The Rapid Update Cycle (RUC), an operational regional analysis–forecast system among the suite of models at the National Centers for Environmental Prediction (NCEP), is distinctive in two primary aspects: its hourly assimilation cycle and its use of a hybrid isentropic–sigma vertical coordinate. The use of a quasi-isentropic coordinate for the analysis increment allows the influence of observations to be adaptively shaped by the potential temperature structure around the observation, while the hourly update cycle allows for a very current analysis and short-range forecast. Herein, the RUC analysis framework in the hybrid coordinate is described, and some considerations for high-frequency cycling are discussed. A 20-km 50-level hourly version of the RUC was implemented into operations at NCEP in April 2002. This followed an initial implementation with 60-km horizontal grid spacing and a 3-h cycle in 1994 and a major upgrade including 40-km horizontal grid spacing in 1998. Verification of forecasts...

An Hourly Assimilation–Forecast Cycle: The RUC

This paper presents recent efforts to understand the relative accuracies of different instrumentation and gauges with various windshield configurations to measure snowfall. Results from the National Center for Atmospheric Research (NCAR) Marshall Field Site will be highlighted. This site hosts a test bed to assess various solid precipitation measurement techniques and is a joint collaboration between the National Oceanic and Atmospheric Administration (NOAA), NCAR, the National Weather Service (NWS), and Federal Aviation Administration (FAA). The collaboration involves testing new gauges and other solid precipitation measurement techniques in comparison with World Meteorological Organization (WMO) reference snowfall measurements. This assessment is critical for any ongoing studies and applications, such as climate monitoring and aircraft deicing, that rely on accurate and consistent precipitation measurements.

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How Well Are We Measuring Snow: The NOAA/FAA/NCAR Winter Precipitation Test Bed

Accurate storm identification and tracking are basic and essential parts of radar and severe weather warning operations in today’s operational meteorological community. Improvements over the original WSR-88D storm series algorithm have been made with the Storm Cell Identification and Tracking algorithm (SCIT). This paper discusses the SCIT algorithm, a centroid tracking algorithm with improved methods of identifying storms (both isolated and clustered or line storms). In an analysis of 6561 storm cells, the SCIT algorithm correctly identified 68% of all cells with maximum reflectivities over 40 dB Z and 96% of all cells with maximum reflectivities of 50 dBZ or greater. The WSR-88D storm series algorithm performed at 24% and 41%, respectively, for the same dataset. With better identification performance, the potential exists for better and more accurate tracking information. The SCIT algorithm tracked greater than 90% of all storm cells correctly. The algorithm techniques and results of a detailed performance evaluation are presented. This algorithm was included in the WSR-88D Build 9.0 of the Radar Products Generator software during late 1996 and early 1997. It is hoped that this paper will give new users of the algorithm sufficient background information to use the algorithm with confidence.

The Storm Cell Identification and Tracking Algorithm: An Enhanced WSR-88D Algorithm

Climate change is expected to accelerate the hydrologic cycle, increase the fraction of precipitation that is rain, and enhance snowpack melting. The enhanced hydrological cycle is also expected to increase snowfall amounts due to increased moisture availability. These processes are examined in this paper in the Colorado Headwaters region through the use of a coupled high-resolution climate–runoff model. Four high-resolution simulations of annual snowfall over Colorado are conducted. The simulations are verified using Snowpack Telemetry (SNOTEL) data. Results are then presented regarding the grid spacing needed for appropriate simulation of snowfall. Finally, climate sensitivity is explored using a pseudo–global warming approach. The results show that the proper spatial and temporal depiction of snowfall adequate for water resource and climate change purposes can be achieved with the appropriate choice of model grid spacing and parameterizations. The pseudo–global warming simulations indicate enha...

High resolution coupled climate-runoff simulations of seasonal snowfall over Colorado: A process study of current and warmer climate

The International H2O Project (IHOP_2002) is one of the largest North American meteorological field experiments in history. From 13 May to 25 June 2002, over 250 researchers and technical staff from the United States, Germany, France, and Canada converged on the Southern Great Plains to measure water vapor and other atmospheric variables. The principal objective of IHOP_2002 is to obtain an improved characterization of the time-varying three-dimensional water vapor field and evaluate its utility in improving the understanding and prediction of convective processes. The motivation for this objective is the combination of extremely low forecast skill for warm-season rainfall and the relatively large loss of life and property from flash floods and other warm-season weather hazards. Many prior studies on convective storm forecasting have shown that water vapor is a key atmospheric variable that is insufficiently measured. Toward this goal, IHOP_2002 brought together many of the existing operational and new st...

