extended to the western slopes of the Sierra Nevada for the winter of 2004-2005. The year's 2012 through 2014 are expected to be transition years when legacy instrumentation will be permanently installed within California. In addition, decision support tools will be developed and deployed to better utilize these observations by forecasters and decision makers. (see http://hmt.noaa.gov/resources/pdf/hmt_impl_plan_pullout.pdf for a complete description of the HMT program and science plan).

Over the past decade, HMT West has made significant progress in communicating its research results to the operational forecast community along with transitioning key observation systems to operational status and incorporating well designed decision support systems. For example, HMT has strived to build relationships between the research community and the operational hydrometeorological forecast community. These relationships have fostered an understanding by the HMT research community of the needs and requirements of the operational forecaster. In addition it has allowed the operational forecaster to participate in research therefore providing them with a valuable learning experience that can be shared with their operational colleagues.

This presentation will describe the various methodologies used to transfer key research results into operations. It will also discuss current and future plans for transitioning on-going research into the operational forecast process, and describe plans for incorporating new observing platforms into operational decision support systems.

Extreme precipitation events in the Southeast United States: Climatology, environmental properties, and predictability

Benjamin Moore, CIRES scientist working at NOAA’s Earth System Research Laboratory
Session 7
4:45–5:30 p.m.; Ballroom A (Austin Convention Center)

The Southeast U.S. can experience extreme precipitation in all seasons in connection with a variety of phenomena, making quantitative precipitation forecasting in this region difficult. An enhanced understanding of the key meteorological processes and the forecast challenges associated with extreme precipitation in the Southeast can likely provide useful guidance to operational forecasters in identifying and predicting the occurrence of extreme precipitation.

In this study, a climatology of extreme precipitation events in the southeastern United States during 2002–2011 is derived using daily (1200 UTC–1200 UTC accumulations) 4-km NCEP Stage-IV quantitative precipitation estimates. Events in the climatology are classified as “tropical” if they were produced directly by a tropical cyclone or its remnants and “non-tropical” otherwise. Results of the climatology indicate that non-tropical extreme precipitation events in the Southeast occurred most frequently in the spring and fall and least frequently in the summer. In the winter and spring, non-tropical events occurred most frequently in the interior Southeast, west of the Appalachian Mountains, often in connection with strong synoptic-scale weather systems, while in the fall non-tropical events were most frequent east of the Appalachian Mountains. Tropical events occurred most frequently in the late summer and early fall and predominately affected the eastern portion of the Southeast.

Synoptic-scale composites are produced in order to examine the key environmental properties of non-tropical extreme events, with a focus on differentiating events featuring strong water vapor transport from low latitudes and strong dynamics from those featuring weak water vapor transport and weak dynamics. Subsets of non-tropical events are selected for composite analysis based upon the magnitude of time-integrated vertically integrated water vapor transport (IVT) associated with each event. The composite synoptic-scale environments of “strong IVT” and “weak IVT” events are then examined. In general, the strong IVT events feature a deep upstream upper-level trough, strong low-level winds, and a plume of moist, unstable air extending poleward from low latitudes, whereas the weak IVT events feature minimally amplified upper-level flow, weak low-level winds, and very moist and unstable conditions.

Lastly, verification of deterministic and probabilistic precipitation forecasts from the Hydrometeorological Prediction Center as well as the NOAA/Earth System Research Laboratory Reforecast ensemble is conducted for each event in the climatology in order to explore the general predictability of extreme precipitation events in the Southeast and to identify scenarios associated with exceptionally high/low predictability. This verification analysis motivates further observation- and numerical model-based investigations of the physical processes and environmental properties associated with high- and low-predictability events.


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