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An Overview of the International H2O Project (IHOP_2002) and Some Preliminary Highlights

A methodology is presented for the real-time automated identification, tracking, and short-term forecasting of thunderstorms based on volume-scan weather radar data. The emphasis is on the concepts upon which the methodology is based. A “storm” is defined as a contiguous region exceeding thresholds for reflectivity and size. Storms defined in this way are identified at discrete time intervals. An optimization scheme is employed to match the storms at one time with those at the following time, with some geometric logic to deal with mergers and splits. The short-term forecast of both position and size is based on a weighted linear fit to the storm track history data. The performance of the detection and forecast were evaluated for the summer 1991 season, and the results are presented.

TITAN: Thunderstorm Identification, Tracking, Analysis, and Nowcasting—A Radar-based Methodology

This paper reviews the status of forecasting convective precipitation for time periods less than a few hours (nowcasting). Techniques for nowcasting thunderstorm location were developed in the 1960s and 1970s by extrapolating radar echoes. The accuracy of these forecasts generally decreases very rapidly during the first 30 min because of the very short lifetime of individual convective cells. Fortunately more organized features like squall lines and supercells can be successfully extrapolated for longer time periods. Physical processes that dictate the initiation and dissipation of convective storms are not necessarily observable in the past history of a particular echo development; rather, they are often controlled by boundary layer convergence features, environmental vertical wind shear, and buoyancy. Thus, successful forecasts of storm initiation depend on accurate specification of the initial thermodynamic and kinematic fields with particular attention to convergence lines. For these reasons ...

https://opensky.ucar.edu/islandora/object/articles%3A3309/datastream/PDF/view

Nowcasting Thunderstorms: A Status Report

The objective of this study is to develop a technique to use lightning observations for estimating convective rainfall. A framework for rainfall estimation is developed in which key elements are 1) the rainfall–lightning ratio, that is, the convective rainfall mass per cloud-to-ground lightning flash; 2) the spatial distribution of rainfall relative to flash locations; and 3) the temporal distribution of rainfall relative to the time of lightning occurrence. These three elements are examined through a study of 22 summer thunderstorms in the domain covered by the Melbourne, Florida, WSR-88D radar during August of 1992 and 1993. The analyses are carried out by combining lightning observations from the National Lightning Detection Network with storm parameters computed from 3D reflectivity observations using the Thunderstorm Identification Tracking and Nowcasting storm-tracking and analysis algorithms. The effect of the prevailing convective regime on the variability of lightning–rainfall relationsh...

Estimation of Convective Rainfall from Lightning Observations

The identification and mitigation of anomalous propagation (AP) and normal propagation (NP) ground clutter is an ongoing problem in radar meteorology Scatter from ground-clutter targets routinely contaminates radar data and masks weather returns causing poor data quality The problem is typically mitigated by applying a clutter filter to all radar data, but this also biases weather data at near-zero velocity Modern radar processors make possible the real-time identification and filtering of AP clutter A fuzzy logic algorithm is used to distinguish between clutter echoes and precipitation echoes and, subsequently, a clutter filter is applied to those radar resolution volumes where clutter is present In this way, zero-velocity weather echoes are preserved while clutter echoes are mitigated Since the radar moments are recalculated from clutter-filtered echoes, the underlying weather echo signatures are revealed, thereby significantly increasing the visibility of weather echo This paper describ

Weather Radar Ground Clutter. Part II: Real-Time Identification and Filtering

Real-time ground-clutter identification and subsequent filtering of clutter-contaminated data is addressed in this two-part paper. Part I focuses on the identification, modeling, and simulation of S-band ground-clutter echo. A new clutter identification parameter, clutter phase alignment (CPA), is presented. CPA is a measure primarily of the phase variability of the in-phase and quadrature-phase time series samples for a given radar resolution volume. CPA is also a function of amplitude variability of the time series. It is shown that CPA is an excellent discriminator of ground clutter versus precipitation echoes. A typically used weather model, time series simulatoris shown to inadequately describe experimentally observed CPA. Thus, a new technique for the simulation of ground-clutter echo is developed that better predicts the experimentally observed CPA. Experimental data from the Denver Next Generation Weather Radar (NEXRAD) at the Denver, Colorado, Front Range Airport (KFTG), and NCAR’s S-band dual-polarization Doppler radar (S-Pol) are used to illustrate CPA. In Part II, CPA is used in a fuzzy logic algorithm for improved clutter identification.

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Michael Dixon

Papers

TITAN: Thunderstorm Identification, Tracking, Analysis, and Nowcasting—A Radar-based Methodology

Nowcasting Thunderstorms: A Status Report

Estimation of Convective Rainfall from Lightning Observations

Weather Radar Ground Clutter. Part II: Real-Time Identification and Filtering

Weather radar ground clutter. Part I: Identification, modeling, and simulation